Method for evaluating a spectral unmixing matrix and system therefor

Abstract

Aspects of the present disclosure include methods for evaluating a spectral unmixing matrix (e.g., for determining suitability of a fluorescent dye panel in generating flow cytometry data). Methods according to certain embodiments include receiving a computed spectral unmixing matrix in a computer cache, evaluating a matrix quality parameter of the computed spectral unmixing matrix, adjusting a cache input into the spectral unmixing matrix based on the evaluated matrix quality parameter, recomputing the spectral unmixing matrix using the adjusted input, and displaying updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface on a display in response to the adjusted input. Systems configured to perform the subject methods and non-transitory computer-readable storage media are also provided.

Description

Technical Field

[0001] This invention relates to a method and system for evaluating spectral unmixing matrices used in flow cytometry data analysis. Background Technology

[0002] Characterization of analytes in biofluids has become an important part of biological research, medical diagnostics, and overall patient health and wellness assessment. Detection of analytes in biofluids (such as human blood or blood-derived products) can provide results that can play a role in determining treatment options for patients with a variety of diseases.

[0003] Flow cytometry is a technique used to characterize biological materials (such as cells in a blood sample or particles of interest in another type of biological or chemical sample) and is often used to sort such materials. A flow cytometer typically includes a sample reservoir for receiving a fluid sample (e.g., a blood sample) and a sheath fluid reservoir containing sheath fluid. The flow cytometer delivers particles (including cells) from the fluid sample as a cell stream into a flow cell, while simultaneously guiding the sheath fluid into the flow cell. To characterize the composition of the flow stream, light is used to illuminate it. Changes in the material within the flow stream (e.g., the presence of morphology or fluorescent labels) can cause changes in the observed light, and these changes allow for characterization and separation. To characterize the components in the flow stream, light must act on the flow stream and be collected. The light source in a flow cytometer can be varied and may include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream, and the optical response from the illuminated particles is collected and quantified.

[0004] Biological particle separation is achieved by adding sorting or collection capabilities to flow cytometers. Particles detected in the separation stream and possessing one or more desired characteristics are individually separated from the sample stream by mechanical or electrostatic removal. Common flow sorting techniques use droplet sorting, where a flow containing linearly separated particles is broken down into droplets. The droplet containing the particle of interest is charged and deflected through an electric field into a collection tube. Typically, linearly separated particles in the flow are characterized as they pass through an observation point located directly below the nozzle tip. Once a particle is identified as meeting one or more desired criteria, the time it will reach the droplet break-off point and detach from the flow as a droplet can be predicted. Ideally, a brief charge is applied to the fluid flow shortly before the droplet containing the selected particle detaches from the flow, and then grounded immediately after the droplet detaches. The droplet to be sorted remains charged when it detaches from the fluid flow, while all other droplets are uncharged.

[0005] Flow cytometry is based on semi-quantitative measurements of fluorescence intensity on single cells, and is often multiplexed, in which case multiple fluorescent dyes with different spectra are measured simultaneously, and the signals are subsequently deconvolved using spectral compensation or demixing. Due to the physical nature of fluorescence detection, the variance (and signal-to-noise ratio) of measuring a fluorescent dye in a flow cytometry experiment can be affected by the presence of other fluorescent dyes in the experiment. Overflow diffusion error is measured by calculating the standard deviation of the demixed or compensated signal of the first fluorescent dye, measured on a single-staining cell population expressing the second fluorescent dye. This standard deviation is then compared to the standard deviation of the unstained population, and the difference is normalized to the intensity of the second fluorescent dye, thus yielding the overflow diffusion error. This calculation captures the diffusion caused by the second fluorescent dye entering the first fluorescent dye. Summary of the Invention

[0006] The inventors have recognized that the quality of the spectral unmixing matrix significantly impacts the reliability and accuracy of flow cytometry data generated using fluorescent dye sets. In particular, when determining the suitability of a fluorescent dye set for generating flow cytometry data, the spectral unmixing matrix will (e.g., by reducing spectral diffusion or spectral variance) affect the resolution and quality of the data, and determine the accuracy of sorting gates and sorting decisions. Embodiments of this disclosure address the aforementioned problems, as well as others. The inventors have determined that intuitive visualization and real-time (or near-real-time) updates of the spectral unmixing matrix can enable high-precision data generation in flow cytometry experiments and rapid identification and repair of problematic fluorescent dyes or autofluorescence within the fluorescent dye set. The inventors have discovered that when adjusting a spectral unmixing matrix with parallelism and caching mechanisms, evaluation matrix quality parameters can be used to optimize flow cytometry data generated using the selected fluorescent dye set for sample analysis.

[0007] This disclosure includes methods for evaluating spectral unmixing matrices (e.g., for determining the suitability of fluorescent dye sets in generating flow cytometry data). A method according to some embodiments includes: receiving a calculated spectral unmixing matrix in a computer buffer; evaluating matrix quality parameters of the calculated spectral unmixing matrix; adjusting the buffered input of the spectral unmixing matrix based on the evaluated matrix quality parameters; recalculating the spectral unmixing matrix with the adjusted input; and displaying the updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface of a display in response to the adjusted input. A system and a non-transitory computer-readable storage medium configured to perform the subject method are also provided.

[0008] In some embodiments, the method includes evaluating matrix quality parameters of the computed spectral unmixing matrix, the matrix quality parameters being selected from the group consisting of: complexity index, similarity index, hotspot matrix, conditional index, variance decomposition scaling matrix (VDP), diffusion correlation matrix, collinearity, variance inflation factor, and covariance matrix. In some instances, the similarity index of the computed spectral unmixing matrix is ​​evaluated. In some instances, the complexity index of the computed spectral unmixing matrix is ​​evaluated. In some instances, the hotspot matrix of the computed spectral unmixing matrix is ​​evaluated. In some instances, evaluating the matrix quality parameters includes visualizing a data plot of the computed spectral unmixing matrix.

[0009] In embodiments, the cached input of the spectral unmixing matrix in the computer cache is adjusted based on the evaluated matrix quality parameters. In some instances, the method includes determining whether the cache includes results of one or more control data used to calculate the spectral unmixing matrix. If no control results are cached, the method includes calculating statistics and results for the control data. In some instances, the input includes monostained positive control data. In some instances, the monostained positive control data is generated from monostained control microbeads. In some instances, the monostained positive control data is generated from monostained control cells. In some instances, the input includes autofluorescence control data. In some instances, the input includes unstained control data. In some instances, the input includes negative control data. In some instances, the input is raw control data. In some instances, the input includes calculated values ​​for control data. In some instances, the input includes control data statistics (e.g., calculated from control data). In some instances, the input includes calculated gate statistics. In some embodiments, a graphical user interface is used to adjust the input.

[0010] In some embodiments, the method includes storing input control data in a computer cache and generating one or more input control data calculations and input control data statistics based on the stored input control data. In some instances, the method further includes clearing the stored input control data from the computer cache when the input control data is adjusted. In some instances, the method includes determining whether the input control data is stored in the computer cache. In some instances, when it is determined that the input control data does not exist in the computer cache, generating control data calculations and control data statistics, and storing the generated control data calculations and control data statistics in the computer cache. In some instances, the method includes: determining that the input control data includes an uncached negative control; generating data calculations and data statistics for the uncached negative control; and caching the data calculations and data statistics for the uncached negative control. In some instances, the method includes: determining that the input control data includes a cached negative control; retrieving data calculations and data statistics for the cached negative control; and recalculating the data calculations and data statistics for the cached negative control. In some instances, the method includes: determining that the input control data includes uncached negative controls; retrieving input control data that includes cached negative controls; and generating data calculations and statistics for both the uncached and cached negative controls. In some instances, the spectral unmixing matrix is ​​recalculated using updated input control data stored in the cache.

[0011] In some instances, minor adjustments are made to the input to the calculated spectral unmixing matrix. In some instances, the spectral unmixing matrix is ​​recalculated when the adjusted input includes variations equal to or below a predetermined threshold. In some instances, the spectral unmixing matrix is ​​recalculated when the adjusted input includes variations above a predetermined threshold. In some instances, the calculated input is retrieved from a computer cache before adjusting the input. In some instances, the cached input is cleared before adjusting the input. In some instances, the method includes clearing the cached input and applying the new input to the spectral unmixing matrix. In some instances, adjusting the cached input includes excluding certain control data when recalculating the spectral unmixing matrix. In some instances, adjusting the cached input includes applying new control data when recalculating the spectral unmixing matrix. In some instances, adjusting the cached input includes changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0012] In some embodiments, the method includes displaying multiple different matrix quality parameters on a graphical user interface. In some instances, updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface in near real-time in response to adjusted input. In other instances, updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface in real-time in response to adjusted input.

[0013] In some embodiments, the method includes generating fluorescence (and autofluorescence) data for calculating a spectral unmixing matrix. In some instances, the method includes illuminating a sample containing particles with a light source and measuring the light from the illuminating sample. In some instances, the particles are control microbeads. In some instances, the particles are control cells. In some instances, the particles are unstained particles. In some instances, the particles are monostained particles.

[0014] In some instances, the light source includes one or more lasers. In some instances, light is detected using a light detection system with multiple photodetectors. In some embodiments, one or more photodetectors are photomultiplier tubes. In some embodiments, one or more photodetectors are photodiodes (e.g., avalanche photodiodes, APDs). In some embodiments, the light detection system includes a photodetector array, such as a photodetector array with multiple photodiodes or charge-coupled devices (CCDs).

[0015] This disclosure also includes systems for practicing the subject method. A system according to certain embodiments includes a processor having memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: receive a computed spectral unmixing matrix in a computer cache of the system processor; evaluate a matrix quality parameter of the computed spectral unmixing matrix; adjust a cached input to the spectral unmixing matrix based on the evaluated matrix quality parameter; recompile the spectral unmixing matrix with the adjusted input; and display the updated matrix quality parameter of the spectral unmixing matrix on a graphical user interface of a display in response to the adjusted input.

[0016] In some embodiments, the memory includes instructions for evaluating matrix quality parameters of the computed spectral unmixing matrix, the matrix quality parameters being selected from the group consisting of: complexity index, similarity index, hotspot matrix, conditional index, variance decomposition scaling matrix (VDP), diffusion correlation matrix, collinearity, variance inflation factor, and covariance matrix. In some instances, the memory includes instructions for evaluating the similarity index of the computed spectral unmixing matrix. In some instances, the memory includes instructions for evaluating the complexity index of the computed spectral unmixing matrix. In some instances, the memory includes instructions for evaluating the hotspot matrix of the computed spectral unmixing matrix. In some instances, the memory includes instructions for evaluating the matrix quality parameters by visualizing a data plot of the computed spectral unmixing matrix.

[0017] In some embodiments, the memory includes instructions for adjusting the cached input of the spectral unmixing matrix in the computer cache based on the evaluated matrix quality parameters. In some instances, the memory includes instructions for determining whether the cache includes results for calculating one or more control data for the spectral unmixing matrix. If no control results are cached, the memory includes instructions for calculating statistics and results for the control data. In some instances, the input includes monostained positive control data. In some instances, the monostained positive control data is generated from monostained control microbeads. In some instances, the monostained positive control data is generated from monostained control cells. In some instances, the input includes autofluorescence control data. In some instances, the input includes unstained control data. In some instances, the input includes negative control data. In some instances, the input is raw control data. In some instances, the input includes calculated values ​​for control data. In some instances, the input includes control data statistics (e.g., generated from calculations of control data). In some instances, the input includes calculated gate statistics. In some embodiments, a graphical user interface is used to adjust the input.

[0018] In some embodiments, the memory includes instructions for storing input control data in a computer cache and generating one or more input control data calculations and input control data statistics based on the stored input control data. In some instances, the memory includes instructions for clearing the stored input control data from the computer cache when the input control data is adjusted. In some instances, the memory includes instructions for determining whether the input control data is stored in the computer cache. In some instances, when it is determined that the input control data does not exist in the computer cache, control data calculations and control data statistics are generated and stored in the computer cache. In some instances, the memory includes instructions for: determining that the input control data includes an uncached negative control; generating data calculations and data statistics for the uncached negative control; and caching the data calculations and data statistics for the uncached negative control. In some instances, the memory includes instructions for: determining that the input control data includes a cached negative control; retrieving data calculations and data statistics for the cached negative control; and recalculating the data calculations and data statistics for the cached negative control. In some instances, the memory includes instructions for: determining that the input control data includes an uncached negative control; retrieving input control data that includes a cached negative control; and generating data calculations and statistics for both the uncached and cached negative controls. In some instances, the memory includes instructions for recalculating the spectral unmixing matrix using updated input control data stored in the cache.

[0019] In some instances, the memory includes instructions for making minor adjustments to the input of the calculated spectral unmixing matrix. In some instances, the memory includes instructions for recalculating the spectral unmixing matrix when the adjusted input includes changes equal to or below a predetermined threshold. In some instances, the memory includes instructions for recalculating the spectral unmixing matrix when the adjusted input includes changes above a predetermined threshold. In some instances, the memory includes instructions for retrieving the calculated input from a computer cache before adjusting the input. In some instances, the memory includes instructions for clearing the cached input before adjusting the input. In some instances, the memory includes instructions for clearing the cached input and applying new input to the spectral unmixing matrix. In some instances, the memory includes instructions for adjusting the cached input by excluding certain control data when recalculating the spectral unmixing matrix. In some instances, the memory includes instructions for adjusting the cached input by applying new control data when recalculating the spectral unmixing matrix. In some instances, the memory includes instructions for adjusting the cached input by changing the autofluorescence gate of autofluorescence control data when recalculating the spectral unmixing matrix.

[0020] In some embodiments, the system includes a display on a graphical user interface (GUI) that visualizes matrix quality parameters, a data graph from the spectral unmixing matrix, and input to the spectral unmixing matrix. In some instances, the memory includes instructions to display multiple different matrix quality parameters on the GUI. In some instances, the memory includes instructions to display updated matrix quality parameters of the spectral unmixing matrix on the GUI in near real-time response to adjusted input. In other instances, the memory includes instructions to display updated matrix quality parameters of the spectral unmixing matrix on the GUI in real-time response to adjusted input.

[0021] In some embodiments, the system includes a light source and a light detection system having one or more photodetectors. In some instances, the light source is a light-emitting diode (LED). In some embodiments, the system includes a light source having one or more lasers (e.g., multiple lasers). In some instances, the light detection system includes multiple photodetectors. In some instances, the photodetectors include one or more photomultiplier tubes. In some instances, the light detection system includes a photodetector array. In some instances, one or more photodetectors in the array are photodiodes (e.g., avalanche photodiodes). In some instances, one or more photodetectors in the array are charge-coupled devices (CCDs). In some instances, the system is configured to detect light in multiple photodetector channels via the light detection system. In some embodiments, the generated spectral data signal is the sum of data signals collected across multiple photodetector channels.

[0022] This disclosure also includes non-transitory computer-readable storage media, for example, for practicing one or more of the computer-implemented methods described herein. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for receiving a computed spectral unmixing matrix in a computer cache, an algorithm for evaluating matrix quality parameters of the computed spectral unmixing matrix, an algorithm for adjusting cached inputs to the spectral unmixing matrix based on the evaluated matrix quality parameters, an algorithm for recompiling the spectral unmixing matrix with the adjusted input, and an algorithm for displaying the updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface of a display in response to the adjusted input.

[0023] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating matrix quality parameters of a computed spectral unmixing matrix, the matrix quality parameters being selected from the group consisting of: complexity index, similarity index, hotspot matrix, conditional index, variance decomposition scaling matrix (VDP), diffusion correlation matrix, collinearity, variance inflation factor, and covariance matrix. In some instances, the storage medium includes instructions for evaluating the similarity index of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the complexity index of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the hotspot matrix of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the matrix quality parameters by visualizing a data graph of the computed spectral unmixing matrix.

[0024] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for adjusting the cached input of the spectral unmixing matrix in the computer cache based on the evaluated matrix quality parameters. In some instances, the non-transitory computer-readable storage medium includes an algorithm for determining whether the cache includes results for calculating one or more control data for the spectral unmixing matrix. In the case where no control results are cached, the non-transitory computer-readable storage medium includes an algorithm for calculating statistics and results for the control data. In some instances, the input includes monostained positive control data. In some instances, the monostained positive control data is generated from monostained control microbeads. In some instances, the monostained positive control data is generated from monostained control cells. In some instances, the input includes autofluorescence control data. In some instances, the input includes unstained control data. In some instances, the input includes negative control data. In some instances, the input is raw control data. In some instances, the input includes calculated values ​​for control data. In some instances, the input includes control data statistics (e.g., generated from calculations of control data). In some instances, the input includes calculated gate statistics. In some embodiments, the non-transitory computer-readable storage medium includes algorithms that adjust input using a graphical user interface.

[0025] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for storing input control data in a computer cache and generating one or more input control data calculations and input control data statistics based on the stored input control data. In some instances, the non-transitory computer-readable storage medium includes an algorithm for clearing the stored input control data from the computer cache when the input control data is adjusted. In some instances, the non-transitory computer-readable storage medium includes an algorithm for determining whether the input control data is stored in the computer cache. In some instances, when it is determined that the input control data does not exist in the computer cache, control data calculations and control data statistics are generated and stored in the computer cache. In some instances, the non-transitory computer-readable storage medium includes an algorithm for: determining that the input control data includes an uncached negative control; generating data calculations and data statistics for the uncached negative control; and caching the data calculations and data statistics for the uncached negative control. In some instances, the non-transitory computer-readable storage medium includes algorithms for: determining that input control data includes cached negative controls; retrieving data calculations and statistics for the cached negative controls; and recalculating data calculations and statistics for the cached negative controls. In some instances, the non-transitory computer-readable storage medium includes algorithms for: determining that input control data includes uncached negative controls; retrieving input control data including cached negative controls; and generating data calculations and statistics for both uncached and cached negative controls. In some instances, the non-transitory computer-readable storage medium includes algorithms for recalculating the spectral unmixing matrix using updated input control data stored in the cache.

