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WO2025006242A1 - Ajustement automatique de matrice de colorant spectral pour réduire la diaphonie spectrale dans des dosages qpcr multiplexés - Google Patents

Ajustement automatique de matrice de colorant spectral pour réduire la diaphonie spectrale dans des dosages qpcr multiplexés Download PDF

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Publication number
WO2025006242A1
WO2025006242A1 PCT/US2024/034295 US2024034295W WO2025006242A1 WO 2025006242 A1 WO2025006242 A1 WO 2025006242A1 US 2024034295 W US2024034295 W US 2024034295W WO 2025006242 A1 WO2025006242 A1 WO 2025006242A1
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Prior art keywords
dye
matrix
dye matrix
data
spectral
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Inventor
Wallace George
Jeffrey Marks
Conor COX
Xinyi YAN
Connor KLOPFER
Rene Vargas-Voracek
Thomas Wessel
Yong Chu
Sitong LIU
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Life Technologies Corp
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Life Technologies Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
    • G01J3/4406—Fluorescence spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/686—Polymerase chain reaction [PCR]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N2021/6417—Spectrofluorimetric devices
    • G01N2021/6421—Measuring at two or more wavelengths
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G01N2021/6439—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks
    • G01N2021/6441—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks with two or more labels
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N2021/6463—Optics
    • G01N2021/6471—Special filters, filter wheel
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6452—Individual samples arranged in a regular 2D-array, e.g. multiwell plates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods
    • G01N2201/1296—Using chemometrical methods using neural networks

Definitions

  • the present disclosure is directed to biological analysis such as, for example, a polymerase chain reaction (PCR), and biological analysis devices such as, for example, instruments for PCR, especially to reduce spectral crosstalk in multiplexed assays and computer systems and computer software relating to methods for reducing spectral crosstalk in multiplexed assays.
  • biological analysis such as, for example, a polymerase chain reaction (PCR)
  • biological analysis devices such as, for example, instruments for PCR, especially to reduce spectral crosstalk in multiplexed assays and computer systems and computer software relating to methods for reducing spectral crosstalk in multiplexed assays.
  • qPCR testing instruments with much higher throughput and reduced per-sample costs. This requires much greater qPCR dye multiplexing.
  • a method for reducing spectral crosstalk in a multiplexed assay includes receiving filter signal data from a multiplexed fluorescence assay and an initial dye matrix including calibrated spectral dye data. The method further includes generating an updated dye matrix based on the initial dye matrix Docket No.
  • TP386609WO1 and estimated crosstalk proxies and then adjusting the updated dye matrix to meet a calculated optimization value.
  • the calculated optimization value is calculated based on at least the estimated crosstalk proxies and the filter signal data.
  • the calculated optimization value may be further calculated based on each adjustment of the updated dye matrix, and a cross-correlation between dyes used in the assay.
  • the method further includes generating an improved dye matrix based on the adjusted updated dye matrix, and then generating spectral adjusted data based on the improved dye matrix.
  • the system includes a detector configured to receive filter signal data from a multiplexed fluorescence assay, and a memory configured to store an initial dye matrix including calibrated spectral dye data.
  • the system further includes a processor configured to generate an updated dye matrix based on the initial dye matrix and estimated crosstalk proxies and adjust the updated dye matrix to meet a calculated optimization value.
  • the calculated optimization value is calculated based on the estimated crosstalk proxies and the filter signal data.
  • the calculated optimization value may be further calculated based on each adjustment of the updated dye matrix, and a cross-correlation between dyes used in the assay.
  • the processor is further configured to generate an improved dye matrix based on the adjusted updated dye matrix, and then generate spectral adjusted data based on the improved dye matrix.
  • a computer-readable medium encoded with computer-readable instructions which when executed by a processor of a computer, causes the computer to carry out a method for reducing spectral crosstalk in a multiplexed assay.
  • the method includes receiving filter signal data from a multiplexed fluorescence assay and an initial dye matrix including calibrated spectral dye data.
  • the method further includes generating an updated dye matrix based on the initial dye matrix and estimated crosstalk proxies, and then adjusting the updated dye matrix to meet a calculated optimization value.
  • the calculated optimization value is calculated based on at least the estimated crosstalk proxies and the filter signal data.
  • FIG. 1 illustrates a flowchart showing a method of reducing spectral crosstalk in a multiplexed assay according to various embodiments described herein.
  • FIG. 2 is a block diagram that illustrates a PCR instrument upon which embodiments of the present teachings may be implemented. [0010] FIG.
  • FIG. 3 depicts an exemplary optical system that may be used for imaging according to embodiments described herein.
  • FIG. 4 illustrates an exemplary computing system for implementing various embodiments described herein.
  • FIG. 5 illustrates an exemplary distributed network system according to various embodiments described herein.
  • FIG. 6 illustrates an example of spectral sampling of a fluorescence signal.
  • FIG. 7 illustrates a normalized dye matrix according to various embodiments described herein.
  • FIG. 8 illustrates a block diagram of spectral crosstalk reducing workflow according to various embodiments described herein.
  • FIG. 9 illustrates a spectral crosstalk reducing workflow according to various embodiments described herein. [0017] FIG.
  • FIG. 10 illustrates a signal discontinuity workflow according to various embodiments described herein.
  • FIG. 11 illustrates an example of a signal discontinuity according to various embodiments described herein. Docket No. TP386609WO1
  • FIGS. 12A-12F illustrate an example of signal discontinuity detection according to various embodiments described herein.
  • FIG. 13 illustrates an adjusted signal based on signal discontinuities according to various embodiments described herein.
  • FIGS. 14A-14D illustrate an example of spectral crosstalk reduction according to various embodiments described herein.
  • FIGS. 15A-15D illustrates another example of spectral crosstalk reduction according to various embodiments described herein. [0023] FIG.
  • FIG. 16 illustrates an optimization workflow for reducing spectral crosstalk according to various embodiments described herein.
  • FIG. 17 illustrates a more detailed portion of an optimization workflow for reducing spectral crosstalk according to various embodiments described herein.
  • FIG. 18 illustrates another portion of an optimization workflow for reducing spectral crosstalk according to various embodiments described herein. DETAILED DESCRIPTION [0026] To provide a more thorough understanding of the present invention, the following description sets forth numerous specific details, such as specific configurations, parameters, examples, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention but is intended to provide a better description of the exemplary embodiments.
