method and system for calibrating biological instruments, and non-transitory computer-readable stora

专利摘要:
In an exemplary embodiment, a method for calibrating an instrument is provided. The instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites. The method includes performing a region of interest (ROI) calibration to determine reaction site positions in an image. The method further includes performing a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye. The method further includes performing an instrument normalization calibration to determine a filter normalization factor. The method includes performing an RNase P validation to validate that the instrument is able to distinguish between two different amounts of sample.
公开号:BR112017016814B1
申请号:R112017016814-6
申请日:2016-02-05
公开日:2021-04-20
发明作者:Yong Chu；Jeffrey Marks；Jacob Freudenthal；Thomas Wessel；David Woo
申请人:Life Technologies Corporation；
IPC主号:

专利说明:

TECHNICAL STATUS
[001] In general, there is a growing need to simplify the installation and configuration of biological analysis systems so that operators can use biological analysis systems more quickly and efficiently for their intended purposes.
[002] The installation and calibration of laboratory instrumentation can be a time-consuming and expensive process. In many cases, the instrument supplier's engineers must be on site to carry out these processes. This cost is usually passed on to the user. In some cases, experienced users can successfully calibrate suitable instruments using multi-step procedures. During this calibration, physical standards and well plates can be used in combination with manual procedures. Manual calibration processing and data inspection are error prone and may depend on ad hoc or subjective measurements. While a final system verification step can provide resilience against accepting suboptimal calibrations, automation offers better objectivity and consistency during these activities. SUMMARY
[003] In an exemplary modality, a method for calibrating an instrument is provided. The instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites. The method includes performing a region of interest (ROI) calibration to determine reaction site positions in an image. The method further includes performing a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye. The method further includes performing an instrument normalization calibration to determine a filter normalization factor. The method includes performing an RNase P validation to validate that the instrument is able to distinguish between two different amounts of sample.
[004] In another exemplary embodiment, a computer-readable storage medium encoded with processor-executable instructions for calibrating an instrument is provided. The instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites. Instructions include instructions for performing a region of interest (ROI) calibration to determine reaction site positions on an image. Instructions include instructions for performing a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye. The instructions also include instructions for performing an instrument normalization calibration to determine a filter normalization factor. Additional instructions include instructions for performing an RNase P validation to validate that the instrument is able to distinguish between two different amounts of sample.
[005] In another exemplary modality, a system to calibrate an instrument is provided. The instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites. The system comprises a processor and a memory, encoded with executable instructions per processor. Instructions include instructions for performing a region of interest (ROI) calibration to determine reaction site positions in an image and performing a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye with a calibration data of the pure spectrum of the fluorescent dye. Additional instructions include performing an instrument normalization calibration to determine a filter normalization factor and performing an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[006] In another exemplary modality, a system to calibrate an instrument is provided. The instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites. The system includes an ROI calibrator configured to determine reaction site positions in an image. The system includes a pure dye calibrator configured to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye. The system further includes an instrument normalization calibrator configured to determine a filter normalization factor. The instrument includes an RNase P validator configured to validate that the instrument is able to distinguish between two different amounts of sample. The system also includes a display mechanism configured to display calibration results. DESCRIPTION OF THE FIGURES
[007] FIG. 1 illustrates a calibration workflow for a biological instrument in accordance with various embodiments described herein.
[008] FIG. 2 is a block diagram illustrating a PCR instrument 200 upon which embodiments of the present teachings can be implemented.
[009] FIG. 3 depicts an exemplary optical system 300 that can be used for imaging in accordance with the embodiments described herein.
[010] FIG. 4 illustrates an exemplary computing system for implementing various embodiments described herein.
[011] FIG. 5 illustrates an exemplary distributed network system in accordance with various embodiments described herein.
[012] FIG. 6 illustrates a sequence of steps used in calibrating qPCR instruments.
[013] FIG. 7 illustrates the regions of interest for a 96-well sample vessel.
[014] FIG. 8 is an image of a FAM dye calibration plate occupying each well of a 96-well calibration plate.
[015] FIGS. 9 and 10 represent an example workflow in accordance with an embodiment of the present disclosure.
[016] FIG. 11A illustrates calibration boards with checkerboard configurations in accordance with an embodiment of the present disclosure.
[017] FIG. 11B is an image of a 4-dyes gridded 96-well calibration plate using the FAM, VIC, ROX, and SYBR dyes in the same configuration illustrated by plate 1100 in FIG. 11 A.
[018] FIG. 12A illustrates dye mixtures used in various embodiments of the present teachings.
[019] FIG. 12B illustrates pure dyes and main channel filter combinations for various embodiments of the present teachings.
[020] FIG. 13 illustrates the % deviation of dye mixtures prior to normalization in accordance with various embodiments of the present teachings.
[021] FIG. 14 illustrates the % deviation of dye mixtures after normalization in accordance with various embodiments of the present teachings.
[022] FIG. 15 illustrates a closer view of the % deviation of dye mixtures after normalization in accordance with various embodiments of the present teachings.
[023] FIG. 16 is a flowchart describing a standardization process in accordance with various embodiments of the present teachings.
[024] FIG. 17 illustrates an exemplary method for validating an instrument in accordance with various modalities described herein.
[025] FIG. 18 illustrates another exemplary method for validating an instrument in accordance with various embodiments described herein.
[026] FIG. 19 illustrates the determination of a plurality of fluorescence thresholds from amplification data in accordance with various embodiments described herein.
[027] FIG. 20 illustrates a system for validating an instrument in accordance with various embodiments described herein.
[028] FIG. 21 illustrates a system for calibrating an instrument in accordance with various embodiments described herein. DETAILED DESCRIPTION
[029] Exemplary systems for methods relating to the various embodiments described herein include those described in US Industrial Design Patent Application Number ___ (Life Technologies Attorney Document Number LTOIOOO DES), and US Provisional Patent Application Number ___ (Life Technologies Attorney Document Number LT01011 PRO), and US Provisional Patent Application Number ___ (Life Technologies Attorney Document Number LT01023 PRO), and US Provisional Patent Application number___ (Life Technologies Attorney Document Number LT01025 PRO) , and US Provisional Patent Application Number ___ (Life Technologies Attorney Document Number LT01028 PRO), and US Provisional Patent Application Number ___ (Life Technologies Attorney Document Number LT01029 PRO), and US Provisional Patent Application Number ___ ( Life Technologies Attorney Document Number LT01032 PRO), and US Provisional Patent Application number ___ (Lif and Technologies Attorney Document Number LT01033 PRO), all of which were filed on February 6, 2015 and are also incorporated herein by reference in their entirety.
[030] To provide a more complete understanding of the present invention, the description below presents several specific details, such as specific settings, parameters, examples and the like. However, it should be recognized that this description is not intended to limit the scope of the present invention, but is intended to provide a better description of the exemplary embodiments.
[031] Advances in the calibration of biological analysis instruments advantageously allow to reduce operator error, reduce operator input and reduce the time required to calibrate a biological analysis instrument, and its various components, for a proper and efficient installation.
[032] As such, according to various embodiments, the present teachings can incorporate expert knowledge into an automated calibration and validation system providing feedback on troubleshooting and pass/fail status when a failure is identified. If an instrument fails the calibration process, an in-service engineer can be called upon. The present teachings can minimize the cost and time required for installation and calibration procedures.
[033] It should be recognized that the methods and systems described in this document can be implemented in various types of systems, instruments and machines, such as biological analysis systems. For example, multiple modalities can be implemented in an instrument, system, or machine that performs polymerase chain reactions (PCR) on a plurality of samples. While it is generally applicable for quantitative polymerase chain reactions (qPCR) where many samples are being processed, it should be recognized that any suitable PCR method can be used according to the various modalities described herein. Suitable PCR methods include, but are not limited to, digital PCR, allele-specific PCR, asymmetric PCR, ligation-mediated PCR, multiplex PCR, nested PCR, qPCR, genome walk and bridging PCR, for example. In addition, as used herein, amplification can include the use of a thermocycler, isothermal amplification, thermal convection, infrared-mediated thermal cycling, or helicase-dependent amplification, for example. GENERAL CALIBRATION WORKFLOW
[034] Biological instruments are often designed to produce accurate and reliable data for experiments. Calibration and regular maintenance of biological instruments ensure proper and optimal instrument function, which can maximize user productivity, minimize costly repairs, address potential problems before they arise, and increase the quality of results.
[035] According to various embodiments of the present teachings, the calibration methods described in this document can be performed separately or in any combination together. In addition, the calibration methods described here can be performed after manufacture for initial calibration or any time after initial installation and use. The calibration methods described here can be performed weekly, monthly, semi-annually, annually or as needed, for example.
[036] According to various modalities described in the present teachings, calibration methods such as region of interest (ROI) calibration, background calibration, uniformity calibration, pure dye calibration, instrument normalization are used to determine the location and intensity of fluorescent signals at each reading, the dye associated with each fluorescent signal, and the significance of the signal. In addition, under various modalities, automatic dye correction, automatic background calibration and plaque detection can be performed to further refine dye detection and readings and determine errors. Validation of proper instrument performance can also be performed automatically by the system using RNase P validation.
[037] FIG. 1 illustrates an exemplary calibration workflow 100 that can be performed on an instrument in accordance with the various modalities described herein. It should be recognized that calibration workflow 100 is an example and that the calibration methods described herein can be performed separately, or as a subset, in any combination and order.
[038] In step 102, an ROI calibration is performed. Generally ROI calibration will yield information that defines the positions of the wells in the detector's field of view. The present teachings can automate ROI calibration by minimizing or eliminating user interaction. Various modalities can automate the process, providing methods and systems that determine the optimal exposure time per filter using histogram analysis and a binary search pattern. ROI calibration, according to the various modalities described here, identifies wells in an image more accurately and with fewer errors than previous methods. ROI calibration methods and systems, according to various modalities, are further described below.
[039] In step 104, a background calibration is performed. Often a detector will read a certain amount of signal even in the absence of a sample that emits a detectable signal. Accounting for this background signal can be important as the background signal can be subtracted from a sample signal reading to obtain a more accurate measure of the sample signal. Background calibration can be performed using a water plate to determine the instrument's background signal for each filter/well combination. The step can be automated to minimize or eliminate user interaction. Automation can be provided which will test whether the correct plate has been used for background calibration. For example, step 104 can look at the signal level and eliminate the possibility of using an incorrect test board, such as the strong signal emission test board used in the ROI calibration. If the signal level exceeds the expected background level, the user can be prompted to insert the appropriate test board. In addition, this step can test for contamination of one or more wells on the test plate, checking for wide divergence of signal levels and, if so verified, trigger a warning that indicates the possible existence of dirty or contaminated wells. Contaminated wells can cause an inadequate background signal level to be subtracted from the sample signal level.
[040] In step 106, a uniformity calibration is performed. In some cases, variations in plate geometry (warp, thickness) can cause intensity readings to vary on a plate despite the presence of equal amounts of fluorescent dye in each well. Uniformity calibrations can calibrate the instrument using a multi-stain plate so that intensity variations due to plate variations can be corrected. Step 106 can be automated and reduce or eliminate user interaction. Parts of this automation can include detecting the use of the wrong calibration plate and detecting and adjusting for empty or contaminated wells in the calibration plate.
