U.S. patent application number 16/010359 was filed with the patent office on 2018-10-11 for methods and systems for biological instrument calibration.
This patent application is currently assigned to Life Technologies Corporation. The applicant listed for this patent is Life Technologies Corporation. Invention is credited to Yong Chu, Jacob Freudenthal, Jeffrey Marks, Thomas Wessel, David Woo.
Application Number | 20180292320 16/010359 |
Document ID | / |
Family ID | 55361999 |
Filed Date | 2018-10-11 |
United States Patent
Application |
20180292320 |
Kind Code |
A1 |
Chu; Yong ; et al. |
October 11, 2018 |
METHODS AND SYSTEMS FOR BIOLOGICAL INSTRUMENT CALIBRATION
Abstract
In one exemplary embodiment, a method for calibrating an
instrument is provided. The instrument includes an optical system
capable of imaging fluorescence emission from a plurality of
reaction sites. The method includes performing a region-of-interest
(ROI) calibration to determine reaction site positions in an image.
The method further includes performing a pure dye calibration to
determine the contribution of a fluorescent dye used in each
reaction site by comparing a raw spectrum of the fluorescent dye to
a pure spectrum calibration data of the fluorescent dye. The method
further includes performing an instrument normalization calibration
to determine a filter normalization factor. The method includes
performing an RNase P validation to validate the instrument is
capable of distinguishing between two different quantities of
sample.
Inventors: |
Chu; Yong; (Castro Valley,
CA) ; Marks; Jeffrey; (Mountain View, CA) ;
Freudenthal; Jacob; (San Jose, CA) ; Wessel;
Thomas; (Pleasanton, CA) ; Woo; David; (Foster
City, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Life Technologies Corporation |
Carlsbad |
CA |
US |
|
|
Assignee: |
Life Technologies
Corporation
Carlsbad
CA
|
Family ID: |
55361999 |
Appl. No.: |
16/010359 |
Filed: |
June 15, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15017249 |
Feb 5, 2016 |
10012590 |
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16010359 |
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62113183 |
Feb 6, 2015 |
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62113077 |
Feb 6, 2015 |
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62113118 |
Feb 6, 2015 |
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62113058 |
Feb 6, 2015 |
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62112964 |
Feb 6, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 21/6428 20130101;
G01N 21/274 20130101; G01N 2201/13 20130101; G01N 21/6456 20130101;
G01N 21/6452 20130101; G01N 2021/6439 20130101; G01N 2021/6471
20130101; C12Q 1/686 20130101; C12Q 1/6851 20130101; G01N 2201/127
20130101; G01N 21/278 20130101; G01N 2333/922 20130101 |
International
Class: |
G01N 21/64 20060101
G01N021/64; C12Q 1/686 20180101 C12Q001/686; G01N 21/27 20060101
G01N021/27 |
Claims
1. A method for calibrating an instrument, wherein the instrument
includes an optical system capable of imaging florescence emission
from a plurality of reaction sites, the method comprising:
performing a region-of-interest (ROI) calibration to determine
reaction site positions in an image; performing a pure dye
calibration to determine the contribution of a fluorescent dye used
in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; performing an instrument normalization calibration
to determine a filter normalization factor; and performing an RNase
P validation to validate the instrument is capable of
distinguishing between two different quantities of sample.
2. The method of claim 1, wherein the ROI calibration comprises:
estimating initial region of interest (ROI) from fluorescence
thresholds from each sample well; estimating the center locations
of each ROI; estimating the size of each ROI; determining the
average size of the ROIs from the plurality of reaction sites;
deriving global gridding models; applying the global gridding
models to the ROIs, wherein the application of the global gridding
models improve the precision of the ROI center locations;
recovering missing ROIs; and adjusting the radius of the ROIs,
wherein the adjustment improves the signal-to-noise ratio of the
optical system.
3. The method of claim 1, wherein the ROI calibration improves
reaction site determination errors by minimizing at least one of
the following group: dye saturation within the plurality of
reaction sites, grid rotation, variation of magnification factors,
and optical radial distortion.
4. The method of claim 1, wherein the pure dye calibration
comprises: imaging a sample holder, loaded into the instrument, at
more than one channel, the sample holder comprising a plurality of
reaction sites and more than one dye type, each dye occupying more
than one reaction site; identifying a peak channel for each dye on
the sample holder; normalizing each channel to the peak channel for
each dye; and producing a dye matrix comprising a set of dye
reference values.
5. The method of claim 1, wherein the optical system comprises a
plurality of excitation filters and a plurality of emission
filters, and wherein the instrument normalization calibration
comprises: determining a first correction factor for each of the
excitation filters and emission filters; calculating a second
correction factor for a pair of filters, wherein each pair of
filters comprises one excitation filter and one emission filter;
and applying the second correction factors to filter data.
6. The method of claim 1, wherein the RNase P validation comprises:
receiving amplification data from a validation plate to generate a
plurality of amplification curves, wherein the validation plate
includes a sample of a first quantity and a second quantity, and
each amplification curve includes an exponential region;
determining a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determining, for each fluorescence threshold of the set, a first
set of cycle threshold (C.sub.t) values of amplification curves
generated from the samples of the first quantity and a second set
of C.sub.t values of amplification curves generated from the
samples of the second quantity; and calculating if the first and
second quantities are sufficiently distinguishable based on C.sub.t
values at each of the plurality of fluorescence thresholds.
7. The method of claim 1, further comprising: performing an
auto-dye correction for real-time spectral calibration of the
multi-component data; performing a plate detection to determine
whether there is a plate loading error; performing an
auto-background calibration to compensate for background changes;
and performing instrument normalization using a reflective material
to detect any changes or variability in fluorescent emissions.
8. A computer readable storage medium encoded with
processor-executable instructions for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites,
the instructions comprising instructions for: performing a
region-of-interest (ROI) calibration to determine reaction site
positions in an image; performing a pure dye calibration to
determine the contribution of a fluorescent dye used in each
reaction site by comparing a raw spectrum of the fluorescent dye to
a pure spectrum calibration data of the fluorescent dye; performing
an instrument normalization calibration to determine a filter
normalization factor; and performing an RNase P validation to
validate the instrument is capable of distinguishing between two
different quantities of sample.
9. The computer readable storage medium of claim 8, wherein the
instructions for ROI calibration comprise instructions for:
estimating initial region of interest (ROI) from fluorescence
thresholds from each sample well; estimating the center locations
of each ROI; estimating the size of each ROI; determining the
average size of the ROIs from the plurality of reaction sites;
deriving global gridding models; applying the global gridding
models to the ROIs, wherein the application of the global gridding
models improve the precision of the ROI center locations;
recovering missing ROIs; and adjusting the radius of the ROIs,
wherein the adjustment improves the signal-to-noise ratio of the
optical system.
10. The computer readable storage medium of claim 8, wherein the
ROI calibration improves reaction site determination errors by
minimizing at least one of the following group: dye saturation
within the plurality of reaction sites, grid rotation, variation of
magnification factors, and optical radial distortion.
11. The computer readable storage medium of claim 8, wherein the
instructions for pure dye calibration comprise instructions for:
imaging a sample holder, loaded into the instrument, at more than
one channel, the sample holder comprising a plurality of reaction
sites and more than one dye type, each dye occupying more than one
reaction site; identifying a peak channel for each dye on the
sample holder; normalizing each channel to the peak channel for
each dye; and producing a dye matrix comprising a set of dye
reference values.
12. The computer readable storage medium of claim 8, wherein the
optical system comprises a plurality of excitation filters and a
plurality of emission filters, and wherein the instructions for
instrument normalization calibration comprise instructions for:
determining a first correction factor for each of the excitation
filters and emission filters; calculating a second correction
factor for a pair of filters, wherein each pair of filters
comprises one excitation filter and one emission filter; and
applying the second correction factors to filter data.
13. The computer readable storage medium of claim 8, wherein the
instructions for RNase P validation comprise instructions for:
receiving amplification data from a validation plate to generate a
plurality of amplification curves, wherein the validation plate
includes a sample of a first quantity and a second quantity, and
each amplification curve includes an exponential region;
determining a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determining, for each fluorescence threshold of the set, a first
set of cycle threshold (C.sub.t) values of amplification curves
generated from the samples of the first quantity and a second set
of C.sub.t values of amplification curves generated from the
samples of the second quantity; and calculating if the first and
second quantities are sufficiently distinguishable based on C.sub.t
values at each of the plurality of fluorescence thresholds.
14. The computer readable storage medium of claim 8, further
comprising instructions for: performing an auto-dye correction for
real-time spectral calibration of the multi-component data;
performing a plate detection to determine whether there is a plate
loading error; performing an auto-background calibration to
compensate for background changes; and performing instrument
normalization using a reflective material to detect any changes or
variability in fluorescent emissions.
15. A system for calibrating an instrument, wherein the instrument
includes an optical system capable of imaging florescence emission
from a plurality of reaction sites, the system comprising: a
region-of-interest (ROI) calibrator configured to determine
reaction site positions in an image; a pure dye calibrator
configured to determine the contribution of a fluorescent dye used
in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; an instrument normalization calibrator configured
to determine a filter normalization factor; an RNase P validator
configured to validate the instrument is capable of distinguishing
between two different quantities of sample; and a display engine
configured to display calibration results.
16. The system of claim 15, wherein the ROI calibrator is
configured to: estimate initial region of interest (ROI) from
fluorescence thresholds from each sample well; estimate the center
locations of each ROI; estimate the size of each ROI; determine the
average size of the ROIs from the plurality of reaction sites;
derive global gridding models; apply the global gridding models to
the ROIs, wherein the application of the global gridding models
improve the precision of the ROI center locations; recover missing
ROIs; and adjust the radius of the ROIs, wherein the adjustment
improves the signal-to-noise ratio of the optical system.
17. The system of claim 15, wherein the pure dye calibrator is
configured to: image a sample holder, loaded into the instrument,
at more than one channel, the sample holder comprising a plurality
of reaction sites and more than one dye type, each dye occupying
more than one reaction site; identify a peak channel for each dye
on the sample holder; normalize each channel to the peak channel
for each dye; and produce a dye matrix comprising a set of dye
reference values.
18. The system of claim 15, wherein the optical system comprises a
plurality of excitation filters and a plurality of emission
filters, and wherein the instrument normalization calibrator is
configured to: determine a first correction factor for each of the
excitation filters and emission filters; calculate a second
correction factor for a pair of filters, wherein each pair of
filters comprises one excitation filter and one emission filter;
and apply the second correction factors to filter data.
19. The system of claim 15, wherein the RNase P validator is
configured to: receive amplification data from a validation plate
to generate a plurality of amplification curves, wherein the
validation plate includes a sample of a first quantity and a second
quantity, and each amplification curve includes an exponential
region; determine a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determine, for each fluorescence threshold of the set, a first set
of cycle threshold (C.sub.t) values of amplification curves
generated from the samples of the first quantity and a second set
of C.sub.t values of amplification curves generated from the
samples of the second quantity; and calculate if the first and
second quantities are sufficiently distinguishable based on C.sub.t
values at each of the plurality of fluorescence thresholds.
20. The system of claim 15, further comprising: an auto-dye
corrector configured to perform real-time spectral calibration of
the multi-component data; a plate detector configured to determine
whether there is a plate loading error; an auto-background
calibrator configured to compensate for background changes; and an
instrument normalizer configured to use a reflective material to
detect any changes or variability in fluorescent emissions.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. application Ser.
No. 15/017,249 filed Feb. 5, 2016 which claims the benefit of
priority of U.S. Provisional Patent Application No. 62/113,183,
U.S. Provisional Patent Application No. 62/113,077, U.S.
Provisional Patent Application No. 62/113,118, U.S. Provisional
Patent Application No. 62/113,058, and U.S. Provisional Patent
Application No. 62/112,964, all filed on Feb. 6, 2015, all of which
are incorporated herein in their entireties by reference.
BACKGROUND
[0002] Generally, there is an increasing need to simplify the
installation and setup of biological analysis systems so that
operators can more quickly and efficiently use biological analysis
systems for their intended purpose.
[0003] Installation and calibration of laboratory instrumentation
can be a time consuming and expensive process. In many cases,
engineers from the instrument supplier must be on site to perform
these processes. This cost is generally passed on to the user. In
some cases, experienced users can successfully calibrate properly
manufactured instruments using multi-step procedures. During such
calibration, physical standards and well plates may be used in
combination with manual procedures. Manual calibration processing
and data inspection is error prone and may rely on ad hoc or
subjective measures. While a final system verification step may
provide resilience against accepting suboptimal calibrations,
automation offers improved objectivity and uniformity during such
activities.
SUMMARY
[0004] In one exemplary embodiment, a method for calibrating an
instrument is provided. The instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites. The method includes performing a region-of-interest
(ROI) calibration to determine reaction site positions in an image.
The method further includes performing a pure dye calibration to
determine the contribution of a fluorescent dye used in each
reaction site by comparing a raw spectrum of the fluorescent dye to
a pure spectrum calibration data of the fluorescent dye. The method
further includes performing an instrument normalization calibration
to determine a filter normalization factor. The method includes
performing an RNase P validation to validate the instrument is
capable of distinguishing between two different quantities of
sample.
[0005] In another exemplary embodiment, a computer readable storage
medium encoded with processor-executable instructions for
calibrating an instrument is provided. The instrument includes an
optical system capable of imaging florescence emission from a
plurality of reaction sites. The instructions include instructions
for performing a region-of-interest (ROI) calibration to determine
reaction site positions in an image. The instructions include
instructions for performing a pure dye calibration to determine the
contribution of a fluorescent dye used in each reaction site by
comparing a raw spectrum of the fluorescent dye to a pure spectrum
calibration data of the fluorescent dye. The instructions further
include instructions for performing an instrument normalization
calibration to determine a filter normalization factor. The
instructions further include instructions for performing an RNase P
validation to validate the instrument is capable of distinguishing
between two different quantities of sample.
[0006] In another exemplary embodiment, a system for calibrating an
instrument is provided. The instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites. The system comprises a processor and a memory,
encoded with processor-executable instructions. The instructions
include instructions for performing a region-of-interest (ROI)
calibration to determine reaction site positions in an image, and
performing a pure dye calibration to determine the contribution of
a fluorescent dye used in each reaction site by comparing a raw
spectrum of the fluorescent dye to a pure spectrum calibration data
of the fluorescent dye. The instructions further include performing
an instrument normalization calibration to determine a filter
normalization factor and performing an RNase P validation to
validate the instrument is capable of distinguishing between two
different quantities of sample.
