U.S. patent application number 14/262040 was filed with the patent office on 2014-10-09 for methods and systems to analyze reactions using an information system.
The applicant listed for this patent is Abbott Laboratories. Invention is credited to John M. Clemens, Tzyy-Wen Jeng, George J. Schneider, Eric B. Shain.
Application Number | 20140302505 14/262040 |
Document ID | / |
Family ID | 34710061 |
Filed Date | 2014-10-09 |
United States Patent
Application |
20140302505 |
Kind Code |
A1 |
Shain; Eric B. ; et
al. |
October 9, 2014 |
METHODS AND SYSTEMS TO ANALYZE REACTIONS USING AN INFORMATION
SYSTEM
Abstract
Disclosed are example methods and systems to determine the
quantity of an analyte initially present in a chemical and or
biological reaction. Also disclosed are computer implemented
methods and systems to automate portions of the analysis comprising
mathematical or graphical analysis of an amplification
reaction.
Inventors: |
Shain; Eric B.; (Glencoe,
IL) ; Clemens; John M.; (Wadsworth, IL) ;
Jeng; Tzyy-Wen; (Vernon Hills, IL) ; Schneider;
George J.; (Barrington, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Abbott Laboratories |
Abbott Park |
IL |
US |
|
|
Family ID: |
34710061 |
Appl. No.: |
14/262040 |
Filed: |
April 25, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13350571 |
Jan 13, 2012 |
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14262040 |
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12189358 |
Aug 11, 2008 |
8099243 |
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13350571 |
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10991025 |
Nov 17, 2004 |
7565250 |
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12189358 |
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60527389 |
Dec 6, 2003 |
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Current U.S.
Class: |
435/6.12 ;
435/287.2; 702/19 |
Current CPC
Class: |
C12Q 1/686 20130101;
C12Q 2561/113 20130101; C12Q 1/686 20130101; G16B 30/00 20190201;
C12Q 1/68 20130101; C12Q 2561/113 20130101; C12Q 2545/101 20130101;
C12Q 2537/165 20130101; C12Q 2537/165 20130101; C12Q 1/6851
20130101; C12Q 1/6851 20130101 |
Class at
Publication: |
435/6.12 ;
435/287.2; 702/19 |
International
Class: |
G06F 19/22 20060101
G06F019/22; C12Q 1/68 20060101 C12Q001/68 |
Claims
1. A method for determining a presence of a target nucleic acid in
a sample, the method comprising: accessing, using a processor, data
associated with a signal related to an amplification of the target
nucleic acid in the sample, the data including a data pair
comprising an efficiency related value and a corresponding
amplification interval; performing a comparison of the data pair to
criteria data, the criteria data including a first set indicative
of amplification of the target nucleic acid and a second set not
indicative of amplification of the target nucleic acid; and
determining, using the processor, the presence of the target
nucleic acid in the sample based on the comparison, wherein if the
data pair falls within the first set, the sample is determined to
contain the target nucleic acid.
2. The method of claim 1, wherein the amplification interval is one
of an increment of time or a cycle number.
3. The method of claim 1, wherein the amplification interval is a
fractional cycle number and further comprising interpolating
additional data between the data to define the efficiency related
value and the fractional cycle number.
4. The method of claim 1, wherein the amplification interval is a
threshold cycle number.
5. The method of claim 1, wherein the amplification interval is an
increment of time and the amplification is an isothermal
amplification.
6. The method of claim 1, wherein the criterion data is one of a
criterion curve or a region partitioned between data pairs
indicative of amplification of the target nucleic acid and data
pairs not indicative of amplification of the target nucleic
acid.
7. The method of claim 6, further comprising: identifying, based on
the criterion curve, a first region indicative of a presence of the
target nucleic acid, a second region indicative of absence of the
target nucleic acid, and a third region indicative of an inhibited
presence of the target nucleic acid, the third region statistically
offset from the first region; and performing a comparison of the
data pair to the first region, the second region, and the third
region; and determining the presence of the target nucleic acid in
the sample based on the comparison.
8. The method of claim 1, further comprising: determining a mean of
data points associated with multiple signals; establishing a
confidence corridor within a range around the mean; and using only
data points within the confidence corridor to determine the
presence of the target nucleic acid.
9. The method of claim 1, wherein the amplification interval is a
fractional cycle number and further comprising determining the
fractional cycle number by: accessing, using the processor, a data
curve representative of the amplification; identifying a peak in
the curve; and determining the fractional cycle number based on a
location of the peak in the curve, wherein the location of the peak
is measured based on one of a time or a cycle number from an
initiation of the amplification.
10. The method of claim 9, wherein the efficiency related value is
a maximum value of the peak.
11. The method of claim 1, wherein the criteria data includes a
third set different from the first set and the second set, and
wherein if the data pair falls within the third set, the sample is
deemed subject to error.
12. A system comprising: an interface to obtain data associated
with a signal related to amplification of a target nucleic acid in
a sample, the data including a data pair comprising an efficiency
related value and a corresponding amplification interval; and a
processor to: perform a comparison of the data pair to criteria
data, the criteria data including a first set indicative of
amplification of the target nucleic acid and a second set not
indicative of amplification of the target nucleic acid; and
determine a presence of the target nucleic acid in the sample based
on the comparison, wherein if the data pair falls within the first
set, the sample is determined to contain the target nucleic
acid.
13. The system of claim 12, wherein the amplification interval is
one of an increment of time or a cycle number.
14. The system of claim 12, wherein the amplification interval is a
fractional cycle number and wherein the processor is to interpolate
additional data points between the data points to define the
efficiency related value and the fractional cycle number.
15. The system of claim 12, wherein the amplification interval is a
threshold cycle number.
16. The system of claim 12, wherein the amplification interval is
an increment of time and the amplification is an isothermal
amplification.
17. The system of claim 12, wherein the criterion data is one of a
criterion curve or a region partitioned between data pairs
indicative of amplification of the target nucleic acid and data
pairs not indicative of amplification of the target nucleic
acid.
18. The system of claim 17, wherein the processor is to: identify,
based on the criterion curve, a first region indicative of a
presence of the target nucleic acid, a second region indicative of
absence of the target nucleic acid, and a third region indicative
of an inhibited presence of the target nucleic acid, the third
region statistically offset from the first region; and perform a
comparison of the data pair to the first region, the second region,
and the third region; and determine the presence of the target
nucleic acid in the sample based on the comparison.
19. The system of claim 12, wherein the processor is to: determine
a mean of data points associated with multiple signals; establish a
confidence corridor within a range around the mean; and use only
data points within the confidence corridor to determine the
presence of the target nucleic acid.
20. The system of claim 12, wherein the amplification interval is a
fractional cycle number and wherein the processor is to: identify a
peak in a data curve representative of the amplification; and
determine the fractional cycle number based on a location of the
peak in the curve, wherein the location of the peak is measured
based on one of a time or a cycle number from an initiation of the
amplification.
21. The system of claim 20, wherein the efficiency related value is
a maximum value of the peak.
22. The system of claim 12, wherein the criteria data includes a
third set different from the first set and the second set, and
wherein if the data pair falls within the third set, the processor
is to deem the sample subject to error.
23. A tangible machine readable medium having instructions, which
when read, cause a machine to at least: access data associated with
a signal related to an amplification of a target nucleic acid in a
sample, the data including a data pair comprising an efficiency
related value and a corresponding amplification interval; perform a
comparison of the data pair to criteria data, the criteria data
including a first set indicative of amplification of the target
nucleic acid and a second set not indicative of amplification of
the target nucleic acid; and determine the presence of the target
nucleic acid in the sample based on the comparison, wherein if the
data pair falls within the first set, the sample is determined to
contain the target nucleic acid.
Description
RELATED APPLICATIONS
[0001] This patent is a continuation of U.S. patent application
Ser. No. 13/350,571, entitled, "Methods and Systems to Analyze
Reactions Using an Information System," which was filed on Jan. 13,
2012, which is a continuation of U.S. patent application Ser. No.
12/189,358, entitled, "Method and System for Analyzing Reactions
Using an Information System," which was filed on Aug. 11, 2008,
which is a divisional application of U.S. patent application Ser.
No. 10/991,025, entitled, "Method and System for Analyzing
Reactions Using an Information System," which was filed on Nov. 17,
2004, which claims the benefit of U.S. Provisional Patent
Application Ser. No. 60/527,389, entitled "Method and/or System for
Analyzing Reactions Using an Information System," which was filed
on Dec. 6, 2003, and all of which are incorporated herein by
reference in their entireties.
COPYRIGHT NOTICE
[0002] Pursuant to 37 C.F.R. 1.71(e), applicants note that this
disclosure contains material that is subject to and for which is
claimed copyright protection, such as, but not limited to, source
code listings, screen shots, user interfaces, user instructions,
and any other aspects of this submission for which copyright
protection is or may be available in any jurisdiction. The
copyright owner has no objection to the facsimile reproduction by
anyone of the patent document or patent disclosure, as it appears
in the records of the Patent and Trademark Office. All other rights
are reserved, and all other reproduction, distribution, creation of
derivative works based on the contents, public display, and public
performance of the application or any part thereof are prohibited
by applicable copyright law.
BACKGROUND
[0003] 1. Field of the Disclosure
[0004] The present disclosure relates to analysis of data of
nucleic acid amplification reactions. More specifically, the
disclosure relates to an information system and method for making
determinations regarding chemical and/or biological reactions. The
disclosure also involves an alternate method of quantifying nucleic
acids in a sample comprising amplification of a target nucleic acid
and analysis of data obtained during the amplification reaction.
The disclosure further involves a diagnostic system and/or kit
using real-time nucleic acid amplification including, but not
limited to, PCR analysis.
[0005] 2. Discussion of the Art
[0006] In many different industrial, medical, biological, and/or
research fields, it is desirable to determine the quantity of a
nucleic acid of interest. Some methods of quantifying nucleic acids
of interest involve amplifying them and observing a signal
proportional to the quantity of amplified products made; other
methods involve generating a signal in response to the presence of
a target nucleic acid, which signal accumulates over the duration
of the amplification reaction. As used herein, nucleic acid
amplification reaction refers both to amplification of a portion of
the sequence of a target nucleic acid and to amplification and
accumulation of a signal indicative of the presence of a target
nucleic acid, with the former often being preferred to the latter.
The quantification of nucleic acids is made more difficult or less
accurate or both because data captured during amplification
reactions are often significantly obscured by signals that are not
generated in response to the target nucleic acid (i.e., noise).
Furthermore, the data captured by many monitoring methods can be
subject to variations and lack of reproducibility due to conditions
that can change during a reaction or change between different
instances of a reaction. In view of the above, there is a need to
develop improved means of quantifying a nucleic acid. Where
quantification of nucleic acids is enabled by amplification
reactions, there is also a need to improve current methods of
detecting suspect or invalid amplification reactions. There further
remains a need to improve current abilities to distinguish between
amplification reactions that do not detect a target nucleic acid
(i.e., negative reactions) from weak signals obtained from
amplification reactions suffering from low quantities of a target
nucleic acid in a sample, a degree of inhibition of the
amplification reaction, or other causes. The present disclosure
provides improvements in these areas as is disclosed below.
[0007] A non-exhaustive list of references providing background
information regarding the present disclosure follows: [0008] Livak,
K. and Schmittgen, T., Analysis of Relative Gene Expression Data
Using Real-Time Quantitative PCR and the 22DDCT Method, METHODS 25:
402-408 (2001) doi:10.1006/meth.2001.1262. [0009] Bustin S A,
Absolute quantification of mRNA using real-time reverse
transcription PCR assays, Journal of Molecular Endocrinology 25:
169-193 (2000). [0010] Bustin S A., Quantification of mRNA using
real-time reverse transcription PCR: trends and problems, J Mol
Endocrinol. 29: 23-29 (2002). While the inventors cannot guarantee
that the following website will remain available and do not
necessarily endorse any opinions expressed therein, an interested
person may wish to refer to the website
www.wzw.turn.de/gene-quantification/index.shtml for useful
background information.
[0011] The discussion of any works, publications, sales, or
activity anywhere in this submission, including in any documents
submitted with this application, is not intended to be an admission
of any manner that any such work constitutes prior art, unless
explicitly stated to the contrary. Similarly, the discussion of any
activity, work, or publication herein is not an admission that such
activity, work, or publication was known in any particular
jurisdiction.
[0012] Real-time PCR is an amplification reaction used for the
quantification of target nucleic acids in a test sample.
Conventionally, skilled artisans typically view the amplification
reaction as comprising three distinct phases. First, there is a
background or baseline phase, in which the target nucleic acid is
being amplified but the signal proportional to the quantity of the
target nucleic acid cannot be detected because it is too small to
be observed relative to signals independent of the target
(sometimes called "background" or "background signal"). Next, there
is a logarithmic phase in which the signal grows substantially
logarithmically because the signal is substantially proportional to
the quantity of target nucleic acid in the amplification reaction
and is greater than the background signal. Finally, the growth in
the signal slows during a "plateau" phase reflecting less than
logarithmic amplification of the target nucleic acid. As is known
in the art, the time at which the logarithmic phase crosses a
threshold value, which is a value somewhat greater than the value
of the background signal, is reproducibly related to the log of the
concentration of the target nucleic acid. This prior art method is
generically referred to as the C.sub.t method, perhaps so named for
the Cycle at which the signal crosses the threshold. C.sub.t
analysis is reasonably reproducible and accurate, but suffers from
some drawbacks, which need not be discussed here to understand the
present disclosure.
