U.S. patent application number 16/724092 was filed with the patent office on 2020-05-07 for metal-antibody tagging and plasma-based detection.
The applicant listed for this patent is Purdue Research Foundation. Invention is credited to Euiwon Bae, Valery P. Patsekin, Bartlomiej P. Rajwa, Joseph Paul Robinson.
Application Number | 20200141873 16/724092 |
Document ID | / |
Family ID | 55459652 |
Filed Date | 2020-05-07 |
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United States Patent
Application |
20200141873 |
Kind Code |
A1 |
Robinson; Joseph Paul ; et
al. |
May 7, 2020 |
METAL-ANTIBODY TAGGING AND PLASMA-BASED DETECTION
Abstract
Various techniques for characterizing a target within a sample
are described. An example method includes applying, to the sample,
a recognition construct that includes a metal and a scaffold,
wherein the scaffold is configured to bind to the target. Energy
can be applied to the sample, wherein the applied energy is
sufficient to transform at least some of the sample into a plasma.
Electromagnetic radiation emitted by the plasma can be detected to
provide an optical-spectrum signal of the sample.
Inventors: |
Robinson; Joseph Paul; (West
Lafayette, IN) ; Rajwa; Bartlomiej P.; (West
Lafayette, IN) ; Patsekin; Valery P.; (West
Lafayette, IN) ; Bae; Euiwon; (West Lafayette,
IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Purdue Research Foundation |
West Lafayette |
IN |
US |
|
|
Family ID: |
55459652 |
Appl. No.: |
16/724092 |
Filed: |
December 20, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15042969 |
Feb 12, 2016 |
10514338 |
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16724092 |
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PCT/US2015/049916 |
Sep 14, 2015 |
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15042969 |
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62049931 |
Sep 12, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 21/718 20130101;
G01N 33/58 20130101; G01N 21/25 20130101; G01N 2469/00 20130101;
G01N 21/67 20130101; G01N 33/569 20130101; G01N 33/56911 20130101;
G01N 2469/10 20130101 |
International
Class: |
G01N 21/71 20060101
G01N021/71; G01N 21/25 20060101 G01N021/25; G01N 33/58 20060101
G01N033/58; G01N 33/569 20060101 G01N033/569; G01N 21/67 20060101
G01N021/67 |
Goverment Interests
STATEMENT OF FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] This invention was made with government support under
Contract No. 59-1935-2-279 awarded by the United States Department
of Agriculture. The government has certain rights in the invention.
Claims
1. A method for characterizing a target within a sample, the method
comprising: applying to the sample a recognition construct
comprising a metal and a scaffold, wherein the scaffold is
configured to bind to the target; retaining the sample on a
substrate; applying energy to the sample, wherein the applied
energy is sufficient to transform at least some of the sample into
a plasma; and detecting electromagnetic radiation emitted by the
plasma to provide an optical-spectrum signal of the sample;
generating a normalized optical-spectrum signal by normalizing the
optical-spectrum signal of the sample with respect to an
optical-spectrum signal of a material of the substrate; and
characterizing the target in the sample based at least in part on
the normalized optical-spectrum signal.
2. The method according to claim 1, wherein the applying energy
comprises heating at least part of the sample.
3. The method according to claim 1, wherein the applying energy
comprises irradiating at least part of the sample using a
laser.
4. The method according to claim 1, wherein the applying energy
comprises applying a spark to at least part of the sample.
5. The method according to claim 1, wherein characterizing the
target in the sample comprises: determining presence of the metal
based at least in part on the normalized optical-spectrum signal by
performing at least spectral unmixing or constrained energy
minimization (CEM).
6. The method according to claim 1, further comprising: preparing
the recognition construct by bonding the metal to the scaffold,
wherein the scaffold comprises a biological scaffold and the metal
comprises a metal atom or ion.
7. The method according to claim 1, wherein the target includes a
microbe and the scaffold comprises an antibody against epitopes
present on a surface of the microbe.
8. The method according to claim 1, wherein the target includes a
biological toxin and the scaffold comprises an antibody against the
biological toxin.
9. The method according to claim 1, wherein: the scaffold is
configured to couple with the target, the metal comprises a metal
atom or ion, and the recognition construct further comprises a
polymer that is coupled to the scaffold, linked to the metal atom
or ion, and comprises a metal-chelating ligand.
10. The method according to claim 9, wherein the metal-chelating
ligand comprises diethylenetriaminepenta-acetic acid (DTPA),
11. The method according to claim 1, wherein characterizing the
target in the sample comprises: determining presence of the metal
by applying the normalized optical-spectrum signal of the sample to
a multi-class classifier selected from the group consisting of a
support vector machine, a kernel estimator, a nearest-neighbor
classifier, a decision tree, a decision forest, a neural network,
or a deep neural network.
12. The method according to claim 1, wherein the scaffold comprises
at least one of: adNectin, iMab, anticalin, designed ankyrin repeat
protein (DARPin), affilin, tetranectin, or avimer.
13. The method according to claim 1, wherein the first metal atom
or ion linked to the metal-chelating ligand comprises a
lanthanide.
14. A method comprising: applying to a sample a recognition
construct comprising a metal and a scaffold, wherein the scaffold
is configured to bind to the target; retaining the sample on a
substrate; applying energy to the sample, wherein the applied
energy is sufficient to transform at least some of the sample into
a plasma; and detecting electromagnetic radiation emitted by the
plasma to provide an optical-spectrum signal of the sample;
generating a normalized optical-spectrum signal by normalizing the
optical-spectrum signal of the sample with respect to an
optical-spectrum signal of a material of the substrate; and
determining presence of the target in the sample based at least in
part on the normalized optical-spectrum signal.
15. The method according to claim 14, wherein the applying energy
comprises at least one of: heating at least part of the sample;
irradiating at least part of the sample using a laser; or applying
a spark to at least part of the sample.
16. The method according to claim 14, wherein determining the
presence of the target in the sample comprises performing at least
one of spectral unmixing or constrained energy minimization (CEM)
on the normalized optical-spectrum signal.
17. The method according to claim 14, wherein determining the
presence of the metal comprises applying the normalized
optical-spectrum signal of to a multi-class classifier comprising
at least one of a support vector machine, a kernel estimator, a
nearest-neighbor classifier, a decision tree, a decision forest, a
neural network, or a deep neural network.
18. The method according to claim 14, wherein: the scaffold is
configured to couple with the target, the metal comprises a metal
atom or ion, and the recognition construct further comprises a
polymer that is coupled to the scaffold, linked to the metal atom
or ion, and comprises a metal-chelating ligand.
19. A method for characterizing a target within a sample, the
method comprising: causing the target to bind with a recognition
construct, the recognition construct comprising a scaffold
configured to bind with the target; a polymer coupled to the
scaffold and comprising a metal-chelating ligand; and a metal atom
or ion linked to the metal chelating ligand, the sample comprising
the target bound with the recognition construct; causing the target
within the sample to be bound to a capture antibody that is coupled
to a silicon substrate; in response to causing the target to be
bound to the capture antibody, applying energy to the sample,
wherein the applied energy is sufficient to transform at least some
of the sample into a plasma; and detecting electromagnetic
radiation emitted by the plasma to provide an optical-spectrum
signal of the sample; generating a normalized optical-spectrum
signal by normalizing the optical-spectrum signal of the sample
with respect to an optical-spectrum signal of silicon; and
identifying presence of the target in the sample by detecting
presence of the metal atom or ion based at least in part on the
normalized optical-spectrum signal.
20. The method of claim 19, wherein: the scaffold comprises at
least one of adNectin, iMab, anticalin, designed ankyrin repeat
protein (DARPin), affilin, tetranectin, or avimer; the
metal-chelating ligand comprises diethylenetriaminepenta-acetic
acid (DTPA); and the first metal atom or ion linked to the
metal-chelating ligand comprises a lanthanide.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present patent application is a divisional of U.S.
patent application Ser. No. 15/042,969, filed Feb. 12, 2016, which
is a continuation-in-part under 35 U.S.C. 365(c) of International
Application No. PCT/US2015/049916, filed Monday, Sep. 14, 2015,
which is related to and claims the priority benefit of U.S.
Provisional Patent Application Ser. No. 62/049,931, filed Sep. 12,
2014, the content of each of which is hereby incorporated by
reference in its entirety into this disclosure.
TECHNICAL FIELD
[0003] The present disclosure generally relates to biological
detection, and in particular to detection of biological pathogens
using antibody tagging.
BACKGROUND
[0004] This section introduces aspects that may help facilitate a
better understanding of the disclosure. Accordingly, these
statements are to be read in this light and are not to be
understood as admissions about what is or is not prior art.
[0005] The fields of microbiology, biosafety, and biosurveillance
employ multiple detection technologies paired with various
reporting modalities. The most common approaches use traditional
optical labeling techniques such as fluorescence, phosphorescence,
or formation of color chromophores. The optical labels are
typically connected to molecular recognition molecules such as
antibodies.
[0006] Other lesser-known methods for pathogen recognition or
detection include detection of antibody immobilized bacteria using
surface plasmon resonance (SPR) sensors, interferometric
biosensors, acoustic wave biosensor platforms based on the
thickness shear mode (TSM) resonator, and piezoelectric-excited
millimeter-sized cantilever (PEMC) sensors. There has been also
experimental work reported on detection involving microfluidic
microchips coated with antibodies. The chips have an electric
current passed through them. When the chip surface comes into
contact with bacteria, the system shows changes in potentiometric,
amperometric, or impedimetric/conductimetric characteristics
demonstrating bacterial presence.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Various objects, features, and advantages will become more
apparent when taken in conjunction with the following description
and drawings wherein identical reference numerals have been used,
where possible, to designate features that are common to the
drawings. The attached drawings are for purposes of illustration
and are not necessarily to scale.
[0008] FIG. 1 is a diagram showing the components of a system for
detecting a biological target in a sample.
[0009] FIG. 2 is a plot showing example data that were collected in
an experiment that where samples containing bacteria were labeled
with two different metal-tagged antibodies, Sb and Pr according to
one embodiment.
[0010] FIG. 3 is an annotated graphical representation of a
photograph of an example configuration of a silicon wafer to hold
sample(s).
[0011] FIG. 4 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Lu and Pr in the
400-600 nm range according to one embodiment.
[0012] FIG. 5 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Lu and Pr in the
320-380 nm range according to one embodiment.