  • qPCR quantitative polymerase chain reaction
  • qPCR instruments require calibrated spectral dye matrices (“DMs”) to convert raw spectral filter data to multi-component dye data (“raw data”).
  • DMs calibrated spectral dye matrices
  • raw data multi-component dye data
  • calibrated spectral dye matrices represent our best estimate of the spectrum of each dye.
  • the dye matrices are obtained by placing the spectrum of each dye along the rows of the dye Docket No. TP386609WO1 matrix and normalizing each spectrum to have a maximum value of one.
  • Dye Matrix (DM) re-calibration could potentially come from: • Time drift from light sources or instrument optical changes • Spectral changes due to changes in ambient temperature and/or moving the instrument • Changes in assay chemistry over time/changes in assay lots • Possible calibration changes/drift within a qPCR run • Use of custom dyes [0028] According to various embodiments of the present teachings, an algorithm can be used to minimize spectral crosstalk by performing Dye Matrix Optimization (DMO) on the dye matrix used for spectral deconvolution.
  • DMO Dye Matrix Optimization
  • DMO is done using Machine Learning techniques, such as constrained multi-target non-linear optimization, to iteratively adjust an initial dye matrix to minimize the cross-correlation and/or crosstalk between the dye amplification curves produced by the adjusted dye matrices.
  • the absolute cross-correlation is the absolute value of the off- diagonal terms in the multicomponent dyes’ Cross-Correlation Matrix. It is important to note that spectral crosstalk occurs during deconvolution due to errors in the Dye Matrix (DM) with respect to the true DM (DM0). If there are no errors in the Dye Matrix, then there would be no spectral crosstalk. During optimization, reducing the error in the DM regarding the true DM (DM0), results in reduced crosstalk.
  • DM Dye Matrix
  • DM0 true DM
  • an algorithm of embodiments of the present teachings optimizes/improves the dye matrices (DMs) by minimizing multi-targets consisting of a specially weighted combination of the absolute cross-correlation between the multicomponent dye amplification Docket No. TP386609WO1 curves, and, also, of the absolute values of probable crosstalk between the dye traces as multi- targets.
  • DMs dye matrices
  • Spectral crosstalk often seen as induced pullup/pull-down in the filter signal data, results from using an incorrect dye matrix when performing the deconvolution of spectral filter data to produce dye data during multi-componenting (spectral deconvolution).
  • reduced spectral crosstalk typically leads to reduced false positives and false negatives at a given Ct threshold for qPCR. There is no need to raise Ct thresholds to extremely high values to avoid all potential spectral crosstalk.
  • LOD Limit of Detection
  • the method to prevent spectral error in extracting dye signals from the raw filter data is to perform a pure dye calibration.
  • the pure dye calibration plate consists of a plate loaded with dye attached to oligonucleotides and sealed with a plate seal.
  • the dyes are attached to oligonucleotides to both enable them to dissolve in solution, and to match the spectral characteristics of real dye labeled probes, since the DNA attached to the dye can affect the spectral characteristics of the dye. Since different DNA nucleotide bases attached to the dye can slightly change the spectral characteristics of the dye, pure dye calibration solutions use degenerate oligonucleotides.
  • TP386609WO1 with any one dye and model the spectral performance across the entire plate by interpolating and extrapolating the spectra for every well in the plate from that subset of wells.
  • all the other calibrations in the system have to be applied. This includes a background subtraction with a plate of just buffer solution, so that we know whatever signal we measure is not affected by background. It also includes a uniformity calibration to compensate for well-to-well signal variation using a uniform plate. It also includes a “color balancing” correction which multiplies a correction factor to each filter combination so that the relative signal levels for each filter combination are consistent across different instruments.
  • FIG. 1 illustrates a flowchart showing a method of reducing spectral crosstalk in a multiplexed assay according to various embodiments described herein. The method includes receiving filter signal data from a multiplexed fluorescence assay in step 102.
  • the method further includes, in step 104, receiving an initial dye matrix, where the initial dye matrix includes calibrated spectral dye data and, in step 106, generating an updated dye matrix based on the initial dye matrix and estimated crosstalk proxies.
  • the method further includes adjusting the updated dye matrix to meet a calculated optimization value in step 108.
  • the calculated optimization value is calculated based on the estimated crosstalk proxies and the filter signal data.
  • the adjusting step may be an iterative process with iterative calculation of the calculated optimization value.
  • the optimization values may further be calculated based on each adjustment of the updated dye matrix, and a cross-correlation between dyes used in the assay. In some embodiments, a plurality of calculated optimization values is met before the adjusting is stopped.
  • Each optimization value may be calculated based on each fluorescent dye used in the assay.
  • the method also includes generating an improved dye matrix based on the adjusted updated dye Docket No. TP386609WO1 matrix, in step 110, and generating spectral adjusted data based on the adjusted updated dye matrix in step 112.
  • Those skilled in the art will recognize that the operations of the various embodiments may be implemented using hardware, software, firmware, or combinations thereof, as appropriate. For example, some processes can be carried out using processors or other digital circuitry under the control of software, firmware, or hard-wired logic.
  • PCR INSTRUMENTS As mentioned above, an instrument that may be utilized according to various embodiments, but is not limited to, is a polymerase chain reaction (PCR) instrument.
  • FIG. 2 is a block diagram that illustrates PCR instrument 200, upon which embodiments of the present teachings may be implemented.
  • PCR instrument 200 may include heated cover 210 that is placed over a plurality of samples 212 contained in a substrate (not shown).
  • a substrate may be a glass or plastic slide with a plurality of sample regions, which sample regions have a cover between the sample regions and heated cover 210.
  • Some examples of a substrate may include, but are not limited to, a multi-well plate, such as a standard microtiter 96-well, a 384-well plate, or a microcard, or a substantially planar support, such as a glass or plastic slide.
  • the reaction sites in various embodiments of a substrate may include depressions, indentations, ridges, and combinations thereof, patterned in regular or irregular arrays formed on the surface of the substrate.
  • PCR instruments include a sample block 214, elements for heating and cooling 216, a heat exchanger 218, control system 220, and user interface 222.
  • Various embodiments of a thermal block assembly according to the present teachings comprise components 214-218 of PCR instrument 200 of FIG. 2. Docket No. TP386609WO1 [0039]
  • Real-time PCR instrument 200 has an optical system 224.