[0013] U.S. Pat. No. 6,303,305 discloses a method of quantification
of nucleic acids employing PCR reactions. The method disclosed
employs the nth derivative of the growth curve of a fluorescent
nucleic acid amplification reaction. This method effectively avoids
the need to perform a baseline correction, but provides no reliable
method of determining reactive from non-reactive samples, and does
not reasonably suggest how to use an nth derivative calculation to
assess the validity of the results obtained. In addition, nucleic
acid amplification signals resulting from any artifacts in the
system (e.g., crosstalk or positive bleedover-defined infra) cannot
be distinguished from true positive responses using the methods
disclosed therein and can lead to false positive results. However,
the first derivative calculation disclosed by U.S. Pat. No.
6,303,305 provides an efficiency related value that is useful in
the context of the present disclosure. The skilled artisan can
refer to U.S. Pat. No. 6,303,305 for additional details relating to
calculation of a first derivative of a nucleic acid amplification
signal growth curve. U.S. Pat. No. 6,303,305 is incorporated by
reference only in the United States of America, and other
jurisdictions permitting incorporation by reference, to the extent
it discloses the calculation of the first derivative of a nucleic
acid amplification growth curve. However, U.S. Pat. No. 6,303,305
does not disclose or suggest the uses of this efficiency related
value described in this disclosure (below).
[0014] Co-owned U.S. Provisional Patent Application No. 60/527,389,
filed Dec. 6, 2003, discloses a method for analyzing a nucleic acid
amplification reaction in which the log of the signal from an
amplification reaction is examined for the maximum gradient or
slope. This value, which for any data set corresponds to a point a
certain period of time or number of cycles after the initiation of
the amplification reaction, is called the MGL of the reaction. The
MGL is useful in certain embodiments of the present disclosure,
particularly in those that distinguish qualitatively those samples
comprising little target nucleic acid from those samples that do
not contain target nucleic acid. U.S. Patent Application No.
60/527,389, filed Dec. 6, 2003 is incorporated herein by reference
in its entirety.
SUMMARY
[0015] The present disclosure provides a method for determining
whether a sample contains a nucleic acid of interest, for
quantifying this nucleic acid, and for assessing the validity or
quality of the data used to reach the preceding qualitative and
quantitative determinations.
[0016] The method of this disclosure comprises contacting a sample
with amplification or detection reagents or both in order to
amplify the nucleic acid (as the term "amplified" is used herein).
The amplification reaction generates signals indicative of the
quantity of the target nucleic acid present in the sample, which
signals are recorded at numerous points during the amplification
reaction. The signal can be measured and recorded as a function of
time value, or in the alternative, cycle number.
[0017] Suitable "efficiency related transforms" viewed or
calculated as a function of time are determined for the
amplification reaction, and the point in the amplification reaction
of the maximum of the efficiency related transform, the magnitude
of the maximum of the efficiency related transform, or the width
(or similar parameter) of a peak in the plot of the efficiency
related transform as a function of time can be used to obtain
information about the reaction. This point in the reaction
represents the point in time or the amplification cycle at which
the maximum of the efficiency related transform occurs.
Advantageously, the maximum of the efficiency related transform for
a particular reaction, as well as the duration and magnitude of
substantial changes in the calculated efficiency related transform,
have consistently reproducible relationships to the initial
concentration of a target nucleic acid in a sample, to the
reliability of the data and information generated by the assay, to
the presence or absence of a bona fide target nucleic acid, and to
other parameters of the reaction. Advantageously, these
relationships hold even in the presence of substantial noise and
unpredictable variations in the signal(s) generated by the
amplification reaction. As used herein, the term "maximum", as
applied to efficiency related transforms, is intended to include
the minimum of the efficiency related transform when the reciprocal
of the efficiency related transform is used. One can use the
inverse ratio, in which, in the case of a curve, the curve will
start at a value of approximately 1 in the baseline region,
decrease during the growth region, and return approximately to one
in the plateau region. The use of this transform would allow one to
use the magnitude and the position of the trough instead of the
magnitude and position of the peak for analysis. This transform is
implemented in a manner that essentially equivalent to the ratio
method in which the maximum of the efficiency related transform for
a particular reaction is employed.
[0018] In all embodiments, signals from the amplification reaction
are measured at intervals of time appropriate for the amplification
reaction during the amplification reaction. These signals can be
referred to as time-based or periodic measurements, such that every
measurement of the signal generated for a particular reaction can
be expressed as a function of time. In some embodiments, the
amplification reaction is cyclical (e.g., as in PCR). Because
cycles often have a substantially uniform duration, it is
frequently convenient to substitute a "cycle number" for a time
measurement. Accordingly, in some embodiments of the present
disclosure, a region of data identified by one or more methods on
an information processing system as described herein can correspond
to a cycle number. However, some cyclical amplification reactions
have cycles of non-uniform duration. For these amplification
reactions, it may be preferable to measure time in non-uniform
measures. For example, the theoretical extent of amplification in a
PCR reaction having cycles of varying duration will be linked more
directly to the number of cycles performed rather than the duration
of the reaction. Accordingly, the skilled artisan will readily
appreciate that the time-based measurements can easily be scaled to
reflect the underlying amplification reaction. As is known in the
art, it is often useful to interpolate data and results between
cycle numbers, which gives rise to the concept of a fractional
cycle number "FCN." Similarly, in reactions where measurements are
based on time, events can be measured in fractional time units.
[0019] In further embodiments, the disclosure advantageously
involves a system or method or both for analyzing a reaction
sample, such as a PCR reaction sample, that uses a substantial set
of available reaction kinetics data to identify a region of
interest, rather than using a very limited data set, such as where
a reaction curve crosses a threshold.
[0020] In certain embodiments, an identified region can be used to
determine one or more qualitative results, or quantitative data
analysis results, or both. The reaction point of the maximum of the
efficiency related transform can be used to determine the
concentration of a target nucleic acid in a sample or to determine
qualitatively whether any target analyte is present in a test
sample. These and other values can be compared with reference
quantities in generally the same way that a threshold cycle number
(CO or fractional threshold cycle number can be used in the prior
art.
[0021] The reaction point corresponding to the maximum of the
efficiency related transform can be understood as indicating or
being derived from a cycle number that is located at a relatively
consistent point with respect to reaction efficiency, such as at a
maximum of reaction efficiency or a region consistently related to
a maximum of reaction efficiency or consistently related to some
other reaction progression. Different methods can be used to
determine a reaction point related to a maximum of reaction
efficiency. This value can comprise adjusted FCN values (e.g.,
FCN.sub.MR Adj. and FCN.sub.Int. Adj.), as described below. In
certain embodiments of this disclosure, methods of the disclosure
can determine FCN values for multiple reaction signals, such as a
target and/or a control and use those values in determining
reaction parameters, including, but not limited to, quantity of
target nucleic acid initially present in a sample and the validity
of the results generated by an amplification reaction.
[0022] The present disclosure can identify a value indicative of
the reaction efficiency (at times, herein, generally referred to as
an "efficiency related value" (ERV)) at one or more regions on a
signal growth curve. A specific efficiency related value is
referred to as a MaxRatio value or MR. MaxRatio refers to one
possible method for calculating an efficiency related value as
further discussed herein. This is one example of a method for
determining an ERV and illustrative examples herein that refer to
MR should also be understood to include other suitable methods for
determining an efficiency related value, including, but not limited
to, the maximum gradient of the log of the growth curve, as
described in co-owned U.S. Patent Application No. 60/527,389, filed
Dec. 6, 2003, the maximum first derivative of the signal obtained
from the amplification reaction (e.g., as disclosed in U.S. Pat.
No. 6,303,305), and the maximum difference between two sequential
signals obtained from the amplification reaction. Thus, this
disclosure is involved with an analytical method that identifies
two values for a reaction curve: (1) one value related to a cycle
number or time value and (2) one value indicating an efficiency
related value. The disclosure can use those two values in analysis
of reaction data performed using an information-handling system and
method of using the system. An example of two such values are FCN
and MR specific embodiments discussed below.
[0023] This disclosure is also involved with a method and system
that uses two values as discussed above that are determined from a
reaction under examination to compare that reaction to one or more
criteria data sets. A criteria comparison can be used to determine
and/or correct any results and/or quantifications as described
herein. Criteria data can be derived by generating pairs of cycle
number related values-efficiency related values (e.g., FCN-MR
pairs) from multiple calibration reactions of known quantity or
known concentration or both.
[0024] This disclosure also involves one or more techniques for
performing efficiency analysis of reaction data. This analysis can
be used separately from or in conjunction with the cycle number
related value-efficiency related value analysis discussed herein.
Efficiency analysis can be used to find a region of interest for
making a determination about reaction data, such as, for comparison
to calibration data sets, in a way similar to C.sub.t analysis as
understood in the art.
[0025] The present disclosure also provides a method for analyzing
a nucleic acid amplification reaction, in which a sample containing
a nucleic acid is contacted with amplification agents and placed
under suitable amplification conditions to amplify a portion of the
nucleic acid in the sample. During the amplification reaction,
signals that are proportional to the amount of the target nucleic
acid present are periodically measured at a suitable interval.
Conveniently, the interval can correspond to the duration of a
cycle for those amplification reactions that are cyclical. The
signals are then manipulated to determine an efficiency related
transform for the amplification reaction. Any suitable efficiency
related transform can be used for the disclosure. Efficiency
related transforms preferred in the context of the present
disclosure include the slope of the line, which can be determined
by many techniques, including, but not limited to, difference
calculations on sequential data points, determining the first
derivative of a line fitted to the growth curve of the reaction
signal, and determining the gradient, slope, or derivative of the
log of the growth curve (i.e., Log (growth curve)). More
preferably, the efficiency related transform is the ratio of
sequential data points, sometimes referred to herein as the ratio
curve. When the efficiency related transform for the reaction is
known, a plot of the efficiency related transform as a function of
time (preferably expressed in the units used to measure the signal)
(or mathematical manipulation yielding information similar to a
plot) can be used to identify a peak value. However, a plot is not
required. The width of the peak in the selected range of acceptable
peak widths can be determined by any suitable technique or method.
However, a preferred method for determining the acceptable peak
width involves statistically analyzing the degree of variance in
peak widths obtained from objectively normal amplification
reactions that are very similar to or even identical to the
amplification method analyzed by the method of this disclosure. In
the reaction analyzed, an unknown test sample is usually used in
place of samples used to characterize the amplification reaction or
an analyte assay. If the peak width of the analyzed amplification
reaction falls within the prescribed range of acceptable peak
widths, the reaction is declared normal; if the peak width of the
analyzed amplification reaction does not fall within the prescribed
range of acceptable peak widths, the reaction is identified as
having provided sub-optimal, aberrant, or otherwise questionable
signals. The width of the leading half of the efficiency related
transform peak is evaluated. This evaluation is a more forgiving
measurement of amplification reaction validity, and therefore may
be preferred in some instances, but generally not in all
instances.
[0026] The disclosure further involves an information system and/or
program able to analyze captured data. Data can be captured as
image data from observable features of the data, and the
information system can be integrated with other components for
capturing, preparing, and/or displaying sample data. Representative
examples of systems in which the disclosure can be employed
include, but are not limited to, the BioRad.RTM. i-Cycler.RTM., the
Stratagene.RTM. MX4000.RTM., and the ABI Prism 7000.RTM. systems.
Similarly, the present disclosure provides a computer product
capable of executing the method of this disclosure.
[0027] Various embodiments of the present disclosure provide
methods and/or systems that can be implemented on a general purpose
or special purpose information handling system by means of a
suitable programming language, such as Java, C++, C#, Cobol, C,
Pascal, Fortran, PL1, LISP, assembly, etc., and any suitable data
or formatting specifications, such as HTML, XML, dHTML, TIFF, JPEG,
tab-delimited text, binary, etc. For ease of discussion, various
computer software commands useful in the context of the present
disclosure are illustrated in MATLAB.RTM. commands. The MATLAB
software is a linear algebra manipulator and viewer package
commercially available from The Mathworks, Natick, Mass. (USA). Of
course, in any particular implementation (as in any software
development project), numerous implementation-specific decisions
can be made to achieve the developer's specific goals, such as
compliance with system-related and/or business-related constraints,
which will vary from one implementation to another. Moreover, it
will be appreciated that such a developmental effort might be
complex and time-consuming, but would nevertheless be a routine
undertaking of software engineering for those of ordinary skill in
the art having the benefit of this disclosure.
[0028] The disclosure will be better understood with reference to
the following drawings and detailed descriptions. For purposes of
clarity, this discussion refers to devices, methods, and concepts
in terms of specific examples. However, the disclosure and aspects
thereof may have applications to a variety of types of devices and
systems.
[0029] Furthermore, it is well known that logic systems and methods
such as those described herein can include a variety of different
components and different functions in a modular fashion. Different
embodiments of the disclosure can include different combinations of
elements and functions and may group various functions as parts of
various elements. For purposes of clarity, the disclosure is
described in terms of systems that include many different
components and combinations of novel components and known
components. No inference should be taken to limit the disclosure to
combinations requiring all of the novel components in any
illustrative embodiment of this disclosure.