[0007] In another exemplary embodiment, a system for calibrating an
instrument is provided. The instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites. The system includes an ROI calibrator configured to
determine reaction site positions in an image. The system includes
a pure dye calibrator configured to determine the contribution of a
fluorescent dye used in each reaction site by comparing a raw
spectrum of the fluorescent dye to a pure spectrum calibration data
of the fluorescent dye. The system further includes an instrument
normalization calibrator configured to determine a filter
normalization factor. The instrument includes an RNase P validator
configured to validate the instrument is capable of distinguishing
between two different quantities of sample. The system further
includes a display engine configured to display calibration
results.
DESCRIPTION OF THE FIGURES
[0008] FIG. 1 illustrates a calibration workflow for a biological
instrument according to various embodiments described herein.
[0009] FIG. 2 is a block diagram that illustrates a PCR instrument
200 upon which embodiments of the present teachings may be
implemented.
[0010] FIG. 3 depicts an exemplary optical system 300 that may be
used for imaging according to embodiments described herein.
[0011] FIG. 4 illustrates an exemplary computing system for
implementing various embodiments described herein.
[0012] FIG. 5 illustrates an exemplary distributed network system
according to various embodiments described herein.
[0013] FIG. 6 illustrates a sequence of steps used in the
calibration of qPCR instruments.
[0014] FIG. 7 illustrates the regions-of-interest for a 96 well
sample container.
[0015] FIG. 8 is an image of a calibration plate with FAM dye
occupying each well of a 96-well calibration plate.
[0016] FIGS. 9 and 10 depict an example workflow according to an
embodiment of the present disclosure.
[0017] FIG. 11A illustrates calibration plates with checkerboard
configurations according to an embodiment of the present
disclosure.
[0018] FIG. 11B is an image of a 4 dye checkerboard 96-well
calibration plate with FAM, VIC, ROX and SYBR dyes in the same
configuration as illustrated by plate 1100 in FIG. 11A.
[0019] FIG. 12A illustrates dye mixtures used in various
embodiments of the present teachings.
[0020] FIG. 12B illustrates pure dyes and main channel filter
combinations for various embodiment of the present teachings.
[0021] FIG. 13 illustrates % deviation of dye mixtures before
normalization according to various embodiments of the present
teachings.
[0022] FIG. 14 illustrates % deviation of dye mixtures after
normalization according to various embodiments of the present
teachings.
[0023] FIG. 15 illustrates a closer view of % deviation of dye
mixtures after normalization according to various embodiments of
the present teachings.
[0024] FIG. 16 is a flow chart depicting a normalization process
according to various embodiments of the present teachings.
[0025] FIG. 17 illustrates an exemplary method for validating an
instrument according to various embodiments described herein.
[0026] FIG. 18 illustrates another exemplary method for validation
an instrument according to various embodiments described
herein.
[0027] FIG. 19 illustrates determining a plurality of fluorescence
thresholds from amplification data according to various embodiments
described herein.
[0028] FIG. 20 illustrates a system for validation of an instrument
according to various embodiments described herein.
[0029] FIG. 21 illustrates a system for calibration of an
instrument according to various embodiments described herein.
DETAILED DESCRIPTION
[0030] Exemplary systems for methods related to the various
embodiments described in this document include those described in
U.S. design patent application Ser. No. 29/516,847 (Life
Technologies Docket Number LT01000 DES), and U.S. provisional
patent application No. 62/112,910 (Life Technologies Docket Number
LT01011 PRO), and U.S. provisional patent application No.
62/113,006 (Life Technologies Docket Number LT01023 PRO), and U.S.
provisional patent application No. 62/113,077 (Life Technologies
Docket Number LT01025 PRO), and U.S. provisional patent application
No. 62/113,058 (Life Technologies Docket Number LT01028 PRO), and
U.S. provisional patent application No. 62/112,964 (Life
Technologies Docket Number LT01029 PRO), and U.S. provisional
patent application No. 62/113,118 (Life Technologies Docket Number
LT01032 PRO), and U.S. provisional patent application No.
62/113,212 (Life Technologies Docket Number LT01033 PRO), all of
which were filed on Feb. 6, 2015 and all of which are each also
incorporated by reference herein in their entirety.
[0031] To provide a more thorough understanding of the present
invention, the following description sets forth numerous specific
details, such as specific configurations, parameters, examples, and
the like. It should be recognized, however, that such description
is not intended as a limitation on the scope of the present
invention, but is intended to provide a better description of the
exemplary embodiments.
[0032] Advances in the calibration of biological analysis
instruments advantageously allow for reduced operator error,
reduced operator input, and reduced time necessary to calibrate a
biological analysis instrument, and its various components, for
proper and efficient installation.
[0033] As such, according to various embodiments of the present
teachings can incorporate expert knowledge into an automated
calibration and validation system providing pass/fail status and
troubleshooting feedback when a failure is identified. If an
instrument should fail the calibration process, then a service
engineer can be called. The present teachings can minimize the cost
of, and time required for, the installation and calibration
procedures.
[0034] It should be recognized that the methods and systems
described herein may be implemented in various types of systems,
instruments, and machines such as biological analysis systems. For
example, various embodiments may be implemented in an instrument,
system or machine that performs polymerase chain reactions (PCR) on
a plurality of samples. While generally applicable to quantitative
polymerase chain reactions (qPCR) where a large number of samples
are being processed, it should be recognized that any suitable PCR
method may be used in accordance with various embodiments described
herein. Suitable PCR methods include, but are not limited to,
digital PCR, allele-specific PCR, asymmetric PCR, ligation-mediated
PCR, multiplex PCR, nested PCR, qPCR, genome walking, and bridge
PCR, for example. Furthermore, as used herein, amplification may
include using a thermal cycler, isothermal amplification, thermal
convection, infrared mediated thermal cycling, or helicase
dependent amplification, for example
Overall Calibration Workflow
[0035] Biological instruments are often relied on to produce
accurate and reliable data for experiments. Regular calibration and
maintenance of biological instruments ensures proper and optimal
operation of the instrument, which can maximize user productivity,
minimize costly repairs by addressing potential problems before
they manifest, and increase quality of results.
[0036] According to various embodiments of the present teachings,
the calibration methods described in this document may be performed
separately or in any combination together. Further, the calibration
methods described herein may be performed after manufacture for
initial calibration or any time after initial installation and use.
The calibration methods described herein may be performed weekly,
monthly, semi-annually, yearly, or as needed, for example.
[0037] According to various embodiments described in the present
teachings, calibration methods such as Region-Of-Interest (ROI)
calibration, background calibration, uniformity calibration, pure
dye calibration, instrument normalization are used to determine the
location and intensity of the fluorescent signals in each read, the
dye associated with each fluorescent signal, and the significance
of the signal. Further, according to various embodiments, auto-dye
correction, auto-background calibration, and plate detection may be
performed to further refine detection and dye readings, and
determine errors. Instrument validation of proper performance may
also be automatically performed by the system using RNase P
validation.
[0038] FIG. 1 illustrates an exemplary calibration workflow 100
that may be performed on an instrument according to various
embodiments described herein. It should be recognized that
calibration workflow 100 is an example and that the calibration
methods described herein may be performed separately, or as a
subset, in any combination and order.
[0039] In step 102, an ROI calibration is performed. Generally ROI
calibration will produce information defining the positions of
wells in the detector's field of view. The present teachings can
automate the ROI calibration through minimization or elimination of
user interaction. Various embodiments can automate the process by
providing methods and systems that determine the optimal exposure
time per filter using histogram analysis and a binary search
pattern. The ROI calibration, according to various embodiments
described herein, identify wells in an image more accurately and
with fewer errors than previous methods. ROI calibration methods
and systems, according to various embodiments, are further
described below.
[0040] In step 104, a background calibration is performed. Often, a
detector will read some amount of signal even in the absence of a
sample emitting detectable signal. Accounting for this background
signal can be important as the background signal can be subtracted
from a sample signal reading in order to get a more accurate
measurement of sample signal. Background calibration can be
performed using a water plate to determine the instrument
background signal for every filter/well combination. The step may
be automated to minimize or eliminate user interaction. Automation
can be provided that will test if the correct plate has been used
for background calibration. For example, step 104 can look at the
signal level and eliminate the possibility of using an incorrect
test plate such as the strong signal emitting test plate used in
the ROI calibration. If the signal level far exceeds the expected
level of the background, the user can be alerted to insert the
proper test plate. Also this stage can test for contamination of
one or more wells in the test plate by checking for wide divergence
of signal levels and if so found, trigger a warning indicating the
possible existence of dirty or contaminated wells. Contaminated
wells can lead to an improper background signal level being
subtracted from the sample signal level.
[0041] In step 106, a uniformity calibration is performed. In some
cases, variations in plate geometry (warping, thickness) can cause
intensity readings to vary across a plate despite the presence of
equal amounts of fluorescent dye in each well. Uniformity
calibrations can calibrate the instrument using a multi-dye plate
so that intensity variations due to plate variations can be
corrected for. Step 106 may be automated and reduce or eliminate
user interaction. Parts of this automation can include detection of
the use of the wrong calibration plate and detection and
adjustments for empty or contaminated wells in the calibration
plate.
[0042] In step 108, a pure dye calibration is performed.
Calibrating fluorescent dyes used in a qPCR instrument allows the
instrument software to use the calibration data collected from dye
standards to characterize and distinguish the individual
contribution of each dye in the total fluorescence collected by the
instrument. According to various embodiments of the present
teachings, after a sample run, the instrument software receives
data in the form of a raw spectra signal for each reading. The
software determines the contribution of each of the fluorescent
dyes used in each reaction site by comparing the raw spectra,
contributed by each dye, to the pure spectra calibration data. When
a user saves an experiment after analysis, the instrument software
stores the pure spectra along with the collected fluorescence data
for that experiment, as well as the contribution of each
fluorescence dye per well. The method is further described below.
Using the pure dye calibration, according to various embodiments of
the present teachings, fewer pure calibration plates may be used,
saving a user cost, and eliminating sources of errors in the
calibration.
[0043] In step 110, an instrument normalization calibration is
performed. One difficulty commonly faced is the inability of
researchers to easily compare results of experiments run on
multiple instruments. Physical variations in the parameters of
components such as light sources, optical elements and fluorescence
detectors, for example, can result in variation in the results of
analyses on what may be identical biological samples. There is,
therefore, a continuing need for methods and apparatus to aid in
minimizing the variations in the components.
[0044] In qPCR, amplification curves are often determined by
normalizing the signal of a reporter dye to a passive reference dye
in the same solution. This normalization can be reported as
normalized fluorescence values labeled or "Rn". Passive reference
normalization enables consistent Rn values even if the overall
signal level is affected by liquid volume, or overall illumination
intensity. Passive reference normalization, however, cannot work
properly if the ratio in signal between the reporter dye and
reference dye varies, such as from instrument-to-instrument
differences in the spectrum of the illumination. According to
various embodiments described herein, instrument normalization
calibration includes reading fluorescence from the dye mixture to
get a "normalization factor" to adjust Rn values requires
additional expense.
[0045] In step 112, an RNase P validation is performed. Performing
a validation test checks to see if an instrument is functioning
properly. For example, RNase P validation determines if an
instrument can accurately distinguish between two different
quantities of sample. Previously, an RNase P validation was
manually performed using a standard curve, with the user doing the
statistical calculations to validate the instrument. According to
various embodiments described in the present teachings, the RNase P
validation may be performed automatically by the system without
using a standard curve. Various embodiments of an RNase P
validation is further described below.
[0046] FIG. 21 illustrates a system 2100 for calibration of an
instrument according to various embodiments described herein.
System 2100 includes ROI calibrator 2102, pure dye calibrator 2104,
instrument normalization calibrator 2108, RNase P validator 2110,
and display engine/GUI 2106. ROI calibrator 2102 is configured to
determine reaction site positions in an image. Pure dye calibrator
2104 is configured to determine the contribution of a fluorescent
dye used in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye. Instrument normalization calibrator 2108 is
configured to determine a filter normalization factor. RNase P
validator 2110 is configured to validate the instrument is capable
of distinguishing between two different quantities of sample.
Display engine 2106 is configured to display calibration
results.
[0047] The present teachings are described with reference to
Real-Time Polymerase Chain Reaction (RT-PCR) instruments. In
particular, an embodiment of the present teachings is implemented
for RT-PCR instruments employing optical imaging of well plates.
Such instruments can be capable of simultaneously measuring signals
from a plurality of samples or spots for analytical purposes and
often require calibration, including but not limited to processes
involving: identifying ROI (Regions of Interest), determining
background signal, uniformity and pure dye spectral calibration for
multicomponent analysis. Calibration may also involve a RT-PCR
validation reaction using a known sample plate with an expected
outcome. One skilled in the art will appreciate that while the
present teachings have been described with examples pertaining to
RT-PCR instruments, their principles are widely applicable to other
forms of laboratory instrumentation that may require calibration
and verification in order to ensure accuracy and/or optimality of
results.
PCR Instruments
[0048] As mentioned above, an instrument that may be utilized
according to various embodiments, but is not limited to, is a
polymerase chain reaction (PCR) instrument. FIG. 2 is a block
diagram that illustrates a PCR instrument 200, upon which
embodiments of the present teachings may be implemented. PCR
instrument 200 may include a heated cover 210 that is placed over a
plurality of samples 212 contained in a substrate (not shown). In
various embodiments, a substrate may be a glass or plastic slide
with a plurality of sample regions, which sample regions have a
cover between the sample regions and heated cover 210. Some
examples of a substrate may include, but are not limited to, a
multi-well plate, such as a standard microtiter 96-well, a 384-well
plate, or a microcard, or a substantially planar support, such as a
glass or plastic slide. The reaction sites in various embodiments
of a substrate may include depressions, indentations, ridges, and
combinations thereof, patterned in regular or irregular arrays
formed on the surface of the substrate. Various embodiments of PCR
instruments include a sample block 214, elements for heating and
cooling 216, a heat exchanger 218, control system 220, and user
interface 222. Various embodiments of a thermal block assembly
according to the present teachings comprise components 214-218 of
PCR instrument 200 of FIG. 2.
[0049] Real-time PCR instrument 200 has an optical system 224. In
FIG. 2, an optical system 224 may have an illumination source (not
shown) that emits electromagnetic energy, an optical sensor,
detector, or imager (not shown), for receiving electromagnetic
energy from samples 212 in a substrate, and optics 240 used to
guide the electromagnetic energy from each DNA sample to the
imager. For embodiments of PCR instrument 200 in FIG. 2 and
real-time PCR instrument 200 in FIG. 2, control system 220, may be
used to control the functions of the detection system, heated
cover, and thermal block assembly. Control system 220 may be
accessible to an end user through user interface 222 of PCR
instrument 200 in FIG. 2 and real-time PCR instrument 200 in FIG.