[0026] In some instances, the non-transitory computer-readable storage medium includes an algorithm for making minor adjustments to the input of the calculated spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for recalculating the spectral unmixing matrix when the adjusted input includes a change equal to or below a predetermined threshold. In some instances, the non-transitory computer-readable storage medium includes an algorithm for recalculating the spectral unmixing matrix when the adjusted input includes a change above a predetermined threshold. In some instances, the non-transitory computer-readable storage medium includes an algorithm for retrieving the calculated input from a computer cache before adjusting the input. In some instances, the non-transitory computer-readable storage medium includes an algorithm for clearing the cached input before adjusting the input. In some instances, the non-transitory computer-readable storage medium includes an algorithm for clearing the cached input and applying the new input to the spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for adjusting the cached input by excluding certain control data when recalculating the spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for adjusting the cached input by applying new control data when recalculating the spectral unmixing matrix. In some instances, non-transitory computer-readable storage media include algorithms that adjust cached inputs by changing the autofluorescence gate of autofluorescence control data when recalculating the spectral unmixing matrix.

[0027] In some instances, the non-transitory computer-readable storage medium includes algorithms for displaying multiple different matrix quality parameters on a graphical user interface. In some instances, the non-transitory computer-readable storage medium includes algorithms for displaying updated matrix quality parameters of a spectral unmixing matrix on a graphical user interface in near real-time in response to adjusted input. In other instances, the non-transitory computer-readable storage medium includes algorithms for displaying updated matrix quality parameters of a spectral unmixing matrix on a graphical user interface in real-time in response to adjusted input. Attached Figure Description

[0028] This disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures:

[0029] Figure 1A A flowchart for evaluating and adjusting the spectral unmixing matrix is ​​depicted according to certain embodiments. Figure 1B A flowchart illustrating the adjustment of the spectral unmixing matrix in a computer cache according to certain embodiments is described. Figure 1C A workflow for setting up an unmixing experiment to calculate compensation results for a fluorescent dye set, according to certain embodiments, is described. Figure 1D Examples of graphical user interfaces for evaluating and adjusting matrix quality parameters of spectral unmixing matrices according to certain embodiments are depicted.

[0030] Figure 2 A flow cytometry system according to certain embodiments is presented.

[0031] Figure 3A An image-enabled particle sorter according to certain embodiments is described. Figure 3B Image-enabled particle sorting data processing according to certain embodiments is described.

[0032] Figure 4A A functional block diagram of a particle analysis system according to certain embodiments is depicted. Figure 4B A flow cytometer according to certain embodiments is described.

[0033] Figure 5 A functional block diagram of an example control system according to certain embodiments is depicted.

[0034] Figure 6A A schematic diagram of a particle sorting system according to certain embodiments is depicted. Figure 6B A schematic diagram of a particle sorting system according to certain embodiments is depicted.

[0035] Figure 7 Aspects of a computer control system according to certain embodiments are shown. Detailed Implementation

[0036] This disclosure includes methods for evaluating spectral unmixing matrices (e.g., for determining the suitability of fluorescent dye sets in generating flow cytometry data). A method according to some embodiments includes: receiving a calculated spectral unmixing matrix in a computer buffer; evaluating matrix quality parameters of the calculated spectral unmixing matrix; adjusting the buffered input of the spectral unmixing matrix based on the evaluated matrix quality parameters; recalculating the spectral unmixing matrix with the adjusted input; and displaying the updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface of a display in response to the adjusted input. A system and a non-transitory computer-readable storage medium configured to perform the subject method are also provided.

[0037] Before describing this disclosure in more detail, it should be understood that the invention is not limited to the specific embodiments described, as these embodiments can certainly be varied. It should also be understood that, since the scope of this disclosure will be limited only by the appended claims, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0038] Where a range of values ​​is provided, it should be understood that, unless the context explicitly specifies otherwise, every intermediate value (to one-tenth of the lower limit unit) between the upper and lower limits of the range, as well as any other specified value or intermediate value within the range, is included in this disclosure. The upper and lower limits of these smaller ranges may be independently included within the smaller range and also within this disclosure, subject to any specific exclusions within the range. Where the range includes one or both of the included limits, the range excluding one or both of those included limits is also included in this disclosure.

[0039] This article provides certain ranges whose numerical values ​​are preceded by the term "approximately". The term "approximately" is used in this article to provide textual support for the exact number that follows it, as well as for numbers that are close to or approximate to the number that follows the term. In determining whether a number is close to or approximate to a specifically listed number, an unlisted number that is close to or approximate to a number can be a number that provides a basic equivalent to the specifically listed number in the context in which it appears.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Although any methods and materials similar to or equivalent to those described herein can be used in the practice or testing of this disclosure, representative illustrative methods and materials are described here.

[0041] All publications and patents referenced in this specification are incorporated herein by reference, as if each individual publication or patent were expressly and individually indicated to be incorporated herein by reference to disclose and describe the methods and / or materials relating to the referenced publication. Any reference to a publication refers to a publication prior to the filing date and should not be construed as an admission that this disclosure has no priority over such publication due to prior art. Furthermore, the publication dates provided may differ from the actual publication dates and may require independent verification.

[0042] It should be noted that, unless the context clearly indicates otherwise, the use of an element in this document and the appended claims without a defined quantity implies the presence of at least one such element. It should also be noted that claims may be drafted to exclude any optional elements. Therefore, this statement is intended as a preliminary basis for the use of exclusive terms such as "unique," "only," etc., when referencing claim elements or using the "negative" limitation.

[0043] As will be apparent to those skilled in the art upon reading this disclosure, each individual embodiment described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of this disclosure. Any enumerated methods may be performed in the order of the enumerated events or in any other logically possible order.

[0044] Although the system and method have been or will be described for grammatical fluency and functional interpretation, it should be clearly understood that, unless expressly provided in 35 U.SC §112, the claims should not be construed as necessarily being bound by “means” or “steps,” but rather should be given the full range of meaning and equivalents provided by the definition of the claims in accordance with the doctrine of judicial equivalence, and if the claims are expressly provided in 35 U.SC §112, they should be given the full range of legal equivalents in accordance with 35 U.SC §112.

[0045] Methods for evaluating spectral unmixing matrices

[0046] This disclosure includes a computer-implemented method for evaluating a spectral unmixing matrix, for example, for determining the suitability of a group of fluorescent dyes in generating flow cytometry data. As described above, the quality of the spectral unmixing matrix significantly impacts the reliability and accuracy of flow cytometry data generated using multiple different fluorescent dyes. An optimized spectral unmixing matrix can precisely separate the contribution of fluorescent dyes to the measured fluorescence signal. The spectral unmixing matrix is ​​used to determine the relative abundance of fluorescent dyes in complex samples with multiple overlapping fluorescence spectra. In embodiments, as described herein, evaluating and adjusting the spectral unmixing matrix can improve the sensitivity and accuracy of flow cytometry data. In some instances, an optimized spectral unmixing matrix provides more accurate spectral unmixing to separate the contributions between the spectra of different components of the detected light, for example, by improving spectral unmixing efficiency by 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, and including 95% or more. In some instances, an optimized spectral unmixing matrix can reduce or eliminate artifacts in the spectral unmixed data. In some instances, the methods described herein are able to reduce or eliminate fluorescent dyes with similar spectra and background signals caused by the autofluorescence of particles in the sample.

[0047] Furthermore, the method for evaluating and adjusting the spectral unmixing matrix as described in this disclosure provides a caching mechanism that maintains the high performance of the flow cytometer and the parallelism between adjusting the spectral unmixing matrix and characterizing and correcting the quality of spectral spillover diffusion between fluorescent dyes. Changes to the input of the spectral unmixing matrix provide substantially real-time changes to the spectral unmixing matrix, allowing for substantially immediate visualization of matrix quality parameters when using control data or calculations with a graphical user interface. In some instances, updates to the matrix quality parameters can be visualized simultaneously when changes are made to control data or control data calculations and statistics. This feedback facilitates intuitive changes to control data for each spectral unmixing matrix and precise optimization of the fluorescent dye set used in flow cytometry experiments. In some instances, evaluating and adjusting the spectral unmixing matrix according to embodiments of this disclosure reduces the time required to determine the optimized fluorescent dye set for flow cytometry experiments by 25% or more, e.g., 30% or more, e.g., 35% or more, e.g., 40% or more, e.g., 45% or more, e.g., 50% or more, e.g., 75% or more, e.g., 90% or more, and including 95% or more.

[0048] In an embodiment, a spectral unmixing matrix for a set of fluorescent dyes is calculated. In some embodiments, the spectral unmixing matrix is ​​calculated in a computer cache. In some instances, the calculated spectral unmixing matrix is ​​stored in a computer cache. Throughout this document, the term "computer cache" is used in its conventional sense to refer to a hardware or software component (e.g., in a computer with a processor) that temporarily stores data, calculated data values, or data statistics. In some instances, the computer cache acts as a cache memory, allowing faster access to spectral unmixing matrix calculations and matrix quality parameters. In an embodiment, the cache may reside in the computer's CPU (computer cache), in the computer's hard drive (hard drive cache), or on a local or remote server communicating with the subject system functions (server cache).

[0049] In this document, the term "spectral matrix" is used in its conventional sense, referring to a matrix containing information about the spectral properties of a set of fluorescent dyes. In embodiments, the spectral matrix of interest includes a set of spectral features associated with each of the fluorescent dye identifiers in a set of fluorescent dye identifiers. A "spectral feature" refers to a characteristic of the fluorescence spectrum of a single fluorescent dye, represented as one or more numerical values. Two entities can be described as "associated" with each other if they are linked and / or correlated with each other in the data space. In other words, the spectral matrix associated with the instrument identifier (also referred to herein as the "input" or "raw" spectral matrix) can represent a "palette" of possible fluorescent dyes or dyes from which the subject set of fluorescent dyes can be selected. In some cases, the spectral matrix contains information related to the spectral properties of each fluorescent dye on the instrument of interest (e.g., a flow cytometer). This information may vary on different machines. For example, differences between different instruments in the number and arrangement of lasers and detection channels can result in different spectral features associated with each instrument. Therefore, embodiments of the subject-matter approach employ a spectral matrix that includes spectral characteristics specific to a particular instrument (or instrument category / type) in which fluorescent dyes from the evaluated group of fluorescent dyes will be hypothetically used. Due to differences between instruments, the same group of fluorescent dyes may be more or less associated with variance in flow cytometry data if the fluorescent dyes are applied to two different types of machines (e.g., flow cytometer). In some instances, obtaining a spectral matrix associated with an instrument identifier allows for the evaluation of the group of fluorescent dyes in an instrument-specific context, thereby enabling those skilled in the art practicing the subject-matter approach to gain more reliable insight into the quality of flow cytometry data (if a given group of fluorescent dyes is used in a particular instrument).

[0050] A “fluorescent dye group” refers to a group of distinct fluorescent molecules (i.e., dyes) that can be used to identify particles in a sample or specific parts or components associated with particles. The “fluorescent dye group” discussed herein can also refer to a set of identifiers (e.g., numerical identifiers) that uniquely identify and associate with a particular fluorescent molecule. These identifiers may be referred to as fluorescent dye identifiers in some instances. Different fluorescent dyes within a fluorescent dye group can differ in properties such as absorption spectra, extinction coefficients, emission spectra, and quantum efficiency (i.e., the number of photons emitted per absorbed photon) or combinations thereof. Therefore, different or distinguishable fluorescent dyes can differ from each other in terms of chemical composition and / or one or more properties of the dye. For example, a given pair of fluorescent dyes can be considered distinct if they differ from each other in terms of excitation and / or emission maxima, wherein in some instances, the magnitude of such difference is 5 nm or greater, e.g., 10 nm or greater, including 15 nm or greater, wherein in some instances, the magnitude of the difference ranges from 5 to 400 nm (e.g., 10 to 200 nm) and includes 15 to 100 nm (e.g., 25 to 50 nm).

[0051] In some embodiments, spectral features are received from experimental data (i.e., the results of experiments performed on a particular instrument). In other cases, spectral features are received from simulated data. The input spectral matrix can be associated with any suitable number of fluorescent dye identifiers. In some embodiments, the number of fluorescent dye identifiers in the input spectral matrix ranges from 2 to 150, for example 2 to 140, 2 to 130, 2 to 120, 2 to 110, 2 to 100, 2 to 90, 2 to 80, 2 to 70, 2 to 60, and includes 2 to 50. A set of fluorescent dye identifiers used in a given embodiment of the invention may be referred to as a fluorescent dye palette, which collectively refers to fluorescent dye identifiers selected from a given group of fluorescent dyes.

[0052] In embodiments, the spectral features include one or more overflow values. An “overflow value” refers to the relative amount of signal emitted by a given fluorescent dye into each detector band. In some cases, the overflow value is normalized to the detector with the largest signal for that fluorescent dye (i.e., the “peak” detector). In some cases, particle-modulated light indicating a particular fluorescent dye is received by one or more detectors in a particle analyzer (e.g., a flow cytometer), and one or more detectors are not one or more peak detectors for the fluorescent dye. Therefore, the light may “overflow” and be detected by non-peak detectors. In other words, a specific fluorescent label used in an experiment and its associated fluorescence emission band can be selected to roughly correspond to certain detectors. However, as more detectors are provided and more labels are used, a perfect correspondence between a specific detector and the fluorescence emission spectrum may not be possible. Typically, although the peak of the emission spectrum of a particular fluorescent molecule may fall within the window of a particular detector, a portion of the emission spectrum of that label will also overlap with the windows of one or more other detectors. This can be referred to as overflow.

[0053] In some embodiments, the spectral matrix described herein may include one or more autofluorescence spectral features. Autofluorescence is an inherent fluorescent signal produced by particles (e.g., cells) when measured in flow cytometry. It originates from endogenous molecules within the cell that exhibit fluorescent activity, such as metabolites. Different cell types (e.g., lymphocytes) may have the same autofluorescence spectrum but with different intensities; for example, larger cells generally tend to have a larger autofluorescence signal. In some instances, different types of particles are associated with different autofluorescence spectra. For example, different types of cells (e.g., lymphocytes vs. monocytes) may not only have different levels of autofluorescence but may also have different autofluorescence spectra (e.g., the spectral features of lymphocyte autofluorescence may differ from those of monocyte autofluorescence). In some cases, such as in spectrocytology, the spectral features of autofluorescence are measured by observing unstained cells and included as one or more additional "fluorescent dye" parameters (if multiple autofluorescence spectra are included) during spectral unmixing.

[0054] As described above, the obtained spectral matrix is ​​associated with the fluorescent dye group. In other words, the obtained spectral matrix is ​​a submatrix of the input spectral matrix (e.g., the spectral matrix associated with the instrument identifier), which includes only the spectral features of the fluorescent dyes in the fluorescent dye group. In this paper, "submatrix" is discussed in its conventional sense to describe a matrix obtained by removing certain rows and / or columns from another matrix.

[0055] In some embodiments, the calculated spectral unmixing matrix is ​​generated from multiple different input controls. In some instances, the input includes monostained positive control data. In some instances, the monostained positive control data is generated from monostained control microbeads. In some instances, the monostained positive control data is generated from monostained control cells. In some instances, the input includes autofluorescence control data. In some instances, the input includes unstained control data. In some instances, the input includes negative control data. In some instances, the input is raw control data. In some instances, the input includes calculated values ​​of control data. In some instances, the input includes control data statistics (e.g., generated from calculated values ​​of control data). In some instances, the input includes calculated gate statistics.

[0056] In some embodiments, the reference data used to calculate and update the spectral unmixing matrix in a computer cache is spectral data. Hereinafter, the term "spectral data" is used in its conventional sense to refer to a data signal extending across one or more predetermined wavelength ranges. The spectral data signal can be generated by light measured by a photodetector at one or more different wavelengths, such as at two or more different wavelengths, for example at five or more different wavelengths, for example at ten or more different wavelengths, for example at 25 or more different wavelengths, for example at 50 or more different wavelengths, for example at 100 or more different wavelengths, for example at 200 or more different wavelengths, for example at 300 or more different wavelengths, and includes measuring light from particles in a flowing stream at 400 or more different wavelengths. In some instances, the light from the sample is spectrally separated into two or more spectral ranges, and each spectral range is detected in one or more photodetector channels, for example, three or more spectral ranges, for example, four or more, for example, eight or more, for example, 16 or more, for example, 32 or more, for example, 64 or more, for example, 128 or more, for example, 256 or more, and including 512 or more different spectral ranges. Therefore, the generated data signal is in response to light with a spectral range of 1 nm or more, for example, 2 nm or more, for example, 5 nm or more, for example, 10 nm or more, for example, 25 nm or more, for example, 50 nm or more, for example, 100 nm or more, for example, 150 nm or more, for example, 200 nm or more, and including spectral ranges of 250 nm or more. In some embodiments, each spectral data signal may be generated in one or more spectral photodetector channels, such as two or more, three or more, four or more, eight or more, twelve or more, sixteen or more, thirty-two or more, and may be generated in 64 or more different spectral photodetector channels. In some embodiments, spectral data may be generated in one or more imaging photodetector channels, such as two or more, three or more, four or more, eight or more, twelve or more, sixteen or more, thirty-two or more, and may be generated in 64 or more different imaging photodetector channels.