[0030] As used herein, "the disclosure" should be understood to
include one or more specific embodiments of the disclosure (unless
explicitly indicated to the contrary). Many variations according to
the disclosure will be understood from the teachings herein to
those of ordinary skill in the art.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] FIG. 1 is a plot of discrete captured reaction data values
from 43 readings (e.g., cycles) taken from a nucleic acid
amplification reaction that can be used in an analysis method
according to embodiments of this disclosure.
[0032] FIG. 2 is a plot illustrating captured reaction data showing
target and control data sets that have been normalized according to
embodiments of this disclosure.
[0033] FIG. 3 is a plot illustrating reaction data showing target
and control data that have been scaled according to embodiments of
this disclosure.
[0034] FIG. 4 is a plot illustrating captured reaction data showing
target and control data after digital filtering according to
embodiments of this disclosure.
[0035] FIG. 5 is a plot illustrating captured reaction data showing
target and control data with slope values removed according to
embodiments of this disclosure.
[0036] FIG. 6 is a plot illustrating ratio transform of reaction
target and control data according to embodiments of this
disclosure.
[0037] FIG. 7 is a plot illustrating shifted ratio transform of
reaction target and control data according to embodiments of this
disclosure.
[0038] FIG. 8 is a plot illustrating interpolated transformed
reaction data showing target and control data that have been
interpolated according to embodiments of this disclosure.
[0039] FIG. 9 is a plot illustrating interposed reaction data
showing identification of the FCN and MR points according to
embodiments of this disclosure.
[0040] FIG. 10 is a flow chart for performing a characterization of
reaction data according to embodiments of this disclosure.
[0041] FIG. 11 is a plot illustrating methods for determining
criteria data according to embodiments of this disclosure.
[0042] FIG. 12 is a plot illustrating two sets of reaction data
that illustrate how reaction curves for same concentration initial
samples can vary due to different reaction anomalies.
[0043] FIG. 13 illustrates peak efficiency calculations for the
data sets in FIG. 12. The figure illustrates the desirability of
using an offset efficiency transform according to specific
embodiments of the present disclosure.
[0044] FIG. 14 illustrates data for an HIV assay run with eight
replicates of known concentration samples at 50, 500, 5,000,
50,000, 500,000 and 5,000,000 copies per mL.
[0045] FIG. 15 is a plot illustrating four linear standard curves
generated from three-point calibration data using four different
cycle number related values (e.g., FCN, FCN2, FCN.sub.MR Adj., and
FCN.sub.Int. Adj.) according to embodiments of this disclosure.
[0046] FIGS. 16A and 16B compare calculated concentrations to known
concentrations for the data illustrated in FIG. 14 using the four
curves illustrated in FIG. 15 according to embodiments of this
disclosure in graphical form (FIG. 16A) and chart form (FIG.
16B).
[0047] FIGS. 17A and 17B illustrate results using a one-point
calibration according to embodiments of this disclosure in
graphical form (FIG. 17A) and chart form (FIG. 17B).
[0048] FIG. 18 illustrates experimental HBV results using MR
analysis with a one-point calibration according to embodiments of
this disclosure.
[0049] FIG. 19 illustrates experimental HBV results using MR
analysis and FCN.sub.MR adj. with a one-point calibration according
to embodiments of this disclosure.
[0050] FIG. 20 illustrates experimental HBV results using C.sub.t
analysis and a one-point calibration according to embodiments of
this disclosure.
[0051] FIG. 21 illustrates experimental HIV results using MR
analysis and one-point calibration, e.g. using 10.sup.3 and
10.sup.7 copies/mL responses as calibrators, according to
embodiments of this disclosure.
[0052] FIG. 22 is a plot illustrating two types of criteria data
according to embodiments of this disclosure wherein the lower
horizontal line represents criteria data suitable for
differentiating negative reactions from positive reactions.
[0053] FIG. 23 is a plot illustrating FCN-MR for HIV data from 50
copies/mL to 5,000,000 copies/mL analyzed by a statistics software
package to apply a curve fit to the data and to determine
confidence intervals according to embodiments of this
disclosure.
[0054] FIG. 24 is a plot illustrating internal control data
analyzed by a statistics software package to determine confidence
intervals according to embodiments of this disclosure.
[0055] FIGS. 25A, 25B, and 25C are flow charts illustrating a logic
analysis tree for assessment of assay validity through analysis of
pairs of cycle number related value-efficiency related value for
both the internal control and the target amplification reactions
according to embodiments of this disclosure.
[0056] FIG. 26 is a flow chart illustrating a logic analysis tree
for reporting target results with validity criteria assessment
using the pairs of cycle number related value-efficiency related
value according to embodiments of this disclosure.
[0057] FIG. 27 illustrates the calculation of peak width
measurements according to embodiments of this disclosure.
[0058] FIG. 28 illustrates experimental HIV results using the full
peak width measurement according to embodiments of this
disclosure.
[0059] FIG. 29 illustrates experimental HIV results using the full
peak width measurement to identify an abnormal response according
to embodiments of this disclosure.
[0060] FIG. 30 illustrates an example of a user interface
displaying an FCN-MR plot according to embodiments of this
disclosure.
[0061] FIG. 31 illustrates an example of a user interface
displaying a shifted ratio plot according to embodiments of this
disclosure.
[0062] FIG. 32 is a block diagram showing a representative example
of a logic device in which various aspects of the present
disclosure may be embodied.
DETAILED DESCRIPTION
[0063] As used herein, the expression "efficiency related value"
means a value that has a consistent relationship to the efficiency
of an amplification reaction. The expression "efficiency related
transform" means a mathematical transformation involving the
response in an amplification reaction that is used to determine an
efficiency related value. The expression "reaction point" means a
point during a reaction at which an efficiency related value
occurs. The reaction point can be a point in time measured from the
beginning of the reaction. Alternatively, the reaction point can be
a point that denotes a cycle measured from the beginning of the
reaction. The term "derivative" means the slope of a curve at a
given point in the curve.
[0064] The present disclosure is directed to the analysis of a
sample containing an analyte. The analyte can be a nucleic acid. In
the context of the present disclosure, copies of a portion of the
analyte are made (hereinafter "amplified") in a manner that
generates a detectable signal during amplification. The signal is
indicative of the progress of the amplification reaction, and
preferably is related either to the quantity of analyte and copies
of the analyte present in a test sample, or is related to the
quantity of the copies of the analyte produced by the reaction. The
amplification is preferably configured to allow logarithmic
accumulation of the target analyte (e.g., as in a PCR reaction),
and in a more preferred embodiment, the amplification is a PCR
reaction in which data are collected at regular time intervals
and/or at a particular point in each PCR cycle.
[0065] Many systems have been developed that are capable of
amplifying and detecting nucleic acids. Similarly, many systems
employ signal amplification to allow the determination of
quantities of nucleic acids that would otherwise be below the
limits of detection. The present disclosure can utilize any of
these systems, provided that a signal indicative of the presence of
a nucleic acid or of the amplification of copies of the nucleic
acid can be measured in a time-dependent or cycle-dependent manner.
Some preferred nucleic acid detection systems that are useful in
the context of the present disclosure include, but are not limited
to, PCR, LCR, 3SR, NASBA, TMA, and SDA.
[0066] Polymerase Chain Reaction (PCR) is well-known in the art and
is essentially described in Saiki et al., Science 230; 1350-1354
(1985); Saiki et al., Science 239:487-491 (1988); Livak et al.,
U.S. Pat. Nos. 5,538,848; 5,723,591; and 5,876,930, and other
references. PCR can also be used in conjunction with reverse
transcriptase (RT) and/or certain multifunctional DNA polymerases
to transform an RNA molecule into a DNA copy, thereby allowing the
use of RNA molecules as substrates for PCR amplification by DNA
polymerase. Myers et al. Biochem. 30: 7661-7666 (1991)
[0067] Ligation chain reactions (LCR) are similar to PCR with the
major distinguishing feature that, in LCR, ligation instead of
polymerization is used to amplify target sequences. LCR is
described inter alia in Backman et al., European Patent 320 308;
Landegren et al., Science 241:1077 (1988); Wu et al., Genomics
4:560 (1989). In some advanced forms of LCR, specificity can be
increased by providing a gap between the oligonucleotides, which
gaps must be filled in by template-dependent polymerization. This
can be especially advantageous if all four dNTPs are not needed to
fill the gaps between the oligonucleotide probes and all four dNTPS
are not supplied in the amplification reagents. Similarly, rolling
circle amplification (RCA) is described by Lisby, Mol. Biotechnol.
12(1):75-99 (1999)), Hatch et al., Genet. Anal. 15(2):35-40 (1999)
and others, and is useful in the context of the present
disclosure.
[0068] Isothermal amplification reactions are also known in the art
and useful in the context of the present disclosure. Examples of
isothermal amplification reactions include 3SR as described by Kwoh
et al., Proc. Nat. Acad. Sci. (USA) 86: 1173-1177 (1989) and
further developed in the art; NASBA as described by Kievits et al.,
J. Virol. Methods 35:273-286 (1991) and further developed in the
art; and Strand Displacement Amplification (SDA) method as
initially described by Walker et al., Proc. Nat. Acad. Sci. (USA)
89:392-396 (1992) and U.S. Pat. No. 5,270,184 and further developed
in the art.
[0069] Thus, many amplification or detection systems requiring only
that signal gains indicative of the quantity of a target nucleic
acid can be measured in a time-dependent or cycle-dependent manner
are useful in the context of the present disclosure. Other systems
having these characteristics are known to the skilled artisan, and
even though not discussed above, are useful in the context of the
present disclosure.
[0070] Analysis of the data collected from the amplification
reaction can provide answers to one or more of the following
questions:
[0071] (1) Was the target sequence found?
[0072] (2) If yes, what was the initial level or quantity of the
target sequence?
[0073] (3) Is the result correct?
[0074] (4) Did the reaction series run correctly?
[0075] (5) Was there inhibition of the desired or expected
reaction?
[0076] (6) Is the sample preparation recovery acceptable?
[0077] (7) Is the calibration to any reference data, if used, still
valid?
[0078] According to some embodiments of this disclosure, one or
more of these questions can be answered by identifying a region of
interest (e.g., an FCN) and an efficiency related value (e.g., an
MR) of a target and/or internal control reaction. In other
embodiments, one or more of these questions can be answered by
comparing such values to data sets herein referred to as criteria
data, criteria curves, and/or criteria data sets. In additional
embodiments, one or more of these questions can be answered by
comparing such values obtained for an internal control, e.g., a
2.sup.nd amplification control reaction, in the same reaction
mixture as its criteria data. In still further embodiments, one or
more of these questions can be answered by comparing such values
obtained for the target reaction to such values obtained for an
internal control reaction in the same reaction mixture as their
respective criteria data.
[0079] For clarity, the disclosure will be illustrated with
reference to real-time PCR reactions, which are one class of
measuring and monitoring techniques of high interest in automated
and manual systems for detecting and quantifying human nucleic
acids, animal nucleic acids, plant nucleic acids, and nucleic acids
of human, non-human animal, and plant pathogens. Real-time PCR is
also well adapted to detection of bio-warfare agents and other
living or viral organisms in the environment. Real-time PCR
combines amplification of nucleic acid (NA) sequence targets with
substantially simultaneous detection of the amplification product.
Optionally, detection can be based on fluorescent probes or primers
that are quenched or are activated depending on the presence of a
target nucleic acid. The intensity of the fluorescence is dependent
on the concentration or amount of the target sequence in a sample
(assuming, of course, that the quantity of the target is above a
minimal detectable limit and is less than any saturation limit).
This quench/fluoresce capability of the probe allows for
homogeneous assay conditions, i.e., all the reagents for both
amplification and detection are added together in a reaction
container, e.g., a single well in a multi-well reaction plate.
Electronic detection systems, target-capture based systems, and
aliquot-analysis systems and techniques are other forms of
detection systems useful in the context of the present disclosure
so long as a given system accumulates data indicative of the
quantity of target present in a sample during various time points
of a target amplification reaction.
[0080] In PCR reactions, the quantity of target nucleic acid
doubles at each cycle until reagents become limiting or are
exhausted, there is significant competition, an inadequate supply
of reactants, or other factors that accumulate over the course of a
reaction. At times during which a PCR reaction causes doubling
(exactly) of the target in a particular cycle, the reaction is said
to have an efficiency (e) of 1 (e.g., e=1). After numerous cycles,
detectable quantities of the target can be created from very small
and initially undetectable quantity of target. Typically, PCR
cycling protocols consist of between around 30-50 cycles of
amplification, but PCR reactions employing more or fewer cycles are
known in the art and useful in the context of the present
disclosure.
[0081] In the real-time PCR reactions described below to illustrate
the present disclosure, the reaction mixture includes an
appropriate reagent cocktail of oligonucleotide primers,
fluorescent dye-labeled oligonucleotide probes capable of being
quenched when not bound to a complementary target nucleic acid,
amplification enzymes, deoxynucleotide triphosphates (dNTPs), and
additional support reagents. Also, a second fluorescent dye-labeled
oligonucleotide probe for detection of an amplifiable "control
sequence" or "internal control" and a "reference dye", which
optionally may be attached to an oligonucleotide that remains
unamplified throughout a reaction series, can be added to the
mixture for a real-time PCR reaction. Thus, some real-time PCR
systems use a minimum of three fluorescent dyes in each sample or
reaction container (e.g., a well). PCR systems using additional
fluorescent probe(s) for the detection a second target nucleic acid
are known in the art and are useful in the context of the present
disclosure.