2. Also a computer system 200, as depicted in FIG. 2, may serve as
to provide the control the function of PCR instrument 200 in FIG.
2, as well as the user interface function. Additionally, computer
system 400 of FIG. 4 may provide data processing, display and
report preparation functions. All such instrument control functions
may be dedicated locally to the PCR instrument, or computer system
400 of FIG. 4 may provide remote control of part or all of the
control, analysis, and reporting functions, as will be discussed in
more detail subsequently.
Optical System for Imaging
[0050] FIG. 3 depicts an exemplary optical system 300 that may be
used for imaging according to embodiments described herein. It
should be recognized that optical system 300 is an exemplary
optical system and one skilled in the art would recognize that
other optical systems may be used to capture images an
object-of-interest. According to various embodiments, an object of
interest may be a sample holder such as, for example, a calibration
plate as described herein. An optical sensor 302 included in a
camera 304, for example, may image an object-of-interest 310. The
optical sensor 302 may be a CCD senor and the camera 304 may be a
CCD camera. Further, the optical sensor includes a camera lens
306.
[0051] Depending on the object of interest, an emission filter 308
can be chosen for imagining the object-of-interest 310 according to
various embodiments. Emission filter 308 may be changed to image
fluorescent emission emitted from the object-of-interest 301 in
other embodiments.
[0052] Optical system 300 may use a reflected light source 312 to
image object-of-interest 310. The light from light source 312 may
be filtered through an asphere 314, a focuser/diverger 316, and
excitation filter 318 before being reflected to the
object-of-interest 310 by beamsplitter 320. Optical system 300 may
also include a field lens 322. Depending on the object of interest,
the excitation filter 318 can be chosen or changed for imagining
the object-of-interest 310 according to various embodiments.
[0053] The following descriptions of various implementations of the
present teachings have been presented for purposes of illustration
and description. It is not exhaustive and does not limit the
present teachings to the precise form disclosed. Modifications and
variations are possible in light of the above teachings or may be
acquired from practicing of the present teachings. Additionally,
the described implementation includes software but the present
teachings may be implemented as a combination of hardware and
software or in hardware alone. The present teachings may be
implemented with both object-oriented and non-object-oriented
programming systems.
Computing System
[0054] FIG. 4 is a block diagram that illustrates a computer system
400 that may be employed to carry out processing functionality,
according to various embodiments. Instruments to perform
experiments may be connected to the exemplary computing system 400.
Computing system 400 can include one or more processors, such as a
processor 404. Processor 404 can be implemented using a general or
special purpose processing engine such as, for example, a
microprocessor, controller or other control logic. In this example,
processor 404 is connected to a bus 402 or other communication
medium.
[0055] Further, it should be appreciated that a computing system
400 of FIG. 4 may be embodied in any of a number of forms, such as
a rack-mounted computer, mainframe, supercomputer, server, client,
a desktop computer, a laptop computer, a tablet computer, hand-held
computing device (e.g., PDA, cell phone, smart phone, palmtop,
etc.), cluster grid, netbook, embedded systems, or any other type
of special or general purpose computing device as may be desirable
or appropriate for a given application or environment.
Additionally, a computing system 400 can include a conventional
network system including a client/server environment and one or
more database servers, or integration with LIS/LIMS infrastructure.
A number of conventional network systems, including a local area
network (LAN) or a wide area network (WAN), and including wireless
and/or wired components, are known in the art. Additionally,
client/server environments, database servers, and networks are well
documented in the art. According to various embodiments described
herein, computing system 400 may be configured to connect to one or
more servers in a distributed network. Computing system 400 may
receive information or updates from the distributed network.
Computing system 400 may also transmit information to be stored
within the distributed network that may be accessed by other
clients connected to the distributed network.
[0056] Computing system 400 may include bus 402 or other
communication mechanism for communicating information, and
processor 404 coupled with bus 402 for processing information.
[0057] Computing system 400 also includes a memory 406, which can
be a random access memory (RAM) or other dynamic memory, coupled to
bus 402 for storing instructions to be executed by processor 404.
Memory 406 also may be used for storing temporary variables or
other intermediate information during execution of instructions to
be executed by processor 404. Computing system 400 further includes
a read only memory (ROM) 408 or other static storage device coupled
to bus 402 for storing static information and instructions for
processor 404.
[0058] Computing system 400 may also include a storage device 410,
such as a magnetic disk, optical disk, or solid state drive (SSD)
is provided and coupled to bus 402 for storing information and
instructions. Storage device 410 may include a media drive and a
removable storage interface. A media drive may include a drive or
other mechanism to support fixed or removable storage media, such
as a hard disk drive, a floppy disk drive, a magnetic tape drive,
an optical disk drive, a CD or DVD drive (R or RW), flash drive, or
other removable or fixed media drive. As these examples illustrate,
the storage media may include a computer-readable storage medium
having stored therein particular computer software, instructions,
or data.
[0059] In alternative embodiments, storage device 410 may include
other similar instrumentalities for allowing computer programs or
other instructions or data to be loaded into computing system 400.
Such instrumentalities may include, for example, a removable
storage unit and an interface, such as a program cartridge and
cartridge interface, a removable memory (for example, a flash
memory or other removable memory module) and memory slot, and other
removable storage units and interfaces that allow software and data
to be transferred from the storage device 410 to computing system
400.
[0060] Computing system 400 can also include a communications
interface 418. Communications interface 418 can be used to allow
software and data to be transferred between computing system 400
and external devices. Examples of communications interface 418 can
include a modem, a network interface (such as an Ethernet or other
NIC card), a communications port (such as for example, a USB port,
a RS-232C serial port), a PCMCIA slot and card, Bluetooth, etc.
Software and data transferred via communications interface 418 are
in the form of signals which can be electronic, electromagnetic,
optical or other signals capable of being received by
communications interface 418. These signals may be transmitted and
received by communications interface 418 via a channel such as a
wireless medium, wire or cable, fiber optics, or other
communications medium. Some examples of a channel include a phone
line, a cellular phone link, an RF link, a network interface, a
local or wide area network, and other communications channels.
[0061] Computing system 400 may be coupled via bus 402 to a display
412, such as a cathode ray tube (CRT) or liquid crystal display
(LCD), for displaying information to a computer user. An input
device 414, including alphanumeric and other keys, is coupled to
bus 402 for communicating information and command selections to
processor 404, for example. An input device may also be a display,
such as an LCD display, configured with touchscreen input
capabilities. Another type of user input device is cursor control
416, such as a mouse, a trackball or cursor direction keys for
communicating direction information and command selections to
processor 404 and for controlling cursor movement on display 412.
This input device typically has two degrees of freedom in two axes,
a first axis (e.g., x) and a second axis (e.g., y), that allows the
device to specify positions in a plane. A computing system 400
provides data processing and provides a level of confidence for
such data. Consistent with certain implementations of embodiments
of the present teachings, data processing and confidence values are
provided by computing system 400 in response to processor 404
executing one or more sequences of one or more instructions
contained in memory 406. Such instructions may be read into memory
406 from another computer-readable medium, such as storage device
410. Execution of the sequences of instructions contained in memory
406 causes processor 404 to perform the process states described
herein. Alternatively hard-wired circuitry may be used in place of
or in combination with software instructions to implement
embodiments of the present teachings. Thus implementations of
embodiments of the present teachings are not limited to any
specific combination of hardware circuitry and software.
[0062] The term "computer-readable medium" and "computer program
product" as used herein generally refers to any media that is
involved in providing one or more sequences or one or more
instructions to processor 404 for execution. Such instructions,
generally referred to as "computer program code" (which may be
grouped in the form of computer programs or other groupings), when
executed, enable the computing system 400 to perform features or
functions of embodiments of the present invention. These and other
forms of non-transitory computer-readable media may take many
forms, including but not limited to, non-volatile media, volatile
media, and transmission media. Non-volatile media includes, for
example, solid state, optical or magnetic disks, such as storage
device 410. Volatile media includes dynamic memory, such as memory
406. Transmission media includes coaxial cables, copper wire, and
fiber optics, including the wires that comprise bus 402.
[0063] Common forms of computer-readable media include, for
example, a floppy disk, a flexible disk, hard disk, magnetic tape,
or any other magnetic medium, a CD-ROM, any other optical medium,
punch cards, paper tape, any other physical medium with patterns of
holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip
or cartridge, a carrier wave as described hereinafter, or any other
medium from which a computer can read.
[0064] Various forms of computer readable media may be involved in
carrying one or more sequences of one or more instructions to
processor 404 for execution. For example, the instructions may
initially be carried on magnetic disk of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem. A
modem local to computing system 400 can receive the data on the
telephone line and use an infra-red transmitter to convert the data
to an infra-red signal. An infra-red detector coupled to bus 402
can receive the data carried in the infra-red signal and place the
data on bus 402. Bus 402 carries the data to memory 406, from which
processor 404 retrieves and executes the instructions. The
instructions received by memory 406 may optionally be stored on
storage device 410 either before or after execution by processor
404.
[0065] It will be appreciated that, for clarity purposes, the above
description has described embodiments of the invention with
reference to different functional units and processors. However, it
will be apparent that any suitable distribution of functionality
between different functional units, processors or domains may be
used without detracting from the invention. For example,
functionality illustrated to be performed by separate processors or
controllers may be performed by the same processor or controller.
Hence, references to specific functional units are only to be seen
as references to suitable means for providing the described
functionality, rather than indicative of a strict logical or
physical structure or organization.
Distributed System
[0066] Some of the elements of a typical Internet network
configuration 500 are shown in FIG. 5, wherein a number of client
machines 502 possibly in a remote local office, are shown connected
to a gateway/hub/tunnel-server/etc 510 which is itself connected to
the internet 508 via some internet service provider (ISP)
connection 510. Also shown are other possible clients 512 similarly
connected to the internet 508 via an ISP connection 514, with these
units communicating to possibly a central lab or office, for
example, via an ISP connection 516 to a gateway/tunnel-server 518
which is connected 520 to various enterprise application servers
522 which could be connected through another hub/router 526 to
various local clients 530. Any of these servers 522 could function
as a development server for the analysis of potential content
management and delivery design solutions as described in the
present invention, as more fully described below.
Region of Interest (ROI) Calibration
[0067] As presented above, the present teachings are described with
reference to Real-Time Polymerase Chain Reaction (RT-PCR)
instruments. In particular, an embodiment of the present teachings
is implemented for RT-PCR instruments employing optical imaging of
well plates. Such instruments can be capable of simultaneously
measuring signals from a plurality of samples or spots for
analytical purposes and often require calibration. An example of a
process that can require calibration is the identification of ROIs
or Regions of Interest.
[0068] Generally ROI calibration can be performed using a plate
with strong emissions in each cell corresponding to all filters.
This can be useful since the ROIs may not be identical for each
filter. Differences in the ROIs between filters can be caused by
slight angular differences in the filters and other filter spectral
characteristics. Thus, various embodiments perform per filter/per
well (PFPW)-ROI calibration. These PFPW-ROI calibrations are useful
to determine locations of the wells in the 96 well-plate for each
filter. ROI calibration can be performed using a method such as the
Adaptive Mask Making teachings as described in U.S. Pat. No.
6,518,068 B1.
[0069] The present teachings can automate the ROI calibration
through minimization or elimination of user interaction. Various
embodiments can automate the process by providing for software that
determine the optimal exposure time per filter using histogram
analysis and a binary search pattern. The exposure time is the
amount of time required to capture an image of the plate. Again,
this value can vary according to a filter's spectral
characteristics. Generally ROI calibration will produce information
defining the positions of wells in the detector's field of view.
This information can be stored as mask files with either a global
mask or multiple masks corresponding to different filters.
[0070] Calibration processes such as what is described above
frequently use row and column projections and intensity profiles.
This can result in ROI determinations being susceptible to
artifacts and saturation inside the wells, grid rotation, variation
of magnification factors and optical radial distortion. It can
therefore be advantageous to have a more robust determination of
ROIs to minimize such susceptibilities and remove distortions and
other unwanted background noise in the detected emission data.
[0071] Background noise may refer to inherent system noise as well
as other undesired signals. For example, some background noise in
the data may be due to physical sources on the substrate, such as
dust particles or scratches, for example. Another example of a
physical source that may provide background noise is a holder or
case holding or enclosing the sample. Other background noise in the
data may be due to natural radiation from the surfaces in the
instrument, such as reflection and natural fluorescence. Other
background noise may also be a result from the optical system
detecting the emission data or the light source, for example.
[0072] The biological instrument may be detecting several hundred
to several thousand samples, all of which may be a very small
volume, such as less than one nanoliter. As such, other background
noise removal methods may be used alone or in combination with the
calibration methods described in this document according to various
embodiments to be able to determine and analyze the emission data
from the sample volumes. In some embodiments, the location of
samples volumes may be more accurately determined within the
substrate to perform a more accurate analysis. For example, in
digital PCR analysis, being able to more accurately distinguish
reactions in sample volumes versus non-reactions may produce more
accurate results. Even further, according to various embodiments
described herein, empty wells or through-holes may be distinguished
from sample volumes in wells or througholes that did not react,
which may also be distinguished from sample volumes in wells or
througholes that did react.
[0073] According to various embodiments described herein,
background noise removal may include image data analysis and
processing. The method may include analyzing intensity values of
the image data to interpolate the background noise that may be
removed from the image of the substrate. In this way, locations of
the regions-of-interest within the image may also be determined.
The background noise removal may also include interpolating data
from areas of the image known to include regions-of-interest. After
determining the background noise over the image, the background
noise may be subtracted from the image data.
[0074] FIG. 6 depicts an exemplary in silico method 600 according
to one embodiment of the present invention. In silico method 600
includes a plurality of set workflow subroutines in a computer
readable format that can include subroutines for a biotechnology
process. FIG. 6 is merely an exemplary method and the skilled
artisan, in light of this disclosure, will realize that the actual
number of subroutines can vary from at least about 2 subbroutines
to many (e.g. 2-10, 2-20, 2-30, 2-n (where n may be any number of
subroutines from 3-100, 3-1000 and so on)). Each set subroutine
310-370 can include a single step or task, or optionally can
include more than 1 step or task, also in a computer readable
format, and each step can further include additional optional
customizable steps or tasks. Each of the optional/customizable
steps or tasks can have one or more optional parameters (options)
that can be viewed, reviewed, set or customized by a user. In some
embodiments, an in silico method of the invention includes
selection by a user of at least one parameter each for each
optional/customizable step of the biotechnological process using a
graphical user interface (GUI) to select the at least one parameter
for each optional/customizable step. In certain embodiments, every
step and every parameter of the subroutines of a workflow are
available to a user to view, and optionally edit. Bioinformatics
programs typically hide some of these parameters and/or steps from
users, which causes user frustration and inefficiency especially
when the result of an in silico designed experiment is not the
expected result for a user.