  • an optical system 224 may have an illumination source (not shown) that emits electromagnetic energy, an optical sensor, detector, or imager (not shown), for receiving electromagnetic energy from samples 212 in a substrate, and optics used to guide the electromagnetic energy from each DNA sample to the imager.
  • an optical system 224 may have an illumination source (not shown) that emits electromagnetic energy, an optical sensor, detector, or imager (not shown), for receiving electromagnetic energy from samples 212 in a substrate, and optics used to guide the electromagnetic energy from each DNA sample to the imager.
  • control system 220 may be used to control the functions of the detection system, heated cover, and thermal block assembly.
  • Control system 220 may be accessible to an end user through user interface 222 of PCR instrument 200 in FIG. 2 and real- time PCR instrument 200 in FIG. 2.
  • computer system 400 as depicted in FIG. 4, may serve as to provide control of the function of PCR instrument 200 in FIG. 2, as well as the user interface function.
  • computer system 400 of FIG. 4 may provide data processing, display and report preparation functions. All such instrument control functions may be dedicated locally to the PCR instrument.
  • FIG. 3 depicts an exemplary optical system 300 that may be used for imaging according to embodiments described herein. It should be recognized that optical system 300 is an exemplary optical system and one skilled in the art would recognize that other optical systems may be used to capture images of an object-of-interest. According to various embodiments, an object-of-interest may be a sample holder, such as a calibration plate, as described herein. An optical sensor 302 included in a camera 304, for example, may image an object-of-interest 310.
  • the optical sensor 302 may be a CCD senor and the camera 304 may be a CCD camera. Further, the optical sensor includes a camera lens 306.
  • an emission filter 308 can be chosen for imagining the object-of-interest 310 according to various embodiments. Emission filter 308 may be changed to image fluorescent emissions emitted from the object-of-interest 301 in other embodiments. Docket No. TP386609WO1 [0042]
  • Optical system 300 may use a reflected light source 312 to image object-of-interest 310.
  • the light from light source 312 may be filtered through an asphere 314, a focuser/diverger 316, and excitation filter 318 before being reflected to the object-of-interest 310 by beamsplitter 320.
  • Optical system 300 may also include a field lens 322.
  • the excitation filter 318 can be chosen or changed for imagining the object-of-interest 310 according to various embodiments.
  • FIG. 4 is a block diagram that illustrates a computer system 400 that may be employed to carry out processing functionality, according to various embodiments. Instruments to perform experiments may be connected to the exemplary computing system 400. Computing system 400 may be locally connected to an instrument according to various embodiments. In other embodiments, computing system 400 may be connected over the internet as a server controlling functionality of an instrument, generating results from data obtained by an instrument, or analyzing and processing data obtained by an instrument. Computing system 400 can include one or more processors, such as a processor 404.
  • Processor 404 can be implemented using a general or special purpose processing engine such as, for example, a microprocessor, controller or other control logic. In this example, processor 404 is connected to a bus 402 or other communication medium.
  • a computing system 400 of FIG. 4 may be embodied in any of a number of forms, such as a rack-mounted computer, mainframe, supercomputer, server, client, a desktop computer, a laptop computer, a tablet computer, hand- Docket No.
  • a computing system 400 can include a conventional network system including a client/server environment and one or more database servers, or integration with LIS/LIMS infrastructure.
  • a number of conventional network systems including a local area network (LAN) or a wide area network (WAN), and including wireless and/or wired components, are known in the art.
  • client/server environments, database servers, and networks are well documented in the art.
  • computing system 400 may be configured to connect to one or more servers in a distributed network.
  • Computing system 400 may receive information or updates from the distributed network. Computing system 400 may also transmit information to be stored within the distributed network that may be accessed by other clients connected to the distributed network. [0046] Computing system 400 may include bus 402 or other communication mechanism for communicating information, and processor 404 coupled with bus 402 for processing information. [0047] Computing system 400 also includes a memory 406, which can be a random access memory (RAM) or other dynamic memory, coupled to bus 402 for storing instructions to be executed by processor 404. Memory 406 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 404. Computing system 400 further includes a read only memory (ROM) 408 or other static storage device coupled to bus 402 for storing static information and instructions for processor 404.
  • ROM read only memory
  • Computing system 400 may also include a storage device 410, such as a magnetic disk, optical disk, or solid state drive (SSD) is provided and coupled to bus 402 for storing information and instructions.
  • Storage device 410 may include a media drive and a removable storage interface.
  • a media drive may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an optical disk drive, a CD or DVD drive (R or RW), flash drive, or other removable or fixed media drive.
  • the storage media may include a computer-readable storage medium having stored therein particular computer software, instructions, or data. Docket No.
  • storage device 410 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into computing system 400.
  • Such instrumentalities may include, for example, a removable storage unit and an interface, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the storage device 410 to computing system 400.
  • Computing system 400 can also include a communications interface 418. Communications interface 418 can be used to allow software and data to be transferred between computing system 400 and external devices.
  • communications interface 418 can include a modem, a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a RS-232C serial port), a PCMCIA slot and card, Bluetooth, etc.
  • Software and data transferred via communications interface 418 are in the form of signals which can be electronic, electromagnetic, optical or other signals capable of being received by communications interface 418. These signals may be transmitted and received by communications interface 418 via a channel such as a wireless medium, wire or cable, fiber optics, or other communications medium.
  • a channel include a phone line, a cellular phone link, an RF link, a network interface, a local or wide area network, and other communications channels.
  • Computing system 400 may be coupled via bus 402 to a display 412, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user.
  • a display 412 such as a cathode ray tube (CRT) or liquid crystal display (LCD)
  • An input device 414 is coupled to bus 402 for communicating information and command selections to processor 404, for example.
  • An input device may also be a display, such as an LCD display, configured with touchscreen input capabilities.
  • cursor control 416 such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor 404 and for controlling cursor movement on display 412.
  • This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.
  • a computing system 400 provides data processing and provides a level of confidence for such data. Consistent with certain Docket No. TP386609WO1 implementations of embodiments of the present teachings, data processing and confidence values are provided by computing system 400 in response to processor 404 executing one or more sequences of one or more instructions contained in memory 406. Such instructions may be read into memory 406 from another computer-readable medium, such as storage device 410. Execution of the sequences of instructions contained in memory 406 causes processor 404 to perform the process states described herein.
  • computer-readable medium and “computer program product” as used herein generally refers to any media that is involved in providing one or more sequences or one or more instructions to processor 404 for execution.