[041] In step 108, a calibration with pure dye is performed. Calibration with fluorescent dyes used in a qPCR instrument allows the instrument software to use the calibration data collected from the dye standards to characterize and distinguish the individual contribution of each dye to the total fluorescence collected by the instrument. In accordance with various embodiments of the present teachings, after a sample test, the instrument software receives data in the form of a raw spectrum signal for each reading. The software determines the contribution of each of the fluorescent dyes used at each reaction site by comparing the raw spectra contributed by each dye with the calibration data of pure spectra. When a user saves an experiment after analysis, the instrument software stores the pure spectra along with the fluorescence data collected for that experiment, as well as the contribution of each fluorescence dye per well. The method is further described below. Using pure dye calibration, in accordance with various modalities of the present teachings, fewer pure calibration plates can be used, saving a user cost and eliminating sources of calibration errors.
[042] In step 110, an instrument normalization calibration is performed. A common difficulty faced is the inability of researchers to easily compare the results of experiments performed on multiple instruments. Physical variations in the parameters of components such as light sources, optical elements and fluorescence detectors, for example, can result in variation in analysis results when they are identical biological samples. There is, therefore, a continuing need for methods and instruments to help minimize variations in components.
[043] In qPCR, amplification curves are often determined by normalizing the signal from the reporter dye to a passive reference dye in the same solution. This normalization can be reported as normalized fluorescence values labeled or "Rn". Passive reference normalization allows for consistent Rn values even if the overall signal level is affected by liquid volume or overall illumination intensity. Passive reference normalization, however, may not work correctly if the signal ratio between the reporter dye and the reference dye varies, such as instrument-to-instrument differences in the illumination spectrum. In accordance with the various embodiments described herein, instrument normalization calibration includes reading the fluorescence of the dye mixture to obtain a "normalization factor" to adjust the Rn values, which requires additional expense.
[044] In step 112, an RNase P validation is performed. When performing a validation test, it verifies that an instrument is working correctly. For example, RNase P validation determines whether an instrument can accurately distinguish between two different amounts of sample. Previously, an RNase P validation was performed manually using a standard curve, with the user performing statistical calculations to validate the instrument. In accordance with various embodiments described in the present teachings, validation of RNase P can be performed automatically by the system without using a standard curve. Various modalities of an RNase P validation are further described below.
[045] FIG. 21 illustrates a system for calibrating an instrument in accordance with various embodiments described herein. The 2100 system includes the 2102 ROI calibrator, 2104 pure dye calibrator, 2108 instrument normalization calibrator, 2110 RNase P validator, and 2106 display/GUI engine. reaction site positions in an image. Perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; The 2108 instrument normalization calibrator is configured to determine a filter normalization factor. The RNase P 2110 validator is configured to validate that the instrument is able to distinguish between two different amounts of sample. The 2106 display engine is configured to display calibration results.
[046] The present teachings are described with reference to Real Time Polymerase Chain Reaction (RT-PCR) instruments. In particular, one embodiment of the present teachings is implemented for RT-PCR instruments that use optical imaging of well plates. Such instruments may be capable of simultaneously measuring signals from a plurality of samples or test points for analytical purposes and often require calibration, including but not limited to processes involving: ROI (Regions of Interest) identification, signal determination background, uniformity and spectral calibration of pure dye for multi-component analysis. Calibration may also involve an RT-PCR validation reaction using a known sample plate with an expected result. One of skill in the art will appreciate that while the present teachings have been described with examples relating to RT-PCR instruments, its principles are broadly applicable to other forms of laboratory instrumentation that may require calibration and verification to ensure accuracy and/or optimization of results. PCR INSTRUMENTS
[047] As mentioned above, an instrument that can be used according to various modalities, but not limited to it, is a polymerase chain reaction (PCR) instrument. FIG. FIG. 2 is a block diagram illustrating a PCR instrument 200 upon which embodiments of the present teachings can be implemented. PCR instrument 200 may include a heated cover 210 that is placed over a plurality of samples 212 contained in a substrate (not shown). In various embodiments, a substrate can be a glass or plastic slide with a plurality of sample regions, which sample regions have a cover between the sample regions and the heated cover 210. Some examples of a substrate may include, but not are limited to, a multi-well plate, such as a standard 96-well microtiter plate, a 384-well microtiter plate, or a microplate, or a substantially flat support, such as a glass or plastic slide. Reaction sites in various modalities of a substrate can include depressions, indentations, ribs and combinations thereof, patterned in regular or irregular arrays formed on the surface of the substrate. Various embodiments of PCR instruments include a sample block 214, heating and cooling elements 216, a heat exchanger 218, control system 220 and user interface 222. Various embodiments of a thermal block assembly in accordance with the present teachings comprise components 214-218 of the PCR instrument 200 of FIG. two.
[048] Real-time PCR Instrument 200 has an optical system 224. In FIG. 2, an optical system 224 may have an illumination source (not shown) that emits electromagnetic energy, an optical sensor, detector or image (not shown) to receive electromagnetic energy from samples 212 on a substrate, and optical elements 240 used to guide the electromagnetic energy of each DNA sample to the imager. For the modalities of the PCR instrument 200 in FIG. 2 and the real-time PCR instrument 200 in FIG. 2, the control system 220 can be used to control the functions of detection system, heated cover and thermal block assembly. Control system 220 may be accessible to an end user via user interface 222 of the PCR instrument 200 in FIG. 2 and the real-time PCR instrument 200 in FIG. 2. In addition, a computing system 200 as depicted in FIG. 2, can serve to provide control of the function of the PCR instrument 200 in FIG. 2, as well as UI function. In addition, computing system 400 of FIG. 4 can provide data processing, display and report preparation functions. All of these instrument control functions can be dedicated locally on the PCR instrument, or the computer system 400 of FIG. 4 can provide remote control of part or all of the control, analysis and reporting functions, as will be discussed in more detail later. OPTICAL SYSTEM FOR IMAGING
[049] FIG. 3 depicts an exemplary optical system 300 that can be used for imaging in accordance with the 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 can be used to capture images of an object of interest. According to various embodiments, an object of interest can be a sample holder such as, for example, a calibration plate as described herein. An optical sensor 302 included in a camera 304, for example, can represent an object of interest 310. The optical sensor 302 can be a CCD sensor and the camera 304 can be a CCD camera. In addition, the optical sensor includes a 306 camera lens.
[050] Depending on the object of interest, an emission filter 308 can be chosen for imaging the object of interest 310 according to various modalities. The emission filter 308 can be altered to image the fluorescent emission emitted from the object of interest 301 in other modalities.
[051] The optical system 300 can use a reflected light source 312 to image the object of interest 310. The light from the light source 312 can be filtered through a sphere 314, a focuser/diverter 316, and the excitation filter 318 before being reflected to object of interest 310 by beam propagation device 320. Optical system 300 may also include a field lens 322. Depending on the object of interest, excitation filter 318 may be chosen or changed to image the object of interest 310 according to various modalities.
[052] The following descriptions of the various implementations of the present teachings have been presented for purposes of illustration and description. They are not exhaustive and do not limit the present teachings to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing the present teachings. Furthermore, the described implementation includes software, but the present teachings can be implemented as a combination of hardware and software or just hardware. The present teachings can be implemented with object-oriented and non-object-oriented programming systems. COMPUTER SYSTEM
[053] FIG. 4 is a block diagram illustrating a computing system 400 that may be employed to perform processing functionality in accordance with various embodiments. Instruments for performing experiments can be connected to the exemplary computer system 400. The computer system 400 can include one or more processors, such as a 404 processor. The 404 processor can be implemented using a general-purpose 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.
[054] In addition, it should be appreciated that a computing system 400 of FIG. 4 can be incorporated in any of several forms, such as a rackmount computer, mainframe, supercomputer, server, client, desktop computer, laptop computer, tablet, portable computing device (eg PDA, cell phone, smartphone, palmtop, etc.), cluster and grid, netbook, embedded systems, or any other type of special or general purpose computing device, as is desirable or appropriate for a particular application or environment. In addition, a computing system 400 may include a conventional network system that includes a client/server environment and one or more database servers, or integration with the 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. Furthermore, client/server environments, database servers and networks are well documented in the art. In accordance with various embodiments described herein, computing system 400 may be configured to connect to one or more servers in a distributed network. Computer system 400 can receive information or updates from the distributed network. Computer system 400 can also transmit information to be stored on the distributed network that can be accessed by other clients connected to the distributed network.
[055] Computer system 400 may include a bus 402 or other communication mechanism for communicating information, and a processor 404 coupled with bus 402 for processing information.
[056] The computing system 400 also includes a memory 406, which can be random access memory (RAM) or another type of dynamic memory, coupled to the bus 402 to store instructions to be executed by the processor 404. The memory 406 is also can be used to temporary storage variables or other intermediate information during the execution of instructions to be executed by processor 404. Computer system 400 further includes a read-only memory (ROM) 408 or other static storage device coupled to bus 402 to store static information and instructions for the 404 processor.
[057] 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 to store information and instructions. Storage device 410 may include a media drive and a removable storage interface. A media drive can include a drive or other mechanism to support fixed or removable storage media, such as a hard drive, a floppy drive, a magnetic tape drive, an optical disc drive, a CD drive, or DVD (R or RW), flash drive, or other removable or fixed media drive. As these examples illustrate, the storage media may include a computer readable storage media having stored thereon specific computer software, instructions or data.
[058] In alternative embodiments, storage device 410 may include other similar instruments to allow computer programs or other instructions or data to be loaded into computing system 400. Such instruments may include, for example, a computer unit. removable storage and an interface, such as a program cartridge and a cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot and other removable storage drives and interfaces that allow software and data are transferred from storage device 410 to computing system 400.
[059] Computer system 400 may also include a communications interface 418. Communications interface 418 may be used to allow software and data to be transferred between computer system 400 and external devices. Examples of a 418 communications interface may include a modem, a network interface (such as an Ethernet or other NIC card), a communication port (such as a USB port, an RS-232C serial port), a slot, and PCMCIA card, Bluetooth, etc. The software and data transferred via the 418 communications interface are in the form of signals which may be electronic, electromagnetic, optical or other signals that can be received by the 418 communications interface. These signals may be transmitted and received by the 418 communications interface through a channel, such as wireless, wired or cable, fiber optic, or other communication media. Some examples of a channel include a telephone line, a cell phone link, an RF link, a network interface, a local or wide area network, and other communication channels.
[060] The computing system 400 can be coupled through the bus 402 to a screen 412, such as a cathode ray tube (CRT) or liquid crystal display (LCD), to display information to a computer user. An input device 414, including alphanumeric and other keys, is coupled to bus 402 to communicate information and command selections to processor 404, for example. An input device can also be a screen, such as an LCD screen, configured with touchscreen input capability. Another type of user input device is the 416 cursor control, such as a mouse, trackball or cursor direction keys to communicate direction information and command selections to a 404 processor and to control cursor movement in the screen 412. This input device typically has two degrees of freedom on two axes, a first axis (eg x) and a second axis (eg y), which allows the device to specify positions in a plane . A computer system 400 provides data processing and provides a level of confidence for such data. Consistent with certain implementations of embodiments of the present teachings, data processing and confidence values are provided by computer 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 other computer-readable media, such as storage device 410. Execution of the instruction sequences contained in memory 406 causes processor 404 to perform the process states described herein. Alternatively, hardware circuitry can be used in place of or in combination with software instructions to implement embodiments of the present teachings. Thus, embodiments implementations of the present teachings are not limited to any specific combination of hardware and software circuitry.