[0082] Systems that plot and display data for each of one, or
possibly more, reactions (e.g., each well in a multi-well plate)
are also useful in the context of the present disclosures. These
systems optionally calculate values representing the fluorescence
intensity of the probe as a function of time or cycle number
(C.sub.N) or both as a two-dimensional plot (y versus x). Thus, the
plotted fluorescence intensity can optionally represent a
calculation from multiple dyes (e.g., the probe dye and/or the
control dye normalized by the reference dye) and can include
subtraction of a calculated background signal. In PCR systems, such
a plot is generally referred to as a PCR amplification curve and
the data plotted can be referred to as the PCR amplification
data.
[0083] In PCR, data analysis can be made difficult by a number of
factors. Accordingly, various steps can be performed to account for
these factors. For example, captured light signals can be analyzed
to account for imprecision in the light detection itself. Such
imprecision can be caused by errors or difficulties in resolving
the fluorescence of an individual dye among a plurality of dyes in
mixture of dyes (described below as "bleedover"). Similarly, some
amount of signal can be present (e.g., "background signal") and can
increase even when no target is present (e.g., "baseline drift").
Thus, a number of techniques for removing the background signal,
preferably including the baseline drift, trend analysis, and
normalization are described herein and/or are known in the art.
These techniques are useful but are not required in the context of
the present disclosure. (Baseline drift or trending can be caused
by many sources, such as, for example, dye instability, lamp
instability, temperature fluctuations, optical alignment, sensor
stability, or combinations of the foregoing. Because of these
factors and other noise factors, automated methods of identifying
and correcting the baseline region are prone to errors.)
[0084] Typically in PCR, the answers of interest are generally
determined from a growth curve, which characteristically starts out
as nearly flat during the early reaction cycles when insufficient
doubling has occurred to cause a detectable signal, and then rises
exponentially until one or more reaction limiting conditions, such
as exhaustion of one or more reactants, begins to influence the
amplification reaction or the detection process.
[0085] A number of methods have been proposed and have been used in
research and other settings to analyze PCR-type reaction data.
Typically, these methods attempt to detect when the reaction curve
has reached a particular point, generally during a period of
exponential or near-exponential signal growth (also known as "the
log-linear phase"). While not wishing to be bound by any theory,
the inventors believe that the earliest point(s) in which the log
linear phase can be observed above the baseline or background
signal provides the most useful information about the reaction and
that the slope of the log-linear phase is a reflection of the
amplification efficiency. Some prior art references erroneously
suggest that for the slope to be an indicator of real amplification
(rather than signal drift), there has to be an inflection point,
which is the point on the growth curve where the log-linear phase
ends. The inflection point can also represent the greatest rate of
change along the growth curve. In some reactions where inhibition
occurs, the end of the exponential growth phase may occur before
the signal emerges from the background.
[0086] In running a PCR analysis, it is generally desired to
determine one or more assay results regarding the initial
amount/concentration of the target molecules. For discussion
purposes, results may be expressed by answers to at least one of
four questions:
[0087] (1) Was the target molecule present at all in the initial
sample (e.g., a positive/negative detection result)?
[0088] (2) What was the absolute quantity of the initial target
present?
[0089] (3) What is the confidence (e.g., sometimes expressed as a
confidence value that the answers to questions 1 or 2 are
correct)?
[0090] (4) What is the relative amount of the target present in two
different samples? A number of methods have been proposed and can
be used in research and other settings to answer one or more of
these questions.
[0091] Data for PCR reactions is often collected one time in each
cycle for each dye that is measured (i.e., fluorescence determined)
in a reaction. While such data is useful in the context of the
present disclosure, more precise quantification can be carried out
by interpolation between the data points acquired at each cycle. In
this way, the data can be analyzed to generate "fractional cycle
numbers", and points of interest can be determined to be coincident
with a particular cycle number or at a reaction point between any
pair of cycle numbers.
[0092] One problem with methods that rely on thresholds,
particularly in diagnostic settings where it is desirable to fix
thresholds, is that theses methods can be susceptible to errors due
to the presence of noise factors, particularly systematic noise
factors, such as, for example, "crosstalk" and "bleedover".
Crosstalk can generally be understood as occurring when a signal
from an assay in one location (such as one well in a multi-well
plate) causes an anomaly in a signal in a different, usually
adjacent assay location. Bleedover can generally be understood as
occurring in situations where more than one signal or data set is
detected from the reaction. While detection dyes for a reaction are
selected to be largely independent from each other and to have
individual fluorescence emission spectra, the emission spectra
sometimes overlap such that the emission spectrum from one dye will
bleedover into the emission spectrum of a different dye.
[0093] Both crosstalk and bleedover can have the effect of either
increasing or decreasing the calculated measurement of interest.
Furthermore, in both cases, there can be situations where the curve
itself can have an anomaly due to either or both of these
phenomena. Systematic noise factors such as crosstalk and bleedover
can be especially difficult to deal with when performing a baseline
correction.
[0094] In some systems of the prior art, in order to detect
low-level signals for either qualitative results or quantitative
results, a low threshold is generally required. However, the use of
a low threshold causes discrimination between a false positive
signal due to crosstalk and a correct positive signal to be
particularly difficult, because either can cause the PCR curve to
rise above an amplification threshold, thereby suggesting that a
target analyte is present. Positive and negative bleedover can also
present problems. Positive bleedover can produce a false-positive
results or cause falsely elevated estimates of the initial quantity
of target in a sample, while negative bleedover can cause falsely
depressed estimates of the initial quantity of target in a sample
or falsely indicate the absence of a target in a test sample.
[0095] The method or system of this disclosure can reproducibly
identify a region in a reaction curve or data, preferably using an
information processing system, which can then be used to provide
results based on the amplification reaction data. The disclosure
can identify this region regardless of the base level of the
signal, even in the presence of substantial noise. The disclosure
can furthermore identify a value that is representative of
efficiency at that region. This value can be used in determining
primary results or in adjusting results or in determining
confidence values as described herein, or all of the foregoing.
[0096] The disclosure can be illustrated by a specific example,
shown below. In this example, an information processing system is
used to analyze data representing the growth curve of an
amplification reaction. In the amplification, a "peak" is generated
by one step in the data analysis. The location of this peak
(measured in time units or in cycles from the initiation of the
amplification reaction) is referred to as the fractional cycle
number (FCN) and the maximum value of the peak is referred to as
the ERV (efficiency related value). These values can be used in a
method to identify an efficiency related value region and to
determine an efficiency related value at this peak. Both of these
values can be understood as being derived from a method that
analyzes the shape of the reaction curve regardless of the
intensity of the amplification signal, which intensity of
amplification signal can vary from reaction to reaction and from
instrument to instrument, despite starting with identical samples.
The reaction curve is a representation of the reaction wherein a
signal substantially indicative of the quantity of target in a
reaction is plotted as a function of time or, when appropriate,
cycle number. The FCN can be understood as being consistently
related to a point of maximum growth efficiency of a reaction
curve, and the ERV can be understood as being consistently related
to the efficiency at that point.
[0097] In some embodiments of this disclosure, analytical methods
can optionally, and advantageously, be employed without use of
baseline correction. In some systems and methods of this
disclosure, a reference dye is not needed.
[0098] The present disclosure allows objective quantification of
the quantity of a target present in a test sample without the need
to calculate a subjective and variable threshold or a C.sub.t
value, as employed in some techniques of the prior art.
Furthermore, the disclosure can use information that is available
for determining the degree of inhibition in a reaction by analyzing
the shape of the PCR amplification curve, including data that
previously has generally been ignored, such as data in cycles after
a C.sub.t.
[0099] General methods for generating and using data pairs
determined from reaction curve data will be understood from the
examples below. For clarity, these examples refer to a specific set
of data and specific functions for analyzing that data, though the
disclosure is not limited to the examples discussed.
Example 1
Captured Data
[0100] By way of example, a typical real-time PCR reaction
detection system generates a data file that stores the signal
generated from one or more detection dyes. FIG. 1 illustrates a
plot of captured reaction data that can be used in an analytical
method according to the present disclosure. In this example, one
dye signal (DYE1) provides the captured target data, another dye
signal (DYE2) provides captured internal control data, and a
further dye signal (DYE3) provides optional captured reference
data. These data represent data from a single reaction, taken from
a standard output file. This particular plot can be understood to
represent initial data to which some type of multi-component
algorithm has been applied. In this plot, the x-axis provides an
indication of cycle number (e.g., 1 to 45) and the y-axis indicates
dye intensity detected, in relative fluorescence units. In this
figure, the three different capture data sets are illustrated as
continuous curves. However, the actual captured data values are
generally discrete signal values captured at each cycle number.
Thus, an initial data set as illustrated in FIG. 1 may consist of
three sets (target, control, and reference) of suitable discrete
values (e.g., about 50 values in this case).
Example 2
Normalization
[0101] Although optional, normalization can be performed on the
captured data in several different ways. One method involves
dividing the target and control values at each cycle reading by the
corresponding reference dye signal. Alternatively, the divisor can
be the average reference value over all cycles or an average over
certain cycles. In another alternative embodiment, the divisor can
be the average of the target dye or the control dye or the target
dye and the control dye over one or more earlier (baseline) cycles,
when no amplification signal is detected. Any known normalization
method can be employed in a data analysis. The disclosure can be
used with data that has already been normalized by a PCR system.
FIG. 2 is a plot of captured reaction data showing target and
control data sets that have been normalized according to the
present disclosure. In this example, as a result of the
normalization, the y-axis scale represents a pure number. In this
case, the number is between about 0 and 9. Other normalization
methods are known in the art and can convert this number to between
about 0 and 100 or to any other desired range.
[0102] Because normalization is optional, the present disclosure
can be used to analyze reaction data without the use of a
normalization or reference dye. Alternatively, the target signal or
the control signal or both can be used for normalization.
Example 3
Scaling
[0103] Scaling is optional but can be performed to make it easier
for a human operator to visualize the data. Scaling does not affect
analytical results. Scaling can be carried out in addition to
normalization, in the absence of normalization, or before or after
normalization.
[0104] One method of scaling involves dividing each data set value
by the average of the values during some early cycles, generally in
the baseline region before any positive data signal is detected. In
this example, readings 4 through 8 were averaged and normalization
was performed first. FIG. 3 is a plot of reaction data showing
target and control data that have been scaled. In this example,
scaling forces the early values of the target and control to one,
and because the early values are less than one, the division forces
the later values to slightly larger pure numbers.
Example 4
Digital Filtering
[0105] One or more digital filtering methods can be applied to the
captured data to "clean up" the signal data sets and to improve the
signal to noise ratio. Many different filtering algorithms are
known. The present disclosure can employ a four-pole filter with no
zeros. This eliminates the potential for overshoot of the filtered
signal. As an example, this can be implemented with the MATLAB
function "filtfilt" provided with the MATLAB Signal Processing
Toolbox, which both forward and backward filters to eliminate any
phase lag (time delays). An example of parameters and MATLAB
function call is as follows:
TABLE-US-00001 b=0.3164; a=[1.0000-1.0000 0.3750-0.0625 0.0039];
data(:,:,assay)=filtfilt(b,a,data(:,:,assay));
data(:,:,ic)=filtfilt(b,a,data(:,:,ic));
[0106] In this example, "b" and "a" contain the filter
coefficients. "data(:,:,assay)" and "data(:,:,ic)" contain the
captured data that may or may not have been normalized, scaled, or
both. In this case, the filtered data is both normalized and
scaled. FIG. 4 is a plot of captured reaction data showing target
and control data after digital filtering. The values are not
changed by the digital filtering, but the data set is "smoothed"
somewhat.
Example 5
Slope Removal/Baselining
[0107] An optional slope removal method can be used to remove any
residual slope that is present in the early baseline signal before
any detectable actual signal is produced. This procedure may also
be referred to as baselining, but in some embodiments, the offset
is not removed, only the slope. According to this disclosure, for
slope removal, both the target (DYE1) and control (DYE2) signals
are examined simultaneously. Whichever signal comes up first
defines the forward regression point, and the method generally goes
back 10 cycles. If 10 cycles back is before cycle 5, then cycle 5
is used as the initial regression point to avoid any earlier signal
transients. A linear regression line is calculated using the signal
data between these points and the slope of the regression for each
dye is subtracted from that dye's signal. In this case, the slope
removal is applied to the normalized, scaled, and filtered data
discussed above. FIG. 5 is a plot of captured reaction data showing
target and control data with slope values removed. In each of these
figures, very little slope was present in early cycles; therefore,
the slope removal does not substantially affect the captured data
values.
Example 6
Transform Calculation
[0108] An embodiment of the method of this disclosure is the
MaxRatio method. In this method, the ratio between sequential
measurements is calculated, thereby yielding a series of ratios,
each of which can be indexed to a time value or cycle number. Many
suitable means of calculating these ratios exist, and any suitable
means can be used. The simplest way of performing this ratio
calculation utilizes the following function:
Ratio ( n ) = s ( n + 1 ) s ( n ) ##EQU00001##
where n represents the cycle number and s(n) represents the signal
at cycle n. This calculation provides a curve that starts at
approximately 1 in the baseline region of the response, increases
to a maximum during the growth region, and returns to approximately
1 in the plateau region. A MATLAB expression that performs this
calculation efficiently is the following:
Ratio=s(2:end,:)./s(1:end-1,:),
[0109] where "s" represents the signal response matrix, with each
column representing a separate response.