[0057] In some instances, input controls include negative and single-positive population controls for one or more monostaining fluorescent dyes in a fluorescent dye set. In this document, the term "negative population" is used in its conventional sense to refer to a population of particles not labeled with a specific fluorescent dye, such as unstained samples for each dye, or a group of cells that does not express the antigen under investigation. In some instances, negative population controls (e.g., unstained populations) allow for the determination of background fluorescence and the establishment of negative control gating strategies. In some instances, negative population controls include a reference unstained sample. In some instances, negative population controls for monostaining fluorescent dyes include a population of particles that does not express the antigen of interest. In some instances, negative population controls are used to determine whether a particular fluorescent dye in a fluorescent dye set is negative for that specific fluorescent dye. In some instances, negative population controls are obtained from a reference database. In some instances, the method includes obtaining negative and single-positive population controls for each monostaining fluorescent dye. In this document, the term "positive population" is used in its conventional sense to refer to a sample containing particles stained with at least one fluorescent dye of interest; for example, this positive population control can be used as a positive control. In some instances, positive population controls include monostaining dyes. In some instances, the positive population includes cells or particles known to contain specific binding antigens. In some embodiments, the single-positive population for a single-staining fluorescent dye is a population of particles that exhibit little or no detectable fluorescence with other fluorescent dyes, for example, the population is distributed around zero symmetry in the respective data channels. In some embodiments, the method includes obtaining at least one single-positive population control for each single-staining fluorescent dye. For each single-staining fluorescent dye sample in a reference flow cytometry record database (e.g., a separate record of leukocytes single-stained with anti-CD4 conjugates of various fluorescent dyes), negative and single-positive populations are defined by gating or clustering (e.g., CD4+ vs. CD- lymphocytes). A single reference unstained sample may be used as a negative reference population instead of the negative population within the record. In some embodiments, the negative and positive populations are assigned qualitative markers indicating the abundance of the positive and negative populations, where expression patterns may include (+,+), (+,-), (-,+), (-,-), where “+” and “-” are qualitative markers indicating the abundance of the positive and negative populations, respectively, and (+,-) for example means, for example, that the first marker is positive and the second marker is negative.

[0058] In embodiments, the method includes evaluating matrix quality parameters of a computed spectral unmixing matrix. In some embodiments, the matrix quality parameters are evaluated by visualizing a data plot generated from the spectral unmixing matrix. For example, evaluating the matrix quality parameters may include visualizing the spectra of the fluorescent dye used to generate the spectral unmixing matrix. In some instances, the spectral unmixing matrix is ​​evaluated based on gate statistics generated using the spectral unmixing matrix. In some instances, evaluating the matrix quality parameters includes visualizing one or more spectral gates for the fluorescent dye. In some instances, evaluating the spectral unmixing matrix includes evaluating spillover diffusion. In some instances, evaluating the spectral unmixing matrix includes visualizing autofluorescence spectral collinearity in the generated data. In some instances, the visualization highlights autofluorescence spectra associated with variance in the generated data. In some instances, evaluating the spectral unmixing matrix includes visualizing a group hotspot matrix. In some instances, evaluating the spectral unmixing matrix includes visualizing the diagonal of the group hotspot matrix. In some instances, evaluating the spectral unmixing matrix includes visualizing a diffusion correlation matrix. In some instances, evaluating the spectral unmixing matrix includes visualizing a cosine similarity matrix. In some instances, evaluating the spectral unmixing matrix includes visualizing a covariance matrix.

[0059] In some instances, the computed spectral unmixing matrix is ​​evaluated by determining a complexity index of the spectral unmixing matrix. In this paper, the term "complexity index" is used in its conventional sense, referring to a measure of the similarity of the fluorescent dyes. In some instances, the complexity index is evaluated to determine whether it exceeds a predetermined threshold. In some instances, the method involves determining a minimum complexity index for the fluorescent dye and calculating the difference between the complexity index of the computed spectral unmixing matrix and the minimum complexity index of the fluorescent dye.

[0060] In some embodiments, evaluating the matrix quality parameters of the computed spectral unmixing matrix includes determining a similarity index for the spectral unmixing matrix. In this document, the term "similarity index" conventionally refers to cosine similarity, a measure of how similar the spectral characteristics of two fluorophores are. In some instances, a cosine similarity matrix is ​​computed against a single-stain positive control, providing a pairwise comparison of the similarity between the emission spectra of all fluorescent dyes used in the staining group. This cosine similarity matrix can be used to indicate the degree of similarity between fluorescent dyes. A high cosine similarity score (close to 1) between fluorescent dye pairs indicates a higher compensation value and greater spillover diffusion between the two fluorescent dyes. Using a combination of fluorescent dyes with a low cosine similarity score (close to 0) results in a lower compensation value and lower spillover diffusion, thus providing higher sensitivity for detecting low-expressed antigens within a population co-expressing that pair of fluorescent dyes. In some instances, the similarity index ranges from 0 to 1. In some instances, the method includes minimizing the spectral overlap between fluorescent dyes and adjusting the input such that the similarity index is minimized.

[0061] In some embodiments, evaluating the matrix quality parameters of the computed spectral unmixing matrix includes generating a hotspot matrix for the fluorescent dye. Hereinafter, the term "hotspot matrix" is used in its conventional sense to refer to the mechanism used to mathematically describe and / or visualize the effect of diffusion on autofluorescence (and / or fluorescent dyes). In selected cases, a group hotspot matrix is ​​used for the visualization described above. In some embodiments, the group hotspot matrix is ​​a diagonal matrix. In some instances, the group hotspot matrix can be computed by obtaining the square root of the absolute value of the inverse matrix (e.g., the Gramm-inverse matrix). Computing the group hotspot matrix can result in two distinct metrics: the pseudo-inverse matrix row norm and off-diagonal entries. The pseudo-inverse matrix row norm (i.e., the diagonal of the group hotspot matrix) indicates which autofluorescence (and / or fluorescent dyes) are most affected by unmixing-dependent diffusion. In some cases, the diagonal entries are the 2-norm of the pseudo-inverse for each autofluorescence (and / or fluorescent dye). In some versions, the pseudo-inverse matrix row norm can be represented on a scale corresponding to a factor by which the standard deviation of the unmixed data in the autofluorescence (and / or fluorescent dye) will be amplified due to unmixing in that group. For example, 1 indicates no effect, while 2 indicates 2x diffusion, and so on. Examining the off-diagonal entries in the complete group hotspot matrix reveals problematic combinations of autofluorescence (and / or fluorescent dyes) within the group. An off-diagonal entry is the dot product of the pseudo-inverse rows and columns of the corresponding autofluorescence (and / or fluorescent dye) spectra. The off-diagonal value represents the magnitude of the covariance between two pseudo-inverse entries of the autofluorescence (and / or fluorescent dye) spectra. For example, in some embodiments, an off-diagonal value of 0 indicates no covariance, while higher values ​​indicate a correspondingly higher level of covariance.

[0062] In some embodiments, the method includes evaluating a similarity index, a complexity index, or performing hotspot analysis (to generate a hotspot matrix), as described in U.S. Patent Publication No. 2023 / 0393049, filed May 24, 2023, the disclosure of which is incorporated herein by reference.

[0063] In some embodiments, evaluating the matrix quality parameters of the calculated spectral unmixing parameters includes evaluating the variance decomposition ratio (VDP). In some instances, evaluating the matrix quality parameters of the calculated spectral unmixing parameters includes evaluating the variance decomposition ratio via singular value decomposition (SVD). In some instances, the variance decomposition ratio is used to identify collinearity in the spectra of fluorescent dyes. In some instances, the variance decomposition ratio is used to evaluate collinearity in autofluorescence spectra calculated from input control data. In some instances, the conditional index is calculated as the ratio of the largest calculated singular value to each individually calculated singular value. In some instances, the method includes identifying variance decomposition proportions corresponding to a condition index of 10 or higher, such as 11 or higher, 12 or higher, 13 or higher, 14 or higher, 15 or higher, 16 or higher, 17 or higher, 18 or higher, 19 or higher, 20 or higher, 21 or higher, 22 or higher, 23 or higher, 24 or higher, 25 or higher, 26 or higher, 27 or higher, 28 or higher, 29 or higher, 30 or higher, and includes identifying variance decomposition proportions corresponding to a condition index of 35 or higher. In some instances, the method includes identifying control spectra whose variance decomposition proportions of condition indices exceed a large fraction (e.g., 0.3 or higher, 0.4 or higher, 0.5 or higher, 0.6 or higher, including 0.7 or higher). In some instances, the method includes identifying control spectra whose variance decomposition proportions are greater than 0.3 and whose condition indices are greater than 15.

[0064] In some cases, evaluating the matrix quality parameters of a spectral unmixing matrix includes evaluating the diffusion correlation matrix. As described herein, a "diffusion correlation matrix" is a mechanism for mathematically describing and / or visualizing the effect of a particular fluorescence or autofluorescence spectrum on certain data populations (e.g., double-negative populations). In embodiments, the diffusion correlation matrix can be used to predict skewness in double-negative populations. Here, "skewness" refers to a measure of the degree to which a population (e.g., a double-negative population) shifts in a particular direction (e.g., corresponding to positive or negative correlation) due to the manner of data collection and / or preparation. In some embodiments, preparing the diffusion correlation matrix includes treating the Gram inverse matrix as a covariance matrix and normalizing each row and each column to the square root of its diagonal elements to compute the correlation matrix.

[0065] In the embodiments, one or more matrix quality parameters of the spectral unmixing matrix are evaluated, such as two or more, three or more, four or more, five or more, six or more, seven or more, and eight or more matrix quality parameters of the spectral unmixing matrix are evaluated.

[0066] In some embodiments, the cached input to the spectral unmixing matrix is ​​adjusted based on one or more evaluated matrix quality parameters. In some instances, the method includes determining whether the cache includes results from one or more control data used to compute the spectral unmixing matrix. If no control results are cached, the method includes computing statistics and results for the control data. In some instances, the method includes modifying the monochromatic positive control. In some instances, a different monochromatic positive control is used (i.e., one monochromatic positive control is replaced with another). In some instances, the method includes changing control data generated using monochromatic control beads to control data generated using monochromatic control cells. In some instances, the method includes recalculating control statistics or calculated values ​​after adding or excluding positive or negative controls in the computation of the spectral unmixing matrix.

[0067] In some instances, the method involves modifying autofluorescence control data. In some instances, autofluorescence gates are included or excluded in the calculation of the spectral unmixing matrix. In some instances, autofluorescence spectra in the autofluorescence spectrum set are adjusted by replacing autofluorescence spectra with different autofluorescence spectra in the spectral unmixing matrix. In some instances, the method involves replacing individual autofluorescence spectra in the autofluorescence spectrum set. In some cases, the method involves maintaining an autofluorescence spectrum set of constant size. For example, an evaluated autofluorescence spectrum set with N autofluorescence spectra will continue to have N autofluorescence spectra after adjustment. In some instances, autofluorescence spectra in the autofluorescence spectrum set are not replaced with autofluorescence spectra already present in that set. In some instances, the method involves identifying autofluorescence spectra in the autofluorescence spectrum set that are associated with the variance generated using the autofluorescence spectrum set (e.g., by analyzing the calculated inverse matrix). In some embodiments, the method includes identifying autofluorescence controls associated with smaller variance, and clearing autofluorescence controls from a cache and entering autofluorescence controls associated with smaller variance. In some cases, the method includes clearing autofluorescence controls with autofluorescence spectra that are associated with larger variance.

[0068] In some instances, the spectral unmixing matrix is ​​recalculated when the adjusted input includes variations equal to or below a predetermined threshold. In some instances, the spectral unmixing matrix is ​​recalculated when the adjusted input includes variations above a predetermined threshold. In some instances, the calculated input is retrieved from a computer cache before adjusting the input. In some instances, the cached input is cleared before adjusting the input. In some instances, the method includes clearing the cached input and applying the new input to the spectral unmixing matrix. In some instances, adjusting the cached input includes excluding certain control data when recalculating the spectral unmixing matrix. In some instances, adjusting the cached input includes applying new control data when recalculating the spectral unmixing matrix. In some instances, adjusting the cached input includes changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0069] In some embodiments, the method includes storing input control data in a computer cache and generating one or more input control data calculations and input control data statistics based on the stored input control data. In some instances, the method further includes clearing the stored input control data from the computer cache when the input control data is adjusted. In some instances, the method includes determining whether the input control data is stored in the computer cache. In some instances, when it is determined that the input control data does not exist in the computer cache, generating control data calculations and control data statistics, and storing the generated control data calculations and control data statistics in the computer cache. In some instances, the method includes: determining that the input control data includes an uncached negative control; generating data calculations and data statistics for the uncached negative control; and caching the data calculations and data statistics for the uncached negative control. In some instances, the method includes: determining that the input control data includes a cached negative control; retrieving data calculations and data statistics for the cached negative control; and recalculating the data calculations and data statistics for the cached negative control. In some instances, the method includes: determining that the input control data includes uncached negative controls; retrieving input control data that includes cached negative controls; and generating data calculations and statistics for both the uncached and cached negative controls. In some instances, the spectral unmixing matrix is ​​recalculated using updated input control data stored in the cache.

[0070] As described above, the method includes recalculating the spectral unmixing matrix with the adjusted input. In some instances, the spectral unmixing matrix is ​​recalculated in real time when the control data input (including original control data, calculated data values, or data statistics) is adjusted, for example, recalculating the spectral unmixing matrix within 1 second or less after the control data input is adjusted, for example, within 0.5 seconds or less, for example, within 0.1 seconds or less, for example, within 0.05 seconds or less, for example, within 0.01 seconds or less, for example, within 0.005 seconds or less, for example, within 0.001 seconds or less, for example, within 0.0005 seconds or less, and including within 0.0001 seconds or less. In some instances, the spectral unmixing matrix is ​​recalculated immediately when the control data input is adjusted, for example, the spectral unmixing matrix is ​​updated in real time. In some embodiments, two or more inputs are adjusted, and then the spectral unmixing matrix is ​​recalculated, for example, adjusting three or more, such as four or more, five or more, six or more, and including adjusting ten or more different inputs. In some instances, the recalculation is user-initiated by a graphical user interface, for example, via a user-interactive icon on a graphical user interface, to recalculate the spectral unmixing matrix after two or more different adjustments to the input control data.

[0071] In some embodiments, the method includes updating matrix quality parameters in response to a recalculated spectral unmixing matrix. In some instances, the recalculated spectral unmixing matrix is ​​cached, and the matrix quality parameters are automatically updated for the recalculated spectral unmixing matrix. In some instances, the updated matrix quality parameters are displayed on a graphical user interface of a display. In some instances, the matrix quality parameters are updated near real-time on the graphical user interface, for example, updating the matrix quality parameters within 1 second or less after the control data input (including raw control data, calculated data values, or data statistics) is adjusted, for example, after 0.5 seconds or less, for example, after 0.1 seconds or less, for example, after 0.05 seconds or less, for example, after 0.01 seconds or less, for example, after 0.005 seconds or less, for example, after 0.001 seconds or less, for example, after 0.0005 seconds or less, and including after 0.0001 seconds or less. In some instances, the updated matrix quality parameters are displayed simultaneously when the control data input is adjusted; for example, the matrix quality parameters are updated and displayed in real time. Therefore, adjustments to the input control data or calculations can provide intuitive changes to the matrix quality parameters, which can be immediately visualized and further adjusted as needed. In some embodiments, two or more inputs are adjusted, and then the matrix quality parameters are updated, for example, adjusting three or more, such as four or more, five or more, six or more, and including adjusting ten or more different inputs, and then updating and displaying the matrix quality parameters.

[0072] In some embodiments, the method includes further adjusting one or more inputs based on the recalculated spectral unmixing matrix. In some instances, one or more updated matrix quality parameters are evaluated, and the input control data or calculations may be further adjusted. The process can be repeated two or more times using a graphical user interface, simultaneously visualizing the updated matrix quality parameters of the cached recalculated spectral unmixing matrix, for example, repeating three or more times, such as four or more times, such as five or more times, such as ten or more times, such as twenty or more times, and including fifty or more times. In some instances, one or more inputs are iteratively adjusted and the updated matrix quality parameters are evaluated to optimize the spectral unmixing matrix as needed.

[0073] Figure 1A A flowchart illustrating the evaluation and adjustment of a spectral unmixing matrix according to certain embodiments is described. At step 101, the calculated spectral unmixing matrix is ​​received in a computer cache. The cache may reside on the CPU or hard disk drive of a local computer (CPU or hard disk cache), or it may be a remote cache, for example, where the spectral unmixing matrix is ​​cached on a local or remote server (server cache). At step 102, one or more matrix quality parameters of the calculated spectral unmixing matrix are evaluated. In some instances, the calculated and evaluated quality metrics include a complexity index, a similarity index (cosine similarity matrix), or a hotspot matrix. In some instances, autofluorescence collinearity, for example, by variance decomposition ratio and condition number, is evaluated as a quality metric for the spectral unmixing matrix. (Step 102a) At step 103, the cache input is adjusted based on the evaluated matrix quality parameters. In some instances, the cache input includes calculated values ​​or statistics from control data. In some instances, control data may be added, excluded, or modified, such as single-stain control data, negative control data, autofluorescence control data, and calculated gate statistics. (Step 103a) In some instances, when the control is not yet cached, the method includes retrieving the control data or calculation into a cache before adjusting and recalculating the spectral unmixing matrix. At step 104, the spectral unmixing matrix is ​​recalculated with the adjusted input. At step 105, the updated matrix quality parameters are displayed on the graphical user interface. In some instances, the matrix quality parameters are updated and displayed simultaneously with the input to the spectral unmixing matrix adjusted on the graphical user interface, making the process occur in real time.

