U.S. patent application number 16/855535 was filed with the patent office on 2020-08-06 for automated priming and library loading device.
The applicant listed for this patent is Clear Labs, Inc.. Invention is credited to Adam ALLRED, Sasan AMINI, Julius BARSI, Christopher HANEY, Ramin KHAKSAR, Sima MORTAZAVI, Hossein NAMAZI, Stephanie POLLARD, Shadi SHOKRALLA, Michael TAYLOR, David TRAN, Pavan VAIDYANATHAN.
Application Number | 20200251181 16/855535 |
Document ID | / |
Family ID | 1000004828692 |
Filed Date | 2020-08-06 |
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United States Patent
Application |
20200251181 |
Kind Code |
A1 |
AMINI; Sasan ; et
al. |
August 6, 2020 |
AUTOMATED PRIMING AND LIBRARY LOADING DEVICE
Abstract
Provided herein are automated apparatus for the identification
of microorganisms in various samples. The disclosure solves
existing challenges encountered in identifying and distinguishing
various types of microorganisms, including viruses and bacteria in
a timely, efficient, and automated manner by sequencing.
Inventors: |
AMINI; Sasan; (Redwood City,
CA) ; KHAKSAR; Ramin; (Redwood City, CA) ;
TAYLOR; Michael; (Kensington, MD) ; SHOKRALLA;
Shadi; (Danville, CA) ; HANEY; Christopher;
(Mountain View, CA) ; VAIDYANATHAN; Pavan; (Palo
Alto, CA) ; POLLARD; Stephanie; (Pleasanton, CA)
; ALLRED; Adam; (Menlo Park, CA) ; MORTAZAVI;
Sima; (Foster City, CA) ; TRAN; David; (Santa
Rosa, CA) ; NAMAZI; Hossein; (Menlo Park, CA)
; BARSI; Julius; (Menlo Park, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Clear Labs, Inc. |
Menlo Park |
CA |
US |
|
|
Family ID: |
1000004828692 |
Appl. No.: |
16/855535 |
Filed: |
April 22, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US18/67750 |
Dec 27, 2018 |
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16855535 |
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62611846 |
Dec 29, 2017 |
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62646135 |
Mar 21, 2018 |
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62730288 |
Sep 12, 2018 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G16B 35/10 20190201;
G16B 35/20 20190201; G16B 25/20 20190201; G16B 30/20 20190201 |
International
Class: |
G16B 35/10 20060101
G16B035/10; G16B 35/20 20060101 G16B035/20; G16B 25/20 20060101
G16B025/20; G16B 30/20 20060101 G16B030/20 |
Claims
1. A nucleic acid sequencing apparatus comprising: (a) a nucleic
acid library preparation compartment comprising two or more
chambers configured to prepare a plurality of nucleic acids from a
sample for a sequencing reaction; (b) a nucleic acid sequencing
chamber, wherein said nucleic acid sequencing chamber comprises one
or more flow cells comprising a plurality of pores or sequencing
cartridges configured for the passage of a nucleic acid strand,
wherein said nucleic acid library preparation compartment is
operatively connected to said nucleic acid sequencing chamber; and
(c) an automated platform, wherein said automated platform is
programmed to robotically move a sample from said nucleic acid
library preparation compartment into said nucleic acid sequencing
chamber.
2. The nucleic acid sequencing apparatus of claim 1, wherein said
automated platform moves a second sample from said nucleic acid
library preparation compartment into said nucleic acid sequencing
chamber upon detecting a failure of a sequencing reaction.
3. The nucleic acid sequencing apparatus of claim 1, wherein said
automated platform moves a second sample from said nucleic acid
library preparation compartment into said nucleic acid sequencing
chamber upon detecting a completion of a sequencing reaction.
4. The nucleic acid sequencing apparatus of claim 1, further
comprising adding a barcode to a plurality of nucleic acids in said
two or more chambers of (a), thereby providing a plurality of
barcoded nucleic acids for said sequencing reaction.
5. The nucleic acid sequencing apparatus of claim 1, further
comprising adding a plurality of mutually exclusive barcodes to a
plurality of nucleic acids in said two or more chambers of (a),
thereby providing a plurality of mutually exclusive barcoded
nucleic acids.
6. The nucleic acid sequencing apparatus of claim 5, wherein said
automated platform robotically moves two or more of said mutually
exclusive barcoded nucleic acids into said nucleic acid sequencing
chamber.
7. The nucleic acid sequencing apparatus of claim 5, wherein said
automated platform robotically moves two or more of said mutually
exclusive barcoded nucleic acids into a same flow cell of said one
or more flow cells.
8. The nucleic acid sequencing apparatus of claim 5, wherein said
sample is a food or an environmental sample.
9. The nucleic acid sequencing apparatus of claim 5, wherein said
sample is a non-food sample.
10. The nucleic acid sequencing apparatus of claim 9, wherein said
sample comprise blood, plasma, urine, tissue, feces, bone marrow,
saliva or cerebrospinal fluid.
11. The nucleic acid sequencing apparatus of claim 1, wherein the
apparatus comprises two or more flow cells.
12. The nucleic acid sequencing apparatus of claim 11, wherein one
or more of the flow cells are juxtaposed to one another.
13. The nucleic acid apparatus of claim 1, wherein said automated
platform is programmed to flush at least one of said one or more
flow cells.
14. The nucleic acid apparatus of claim 1, wherein said automated
platform is programmed to move a liquid sample of about 0.1 .mu.l
to about 1000 .mu.l from said nucleic acid library preparation
compartment into said sample input port.
15. The nucleic acid apparatus of claim 1, wherein said plurality
of nucleic acid comprises a nucleic acid from a microorganism.
16. The nucleic acid apparatus of claim 15, wherein said
microorganism is a bacteria, a fungus, a parasite, a protozoa, or a
virus.
17. The nucleic acid apparatus of claim 16, wherein said
microorganism is a virus.
18. The nucleic acid apparatus of claim 16, wherein said
microorganism is a bacteria.
19. The nucleic acid apparatus of claim 1, wherein said sequencing
reaction is a nanopore sequencing.
20. The nucleic acid apparatus of claim 1, wherein said sequencing
reaction is a sequencing-by-synthesis reaction.
Description
CROSS-REFERENCE
[0001] This application is a continuation of PCT Application No.
PCT/US18/67750, filed Dec. 27, 2018; which claims priority to
provisional patent application Ser. No. 62/611,846, filed on Dec.
29, 2017; provisional patent application Ser. No. 62/646,135 filed
on Mar. 21, 2018; and provisional patent application Ser. No.
62/730,288, filed on Sep. 12, 2018; all of which are incorporated
herein by reference in their entirety.
BACKGROUND
[0002] Food producers recall their products from the marketplace
when the products are mislabeled or when the food may present a
health hazard to consumers because the food is contaminated or has
caused a foodbome illness outbreak. Although these producers rely
on several existing monitoring programs for pathogens, natural
toxins, pesticides, and other contaminants about 48 million cases
of foodborne illness are still identified annually in the United
States alone--the equivalent of sickening 1 in 6 Americans each
year. And each year these illnesses result in an estimated 128,000
hospitalizations and 3,000 deaths. The threats are numerous and
varied, with symptoms ranging from relatively mild discomfort to
very serious, life-threatening illness. While the very young, the
elderly, and persons with weakened immune systems are at greatest
risk of serious consequences from most foodbome illnesses, some of
the microorganisms detected in foods pose grave threats to all
persons.
SUMMARY
[0003] In some aspects the disclosure provides a method comprising:
(a) deploying an assay to one or more food processing facilities;
(b) performing a sequencing reaction of a food sample or of an
environmental sample from said one or more food processing
facilities; (c) transmitting an electronic communication comprising
a data set associated with said sequencing reaction of said food
sample or of said environmental sample from said one or more food
processing facilities to a server; and (d) scanning, by a computer,
at least a fraction of said transmitted data set for one or more
polymorphic regions associated with a microorganism.
[0004] In some aspects the disclosure provides a method comprising:
(a) obtaining a plurality of nucleic acid sequences from a sample;
(b) scanning, by a computer, at least a fraction of said plurality
of said nucleic acid sequences for a plurality of nucleic acid
regions from one or more microorganisms selected from the group
consisting of: a microorganism of the Salmonella genus, a
microorganism of the Campylobacter genus, a microorganism of the
Listeria genus, and a microorganism of the Escherichia genus,
wherein said scanning characterizes said one or more microorganisms
with greater than 99.5% sensitivity.
[0005] In some aspects the disclosure provides a method comprising:
(a) sequencing a plurality of nucleic acid sequences from a food
sample or from an environmental sample associated with said food
sample for a period of time; and (b) performing an assay on said
food sample or said environment associated with said food sample if
said sequencing for said period of time identifies a threshold
level of nucleic acid sequences from a microorganism in said food
sample.
[0006] In some aspects the disclosure provides a method comprising:
(a) obtaining a first plurality of nucleic acid sequences from a
first sample of a food processing facility; (b) creating a data
file in a computer that associates one or more of said first
plurality of nucleic acid sequences with said food processing
facility; (c) obtaining a second plurality of nucleic acid
sequences from a second sample of said food processing facility;
and (d) scanning a plurality of sequences from said second
plurality of nucleic acid sequences for one or more sequences
associated with said food processing facility in (b).
[0007] In some aspects, the disclosure provides a method
comprising: (a) obtaining a first sample of a food processing
facility; (b) sequencing said first sample of said food processing
facility, thereby generating a first set of sequencing data from
said food processing facility; (c) obtaining a second sample of
said food processing facility; (d) sequencing said second sample of
said food processing facility, thereby generating a second set of
sequencing data from said food processing facility; and (e)
comparing said second set of sequencing data to said first set of
sequencing data; and (d) decontaminating said food processing
facility if said comparing identifies a pathogenic microorganism in
said food processing facility.
[0008] In some aspects, the disclosure provides a method
comprising: (a) obtaining a first plurality of nucleic acid
sequences from a first sample of a food processing facility; (b)
obtaining a second plurality of nucleic acid sequences from a
second food sample of said food processing facility; and (c)
performing sequence alignments in a computer between said first
plurality of nucleic acid sequences and said second plurality of
nucleic acid sequences thereby determining a similarity between
said first sample and said second sample from said food processing
facility.
[0009] In some aspects the disclosure provides a method comprising:
(a) adding a reagent to a plurality of nucleic acid molecules from
a food sample or from an environmental sample associated with said
food sample, thereby forming a modified plurality of nucleic acid
molecules, whereby said reagent: (i) modifies a structure of or
interacts with a plurality of nucleic acid molecules derived from
one or more dead microorganisms; and (ii) does not modify a
structure of a nucleic acid molecule derived from one or more live
microorganisms; thereby providing a modified plurality of nucleic
acid molecules; and (b) sequencing by a sequencing reaction said
modified plurality of nucleic acid molecules, thereby
distinguishing one or more live organisms from said food sample or
from said environmental sample associated with said food
sample.
[0010] In some aspects the disclosure provides a method comprising
performing a pore sequencing reaction on a plurality of nucleic
acid molecules from a food sample or from an environmental sample
associated with said food sample, whereby said pore sequencing
reaction distinguishes one or more nucleic acid molecules derived
from a dead microorganism from one or more nucleic acid molecules
derived from a live microorganism based on a methylation pattern or
another epigenetic pattern of said one or more nucleic acid
molecules derived from said dead microorganism.
[0011] In some aspects the disclosure provides a method comprising:
(a) obtaining a plurality of nucleic acid sequences of a food
sample or of an environmental sample from a food processing
facility; (b) performing a first assay in said plurality of nucleic
acid sequences of said food sample, whereby said assay predicts a
presence or predicts an absence of a microorganism in said food
sample; and (c) determining, based on said predicted presence or
said predicted absence of said microorganism of (b) whether to
perform a second assay, whereby a sensitivity of said second assay
is selected to determine a genus, a species, a serotype, a
sub-serotype, or a strain of said microorganism.
[0012] In some aspects, the disclosure provides a method
comprising: (a) detecting a presence or an absence of a
non-pathogenic microorganism in a sample; (b) predicting, by a
computer system, a presence or an absence of a pathogenic
microorganism in said sample based on said presence or said absence
of said non-pathogenic microorganism.
[0013] In some aspects, the disclosure provides a method
comprising: (a) detecting a presence or an absence of a
microorganism in a sample or in a facility associated with said
sample; and (b) predicting, by a computer system, a risk presented
by said facility based on said presence or said absence of said
microorganism.
[0014] In some aspects, the disclosure provides a method
comprising: (a) adding a first barcode to a first plurality of
nucleic acid sequences from a sample, thereby providing a first
plurality of barcoded nucleic acid sequences; and (b) performing a
first sequencing reaction on said first plurality of barcoded
nucleic acid sequences, wherein said sequencing reaction is
performed on a sequencing apparatus comprising a flow cell; (c)
adding a second barcode to a second plurality of nucleic acid
sequences from a second sample, thereby providing a second
plurality of barcoded nucleic acid sequences; and (d) performing a
second sequencing reaction on said second plurality of barcoded
nucleic acid sequences, wherein said second sequencing reaction is
performed on said sequencing apparatus comprising said flow cell,
thereby reusing said flow cell.
[0015] In some aspects, the disclosure provides a nucleic acid
sequencing apparatus comprising: (a) a nucleic acid library
preparation compartment comprising two or more chambers configured
to prepare a plurality of nucleic acids from a sample for a
sequencing reaction, wherein said compartment is operatively
connected to a nucleic acid sequencing chamber; (b) a nucleic acid
sequencing chamber, wherein said nucleic acid sequencing chamber
comprises: (i) one or more flow cells comprising a plurality of
pores or sequencing cartridges configured for the passage of a
nucleic acid strand, wherein two or more of the one or more flow
cells are juxtaposed to one another; and (c) an automated platform,
wherein said automated platform is programmed to robotically move a
sample from said nucleic acid library preparation compartment into
said nucleic acid sequencing chamber.
[0016] In some aspects, the disclosure provides a method
comprising: (a) adding a first molecular index to a first plurality
of nucleic acid sequences from a sample, thereby providing a first
plurality of indexed nucleic acid sequences; and (b) adding a
second molecular index to said first plurality of nucleic acid
sequences from said first sample, thereby providing a second
plurality of indexed nucleic acid sequences; and (c) adding a third
molecular index to said first plurality of nucleic acid sequences
from said first sample, thereby providing a third plurality of
indexed nucleic acid sequences; (d) performing a sequencing
reaction on said third plurality of nucleic acid sequences; and (e)
demultiplexing, by a computer system, said third plurality of
nucleic acid sequences comprising said first molecular index, said
second molecular index, and said third molecular index.
[0017] In some aspects, the present disclosure describes a device
capable of detecting and distinguishing microorganisms, including
food-borne pathogens. Food-borne pathogens may include any of the
numerous organisms that spread via food consumption, including
enterotoxic E. Coli and Salmonella bacteria. These microorganisms
can often survive in a wide variety of environments, including food
preparation surfaces and food processing equipment, as well as on
food itself. Tracing the origins and movements of food-borne
pathogen outbreaks often necessitates detecting one or more
microorganisms from a variety of sample types, including swabs,
food samples, and stool samples. Because outbreaks may be tied to a
particular strain of a microorganism, e.g. E. coli O157:H7, and
because its detection is critical to stopping its spread, detection
must be rapid and accurate.
[0018] A food-borne pathogen detection system may be designed for
numerous purposes, including deployable systems that can be moved
to any environment, e.g. a farm field, or grounded devices for
laboratory settings where collected samples are brought to the
device. In most cases, it is highly desirable to have a device that
is highly automated to reduce the number of steps that a user must
be involved in to increase the ease of usage and reduce the risk of
contamination or other sources of process failure.
INCORPORATION BY REFERENCE
[0019] All publications, patents, and patent applications mentioned
in this specification are herein incorporated by reference to the
same extent as if each individual publication, patent, or patent
application was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The novel features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the present invention will be obtained
by reference to the following detailed description that sets forth
illustrative embodiments, in which the principles of the invention
are utilized, and the accompanying drawings of which:
[0021] FIG. 1 (FIG. 1): illustrates the deploying of a sequencing
assay 101 to one or more food processing facilities, food testing
lab, or any other diagnostic lab 102 for performing a sequencing
reaction of a food sample or of an environmental sample from said
food processing facilities such as, for example, soil, water, air,
animal product(s), feed, manure, crop production, or any sample
associated with a manufacturing plant.
[0022] FIG. 2 (FIG. 2): illustrates a transmission of an electronic
communication comprising a data set associated with a sequencing
reaction from one or more food processing facilities to a
server.
[0023] FIG. 3 (FIG. 3): is a chart illustrating that a redundancy
in genetic markers decreases a false negative rate of a method of
the disclosure.
[0024] FIG. 4 (FIG. 4): illustrates a process for predictive risk
assessment based on a detection of a non-pathogenic
microorganism.
[0025] FIG. 5 (FIG. 5): is a heat map illustrating predictive
pathogen detection through machine learning.
[0026] FIG. 6 (FIG. 6): illustrates a process for predicting a
shelf-life of a food based on the detection of a microorganism.
[0027] FIG. 7 (FIG. 7): is a diagram illustrating the tunable
resolution of various assays.
[0028] FIG. 8 (FIG. 8): is a schematic illustrating various
serotypes of various microorganisms that can be detected by an
analysis of a plurality of nucleic acid sequences as described
herein and further validated with a serotyping assay.
[0029] FIG. 9 (FIG. 9): is a schematic illustrating one process for
distinguishing a live microorganism from a food or from an
environmental sample.
[0030] FIG. 10 (FIG. 10): illustrates a process for re-using flow
cells with distinct indexes.
[0031] FIG. 11 (FIG. 11): illustrates an automated sequencing
apparatus of the disclosure.
[0032] FIG. 12 (FIG. 12): illustrates a sequencing process with no
human touch points after enrichment.
[0033] FIG. 13 (FIG. 13): illustrates the PMAxx-induced removal of
free-floating DNA.
[0034] FIG. 14 (FIG. 14): illustrates a priming port in a flow
cell.
[0035] FIG. 15 (FIG. 15): illustrates a dispensing of a loading
library on a flow cell.
[0036] FIG. 16 (FIG. 16): illustrates the simultaneous targeting of
multiple pathogens.
[0037] FIG. 17 (FIG. 17): illustrates the in silico prediction of
primer sensitivity/specificity.
[0038] FIG. 18 (FIG. 18): illustrates the reuse of MinION/GridION
flow cells.
[0039] FIG. 19 (FIG. 19): illustrates the number of reads per
sample during reuse of MinION/GridION flow cells.
[0040] FIG. 20 (FIG. 20): illustrates the performance of the
disclosed automated handling system on samples spiked with 10
different Salmonella serotypes (Enteritidis, Thyphimurium, I
4_[5]_12: i:, Newport, Javiana, Infantis, Montevideo, Heidelberg,
Muenchen).
[0041] FIG. 21 (FIG. 21): illustrates a principal component
analysis to chicken wing chicken data sets.
[0042] FIG. 22 (FIG. 22): illustrates a principal component
analysis to ground chicken data sets.
[0043] FIG. 23 (FIG. 23): illustrates periodic and nonperiodic
barcode designs.
[0044] FIG. 24 (FIG. 24): illustrates a principle component
analysis of Listeria sequences identifying clusters of closely
related bacteria which likely originated from the same source.
[0045] FIG. 25 (FIG. 25): illustrates an exemplary automatable
nanopore flow cell suitable for use with the methods according to
this disclosure.
[0046] FIG. 26 (FIG. 26): illustrates an exemplary automatable
nanopore flow cell with an alternative sample input port plug as
described herein.
[0047] FIG. 27 (FIG. 27): illustrates the phase-separation
microbial concentration method described in Example 23.
DETAILED DESCRIPTION
[0048] Food safety is a complex issue that has an impact on
multiple segments of society. Usually a food is considered to be
adulterated if it contains: (1) a poisonous or otherwise harmful
substance that is not an inherent natural constituent of the food
itself, in an amount that poses a reasonable possibility of injury
to health, or (2) a substance that is an inherent natural
constituent of the food itself; is not the result of environmental,
agricultural, industrial, or other contamination; and is present in
an amount that ordinarily renders the food injurious to health. The
first includes, for example, a pathogenic bacterium, fungus,
parasite or virus, if the amount present in the food may be
injurious to health. An example of the second is the tetrodotoxin
that occurs naturally in some organs of some types of pufferfish
and that ordinarily will make the fish injurious to health. In
either case, foods adulterated with these agents are generally
deemed unfit for consumption.
[0049] Many different disease-causing microorganisms can
contaminate foods, and there are many different foodborne
infections. Although our scientific understanding of pathogenic
microorganisms and their toxins is continually advancing, some of
the most common microorganisms associated with foodborne illnesses
include microorganisms of the Salmonella, Campylobacter, Listeria,
and Escherichia genus.
[0050] Salmonella for example is widely dispersed in nature. It can
colonize the intestinal tracts of vertebrates, including livestock,
wildlife, domestic pets, and humans, and may also live in
environments such as pond-water sediment. It is spread through the
fecal-oral route and through contact with contaminated water.
(Certain protozoa may act as a reservoir for the organism). It may,
for example, contaminate poultry, red meats, farm-irrigation water
(thereby contaminating produce in the field), soil and insects,
factory equipment, hands, and kitchen surfaces and utensils.
[0051] Campylobacter jejuni is estimated to be the third leading
bacterial cause of foodborne illness in the U.S. The symptoms this
bacterium causes generally last from 2 to 10 days and, while the
diarrhea (sometimes bloody), vomiting, and cramping are unpleasant,
and they usually go away by themselves in people who are otherwise
healthy. Raw poultry, unpasteurized ("raw") milk and cheeses made
from it, and contaminated water (for example, unchlorinated water,
such as in streams and ponds) are major sources, but C. jejuni also
occurs in other kinds of meats and has been found in seafood and
vegetables.
[0052] Although the number of people infected by foodborne Listeria
is comparatively small, this bacterium is one of the leading causes
of death from foodborne illness. It can cause two forms of disease.
One can range from mild to intense symptoms of nausea, vomiting,
aches, fever, and, sometimes, diarrhea, and usually goes away by
itself. The other, more deadly, form occurs when the infection
spreads through the bloodstream to the nervous system (including
the brain), resulting in meningitis and other potentially fatal
problems.
[0053] Escherichia microorganisms are also diverse in nature. For
instance, at least four groups of pathogenic Escherichia coli have
been identified: a) Enterotoxigenic Escherichia coli (ETEC), b)
Enteropathogenic Escherichia colt (EPEC), c) Enterohemorrhagic
Escherichia coli (EHEC), and Enteroinvasive Escherichia colt
(EIEC). While ETEC is generally associated with traveler's diarrhea
some members of the EHEC group, such as E. coli O157:H7, can cause
bloody diarrhea, blood-clotting problems, kidney failure, and
death. Thus, it is important to be able not only to identify
individual microorganism, but also to distinguish them.
[0054] Provided herein are methods and apparatus for the
identification of pathogenic and non-pathogenic microorganisms in
food and environmental samples. The disclosure solves existing
challenges encountered in identifying food borne pathogens,
including pathogens of the Salmonella, Campylobacter, Listeria, and
Escherichia genus in a timely and efficient manner. The disclosure
also provides methods for differentiating a transient versus a
resident pathogen, correlating presence of non-pathogenic with
pathogenic microorganisms, and distinguishing live versus dead
microorganisms by sequencing, amongst others.
[0055] As used herein, the term "food processing facility" includes
facilities that manufacture, process, pack, or hold food in any
location globally. A food processing facility can, for example,
determine the location and source of an outbreak of food-borne
illness or a potential bioterrorism incident.
[0056] As used herein, the term "food" includes any nutritious
substance that people or animals eat or drink, or that plants
absorb, in order to maintain life and growth. Non-limiting examples
of foods include red meat, poultry, fruits, vegetables, fish, pork,
seafood, dairy products, eggs, egg shells, raw agricultural
commodities for use as food or components of food, canned foods,
frozen foods, bakery goods, snack food, candy (including chewing
gum), dietary supplements and dietary ingredients, infant formula,
beverages (including alcoholic beverages and bottled water), animal
feeds and pet food, and live food animals. The term "environmental
sample," as used herein, includes all food contact substances or
items from a food processing facility. The term environmental
sample includes a surface swab of a food contact substance, a
surface rinse of a food contact substance, a food storage
container, a food handling equipment, a piece of clothing from a
subject in contact with a food processing facility, or another
suitable sample from a food processing facility. The term "sample"
as used herein, generally refers to any sample that can be
informative of an environment or a food, such as a sample that
comprises soil, water, water quality, air, animal production, feed,
manure, crop production, manufacturing plants, environmental
samples or food samples directly. The term "sample" may also refer
to other non-food sample, such as samples derived from a subject,
such as comprise blood, plasma, urine, tissue, faces, bone marrow,
saliva or cerebrospinal fluid. Such samples may be derived from a
hospital or a clinic.
[0057] As used herein, the term "subject," can refer to a human or
to another animal. An animal can be a mouse, a rat, a guinea pig, a
dog, a cat, a horse, a rabbit, and various other animals. A subject
can be of any age, for example, a subject can be an infant, a
toddler, a child, a pre-adolescent, an adolescent, an adult, or an
elderly individual.
[0058] As used herein, the term "disease," generally refers to
conditions associated with the presence of a microorganism in a
food, e.g., outbreaks or incidents of foodborne disease.
[0059] The term "nucleic acid" or "polynucleotide," as used herein,
refers to a polymeric form of nucleotides of any length, either
ribonucleotides or deoxyribonucleotides. Polynucleotides include
sequences of deoxyribonucleic acid (DNA), ribonucleic acid (RNA),
or DNA copies of ribonucleic acid (cDNA).
[0060] The term "polyribonucleotide," as used herein, generally
refers to polynucleotide polymers that comprise ribonucleic acids.
The term also refers to polynucleotide polymers that comprise
chemically modified ribonucleotides. A polyribonucleotide can be
formed of D-ribose sugars, which can be found in nature, and
L-ribose sugars, which are not found in nature.
[0061] The term "polypeptides," as used herein, generally refers to
polymer chains comprised of amino acid residue monomers which are
joined together through amide bonds (peptide bonds). The amino
acids may be the L-optical isomer or the D-optical isomer.
[0062] The term "barcode," as used herein, generally refers to a
label, or identifier, that conveys or is capable of conveying
information about one or more nucleic acid sequences from a food
sample or from an environmental sample associated with said food
sample. A barcode can be part of a nucleic acid sequence. A barcode
can be independent of a nucleic acid sequence. A barcode can be a
tag attached to a nucleic acid molecule. A barcode can have a
variety of different formats. For example, barcodes can include:
polynucleotide barcodes; random nucleic acid and/or amino acid
sequences; and synthetic nucleic acid and/or amino acid sequences.
A barcode can be added to, for example, a fragment of a
deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sample
before, during, and/or after sequencing of the sample. Barcodes can
allow for identification and/or quantification of individual
sequencing-reads. Examples of such barcodes and uses thereof, as
may be used with methods, apparatus and systems of the present
disclosure, are provided in U.S. Patent Pub. No. 2016/0239732,
which is entirely incorporated herein by reference. In some
instances, as described herein, a "molecular index" can either be a
barcode itself or it can be a building block, i.e., a component or
portion of a larger barcode.
[0063] The term "sequencing," as used herein, generally refers to
methods and technologies for determining the sequence of nucleotide
bases in one or more nucleic acid polymers, i.e., polynucleotides.