  • Such instructions generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 400 to perform features or functions of embodiments of the present invention.
  • Non-volatile media includes, for example, solid state, optical or magnetic disks, such as storage device 410.
  • Volatile media includes dynamic memory, such as memory 406.
  • Transmission media includes coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 402.
  • Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.
  • Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor 404 for execution.
  • the instructions may initially be carried on magnetic disk of a remote computer.
  • the remote computer can load the instructions into its dynamic memory and send the instructions over a Docket No.
  • a modem local to computing system 400 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infra-red signal.
  • An infra-red detector coupled to bus 402 can receive the data carried in the infra-red signal and place the data on bus 402.
  • Bus 402 carries the data to memory 406, from which processor 404 retrieves and executes the instructions.
  • the instructions received by memory 406 may optionally be stored on storage device 410 either before or after execution by processor 404.
  • DISTRIBUTED SYSTEM [0056] Some of the elements of a typical Internet network configuration 500 are shown in FIG. 5, wherein a number of client machines 502 possibly in a remote local office, are shown connected to a gateway/hub/tunnel-server/etc. 510 which is itself connected to the internet 508 via some internet service provider (ISP) connection 510.
  • ISP internet service provider
  • Filter signal data refers to the temporal and spectral sampling of the fluorescence caused by different dyes present within a multiplex qPCR reaction site.
  • An array of different filters is used to generate the filter signal data, or filter data.
  • Each filter detects fluorescence within a different narrow band of the light spectrum (FIG. 6), and the array of filters thus allows to obtain a sampling of the overall fluorescence generated by the different dyes with a well, at specific points in time or cycles Docket No. TP386609WO1 within the qPCR process.
  • FIG. 6 illustrates an example of spectral sampling of a fluorescence signal.
  • the filter signal data then defines a two-dimensional array of data composed of spectrally separated measurements (identified by Filter number or ID) acquired at specific time points defined by the cycle sequence in a qPCR experiment.
  • FIG. 6 is an example of the spectral sampling of the fluorescence within a reaction site (continuous line 602) at a fixed time point or cycle of a qPCR process.
  • an array of 10, spectrally distributed filters are used to measure the fluorescence caused by several dyes within a reaction site.
  • the dye matrix data refers to the two-dimensional array of values that relate the fluorescence of individual dyes with the spectral measurements obtained by individual filters in the array of filters used in a multiplex qPCR experiment.
  • FIG. 7 illustrates an example of a normalized dye matrix according to various embodiments described herein.
  • FIG. 7 shows the normalized Dye Matrix (DM) values vs Filter numbers for a dye system with 4 dyes (FAM, VIC, ABY, and JUN) measured using 10 filters.
  • the dye matrices are typically normalized to have a maximum value of 1 for each dye.
  • Table 1 illustrates the normalized Dye Matrix values associated with the example in FIG. 7.
  • the table shows the normalized Dye Matrix (DM) values vs Filter numbers for a dye system with 4 dyes (FAM, VIC, ABY, and JUN) measured using 10 filters Normalized Dye Matrix Values Filter Numbers 1 2 3 4 5 6 7 8 9 10 FAM 1 0.397 0.085 0.029 0.001 0.001 0.002 0.000 -0.001 0.000 VIC 0.084 0.573 1.0 0.370 0.303 0.154 0.007 0.001 -0.001 -0.001 Dyes ABY 0.001 0.042 0.141 0.378 1.0 0.389 0.407 0.053 0.001 0.000 JUN 0.001 0.002 0.001 0.007 0.025 0.429 1.00 0.201 0.020 0.008 Table 1 [0068] Inverting equation 2 using straight forward
  • spectral deconvolution will not produce spectral errors or spectral crosstalk if the dye matrix is calibrated, optimized, or improved with minimal or no error.
  • spectral crosstalk results.
  • a dye matrix may be iteratively updated to get close to the true dye matrix. Mathematically this can be stated as, the closer our current updated dye matrix gets to the true dye matrix, then the closer the spectral crosstalk matrix (C) gets to becoming the identity matrix.
  • the resulting adjusted updated dye matrix is an improved dye matrix to use to deconvolve filter signal data to reduce spectral crosstalk.
  • D0 F * pinv(DM0), (4a) where D0 is the true dye data matrix, or calibrated spectral dye data, without any errors (of size numScans x numDyes) with the dye data for each scan down each row, F is the filter data matrix (of size numScans x numFilters) with the filter data for each scan down each row, and DM 0 is the true dye matrix.
  • Equations 7 and 8 gives us the assurance that an optimization process that updates the initial dye matrix (DM) closer to the true dye matrix (DM0) by making the dye matrix error Docket No. TP386609WO1 (DMe) get closer to zero will result in decreased spectra crosstalk, adjusting updated DM to an improved dye matrix.
  • DM initial dye matrix
  • DMe dye matrix error Docket No. TP386609WO1
  • the element Cij of the crosstalk matrix which is the ith row and jth column of the crosstalk matrix C, represents the spectral crosstalk from dye i into dye j.
  • a set of novel preferred optimization criteria are to minimize multi-targets consisting of a specially weighted combination of the absolute cross- correlation between the multicomponent dye amplification curves, and, also, of the absolute values of probable crosstalk between the dye traces as multi-targets.
  • the dye matrix is of size numDyes x numFilters.
  • numDyes is the number of dyes
  • numFilters is the number of filters.
  • the second preferred embodiment should be faster than the first preferred embodiment since the number of variables being optimized for is much smaller; and optimization speed tends to scale by at least the cube of the number of variables being optimized for.
  • the constraints (bounds) that need to be placed around the values of the dye matrix can be directly expressed in terms of the values of the dye matrix. As a result, the optimizations are more robust.
  • dyes that are spectrally further from each other to each other tend to have less potential for crosstalk with respect to each other.
  • the spectral distance between dyes is approximately inversely proportional to the potential for crosstalk between dyes. That is, dyes that are spectrally close together should have wider constraints on the potential values of the crosstalk between them during optimization, as reflected in the corresponding off-diagonal values of the crosstalk matrix (C) being optimized. Conversely, dyes that are spectrally farther apart should Docket No. TP386609WO1 have tighter constraints on the potential values of the crosstalk between them during optimization, as reflected in the corresponding off-diagonal values of the crosstalk matrix (C) being optimized.
  • the concept of spectral distance is can be defined in terms of the spectral overlap between the dyes.