[0074] Figure 1BA flowchart illustrating the adjustment of a spectral unmixing matrix in a computer cache according to certain embodiments is described. In the computer cache, the spectral unmixing matrix is ​​received and overflows are calculated to generate matrix quality parameters. Here, control results are cached and stored when the spectral unmixing matrix calculation is complete. In some embodiments, controls are retained in the cache until the user changes or removes the control from the cache. First, compensation results for the fluorescent dye set are calculated and received by the computer cache. Next, it is determined whether the controls for the fluorescent dye set have cached results. If the controls do not have cached results, results are calculated for one or more input control data. In some instances, controls have uncached, unstained negative control data. In these instances, statistics for the uncached, unstained negative control data are calculated, and then the negative control statistics are cached. If the controls have cached, unstained negative control data, the calculated statistics are retrieved, and further statistics can then be calculated based on the updated control data. Overflows are calculated in the cache to compute the spectral unmixing matrix. Based on the computed spectral unmixing matrix, matrix quality parameters (e.g., complexity, similarity, and hotspots) indicating matrix quality are returned to the user via a graphical user interface. Based on the evaluated matrix quality metric, one or more cached controls can be adjusted or excluded, or uncached controls can be added to the cache to adjust the spectral unmixing matrix. Since the control data and the calculated overflows (used to calculate the spectral unmixing matrix) are stored in the computer cache, the updated matrix quality metric results can be immediately returned to the graphical user interface in real time. In some instances, the cached controls are iteratively adjusted one or more times via the graphical user interface, and the iterative updates to the matrix quality metric are evaluated to identify the optimal spectral unmixing matrix.

[0075] Figure 1C A workflow for setting up an unmixing experiment to calculate compensation results for a fluorescent dye set, according to certain embodiments, is described. In this workflow, compensation results are generated and returned to the user, who can then use these results to calculate and optimize a spectral unmixing matrix as described herein. Using a graphical user interface, the unmixing experiment is set up, generating a preview of the unmixing matrix. The compensation results are calculated and returned to the user, for example, to a computer cache. As described above, based on matrix quality parameters evaluated for the calculated spectral unmixing matrix, control data and calculated values ​​can be adjusted in the computer cache to provide real-time adjustments to the spectral unmixing matrix.

[0076] Figure 1DExamples of graphical user interfaces (GUIs) for evaluating matrix quality parameters and adjusting the spectral unmixing matrix according to certain embodiments are depicted. The GUI includes user-interactive panels for modifying cached control data and calculated values, and for visualizing matrix quality metrics of the recalculated spectral unmixing matrix. The GUI includes a Spectral Matrix Setup panel 201, which lists control data and calculated values ​​for setting the spectral unmixing matrix. Here, one or more control data or calculated inputs can be retrieved, adjusted, or excluded from the computer cache to change the calculated matrix. Autofluorescence data controls can also be adjusted in the Spectral Matrix Setup panel 202. A matrix preview is displayed in a Preview Spectral Matrix panel 203, which allows the user to visualize data plots (e.g., bi-exponential plots, scatter plots) and visualize updated matrix quality parameters, such as spectral feature maps, similarity matrices, or hotspot matrices. The Preview Spectral Matrix panel updates in real time as changes are made to the Spectral Matrix Setup section.

[0077] In some embodiments, the method further includes generating spectral data for calculating compensation and spectral unmixing matrices for the fluorescent dye set. In some instances, the method includes generating spectral data for one or more controls as described above. For example, in some instances, the method includes generating spectral data for a single-stained positive control. In some instances, the method includes generating spectral data for a negative control. In some instances, the method includes generating spectral data for an autofluorescence control. In some instances, the method includes generating spectral data using single-stained microbeads for positive controls. In some instances, the method includes generating spectral data using single-stained cells for positive controls. When practicing the subject method according to some embodiments, a sample having particles (e.g., single-stained microbeads, single-stained cells, unstained microbeads, unstained cells) is irradiated with light from a light source. In some embodiments, the light source is a broadband light source that emits light with a wide wavelength range, such as spanning or greater than 50 nm, for example, 100 nm or greater than 100 nm, for example, 150 nm or greater than 150 nm, for example, 200 nm or greater than 200 nm, for example, 250 nm or greater than 250 nm, for example, 300 nm or greater than 300 nm, for example, 350 nm or greater than 350 nm, for example, 400 nm or greater than 400 nm, and including spanning or greater than 500 nm. For example, a suitable broadband light source emits light with wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light with wavelengths from 400 nm to 1000 nm. When the method involves illumination using a broadband light source, options for the broadband light source of interest include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stable fiber-coupled broadband light sources, broadband LEDs with continuous spectra, superluminescent diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, and other broadband light sources or any combination thereof.

[0078] In other embodiments, the method includes illumination using a narrowband light source that emits a specific wavelength or a narrow wavelength range, such as a light source emitting light in a narrow wavelength range, such as 50 nm or less, 40 nm or less, 30 nm or less, 25 nm or less, 20 nm or less, 15 nm or less, 10 nm or less, 5 nm or less, or 2 nm or less, and includes a light source emitting light of a specific wavelength (i.e., monochromatic light). When the method includes illumination using a narrowband light source, options of narrowband light source of interest include, but are not limited to, narrow-wavelength LEDs, laser diodes, broadband light sources coupled with one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0079] In some embodiments, the method includes irradiating the flow stream with one or more lasers. The type and number of lasers vary depending on the sample and the desired light to be collected, and may be gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other instances, the method includes irradiating the flow stream with dye lasers, such as stilbene, coumarin, or rhodamine lasers. In still other instances, the method includes irradiating the flow stream with metal vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, or combinations thereof. In other instances, the method involves irradiating the flow with a solid-state laser, such as a ruby ​​laser, an Nd:YAG laser, an NdCrYAG laser, an Er:YAG laser, an Nd:YLF laser, an Nd:YVO4 laser, an Nd:YCa4O(BO3)3 laser, an Nd:YCOB laser, a titanite sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, or combinations thereof.

[0080] The flowing sample can be illuminated by one or more of the aforementioned light sources, such as two or more light sources, three or more light sources, four or more light sources, five or more light sources, and including ten or more light sources. The light sources can include any combination of light source types. For example, in some embodiments, the method includes illuminating the sample in the flowing stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0081] The wavelength range for irradiating the sample can be from 200 nm to 1500 nm, for example, 250 nm to 1250 nm, for example, 300 nm to 1000 nm, for example, 350 nm to 900 nm, and including 400 nm to 800 nm. For example, when the light source is a broadband light source, the sample can be irradiated with wavelengths from 200 nm to 900 nm. In other cases, when the light source comprises multiple narrowband light sources, the sample can be irradiated with light of a specific wavelength from 200 nm to 900 nm. For example, the light source can be multiple narrowband LDEs (1 nm to 25 nm), each independently emitting light of a wavelength from 200 nm to 900 nm. In other embodiments, the narrowband light source comprises one or more lasers (e.g., a laser array), and the sample is irradiated with a specific wavelength from 200 nm to 700 nm, for example, using a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers as described above.

[0082] When more than one light source is used, the sample can be illuminated by the light sources simultaneously, sequentially, or in combination. For example, the sample can be illuminated simultaneously by each of the light sources. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When more than one light source is used to sequentially illuminate the sample, the duration for which each light source illuminates the sample can independently be 0.001 microseconds or greater than 0.001 microseconds, such as 0.01 microseconds or greater than 0.01 microseconds, such as 0.1 microseconds or greater than 0.1 microseconds, such as 1 microsecond or greater than 1 microsecond, such as 5 microseconds or greater than 5 microseconds, such as 10 microseconds or greater than 10 microseconds, such as 30 microseconds or greater than 30 microseconds, and includes 60 microseconds or greater than 60 microseconds. For example, the method may include illuminating the sample with a light source (e.g., a laser) for a duration of 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, and includes 5 microseconds to 10 microseconds. In embodiments where the sample is sequentially illuminated by two or more light sources, the duration of illumination by each light source may be the same or different.

[0083] The time interval between illumination by each light source can vary as needed, independently separated by delays of 0.001 microseconds or greater, such delays as 0.01 microseconds or greater, 0.1 microseconds or greater, 1 microsecond or greater, 5 microseconds or greater, 10 microseconds or greater, 15 microseconds or greater, 30 microseconds or greater, and including 60 microseconds or greater. For example, the time interval between illumination by each light source can be from 0.001 microseconds to 60 microseconds, from 0.01 microseconds to 50 microseconds, from 0.1 microseconds to 35 microseconds, from 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In some embodiments, the time interval between illumination by each light source is 10 microseconds. In embodiments where the sample is sequentially illuminated by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0084] The sample may be illuminated continuously or at discrete intervals. In some cases, the method involves illuminating the sample continuously with a light source. In other cases, the sample is illuminated by a light source at discrete intervals, such as once every 0.001 milliseconds, once every 0.01 milliseconds, once every 0.1 milliseconds, once every 1 millisecond, once every 10 milliseconds, once every 100 milliseconds, and including once every 1000 milliseconds, or other intervals.

[0085] Depending on the light source, the sample can be illuminated from varying distances, such as 0.01 mm or greater, for example 0.05 mm or greater, 0.1 mm or greater, 0.5 mm or greater, 1 mm or greater, 2.5 mm or greater, 5 mm or greater, 10 mm or greater, 15 mm or greater, 25 mm or greater, and including 50 mm or greater. Simultaneously, the illumination angle can also vary from 10° to 90°, for example 15° to 85°, 20° to 80°, 25° to 75°, and including 30° to 60°, such as a 90° angle.

[0086] In some embodiments, the method includes illuminating a sample with two or more beams of frequency-shifted light. As described above, a beam generator component having a laser and an acousto-optic device for frequency-shifting the laser can be used. In these embodiments, the method includes illuminating the acousto-optic device with a laser. Depending on the desired wavelength of the light generated in the output laser beam (e.g., for illuminating a sample in a flowing stream), the specific wavelength of the laser can vary from 200 nm to 1500 nm, for example, 250 nm to 1250 nm, for example, 300 nm to 1000 nm, for example, 350 nm to 900 nm, and includes 400 nm to 800 nm. The acousto-optic device can be illuminating with one or more lasers, for example, two or more lasers, such as three or more lasers, such as four or more lasers, such as five or more lasers, and includes ten or more lasers. The lasers can include any combination of laser types. For example, in some embodiments, the method includes irradiating an acousto-optic device with a laser array, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0087] When more than one laser is used, the acousto-optic device can be illuminated by the laser simultaneously, sequentially, or in combination. For example, the acousto-optic device can be illuminated simultaneously by each of the lasers. In other embodiments, the acousto-optic device is illuminated sequentially by each of the lasers. When more than one laser is used to sequentially illuminate the acousto-optic device, the duration for which each laser illuminates the acousto-optic device can independently be 0.001 microseconds or greater than 0.001 microseconds, for example, 0.01 microseconds or greater than 0.01 microseconds, for example, 0.1 microseconds or greater than 0.1 microseconds, for example, 1 microsecond or greater than 1 microsecond, for example, 5 microseconds or greater than 5 microseconds, for example, 10 microseconds or greater than 10 microseconds, for example, 30 microseconds or greater than 30 microseconds, and includes 60 microseconds or greater than 60 microseconds. For example, the method may include irradiating the acousto-optic device with a laser for a duration of 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In embodiments where the acousto-optic device is sequentially irradiated by two or more lasers, the duration of irradiation of the acousto-optic device by each laser may be the same or different.

[0088] The time interval between irradiations by each laser can vary as needed, independently separated by delays of 0.001 microseconds or greater, such delays as 0.01 microseconds or greater, 0.1 microseconds or greater, 1 microsecond or greater, 5 microseconds or greater, 10 microseconds or greater, 15 microseconds or greater, 30 microseconds or greater, and including 60 microseconds or greater. For example, the time interval between irradiations by each light source can be from 0.001 microseconds to 60 microseconds, from 0.01 microseconds to 50 microseconds, from 0.1 microseconds to 35 microseconds, from 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In some embodiments, the time interval between irradiations by each laser is 10 microseconds. In embodiments where the acousto-optic device is sequentially irradiated by more than two (i.e., three or more) lasers, the delay between irradiations by each laser can be the same or different.

[0089] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method involves illuminating the acousto-optic device continuously with a laser. In other cases, the acousto-optic device is illuminated by a laser at discrete intervals, such as once every 0.001 milliseconds, once every 0.01 milliseconds, once every 0.1 milliseconds, once every 1 millisecond, once every 10 milliseconds, once every 100 milliseconds, and including once every 1000 milliseconds, or other intervals.

[0090] Depending on the laser, the acousto-optic device can be illuminated from varying distances, such as 0.01 mm or greater, 0.05 mm or greater, 0.1 mm or greater, 0.5 mm or greater, 1 mm or greater, 2.5 mm or greater, 5 mm or greater, 10 mm or greater, 15 mm or greater, 25 mm or greater, and including 50 mm or greater. Simultaneously, the illumination angle can also vary from 10° to 90°, such as 15° to 85°, 20° to 80°, 25° to 75°, and including 30° to 60°, such as a 90° angle.

[0091] In an embodiment, the method includes applying radio frequency (RF) drive signals to an acousto-optic device to generate an angle-deflected laser beam. Two or more RF drive signals can be applied to the acousto-optic device to generate an output laser beam having a desired number of angle-deflected laser beams, such as three or more RF drive signals, four or more RF drive signals, five or more RF drive signals, six or more RF drive signals, seven or more RF drive signals, eight or more RF drive signals, nine or more RF drive signals, ten or more RF drive signals, fifteen or more RF drive signals, twenty-five or more RF drive signals, fifty or more RF drive signals, and including one hundred or more RF drive signals.

[0092] Each angle-deflecting laser beam generated by a radio frequency (RF) drive signal has an intensity based on the amplitude of the applied RF drive signal. In some embodiments, the method includes applying an RF drive signal having an amplitude sufficient to generate an angle-deflecting laser beam with a desired intensity. In some cases, each applied RF drive signal independently has an amplitude of about 0.001V to about 500V, for example about 0.005V to about 400V, for example about 0.01V to about 300V, for example about 0.05V to about 200V, for example about 0.1V to about 100V, for example about 0.5V to about 75V, for example about 1V to about 50V, for example about 2V to about 40V, for example about 3V to about 30V, and including about 5V to about 25V. In some instances, each applied radio frequency drive signal independently has an amplitude of about 0.001V to 100V, for example, about 0.001V to 200V, for example, 0.001V to 300V, for example, 0.001V to 400V, and includes 0.001V to 500V. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001MHz to about 500MHz, for example, about 0.005MHz to about 400MHz, for example, about 0.01MHz to about 300MHz, for example, about 0.05MHz to about 200MHz, for example, about 0.1MHz to about 100MHz, for example, about 0.5MHz to about 90MHz, for example, about 1MHz to about 75MHz, for example, about 2MHz to about 70MHz, for example, about 3MHz to about 65MHz, for example, about 4MHz to about 60MHz, and includes about 5MHz to about 50MHz. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001 MHz to about 100 MHz, for example 0.001 MHz to 200 MHz, for example 0.001 MHz to 300 MHz, for example 0.001 MHz to 400 MHz, for example 0.001 MHz to 500 MHz.

[0093] In these embodiments, the angle-deflected laser beams in the output laser beam are spatially separated. Depending on the applied radio frequency drive signal and the desired illumination distribution of the output laser beam, the angle-deflected laser beams can be spaced 0.001 μm or greater, for example, 0.005 μm or greater, for example, 0.01 μm or greater, for example, 0.05 μm or greater, for example, 0.1 μm or greater, for example, 0.5 μm or greater, for example, 1 μm or greater, for example, 5 μm or greater, for example, 10 μm or greater, for example, 100 μm or greater, for example, 500 μm or greater, for example, 1000 μm or greater, including 5000 μm or greater. In some embodiments, the angle-deflected laser beams overlap, for example, along the horizontal axis of the output laser beam with adjacent angle-deflected laser beams. The overlap between adjacent angle-deflected laser beams (such as the overlap of beam spots) can be 0.001 μm or greater, for example, 0.005 μm or greater, such as 0.01 μm or greater, 0.05 μm or greater, 0.1 μm or greater, 0.5 μm or greater, 1 μm or greater, 5 μm or greater, 10 μm or greater, including 100 μm or greater.

[0094] In some instances, a flow is illuminated with multiple frequency-shifted beams, and particles within the flow are imaged, as described by Diebold et al. in Nature Photonics, Vol. 7 (10); 806-810 (2013), and as in U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; 10 The disclosures of these patents are described in ,684,211; 10,845,295; 10,935,482; 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851; the disclosures of these patents are incorporated herein by reference.

[0095] In implementing the subject method, light from each particle is detected by a photodetector system. In embodiments, the photodetector system includes one or more photodetectors, such as two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more photodetectors. The photodetectors used to implement the subject method can be any convenient light detection scheme, including but not limited to photosensors or photodetectors such as avalanche photodiodes (APDs), active pixel sensors (APSs), quadrant photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or combinations thereof, and other photodetectors. In some embodiments, the photodetector is a photomultiplier tube, for example, the effective detection surface area per region is in the range of 0.01 cm². 2 Up to 10cm 2 Photomultiplier tubes, for example, 0.05 cm 2 Up to 9cm 2 For example, 0.1cm 2 Up to 8cm 2 For example, 0.5cm 2 Up to 7cm 2 And including 1cm 2 up to 5cm 2 In an embodiment, light from the irradiated sample is detected in two or more photodetector channels, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twelve or more, sixteen or more, twenty-four or more, thirty-two or more, sixty-four or more, twenty-eight or more, twenty-five-six or more, and including 512 or more photodetector channels.

[0096] A photodetector can be used to measure light at one or more wavelengths, such as at two or more wavelengths, at five or more different wavelengths, at ten or more different wavelengths, at 25 or more different wavelengths, at 50 or more different wavelengths, at 100 or more different wavelengths, at 200 or more different wavelengths, at 300 or more different wavelengths, and including measuring light of particles in a flowing stream at 400 or more different wavelengths. The light can be measured continuously or at discrete intervals. In some instances, the detector of interest is configured to measure light continuously. In other instances, the detector of interest is configured to perform measurements at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, and including measuring light every 1000 milliseconds, or some other kind of interval.