[0013] FIG. 6 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with .sup.156Gd and a blank
sample in the 340-380 nm range according to one embodiment.
[0014] FIG. 7 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Pr in the 340-460 nm
range according to one embodiment.
[0015] FIG. 8 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Dy in the 240-360 nm
range according to one embodiment.
[0016] FIG. 9 is a plot showing initial dose response to two
different agents, Shiga Toxin Stx-2-2 labeled with .sup.141Pr and
Ricin labeled with .sup.162Lu.
[0017] FIG. 10 is a plot showing spectral measurement of the 240
nm-360 nm window, where there are possible peaks that only exists
on certain regions of the spectra (for Pr, Lu, Gd, and Dy).
[0018] FIG. 11 is a high-level depiction of an example recognition
construct.
[0019] FIG. 12 illustrates an example process for analysis of a
sample using spark-induced breakdown spectroscopy (SIBS).
[0020] FIG. 13 illustrates an example process for preparing a
recognition construct.
[0021] FIG. 14A is a plot of measured spectral data of a
substrate.
[0022] FIG. 14B is a plot of measured spectral data of a tagged
recognition construct.
[0023] FIG. 14C is a plot of measured spectral data of another
tagged recognition construct.
[0024] FIG. 15 schematically illustrates an example process for
analysis of a sample using laser-induced breakdown spectroscopy
(LIBS).
[0025] FIGS. 16A-16H schematically illustrate an example process
for analysis of a sample using SIBS.
[0026] FIG. 17A is a schematic of a LIBS system.
[0027] FIG. 17B is a plot of measured temporal profiles of example
laser pulses.
[0028] FIG. 18 illustrates example validation spectra prediction
results.
[0029] FIG. 19 illustrates example spectral measurements according
to the prior art.
[0030] FIG. 20A illustrates example spectral measurements.
[0031] FIG. 20B illustrates example spectral measurements.
[0032] FIG. 21 graphically depicts structural components of an
antibody useful with various examples.
[0033] FIG. 22 schematically illustrates a process of preparing a
recognition construct including a metal-tagged antibody.
[0034] FIGS. 23A-23C schematically illustrate steps in a process of
analyzing a sample.
[0035] FIG. 24 is a schematic diagram of a bead carrying capture
antibodies.
[0036] FIG. 25 is a plot of measured spectral data.
[0037] FIG. 26 is an example dose-response curve.
[0038] FIG. 27 is a schematic diagram of portions of a SIBS
system.
[0039] FIG. 28 is a schematic diagram of portions of a LIBS
system.
[0040] FIG. 29 is a schematic diagram of portions of a SIBS
system.
[0041] FIG. 30 is a flow diagram of an example process for
analyzing a sample.
[0042] FIG. 31 is a flow diagram of an example process for
analyzing a sample.
DETAILED DESCRIPTION
Overview
[0043] Many prior schemes do not offer good multiplexing
capability, as they are specifically designed to announce the
presence of a specific type or category of bacteria. They are also
not easily extendable to detect other biological hazards, such as
present of biological toxins. Therefore, improvements are needed in
the field. However, the claimed subject matter is not limited to
implementations that solve any or all of these disadvantages or
other herein-described disadvantages or limitations of prior
schemes.
[0044] Various aspects herein provide optical detection of targets
in samples. The target can be, e.g., biological or metallic. As
used herein, the term "light" can include, but is not limited to,
electromagnetic radiation in a human-visible wavelength range of,
e.g., k=400 nm-700 nm, or in a wavelength range of, e.g., 240
nm-360 nm. The term "optical" relates to structures and techniques
for detecting electromagnetic radiation, e.g., in the above noted
wavelength ranges or in other wavelength ranges. For example,
optical detection as described herein can be used to, e.g., detect
viruses on surfaces; detect pesticides or dioxins in fats or oils;
analyze food-related samples for the presence of biological,
biochemical, or chemical contaminants, e.g., bacteria, molds,
biological toxins, pharmaceuticals, or heavy metals; detect
microorganisms such as E. coli and Salmonella species; or detect
toxins such as Botulinum neurotoxins (e.g., serotypes A, B, E, or
F), Shiga toxins, Ricin, Abrin, mycotoxins, or bacterial toxins.
Examples can include detecting Shiga or other toxins carried by
bacteria, e.g., E. coli.
[0045] Various aspects herein provide methods and apparatus for
metal-antibody tagging and plasma-based detection (MAPD), which
involves the use of metal-labeled recognition macromolecules to tag
infectious agents (such as bacterial cells) or toxic biological
products and substances for subsequent detection using
laser-induced breakdown spectroscopy (LIBS), spark induced
breakdown spectroscopy (SIBS), laser ablation molecular isotopic
spectrometry (LAMIS) or other detection modalities using optical
spectra evaluation after plasma formation. Various herein-described
detection techniques use optical emission spectroscopy. They employ
a laser and a focusing lens (LIBS and LAMIS), or a spark (SIBS) to
generate a plasma from the vaporized tagged sample. Various
examples use microwaves or glow-discharge microwave to vaporize
tagged samples to generate the plasma.
[0046] Breakdown spectroscopy as described herein allows detection
of various broad classes of biological contaminants (such as
microorganisms), and can serve as a sensitive detector of
toxicologically important metals (e.g., light metals or heavy
metals), including cadmium, arsenic, beryllium, chromium, copper,
lead, mercury, thallium, nickel, and zinc. Various aspects can test
for the presence of toxic organic compounds (such as polybrominated
biphenyls and polybrominated diphenyl ethers), pesticides (aldrin,
dieldrin, chlorpyrifos, parathion), and dioxins. Various examples
do not require labeling the targets to be detected, but instead
label recognition constructs as described below. Detecting targets
without labeling those targets can permit detecting a wider range
of compounds and more reliably attaching and detecting labels,
compared to prior schemes.
[0047] In various aspects, a method for characterizing a target,
e.g., a microbe or a biological toxin, includes labeling the target
with a biomolecular recognition construct and measuring an
optical-spectrum signal of the biomolecular recognition construct.
The biomolecular recognition construct may be formed by tagging a
biological or other recognition scaffold with a metal atom or ion.
The target may include microbe(s), e.g., bacteria, and the
biological scaffold may comprise an antibody against epitopes
present on bacterial surface, the antibody linked to a heavy metal.
The method can include heating the labeled target before measuring
the optical-spectrum signal. The optical-spectrum signal can be
measured by performing laser-induced breakdown spectroscopy (LIBS).
The optical-spectrum signal can be measured by performing spark
induced breakdown spectroscopy (SIBS). Data of the optical-spectrum
signal can be classified using a computer-based classifier and a
classification score can be assigned to the analyzed sample (e.g.,
spectral unmixing or spectral fingerprint classification).
[0048] Various examples incorporate at least: stable,
readily-synthesizable metallic labels; a disposable solid-surface
format that can be functionalized and that provides robust
reference signals for calibration and data alignment; or an
inexpensive readout apparatus linked to a computer-based
data-processing system.
[0049] Various aspects include a transportable system able to
replace a number of dedicated prior contamination detection
technologies developed for single specific classes of adulterants.
Various aspects process an optically-detected spectral readout
using machine-learning algorithms or a database of plasma patterns
to provide high-content information about a variety of tested
samples. Various aspects provide a universal readout format
compatible with at least three classes of contaminants (biological,
organic-chemical, and inorganic-chemical).
[0050] Various aspects herein include at least an apparatus or
method for characterizing a target, e.g., a microbe or a biological
toxin. Apparatus can be configured to perform, or the method can
include, labeling the target with a biomolecular recognition
construct and measuring an optical-spectrum signal of the
biomolecular recognition construct. The method can include heating
the labeled target before measuring the optical-spectrum signal.
The optical-spectrum signal can be measured by performing
laser-induced breakdown spectroscopy. The optical-spectrum signal
can be measured by performing spark induced breakdown spectroscopy.
The biomolecular recognition construct can be prepared by tagging a
biological scaffold with a metal atom or ion. In an aspect in which
the target includes a microbial sample, the biological scaffold can
include an antibody against epitopes present on bacterial surface,
the antibody linked to a heavy metal. In an aspect in which the
target includes a biological toxin, the biological scaffold can
include an antibody against the biological toxin linked to heavy
metals.
[0051] One prior scheme is dissociation-enhanced lanthanide
fluorescence immunoassay (DELFIA), a fluorescence-based assay.
DELFIA uses narrow-banded emission and large Stokes shift of
lanthanide-diketone chelates (e.g., europium) detected via
time-resolved fluorescence. However, in DELFIA, that binding
between the La ions and the .beta.-diketone ligands is too weak for
the chelate-antibody conjugation. Therefore, a multistep approach
is used in which the antibody is linked with stable chelates (e.g.,
based on EDTA derivatives) prior to use. Subsequently the La ions
are dissociated using a low-pH enhancer solution and re-chelated
with .beta.-diketone or its derivatives.
[0052] Various aspects herein, compared to DELFIA, require fewer
steps of incubation, since DELFIA requires the lanthanide ions be
released from the chelating polymers. The reduction in incubation
provides various aspects with higher throughput than DELFIA.
Various aspects can use more metals than the four lanthanides be
detectable using DELFIA (europium, terbium, samarium and
dysprosium). Various aspects can use five or more different metal
tags in a single sample, to multiplex more than the four-way
multiplexing supported by DELFIA. Various aspects provide more
metal ions per antibody than the small number provided in DELFIA,
due to DELFIA's low-efficiency chelation process. A higher number
of ions per antibody can provide various aspects with higher
signal-to-noise ratios than DELFIA. Various aspects tag monoclonal
antibodies (mAbs) or other antibodies without interfering with
antibody functionality, unlike the tagging process used in DELFIA,
which may compromise antibody functionality.
[0053] Another prior scheme is mass cytometry, often referred to as
CYTOF, in which rare-earth elements are attached to cells of
interest. The cells are vaporized and any metals in those cells are
detected by time-of-flight mass spectrometry. However, CYTOF
requires expensive, precision equipment that may not be readily
adaptable to uses in the field. In contrast, various aspects herein
provide portable, inexpensive instruments that can detect metals
without requiring a mass spectrometer. Various aspects herein use
optical signals, e.g., intensity of plasma emission as a function
of wavelength, instead of mass-spectrometer data, e.g., number of
ions as a function of mass-to-charge ratio.