FIG. 6 shows an example of this ratio transform. Because of the
intrinsic background fluorescence, the ratio does not reach 2 as
would be expected of a PCR reaction if the signal were doubling.
Regardless, the magnitude of the peak is independent of
multiplicative intensity variations and is proportional to the rate
of growth or efficiency at that point. The method of calculating
ratios is simple and efficiently calculated. Other equivalent
calculations could be made. An example would involve calculating
the forward and reverse ratios and then averaging them. On can use
the inverse of the ratio, in which case the curve will begin at a
value of approximately 1 in the baseline region, decrease in the
growth region, and return to a value of approximately 1 in the
plateau region. One would then use the magnitude and location of
the trough instead of a peak for analysis. This transform can be
implemented in a manner essentially equivalent to the ratio
method.
[0110] Although the MaxRatio algorithm is usable as described, it
is convenient to shift the curve by subtracting a constant, e.g.,
about one (1), from each point. This operation provides a
transformation of the original response, which starts near zero in
the baseline region, rises to a peak in the growth region of the
curve, and returns near zero in the plateau region. This shifted
ratio calculation is described by the following function:
Ratio ( n ) = s ( n + 1 ) s ( n ) - 1. ##EQU00002##
FIG. 7 shows the output of this shifted ratio calculation. The
reaction point and magnitude of the peak of the shifted ratio curve
is then determined. The reaction point (i.e., distance along the
x-axis) specifies the FCN value of the MR and the magnitude
specifies the efficiency related value MR (Maximum of the
Ratio).
Example 7
Interpolation
[0111] In order to enhance cycle number resolution, an
interpolation can be performed. Many ways of accomplishing this
operation are known in the art. One method of interpolating in the
context of the disclosure is cubic spline interpolation, which
provides a smooth interpolation, so that even the second derivative
of the captured data sets will be continuous. The disclosure can be
used to interpolate the entire data series. The disclosure can be
used to determine a region of interest and then to interpolate only
in that region to achieve sub-periodic, or sub-cycle, resolution.
An example of a MATLAB command for performing a cubic spline
interpolation is as follows:
out=interp1(x,in,x2,`spline`)
where "x" represents the period (or cycle) numbers (1, 2, 3 . . .
), in represents the uninterpolated signal at those cycles, "x2"
represents the higher resolution period (or cycle) vector (1.00,
1.01, 1.02, . . . ) and out represents the interpolated signal that
corresponds to the fractional cycles in "x2". FIG. 8 is a plot of
captured reaction data showing target and control data that have
been interpolated to provide function continuity. As a result of an
interpolation, the number of values in the data set will generally
increase substantially, for example from 43 values to 4201
values.
[0112] It should be understood that the steps described above can
be performed in different orders, such as, for example, filtering
first, followed by baselining before scaling. However, if the
interpolation is performed before the ratio calculation, care must
be taken to select the appropriate interpolated response values for
the ratio calculation. It is important that the interval between
ratio values remain the same. Thus, if cycles are used as the
period of measurement, and interpolation increases the time
resolution to 0.01 cycles, then the shifted ratio at x=2.35 would
be R=s(3.35)/s(2.35)-1.
Example 8
Finding Peaks to Determine FCN and ERV (e.g., MR) of Target and
Control
[0113] Another step is to select peaks in the data series. This
operation involves the steps of (1) finding local peaks and (2)
selecting from local peaks one or more peaks for further analysis,
optionally using criteria data (defined infra).
[0114] A peak-finding algorithm identifies where the slope of the
curve changes from positive to negative, which represents a local
maximum. The algorithm identifies the locations and the magnitude
of the peaks. An example of a MATLAB function to do this
calculation is as follows:
TABLE-US-00002 function [ind,peaks] = findpeaks(y) % FINDPEAKS Find
peaks in real vector. % ind = findpeaks(y) finds the indices (ind)
which are % local maxima in the sequence y. % [ind,peaks] =
findpeaks(y) returns the value of the % peaks at these locations,
i.e. peaks=y(ind); y = y(:)'; switch length(y) case 0 ind = [ ];
case 1 ind = 1; otherwise dy = diff(y); not_plateau_ind =
find(dy~=0); ind = find( ([dy(not_plateau_ind) 0]<0) & ([0
dy(not_plateau_ind)]>0) ); ind = not_plateau_ind(ind); end if
nargout > 1 peaks = y(ind); end
[0115] FIG. 9 is a of an efficiency calculation showing identified
FCN and MR values of the target and internal control dyes and a
criteria curve according to embodiments of the present disclosure.
For the target data, FINDPEAKS located one peak at cycle axis
x=19.42 with a magnitude of 0.354. For the internal control data,
FINDPEAKS found peaks at: x=2.03, 5.29, 7.67, 12.83, 22.70, 37.86,
with respective magnitudes 0.0027, 0.0027, 0.0022, 0.0058, 0.1738,
0.0222.
Example 9
Selecting Peaks to Determine FCN and ERV (e.g., MR) of Target and
Control
[0116] In the method discussed above, a number of local maximum
peaks are often identified for both the target data and the control
data. Various methods can be used for selecting which of these
local maximum peaks will be used for determining an FCN and
ERV.
[0117] Typically, and in particular during well-behaved reactions,
the highest peak or maximum peak is selected. In many situations,
this selection provides the most reproducible reaction point from
which to perform further calculations as discussed herein. However,
in some situations, a first peak, or first peak above a particular
cutoff or after a particular number of cycles is preferable. Thus,
in particular examples, a Max Peak or First Peak selection can be
employed where Max Peak finds the largest peak in the shifted ratio
curve while First Peak finds the first peak that is higher than
some selected value.
[0118] Once criteria data are determined, these data can also be
used to determine which peak to select for an ERV determination
during actual operation, particularly for weak or noisy
signals.
[0119] In FIG. 9, for example, for the DYE2 data, the peak-finding
algorithm found six local peaks, but the fifth peak was the maximum
peak and was also the only one that was above the criteria curve.
Thus, in this example, an FCN determined for DYE2 is 22.70 and the
MR determined for DYE 2 is 0.1738.
[0120] An information appliance or system apparatus can also be
used to perform the methods of this disclosure. FIG. 10 is a flow
chart for performing a reaction data characterization according to
embodiments of the present disclosure. Further details of this
general method will be understood from the discussion below.
[0121] The analytic methods described herein can be applied to
reactions containing either known or unknown target concentrations.
In one embodiment, known target nucleic acid concentrations will be
included in calibration wells in a reaction carried out in a
multi-well reaction plate, and the ERV and value of the reaction
point will be used from these known concentration samples to
perform quantification. Known concentrations may also be used to
develop criteria data as further described herein.
Example 10
Determining Criteria Curve/Criteria Data Sets
[0122] In other embodiments, efficiency related values (e.g., MR
values) can be plotted as a function of their reaction point values
(FCN values) for a number of data sets of known concentration in
order to generate a characteristic criteria curve for a particular
assay. The criteria curve is characteristic of a particular assay
formulation and detection protocol and can be used to reliably
determine positive/negative results, to determine whether a
particular result should be discarded as unreliable, to determine a
confidence measure of a result, or any combination of the
foregoing. In general, pairs of reaction data that lie below a
criteria curve indicate non-reactive samples, or non-functional
reactions, such as reactions encountering significant
inhibition.
[0123] Criteria data can be used to select which peaks to report or
to use in reaction analysis, or both. Criteria data provide an
automatic and reliable method for discriminating between negative
results (e.g., target not present at all) and results showing low
amount of target.
[0124] FIG. 11 is a plot in which the MR of six sets of reactions
of known concentration (i.e., standards or calibrators) and one set
of negative reactions are plotted as a function of the calculated
FCN value of the MR value. This plot allows a criteria curve to be
selected. A criteria curve, which was described previously, is any
curve or line that separates positive results from negative
results. The criteria curve is preferably selected so that it is
relatively close to and above the negative reaction data (in the
x-y space of the plot). In FIG. 11, pairs of MR-FCN data from a
number of samples of known concentrations determined under the same
or similar assay conditions are plotted together with pairs of
MR-FCN data from samples that do not contain the target of the
assay, which samples are also referred to as negatives. Although
the negatives should exhibit no amplification response, the
analytical method does determine an MR-FCN data pair for these
samples. These data for negative samples usually correspond to
noise driven maxima on the response output, which is generally a
random response. The MR value determined from noise is very low and
far removed from the responses from samples of known
concentrations. MR-FCN pairs for negative reactions can cluster if
there is a systematic noise source, such as bleedover, in which
case the MR-FCN pairs may falsely appear to be positive reaction
signals. In characterizing the MR-FCN response of true positives
versus true negatives, one can identify a clear region of
separation between these two sets of data, which is represented by
the broken line or curve in FIG. 11, the criteria curve. In this
figure, each circle represents a FCN-MR data pair. In this case,
each of the clusters of circles represents multiple responses at
known concentrations of the target. There are eight different
replicates at six known concentrations within this example. From
the right of the plot, for example, these known concentrations can
represent concentrations of 50 copies/ml, 5.times.10.sup.2
copies/ml, 5.times.10.sup.3 copies/ml, 4.times.10.sup.4 copies/ml,
5.times.10.sup.5 copies/ml, and 5.times.10.sup.6 copies/ml. These
criteria data clusters can be used to generate a criteria
curve.
[0125] Multiple, relatively simple criteria data sets can be used
to provide characteristic criteria curves for a number of assays.
One useful approach involves taking the mean of the MR values for
the set of negative responses and adding to this value a multiple
of the standard deviation of the MR values for the negative
responses. For the example shown in FIG. 11, the criteria curve was
set to be a horizontal line equal to the mean plus 10 standard
deviations of the MR values for the negative responses. The
criteria value in this example was calculated to be about 0.026. In
some systems, other considerations can make modification of the
criteria value (e.g., an FCN-MR value) desirable to account for
potential signal anomalies, such as, for example, crosstalk or
positive bleedover. Crosstalk can result from signal in a positive
well of a multi-well instrument and influence the signal from a
different well. As much as 2% crosstalk has been observed in
certain instruments. For this reason, the criteria may be increased
so as to avoid classifying true negative samples as positive
samples. For the assay data represented in FIG. 11, the highest MR
values for positive assays are about 0.50. Two percent of this
value is 0.010. Increasing the criteria by 0.010 should eliminate
false positives due to crosstalk. Because the highest MR values in
this assay only occur with samples of higher concentration that
have smaller FCN values, the criteria may be increased only at
smaller FCN values, where crosstalk is likely to occur. This
modified criteria set can be described by a series of data pairs
(X.sub.n, Y.sub.n), which describe a multi-element curve. For
example, the modified criteria curve shown in FIG. 11 can be
specified by the criteria data set:
[0126] (X.sub.1,Y.sub.1)=(1,0.036)
[0127] (X.sub.2,Y.sub.2)=(20,0.036)
[0128] (X.sub.3,Y.sub.3)=(25,0.026)
[0129] (X.sub.4,Y.sub.4)=(45,0.026)
As a further example, the criteria curve shown in FIG. 10 can be
specified by the criteria data set:
[0130] (X.sub.1,Y.sub.1)=(1,0.10)
[0131] (X.sub.2,Y.sub.2)=(10,0.10)
[0132] (X.sub.3,Y.sub.3)=(20,0.05)
[0133] (X.sub.4,Y.sub.4)=(40,0.05)
[0134] Criteria curves and/or criteria data sets, including sets
having different shapes or more complex shapes or both, can be
determined without undue experimentation. The intended use of the
PCR application will call for different approaches to establishing
criteria lines. The skilled artisan will readily appreciate that
when high sensitivity is desired in an assay, a low criteria line
is used. For example, if an assay is designed for differentiating
sequence variants, such as population consensus sequence (i.e., a
"wild type" sequence) versus polymorphic or variant sequences
(e.g., a "single nucleotide polymorphism"), then a criteria line of
higher value can be used, because the detection of limiting
quantities of target nucleic acid is not usually required in the
determination of sequence variants.
[0135] The particular example shown in FIG. 11 does not exhibit
positive bleedover from the internal control (IC) signal response
to the assay signal response. If positive IC signal response to
assay bleedover were to be present, a similar modification to the
criteria could be made. Because the IC signal response should only
occur over a narrow range of FCN values, the criteria could be
increased only in that limited range.
[0136] Generally, as further discussed herein, a FCN-MR response is
determined for samples of known concentration across the target
concentration range of interest to define the "normal" response.
Additional studies in a population of samples that challenge the
assay reaction may be run to see how much deterioration in MR is
acceptable before the assay performance is compromised. These types
of characterization analyses can be used to establish criteria data
or sets of criteria data independently of the standard deviation or
other characteristics of the noise or baseline observed when
samples that do not contain target nucleic acid are treated under
amplification conditions.
[0137] According to other embodiments of the disclosure, criteria
data also can be determined in ways similar to determining a
C.sub.t, for C.sub.t analysis as has been done in the prior art. A
particular assay under design can be performed a number of times to
characterize it's typical MR-FCN response. From this typical
response, the criteria data set can be defined. However, unlike
C.sub.t analysis, in FCN-MR, the response is independent of
intensity of signal and is easily reproducible, even across
instruments of a particular type that produce highly variable
results with identical samples.