[0097] In some embodiments, the light detected from the sample is scattered light. The term "scattered light" is used herein in its conventional sense to refer to the propagation of light energy from particles in the sample (e.g., flowing in a flow stream), which are deflected from the path of the incident beam, for example, by reflection, refraction, or deflection. In some instances, the scattered light detected from particles in the flow stream is forward scattered light (FSC). In other instances, the scattered light detected from particles in the flow stream is side scattered light (SSC). And in still other instances, the scattered light detected from particles in the flow stream is backscattered light (BSC).

[0098] In some embodiments, the light detected from each particle in the sample is transmitted light, such as light detected using a bright-field detector. In other embodiments, the light detected from each particle in the sample is emitted light, such as particle luminescence (i.e., fluorescence or phosphorescence), as described in more detail below.

[0099] In this embodiment, the autofluorescence produced by the irradiated particles is measured. The term “autofluorescence” is used herein in its conventional sense to refer to the light emitted by the structure or components of a particle (e.g., cellular components within a cell) when illuminated by a light source. Autofluorescence is not derived from and is distinct from added fluorescent markers. In some instances, the particles are cells, and cellular autofluorescence includes the light emission of autofluorescent molecules such as NADPH, flavins, proteins, and amino acids such as tryptophan, tyrosine, and phenylalanine. In some cases, cellular autofluorescence includes the light emission of intrinsic properties of collagen or elastin. The autofluorescence of particles may vary in certain cases depending on the state of the cell, such as whether the cell is unstimulated, stimulated, activated, or in some other biological or physical state. As described herein, the method includes assessing collinearity of the autofluorescence spectra from particles (e.g., cellular autofluorescence). In some embodiments, the contribution of the autofluorescence spectrum to the spectral unmixing matrix is ​​considered, reduced, and eliminated as needed.

[0100] In some instances, the sample comprises two or more particles of different types, such as three or more, four or more, five or more, ten or more, fifteen or more, twenty-five or more, fifty or more, seventy-five or more, and even one hundred or more particles of different types. In some instances, the particles are cells. In some instances, the cells are in two or more different states, such as one or more cells being in stimulated, unstimulated, activated, and inactivated states.

[0101] In some embodiments, the particles in the sample comprise one or more fluorescent dyes that emit fluorescence in response to illumination by a light source. In some embodiments, each particle in the control sample comprises a single type of fluorescent dye (i.e., a monostain control). In other embodiments, each particle may comprise two or more fluorescent dyes, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more fluorescent dyes. The subject fluorescent dye set may include any suitable fluorescent dye set. The maximum excitation range of the fluorescent dye of interest according to some embodiments is from 100 nm to 800 nm, such as 150 nm to 750 nm, such as 200 nm to 700 nm, such as 250 nm to 650 nm, such as 300 nm to 600 nm, and including 400 nm to 500 nm. According to some embodiments, the maximum emission range of the fluorescent dye of interest is 400 nm to 1000 nm, for example 450 nm to 950 nm, for example 500 nm to 900 nm, for example 550 nm to 850 nm, and includes 600 nm to 800 nm. In some instances, the fluorescent dye is a luminescent dye, such as a fluorescent dye with a peak emission wavelength of 200 nm or longer, for example 250 nm or longer, for example 300 nm or longer, for example 350 nm or longer, for example 400 nm or longer, for example 450 nm or longer, for example 500 nm or longer, for example 550 nm or longer, for example 600 nm or longer, for example 650 nm or longer, for example 700 nm or longer, for example 750 nm or longer, for example 800 nm or longer, for example 850 nm or longer, for example 900 nm or longer, for example 950 nm or longer, for example 1000 nm or longer, and includes 1050 nm or longer. For example, the fluorescent dye may be a fluorescent dye with a peak emission wavelength range of 200 nm to 1200 nm, such as 300 nm to 1100 nm, such as 400 nm to 1000 nm, such as 500 nm to 900 nm, and includes fluorescent dyes with a peak emission wavelength range of 600 nm to 800 nm.

[0102] In some embodiments, the fluorescent dyes of interest may include, but are not limited to, dyes suitable for analytical applications (e.g., flow cytometry, imaging, etc.), such as acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes (e.g., diphenylmethane dyes), chlorophyll-containing dyes, triarylmethane dyes (e.g., triphenylmethane dyes), azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, anthocyanin dyes, asymmetric anthocyanin dyes, quinone imine dyes, acridine dyes, eurhodin dyes, safranin dyes, indamins, indophenol dyes, and fluorine dyes. Oxazine dyes, oxazone dyes, thiazine dyes, thiazolium dyes, xanthan dyes, fluorene dyes, pyrrolidine dyes, fluorine dyes, and dyes combining two or more of the above dyes (e.g., in tandem), polymer dyes having one or more monomer dye units, and mixtures of two or more of the above dyes. A wide variety of dyes are commercially available from various sources, such as Molecular Probes (Eugene, OR), Dyomics GmbH (Jena, Germany), Sigma-Aldrich (St. Louis, MO), Sirigin, Inc. (Santa Barbara, CA), Becton Dickinson (BD) and Company (Franklin Lakes, NJ), and Exciton (Dayton, OH).For example, fluorophores may include 4-acetamido-4'-isothiocyanate-stilbene-2,2'-disulfonic acid; acridine and its derivatives, such as acridine, acridine orange, acridine yellow, acridine red, and acridine isothiocyanate; allophycocyanin (APC), phycoerythrin (PE), polydinophyll-chlorophyll protein, 5-(2'-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinylsulfonyl)phenyl]naphthalimide-3,5-disulfonate (fluorescein VS); N-(4-anilino-1-naphthyl)maleimide; ortho- Aminobenzamide; Brilliant Yellow; Coumarins and their derivatives, such as coumarin, 7-amino-4-methylcoumarin (AMC, coumarin 120), 7-amino-4-trifluoromethylcoumarin (coumarin 151); Anthocyanins and their derivatives, such as cyanosine, Cy3, Cy3.5, Cy5, Cy5.5 and Cy7; 4',6-Diamidinyl-2-phenylindole (DAPI); 5',5"-Dibromopyrogallol-sulfophthalein (bromopyrogallol red); 7-Diethylamino-3-(4'-isothiocyanate-phenyl)-4-methylcoumarin Coumarin; diethylaminocoumarin; diethylenetriaminepentaacetic acid; 4,4'-diisothiocyanodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid; 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylamineazobenzene)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl-4'-isothiocyanate (DABITC); eosin and its derivatives, such as eosin and eosin isothiocyanate; erythrosine and its derivatives, such as erythrosine B and erythrosine isothiocyanate. Cyanide esters; ethidium bromide; fluorescein and its derivatives, such as 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2-yl)aminofluorescein (DTAF), 2'7'-dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein isothiocyanate (FITC), fluorescein chlorotriazinyl, naphthofluorescein and QFITC ​​(XRITC); fluorescent amines; IR144; IR1446; green fluorescent protein (GFP); coral reef fluorescent protein (RCFP); Lissamine. TM Lissamine, Lucifer yellow; Malachite green isothiocyanate; 4-methylumbelliferone; o-cresolphthalein; nitrotyrosine; parasitic red; Nile red; Oregon green; phenol red; β-phycoerythrin; phthalaldehyde; pyrene and its derivatives, such as pyrene, pyrene butyrate, and succinimidyl 1-pyrene butyrate; Reactive Red 4 (Cibacron)TM Bright red 3B-A); Rhodamine and its derivatives, such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), 4,7-dichlororhodamine lissamine, rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulfonylrhodamine B, sulfonylrhodamine 101, sulfonylrhodamine 101 sulfonyl chloride derivatives (Texas Red), N,N,N',N'-tetramethyl-6-carboxyrhodamine. (TAMRA), tetramethylrhodamine and tetramethylrhodamine isothiocyanate (TRITC); riboflavin; rosemary acid and terbium chelate derivatives; xanthones; dye conjugated polymers (i.e., polymer-linked dyes), such as fluorescein isothiocyanate-dextran, and dyes combining two or more of the above dyes (such as tandem dyes or protein complex tandem dyes), such as phycoerythrin (PE) tandem dyes or allophycocyanin (APC) tandem dyes, such as phycoerythrin-CF594 (PE-CF594) tandem, phycoerythrin-cyanine 5 tandem (PE-Cy5), phycoerythrin-cyanine 5.5 tandem (PE-Cy5.5), phycoerythrin-cyanine 7 tandem (PE-Cy7), allophycocyanin-R700 tandem (APC-R700), allophycocyanin-cyanine 7 (APC-Cy7), polymeric dyes having one or more monomeric dye units, and mixtures or combinations of the above two or more dyes.

[0103] In some instances, the fluorophore (i.e., the dye) is a fluorescent polymer dye. The fluorescent polymer dyes used in the subject method and system are diverse. In some instances of this method, the polymer dye includes conjugated polymers. Conjugated polymers (CPs) are characterized by a delocalized electronic structure comprising a backbone of alternating unsaturated bonds (such as double and / or triple bonds) and saturated bonds (such as single bonds), where π electrons can move from one bond to another. Therefore, the conjugated backbone can impart an extended linear structure to the polymer dye, with finite bond angles between the repeating units of the polymer. For example, proteins and nucleic acids, although also polymerized, do not form extended rod-like structures in some cases, but rather fold into higher-order three-dimensional shapes. Furthermore, CPs may form “rigid rod-like” polymer backbones and experience finite twist (e.g., torsion) angles between the monomeric repeating units along the polymer backbone. In some instances, the polymer dye includes conjugated polymers with rigid rod-like structures. As described above, the structural features of the polymer dye influence the fluorescence properties of the molecule.

[0104] Any readily available polymer dye can be used in the subject methods and systems. In some instances, the polymer dye is a multi-chromophore with a structure capable of trapping light to amplify the fluorescence output of a fluorophore. In some cases, the polymer dye is capable of trapping light and efficiently converting it into emitted light of a longer wavelength. In some cases, the polymer dye has a light-trapping multi-chromophore system that can efficiently transfer energy to a nearby luminescent substance (e.g., a “signal chromophore”). Mechanisms of energy transfer include, for example, resonant energy transfer (e.g., Forster (or fluorescent) resonant energy transfer, FRET), quantum charge exchange (Dexter energy transfer), etc. In some instances, the range of these energy transfer mechanisms is relatively short; that is, the close proximity of the light-trapping multi-chromophore system to the signal chromophore provides efficient energy transfer. Under conditions of effective energy transfer, when the number of individual chromophores in a light-trapping multi-chromophore system is large, the emission of the signal chromophore is amplified; that is, when the wavelength of the incident light ("excitation light") is absorbed by the light-trapping multi-chromophore system, the emission from the signal chromophore is stronger than when the signal chromophore is directly excited by the pump light.

[0105] Multichromophores can be conjugated polymers. Conjugated polymers (CPs) possess delocalized electronic structures, making them highly responsive optical reporters for chemical and biological targets. Because the effective conjugation length is much smaller than the polymer chain length, the backbone contains a large number of closely adjacent conjugated segments. Therefore, conjugated polymers are effective for light trapping and can achieve optical amplification through energy transfer.

[0106] In some instances, polymers can be used as direct fluorescent reporters, such as fluorescent polymers with high extinction coefficients, high brightness, etc. In other instances, polymers can be used as strong chromophores, where color or optical density serves as an indicator.

[0107] Polymer dyes of interest include, but are not limited to, those described in U.S. Publications 20040142344, 20080293164, 20080064042, 20100136702, 20110256549, 20120028828, 20120252986, 20130190193, and 20160025735, the disclosures of which are incorporated herein by reference in their entirety; and Gaylord et al., J. Am. Chem. Soc., 2001, 123 (26), pp 6417–6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411–2419; and Traina et al., J. Am. Chem. Soc., 2011, 133 (32), pp The dyes described in 12600–12607 are incorporated herein by reference in their entirety.

[0108] In some embodiments, the method includes performing flow cytometry experiments using a set of fluorescent dyes, determined, for example, by the spectral unmixing matrix calculated above. In some instances, the method includes irradiating a sample containing particles and measuring the light sample using a light detection system described in detail above. In some instances, the sample analyzed in the flow cytometry experiment is a biological sample. The term "biological sample," used in its conventional sense, refers to a subset of a whole organism, plant, fungus, or animal tissue, cell, or component, which in some instances can be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, "biological sample" refers both to a natural organism or a subset of its tissues and to homogenates, lysates, or extracts prepared from a subset of an organism or its tissues, including but not limited to, for example, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular and genitourinary tract, tears, saliva, breast milk, blood cells, tumors, and organs. Biological samples can be any type of biological tissue, including both healthy and diseased tissues (e.g., cancerous, malignant, necrotic tissue, etc.). In some embodiments, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc. In some instances, the sample is a blood sample, including whole blood, such as blood obtained by venipuncture or finger prick (where the blood may or may not be bound to any reagents such as preservatives, anticoagulants, etc. before testing).

[0109] In some embodiments, the source of the sample is "mammal" or "milk animal," terms that are widely used to describe organisms within the class Mammalia, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some instances, the subject is a human. The method can be applied to samples obtained from human subjects of both sexes and at any developmental stage (i.e., newborns, infants, adolescents, teenagers, and adults), wherein in some embodiments, the human subject is an adolescent, teenager, or adult. While this disclosure can be applied to samples from human subjects, it should be understood that the method can also be performed on samples from other animal subjects (i.e., "non-human subjects") (e.g., but not limited to birds, mice, rats, dogs, cats, livestock, and horses).

[0110] Cells of interest can be targeted to characterize them according to various parameters, such as phenotypic features identified by attaching specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analytical droplets identified as including target cells. A wide variety of cells can be characterized using the methods of this subject matter. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells with readily available cell surface markers or antigens that can be captured or labeled by readily available affinity agents or conjugates thereof. For example, target cells may include cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminal), SSEA-3, TRA-1-60 antigen, disialotetrahexosylganglioside GD2, and CD71. In some embodiments, target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) derived from whole blood, bone marrow, or umbilical cord blood.

[0111] When implementing the methods of this subject matter according to certain embodiments, a certain amount of initial fluid sample is injected into a flow cytometer. The sample volume injected into the particle sorting module can vary, for example, ranging from 0.001 mL to 1000 mL, such as 0.005 mL to 900 mL, 0.01 mL to 800 mL, 0.05 mL to 700 mL, 0.1 mL to 600 mL, 0.5 mL to 500 mL, 1 mL to 400 mL, 2 mL to 300 mL, including samples from 5 mL to 100 mL.

[0112] In some embodiments, the method includes counting and optionally sorting labeled particles (such as target cells) in a sample. In implementing the method of this subject matter, a fluid sample containing particles is first introduced into a flow nozzle of the system. After exiting the flow nozzle, the particles pass substantially one-by-one through a sample detection region, where each particle is illuminated by a light source, and measurements of light scattering parameters for each particle are recorded individually. In some instances, fluorescence emission measurements are also recorded as needed (e.g., two or more light scattering parameter measurements and one or more fluorescence emission measurements). Depending on the characteristics of the detected flow, regions of 0.001 mm or longer in the flow may be illuminated, such as 0.005 mm or longer, 0.01 mm or longer, 0.05 mm or longer, 0.1 mm or longer, 0.5 mm or longer, including regions of 1 mm or longer in the flow that may be illuminated. In some embodiments, the method includes illuminating a planar cross-section of the flow in the sample detection region, for example, using a laser (as described above). In other embodiments, the method includes irradiating a flow of predetermined length in the sample detection region, for example, corresponding to the irradiation profile of a diffuse laser beam or lamp.

[0113] In some embodiments, the method includes irradiating the flow at or near the flow tank nozzle orifice. For example, the method may include irradiating the flow at a location approximately 0.001 mm or more from the nozzle orifice, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, including a location 1 mm or more from the nozzle orifice. In some embodiments, the method includes irradiating the flow at a location immediately adjacent to the flow nozzle orifice.

[0114] As in embodiments of this method, detectors, such as photomultiplier tubes (PMTs), are used to record light passing through each particle (in some cases referred to as forward scattering), light reflected orthogonally to the flow direction of the particle through the detection zone (in some cases referred to as orthogonal or lateral scattering), and fluorescence emitted from the particle as it passes through the detection zone and is illuminated by energy, if the particle is labeled with one or more fluorescent markers. Each of forward scattering (FSC), lateral scattering (SSC), and fluorescence emission includes a separate parameter for each particle (or each "event"). Thus, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle is analyzed in real time as needed or stored in a data storage and analysis device, such as a computer.

[0115] In some embodiments, particles are detected and uniquely identified, as needed, by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels. The fluorescence emitted in the detection channels, used to identify the particles and the binding complexes associated with them, can be measured after excitation using a single light source, or separately after excitation using different light sources. If separate excitation sources are used to excite the particle markers, the markers can be selected such that all markers can be excited by each excitation source used.

[0116] The method in some embodiments also includes data acquisition, analysis, and recording (e.g., using a computer), wherein multiple data channels record data from each detector, each detector being used for light scattering and fluorescence emitted by each particle as it passes through a sample detection area of ​​a particle sorting module. In these embodiments, the analysis includes classifying and counting particles such that each particle exists as a set of digitized parameter values. The subject system can be configured to trigger on selected parameters to distinguish particles of interest from background and noise. "Trigger" refers to a preset threshold for the detection parameter, which can be used as a means of detecting particles passing through a light source. Detecting an event exceeding the selected parameter threshold triggers the acquisition of light scattering and fluorescence data of the particles. Data on particles or other components in the analyzed medium that elicit a response below the threshold are not acquired. The trigger parameter can be the detection of forward scattered light caused by particles passing through a light beam. Flow cytometry then detects and collects the light scattering and fluorescence data of the particles.

[0117] The sample is then further analyzed for specific subpopulations of interest by “gating” based on data collected for the entire population. To select an appropriate gate, the data is plotted to obtain the best possible subpopulation separation. This process can be performed by plotting forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC) on a two-dimensional dot plot. Particle subpopulations (i.e., those cells within the gate) are then selected, and particles not within the gate are excluded. If needed, gates can be selected by drawing lines around the desired subpopulation on the computer screen using a cursor. Those particles within the gate are then further analyzed only by plotting other parameters of these particles (e.g., fluorescence). If necessary, the above analysis can be configured to generate counts of particles of interest in the sample.