Illustrative Embodiments
[0054] For the purposes of promoting an understanding of the
principles of the present disclosure, reference will now be made to
the embodiments illustrated in the drawings, and specific language
will be used to describe the same. It will nevertheless be
understood that no limitation of the scope of this disclosure is
thereby intended.
[0055] FIG. 1 is a diagram showing the components of an example
recognition system 101 for analyzing sample data and performing
other analyses described herein, and related components. The system
101 includes a processor 186, a peripheral system 120, a user
interface system 130, and a data storage system 140. The peripheral
system 120, the user interface system 130, and the data storage
system 140 are communicatively connected to the processor 186.
Processor 186 can be communicatively connected to network 150
(shown in phantom), e.g., the Internet or a leased line, as
discussed below. Lasers, sample preparation or addition devices,
substrate handlers, and other devices herein can each include one
or more processor(s) 186 or one or more of systems 120, 130, 140,
and can each connect to one or more network(s) 150. Processor 186,
and other processing devices described herein, can each include one
or more microprocessors, microcontrollers, field-programmable gate
arrays (FPGAs), application-specific integrated circuits (ASICs),
programmable logic devices (PLDs), programmable logic arrays
(PLAs), programmable array logic devices (PALs), or digital signal
processors (DSPs).
[0056] Processor 186 can implement processes of various aspects
described herein. Processor 186 and related components can, e.g.,
carry out processes for performing assays using recognition
macromolecules as described in Paper 1.
[0057] Processor 186 can be or include one or more device(s) for
automatically operating on data, e.g., a central processing unit
(CPU), microcontroller (MCU), desktop computer, laptop computer,
mainframe computer, personal digital assistant, digital camera,
cellular phone, smartphone, or any other device for processing
data, managing data, or handling data, whether implemented with
electrical, magnetic, optical, biological components, or
otherwise.
[0058] The phrase "communicatively connected" includes any type of
connection, wired or wireless, for communicating data between
devices or processors. These devices or processors can be located
in physical proximity or not. For example, subsystems such as
peripheral system 120, user interface system 130, and data storage
system 140 are shown separately from the processor 186 but can be
embodied or integrated completely or partially within the processor
186. In an example, processor 186 includes an ASIC including a
central processing unit connected via an on-chip bus to one or more
core(s) implementing function(s) of systems 120, 130, or 140.
[0059] The peripheral system 120 can include or be communicatively
connected with one or more devices configured or otherwise adapted
to provide digital content records to the processor 186 or to take
action in response to signals or other instructions received from
processor 186. For example, the peripheral system 120 can include
digital still cameras, digital video cameras, spectroscopic
detector 196, or other data processors. The processor 186, upon
receipt of digital content records from a device in the peripheral
system 120, can store such digital content records in the data
storage system 140.
[0060] Processor 186 can, via peripheral system 120, control
subsystems 190, 192, 194, and spectroscopic detector 196.
Biological sample 198 is carried on substrate 199, which can be or
include, e.g., a silicon (Si) wafer or a polystyrene (PS) sheet.
Substrate 199 can be manipulated by a wafer-handling or other
motion subsystem (not shown). Target 197 is shown in sample 198 for
illustration. Sample 198 can include liquid, gas, powder, bulk
solid, or any combination or mixture thereof. In some examples,
target 197, e.g., toxin molecules or bacteria, is captured and
immobilized on substrate 199 for subsequent detection as described
herein.
[0061] Subsystem 190, e.g., a preparation subsystem (graphically
represented as an eyedropper), is configured or otherwise adapted
to add a biomolecular recognition construct to the sample 198,
e.g., a dispenser or sample-deposition device such as those used in
automatic dry- or wet-slide bioassays or in flow cytometry. The
recognition construct can include a metal. Subsystem 192 is
configured to wash at least some unbound recognition construct out
of the sample 198 to provide washed sample. Subsystem 194 is
configured to heat the sample-construct mixture, e.g., the sample
before washing or the washed sample, so that at least some of the
metal in the biomolecular recognition construct in the washed
sample emits electromagnetic radiation, e.g., comprising photons at
characteristic wavelength(s). This subsystem 194 can include a
laser, e.g., of a type used in laser-induced breakdown spectroscopy
(LIBS). Subsystem 194 can also include a spark induced breakdown
spectroscopy (SIBS) spark generator, e.g., a closely-spaced
electrode pair connected to a high-voltage power supply so that a
high voltage can be introduced across the electrodes to produce a
spark. Spectroscopic detector 196 (depicted as a camera;
dashed-line connector used for clarity only) is configured to
detect at least some of the electromagnetic radiation emitted by
the metal, e.g., by metal atoms or ions in the recognition
macromolecules. In some examples, at least some of the metal emits
the electromagnetic radiation in response to the heating of the
washed sample by subsystem 194, e.g., in response to energy added
to the washed sample by subsystem 194.
[0062] In the illustrated example, apparatus for detecting a target
197 in a sample 198 includes subsystem 190 for adding a
biomolecular recognition construct to the sample, subsystem 192 for
washing unbound recognition construct out of the sample, and
subsystem 194 for ionizing the sample-construct mixture into a
plasma. Subsystem 194 can transform a metal in the sample into a
plasma, e.g., can heat the metal until at least some of the metal
vaporizes and then passes to the plasma state. The electromagnetic
energy emitted by plasmas of or containing atomic and ionic species
of the metals used to tag the antibodies attached to the sample can
be collected by a spectrometer. The metals emit photons at
characteristic wavelengths, and spectroscopic detector 196 is used
for detecting photons emitted by the metal ions. The heating
subsystem 194, e.g., a plasma generation subsystem, can include a
laser, or can include at least two electrodes and a high-voltage
power supply connected to the at least two electrodes and
configured to selectively produce a spark across the at least two
electrodes.
[0063] The user interface system 130 can convey information in
either direction, or in both directions, between a user 138 and the
processor 186 or other components of system 101. The user interface
system 130 can include a mouse, a keyboard, another computer
(connected, e.g., via a network or a null-modem cable), or any
device or combination of devices from which data is input to the
processor 186. The user interface system 130 also can include a
display device, a processor-accessible memory, or any device or
combination of devices to which data is output by the processor
186. The user interface system 130 and the data storage system 140
can share a processor-accessible memory.
[0064] In various aspects, processor 186 includes or is connected
to communication interface 115 that is coupled via network link 116
(shown in phantom) to network 150. For example, communication
interface 115 can include an integrated services digital network
(ISDN) terminal adapter or a modem to communicate data via a
telephone line; a network interface to communicate data via a
local-area network (LAN), e.g., an Ethernet LAN, or wide-area
network (WAN); or a radio to communicate data via a wireless link,
e.g., WIFI or GSM (Global System for Mobile Communications).
Communication interface 115 can send and receives electrical,
electromagnetic, or optical signals that carry digital or analog
data streams representing various types of information across
network link 116 to network 150. Network link 116 can be connected
to network 150 via a switch, gateway, hub, router, or other
networking device.
[0065] In various aspects, system 101 can communicate, e.g., via
network 150, with other data processing system(s) (not shown),
which can include the same types of components as system 101 but is
not required to be identical thereto. System 101 and other systems
not shown can be communicatively connected via the network 150.
System 101 and other systems not shown can execute computer program
instructions to measure constituents of samples or exchange spectra
or other data, e.g., as described herein.
[0066] Processor 186 can send messages and receive data, including
program code, through network 150, network link 116, and
communication interface 115. For example, a server can store
requested code for an application program (e.g., a JAVA applet) on
a tangible non-volatile computer-readable storage medium to which
it is connected. The server can retrieve the code from the medium
and transmit it through network 150 to communication interface 115.
The received code can be executed by processor 186 as it is
received, or stored in data storage system 140 for later
execution.
[0067] Data storage system 140 can include or be communicatively
connected with one or more processor-accessible memories configured
or otherwise adapted to store information. The memories can be,
e.g., within a chassis or as parts of a distributed system. The
phrase "processor-accessible memory" is intended to include any
data storage device to or from which processor 186 can transfer
data (e.g., using components of peripheral system 120). A
processor-accessible memory can include one or more data storage
device(s) that are volatile or nonvolatile, that are removable or
fixed, or that are electronic, magnetic, optical, chemical,
mechanical, or otherwise. Example processor-accessible memories
include but are not limited to: registers, floppy disks, hard
disks, tapes, bar codes, Compact Discs, DVDs, read-only memories
(ROM), erasable programmable read-only memories (EPROM, EEPROM, or
Flash), and random-access memories (RAMs). One of the
processor-accessible memories in the data storage system 140 can be
a tangible non-transitory computer-readable storage medium, i.e., a
non-transitory device or article of manufacture that participates
in storing instructions that can be provided to processor 186 for
execution.
[0068] In an example, data storage system 140 includes code memory
141, e.g., a RAM, and disk 143, e.g., a tangible computer-readable
rotational storage device or medium such as a hard drive. In this
example, computer program instructions are read into code memory
141 from disk 143. Processor 186 then executes one or more
sequences of the computer program instructions loaded into code
memory 141, as a result performing process steps described herein.
In this way, processor 186 carries out a computer implemented
process. For example, steps of methods described herein, blocks of
block diagrams herein, and combinations of those, can be
implemented by computer program instructions. Code memory 141 can
also store data.
[0069] Various aspects described herein may be embodied as systems
or methods. Accordingly, various aspects herein may take the form
of an entirely hardware aspect, an entirely software aspect
(including firmware, resident software, micro-code, etc.), or an
aspect combining software and hardware aspects These aspects can
all generally be referred to herein as a "service," "circuit,"
"circuitry," "module," or "system."
[0070] Furthermore, various aspects herein may be embodied as
computer program products including computer readable program code
("program code") stored on a computer readable medium, e.g., a
tangible non-transitory computer storage medium or a communication
medium. A computer storage medium can include tangible storage
units such as volatile memory, nonvolatile memory, or other
persistent or auxiliary computer storage media, removable and
non-removable computer storage media implemented in any method or
technology for storage of information such as computer-readable
instructions, data structures, program modules, or other data. A
computer storage medium can be manufactured as is conventional for
such articles, e.g., by pressing a CD-ROM or electronically writing
data into a Flash memory. In contrast to computer storage media,
communication media may embody computer-readable instructions, data
structures, program modules, or other data in a modulated data
signal, such as a carrier wave or other transmission mechanism. As
defined herein, "computer storage media" do not include
communication media. That is, computer storage media do not include
communications media consisting solely of a modulated data signal,
a carrier wave, or a propagated signal, per se.