[0075] An exemplary in silico method of the disclosure illustrated
generally in FIG. 6 can be carried out (performed) by generating at
least one method file in a computer system (such as shown in FIG.
4), the method file comprising computer readable instructions for a
plurality subroutines (10, 20, 30 . . . ) of customizable steps (A,
B, C) each of which may have one or more parameters that may be
viewed, selected, changed or inputted; and performing the
biotechnological process in silico comprising executing the at
least one method file comprising computer readable instructions by
the computer system to obtain at least one biotechnology
product.
[0076] In some embodiments, at least one customizable/optional
parameter is selected from a default parameter, wherein the default
parameter is stored in a component of the computer system (such as
storage, database etc.).
[0077] Referring again to FIG. 6, the first step in calculating ROI
locations is to estimate the initial ROI centers from the
fluorescence threshold in step 610. A sample plate configured to
contain a plurality of biological samples is provided and inserted
into an analytical instrument capable of analyzing biological
samples through the process of PCR. Each biological sample is
contained in a sample well and can be excited by a light source and
in response to the excitation can fluoresce at a predetermined
wavelength which can be detected by a fluorescence detector. As
presented above with regards to FIG. 2, light source 202 can be a
laser, LED or other type of excitation source capable of emitting a
spectrum that interacts with spectral species to be detected by
system 200. Additionally, biological samples can include spectrally
distinguishable dyes such as one or more of FAM, SYBR Green, VIC,
JOE, TAMRA, NED, CY-3, Texas Red, CY-5, ROX (passive reference) or
any other fluorochromes that emit a signal capable of being
detected.
[0078] Prior to exciting the biological samples input parameters
and algorithm parameters are set to provide a starting point for
the ROI determination. Input parameters can include well size, well
center-to-center distance, optical pixels per millimeter and plate
layout. The plate layout can include the total number of wells and
the configuration of the sample wells. A frequently used
configuration can be a rectangular array comprising a plurality of
rows and a plurality of columns, however one skilled in the art
will understand that the configuration can be any geometry suitable
for the instrument being used. Further, the total number of wells
can vary. One skilled in the art will be familiar with
configurations totaling from 1 well to thousands of wells in a
single sample plate or sample containment structure. The ROI
finding algorithm parameters can set acceptable ranges for well
size, well center-to-center distance and minimum circularity.
Circularity is a calculated value and can be a ratio of the
perimeter to the area.
[0079] Once the input parameters and the algorithm parameters have
been determined, the plurality of samples are excited with energy
from an appropriate light source, and images are collected of the
fluorescence emitted from each sample well in the sample plate. The
fluorescence images of the sample plate are further analyzed to
select ROI candidates based on the input parameters and the
algorithm parameters. The ROI candidates that satisfy the
parameters are saved for further analysis and the size and
circularity of each well is determined in step 620. ROI candidates
that do not satisfy the parameters can be discarded along with any
locations that did not fluoresce. The retained ROI candidates are
further evaluated to determine the distance between ROIs based on
the well-to-well spacing parameter and the allowed range parameter
for the well-to-well spacing. ROIs that have centers that are in
close proximity to each other based on the well-to-well parameters
can be considered to be the same sample well, and the one with the
best circularity is selected as the ROI for that well. Once all the
ROI candidates have been determined, the average well size is
calculated, the average is assigned to each sample well ROI in step
630 and the initial estimated ROIs are saved.
[0080] The expected well locations are arranged in a grid pattern
determined based on the plate layout parameter. This parameter can
include the number of wells, number of columns and number of rows
where each well has an expected set of XY grid co-ordinates based
on the plate layout parameter. Further analysis can now be
initiated on the initial estimated ROIs to better define the
locations of each initial ROI and can be referred to as global
gridding. The first step in global gridding is to analyze the
centers of the initial estimated ROIs to find adjacent ROIs. This
can be determined by comparing the center-to-center distance
between ROIs to the grid co-ordinates based on the plate layout.
The XY grid co-ordinates can then be determined for each of the
initial estimated ROIs based on the spatial relationship between
ROIs.
[0081] In order to improve the precision of the ROI locations it
would be advantageous to relate the center-to-center ROI
co-ordinates to the grid co-ordinates of the plate layout. This can
be accomplished by determining and applying mapping functions.
Mapping functions are a pair of 2-dimensional quadratic polynomial
functions. These functions are calculated to map X (or Y) grid
locations to the ROI center locations in the X (or Y) direction.
Once the mapping functions have been determined, they can be
applied to the expected grid co-ordinates to provide two benefits.
First the precision of the ROI center locations can be improved,
and second it can be possible to recover ROIs that were missing
during the initial ROI finding.
[0082] Further adjustment of ROIs can provide additional benefits
to optical performance. The inventors discovered that there was a
relationship between ROI size and the signal-to-noise ratio (SNR)
of the optical system. One skilled in the art would know that there
are several equations to calculate SNR of electrical and optical
systems. SNR can be characterized with Equation 1 below, for
example:
SNR = S dye plate - S BG S dye + S BG - 2 N .times. offset G + 2 N
.sigma. R , y 2 ##EQU00001##
where: [0083] SNR=Signal to Noise Ratio [0084] Sdye plate=the sum
of all pixel intensities within ROIs from the dye images [0085]
S.sub.BG=the sum of all pixel intensities within ROIs from
background images [0086] Sdye=the sum of all pixel intensities
within ROIs from the dye images [0087] N=the number of pixels
within an ROI [0088] offset=the camera offset [0089] G=the camera
gain [0090] .delta.2R,y=the read noise
[0091] An experiment was conducted using an optical system that
included six pairs of filters. Each pair of filters included an
excitation filter (Xn) and an emission filter (Mn). Each filter was
sensitive to a narrow band of wavelengths that correspond to the
excitation frequency and emission frequency of dye configured to be
compatible with the PCR process. In addition ROIs were optimized
according to the teachings presented in this document. In order to
study the effect of ROI size on signal-to-noise, fluorescence was
detected from a 96 well sample plate using 6 pairs of filters. The
radius of each ROI was extended incrementally by 1 pixel. Equation
1 was used to calculate the SNR for each of 6 filter pairs and each
pixel increment. The results of the experiment are shown below in
Table 1:
TABLE-US-00001 TABLE 1 SNR X1M1 X2M2 X3M3 X4M4 X5M5 X6M6 .DELTA.R =
0 1709.5 2502.7 1840.3 1613.8 1632.4 475.5 .DELTA.R = 1 1808.2
2642.0 1942.7 1706.3 1709.2 496.8 .DELTA.R = 2 1826.6 2677.8 1964.2
1722.7 1718.8 491.2 .DELTA.R = 3 1818.7 2678.7 1958.4 1714.4 1708.2
479.0 .DELTA.R = 4 1802.5 2667.3 1943.1 1697.6 1690.8 464.7
[0092] The bold entries identify the highest SNR for each of the 6
filter pairs, and a 2 pixel radius extension provides an overall
improvement in SNR of approximately 6% across the 6 filter
pairs.
[0093] FIG. 7 shows an image of a sample plate with 96 wells 710.
Each of the wells 710 produced a fluorescent image. After applying
the teachings of this document ROIs were optimized and the blue
circles identify the ROI for each well position.
Pure Dye Calibration
[0094] As described above, there is an increasing need to simplify
the installation and setup of biological analysis systems so that
operators can more quickly and efficiently use biological analysis
systems for their intended purpose. This need is evident in, for
example, calibrating a biological analysis instrument and
associated components. One exemplary calibration is the calibrating
of fluorescent dyes used for fluorescence detection in biological
analysis systems such as, for example, qPCR systems.
[0095] Calibrating fluorescent dyes used in a qPCR instrument
allows the instrument software to use the calibration data
collected from dye standards to characterize and distinguish the
individual contribution of each dye in the total fluorescence
collected by the instrument. After a sample run, the instrument
software receives data in the form of a raw spectra signal for each
reading. The software determines the contribution of each of the
fluorescent dyes used in each reaction site by comparing the raw
spectra, contributed by each dye, to the pure spectra calibration
data. When a user saves an experiment after analysis, the
instrument software stores the pure spectra along with the
collected fluorescence data for that experiment, as well as the
contribution of each fluorescence dye per well.
[0096] The product of a dye calibration in a qPCR instrument, for
example, is a collection of spectral profiles that represent the
fluorescence signature of each dye standard for each reaction site.
Each profile consists of a set of spectra that correspond to the
fluorescence collected from reaction sites, such as wells, of a
sample holder such as, for example, a calibration plate or array
card. Following the calibration of each dye, the instrument
software "extracts" a spectral profile for each dye at each
reaction site. The software plots the resulting data for each
profile in a graph of fluorescence versus filter. When the software
extracts the dye calibration data, it evaluates the fluorescence
signal generated by each well in terms of the collective spectra
for the entire calibration plate or array card. Dye spectra are
generally acceptable if they peak within the same filter as their
group, but diverge slightly at other wavelengths.
[0097] When running dye calibration on a sample holder, such as a
calibration plate, the reaction sites (e.g., wells) generally
contain identical concentrations of dye to allow generation of a
pure spectra value at each well of the plate. FIG. 8 displays an
image of a calibration plate with a single dye (in this case, FAM
dye), occupying each well of a 96-well calibration plate. This
allows for the comparison of fluorescence signal generated by each
well in a run to a pure spectra read for that well. By using a
single dye for each well of a calibration plate, the resulting
signals for the wells should be similar. Variations in spectral
position and peak position can be caused, for example, by minor
differences in the optical properties and excitation energy between
the individual wells. Taking these variations into account in dye
calibration theoretically leads to a more accurate dye
calibration.
[0098] However, the use of a single dye per calibration plate could
be time intensive and complicated, particularly when calibrating
numerous dyes. Non-limiting examples of fluorescent dyes include
FAM, VIC, ROX, SYBR, MP, ABY, JUN, NED, TAMRA and CY5. Therefore, a
need exists to simplify the dye calibration process and reduce the
time required for calibration while maintaining the same quality of
results of the dye calibration.
[0099] FIGS. 9 and 10 illustrate a flowchart depicting an exemplary
method 900 of calibrating fluorescent dye(s) according to
embodiments described herein. The steps of method 900 may be
implemented by a processor 404, as shown in FIG. 4. Furthermore,
instructions for executing the method by processor 404 may be
stored in memory 406.
[0100] With reference to FIG. 9, in step 902, calibration plates
are prepared by loading dyes into reaction sites of a substrate for
processing. The substrate, in this case, is a 96-well plate, though
different substrates may be used including, for example, a 384-well
plate. In various embodiments, the substrate may be a glass or
plastic slide with a plurality of sample regions. Some examples of
a substrate may include, but are not limited to, a multi-well
plate, such as a standard microtiter 96-well plate, a 384-well
plate, or a microcard, a substantially planar support, such as a
glass or plastic slide, or any other type of array or microarray.
The reaction sites in various embodiments of a substrate may
include wells, depressions, indentations, ridges, and combinations
thereof, patterned in regular or irregular arrays formed on the
surface of the substrate. Heretofore, reference to wells or plates
are just for exemplary purposes only and not in any way to limit
the type of reaction site or sample holder useable herein.
[0101] The calibration plates may be prepared in a checkerboard
pattern as illustrated in FIG. 11A. As illustrated in calibration
plates 1100, 1120 and 1140, the plates themselves may be of a
96-well format, though the number of wells on the calibration plate
can be varied as needed depending on, for example, the number of
dyes requiring calibration, the sample block 314 (see FIG. 3)
format accepting the calibration plate, or the capabilities of the
instrument (PCR instrument 300 for example) to image plates of
different well densities.
[0102] The checkerboard pattern of dye distribution allows multiple
dyes to be calibrated per calibration plate. As opposed to
calibrating one dye per calibration plate, the checkerboard pattern
advantageously allows a user to use fewer plates to calibrate a dye
set, thus decreasing time and process steps needed for dye
calibration.
[0103] In the embodiment illustrated in FIG. 11A, three plates are
used to calibrate ten separate dyes. Each calibration plate
1100/1120/1140 is configured to accommodate four different dyes in
a repeating pattern of alternating dyes along wells in each row of
the plate such that each well presents a specific dye in the
repeating pattern (dye presented well). For example, plate 1100
accommodates FAM, VIC, ROX and SYBR dyes in alternating wells
exemplified by wells 1102 (FAM), 1104 (VIC), 1106 (ROX) and 1108
(SYBR); plate 1120 accommodates a buffer, MP dye, ABY dye and JUN
dye in alternating wells exemplified by wells 1122 (buffer), 1124
(MP), 1126 (ABY) and 1128 (JUN); and plate 1140 accommodates NED
dye, TAMRA dye, CY5 dye and a buffer in alternating wells
exemplified by wells 1142 (NED), 1144 (TAMRA), 1146 (CY5) and 1148
(buffer). In this embodiment, since only ten dyes are being
calibrated, buffers are used in plates 1120 and 1140 as filler for
wells not accommodating a dye to be calibrated.
[0104] It should be appreciated that the embodiment in FIGS. 11A
and 11B is an example only, and that the number of total dyes
calibrated, the number of dyes per plate, and the number of plates,
can all vary as needed based, for example, on a user's calibration
needs, the number of wells on the plate, and capacity of the
instrument handling the calibration. For example, if 12 dyes were
being calibrated in the embodiment illustrated in FIG. 11A, a
buffer would not be needed in plates 1120 and 1140, as four dyes
could be calibrated in each of the three calibration plates
1100/1120/1140 for a total of 12 dyes.
[0105] Moreover, the number of dyes per plate can be two or more,
with the maximum number of dyes per plate based on, for example,
the number of wells on the calibration plate, the capability of the
instrument used to properly model a full plate (see below for
further explanation), and the capability of the imaging system to
obtain usable fluorescence data from the plate chosen. For example,
rather than using a 96-well plate as illustrated in FIG. 11A, one
may have a sufficiently robust instrument and associated imaging
system to be able to use a 384-well calibration plate. With the
additional well density provided, one could calibrate more dyes per
plate, for example 16 dyes per plate, and still get the same number
of data points (i.e., dye presented wells) per dye (e.g., 24)
needed to get a sufficient global model (discussed in more detail
below). For example, with a 384-well plate, 10 dyes can be
calibrated using two plates and five dyes per plate.