[0118] Methods of interest may also include the use of particles in research, laboratory testing, or treatment. In some embodiments, the subject method includes obtaining single cells prepared from a target fluid or tissue biological sample. For example, the subject method includes obtaining cells from a fluid or tissue sample for use as a research or diagnostic specimen for diseases such as cancer. Similarly, the subject method includes obtaining cells from a fluid or tissue sample for therapeutic purposes. Cell therapy protocols are those that can prepare living cellular materials, including, for example, cells and tissues, and introduce them into a subject for therapeutic treatment. Situations where treatment can be performed by applying samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system disorders, organ damage, etc.

[0119] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, gene modification, in vitro culture and expansion, cell harvesting, sample volume reduction and washing, biopreservation, storage, and introduction of cells into a subject. The protocol can begin with the collection of live cells and tissues from the subject's source tissue to produce cell and / or tissue samples. Samples can be collected using any suitable procedure, including, for example, administration of cell mobilizing agents to the subject, drawing blood from the subject, or extracting bone marrow from the subject. After sample collection, cells can be enriched using several methods, including, for example, centrifugation-based methods, filter-based methods, elutriation, magnetic separation methods, fluorescence-activated cell sorting (FACS), etc. In some instances, the enriched cells can be gene-modified using any convenient method, such as nuclease-mediated gene editing. Genetically modified cells can then be cultured, activated, and expanded in vitro. In some instances, cells are preserved, for example, cryopreserved, and stored for future use, where the cells are thawed and then administered to the patient, for example, cells can be infused into the patient.

[0120] system

[0121] This disclosure also includes systems for implementing the methods described above. A system according to certain embodiments includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: receive a computed spectral unmixing matrix in a computer cache of the system processor; evaluate matrix quality parameters of the computed spectral unmixing matrix; adjust cached inputs to the spectral unmixing matrix based on the evaluated matrix quality parameters; recompile the spectral unmixing matrix using the adjusted inputs; and, in response to the adjusted inputs, display the updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface on a display.

[0122] The programmable logic can be implemented in any of a variety of devices, such as specially programmed event processing computers, wireless communication devices, integrated circuit devices, etc. In some embodiments, the programmable logic can be executed by a specially programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Combinations of computing devices, such as a combination of DSPs and microprocessors, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration in at least a portion of the data connections, can implement one or more of the features described herein.

[0123] In some instances, the system is or includes a particle analyzer. A particle analyzer of interest may include a flow cell for transporting particles in a flow stream, a light source for illuminating particles in the flow stream at an interrogation point, and a particle-modulated light detector for detecting particle-modulated light. In some embodiments, the particle analyzer is a flow cytometer. In some instances, where the particle analyzer is a flow cytometer, the flow cytometer is a full-spectrum flow cytometer.

[0124] As discussed herein, a "flow cell," described in its conventional sense, refers to a component, such as a cuvette, that includes a flow channel having a liquid flow stream for transporting particles in a sheath fluid. A cuvette of interest includes a container having a passageway therethrough. The flow stream may include a liquid sample injected from a sample tube. A flow cell of interest includes a light-accessible flow channel. In some instances, the flow cell includes a transparent material (e.g., quartz) that allows light to pass through. In some embodiments, the flow cell is an airflow flow cell, wherein the light interrogation of the particles occurs outside the flow cell (i.e., in free space).

[0125] In some instances, the flow stream is configured to illuminate the interrogation point with light from a light source. The flow stream configured with flow channels may include a liquid sample injected from a sample tube. In some embodiments, the flow stream may include a narrow, fast-flowing liquid stream arranged such that linearly separated particles transported therein are separated from each other in a single file. The term "interrogation point" discussed herein refers to a region within the flow cell where particles are illuminated by light from a light source, for example, for analysis. The size of the interrogation point can vary as needed. For example, when 0 μm represents the axis of light emitted by the light source, the range of the interrogation point can be from -100 μm to 100 μm, for example -50 μm to 50 μm, for example -25 μm to 40 μm, and includes -15 μm to 30 μm.

[0126] When particles in a moving pool are irradiated, particle-modulated light can be observed. "Particle-modulated light" refers to the light received from the particles after they have been irradiated with light from a light source in a flowing stream. In some instances, particle-modulated light is side-scattered light. As described herein, side-scattered light refers to light refracted and reflected from the surface and internal structure of the particles. In other embodiments, particle-modulated light includes forward-scattered light (i.e., light that propagates primarily in the positive direction through or around the particles). In still other cases, particle-modulated light includes fluorescence (i.e., light emitted from a fluorescent dye after irradiation with light of the excitation wavelength).

[0127] The system according to some embodiments includes a light source configured to irradiate particles of a sample. In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit light with wavelengths varying in the range of 200 nm to 1500 nm, such as 250 nm to 1250 nm, 300 nm to 1000 nm, 350 nm to 900 nm, and including 400 nm to 800 nm. For example, the light source can include a broadband light source that emits light with wavelengths in the range of 200 nm to 900 nm. In other instances, the light source includes a narrowband light source that emits wavelengths in the range of 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) that emits light with wavelengths in the range of 200 nm to 900 nm. In some embodiments, the light source is a laser. In some instances, the subject system includes gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other instances, the subject system includes dye lasers such as stilbene, coumarin, or rhodamine lasers. In still other instances, lasers of interest include metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, or combinations thereof. In other instances, the subject system includes solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers and combinations thereof.

[0128] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, and light-emitting diodes (LEDs), such as broadband LEDs with a continuous spectrum, superluminescent LEDs, semiconductor LEDs, broadband LED white light sources, and integrated multi-LEDs. In some instances, the non-laser light source is a stable fiber-coupled broadband light source, a white light source, and other light sources or any combination thereof.

[0129] The light source can be positioned at any suitable distance from the sample (e.g., the flow stream in a flow cytometer), such as 0.001 mm or more, 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, and including distances of 100 mm or more. Furthermore, the light source illuminates the sample at any suitable angle (e.g., relative to the vertical axis of the flow stream), such as angles from 10° to 90°, 15° to 85°, 20° to 80°, 25° to 75°, and including angles from 30° to 60°, such as 90°.

[0130] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some instances, the system includes a light source configured to continuously illuminate the sample, such as a continuous-wave laser that continuously illuminates the flow stream at the interrogation point in a flow cytometer. In other instances, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds, or some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to provide intermittent illumination of the sample with the light source. For example, the subject system in these embodiments may include one or more laser beam choppers, manually or computer-controlled beam blockers, for blocking and exposing the sample to the light source.

[0131] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous-wave lasers. For example, the laser can be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser or a combination thereof; a solid-state laser, such as a ruby ​​laser, an Nd:YAG laser, an NdCrYAG laser, an Er:YAG laser, an Nd:YLF laser, an Nd:YVO4 laser, or an Nd: YCa4O(BO3)3 laser, Nd:YCOB laser, Ti:sapphire laser, thulium YAG laser, ytterbium YAG laser, Yb2O3 laser or cerium-doped laser and combinations thereof; second or third harmonic implementation of semiconductor diode laser, optically pumped semiconductor laser (OPSL) or any of the above lasers.

[0132] In some embodiments, the light source is a beam generator configured to generate two or more frequency-shifted beams. In some instances, the beam generator includes a laser, a radio frequency (RF) generator configured to apply an RF drive signal to the acousto-optic device to generate two or more angle-deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, the laser in the beam generator of interest can be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; or a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, or a helium-selenium (H) laser. eSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titania-sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers and combinations thereof.

[0133] The acousto-optic device can be any convenient acousto-optic scheme configured to frequency-shift a laser using applied acoustic waves. In some embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate an angle-deflected laser beam based on light from the laser and an applied radio frequency (RF) drive signal. The RF drive signal can be applied to the acousto-optic device using any suitable RF drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0134] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a laser beam with a desired number of angular deflections in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0135] In some instances, in order to generate an intensity distribution of an angle-deflected laser beam in the output laser beam, the controller is configured to apply an RF drive signal with varying amplitudes, such as from about 0.001 V to about 500 V, for example from about 0.005 V to about 400 V, for example from about 0.01 V to about 300 V, for example from about 0.05 V to about 200 V, for example from about 0.1 V to about 100 V, for example from about 0.5 V to about 75 V, for example from about 1 V to 50 V, for example from about 2 V to about 40 V, for example from 3 V to about 30 V, and including from about 5 V to about 25 V. In some embodiments, the frequency of each applied radio frequency drive signal is from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and includes about 5 MHz to about 50 MHz.

[0136] In some embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with angular deflections of a desired intensity distribution. For example, the memory may include instructions to generate two or more laser beams with the same intensity at different angles, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and may also include instructions to generate 100 or more laser beams with the same intensity at different angles. In other embodiments, the memory may include instructions to generate two or more laser beams with different intensities at different angles, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and may also include instructions to generate 100 or more laser beams with different intensities at different angles.

[0137] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity that increases along a horizontal axis from the edge to the center. In these examples, the intensity of the angled laser beam at the center of the output beam along the horizontal axis can be from 0.1% to about 99%, for example, from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, and includes from about 10% to about 50% of the intensity of the angled laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity that increases along a horizontal axis from the edge to the center of the output laser beam. In these examples, the intensity of the angled laser beam at the edge of the output laser beam along the horizontal axis can be from 0.1% to about 99%, for example, from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, and includes from about 10% to about 50% of the intensity of the angled laser beam along the horizontal axis at the center of the output laser beam. In other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam with a Gaussian intensity distribution along a horizontal axis. In yet another embodiment, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam with a top-hat intensity distribution along a horizontal axis.

[0138] In embodiments, the beam generator of interest can be configured to generate spatially separated, angle-deflected laser beams within the output laser beam. Depending on the applied radio frequency drive signal and the desired illumination distribution of the output laser beam, the angle-deflected laser beams can be separated by 0.001 µm or more, for example, 0.005 µm or more, for example, 0.01 µm or more, for example, 0.05 µm or more, for example, 0.1 µm or more, for example, 0.5 µm or more, for example, 1 µm or more, for example, 5 µm or more, for example, 10 µm or more, for example, 100 µm or more, for example, 500 µm or more, for example, 1000 µm or more, and including 5000 µm or more. In some embodiments, the system is configured to generate overlapping angle-deflected laser beams within the output laser beam, for example, overlapping with adjacent angle-deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., the overlap of beam points) can be 0.001 µm or more, such as 0.005 µm or more, such as 0.01 µm or more, such as 0.05 µm or more, such as 0.1 µm or more, such as 0.5 µm or more, such as 1 µm or more, such as 5 µm or more, such as 10 µm or more, and includes 100 µm or more.

[0139] In some instances, beam generators configured to produce two or more frequency-shifted beams include laser excitation modules, as described by Diebold et al. in Nature Photonics Vol. 7(10); 806-810 (2013); and as described in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and 10,684,211. The contents of 10,845,295, 10,935,482, 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369 and 11,946,851 are described herein; their public information is incorporated herein by reference.

[0140] In some embodiments, the system includes a light detection system with a photodetector configured to detect light emitted by irradiated particles. In some embodiments, the light detection system is configured to detect scattered light. In some instances, the light detection system includes a side-scattering light detector. In some instances, the light detection system includes a forward-scattering light detector. In other embodiments, the light detection system includes a plurality of scattered light detectors, such as two or more, three or more, four or more, and including five or more. In some embodiments, the light detection system further includes a fluorescence detector configured to detect light at one or more fluorescence wavelengths. In other embodiments, the light detection system includes a plurality of fluorescence detectors, such as two or more, three or more, four or more, five or more, ten or more, fifteen or more, and including twenty or more.

[0141] The detector of interest may include, but is not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, radiative thermal analyzers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof. In some embodiments, a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor is used to measure the collected light. In some embodiments, the detector is a photomultiplier tube, such as a photomultiplier tube with an effective detection surface area of ​​0.01 cm² to 10 cm² per region, for example 0.05 cm² to 9 cm², for example 0.1 cm² to 8 cm², for example 0.5 cm² to 7 cm², and including 1 cm² to 5 cm².

[0142] In cases where the subject system includes multiple fluorescence detectors, each fluorescence detector can be identical, or the set of fluorescence detectors can be a combination of detectors of different types. For example, in cases where the subject system includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device, and the second fluorescence detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first and second fluorescence detectors are CCD-type devices. In yet another embodiment, both the first and second fluorescence detectors are CMOS-type devices. In yet another embodiment, the first fluorescence detector is a CCD-type device, and the second fluorescence detector is a photomultiplier tube (PMT). In yet another embodiment, the first fluorescence detector is a CMOS-type device, and the second fluorescence detector is a photomultiplier tube. In yet another embodiment, both the first and second fluorescence detectors are photomultiplier tubes.

[0143] In embodiments of this disclosure, the fluorescence detector of interest is configured to measure collected light at one or more wavelengths, such as two or more wavelengths, five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths, and includes measuring light emitted by a sample in a flowing stream at 400 or more different wavelengths. In some embodiments, two or more detectors in the module described herein are configured to measure collected light at the same or overlapping wavelengths.

[0144] In some embodiments, the fluorescence detector of interest is configured to measure collected light within a wavelength range (e.g., 200 nm – 1000 nm). In some embodiments, the detector of interest is configured to collect a spectrum within a wavelength range. For example, a flow cytometer may include one or more detectors configured to collect a spectrum within one or more wavelength ranges of 200 nm – 1000 nm. In still other embodiments, the detector of interest is configured to measure light emitted by a sample in a flowing stream at one or more specific wavelengths. For example, the module may include one or more detectors configured to measure light at one or more of the following wavelengths and any combination thereof: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm. In some embodiments, one or more detectors may be configured to pair with specific fluorophores, such as those used with the sample in a fluorescence assay.

[0145] In some embodiments, the optical detection system described herein is part of a flow cytometer. The flow cytometer may include any suitable mechanisms for supplying sheath fluid and sample fluid to a sample fluid input coupler and a sheath fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., a conduit) that is fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line that is fluidly connected to a sheath fluid reservoir. Similarly, the flow cytometer may include any suitable mechanisms for managing waste fluid from the flowing stream. A fluid output coupler may be fluidly connected to a waste fluid line that is fluidly connected to a waste fluid reservoir. A fluid management system suitable for the subject flow cytometer is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0146] In some embodiments, a flow cytometer includes a flow cell. The flow cell of interest includes a cuvette configured to transport particles in a flow stream. As discussed herein, a “flow cell” generally refers to an assembly containing a flow channel for transporting particles in a sheath fluid. The cuvette of interest has a passageway (i.e., a flow channel) running through it. The flow stream configured in the flow channel may include a liquid sample injected from a sample tube. In some instances, the flow cell includes a light-accessible flow channel. The cuvette may be made of, for example, quartz, glass, transparent plastic, etc. In some embodiments, the cuvette is formed of silica, for example, fused silica. In some instances, the flow cell is configured to be illuminated with light from a light source at one or more detection points. A “detection point” discussed herein refers to an area within the flow cell where particles are illuminated by light from a light source, for example, for analysis. The size of the detection point can vary as needed. For example, when 0 µm represents the optical axis of the light emitted by the light source, the detection point range can be from -50 µm to 50 µm, such as from -25 µm to 40 µm, and includes from -15 µm to 30 µm. Depending on certain considerations (e.g., the number and arrangement of lasers), multiple irradiation points can exist within the flow cell.

[0147] In some embodiments, the flow cell includes or is configured to be used with a sample injection port, which is configured to provide a sample to the flow cell. In embodiments, the sample injection system is configured to provide a suitable sample flow into the flow cell cavity (i.e., the flow channel). Depending on the desired flow characteristics, the sample rate delivered from the sample injection port to the flow cell cavity can be 1 µL / min or more, for example 2 µL / min or more, for example 3 µL / min or more, for example 5 µL / min or more, for example 10 µL / min or more, for example 15 µL / min or more, for example 25 µL / min or more, for example 50 µL / min or more, and including 100 µL / min or more. In some instances, the sample rate delivered from the sample injection port to the flow cell cavity is 1 µL / s or more, for example 2 µL / s or more, for example 3 µL / s or more, for example 5 µL / s or more, for example 10 µL / s or more, for example 15 µL / s or more, for example 25 µL / s or more, for example 50 µL / s or more, and including 100 µL / s or more.

[0148] The sample injection port can be an orifice located in the inner wall of the lumen or a conduit located proximal to the lumen. When the sample injection port is an orifice located in the inner wall of the lumen, the orifice can be of any suitable shape. Cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses, etc.; and irregular shapes, such as parabolic bottoms coupled to a flat top. In some embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice can vary depending on its shape. In some instances, the opening is 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0149] In some instances, the sample injection port is a conduit located proximal to the flow cell lumen. For example, the sample injection port may be a conduit positioned so that its orifice is aligned with the flow cell orifice. When the sample injection port is a conduit positioned aligned with the flow cell orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, including but not limited to: straight cross-sectional shapes such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes such as circles, ellipses, etc.; and irregular shapes, such as parabolic bottoms coupled to a flat top. The orifice of the conduit can vary depending on its shape; in some instances, the orifice opening is 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip with a bevel angle of 1° to 10°, such as 2° to 9°, such as 3° to 8°, such as 4° to 7°, and including a 5° bevel.

[0150] In some embodiments, the flow cell further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid into the flow cell cavity, for example, in combination with a sample to generate a laminar flow of sheath fluid around the sample. Depending on the desired flow characteristics, the rate of sheath fluid delivered to the flow cell cavity can be 25 µL / s or more, for example 50 µL / s or more, for example 75 µL / s or more, for example 100 µL / s or more, for example 250 µL / s or more, for example 500 µL / s or more, for example 750 µL / s or more, for example 1000 µL / s or more, and includes 2500 µL / s or more.