[0071] The program code can include computer program instructions
that can be loaded into processor 186 (and possibly also other
processors), and that, when loaded into processor 186, cause
functions, acts, or operational steps of various aspects herein to
be performed by processor 186 (or other processor). The program
code for carrying out operations for various aspects described
herein may be written in any combination of one or more programming
language(s), and can be loaded from disk 143 into code memory 141
for execution. The program code may execute, e.g., entirely on
processor 186, partly on processor 186 and partly on a remote
computer connected to network 150, or entirely on the remote
computer.
[0072] Using the system 101, the biomolecular recognition construct
can be prepared by tagging a biological scaffold with a metal atom
or ion. The biological scaffold may comprise adNectins, iMabs,
anticalins, microbodies, peptide aptamers, designed ankyrin repeat
proteins (DARPins), affilins, tetranectins, avimers, or other
molecules configured to bind to targets. In an aspect in which the
target includes a microbe such as a bacterium, the biological
scaffold can include an antibody against epitopes present on the
bacterial surface, said antibody linked to a heavy metal. In an
aspect in which the target includes a biological toxin, the
biological scaffold can include an antibody against the biological
toxin linked to heavy metals.
[0073] The construct for the molecular recognition system may be
tagged using various metallic elements such as Al, Ca, Cr, Cu, Fe,
Mg, Mn, Pb, Si, Ti, V and Zn. However, in order to minimize the
background it is advisable to use lanthanide metals (rare earth
elements) which are typically not present in biological material
such as La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and
Lu. The probes can be prepared by coupling the scaffold for
molecular recognition to polymers equipped with metal-binding
ligands. These polymers contain a functional group enabling them to
be covalently attached to biological macromolecules such as
antibodies, while simultaneously binding to one or more metals,
e.g., metal atoms or ions. Various aspects use lanthanide metals
(rare earth elements), which are typically not present in
biological material (La, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho,
Er, Tm, Yb, and Lu). However, various aspects herein can be also
employed with chelated heavy metal ions, assuming that the heavy
metals used as labels in a particular test are not themselves
targets to be detected in that test. For example, in tests for
heavy-metal contamination of food, metals other than heavy metals
can be used as tags.
[0074] Example recognition constructs described herein can be
prepared by coupling the scaffold for molecular recognition to
polymers equipped with metal-binding ligands (e.g., metal-chelating
polymers, MCPs). These specially designed polymers contain a
functional group enabling them to be covalently attached to
biological macromolecules, while simultaneously binding multiple
ions of metals. Various aspects include antibodies tagged via a
reaction involving selective reduction of disulfide bonds in their
hinge region, followed by thiol addition to a maleimide group at
one end of the metal-chelating polymer (MCP). Owing to unique and
distinguishable atomic spectral signals from many other metals,
various aspects can also take advantage of alternative non-MCP
labeling strategies, for example using HgS nanoparticles, silver
nanoparticles, organic mercury compounds, or ruthenium compounds.
Additionally ions derived from cadmium, mercury, cobalt, arsenic,
copper, chromium and selenium can also be identified. In some
examples, molybdenum, vanadium, strontium, europium, terbium,
samarium, or dysprosium can be used as tags.
[0075] The prepared recognition biomolecular recognition constructs
(macromolecules) are subsequently used to perform the assay. There
are many possible ways for the recognition biomolecular recognition
construct to be used. In one example, a biological specimen
containing pathogens or toxins can be attached to an inert surface
(e.g. such as a silicon wafer). A metal-tagged antibody or an
alternative molecular recognition construct is applied over the
surface binding to the exposed antigens. The excess antibody or
other recognition macromolecule can be removed by washing the
substrate
[0076] In another example, in an indirect setting following the
attachment of the bacteria cells or toxin macromolecules to the
surface of the inert sample holder a primary antibody or other
recognition macromolecule is added, binding specifically to the
antigens of interest. This primary molecular recognition system is
not tagged, in contrast to the reagents described above.
Subsequently a secondary (metal-tagged) macromolecule is added
binding to the primary macromolecule.
[0077] In a further example, an inert surface is functionalized and
covered with recognition macromolecules. The biological specimen is
added and the antigens of interest are captured by the
surface-bound recognition macromolecules. In the last step, the
metal-tagged recognition macromolecules are added binding to the
immobilized antigen. At least some of the excess unbound
macromolecules are removed by a wash, e.g., by subsystem 192, FIG.
1.
[0078] Following the tagging step in these and other aspects, the
specimen containing the sample of interest labeled by metal-tagged
recognition molecules is analyzed using system 101 by employing one
of the optical-spectroscopy techniques mentioned above. In an
aspect using LIBS spectroscopy, subsystem 194 focuses a laser beam
onto the inert surface (e.g., the silicon wafer) where the sample
198 is deposited. Owing to the large power density of the laser the
tagged material starts to evaporate leading to the generation of
plasma. The chemical constituents of the biological material are
excited by the laser beam and emit electromagnetic radiation
(light, e.g., human-visible or otherwise) which is element
specific, upon which the radiation is detected by detector 196.
[0079] In the described settings, simultaneous (multiplexed)
analysis of many targets 197 within the sample 198 is possible by
utilizing a cocktail of recognition macromolecules (e.g., a mixture
of antibodies), each class of recognition macromolecules labeled
with a different metal. Owing to distinguishability and specificity
of optical spectra produced by plasmas of different metals, this
tagging arrangement permits effective multiplexing, i.e.,
simultaneous detection of multiple targets (for instance, different
bacterial pathogens or toxins).
[0080] The plasma signal emitted by atomic and ionic species of the
metals used to tag the antibodies attached to the sample can be
collected using a spectrometer, such as detector 196. The naturally
occurring chemical constituents of the biological sample 198 can
also contribute to the spectra signal. In fact, it has been
disclosed and demonstrated that the LIBS signal from bacteria alone
may lead to recognition of some bacterial species. However, owing
to a high similarity in biochemical composition of bacterial
species, the classification ability of the label-free methods is
relatively low. The spectra are used to determine the elemental
constituents of the sample 198, and such constituents are similar
for many bacteria or other targets. In various aspects, since the
metals used to label the antibodies are either not naturally
present in the tested sample 198 of interest or present only in
very small quantities, the detection of the spectra of those metals
is a direct indicator of a sample type and origin.
[0081] Various aspects include digitization of the recorded
spectra, followed by spectral unmixing (allowing for the
determination of the individual spectral constituents) or spectral
fingerprint classification (involving matching the obtained
spectrum to other spectra present in the database).
[0082] The disclosed system 101 therefore offers faster and more
sensitive detection with reduced sample processing and preparation
compared to prior art schemes. The presently disclosed detection
format allows for multiplexing, e.g. simultaneous detection of
multiple bacterial species, biological toxins, or other
targets.
[0083] Some examples implement system 101 as a bench based device,
operating on a conventional laboratory power source. Various
aspects permit easy access to samples. Various aspects of system
101 include a sample collection station configured to accept a
disposable single-use device that will carry the sample and final
assay combination. Various examples measure panels of potential
antigens, e.g., a toxin panel, a Salmonella panel, or a panel of
common water-borne pathogens
[0084] According to various aspects, system 101 is configured to
characterize a target 197 within a sample 198. System 101
comprises, in some examples, an energy source (e.g., subsystem 194)
configured to transform a metal in the sample 198 into a plasma. An
optical spectroscopic detector (e.g., detector 196) is configured
to detect electromagnetic radiation emitted by the plasma to
provide an optical-spectrum signal. In some examples, substrate 199
of system 101 is configured to retain the sample 198 in operative
arrangement with the energy source (194) to receive energy from the
energy source (194). In some examples, the substrate 199 comprises
silicon or polystyrene. The substrate 199 can further comprise
recognition macromolecules, e.g. capture antibodies 2302 or
detection antibodies 2306, FIG. 23.
[0085] In some examples, processor 186 executes instructions stored
in a processor-accessible memory (e.g., data storage system 140).
The instructions cause the processor to determine presence of the
metal in the sample based at least in part on the optical-spectrum
signal. Examples are discussed below, e.g., with reference to step
3104, FIG. 31. In some examples, the processor 186 performs
spectral unmixing or spectral fingerprint classification on the
optical-spectrum signal, as discussed below.
[0086] In some examples, processor 186 determines presence of a
second metal in the sample based at least in part on the
optical-spectrum signal. The second metal is different from the
metal. In some examples, any number of different metals can be
detected in sample 198. The different metals can be incorporated in
different recognition constructs to detect different targets 197 in
the sample 198.
[0087] In some examples, components of system 101 constitute
apparatus for detecting a biological target 197 in a sample 198.
The apparatus can include a preparation subsystem (e.g., subsystem
190) configured to add a recognition construct (e.g., construct
1100, FIG. 11) to the sample 198. The recognition construct can
include a metal (e.g., ions 1106, FIG. 11). A washing subsystem 192
can be configured to wash unbound recognition construct out of the
sample 198. A heating subsystem 194 can be configured to heat the
washed sample 198 to cause the metal in the washed sample 198 to
emit photons at characteristic wavelengths. A spectroscopic
detector 196 can be configured to detect at least some of the
photons. In some examples, heating subsystem 194 includes a laser.
Examples are discussed below, e.g., with reference to FIGS. 17A and
28. In some examples, the heating subsystem comprises two
electrodes 2908, 2910 (FIG. 29) and a high-voltage power supply
2906 connected to the two electrodes 2908, 2910 and configured to
selectively produce a spark 2912 across the two electrodes 2908,
2910.
[0088] FIG. 2 shows an example plot of an experiment in which
samples containing bacteria were labelled with two different types
of antibodies. The Sb-tagged antibodies (indicated by Sb) attached
to E. coli can be readily distinguished from Pr-tagged antibodies
(indicated by Pr).
[0089] FIG. 3 shows an example of a Silicon wafer with spotted
samples (numbered 1-6) on the surface. Each spot is analyzed using
the techniques described above. Results described herein were based
on measurements made in this manner.
[0090] FIG. 4 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Lu and Pr in the
400-600 nm range.
[0091] FIG. 5 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Lu and Pr in the
320-380 nm range.