  • the spectral overlap is given as: S0 ⁇ DM *DM T , (15) where is the dye spectral overlap matrix.
  • the corresponding spectral overlap matrix is shown next in Table 2.
  • the peaks of the dyes FAM, VIC, ABY, and JUN are in filter numbers 1, 3, 5, and 7 respectively.
  • FAM is spectrally furthest from JUN.
  • Table 2 for instance, that the spectral overlap between FAM and JUN is 0.004.
  • the dyes spectral overlap matrix can be normalized to have a maximum value of 1 along each row, by dividing each row by its maximum value. This produces a normalized dye spectral overlap matrix, SNO.
  • Table 3 shows the normalized Dyes Spectral Overlap Matrix values obtained from Table 2. It is associated with the example in FIG. 7 and Table 1.
  • the normalized dye spectral overlap matrix, SNO shows this inverse relationship between spectral distance and the off-diagonal terms of the normalized dyes spectral overlap very clearly. For example, FAM is spectrally furthest from JUN.
  • TP386609WO1 far apart has much small potential for crosstalk with respect to each other.
  • cUpper bound is a parameter which sets the off-diagonal elements of the crosstalk matrix allowed during optimization
  • cmin bound is a parameter which sets the desired minimum absolute value of the upper bound
  • cmax bound is a parameter which sets the desired maximum absolute value of the upper bound
  • SNO is the normalized spectral overlap defined previously
  • I is the identity matrix with 1s on the diagonal and zeros elsewhere.
  • cmin bound would typically be set to a small value ⁇ 0.005 ( ⁇ 0.5%).
  • cmax bound would typically be ⁇ 0.15 ( ⁇ 15%) but can be set to be bigger or smaller depending on the dye set and qPCR instrument.
  • the term (SNO - I) means that the on-diagonal terms are zero reflecting zero crosstalk of dyes unto themselves.
  • the upper bounds on the diagonal elements are given by: cUpper bound ⁇ cmin bound + I, (16b) [00100]
  • DM0 C * DM (13)
  • equation 14 we start with a method of independently estimating the elements of the crosstalk matrix (C i ) at each iteration of the optimization. This is difficult to do in qPCR, in general, since we typically only have 40-45 cycles to work with and amplification in each dye is guaranteed to start in different, well-separated, and characteristic cycle ranges. If amplification in each dye for an assay is expected to start in different, well- separated, and characteristic cycle ranges, then it may be possible to produce reasonable estimates of the elements of the crosstalk matrix (C i ) at each iteration of the optimization.
  • FIG. 8 illustrates block diagram 800 of spectral crosstalk reducing workflow according to various embodiments described herein.
  • a system 804 is configured to reduce spectral crosstalk is also operable with another system 802.
  • System 802 may provide a user interface or other analysis functionality of qPCR data.
  • System 804 receives inputs of filter signal data, which is a multiplexed fluorescence signal, calibrated spectral dye matrix data, and optimization values.
  • System 804 includes a signal discontinuity detection component 806 and a dye matrix optimization component 808.
  • Signal Discontinuity component 806 is configured to detect and correct sudden drops or increases in the filter signal data. Such sudden discontinuities can create false cross- correlations which can lead to errant “crosstalk” identification and reduction. These sudden signal discontinuities are constrained to occur simultaneously in each of the filter temporal data sequences within two points of temporal data acquisition (qPCR cycles).
  • the objective of the Dye-Matrix Optimization component 808 is to minimize the effect of “crosstalk” observed in multiplex qPCR essays where signals for several dyes are acquired at the same time.
  • Crosstalk refers to the effect in which the measured increase of the Docket No. TP386609WO1 signal corresponding to one of the dyes, causes a corresponding increase or decrease in the signal of another dye signal.
  • system 904 is configured to reduce spectral crosstalk is also operable with another system 902.
  • System 902 may provide a user interface or other analysis functionality of qPCR data.
  • System 904 receives inputs of filter signal data, which is a multiplexed fluorescence signal, calibrated spectral dye matrix data, and optimization values.
  • filter signal data which is a multiplexed fluorescence signal, calibrated spectral dye matrix data, and optimization values.
  • the output of system 904 depends on the type of data received from external system 902. As described by the diagram in FIG.
  • system 904 runs signal discontinuity detection component 906 and outputs the adjusted filter signal data based on the detected discontinuities. [00114] If at decision 908, it is determined that calibrated spectral dye matrix data is also provided, system 904 also runs the dye matrix optimization component 910 to correct for any crosstalk effect if present. At decision 912, if there is an issue that prevents the algorithm from running, the original input data is returned unchanged. [00115] The following sections describe signal discontinuity detection component 906 and dye matrix optimization component 910 in more detail. SIGNAL DISCONTINUITY COMPONENT [00116] FIG.
  • signal discontinuity detection component 1000 illustrates a signal discontinuity workflow of signal discontinuity detection component 1000 according to various embodiments described herein.
  • the input to signal discontinuity detection component 1000 are single well filter signal data.
  • Signal discontinuity detection is made in signal discontinuity detection component 1002. If at decision 1008, a signal discontinuity is not detected, it leaves the filter signal data sequences unmodified. If at decision 1008, a signal discontinuity is detected, the location of the signal discontinuity is estimated in signal discontinuity position estimation component 1004. Once the location of the signal Docket No. TP386609WO1 discontinuity is calculated, the effect of the signal discontinuity on the filter signal data sequences is removed in signal discontinuity correction component 1006. With decision 1010, the process is then repeated to detect additional signal discontinuities.
  • signal discontinuity detection component 1000 limits the number of signal discontinuities to be detected and corrected to an internally set limit of 5 since a larger number of signal discontinuities would likely be associated with noisy measurement data which would not be reliable to analyze any further.
  • Signal discontinuity detection component 1002 detects a signal discontinuity through a signature waveform that is obtained through several steps of processing the filter data sequences and combining the results.
  • FIG. 11 illustrates an example of a signal discontinuity in filter signal data according to various embodiments described herein.
  • a plot 1100 of filter signal data 1100 shows an evident sudden change (dashed line) occurring in all filter signal data sequences at the same Cycle measurement point. Such a characteristic sudden change is associated with the presence of a signal discontinuity in the reaction site.
  • FIG. 12C illustrates step S3.
  • S2 abs(S2). Once the “up” “down” signal discontinuity type is identified, the absolute value of the difference filter signal data in S2 is obtained.
  • FIG. 12D illustrates step S4.