Sequencing can be performed by various systems currently available,
such as, without limitation, a sequencing system by Illumina.RTM.,
Pacific Biosciences (PacBio.RTM.), Oxford Nanopore.RTM., Genia
(Roche) or Life Technologies (Ion Torrent.RTM.). Alternatively or
in addition, sequencing may be performed using nucleic acid
amplification, polymerase chain reaction (PCR) (e.g., digital PCR,
quantitative PCR, or real time PCR), or isothermal amplification.
Such systems may provide a plurality of raw data corresponding to
the genetic information associated with a food sample or an
environmental sample. In some examples, such systems provide
nucleic acid sequences (also "reads" or "sequencing reads" herein).
The term also refers to epigenetics which is the study of heritable
changes in gene function that do not involve changes in the DNA
sequence. A read may include a string of nucleic acid bases
corresponding to a sequence of a nucleic acid molecule that has
been sequenced.
Analyzing Sequences Requested by a Customer
[0064] Many food poisoning outbreaks have been associated with
pathogenic microorganisms including pathogens of the Salmonella,
Campylobacter, Listeria, and Escherichia genus. Examples of foods
that have been associated with such outbreaks include milk,
cheeses, vegetables, meats (notably beef and poultry), fish,
seafood, and many others. Potential contamination sources for
various pathogens include raw materials, food workers, incoming
air, water, and food processing environments. Among those,
post-processing contamination at food-contact surfaces in a food
processing facility poses a great threat to product
contamination.
[0065] There are many challenges in ensuring the safety of our food
supply. Some of these challenges include changes in a food
processing environment that lead to food contamination, such as the
introduction of a new lot of contaminated raw products. Other
challenges include changes in food production and supply, which
include importing and exporting foods from different jurisdictions,
which may have distinct standards to assess a risk associated with
a food. In addition, new and emerging bacteria strains, toxins, and
antibiotic resistance may not be detected by traditional serotyping
or PCR methods of detection.
[0066] In some aspects, the disclosure provides a method for the
identification of a microorganism associated with a food or with a
food processing facility. In some aspects the method comprises
deploying an assay to one or more food processing facilities;
performing a sequencing reaction of a food sample or of an
environmental sample from said one or more food processing
facilities; transmitting an electronic communication comprising a
data set associated with said sequencing reaction of said food
sample or of said environmental sample from said one or more food
processing facilities to a server; and scanning, by a computer, at
least a fraction of said transmitted data set for one or more genes
associated with a microorganism. In some embodiments, the method
comprises deploying an assay to one or more food processing
facilities; receiving via a server an electronic communication
comprising a data set associated with a sequencing reaction,
wherein the sequencing reaction characterizes a food sample or of
an environmental sample from said one or more food processing
facilities; and scanning, by a computer, at least a fraction of
said transmitted data set for one or more genes associated with a
microorganism.
[0067] In some aspects, the disclosure provides a method for the
identification of a microorganism associated with a food or with a
food processing facility. In some aspects the method comprises
receiving an assay at one or more food processing facilities;
performing a sequencing reaction of a food sample or of an
environmental sample from said one or more food processing
facilities; transmitting an electronic communication comprising a
data set associated with said sequencing reaction of said food
sample or of said environmental sample from said one or more food
processing facilities to a server; and scanning, by a computer, at
least a fraction of said transmitted data set for one or more genes
associated with a microorganism. In some embodiments, the method
comprises receiving an assay at one or more food processing
facilities; receiving via a server an electronic communication
comprising a data set associated with a sequencing reaction,
wherein the sequencing reaction characterizes a food sample or of
an environmental sample from said one or more food processing
facilities; and scanning, by a computer, at least a fraction of
said transmitted data set for one or more genes associated with a
microorganism.
[0068] In some aspects, the disclosure provides a method for the
identification of a microorganism associated with a food or with a
food processing facility. In some aspects the method comprises
deploying an assay to one or more food processing facilities;
receiving an electronic communication comprising a data set
associated with a sequencing reaction of a food sample or an
environmental sample from said one or more food processing
facilities to a server; and scanning, by a computer, at least a
fraction of said transmitted data set for one or more genes
associated with a microorganism. In some embodiments, the method
comprises deploying an assay to one or more food processing
facilities; receiving via a server an electronic communication
comprising a data set associated with a sequencing reaction,
wherein the sequencing reaction characterizes a food sample or of
an environmental sample from said one or more food processing
facilities; and scanning, by a computer, at least a fraction of
said transmitted data set for one or more genes associated with a
microorganism.
[0069] In some instances, the scanning scans fewer than 1%, fewer
than 0.1%, fewer than 0.001% of said transmitted data set for one
or more genes associated with said microorganism. Said scanning can
be performed to identify a variety of polymorphic gene regions
(comprising SNP's, RFLP's, STRs, VNTR's, hypervariable regions,
minisatellites, dinucleotide repeats, trinucleotide repeats,
tetranucleotide repeats, simple sequence repeats, indels, and
insertion elements) associated with a wide diversity of
microorganisms. The variety of polymorphic regions to be searched
for can be determined by creating a large database of sequences
from dozens, hundreds and thousands of food and environmental
samples. For instance, a database of such polymorphic regions can
be constructed by performing sequencing reactions on at least
5,000, at least 10,000, at least 15,000, at least 20,000, at least
25,000, at least 30,000, at least 35,000, at least 40,000, at least
45,000, at least 50,000 different food or environmental samples.
The sequences obtained can be used to compile information in a
database that includes: a) the composition of each sample; and b)
the presence or absence of a variety of pathogenic and
non-pathogenic organisms associated on each sample. In addition to
containing information about various types of genus and species,
such databases comprise data from polymorphic gene regions of a
variety of strains that are variants of a single species. For
example, a plurality of sequences in the database might correspond
to one or more serovars, morphovars, biovars, or other strain
specific information.
[0070] A variety of sequencing techniques, such as a pore
sequencing reaction, a next generation sequencing reaction, a
shotgun next generation sequencing, or Sanger sequencing can be
used to create a collection of polymorphic regions. In some
instances, said sequencing reaction is a pore sequencing reaction
and said pore sequencing reaction distinguishes an epigenetic
pattern on a nucleic acid from said food sample or from said
environmental sample.
[0071] In some cases, said microorganism may be pre-selected by a
customer. A customer can be an individual or an entity, such as one
or more food processing facilities. For example, a customer can be
a food packaging facility; a food distribution center; a food
storage center; a facilities handling meat, poultry, egg, or
another edible product; a farm; a retail food establishment; a
fishing vessel; or another type of facility that also manufactures,
processes, packs, or holds foods for any period of time.
[0072] A customer may pre-select a microorganism of interest to be
identified with any of the methods disclosed herein. For example,
raw or undercooked ground beef and beef products are vehicles often
implicated in E. coli O157:H7 outbreaks. Produce, including bagged
lettuce, spinach, and alfalfa sprouts, are also increasingly being
implicated in E. coli O157:H7 outbreaks. A food processing facility
producing raw meats or other produce associated with E. coli
O157:H7 may be a customer that pre-selects E. coli as a
microorganism for analysis. A customer may pre-select one or more
types of microorganisms for analysis. A microorganism can be one or
more of types of bacteria, fungus, parasites, protozoa, and
viruses.
[0073] Non-limiting examples of bacteria that can be pre-selected
by a customer and detected with the methods of the disclosure
include: bacteria in the Escherichia genus, including
enterotoxigenic Escherichia coli (ETEC), enteropathogenic
Escherichia coli (EPEC), enterohemorrhagic Escherichia coli (EHEC),
and enteroinvasive Escherichia coli (EIEC); bacteria of the
Salmonella genus; bacteria of the Campylobacter genus; bacteria of
the Listeria genus; bacteria of the Yersinia genus; bacteria of the
Shigella genus; bacteria of the Vibrio genus; bacteria of the
Coxiella genus; bacteria of the Mycobacterium genus; bacteria of
the Brucella genus; bacteria of the Vibrio genus; bacteria of the
Cronobacter genus; bacteria of the Aeromonas genus; bacteria of the
Plesiomonas genus; bacteria of the Clostridium genus; bacteria of
the Staphylococcus genus; bacteria of the Bacillus genus; bacteria
of the Streptococcus genus; bacteria of the Clostridium genus; and
bacteria of the Enterococcus genus.
[0074] A microorganism can be a virus. Non-limiting examples of
viruses that can be pre-selected by a customer and detected with
the methods of the disclosure include: noroviruses, Hepatitis A
virus, Hepatitis E virus, rotavirus.
[0075] The performing of a sequencing reaction of a food sample or
of an environmental sample from said one or more food processing
facilities often generates a plurality of nucleic acids sequences
that contain redundant information or information associated with
genes that are not from a microorganism. In some aspects, the
disclosed methods empower efficient data analysis by facilitating
the targeted analysis of a smaller data set. The generated data
could be in the range of Kb, Mb, Gb, Tb or more per analyzed
sample. In some aspects, said scanning scans fewer than 1/10, fewer
than 1/20, fewer than 1/30, fewer than 1/40, fewer than 1/50, fewer
than 1/60, fewer than 1/70, fewer than 1/80, fewer than 1/90, fewer
than 1/100, fewer than 1/200, fewer than 1/300, fewer than 1/400,
fewer than 1/500, fewer than 1/600, fewer than 1/700, fewer than
1/800, fewer than 1/900, fewer than 1/1,000, fewer than 1/10,000,
or fewer than 1/100,000 of a data set, such as a transmitted data
set for one or more genes associated with a microorganism. In some
aspects, said scanning scans at least a fraction of said
transmitted data set for one or more genes associated with two or
more, three or more, four or more, five or more, six or more, seven
or more, eight or more, nine or more, ten or more microorganisms or
another suitable number. In some instances, said scanning comprises
scanning said transmitted data set for one or more polymorphic gene
regions. In some instances, said one or more polymorphic regions
comprise one or more single nucleotide polymorphisms (SNP's), one
or more restriction fragment length polymorphisms (RFLP's), one or
more short tandem repeats (STRs), one or more variable number of
tandem repeats (VNTR's), one or more hypervariable regions, one or
more minisatellites, one or more dinucleotide repeats, one or more
trinucleotide repeats, one or more tetranucleotide repeats, one or
more simple sequence repeats, one or more indel, or one or more
insertion elements. In some instances said one or more polymorphic
regions comprise one or more single nucleotide polymorphisms
(SNP's). A data set associated with a sequencing reaction of a food
sample or of an environmental sample can be transmitted to a server
and scanned by a computer.
[0076] In some cases, a method can detect a microorganism selected
from the group consisting of: a microorganism of the Salmonella
genus, a microorganism of the Campylobacter genus, a microorganism
of the Listeria genus, and a microorganism of the Escherichia
genus. The detected microorganisms may be of any serotype and a
scanning, by a computer, of one or more genes associated with a
microorganism may detect a microorganism independently of its
serotype.
[0077] In some cases, a sequencing reaction of a food sample, an
environmental sample, or another sample is a pore sequencing
reaction, such as an Oxford Nanopore.RTM. sequencing reaction. In
some instances, at least one barcode is added to one or more
nucleic acid polymers derived from a food sample, from an
environmental sample, or from another sample prior to performing
said sequencing reaction. In some instances, a plurality of
mutually exclusive barcodes are added to a plurality of food
processing facilities, thereby creating a barcode identifier that
can be associated with each food processing facility. For instance,
a barcoded sequencing read comprising sequences from a pathogenic
microorganism can be associated with a food or processing facility.
In some aspects, a method disclosed herein further comprises
creating, in a computer, a data file that associates said at least
one barcode with a source of said food sample, of said
environmental sample, or of another sample.
[0078] In some aspects, the disclosed methods comprise computer
systems or devices utilizing computer systems that are programmed
to implement methods of the disclosure. FIG. 1 illustrates the
deploying of a sequencing assay 101 to one or more food processing
facilities 102, food testing lab, or any other diagnostic lab and
performing a sequencing reaction of a food sample or of an
environmental sample from said one or more food processing
facilities 102. The food processing facility, food testing lab, or
any other diagnostic lab may have one or more computer systems that
can be used to transmit the results of the sequencing reads to a
server, either on premise or remotely deployed cloud environment.
FIG. 2 illustrates a transmission of an electronic communication
comprising a data set associated with a sequencing reaction from
one or more food processing facilities, food testing labs, or any
other diagnostic labs to a server.
[0079] The raw sequence data collected from the sequencing reaction
includes a large set of data that includes all individual sequences
as well as the quality at each base. From this large data set, the
Clear Labs bioinformatics pipeline extracts a final report that is
orders of magnitudes smaller. The final report (e.g. electronic
communication) is essentially limited to the presence or absence of
an organism of interest, for instance pathogens, and a further
classification of the organism in terms of serotypes, strains, or
other subclassifications. The collected data not used in the report
comprises the following:
[0080] (a) Read quality: The raw sequences include information on
the quality of the sequences per base. The quality scores can be
used in a Bayesian model where classifications are statistically
sensitive to these quality scores. Furthermore the quality scores
can reveal more on possible relations that content of samples have
with the accuracy of sequencing platform.
[0081] (b) Sequence time: The raw sequences also include
information on the time when the sequence was read by the
sequencer. The number of sequences form the same source as a
function of time can reveal a lot more information than we
currently have. In addition, these time data, can be useful in
generating reports for all or some of the samples earlier than it
is currently done.
[0082] (c) Trimmed portions of sequences: During demultiplexing of
the sequences initial and terminal portions of those sequences are
trimmed. Those portions include adapters, index barcodes, and
primers. The main data extracted from the trimmed portions,
identifies which sample the sequence belonged to. This decision
however is influenced by sequencing errors, and special properties
of the involved sequences. The information on accuracy of this
decision, and other factors is lost with trimming. Moreover the
quality of these portions can be used as an indicator for the
quality of the entire sequence.
[0083] (d) Clustering: An important step in the pipeline involves
clustering sequences that are close enough to each other and
representing all the sequences within a cluster by a consensus
sequence. This reduces the data significantly and make is easier to
classify these sequences. However these differences, even if
minute, carry information that gets lost with clustering.
Clustering with more stringent criteria, or no clustering can lead
into higher resolution and perhaps finer classification.
[0084] A computer system 201 can be programmed or otherwise
configured to process and transmit a data set from a food
processing facility, food testing labs, or any other diagnostic
labs. The computer system 201 includes a central processing unit
(CPU, also "processor" and "computer processor" herein) 204, which
can be a single core or multi core processor, or a plurality of
processors for parallel processing. The computer system 201 also
includes memory or memory location 205 (e.g., random-access memory,
read-only memory, flash memory), electronic storage unit 206 (e.g.,
hard disk), communication interface 202 (e.g., network adapter) for
communicating with one or more other systems, such as for instance
transmitting a data set associated with said sequencing reads, and
peripheral devices 204, such as cache, other memory, data storage
and/or electronic display adapters. The memory 205, storage unit
206, interface 202 and peripheral devices 203 are in communication
with the CPU 204 through a communication bus (solid lines), such as
a motherboard. The storage unit 206 can be a data storage unit (or
data repository) for storing data. For instance, in some cases, the
data storage unit 206 can store a plurality of sequencing reads and
provide a library of sequences associated with one or more strains
from one or more microorganisms associated with a food processing
facility, food testing labs, or any other diagnostic labs.
[0085] The computer system 201 can be operatively coupled to a
computer network ("network") 207 with the aid of the communication
interface 202. The network 207 can be the Internet, an internet
and/or extranet, or an intranet and/or extranet that is in
communication with the Internet. The network 207 in some cases is a
telecommunication and/or data network. The network 207 can include
one or more computer servers, which can enable distributed
computing, such as cloud computing. The network 207, in some cases
with the aid of the computer system 201, can implement a
peer-to-peer network, which may enable devices coupled to the
computer system 201 to behave as a client or a server.
High Sensitivity Detection of Microorganisms
[0086] Some families of microorganisms comprise both harmless and
highly pathogenic bugs. The Escherichia family of pathogens, for
example, comprise lethal and harmless strains of E. coli. Thus it
is not only relevant to be able to identify a pathogen in a sample,
but it is also relevant to be able to characterize it with high
sensitivity. In some aspects, the disclosure provides a method
comprising obtaining a plurality of nucleic acid sequences from a
food sample, from an environment associated with said food sample
or from another sample, such as non-food derived samples from
clinical sources, including blood, plasma, urine, tissue, faces,
bone marrow, saliva or cerebrospinal fluid samples; scanning, by a
computer, at least a fraction of said plurality of said nucleic
acid sequences for a plurality of nucleic acid regions from one or
more microorganisms selected from the group consisting of: a
microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, and a
microorganism of the Escherichia genus, wherein said scanning
characterizes said one or more microorganisms with greater than 98%
sensitivity, greater than 98.5% sensitivity, greater than 99%
sensitivity, greater than 99.5% sensitivity, or greater than 99.9%
sensitivity. In some aspects, said scanning characterizes said one
or more microorganisms with greater than 98% specificity, greater
than 98.5% specificity, greater than 99% specificity, greater than
99.5% specificity, or greater than 99.9% specificity. Sensitivity
can be a measure of a microorganism that is correctly identified
(e.g. the percentage of a microorganism that can be correctly
identified based on sequencing read analyses). Specificity (also
called the true negative rate) measures the proportion of negatives
that are correctly identified as such (e.g. the percentage of food
samples or environmental samples that are correctly identified as
not having the microorganism therein). In some instances, said
method can distinguish a genetic variant or subtype of a
microorganism (e.g., one or more bacterial strains).
[0087] In some instances said plurality of nucleic acid sequences
comprise complementary DNA (cDNA) sequences, ribonucleic acid (RNA)
sequences, genomic deoxyribonucleic acid (gDNA) sequences or a
mixture of cDNA, RNA, and gDNA sequences. In some instances, the
high sensitivity of the disclosed method, the high specificity of
the disclosed method, or both, can be accomplished by scanning said
plurality of said nucleic acid sequences for one or more
polymorphic gene regions associated with said microorganisms. In
some instances, said one or more polymorphic regions is selected
from the group consisting of one or more single nucleotide
polymorphisms (SNP's), one or more restriction fragment length
polymorphisms (RFLP's), one or more short tandem repeats (STRs),
one or more variable number of tandem repeats (VNTR's), one or more
hypervariable regions, one or more minisatellites, one or more
dinucleotide repeats, one or more trinucleotide repeats, one or
more tetranucleotide repeats, one or more simple sequence repeats,
one or more indel, or one or more insertion elements. In some
instances, said scanning compares a scanned polymorphism with a
library of sequences comprising sequences from dozens, hundreds, or
thousands of unique strains of a microorganism. The higher
sensitivity is achieved by comparing the sequence information of
the target region that can discriminate different microorganisms
through the lens of SNPs, indels or other non-universal target
specific markers that are only present within the genome of target
micromicroorganisms.
[0088] In some aspects, an analysis of a redundancy in genetic
markers increases a specificity and sensitivity of a method
disclosed herein. FIG. 3 is a chart illustrating that a redundancy
in genetic markers decreases a false negative rate of a method of
the disclosure and increases its sensitivity as compared to PCR
based methods. As shown in FIG. 3, three commercially available
q/PCR based pathogen detection kits revealed that they would not
detect all known Salmonella or Listeria genomes. 301 illustrates
percentages of Salmonella detection by existing commercial kits.
302 illustrates percentages of Listeria detection by existing
commercial kits.
[0089] A scanning of a plurality of nucleic acid regions within
said plurality of nucleic acid sequences can characterize said one
or more microorganisms with a desired specificity, sensitivity, or
both. In some aspects, a scanning of no more than 0.001%, 0.01%,
0.1%, 1%, 5%, 10%, 25%, 50%, 90%, 99%, 100% or any number in
between of nucleic acid regions within said plurality of nucleic
acid sequences characterizes said one or more microorganisms with
greater than 90%, 95%, 98%, 99%, 99.9%, 99.99% and 99.999%
sensitivity. In some aspects, the method has fewer than 2%, fewer
than 1.5%, fewer than 1.0%, fewer than 0.5%, or fewer than 0.1% of
a false positive identification rate. In some aspects, a scanning
of no more than 1% of a whole genome can characterize said
microorganism.
[0090] In some instances, the high sensitivity and specificity of
the disclosed methods are independent of a serotype of the
microorganism. For instance, a scanning of a plurality of nucleic
acid regions can identify a microorganism of the Salmonella genus
that has a serotype selected from the group consisting of:
Enteritidis, Typhimurium, Newport, Javiana, Infantis, Montevideo,
Heidelberg, Muenchen, Saintpaul, Oranienburg, Braenderup, Paratyphi
B var. L(+) Tartrate+, Agona, Thompson, and Kentucky; a
microorganism of the Escherichia genus has a serotype selected from
the group consisting of: O103, O111, O121, O145, O26, O45, and
O157; a microorganism of the Listeria genus that has a serotype
selected from the group consisting of: 2a, 1/2b, 1/2c, 3a, 3b, 3c,
4a, 4b, 4ab, 4c, 4d, and 4e; a microorganism of the Campylobacter
genus with the C. jejuni, C. lari, or C. coli serotype and
others.
[0091] A non-pathogenic strain of Citrobacter, namely Citrobacter
sedlakii, expresses the Escherichia coli O157:H7 antigen. This is
usually associated with a false positive detection of E. coli in a
sample. Typically, when Citrobacter is erroneously classified as E.
coli, a food lot may be unnecessarily disposed of and a food
processing facility may be erroneously classified as a contaminated
facility. In some aspects, the high sensitivity of the disclosed
methods can be used to distinguish a microorganism from the
Escherichia genus from a microorganism of the Citrobacter genus. In
some instances, the disclosure provides a method comprising:
scanning, by a computer, a plurality of sequencing reads from a
food sample or from an environment associated with said food
sample, whereby said scanning distinguishes a microorganism of a
Citrobacter genus from a microorganism of an Escherichia genus by
identifying one or more single nucleotide polymorphisms that are
associated with either said Citrobacter genus or said Escherichia
genus. Other examples include E. coli O157:H7 assay cross-reacting
with E. coli O55 (which is not an STEC). Also some assays deliver
false positives against E. coli O104 (which is not an STEC).
Citrobacter is also a long-understood challenge for the some
systems E. coli O157:H7.
[0092] In many cases, disease outbreaks require a rapid response,
often including multijurisdictional coordination. In some aspects,
the disclosure provides methods for the rapid identification of a
microorganism from a food sample. In some instances, the disclosure
provides a method for sequencing a plurality of nucleic acid
sequences from a food sample, from an environmental sample
associated with said food sample or from another sample (such as a
clinically derived sample) for a period of time; and performing an
assay on said food sample or said environment associated with said
food sample if said sequencing for said period of time identifies a
threshold level of nucleic acid sequences from a microorganism in
said food sample. In some instances said period of time is less
than 12 hours, less than 6 hours, less than 4 hours, less than 2
hours, less than 1 hour, less than 30 minutes, less than 20
minutes, less than 15 minutes or another suitable time. FIG. 4 is a
schematic illustrating a sequencing of a plurality of nucleic acid
sequences from a food sample for a period of time and the
advantages of performing an assay on said food sample if said
sequencing for said period of time identifies a threshold level of
nucleic acid sequences from a microorganism in said food
sample.
Pathogenic Microorganisms
[0093] In general, a microorganism that can injure its host, e.g.,
by competing with it for metabolic resources, destroying its cells
or tissues, or secreting toxins can be considered a pathogenic
microorganism. Examples of classes of pathogenic microorganisms
include viruses, bacteria, mycobacteria, fungi, protozoa, and some
helminths. In some aspects, the disclosure provides methods for
detecting one or more microorganisms from a food sample or from an
environment associated with said food sample--such as from a table,
a floor, a boot cover, an equipment of a food processing
facility--or from a food related sample that comprise soil, water,
water quality, air, animal production, feed, manure, crop
production, manufacturing plants, environmental samples, or
non-food derived samples, such as samples from clinical sources
that comprise blood, plasma, urine, tissue, faces, bone marrow,
saliva or cerebrospinal fluid by analyzing a plurality of nucleic
acid sequencing reads from such samples.
[0094] Many pathogenic microorganisms are further subdivided into
serotypes, which can differentiate strains by their surface and
antigenic properties. For instance Salmonella species are commonly
referred to by their serotype names. For example, Salmonella
enterica subspecies enterica is further divided into numerous
serotypes, including S. enteritidis and S. typhimurium. In some
aspects, the methods of the disclosure can distinguish between such
subspecies of a variety of Salmonella by analyzing their nucleic
acid sequences.
[0095] Escherichia coli (E. coli) bacteria normally live in the
intestines of people and animals. Many E. coli are harmless and in
some aspects are an important part of a healthy human intestinal
tract. However, many E. coli can cause illnesses, including
diarrhea or illness outside of the intestinal tract and should be
distinguished from less pathogenic strains. In some aspects, the
methods of the disclosure can distinguish between various
subspecies of a variety of Escherichia bacteria by analyzing their
nucleic acid sequences.
[0096] Listeria is a harmful bacterium that can be found in
refrigerated, ready-to-eat foods (meat, poultry, seafood, and
dairy--unpasteurized milk and milk products or foods made with
unpasteurized milk), and produce harvested from soil contaminated
with, for example, L. monocytogenes. Many animals can carry this
bacterium without appearing ill, which increases the challenges in
identifying the pathogen derived from a food source. In addition,
some species of Listeria can grow at refrigerator temperatures
where most other foodborne bacteria do not, another factor that
increases the challenges of identifying Listeria. When eaten,
Listeria may cause listeriosis, an illness to which pregnant women
and their unborn children are very susceptible. In some aspects,
the methods of the disclosure can distinguish between various
subspecies of a variety of Listeria bacteria by analyzing their
nucleic acid sequences.
[0097] Campylobacter jejuni is estimated to be the third leading
bacterial cause of foodborne illness in the United States. Raw
poultry, unpasteurized ("raw") milk and cheeses made from it, and
contaminated water (for example, unchlorinated water, such as in
streams and ponds) are major sources of Campylobacter, but it also
occurs in other kinds of meats and has been found in seafood and
vegetables. In some aspects, the methods of the disclosure can
distinguish between various subspecies of a variety of
Campylobacter bacteria by analyzing their nucleic acid
sequences.
[0098] Non-limiting examples of pathogenic microorganisms that can
be detected with the methods of the disclosure include: pathogenic
Escherichia coli group, including Enterotoxigenic Escherichia coli
(ETEC), Enteropathogenic Escherichia coli (EPEC), Enterohemorrhagic
Escherichia coli (EHEC), Enteroinvasive Escherichia coli (EIEC),
Salmonella spp., Campylobacter jejuni, Listeria, Yersinia
enterocolitica, Shigella spp., Vibrio parahaemolyticus, Coxiella
burnetii, Mycobacterium bovis, Brucella spp., Vibrio cholera,
Vibrio vulnificus, Cronobacter, Aeromonas hydrophila and other
spp., Plesiomonas shigelloides, Clostridium perfringens,
Clostridium botulinum, Staphylococcus aureus, Bacillus cereus and
other Bacillus spp., Listeria monocytogenes, Streptococcus spp.,
Enterococcus, and others.
Identifying a New Microorganism in an Environment
[0099] Disclosed herein are methods and apparatuses that allow the
distinction of a microorganism that has been newly introduced into
a food processing facility or any other environmental setting in
which tracking hygiene is critical, such as a hospital or a clinic.
In some instances, resident microorganisms reflect a persistent
contamination within a location, e.g., a food processing facility
or a hospital, that is very different than the transient pathogens
that are being repeatedly introduced into the locations.