[061] The term "computer readable media" and "computer program product" as used herein refers generally to any medium that is involved in providing one or more sequences or one or more instructions to the 404 processor for execution . Such instructions, commonly referred to as "computer program code" (which may be grouped as computer programs or other groups), when performed, allow computer system 400 to perform functions or features of embodiments of the present invention. These and other forms of non-transient computer readable media can take many forms, including, but not limited to, non-volatile media, volatile media, and broadcast media. Non-volatile media include, for example, solid state, optical or magnetic disks, such as storage device 410. Volatile media include dynamic memory, such as memory 406. Transmission media include coaxial cables, copper wire and optical fibers, including the wires that make up the 402 bus.
[062] Common forms of computer readable media include, for example, a floppy disk, floppy disk, hard disk, magnetic tape or any other magnetic media, a CD-ROM, any other optical media, punched cards, paper tape , any other physical medium with hole patterns, a RAM, PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave, as described below, or any other medium from which a computer can read.
[063] Various forms of computer readable media may be involved in carrying out one or more sequences of one or more instructions to the 404 processor for execution. For example, instructions can initially be carried out on a remote computer's magnetic disk. The remote computer can load the instructions into its dynamic memory and send the instructions over a phone line using a modem. A local modem to computer system 400 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus 402 can receive the data carried on the infrared signal and place the data on the bus 402. The bus 402 carries the data to memory 406, from which processor 404 retrieves and executes the instructions. Instructions received by memory 406 may optionally be stored in storage device 410 either before or after execution by processor 404.
[064] It will be noted that, for purposes of clarity, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be evident that any suitable distribution of functionality between different units, processors or functional domains can be used without prejudice to the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Thus, references to specific functional units should only be seen as references to adequate means of providing the described functionality, rather than indicative of a rigorous logical or physical organization or structure. DISTRIBUTED SYSTEM
[065] Some of the elements of a typical Internet 500 network configuration are shown in FIG. 5, in which numerous client machines 502, possibly at a remote local office, are shown connected to a gateway/hub/server-tunnel/etc. 510, which is itself connected to the internet 508 through some Internet Service Provider (ISP) 510 connection. Other similar possible 512 clients connected to the internet 508 through an ISP connection 514 are also shown, with these units communicating , possibly with a laboratory or central office, for example, via an ISP connection 516 to a port/tunnel-server 518 that is connected 520 to various 522 enterprise application servers that could be connected via another hub/router 526 to multiple local clients 530. Any one of these servers 522 can function as a development server for potential content control analysis and provision design solutions, as described in the present invention, as more fully described below. CALIBRATION OF THE REGION OF INTEREST (ROI)
[066] As presented above, the present teachings are described with reference to the Real Time Polymerase Chain Reaction (RT-PCR) instruments. In particular, one embodiment of the present teachings is implemented for RT-PCR instruments that use optical imaging of well plates. Such instruments may be able to simultaneously measure signals from a plurality of samples or test points for analytical purposes and often require calibration. An example of a process that might require calibration is identifying ROIs or regions of interest.
[067] In general, ROI calibration can be performed using a plate with strong emissions in each cell corresponding to all filters. This can be useful as the ROI may not be identical for each filter. Differences in ROIs between filters can be caused by slight angular differences in the filters and other spectral characteristics of the filter. Thus, several modalities perform per-filter/per-well (PFPW) ROI calibration. These ROI-PFPW calibrations are useful for determining well locations on the 96-well plate for each filter. Calibration of the ROI can be performed using a method such as in the Fitting Mask Production teachings as described in U.S. Patent No. 6,518,068 B1.
[068] The present teachings can automate ROI calibration by minimizing or eliminating user interaction. Various modalities can automate the process by providing software that determines the optimal exposure time per filter using histogram analysis and a binary search pattern. Exposure time is the amount of time required to capture an image of the plate. Again, this value can vary depending on the spectral characteristics of a filter. Generally ROI calibration will yield information that defines the positions of the wells in the detector's field of view. This information can be stored as mask files 304 or with a global mask or multiple masks corresponding to different filters.
[069] Calibration processes such as the one described above often use row and column projections and intensity profiles. This can result in ROI determinations being susceptible to artifacts and saturation within the wells, grid rotation, varying magnification factors and optical radial distortion. As a consequence, it may be advantageous to have a more robust determination of ROIs to minimize such susceptibilities and remove distortions and other undesirable background noise in the detected emission data.
[070] Background noise can refer to inherent system noise as well as other undesirable signals. For example, some background noise in the data could be due to physical sources on the substrate, such as dust particles or scratches, for example. Another example of a physical source that can provide background noise is a support or box that supports or surrounds the sample. Other background noise in the data may be due to natural radiation from instrument surfaces, such as reflection and natural fluorescence. Other background noise can also be a result of the optical system detecting the emission data or the light source, for example.
[071] The biological instrument can detect from several hundreds to several thousands of samples, which may be of a very small volume, such as less than one nanoliter. As such, other background noise removal methods can be used separately or in combination with the calibration methods described in this document, according to various modalities to be able to determine and analyze the emission data from the sample volumes. In some embodiments, the location of sample volumes can be more accurately determined within the substrate to perform a more accurate analysis. For example, in digital PCR analysis, being able to more precisely distinguish reactions in sample volumes versus non-reactions can produce more accurate results. Furthermore, in accordance with various embodiments described herein, empty wells or through holes can be distinguished from sample volumes in unreacted wells or through holes, which can also be distinguished from sample volumes in wells or through holes who reacted.
[072] According to various modalities described here, the removal of background noise can include the analysis and processing of image data. The method may include analyzing image data intensity values to interpolate background noise that can be removed from the substrate image. In this way, the locations of the regions of interest within the image can also be determined. Background noise removal can also include interpolation data from image areas known to include regions of interest. After determining the background noise on the image, the background noise can be subtracted from the image data.
[073] FIG. 6 depicts an example of an in silico (computer-performed) method 600 in accordance with an embodiment of the present invention. The in silico method 600 includes a plurality of assembly workflow subroutines in a computer readable format that can include subroutines from a biotech process. FIG 6 is merely an exemplary method and one of ordinary skill in the art, in light of the present disclosure, will appreciate that the actual number of subroutines can range from at least about 2 subroutines to many (e.g., 2-10 , 2-20, 2-30, 2-n (where n can be any number of subroutines from 3-100, 3-1000, and so on)). Each set 310-370 stanza may include a single step or task, or optionally it may include more than one step or task, also in a computer-readable format, and each step may further include additional customizable steps or tasks optional. Each of the optional/customizable steps or tasks can have one or more optional parameters (options) that can be viewed, analyzed, defined or customized by the user. In some embodiments, an in silico method of the invention includes selection by a user of at least one parameter of each for each optional/customizable step of the biotechnology process using a graphical user interface (GUI) to select at least one parameter for each step optional/customizable. In certain modalities, each step and all the subroutine parameters of a workflow are available for a user to view and optionally edit. Bioinformatics programs usually hide some of these parameters and/or steps from users, which causes user frustration and inefficiency especially when the result of an experiment designed in silico is not the expected result for a user.
[074] An exemplary in silico method of the disclosure illustrated generally in FIG. 6 can be performed (executed) by generating at least one method file in a computing system (as shown in FIG. 4), the method file comprising computer readable instructions for a plurality of subroutines (10 , 20, 30.) of the customization steps (A, B, C), each of which may have one or more parameters that can be viewed, selected, changed or entered; and performing the in silico biotechnology process comprising performing at least one method file comprising computer readable instructions by the computer system to obtain at least one biotechnology product.
[075] In some embodiments, at least one optional/customizable parameter is selected from a default parameter, where the default parameter is stored in a computer system component (eg storage, database, etc.) .
[076] Referring again to FIG. 6, the first step in calculating ROI locations is to estimate the initial ROI centers from the fluorescence threshold in step 610. A sample plate configured to contain a plurality of biological samples is provided and inserted into an analytical instrument capable of analyzing samples through the PCR process. Each biological sample is contained in a sample well and can be excited by a light source and, in response to the excitation, can fluoresce at a predetermined wavelength, which can be detected by a fluorescence detector. As shown above with reference to 2, the light source 202 may be a laser, LED, or other type of excitation source capable of emitting a spectrum that interacts with the spectral species to be detected by the system 200. In addition, the biological samples may include spectrally distinct dyes such as one or more of FAM, SYBR Green, VIC, JOE, TAMRA, NED, CY-3, Texas red, CY-5, ROX (passive reference) or any other fluorochromes that emit a capable signal to be detected.
[077] Prior to excitation, the biological samples input parameters and algorithm parameters are defined to provide a starting point for determining the ROI. Input parameters can include well size, well center-to-center distance, optical pixels per millimeter, and plate layout. Plate layout can include the total number of wells and the configuration of sample wells. A frequently used configuration may be a rectangular matrix comprising a plurality of rows and a plurality of columns, however, one skilled in the art will understand that the configuration can be any geometry suitable for the instrument being used. Also, the total number of wells may vary. One skilled in the art will be familiar with configurations totaling from 1 well to thousands of wells in a single sample plate or sample containment structure. The ROI verification algorithm parameters can define acceptable ranges for well size, well center-to-center distance, and minimum roundness. Roundness is a calculated value and can be a ratio of perimeter to area.
[078] Once the input parameters and algorithm parameters have been determined, the plurality of samples are excited with energy from an appropriate light source, and images are collected of the fluorescence emitted from each sample well in the plate. sample. Fluorescence images from the sample plate are further analyzed to select ROI candidates based on input parameters and algorithm parameters. ROI candidates that meet the parameters are saved for further analysis and the size and circularity of each well is determined in step 620. ROI candidates that do not meet the parameters can be discarded along with any sites that do not fluoresce. Retained ROI candidates are further evaluated to determine the distance between the ROI based on the well-to-well spacing parameter and the allowable range parameter for the well-to-well spacing. ROIs that have centers that are in close proximity to each other based on well-to-well parameters can be considered to be the same sample well, and the one with the best roundness is selected as the ROI for that well. Once all ROI candidates have been determined, the average well size is calculated, the average is assigned to each sample ROI well in step 630, and the initial estimated ROIs are saved.
[079] The expected well locations are arranged in a grid pattern determined based on the plate layout parameter. This parameter can include the number of wells, the number of columns, and the number of rows where each well has an expected set of X-Y grid coordinates based on the plate layout parameter. Further analysis can now be initiated on initial estimated ROIs to better define the locations of each initial ROI and may be referred to as the global grid. The first step in the global grid is to analyze the centers of the initial estimated ROIs to find the adjacent ROIs. This can be determined by comparing the center-to-center distance between the ROIs to the grid coordinates based on the plate layout. The X-Y grid coordinates can then be determined for each of the initial estimated ROIs based on the spatial relationship between the ROIs.
[080] In order to improve the accuracy of the ROI locations it would be advantageous to relate the center-to-center ROI coordinates to the grid coordinates of the plate layout. This can be accomplished by determining and applying mapping functions. Mapping functions are a pair of two-dimensional quadratic polynomial functions. These functions are calculated to map the X (or Y) grid locations to the ROI center locations in the X (or Y) direction. Once the mapping functions have been determined, they can be applied to the expected grid coordinates to provide two benefits. First the accuracy of ROI center locations can be improved, and second it may be possible to recover the ROIs that were missing during the initial ROI finding.