  • FIG. 12E illustrates step S5.
  • S5 Sum(S4).
  • the data sequences from S4 are added together, resulting in a single data sequence reflecting the normalized cumulative effect of the signal discontinuity for all filter signal data sequences.
  • FIG. 12F illustrates step S6.
  • S6 diff(S5).
  • a difference operation is finally implemented to the data sequence from S5, resulting in a sequence with a signature “up-down” behavior of the data points around the position of the signal discontinuity in the original filter signal data sequences.
  • the signal discontinuity component Based on the steps described above, if the signature “up-down” behavior is found within consecutive data points in the associated data sequence S6, then the signal discontinuity component detects a signal discontinuity in the associated filter signal data set.
  • the signal discontinuity position estimation component 1004 estimates the cycle at which the signal discontinuity occurs based on the location of the maximum value of the sequence signal obtained in step S5.
  • FIG. 13 illustrates an adjusted signal based on signal discontinuities according to various embodiments described herein.
  • a polynomial curve of third order 1302 is fitted to each one of the filter signal data curves 1300 using 5 measurement points before the estimated Docket No. TP386609WO1 signal discontinuity position.
  • the fixed measurement value 1304 at the location of the signal discontinuity is estimated by extrapolation. The value found is then used to estimate the correction value (shift) 1306 to be added to all measured values after the signal discontinuity location to generate adjusted filter signal data 1308.
  • the third order of the polynomial fit allows for better correction if there is an exponential or transition phase.
  • DYE MATRIX OPTIMIZATION COMPONENT The purpose of the dye matrix optimization component according to various embodiments of the present teachings is to minimize the effect of crosstalk between dye signals associated with a corresponding set of targets in a multiplex assay.
  • Crosstalk refers to the induced positive or negative amplification of a dye/target signal caused by the amplification of another dye signal. When crosstalk is present, it can be assumed that the amplification in one dye signal is positively or negatively correlated to some degree to the amplification of another dye signal.
  • FIGS. 14A-14D illustrate an example of spectral crosstalk reduction according to various embodiments described herein.
  • cross-correlation is used as one of the optimization values.
  • FIGS. 14A-14D shows a 5-dye set system with the dyes ABY, FAM, VIC, JUN, and AF647.
  • Table 1402 in FIG. 14A shows the cross-correlation matrix for the signal dye data (amplitude curves) prior to the application of the dye matrix optimization.
  • FIG. 14C the plot of the amplitude of the dyes over the PCR cycles before dye matrix optimization is shown in plots 1404 show the resulting crosstalk between dyes that are not excited. Since the two excited dyes are VIC and JUN, the relevant elements of the cross- correlation matrix are the rows corresponding to VIC and JUN.
  • Table 1408 in FIG. 14B shows the cross-correlation matrix for the signal dye data (amplitude curves) after to the application of the dye matrix optimization.
  • FIG. 14D the plot of amplitude of dyes over PCR cycles after dye matrix optimization is shown in plot 1406.
  • FIGS. 15A-15D illustrate another example of spectral crosstalk reduction according to various embodiments described herein. In this example, cross-correlation is used as one of the optimization values.
  • This example shows a 5-dye set system with the dyes ABY, FAM, VIC, JUN, and AF647.
  • Table 1502 shows the cross-correlation matrix for the signal data (amplitude curves) prior to the application of dye matrix optimization.
  • FIG. 15C the plot of the amplitude of the dyes over the PCR cycles before dye matrix optimization is shown in plots 1504 showing a great amount of crosstalk between the VIC and FAM dyes. Since the excited dyes are FAM and JUN, the relevant elements of the cross-correlation matrix are the rows corresponding to FAM and JUN. The other rows, since the signal levels are low, do not carry that much information.
  • Table 1508 shows the cross-correlation matrix for the signal data (amplitude curves) after to the application of the dye matrix optimization.
  • FIG. 15D the plot of the amplitude of the dyes over the PCR cycles after dye matrix optimization is shown in plots 1506 showing a reduction of FAM to VIC crosstalk.
  • the relevant elements of the cross-correlation matrix are the rows corresponding to FAM and JUN.
  • the dye- matrix optimization component evaluates the effect of crosstalk by focusing on the off-diagonal elements of the cross-correlation matrix between all dye signals, since the off-diagonal elements of a cross-correlation matrix (as shown in FIGS. 14A-14D and 15A-15D) reflect the correlation between the different pairs of dye signals.
  • dye signals are estimated based on a system of equations (defining the dye-matrix) that relate a set of discrete measurements of the combined spectral characteristics of the dyes (the filter or raw data) with each one of the dyes (the multicomponent data).
  • Crosstalk is an additional overlapping effect on the spectral measurements or filter signal data.
  • crosstalk effect is considered by the system of equations or dye-matrix and can be corrected, such that the resulting estimated dye signals reflect only corresponding amplification trends.
  • the effect of crosstalk is modeled within the definition of the dye- matrix.
  • the dye-matrix optimization component works by taking a dye matrix through an optimizer which modifies the dye matrix values such that when a modified cross-correlation is performed on all multicomponent data it minimizes the off-diagonal elements.
  • Optimization Process [00141] The general optimization process and how it fits into the dye-matrix optimization is described in FIGS. 16, 17, and 18. Docket No. TP386609WO1 [00142] FIG. 16 illustrates an optimization workflow 1600 for reducing spectral crosstalk according to various embodiments described herein. [00143]
  • the dye matrix optimization component initial setup 1602 is for defining the input data and parameters that are necessary for the optimization approach.
  • the input data is the signal discontinuity adjusted data and the initial dye matrices (if no signal discontinuity was detected).
  • the initial cycle point from which all filter signal data is to be considered. This parameter is used to avoid including the usually noisy measurements on early qPCR cycles.
  • Other parameters are used to control the operation of the optimization value calculator 1604, such as the maximum number of iterations and the optimization target value.
  • the dye matrix optimization component further uses a Constrained “DogLeg” Solver (“CoDoSol”) 1606. Minimization [00144]
  • FIG. 17 illustrates a multitarget constrained optimization utilizing (CoDoSol) component 1700 for reducing spectral crosstalk according to various embodiments described herein.
  • the Constrained “DogLeg” Solver (CoDoSol) is a multi-target constrained optimization method for the solution of non-linear systems/problems.