Discriminating resident and transient pathogens provides more
clarity for differentiation of source of contaminations and
intervention strategies. This strategy can be used, for example, to
manage contaminations with managing contaminations with Listeria
monocytogensis. For example, Campylobacter is part of the natural
gut microflora of most food-producing animals, such as chickens,
turkeys, swine, cattle, and sheep. Typically, each contaminated
poultry carcass can carry from about 100 to about 100,000
Campylobacter cells. On one hand, given the fact that less than 500
Campylobacter cells can cause infection, poultry products pose a
significant risk for consumers who mishandle fresh or processed
poultry during preparation or who undercook it. On another hand,
one must be able to distinguish a normal level of a Campylobacter
on a food carcass from a Campylobacter overgrowth in a sample or
from the presence of a new strain of Campylobacter in a food
processing facility, environment, or food sample. One must also be
able to identify a new source of contamination in a facility from
existing sources. FIG. 4 illustrates a process for predictive risk
assessment based on a detection of a non-pathogenic microorganism.
Briefly, a food sample, such as a steak sample illustrated as 401
is processed and an assay, such as a nucleic acid sequencing
reaction is performed. An analysis of a plurality of nucleic acid
sequencing reads from 401 may, in some instances, not detect a
particular pathogen, such as the E. coli pathogen illustrated in
this example. Nevertheless, an analysis 403 of the microbiome 402
of the food sample 401 may indicate high risk for a presence of a
pathogen, such as E. coli. In such instances, the food sample may
be re-sampled and re-processed to confirm the presence of a
pathogenic microorganism therein.
[0100] In some instances, the methods disclosed herein further
comprise performing an additional assay to confirm the presence of
the pathogenic microorganism in the sample, such as a serotyping
assay, a polymerase chain reaction (PCR) assay, an enzyme-linked
immunosorbent (ELISA) assay, or an enzyme-linked fluorescent assay
(ELFA) assay, restriction fragment length polymorphisms (RFLP)
assay, pulse field gel electrophoresis (PFGE) assay, multi-locus
sequence typing (MLST) assay, targeted DNA sequencing assay, whole
genome sequencing (WGS) assay, or shotgun sequencing assay.
[0101] In some aspects, the disclosure provides a method comprising
obtaining a first plurality of nucleic acid sequences from a first
sample of a food processing facility; creating a data file in a
computer that associates one or more of said first plurality of
nucleic acid sequences with said food processing facility;
obtaining a second plurality of nucleic acid sequences from a
second food sample of said food processing facility; and scanning a
plurality of sequences from said second plurality of nucleic acid
sequences for one or more sequences associated with said food
processing facility in the created data file.
[0102] One or more data files can be created that associate a
microorganism with a food processing facility. In some instances, a
data file can provide a collection of sequencing reads that can be
associated with one or more strains of a microorganism present in
the processing facility. In some cases, more than 10, 15, 20, 25,
30, 35, 40, 45, 50, 60, 70, 80, 90, 100, or 1000 bacterial strains
can be associated with one or more food processing facilities.
Correlating a Presence of a Microorganism with the Risk Associated
with a Food Sample
[0103] The instance disclosure recognizes that a presence of some
non-pathogenic microorganisms, i.e. indicator microorganisms, can
be correlated with a presence of pathogenic bacteria in food, in
environmental samples, or another sample. In some aspects the
disclosure provides a method comprising detecting a presence or an
absence of a non-pathogenic microorganism in a food sample, an
environment associated with said food sample, or another sample
described herein, by a computer system, and a presence or an
absence of a pathogenic microorganism in said food sample,
environment associated, or another sample based on said presence or
said absence of said non-pathogenic microorganism. FIG. 5 is a heat
map illustrating predictive pathogen detection through machine
learning using associated non-pathogenic microorganisms. Data was
collected from more than 20,000 food samples varying over the food
categories identified by CODEX, with presentation proportional to
their market share. Among those about 950 samples were identified
to have pathogens present. The pathogens were detected via Clear
Labs sequencing platform, as well as, with traditional culturing.
Via sequencing multiple regions, the bacteria present in the
samples were detected and quantified (relative to each other) at
the species level.
[0104] The data was supplemented by alpha diversity measures
including Shannon entropy, number of observed OTUs, and Faith's
phylogenetic diversity measure. The quantification of the bacteria
in the samples and these supplemented measures, provided
coordinates for the data points used in the final classification.
The distance between the data points was computed as a combination
of unifrac distance and the euclidean distance restricted to the
supplemented coordinates.
[0105] The data points were split into training and test subsets.
We used stratified 10-fold cross validation to train support vector
machine model on the training set. The performance of the model was
measured on the previously separated test set. The scores with
regard to detection of some of the pathogens is presented in FIG.
5.
[0106] The coefficients of the support vector machine classifier
were used to determine bacteria that play significance in
determining presence or absence of the pathogens and therefore to
provide signatures that can be used independently of the model.
This analysis determined a set of non-pathogenic microorganisms
that had statistically significant correlation with the presence of
pathogenic organisms, including members of the genus Enterobacter.
Enterobacter asburiae, Enterobacter bugandensis, Enterobacter
cancerogenus, Enterobacter cloacae, Enterobacter endosymbiont,
Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii,
and Enterobacter soli were among the top 9 examples of
non-pathogenic bacteria associated with our set of pathogenic
bacteria. For example, Yersinia pseudotuberculosis was associated
with Enterobacter asburiae; Vibrio vulnificus was associated with
Enterobacter bugandensis, Enterobacter endosymbiont, and
Enterobacter soli; Escherichia coli, Salmonella enterica, and
Shigella boydii were associated with Enterobacter cancerogenus,
Enterobacter cloacae, and Enterobacter hormaechei; Staphylococcus
Aureus was associated with Enterobacter kobei; and Yersinia
pseudotuberculosis was associated with Enterobacter asburiae and
Enterobacter ludwigii.
[0107] Without being limited by theory, a variety of other samples
described herein can be analyzed as described. Briefly, a sample
may be screened with any one of the methods described herein and a
plurality of nucleic acid sequences may be obtained. Numerous
sequences within said plurality of nucleic acid sequences may be
correlated by a machine learning algorithm with a variety of
microorganisms. A prediction can then be created and a visual
output of such prediction, such as the illustrated a heat map can
be created by detecting statistically significant correlations. For
instance, a heat map created by a machine learning algorithm may
illustrate a correlation between a presence of E. coli, Salmonella
enterica, and Shigella boydii of one or more non-pathogenic
microorganisms from the Enterobacter genus, such as Enterobacter
cancerogenus, Enterobacter cloacae, and Enterobacter hormaechei or
any other bacterial genera. In some aspects, a machine learning
algorithm, including the machine learning algorithms described
herein, can be used to create such predictions.
[0108] A statistical analysis can be performed to identify the top
nonpathogenic species/food ingredients associated with the presence
of Vibrio/Staphylococcus/Yersinia/Shigella/Salmonella/Escherichia
(an illustrative cluster-based representation of such analysis is
presented in FIG. 5). This analysis determined a set of
non-pathogenic microorganisms that had statistically significant
correlation with the presence of pathogenic organisms, including
members of the genus Enterobacter. Enterobacter asburiae,
Enterobacter bugandensis, Enterobacter cancerogenus, Enterobacter
cloacae, Enterobacter endosymbiont, Enterobacter hormaechei,
Enterobacter kobei, Enterobacter ludwigii, and Enterobacter soli
were among the top 9 examples of non-pathogenic bacteria associated
with our set of pathogenic bacteria. For example, Yersinia
pseudotuberculosis was associated with Enterobacter asburiae;
Vibrio vulnificus was associated with Enterobacter bugandensis,
Enterobacter endosymbiont, and Enterobacter soli; Escherichia colt,
Salmonella enterica, and Shigella boydii were associated with
Enterobacter cancerogenus, Enterobacter cloacae, and Enterobacter
hormaechei; Staphylococcus Aureus was associated with Enterobacter
kobei; and Yersinia pseudotuberculosis was associated with
Enterobacter asburiae and Enterobacter ludwigii.
[0109] Food is a chemically complex matrix. Predicting whether, or
how fast, microorganisms will grow in a food, or how quickly a food
may spoil, is difficult. For instance, most foods contain
sufficient nutrients to support microbial growth. Furthermore,
there are many additional factors that encourage, prevent, or limit
growth of microorganisms in foods including pH, temperature, and
relative humidity. In some aspects, the instant disclosure
recognizes that a presence of some microorganism, whether or not
pathogenic, can be correlated with a sell-by date, i.e., a spoilage
date of a food. In some aspects the disclosure provides a method
comprising: detecting a presence or an absence of a microorganism
in a food sample or in an environmental sample from a food
processing facility; and predicting, by a computer system, a risk
presented by said food sample or by said food processing facility
based on said presence or said absence of said microorganism.
[0110] FIG. 6 illustrates a process for predicting a shelf-life of
a food based on machine learning. Briefly, FIG. 6 illustrates a
screening of a sample, such as a screening of a plurality of
nucleic acid sequencing reads. Subsequently, a machine learning
algorithm is used to create a risk profile, whereby said risk
profile associates a presence of some microorganism with a low or a
high likelihood of food spoilage, thereby predicting the sell-by
date of a food.
[0111] A machine learning algorithm can be used to associate any
number of sequencing reads with a presence of microorganism in a
food sample, a food related sample, or another sample. Similarly, a
machine learning algorithm may be able to associate any number of
sequencing reads with a presence of a pathogenic microorganism,
even if the sequence reads themselves are not from the pathogenic
microorganism. Computer-implemented methods for generating a
machine learning-based classifier in a system may require a number
of input datasets in order for the classifier to produce highly
accurate predictions. Depending on the microorganism, matrix, and
the microorganisms abundance in the real life samples of the
matrix, the data can be in range of 100, 1000, 10000, 100000,
1000000, 10000000, 100000000 sequencing reads. A machine learning
algorithm is selected from the group consisting of: a support
vector machine (SVM), a Naive Bayes classification, a random
forest, Logistic regression and a neural network.
Tuning an Assay Resolution
[0112] One can tune the resolution for the detection of a
microorganism based on the source of the sample, e.g., food versus
surface swab; and the sensitivity of the assay itself, e.g., genus,
species, serotype, versus strain (obtained via whole genome
sequencing). FIG. 7 is a diagram illustrating the tunable
resolution of various assays. Briefly, one or more assays can be
used sequentially to obtain a desired level of sensitivity, such as
to determine a genus, a species, a serotype, a sub-serotype, or a
strain of said microorganism. The assays can be identical or they
can be distinct. FIG. 7 illustrates that a sequencing assay can be
used to identify a strain or a sub-serotype of a microorganism
whereas a PCR reaction may be able to identify a species or, in
some cases, a serotype of a particular microorganism.
[0113] In some aspects, the disclosure provides a method
comprising: obtaining a plurality of nucleic acid sequences of a
food sample, of an environmental sample or of another non-food
derived sample from a food processing facility or another facility;
performing a first assay in said plurality of nucleic acid
sequences of said food sample, whereby said assay predicts a
presence or predicts an absence of a microorganism in said food
sample; and determining, based on said predicted presence or said
predicted absence of said microorganism of the first assay whether
to perform a second assay, whereby a sensitivity of said second
assay is selected to determine a genus, a species, a serotype, a
sub-serotype, or a strain of said microorganism.
[0114] There are various approaches for processing nucleic acids
from food samples or from environmental samples, such as polymerase
chain reaction (PCR) and sequencing. In some cases said assay is a
sequencing assay that provides the ability to obtain
sequencing-reads in real time, such as pore sequencing assay.
Sequencing can be performed by various systems currently available,
such as, without limitation, a sequencing system by Illumina.RTM.,
Pacific Biosciences (PacBio.RTM.), Oxford Nanopore.RTM., Genia
(Roche) or Life Technologies (Ion Torrent.RTM.). Alternatively or
in addition, sequencing may be performed using nucleic acid
amplification, polymerase chain reaction (PCR) (e.g., digital PCR,
quantitative PCR, or real time PCR), or isothermal
amplification.
[0115] Various strategies may be used for amplification. In some
cases, the nucleic acid amplification, polymerase chain reaction
(PCR) (e.g., digital PCR, quantitative PCR, or real time PCR), or
isothermal amplification involves amplification with fully or
partially degenerate primers. In some cases, the nucleic acid
amplification, polymerase chain reaction (PCR) (e.g., digital PCR,
quantitative PCR, or real time PCR), or isothermal amplification
involves targeted amplification of particular gene or genomic
regions. In some cases, targeted amplification of particular gene
or genomic regions involves targeted amplification of regions
containing and/or circumscribing SNPs, RFLPs, STRs, VNTRs,
hypervariable regions, minisatellites, dinucleotide repeats,
trinucleotide repeats, tetranucleotide repeats, simple sequence
repeats, indels, and/or insertion elements associated with or
variable between individual microorganisms or microorganism
serotypes. The targeted amplification of the particular gene or
genomic regions may involve the use of multiple sets of
oligonucleotide primers that are partially or fully complementary
to regions flanking the SNPs, RFLPs, STRs, VNTRs, hypervariable
regions, minisatellites, dinucleotide repeats, trinucleotide
repeats, tetranucleotide repeats, simple sequence repeats, indels,
and/or insertion elements. In some embodiments, the targeted
amplification uses at least one, 100, 200, 300, 400, 500, 600, 700,
or 800 pairs of oligonucleotide primers to amplify particular gene
or genomic regions from the nucleic acids.
[0116] In some cases, the assay is a serotyping assay. The
serotyping assay may comprise an enzyme-linked immunosorbent
(ELISA) assay or an enzyme-linked fluorescent assay (ELFA) assay. A
serotype or serovar is a distinct variation within a species of
bacteria or virus. These microorganisms can be classified together
based on their cell surface antigens, allowing the epidemiologic
classification of microorganisms to the sub-species level. A group
of serovars with common antigens is called a serogroup or sometimes
serocomplex. In some aspects, the disclosure provides methods for
performing a sequencing assay on a plurality of nucleic acids
derived from a sample and a then performing a serotyping assay on a
derivative of said sample. FIG. 8 is a schematic illustrating
various serotypes of various microorganisms that can be detected by
an analysis of a plurality of nucleic acid sequences as described
herein and further validated with a serotyping assay.
Differentiating Live Versus Dead Microorganisms
[0117] Nucleic acid-based targeted analytical methods, such as PCR
provide only limited information on the activities and
physiological states of microorganisms in samples and cannot
distinguish viable cells from dead cells. In some aspects, the
disclosure provides methods for distinguishing a live microorganism
in a food sample or in another sample, from a dead microorganism
within the same sample. FIG. 9 is a schematic illustrating one
process for distinguishing a live microorganism from a food or from
an environmental sample. Briefly, FIG. 9 illustrates than an amount
of a microorganism in a sample can be increased, i.e., enriched
901, by growing the microorganism in a rich medium for a period of
time. A reagent, such as a photoreactive DNA-binding dye, a DNA
intercalating reagent, or another suitable reagent may be added to
enriched sample 901. Such reagents distinguish live 902
microorganisms from dead 903 microorganisms by interacting with the
nucleic acid sequence of dead microorganisms only. In some cases,
the disclosure contemplates using propidium monoazide or a
derivative thereof as a dye. The modified sample can be prepared
for a subsequent reaction 904, such as a sequencing reaction
905.
[0118] In some instances the disclosure provides a method
comprising adding a reagent to a plurality of nucleic acid
molecules from a food sample, or food related sample or another
sample described herein thereby forming a modified plurality of
nucleic acid molecules, whereby said reagent (i) interacts with and
modifies a structure of a plurality of nucleic acid molecules
derived from one or more dead microorganisms; and (ii) does not
interact with or modify a structure of a nucleic acid molecule
derived from one or more live microorganisms; thereby providing a
modified plurality of nucleic acid molecules; and sequencing said
modified plurality of nucleic acid molecules, thereby
distinguishing one or more live organisms from said food sample or
from another sample.
[0119] In other aspects the disclosure provides a method comprising
performing a pore sequencing or other DNA sequencing or
hybridization assay on a plurality of nucleic acid molecules from a
food sample or from another sample whereby said pore sequencing
reaction distinguishes one or more nucleic acid molecules derived
from a dead microorganism from one or more nucleic acid molecules
derived from a live microorganism based on a methylation or other
epigenetic pattern of said one or more nucleic acid molecules
derived from said dead microorganism.
[0120] In some embodiments, epigenetic patterns, such as
methylation, can be detected in DNA derived from food or
environmental samples by chemical or enzymatic selection methods
prior to sequencing. Such methods include, but are not limited to,
bisulfite sequencing (including targeted bisfulfite sequencing, see
e.g. Ziller et al. Epigenetics Chromatin. 2016 Dec. 3; 9:55 and
Masser et al. J Vis Exp. 2015; (96): 52488) and
methylation-sensitive restriction digestion (see e.g. Bitinaite et
al. U.S. Pat. No. 9,034,597).
[0121] In some embodiments, epigenetic patterns can be detected in
DNA derived from food or environmental samples by characteristic
changes in ionic current during nanopore sequencing (see e.g.
Wescoe et al. J Am Chem Soc. 2014 Nov. 26; 136(47):16582-7 and
Laszlo et al. Proc Natl Acad Sci USA. 2013 Nov. 19;
110(47):18904-9).
Barcodes
[0122] Unique identifiers, such as barcodes, can be added to one or
more nucleic acids isolated from a sample from a food processing
facility, from a hospital or clinic, or from another sources.
Barcodes can be used to associate a sample with a source; e.g., to
associate an environmental sample with a specific food processing
facility or with a particular location within said food processing
facility. Barcodes can also be used to identify a processing of a
sample, as described in U.S. Patent Pub. No. 2016/0239732, which is
entirely incorporated herein by reference.
[0123] In some aspects, the disclosure provides a method comprising
adding a first barcode to a first plurality of nucleic acid
sequences from a sample, thereby providing a first plurality of
barcoded nucleic acid sequences; performing a first sequencing
reaction on said first plurality of barcoded nucleic acid
sequences, wherein said sequencing reaction is performed on a
sequencing apparatus comprising a flow cell; adding a second
barcode to a second plurality of nucleic acid sequences from a
second sample, thereby providing a second plurality of barcoded
nucleic acid sequences; and performing a second sequencing reaction
on said second plurality of barcoded nucleic acid sequences,
wherein said second sequencing reaction is performed on said
sequencing apparatus comprising said flow cell, thereby reusing
said flow cell. FIG. 10 illustrates a process for re-using flow
cells with distinct indexes as described herein. As illustrated by
FIG. 10 two distinct indexes, 1001 and 1002, such as two different
barcodes, can be added to different samples prior to sequencing
1003. Since a first sample can be associated with a first index
1001 and a second sample can be associated with a second index 1002
this process effectively allows for the re-using of a flow cell.
FIG. 18 and FIG. 19 demonstrate the re-use of MinION/GridION flow
cells. Example 21 demonstrates how certain primer design schemes,
such as a nonperiodic design, can reduce crosstalk in situations
with high multiplexing or closely related sequences, as may happen
with reuse of flow cells.
[0124] One or more barcodes or block of barcodes may be added to a
nucleic acid sequence from a food sample or another sample from a
food processing facility, such as a first, a second, a third, or
any subsequent sample. In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 identical barcodes
are added to such samples. In other cases, distinct barcodes are
added to such samples. In some cases, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 distinct barcodes are
added to such samples. The serial addition of two or more barcodes,
either identical in sequence or distinct in sequence, can provide
an indexing of a sample that is used in its analyses. The presence
of additional barcode or barcode blocks make the system more robust
against any barcode manufacturing error and can also significantly
reduce the chance of cross contamination between barcodes. In some
cases, a barcode is added to a nucleic acid sequence comprising
complementary DNA (cDNA) sequences, ribonucleic acid (RNA)
sequences, genomic deoxyribonucleic acid (gDNA) sequences, or a
mixture of cDNA, RNA, and gDNA sequences.
[0125] Barcodes can have a variety of lengths. In some instances a
barcode is from about 3 to about 25 nucleotides in length, from
about 3 to about 24 nucleotides in length, from about 3 to about 23
nucleotides in length, from about 3 to about 22 nucleotides in
length, from about 3 to about 21 nucleotides in length, from about
3 to about 20 nucleotides in length, from about 3 to about 19
nucleotides in length, from about 3 to about 18 nucleotides in
length, from about 3 to about 17 nucleotides in length, from about
3 to about 16 nucleotides in length, from about 3 to about 15
nucleotides in length, from about 3 to about 14 nucleotides in
length, from about 3 to about 13 nucleotides in length, from about
3 to about 12 nucleotides in length, from about 3 to about 11
nucleotides in length, from about 3 to about 10 nucleotides in
length, from about 3 to about 9 nucleotides in length, from about 3
to about 8 nucleotides in length, or from about 3 to about 7
nucleotides in length.
[0126] In other instances, a barcode is from about 4 to about 25
nucleotides in length, from about 4 to about 24 nucleotides in
length, from about 4 to about 23 nucleotides in length, from about
4 to about 22 nucleotides in length, from about 4 to about 21
nucleotides in length, from about 4 to about 20 nucleotides in
length, from about 4 to about 19 nucleotides in length, from about
4 to about 18 nucleotides in length, from about 4 to about 17
nucleotides in length, from about 4 to about 16 nucleotides in
length, from about 4 to about 15 nucleotides in length, from about
4 to about 14 nucleotides in length, from about 4 to about 13
nucleotides in length, from about 4 to about 12 nucleotides in
length, from about 4 to about 11 nucleotides in length, from about
4 to about 10 nucleotides in length, from about 4 to about 9
nucleotides in length, from about 4 to about 8 nucleotides in
length, or from about 4 to about 7 nucleotides in length.
[0127] a barcode is from about 5 to about 25 nucleotides in length,
from about 5 to about 24 nucleotides in length, from about 5 to
about 23 nucleotides in length, from about 5 to about 22
nucleotides in length, from about 5 to about 21 nucleotides in
length, from about 5 to about 20 nucleotides in length, from about
5 to about 19 nucleotides in length, from about 5 to about 18
nucleotides in length, from about 5 to about 17 nucleotides in
length, from about 5 to about 16 nucleotides in length, from about
5 to about 15 nucleotides in length, from about 5 to about 14
nucleotides in length, from about 5 to about 13 nucleotides in
length, from about 5 to about 12 nucleotides in length, from about
5 to about 11 nucleotides in length, from about 5 to about 10
nucleotides in length, from about 5 to about 9 nucleotides in
length, from about 5 to about 8 nucleotides in length, or from
about 5 to about 7 nucleotides in length.
[0128] a barcode is from about 6 to about 25 nucleotides in length,
from about 6 to about 24 nucleotides in length, from about 6 to
about 23 nucleotides in length, from about 6 to about 22
nucleotides in length, from about 6 to about 21 nucleotides in
length, from about 6 to about 20 nucleotides in length, from about
6 to about 19 nucleotides in length, from about 6 to about 18
nucleotides in length, from about 6 to about 17 nucleotides in
length, from about 6 to about 16 nucleotides in length, from about
6 to about 15 nucleotides in length, from about 6 to about 14
nucleotides in length, from about 6 to about 13 nucleotides in
length, from about 6 to about 12 nucleotides in length, from about
6 to about 11 nucleotides in length, from about 6 to about 10
nucleotides in length, from about 6 to about 9 nucleotides in
length, from about 6 to about 8 nucleotides in length, or from
about 3 to about 7 nucleotides in length.
[0129] Apparatus
[0130] Automated nucleic acid sequencing apparatuses can provide a
robust platform for the generation of nucleic acid sequencing
reads. Unfortunately, many apparatuses have a high rate of failure,
i.e., high rate of error of the sequencing reaction itself, which
require manual intervention in such instances, such as re-loading
of samples into flow cells. In some aspects, the disclosure
provides an automated nucleic acid sequencing apparatus that
requires no manual intervention in the event of a failure of a
sequencing reaction. In some aspects, the disclosure provides a
nucleic acid sequencing apparatus comprising: a nucleic acid
library preparation compartment comprising two or more chambers
configured to prepare a plurality of nucleic acids for a sequencing
reaction, wherein said compartment is operatively connected to a
nucleic acid sequencing chamber; a nucleic acid sequencing chamber,
wherein said nucleic acid sequencing chamber comprises: (i) one or
more flow cells comprising a plurality of pores configured for the
passage of a nucleic acid strand, wherein said two or more flow
cells are juxtaposed to one another; and an automated platform,
wherein said automated platform is programmed to robotically move a
sample from said nucleic acid library preparation compartment into
said nucleic acid sequencing chamber. FIG. 11 illustrates an
automated sequencing apparatus of the disclosure. 1101 is a diagram
of the apparatus comprising the nucleic acid sequencing compartment
1102. Nucleic acid library preparation compartment 1103 shows a
variety of chambers configured to prepare a plurality of nucleic
acids for a sequencing reaction in close proximity to a sequencing
chamber 1104, which comprises one or more flow cells. Briefly, an
automated apparatus of the disclosure is programmed to move one or
more samples from the library preparation chambers 1103 into a
sequencing chamber 1104 upon detecting a failure in a sequencing
reaction. This provides a sequencing process with no human touch
points after a sample is added to the library preparation chamber,
as illustrated in FIG. 12. FIG. 12 illustrates an embodiment where
a sample from a food processing facility, from a hospital or
clinical setting, or from another source can be manually processed
between 6 am to 6 pm or any shorter or longer incubation window by
incubating the sample in a presence of a growth medium (e.g.,
enrichment) and automatically processed after the sample is added
to a nucleic acid preparation chamber 1103.
[0131] The disclosed apparatus is programmed in such a manner that
said automated platform moves one or more samples from said nucleic
acid library preparation compartment into said nucleic acid
sequencing chamber. Upon detecting a failure of a sequencing
reaction, the automated platform moves one or more samples from the
failed sequencing flow cell or apparatus to the next sequencing
flow cell or apparatus. In many cases, such samples comprise
nucleic acid sequences that include one or more barcodes. In some
cases, a plurality of mutually exclusive barcodes are added to a
plurality of nucleic acids in said two or more chambers of the
nucleic acid library preparation compartment 1103, thereby
providing a plurality of mutually exclusive barcoded nucleic acids
within the apparatus. In some instances, the automated platform
robotically moves two or more of said mutually exclusive barcoded
nucleic acids into said nucleic acid sequencing chamber, in some
instances by moving said mutually exclusive barcoded nucleic acids
into a same flow cell of said one or more flow cells.
[0132] The present disclosure describes an apparatus for the
automated detection of food-borne pathogens via the sequencing of
genomic libraries from samples introduced into the instrument. In
some aspects, the apparatus may comprise four main components:
library chambers for library preparation, fluid handling systems,
sequencing flow cells, and automation systems. Within the scope of
the present disclosure, there are numerous possible uses of the
pathogen detection system.
[0133] Library Chambers
[0134] The present disclosure describes a device comprising one or
more library chambers. Each library chamber may be capable of a
broad range of functions including, but not limited to, sample
preparation, sample enrichment, nucleic acid amplification, and
purging. In some aspects, the library preparation may be performed
entirely within a single library chamber. In other aspects, each
library chamber may be reserved for a separate function in the
library preparation process. Depending upon the processes necessary
for library preparation, library chambers may be operatively
connected to each other, and to one or more flow cells, or they may
only have operative connections to a sequencing flow cell.
[0135] A library chamber may comprise one or more chambers and a
securable hatch. The hatch allows access by a user or automated
loading system for sample loading. The opening and closing
mechanism of the hatch may be manual or electrically-actuated. In
some aspects, a library chamber may comprise a main compartment and
a secondary compartment for pre-loading a sample. Pre-loading may
comprise a process of decontamination or other processes to prevent
outside contaminants such as dust and pollen from entering the
apparatus. Following decontamination, the specimen may be
transferred into the main compartment. In other aspects, samples
may be loaded into a single library chamber and the entire library
chamber will be decontaminated.