[0092] FIG. 6 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with .sup.156Gd and a blank
sample in the 340-380 nm range.
[0093] FIG. 7 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Pr in the 340-460 nm
range.
[0094] FIG. 8 is a plot showing spectral measurement of a sample
containing antitoxin antibodies labeled with Dy in the 240-360 nm
range.
[0095] FIG. 9 is a plot showing initial dose response to two
different agents, Shiga Toxin Stx-2-2 labeled with .sup.141Pr and
Ricin labeled with .sup.162Lu.
[0096] FIG. 10 is a plot showing spectral measurement of the 240
nm-360 nm window, where there peaks can be identified on regions of
the spectra representing Pr, Lu, Gd, and Dy simultaneously as
shown.
[0097] FIG. 11 shows an example recognition construct 1100. An
antibody 1102, e.g., a monoclonal antibody (mAb), includes or is
linked (e.g., covalently attached) to a metal-chelating polymer
1104. In some examples, about 20 lanthanide ions 1106 bind to a
single polymer chain. In some examples of multiple recognition
constructs 1100, about 2.4 polymer chains bind to each antibody
1102, on average. In some cases different lengths and different
numbers of polymers can be attached to the antibody. In some
examples, the polymer-linked antibody can be prepared by anionic
ring-opening polymerization. In some examples, more than one type
of metal atom or ion 1106 can be included in recognition construct
1100. Using multiple metals can permit adjusting the spectra to
increase multiplexing factor or reduce noise.
[0098] In some examples, monoclonal antibodies (mAbs) are tagged
with various metallic elements via metal-chelating polymers which
carry multiple copies of individual metal ions and provide a
reactive functionality for attachment. Example ions can include at
least one of .sup.59Pr Praseodymium, .sup.60Nd neodymium, .sup.62Sm
Samarium, .sup.64Gd Gadolinium, .sup.65Tb Terbium, .sup.66Dy
Dysprosium, .sup.67Ho Holmium, .sup.68Er Erbium, .sup.69Tm Thulium,
.sup.70Yb Ytterbium, or .sup.71Lu Lutetium.
[0099] Using specific antibodies and different lanthanides,
multiple targets may be investigated simultaneously in one sample,
e.g., Shiga toxin, ricin, and botulinum toxin. In some examples of
detecting botulinum, mAbs can include F1-2 (2.3 mg/mL, 0.2 mL) and
F1-51. Both of these bind the heavy chain of botulinum neurotoxin
(BoNT) and can be used in a sandwich assay such as those described
below with reference to FIGS. 23 and 24. In some examples of
detecting Ricin, mAbs can include RBV-11 (1.7 mg/mL, 0.4 mL); RC-91
mL ascites; RF-5 1 mL ascites; TFT B194102701 (binds to the
(3-chain); or TAZ E12 3C11 (1.2 mg/mL 0.25 mL). These can be used
in a sandwich assay such as those described below with reference to
FIGS. 23 and 24. In some examples of detecting Shiga-like toxin 2
(SLT2), mAbs can include Stx 2-1, which binds the A subunit of an
SLT2, and Stx2-2, which binds the B subunit. An example SLT2 is
A1B5, i.e., has 5 B subunits to each A subunit (on average).
[0100] In some examples of system 101, FIG. 1, the sample 198
comprises a recognition construct such as construct 1100. The
metal, e.g., ion 1106, can be included in or linked to the
recognition construct 1100. In some examples, the recognition
construct 1100 comprises at least an antibody, adNectin, or other
scaffold described above with reference to system 101.
[0101] FIG. 12 schematically illustrates an example process 1200
for analysis of a sample using SIBS. At 1202, sample 198 is applied
to or disposed in or over substrate 199. At 1204, a spark pulse is
applied to sample 198. At 1206, energy from the spark has begun to
vaporize at least part of sample 198 to generate a vapor or plasma.
In addition to or instead of initiating the breakdown process by a
high-voltage spark, the plasma formation can induced using a laser,
microwaves or glow-discharge microwave. At 1208, the vapor pulse
has expanded and heated. At 1210, at least some of the plasma has
become vapor and emits electromagnetic energy. The energy can be
detected by spectroscopic detector 196, FIG. 1. At 1212, the sample
has cooled and vapor or plasma has dissipated. In some examples,
the substrate 199 can be reused beginning again at 1202.
[0102] FIG. 13 schematically illustrates steps in a process 1300 of
preparing a recognition construct such as construct 1100, FIG. 11.
The illustrated construct 1100 includes an antibody 1102 tagged
with ions, e.g., ions 1106, FIG. 11. Example process 1300 includes
mAbs tagging with lanthanides to prepare compounds useful for
label-based detection as described herein. In the illustrated
example, diethylenetriaminepenta-acetic acid (DTPA) is used as a
chelator to create high-affinity complexes with Ln.sup.+3 ions.
[0103] The antibody 1102 of interest is placed in a reaction
vessel, e.g., a test tube (as depicted). Antibody 1102 is subjected
to selective reduction 1302 of --S--S-- groups to produce reactive
--SH groups. Polymer attachment 1304 is then carried out, which can
include reacting the --SH groups with the terminal maleimide groups
of polymer 1104 bearing metal-chelating ligands 1306 along its
backbone. In some examples, the ligands 1306 include
diethylenetriaminepenta-acetic acid (DTPA), which is used as a
chelator to create high-affinity complexes with Ln.sup.+3 ions.
Other ligands 1306 can be used.
[0104] The polymer-bearing antibodies are purified, treated 1308
with, e.g., lanthanide (Ln.sup.+3) ions, and then purified again.
In some examples, the result is a complex 1310 of ligand 1306 and
ion 1106. In some examples, process 1300 is used to prepare
multiple constructs 1100, e.g., having respective, different types
of antibodies 1102 and respective, different element labels (e.g.,
ions 1106). In some examples, each type of antibody is labeled with
a different element.
[0105] In some examples, metal-chelating polymers (MCPs) such as
polymer 1104 include a functional group enabling them to be
covalently attached to biological macromolecules such as antibody
1102, and to concurrently binding multiple ions 1106 of metals. The
illustrated reaction involves selective reduction of disulfide
bonds in the hinge region of antibody 1102, followed by thiol
addition to a maleimide group at one end of the polymer 1104.
[0106] FIGS. 14A-C illustrate measured data of examples of
breakdown spectroscopy signals obtained from mAbs labeled with two
different lanthanides and deposited on a silicon dioxide surface.
The abscissas are wavelength in nm and the ordinates are intensity
(in arbitrary units) measured by detector 196, FIG. 1.
[0107] FIG. 14A shows a measured spectrum of a silicon dioxide
surface, e.g., of a clean silicon wafer substrate 199. The Si peaks
provide alignment and calibration markers. For example, in a
spectral measurement, the characteristic signal of silicon can be
used to align and normalize multiple spectra so that the Si peaks
coincide in a normalized space. The unique spectra of the metal
ions can be defined by presence or absence of spectral features. In
FIGS. 14B and 14C, for clarity of illustration, the silicon peaks
are masked by vertical rectangles to show the remaining spectral
features.
[0108] FIG. 14B shows a measured spectrum of a silicon wafer
substrate 199 bearing recognition constructs such as constructs
1100. The recognition constructs in this example included mAb
antibodies Stx 2-2 against SLT2 and were labeled with
.sup.141Pr.
[0109] FIG. 14C shows a measured spectrum of a silicon wafer
substrate 199 bearing a different recognition construct. The
recognition construct included mAbs Stx 2-1 against SLT2 and was
labeled with .sup.175Lu. As shown, the pattern of peaks differs
between FIGS. 14A and 14B, permitting distinguishing Stx 2-2 from
Stx 2-1, and thus permitting distinguishing A and B subunits of an
SLT2.
[0110] FIG. 15 schematically illustrates an example process 1500
for analysis of a sample using laser-induced breakdown spectroscopy
(LIES). At 1502, a pulsed laser beam is focused onto, or otherwise
directed to irradiate, a sample of a substance to be analyzed. The
sample can be on a substrate, or can be a solid-phase sample
without a substrate. At 1504, the energy applied to the sample is
sufficient to cause the sample to begin to evaporate. At 1506, the
material vapor and the surrounding atmosphere form a plasma. At
1508, the material constituents of the plasma are excited and
spontaneously emit electromagnetic radiation. This emission is
resolved spectrally and is detected by a spectrometer, e.g.,
detector 196. At 1510, the plasma cools, resulting in substrate
1512 having crater 1514. Substrate 1512 can be disposed, or can be
reused.
[0111] FIGS. 16A-16H schematically illustrate an example process
for analysis of a sample using SIBS. Each of FIGS. 16A-16H is
labeled with an example time from the beginning of the irradiation
("S" for seconds).
[0112] FIG. 16A shows laser beam LB irradiating sample S, which can
be, e.g., in or over a substrate 199, FIG. 1. FIG. 16B shows
material H heated by the electromagnetic radiation from laser beam
LB. FIG. 16C shows a vapor bubble forming. FIG. 16D shows further
heating of the vapor to form a plasma, which then emits
electromagnetic radiation. FIG. 16E shows further emission,
approximately 45 ns after the onset of emission. This 45 ns
emission time can be sufficient to capture an optical measurement.
FIG. 16F shows that, even after 5 .mu.s, some emission is still
present. FIG. 16G shows that, even after 20 .mu.s, a small amount
of emission remains. The long duration of emission can permit using
a variety of spectroscopic detectors 196, e.g., less-expensive,
longer-integration-time detectors 196 for portable uses such as
in-the-field food testing, or more-expensive,
shorter-integration-time detectors 196 for benchtop or
high-throughput-screening uses. FIG. 16H shows particles PT
escaping from the sample, in which a crater CR has been formed.
[0113] FIG. 17A is a schematic of a system 1700 according to
various aspects. System 1700 includes CO.sub.2 laser 1702 and
Nd:YAG ("YAG") laser 1704 driven by programmable timing generators
PTG. Laser light is directed through one or more apertures, mirrors
M, waveplates WP, cube beamsplitters (between WP and M), lenses L,
or other optical, optoelectronic, or optomechanical components in
order to direct energy to sample 198 on substrate 199. In some
examples, at least some energy, e.g., of a non-desired
polarization, is directed to beam dump BD. In some examples, angle
.beta. between incident beams is substantially equal to
5.degree..