  • CoDoSol component 1700 can be parameterized to optimize a single optimization target to minimize. This is the standard way that we commonly think about optimization problems. It can also be parameterized to minimize several targets simultaneously coupled manner. This is a more sophisticated form of multi-target optimization.
  • the targets for CoDoSol component 1700 are typically set up for each dye and are described later in equations 19 and 20. These are a weighted square of the off-diagonal cross correlative elements of the multicomponent dye data of each non-active dye. Off diagonal elements involving two active dyes are not included.
  • the variables the CoDoSol component 1700 changes are the elements of dye matrix.
  • the variables that the CoDoSol component 1700 is adjusting are the elements of the dye matrix starting from an initial dye matrix derived from the filter signal data. Docket No. TP386609WO1
  • the variable the CoDoSol component 1700 changes is the elements of the estimated crosstalk.
  • the Jacobian matrix is calculated. Mathematically, the Jacobian matrix is defined as the gradient of the target or targets with respect to minor changes in the variables being optimized. The inverse of this Jacobian matrix gives a proposed direction that the optimizer needs to make changes to the variables being optimized to move the optimization in the direction of a minimum.
  • the variables that the optimizer is adjusting are the elements of the crosstalk matrix starting from an initial dye matrix.
  • An updated dye matrix, at the ith optimization iteration, is obtained using a slight update to equation 13.
  • DM0, i Cj * DM, (13a)
  • DM 0 , i the updated dye matrix at the ith iteration, and is the estimate of the spectral crosstalk that would be produced by the initial dye matrix, DM.
  • DM 0 , i is the updated dye matrix at the ith iteration, and is the estimate of the spectral crosstalk that would be produced by the initial dye matrix, DM.
  • These variants are used to deconvolve the filter signal data to produce the multicomponent dye data which is then used to produce the cross-correlation matrix.
  • These variants are used by the optimization value calculator 1706 to return associated optimization values for each target.
  • the CoDoSol optimizer assembles these optimization target values to form a Jacobian Matrix.
  • the inverse of this Jacobian matrix points towards the lowest gradient direction for the current updated Dye Matrix. This is the direction to update the updated Dye Matrix to produce a reduction in the target values.
  • a reduction in the target value is associated with a reduction in crosstalk.
  • This data is then used to recompute the multicomponent data and fed back to be baselined.
  • FIG. 18 illustrates optimization value calculator component 1800 for reducing spectral crosstalk according to various embodiments described herein.
  • Optimization value calculator component 1800 includes a deconvolution component 1804, baseline adjustment component 1806, and weighted cross-correlation component 1808.
  • Deconvolution Optimization value calculator component 1800 operates at the individual well level.
  • the inputs 1802 include signal discontinuity adjusted data, an initial dye matrix including calibrated spectral dye matrix, and parameters.
  • the parameters include the minimum cycle to begin the optimization process and the DyeFractThresh, a parameter that determines the dye fraction threshold, which determines an upper bound for the fractional range that nonactive dyes should be more actively processed.
  • the deconvolution component 1804 adjusted filter signal data based on detected signal discontinuities are mapped to the specific dye-matrix data by multiplying the filter data by the inverse (pseudo inverse if the number of filters is different to the number of dyes) of the dye matrix.
  • the dye data set is also referred to as the multicomponent data set.
  • the baseline adjustment step smooths and adjusts the initial dye matrix data to reduce measurement noise and generate a flat baseline dye signal with a uniform level across all dyes.
  • the baseline adjustment step can be performed by known baseliner algorithms.
  • Weighted Cross-Correlation [00154] The cross-correlation between two dye signal curves results in a square matrix in which, if the two dye signals are uncorrelated, the off-diagonal elements have absolute values close to zero. If the two dye signals are correlated, for example due to crosstalk, the off-diagonal elements have absolute values closer to 1. To emphasize regions where amplification is measured, the cross-correlation is weighted by the measured value of the dye signal. For signals where crosstalk is present, the off-diagonal elements are non-zero, and thus become the minimization objective of the overall crosstalk reduction method of the various embodiments described herein. Docket No.
  • the dyes to be minimized are chosen to be each of the non-active dyes.
  • powMultiple is typically 1 but can be parameterized to be values from 0.125 to 4
  • isPossibleCrosstalkInfo[dye1][dye2] typically have values of 0 or 1 but can have values between 0 and 1.
  • isPossibleCrosstalkInfo[dye1][dye2] is typically a either 0 or 1 obtained from applying a threshold to dye and dye 2 of the normalized dye spectral overlap matrix, SNO, so that values of the spectral overlap matrix for the dye1-dye2 Docket No.
  • TP386609WO1 pair below the threshold (for instance 0.1) is results in a 0, and values above results in a 1. Or it can be the dye spectral overlap matrix value for the dye1-dye2 pair itself.
  • Our optimization approach offers a very rich set of optimization strategies focusing on calculated optimization values. 1) optTarget can be made to be a single target independent of the dyes. 2) optTarget can be made to be multiple targets tracking each dye. 3) optTarget can be made to be multiple targets tracking each active dye. 4) optTarget can be made to be multiple targets tracking each dye-pair. 5) optTarget can be made to be multiple targets tracking each active dye-pair.
  • the weighting can be set to be a constant (e.g., with any loss of generality, 1). This leads to the optimization targets being based on unweighted powers (powPos) of the elements of the correlation matrix. powPos is typically set to be 2 but can be values in the typical range 0.125 to 8. This places the emphasis solely on the optimizer’s reduction/minimization of the cross-correlation between the dye curves. That leads to an optimization/target value minimization based solely on an unweighted minimization of the off-diagonal elements of the cross-correlation matrix.
  • the weighting can be set to be based on the relative strength of active (excited) dyes signals. Note that in the discussion of Figures 9 and 10, it was observed that it was only the cross-correlation of the active dyes relative to the non-active (non-excited) dyes the contains direct information that we should target for optimization. The cross-correlation of the non-active dyes relative to the other non-active dyes the contains in-direct information about mutual crosstalk that is being fundamentally driven by the active dyes. 3) The weighting can be set to be based on the strength of the apparent crosstalk signals in the non-active dyes.
  • the strength of the apparent crosstalk signals in the non-active dyes is measured by the variables cumAbsSumEmphasizeNegs[dye2] which is the cumulative absolute sum of the values of baselined curve for dye2 done in such a manner that Docket No. TP386609WO1 negative values are emphasized, and cumNormedPositiveSum[dye1] which is a cumulative normalized sum of the positive values for the baselined curve for dye1.