[0136] The library chambers may be configured to accommodate a
broad range of samples. When tracing the outbreak and spread of a
food-borne pathogen, many possible sample types may be tested,
including, but not limited to, soil samples, crop samples, tissue
samples, cloth swabs, stool samples, and fluid samples. In some
aspects, the present disclosure may provide a dynamic platform that
is capable of enriching a detectable amount of nucleic acids from a
sample. In some aspects, the library chamber may comprise a fixed
unit of the apparatus described in the present enclosure and is
capable of repeated reuse. In some aspects, a device of the
disclosure comprises at least 5, at least 10, at least 15, at least
20, or another suitable number of library chambers 1103. In other
aspects, the library chamber may comprise a cartridge for sample
collection in the field. In such an embodiment, the library chamber
would be loaded into the sequencing apparatus manually before the
commencement of an automated library preparation and sequencing
assay.
[0137] For the present disclosure, a library chamber may be
configured in multiple ways depending upon how it will be utilized.
A library chamber may comprise one or more inlet ports for the
addition of reagents, gases, or any other necessary materials for
library preparation. The inlet ports may be physically positioned
at any portion of the library chamber depending upon the function
of the inlet port. In some aspects, a library chamber may comprise
one or more inlet ports. In some aspects, a library chamber may
comprise 1 to 10 inlet ports, 2 to 10 inlet ports, 3 to 10 inlet
ports, 4 to 10 inlet ports, 5 to 10 inlet ports, 6 to 10 inlet
ports, 7 to 10 inlet ports, 8 to 10 inlet ports, or 9 to 10 inlet
ports. In some aspects, an inlet port may be configured for the
introduction of gases, liquids, or solids. In some aspects, an
inlet port may be positioned near the top of the library chamber
for uses such as the addition of liquid media. In other aspects, an
inlet port may be positioned near the bottom of the library chamber
for uses such as gas bubbling or sparging. A library chamber may
also comprise one or more exit ports. In some aspects, a library
chamber may comprise one or more exit ports. In some aspects, a
library chamber may comprise 1 to 10 exit ports, 2 to 10 exit
ports, 3 to 10 exit ports, 4 to 10 exit ports, 5 to 10 exit ports,
6 to 10 exit ports, 7 to 10 exit ports, 8 to 10 exit ports, or 9 to
10 exit ports. In some aspects, an exit port may be configured for
the removal of gases, liquids, or solids.
[0138] The preparation of nucleic acid libraries may require the
enrichment of a sample by culturing such samples in nutritious
media that supports the enrichment of a sufficient amount of
nucleic acids to perform a sequencing assay. In some aspects, the
library chamber may serve to enrich a sample by serving as a
cell-culturing chamber. In such a configuration, the library
chamber may be filled with a cell-growth medium and any other
reagents necessary to promote cell growth. In some aspects, the
library chamber may be connected to modules of thermal regulation,
including both heating and cooling, to promote optimal cell growth.
The library chamber may be capable of aerobic or anaerobic
operation. In some aspects, aerobic operation may comprise bubbling
or sparging with oxygen or air. In other aspects, anaerobic
operation may comprise bubbling or sparging the library chamber
with a non-oxygenated gas including, but not limited to, nitrogen,
helium, carbon dioxide or hydrogen. Mechanical agitation of cell
culture may be necessary to prevent sedimentation of cells. In some
aspects, agitation may be provided by sufficient bubbling of gases
through the library chamber. In other aspects, a micro-impeller may
provide mechanical mixing to the library chamber. In some aspects,
a micro-impeller may comprise an impeller blade connected to a
motor through a sealed bearing in a surface of the library chamber.
In other aspects, a micro-impeller may comprise an impeller blade
and shaft entirely contained within the library chamber. In such an
embodiment, the impeller blade and shaft may comprise a
magnetically-susceptible material such that the operation of an
electromagnet in close proximity to the library surface may induce
the spinning of the blade. In some aspects, a library chamber may
be used to lyse cells as a method of freeing the nucleic acids
contained within the cells. In some aspects, cell lysing and
nucleic acid capture may be performed within one library chamber.
In other aspects, cell lysing and nucleic acid capture may be
performed in successive library chambers via a series of assays and
material transfer between library chambers.
[0139] In some aspects, a library chamber in the present disclosure
may comprise a DNA amplification and manipulation device. The
library chamber may be a platform for any DNA amplification
technique, including, but not limited to emulsion PCR. As a PCR
platform, the library chamber may include a themocycler. The
library chamber may also comprise a device for a variety of other
DNA manipulation techniques including, but not limited to,
restriction assays and ligation assays. In some aspects, the
present disclosure may comprise a means to amplify a nucleic acid
library. The library chamber may be used to fragment larger pieces
of genomic DNA and add identifying sequences such as barcodes to
nucleic acid fragments. All DNA manipulations may be performed in a
single library chamber or multiple assays may be performed in one
or more successive library chambers.
[0140] A library chamber may be comprised of a variety of materials
depending upon the assays to be performed in it. The library may be
comprised of materials such as metal, glass, ceramic or plastic.
Library chambers may comprise metals that are non-magnetic,
paramagnetic or ferromagnetic. In the present disclosure, library
chambers may comprise metals such as aluminum, tungsten, tungsten
oxide, austenitic stainless steel, or ferritic stainless steel. A
library chamber may comprise a thermoplastic or a machinable
plastic. The library chamber may comprise a plastic such as
polyethylene, polypropylene, polyester, or polycarbonate. The
chamber material may be chosen for a variety of properties
including, but not limited to, biocompatibility, corrosion
resistance, chemical reactivity, surface energy, electrical
capacitance, electrical resistivity, electrical conductivity,
magnetic properties, ductility, durability, elasticity,
flexibility, hardness, malleability, mass density, tensile
strength, surface roughness, machinability, light absorbance, light
transmittance, index of refraction, light emissivity, thermal
expansion, specific heat, and thermal conductivity. In some
aspects, a library chamber may be composed of a single material
that is acceptable for all intended uses. In other aspects, a
library chamber may be composed of multiple materials, e.g. a glass
chamber with metal inserts for connections to inlet and outlet
ports. In some aspects, library chamber surfaces may comprise a
chosen material with an applied coating. Such coatings may be used
for a variety of purposes including, but not limited to,
anti-corrosion, anti-friction, hydrophobicity, hydrophilicity,
anti-agglomeration, anti-adsorption, pro-adsorption, anti-fouling,
anti-static, chemical reactivity and chemical inertness. In the
present disclosure, the library preparation portion of the
apparatus may comprise multiple library chambers arranged in
parallel or series configurations for a variety of purposes. In
either case, each library chamber in the apparatus may comprise a
different material design specifically chosen for the intended
application of the library chamber.
[0141] Library chambers may be designed to include inline
detection. The purpose of detection systems may include measuring
system properties or detecting failed assays. Detection systems may
be used to measure a variety of system properties, including, but
not limited to, cell density, nucleic acid concentration, nucleic
acid purity, pH, temperature, pressure, oxygen concentration, fluid
density, fluid viscosity, dielectric constant, absorption spectrum,
and heat capacity. In some aspects, a library chamber may include
an optical port comprised of an optically-opaque material such as
quartz glass. In some aspects, the transmittance, absorption,
reflection or refraction of visible, infrared, microwave, or
ultraviolet light sources may be measured using embedded optical
ports. In some aspects, the library chambers may include mechanical
ports for inserting measurement devices including, but not limited
to pH probes, thermocouples, pressure gauges and dielectric probes.
In some aspects, library chambers may be designed to allow fluid to
be drawn out of fluid inlet or outlet ports for measurement at
downstream instrumentation.
[0142] Fluid Handling Systems
[0143] In the present disclosure, fluid transfer may occur between
one or more library chambers and one or more sequencing flow cells.
The apparatus may comprise systems for ensuring the accurate
transfer of fluids. Fluid transfer may also be involved in many
other aspects of device operation, including, but not limited to
cell culturing, cell lysis, nucleic acid purification, nucleic acid
amplification, nucleic acid ligation, nucleic acid fragmentation,
nucleic acid sequencing, sequence flow cell priming, chamber
mixing, chamber cleaning, and chamber purging. The sequencing flow
cell, as designed, must maintain a gas-free operational state for
its entire life. In many embodiments of the present disclosure, the
fluid handling system will be designed to ensure that no gas may be
transferred from the library preparation system to the sequencing
flow cell system.
[0144] Numerous fluids may be involved in the operation of the
food-borne pathogen detection described in the present disclosure.
Liquids used may include buffers, acids, bases, surfactants,
emulsions, suspensions, chelating agents, and solutions. Liquids
used may include, but are not limited to, deionized water, HCl,
H.sub.2SO.sub.4, HNO.sub.3, NaOH, KOH, NaCl, KCl, CaCl.sub.2,
MgCl.sub.2, EDTA, ethanol, and methanol. Gases used may include
inert gases, oxidizing gases and reducing gases. Gases used may
include, but are not limited to N.sub.2, air, O.sub.2, He, Ar, Hz,
and CO.sub.2. Commonly used liquids may be stored in the device. In
some aspects, liquids may be stored in onboard chambers. In other
aspects, liquids may be stored in cartridges that can be added or
removed manually or via an automated system. In some aspects, gases
may be delivered via external tanks or plumbed lines via inlet
ports in the apparatus.
[0145] Fluids may be moved through a food-borne pathogen detection
apparatus by a variety of mechanisms. Fluid movement devices may
include pumps, compressors, regulators, blowers, and fans. The
apparatus in the present disclosure may comprise one or more pumps
for liquid transfer. These pumps may be responsible for moving
fluids into library chambers, emptying library chambers,
transferring fluids from library chambers to sequencing flow cells,
moving fluids through flow cells, preventing sedimentation of
solids, clearing filters and draining waste fluids from the
apparatus. Depending upon the specific applications, pumps included
in the described apparatus may comprise positive-displacement
pumps, peristaltic pumps, gear pumps, rotary pumps, screw pumps,
piston pumps, or diaphragm pumps. Pumps and compressors may also be
used for gas transfer. Regulators and compressors may be used to
adjust gas pressures in the apparatus. Vacuum pumps may be used to
void library chambers during purging operations. Fluid transfer may
also be achieved via passive mechanisms such as gravity feeding and
capillary action. A pathogen or a non-pathogenic microorganism
detection device may comprise one or more valves for fluid control.
Valves may be located in any flow line, including at inlet and exit
ports for library chambers, at inlets and exits to the sequencing
flow cells, and at inlet and drainage ports for the apparatus.
Valves may be capable of manual control or automated control. Fluid
transfer may be controlled by devices such as mass flow controllers
and rotameters. Fluid transfer regulation may be achieved via
manual controls on the apparatus, analog or digital electronic
control systems on the apparatus, or via computer systems
interfaced with a remote sequencing apparatus.
[0146] For the present disclosure, connectivity between device
inlets, library chambers, sequencing flow cells, and drainage ports
may be pursued. Connectivity may be achieved via direct coupling of
components at otherwise sealed junctions or junctions that have
movable openings. Connectivity between components may be achieved
by any suitable method, including, but not limited to, mated
flanges, compression fittings, friction fittings, hose barbs, and
magnetic couplings. In some aspects, a seal may be needed between
two connected components. Depending upon the design, a seal may
need to be air-tight, leak-free, detachable, or permanent. A seal
may comprise a gasket, O-ring, metal compression fitting or plastic
compression fitting. Seals may be chosen from a variety of
materials including, but not limited to polypropylene,
polycarbonate, rubber, copper, or graphite. Connectivity may also
comprise piping or tubing between system components. Piping or
tubing may comprise any material suitable to the chosen
application. Materials may be chosen to for properties including
anti-fouling, anti-friction, hydrophobicity, hydrophilicity,
durability, flexibility, strength, cost, and biocompatibility.
Piping or tubing materials may include, but are not limited to,
stainless steel tubing, copper tubing, aluminum tubing, brass
tubing, rigid plastic tubing, or flexible plastic tubing. In some
aspects, piping or tubing may be fitted permanently in the device.
In other aspects, piping or tubing lines may be disposable.
[0147] Other devices may necessarily be part of the flow system for
the device described in the present disclosure. In some aspects,
various flow lines may be equipped with one or more filters. Filter
may be for liquids or gases. Filters may be for various purposes
including capturing cells or cellular components, maintaining
sterility from outside fluid sources, capturing any particle
contaminants, or any other debris that may need to be excluded from
the library chambers or sequencing flow cells. In some aspects, one
or more bubblers may be included at the junction between a library
chamber and an external gas line. Bubblers may comprise a fritted
metal, fritted glass or any other porous material that distributes
flowing gas. In some aspects, a separation device may comprise a
connection between units in the described apparatus. Separation
devices may be used to perform ultrafiltration, adsorption, reverse
osmosis, extraction, chromatography, sedimentation, sieving or
vapor-liquid separation. In some aspects, a device may be placed
between a library chamber and a sequencing flow cell to ensure the
removal of all gas bubbles.
[0148] In some aspects, the sterility of the sequencing apparatus
may be maintained to promote a higher efficiency operation or a
sequencing that may be exposed to fewer contaminants. The internal
and automated portions of the device may be enclosed within a
sealed housing to prevent the intrusion of any airborne particles
and debris such as dust, mold, mildew, pollen, bacteria, viruses
and lint. The sealed housing may be purged of ambient air by use of
a vacuum pump or may be held under positive pressure via an
attached source of compressed gas. Certain external portions of the
device may require frequent exposure to the outside environment,
presenting potential sources of contamination, e.g. library
chambers during sample insertion. Any external port, inlet or
chamber may be held under positive pressure to minimize the chances
of unwanted debris or biological entities depositing into the
system during operation. Any and all chambers, cells and fluid
transfer systems may undergo one or more washing, cleaning or
purging processes to remove contaminants or residual matter from
normal operations. Washing, cleaning or purging may comprise the
use of detergents, surfactants, acids, bases, alcohols, deionized
water, DNAses, RNAses, proteases, lipases, or any other cleansing
method. Washing, cleaning or purging may involve heat treatments or
vacuum evacuation. Any and all fluid transfer systems may comprise
materials with anti-fouling or biocidal coatings or surface
functionalization to minimize the deposition of contaminants,
especially in regions with fluid stagnation. Fluid flow may be
laminar to increase residence time in a portion of the apparatus,
e.g. a prolonged cleansing step, or may be turbulent to decrease
residence time or decrease mixing, e.g. rapid cell movement to
reduce sedimentation.
Automation Systems
[0149] The food-borne pathogen detection apparatus described in the
present disclosure is intended for autonomous or semi-autonomous
operation. In some aspects, the apparatus may only require manual
intervention for the input of samples and reagents, and all further
operations may be handled via an automated software/hardware
system. In other aspects, the apparatus may require manual input of
information, instructions or physical materials, such as reagents,
at particular times in the instrument's operations. The device may
operate using customized algorithms for each operation or may
utilize standard algorithms. Algorithms may be manually input via
onboard control systems or sent from a remote computer system. The
device may be hardwired to an external computer system or
communicate wirelessly. The sequencing apparatus may be capable of
exporting data in packets or transmitting data in real-time as
sequencing is performed. In some aspects, the apparatus will
automatically detect failed operations including, but not limited
to, failed bacterial enrichment, failed DNA amplification or
purification, and failed sequencing. In some aspects, the system
may include diagnostic or analytical devices at inlet or exit
ports, in library chambers, or in any flow line to provide data on
the status of ongoing operations.
[0150] The apparatus in the present disclosure may operate via
electrical supply from an external power supply, e.g. a wall
outlet, or run via a self-contained battery system. Field portable
versions of the device may be intended to run in conjunction with
portable power systems such as solar panels or portable generators.
In some aspects, the apparatus will comprise all necessary
electrical components to accept either DC or AC power, as the power
supply source dictates.
[0151] The sequencing device may utilize robotics for automated
operation. In some aspects, robotics may be responsible for any and
all internal operations, including, but not limited to moving
fluids, opening and closing valves, adding reagents, performing
cleaning operations, performing and monitoring bacterial growth
operations, performing and monitoring DNA amplification and
purification operations, performing sequencing assays, priming or
reconditioning flow cells, and discharging waste from the
apparatus. In some aspects, all components of a sequencing device
may be fixed in their positions, with robotics used primarily to
control the movement of liquids, gases and other materials through
the system. In other aspects, robotics may be used to move library
chambers to a point of direct connectivity with another portion of
the system, e.g. a sequencing flow cell.
[0152] In some aspects, fluid transfer operations may be mediated
by one or more automated pipette systems. In some aspects, a
pipette system may comprise a single pipette. In other aspects, a
pipette system may comprise an array of pipettes arranged in
multiplexed fashion. One or more pipettes may be capable of
dispensing fluids via positive pressure-driven flow or removing
fluids via a negative pressure differential (vacuum). In some
aspects, one or more pipettes may be configured to dispense or
withdraw fluids individually. In other aspects, one or more
pipettes may be configured to dispense or withdraw fluids
simultaneously. Fluids may be dispensed or withdrawn in a
continuous or metered fashion. In some aspects, a metered pipette
may dispense or withdraw fluid volumes of about 0.1 .mu.l to about
1000 .mu.l. In some aspects a metered pipette may dispense or
withdraw fluid volumes of about 0.1 .mu.l to 10 .mu.l, 0.1 .mu.l to
20 .mu.l, 0.1 .mu.l to 30 .mu.l, 0.1 .mu.l to 40 .mu.l, 0.1 .mu.l
to 50 .mu.l, 0.1 .mu.l to 60 .mu.l, 0.1 .mu.l to 70 .mu.l, 0.1
.mu.l to 80 .mu.l, 0.1 .mu.l to 90 .mu.l, 0.1 .mu.l to 100 .mu.l, 1
.mu.l to 10 .mu.l, 1 .mu.l to 20 .mu.l, 1 .mu.l to 30 .mu.l, 1
.mu.l to 40 .mu.l, 1 .mu.l to 50 .mu.l, 1 .mu.l to 60 .mu.l, 1
.mu.l to 70 .mu.l, 1 .mu.l to 80 .mu.l, 1 .mu.l to 90 .mu.l, 1
.mu.l to 100 .mu.l, 10 .mu.l to 20 .mu.l, 10 .mu.l to 30 .mu.l, 10
.mu.l to 40 .mu.l, 10 .mu.l to 50 .mu.l, 10 .mu.l to 60 .mu.l, 10
.mu.l to 70 .mu.l, 10 .mu.l to 80 .mu.l, 10 .mu.l to 90 .mu.l, 10
.mu.l to 100 .mu.l, 10 .mu.l to 200 .mu.l, 10 .mu.l to 300 .mu.l,
10 .mu.l to 400 .mu.l, 10 .mu.l to 500 .mu.l, 10 .mu.l to 1000
.mu.l, 100 .mu.l to 200 .mu.l, 100 .mu.l to 300 .mu.l, 100 .mu.l to
400 .mu.l, 100 .mu.l to 500 .mu.l, 100 .mu.l to 1000 .mu.l, 250
.mu.l to 500 .mu.l, or about 250 .mu.l to 1000 .mu.l. In some
aspects, each pipette may comprise a separate pressure actuator. In
other aspects, two or more pipettes may be controlled by the same
pressure actuator. Pipette tips may be permanent or disposable. In
some aspects, disposable pipette tips may comprise a hollow plastic
piece that mates to a permanent surface. Disposable pipette tips
may be secured to the permanent surface via downward pressure on
the permanent surface onto the plastic. An automated fluid transfer
system may comprise an automated method for removing disposable
pipette tips.
[0153] An array of pipettes with pressure actuators or connectivity
to pressure actuators may be mounted on an automated translation
stage capable of movement in one or more dimensions. In some
aspects, an automated translation stage may be capable of
3-dimensional movement. In some aspects, an automated translation
stage may be capable of rotational movement. In some aspects, an
automated translation system may be coupled to one or more motors,
pneumatic devices, or any other method of producing linear motion.
Translation may be produced in continuous or incremented, step-wise
fashion. Translational movements may be produced on the order of
about 1 .mu.m, 2 .mu.m, 3 .mu.m, 4 .mu.m, 5 .mu.m, 6 .mu.m, 7
.mu.m, 8 .mu.m, 9 .mu.m, 10 .mu.m, 20 .mu.m, 30 .mu.m, 40 .mu.m, 50
.mu.m, 60 .mu.m, 70 .mu.m, 80 .mu.m, 90 .mu.m, 100 .mu.m, 200
.mu.m, 300 .mu.m, 400 .mu.m, 500 .mu.m, 600 .mu.m, 700 .mu.m, 800
.mu.m, 900 .mu.m, 1 mm, 5 mm, 10 mm, 20 mm, 30 mm, 40 mm, 50 mm,
100 mm, 200 mm, 300 mm, 400 mm, 500 mm, 600 mm, 700 mm, 800 mm, 900
mm, or 1000 mm.
Automated Priming and Library Loading Device
[0154] Oxford Nanopore flow cells have a flow path as shown in FIG.
25, (which depicts a schematic cross-section along the flow path,
top, and the corresponding features on a picture of a flow cell,
bottom) and are pre-filled with a conditioning liquid to protect
the nanopore membrane during storage. Further description of such
flow cells can be found e.g., in WO2018007819A1. However, the
commercial form of the oxford nanopore flow cell (e.g.
GridIONx5.TM. cell) is not provided in a form where all the
preparation steps for sequencing can be performed by an automated
process. Particularly, all steps of storage buffer replacement and
priming of liquid flow through the flow cell are difficult to
automate because of a flat plastic removable seal that covers the
sample input port (the presence and absence of which is
demonstrated in FIG. 14 and FIG. 15 which cannot be conveniently
removed by automated processes.
[0155] The nanopore flow cell device comprises a sensor provided in
a sensing chamber (2505); a flow path comprising a sensing chamber
inlet (2509) and a sensing chamber outlet (2504) connecting to the
sensing chamber for respectively passing liquid into and out of the
sensing chamber, and a sample input port (2506) in fluid
communication with the inlet; and a liquid collection channel
(2503) downstream of the outlet. The device additionally has a flow
path interruption (2502, e.g. a valve activated by an actuatable
lever accessible from the top surface of the device) between the
sensing chamber outlet (2501) and the liquid collection channel
(2503), preventing liquid from flowing into the liquid collection
channel (2503) from upstream, and the device may be activated by
completing the flow path between the sample input port (2506) and
the liquid collection channel (2503), such as by opening a valve
when a valve is in place of the flow path interruption (2502). When
provided by the manufacturer as a new flow cell, conditioning
liquid fills from the sample input port (2506) to the flow path
interruption (2502) such that the sensor (within 2505) is covered
by liquid and is unexposed to a gas or gas/liquid interface. The
device additionally has a buffer input port (2507) in fluid
communication with the sensing chamber inlet (2509), a flow path
interruption (2502, e.g. a valve activated by an actuatable lever
accessible from the top surface of the device, which is the same as
the valve controlling the flow between the sensing chamber outlet
and the liquid collection channel), and a flat plastic removable
plug covering the sample input port (2506).
[0156] Before use, the conditioning liquid filling the flow cell
must be replaced by priming buffer suitable for operation of the
device, but buffer must be introduced in such a way that it does
not allow the sensor in the sensing chamber (2505) to come in
contact with bubbles or gas/liquid interface, which damage the
sensor. Thus, the normal method for buffer replacement involves the
removal of the flow path obstruction(s) (2506), also known as
activation (which allows liquid to flow through the device),
followed by buffer introduction into the buffer input port (2507),
which displaces the conditioning liquid within the device. The flat
plastic plug is then removed from the input port (2506), and
priming buffer is applied via the input port so there is a
continuous fluid channel from the input port (2506) through the
sensing chamber outlet (2501) that is ready to receive sample.
Because there is a continuous fluid channel from the input port
(2506) through the sensing chamber outlet (2501), application of
one or more volumes of test liquid to the wet surface of the input
port provides a net driving for sufficient to introduce the one or
more volumes of test liquid into the device and displace buffer
liquid into the liquid collection channel (2503), allowing normal
operation of the device (e.g. flow of nucleic acids within the test
liquid through the sensor chamber).
[0157] Because the flow path interruptions (2506) are provided as a
valve with a horizontally-actuatable lever on the surface of the
device, opening of the valve during the priming process can be
automated e.g. via a robotic arm. The removal of the flat sample
input port plug is difficult to automate.
[0158] Replacement of the flat sample input port plug with an
alternative plug that is amenable to robotic removal, however,
allows the opening of the sample input port to be opened during an
automated process. In an example, the alternative plug is
cylindrical, consisting of a first flat end and a second conical
tapered end, wherein the conical tapered end tapers to a size
sufficient to completely fill and obstruct the sample input port.
Such a cylindrical plug will project a sufficient distance (e.g.
1-3 cm, or 1.0, 1.5, 2.0, 2.5, or 3 cm) above the surface of the
flow cell when used to plug the sample input port via its tapered
end so that it can be conveniently removed by a robotic arm without
touching the surface of the flow cell. Such an exemplary
alternative plug is depicted in FIG. 26. In some instances, the
alternative plug is a shape that is not cylindrical (e.g.
rectangular, square, triangular), but which projects at least a
sufficient distance (e.g. 1-3 cm, or 1.0, 1.5, 2.0, 2.5, or 3 cm)
above the surface of the flow cell when used to plug the sample
input port that it can be removed without disturbing the surface of
the flow cell and at least tapers to a size sufficient to plug the
sample input port. In some embodiments, the alternative plug is
constructed of a ferromagnetic material such as ferritic stainless
steel, so that handling (placement and/or removal) of the plug can
be accomplished with an electromagnet. In some embodiments, the
alternative plug comprises a metallic material. In some
embodiments, the alternative plug comprises tungsten, aluminum,
austenic stainless steel, ferritic stainless steel, or another
material that is resistant to dilute nitric acid (HNO.sub.3), 1M
NaOH, or dilute NaOCl for removal of RNA/DNA/RNAse contamination.
In some embodiments, the alternative plug comprises polypropylene
or polycarbonate.
[0159] The automated removal of the alternative plug can be
incorporated into the process of Example 13 to accomplish fully
automated nanopore pore cell priming and sample loading of one or
multiple flow cells simultaneously. An exemplary automated process
involving the use of such an alternative plug described above
involves first replacing the flat plastic sample input plug port
(the "SpotON" plug depicted in FIG. 14) with an embodiment of the
alternative plug described above (e.g. manually). The flow path
interruptions/valves (2506) of the flow cell are opened, and the
device is placed inside an automated sequencing apparatus as
described above, in Example 13, or in FIG. 13. In some embodiments,
the flow path interruptions/valves (2506) of the flow cell are
opened via an automated process after the flow cell has been
manually placed in the automated sequencing apparatus. The
automated sequencing apparatus then provides priming buffer to the
buffer input port (2506), such as the buffers described in Example
13, and after conditioning buffer in the flow cell has been
displaced, the alternative plug is removed (e.g. by a robotic arm)
and sample is provided to the sample input port via the automated
sequencing apparatus. The sequencing process then proceeds as
otherwise described in Example 13 with automated handling and fluid
addition. In some embodiments, the alternative plug is replaced
after the sequencing run is completed so that the flow cell can be
flushed and re-primed for at least an additional run. In this way,
the nanopore flow cells can be repeatedly re-primed and reused e.g.
for running additional samples on the same flow cell, or for
running repeats of samples where the flow sequencing has failed,
data recording has failed, or the PCR amplification of nucleic
acids derived from the food or environmental sample have failed. In
some embodiments, the flow cells in the automated sequencing
apparatus are re-primed and reused at least several times (e.g. at
least 2, 3, 4, 5, 6, 7, 8, 9 or 10 times). Importantly, the
alternative plug provides a way of re-priming the cell on-demand
without manual intervention. This is particularly important in the
case of sequencing/amplification/data transmission failure, as it
enables automated repeating of sample runs after hours or at other
times/locations where the automated sequencing apparatus is
unattended.