[0114] Electromagnetic energy from lasers 1702 or 1704 irradiates
sample 198 on substrate 199, producing plasma plume P. Optical
collector 1706, e.g., a lens, passes at least some electromagnetic
radiation from plasma plume P through optical fiber 1708 to
spectrograph 1710 of detector 196. In some examples, angle .alpha.
between one incident beam and the angle of detection of collector
1706 is 50.degree.. Detector 196 detects electromagnetic radiation
(e.g., the intensity thereof) as a function of wavelength, e.g.,
continuously or in discrete bins. For example, spectrograph 1710
can spatially disperse electromagnetic radiation carried by optical
fiber 1708 across the surface of an intensified charge-coupled
device (ICCD) linear or area sensor, and read the resulting
intensity distribution at the active surface of the ICCD.
[0115] FIG. 17B shows temporal profiles of Nd:YAG and CO2 laser
pulses. The abscissa is time and the ordinate is intensity in
arbitrary units. As shown, the Nd:YAG laser has a longer, more
intense pulse, and the CO.sub.2 laser has a shorter, less intense
pulse. In some examples, the CO.sub.2 laser can be used for
precisely-timed excitation and the Nd:YAG laser can be used to
supply higher amounts of excitation energy.
[0116] FIG. 18 illustrates example results. Sample A is an S.
aureus LP9, and Sample B is an E. coli DH5.alpha.. As shown, the
samples can be readily distinguished with this type of plot.
[0117] FIG. 19 illustrates example spectral measurements according
to the prior art, with intensity (a.u.) as a function of
wavelength. Illustrated are the LIBS spectra from 380 to 410 nm for
B. megaterium PV361, B. thuringiensis, B. megaterium QM B1551, B.
subtilis, E. coli, and LB (a culture medium). The emission peaks at
393.7 and 396.9 nm are attributed to calcium atomic transitions.
However, the peaks are not strong and have a significant overlap
between different organisms.
[0118] FIG. 20A illustrates example measurements of LIBS detection
of Gd in an aqueous solution.
[0119] FIG. 20B illustrates example measurements of LIB S detection
of Eu and Sm in an aqueous solution.
[0120] FIGS. 20A and 20B show that rare earth elements can be
effectively distinguished from each other in LIB S
measurements.
[0121] FIG. 21 illustrates structural details of an example
antibody 2100, which can represent construct 1100, FIG. 11. In some
examples, a maleimide group of a polymer 1104, FIG. 11, can connect
to the amino acid cysteine in the hinge region 2102 of the antibody
2100. The exact area where polymer 1104 bonds to antibody 2100 does
not affect specificity of the antibody, in some examples.
[0122] FIG. 22 schematically illustrates steps in a process 2200 of
preparing a recognition construct 1100 including a metal-tagged
antibody. Compound 2202 is reacted with compound 2204 in an acetate
buffer, e.g., pH=5.5, during operation 2206 to form compound 2208.
Operation 2206 can be performed, e.g., at room temperature for 27
hours.
[0123] Process 2200 is an example of Diels-Alder End-Group
Functionalization between the DTPA-containing polymer (compound
2202) and the bismaleimide reactive group (compound 2204) added at
the end of the polymer. Once compound 2208 is formed, it can be
reacted with antibody 1102 via mild reduction of disulfide bonds of
the antibody 1102, using tris(2-carboxyethyl)phosphine (TCEP) to
convert the reduced antibody, to form construct 1100.
[0124] FIGS. 23A-C schematically illustrate steps in a process of
analyzing a sample. The illustrated process is a sandwich assay, in
which an inert surface is functionalized and covered with
recognition macromolecules. The biological specimen is added and
the antigens of interest are captured by the surface-bound
recognition macromolecules. Finally, the metal-tagged mAbs are
added, binding to the immobilized antigen.
[0125] FIG. 23A shows capture antibody 2302 attached to a
substrate, e.g., substrate 199, FIG. 1. In some examples, capture
antibodies 2302 are immobilized on substrate 199 by the
carbodiimide reaction: substrate 199, e.g., polystyrene or silica
microbeads, prefunctionalized with carboxyl groups, are activated
with equimolar concentration of
1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide (EDC or EDAC) and
N-hydroxysulfosuccinimide (Sulfo-NHS) or N-hydroxysuccinimide
(NETS). Activation stabilizes the amine-reactive intermediate by
converting it to a semistable amine-reactive sulfo-NHS or NHS
ester. Then excess of antibody-protein is added for a reaction time
of, e.g., 30 min-2 h. Remaining active groups can then be quenched
with BSA, glycine or ethanolamine solution.
[0126] FIG. 23B shows a target protein 2304 that has been captured
by capture antibody 2302. Target protein 2304 represents a target,
e.g., a pathogen or toxin. Non-captured components or solutions can
be washed off the substrate to reduce measurement noise.
[0127] FIG. 23C shows detection antibody 2306 bound to target
protein 2304. Detection antibody 2306 is part of a recognition
construct 1100 that also includes metal tag 2308. Metal tag 2308
can be vaporized as described above, e.g., with reference to FIG.
12 or 16, to provide detectable electromagnetic energy.
[0128] In some examples, the substrate, tagged with the relevant
antibody, is incubated with the mixture of detection mAbs labeled
with metal ions and a solution obtained from a filtered sample. In
these examples, FIGS. 23B and 23C can take place concurrently. For
example, time of incubation can be approximately 20-30 min at room
temperature.
[0129] FIG. 24 is a schematic diagram of a bead 2402 carrying
capture antibodies 2302, depicted as "Y" shapes attached to the
bead 2402. Bead 2402 can include a silicon or polyester bead. Using
beads rather than a flat surface can increase surface area,
enhancing the assay sensitivity. Tagged antibodies can be
immobilized on the surface of microbeads by the standard
carbodiimide reaction. Polystyrene or silica microbeads
prefunctionalized with carboxylic acid groups can be activated by
an equimolar amounts of [1-ethyl-3-(3-dimethyl-aminopropyl)
carbodiimide hydrochloride]. Sample can be brought into contact
with the antibodies as discussed above with reference to FIG. 23.
The capture antibodies are thus bound to targets 2404 of interest
(depicted as diamonds) in the sample. Metal-tagged detection
antibodies 2306 (depicted "Y" shapes with attached starbursts) are
also bound to the targets. Metal tags 2308 (depicted as starbursts)
can then be vaporized to provide detectable electromagnetic
radiation.
[0130] The examples in FIGS. 23 and 24 are sandwich assays, though
direct-detection assays can also be performed. In some examples,
substrate 199 includes or is located in a plastic dish holding the
capture antibody. Example solid substrates can include plastic or
glass, and can be shapes as a sheet; filter, bead, or other shapes.
Some examples use heavy-metal tagging and a washing (or other
background removal) step. Compared to prior schemes using
fluorescent or color change (colorimetric) assays, SIBS or LIBS
assays as described herein can provide more accurate, more rapid
measurements.
[0131] In some examples of assays described herein, data was
collected in the 340 nm-460 nm range. A silicon wafer with no
sample exhibited a strong peak at .about.390 nm. A sample of
phosphate-buffered saline (PBS) on the silicon wafer did not
substantially change the position of the Si peak. A sample of Shiga
toxin Stx 2-2 using a .sup.141Pr label showed strong peaks at
.about.395 nm and .about.420 nm, plus the Si peak. A sample of
Ricin using a .sup.162Dy label showed strong peaks at .about.388 nm
and .about.395 nm, plus the Si peak. The difference in the peak
patterns permits distinguishing tags using optical data (e.g.,
using only optical data), and thus permits distinguishing targets
tagged with recognition constructs having different tags, e.g.,
different metals.
[0132] FIG. 25 illustrates measured spectral data. As shown, three
wavelength peaks of a Pr tag are clearly visible.
[0133] FIG. 26 illustrates an example dose-response curve
determined based on measurements in the 320 nm-450 nm window using
heavy metal Pr as a tag. FIG. 26, in the inset, shows statistical
properties of the illustrated fit line to the illustrated measured
data points (squares).
[0134] FIG. 27 illustrates an example of application of energy to a
sample, e.g., using SIBS. Voltage is applied between an electrode E
and a substrate S to create one of more sparks SP that strike the
sample, thereby applying the sparks to the sample, vaporizing the
sample, and creating a plasma. It is not necessary that the spark
directly ionize the sample; heat or electromagnetic radiation
emitted by the spark can also contribute to plasma formation. In
some examples, distance A is .about.3 mm-.about.5 mm and the crater
is .about.5 mm in diameter. Configurations such as that depicted
can interrogate, in one measurement, a relatively large sample
area. This can increase the throughput of measurements. Increasing
throughput can be useful, e.g., in field tests for contamination of
food or water.
[0135] FIG. 28 illustrates an example of application of energy to a
sample, e.g., using LIBS. A laser beam is focused on a substrate S
to create a plasma P of a sample. In some examples, distance B
between the lens and the sample is .about.200 mm-.about.1500 mm,
and the crater is .about.100 .mu.m in diameter. Configurations such
as that depicted can interrogate, in one measurement, a relatively
small sample area. This can increase the precision of measurements.
Increasing precision can be useful, e.g., in laboratory tests.
[0136] FIG. 29 illustrates an example 2900 of application of energy
to a sample, e.g., using SIBS. Substrate 2902 holds sample 2904.
Electrical supply 2906, e.g., under control of processor 186, FIG.
1, applies voltage or current pulses to electrodes 2908, 2910 to
cause the air, sample, other substances, or vacuum between
electrode 2908 and electrode 2910 to become conductive (e.g., break
down), forming spark 2912. Spark 2912 can pass directly through at
least some of sample 2904, or (as shown) can travel in proximity to
sample 2904. Energy from spark 2912 can be provided to sample 2904
to transform at least part of sample 2904 into a plasma. Example
forms of energy from spark 2912 can include, e.g., kinetic energy
of ionized air or sample within spark 2912, electromagnetic energy
radiated by spark 2912, or thermal energy conducted from spark 2912
to substances around it, such as sample 2904.
[0137] FIG. 30 shows a flowchart illustrating an example process
3000 for analyzing a sample. Some examples permit characterizing a
target, e.g., a biological target, within a sample. The steps can
be performed in any order except when otherwise specified, or when
data from an earlier step is used in a later step. In at least one
example, processing begins with step 3002. For clarity of
explanation, reference is herein made to various components shown
in FIGS. 1-29 that can carry out or participate in the steps of the
example method. It should be noted, however, that other components
can be used; that is, example method(s) shown in FIGS. 30 and 31
are not limited to being carried out by the identified
components.