[081] In addition, tuning ROIs can provide additional benefits for optical performance. The inventors discovered that there is a relationship between the size of the ROI and the signal-to-noise ratio (SNR) of the optical system. A person skilled in the art would know that there are several equations for calculating the SNR of electrical and optical systems. The SNR can be characterized with Equation 1 below, for example:

[082] where SNR = Signal to Noise ratio SColor board = the sum of all pixel intensities within the ROIs from the SBG dye images = the sum of all pixel intensities within the Scouring background images ROIs = the sum of all pixel intensities within the dye ROIs N = the number of pixels within an ROI offset = the camera offset G = camera gain δ2R,y = read noise
[083] An experiment was conducted using an optical system that included six pairs of filters. Each pair of filters included an excitation filter (Xn) and an emission filter (Mn). Each filter was sensitive to a narrow band of wavelengths corresponding to the excitation frequency and emission frequency of the dye configured to be compatible with the PCR process. In addition, ROIs have been optimized according to the teachings presented in this document. In order to study the effect of ROI size on the signal to noise ratio, fluorescence was detected from a 96-well sample plate using 6 pairs of filters. The radius of each ROI was extended in 1-pixel increments. Equation 1 was used to calculate the SNR for each of the 6 filter pairs and each pixel increment. The results of the experiment are shown below in Table 1:

[084] Bold entries identify the highest SNR for each of the 6 filter pairs, and a radius length of 2 pixels provides an overall SNR improvement of about 6% among the 6 filter pairs.
[085] FIG. 7 shows an image of a 96-well sample plate 710. Each of the 710 wells produced a fluorescent image. After applying the teachings in this document, the ROIs were optimized and the blue circles identify the ROI for each well position. CALIBRATION OF PURE DYE
[086] As described above, there is a growing need to simplify the installation and configuration of bioassay systems so that operators can use bioassay systems more quickly and efficiently for their intended purpose. This need is evident, for example, in the calibration of a biological analysis instrument and associated components. An exemplary calibration is the calibration of fluorescent dyes used for the detection of fluorescence in biological analysis systems such as, for example, qPCR systems.
[087] Calibration with fluorescent dyes used in a qPCR instrument allows the instrument software to use the calibration data collected from the dye standards to characterize and distinguish the individual contribution of each dye to the total fluorescence collected by the instrument. After a sample test, the instrument's software receives data in the form of a raw spectra signal for each reading. The software determines the contribution of each of the fluorescent dyes used at each reaction site by comparing the raw spectra contributed by each dye to the calibration data of the pure spectra. When a user saves an experiment after analysis, the instrument software stores the pure spectrum, along with the fluorescence data collected for that experiment, as well as the contribution of each fluorescence dye per well.
[088] The product of a dye calibration on a qPCR instrument, for example, is a collection of spectral profiles that represent the fluorescence signature of each dye standard for each reaction site. Each profile consists of a set of spectra that correspond to the fluorescence collected from reaction sites, such as wells, of a sample holder, such as, for example, a calibration plate or matrix. After calibrating each dye, the instrument software “draws” a spectral profile for each dye at each reaction site. The software plots the resulting data for each of the profiles on a graph of fluorescence versus filter. When the software extracts the dye calibration data, it evaluates the fluorescence signal generated by each well, in terms of the collective spectra for the entire calibration plate or matrix card. Dye spectra are generally acceptable if they peak within the same filter as their group, but diverge slightly at other wavelengths.
[089] When performing dye calibration on a sample holder, such as a calibration plate, the reaction sites (eg, wells) generally contain identical concentrations of dye to allow the generation of a pure spectrum value in each plate well. FIG 8 shows an image of a single dye calibration plate (in this case FAM dye) occupying each well of a 96-well calibration plate. This allows comparison of the fluorescence signal generated by each well, in a test of a pure spectrum read for that well. When using a single dye for each well of a calibration plate, the resulting signals for the wells should be similar. Variations in spectral position and peak position can be caused, for example, by small differences in optical properties and excitation energy between individual wells. Taking these variations in dye calibration into account theoretically results in a more accurate dye calibration.
[090] However, the use of a single dye per calibration plate can be time-consuming and complicated, particularly when calibrating numerous dyes. Non-limiting examples of fluorescent dyes include, FAM, VIC, ROX, SYBR, MP, ABY, JUN, NED, TAMRA and CY5. Therefore, there is a need to simplify the dye calibration process and reduce the time required for calibration while maintaining the same quality of dye calibration results.
[091] FIGS. 9 and 10 illustrate a flowchart depicting an exemplary method 900 of calibrating fluorescent dye(s) in accordance with the modalities described herein. The steps of method 900 can be implemented by a processor 404 as shown in FIG. 4. In addition, instructions for performing the method by processor 404 can be stored in memory 406.
[092] With reference to FIG. 9, in step 902, calibration plates are prepared by loading dyes into reaction sites of a substrate for processing. The substrate in this case is a 96-well plate, although different substrates may be used, including, for example, a 384-well plate. In various embodiments, the substrate can be a glass or plastic slide with a plurality of sample regions. Some examples of a substrate may include, but are not limited to, a multi-well plate, such as a standard 96-well microtiter plate, a 384-well plate, or a microplate, a substantially flat support, such as a slide glass or plastic, or any other type of matrix or micro-matrix. Reaction sites in various modalities of a substrate can include pits, depressions, indentations, grooves, and combinations thereof, patterned into regular or irregular arrays, formed on the substrate surface. Heretofore, references to wells or plates are for exemplary purposes only and do not in any way limit the type of reaction site or sample holder usable herein.
[093] Calibration plates can be prepared in a grid pattern as illustrated in FIG. 11 A. As illustrated in the 1100, 1120 and 1140 calibration plates, the plates themselves can be of a 96-well format, although the number of wells in the calibration plate can be varied as needed depending on, for example, the number of dyes requiring calibration, the format of the 314 sample block (see FIG. 3) that the calibration plate receives, or the capabilities of the instrument (PCR 300 instrument, for example) to image plates at different well densities .
[094] The checkered dye distribution pattern allows multiple dyes to be calibrated per calibration plate. As opposed to calibrating one dye per calibration plate, the checkered pattern advantageously allows a user to use fewer plates to calibrate a set of dyes, thus decreasing the time and process steps required for dye calibration.
[095] In the embodiment illustrated in FIG. 11A, three plates are used to calibrate ten separate dyes. Each 1100/1120/1140 Calibration Plate is configured to accommodate four different dyes in a repeat pattern of alternating dyes across the wells in each row of the plate such that each well has a specific dye in the repeat pattern (dye presented in the well). For example, plate 1100 accommodates FAM, VIC, ROX, and SYBR dyes in alternate wells exemplified by wells 1102 (FAM), 1104 (VIC), 1106 (ROX), and 1108 (SYBR); plate 1120 accommodates a buffer, MP dye, ABY dye and JUN dye in alternate wells exemplified by wells 1122 (buffer), 1124 (MP), 1126 (ABY) and 1128 (JUN); and plate 1140 accommodates NED dye, TAMRA dye, CY5 dye and a buffer in alternate wells exemplified by wells 1142 (NED), 1144 (TAMRA), 1146 (CY5) and 1148 (buffer). In this modality, since only ten dyes are being calibrated, the buffers are used in plates 1120 and 1140 as fillers for wells that do not accommodate a dye to be calibrated.
[096] It should be understood that the embodiment in FIGS. 11A and 11B is just an example, and that the number of total dyes calibrated, the number of dyes per plate, and the number of plates may vary as needed, based, for example, on a user's calibration needs, in the number of wells on the plate, and the instrument's calibration handling capability. For example, if 12 dyes are being calibrated in the modality illustrated in FIG. 11A, a buffer would not be required on the 1120 and 1140 plates as four dyes can be calibrated on each of the three 1100/1120/1140 calibration plates for a total of 12 dyes.
[097] In addition, the number of dyes per plate can be two or more, with the maximum number of dyes per plate based on, for example, the number of wells in the calibration plate, the ability of the instrument used to properly model a complete plate (see below for further explanation), and the ability of the imaging system to obtain usable fluorescence data from the chosen plate. For example, instead of using a 96-well plate as illustrated in FIG. 11A, one may have a sufficiently robust instrument and associated imaging system to be able to use a 384-well calibration plate. With the additional well density provided, you can calibrate more dyes per plate, eg 16 dyes per plate, and still get the same number of data points (ie, dye-bearing wells) per dye (eg 24) needed to obtain a sufficient global template (discussed in more detail below). For example, with a 384-well plate, 10 dyes can be calibrated using two plates and five dyes per plate.
[098] Even the type of sample holder and type of reaction site can affect the number of possible dyes. As stated above, other types of sample holders and reaction sites can be used for calibration.
[099] Returning to FIG. 9, at step 904, the prepared gridded calibration plates can be loaded into the instrument. The number of plates loadable in an instrument at one time depends on the capabilities and capacity of the instrument used. For example, a standard qPCR thermal cycler with a 96-well block will only accept one calibration plate at a time. However, multiblock thermal cyclers can offer multiple blocks that can each receive a calibration plate. Furthermore, if a calibration plate is not used, according to the format of the sample holder used (for example, a microarray or microchip array), multiple sample holders can be received on a single instrument using, for example, a charging assembly that fits the instrument.
[0100] In step 906 of FIG. 9, the instrument, using the associated optical imaging system (see, for example, FIG. 3) acquires images from the loaded calibration plate, or from plates in series or in parallel. The acquired images and associated data can be stored, for example, in memory 406 or storage device 410 of computing system 400 in FIG. 4. The optical imaging system can acquire images from each of the plates in each optical channel. The number of channels depends on the number of excitation and emission filters provided in the imaging system. For example, for an optical imaging system having 6 excitation filters (X filters) and 6 emission filters (M filters), the total number of channels is 21, represented by the following filter combinations: XI M1, X1M2, X1M3, X1M4, X1M5, X1M6, X2M2, X2M3, X2M4, X2M5, X2M6, X3M3, X3M4, X3M5, X3M6, X4M4, X4M5, X4M6, X5M5, X5M6 and X6M6. The number of images or exposures acquired on each channel may vary. For example, the imaging system can acquire two images or exposures per channel. The number of images or exposures taken depends on the user's needs, as having fewer images or exposures per channel can decrease the time needed to acquire images or exposures, and having more images or exposures per channel provides a higher probability of quality data .
[0101] In step 908 of FIG. 9, the instrument, using the data gathered from the images or exposures acquired by the optical imaging system (see, for example, FIG. 3), identifies the peak channel for each dye on the calibration plate. This peak channel for each reaction site is the channel in which the specific dye analyzed shows the greatest fluorescence for the reaction site. Peak channel identification can occur when, for example, 95% or more reaction sites are occupied by dye, in this case allowing no more than 5% outlier reaction sites during calibration. The percentage of outliers allowed may vary. Outliers can then be discarded from further calculation and analysis. Outliers can occur, for example, when the wrong dyes are loaded, dyes are loaded in the wrong setting, when there is improper dye loading, or optical components become dirty (eg dust particles). The peak channel for each dye on the calibration plate can be identified, for example, by processor 404 of computer system 400 using data stored in memory 406. Identification results can be stored, for example, in memory 406 or storage device 410 of computer system 400.
[0102] Alternatively, the fluorescence data obtained gathered from the images or exposures acquired by the optical imaging system for each filter combination at each reaction site can be corrected by background and uniformity correction before identifying the channel. peak, using background component and uniformity factors determined using background and uniformity calibration methods known in the art.
[0103] In step 910 of FIG. 9, the instrument, using the data gathered from the images or exposures acquired by the optical imaging system (see, for example, FIG. 2), normalizes each channel to the peak channel identified from step 908 for all wells that showed dye. Each channel can be normalized to the peak channel identified, for example, by processor 404 of computing system 200, using data stored in memory 406. Normalization results can be stored, for example, in memory 406 or device. storage 410 of computing system 400.