Configurations and Methods of Operation
[0160] Described herein are methods for operating an automated
sequencing apparatus for food-borne pathogen detection. Such a
system may be comprised in numerous fashions. In some aspects, the
apparatus may comprise a fixed device with minimal moving parts. In
other aspects, the device may comprise a dynamic, robotic system
with numerous moving parts.
[0161] Regardless of configuration, a sequencing operation may
comprise the steps of sample loading, library generation, library
transfer, sequencing and data communication. Each step may comprise
numerous embodiments. The methods described herein are exemplary
and do not constrain the possible mode of operation for any
embodiment of the food-borne pathogen detection system.
[0162] Sample loading may comprise any process for the emplacement
of a specimen in a library chamber. In some aspects, a specimen may
be manually placed into the library chamber. In other aspects a
sample may be loaded by an automated system. In some aspects,
samples may be captured in cartridges that can be loaded into the
library chamber by an automated sample handling system. Sample
loading may comprise processes for decontaminating library chambers
of environmental contaminants from the loading process.
[0163] Library generation may comprise a sequence of assays and
methods depending upon the methodology of library generation.
Assays may include cell culture, cell lysis, DNA amplification, DNA
purification, washings, extractions, purifications, dilutions,
concentrations, buffer exchanges, restriction assays, barcoding,
and any other biochemical method necessary to generate a DNA
library. In some aspects, a single library chamber may be utilized
for all processing steps. In other aspects, a sample may be relayed
between multiple chambers for each processing step with emptied
library chambers undergoing wash procedures to remove excess
reagents.
[0164] Sequencing of the DNA library may occur in one or more flow
cells. A DNA library may be distributed into multiple libraries to
speed the processing of a sample. Sequencing may comprise the
real-time transmission of data or staged transmission of packets of
data. Data processing may occur in one or more onboard processors
in the sequencing apparatus, or may occur at a remote terminal.
[0165] Although many details of the operation may be found in all
embodiments of the food-borne pathogen detection system, there may
be numerous methods of configuring the system to achieve the
desired level of performance and accuracy within an allowable
footprint. The configuration may be motivated in response to the
application of the system. In some aspects, the sequencing
apparatus may be field-portable for rapid deployment in difficult
environments such as farm fields or restaurant kitchens. Such a
device may have a limited footprint with room for few library
chambers or sequencing flow cells. In other aspects, the device may
comprise a lab-scale fixture with an effectively unconstrained
footprint. Such an instrument may comprise hundreds to thousands of
library chambers and sequencing flow cells with a robotic system
for sample management. Described below are several exemplary
embodiments of apparatus configurations. Other embodiments are
possible within the scope this disclosure.
[0166] Static Operation
[0167] A food-borne pathogen detection apparatus may comprise a
fixed or static device. In such a configuration, the library
chambers may be positioned permanently relative to the sequencing
flow cells. Connectivity between library chambers may comprise a
system of tubing, pumps and valves. The fluid flow system would be
capable of performing all necessary fluid transfer operations
during operation without manual intervention. In some aspects, one
or more library chambers may comprise a sequencing apparatus. The
library chambers may be arranged in a serial or parallel
fashion.
[0168] A sequencing apparatus may comprise a single library chamber
and one or more sequencing flow cells. In some aspects, the
connection between the library chamber and sequencing cell may
comprise a flow line and one or more valves. Such an embodiment may
comprise the simplest device with the most compact footprint.
[0169] Two or more library chambers arranged in parallel fashion
may comprise a sequencing apparatus. In some aspects, a plurality
of library chambers may have connectivity with a single flow cell.
In other aspects, each library may be connected to a single flow
cell. In some aspects, parallel operation of library chambers may
comprise performing all aspects of sample preparation and library
generation within a single library chamber. Following library
generation, nucleic acid from a library chamber may be transferred
to one or more sequencing flow cells. Multiple flow cells may be
used for a single DNA library to speed the sequencing process.
[0170] Two or more chambers may also be arranged in a serial
fashion. A serial operation may comprise a staged operation with
each library chamber specialized to perform a specific operation
within the device methodology. A serial arrangement of library
chambers may comprise a larger footprint than a system comprising a
single library chamber or parallel library chambers. A serial
arrangement of chambers may comprise a more complicated flow system
with additional valves and pumps needed to actuate all necessary
fluid transfer steps. A serial configuration may offer more
efficient operation because each library chamber is designed
specifically for its function.
[0171] Conveyer Operation
[0172] In some aspects, a food-borne pathogen detection apparatus
may comprise a series of two or more library chambers on a conveyer
system. The conveyer may comprise a linear or circular system. A
sequencing apparatus may comprise one or more conveyer systems.
Each conveyer unit may couple to one or more sequencing flow cells.
The purpose of the conveyer system is to move a library chamber
into connectivity with a sequencing cell when the nucleic acid
library has been prepared. Each library chamber on the conveyer
system may have connectivity with the necessary components to carry
out library preparation procedures. When library preparation is
completed, the library chamber may be moved into position by the
conveyer and coupled to the sequencing flow cell. Upon completion
of fluid transfer from the library chamber to the sequencing cell,
the conveyer may move the completed library chamber and out and
replace the flow cell plug or place a new library chamber in
connectivity with the flow cell. In some aspects, the conveyer
system comprises a circular conveyer with four library chambers and
two flow cells mounted along an axis. In this configuration, two
library chambers have connectivity to flow cells while two library
chambers conduct library preparation procedures. When new libraries
are ready for sequencing, the conveyer may rotate 90o to connect
the new library chambers, while the previously-sequenced chambers
being new library preparations.
[0173] Compartment Operation
[0174] A compartment-style sequencing apparatus may comprise a
system of hundreds or thousands of library chambers in a
large-footprint device. In some aspects, a library chamber may
comprise a cartridge that is loaded with a specimen external to the
sequencing apparatus. The cartridge may be transferred into the
apparatus and then moved to a docking station comprising one or
more connective ports by a plurality of robotic conveyances. The
docking ports may provide all necessary fluid transfer operations
to complete library preparation within the library chamber. When
library preparation is complete, the cartridge may be transferred
to an available sequencing flow cell by a plurality of robotic
conveyances. In some aspects, a cartridge-style library chamber may
be simultaneously connected to fluid transfer ports and a
sequencing flow cell to create a semi-robotic system with a reduced
footprint.
[0175] FIG. 11 illustrates a compartmentalized automated sequencing
apparatus of the disclosure with a desktop footprint. 1101 is a
diagram of the apparatus comprising the nucleic acid sequencing
compartment 1102. Nucleic acid library preparation compartment 1103
shows a variety of chambers configured to prepare a plurality of
nucleic acids for a sequencing reaction in close proximity to a
sequencing chamber 1104, which comprises one or more flow cells.
Briefly, an automated apparatus of the disclosure is programmed to
move one or more samples from the library preparation chambers 1103
into a sequencing chamber 1104 upon detecting a failure in a
sequencing reaction. This provides a sequencing process with no
human touch points after a sample is added to the library
preparation chamber, as illustrated in FIG. 12. FIG. 12 illustrates
an embodiment where a sample from a food processing facility, from
a hospital or clinical setting, or from another source can be
manually processed between 6 am to 6 pm or any shorter or longer
incubation window by incubating the sample in a presence of a
growth medium (e.g., enrichment) and automatically processed after
the sample is added to a nucleic acid preparation chamber 1103.
[0176] The disclosed apparatus is programmed in such a manner that
said automated platform moves one or more samples from said nucleic
acid library preparation compartment into said nucleic acid
sequencing chamber. Upon detecting a failure of a sequencing
reaction, the automated platform moves one or more samples from the
failed sequencing flow cell or apparatus to the next sequencing
flow cell or apparatus. In many cases, such samples comprise
nucleic acid sequences that include one or more barcodes. In some
cases, a plurality of mutually exclusive barcodes are added to a
plurality of nucleic acids in said two or more chambers of the
nucleic acid library preparation compartment 1103, thereby
providing a plurality of mutually exclusive barcoded nucleic acids
within the apparatus. In some instances, the automated platform
robotically moves two or more of said mutually exclusive barcoded
nucleic acids into said nucleic acid sequencing chamber, in some
instances by moving said mutually exclusive barcoded nucleic acids
into a same flow cell of said one or more flow cells.
[0177] Classification
[0178] Microbiome data (data representing the presence or absence
of particular species or serotypes of microbes as determined by
sequencing) of the invention can be used to classify a sample. For
example, a sample can be classified as, or predicted to be: a)
containing a particular pathogenic microbe, b) containing a
particular serotype of a pathogenic microbe, and/or c) contaminated
with at least one species/serotype of pathogenic microbe. Many
statistical classification techniques are known to those of skill
in the art. In supervised learning approaches, a group of samples
from two or more groups (e.g. contaminated with a pathogen and not)
are analyzed with a statistical classification method. Microbe
presence/absence data can be used as a classifier that
differentiates between the two or more groups. A new sample can
then be analyzed so that the classifier can associate the new
sample with one of the two or more groups. Commonly used supervised
classifiers include without limitation the neural network
(multi-layer perceptron), support vector machines, k-nearest
neighbours, Gaussian mixture model, Gaussian, naive Bayes, decision
tree and radial basis function (RBF) classifiers. Linear
classification methods include Fisher's linear discriminant,
logistic regression, naive Bayes classifier, perceptron, and
support vector machines (SVMs). Other classifiers for use with the
invention include quadratic classifiers, k-nearest neighbor,
boosting, decision trees, random forests, neural networks, pattern
recognition, Bayesian networks and Hidden Markov models. One of
skill will appreciate that these or other classifiers, including
improvements of any of these, are contemplated within the scope of
the invention.
[0179] Classification using supervised methods is generally
performed by the following methodology:
[0180] In order to solve a given problem of supervised learning
(e.g. learning to recognize handwriting) one has to consider
various steps:
[0181] 1. Gather a training set. These can include, for example,
samples that are from a food or environment contaminated or not
contaminated with a particular microbe, samples that are
contaminated with different serotypes of the same microbe, samples
that are or are not contaminated with a combination of different
species and serotypes of microbes, etc. The training samples are
used to "train" the classifier.
[0182] 2. Determine the input "feature" representation of the
learned function. The accuracy of the learned function depends on
how the input object is represented. Typically, the input object is
transformed into a feature vector, which contains a number of
features that are descriptive of the object. The number of features
should not be too large, because of the curse of dimensionality;
but should be large enough to accurately predict the output. The
features might include a set of bacterial species or serotypes
present in a food or environmental sample derived as described
herein.
[0183] 3. Determine the structure of the learned function and
corresponding learning algorithm. A learning algorithm is chosen,
e.g., artificial neural networks, decision trees, Bayes classifiers
or support vector machines. The learning algorithm is used to build
the classifier.
[0184] 4. Build the classifier (e.g. classification model). The
learning algorithm is run on the gathered training set. Parameters
of the learning algorithm may be adjusted by optimizing performance
on a subset (called a validation set) of the training set, or via
cross-validation. After parameter adjustment and learning, the
performance of the algorithm may be measured on a test set of naive
samples that is separate from the training set.
[0185] Once the classifier (e.g. classification model) is
determined as described above, it can be used to classify a sample,
e.g., that of food sample or environment that is being analyzed by
the methods of the invention.
[0186] Unsupervised learning approaches can also be used with the
invention. Clustering is an unsupervised learning approach wherein
a clustering algorithm correlates a series of samples without the
use the labels. The most similar samples are sorted into
"clusters." A new sample could be sorted into a cluster and thereby
classified with other members that it most closely associates.
Digital Processing Device
[0187] In some aspects, the disclosed provides quality control
methods or methods to assess a risk associated with a food, with a
hospital, with a clinic, or any other location where the presence
of a bacterium poses a certain risk to one or more subjects. In
many instances, systems, platforms, software, networks, and methods
described herein include a digital processing device, or use of the
same. In further embodiments, the digital processing device
includes one or more hardware central processing units (CPUs),
i.e., processors that carry out the device's functions, such as the
automated sequencing apparatus disclosed herein or a computer
system used in the analyses of a plurality of nucleic acid
sequencing reads from samples derived from a food processing
facility or from any other facility, such as a hospital a clinical
or another. In still further embodiments, the digital processing
device further comprises an operating system configured to perform
executable instructions. In some embodiments, the digital
processing device is optionally connected a computer network. In
further embodiments, the digital processing device is optionally
connected to the Internet such that it accesses the World Wide Web.
In still further embodiments, the digital processing device is
optionally connected to a cloud computing infrastructure. In other
embodiments, the digital processing device is optionally connected
to an intranet. In other embodiments, the digital processing device
is optionally connected to a data storage device. In other
embodiments, the digital processing device could be deployed on
premise or remotely deployed in the cloud.
[0188] In accordance with the description herein, suitable digital
processing devices include, by way of non-limiting examples, server
computers, desktop computers, laptop computers, notebook computers,
sub-notebook computers, netbook computers, netpad computers,
set-top computers, handheld computers, Internet appliances, mobile
smartphones, tablet computers, personal digital assistants, video
game consoles, and vehicles. Those of skill in the art will
recognize that many smartphones are suitable for use in the system
described herein. Those of skill in the art will also recognize
that select televisions, video players, and digital music players
with optional computer network connectivity are suitable for use in
the system described herein. Suitable tablet computers include
those with booklet, slate, and convertible configurations, known to
those of skill in the art. In many aspects, the disclosure
contemplates any suitable digital processing device that can either
be deployed to a food processing facility, or is used within said
food processing facility to process and analyze a variety of
nucleic acids from a variety of samples.
[0189] In some embodiments, a digital processing device includes an
operating system configured to perform executable instructions. The
operating system is, for example, software, including programs and
data, which manages the device's hardware and provides services for
execution of applications. Those of skill in the art will recognize
that suitable server operating systems include, by way of
non-limiting examples, FreeBSD, OpenBSD, NetBSD.RTM., Linux,
Apple.RTM. Mac OS X Server.RTM., Oracle.RTM. Solaris.RTM., Windows
Server.RTM., and Novell.RTM. NetWare. Those of skill in the art
will recognize that suitable personal computer operating systems
include, by way of non-limiting examples, Microsoft.RTM.
Windows.RTM., Apple.RTM. Mac OS X.RTM., UNIX.RTM., and UNIX-like
operating systems such as GNU/Linux.RTM.. In some embodiments, the
operating system is provided by cloud computing. Those of skill in
the art will also recognize that suitable mobile smart phone
operating systems include, by way of non-limiting examples,
Nokia.RTM. Symbian.RTM. OS, Apple.RTM. iOS.RTM., Research In
Motion.RTM. BlackBerry OS.RTM., Google.RTM. Android.RTM.,
Microsoft.RTM. Windows Phone.RTM. OS, Microsoft.RTM. Windows
Mobile.RTM. OS, Linux.RTM., and Palm.RTM. WebOS.RTM..
[0190] In some embodiments, a digital processing device includes a
storage and/or memory device. The storage and/or memory device is
one or more physical apparatuses used to store data or programs on
a temporary or permanent basis. In some embodiments, the device is
volatile memory and requires power to maintain stored information.
In some embodiments, the device is non-volatile memory and retains
stored information when the digital processing device is not
powered. In further embodiments, the non-volatile memory comprises
flash memory. In some embodiments, the non-volatile memory
comprises dynamic random-access memory (DRAM). In some embodiments,
the non-volatile memory comprises ferroelectric random access
memory (FRAM). In some embodiments, the non-volatile memory
comprises phase-change random access memory (PRAM). In other
embodiments, the device is a storage device including, by way of
non-limiting examples, CD-ROMs, DVDs, flash memory devices,
magnetic disk drives, magnetic tapes drives, optical disk drives,
and cloud computing based storage. In further embodiments, the
storage and/or memory device is a combination of devices such as
those disclosed herein.
[0191] In some embodiments, a digital processing device includes a
display to send visual information to a user. In some embodiments,
the display is a cathode ray tube (CRT). In some embodiments, the
display is a liquid crystal display (LCD). In further embodiments,
the display is a thin film transistor liquid crystal display
(TFT-LCD). In some embodiments, the display is an organic light
emitting diode (OLED) display. In various further embodiments, on
OLED display is a passive-matrix OLED (PMOLED) or active-matrix
OLED (AMOLED) display. In some embodiments, the display is a plasma
display. In other embodiments, the display is a video projector. In
still further embodiments, the display is a combination of devices
such as those disclosed herein.
[0192] In some embodiments, a digital processing device includes an
input device to receive information from a user. In some
embodiments, the input device is a keyboard. In some embodiments,
the input device is a pointing device including, by way of
non-limiting examples, a mouse, trackball, track pad, joystick,
game controller, or stylus. In some embodiments, the input device
is a touch screen or a multi-touch screen. In other embodiments,
the input device is a microphone to capture voice or other sound
input. In other embodiments, the input device is a video camera to
capture motion or visual input. In still further embodiments, the
input device is a combination of devices such as those disclosed
herein.
[0193] In some embodiments, a digital processing device includes a
digital camera. In some embodiments, a digital camera captures
digital images. In some embodiments, the digital camera is an
autofocus camera. In some embodiments, a digital camera is a
charge-coupled device (CCD) camera. In further embodiments, a
digital camera is a CCD video camera. In other embodiments, a
digital camera is a complementary metal-oxide-semiconductor (CMOS)
camera. In some embodiments, a digital camera captures still
images. In other embodiments, a digital camera captures video
images. In various embodiments, suitable digital cameras include 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and higher megapixel
cameras, including increments therein. In some embodiments, a
digital camera is a standard definition camera. In other
embodiments, a digital camera is an HD video camera. In further
embodiments, an HD video camera captures images with at least about
1280.times.about 720 pixels or at least about 1920.times.about 1080
pixels. In some embodiments, a digital camera captures color
digital images. In other embodiments, a digital camera captures
grayscale digital images. In various embodiments, digital images
are stored in any suitable digital image format. Suitable digital
image formats include, by way of non-limiting examples, Joint
Photographic Experts Group (JPEG), JPEG 2000, Exchangeable image
file format (Exif), Tagged Image File Format (TIFF), RAW, Portable
Network Graphics (PNG), Graphics Interchange Format (GIF),
Windows.RTM. bitmap (BMP), portable pixmap (PPM), portable graymap
(PGM), portable bitmap file format (PBM), and WebP. In various
embodiments, digital images are stored in any suitable digital
video format. Suitable digital video formats include, by way of
non-limiting examples, AVI, MPEG, Apple.RTM. QuickTime.RTM., MP4,
AVCHD.RTM., Windows Media.RTM., DivX.TM., Flash Video, Ogg Theora,
WebM, and RealMedia.
Non-Transitory Computer Readable Storage Medium
[0194] In many aspects, the systems, platforms, software, networks,
and methods disclosed herein include one or more non-transitory
computer readable storage media encoded with a program including
instructions executable by the operating system of an optionally
networked digital processing device. For instance, in some aspects,
the methods comprise creating data files associated with a
plurality of sequencing reads from a plurality of samples
associated with a food processing facility. In further embodiments,
a computer readable storage medium is a tangible component of a
digital processing device. In still further embodiments, a computer
readable storage medium is optionally removable from a digital
processing device. In some embodiments, a computer readable storage
medium includes, by way of non-limiting examples, CD-ROMs, DVDs,
flash memory devices, solid state memory, magnetic disk drives,
magnetic tape drives, optical disk drives, cloud computing systems
and services, and the like. In some cases, the program and
instructions are permanently, substantially permanently,
semi-permanently, or non-transitorily encoded on the media.
Computer Program
[0195] In some embodiments, the systems, platforms, software,
networks, and methods disclosed herein include at least one
computer program. A computer program includes a sequence of
instructions, executable in the digital processing device's CPU,
written to perform a specified task. In light of the disclosure
provided herein, those of skill in the art will recognize that a
computer program may be written in various versions of various
languages. In some embodiments, a computer program comprises one
sequence of instructions. In some embodiments, a computer program
comprises a plurality of sequences of instructions. In some
embodiments, a computer program is provided from one location. In
other embodiments, a computer program is provided from a plurality
of locations. In various embodiments, a computer program includes
one or more software modules. In various embodiments, a computer
program includes, in part or in whole, one or more web
applications, one or more mobile applications, one or more
standalone applications, one or more web browser plug-ins,
extensions, add-ins, or add-ons, or combinations thereof
Web Application
[0196] In some embodiments, a computer program includes a web
application. In light of the disclosure provided herein, those of
skill in the art will recognize that a web application, in various
embodiments, utilizes one or more software frameworks and one or
more database systems. In some embodiments, a web application is
created upon a software framework such as Microsoft.RTM. .NET or
Ruby on Rails (RoR). In some embodiments, a web application
utilizes one or more database systems including, by way of
non-limiting examples, relational, non-relational, object oriented,
associative, and XML database systems. In further embodiments,
suitable relational database systems include, by way of
non-limiting examples, Microsoft.RTM. SQL Server, mySQL.TM., and
Oracle.RTM.. Those of skill in the art will also recognize that a
web application, in various embodiments, is written in one or more
versions of one or more languages. A web application may be written
in one or more markup languages, presentation definition languages,
client-side scripting languages, server-side coding languages,
database query languages, or combinations thereof. In some
embodiments, a web application is written to some extent in a
markup language such as Hypertext Markup Language (HTML),
Extensible Hypertext Markup Language (XHTML), or eXtensible Markup
Language (XML). In some embodiments, a web application is written
to some extent in a presentation definition language such as
Cascading Style Sheets (CSS). In some embodiments, a web
application is written to some extent in a client-side scripting
language such as Asynchronous Javascript and XML (AJAX), Flash.RTM.
Actionscript, Javascript, or Silverlight.RTM.. In some embodiments,
a web application is written to some extent in a server-side coding
language such as Active Server Pages (ASP), ColdFusion.RTM., Perl,
Java.TM., JavaServer Pages (JSP), Hypertext Preprocessor (PHP),
Python.TM., Ruby, Tcl, Smalltalk, WebDNA.RTM., or Groovy. In some
embodiments, a web application is written to some extent in a
database query language such as Structured Query Language (SQL). In
some embodiments, a web application integrates enterprise server
products such as IBM.RTM. Lotus Domino.RTM.. A web application for
providing a career development network for artists that allows
artists to upload information and media files, in some embodiments,
includes a media player element. In various further embodiments, a
media player element utilizes one or more of many suitable
multimedia technologies including, by way of non-limiting examples,
Adobe.RTM. Flash.RTM., HTML 5, Apple.RTM. QuickTime.RTM.,
Microsoft.RTM. Silverlight.RTM., Java.TM., and Unity.RTM..
Mobile Application
[0197] In some embodiments, a computer program includes a mobile
application provided to a mobile digital processing device. In some
embodiments, the mobile application is provided to a mobile digital
processing device at the time it is manufactured. In other
embodiments, the mobile application is provided to a mobile digital
processing device via the computer network described herein.
[0198] In view of the disclosure provided herein, a mobile
application is created by techniques known to those of skill in the
art using hardware, languages, and development environments known
to the art. Those of skill in the art will recognize that mobile
applications are written in several languages. Suitable programming
languages include, by way of non-limiting examples, C, C++, C #,
Objective-C, Java.TM., Javascript, Pascal, Object Pascal,
Python.TM., Ruby, VB.NET, WML, and XHTML/HTML with or without CSS,
or combinations thereof.
[0199] Suitable mobile application development environments are
available from several sources. Commercially available development
environments include, by way of non-limiting examples, AirplaySDK,
alcheMo, Appcelerator.RTM., Celsius, Bedrock, Flash Lite, .NET
Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other
development environments are available without cost including, by
way of non-limiting examples, Lazarus, MobiFlex, MoSync, and
Phonegap. Also, mobile device manufacturers distribute software
developer kits including, by way of non-limiting examples, iPhone
and iPad (iOS) SDK, Android.TM. SDK, BlackBerry.RTM. SDK, BREW SDK,
Palm.RTM. OS SDK, Symbian SDK, webOS SDK, and Windows.RTM. Mobile
SDK.
[0200] Those of skill in the art will recognize that several
commercial forums are available for distribution of mobile
applications including, by way of non-limiting examples, Apple.RTM.
App Store, Android.TM. Market, BlackBerry.RTM. App World, App Store
for Palm devices, App Catalog for webOS, Windows.RTM. Marketplace
for Mobile, Ovi Store for Nokia.RTM. devices, Samsung.TM. Apps, and
Nintendo.RTM. DSi Shop.
Standalone Application
[0201] In some embodiments, a computer program includes a
standalone application, which is a program that is run as an
independent computer process, not an add-on to an existing process,
e.g., not a plug-in. Those of skill in the art will recognize that
standalone applications are often compiled. A compiler is a
computer program(s) that transforms source code written in a
programming language into binary object code such as assembly
language or machine code. Suitable compiled programming languages
include, by way of non-limiting examples, C, C++, Objective-C,
COBOL, Delphi, Eiffel, Java.TM., Lisp, Python.TM., Visual Basic,
and VB .NET, or combinations thereof. Compilation is often
performed, at least in part, to create an executable program. In
some embodiments, a computer program includes one or more
executable complied applications.
Software Modules
[0202] The systems, platforms, software, networks, and methods
disclosed herein include, in various embodiments, software, server,
and database modules. In view of the disclosure provided herein,
software modules are created by techniques known to those of skill
in the art using machines, software, and languages known to the
art. The software modules disclosed herein are implemented in a
multitude of ways. In various embodiments, a software module
comprises a file, a section of code, a programming object, a
programming structure, or combinations thereof. In further various
embodiments, a software module comprises a plurality of files, a
plurality of sections of code, a plurality of programming objects,
a plurality of programming structures, or combinations thereof. In
various embodiments, the one or more software modules comprise, by
way of non-limiting examples, a web application, a mobile
application, and a standalone application. In some embodiments,
software modules are in one computer program or application. In
other embodiments, software modules are in more than one computer
program or application. In some embodiments, software modules are
hosted on one machine. In other embodiments, software modules are
hosted on more than one machine. In further embodiments, software
modules are hosted on cloud computing platforms. In some
embodiments, software modules are hosted on one or more machines in
one location. In other embodiments, software modules are hosted on
one or more machines in more than one location.
Embodiments
[0203] EMBODIMENT 1. An embodiment comprising: (a) deploying an
assay to one or more food processing facilities; (b) performing a
sequencing reaction of a food sample or of an environmental sample
from said one or more food processing facilities; (c) transmitting
an electronic communication comprising a data set associated with
said sequencing reaction of said food sample or of said
environmental sample from said one or more food processing
facilities to a server; and (d) scanning, by a computer, at least a
fraction of said transmitted data set for one or more genes
associated with a microorganism.
[0204] EMBODIMENT 2. The method of embodiment 1, wherein said
scanning scans fewer than 0.001%, 0.01%, 0.1%, 1% of said
transmitted data set for one or more genes associated with said
microorganism.
[0205] EMBODIMENT 3. The method of embodiment 1, wherein said
sequencing reaction is a pore sequencing reaction, a next
generation sequencing reaction, a shotgun next generation
sequencing, or Sanger sequencing.
[0206] EMBODIMENT 4. The method of embodiment 3, wherein said
sequencing reaction is a pore sequencing reaction.