[0138] At 3002, a recognition construct is applied to the sample.
The recognition construct can include a metal and a scaffold, e.g.,
a biological scaffold. The scaffold can be configured to bind to
the target. Examples are discussed above with reference to FIGS.
11, 13, and 21-24. In some examples, the recognition construct can
consist of at least one metal or salt. In some examples, the
recognition construct can exclude, i.e., can be substantially free
from, one or more, or all, of the following: adNectins, iMabs,
anticalins, microbodies, peptide aptamers, designed ankyrin repeat
proteins (DARPins), affilins, tetranectins, or avimers.
[0139] In some examples, the sample can be free of one or more of
the compound types listed in the preceding paragraph. For example,
the sample can exclude antibodies, or can exclude recognition
constructs such as recognition construct 1100, FIG. 11. In some
examples, the sample can include only antibodies (or other compound
types listed in the previous paragraph) that are naturally
occurring in the sample, as opposed to being added as described in
step 3002.
[0140] In some examples, processing can begin with step 3004. In
some examples, steps 3004 and 3006 can be performed, e.g., to test
for the presence of metals, salts, or other distinguishable targets
in the sample. As used herein, "distinguishable targets" are
targets that, when suitably energized, emit electromagnetic
radiation having an optical spectrum that can be identified in the
collected optical-spectrum signal, e.g., using techniques discussed
below with reference to step 3104. Samples of food, water, or other
items useful to human biology, for example, can be measured as
described with reference to steps 3004 and 3006, e.g., to test for
lead or other heavy metals, or other contaminants, in those
samples. Moreover, samples can be tested, e.g., for the presence of
salts such as metal salts.
[0141] In some examples, a sample 198 can contain metal(s) (or
other targets) included in recognition constructs added at step
3002, and can also contain metal(s) (or other distinguishable
targets) present in the sample before step 3002, or otherwise
present in the sample but not part of a recognition construct.
Various examples herein can, in a single measurement, detect both
targets in constructs 1100 and distinguishable targets outside of
recognition constructs. This can permit, e.g., performing a single
measurement of, e.g., a food or water sample, to test for both
bacterial contamination (using recognition constructs that bind to
bacterial species of interest) and heavy-metal contamination (the
heavy metals themselves being distinguishable targets different
from the metal ions 1106 used in the recognition constructs).
[0142] At 3004, energy is applied to the sample. The energy applied
at step 3004 is sufficient to transform at least some of the sample
into a plasma, e.g., to convert at least some of the sample from a
solid, liquid, or gaseous phase to a plasma phase. Examples are
discussed above with reference to FIGS. 12, 15, 16A-16H, 17, 27,
and 28.
[0143] In some examples, step 3004 includes applying energy to the
sample before adding recognition construct to the sample or without
first adding recognition construct to the sample.
[0144] In some examples, the applying energy comprises heating at
least part of the sample, e.g., using a flame, resistive heater,
inductive heater, or other heat source. In some examples, the
applying energy comprises irradiating at least part of the sample
using a laser. Examples are discussed above, e.g., with reference
to FIG. 15 or 28. In some examples, the applying energy comprises
applying a spark to at least part of the sample. For example, a
spark can be generated between an electrode and a substrate
retaining the sample, e.g., as in FIG. 12 or 27. In another
example, a spark can be generated between two electrodes in
proximity to the sample, e.g., as discussed above with reference to
FIG. 29. In some examples, multiple sparks can be applied to
provide sufficient energy to the at least part of the sample to
form a plasma emitting a detectable amount of electromagnetic
radiation.
[0145] At 3006, electromagnetic radiation emitted by the plasma is
detected to provide an optical-spectrum signal of the sample.
Wavelength-specific peaks in the optical-spectrum signal, or other
characteristics of the detected electromagnetic radiation as a
function of wavelength, can be correlated with the metal in the
recognition construct. Examples are discussed above with reference
to FIGS. 12, 15, 16A-16H, and 17.
[0146] FIG. 31 shows a flowchart illustrating an example method
3100 for analyzing a sample according to various aspects. The
sample can be analyzed to determine presence of a target.
Processing can begin at step 3102.
[0147] At step 3102, a recognition construct is prepared. Some
examples include preparing the recognition construct including a
tagged scaffold. Examples are discussed above, e.g., with reference
to FIG. 11, 13, 21, or 22.
[0148] In some examples, step 3102 can include preparing the
recognition construct by bonding a metal to a scaffold. Examples
are discussed above, e.g., with reference to FIG. 11, 13, 21, or
22. The scaffold can include, e.g., a biological scaffold. The
metal can include, e.g., a metal atom or ion. In some examples, the
target includes a microbe and the scaffold comprises an antibody
against epitopes present on a surface of the microbe. In some
examples, the target includes a biological toxin and the scaffold
comprises an antibody against the biological toxin. Step 3102 can
be followed by step 3002.
[0149] In some examples, e.g., of analyzing food or water samples
as described above with reference to FIG. 30, processing can begin
at step 3004. Measurements can be taken as described above with
reference to steps 3004 and 3006. At least one of steps 3002, 3004,
or 3006 can be followed by step 3104.
[0150] At step 3104, presence of the metal in the sample is
determined based at least in part on the optical-spectrum signal.
Step 3104 can include, e.g., performing at least one of spectral
unmixing, constrained energy minimization (CEM), pattern
recognition, or classification. Various examples of step 3104 can
provide rapid processing of complex optical spectra originating
from label-free and label-based measurements. For example, given a
library of possible spectra of constituents of the sample, spectral
unmixing can be used to determine the most likely proportions of
those constituents. In other examples, blind unmixing can be used
to estimate a most-likely collection of constituent spectra without
reference to a library of spectra.
[0151] In some examples, readouts provided by systems described
herein can include multispectral data sets. Let r denote the
normalized vector of observations (digitized readouts from the
spectrometer), M an L.times.p spectral-signature matrix (p being
the number of metal labels used in the test, and L the number of
bands employed in the spectrometer), and a the vector of fractional
abundances of the j-th label in the measured sample. Also, let n
denote noise. Therefore, for a measured sample, Eq. (1) holds.
r=Ma+n. (1)
[0152] This simple mixture model assumes that a multiband spectrum
measured from a sample can be expressed as a linear combination of
the spectral signatures of the labels used and the intrinsic
spectra of the biological material and matrix with appropriate
fractions a=[a.sub.1, a.sub.2, . . . , a.sub.n]. The physical
constraints of spectral analysis require that an estimate of a be
positive and that .SIGMA..sub.i=1.sup.pa.sub.i=1. Therefore, the
constrained least-squares estimator of a is Eq. (2).
a ^ = min a .di-elect cons. .DELTA. { ( r - Ma ) T ( r - Ma ) } s .
t . .DELTA. = { a | ( j = 1 p a i = 1 ) ( a i .gtoreq. 0 ) } . ( 2
) ##EQU00001##
[0153] Consequently, multiplying the fractions vector a by the
integral of the optical spectrum signal collected by the detector
allows retrieval of the signals originating from every single
label, as well as the signal of the matrix/unlabeled sample.
[0154] In some examples, the spectral constituents of a sample may
not be completely known. Moreover, the signal of the unlabeled
matrix may be unavailable. In such situations, blind unmixing of
the labels can be performed.
[0155] Various aspects use constrained energy minimization (CEM) to
determine a label of interest. CEM implements a finite-impulse
response (FIR) filter in such a manner that the filter output
energy is minimized subject to a constraint imposed by the desired
spectral signature of interest. CEM does not assume the linear
mixture model or any particular noise characteristics. An example
CEM filter w is in Eq. (3). Other filters known in hyperspectral
analysis can be used.
w.sub.CEM=(d.sup.TR.sub.r.sup.-1d).sup.-1R.sub.r.sup.-1d. (3)
where R is the sample correlation matrix R=<rr.sup.T>.
[0156] A CEM-based filter as shown in Eq. (3) is designed to detect
the desired target d while mathematically minimizing the filter
output energy caused by other (presumably unknown) undesired signal
sources. Various examples of CEM can obtain concentrations of
single metallic labels of interest, producing a result
substantially equal to a result of unmixing on the same
measurements. For example, when the mixture is linear and the label
of interest co-occurs with other labels in proportion to their
global average concentrations in the tested material, CEM can
accurately identify the metal tags by their spectra.
[0157] The CEM filter can be expanded to operate on multiplexed and
multi-label assays. For instance, if a particular complex
contaminant in a sample is defined by the presence of three
chemical constituents and absence of two other markers, a CEM-based
filter can be designed to produce a desired signature. Let F denote
a multi-label signature F={d.sub.1,d.sub.2,d.sub.3}. An example
constraint for a CEM filter to detect the desired target F while
mathematically minimizing the remaining signals is shown in Eq.
(4).
min w { w T R r w } s . t . F T w = 1. ( 4 ) ##EQU00002##
[0158] The solution to Eq. (4) is a CEM-based filter w*:
w.sub.CEM*=R.sub.r.sup.-1F(F.sup.TR.sub.r.sup.-1F).sup.-11. (5)
[0159] In some examples, step 3104 can include determining presence
of the metal in the sample based at least in part on results of a
multi-class classifier trained on training data including spectra
of metals that may possibly occur in the sample. Example
classifiers can include support vector machines, kernel estimators
such as nearest-neighbor classifiers, decision trees or forests, or
neural networks such as deep neural networks.
[0160] In some examples, step 3104 can include determining presence
of the metal in the sample based at least in part on at least a
non-fluorescence portion of the optical-spectrum signal. For
example, the THERMOFISHER CELLTRACKER Orange CMRA fluorescent dye
has a fluorescent emission peak at approximately 578 nm and a full
width at half-maximum (FWHM) of the emission spectrum of 42 nm
(.about.560 nm-.about.602 nm). In some examples, step 3104 can
include analyzing the portion outside the FWHM of a particular
fluorescent emission, e.g., 450 nm-560 nm and 602 nm-750 nm. In
some examples, step 3104 can include analyzing the full range of
the captured spectrum, e.g., 450 nm-750 nm, or a spectral range
wider than, e.g., 50 nm, 100 nm, 150 nm, 200 nm, or 250 nm.