[0104] All wells shown with dyes are taken as a quantitative baseline value from which to normalize. Generally, the greater the quantitative value, the greater the fluorescence detected. Therefore, the peak channel identified for a given dye would have the highest quantitative value for that dye in the dye-bearing wells, except for outlier peak channel values. Regardless of the peak channel quantitative value, to normalize, the quantitative value in the channel is reset to a value of one. The remaining quantitative values for the same dye in other channels are then adjusted according to the reset value of one for the peak channel. For example, if it is the dye of X, peak channel A had a quantitative value of 100 in the wells, and another channel B had a quantitative value of 40 in the wells, after normalization, peak channel A is set to 1 .0 and channel B is set to 0.40. This normalized value can also be referred to as a calibration factor, with the calibration factor for the peak channel being set to 1.0 as discussed above.
[0105] In the embodiment illustrated in FIGS. 11A and B, when four dyes are evenly dispersed among the wells of a 96-well plate, the number of wells that presented dye per dye would be 24. The number of wells displayed with dyes may vary for reasons discussed above, such as, for eg the number of reaction sites (eg wells) on the sample holder (eg calibration plate), the number of dyes per dispersion in the sample holder. For example, in a 96-well plate, if three dyes are dispersed, the number of wells shown with dyes would be 32 per dye. If there are six dyes dispersed in the 96-well plate, it would be 16 wells displayed with dyes per dye.
[0106] Referring now to FIG. 10, in step 912, the instrument performs global modeling for all dye wells. To calibrate a dye for all wells of a sample holder format, the instrument can use the well data presented with dyes for a specific dye to model all wells, including wells without a specific dye. Global modeling can be performed, for example, by processor 404 of computer system 400, using well data presented with dyes for a specific dye to model all wells. The resulting template can be stored, for example, in memory 406 or storage device 410. Referring to FIG. 11A, for the FAM dye present in 24 wells 1102 of plate 1100, the other 112 wells on the plate would be FAM dye not shown. The same distribution of 24 shown/72 not shown would apply to each dye in FIG. 11 A. The number of wells not shown with dyes depends on the number of wells shown with dyes, which, as discussed above, may depend for several reasons. Regardless, the sum of displayed and undisplayed wells with dyes for a given plate is equal to the number of wells on that plate. FIG 11 B is an image of a 4-dyes gridded 96-well calibration plate using the FAM, VIC, ROX, and SYBR dyes in the same configuration illustrated by plate 1100 in FIG. 11 A.
[0107] In an alternative modality, the instrument performs global shaping for all channels or channels that have a normalized value, for example, greater than 0.01, or 1% of the identified peak channel. For those channels below this threshold, the instrument could perform local modeling (see step 922 of FIG. 10) rather than performing global modeling. Global shaping may become unnecessary at such low levels in certain channels that the fluorescence detected is primarily a result of, for example, noise or other disturbance, rather than the contribution of the actual dye being calibrated.
[0108] A global modeling algorithm can work on a dye calibration to derive a model of dye calibration factors for each filter channel for each dye based on the measured dye calibration factors from the wells presented with specific dye. For example, if 24 wells are presented in the 96-well gridded plate for a specific dye, the global modeling uses the dye calibration factors from these 24 wells to derive the calibration factors for all wells, including the other 72 wells not shown with dyes and thus produce a template for the entire plate per channel, per dye.
[0109] The two-dimensional (2D) quadratic polynomial function is an example of a function that can be applied as a global model for dye calibration factors. Other global modeling functions are known and can be used here. A nonlinear least squares solver can be used to derive the 2D quadratic polynomial function from the dye calibration factors measured in the wells presented with specific dye, minimizing modeling residues (the difference between the values calculated from the model and the measured dye calibration factors). The Levenberg-Marquardt confidence region algorithm can be used as the optimization algorithm in this solver. Although many other optimization algorithms are usable here, another example is the Dogleg method, whose key idea is to use both the Gauss-Newton and Cauthy methods to calculate the optimization step to optimize the nonlinear objective. This approach approximates the objective function using a model function (often quadratic) over a subset of the search space known as the confidence region. If the model function manages to minimize the true objective function, the trust region is expanded. On the other hand, if the approximation is weak, then the region is contracted and the model function is applied again. A loss function, for example, can also be used to reduce the influence of high residuals (greater difference between calculated and measured calibration factors). These high residuals often constitute outliers on optimization.
[0110] In step 914 of FIG. 10, after all wells are modeled for a given dye or dyes, the instrument performs a quality of fit check (GOF). This can ensure that the global modeling step is sufficiently reliable. A GOF check can be performed, for example, by processor 404 of computing system 400, with the results stored, for example, in memory 406 or storage device 410. Quality of fit measures typically summarize the discrepancy between values observed and expected values under the model in question. GOF can be determined in many ways including, for example, coefficient of determination of R squared and root mean square error (RMSE) values. R squared, for example, is a statistic that will generate some information about the goodness of fit of a model. In regression, the R-squared coefficient of determination is a statistical measure of how well the regression line approaches the actual data points. An R squared of 1 indicates that the regression line fits the data perfectly. The RMSE is the square root of the mean squared of the differences or residuals between the observed values and the expected values under the model in question. The RMSE is a good measure of the model's predictive accuracy. A RMSE of 0 indicates that the expected values under the model are exactly equal to the observed values.
[0111] In step 916 of FIG. 10, if there is a good fit, then the instrument produces a dye matrix in step 918 of FIG. 9. A good statistical fit can occur, in R squared analysis, for example, when R squared values are, for example, greater than or equal to 0.85, or RMSE values that are, for example , less than or equal to 0.01, as illustrated in FIG. 10. The dye matrix can be prepared, for example, by the processor 204 of computer system 200, and transmitted to the screen 212.
[0112] In step 920 of figure 10, if there is a bad fit, then the instrument performs a local modeling, in step 922 of FIG. 10. This may become necessary, for example, if the R value calculated for a GOF check is less than 0.85, for example, and the RMSE values are greater than 0.01 for example. Local modeling can be performed, for example, by processor 404 of computer system 400, using data from wells displayed with dyes for a specific dye to model the remaining wells not displayed with dyes. The resulting template can be stored, for example, in memory 406 or storage device 410.
[0113] A local modeling method may include, for example, the use of calibration factors from the wells presented with surrounding dye for the same dye on the plate. For example, to determine the calibration factor value in a well not presented with dye for a specific dye, the local model can take the average value of all wells presented with specific dye of the same dye that are within a local window of 5x5 of the surrounding wells or the entire plate. This average value is determined until a complete modeling of the plate is completed. The local modeling output can then override the global modeling output.
[0114] At the completion of the local modeling, the dye matrix is sufficient so that the instrument produces the dye matrix, in step 918 of FIG. 10. This dye matrix serves as a profile of the fluorescence signature of each calibrated dye. After each test, the instrument receives data in the form of a raw spectra signal for each reading. The instrument determines the contribution of the fluorescent dyes used in each reaction by comparing the raw spectra to the calibration data of the pure dye matrix spectra. The instrument uses the calibration data obtained from the dye standards (ie the dye matrix) to characterize and distinguish the individual contribution of each dye to the total fluorescence collected by the instrument. INSTRUMENT STANDARDIZATION CALIBRATION
[0115] Currently, genomic analysis, including that of approximately 30,000 human genes, is an important focus of basic and applied biochemical and pharmaceutical research. Such analysis can aid in the development of diagnostics, drugs, and therapies for a wide variety of disorders. However, the complexity of the human genome and the interrelated functions of genes often make this task difficult. A common difficulty faced is the inability of researchers to easily compare the results of experiments performed on multiple instruments. Physical variations in the parameters of components such as light sources, optical elements and fluorescence detectors, for example, can result in variation in analysis results when they are identical biological samples. There is, therefore, a continuing need for methods and instruments to help minimize variations in components.
[0116] In qPCR, amplification curves are often determined by normalizing the signal from a reporter dye to a passive reference dye in the same solution. Examples of reporter dyes may include, but are not limited to, FAM, SYBR Green, VIC, JOE, TAMRA, NED CY-3, Texas Red, CY-5. An example of a passive referral can be, but is not limited to, ROX. This normalization can be classified as labeled normalized fluorescence values or "Rn". Passive reference normalization allows for consistent Rn values even if the overall signal level is affected by liquid volume or overall illumination intensity. Passive reference normalization, however, may not work correctly if the signal ratio between the reporter dye and the reference dye varies, such as instrument-to-instrument differences in the illumination spectrum. In order to adjust, normalization solutions can be fabricated to normalize the reporter to passive reference ratio. An example of such a normalizing solution may be a 50:50 mixture of FAM and ROX, which may be referred to as a "FAM/ROX" normalizing solution.
[0117] This current method of instrument normalization, including reading the dye mixture fluorescence to obtain a "normalization factor" to adjust the Rn values, requires additional expense. Typically, it requires the fabrication of standardization solutions and standardization boards, and time to perform additional calibrations. Also, this method only works for dye mixtures that you are calibrating with a standard paired filter set. A set of matched filters can be a combination of an excitation filter and an emission filter. One skilled in the art will understand that adding an additional dye would require a different normalization and calibration solution.
[0118] The manufacturing processes to produce the standardization solutions also contribute to variations in the response of dyes. It has been found that controlling dye concentrations can be difficult due to the lack of an absolute fluorescence standard. In order to minimize these errors and variations, it may be advantageous to target the solution's dye ratio to within +/-15% of the desired blend, or within +/- 10% of the desired blend from the manufacturing process . The manufacturing process is usually not well controlled enough to simply mix a 50:50 blend of the dyes and meet these specifications, so an additional step in the process is needed to adjust the dye mix with a fluorimeter.
[0119] The acceptable percentage variations described above were determined by studying the relationship between the variation in the dye mixture and CTS. A Ct is a common abbreviation for a "limit cycle". Quantitative PCR (qPCR) can provide a method to determine how much of a target sequence or gene is present in a sample. During PCR, a biological sample is subjected to a series of 35 or 40 temperature cycles. A cycle can have multiple temperatures. For each temperature cycle the amount of target sequence could theoretically double and is dependent on a number of factors not presented here. Since the target sequence contains a fluorescent dye, as the amount of target sequence increases, ie it is amplified during the 35 or 40 temperature cycles, the sample solution fluoresces brighter and brighter with each cycle. thermal. The amount of required fluorescence that must be measured by a fluorescence detector is often referred to as a "threshold", and the cycle number in which fluorescence is detected is referred to as the "threshold cycle" or Ct. Therefore, by knowing how efficient the amplification and Ct is, the amount of target sequence in the original sample can be determined.
[0120] The percent tolerated variation described above may also be related to the standard deviation of the Ct deviations in the instrument. It has been determined that a +/- 15% variation in the dye mixture can result in a standard deviation of 0.2 Ct, which can be 2 standard deviations.
[0121] As shown above, the ability to reliably compare experimental results from multiple instruments is desirable and instrument-to-instrument variability is often an issue. This variability can result from two sources; the variability of components within instruments such as lamps and filters and the variability over time such as lamp and filter aging. It would be advantageous to implement a process whereby experimental results from multiple instruments can be compared reliably, easily and inexpensively. The teachings found here disclose such a process.
[0122] The amount of fluorescence signal from a sample of an optical system can be dependent on several factors. Some of the factors may include, but are not limited to, the wavelength of fluorescence light, the efficiency of the detector at that wavelength of fluorescence light, the emission filter efficiency, the excitation filter efficiency, and the efficiency of the dye. The present teachings suggest that instrument-to-instrument variability can be minimized if the physical optical elements of the instruments can be normalized.