[0207] EMBODIMENT 5. The method of embodiment 4, wherein said pore
sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0208] EMBODIMENT 6. The method of embodiment 5, wherein said
epigenetic pattern is a methylation pattern.
[0209] EMBODIMENT 7. The method of embodiment 1, wherein said
microorganism is pre-selected by a customer.
[0210] EMBODIMENT 8. The method of embodiment 1, further comprising
scanning, by a computer, at least a fraction of said transmitted
data set for one or more genes associated with two or more
microorganisms.
[0211] EMBODIMENT 9. The method of embodiment 1, wherein said
microorganism is selected from the group consisting of: a
microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, and a
microorganism of the Escherichia genus.
[0212] EMBODIMENT 10. The method of embodiment 1, wherein said food
sample is a perishable.
[0213] EMBODIMENT 11. The method of embodiment 10, wherein said
perishable is a meat.
[0214] EMBODIMENT 12. The method of embodiment 11, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0215] EMBODIMENT 13. The method of embodiment 8, wherein said
perishable is a fruit, an egg, a vegetable, a produce or a
legume.
[0216] EMBODIMENT 14. The method of embodiment 1, wherein said
environmental sample is a surface swab or a surface rinse of said
one or more food processing facilities.
[0217] EMBODIMENT 15. The method of embodiment 1, wherein said
environmental sample is a food storage container, a food handling
equipment from said one or more food processing facilities, or a
piece of clothing from a worker of said one or more food processing
facilities.
[0218] EMBODIMENT 16. The method of embodiment 1, further
comprising amplifying or enriching one or more nucleic acids of
said food sample or of said environmental sample prior to
performing said sequencing reaction.
[0219] EMBODIMENT 17. The method of embodiment 1, further
comprising adding at least one barcode to one or more nucleic acids
of said food sample or of said environmental sample prior to
performing said sequencing reaction.
[0220] EMBODIMENT 18. The method of embodiment 17, further
comprising creating, in a computer, a data file that associates
said at least one barcode with a source of said food sample or of
said environmental sample.
[0221] EMBODIMENT 19. The method of embodiment 17, further
comprising adding a plurality of mutually exclusive barcodes to a
plurality of food processing facilities.
[0222] EMBODIMENT 20. The method of embodiment 1, wherein said
scanning comprises scanning said transmitted data set for one or
more polymorphic gene regions.
[0223] EMBODIMENT 21. The method of embodiment 20, wherein said one
or more polymorphic regions comprise one or more single nucleotide
polymorphisms (SNP's), one or more restriction fragment length
polymorphisms (RFLP's), one or more short tandem repeats (STRs),
one or more variable number of tandem repeats (VNTR's), one or more
hypervariable regions, one or more minisatellites, one or more
dinucleotide repeats, one or more trinucleotide repeats, one or
more tetranucleotide repeats, one or more simple sequence repeats,
one or more indel, or one or more insertion elements.
[0224] EMBODIMENT 22. The method of embodiment 20, wherein said one
or more polymorphic regions comprise one or more single nucleotide
polymorphisms (SNP's).
[0225] EMBODIMENT 23. The method of embodiment 1, wherein said
sequencing reaction differentiates a live microorganism from a dead
microorganism.
[0226] EMBODIMENT 24. The method of embodiment 1, wherein said
sequencing reaction differentiates a resident microorganism as
compared to a transient microorganism.
[0227] EMBODIMENT 25. The method of embodiment 1, wherein said
method distinguishes a microorganism from an Escherichia genus from
a microorganism of a Citrobacter genus or a Shiga-Toxin producing
E. coli (STEC) from a non-STEC E. coli.
[0228] EMBODIMENT 26. An embodiment comprising: (a) deploying an
assay to one or more food processing facilities; (b) performing a
sequencing reaction of a food sample or of an environmental sample
from said one or more food processing facilities; (c) transmitting
an electronic communication comprising a data set associated with
said sequencing reaction of said food sample or of said
environmental sample from said one or more food processing
facilities to a server; and (d) scanning, by a computer, at least a
fraction of said transmitted data set for one or more genes
associated with a microorganism. Copy language of issued claim
[0229] EMBODIMENT 27. An embodiment comprising: (a) deploying an
assay to one or more food processing facilities; (b) performing a
sequencing reaction of a food sample or of an environmental sample
from said one or more food processing facilities, wherein said
sample comprises a target nucleic acid comprising a periodic or a
non-periodic barcode; (c) transmitting an electronic communication
comprising a data set associated with said sequencing reaction of
said food sample or of said environmental sample from said one or
more food processing facilities to a server; and (d) scanning, by a
computer, at least a fraction of said transmitted data set for one
or more genes associated with a microorganism, wherein said
fraction is in comparison to a data set of a substantially complete
sequencing reaction, wherein said fraction of said transmitted data
set comprises a number of sequencing reads or a number of sequenced
nucleotide bases.
[0230] EMBODIMENT 28. An embodiment comprising: (a) obtaining a
plurality of nucleic acid sequences from a sample; (b) scanning, by
a computer, at least a fraction of said plurality of said nucleic
acid sequences for a plurality of nucleic acid regions from one or
more microorganisms selected from the group consisting of: a
microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, and a
microorganism of the Escherichia genus, wherein said scanning
characterizes said one or more microorganisms with greater than
99.5% sensitivity.
[0231] EMBODIMENT 29. The method of embodiment 28, wherein said
sample is a food sample or an environmental sample associated with
said food sample.
[0232] EMBODIMENT 30. The method of embodiment 28, wherein said
sample is a non-food sample.
[0233] EMBODIMENT 31. The method of embodiment 28, wherein said
sample comprises blood, plasma, urine, tissue, faces, bone marrow,
saliva or cerebrospinal fluid.
[0234] EMBODIMENT 32. The method of embodiment 28, wherein said
scanning characterizes said one or more microorganisms with greater
than 99% sensitivity.
[0235] EMBODIMENT 33. The method of embodiment 32, wherein said
scanning characterizes said one or more microorganisms with greater
than 99.9%, 99.99%, or 99.999% sensitivity.
[0236] EMBODIMENT 34. The method of embodiment 33, wherein said
scanning characterizes said one or more microorganisms with greater
than 99.5% specificity.
[0237] EMBODIMENT 35. The method of embodiment 34, wherein said
scanning characterizes said one or more microorganisms with greater
than 99% specificity.
[0238] EMBODIMENT 36. The method of embodiment 35, wherein said
scanning characterizes said one or more microorganisms with greater
than 99.9%, 99.99%, or 99.999% specificity.
[0239] EMBODIMENT 37. The method of embodiment 28, wherein said
scanning characterizes said one or more microorganisms with greater
than 99.5% sensitivity and greater than 99% specificity.
[0240] EMBODIMENT 38. The method of embodiment 28, wherein a
scanning of no more than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid
regions within said plurality of nucleic acid sequences
characterizes said one or more microorganisms with greater than
99.5% sensitivity.
[0241] EMBODIMENT 39. The method of embodiment 38, wherein a
scanning of no more than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid
regions within said plurality of nucleic acid sequences
characterizes said one or more microorganisms with greater than
99.9% sensitivity.
[0242] EMBODIMENT 40. The method of embodiment 28, wherein a
scanning of no more than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid
regions within said plurality of nucleic acid sequences
characterizes said one or more microorganisms with greater than
99.5% specificity.
[0243] EMBODIMENT 41. The method of embodiment 40, wherein a
scanning of no more than 0.001%, 0.01%, 0.1%, or 1% of nucleic acid
regions within said plurality of nucleic acid sequences
characterizes said one or more microorganisms with greater than
99.9% specificity.
[0244] EMBODIMENT 42. The method of embodiment 41, wherein said
method has fewer than 0.1% of a false positive identification
rate.
[0245] EMBODIMENT 43. The method of embodiment 28, wherein said
plurality of nucleic acid sequences comprise complementary DNA
(cDNA) sequences.
[0246] EMBODIMENT 44. The method of embodiment 28, wherein said
plurality of nucleic acid sequences comprise ribonucleic acid (RNA)
sequences.
[0247] EMBODIMENT 45. The method of embodiment 28, wherein said
plurality of nucleic acid sequences comprise genomic
deoxyribonucleic acid (gDNA) sequences.
[0248] EMBODIMENT 46. The method of embodiment 28, wherein said
plurality of nucleic acid sequences comprise a mixture of cDNA,
RNA, and gDNA sequences.
[0249] EMBODIMENT 47. The method of embodiment 28, wherein said
scanning comprises scanning said plurality of said nucleic acid
sequences for one or more polymorphic gene regions associated with
said microorganisms.
[0250] EMBODIMENT 48. The method of embodiment 47, wherein said one
or more polymorphic regions comprise a gene coding region
associated with said microorganisms.
[0251] EMBODIMENT 49. The method of embodiment 47, wherein said one
or more polymorphic regions comprise a regulatory region associated
with said microorganisms.
[0252] EMBODIMENT 50. The method of embodiment 47, wherein said one
or more polymorphic regions is selected from the group consisting
of one or more single nucleotide polymorphisms (SNPs), one or more
restriction fragment length polymorphisms (RFLPs), one or more
short tandem repeats (STRs), one or more variable number of tandem
repeats (VNTRs), one or more hypervariable regions, one or more
minisatellites, one or more dinucleotide repeats, one or more
trinucleotide repeats, one or more tetranucleotide repeats, one or
more simple sequence repeats, one or more insertion elements, or
one or more epigenetic modifications.
[0253] EMBODIMENT 51. The method of embodiment 28, wherein said
obtaining of said plurality of nucleic acid sequences comprises
sequencing or hybridizing said plurality of nucleic acid
sequences.
[0254] EMBODIMENT 52. The method of embodiment 51, wherein said
sequencing reaction is a pore sequencing reaction, a next
generation sequencing reaction, a shotgun next generation
sequencing, or Sanger sequencing.
[0255] EMBODIMENT 53. The method of embodiment 52, wherein said
sequencing reaction is a pore sequencing reaction.
[0256] EMBODIMENT 54. The method of embodiment 53, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0257] EMBODIMENT 55. The method of embodiment 54, wherein said
epigenetic pattern is a methylation pattern.
[0258] EMBODIMENT 56. The method of embodiment 28, wherein said
microorganism of the Salmonella genus has a serotype selected from
the group consisting of: Enteritidis, Typhimurium, Newport,
Javiana, Infantis, Montevideo, Heidelberg, Muenchen, Saintpaul,
Oranienburg, Braenderup, Paratyphi B var. L(+) Tartrate+, Agona,
Thompson, and Kentucky.
[0259] EMBODIMENT 57. The method of embodiment 56, wherein said
microorganism of the Salmonella genus is of the serotype
Enteritidis.
[0260] EMBODIMENT 58. The method of embodiment 56, wherein said
microorganism of the Salmonella genus is of the serotype
Typhimurium.
[0261] EMBODIMENT 59. The method of embodiment 56, wherein said
microorganism of the Salmonella genus is of the serotype
Newport.
[0262] EMBODIMENT 60. The method of embodiment 56, wherein said
microorganism of the Salmonella genus is of the serotype
Javiana.
[0263] EMBODIMENT 61. The method of embodiment 56, wherein said
microorganism of the Escherichia genus has a serotype selected from
the group consisting of: O103, O111, O121, O145, O26, O45, and
O157.
[0264] EMBODIMENT 62. The method of embodiment 61, wherein said
microorganism of the Escherichia genus is E. coli O157:H7.
[0265] EMBODIMENT 63. The method of embodiment 28, wherein said
scanning distinguishes said microorganism of the Escherichia genus
from a microorganism of the Citrobacter genus.
[0266] EMBODIMENT 64. The method of embodiment 28, wherein said
microorganism of the Listeria genus has a serotype selected from
the group consisting of: 2a, 1/2b, 1/2c, 3a, 3b, 3c, 4a, 4b, 4ab,
4c, 4d, and 4e.
[0267] EMBODIMENT 65. The method of embodiment 28, wherein said
microorganism of the Campylobacter genus is C. jejuni. C. lari, and
C. coli.
[0268] EMBODIMENT 66. An embodiment comprising: (a) sequencing a
plurality of nucleic acid sequences from a food sample or from an
environmental sample associated with said food sample for a period
of time; and (b) performing an assay on said food sample or said
environment associated with said food sample if said sequencing for
said period of time identifies a threshold level of nucleic acid
sequences from a microorganism in said food sample.
[0269] EMBODIMENT 67. The method of embodiment 66, wherein said
period of time is less than 30 minutes.
[0270] EMBODIMENT 68. The method of embodiment 66, wherein said
period of time is less than 20 minutes.
[0271] EMBODIMENT 69. The method of embodiment 66, wherein said
threshold is no more than 0.001%, 0.01%, 0.1%, or 1%, of nucleic
acid sequences from said microorganism.
[0272] EMBODIMENT 70. The method of embodiment 66, further
comprising performing an amplification reaction on said plurality
of nucleic acid sequences prior to sequencing.
[0273] EMBODIMENT 71. The method of embodiment 66, wherein said
sequencing is a pore sequencing reaction.
[0274] EMBODIMENT 72. The method of embodiment 66, wherein said
assay is a serotyping assay, a culturing assay, a Pulse Field Gel
Electrophoresis (PFGE) assay, a RiboPrinter.RTM. assay, a q-PCR
assay, a Sanger sequencing assay, an ELISA assay, a Whole Genome
Sequencing (WGS) assay, a targeted sequencing assay, or a shotgun
metagenomics assay.
[0275] EMBODIMENT 73. The method of embodiment 66, wherein said
microorganism is selected from the group consisting of: a
microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, and a
microorganism of the Escherichia genus.
[0276] EMBODIMENT 74. The method of embodiment 73, wherein said
microorganism of the Salmonella genus has a serotype selected from
the group consisting of: Enteritidis, Typhimurium, Newport,
Javiana, Infantis, Montevideo, Heidelberg, Muenchen, Saintpaul,
Oranienburg, Braenderup, Paratyphi B var. L(+) Tartrate+, Agona,
Thompson, and Kentucky.
[0277] EMBODIMENT 75. The method of embodiment 73, wherein said
microorganism of the Escherichia genus has a serotype selected from
the group consisting of: O103, O111, O121, O145, O26, O45, and
O157.
[0278] EMBODIMENT 76. The method of embodiment 73, wherein said
microorganism of the Escherichia genus is E. coli O157:H7.
[0279] EMBODIMENT 77. The method of embodiment 73, wherein said
microorganism of the Listeria genus has a serotype selected from
the group consisting of: 2a, 1/2b, 1/2c, 3a, 3b, 3c, 4a, 4b, 4ab,
4c, 4d, and 4e.
[0280] EMBODIMENT 78. The method of embodiment 73, wherein said
microorganism of the Campylobacter genus is C. jejunis, C. lari, or
C. coli.
[0281] EMBODIMENT 79. The method of embodiment 66, wherein said
plurality of nucleic acid sequences comprise complementary DNA
(cDNA) sequences.
[0282] EMBODIMENT 80. The method of embodiment 66, wherein said
plurality of nucleic acid sequences comprise ribonucleic acid (RNA)
sequences.
[0283] EMBODIMENT 81. The method of embodiment 66, wherein said
plurality of nucleic acid sequences comprise genomic
deoxyribonucleic acid (gDNA) sequences.
[0284] EMBODIMENT 82. The method of embodiment 66, wherein said
plurality of nucleic acid sequences comprise a mixture of cDNA,
RNA, and gDNA sequences.
[0285] EMBODIMENT 83. The method of embodiment 66, wherein said
food sample is a perishable.
[0286] EMBODIMENT 84. The method of embodiment 83, wherein said
perishable is a meat.
[0287] EMBODIMENT 85. The method of embodiment 84, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0288] EMBODIMENT 86. The method of embodiment 83, wherein said
perishable is a fruit, an egg, a vegetable, a produce or a
legume.
[0289] EMBODIMENT 87. The method of embodiment 66, wherein said
environmental sample is a surface swab or a surface rinse of said
environment.
[0290] EMBODIMENT 88. The method of embodiment 66, wherein said
environmental sample is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food sample.
[0291] EMBODIMENT 89. An embodiment comprising: (a) obtaining a
first plurality of nucleic acid sequences from a first sample of a
food processing facility; (b) creating a data file in a computer
that associates one or more of said first plurality of nucleic acid
sequences with said food processing facility; (c) obtaining a
second plurality of nucleic acid sequences from a second sample of
said food processing facility; and (d) scanning a plurality of
sequences from said second plurality of nucleic acid sequences for
one or more sequences associated with said food processing facility
in (b).
[0292] EMBODIMENT 90. The method of embodiment 89, wherein said
data file associates a strain of said microorganism with said food
processing facility.
[0293] EMBODIMENT 91. The method of embodiment 89, wherein said
first sample, said second sample, or both comprises a plurality of
sequences from a plurality of microorganisms.
[0294] EMBODIMENT 92. The method of embodiment 89, wherein at least
one of said plurality of microorganisms is a non-pathogenic
microorganism.
[0295] EMBODIMENT 93. The method of embodiment 89, wherein at least
one of said plurality of microorganisms is a pathogenic
microorganism.
[0296] EMBODIMENT 94. The method of embodiment 93, wherein said
pathogenic microorganism is selected from the group consisting of a
gram-negative bacteria, a gram-positive bacteria, a protozoa, a
viruses, and a fungi.
[0297] EMBODIMENT 95. The method of embodiment 94, wherein said
gram-negative bacteria is a Salmonella bacterium.
[0298] EMBODIMENT 96. The method of embodiment 94, wherein said
gram-negative bacteria is an Escherichia bacterium.
[0299] EMBODIMENT 97. The method of embodiment 94, wherein said
gram-positive bacteria is a Listeria bacterium.
[0300] EMBODIMENT 98. The method of embodiment 94, wherein said
gram-negative bacteria is a Campylobacter bacterium.
[0301] EMBODIMENT 99. The method of embodiment 89, further
comprising obtaining a third plurality of nucleic acid sequences
from an additional sample of said food processing facility.
[0302] EMBODIMENT 100. The method of embodiment 89, wherein said
first sample, said second sample, or both is a perishable.
[0303] EMBODIMENT 101. The method of embodiment 100, wherein said
perishable is a meat.
[0304] EMBODIMENT 102. The method of embodiment 100, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0305] EMBODIMENT 103. The method of embodiment 101, wherein said
perishable item is a fruit, an egg, a vegetable, a produce or a
legume.
[0306] EMBODIMENT 104. The method of embodiment 89, wherein said
first sample, said second sample, or both is a surface swab or a
surface rinse of said environment.
[0307] EMBODIMENT 105. The method of embodiment 89, wherein said
first sample, said second sample, or both is a food storage
container, a food handling equipment, or a piece of clothing from a
worker of said environment associated with said food sample.
[0308] EMBODIMENT 106. The method of embodiment 89, wherein at
least one barcode is added to said first plurality of nucleic acid
sequences, said second plurality of nucleic acid sequences or
both.
[0309] EMBODIMENT 107. The method of embodiment 106, wherein said
at least one barcode is associated with said data file of (b),
thereby associating said at least one barcode with said food
processing facility.
[0310] EMBODIMENT 108. The method of embodiment 89, wherein
obtaining said first plurality of nucleic acid sequences, said
second plurality of nucleic acid sequences, or both comprises
performing a sequencing reaction or a hybridization assay.
[0311] EMBODIMENT 109. The method of embodiment 105, wherein said
sequencing reaction is a pore sequencing reaction, a next
generation sequencing reaction, a shotgun next generation
sequencing, or Sanger sequencing.
[0312] EMBODIMENT 110. The method of embodiment 109, wherein said
sequencing reaction is a pore sequencing reaction.
[0313] EMBODIMENT 111. The method of embodiment 110, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0314] EMBODIMENT 112. The method of embodiment 110, wherein said
epigenetic pattern is a methylation pattern.
[0315] EMBODIMENT 113. An embodiment comprising: (a) obtaining a
first sample of a food processing facility; (b) sequencing said
first sample of said food processing facility, thereby generating a
first set of sequencing data from said food processing facility;
(c) obtaining a second sample of said food processing facility; (d)
sequencing said second sample of said food processing facility,
thereby generating a second set of sequencing data from said food
processing facility; and (e) comparing said second set of
sequencing data to said first set of sequencing data; and (d)
decontaminating said food processing facility if said comparing
identifies a pathogenic microorganism in said food processing
facility.
[0316] EMBODIMENT 114. An embodiment comprising (a) obtaining a
first plurality of nucleic acid sequences from a first sample of a
food processing facility; (b) obtaining a second plurality of
nucleic acid sequences from a second food sample of said food
processing facility; and (c) performing sequence alignments in a
computer between said first plurality of nucleic acid sequences and
said second plurality of nucleic acid sequences thereby determining
a similarity between said first sample and said second sample from
said food processing facility.
[0317] EMBODIMENT 115. An embodiment comprising: (a) adding a
reagent to a plurality of nucleic acid molecules from a food sample
or from an environmental sample associated with said food sample,
thereby forming a modified plurality of nucleic acid molecules,
whereby said reagent (i) modifies a structure of or interacts with
a plurality of nucleic acid molecules derived from one or more dead
microorganisms; and (ii) does not modify a structure of a nucleic
acid molecule derived from one or more live microorganisms; thereby
providing a modified plurality of nucleic acid molecules; and (b)
sequencing by a sequencing reaction said modified plurality of
nucleic acid molecules, thereby distinguishing one or more live
organisms from said food sample or from said environmental sample
associated with said food sample.
[0318] EMBODIMENT 116. The method of embodiment 113, wherein said
sequencing reaction comprises pore sequencing.
[0319] EMBODIMENT 117. The method of embodiment 113, wherein said
food sample is stressed, shocked or processed prior to adding said
reagent to said plurality of nucleic acid molecules.
[0320] EMBODIMENT 118. The method of embodiment 113, further
comprising incubating said food sample in a growth medium prior to
performing said sequencing reaction.
[0321] EMBODIMENT 119. The method of embodiment 113, wherein said
reagent is a photoreactive DNA-binding dye.
[0322] EMBODIMENT 120. The method of embodiment 119, wherein said
photoreactive DNA-binding dye is propidium monoazide or a
derivative thereof.
[0323] EMBODIMENT 121. The method of embodiment 113, wherein said
reagent is a DNA intercalating reagent.
[0324] EMBODIMENT 122. The method of embodiment 113, further
comprising performing an amplification reaction prior to sequencing
said modified plurality of nucleic acid molecules.
[0325] EMBODIMENT 123. The method of embodiment 113, wherein said
food sample is a perishable.
[0326] EMBODIMENT 124. The method of embodiment 123, wherein said
perishable is a meat.
[0327] EMBODIMENT 125. The method of embodiment 124, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0328] EMBODIMENT 126. The method of embodiment 123, wherein said
perishable is a fruit, an egg, a vegetable, or a legume.
[0329] EMBODIMENT 127. The method of embodiment 115, wherein said
environmental sample is a surface swab or a surface rinse of said
environment.
[0330] EMBODIMENT 128. The method of embodiment 115, wherein said
environmental sample is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food sample.
[0331] EMBODIMENT 129. An embodiment comprising performing a pore
sequencing reaction on a plurality of nucleic acid molecules from a
food sample or from an environmental sample associated with said
food sample, whereby said pore sequencing reaction distinguishes
one or more nucleic acid molecules derived from a dead
microorganism from one or more nucleic acid molecules derived from
a live microorganism based on a methylation pattern or another
epigenetic pattern of said one or more nucleic acid molecules
derived from said dead microorganism.
[0332] EMBODIMENT 130. The method of embodiment 129, wherein said
pore sequencing reaction is a nanopore sequencing reaction.
[0333] EMBODIMENT 131. A method comprising: (a) obtaining a
plurality of nucleic acid sequences of a food sample or of an
environmental sample from a food processing facility; (b)
performing a first assay in said plurality of nucleic acid
sequences of said food sample, whereby said assay predicts a
presence or predicts an absence of a microorganism in said food
sample; and (c) determining, based on said predicted presence or
said predicted absence of said microorganism of (b) whether to
perform a second assay, whereby a sensitivity of said second assay
is selected to determine a genus, a species, a serotype, a
sub-serotype, or a strain of said microorganism.
[0334] EMBODIMENT 132. The method of embodiment 131, wherein said
first assay and said second assay are identical.
[0335] EMBODIMENT 133. The method of embodiment 131, wherein said
first assay and said second assay have distinct sensitivities.
[0336] EMBODIMENT 134. The method of embodiment 131, wherein said
first assay, said second assay or both comprise a sequencing
assay.
[0337] EMBODIMENT 135. The method of embodiment 134, wherein said
sequencing assay comprises a pore sequencing reaction, a next
generation sequencing reaction, a shotgun next generation
sequencing, or Sanger sequencing.
[0338] EMBODIMENT 136. The method of embodiment 134, wherein said
sequencing reaction is a pore sequencing reaction.
[0339] EMBODIMENT 137. The method of embodiment 134, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0340] EMBODIMENT 138. The method of embodiment 137, wherein said
epigenetic pattern is a methylation pattern.
[0341] EMBODIMENT 139. The method of embodiment 131, wherein said
first assay, said second assay, or both comprise a polymerase chain
reaction (PCR) assay.
[0342] EMBODIMENT 140. The method of embodiment 131, wherein said
first assay, said second assay, or both comprise an enzyme-linked
immunosorbent (ELISA) assay.
[0343] EMBODIMENT 141. The method of embodiment 131, wherein said
first assay, said second assay, or both comprise an enzyme-linked
fluorescent assay (ELFA) assay.
[0344] EMBODIMENT 142. The method of embodiment 131, wherein said
first assay, said second assay, or both comprise a serotyping
assay.
[0345] EMBODIMENT 143. The method of embodiment 131, wherein said
microorganism is selected from the group consisting of: a
microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, a
microorganism of the Escherichia genus, a virus, a parasite, and a
fungi.
[0346] EMBODIMENT 144. The method of embodiment 131, wherein said
food sample is a perishable.
[0347] EMBODIMENT 145. The method of embodiment 144, wherein said
perishable is a meat.
[0348] EMBODIMENT 146. The method of embodiment 145, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0349] EMBODIMENT 147. The method of embodiment 144, wherein said
perishable is a fruit, an egg, a vegetable, a produce or a
legume.
[0350] EMBODIMENT 148. The method of embodiment 144, wherein said
environmental sample is a surface swab or a surface rinse of said
environment.
[0351] EMBODIMENT 149. The method of embodiment 144, wherein said
environmental sample is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food processing facility.
[0352] EMBODIMENT 150. The method of embodiment 131, wherein said
performing of said first assay and said performing of said second
assay predicts said presence or predicts said absence of said
microorganism with greater than 90%, 95%, 98%, 99%, 99.9%, 99.99%
or greater than 99.999% sensitivity.
[0353] EMBODIMENT 151. An embodiment comprising: (a) detecting a
presence or an absence of a non-pathogenic microorganism in a
sample; (b) predicting, by a computer system, a presence or an
absence of a pathogenic microorganism in said sample based on said
presence or said absence of said non-pathogenic microorganism.
[0354] EMBODIMENT 152. The method of embodiment 151, wherein said
predicting is performed by a machine learning algorithm in a
computer.
[0355] EMBODIMENT 153. The method of embodiment 152, wherein said
machine learning algorithm is selected from the group consisting
of: a support vector machine (SVM), a Naive Bayes classification, a
random forest, Logistic Regression, and a neural network.
[0356] EMBODIMENT 154. The method of embodiment 152, wherein said
sample is a food sample or an environmental sample associated with
said food sample.
[0357] EMBODIMENT 155. The method of embodiment 154, wherein said
food sample is a perishable.
[0358] EMBODIMENT 156. The method of embodiment 155, wherein said
perishable is a meat.