Example 11
Alternative Region of Interest
[0138] It has been empirically found that the FCN value of an
efficiency related value as determined above can be advantageously
adjusted to provide an even more reproducible quantification value.
For example, FIG. 12 is a plot of two sets of reaction data that
illustrate how reaction curves for samples having the same initial
concentration can vary due to different reaction anomalies. This
figure illustrates two responses for samples containing equal
quantities of an HIV target nucleic acid. However, in one response,
the signal obtained from the reaction falls off early due to an
anomaly in the reaction. This fall off can cause a FCN value
determined from the maximum of the shifted ratio curve to vary
substantially between the two samples, as illustrated in FIG. 13.
However, the figure also shows that the two gradient curves are
more substantially similar at early time or cycle number, which is
plotted on the x-axis of the graph.
[0139] Thus, the disclosure involves determining an offset from the
cycle number of maximum efficiency value (herein referred to as an
FCN2 value), which is the location of another point on a reaction
curve that can be used for analysis as described herein. In further
embodiments, an Efficiency Related Value Threshold (ERVT) or Ratio
Threshold (RT) value can be selected and used to determine a cycle
number region of interest. An ERVT or RT can be an automatically or
empirically determined value for a particular assay. The RT value
can be set near to or at a criteria data level that is determined
at the latter cycles during assay calibration.
[0140] One embodiment of a method of this disclosure starts at the
FCN value on the shifted ratio curve and determines an earlier
reaction point where the curve crosses the RT value. This reaction
point is reported as an FCN2 value. It is believed that the FCN2
value provides improved linearity in samples having low copy
numbers, in contrast with FCN values for certain assays, such as
reactions where non-specific product formation reduces the
efficiency of product formation in samples having low copy
numbers.
[0141] FIG. 13 illustrates the desirability of using an offset
efficiency value. This figure shows the shifted ratio curves for
the responses shown in FIG. 12 and an RT line at 0.03. For this
example, the FCN and FCN2 values are shown in Table 1.
TABLE-US-00003 TABLE 1 Response FCN FCN2 MR Well 41 28.81 22.85
0.129 Well 42 28.06 22.92 0.097 Difference 0.75 0.07 0.032
[0142] In this example, the curve of one response flattens out
early and differs in shape from the curve of the other response,
and the shifted ratio curve shows a difference. The early
flattening can cause the earlier peak. In this example, the FCN2
values are more closely matched than the FCN values. In general,
FCN and FCN2 values have been found to be more precise (lower
standard deviations) than C.sub.t values. While these examples
focus on use of the MR, it will be appreciated that other measures
of the efficiency of the amplification reaction can be employed in
the FCN and FCN2 embodiments of the present disclosure. Other
efficiency related transforms useful in the context of the present
disclosure include, but are not limited to, (a) use of first
derivative, (b) use of the differences between sequential periodic
data points, and (c) use of the slope or gradient of the log of the
growth curve.
Example 12
Quantification Using MR-FCN Analysis
[0143] Quantification is often desired in various types of reaction
analysis. In PCR reactions, for example, quantification generally
refers to an analysis of a reaction to estimate a starting amount
or concentration of a target having an unknown concentration. The
disclosure involves methods or systems or both for using an
efficiency related value and a cycle number value (e.g., FCN) to
perform a quantification. Specifically, the ERV of a test sample is
compared to one or more of the ERV of at least one calibrator,
preferably at least two calibrators, and, optionally, 3, 4, 5, or 6
calibrators, each of which contains a known quantity of a target
nucleic acid.
[0144] In further embodiments, quantification can generally be
understood as involving one or more calibration data captures and
one or more quantification data captures. The calibration data and
quantification are related using a quantification relationship or
equation.
[0145] In calibration, a relationship between captured data, or a
value derived from captured data (such as an FCN, FCN2, or MR, or
combination of the foregoing), and one or more known starting
concentration reactions is used to establish one or more parameters
for a quantification equation. These parameters can then be used to
determine the starting concentrations of one or more unknown
reactions.
[0146] Various methods and techniques are known in the art for
performing quantification and/or calibration in reaction analysis.
For example, in diagnostic PCR settings, it is not uncommon to
analyze test samples in a 96-well reaction plate. In each 96-well
reaction plate, some wells are dedicated to calibration reactions
with samples having known initial concentrations of target. The
calibration values determined for these samples can then be used to
quantify the samples of unknown concentration in the well.
[0147] Two general types of calibration methods are referred to as
one-point calibration and standard curve (e.g., multiple points)
calibration. Examples of these types are set forth below. Any
suitable calibration method, however, can be used in the context of
the present disclosure.
[0148] When there is no inhibition or interference, the PCR
reaction proceeds with the target sequence showing exponential
growth, so that after N cycles of replication, the initial target
concentration has been amplified according to the relationship:
Conc.sub.N.varies.Conc.sub.0(1+e).sup.N
which can also be expressed as:
Conc 0 .varies. Conc N .times. 1 ( 1 + e ) N ##EQU00003##
where Conc.sub.N represents the concentration of amplified target
after N reaction cycles, Conc.sub.0 represents the initial target
concentration before amplification, N represents the cycle number
and e represents the efficiency of the target amplification.
Quantitative data analysis is used to analyze real time PCR
reaction curves so as to determine Conc.sub.0 to an acceptable
degree of accuracy. Previous C.sub.t analysis methods attempt to
determine a cycle number at a reaction point where the Conc.sub.N
is the same for all reactions under analysis. The FCN value
determined by the methods of the disclosure provides a good
estimate for the cycle number N for an assay in which no
significant inhibition or signal degradation over the dynamic range
of input target concentrations is demonstrated. The following
proportionality relationship between a starting concentration and
FCN can be used:
Conc.sub.0(FCN).varies.1/(1+e).sup.FCN
where Conc.sub.0 (FCN) represents the estimate of the initial
target concentration determined by using the FCN value as
determined by the methods of this disclosure. In other words, the
lower the starting concentration of target, the higher the FCN
value determined for the PCR reaction. This relationship can be
used for both calibration data and for quantification data.
[0149] This proportionality relationship can also be expressed as
an equivalence, such as
Conc.sub.0(FCN)=K.times.1/(1+e).sup.FCN
where K represents a calibration proportionality constant. For
calibration data, Conc.sub.0 (FCN) represents a known
concentration, such as 500,000 copies of target nucleic acid/mL;
the exponent FCN is a FCN cycle number determined as described
above; and e represents the efficiency value for a reaction, with
e=1 indicating a doubling each cycle. These factors combine to form
a relationship to allow for determination of the proportionality
constant. Determination of the proportionality constant can only be
made if there is a priori knowledge of the efficiency, e, of the
amplification reaction. This a priori knowledge enables a one-point
calibration. For quantification data, FCN values are determined for
reactions involving samples having unknown concentrations of
target. The FCN values are then converted to concentration values
by use of the above equation. If the efficiency, e, is not known a
priori, then a standard curve quantification method can be used. In
this case, for calibration data, different samples having different
levels of known concentration are amplified, and the FCN values of
the samples are determined. These FCN values can be plotted against
the log (base 10) of the known concentrations to describe a log
(concentration) vs. FCN response. For an assay that demonstrates no
significant inhibition or signal degradation over the dynamic range
of input target concentrations, this response is typically
well-fitted by a linear curve. The following equation describes the
form of this standard curve:
Log.sub.10(Conc.sub.0(FCN))=m.times.FCN+b
where Log.sub.10(Conc.sub.0(FCN)) represents the log (base 10) of
the initial target concentration, m represents the slope of the
linear standard curve, and b represents the intercept of the linear
standard curve. By using two or more known concentration
calibration samples, a linear regression can be applied to
determine the slope, m, and intercept, b, of the standard curve.
For quantification data, FCN values are determined for reactions
involving test samples of unknown concentration, which values are
then converted to log (concentration) values by use of the above
linear equation. Results can be reported in either log
(concentration) or concentration units by the appropriate
conversion.
[0150] It should be noted that the one-point calibration equation
is easily converted to this linear standard curve form:
Conc.sub.0(FCN)=K.times.1/(1+e).sup.FCN
Log.sub.10(Conc.sub.0(FCN))=-log.sub.10(1+e).times.FCN+log.sub.10(K).
The linear coefficient m can be used to calculate the efficiency of
the particular PCR reaction.
Example 13
Quantification Adjustments
[0151] When PCR reactions are subjected to inhibition, the
resulting real-time PCR signal intensity can be depressed or
delayed. The effect of this signal degradation on an efficiency
related value such as MR is a reduction in that value. In addition,
the effect of signal degradation on the fractional cycle number is
generally to identify the FCN at an earlier cycle number than would
be expected for the uninhibited reaction. These factors cause the
plot of log (concentration) as a function of FCN to be less well
described by a linear curve fitting function. Although higher order
curve fitting functions can be applied for a standard curve, a
linear fit requires fewer calibration levels and is simpler to
calculate.
[0152] Some of these problems can be addressed in a standard curve
analysis by incorporating an ERV or Intensity value into the
quantification relationships as discussed above. Thus, the
equations above can be rewritten a:
Conc.sub.0(FCN.sub.Intensity
Adj).varies.Intensity/(1+e).sup.FCN
Conc.sub.0(FCN.sub.MR Adj).varies.MR/(1+e).sup.FCN
where Intensity represents the response intensity (above
background) at the determined FCN value, MR represents the MR value
as described previously. Conc.sub.0 (FCN.sub.Intensity Adj)
represents the estimate of the initial concentration of the target
determined by using the FCN value adjusted by using the Intensity
value and Conc.sub.0 (FCN.sub.MR Adj) represents the estimate of
the initial concentration of the target determined by using the FCN
value adjusted by using the MR value.
[0153] These expressions take advantage of the relationship
observed between the intensity at the selected FCN cycle or the MR
determined at the selected FCN cycle, or both, and the change to
the FCN value in the presence of inhibition, as discussed above.
The net effect is that the right hand side of the proportionality
expressions above is relatively insensitive to inhibition and other
factors that affect the PCR amplification curve, and, therefore,
provide significant robustness as expressions for determining the
concentration values of the target.
[0154] The following discussion further explains the properties and
relationships of FCN, FCN.sub.Intensity Adj, and FCN.sub.MR Adj.
Assuming the efficiency is 1, the previous can be simplified
to:
Conc.sub.0(FCN).varies.1/2.sup.FCN
Conc.sub.0(FCN.sub.Intensity Adj).varies.Intensity/2.sup.FCN
Conc.sub.0(FCN.sub.MR Adj).varies.MR/2.sup.FCN
Taking the Log base two of the expressions yields:
Log.sub.2(Conc.sub.0(FCN)).varies.FCN
Log.sub.2(Conc.sub.0(FCN.sub.Intensity
Adj)).varies.FCN-Log.sub.2(Intensity)
Log.sub.2(Conc.sub.0(FCN.sub.MR Adj)).varies.FCN-Log.sub.2(MR)
From the right sides of the expressions come the values for
compensating for intensity or MR to adjust the FCN value by means
of the following formulas:
FCN.sub.Int. Adj.=FCN-Log.sub.2(Intensity)
FCN.sub.MR. Adj.=FCN-Log.sub.2(MR).
This calculation then provides quantification by using adjusted FCN
values analogous to using FCN values or C.sub.t values. It should
be noted that the use of these adjusted FCN values provide
significant robustness to inhibition and other factors that affect
PCR amplification, such as C.sub.t values used in determining the
concentrations of the target in the unknown samples. The plot of
Log (concentration) vs. these adjusted FCN values is generally well
fitted by a linear standard curve. Thus, the present disclosure
provides a method for determining the quantity of a target nucleic
acid in a sample comprising involving the steps of (a) finding the
period of time or cycle number of an amplification reaction
corresponding to a maximum of an efficiency related value,
preferably of an MR, and (b) adjusting that value by subtracting a
logarithm of the Intensity or a logarithm of the MR, and (c)
comparing the value obtained to calibration data obtained using the
same methodology.
Example 14
Standard Curve Calibration
[0155] Development of a standard curve from known concentrations
and use thereof for quantification is well known in the art and can
be further understood from the following example. In a typical
case, a number of calibration reactions (such as in wells in which
the initial concentrations are known) are used during each
amplification or series of amplifications to perform the
calibration operation. One problem that arises with attempting to
quantify a target nucleic acid in a sample through a large range of
possible initial concentrations is that quantification of lower
quantities of target nucleic acid in any particular reaction
becomes more difficult. For example, FIG. 14 illustrates data for
an assay designed to quantify the amount of HIV in test samples.
The reactions were performed with eight replicates of six known
concentrations of target nucleic acid, which were 50; 500; 5,000;
50,000; 500,000; and 5,000,000 copies per mL. The assay data show
significant signal suppression in reactions where the copy number
is low (the curves farthest to the right). While quantity of the
four highest concentrations of target nucleic acid (the curve sets
to the left) yielded precise results with low coefficients of
variability, the two lowest concentrations produced less precise
curves. The imprecision caused by the difficulties in quantifying
low concentrations of target nucleic acids in assays having a
dynamic range of 100,000 to 1 or more can be addressed by the
following methods of this disclosure.