  • the constants corrAddConst and corrMultiplierConst (which is equal to (1.0 - corrAddConst)) adjust the relative importance of setting the target to be the weighting term alone relative to the product of the cross-correlation and the weighting term.
  • setting corrAddConst to be 1 is equivalent to setting the target to be based solely on the strength of the apparent crosstalk signals in the non-active dyes.
  • setting corrAddConst to be 0 is equivalent to setting the target to be based solely on the product of the cross-correlation and a weighting term based on the strength of the apparent crosstalk signals in the non-active dyes. Examples [00161] The following numbered examples are embodiments: 1.
  • a method for reducing spectral crosstalk in a multiplexed assay comprising: receiving filter signal data from a multiplexed fluorescence assay; receiving an initial dye matrix, wherein the initial dye matrix includes calibrated spectral dye data; generating an updated dye matrix based on the initial dye matrix and estimated crosstalk proxies; adjusting the updated dye matrix to meet a calculated optimization value, wherein a calculated optimization value is calculated based on the estimated crosstalk proxies and the filter signal data; generating an improved dye matrix based on the adjusted updated dye matrix; and generating spectral adjusted data based on the improved dye matrix.
  • adjusting the updated dye matrix further includes meeting a plurality of calculated optimization values, wherein each calculated optimization Docket No.
  • TP386609WO1 value is calculated based on each fluorescent dye used in the multiplexed fluorescence assay. 3. The method of any one of the examples 1 to 2, wherein the calculated optimization value is further calculated based on an intermediate updated dye matrix, and a cross-correlation between dyes used in the multiplexed fluorescence assay. 4. The method of any one of the examples 1 to 3, further comprising: deconvolving the filter signal data to determine individual dye data; adjusting the baseline of the discontinuity fixed data to reduce measurement noise; calculating a cross-correlation matrix for each fluorescent dye signal used in the multiplexed fluorescent assay; and generating the calculated optimization value using the estimated crosstalk proxies for weighting data in the cross-correlation matrix. 5.
  • DM improved approximates the true dye matrix, DM 0 , and is: DMimproved ⁇ DM - DMe , where DMimproved is the improved dye matrix, DM is the initial dye matrix, and DMe is the error in the initial dye matrix. 6.
  • adjusting the updated dye matrix is an iterative process. 7.
  • TP386609WO1 detecting discontinuities in the filter signal data, wherein a discontinuity is a significant increase or decrease between two temporal points of the filter signal data; and generating adjusted filter signal data based on the detected discontinuities, wherein the calculated optimization value is further based on the adjusted filter signal data.
  • generating the adjusted filter signal data based on the detected discontinuities includes using a polynomial fit curve.
  • a system for reducing spectral crosstalk in a multiplexed assay comprising: a detector configured to receive filter signal data from a multiplexed fluorescence assay; a memory configured to store an initial dye matrix, wherein the initial dye matrix includes calibrated spectral dye data; a processor configured to: generate an updated dye matrix based on the initial dye matrix and estimated crosstalk proxies; adjust the updated dye matrix to meet a calculated optimization value, wherein a calculated optimization value is calculated based on the estimated crosstalk proxies and the filter signal data; generate an improved dye matrix based on the adjusted updated dye matrix; and generate spectral adjusted data based on the improved dye matrix. 11.
  • the processor is further configured to: adjust the updated dye matrix to meet a plurality of calculated optimization values, wherein each calculated optimization value is calculated based on each fluorescent dye used in the multiplexed fluorescence assay. Docket No. TP386609WO1 12. The system of any one of the examples 10 to 11, wherein the calculated optimization value is further calculated based on an intermediate updated dye matrix, and a cross- correlation between dyes used in the multiplexed fluorescence assay. 13.
  • the processor is further configured to: deconvolve the filter signal data to determine individual dye data; adjust the baseline of the discontinuity fixed data to reduce measurement noise; calculate a cross-correlation matrix for each fluorescent dye signal used in the multiplexed fluorescent assay; and generate the calculated optimization value using the estimated crosstalk proxies for weighting data in the cross-correlation matrix.
  • the improved dye matrix (DMimproved) approximates the true dye matrix, DM0, and is: DMimproved ⁇ DM - DMe , where DM improved is the improved dye matrix, DM is the initial dye matrix, and DM e is the error in the initial dye matrix.
  • any one of the examples 10 to 14 wherein the processor is configured to adjust the updated dye matrix in an iterative process. 16.
  • the processor is further configured to: generate a quantification result using the spectral adjusted data.
  • the processor is further configured to: Docket No. TP386609WO1 detect discontinuities in the filter signal data, wherein a discontinuity is a significant increase or decrease between two temporal points of the filter signal data; and generate adjusted filter signal data based on the detected discontinuities, wherein the calculated optimization value is further based on the adjusted filter signal data. 18.
  • the system of example 17, wherein the processor generates the adjusted filter signal data based on the detected discontinuities includes using a polynomial fit curve. 19.
  • a system comprising a processor, and a storage medium storing instruction, which when executed by a processor, causes the system to carry out the method of any one of examples 1 to 9.

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Abstract

L'invention concerne un procédé de réduction de la diaphonie spectrale dans un dosage multiplexé. Le procédé comprend la réception de données de signal de filtre à partir d'un dosage de fluorescence multiplexé et d'une matrice de colorant initiale comprenant des données de colorant spectral étalonnées. Le procédé comprend en outre la génération d'une matrice de colorant mise à jour sur la base de la matrice de colorant initiale et de proxies de diaphonie estimés, puis l'ajustement de la matrice de colorant mise à jour pour satisfaire une valeur d'optimisation calculée. La valeur d'optimisation calculée est calculée sur la base d'au moins les proxies de diaphonie estimés et des données de signal de filtre. La valeur d'optimisation calculée peut en outre être calculée sur la base de chaque ajustement de la matrice de colorant mise à jour, et d'une corrélation croisée entre des colorants utilisés dans le dosage. Le procédé comprend en outre la génération d'une matrice de colorant améliorée sur la base de la matrice de colorant mise à jour ajustée, puis la génération de données ajustées spectrales sur la base de la matrice de colorant améliorée.
PCT/US2024/034295 2023-06-30 2024-06-17 Ajustement automatique de matrice de colorant spectral pour réduire la diaphonie spectrale dans des dosages qpcr multiplexés Pending WO2025006242A1 (fr)

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