[0106] Even the type of sample holder and type of reaction site may
affect the number of dyes possible. As stated above, other types of
sample holders and reaction sites may be used for calibration.
[0107] Returning to FIG. 9, in step 904, prepared checkerboard
calibration plates can be loaded into the instrument. The number of
plates loadable into an instrument at one time depends on the
capabilities and capacity of the instrument used. For example, a
standard qPCR thermal cycler with a 96-well block will only accept
on calibration plate at a time. However, multi-block thermal
cyclers may offer multiple blocks that can each accept a
calibration plate. Moreover, if a calibration plate is not used,
depending on the format of the sample holder used (e.g., a
microarray or microchip array), multiple sample holders may be
received in a single instrument using, for example, a loading
assembly that fits into the instrument.
[0108] In step 906 of FIG. 9, the instrument, using its associated
optical imaging system (see, for example, FIG. 3), acquires images
of the loaded calibration plate, or plates, in series or parallel.
The acquired images and associated data can be stored, for example,
on memory 406 or storage device 410 of computing system 400 in FIG.
4. The optical imaging system can acquire images of each plate at
each optical channel. The number of channels depends on the number
of excitation and emission filters provided in the imaging system.
For example, for an optical imaging system having 6 excitation
filters (X filters) and 6 emission filters (M filters), the total
number of channels is 21, represented by the following filter
combinations: X1M1, X1M2, X1M3, X1M4, X1M5, X1M6, X2M2, X2M3, X2M4,
X2M5, X2M6, X3M3, X3M4, X3M5, X3M6, X4M4, X4M5, X4M6, X5M5, X5M6,
and X6M6. The number of images or exposures acquired at each
channel can vary. For example, the imaging system can acquire two
images or exposures per channel. The number of images or exposures
taken depends on user needs, as taking fewer images or exposures
per channel may decrease the time needed to acquire images or
exposures, while taking more images or exposures per channel
provides greater likelihood of quality data.
[0109] In step 908 of FIG. 9, the instrument, using the data
gathered from the images or exposures acquired by the optical
imaging system (see, for example, FIG. 3), identifies the peak
channel for each dye on the calibration plate. This peak channel
for each reaction site is the channel where the specific dye
analyzed shows the greatest fluorescence for that reaction site.
The peak channel identification can occur when, for example, 95% or
more reaction sites are dye occupied, in this case allowing no more
than 5% outlier reaction sites during calibration. The percentage
of allowable outliers can vary. The outlier reaction sites can then
be discarded from future calculation and analysis. Outliers can
occur, for example, when the wrong dyes are loaded, the dyes are
loaded in the incorrect configuration, there is improper loading of
dyes, or optical components become dirty (e.g., dust particles).
The peak channel for each dye on the calibration plate can be
identified, for example, by processor 404, of computing system 400,
utilizing data stored on memory 406. The identification results can
be stored, for example, on memory 406 or storage device 410 of
computing system 400.
[0110] Alternatively, the collected fluorescence data gathered from
the images or exposures acquired by the optical imaging system for
each filter combination on each reaction site can be corrected by
background and uniformity correction before peak channel
identification, using background component and uniformity factors
determined using background and uniformity calibrations methods
known in the art.
[0111] In step 910 of FIG. 9, the instrument, using the data
gathered from the images or exposures acquired by the optical
imaging system (see, for example, FIG. 2), normalizes each channel
to the identified peak channel of step 908 for all the dye
presented wells. Each channel can be normalized to the identified
peak channel, for example, by processor 404, of computing system
200, utilizing data stored on memory 406. The results of the
normalization can be stored, for example, on memory 406 or storage
device 410 of computing system 400.
[0112] All dye presented wells are given a baseline quant value
from which to normalize from. Generally, the greater the quant
value, the greater the detected fluorescence. Therefore, the
identified peak channel for a given dye would have the largest
quant value for that dye in the dye presented wells, excluding peak
channel outliers. Regardless of the quant value in that peak
channel, to normalize, that quant value at that channel is reset to
a value of one. The remaining quant values for that same dye at the
other channels are then adjusted according to the reset value of
one for the peak channel. For example, if for dye X, the peak
channel A had a quant value of 100 in the wells, and other channel
B had a quant value of 40 in the wells, upon normalization, peak
channel A gets set to 1.0 and channel B gets set to 0.40. This
normalized value can also be referred to as a calibration factor,
with the calibration factor for the peak channel being set to 1.0
as discussed above.
[0113] In the embodiment illustrated in FIGS. 11A and B, where four
dyes are equally dispersed among the wells of a 96-well plate, the
number of dye presented wells per dye would be 24. The number of
dye presented wells can vary for reasons discussed previously such
as, for example, the number of reaction sites (e.g., wells) on the
sample holder (e.g., calibration plate), the number of dyes per
dispersed on the sample holder. For example, on a 96-well plate, if
three dyes are dispersed, the number of dye presented wells would
be 32 per dye. If there are six dyes dispersed on the 96-well
plate, there would be 16 dye presented wells per dye.
[0114] With reference now to FIG. 10, in step 912, the instrument
performs global modeling for all wells per dye. In order to
calibrate a dye for all wells of a sample holder format, the
instrument can use the data from the dye presented wells for a
specific dye to model for all wells, including the ones without a
specific dye. The global modeling can be performed, for example, by
processor 404, of computing system 400, by using the data from the
dye presented wells for a specific dye to model for all wells. The
resulting model can be stored, for example, on memory 406 or
storage device 410. Referring to FIG. 11A, for the FAM dye present
in 24 wells 1102 of plate 1100, the other 112 wells on that plate
would be FAM dye unpresented. The same 24 presented/72 unpresented
distribution would apply to each dye in FIG. 11A. The number of dye
unpresented wells depends on the number of dye presented wells,
which, as discussed above, can depend for various reasons.
Regardless, the sum of dye presented and unpresented wells for a
given plate equals the number of wells on that plate. FIG. 11B is
an image of a 4 dye checkerboard 96-well calibration plate with
FAM, VIC, ROX and SYBR dyes in the same configuration as
illustrated by plate 1100 in FIG. 11A.
[0115] In an alternative embodiment, the instrument performs global
modeling for all channels or those channels that have a normalized
value, for example, greater than 0.01, or 1% of the identified peak
channel. For those channels below this threshold, the instrument
would perform a local modeling (see step 922 of FIG. 10) instead of
performing global modeling. Global modeling may become unnecessary
at such low levels at certain channels such that detected
fluorescence is primarily a result of, for example, noise or other
disturbance, rather than contribution of the actual dye being
calibrated.
[0116] A global modeling algorithm can function in a dye
calibration to derive a model of dye calibration factors for each
filter channel for each dye based on the measured dye calibration
factors of the specific dye presented wells. For example, if 24
wells are presented on the 96-well checkerboard plate for a
specific dye, global modeling utilizes the dye calibration factors
of those 24 wells to derive calibration factors for all the wells
including the other dye unpresented 72 wells, and thus produce a
model for the whole plate per channel, per dye.
[0117] The two-dimensional (2D) quadratic polynomial function is an
example of a function that can be applied as a global model for dye
calibration factors. Other global modeling functions are known and
can be used herein. A non-linear least square solver can be used to
derive the 2D quadratic polynomial function from the measured dye
calibration factors on the specific dye presented wells by
minimizing the modeling residuals (the difference between the
values calculated from the model and the measured dye calibration
factors). Levenberg-Marquardt Trust region algorithm can be used as
the optimization algorithm in this solver. While many other
optimization algorithms are useable herein, one other example is
the Dogleg method, whose key idea is to use both Gauss-Newton and
Cauthy methods to calculate the optimization step to optimize the
non-linear objective. This approach approximates the objective
function using a model function (often a quadratic) over a subset
of the search space known as the trust region. If the model
function succeeds in minimizing the true objective function, the
trust region is expanded. Conversely, if the approximation is poor,
then the region is contracted and the model function is applied
again. A loss function, for example, may also be used to reduce the
influence of the high residuals (greatest difference between
calculated and measured calibration factors). These high residuals
usually constitute outliers on the optimization.
[0118] In step 914 of FIG. 10, after all wells are modeled for a
given dye or dyes, the instrument performs a goodness of fit (GOF)
check. This can ensure that the global modeling step is
sufficiently reliable. A GOF check can be performed, for example,
by processor 404 of computing system 400, with the results stored,
for example, on memory 406 or storage device 410. Measures of
goodness of fit typically summarize the discrepancy between
observed values and the values expected under the model in
question. GOF can be determined in many ways including, for
example, coefficient of determination R-squared and
root-mean-square error (RMSE) values. R-squared, for example, is a
statistic that will give some information about the goodness of fit
of a model. In regression, the R-squared coefficient of
determination is a statistical measure of how well the regression
line approximates the real data points. An R-squared of 1 indicates
that the regression line perfectly fits the data. RMSE is the
square root of the mean square of the differences or residuals
between observed values and the values expected under the model in
question. RMSE is a good measure of the predication accuracy of the
model. A RMSE of 0 indicates the values expected under the model
are exactly matched to the observed values.
[0119] In step 916 of FIG. 10, if there is a good fit, then the
instrument outputs a dye matrix at step 918 of FIG. 9. A
statistical good fit may occur, in R-squared analysis for example,
when R-squared values are, for example, greater than or equal to
0.85, or RMSE values that are, for example, less than or equal to
0.01, such as that illustrated in FIG. 10. The dye matrix can be
prepared, for example, by processor 204 of computing system 200,
and outputted to display 212.
[0120] In step 920 of FIG. 10, if there is a bad fit, then the
instrument performs a local modeling at step 922 of FIG. 10. This
can become necessary, for example, if the calculated R.sup.2 value
for a GOF check is less than 0.85, for example, and RMSE values are
greater than 0.01, for example. The local modeling can be
performed, for example, by processor 404, of computing system 400,
by using the data from the dye presented wells for a specific dye
to model for the remaining dye unpresented wells. The resulting
model can be stored, for example, on memory 406 or storage device
410.
[0121] A local modeling method can include, for example, using the
calibration factors from the surrounding dye presented wells for
the same dye on the plate. For example, to determine the
calibration factor value in a dye unpresented well for a specific
dye, the local model can take the median value of all specific dye
presented wells of the same dye that are within a 5.times.5 local
window of surrounding wells or from the whole plate. That median
value is determined until a full modeling of the plate is
completed. The local modeling output can then replace the global
modeling output.
[0122] At the conclusion of the local modeling, the dye matrix is
sufficient such that the instrument outputs the dye matrix at step
918 of FIG. 10. This dye matrix serves as a profile of the
fluorescence signature of each calibrated dye. After each run, the
instrument receives data in the form of a raw spectra signal for
each reading. The instrument determines the contribution of the
fluorescent dyes used in each reaction by comparing the raw spectra
to the pure spectra calibration data of the dye matrix. The
instrument uses the calibration data collected from the dye
standards (i.e., the dye matrix) to characterize and distinguish
the individual contribution of each dye in the total fluorescence
collected by the instrument.
Instrument Normalization Calibration
[0123] Currently, genomic analysis, including that of the estimated
30,000 human genes is a major focus of basic and applied
biochemical and pharmaceutical research. Such analysis may aid in
developing diagnostics, medicines, and therapies for a wide variety
of disorders. However, the complexity of the human genome and the
interrelated functions of genes often make this task difficult. One
difficulty commonly faced is the inability of researchers to easily
compare results of experiments run on multiple instruments.
Physical variations in the parameters of components such as light
sources, optical elements and fluorescence detectors, for example,
can result in variation in the results of analyses on what may be
identical biological samples. There is, therefore, a continuing
need for methods and apparatus to aid in minimizing the variations
in the components.
[0124] In qPCR, amplification curves are often determined by
normalizing the signal of a reporter dye to a passive reference dye
in the same solution. Examples of reporter dyes can include, but
not be limited to FAM, SYBR Green, VIC, JOE, TAMRA, NED CY-3, Texas
Red, CY-5. An example of a passive reference can be, but not
limited to ROX. This normalization can be reported as normalized
fluorescence values labeled or "Rn". Passive reference
normalization enables consistent Rn values even if the overall
signal level is affected by liquid volume, or overall illumination
intensity. Passive reference normalization, however, cannot work
properly if the ratio in signal between the reporter dye and
reference dye varies, such as from instrument-to-instrument
differences in the spectrum of the illumination. In order to adjust
for this, normalization solutions can be manufactured to normalize
the ratio of reporter to passive reference. An example of such a
normalization solution can be a 50:50 mixture of FAM and ROX, which
can be referred to as a "FAM/ROX" normalization solution.
[0125] This current method of instrument normalization, including
reading fluorescence from the dye mixture to get a "normalization
factor" to adjust Rn values requires additional expense. Typically,
it can require the manufacture of normalization solutions and
normalization plates, and the time to run the additional
calibrations. Further, this method only works for the dye mixtures
you are calibrating with a standard paired filter set. A paired
filter set can be a combination of an excitation filter and an
emission filter. One skilled in the art will understand that the
addition of an additional dye would require a different
normalization solution and calibration.
[0126] Manufacturing processes for producing the normalization
solutions also contribute to variations in the response of the
dyes. It has been found that it can be difficult to control dye
concentrations due to the lack of an absolute fluorescence
standard. In order to minimize these errors and variations it can
be advantageous to target the dye ratio of the solution to within
+/-15% of the desired mix, or within +/-10% of the desired mix from
the manufacturing process. The manufacturing process is typically
not controlled well enough to simply mix a 50:50 mixture of the
dyes and meet those specifications, so an additional step in the
process is necessary to adjust the dye mixture with a
fluorimeter.
[0127] Acceptable percent variations disclosed above have been
determined by studying the relationship between variation in dye
mixture and Cts. A Ct is a common abbreviation for a "threshold
cycle". Quantitative PCR (qPCR) can provide a method for
determining the amount of a target sequence or a gene that is
present in a sample. During PCR a biological sample is subjected to
a series of 35 or 40 temperature cycles. A cycle can have multiple
temperatures. For each temperature cycle the amount of target
sequence can theoretically double and is dependent on a number of
factors not presented here. Since the target sequence contains a
fluorescent dye, as the amount of target sequence increases i.e
amplified over the 35 or 40 temperature cycles the sample solution
fluoresces brighter and brighter with each thermal cycle. The
amount of fluorescence required to be measured by a fluorescence
detector is frequently referred to as a "threshold", and the cycle
number at which the fluorescence is detected is referred to as the
"threshold cycle" or Ct. Therefore by knowing how efficient the
amplification is and the Ct, the amount of target sequence in the
original sample can be determined.