[0156] Because calibration runs in a reaction plate are relatively
expensive, it is conventional to collect a minimal acceptable
number of calibration data sets. For example, in one
implementation, the average of two replicates each of the 500;
50,000; and 5,000,000 copy/mL samples are run along with the
diagnostic assays, thereby requiring perhaps six wells in a 96 well
plate to be used for calibration reactions.
[0157] Because the relationship between the cycle numbers and the
log of the calibrator concentration is substantially linear, a
linear regression can be performed between a log (e.g., log.sub.10)
of the calibrator concentrations and the cycle number. This
regression can easily be performed via the Excel program and other
mathematical analysis software. FIG. 15 illustrates four linear
standard curves generated from three-point calibration data using
four different cycle number related values (e.g., FCN, FCN2,
FCN.sub.MR Adj., and FCN.sub.Int. Adj.).
[0158] In each of the curve fit equations, the x-axis displays
values of the Log.sub.10 [Target] actual or known concentration.
Thus, solving for x provides an expression for converting from
cycle number related values to Log.sub.10(Target) calculated
concentration of the assay. If the assay response is not linear
with Log(Target), a higher order or more complex regression, or a
larger number of calibration reactions, or both, can be used. In
this example, the following equations were determined:
FCN=-3.0713*Log.sub.10(Conc.sub.0)+31.295
FCN2=-3.0637*Log.sub.10(Conc.sub.0)+25.006
FCN.sub.MR adj=-3.2344*Log.sub.10(Conc.sub.0)+33.271
FCN.sub.Int. adj=-3.2870*Log.sub.10(Conc.sub.0)+32.775
Example 15
Comparing Quantification Using Different Cycle Number Related
Values
[0159] In order to examine the different characteristics of
calibrations using the different cycle number related values
described above, quantification can be performed on various samples
having known concentrations, and the concentrations calculated
compared with the known concentrations. In one example of such a
comparison, the standard curves having the parameters generated
above were used to carry out quantification of the assay responses
shown in FIG. 14. The mean of the calculated concentrations of the
eight replicates at each known concentration was compared to the
known concentration value. FIGS. 16A and 16B compare log.sub.10 of
the known concentration values (x-axis) to the means of the
log.sub.10 of each of the calculated concentrations for the eight
samples at each concentration.
[0160] As indicated by FIGS. 16A and 16B, the 50 copies/mL samples
(log (concentration)=1.7) are slightly over-quantified (i.e.,
higher than the actual concentration) using FCN, while the accuracy
for the FCN method (of the MR) at the higher concentrations is very
good. FCN2 is more accurate at the lowest concentration, but
somewhat under-quantified (i.e., lower than the actual
concentration), and exhibit less linearity and accuracy at some
higher concentrations. FCN.sub.MR Adj., showed very accurate and
linear quantification throughout the concentration range.
FCN.sub.Int. Adj. also showed substantial improvement in accuracy
and linearity compared to FCN, except for very slight
under-quantification at the lowest concentration. Accordingly, all
four methods work well, but some are better than others for
particular situations. Therefore, the skilled artisan can easily
select an appropriate method for any particular application to
obtain excellent results.
Example 16
Quantification Using One-Point Calibration
[0161] A one-point calibration can be used for quantification. In
this case, two wells at the 50,000 copies/mL concentration
(Log(4.7)) were used for calibration. In order to calculate the
calibration constant, the following equation is used:
K=Conc.sub.0*2.sup.FCN, where K represents the calibration
constant, Conc.sub.0 represents the known concentration of the
calibrator, FCN represents the fractional cycle number of the
calibrator, and the efficiency of the reaction, e, as described
earlier, is assumed to be 1. Similar calibration constants can be
generated using the proportionality relationships such as FCN2,
FCN.sub.MR Adj. and FCN.sub.Int. Adj.
[0162] In this case, the constant was generated for two wells and
the average was used. Once the calibration constant is generated,
the concentration for each assay is calculated with the following
equation: Conc=K.sub.FCN/2.sup.FCN. FIGS. 17A and 17B illustrate
results from a one-point calibration.
[0163] As can be seen, the FCN results are elevated at the lowest
two concentrations and accurate from log(Conc) equals 3.7 and
above. FCN2 shows improved accuracy at low concentrations compared
to FCN, but under-quantifies at log(Target) equal to 5.7 and 6.7.
FCN-MR adjusted shows good linearity over the entire range with
slight over-quantification at the two lowest concentrations.
FCN-Intensity adjusted also shows good linearity with very slight
under-quantification at the lowest two concentrations. Accordingly,
each of these embodiments works well and the skilled artisan can
readily select from among these options.
[0164] As discussed above, an FCN-MR analysis can be used to
characterize a particular reaction as positive or negative or to
compare the reaction to criteria data, or both. These values can be
used to quantify a reaction. A variety of quantification methods
can benefit from FCN-MR analysis rather than C.sub.t analysis.
[0165] In one embodiment, a FCN value, a FCN2 value, or a FCN
adjusted value can be used in any way that a C.sub.t value has been
used in the prior art. Typically, but not necessarily,
FCN-adjusted, FCN2-adjusted, or FCN-adjusted analysis can be
applied to various sets of calibration data to thereby develop
reference data curves or an equation for comparing the result of a
reaction in which the concentration of target is unknown to the
results of reactions in which the concentration of target is known.
Thus, the present disclosure can be used to develop reference data
and to perform a comparison wherein two values (e.g., FCN-MR) are
used both for developing reference data and also for making a
comparison to that data.
[0166] While experiments using the MR method regularly used
different preprocessing steps on the captured data set before
processing the data set with a ratio function, most of these steps
are not required. In particular, experimental results have
indicated that scaling, normalization by a reference dye,
baselining (both offset and slope correction), and filtering are
not required. However, filtering has generally been found to be
desirable as it improves performance in the presence of noise.
Slope correction (for the baseline region) has also been found to
be desirable as it slightly improves discrimination between samples
that do not contain target nucleic acid and those that contain very
little target nucleic acid or suffer from significant inhibition of
the amplification reaction. Generally, however, when
FCN.sub.Intensity adj is used, it is preferable to use a
normalization technique, such as, but not limited to, scaling or
normalization to a reference dye.
Example 17
MR Algorithm Applied to HBV Data Using a One-Point Calibration
[0167] HBV assays of control solutions ranging from 10
copies/reaction to 10.sup.9 copies/reaction and negatives were
processed on an ABI Prism 7000 with six replicates at each
concentration. The captured data was processed using only a digital
filter. FCN values were then calculated using a MR algorithm as
described above. The concentrations were calculated by means of a
one-point calibration using the three of the responses at 10.sup.9
copies/reaction as a reference calibrator.
[0168] Even without normalization, scaling, or baselining, the
resulting quantification was very good, with the exception of an
acceptable amount of over-quantification of the 10 copies/reaction
and 100 copies/reaction samples (i.e., the Log(Target)=1 and 2
samples). There was a very clear distinction between the negatives
and the 10 copies/reaction assays, with no false positives or false
negatives. Additional results indicated that when the same data was
quantified with C.sub.t analysis, the 10 copies/reaction and 100
copies/reaction assays are also slightly over-quantified, and the
precision at all concentrations above 10 copies/reaction is better
with the MR analysis. In this case, the C.sub.t results were
normalized, baselined, and calibrated by means of a two-point
calibration with three replicates each at concentrations 10.sup.3
and 10.sup.7 copies/reaction.
[0169] FIG. 19 illustrates an example of the same HBV data using MR
analysis and with FCN.sub.MR adj., correction. Again, the
quantification was performed by means of a one-point calibration
with three responses at the 10.sup.9 copies/reaction with no
normalization, scaling, or baselining. As can be seen, the
over-quantification of the low concentrations is significantly
reduced, i.e., the quantitative results are significantly
improved.
Example 18
MR Algorithm Applied to HIV Data
[0170] In this example, HIV assays of control solution were
performed at concentrations of negatives, 50 copies/mL, and 100
copies/mL, through 10.sup.6 copies/mL in replicates of six. The
responses were processed by means of the MR algorithm using
FCN.sub.MR Adj. with normalizing and baselining. FIG. 21
illustrates results the example using MR analysis and two-point
calibration, e.g., using two replicates of the 10.sup.2 and
10.sup.5 copies/mL responses as calibrators. There was clear
differentiation between the negatives and the 50 copies/mL assays
with no false positives or false negatives. As can be seen, there
is good linearity and precision.
Example 19
Validity Determination Using Target and IC (FCN, MR) Pairs
[0171] It has been found that pairs of reaction time or cycle
number values and efficiency related values (e.g., pairs of FCN-MR
values) can provide valuable information about a nucleic acid
amplification reaction, e.g., a PCR reaction, which can be further
enhanced by considering data pairs for both the internal control
and target amplification reactions. While pairs for a target
reaction alone carry important information about reaction
efficiency and can be used for comparison with criteria data,
additional factors that arise in processing samples or in the
samples themselves may be better analyzed by considering control
data as well.
[0172] For example, in processing specimens for use in PCR or other
suitable amplification reactions, the sample can carry various
inhibitors into the reaction, which might be detectable through
assessment of target data only. However, abnormal recovery of
target nucleic acid during sample preparation typically would not
be detected by analysis of a single amplification reaction.
Furthermore, a target nucleic acid may possess polymorphic
sequences that could impair detection of the target nucleic acid,
e.g., if a probe is used that binds to a polymorphic region of the
sequence. Mismatches caused by the polymorphic sequence in this
region would affect the detected signal, and, consequently, the
amplification might not appear as abnormal or inhibited using the
evaluation of data pairs for a single amplification. Co-analysis of
an internal control together with analysis of the target
amplification responses can provide accurate quantification of the
target nucleic acid in such samples when other methods would
typically indicate an invalid reaction.
[0173] Thus, pairs of reaction time or cycle number values and
efficiency related values can be used together to assess the
validity of a given reaction, such as in a given container or well.
One could design the internal control (IC) amplification reaction
to be comparable in robustness to the target amplification
reaction, or slightly less robust. Robustness in this context means
the sensitivity of the reaction performance to factors that can
affect the PCR processing pathway, such as inhibition that results
from sample preparation or the samples themselves, or to
variability in transferring of the reaction mixture by pipette,
such as transferring inaccurate amounts of amplification reagents
by pipette.
Example 20
Multiple Criteria Data Curves
[0174] Multiple criteria curves for the pairs of cycle number
value-efficiency related value (e.g., FCN-MR pairs) can be
developed and can have different uses or levels of importance,
particular for use with validity determination. For example, a
first criteria curve can be selected so as to be able to
discriminate reactive amplification signals from non-reactive
responses. A second criteria curve can be selected so as to be more
constraining than the first type, so that it would be useful in
identifying sample responses that lead to accurate quantification
in contrast to those having partial inhibition that might have
lower confidence in quantification. FIG. 22 is a plot illustrating
two types of criteria data, wherein the lower horizontal line
represents criteria data suitable for differentiating negative from
reactive reactions. The second set of lines represents criteria
data indicating the normal range for the FCN-MR pair responses.
These criteria can be used to distinguish high confidence in
quantification in contrast to a lower confidence that might be
associated with a value outside this range due to partial reaction
inhibition.
[0175] For example, the first type of criteria data that
differentiates reactive and non-reactive amplification reaction can
be referred to as "MR criteria data." These data act as a cutoff
threshold-reactive responses will have MR values that exceed the MR
criteria data, whereas negative samples will have MR values that
will not exceed the criteria value or criterion line. The criteria
data is preferably set so that noise in the response signal does
not exceed the criteria, nor will such biases as cross-talk or
bleedover.
[0176] The second type of criteria data is referred to as the MR
normal range. This range would be the range of MR values for a
given FCN over which quantification of the sample is accurate. If a
signal response is suppressed, the MR value observed will drop. As
the MR value decreases due to inhibition, the FCN value can shift
to earlier cycles, whereas a threshold based C.sub.t might shift to
later cycles. The MR normal range would be the range for MR values
in a criteria data set for which a chosen value related to a cycle
number would provide an accurate quantitative result for the sample
when used to determine the concentration of target in the sample
from the assay standard curve.
[0177] The "MR normal range" can be developed using a Bivariate Fit
of the MR by FCN as will be understood in the art. FIG. 23, for
example, shows a FCN-MR plot for HIV data from 50 copies/mL to
5,000,000 copies/mL. The data was analyzed by means of a statistics
software package (such as JMP (SAS Institute, Inc.)) to apply a
cubic curve fit to the data. This cubic curve fit is represented by
the solid line in middle of the figure. The upper and lower dashed
curves represent the confidence interval generated using a
confidence interval individual analysis option with an alpha level
of 0.001. TABLES 2A, 2B, and 2C illustrate sample data input and
output related to FIG. 23.