[0151] In some embodiments, the sheath fluid injection port is an orifice located in the inner wall of the cavity. The sheath fluid injection port orifice can be of any suitable shape, with cross-sectional shapes of interest including but not limited to: straight cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses, etc.; and irregular shapes, such as parabolic bottoms coupled to a flat top. The size of the sheath fluid injection port orifice can vary depending on the shape, and in some instances, the opening of the orifice is 0.1 mm to 5.0 mm, for example 0.2 mm to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0152] In some embodiments, the system includes a flow cytometer or is operatively coupled to a flow cytometer. Suitable flow cytometry systems may include, but are not limited to, the flow cytometry systems described below: Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, HumanaPress (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan;49(pt 1):17-28; Linden, et al., Semin ThromHemost. 2004 Oct;30(5):502-11; Alison, et al. J Pathol, 2010 Dec; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255; its published content is incorporated herein by reference.In some instances, flow cytometry systems of interest include the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSR Fortessa™ flow cytometer, and BD Biosciences LSR Fortessa™ flow cytometer. BD Biosciences X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, BD Biosciences FACSSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, BD Biosciences FACSDiscover™ cell sorter, etc.

[0153] In some embodiments, the subject system is a flow cytometry system, such as U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,145,793, 10,113,967, 10,006,852, 9,952,076, 9,933,341, 9,726,527, 9,453,789, 9,200,334, 9,097,640, and 9,09 The flow cytometry systems described in 5,494, 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,140,300, 7,544,326, 7,201,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766 are disclosed herein by reference in their entirety.

[0154] In some embodiments, the flow cytometer is configured as an imaging flow cytometer. For example, in some instances, the subject system is a flow cytometer system configured to image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), such as those described in Diebold et al., Nature Photonics, Vol. 7(10); 806-810 (2013), and U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and 10,684,211. The flow cytometry systems described in 10,845,295, 10,935,482, 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369, and 11,946,851, the disclosures of which are incorporated herein by reference.

[0155] Figure 2A system 200 for a flow cytometer according to an illustrative embodiment of the present disclosure is shown. System 200 includes a laser 201 configured to irradiate particles 211 in a flow stream 214 at a detection point 215 within a flow cell 210. Although Figure 2 The example shows a single laser, but it should be understood that multiple lasers can also be used. The laser beam from laser 201 is directed to focusing lens 202, which focuses the beam onto the fluid flow portion within flow cell 210 where sample particles 211 reside. Flow cell 210 is part of a fluid system that directs particles (typically one at a time) in the flow to the focused laser beam for detection. Alternatively, in the case of an air flow cytometer, a nozzle top can be used.

[0156] like Figure 2 As shown, flow cell 210 is fluidly connected to a sheath fluid reservoir 203 containing sheath fluid and a sample fluid reservoir 204 containing sample fluid. Sheath fluid from sheath fluid reservoir 203 is supplied to at least one sheath fluid injection port 208 via a conduit (i.e., sheath fluid line) 207. Furthermore, sample fluid containing particles 211 from sample fluid reservoir 204 is supplied to sample injection port 206 via a conduit (i.e., sample fluid line) 205. Sample injection port 206 is fluidly connected to a sample injector 213 (e.g., a sample injection needle), configured to introduce particles 211 into the interior of flow cell 210. The particles 211 are hydrodynamically focused by the sheath fluid entering from sheath fluid injection port 208, such that a flow stream 214 forms downstream of the conical portion 212 of flow cell 210. Particles discharged at the distal end of flow cell 210 can be disposed of and / or collected by any suitable method. For example, depending on the type of flow cytometer being used, particles can be collected at the distal end of the flow cell 210, for example, via a waste line. Alternatively, the particles can be sorted.

[0157] Depending on the characteristics of the particles (e.g., particle size, internal structure) and the presence of one or more fluorescent molecules attached to or naturally present on or within the particles, light from a laser beam interacts with the particles in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various wavelengths. The fluorescence emission, as well as the diffracted, refracted, reflected, and scattered light, can be directed to one or more detectors. Specifically, forward scattered light (FSC) is directed to forward scattered light detector 223. Forward scattered light detector 223 is positioned slightly off-center from the axis of the direct beam passing through flow cell 210 and is configured to detect diffracted light, i.e., excitation light that passes primarily in the forward direction through or around the particles. The intensity of the light detected by forward scattered light detector 223 depends on the overall size of the particles. The forward scattered detector may include, for example, a photodiode. Between forward scattered light detectors 223 are optical filters 221a and scattering strips 222. Optical filter 221a can be configured to filter out non-FSC light of at least one wavelength, while scattering bar 222 can be configured to prevent the incident beam (i.e. non-scattered light) from laser 201 from being detected by forward scattering detector 223.

[0158] Furthermore, side-scattered light (SSC) is detected by side-scattered light detector 224. In other words, side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structures of particle 211, which tend to increase with increasing particle structure complexity. Figure 2In the example, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to a side-scatter light detector 224 while allowing non-SSC light (e.g., fluorescence) to pass through. An optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by the side-scatter light detector 224. Fluorescence detectors 225a-225c are also shown, each configured to detect fluorescence of a different wavelength. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or a first wavelength range) to the fluorescence detector 225a while allowing light of other wavelengths to pass through. The optical filter 221c may be configured to prevent light of at least one wavelength not corresponding to the first wavelength (or the first wavelength range) from being detected by the fluorescence detector 225a. Similarly, the dichroic mirror 220c is configured to reflect FL light corresponding to a second wavelength (or a second wavelength range) to the fluorescence detector 225b while allowing light of a third wavelength (or a third wavelength range) to pass through for detection by the fluorescence detector 225c. Optical filter 221d is configured to prevent light of at least one wavelength that does not correspond to the second wavelength (or the second wavelength range) from being detected by fluorescence detector 225b. Furthermore, optical filter 221e is configured to prevent light of at least one wavelength that does not correspond to the third wavelength (or the third wavelength range) from being detected by fluorescence detector 225c.

[0159] Those skilled in the art will recognize that the flow cytometers according to embodiments of this disclosure are not limited to... Figure 2 The flow cytometer shown is not limited to any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors with various wavelengths and different configurations. For example, although... Figure 2 The embodiment shown has three fluorescence detectors for illustrative purposes, but it should be understood that any suitable number of fluorescence detectors can be used.

[0160] During operation, the controller / processor 290 controls the operation of the flow cytometer, and measurement data from the detector can be stored in memory 295 and processed by the controller / processor 290. Although not explicitly shown, the controller / processor 290 is coupled to the detector to receive output signals from the detector, and the controller / processor 290 can also be coupled to the electrical and electromechanical components of the flow cytometer to control the laser 201, fluid flow parameters, etc. Input / output (I / O) functionality 297 can also be provided in the system. Memory 295, controller / processor 290, and I / O 297 can be provided entirely as part of the flow cytometer. In this embodiment, a display can also be formed as part of the I / O functionality 297 for presenting experimental data to the user of the flow cytometer 200. Alternatively, some or all of the memory 295 and controller / processor 290, as well as the I / O functionality, can be part of one or more external devices (e.g., a general-purpose computer). In some embodiments, some or all of the memory 295 and controller / processor 290 can communicate wirelessly or wiredly with the flow cytometer 210. The controller / processor 290, combined with memory 295 and I / O 297, can be configured to perform various functions related to the preparation and analysis of flow cytometry experiments.

[0161] Different fluorescent molecules in the fluorescent dye panel used in flow cytometry experiments will emit light in their own characteristic wavelengths. Specific fluorescent labels and their associated fluorescence emission bands can be selected for the experiment, making them approximately match the detector's filter window. I / O 297 can be configured to receive data about a flow cytometry experiment having a fluorescently labeled panel and multiple cell populations with multiple labels, each cell population having a subset of those labels. I / O 297 can also be configured to receive biological data assigning one or more labels to one or more cell populations, label density data, emission spectral data, data on label assignment to one or more labels, and flow cytometry configuration data. Flow cytometry experiment data, such as label spectral characteristics and flow cytometry configuration data, can also be stored in memory 295. Controller / processor 290 can be configured to evaluate the assignment of one or more labels to labels.

[0162] In some embodiments, the subject system is a particle sorting system configured to sort particles via a closed particle sorting module, such as the module described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017 (the disclosure of which is incorporated herein by reference). In a particular embodiment, particles (e.g., cells) in a sample are sorted via a sorting decision module having multiple sorting decision units, such as the module described in U.S. Patent Publication No. 2020 / 0256781, filed December 23, 2019 (the disclosure of which is incorporated herein by reference). In some embodiments, a system for sorting components of a sample includes a particle sorting module with deflection plates, such as the module described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017 (the disclosure of which is incorporated herein by reference).

[0163] In a particular embodiment, the system is a fluorescence imaging system using an RFID-tagged image-guided particle sorter, as shown in Figure 3. The particle sorter 300 includes an illumination component 300a, which includes a light source 301 (e.g., a 488nm laser). The light source 301 generates an output beam 301a, which is split into beams 302a and 302b by a beam splitter 302. Beam 302a propagates through an acousto-optic device 303 (e.g., an acousto-optic deflector, AOD) to generate an output beam 303a with one or more angle deflections. In some instances, the output beam 303a generated by the acousto-optic device 303 includes a local oscillator beam and multiple RF comb beams. Beam 302b propagates through an acousto-optic device 304 (e.g., an acousto-optic deflector, AOD) to generate an output beam 304a with one or more angle deflections. In some instances, the output beam 304a generated by the acousto-optic device 304 includes a local oscillator beam and multiple RF comb beams. Output beams 303a and 304a, generated by acousto-optic devices 303 and 304 respectively, are combined by beam splitter 305 to generate output beam 305a. Output beam 305a is transmitted through optical component 306 (such as an objective lens) to irradiate particles in flow cell 307. In a particular embodiment, acousto-optic device 303 (AOD) splits a single laser beam into an array of small beams, each small beam having a different optical frequency and angle. A second AOD 304 tunes the optical frequency of a reference beam, which is then overlapped with the array of small beams at beam combiner 305. In a particular embodiment, the light illumination system having a light source and acousto-optic devices may also include those devices described in Schraivogel et al., (“High-speed fluorescence image-enabled cell sorting” Science (2022), 375 (6578): 315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosure of which is incorporated herein by reference.

[0164] Output beam 305a irradiates sample particles 308 propagating through flow cell 307 (e.g., using sheath fluid 309) at irradiation region 310. As shown in irradiation region 310, multiple beams (e.g., RF frequency-shifted beams deflected at an angle, represented by a point across irradiation region 310) overlap with a reference local oscillator beam (represented by a shaded line across irradiation region 310). Due to their different optical frequencies, the overlapping beams exhibit beat frequency behavior, such that each small beam carries a different frequency f. 1-n Sine modulation.

[0165] Light from the illuminated sample is transmitted to a light detection system 300b, which includes multiple photodetectors. The light detection system 300b includes a forward-scattering photodetector 311 for generating a forward-scattering image 311a and a side-scattering photodetector 312 for generating a side-scattering image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattering detector 311 and the side-scattering detector 312 are photodiodes (e.g., avalanche photodiodes, APDs); in some instances, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is detected by fluorescence photodetectors 314-317. In some instances, photodetectors 314-317 are photomultiplier tubes. Light from the illuminated sample is directed through a beam splitter 320 to the side-scattering detection channel 312 and the fluorescence detection channels 314-317. The optical detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for propagating light of a predetermined wavelength to photodetectors 314-317. In some examples, optical component 321 has a 534nm / 40nm bandpass. In some examples, optical component 322 has a 586nm / 42nm bandpass. In some examples, optical component 323 has a 700nm / 54nm bandpass. In some examples, optical component 324 has a 783nm / 56nm bandpass. The first value represents the center wavelength of the spectral band, and the second value represents the range of the spectral band. Therefore, the 510 / 20 filter extends 10nm on each side of the center of the spectral band, or from 500nm to 520nm.

[0166] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright field light detection channel 313, and fluorescence detection channels 314-317 are digitally processed in real time by processors 350 and 351. Based on the data signals generated in processors 350 and 351, images 311a-317a can be generated in each light detection channel. Image-guided sorting is performed in response to a sorting signal generated by sorting trigger 352. Sorting assembly 300c includes a deflection plate 331 for deflecting particles to sample container 332 or waste stream 333. In some instances, sorting assembly 300c is configured to sort particles via a closed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017 (the disclosure of which is incorporated herein by reference). In a particular embodiment, the sorting component 300c includes a sorting decision module having multiple sorting decision units, such as those described in U.S. Patent Publication No. 2020 / 0256781 (the disclosure of which is incorporated herein by reference).

[0167] In some embodiments, the system is a particle analyzer, wherein particle analysis system 401 (Figure 4) can be used to analyze and characterize particles, regardless of whether the particles are physically sorted into a collection container. Figure 4 shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 includes a fluid system 402. Fluid system 402 may include or be coupled to a sample tube 405 and a flowing liquid column within the sample tube, wherein particles 403 (such as cells) in the sample move along a common sample path 409 within the sample tube.

[0168] The particle analysis system 401 includes a detection system 404 configured to collect signals from each particle as it passes through one or more detection stations along a common sample path. A detection station 408 generally refers to a monitoring area 407 of the common sample path. In some embodiments, detection may include detecting light or one or more other characteristics of particle 403 as it passes through monitoring area 407. Figure 4 illustrates a detection station 408 with a monitoring area 407. Some embodiments of the particle analysis system 401 may include multiple detection stations. Furthermore, some detection stations may monitor more than one area.

[0169] Each signal is assigned a signal value to form a data point for each particle. As previously mentioned, this data may be referred to as event data. The data point can be a multi-dimensional data point, including corresponding properties measured for the particle. The detection system 404 is configured to collect a series of such data points within a first time interval.

[0170] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The control system shown may be operationally associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the number of data points collected by the detection system 404 within the first time interval. The control system 406 may also be configured to generate an experimental signal frequency based on the number of data points in a portion of the first time interval. The control system 406 may also compare the experimental signal frequency with the calculated signal frequency or a predetermined signal frequency.

[0171] Figure 5 A functional block diagram of an example particle analyzer control system is shown, which is, for example, an analysis controller (i.e., processor) 500 for analyzing and displaying biological events. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0172] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to an analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 through this data communication channel. The analysis controller 500 can be a processor configured to perform the method of the present invention, for example, by applying a distance-based classification model in a size-based analyte feature space to determine a density discrimination threshold, applying a density-based clustering algorithm to separate the analyte data into high-density clusters and low-density clusters according to the density threshold, and classifying the analyte data based on the high-density clusters and low-density clusters according to the size-based analyte feature space.

[0173] Analysis controller 500 may be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 may include flow cytometry event data. Analysis controller 500 may be configured to provide a graphical display to display device 506 including a first image of the biological event data. Analysis controller 500 may also be configured to render regions of interest as gates around the group of biological event data shown on display device 506, for example, overlaid on the first image. In some embodiments, a gate may be a logical combination of regions of interest plotted on a single parameter histogram or bivariate plot of one or more diagrams. In some embodiments, the display device may be used to display particle parameter or saturation detector data.

[0174] The analysis controller 500 may be further configured to display biological event data on the display device 506, where the events in the biological event data inside the door differ from those in other biological event data outside the door. For example, the analysis controller 500 may be configured to set the color of the biological event data contained inside the door to a different color than the biological event data outside the door. The display device 506 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0175] The analysis controller 500 can be configured to receive a door selection signal for identifying a door from a first input device. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a door selection signal to the analysis controller 500, identifying a door that needs to be displayed on or operated via the display device 506 (e.g., by clicking on or inside the desired door when the cursor is over it). In some embodiments, the first device can be implemented as a keyboard 508 or other means of providing input signals to the analysis controller 500, such as a touchscreen, stylus, optical detector, or voice recognition system. Some input devices may include multiple input functions, in which case each input function can be considered an input device. For example, such as... Figure 5 As shown, mouse 510 may include a right mouse button and a left mouse button, and each mouse button can generate a trigger event.

[0176] Triggering events can cause the analysis controller 500 to change the data display method, adjust the actual data displayed on the display device 506, and / or provide input for subsequent processing (such as selecting groups of interest for particle sorting).

[0177] In some embodiments, the analysis controller 500 may be configured to detect when a gate selection is triggered by a mouse 510. The analysis controller 500 may also be configured to automatically modify the plotting visualization to assist the gate setting process. Modifications may be based on the specific distribution of biological event data received by the analysis controller 500.

[0178] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. Furthermore, the storage device 504 can be configured to allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

[0179] Display device 506 can be configured to receive display data from analysis controller 500. The display data may include images of biological event data and gates for outlining the image partitions. Display device 506 can also be configured to change the presented information based on input from analysis controller 500, combined with input from particle analyzer 502, storage device 504, keyboard 508, and / or mouse 510.

[0180] In some implementations, the analysis controller 500 may generate a user interface to receive sample events for sorting. For example, the user interface may include control components for receiving sample events or sample images. Sample events, images, or sample gates may be provided before collecting event data for the samples, or based on an initial set of events from a portion of the samples.

[0181] Figure 6A This is a schematic diagram of a particle sorting system 600 (e.g., a particle analyzer or sorting system 502) according to one embodiment described herein. In some embodiments, the particle sorting system 600 is a cell sorting system. Figure 6A As shown, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, include, or be itself a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606, including particles 609, into a moving liquid column 608 (e.g., a flow). Within the moving liquid column 608, particles 609 (e.g., cells) align in a single file across a monitoring region 611 (e.g., a laser-flow junction) and are irradiated by an irradiation source 612 (e.g., a laser). Vibration of the droplet-forming transducer 602 causes the moving liquid column 608 to break into multiple droplets 610, some of which contain particles 609.