[0128] The tolerated percent variation described above can also be
related to the standard deviation of Ct shifts in the instrument.
It has been determined that a +/-15% variation in dye mixture can
result in a standard deviation of 0.2 Cts which can be 2 standard
deviations.
[0129] As presented above the ability to reliably compare
experimental results from multiple instruments is desirable and
instrument-to-instrument variability is frequently an issue. This
variability can result from two sources; variability of components
within the instruments such as, for example, lamps and filters and
variability over time such as, for example lamp and filter aging.
It would be advantageous to implement a process through which
experimental results from multiple instruments can be reliably,
easily and inexpensively compared. The teachings found herein
disclose such a process.
[0130] The amount of fluorescent signal of a sample in an optical
system can be dependent on several factors. Some of the factors can
include, but not be limited to, the wavelength of the fluorescence
light, the detector efficiency at that wavelength of fluorescence
light, the efficiency of the emission filter, the efficiency of the
excitation filter and the efficiency of the dye. The present
teachings suggest that instrument-to-instrument variability can be
minimized if the physical optical elements of the instruments could
be normalized.
[0131] In one embodiment the normalization factors can be derived
from pure dye spectra rather than from dye mixtures. Pure dyes can
be easier to manufacture than dye mixtures, because the
concentrations do not have to be exact, and there is only one
fluorescent component. This concept was tested by normalizing 2
filter sets in an instrument using 10 pure dyes and comparing the
results to the normalization obtained from using dye mixtures. The
normalization was implemented by determining a correction factor
for each excitation filter and emission filter. The resulting
correction factors can be used to normalize any combination of
dyes, even from different instruments.
[0132] In another embodiment, the normalization taught above was
applied to multiple instruments of various types. Eight dye mixture
solutions and 10 pure dye solutions were created. Each solution was
pipetted into 8 wells of three 96 well plates. Potential spatial
crosstalk was minimized by pipetting into every other well. The dye
mixtures used are shown in FIG. 12A and the pure dyes used are
shown in FIG. 4B. In addition, the instruments used included 6 sets
of filters. FIG. 12B further identifies the filter pairs for the
main optical channel for each pure dye. The excitation filter is
depicted with an "X" and the emission filter is depicted with an
"M".
[0133] In an effort to quantify the effectiveness of the
normalization process, the dye ratios were measured before and
after normalization. FIG. 13 shows the percent deviation of dye
mixtures from the average ratio for 17 tested instruments. The
instruments are labeled on the X-axis and the percent deviation is
on the Y-axis. One skilled in the art will notice that the
deviations across the instruments is frequently greater than the
desired +/-15% previously discussed. This data, therefore, shows a
need for an improved normalization process such as the current
teachings.
[0134] The current teachings were applied to all 17 instruments.
The normalization method determines a correction factor for each
individual filter rather than for each dye ratio. Because the
instruments provided 6 excitation and 6 emission filters, 12
factors were determined. The process is shown in FIG. 16 and
flowchart 1600. In step 1605, calibration spectra were generated
for multiple dyes across multiple filter combinations. For the
instruments being normalized, there were 10 pure dyes and 21 filter
combinations. In step 1610, the spectra were normalized so the
maximum signal is 1. In step 1615 the dye spectra are averaged
across multiple wells. This averaging will result in producing one
spectrum per dye. Collectively, the dye spectra can be referred to
as a dye matrix "M" containing dye and filter combinations. At this
point, a reference instrument is identified. The reference
instrument would an instrument or group of instruments that the
test instruments will be normalized to. The same set of dye spectra
used in the test instrument can be obtained from the reference
instrument(s). In some embodiments the reference can be a group of
instruments. In such an embodiment the spectra for each dye can be
averaged across the group. This step is represented in flowchart
1600 at step 1620. As an example, the reference spectra can be
referred to as matrix "Mref".
[0135] In step 1625 each of the 12 filters has an adjustment factor
initially set to 1. What is desired, is to multiply the adjustment
factors times matrix "M" while iteratively modifying the adjustment
factors between 0 and land preferably between 0.04 and 1 until the
difference between matrix "M" and matrix Mref" is minimized as
shown in step 1630. In step 1635, correction factors each filter
pair are calculated. The correction factor for each filter pair is
the product of the emission filter factor times the excitation
filter factor. The main channel filter pairs are shown in FIG. 4B.
Once the correction factors for each filter pair has been
determined, each filter pair factor can then be multiplied by the
fluorescence data for the test instrument as well as for the pure
dye spectra. The corrected pure dye spectra can then be
renormalized to a maximum value of 1 as shown in step 1645. The
final step in the process at step 1650 is to generate
multicomponent data. One skilled in the art will understand the
multicomponenting procedure to be the product of the fluorescence
data and the pseudo-inverse of the dye matrix. The multicomponent
values are already normalized so it would not be necessary to make
dye specific corrections since the data has been normalized at the
filter level.
[0136] At the completion of normalization the % deviation of dye
mixtures from the average ratio were calculated across all 17
instruments. The results are shown in FIG. 14. These results are
significantly improved as compared to the data in FIG. 13 before
normalization. A closer view of the normalized data is shown in
FIG. 15, where the deviations after normalization have been reduced
to +/-8% which is well below the target of +/-15% as presented
previously.
RNase P Validation
[0137] As mentioned above, it is important to validate an
instrument to be sure it is working properly especially after a new
installation or after several uses. In this way, a user may be sure
experimental results and analyses are accurate and reliable.
Previously, a validation assay was run on the instrument by a user
and the user manually performed data analysis on the amplification
data from the verification assay to validate the instrument.
Because the data analysis was performed manually by the user, the
validation process was more prone to error and took time.
[0138] According to various embodiments of the present teachings,
automated validation methods and systems are provided. An example
of a validation assay is an RNase P assay. However, as used herein,
validation assay may be any assay that has known and reliable
properties and can be used to validate an instrument.
[0139] After installation and after several uses, it is important
to validate that the instrument is working properly. Often, a user
will manually run a known assay to validate an instrument, such as
an RNase P assay. The RNase P gene is a single-copy gene encoding
the RNA moiety of the RNase P enzyme. It is often used as a
validation assay because of its known properties and
characteristics.
[0140] A validation plate is preloaded with the reagents necessary
for the detection and quantitation of genomic copies of the sample.
For example, in an RNase P validation plate, each well contains PCR
master mix, RNase P primers, FAM.TM. dye-labeled probe, and a known
concentration of human genomic DNA template.
[0141] In a traditional RNase P assay example, a standard curve is
generated from the Ct (cycle threshold) values obtained from a set
of replicate standards (1,250, 2,500, 5,000, 10,000 and 20,000
copies). The standard curve is then used to determine the copy
number for two sets of unknown templates (5,000 and 10,000
replicate populations). The instrument is validated if it can
demonstrate the ability to distinguish between 5,000 and 10,000
genomic equivalents with a 99.7% confidence level for a subsequent
sample run in a single well.
[0142] To pass installation, the instruments must demonstrate the
ability to distinguish between 5,000 and 10,000 genomic equivalents
with a 99.7% confidence level for a subsequent sample run in a
single well.
[0143] According to various embodiments, the present teachings can
incorporate expert knowledge into an automated calibration and
validation system providing pass/fail status and troubleshooting
feedback when a failure is identified. If an instrument should fail
the validation process, then the user knows that a service engineer
can be called, for example. The present teachings can minimize the
cost of, and time required for, the installation and calibration
procedures.
[0144] As stated above, according to various embodiments described
herein, the goal of a validation analysis is to confirm that two
quantities of the same sample are sufficiently distinguishable by
the instrument. This way, the instrument performance may be
validated.
[0145] According to various embodiments of the present teachings,
an automated validation method and system is provided. Cycle
threshold values (C.sub.ts) of a validation assay are analyzed and
compared by a system to determine if an instrument can sufficiently
distinguish two quantities of a sample. An example of a validation
assay is the RNase P assay. In this example, a system determines
C.sub.t values generated for RNase P samples of 5000 and 10000
genomic copies to determine if the data from the 5000 and 10000
genomic copies are sufficiently distinguishable. Sufficiently
distinguishable, according to the embodiments described herein,
means at least 3 standard deviations (3.sigma.) (.about.99.7%)
separate the 5000 and 10000 genomic copy amplification data. The
method according to various embodiments is described further below
with reference to FIGS. 17 and 18.
[0146] FIG. 17 illustrates an exemplary method for validating an
instrument according to various embodiments described herein. In
general, the begins in step 1702 by receiving amplification data
from a validation assay plate to generate a plurality of
amplification curves, each corresponding to a well on the
plate.
[0147] Plates contain a plurality of wells. In some examples, a
plate contains 96 wells. In other examples, a plate contains 384
wells. A portion of the wells in the plate may contain a sample of
a first quantity and another portion of the wells in the plate may
contain a sample of a second quantity. The first quantity and the
second quantity are different. The second quantity is greater than
the first quantity in various embodiments described herein. The
second quantity may be a 1.5 fold difference than the first
quantity in some embodiments. In other embodiments, the second
quantity may be a 2 fold difference than the first quantity.
According to various embodiments described herein, the second
quantity may be any fold difference than the first quantity. In
some embodiments, the first quantity may be 5000 genomic copies per
well and the second quantity may be 10000 genomic copies per
well.
[0148] With reference back to FIG. 17, in step 1704, a plurality of
fluorescence thresholds are determined based on the plurality of
generated amplification curves. Exponential regions of the
plurality of amplification curves are compared to determine a range
of fluorescence values where the exponential regions fall. For
example, the range of fluorescence values from the lowest
fluorescence value of a bottom of an exponential region to the
highest fluorescence value of a top of an exponential region of the
plurality of amplification curves is determined. The fluorescence
value range is used in the automated analysis of the plurality of
amplification curves to validate the instrument according to
embodiments of the present teachings.
[0149] With reference to FIG. 19, a plurality of amplification
curves and determination of a range of fluorescence values and
corresponding cycle threshold is illustrated. Each of the plurality
of amplification curves includes an exponential region of the
curve. Axis 1902 indicates fluorescence values. Axis 1904
illustrates cycle numbers. Fluorescence range 1906 shows the range
of fluorescence values from the lowest fluorescent value of a
determined bottom of an exponential region of the plurality of
exponential regions and highest fluorescent value of a determined
top of an exponential region of the plurality of exponential
regions. According to various embodiments, the range of
fluorescence values is divided evenly by a predetermined number to
generate a set of fluorescence values for automated analysis by the
system. In one example, the range of fluorescence values 1906 is
divided by 100 to determine 100 fluorescence values for a set of
fluorescence thresholds. In some embodiments, the top 5
fluorescence values and the bottom 5 fluorescence values are
discarded so that analysis proceeds with a set of 90 fluorescence
thresholds.
[0150] With reference back to FIG. 17, in step 1706, for each
fluorescence value of the set of fluorescence values, the cycle
threshold (C.sub.t) is determined for each of the plurality of
amplification curves generated from wells containing the first
quantity of the sample. Similarly, for each fluorescence value of
the set of fluorescence values, the cycle threshold (C.sub.t) is
determined for each of the plurality of amplification curves
generated from wells containing the second quantity of the
sample.
[0151] In step 1708, using the C.sub.t values for the first and
second quantities for each of the fluorescence values of the set,
it is determined if the first and second quantities are
sufficiently distinguishable. Sufficiently distinguishable,
according to various embodiments, means that, using equation (1),
yields a positive result for at least one of the fluorescence
values of the set:
((.mu.C.sub.tquant1-3.sigma.C.sub.tquant1)-(.mu.C.sub.tquant2+3.sigma.C.-
sub.tquant2)) (1)
[0152] Equation 1 determines if a first and second quantity are
sufficiently distinguishable, where quant2 is greater than quant1,
according to the embodiments described herein. Sufficiently
distinguishable means at least 3 standard deviations (3.sigma.)
(.about.99.7%) separate the C.sub.t values of the first and second
quantities. If it is found that the quantities are sufficiently
distinguishable, an indication is provided to the user that the
instrument is validated.
[0153] FIG. 18 illustrates another exemplary method for validation
an instrument according to various embodiments described herein. In
step 1802, amplification data is received from a plurality of
samples included in wells of a validation plate. A portion of the
wells in the validation plate contain a sample in a first quantity.
Another portion of the wells of the validation plate contain the
sample in a second quantity. The first quantity and the second
quantity are different. The second quantity may be a 1.5 fold
difference than the first quantity in some embodiments. In other
embodiments, the second quantity may be a 2 fold difference than
the first quantity. According to various embodiments described
herein, the second quantity may be any fold difference than the
first quantity. In some embodiments, the first quantity may be 5000
genomic copies per well and the second quantity may be 10000
genomic copies per well.
[0154] In step 1804, a first set of fluorescence thresholds are
determined based on the plurality of generated amplification
curves. Exponential regions of the plurality of amplification
curves are compared to determine a range of fluorescence values
where the exponential regions fall. For example, the range of
fluorescence values from the lowest fluorescence value of a bottom
of an exponential region to the highest fluorescence value of a top
of an exponential region of the plurality of amplification curves
is determined. The fluorescence value range is used in the
automated analysis of the plurality of amplification curves to
validate the instrument according to embodiments of the present
teachings.
[0155] According to various embodiments, the range of fluorescence
values is divided evenly by a predetermined number to generate a
set of fluorescence values for automated analysis by the system. In
one example, the range of fluorescence values 1906 is divided by
100 to determine 100 fluorescence values for a set of fluorescence
thresholds. In some embodiments, the top 5 fluorescence values and
the bottom 5 fluorescence values are discarded so that analysis
proceeds with a set of 90 fluorescence thresholds.
[0156] In step 1806, for each fluorescence threshold of the set, a
first set of C.sub.t values for the amplification curves
corresponding to the first quantity is determined. Similarly, for
each fluorescence threshold of the set, a second set of C.sub.t
values for the amplification curves corresponding to the first
quantity is determined. This is repeated for every fluorescence
threshold in the set.
[0157] In some embodiments, a predetermined number of outlier
C.sub.t values are removed from each set of C.sub.t values before
further calculations are performed. For example, in some
embodiments, if a 96 well plate is used, 6 outliers are removed
from each set of C.sub.t values. An outlier is the C.sub.t values
furthest away from the mean value of the set of C.sub.t values. In
another example, if a 364 well plate is used, 10 outliers are
removed from each set of C.sub.t values. After the outliers are
removed, the remaining C.sub.t values of each set are used in the
remaining steps of the method.
[0158] In step 1808, for each set of C.sub.t values, a mean is
calculated. In other words, a first C.sub.t mean is calculated for
the first quantity amplification curves and a second C.sub.t mean
is calculated for the second quantity amplification curves for each
fluorescence threshold of the set determined in step 1804.