[0359] EMBODIMENT 157. The method of embodiment 156, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0360] EMBODIMENT 158. The method of embodiment 155, wherein said
perishable is a fruit, an egg, a vegetable, a produce, or a
legume.
[0361] EMBODIMENT 159. The method of embodiment 154, wherein said
environmental sample is a surface swab or a surface rinse of said
environment.
[0362] EMBODIMENT 160. The method of embodiment 154, wherein said
environmental sample is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food processing facility.
[0363] EMBODIMENT 161. The method of embodiment 151, wherein said
sample is a non-food sample.
[0364] EMBODIMENT 162. The method of embodiment 151, wherein said
sample comprises blood, plasma, urine, tissue, faces, bone marrow,
saliva or cerebrospinal fluid.
[0365] EMBODIMENT 163. The method of embodiment 151, wherein said
non-pathogenic microorganism.
[0366] EMBODIMENT 164. The method of embodiment 151, wherein said
non-pathogenic microorganism is selected from the group consisting
of: Enterobacter asburiae, Enterobacter bugandensis, Enterobacter
cancerogenus, Enterobacter cloacae, Enterobacter endosymbiont,
Enterobacter hormaechei, Enterobacter kobei, Enterobacter ludwigii,
Enterobacter mori, and Enterobacter soli.
[0367] EMBODIMENT 165. The method of embodiment 151, wherein said
pathogenic microorganism is selected from the group consisting of:
a microorganism of the Salmonella genus, a microorganism of the
Campylobacter genus, a microorganism of the Listeria genus, and a
microorganism of the Escherichia genus.
[0368] EMBODIMENT 166. The method of embodiment 151, wherein said
pathogenic microorganism is selected from the group consisting of
Vibrio parahaemolyticus, Vibrio cholera, Vibrio vulnificus,
Escherichia coli, Salmonella enterica, Shigella boydii,
Campylobacter jejuni, Staphylococcus aureus, Listeria
monocytogenes, Clostridium botulinum, Yersinia pseudotuberculosis,
Clostridium perfringens, Yersinia enterocolitica, Coxiella
burnetii, Yersinia pseudotuberculosis, Vibrio parahaemolyticus,
Bacillus cereus, Mycobacterium tuberculosis, Shigella flexneri,
Shigella boydii, Shigella dysenteriae, and Shigella sonnei.
[0369] EMBODIMENT 167. The method of embodiment 151, wherein said
detecting comprises a nucleic acid characterization assay selected
from the group consisting of a pore sequencing reaction, a next
generation sequencing reaction, a shotgun next generation
sequencing, Sanger sequencing, or hybridization assay.
[0370] EMBODIMENT 168. The method of embodiment 167, wherein said
nucleic acid characterization assay is a pore sequencing
reaction.
[0371] EMBODIMENT 169. The method of embodiment 168, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0372] EMBODIMENT 170. The method of embodiment 151, further
comprising performing an assay to confirm the prediction of
(b).
[0373] EMBODIMENT 171. The method of embodiment 170, wherein said
assay is a serotyping reaction.
[0374] EMBODIMENT 172. The method of embodiment 170, wherein said
assay is a polymerase chain reaction (PCR) assay.
[0375] EMBODIMENT 173. The method of embodiment 172, wherein said
assay is an enzyme-linked immunosorbent (ELISA) assay.
[0376] EMBODIMENT 174. The method of embodiment 172, wherein said
assay is an enzyme-linked fluorescent assay (ELFA) assay.
[0377] EMBODIMENT 175. An embodiment comprising: (a) detecting a
presence or an absence of a microorganism in a sample or in a
facility associated with said sample; and (b) predicting, by a
computer system, a risk presented by said facility based on said
presence or said absence of said microorganism.
[0378] EMBODIMENT 176. The method of embodiment 175, wherein said
sample is a food sample or an environmental sample associated with
said food sample.
[0379] EMBODIMENT 177. The method of embodiment 176, wherein said
food sample is a perishable.
[0380] EMBODIMENT 178. The method of embodiment 177, wherein said
perishable is a meat.
[0381] EMBODIMENT 179. The method of embodiment 178, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0382] EMBODIMENT 180. The method of embodiment 177, wherein said
perishable is a fruit, an egg, a vegetable, a produce, or a
legume.
[0383] EMBODIMENT 181. The method of embodiment 176, wherein said
environmental sample is a surface swab or a surface rinse of said
environment.
[0384] EMBODIMENT 182. The method of embodiment 176, wherein said
environmental sample is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food processing facility.
[0385] EMBODIMENT 183. The method of embodiment 175, wherein said
sample is a non-food sample.
[0386] EMBODIMENT 184. The method of embodiment 175, wherein said
sample comprises blood, plasma, urine, tissue, faces, bone marrow,
saliva or cerebrospinal fluid.
[0387] EMBODIMENT 185. The method of embodiment 175, wherein said
facility is a food processing facility.
[0388] EMBODIMENT 186. The method of embodiment 175, wherein said
facility is a hospital or a clinic.
[0389] EMBODIMENT 187. The method of embodiment 175, wherein said
method predicts said presence or said absence of said microorganism
with greater than 90%, 95%, 98%, 99%, 99.9%, 99.99% or 99.999%
sensitivity.
[0390] EMBODIMENT 188. The method of embodiment 175, wherein said
method predicts said presence or said absence of said microorganism
with greater than 90%, 95%, 98%, 99%, 99.9%, 99.99% or 99.999%
specificity.
[0391] EMBODIMENT 189. The method of embodiment 175, wherein said
risk informs an insurance for said facility.
[0392] EMBODIMENT 190. The method of embodiment 175, wherein said
microorganism is a pathogenic or a non-pathogenic
microorganism.
[0393] EMBODIMENT 191. The method of embodiment 175, wherein said
detecting comprises a sequencing reaction or a hybridization
assay.
[0394] EMBODIMENT 192. The method of embodiment 191, wherein said
sequencing reaction is selected from the group consisting of a pore
sequencing reaction, a next generation sequencing reaction, a
shotgun next generation sequencing, Sanger sequencing.
[0395] EMBODIMENT 193. The method of embodiment 192, wherein said
sequencing reaction is a pore sequencing reaction.
[0396] EMBODIMENT 194. The method of embodiment 193, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0397] EMBODIMENT 195. The method of embodiment 175, further
comprising performing an assay to confirm the prediction of
(b).
[0398] EMBODIMENT 196. The method of embodiment 195, wherein said
assay is a serotyping reaction.
[0399] EMBODIMENT 197. The method of embodiment 195, wherein said
assay is a polymerase chain reaction (PCR) assay.
[0400] EMBODIMENT 198. The method of embodiment 195, wherein said
assay is an enzyme-linked immunosorbent (ELISA) assay.
[0401] EMBODIMENT 199. The method of embodiment 195, wherein said
assay is an enzyme-linked fluorescent assay (ELFA) assay.
[0402] EMBODIMENT 200. An embodiment comprising:(a) adding a first
barcode to a first plurality of nucleic acid sequences from a
sample, thereby providing a first plurality of barcoded nucleic
acid sequences; and (b) performing a first sequencing reaction on
said first plurality of barcoded nucleic acid sequences, wherein
said sequencing reaction is performed on a sequencing apparatus
comprising a flow cell; (c) adding a second barcode to a second
plurality of nucleic acid sequences from a second sample, thereby
providing a second plurality of barcoded nucleic acid sequences;
and (d) performing a second sequencing reaction on said second
plurality of barcoded nucleic acid sequences, wherein said second
sequencing reaction is performed on said sequencing apparatus
comprising said flow cell, thereby reusing said flow cell.
[0403] EMBODIMENT 201. The method of embodiment 200, wherein said
first barcode and said second barcode are between 1 nucleotide and
18 nucleotides in length.
[0404] EMBODIMENT 202. The method of embodiment 200, wherein said
first barcode and said second barcode are about 9 nucleotides in
length.
[0405] EMBODIMENT 203. The method of embodiment 200, wherein said
first barcode and said second barcode have identical sequences.
[0406] EMBODIMENT 204. The method of embodiment 200, wherein said
first barcode and said second barcode have distinct sequences.
[0407] EMBODIMENT 205. The method of embodiment 200, further
comprising adding a third barcode to a third plurality of nucleic
acid sequences from a third food sample or from a third
environmental sample associated with said third food sample,
thereby providing a third plurality of barcoded nucleic acid
sequences.
[0408] EMBODIMENT 206. The method of embodiment 205, further
comprising performing a third sequencing reaction on said third
plurality of barcoded nucleic acid sequences, wherein said third
sequencing reaction is performed on said sequencing apparatus
comprising said flow cell, thereby reusing said flow cell for a
third time.
[0409] EMBODIMENT 207. The method of embodiment 200, wherein said
first barcode, said second barcode, and said third barcode have
identical sequences.
[0410] EMBODIMENT 208. The method of embodiment 200, wherein said
first barcode, said second barcode, and said third barcode have
distinct sequences.
[0411] EMBODIMENT 209. The method of embodiment 200, further
comprising performing an amplification reaction or nucleic acid
enrichment on said plurality of nucleic acid sequences prior to
sequencing of (b), (d), or both.
[0412] EMBODIMENT 210. The method of embodiment 200, wherein said
sequencing is selected from the group consisting of a pore
sequencing reaction, a next generation sequencing reaction, a
shotgun next generation sequencing, or Sanger sequencing.
[0413] EMBODIMENT 211. The method of embodiment 200, wherein said
sequencing reaction is a pore sequencing reaction.
[0414] EMBODIMENT 212. The method of embodiment 211, wherein said
pore sequencing reaction distinguishes an epigenetic pattern on a
nucleic acid from said food sample or from said environmental
sample.
[0415] EMBODIMENT 213. The method of embodiment 211, wherein said
epigenetic pattern is a methylation pattern.
[0416] EMBODIMENT 214. The method of embodiment 200, wherein said
plurality of nucleic acid sequences comprise complementary DNA
(cDNA) sequences.
[0417] EMBODIMENT 215. The method of embodiment 200, wherein said
plurality of nucleic acid sequences comprise ribonucleic acid (RNA)
sequences.
[0418] EMBODIMENT 216. The method of embodiment 200, wherein said
plurality of nucleic acid sequences comprise genomic
deoxyribonucleic acid (gDNA) sequences.
[0419] EMBODIMENT 217. The method of embodiment 200, wherein said
plurality of nucleic acid sequences comprise a mixture of cDNA,
RNA, and gDNA sequences.
[0420] EMBODIMENT 218. The method of embodiment 200, wherein said
first sample is a first food sample or a first environmental sample
associated with said first food sample.
[0421] EMBODIMENT 219. The method of embodiment 200, wherein said
second sample is a second food sample or a second environmental
sample associated with said first food sample.
[0422] EMBODIMENT 220. The method of embodiments 218 or 219,
wherein said first food sample, said second food sample, or both
are a perishable.
[0423] EMBODIMENT 221. The method of embodiment 220, wherein said
perishable is a meat.
[0424] EMBODIMENT 222. The method of embodiment 221, wherein said
meat is a poultry, a red meat, a fish, or a swine.
[0425] EMBODIMENT 223. The method of embodiment 221, wherein said
perishable is a fruit, an egg, a vegetable, a produce or a
legume.
[0426] EMBODIMENT 224. The method of embodiments 218 or 219,
wherein said first environmental sample, said second environmental
sample, or both is a surface swab or a surface rinse of said
environment.
[0427] EMBODIMENT 225. The method of embodiments 218 or 219,
wherein said first environmental sample, said second environmental
sample, or both is a food storage container, a food handling
equipment, or a piece of clothing from a worker of said environment
associated with said food processing facility.
[0428] EMBODIMENT 226. The method of embodiment 200, wherein said
sample is a non-food sample.
[0429] EMBODIMENT 227. The method of embodiment 226, wherein said
sample comprises blood, plasma, urine, tissue, feces, bone marrow,
saliva or cerebrospinal fluid.
[0430] EMBODIMENT 228. A nucleic acid sequencing apparatus
comprising: (a) a nucleic acid library preparation compartment
comprising two or more chambers configured to prepare a plurality
of nucleic acids from a sample for a sequencing reaction, wherein
said compartment is operatively connected to a nucleic acid
sequencing chamber; (b) a nucleic acid sequencing chamber, wherein
said nucleic acid sequencing chamber comprises: (i) one or more
flow cells comprising a plurality of pores or sequencing cartridges
configured for the passage of a nucleic acid strand, wherein two or
more of the one or more flow cells are juxtaposed to one another;
and (c) an automated platform, wherein said automated platform is
programmed to robotically move a sample from said nucleic acid
library preparation compartment into said nucleic acid sequencing
chamber.
[0431] EMBODIMENT 229. The nucleic acid sequencing apparatus of
embodiment 228, wherein said automated platform moves a second
sample from said nucleic acid library preparation compartment or
from previously failed sequencing chamber into said nucleic acid
sequencing chamber upon detecting a failure of a sequencing
reaction.
[0432] EMBODIMENT 230. The nucleic acid sequencing apparatus of
embodiment 228, wherein said automated platform moves a second
sample from said nucleic acid library preparation compartment into
said nucleic acid sequencing chamber upon detecting a completion of
a sequencing reaction.
[0433] EMBODIMENT 231. The nucleic acid sequencing apparatus of
embodiment 228, further comprising adding a barcode to a plurality
of nucleic acids in said two or more chambers of (a), thereby
providing a plurality of barcoded nucleic acids for said sequencing
reaction.
[0434] EMBODIMENT 232. The nucleic acid sequencing apparatus of
embodiment 228, further comprising adding a plurality of mutually
exclusive barcodes to a plurality of nucleic acids in said two or
more chambers of (a), thereby providing a plurality of mutually
exclusive barcoded nucleic acids.
[0435] EMBODIMENT 233. The nucleic acid sequencing apparatus of
embodiment 232, wherein said automated platform robotically moves
two or more of said mutually exclusive barcoded nucleic acids into
said nucleic acid sequencing chamber.
[0436] EMBODIMENT 234. The nucleic acid sequencing apparatus of
embodiment 232, wherein said automated platform robotically moves
two or more of said mutually exclusive barcoded nucleic acids into
a same flow cell of said one or more flow cells.
[0437] EMBODIMENT 235. The nucleic acid sequencing apparatus of
embodiment 232, wherein said sample is a food or an environmental
sample.
[0438] EMBODIMENT 236. The nucleic acid sequencing apparatus of
embodiment 232, wherein said sample is a non-food sample.
[0439] EMBODIMENT 237. The nucleic acid sequencing apparatus of
embodiment 236, wherein said sample comprise blood, plasma, urine,
tissue, faces, bone marrow, saliva or cerebrospinal fluid.
[0440] EMBODIMENT 238. An embodiment comprising: (a) adding a first
molecular index to a first plurality of nucleic acid sequences from
a sample, thereby providing a first plurality of indexed nucleic
acid sequences; and (b) adding a second molecular index to said
first plurality of nucleic acid sequences from said first sample,
thereby providing a second plurality of indexed nucleic acid
sequences; and (c) adding a third molecular index to said first
plurality of nucleic acid sequences from said first sample, thereby
providing a third plurality of indexed nucleic acid sequences; (d)
performing a sequencing reaction on said third plurality of nucleic
acid sequences; and (e) demultiplexing, by a computer system, said
third plurality of nucleic acid sequences comprising said first
molecular index, said second molecular index, and said third
molecular index.
[0441] EMBODIMENT 239. The method of embodiment 238, wherein said
first molecular index, said second molecular index, and said third
molecular index are between 1 nucleotide and 18 nucleotides in
length.
[0442] EMBODIMENT 240. The method of embodiment 238, herein said
first molecular index, said second molecular index, and said third
molecular index are about 9 nucleotides in length.
[0443] EMBODIMENT 241. The method of embodiment 238, wherein said
first molecular index, said second molecular index, and said third
molecular index have identical sequences.
[0444] EMBODIMENT 242. The method of embodiment 238, wherein said
first molecular index, said second molecular index, and said third
molecular index have distinct sequences.
[0445] EMBODIMENT 243. The method of embodiment 238, wherein said
first plurality of indexed nucleic acid sequences, said second
plurality of indexed nucleic acid sequences, and said third
plurality of indexed nucleic acid sequences form a barcode
comprising a periodic block design.
[0446] EMBODIMENT 244. The method of embodiment 243, wherein said
periodic block design has a defined Levenshtein distance between
each of said first plurality of indexed nucleic acid sequences,
said second plurality of indexed nucleic acid sequences, and said
third plurality of indexed nucleic acid sequences.
[0447] EMBODIMENT 245. The method of embodiment 238, wherein said
first plurality of indexed nucleic acid sequences, said second
plurality of indexed nucleic acid sequences, and said third
plurality of indexed nucleic acid sequences form a barcode
comprising a nonperiodic block design.
[0448] EMBODIMENT 246. The method of embodiment 245, wherein said
nonperiodic block design has a defined Levenshtein distance between
each of said first plurality of indexed nucleic acid sequences,
said second plurality of indexed nucleic acid sequences, and said
third plurality of indexed nucleic acid sequences.
[0449] EMBODIMENT 247. The method of embodiment 246, wherein said
Levenshtein distance between each of said first plurality of
indexed nucleic acid sequences, said second plurality of indexed
nucleic acid sequences, and said third plurality of indexed nucleic
acid sequences is the maximum possible Levenshtein distance.
[0450] EMBODIMENT 248. An automatable microfluidic device for
analysing a test liquid comprising: a sensor provided in a sensing
chamber; a flow path comprising a sensing chamber inlet and a
sensing chamber outlet connecting to the sensing chamber for
respectively passing liquid into and out of the sensing chamber,
and a sample input port in fluid communication with the inlet; a
liquid collection channel downstream of the outlet; a flow path
interruption between the sensing chamber outlet and the liquid
collection channel, preventing liquid from flowing into the liquid
collection channel from upstream, whereby the device may be
activated by completing the flow path between the sample input port
and the liquid collection channel; a conditioning liquid filling
from the sample input port to the flow path interruption such that
the sensor is covered by liquid and unexposed to a gas or
gas/liquid interface; wherein the device is configured such that
following activation of the device, the sensor remains unexposed to
a gas or gas/liquid interface and the application of respectively
one or more volumes of test liquid to a wet surface of the input
port provides a net driving force sufficient to introduce the one
or more volumes of test liquid into the device and displace buffer
liquid into the liquid collection channel, wherein the device
further comprises a removable seal for the sample input port,
wherein the removable seal has a body that projects at least 1 cm
above the surface of the microfluidic device when seated in the
sample input port.
[0451] EMBODIMENT 249. The device of embodiment 248, wherein the
removable seal projects at least 1, 2.0, 2.5, 3, or 3.5 cm above
the surface of the device.
[0452] EMBODIMENT 250. The device of embodiment 248, wherein the
removable seal is cylindrical, with a first flat end and a second
tapered end that tapers to a size sufficient to plug the sample
input port on the device.
[0453] EMBODIMENT 251. The device of embodiment 248, wherein the
removable seal comprises a metallic material.
[0454] EMBODIMENT 252. The device of embodiment 249, wherein the
removable seal comprises tungsten, aluminum, austenic stainless
steel, or ferritic stainless steel.
[0455] EMBODIMENT 253. The device of embodiment 249, wherein the
removable seal is resistant to decontamination in dilute nitric
acid, 1M NaOH, or dilute sodium hypochlorite.
[0456] EMBODIMENT 254. The device of embodiment 249, wherein the
removable seal comprises polypropylene or polycarbonate.
[0457] EMBODIMENT 255. The nucleic acid sequencing apparatus of
embodiment 228, wherein the one or more flow cells comprising a
plurality of pores or sequencing cartridges is the automatable
microfluidic device of any one of claims 248-255.
EXAMPLES
Example 1: Preparation of Food and Environmental Samples
[0458] Food and environmental samples may be processed for various
purposes, such as the enrichment of one or more microorganism from
the sample, or the isolation of one or more microorganism from the
sample. The following protocol was used in the preparation of
various food and environmental samples including: carcass rinses,
stainless steel, primary production boot covers, dry pet food and
shell eggs.
TABLE-US-00001 TABLE 1 Food and Environmental Sample Preparation
Table 1: Food and Environmental Sample Preparation Enrichment
Amount determined by volume or Matrix Sample Size weight Incubation
Carcass Rinse 30 .+-. 0.6 mL sample rinse fluid 20 .+-. 0.5 mL of
Clear 42 .+-. 1.degree. C. for Salmonella media (CSM) 9-24 h
Stainless Steel 1 sponge pre moistened with 10 10 .+-. 0.5 mL Clear
42 .+-. 1.degree. C. for mL tris-buffered saline Salmonella media
(CSM) 9-24 h Environmental 1 environmental sampling bootie 50 .+-.
1 mL Clear 42 .+-. 1.degree. C. for Boot Cover pre-moistened with
10 mL skim Salmonella media (CSM) 9-24 h milk Pet Food 25 .+-. 0.5
g 100 .+-. 1 mL Clear 42 .+-. 1.degree. C. for Salmonella media
(CSM) 9-24 h Shell Eggs 100 .+-. 2 g 200 .+-. 2 mL Clear 42 .+-.
1.degree. C. for Salmonella media (CSM) 9-24 h
Example 2: Obtaining a Carcass Food Sample
[0459] In this example, carcass food samples are generated by
aseptically draining excess fluid from a carcass and transferring
the carcass to a large sterile sampling bag. 100 mL of an enriched
broth, in this case, Clear Salmonella media (CSM) was poured into
the cavity of the carcass in the sampling bag. The carcass was
rinsed inside and out with a rocking motion for about one minute,
while assuring that all surfaces (interior and exterior of the
carcass) were rinsed. About 20.+-.0.5 mL of the CSM was added to
the sample bag and homogenized by massaging sample bag for
approximately 1.5-2 min. The sample was incubated at
42.+-.1.degree. C. for 9-24 h, providing an enriched sample.
Example 3: Obtaining an Environmental Sample from a Stainless Steel
Surface
[0460] In this example, a stainless steel surface environmental
sample was generated by moistening a sterile sampling sponge in 10
mL of Dey-Engley Broth prior to sampling, or using a sponge
pre-moistened in the same. The sponge was used to touch, scrub, or
otherwise contact the stainless steel surface and it was
subsequently placed into a sampling bag. About 10.+-.0.5 mL of CSM
was added to the sampling sponge. Subsequently, the sponge was
pressed to expel the collection broth into the CSM solution. The
sample was incubated at 42.+-.1.degree. C. for 9-24 h, providing an
enriched sample.
Example 4: Obtaining an Environmental Sample from a Boot Cover
[0461] In this example, an environmental sample from a boot cover
was first pre-moistened in skim milk. About 50.+-.1 mL of CSM was
then added to the sampling bag containing boot cover environmental
sample. The contents were mixed thoroughly for approximately 1.5-2
min, and incubated at 42.+-.1.degree. C. for 9-24 h, thereby
providing an enriched sample. The enriched sample was removed from
incubator and briefly mixed.
Example 5: Obtaining a Pet Food Sample
[0462] In this example, about 25.+-.0.5 g of a pet food sample were
added into a filtered sampling bag. About 100.+-.1 mL CSM was then
added to the sampling bag containing said pet food. The contents
were mixed thoroughly for approximately 1.5-2 min, and incubated at
42.+-.1.degree. C. for 9-24 h, thereby providing an enriched
sample. The enriched sample was removed from incubator and briefly
mixed.
Example 6: Obtaining a Shell Egg Food Sample
[0463] In this example, about 100.+-.2 g of a homogenized egg
sample was added to a filtered sampling bag. About 200.+-.2 mL CSM
was then added to the sampling bag containing said homogenized egg
sample. The contents were mixed thoroughly for approximately 1.5-2
min, and incubated at 42.+-.1.degree. C. for 9-24 h, thereby
providing an enriched sample. The enriched sample was removed from
incubator and briefly mixed.
Example 7: Photoreactive DNA-Binding Dye Treatment
[0464] In this example, a photoreactive DNA-binding dye, namely
propidium monoazide (PMA) was added to various food and
environmental samples, including the samples described in Examples
1-6. In general, 5 .mu.L of a PMAxx solution was added to a well in
a 200 .mu.L 96-well PCR plate. Approximately 45 .mu.L of each
enriched sample from the sampling bags described in Examples 1-6
was added to individual wells in PCR plate containing PMAxx. The
samples were mixed thoroughly by gentle pipetting and placed in the
dark for 10 min at room temperature. Subsequently, the plates were
incubated under a blue LED light for 20 min. 10 .mu.L of each
sample were then diluted with 90 .mu.L of Lysis Buffer in a new 200
.mu.L 96-well PCR plate. The plate was then incubated in a
thermocycler as shown below. Alternatively the sample could have
been incubated in a water bath.
TABLE-US-00002 Step Temperature Time 1 37.degree. C. 20 min 2
95.degree. C. 10 min
Example 8: PMAxx-Induced Removal of Free-Floating DNA
[0465] This example demonstrates that addition of a solution of the
photoreactive DNA-binding dye PMAxx to a sample solution reduced
the number of free-floating and contaminating DNA in said sample.
Specifically, 45 .mu.L of each enriched sample from the sampling
bags as described in Examples 1-7 was added to individual wells of
the 96-well PCR plate containing 25 .mu.L of PMAxx solution. The
sample solutions were mixed thoroughly by gentle pipetting and
placed in the dark for 10 min at room temperature. Subsequently,
the plates were incubated under a blue LED light for 20 min. 10
.mu.L of each sample were then diluted with 90 .mu.L of Lysis
Buffer in a new 200 .mu.L 96-well PCR plate. The plate was then
incubated in a thermocycler as shown below. Analysis of the sample
readouts showed that the addition of PMAxx solution (25 .mu.L) to
the sample solution was sufficient to reduce the number of
free-floating DNA by at least 2 orders of magnitude, as shown in
FIG. 13.
Example 9: Amplification Reaction
[0466] In this example, the samples described in Examples 1-8 were
subjected to an amplification reaction. Briefly 15 .mu.L of primer
cocktail and polymerase master mix was added to individual wells of
an empty 200 .mu.L 96-well PCR plate. About 5 .mu.l of each sample
treated with a photoreactive DNA-binding dye treatment was added to
the respective wells containing the polymerase master mix. The
solution was mixed gently by pipetting up and down and placed in a
thermocycler with the conditions described below.
TABLE-US-00003 Step Temperature Time 1 95.degree. C. 3 min 2
95.degree. C. 30 sec 3 57.degree. C. 1 min 4 72.degree. C. 1 min 5
Go to step 2, 37 times 6 72.degree. C. 10 min 7 10.degree. C.
Hold
Example 10: Library Preparation
[0467] In this example, Solid Phase Reversible Immobilization
(SPRI) Magnetic Beads were used to purify and quantify one or more
of the samples described in Examples 1-9. Briefly, the SPRI beads
were removed from 4.degree. C. storage and allowed to reach room
temperature for approximately 15 min. About 1 mL of 80% ethanol was
prepared by combining 800 .mu.L of ethanol and 200 .mu.L of
molecular biology grade water. Equal volumes of each samples
amplification product (described in Example 9) was used to obtain
at least 100 .mu.L of pooled products, which was purified using the
SPRI beads along with standard manufacturing protocols. Briefly,
100 .mu.L of vortexed, pooled PCR product was pipetted into a 0.2
mL PCR tube and add 60 .mu.L of SPRI beads. The tube was mixed
thoroughly by pipetting up and down approximately 10 times and
incubated at room temperature for 5 min. The sample/bead mixture
was placed in a magnetic stand and the beads were allowed to pellet
in a ring for approximately 30-60 s, leaving a clear supernatant.