[0123] In one embodiment, normalization factors can be derived from pure dye spectra rather than from dye mixtures. Pure dyes can be easier to manufacture than dye mixtures because the concentrations don't have to be exact, and there is only one fluorescent component. This concept was tested by normalizing 2 sets of filters on one instrument using 10 pure dyes and comparing the results with the normalization obtained using dye mixtures. Normalization was implemented by determining a correction factor for each excitation filter and emission filter. The resulting correction factors can be used to normalize any combination of dyes, even from different instruments.
[0124] In another modality, the normalization taught above has been applied to multiple instruments of various types. Eight dye mixture solutions and 10 pure dye solutions were created. Each solution was pipetted into 8 wells of three 96-well plates. Potential spatial interference was minimized by pipetting into all other wells. The dye mixtures used are shown in FIG. 12A and the pure dyes used are shown in FIG. 12A and the pure dyes used are shown in FIG. 4B. In addition, the instruments used included 6 sets of filters. FIG 12B further identifies the filter pairs for the main optical channel for each pure dye. The excitation filter is represented with an "X" and the emission filter is represented with an "M".
[0125] In an effort to quantify the effectiveness of the normalization process, dye ratios were measured before and after normalization. FIG 13 shows the percent deviation of dye mixtures from the average ratio for 17 instruments tested. The instruments are marked on the X-axis and the percent deviation is on the Y-axis. One Skilled in the Art These data, as a consequence, demonstrate the need for an improved normalization process, such as the present teachings. The present teachings were applied to all 17 instruments.
[0126] Current teachings have been applied to all 17 instruments. The normalization method determines a correction factor for each individual filter rather than for each dye ratio. Since the instruments provided with 6 excitation and 6 emission filters, 12 factors were determined. The process is shown in FIG. 16 and in flowchart 1600. In step 1605, calibration spectra were generated by multiple dyes in multiple filter combinations. For the instruments to be normalized, there were 10 pure dyes and 21 filter combinations. In step 1610, the spectra were normalized so that the maximum signal is 1. In step 1615 the dye spectra are calculated across multiple wells. This calculation will result in the production of a spectrum per dye. Collectively, the dye spectra can be referred to as an "M" dye matrix containing the dye and filter combinations. At this point, a reference instrument is identified. The reference instrument would be an instrument or group of instruments on which test instruments will be standardized. The same set of dye spectra used in the test instrument can be obtained from the reference instrument(s). In some modalities, the reference can be a group of instruments. In such an embodiment, the spectra for each dye can be calculated over the entire group. This step is represented in flowchart 1600 at step 1620. As an example, the reference spectra can be referred to as the 'Mref' matrix.
[0127] In step 1625 each of the filters 12 has an adjustment factor initially set to 1. What is desired is to multiply the adjustment factors by the matrix "M" while iteratively modifying the adjustment factors between 0 and ground, from preferably, between 0.04 and 1 until the difference between the matrix "M" and the matrix Mref is minimized as shown in step 1630. In step 1635, the correction factors of each pair of filters are calculated. The correction factor for each filter pair is the product of the emission filter factor times the excitation filter factor. The main channel filter pairs are shown in FIG. 4B. Once the correction factors for each filter pair have been determined, each filter pair factor can then be multiplied by the fluorescence data for the test instrument as well as the pure dye spectra. The corrected pure dye spectra can then be renormalized to a maximum value of 1 as shown in step 1645. The final step of the process in step 1650 is to generate multi-component data. One of skill in the art will understand the multi-component procedure as being the product of the fluorescence data and the pseudo-inverse of the dye matrix. The multi-component values are already normalized so it would not be necessary to make dye-specific corrections as the data has already been normalized to the filter level.
[0128] Upon completion of normalization, the % deviation of the dye mixtures from the mean ratio was calculated across all 17 instruments. The results are shown in FIG. 14. These results are significantly improved compared to the data in FIG. 13 before standardization. A closer view of the normalized data is shown in FIG. 15, in which the deviations after normalization were reduced to +/- 8%, which is well below the target of +/- 15% as shown above. RNASE P VALIDATION
[0129] As mentioned above, it is important to validate an instrument to make sure it is working properly, especially after a fresh installation or after multiple uses. In this way, a user can be assured that experimental results and analyzes are accurate and reliable. Previously, a validation assay was performed on the instrument by a user and the user manually performed data analysis on the amplification data from the verification assay to validate the instrument. Because data analysis was performed manually by the user, the validation process was more error-prone and time-consuming.
[0130] In accordance with various embodiments of the present teachings, automated validation methods and systems are provided. An example of a validation assay is an RNase P test. An example of a validation assay is an RNase P assay. However, as used here, the validation assay can be any assay that has known and reliable properties and that can be used to validate an instrument.
[0131] After installation and after several uses, it is important to validate that the instrument is working properly. After installation and after several uses, it is important to validate that the instrument is working properly. The RNase P gene is a single copy gene that encodes the RNA portion of the RNase P enzyme. It is often used as a validation assay because of its known properties and characteristics.
[0132] A validation plate is preloaded with the necessary reagents for detection and quantification of sample genomic copies. For example, in an RNase P validation plate, each well contains PCR master mix, RNase P primers, probe labeled with FAM™ dye, and a known concentration of human genomic DNA template.
[0133] In a traditional RNAase P assay example, a standard curve is generated from the Ct (cycle limit) values obtained from a set of replicated standards (1250, 2500, 5000, 10000 and 20000 copies). The standard curve is then used to determine the copy number for two sets of unknown templates (replicata populations 5000 and 10000). The instrument is validated if it can demonstrate the ability to distinguish between 5,000 and 10,000 genomic equivalents with a 99.7% confidence level for a subsequent single-well sample test.
[0134] For installation approval, instruments must demonstrate the ability to distinguish between 5,000 and 10,000 genomic equivalents with a 99.7% confidence level for a subsequent sample test in a single well.
[0135] Under various embodiments, the present teachings can incorporate expert knowledge into an automated calibration and validation system providing pass/fail status and troubleshooting feedback when a failure is identified. If an instrument fails the validation process, the user knows that an on-duty engineer can be called in, for example. The present teachings can minimize the cost and time required for installation and calibration procedures.
[0136] As indicated above, according to the various modalities described here, the purpose of a validation analysis is to confirm that the two quantities of the same sample are sufficiently distinguishable by the instrument. In this way, the instrument's performance can be validated.
[0137] In accordance with various embodiments of the present teachings, an automated validation method and system is provided. Cycle threshold values (Cts) from a validation run are analyzed and compared by a system to determine whether an instrument can sufficiently distinguish two quantities of a sample. An example of a validation assay is an RNase P test. An example of a validation assay is the RNAase P assay. In this example, a system determines the Ct values generated for RNase P samples from genomic copies of 5000 and 10000 to determine whether the data from the 5000 and 10000 genomic copies are sufficiently distinguishable. Sufficiently distinguishable, according to the modalities described here, means at least 3 standard deviations (3°) (-99.7%) separate from the amplification data of the genomic copies of 5000 and 10000. The method according to various modalities is described further below with reference to FIGS. 17 and 18.
[0138] FIG. 17 illustrates an exemplary method for validating an instrument in accordance with various modalities described herein. In general, step 1702 begins by receiving amplification data from a validation assay plate to generate a plurality of amplification curves, each corresponding to a well in the plate.
[0139] Plates contain a plurality of wells. In some examples, a plate contains 96 wells. In other examples, a plate contains 384 wells. A portion of the wells on the plate may contain a sample of a first amount and another portion of the wells on the plate may contain a sample of a second amount. The first quantity and the second quantity are different. The second amount is greater than the first amount of the various modalities described here. The second amount can be a 1.5 times difference from the first amount in some modalities. In other embodiments, the second amount may be a 2-fold difference from the first amount. In accordance with various embodiments described herein, the second amount can be any number of times difference from the first amount. In some embodiments, the first amount can be 5000 genomic copies per well and the second amount can be 10,000 genomic copies per well.
[0140] Referring again to FIG. 17, in step 1704, a plurality of fluorescence thresholds is determined based on the plurality of amplification curves generated. The exponential regions of the plurality of amplification curves are compared to determine a range of fluorescence values when the exponential regions fall. For example, the range of fluorescence values from the smallest fluorescence value of a lower part of an exponential region to the largest fluorescence value of an upper part of an exponential region of the plurality of amplification curves is determined. The range of fluorescence values is used in automated analysis of a plurality of amplification curves to validate the instrument in accordance with the embodiments of the present teachings.
[0141] With reference to FIG. 19, a plurality of curves for amplifying and determining a range of fluorescence values and corresponding cycle thresholds is illustrated. Each of the plurality of amplification curves includes an exponential region of the curve. The 1902 axis indicates the fluorescence values. The 1904 axis illustrates cycle numbers. Fluorescence range 1906 shows the range of fluorescence values from the smallest fluorescence value of a determined lower part of an exponential region of the plurality of exponential regions and the largest fluorescence value of a determined upper part of an exponential region of the plurality of exponential regions. Under various embodiments, the range of fluorescence values is evenly divided by a predetermined number to generate a set of fluorescence values for automated analysis by the system. In one example, the range of 1906 fluorescence values is divided by 100 to determine 100 fluorescence values for a set of fluorescence thresholds. In some embodiments, the top 5 fluorescence values and the bottom 5 fluorescence values are discarded so that the analysis proceeds with a set of 90 fluorescence thresholds.
[0142] Referring again to FIG. 17, at step 1706, for each fluorescence value from the set of fluorescence values, the cycle threshold (Ct) is determined for each plurality of amplification curves generated from the wells containing the first quantity of sample. Similarly, for each fluorescence value from the set of fluorescence values, the cycle threshold (Ct) is determined for each plurality of amplification curves generated from wells containing the second amount of sample.
[0143] In step 1708, using the Ct values for the first and second quantities for each of the fluorescence values of the set, it is determined whether the first and second quantities are sufficiently distinguishable. Sufficiently distinguishable, according to various modalities, means that, using equation (1), a positive result is obtained for at least one of the fluorescence values in the set:

[0144] Equation 1 determines if a first and a second quantity are sufficiently distinguishable, where quant2 is greater than quant1, according to the modalities described here. Sufficiently distinguishable means that at least 3 standard deviations (3°) (-99.7%) separate the Ct values of the first and second quantities. If the quantities are found to be sufficiently distinguishable, an indication is provided to the user that the instrument is validated.
[0145] FIG. 18 illustrates another exemplary method for validating an instrument in accordance with various embodiments described herein. At step 1802, amplification data is received from a plurality of samples comprised in the wells of a validation plate. A portion of the wells on the validation plate contain a sample in a first quantity. Another portion of the validation plate wells contains the sample in a second amount. The first quantity and the second quantity are different. The second amount can be a 1.5 times difference from the first amount in some modalities. In other embodiments, the second amount may be a 2-fold difference from the first amount. In accordance with various embodiments described herein, the second amount can be any number of times difference from the first amount. In some embodiments, the first amount can be 5000 genomic copies per well and the second amount can be 10,000 genomic copies per well.
[0146] In step 1804, a first set of fluorescence thresholds is determined based on the plurality of generated amplification curves. The exponential regions of the plurality of amplification curves are compared to determine a range of fluorescence values when the exponential regions fall. For example, the range of fluorescence values from the smallest fluorescence value of a lower part of an exponential region to the largest fluorescence value of an upper part of an exponential region of the plurality of amplification curves is determined. The range of fluorescence values is used in automated analysis of a plurality of amplification curves to validate the instrument in accordance with the embodiments of the present teachings.