[0159] Similar to step 1808, in step 1810, 3 standard deviations of
each set of C.sub.t values is calculated. In other words, a first 3
standard deviations is calculated for the first quantity
amplification curves and a second 3 standard deviations is
calculated for the second quantity amplification curves for each
fluorescence threshold of the set determined in step 1804.
[0160] To determine if the C.sub.t values of the first quantity and
the second quantity or sufficiently distinguishable, the C.sub.t
values at a fluorescence value, according to various embodiments,
the C.sub.t values are compared. According to various embodiments,
equation (2) is used for the comparison.
((.mu.C.sub.tquant1-3.sigma.C.sub.tquant1)-(.mu.C.sub.tquant2-3.sigma.C.-
sub.tquant2)) (2)
[0161] Equation 2 determines if a first and second quantity are
sufficiently distinguishable, according to the embodiments
described herein. Sufficiently distinguishable means at least 3
standard deviations (3.sigma.) (.about.99.7%) separate the C.sub.t
values of the first and second quantities.
[0162] In step 1814, the results of equation (2) for all
fluorescence thresholds of the set are compared to determine a
maximum value. If the maximum value is a positive number, the
instrument can sufficiently distinguish between the first and
second quantity and an indication that the instrument is validated
is provided to the user in step 1816. If the maximum value is a
negative number, the instrument cannot sufficiently distinguish
between the first and second quantity and an indication the
instrument failed validation is provided to the user in step
1818.
[0163] FIG. 20 illustrates system 2000 for validation of an
instrument according to various embodiments described herein.
System 2000 includes PCR instrument interface 2002, C.sub.t
database 2004, display engine/GUI 2006, C.sub.t calculator 2008,
and validator 2010.
[0164] PCR instrument interface 2002 receives the amplification
data from the PCR instrument to generate amplification curves. As
described above, the PCR instrument amplifies the samples contained
in the validation plate. The validation plate includes a portion of
wells containing a sample of a first quantity and another portion
of wells containing a sample of a second quantity. Fluorescence
data generated from amplification of the samples is received by PCR
instrument interface 2002.
[0165] After a set of fluorescence thresholds are determined as in
steps 1704 and 1804, with reference to FIGS. 17 and 18,
respectively, C.sub.t calculator 2006 calculates a first and second
set of C.sub.t values corresponding to the amplification curves
generated from the samples of the first quantity and the second
quantity, respectively. A first and second set of C.sub.t values is
calculated for each fluorescence threshold in the set of
fluorescence thresholds. The plurality of sets of C.sub.t values
are stored in C.sub.t database 2004.
[0166] Validator 2010 determines whether the first and second
quantities are sufficiently distinguishable as described in step
1708 in FIG. 17 and steps 1810 and 1812 in FIG. 18.
[0167] Display engine/GUI displays the plurality of amplification
curves to the user. Further, after validator 2010 determines
whether the first and second quantities are sufficiently
distinguishable, display engine/GUI 2006 displays an indication of
validation or failed validation to the user.
[0168] Furthermore, an optimal fluorescence threshold can be
determined. The optimal fluorescence threshold may be determined
by, according to various embodiments, selecting the C.sub.t value
that resulting in the maximum separation between
(.mu.C.sub.tquant1-3.sigma.C.sub.tquant1) and
(.mu.C.sub.tquant2+3.sigma.C.sub.tquant2). Moreover, the optimal
fluorescence threshold may also be selected based on the Ct value
which resulted in the fewest number of determined outliers. The
optimal fluorescence threshold may also be selected based on the Ct
value which resulted in the maximum separation between
(.mu.C.sub.tquant1-3.sigma.C.sub.tquant1) and
(.mu.C.sub.tquant2+3.sigma.C.sub.tquant2), and with the fewest
number of determined outliers.
Auto-Dye Correction
[0169] According to various embodiments of the present teachings,
auto-dye correction methods may be used to perform a real-time
spectral calibration of the multi-component data. Auto-dye
correction may be performed in real-time or after amplification
data is collected and secondary analysis is performed. In the
auto-dye correction algorithm, a multicomponent correlation matrix
is generated. According to various embodiments, an auto-dye
correction algorithm adjusts the elements of the dye matrix so that
the off diagonal terms in the multicomponent correlation matrix are
minimized. In this way, errors in Ct determinations are
minimized.
Auto-Background Calibration
[0170] According to various embodiments of the present teachings,
an auto-background calibration may be performed to reduce the need
for a background calibration plate and improve the overall efficacy
of background correction.
[0171] Physical contaminants in the block (particulate or chemical)
that occur over use of the instrument can negatively-impact the
analysis results of the system by artificially inflating certain
spectral components of the analyzed wells that are impacted by
contamination. A re-calibration can address this problem. However,
to prolong periods between required calibrations, a method of
automatically-calculating/compensating for background changes after
background calibration is described. To accomplish auto-background
calibration, a method is performed using the empty/unoccupied
block. The effective signal bleed-through for consumables is known
(empirically determined), and effective background calibration
slopes and offsets can be approximated using scaling factors that
address the effective signal bleed-through.
Plate Detection
[0172] According to various embodiments described herein, plate
detection methods may be performed to identify errors in plate
placement in the instrument.
[0173] During instrument use, the optics of the system are
positioned at either the upper limit (during idle periods) or at
the lower limit (during operation) of travel. The ability to
readout the optics position at an intermediate location between the
travel limits was not designed into the hardware; as such, one
cannot rely on the motor position value to determine if a plate or
tube is present or absent (where the difference in optics position
would be caused by the added material thickness from the tube or
plate present). Without needing an added component for plate or
tube detection (such as a depression switch or positional sensor),
the detection camera in the system is used for sample detection.
However, since only a small portion of the block region is captured
through the use of a discrete and segregated well lens array (each
lens in the array focuses and collects light from one and only one
well), a traditional `photo` of the consumable plane capturing the
entire block region cannot be acquired for image processing. Since
only focused light from each well is collected and manifests as a
circulate spot of brightness on the detector, there is no spatial
or dynamic range in the detected image. However, if the optics are
moved to an intermediate position that allows for focusing on the
seal or lid of a container, this focus spot can be captured as a
reflected image (contrasted with fluorescence, which is the normal
signal collected by the system), and used for plate/tube detection.
The spot of focus would be smaller than a well, and this would
manifest in the captured image as a small bright region relative to
the size of a well (known as the region of investigation, ROI).
Understanding that the focus spot would yield bright pixels and all
other regions would yield darker pixels, a numerical analysis of
the pixel-level information can yield a presence/absence
determination, according to various embodiments described
herein.
Instrument Normalization Using a Reflective Material
[0174] According to various embodiments of the present teachings,
instrument normalization using a reflective material, such as a
photodiode, may be used to auto-calibrate the instrument after any
initial calibrations done after manufacturing or installation.
[0175] According to various embodiments, a stable reflective
material is measured during manufacturing as a control. The
reflective material may be placed above the heated cover.
Subsequently, the stable reflective material can be measured in all
channels to detect any changes or variability. Any changes or
variability may be used to adjust color balance factors, as
described above in the instrument normalization calibration method
to re-normalize for the changes in the excitation light.
EXAMPLES
[0176] In example 1, a method for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites, is
provided comprising: performing a region-of-interest (ROI)
calibration to determine reaction site positions in an image;
performing a pure dye calibration to determine the contribution of
a fluorescent dye used in each reaction site by comparing a raw
spectrum of the fluorescent dye to a pure spectrum calibration data
of the fluorescent dye; performing an instrument normalization
calibration to determine a filter normalization factor; and
performing an RNase P validation to validate the instrument is
capable of distinguishing between two different quantities of
sample.
[0177] In example 2, example 1 is provided, wherein the ROI
calibration comprises: estimating initial region of interest (ROI)
from fluorescence thresholds from each sample well; estimating the
center locations of each ROI; estimating the size of each ROI;
determining the average size of the ROIs from the plurality of
reaction sites; deriving global gridding models; applying the
global gridding models to the ROIs, wherein the application of the
global gridding models improve the precision of the ROI center
locations; recovering missing ROIs; and adjusting the radius of the
ROIs, wherein the adjustment improves the signal-to-noise ratio of
the optical system.
[0178] In example 3, example 1 is provided, wherein the ROI
calibration improves reaction site determination errors by
minimizing at least one of the following group: dye saturation
within the plurality of reaction sites, grid rotation, variation of
magnification factors, and optical radial distortion.
[0179] In example 4, example 1 is provided, wherein the pure dye
calibration comprises: imaging a sample holder, loaded into the
instrument, at more than one channel, the sample holder comprising
a plurality of reaction sites and more than one dye type, each dye
occupying more than one reaction site; identifying a peak channel
for each dye on the sample holder; normalizing each channel to the
peak channel for each dye; and producing a dye matrix comprising a
set of dye reference values.
[0180] In example 5, example 4 is provided, wherein imaging the
sample holder is performed four times for imaging four different
sample holders.
[0181] In example 6, example 1 is provided, wherein the optical
system comprises a plurality of excitation filters and a plurality
of emission filters, and wherein the instrument normalization
calibration comprises: determining a first correction factor for
each of the excitation filters and emission filters; calculating a
second correction factor for a pair of filters, wherein each pair
of filters comprises one excitation filter and one emission filter;
and applying the second correction factors to filter data.
[0182] In example 7, example 1 is provided, wherein the filter
normalization factor allows data from the instrument to be compared
with data from a second instrument.
[0183] In example 8, example 1 is provided, wherein the RNase P
validation comprises: receiving amplification data from a
validation plate to generate a plurality of amplification curves,
wherein the validation plate includes a sample of a first quantity
and a second quantity, and each amplification curve includes an
exponential region; determining a set of fluorescence thresholds
based on the exponential regions of the plurality of amplification
curves; determining, for each fluorescence threshold of the set, a
first set of cycle threshold (Ct) values of amplification curves
generated from the samples of the first quantity and a second set
of Ct values of amplification curves generated from the samples of
the second quantity; and calculating if the first and second
quantities are sufficiently distinguishable based on Ct values at
each of the plurality of fluorescence thresholds.
[0184] In example 9, example 1 is provided, wherein the RNase P
validation is performed by a processor connected to the
instrument.
[0185] In example 10, example 8 is provided, wherein the RNase P
validation further comprises: displaying an indication of
instrument validation or failure on a display screen.
[0186] In example 11, example 1 is provided, further comprising:
performing an auto-dye correction for real-time spectral
calibration of the multi-component data; performing a plate
detection to determine whether there is a plate loading error;
performing an auto-background calibration to compensate for
background changes; and performing instrument normalization using a
reflective material to detect any changes or variability in
fluorescent emissions.
[0187] In example 12, a system for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites, is
provided comprising: a processor; and a memory, encoded with
processor-executable instructions, the instructions including
instructions for: performing a region-of-interest (ROI) calibration
to determine reaction site positions in an image; performing a pure
dye calibration to determine the contribution of a fluorescent dye
used in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; performing an instrument normalization calibration
to determine a filter normalization factor; and performing an RNase
P validation to validate the instrument is capable of
distinguishing between two different quantities of sample.
[0188] In example 13, example 12 is provided, wherein the
instructions for ROI calibration comprise instructions for:
estimating initial region of interest (ROI) from fluorescence
thresholds from each sample well; estimating the center locations
of each ROI; estimating the size of each ROI; determining the
average size of the ROIs from the plurality of reaction sites;
deriving global gridding models; applying the global gridding
models to the ROIs, wherein the application of the global gridding
models improve the precision of the ROI center locations;
recovering missing ROIs; and adjusting the radius of the ROIs,
wherein the adjustment improves the signal-to-noise ratio of the
optical system.
[0189] In example 14, example 12 is provided, wherein the ROI
calibration improves reaction site determination errors by
minimizing at least one of the following groups: dye saturation
within the plurality of reaction sites, grid rotation, variation of
magnification factors, and optical radial distortion.
[0190] In example 15, example 12 is provided, wherein the
instructions for pure dye calibration comprise instructions for:
imaging a sample holder, loaded into the instrument, at more than
one channel, the sample holder comprising a plurality of reaction
sites and more than one dye type, each dye occupying more than one
reaction site; identifying a peak channel for each dye on the
sample holder; normalizing each channel to the peak channel for
each dye; and producing a dye matrix comprising a set of dye
reference values.
[0191] In example 16, example 15 is provided, wherein imaging the
sample holder is performed four times for imaging four different
sample holders.
[0192] In example 17, example 12 is provided, wherein the optical
system comprises a plurality of excitation filters and a plurality
of emission filters, and wherein the instrument normalization
calibration comprises: determining a first correction factor for
each of the excitation filters and emission filters; calculating a
second correction factor for a pair of filters, wherein each pair
of filters comprises one excitation filter and one emission filter;
and applying the second correction factors to filter data.
[0193] In example 18, example 12 is provided, wherein the filter
normalization factor allows data from the instrument to be compared
with data from a second instrument.
[0194] In example 19, example 12 is provided, wherein the
instructions for RNase P validation comprise instructions for:
receiving amplification data from a validation plate to generate a
plurality of amplification curves, wherein the validation plate
includes a sample of a first quantity and a second quantity, and
each amplification curve includes an exponential region;
determining a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determining, for each fluorescence threshold of the set, a first
set of cycle threshold (Ct) values of amplification curves
generated from the samples of the first quantity and a second set
of Ct values of amplification curves generated from the samples of
the second quantity; and calculating if the first and second
quantities are sufficiently distinguishable based on Ct values at
each of the plurality of fluorescence thresholds.
[0195] In example 20, example 12 is provided, wherein the RNase P
validation is performed by a processor connected to the
instrument.
[0196] In example 21, example 19 is provided, wherein the
instructions for RNase P validation further comprise instructions
for: displaying an indication of instrument validation or failure
on a display screen.
[0197] In example 22, example 12 is provided, further comprising
instructions for: performing an auto-dye correction for real-time
spectral calibration of the multi-component data; performing a
plate detection to determine whether there is a plate loading
error; performing an auto-background calibration to compensate for
background changes; and performing instrument normalization using a
reflective material to detect any changes or variability in
fluorescent emissions.
[0198] In example 23, a computer readable storage medium encoded
with processor-executable instructions for calibrating an
instrument, wherein the instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites, is provided, the instructions comprising
instructions for: performing a region-of-interest (ROI) calibration
to determine reaction site positions in an image; performing a pure
dye calibration to determine the contribution of a fluorescent dye
used in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; performing an instrument normalization calibration
to determine a filter normalization factor; and performing an RNase
P validation to validate the instrument is capable of
distinguishing between two different quantities of sample.