The supernatant was discarded by leaving the tube in the magnetic
stand while placing the pipette tip to the bottom center of the
tube when aspirating to avoid disturbing the beads. 190 .mu.L of
80% ethanol was then added to the tube, and incubated for 5-10 s.
The tube was aspirated fully and the ethanol solution discarded.
The process was repeated twice. The sample was allowed to dry for
3-5 min at room temperature, or until no visible ethanol remained.
Once thoroughly dry, the tube was removed from the magnetic stand
and re-suspended in 50 .mu.L of 10 mM RSB into the tube. The tube
was mixed thoroughly by gently pipetting up and down approximately
10 times and incubate at room temperature for 2 min. The tube was
moved to a magnetic stand and incubated at room temperature for 2
min to allow the beads to pellet. 50 .mu.L of the eluate was
removed and retained.
Example 11: End Repair
[0468] In this example, the terminal ends of fragment nucleic acids
described in Example 10 were repaired as described below. First,
the following reagents were combined and mixed well by pipetting up
and down approximately 10 times.
TABLE-US-00004 Reagent Volume Purified Pooled Libraries 45 .mu.L
NEB Ultra II end-prep reaction buffer 7 .mu.L NEB Ultra II End-prep
enzyme mix 3 .mu.L ONT DNA CS (DCS) 5 .mu.L Total 60 .mu.L
[0469] The samples were then spun for approximately 5 s using a
benchtop minifuge. End-repair was performed in a thermal cycler
with the following conditions:
TABLE-US-00005 Step Temperature Time 1 20.degree. C. 5 min 2
65.degree. C. 5 min 3 25.degree. C. 5 min
[0470] Subsequently, the samples were spun for approximately 5 s
using a benchtop minifuge. 60 .mu.L of SPRI beads were added to the
end-repaired product and mixed by pipetting up and down
approximately 10 times. The samples were incubated for 5 min at
room temperature. The sample/bead mixture was placed in a magnetic
stand and the beads were allowed to pellet in a ring around the
middle portion of the tube for approximately 30-60 s, leaving a
clear supernatant. The supernatant was discarded by leaving the
tube in the magnetic stand while placing the pipette tip to the
bottom center of the tube when aspirating to avoid disturbing the
beads. 190 .mu.L of 80% ethanol was added to the samples. The 80%
ethanol solution was incubated in the tube for 5-10 s, and the
ethanol was aspirated and discarded. This process was repeated
twice. The sample was allowed to dry for 5 min at room temperature,
or until no visible ethanol remained. The beads were resuspended
with 31 .mu.L molecular biology grade water and mixed by gently
pipetting up and down approximately 10 times and incubate for 2 min
at room temperature. The tube was moved to a magnetic stand and the
beads were allowed to pellet for approximately 30-60 s. The eluate
was retained as the "end-repaired product".
Example 12: Ligation
[0471] In this example, using the end-repaired product of Example
11, the following reagents were combined:
TABLE-US-00006 Reagent Volume End-repaired product 30 .mu.L ONT
Adapter Mix (AMX 1D) 20 .mu.L NEB Blunt/TA Ligase Master Mix 50
.mu.L Total 100 .mu.L
[0472] The reagents were gently mixed by pipetting up and down
approximately 10 times and were incubated at room temperature for
10 min. About 40 .mu.L of SPRI beads were added to the mixture,
gently mixed, and incubated at room temperature for 5 min. The
sample/bead mixture was placed in a magnetic stand and the beads
were allowed to pellet in a ring around the middle portion of the
tube for approximately 30-60 s, leaving a clear supernatant. The
supernatant was discarded by leaving the tube in the magnetic stand
while placing the pipette tip to the bottom center of the tube when
aspirating to avoid disturbing the beads. The tube was removed from
the magnetic rack and 140 .mu.L of ONT-Adapter Bead Binding buffer
was pipetted onto the beads. The sample was mixed by gently
pipetting up and down approximately 10 times to resuspend the
pellet. The tube was returned to the magnetic stand and the beads
were allowed to pellet in a ring around the middle portion of the
tube for approximately 30-60 s, leaving a clear supernatant. The
supernatant was discarded by leaving the tube in the magnetic stand
while placing the pipette tip to the bottom center of the tube when
aspirating to avoid disturbing the beads. The tube was removed from
the magnetic rack and an additional 140 .mu.L of Adapter Bead
Binding buffer was added and pipetted up and down to resuspend the
pellet. The sample/bead mixture was placed in a magnetic stand and
the beads were allowed to pellet into a ring around the middle
portion of the tube for approximately 30-60 s, leaving a clear
supernatant. The supernatant was discarded by leaving the tube in
the magnetic stand while placing the pipette tip to the bottom
center of the tube when aspirating to avoid disturbing the beads.
The tube was then removed from the magnetic stand. About 15 .mu.L
of Elution Buffer (ELB) was added to the beads, and the beads were
mixed thoroughly by pipetting up and down approximately 10 times
and incubate for 10 minutes at room temperature for 5 min. The
tubes were moved to a magnetic stand and the beads allowed to
pellet for approximately 30-60 s. About 15 .mu.L of eluate was
remove and retained as the "final ligated product" for
sequencing.
Example 13: Pore Sequencing
[0473] In this example, a food or an environmental sample was
processed by pore sequencing using standard manufacturer protocols.
Briefly, one or more flow cells were primed by combining the
following reagents per flow cell:
TABLE-US-00007 Reagent Volume ONT-Running Buffer with Fuel Mix
(RBF) 480 .mu.L Molecular grade H.sub.2O 520 .mu.L Total 1,000
.mu.L
[0474] A loading library was prepared by combining the following
reagents:
TABLE-US-00008 Reagent Volume ONT-Running Buffer with Fuel Mix
(RBF) 35 .mu.L ONT-Library Loading Beads (LLB) 25.5 .mu.L Final
ligated product 12 .mu.L Molecular grade H.sub.2O 2.5 .mu.L Total
75 .mu.L
[0475] The priming port on the Flow Cell was gently opened and
approximately 50 .mu.L of the preservative buffer and any small
bubbles were removed, as illustrated by FIG. 14. About 800 .mu.L of
the priming mix was added into the priming port of the Flow Cell.
Subsequently, 200 .mu.L of the priming mix was dispensed into the
Priming port. The final loading library was mixed thoroughly and 75
.mu.L were added into the SpotON port, as illustrated by FIG. 15.
The lid of the pore sequencing device was closed and the sequencing
was executed.
Example 14: Data Analysis and Interpretation
[0476] In this example, an electronic communication comprising a
data set associated with the sequencing reaction described in
Example 13 was transmitted over the cloud for analysis. The results
of the analysis were reported back to customer. FIG. 16 in this
particular example, the customer requested an analysis of the
sample for the presence or absence of Listeria, Salmonella,
Campylobacter, and E. coli, which required the simultaneous
targeting of multiple pathogens.
Example 15: Identification of a Microorganism in a Food,
Environmental Sample, or in a Non-Food Associated Sample by
Microbiome Metagenomics and Supervised Learning
[0477] In this example, data from pore sequencing was used to
identify foodborne disease-causing microorganisms. Briefly, the
methods and processes described in Examples 1-13 were used to
identify food or environmental samples comprising one or more of
the organism shown below.
TABLE-US-00009 TABLE 2 Table 2: Exemplary Pathogenic Microorganisms
Identified by Methods According to This Disclosure Onset Common
Name Time After Signs & Duration Organism of Illness Ingesting
Symptoms of Ilness Food Sources Bacillus B. cereus food 10-16 hrs
Abdominal 24-48 hours Meats, stews, cereus poisoning cramps, watery
gravies, vanilla diarrhea, nausea sauce Campylobacter
Campylobacteriosis 2-5 days Diarrhea, cramps, 2-10 days Raw and
jejuni fever, and undercooked vomiting; diarrhea poultry, may be
bloody unpasteurized milk, contaminated water Clostridium Botulism
12-72 hours Vomiting, Variable Improperly botulinum diarrhea,
blurred canned foods, vision, double especially vision, difficulty
home-canned in swallowing, vegetables, muscle weakness. fermented
fish, Can result in baked potatoes respiratory failure in aluminum
and death foil Perfringens Perfringens food 8-16 hours Intense
abdominal Usually Meats, poultry, poisoning cramps, watery 24 hours
gravy, dried or diarrhea precooked foods, time and/or temperature-
abused foods Cryptosporidium Intestinal 2-10 days Diarrhea (usually
May be Uncooked food cryptosporidiosis watery), stomach remitting
and or food cramps, upset relapsing over contaminated stomach,
slight weeks to by an ill food fever months handler after cooking,
contaminated drinking water Cyclospora Cyclosporiasis 1-14 days,
Diarrhea (usually May be Various types cayetanensis usually at
watery), loss of remitting and of fresh least 1 appetite, relapsing
over produce week substantial loss of weeks to (imported weight,
stomach months berries, lettuce, cramps, nausea, basil) vomiting,
fatigue E. coli E. coli infection 1-3 days Watery diarrhea, 3-7 or
Water or food (Escherichia (common cause of abdominal more days
contaminated coli) "travelers' cramps, some with human producing
diarrhea") vomiting feces toxin E. coli Hemorrhagic 1-8 days Severe
(often 5-10 days Undercooked O157:H7 colitis or bloody) diarrhea,
beef (especially E. coli O157:H7 abdominal pain hamburger),
infection and vomiting. unpasteurized Usually, little or milk and
juice, no fever is raw fruits and present. More vegetables (e.g.
common in sprouts), and children 4 years contaminated or younger.
Can water lead to kidney failure. Hepatitis A Hepatitis 28 days
Diarrhea, dark Variable, Raw produce, average urine, jaundice, 2
weeks-3 months contaminated (15-50 days) and flu-like drinking
water, symptoms, i.e., uncooked fever, headache, foods and nausea,
and cooked foods abdominal pain that are not reheated after contact
with an infected food handler; shellfish from contaminated waters
Lisieria Listeriosis 9-48 hrs for Fever, muscle Variable
Unpasteurized monocytogenes gastro- aches, and nausea milk, soft
intestinal or diarrhea. cheeses symptoms, Pregnant women made with
2-6 weeks may have mild unpasteurized for invasive flu-like
illness, milk, ready-to- disease and infection can eat deli meats
lead to premature delivery or stillbirth. The elderly or immuno-
compromised patients may develop bacteremia or meningitis.
Noroviruses Variously called 12-48 hrs Nausea, vomiting, 12-60 hrs
Raw produce, viral abdominal contaminated gastroenteritis,
cramping, drinking water, winter diarrhea, diarrhea, fever,
uncooked acute non- bacterial headache. foods and gastroenteritis,
Diarrhea is more cooked foods food poisoning, prevalent in that are
not and food infection adults, vomiting reheated after more common
in contact with an children. infected food handler; shellfish from
contaminated waters Salmonella Salmonellosis 6-48 hours Diarrhea,
fever, 4-7 days Eggs, poultry, abdominal meat, cramps, vomiting
unpasteurized milk or juice, cheese, contaminated raw fruits and
vegetables Shigella Shigellosis or 4-7 days Abdominal 24-48 hrs Raw
produce, Bacillary dysentery cramps, fever, and contaminated
diarrhea. Stools drinking water, may contain blood uncooked and
mucus. foods and cooked foods that are not reheated after contact
with an infected food handler Staphylococcus Staphylococcal 1-6
hours Sudden onset of 24-48 hours Unrefrigerated aureus food
poisoning severe nausea and or improperly vomiting. refrigerated
Abdominal meats, potato cramps. Diarrhea and egg salads, and fever
may be cream pastries present. Vibrio V. 4-96 hours Watery 2-5 days
Undercooked parahaemolyticus parahaemolyticus- (occasionally or raw
seafood, infection bloody) diarrhea, such as abdominal shellfish
cramps, nausea, vomiting, fever Vibrio V. vulnificus 1-7 days
Vomiting, 2-8 days Undercooked vulnificus infection diarrhea, or
raw seafood, abdominal pain, such as blood borne shellfish
infection. Fever, (especially bleeding within oysters) the skin,
ulcers requiring surgical removal. Can be fatal to persons with
liver disease or weakened immune systems.
[0478] First, a database was constructed using data from
approximately 35,000 food or environmental samples (of which about
10% contained traces of pathogenic microorganisms as shown in Table
3) using two components: microorganism presence and chemical
composition. Pore sequencing in combination with use of
characteristic polymorphic gene regions (comprising SNP's, RFLP's,
STRs, VNTR's, hypervariable regions, minisatellites, dinucleotide
repeats, trinucleotide repeats, tetranucleotide repeats, simple
sequence repeats, indels, and insertion elements) associated with a
wide diversity of microorganisms were used to analyze each sample
for the presence or absence of 17,800 different bacterial species
(representing both pathogenic and non-pathogenic bacterial
species). Additionally, data on sample composition was collected
for 4,600 food ingredients in each environmental/food sample.
[0479] The data using the top bacteria associated with pathogen
contamination (exemplified in FIG. 5) was used to train a
classification model, which was tested for overfitting by machine
learning techniques.
[0480] We further tested the performance of the model by testing a
set of unknown food or environmental samples (50% of each). The
full results of and ROC analysis of accuracy and precision of the
classification models are presented in Table 3. In the cases of all
the pathogens in Table 3, the metagenomics-based classification
model had higher than 95% precision and 97% accuracy for pathogen
detection.
TABLE-US-00010 TABLE 3 Table 3: Independent Validation of Pathogen
Prediction in Unknown Samples Accuracy Precision Pathogen Score
Score Vibrio parahaemolyticus 99.78% 96.55% Staphylococcus aureus
99.67% 100.00% Yersinia pseudotuberculosis 99.45% 100.00% Vibrio
vulnificus 99.12% 100.00% Shigella boydii 99.12% 100.00% Salmonella
enterica 96.16% 94.39% Escherichia coli 97.48% 98.40%
Example 16: In Silico Evaluation of Primer Sensitivity and
Specificity
[0481] This example describes the in silico evaluation of primer
sensitivity and specificity for pathogen detection in PCR assays.
First, a candidate primer pair was mapped against inclusion and
exclusion sequences in sequence databases. Secondly, the identified
hits are tabulated based on predicted amplification patterns in
order to then determine the sensitivity and specificity of the
primer pair in silico.
[0482] Specifically, a primer pair was designed to target
Salmonella Montevideo and Salmonella Oranienburg. The composition
of the sequence database for in silico evaluation contained 7705
Salmonella genomes, including 98 Montevideo/Oranienburg genomes,
and 1707 non-Salmonella genomes (total of 9412 genomes). Tabulation
of the analysis results showed that the exact number of 98
Salmonella Montevideo and Oranienburg genomes was identified as
true positive hits. The remaining 9314 (which equals the total
number of 9412 genomes minus the 98 true positive hits identified)
genomes were characterized as true negative results. The results
are shown in FIG. 17.
Example 17: Reuse of Flow Cells
[0483] This example shows that the MinION/GridION flow cell can be
reused for sequence sample analysis for at least 2 times. Between
each sample analysis (50 samples analyzed in each analysis) the
flow cell was washed with a buffer system resulting in 30,000 reads
and 26,000 reads per sample during the second and third reuse,
respectively, compared to 36,000 reads per sample when using a new
flow cell (FIG. 18). FIG. 19 illustrates that the number of reads
per sample for reused MinION/GridION flow cells was well above the
acceptable minimum threshold of 10,000 (10 K) reads per sample.
Example 19: Automated Pathogen Risk Detection
[0484] A significant source of confounding data in pathogen risk
detection is contamination of samples by resident microorganisms on
human handlers. Accordingly, we deployed a biomek-based sample
sequencing platform that requires no human handling after
enrichment (see FIG. 11 and FIG. 12) to implement the methods of
Examples 10-13 and 15. Automation included every step of library
preparation post incubation of the samples as in Examples 1-6, and
included cell lysis, PCR, clean up, and sequencing. An automated
handling system is illustrated in FIG. 11.
[0485] To determine the performance of our automated handling
system, we analyzed samples spiked with 10 different Salmonella
serotypes (Enteritidis, Thyphimurium, I 4_[5]_12: i:-, Newport,
Javiana, Infantis, Montevideo, Heidelberg, Muenchen) by automated
or manual handling. The results are presented in FIG. 20. Serotype
detection accorded 100% between manual and automatic handling, and
a student's T-test of the number of sequencing reads generated
indicated no significant difference between manual and automated
handling.
Example 20: Detection of Food Product Expiration/Shelf Life by
Microbiome Metagenomics
[0486] A significant limitation of existing environmental pathogen
detection methods is that they involve culturing, which involves
the use of multiple different specialized media to detect different
classes of pathogens (e.g. bacteria autotrophic for one or more
nutrient vs those not). This severely limits the ability to detect
food contamination during storage. Accordingly, we applied our
environmental sampling/pore sequencing technique as outlined in
Examples 1-13 on 100 samples of chicken wings and 100 samples of
ground chicken. Each sample was analyzed for the presence/absence
of 17,800 pathogenic and non-pathogenic bacteria.
[0487] We applied a principle components analysis to the whole or
ground chicken data sets, which is presented in FIG. 21 and FIG.
22. Data points for both whole and ground chicken samples cluster
along a discernable trajectory more than 2 days prior to their
expiration date (see movement along PC2 in the whole chicken sample
and PC1/PC3 in the ground chicken sample), while data points 1-2
days from expiration begin to rapidly diverge.
[0488] The principle components analysis suggested a classification
model could be built to detect whether or not a whole or ground
chicken sample had expired. The data on the presence/absence of
17,800 pathogenic and non-pathogenic bacteria was used to generate
a classification model. When tested on an independent data set of
samples, this classifier showed 97% accuracy in detecting samples
past their expiration date using an ROC analysis.
Example 21: Comparison of Periodic and Nonperiodic Block Design for
Sequencing Sample Barcodes; Reduction of Crosstalk Using
Non-Periodic Block Primer Design
[0489] To improve detection of desired sequences during sequencing
runs, we tested the performance of different barcoding designs on
sequence detection. We generated unique sequences of nucleotides
with maximum Levenschtein distances from each other and used them
to generate two formats of barcodes to be applied to sequences
during library preparation: a) a periodic block design, in which
each barcode consisted of a unique block sequence repeated 3 times,
and b) a nonperiodic block design, in which 3 unique blocks were
combined in tandem for each barcode sequence.
[0490] We tested these nonperiodic and periodic block designs
alongside a conventional barcode design (which were designed
barcodes provided by our sequencing platform provider) when applied
to the same samples in test sequencing runs (see FIG. 23). Briefly,
a defined Levenshtein distance between each "building block" or
molecular index can be used to form larger barcodes. Such larger
barcodes can have a period block design, such as barcodes created
by repeating each block multiple times with the largest possible
Levenshtein distance between the individual blocks (see FIG. 23).
Alternatively, such barcodes can also have a nonperiodic block
design, such as barcodes created by concatenative multiple blocks
that are unique to each barcode with the largest possible
Levenshtein distance between the individual blocks (see FIG.
23).
[0491] We performed 10 ONT MinION runs and averaged the % of
retained sequences and crosstalk for each run. The results are
presented in Table 4. Both periodic and nonperiodic barcode designs
showed improvements in retention and crosstalk versus the
conventional design, with the nonperiodic design being the best in
both metrics.
[0492] Both barcode designs present distinct advantages. Both
increase the number of retained sequences and allow for adjustable
precision by choosing 1, 2, or 3 blocks in demultiplexing, but the
periodic design requires fewer repeat blocks and presents less
complexity in demultiplexing, whereas the nonperiodic design allows
for improved crosstalk prevention. The improved crosstalk
prevention of the nonperiodic design suggests a method of reducing
crosstalk during highly multiplexed runs or when a flowcell is
reused.
TABLE-US-00011 TABLE 4 Table 4: Performance of Conventional Barcode
Design vs Periodic and Nonperiodic Block Designs Conventional
Periodic Block Nonperiodic Design Design Block Design Retained
Sequences 85% 96% 98% Crosstalk 6% 5% 2%
Example 22: Detection of Transient Vs Resident Microbes by
Metagenomics
[0493] Listeria-containing food and environmental samples were
prepared, libraries were constructed, and sequencing was performed
as in Examples 1-13 and 15. Samples were analyzed for the presence
of Listeria by analyzing highly polymorphic genetic markers. A
principle component analysis of the Listeria sequences isolated
from sequencing (see FIG. 24) identified clusters of closely
related bacteria which likely originated from the same source.
Example 23: Detection of Microbial Serotype Early in Sequencing
Run
[0494] The length of time for a full sequencing run represents a
major limitation in the speed of detection or serotyping of
pathogenic bacterial strains by high-throughput sequencing. We
hypothesized that using "live" detection calls during sequencing
runs (which can be performed as early as 1 hour for ONT MinION and
GridION, and 5 hours for Illumina MiSeq) would allow for certain
bacteria to be detected/serotyped on a preliminary basis based on
sequencing, with follow-up confirmation by other
non-sequencing-based tests (e.g. Q-PCR).
[0495] We performed a test analysis of 50 environmental samples
with about 15% positive for one of the pathogens identified in
Table 3; positive samples were spiked with Salmonella, Listeria, E.
coli, and campylobacter (2 samples each) from the top known
pathogenic top strain/serotypes. Pathogen species was detected by
detection of characteristic genomic markers. We compared the
accuracy of species detection and serotyping at "live" and complete
timepoints for the sequencing runs. The results are presented in
Table 5. Early detection (1 hour for ONT MinION, and 5 hours for
Illumina MiSeq) was 100% accurate for both formats, while MinION
showed improved accuracy for serotyping.
TABLE-US-00012 TABLE 5 Table 5: "Early call" Detection of Bacterial
Species and Serotype Sequences at Detection Serotyping Final
Platform early call calls calls serotyping call MiSeq 425,000 100%
20% 100% MinION 630,000 100% 60% 100%
Example 24: Cell Concentration from Prepared Food or Environmental
Microbial Samples
[0496] Food or environmental samples of microbes prepared as in
Examples 1-6 are ideally subjected to a concentration step to
maximize the concentration of pathogen associated nucleic acids
(e.g. represented in CFU/.mu.l) and improve downstream detection by
sequencing. A filter-free method involving phase separation is used
to maximize throughput in sample preparation.
[0497] Briefly, a small volume of a liquid formulation that is
designed to be to a) not be miscible with the enrichment media; b)
possess a density of mass similar to that of the desired cell type;
c) be unreactive with downstream applications; d) spontaneously
separate into a distinct layer after mixing with the enrichment
media output from the processes of Examples 1-6 is added to the
enrichment media, and the sample is allowed to equilibrate in a
conical tube to reach a state shown in FIG. 27, which illustrates
the process with microbeads instead of cells. In some embodiments,
the equilibration occurs with or without centrifugation-assisted
phase separation. The aqueous liquid formulation added can contain
a mixture of polymers capable of forming step-gradients in density
(e.g. Ficoll, PEG, glycerol). The desired cell material (e.g.
microbeads shown in FIG. 27), is then collected by directly
pipetting the desired layer and collecting it via a flow-fraction
collection method.
Example 25: Re-Using Flow Cells
[0498] 3 groups of 96 samples (including a mixture of samples
either target pathogen positive as positive samples or non-target
pathogen as negative samples) were prepared according to the
methods described in examples 7-12. Samples were barcoded by
transfer of the libraries to 96-well plates containing a uniquely
indexed barcode specific to each well of the 96-well plate. Each
group of samples from the 96 well plates were pooled into a single
solution and each sample was run successively on an Oxford Nanopore
flow cell. Each cell was washed with buffer in between the runs.
Different numbers of Salmonella-positive and -negative samples were
provided between the runs to introduce sequence variety into each
group. These samples were apportioned into different wells of
barcode-indexed plates. The index plates and barcode assignments
for each group are presented in the table below. Tables 6-8
illustrate on a 96 well grid the sample assignment (positive or
negative) to each well/unique barcode index for each of the 3
successive runs.
[0499] Table 6 illustrates index and well assignments of positive
and negative Salmonella samples for each run on the same nanopore
flow cell. Table 6 illustrates a first run, plate IP1.
TABLE-US-00013 TABLE 6 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + +
+ + + + B - - - - - - - - - - - - C + + + + + + + + + + + + D + + +
+ + + + + + + + + E + + + + + + + + + + + + F + + + + + + + + + + +
+ G - - - - - - - - - - - - H + + + + + + + + + + + +
[0500] Table 7 illustrates a 2nd run, plate IP2.
TABLE-US-00014 TABLE 7 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + +
+ + + + B + + + + + + + + + + + + C - - - - - - - - - - - - D - - -
- - - - - - - - - E - - - - - - - - - - - - F - - - - - - - - - - -
- G + + + + + + + + + + + + H + + + + + + + + + + + +
[0501] Table 8 illustrates a 3rd run, plate IP3.
TABLE-US-00015 TABLE 8 1 2 3 4 5 6 7 8 9 10 11 12 A + + + + + + + +
+ + + + B + + + + + + + + + + + + C + + + + + + + + + + + + D - - -
- - - - - - - - - E - - - - - - - - - - - - F + + + + + + + + + + +
+ G + + + + + + + + + + + + H + + + + + + + + + + + +
[0502] Data from the sequencing runs was analyzed and is presented
in Table 9. Table 9 summarizes the performance parameters for each
run, showing the number of multiplexed samples, whether the samples
were identified as positive or negative for Salmonella, the number
of active nanopore sequencing pores available in each run, the
number of total reads generated for each run, and the number of
false positive (FP) or false negative (FN) calls for Salmonella
presence in each run.
[0503] Table 9 illustrates sample classification as positive or
negative for Salmonella and Performance of Nanopore Sequencing for
each of 3 successive runs on the same flow cell.
TABLE-US-00016 TABLE 9 Total Index Active Total Run Id samples
plate Positives Negatives Flow cell pores reads FP FN 1 96 IP1 72
24 New 1485 1.85 M 0 0 2 96 IP2 48 48 Run1 washed 1104 1.22 M 0 0 3
96 IP3 72 24 Run2 washed 865 1.03 M 0 0
[0504] Surprisingly, high numbers of reads (1.03-1.85 million) were
generated for each run (well above the minimum acceptable minimum
threshold of 10K reads per sample). Additionally, the data from
each run allowed for 100% accuracy in correctly calling the samples
as positive or negative for Salmonella presence (e.g. zero false
positive or false negative calls) and the accuracy in calls did not
decline between runs.
[0505] The results in Table 9 thus demonstrate that, unexpectedly,
the claimed method is capable of correctly distinguishing as many
as 96 uniquely-barcoded samples stacked/multiplexed together in a
single sequencing run on a nanopore flow cell, and that this can be
repeated on the same nanopore flow cell as many as 3 times with no
functional decline in data quality.
[0506] While preferred embodiments of the present invention have
been shown and described herein, such embodiments are provided by
way of example only. It is not intended that the invention be
limited by the specific examples provided within the specification.
While the invention has been described with reference to the
aforementioned specification, the descriptions and illustrations of
the embodiments herein are not meant to be construed in a limiting
sense. Numerous variations, changes, and substitutions will now
occur to those skilled in the art without departing from the
invention. Furthermore, it shall be understood that all aspects of
the invention are not limited to the specific depictions,
configurations or relative proportions set forth herein which
depend upon a variety of conditions and variables. It should be
understood that various alternatives to the embodiments of the
invention described herein may be employed in practicing the
invention. It is therefore contemplated that the invention shall
also cover any such alternatives, modifications, variations or
equivalents. It is intended that the following claims define the
scope of the invention and that methods and structures within the
scope of these claims and their equivalents be covered thereby.
* * * * *