[0147] According to various embodiments, the range of fluorescence values is divided evenly by a predetermined number to generate a set of fluorescence values for automated analysis by the system. In one example, the range of 1906 fluorescence values is divided by 100 to determine 100 fluorescence values for a set of fluorescence thresholds. In some embodiments, the top 5 fluorescence values and the bottom 5 fluorescence values are discarded so that the analysis proceeds with a set of 90 fluorescence thresholds.
[0148] In step 1806, for each fluorescence threshold of the set, a first set of Ct values for the amplification curves corresponding to the first quantity is determined. Similarly, for each fluorescence threshold in the set, a second set of Ct values for the amplification curves corresponding to the first amount is determined. This is repeated for each fluorescence threshold in the pool.
[0149] In some embodiments, a predetermined number of outliers Ct values are removed from each set of Ct values before further calculations are performed. For example, in some embodiments, if a 96-well plate is used, 6 outliers are removed from each set of Ct values. An outlier is a Ct value further away from the mean value of the set of Ct values. In another example, if a plate of 364 is used, 10 of outliers are removed from each set of Ct values. After the outliers are removed, the remaining Ct values from each set are used in the remaining steps of the method.
[0150] In step 1808, for each set of Ct values, an average is calculated. In other words, a first average of Ct is calculated for the first number of amplification curves and a second average of Ct is calculated for the second number of amplification curves for each fluorescence threshold of the set determined in step 1804.
[0151] Similar to step 1808, in step 1810, 3 standard deviations of each set of Ct values are calculated. In other words, a first of 3 standard deviations is calculated for the first amount of amplification curves and a second of 3 standard deviations is calculated for the second amount of amplification curves for each fluorescence threshold of the set determined in step 1804.
[0152] To determine whether the Ct values of the first quantity and the second quantity, or sufficiently distinguishable, the Ct values in a fluorescence value, according to various modalities, and the Ct values are compared. According to various modalities, equation (2) is used for the comparison.

[0153] Equation 2 determines whether a first and a second quantity are sufficiently distinguishable, according to the modalities described here. Sufficiently distinguishable means that at least 3 standard deviations (3°) (-99.7%) separate the Ct values of the first and second quantities.
[0154] In step 1814, the results of equation (2) for all fluorescence thresholds in the set are compared to determine a maximum value. If the maximum value is a positive number, the instrument can sufficiently distinguish between the first and second quantities and an indication that the instrument is validated is provided to the user in step 1816. If the maximum value is a negative number, the instrument can not sufficiently distinguishing between the first and second quantity and an indication that the instrument failed validation is provided to the user in step 1818. FIG. 20 illustrates a system 2000 for validating an instrument according to the various modalities described herein. System 2000 includes PCR instrument 2002 interface, Ct database 2004, display engine /GUI 2006, Ct 2008 calculator and 2010 validator.
[0155] The 2002 PCR instrument interface receives the amplification data from the PCR instrument to generate amplification curves. As described above, the PCR instrument amplifies the samples contained in the validation plate. The validation plate includes a portion of wells containing a sample of a first amount and the other portion of wells containing a sample of a second amount. The fluorescence data generated from the amplification of the samples is received by the 2002 PCR instrument interface.
[0156] After a set of fluorescence thresholds is determined as in steps 1704 and 1804, with reference to FIGS. 17 and 18, respectively, the Ct calculator 2006 calculates a first and second set of corresponding Ct values for the amplification curves generated from the samples of the first quantity and the second quantity, respectively. A first and second set of Ct values are calculated for each fluorescence threshold in the set of fluorescence thresholds. The plurality of sets of Ct values are stored in Ct 2004 database.
[0157] The 2010 validator determines if the first and second quantities are sufficiently distinguishable as described in step 1708 in FIG. 17 and at steps 1810 and 1812 in FIG. 18.
[0158] The display/GUI engine shows the plurality of amplification curves to the user. Also, after the 2010 validator determines whether the first and second quantities are sufficiently distinguishable, the 2006 GUI/View Engine shows an indication of validation or validation failure to the user.
[0159] In addition, an optimal fluorescence threshold can be determined. The optimal fluorescence threshold can be determined by, according to various modalities, selecting the Ct value that results in the maximum separation between
. In addition, the optimal fluorescence threshold can also be selected based on the Ct value that resulted in the fewest possible outliers determined. The optimal fluorescence threshold can also be selected based on the Ct value that resulted in the maximum separation between
with the smallest number of outliers determined. AUTOMATIC DYE CORRECTION
[0160] In accordance with various embodiments of the present teachings, automatic dye correction methods can be used to perform a real-time spectral calibration of multiple component data. Automatic dye correction can be performed in real-time or after amplification data is collected and secondary analysis is performed. In the automatic dye correction algorithm, a multi-component correlation matrix is generated. Under various embodiments, an automatic dye correction algorithm adjusts the elements of the dye matrix so that the off-diagonal terms of the multi-component correlation matrix are minimized. In this way, errors in Ct determinations are minimized. AUTOMATIC BACKGROUND CALIBRATION
[0161] In accordance with various embodiments of the present teachings, an automatic background calibration can be performed to reduce the need for a background calibration plate and improve the overall effectiveness of the background correction.
[0162] Physical contaminants in the block (particles or chemistry) that occur during instrument use can negatively impact system analysis results by artificially inflating certain spectral components of the analyzed wells that are impacted by the contamination. Recalibration can solve this problem. However, to extend the periods between required calibrations, a method to automatically calculate/compensate for background changes after background calibration is described. To perform automatic background calibration, a method is performed using the empty/unoccupied block. The effective leak signal for consumables is known (determined empirically), and the effective background calibration slopes and offsets can be approximated using scale factors that make the effective leak signal. PLATE DETECTION
[0163] According to various modalities described here, the plate detection methods can be performed to identify errors in the positioning of the plate in the instrument.
[0164] During instrument use, the optical elements of the system are positioned either at the upper limit (during rest periods) or at the lower limit (during operation) of the path. The ability to read the position of optical elements at an intermediate location between path boundaries was not designed for hardware; as such, one cannot rely on the motor position value to determine whether a plate or tube is present or absent (where the difference in the position of the optical elements would be caused by the thickness of material added from the tube or plate present). Without the need for an additional component for the sensing plate or tube (such as a vacuum switch or position sensor), the sensing chamber in the system is used for sample detection. However, since only a small portion of the block region is captured through the use of a discrete and segregated well lens array (each lens in the array focuses and collects light from one and only one well), a traditional 'photo' of the consumable plane that captures the entire block region cannot be acquired for image processing. Since only the focused light from each well is collected and manifests as a circular glow point in the detector, there is no spatial or dynamic band in the detected image. However, if the optical elements are moved to an intermediate position that allows them to focus on the seal or lid of a container, this focus point can be captured as a reflected image (in contrast to fluorescence, which is the normal collected signal system), and used for plate/tube detection. The focal point would be smaller than a well, and this would manifest in the captured image as a small bright region relative to the size of a well (known as the investigation region, ROI). Understanding that the focus point can produce bright pixels and all other regions can produce darker pixels, a numerical analysis of the pixel level information can produce a presence/absence determination, in accordance with various embodiments described herein. STANDARDIZATION OF THE INSTRUMENT USING A REFLECTIVE MATERIAL
[0165] In accordance with various embodiments of the present teachings, instrument normalization using a reflective material, such as a photodiode, can be used for automatic instrument calibration after any initial calibrations made after fabrication or installation.
[0166] According to various modalities, a stable reflective material is measured during fabrication as a control. Reflective material can be placed over the heated lid. Subsequently, stable reflective material can be measured in all channels to detect any changes or variability. Any changes or variability can be used to adjust the color balance factors as described above in the instrument normalization calibration method to renormalize changes in excitation light. EXAMPLES
[0167] In example 1, a method for calibrating an instrument is provided, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0168] In example 2, example 1 is provided, in which the ROI calibration comprises: estimating the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0169] In example 3, example 1 is provided, in which the ROI calibration improves the errors in determining the reaction site by minimizing at least one of the following groups: dye saturation within the plurality of reaction sites, rotation of the grid, varying magnification factors, and optical radial distortion.
[0170] In example 4, example 1 is provided, wherein the calibration of the pure dye comprises: imaging a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of locations of reaction and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0171] In example 5, example 4 is provided, wherein imaging of the sample holder is performed four times for imaging four different sample holders.
[0172] In example 6, example 1 is provided, wherein the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0173] In example 7, example 1 is provided, where the filter normalization factor allows instrument data to be compared with data from a second instrument.
[0174] In example 8, example 1 is provided, wherein the validation of RNase P comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0175] In example 9, example 1 is provided, where the validation of RNase P is performed by a processor connected to the instrument.
[0176] In example 10, example 8 is provided, in which the validation of RNase P further comprises: displaying an indication of instrument validation or failure on a display screen.
[0177] In example 11, example 1 is provided, further comprising: performing an automatic dye correction for real-time spectral calibration of multiple component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0178] In example 12, a method for calibrating an instrument is provided, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0179] In example 13, example 12 is provided, in which the ROI calibration comprises: estimating the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0180] In example 14, example 12 is provided, in which the ROI calibration improves reaction site determination errors by minimizing at least one of the following groups: dye saturation within the plurality of reaction sites, rotation of the grid, varying magnification factors, and optical radial distortion.
[0181] In example 15, example 12 is provided, wherein the calibration of the pure dye comprises: imaging a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of locations of reaction and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0182] In example 16, example 15 is provided, wherein imaging of the sample holder is performed four times for imaging four different sample holders.
[0183] In example 17, example 12 is provided, wherein the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0184] In example 18, example 12 is provided, where the filter normalization factor allows the instrument data to be compared with data from a second instrument.
[0185] In example 19, example 12 is provided, wherein the validation of RNase P comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0186] In example 20, example 12 is provided, in which the validation of RNase P is performed by a processor connected to the instrument.
[0187] In example 21, example 19 is provided, in which the instructions for the validation of RNase P further comprise instructions to: display an indication of instrument validation or failure on a display screen.
[0188] In example 22, example 12 is provided, further comprising: performing an automatic dye correction for real-time spectral calibration of multiple component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0189] In example 23, a method for calibrating an instrument, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites is provided comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0190] In example 24, example 23 is provided, in which the ROI calibration comprises: estimating the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0191] In example 25, example 23 is provided, in which the ROI calibration improves reaction site determination errors by minimizing at least one of the following groups: dye saturation within the plurality of reaction sites, rotation of the grid, varying magnification factors, and optical radial distortion.
[0192] In example 26, example 23 is provided, wherein the calibration of the pure dye comprises: imaging a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of locations of reaction and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0193] In example 27, example 26 is provided, wherein imaging of the sample holder is performed four times for imaging four different sample holders.
[0194] In example 28, example 23 is provided, wherein the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0195] In example 29, example 23 is provided, where the filter normalization factor allows the instrument data to be compared with data from a second instrument.
[0196] In example 30, example 23 is provided, wherein the validation of RNase P comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0197] In example 31, example 23 is provided, in which the validation of RNase P is performed by a processor connected to the instrument.
[0198] In example 32, example 30 is provided, in which the instructions for the validation of RNase P further comprise instructions to: display an indication of instrument validation or failure on a display screen.
[0199] In example 33, example 23 is provided, further comprising: performing an automatic dye correction for real-time spectral calibration of multiple component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0200] In example 34, a system for calibrating an instrument, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites, is provided, which comprises: a calibrator of region of interest (ROI) configured to determine reaction site positions in an image; a pure dye calibrator configured to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; an instrument normalization calibrator configured to determine a filter normalization factor; an RNase P validator configured to validate that the instrument is able to distinguish between two different amounts of sample; and a display mechanism configured to display calibration results.