[0199] In example 24, example 23 is provided, wherein the
instructions for ROI calibration comprise instructions for:
estimating initial region of interest (ROI) from fluorescence
thresholds from each sample well; estimating the center locations
of each ROI; estimating the size of each ROI; determining the
average size of the ROIs from the plurality of reaction sites;
deriving global gridding models; applying the global gridding
models to the ROIs, wherein the application of the global gridding
models improve the precision of the ROI center locations;
recovering missing ROIs; and adjusting the radius of the ROIs,
wherein the adjustment improves the signal-to-noise ratio of the
optical system.
[0200] In example 25, example 23 is provided wherein the ROI
calibration improves reaction site determination errors by
minimizing at least one of the following group: dye saturation
within the plurality of reaction sites, grid rotation, variation of
magnification factors, and optical radial distortion.
[0201] In example 26, example 23 is provided, wherein the
instructions for pure dye calibration comprise instructions for:
imaging a sample holder, loaded into the instrument, at more than
one channel, the sample holder comprising a plurality of reaction
sites and more than one dye type, each dye occupying more than one
reaction site; identifying a peak channel for each dye on the
sample holder; normalizing each channel to the peak channel for
each dye; and producing a dye matrix comprising a set of dye
reference values.
[0202] In example 27, example 26 is provided, wherein imaging the
sample holder is performed four times for imaging four different
sample holders.
[0203] In example 28, example 23 is provided, wherein the optical
system comprises a plurality of excitation filters and a plurality
of emission filters, and wherein the instructions for instrument
normalization calibration comprise instructions for: determining a
first correction factor for each of the excitation filters and
emission filters; calculating a second correction factor for a pair
of filters, wherein each pair of filters comprises one excitation
filter and one emission filter; and applying the second correction
factors to filter data.
[0204] In example 29, example 23 is provided, wherein the filter
normalization factor allows data from the instrument to be compared
with data from a second instrument.
[0205] In example 30, example 23 is provided, wherein the
instructions for RNase P validation comprise instructions for:
receiving amplification data from a validation plate to generate a
plurality of amplification curves, wherein the validation plate
includes a sample of a first quantity and a second quantity, and
each amplification curve includes an exponential region;
determining a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determining, for each fluorescence threshold of the set, a first
set of cycle threshold (Ct) values of amplification curves
generated from the samples of the first quantity and a second set
of Ct values of amplification curves generated from the samples of
the second quantity; and calculating if the first and second
quantities are sufficiently distinguishable based on Ct values at
each of the plurality of fluorescence thresholds.
[0206] In example 31, example 23 is provided, wherein the RNase P
validation is performed by a processor connected to the
instrument.
[0207] In example 32, example 30 is provided, wherein the
instructions for RNase P validation further comprise instructions
for: displaying an indication of instrument validation or failure
on a display screen.
[0208] In example 33, example 23 is provided, further comprising
instructions for: performing an auto-dye correction for real-time
spectral calibration of the multi-component data; performing a
plate detection to determine whether there is a plate loading
error; performing an auto-background calibration to compensate for
background changes; and performing instrument normalization using a
reflective material to detect any changes or variability in
fluorescent emissions.
[0209] In example 34, a system for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites, is
provided, comprising: a region-of-interest (ROI) calibrator
configured to determine reaction site positions in an image; a pure
dye calibrator configured to determine the contribution of a
fluorescent dye used in each reaction site by comparing a raw
spectrum of the fluorescent dye to a pure spectrum calibration data
of the fluorescent dye; an instrument normalization calibrator
configured to determine a filter normalization factor; an RNase P
validator configured to validate the instrument is capable of
distinguishing between two different quantities of sample; and a
display engine configured to display calibration results.
[0210] In example 35, example 34 is provided, wherein the ROI
calibrator is configured to: estimate initial region of interest
(ROI) from fluorescence thresholds from each sample well; estimate
the center locations of each ROI; estimate the size of each ROI;
determine the average size of the ROIs from the plurality of
reaction sites; derive global gridding models; apply the global
gridding models to the ROIs, wherein the application of the global
gridding models improve the precision of the ROI center locations;
recover missing ROIs; and adjust the radius of the ROIs, wherein
the adjustment improves the signal-to-noise ratio of the optical
system.
[0211] In example 36, example 34 is provided, wherein the ROI
calibrator improves reaction site determination errors by
minimizing at least one of the following group: dye saturation
within the plurality of reaction sites, grid rotation, variation of
magnification factors, and optical radial distortion.
[0212] In example 37, example 34 is provided, wherein the pure dye
calibrator is configured to: image a sample holder, loaded into the
instrument, at more than one channel, the sample holder comprising
a plurality of reaction sites and more than one dye type, each dye
occupying more than one reaction site; identify a peak channel for
each dye on the sample holder; normalize each channel to the peak
channel for each dye; and produce a dye matrix comprising a set of
dye reference values.
[0213] In example 38, example 37 is provided, wherein the
calibrator is configured to image the sample holder four times for
imaging four different sample holders.
[0214] In example 39, example 34 is provided, wherein the optical
system comprises a plurality of excitation filters and a plurality
of emission filters, and wherein the instrument normalization
calibrator is configured to: determine a first correction factor
for each of the excitation filters and emission filters; calculate
a second correction factor for a pair of filters, wherein each pair
of filters comprises one excitation filter and one emission filter;
and apply the second correction factors to filter data.
[0215] In example 40, example 34 is provided, wherein the filter
normalization factor allows data from the instrument to be compared
with data from a second instrument.
[0216] In example 41, example 34 is provided, wherein the RNase P
validator is configured to: receive amplification data from a
validation plate to generate a plurality of amplification curves,
wherein the validation plate includes a sample of a first quantity
and a second quantity, and each amplification curve includes an
exponential region; determine a set of fluorescence thresholds
based on the exponential regions of the plurality of amplification
curves; determine, for each fluorescence threshold of the set, a
first set of cycle threshold (Ct) values of amplification curves
generated from the samples of the first quantity and a second set
of Ct values of amplification curves generated from the samples of
the second quantity; and calculate if the first and second
quantities are sufficiently distinguishable based on Ct values at
each of the plurality of fluorescence thresholds.
[0217] In example 42, example 41 is provided, wherein the RNase P
validator is further configured to: display an indication of
instrument validation or failure on the display engine.
[0218] In example 43, example 34 is provided, further comprising:
an auto-dye corrector configured to perform real-time spectral
calibration of the multi-component data; a plate detector
configured to determine whether there is a plate loading error; an
auto-background calibrator configured to compensate for background
changes; and an instrument normalizer configured to use a
reflective material to detect any changes or variability in
fluorescent emissions.
[0219] In alternate example 44, a method for calibrating an
instrument, wherein the instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites, is provided comprising: performing a
region-of-interest (ROI) calibration to determine reaction site
positions in an image; performing a pure dye calibration to
determine the contribution of a fluorescent dye used in each
reaction site by comparing a raw spectrum of the fluorescent dye to
a pure spectrum calibration data of the fluorescent dye; performing
an instrument normalization calibration to determine a filter
normalization factor; and performing an RNase P validation to
validate the instrument is capable of distinguishing between two
different quantities of sample.
[0220] In example 45, a system for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites, is
provided comprising: a processor; and a memory, encoded with
processor-executable instructions, the instructions including
instructions for: performing a region-of-interest (ROI) calibration
to determine reaction site positions in an image; performing a pure
dye calibration to determine the contribution of a fluorescent dye
used in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; performing an instrument normalization calibration
to determine a filter normalization factor; and performing an RNase
P validation to validate the instrument is capable of
distinguishing between two different quantities of sample.
[0221] In example 46, a computer readable storage medium encoded
with processor-executable instructions for calibrating an
instrument, wherein the instrument includes an optical system
capable of imaging florescence emission from a plurality of
reaction sites, is provided, the instructions comprising
instructions for: performing a region-of-interest (ROI) calibration
to determine reaction site positions in an image; performing a pure
dye calibration to determine the contribution of a fluorescent dye
used in each reaction site by comparing a raw spectrum of the
fluorescent dye to a pure spectrum calibration data of the
fluorescent dye; performing an instrument normalization calibration
to determine a filter normalization factor; and performing an RNase
P validation to validate the instrument is capable of
distinguishing between two different quantities of sample.
[0222] In example 47, a system for calibrating an instrument,
wherein the instrument includes an optical system capable of
imaging florescence emission from a plurality of reaction sites, is
provided, comprising: a region-of-interest (ROI) calibrator
configured to determine reaction site positions in an image; a pure
dye calibrator configured to determine the contribution of a
fluorescent dye used in each reaction site by comparing a raw
spectrum of the fluorescent dye to a pure spectrum calibration data
of the fluorescent dye; an instrument normalization calibrator
configured to determine a filter normalization factor; an RNase P
validator configured to validate the instrument is capable of
distinguishing between two different quantities of sample; and a
display engine configured to display calibration results.
[0223] In alternate example 48, example 44, 45, 46, 47, or any
preceding example is provided, wherein the ROI calibration
comprises: estimating initial region of interest (ROI) from
fluorescence thresholds from each sample well; estimating the
center locations of each ROI; estimating the size of each ROI;
determining the average size of the ROIs from the plurality of
reaction sites; deriving global gridding models; applying the
global gridding models to the ROIs, wherein the application of the
global gridding models improve the precision of the ROI center
locations; recovering missing ROIs; and adjusting the radius of the
ROIs, wherein the adjustment improves the signal-to-noise ratio of
the optical system.
[0224] In alternate example 49, example 44, 45, 46, 47, or any
preceding example is provided, wherein the ROI calibration improves
reaction site determination errors by minimizing at least one of
the following group: dye saturation within the plurality of
reaction sites, grid rotation, variation of magnification factors,
and optical radial distortion.
[0225] In alternate example 50, example 44, 45, 46, 47, or any
preceding example is provided, wherein the pure dye calibration
comprises: imaging a sample holder, loaded into the instrument, at
more than one channel, the sample holder comprising a plurality of
reaction sites and more than one dye type, each dye occupying more
than one reaction site; identifying a peak channel for each dye on
the sample holder; normalizing each channel to the peak channel for
each dye; and producing a dye matrix comprising a set of dye
reference values.
[0226] In alternate example 51, example 44, 45, 46, 47, 50 or any
preceding example is provided is provided, wherein imaging the
sample holder is performed four times for imaging four different
sample holders.
[0227] In alternate example 52, example 44, 45, 46, 47, or any
preceding example is provided, wherein the optical system comprises
a plurality of excitation filters and a plurality of emission
filters, and wherein the instrument normalization calibration
comprises: determining a first correction factor for each of the
excitation filters and emission filters; calculating a second
correction factor for a pair of filters, wherein each pair of
filters comprises one excitation filter and one emission filter;
and applying the second correction factors to filter data.
[0228] In alternate example 53, example 44, 45, 46, 47, or any
preceding example is provided, wherein the filter normalization
factor allows data from the instrument to be compared with data
from a second instrument.
[0229] In alternate example 54, example 44, 45, 46, 47, or any
preceding example is provided, wherein the RNase P validation
comprises: receiving amplification data from a validation plate to
generate a plurality of amplification curves, wherein the
validation plate includes a sample of a first quantity and a second
quantity, and each amplification curve includes an exponential
region; determining a set of fluorescence thresholds based on the
exponential regions of the plurality of amplification curves;
determining, for each fluorescence threshold of the set, a first
set of cycle threshold (Ct) values of amplification curves
generated from the samples of the first quantity and a second set
of Ct values of amplification curves generated from the samples of
the second quantity; and calculating if the first and second
quantities are sufficiently distinguishable based on Ct values at
each of the plurality of fluorescence thresholds.
[0230] In alternate example 55, example 44, 45, 46, 47, or any
preceding example is provided, wherein the RNase P validation is
performed by a processor connected to the instrument.
[0231] In alternate example 56, example 44, 45, 46, 47, 54 or any
preceding example is provided is provided, wherein the RNase P
validation further comprises: displaying an indication of
instrument validation or failure on a display screen.
[0232] In alternate example 57, example 44, 45, 46, 47, or any
preceding example is provided, further comprising: performing an
auto-dye correction for real-time spectral calibration of the
multi-component data; performing a plate detection to determine
whether there is a plate loading error; performing an
auto-background calibration to compensate for background changes;
and performing instrument normalization using a reflective material
to detect any changes or variability in fluorescent emissions.
[0233] Exemplary systems for methods related to the various
embodiments described in this document include those described in
following applications: [0234] U.S. design patent application Ser.
No. 29/516,847, filed on Feb. 6, 2015; and [0235] U.S. design
patent application Ser. No. 29/516,883; filed on Feb. 6, 2015; and
[0236] U.S. provisional patent application No. 62/112,910, filed on
Feb. 6, 2015; and [0237] U.S. provisional patent application No.
62/113,006, filed on Feb. 6, 2015; and [0238] U.S. provisional
patent application No. 62/113,077, filed on Feb. 6, 2015; and
[0239] U.S. provisional patent application No. 62/113,058, filed on
Feb. 6, 2015; and [0240] U.S. provisional patent application No.
62/112,964, filed on Feb. 6, 2015; and [0241] U.S. provisional
patent application No. 62/113,118, filed on Feb. 6, 2015; and
[0242] U.S. provisional patent application No. 62/113,212, filed on
Feb. 6, 2015; and [0243] U.S. patent application Ser. No.
15/017,488 (Life Technologies Docket Number LT01011), filed on Feb.
5, 2016; and [0244] U.S. patent application Ser. No. 15/017,136
(Life Technologies Docket Number LT01023), filed on Feb. 5, 2016;
and [0245] U.S. patent application Ser. No. 15/016,485 (Life
Technologies Docket Number LT01025), filed on Feb. 5, 2016; and
[0246] U.S. patent application Ser. No. 15/016,564 (Life
Technologies Docket Number LT01028), filed on Feb. 5, 2016; and
[0247] U.S. patent application Ser. No. 15/016,713 (Life
Technologies Docket Number LT01029), filed on Feb. 5, 2016; and
[0248] U.S. patent application Ser. No. 15/017,034 (Life
Technologies Docket Number LT01032), filed on Feb. 5, 2016; and
[0249] U.S. patent application Ser. No. 15/017,393 (Life
Technologies Docket Number LT01033), filed on Feb. 5, 2016, all of
which are also herein incorporated by reference in their
entirety.
[0250] Although various embodiments have been described with
respect to certain exemplary embodiments, examples, and
applications, it will be apparent to those skilled in the art that
various modifications and changes may be made without departing
from the present teachings.
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