TABLE-US-00004 TABLE 2A Summary of Fit RSquare 0.971668 RSquare Adj
0.969737 Root Mean Square Error 0.023918 Mean of Response 0.401317
Observations (or Sum Wgts) 48 Polynomial Fit Degree = 3 MR =
0.6710196-0.0101107 FCN-0.0039387 (FCN-18.3056){circumflex over (
)}2-0.0004202 (FCN-18.3056){circumflex over ( )}3
TABLE-US-00005 TABLE 2B Analysis of Variance Sum of Source DF
Squares Mean Square F Ratio Model 3 0.86331003 0.287770 503.0120
Error 44 0.02517212 0.000572 Prob > F C. Total 47 0.88848215
<.0001
TABLE-US-00006 TABLE 2C Parameter Estimates Term Estimate Std Error
t Ratio Prob > |t| Intercept 0.6710196 0.036617 18.33 <.0001
FCN -0.010111 0.002006 -5.04 <.0001 (FCN-18.3056){circumflex
over ( )}2 -0.003939 0.000198 -19.92 <.0001
(FCN-18.3056){circumflex over ( )}3 -0.00042 0.000047 -8.93
<.0001
[0178] A statistically derived confidence interval, as shown, is a
systematic approach to determining which data points represent
"normal" responses and should therefore be quantified. Data points
lying outside this interval are exceptional and are preferably
identified to a human operator by a software program so that
further investigation can be made.
[0179] In alternative embodiments, such a curve can be simplified
in the form of one or more straight-line segments. This
simplification can in some cases be performed by a technician
viewing the raw data or may be derived from an alpha interval as
discussed above.
[0180] A similar statistical fit can be performed on the internal
control (IC) data. FIG. 24, for example, shows a plot of MR as a
function of FCN for IC data, namely IC data associated with the
data shown in FIG. 23. This data can be used to determine an IC
criteria, which, for example, can be a single value that is five
standard deviations below the mean of the MR values of the IC or
can be a range or box of values, for example, based on the
mean.+-.5 standard deviations of the MR and FCN values.
[0181] Thus, the present disclosure also provides a method for
analyzing an amplification reaction, the method comprising
establishing a "confidence corridor", which is a range of selected
values provided in pairs in which the first value is a maximum
efficiency related value (which is preferably the MR), and the
second value is a time value or cycle number value at a reaction
point (which optionally can be fractional). The method further
comprises determining whether a maximum efficiency value occurring
at any particular periodic time value or cycle number value at a
reaction point (which optionally can be fractional) falls within
the selected range. If the value does not, then further
investigation, or disregarding the results, is indicated. Any
suitable method can be used to establish the selected confidence
corridor. Preferred methods include setting the confidence corridor
about 1, 2, 3, 5, 10, or any other suitable number of standard
deviations from the mean of data obtained from a set of reactions
used to characterize the assay. Another suitable method involves
modifying the confidence corridor by observing known aberrant or
discrepant results and modifying the confidence corridor to exclude
a portion of those aberrant or discrepant results in future assays.
The use of the confidence corridor of the present disclosure can be
applied to target nucleic acid quantification, analysis of any of
standards, calibrators, controls, or to combinations of the
foregoing.
Example 21
Validity Analysis
[0182] FIGS. 25A, 25B, and 25C are flow charts illustrating a logic
analysis tree for assessment of assay validity through analysis of
pairs of cycle number (e.g., FCN) minus of ERV (e.g., MR) for both
the internal control and the target amplification reactions. FIG.
26 is a flow chart illustrating a logic analysis tree for reporting
target results with validity criteria assessment using pairs of
cycle number (e.g., FCN) minus ERV (e.g., MR). In the flow charts,
FCN is used for clarity of illustration, but as noted elsewhere
herein, other methods can be used to generate the reaction point
value, for example, C.sub.t method, FCN2, FCN.sub.MR Adj. or
FCN.sub.Int. Adj, or other suitable method.
[0183] Thus, a validity check can optionally proceed as a series of
questions regarding the internal control (IC) and/or target
data.
[0184] In FIGS. 25A, 25B, and 25C, the left-most arrow blocks
provide general descriptions of the steps of the method. Details of
method(s) can be understood further by considering the following.
The method analyzes a cycle number/ERV pair from both a target and
control (IC) reaction. Initially, if (1) the IC MR is above the IC
MR criteria data, and then if (2) the IC FCN is within the normal
range, and further if (3) the IC MR is within the normal range,
then reaction validity is confirmed.
[0185] As shown in the figure, an invalid result can be further
characterized or explained by considering one or more
characteristics of the target MR.
[0186] FIG. 26 illustrates a method for analyzing the target data
for valid reactions to further characterize a valid result as
indicating (1) a non-reactive target sample, (2) a target at a
concentration of less than the detecting limit of the assay, (3) a
target present but with a quantification inhibited, possibly due to
sub-type mismatch, or (4) a valid, quantifiable target
reaction.
[0187] Thus, by combining the analysis based on multiple targets
and using both cycle number and efficiency related values, one can
distinguish an inhibited sample from a sample that suffered from
poor nucleic acid recovery during sample preparation. The analysis
makes use of pre-established knowledge of the assay that is
contained in the internal control and target criteria data.
Example 22
Validity Determination Using Peak Width
[0188] In contrast to the conventional C.sub.t analyses in the
prior art, which only presents a single value describing an
amplification response, an efficiency related value analysis (and
preferably an MR analysis) can provide an efficiency related
transform curve with data corresponding to the time value or cycle
number value of the entire amplification reaction or any portion
thereof. It has been discovered that within a specific assay
formulation, normal assay responses generate highly reproducible
efficiency related transform curves. One characteristic in
particular is the width of the peak of the efficiency related
transform curve. It has been found that the width of the peak of
the efficiency related transform, e.g., as defined by its width at
the half maximum height, varies very little even when the magnitude
of the fluorescence intensity varies greatly.
[0189] Any suitable method can be used to determine the width of
the peak of the efficiency related value. FIG. 27 depicts one
suitable method for determining the width of an efficiency related
value peak. In FIG. 27, the full peak width is the width in cycles
of the peak at it half maximum level. The HIV responses in FIG. 14
show normalized fluorescence for samples of higher concentration at
approximately 8, while the normalized fluorescence for the samples
of low concentration is as low as about 1. Using the shifted ratio
method to calculate an efficiency related transform for each
amplification reaction and computing the full peak width provides
the results shown in FIG. 28. Even with an eight-fold change in
final fluorescence intensity, the peak widths are surprisingly
conserved within a narrow range. Accordingly, the present
disclosure provides amplification reaction validity criteria,
wherein an amplification reaction is deemed valid when the width of
the peak of an efficiency related value is contained within a
selected range characteristic of the amplification reaction. In
FIG. 28, the dashed horizontal lines in bold type represent the
mean of the width measurements plus and minus 10 standard
deviations. Width measurements that are not within the range of
about 5.5 and 8.0 (as shown in FIG. 28) are considered invalid or
at least suspect. The skilled artisan can readily vary the
parameters describing the acceptance interval, depending on the
requirements of the particular assay and without undue
experimentation.
[0190] Peak width can be used to detect an abnormal assay response.
The full peak width calculation was applied to the assay data that
contained the abnormal response shown in FIG. 12. The results are
presented in FIG. 29. As can be seen, normal responses for this
data set produce full peak widths between about 6 and 9 cycles
whereas full peak width of well 42 is 17.42. Accordingly, the
amplification reaction of well 42 is abnormal and is
disregarded.
[0191] The full peak width calculation will be affected by abnormal
variations in amplification response that occur both before and
after the reaction point value (e.g., the FCN) of the efficiency
related value. Abnormal variations that occur after the reaction
point value of the efficiency related value are not considered for
an assay validity test, because they cannot affect assay
quantification by the MR method. This option can readily be
achieved using the half peak width calculation illustrated in FIG.
27 or its equivalent. In the illustrated example, only the width in
periodic time units from about the half-maximum efficiency related
transform up to about the reaction point value of the maximum
efficiency related value is used. Of course, other suitable methods
for measuring peak widths and half-peak widths are known in the
art.
Example 23
Software Embodiments
[0192] The systems of this disclosure can be incorporated into a
multiplicity of suitable computer products or information
instruments. Some details of a MR software implementation are
provided below. Specific user interface descriptions and
illustrations are taken to illustrate specific embodiments only and
any number of different user interface methods known in the
information processing art can be used in systems embodying this
disclosure. The disclosure can also be used in systems where
virtually all of the options described below are preset,
calculated, or provided by an information system, and,
consequently, provide little or no user interface options. In some
cases, details and/or options of a prototype system are described
for exemplification purposes; many of these options and/or details
may not be relevant or available for a production system.
[0193] Furthermore, software embodiments can include various
functionalities, such as, for example, processing reactions with
one or two target reactions, or one or more internal control
reactions, or reference data, or combinations of the foregoing. A
software system suitable for use in this disclosure can provide any
number of standard file handling functions such as open, close,
printing, saving, etc.
[0194] FIG. 30 illustrates a user interface for processing PCR data
according to this disclosure. In this interface, the selection of
appropriate dye(s) corresponding to the target assay, internal
control, and reference responses are selected from popup lists in
the upper left portion of the window. Tabs for selecting different
viewing options are positioned in the middle of the window and are
arranged horizontally. FIG. 30 shows that the tab displaying the
MR-FCN plot has been selected. FIG. 31 illustrates a user interface
showing the same data for well 1, but displaying the shifted ratio
curve. Other tabs allow viewing of the raw fluorescence data,
normalized fluorescence, and baselined data for all the responses.
In addition, a tab allows inspection of each response individually.
Fields to the right of the plot show calculated response values
such as MR, FCN, C.sub.t, and standard deviation in the baseline
region. Below these calculated values are radio buttons allowing
the user to display either the assay data or the internal control
data.
Embodiment in a Programmed Information Appliance
[0195] FIG. 32 is a block diagram showing an example of a logic
device in which various aspects of the present disclosure may be
embodied. As will be understood from the teachings provided herein,
the disclosure can be implemented in hardware or software or both.
In some embodiments, different aspects of the disclosure can be
implemented in either client-side logic or server-side logic.
Moreover, the disclosure or components thereof can be embodied in a
fixed media program component containing logic instructions or
data, or both, that when loaded into an appropriately configured
computing device can cause that device to perform according to the
disclosure. A fixed media component containing logic instructions
can be delivered to a viewer on a fixed medium for physically
loading into a viewer's computer or a fixed medium containing logic
instructions can reside on a remote server that a viewer can access
through a communication medium in order to download a program
component.
[0196] FIG. 32 shows an information instrument or digital device
700 that can be used as a logical apparatus for performing logical
operations regarding image display or analysis, or both, as
described herein. Such a device can be embodied as a
general-purpose computer system or workstation running logical
instructions to perform according to various embodiments of the
present disclosure. Such a device can also be customized and/or
specialized laboratory or scientific hardware that integrates logic
processing into a machine for performing various sample handling
operations. In general, the logic processing components of a device
according to the present disclosure are able to read instructions
from media 717 or network port 719, or both. The central processing
unit can optionally be connected to server 720 having fixed media
722. Apparatus 700 can thereafter use those instructions to direct
actions or perform analysis as described herein. One type of
logical apparatus that can embody the disclosure is a computer
system as illustrated in 700, containing CPU 707, optional input
devices 709 and 711, storage media 715, e.g., disk drives, and
optional monitor 705. Fixed media 717, or fixed media 722 over port
719, can be used to program such a system and can represent
disk-type optical or magnetic media, magnetic tape, solid state
dynamic or static memory, etc. The disclosure can also be embodied
in whole or in part as software recorded on this fixed media.
Communication port 719 can also be used to initially receive
instructions that are used to program such a system and represents
any type of communication connection.
[0197] FIG. 32 shows additional components that can be part of a
diagnostic system. These components include a viewer or detector
750 or microscope, sample handler 755, UV or other light source 760
and filters 765, and a CCD camera or capture device 780 for
capturing signal data. These additional components can be
components of a single system that includes logic analysis and/or
control. These devices may also be essentially stand-alone devices
that are in digital communication with an information instrument
such as 700 via a network, bus, wireless communication, etc., as
will be understood in the art. Components of such a system can have
any convenient physical configuration and/or appearance and can be
combined into a single integrated system. Thus, the individual
components shown in FIG. 42 represent just one example system.
[0198] The disclosure can also be embodied in whole or in part
within the circuitry of an application specific integrated circuit
(ASIC) or a programmable logic device (PLD). In such a case, the
disclosure can be embodied in a computer understandable descriptor
language, which may be used to create an ASIC, or PLD, that
operates as described herein.
Other Embodiments
[0199] The disclosure has now been described with reference to
specific embodiments. Other embodiments will be apparent to those
of skill in the art. In particular, a viewer digital information
appliance has generally been illustrated as a computer workstation
such as a personal computer. However, the digital computing device
is meant to be any information appliance suitable for performing
the logic methods of the disclosure, and could include such devices
as a digitally enabled laboratory systems or equipment, digitally
enabled television, cell phone, personal digital assistant, etc.
Modification within the spirit of the disclosure will be apparent
to those skilled in the art. In addition, various different actions
can be used to effect interactions with a system according to
specific embodiments of the present disclosure. For example, a
voice command may be spoken by an operator, a key may be depressed
by an operator, a button on a client-side scientific device may be
depressed by an operator, or selection using any pointing device
may be effected by the user.
[0200] It is understood that the examples and embodiments described
herein are for illustrative purposes and that various modifications
or changes in light thereof will be suggested by the teachings
herein to persons skilled in the art and are to be included within
the spirit and purview of this application and scope of the
claims.
[0201] All publications, patents, and patent applications cited
herein or filed with this application, including any references
filed as part of an Information Disclosure Statement, are
incorporated by reference in their entirety.
* * * * *
References