[0201] In example 35, example 34 is provided, in which the ROI calibrator is configured to: estimate the initial region of interest (ROI) from the fluorescence thresholds of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0202] In example 36, example 34 is provided, in which the ROI calibration improves reaction site determination errors by minimizing at least one of the following groups:: dye saturation within the plurality of reaction sites, rotation of the grid, magnification factor variation, and optical radial distortion.
[0203] In example 37, example 34 is provided, wherein the pure dye calibrator is configured to: image a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of reaction sites and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0204] In example 38, example 37 is provided, wherein the calibrator is configured to image the sample holder four times for imaging four different sample holders.
[0205] In example 39, example 34 is provided, wherein the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0206] In example 40, example 34 is provided, where the filter normalization factor allows the instrument data to be compared with data from a second instrument.
[0207] In example 41, example 34 is provided, wherein the validation of RNase P comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0208] In example 42, example 41 is provided, where the RNase P validator is further configured to: display an indication of instrument validation or failure in the display engine.
[0209] In example 43, example 34 is provided, which further comprises: an automatic dye corrector configured to perform real-time spectral calibration of multiple component data; a plate detector configured to determine if there is a plate loading error; an automatic background calibrator configured to compensate for background changes; and a normalizing instrument configured to use a reflective material to detect any changes or variability in fluorescent emissions.
[0210] In example 44, a method for calibrating an instrument is provided, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0211] In example 45, a method for calibrating an instrument is provided, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0212] In example 46, a method for calibrating an instrument is provided, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites comprising: performing a calibration of the region of interest (ROI) to determine reaction site positions in an image; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and perform an RNase P validation to validate that the instrument is able to distinguish between two different sample quantities.
[0213] In example 47, a system for calibrating an instrument, wherein the instrument includes an optical system capable of imaging the fluorescence emission from a plurality of reaction sites, is provided, which comprises: a calibrator of region of interest (ROI) configured to determine reaction site positions in an image; a pure dye calibrator configured to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; an instrument normalization calibrator configured to determine a filter normalization factor; an RNase P validator configured to validate that the instrument is able to distinguish between two different amounts of sample; and a display mechanism configured to display calibration results.
[0214] In example 48, example 45 is provided, in which the ROI calibration comprises: estimating the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0215] In alternative example 49, example 44, 45, 46, 47, or any previous example is provided, where the ROI calibration improves the errors in determining the reaction site by minimizing at least one of the following groups:: dye saturation within the plurality of reaction sites, grating rotation, varying magnification factors, and optical radial distortion.
[0216] In example 50, example 45 is provided, wherein the calibration of the pure dye comprises: imaging a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of locations of reaction and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0217] In alternative example 51, example 44, 45, 46, 47, 50 or any previous example is provided, where imaging of the sample holder is performed four times for imaging four different sample holders.
[0218] In example 52, example 45 is provided, wherein the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0219] In alternative example 53, example 44, 45, 46, 47, or any previous example is provided, where the filter normalization factor allows the instrument data to be compared with data from a second instrument.
[0220] In example 54, example 46 is provided, wherein the validation of RNase P comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0221] In alternative example 55, example 44, 45, 46, 47, or any previous example is provided, where the validation of RNase P is performed by a processor connected to the instrument.
[0222] In alternative example 56, example 44, 45, 46, 47, 54 or any previous example is provided, where RNase P validation further comprises: displaying an indication of instrument validation or failure on a display screen .
[0223] In alternative example 57, example 44, 45, 46, 47, or any previous example is provided, further comprising: performing an automatic dye correction for real-time spectral calibration of multi-component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0224] Exemplary systems for methods related to the various modalities described herein include those described in the following documents: • U.S. Industrial Design Patent Application No. 29/516,847, filed February 6, 2015; and • U.S. Industrial Design Patent Application No. 29/516,883; filed February 6, 2015; and • U.S. provisional patent application number 62/112,910, filed February 6, 2015; and • U.S. provisional patent application number 62/113,006, filed February 6, 2015; and • U.S. Provisional Patent Application number 62/113,077, filed February 6, 2015; and • U.S. provisional patent application number 62/113,058, filed February 6, 2015; and • U.S. provisional patent application number 62/112,964, filed February 6, 2015; and • U.S. provisional patent application number 62/113,118, filed February 6, 2015; and • U.S. provisional patent application number 62/113,212, filed February 6, 2015; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01011), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01023), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01025), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01028), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01029), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01032), filed February 5, 2016; and • U.S. Patent Application Number ___ (Life Technologies Attorney Document Number LT01033), filed February 5, 2016; and all of which are also incorporated herein by reference in their entirety.
[0225] Although various embodiments have been described with respect to certain exemplary embodiments, examples and applications, it will be apparent to those skilled in the art that various modifications and changes can be made without departing from the present teachings.

权利要求:
Claims (20)
[0001]
1. Method for calibrating an instrument, in which the instrument includes an optical system capable of imaging fluorescence emission from a plurality of reaction sites, the method CHARACTERIZED by the fact that it comprises: performing a calibration of the region of interest (ROI ) to determine reaction site positions in an image, where ROI calibration minimizes at least one of the following groups of reaction site determination errors: dye saturation within the plurality of reaction sites, grid rotation, variation of magnification factors, and optical radial distortion; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and performing a validation test to validate that the instrument is able to distinguish between two different amounts of sample.
[0002]
2. Method, according to claim 1, CHARACTERIZED by the fact that the ROI calibration comprises: estimating the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0003]
3. Method, according to claim 1, CHARACTERIZED by the fact that the validation assay comprises an assay of the RNase P gene.
[0004]
4. The method according to claim 1, CHARACTERIZED in that pure dye calibration comprises: imaging a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of locations of reaction and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0005]
5. Method according to claim 1, CHARACTERIZED by the fact that the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibration comprises: determining a first factor of correction for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0006]
6. Method according to claim 1, CHARACTERIZED by the fact that the validation assay comprises: receiving amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0007]
7. Method according to claim 1, CHARACTERIZED by the fact that it further comprises: performing an automatic dye correction for real-time spectral calibration of multiple component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0008]
8. Non-transient computer readable storage media, CHARACTERIZED by the fact that it is encoded with processor executable instructions for calibrating an instrument, wherein the instrument includes an optical system capable of imaging fluorescence emission from a plurality of locations of reaction, instructions comprising instructions to: perform a region of interest (ROI) calibration to determine reaction site positions in an image, where the ROI calibration minimizes at least one of the following groups of site determination errors reaction: dye saturation within the plurality of reaction sites, grating rotation, varying magnification factors, and optical radial distortion; perform a pure dye calibration to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; perform an instrument normalization calibration to determine a filter normalization factor; and performing a validation test to validate that the instrument is able to distinguish between two different amounts of sample.
[0009]
9. Non-transient computer readable storage media, in accordance with claim 8, CHARACTERIZED by the fact that the instructions for the calibration of the ROI comprise instructions to: estimate the initial region of interest (ROI) from fluorescence thresholds of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0010]
10. Non-transient computer readable storage media, according to claim 8, CHARACTERIZED by the fact that the validation assay comprises an RNase P gene assay.
[0011]
11. Non-transient computer readable storage media, according to claim 8, CHARACTERIZED by the fact that the instructions for calibration with pure dye comprise instructions to: image a sample holder, loaded into the instrument, in more than one a channel, the sample holder comprising a plurality of reaction sites and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0012]
12. Non-transient computer readable storage media according to claim 8, CHARACTERIZED by the fact that the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instructions for the calibration of Instrument normalization comprises instructions for: determining a first correction factor for each of the excitation filters and the emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0013]
13. Non-transient computer readable storage media according to claim 8, CHARACTERIZED by the fact that the instructions for the validation assay comprise instructions to: receive amplification data from a validation board to generate a plurality of amplification curves, wherein the validation plate includes a sample of a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0014]
14. Non-transient computer-readable storage media, in accordance with claim 8, CHARACTERIZED by the fact that it further comprises instructions for: performing an automatic dye correction for real-time spectral calibration of multi-component data; perform a plate detection to determine if there is a plate loading error; perform an automatic background calibration to compensate for background changes; and perform instrument normalization using a reflective material to detect any changes or variability in fluorescent emissions.
[0015]
15. System for calibrating an instrument, in which the instrument includes an optical system capable of imaging the emission of fluorescence from a plurality of reaction sites, the system CHARACTERIZED by the fact that it comprises: a calibrator of the region of interest (ROI ) configured to determine reaction site positions in an image, where ROI calibration minimizes at least one of the following groups of reaction site determination errors: dye saturation within the plurality of reaction sites, grid rotation , variation of magnification factors, and optical radial distortion; a pure dye calibrator configured to determine the contribution of a fluorescent dye used at each reaction site by comparing a raw spectrum of the fluorescent dye to a calibration data of the pure spectrum of the fluorescent dye; an instrument normalization calibrator configured to determine a filter normalization factor; a validator using a known assay configured to validate that the instrument is able to distinguish between two different amounts of sample; and a display mechanism configured to display calibration results.
[0016]
16. System, according to claim 15, CHARACTERIZED by the fact that the ROI calibrator is configured to: estimate the initial region of interest (ROI) from the fluorescence limits of each sample well; estimate the center locations for each ROI; estimate the size of each ROI; determine the average size of the ROIs from the plurality of reaction sites; derive global grid templates; apply global grid models to ROIs, where applying global grid models improves the accuracy of ROI center locations; recover missing ROIs; and adjust the radius of the ROIs, where the adjustment improves the signal-to-noise ratio of the optical system.
[0017]
17. The system according to claim 15, CHARACTERIZED by the fact that the pure dye calibrator is configured to: image a sample holder, loaded into the instrument, in more than one channel, the sample holder comprising a plurality of reaction sites and more than one type of dye, each dye occupying more than one reaction site; identify a peak channel for each dye on the sample holder; normalize each channel to the peak channel for each dye; and producing a dye matrix comprising a set of dye reference values.
[0018]
18. The system of claim 15, CHARACTERIZED by the fact that the optical system comprises a plurality of excitation filters and a plurality of emission filters, and wherein the instrument normalization calibrator is configured to: determine a first correction factor for each of the excitation and emission filters; calculating a second correction factor for a pair of filters, each pair of filters comprising an excitation filter and an emission filter; and apply the second correction factors to the filter data.
[0019]
19. The system according to claim 15, CHARACTERIZED by the fact that the validator is configured to: receive amplification data from a validation plate to generate a plurality of amplification curves, wherein the validation plate includes a sample of a first amount and a second amount, and each amplification curve includes an exponential region; determining a set of fluorescence thresholds based on the exponential regions of the plurality of amplification curves; determine, for each fluorescence threshold of the set, a first set of cycle threshold (Ct) values of amplification curves generated from the samples of the first quantity and a second set of Ct values of the amplification curves generated from the samples of the second quantity; and calculating whether the first and second amounts are sufficiently distinguishable based on Ct values at each of the plurality of fluorescence thresholds.
[0020]
20. System according to claim 15, CHARACTERIZED by the fact that it further comprises: an automatic dye corrector configured to perform real-time spectral calibration of multiple component data; a plate detector configured to determine if there is a plate loading error; an automatic background calibrator configured to compensate for background changes; and a normalizing instrument configured to use a reflective material to detect any changes or variability in fluorescent emissions.

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