Analyzing portions of the spectrum that do not correspond to
fluorescent dyes that may be present in a sample, or that are wider
than the FWHMs of fluorescence peaks of such dyes, can permit
distinguishing more tags from each other than some prior
fluorescence-based schemes. Such analyses can additionally or
alternatively reduce noise due to autofluorescence by the
sample.
Example Clauses
[0161] Various aspects can include at least one of the following
provisions.
[0162] A: A method for characterizing a biological target within a
sample, the method comprising: labeling the target with a
biomolecular recognition construct; and measuring an
optical-spectrum signal of the biomolecular recognition
construct.
[0163] B: The method according to paragraph A, further comprising
heating the labeled target before measuring the optical-spectrum
signal.
[0164] C: The method according to paragraph A or B, further
comprising measuring the optical-spectrum signal by performing
laser-induced breakdown spectroscopy.
[0165] D: The method according to any of paragraphs A-C, further
comprising measuring the optical-spectrum signal by performing
spark induced breakdown spectroscopy.
[0166] E: The method according to any of paragraphs A-D, further
comprising classifying data of the optical-spectrum signal using a
computer-based classifier and assigning a classification score to
the analyzed sample.
[0167] F: The method according to any of paragraphs A-E, further
comprising preparing the biomolecular recognition construct by
tagging a scaffold, e.g., a biological scaffold, with a metal atom
or ion.
[0168] G: The method according to paragraph F, wherein the target
includes a microbe, e.g., a bacterium, and the biological scaffold
comprises an antibody against epitopes present on the surface of
the microbe, e.g., the bacterial surface.
[0169] H: The method according to paragraph F or G, wherein the
metal atom or ion comprises a heavy metal atom or ion.
[0170] I: The method according to any of paragraphs F-H, wherein
the target includes a biological toxin and the biological scaffold
comprises an antibody against the biological toxin linked to heavy
metals.
[0171] J: A system for characterizing a target within a sample, the
system comprising: an energy source configured to transform a metal
in the sample into a plasma; and an optical spectroscopic detector
configured to detect electromagnetic radiation emitted by the
plasma and to provide an optical-spectrum signal corresponding to
at least some of the electromagnetic radiation.
[0172] K: The system according to paragraph J, further comprising:
a processor; and a memory, e.g., a processor-accessible memory or
at least one computer storage medium, storing instructions
executable by the processor to cause the processor to perform
operations comprising determining presence of the metal in the
sample based at least in part on the optical-spectrum signal.
[0173] L: The system according to paragraph K, the operations
(e.g., the operations for determining) further comprising
performing spectral unmixing or spectral fingerprint classification
on the optical-spectrum signal.
[0174] M: The system according to paragraph K or L, the operations
further comprising determining presence of a second metal in the
sample based at least in part on the optical-spectrum signal,
wherein the second metal is different from the metal.
[0175] N: The system according to any of paragraphs K-M, the
operations further comprising determining presence of the metal in
the sample based at least in part on at least a non-fluorescence
portion of the optical-spectrum signal.
[0176] O: The system according to any of paragraphs K-N, the
operations further comprising determining presence of the metal in
the sample based at least in part on a portion of the
optical-spectrum signal extending over a spectral range wider than
at least one of 50 nm, 100 nm, 150 nm, 200 nm, or 250 nm.
[0177] P: The system according to any of paragraphs J-O, further
comprising a substrate configured to retain the sample in operative
arrangement with the energy source.
[0178] Q: The system according to paragraph P, wherein the
substrate comprises silicon or polystyrene.
[0179] R: The system according to paragraph P or Q, wherein the
substrate comprises recognition macromolecules.
[0180] S: The system according to any of paragraphs J-R, further
comprising the sample, wherein the sample comprises: a scaffold;
and the metal linked to the scaffold.
[0181] T: The system according to paragraph S, wherein the scaffold
comprises at least one of an antibody, adNectin, iMab, anticalin,
microbody, peptide aptamer, designed ankyrin repeat protein
(DARPin), affilin, tetranectin, or avimer.
[0182] U: The system according to any of paragraphs J-T, wherein
the metal is not a heavy metal.
[0183] V: The system according to any of paragraphs J-U, wherein
the metal is not toxic to humans.
[0184] W: A method for characterizing a target within a sample, the
method comprising: applying to the sample a recognition construct
comprising a metal and a scaffold, wherein the scaffold is
configured to bind to the target; applying energy to the sample,
wherein the applied energy is sufficient to transform at least some
of the sample into a plasma; and detecting electromagnetic
radiation emitted by the plasma to provide an optical-spectrum
signal of the sample.
[0185] X: The method according to paragraph W, wherein the applying
energy comprises heating at least part of the sample.
[0186] Y: The method according to paragraph W or X, wherein the
applying energy comprises irradiating at least part of the sample
using a laser.
[0187] Z: The method according to any of paragraphs W-Y, wherein
the applying energy comprises applying a spark to at least part of
the sample.
[0188] AA: The method according to any of paragraphs W-Z, further
comprising: determining presence of the metal in the sample based
at least in part on the optical-spectrum signal by performing at
least spectral unmixing or constrained energy minimization
(CEM).
[0189] AB: The method according to any of paragraphs W-AA, further
comprising: preparing the recognition construct by bonding the
metal to the scaffold, wherein the scaffold comprises a biological
scaffold and the metal comprises a metal atom or ion.
[0190] AC: The method according to any of paragraphs W-AB, wherein
the target includes a microbe and the scaffold comprises an
antibody against epitopes present on a surface of the microbe.
[0191] AD: The method according to any of paragraphs W-AC, wherein
the target includes a biological toxin and the scaffold comprises
an antibody against the biological toxin.
[0192] AE: The method according to any of paragraphs W-AD, further
comprising determining presence of the metal in the sample based at
least in part on at least a non-fluorescence portion of the
optical-spectrum signal.
[0193] AF: The method according to any of paragraphs W-AE, further
comprising determining presence of the metal in the sample based at
least in part on a portion of the optical-spectrum signal extending
over a spectral range wider than at least one of 50 nm, 100 nm, 150
nm, 200 nm, or 250 nm.
[0194] AG: The method according to any of paragraphs W-AF, wherein
the metal is not a heavy metal.
[0195] AH: The method according to any of paragraphs W-AG, wherein
the metal is not toxic to humans.
[0196] AI: An apparatus for detecting a biological target in a
sample, the apparatus comprising: a preparation subsystem
configured to add a recognition construct to the sample, the
recognition construct comprising a metal; a washing subsystem
configured to form a washed sample by washing at least some unbound
recognition construct out of the sample; a heating subsystem
configured to heat at least some of the washed sample; and a
spectroscopic detector configured to detect at least some
electromagnetic radiation emitted by metal in the at least some of
the washed sample in response to the heating of the washed
sample.
[0197] AJ: The apparatus according to paragraph AI, wherein the
heating subsystem comprises a laser.
[0198] AK: The apparatus according to paragraph AI or AJ, wherein
the heating subsystem comprises two electrodes and a high-voltage
power supply connected to the two electrodes and configured to
selectively produce a spark across the two electrodes.
[0199] AL: The apparatus according to any of paragraphs AI-AK,
further comprising a processor and a processor-accessible memory,
e.g., at least one computer storage medium, storing instructions
executable by the processor to cause the processor to perform
operations.
[0200] AM: The apparatus according to paragraph AL, the operations
comprising determining presence of the metal in the sample based at
least in part on at least a non-fluorescence portion of the
optical-spectrum signal.
[0201] AN: The apparatus according to paragraph AL or AM, the
operations comprising determining presence of the metal in the
sample based at least in part on a portion of the optical-spectrum
signal extending over a spectral range wider than at least one of
50 nm, 100 nm, 150 nm, 200 nm, or 250 nm.
[0202] AO: The apparatus according to any of paragraphs AI-AN,
wherein the metal is not a heavy metal.
[0203] AP: The method according to any of paragraphs AI-AO, wherein
the metal is not toxic to humans.
[0204] AQ: A computer-readable medium, e.g., at least one computer
storage medium, having thereon computer-executable instructions,
the computer-executable instructions upon execution configuring a
computer to perform operations as any of paragraphs W-AH
recites.
[0205] AR: A device comprising: a processor; and a
computer-readable medium, e.g., at least one computer storage
medium, having thereon computer-executable instructions, the
computer-executable instructions upon execution by the processor
configuring the device to perform operations as any of paragraphs
W-AH recites.
[0206] AS: A system comprising: means for processing; and means for
storing having thereon computer-executable instructions, the
computer-executable instructions including means to configure the
system to carry out a method as any of paragraphs W-AH recites.
CONCLUSION
[0207] Various examples herein permit at least tagging mAbs with
metals without loss of mAb function, effectively distinguishing
between tagged substances, distinguishing between at least four
different targets in the same sample, or aligning spectra using the
spectral signature of the substrate.
[0208] Steps of various methods described herein can be performed
in any order except when otherwise specified, or when data from an
earlier step is used in a later step. Example method(s) described
herein are not limited to being carried out by components
particularly identified in discussions of those methods.
[0209] In view of the foregoing, various aspects provide
measurement of constituents of a sample. A technical effect of
various aspects is to ablate a small quantity of the sample to form
a plasma and to measure the constituents of the plasma
spectroscopically. A technical effect of various aspects is to
provide a metal-labeled target. A further technical effect of
various aspects is to present a visual representation of the
detected spectra or corresponding abundances of selected
biomolecules on an electronic display. This can permit medical or
scientific personnel to more readily determine whether a sample
contains a target of interest, e.g., at a selected concentration or
quantity.
[0210] The invention is inclusive of combinations of the aspects
described herein. References to "a particular aspect" (or
"embodiment" or "version") and the like refer to features that are
present in at least one aspect of the invention. Separate
references to "an aspect" (or "embodiment") or "particular aspects"
or the like do not necessarily refer to the same aspect or aspects;
however, such aspects are not mutually exclusive, unless otherwise
explicitly noted. The use of singular or plural in referring to
"method" or "methods" and the like is not limiting. The word "or"
is used in this disclosure in a non-exclusive sense, unless
otherwise explicitly noted.
[0211] The invention has been described in detail with particular
reference to certain preferred aspects thereof, but it will be
understood that variations, combinations, and modifications can be
effected within the spirit and scope of the invention.
* * * * *