[0182] In operation, detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses monitoring area 611. Detection station 614 feeds to timing circuit 628, which in turn feeds to flash charge circuit 630. At the droplet break point, a flash charge can be applied to the moving liquid column 608 based on the timing droplet delay (∆t), causing the droplet of interest to carry a charge. The droplet of interest may include one or more particles or cells to be sorted. The charged droplet can then be sorted by activating a deflection plate (not shown) to deflect the droplet into a container such as a collection tube or a porous / microporous sample plate, where the pores or micropores can be associated with a specific droplet of interest. Figure 6A As shown, the droplets can be collected into the drainage container 638.

[0183] A detection system 616 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as a particle of interest passes through a monitoring region 611. A typical droplet boundary detector is described in U.S. Patent 7,679,039, the entire contents of which are incorporated herein by reference. The detection system 616 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 616 may be fed an amplitude signal 620 and / or a phase signal 618, which are then fed (via amplifier 622) to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624 then control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be included in a control system.

[0184] In some embodiments, sorting electronics (e.g., detection system 616, detection station 614, and processor 640) may be coupled to a memory configured to store detected events and sorting decisions based on those events. Sorting decisions may be included in the event data of the particles. In some embodiments, detection system 616 and detection station 614 may be implemented as a single detection unit or implemented as communicatively coupled, such that event measurement results can be collected by one of detection system 616 or detection station 614 and provided to non-collecting elements.

[0185] Figure 6B This is a schematic diagram of a particle sorting system according to an embodiment presented herein. Figure 6B The particle sorting system 600 shown includes deflection plates 652 and 654. Charge can be applied via current-charged lines in the barbs. This generates a droplet stream 610 containing particles 610 for analysis. The particles can be illuminated by one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The particle information can be processed by, for example, sorting electronics or other detection systems (…). Figure 6B (Not shown in the image) Analysis is performed. Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets to guide them toward the collection container of interest (e.g., one of 672, 674, 676, or 678). Figure 6B As shown, deflector plates 652 and 654 can be controlled to guide particles along a first path 662 toward container 674 or along a second path 668 toward container 678. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particles to continue flowing along flow path 164. Such uncharged droplets can be drawn into a waste container via suction device 670.

[0186] It may include sorting electronics for initiating the collection of measurement results, receiving the fluorescence signal of particles, and determining how to adjust the deflection plate to achieve particle sorting. Figure 6B The exemplary implementation of the illustrated embodiment includes the BD FACSAria™ series flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0187] Computer control system

[0188] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, mouse, or similar device. The processing module includes a processor that can access memory storing instructions for performing the steps of the method of the present invention. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, storage devices, input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or other existing or future-available processors. The processor executes an operating system, which interacts with firmware and hardware in a known manner to assist the processor in coordinating and executing the functions of various computer programs written in a variety of programming languages ​​known in the art, such as Java, Perl, C++, Python, other high-level or low-level languages, or combinations thereof. The operating system typically works with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services according to known techniques. In some embodiments, the processor includes analog electronic components that provide feedback control, such as negative feedback control.

[0189] System memory can be any existing or future storage device. Examples include any common random access memory (RAM), magnetic media (such as internal hard drives or magnetic tape), optical media (such as rewritable optical discs), flash memory devices, or other storage devices. Storage devices can be any existing or future devices, including optical disc drives, magnetic tape drives, or floppy disk drives. Such storage devices typically read from and / or write to program storage media (not shown, such as optical discs). Any such program storage media, or other existing or future-developed media, can be considered a computer program product. As those skilled in the art will recognize, these program storage media typically store computer software programs and / or data. Computer software programs (also known as computer control logic) are typically stored in system memory and / or program storage media used in conjunction with storage devices.

[0190] In some embodiments, a computer program product is described, comprising a computer-usable medium storing control logic (computer software program, including program code). The control logic, when executed by a computer processor, causes the processor to perform the functions described in this disclosure. In other embodiments, some functions are implemented primarily in hardware, such as using a hardware state machine. How to implement a hardware state machine to perform the functions described in this disclosure will be apparent to those skilled in the art.

[0191] The memory can be any device suitable for a processor to store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks, optical discs, magnetic tapes, RAM, or any other suitable fixed or portable device). The processor can include a general-purpose digital microprocessor, which is appropriately programmed via a computer-readable medium carrying the necessary program code. The programming can be provided to the processor remotely via a communication channel or pre-stored in a computer program product using any device associated with the memory, such as memory or other portable or fixed computer-readable storage media. For example, a disk or optical disc can carry the programming and can be read by a disk reader / writer. The system of this disclosure also includes writing algorithms (e.g., in the form of a computer program product) to practice the methods described above. The programming according to this disclosure can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media (such as floppy disks, hard disk storage media, and magnetic tape), optical storage media (such as CD-ROMs), electrical storage media (such as RAM and ROMs), portable flash drives, and mixtures of these categories (such as magnetic / optical storage media).

[0192] The processor can also access communication channels to communicate with users at remote locations. Remote locations mean that users do not have direct contact with the system, but instead relay input information to the input manager through external devices such as computers connected to a wide area network (WAN), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones)

[0193] In some embodiments, the system described herein may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or other devices. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth communication protocol, and cellular communication (such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM))).

[0194] In one embodiment, the communication interface is configured to include one or more communication ports (e.g., physical ports or interfaces such as USB ports, USB-C ports, RS-232 ports, or any other suitable electrical connection ports) to allow data communication between the subject system and other external devices (e.g., computer terminals in a doctor's office or hospital environment) configured for similar complementary data communication.

[0195] In one embodiment, the communication interface is configured for infrared communication, Bluetooth communication, or any other suitable wireless communication protocol to enable the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user may use in conjunction with them.

[0196] In one embodiment, the communication interface is configured to provide a data transmission connection using the Internet Protocol (IP), specifically via a mobile network, short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet via a Wi-Fi hotspot.

[0197] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communication interface, such as using common standards like 802.11, Bluetooth® radio frequency protocol, or IrDA infrared protocol. The server device may be other portable devices, such as smartphones, personal digital assistants (PDAs), or laptops, or larger devices, such as desktop computers, appliances, etc. In some embodiments, the server device has a display (such as a liquid crystal display (LCD)) and input devices (such as buttons, keyboards, mice, or touchscreens).

[0198] In some embodiments, the communication interface is configured to automatically or semi-automatically enable data stored in the subject system (e.g., data in an optional data storage unit) to communicate with a network or server device via one or more of the above-described communication protocols and / or mechanisms.

[0199] The output controller may include a controller for any of a variety of known display devices used to present information to a user (whether human or machine, local or remote). If one of the display devices provides visual information, this information is typically logically and / or physically organized as an array of pixels. The graphical user interface (GUI) controller may include any of a variety of known or future software programs used to provide a graphical input / output interface between the system and the user, and to process user input. Functional elements of the computer may communicate with each other via a system bus. In alternative embodiments, some of these communications may be implemented via a network or other types of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, such as via the Internet, telephone, or satellite network, according to known technologies. The way the output manager presents data may be implemented according to a variety of known technologies. For example, the data may include SQL, HTML, or XML documents, emails, or other files, or other forms of data. The data may include Internet URLs, allowing the user to retrieve more SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the subject system may be any type of known computer platform or type to be developed in the future, although they generally fall into the category of computers commonly referred to as servers. However, they may also be host computers, workstations, or other computer types. They can be connected via any known or future cable or other communication system, including wireless systems, whether networked or otherwise. They can be located in the same place or physically separated. Various operating systems can be deployed on any computer platform, depending on the type and / or model of the chosen platform. Suitable operating systems include Windows® NT®, Windows® XP, Windows® 7, Windows® 8, Windows® 10, iOS®, macOS®, Linux®, Ubuntu®, Fedora®, OS / 400®, i5 / OS®, IBM i®, Android™, SGI IRIX®, Oracle Solaris®, and others.

[0200] Figure 7 The overall architecture of an example computing device 700 according to a particular embodiment is described. Figure 7The overall architecture of the computing device 700 shown includes computer hardware facilities and software components. However, for the sake of providing a feasible disclosure, it is not necessary to show all these generally conventional components. As shown, the computing device 700 includes a processing unit 710, a network interface 720, a computer-readable media drive 730, an input / output device interface 740, a display 750, and an input device 760, all of which can communicate with each other via a communication bus. The network interface 720 provides connectivity to one or more networks or computing systems. The processing unit 710 can therefore receive information and instructions from other computing systems or servers via the network. The processing unit 710 can also communicate with a memory 770 and further provide output information to an optional display 750 via the input / output device interface 740. For example, analysis software (e.g., data analysis software or programs, such as FlowJo®, etc.) stored as executable instructions in the non-temporary memory of the analysis system can display flow cytometry event data to a user. The input / output device interface 740 can also accept input from optional input devices 760, such as keyboards, mice, digital pens, microphones, touch screens, gesture recognition systems, voice recognition systems, game controllers, accelerometers, gyroscopes, or other input devices.

[0201] Memory 770 may include computer program instructions (grouped into modules or components in some embodiments) that processing unit 710 executes to implement one or more embodiments. Memory 770 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 770 may store operating system 772, which provides computer program instructions for use by processing unit 710 in the routine management and operation of computing device 700. Data may be stored in data storage device 790. Memory 770 may also include computer program instructions and other information for implementing various aspects of this disclosure.

[0202] Non-transitory computer-readable storage medium

[0203] This disclosure also includes non-transitory computer-readable storage media having instructions for implementing the methods of this subject matter, such as implementing one or more computer-implemented methods described herein. The computer-readable storage medium can be used on one or more computers to fully or partially automate a system performing the methods described herein. In certain embodiments, instructions according to the methods described herein can be encoded in a “programmed” form into a computer-readable medium, wherein the term “computer-readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Suitable examples of non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network attached storage (NAS), whether these devices are internal or external to a computer. Files containing information can be “stored” on a computer-readable medium, where “stored” means recording the information so that it can be accessed and retrieved by a computer later. The computer-implemented methods described herein can be executed using programs written in one or more programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, and many other languages.

[0204] In some embodiments, the non-transitory computer-readable storage medium includes: an algorithm for receiving a computed spectral unmixing matrix in a computer cache; an algorithm for evaluating matrix quality parameters of the computed spectral unmixing matrix; an algorithm for adjusting cached input to the spectral unmixing matrix based on the evaluated matrix quality parameters; an algorithm for recompiling the spectral unmixing matrix using the adjusted input; and an algorithm for displaying the updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface on a display in response to the adjusted input.

[0205] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating matrix quality parameters of a computed spectral unmixing matrix, the matrix quality parameters being selected from the group consisting of a complexity index, a similarity index, a hotspot matrix, a conditional index, a variance decomposition scaling matrix (VDP), an extended correlation matrix, collinearity, a variance inflation factor, and a covariance matrix. In some instances, the storage medium includes instructions for evaluating a similarity index of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating a complexity index of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating a hotspot matrix of the computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating matrix quality parameters by visualizing a data graph of the computed spectral unmixing matrix.

[0206] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for adjusting cached inputs to the spectral unmixing matrix in a computer cache based on evaluated matrix quality parameters. In some instances, the non-transitory computer-readable storage medium includes an algorithm for determining whether the cache includes results for calculating one or more control data for the spectral unmixing matrix. In cases where no cached results are available for controls, the non-transitory computer-readable storage medium includes an algorithm for calculating statistics and results for the control data. In some instances, the input includes monostained positive control data. In some instances, the monostained positive control data is generated from monostained control beads. In some instances, the monostained positive control data is generated from monostained control cells. In some instances, the input includes autofluorescence control data. In some instances, the input includes unstained control data. In some instances, the input includes negative control data. In some instances, the input is raw control data. In some instances, the input includes control data calculations. In some instances, the input includes control data statistics (e.g., generated from calculations of control data). In some instances, the input includes calculated gate statistics. In some embodiments, the non-transitory computer-readable storage medium includes algorithms for adjusting input using a graphical user interface.

[0207] In some embodiments, the non-transitory computer-readable storage medium includes algorithms for storing input control data in a computer cache and generating one or more of input control data calculations and input control data statistics from the stored input control data. In some instances, the non-transitory computer-readable storage medium includes algorithms for clearing the stored input control data from the computer cache when the input control data is adjusted. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining whether the input control data is stored in a computer cache. In some instances, when it is determined that the input control data does not exist in the computer cache, control data calculations and control data statistics are generated and stored in the computer cache. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining that the input control data includes uncached negative controls, generating data calculations and data statistics for uncached negative controls, and caching data calculations and data statistics for uncached negative controls. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining that the input control data includes cached negative controls, retrieving data calculations and data statistics for cached negative controls, and recalculating data calculations and data statistics for cached negative controls. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining that input control data includes uncached negative controls, retrieving input control data including cached negative controls, and generating data calculations and statistics for both uncached and cached negative controls. In some instances, the non-transitory computer-readable storage medium includes algorithms for recalculating the spectral unmixing matrix using updated input control data stored in the cache.

[0208] In some instances, the non-transitory computer-readable storage medium includes algorithms for making minor adjustments to the input to a computed spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes algorithms for recompiling the spectral unmixing matrix when the adjusted input includes an input variation equal to or below a predetermined threshold. In some instances, the non-transitory computer-readable storage medium includes algorithms for recompiling the spectral unmixing matrix when the adjusted input includes an input variation above a predetermined threshold. In some instances, the non-transitory computer-readable storage medium includes algorithms for retrieving computed input from a computer cache before adjusting the input. In some instances, the non-transitory computer-readable storage medium includes algorithms for clearing cached input before adjusting the input. In some instances, the non-transitory computer-readable storage medium includes algorithms for clearing cached input and applying new input to the spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes algorithms for adjusting the cached input by excluding certain control data when recompiling the spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes algorithms for adjusting the cached input by applying new control data when recalculating the spectral unmixing matrix. In some instances, the non-transitory computer-readable storage medium includes algorithms for adjusting the cached input by changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0209] In some instances, the non-transitory computer-readable storage medium includes algorithms for displaying multiple different matrix quality parameters on a graphical user interface. In some instances, the non-transitory computer-readable storage medium includes algorithms for displaying updated matrix quality parameters of a spectral unmixing matrix on a graphical user interface in near real-time response to adjusted input. In other instances, the non-transitory computer-readable storage medium includes algorithms for displaying updated matrix quality parameters of a spectral unmixing matrix on a graphical user interface in real-time response to adjusted input.

[0210] Non-transitory computer-readable storage media can be applied to one or more computer systems having a display and operator input devices. Operator input devices may be, for example, a keyboard, mouse, or similar devices. The processing module includes a processor that can access memory containing instructions for performing the steps of the methods described herein. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, storage devices, input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or other existing or future-available processors. The processor executes the operating system, which interacts with firmware and hardware in a known manner and assists the processor in coordinating and executing the functions of various computer programs, which may use a variety of programming languages, such as those described above, other high-level or low-level languages, and combinations thereof, as is common knowledge in the art. The operating system typically works with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services according to known techniques.

[0211] kit

[0212] Various aspects of this disclosure also include kits, wherein the kits include storage media such as magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network attached storage (NAS). Any of these program storage media, or other existing or future-developed media, may be included in the subject kit. In embodiments, the program storage media includes instructions for evaluating matrix quality parameters of a spectral unmixing matrix as described in the methods and for use with the systems described herein. In embodiments, the instructions (or portions thereof) on a computer-readable medium provided in the subject kit may be implemented as a software component of software for analyzing data. In these embodiments, the computer control system according to this disclosure may function as a software "plug-in" to existing software packages such as FlowJo®.

[0213] In addition to the components described above, the theme kit may also include (in some embodiments) instructions. These instructions may exist in the theme kit in various forms, with one or more forms present in the kit. One form of these instructions is information printed on a suitable medium or substrate, such as single sheets or more (information printed on paper), inside the kit packaging, in an instruction manual inserted into the packaging, and the like. Another form of these instructions is a computer-readable medium, such as a floppy disk, optical disc (CD), portable flash drive, and similar media on which the information is recorded. Yet another possible form of these instructions is a website address accessible via the Internet to retrieve information from a remote site.

[0214] practicality

[0215] The methods, systems, and computer systems described herein can be used in a variety of applications where optimization of optical detection systems (e.g., optical detection systems with photodetectors) is desired, such as in particle analyzers. The methods and systems described herein can also be used in optical detection systems for analyzing and sorting particulate components in samples (e.g., biological samples) in fluid media. This disclosure is also applicable to flow cytometry, where it is desirable to provide flow cytometers with improved cell sorting accuracy, enhanced particle collection, reduced energy consumption, improved particle charging efficiency, more precise particle charging, and enhanced particle deflection during cell sorting. In embodiments, this disclosure reduces the need for user input or manual adjustments when using flow cytometry for sample analysis. In some embodiments, the methods and systems described herein provide a fully automated approach, requiring minimal manual input (if any) for adjustments to the flow cytometer during use.

[0216] Notwithstanding the appended claims, this disclosure is also defined by the following provision: 1. A computer-implemented method comprising:

[0217] The calculated spectral unmixing matrix is ​​received in the computer's cache;

[0218] Evaluate the matrix quality parameters of the calculated spectral unmixing matrix;

[0219] The buffer input to the spectral unmixing matrix is ​​adjusted based on the evaluated matrix quality parameters;

[0220] Recalculate the spectral unmixing matrix using the adjusted input; and

[0221] In response to the adjusted input, the updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface on the display.

[0222] 2. The method according to Clause 1, wherein the method includes evaluating a matrix quality parameter of the computed spectral unmixing matrix, the matrix quality parameter being selected from a group consisting of a complexity index, a similarity index, a hotspot matrix, a conditional index, a variance decomposition proportional matrix (VDP), an extended correlation matrix, collinearity, a variance inflation factor, and a covariance matrix.

[0223] 3. The method according to Clause 2, wherein the method includes evaluating a similarity index of the calculated spectral unmixing matrix.

[0224] 4. The method according to Clause 2, wherein the method includes evaluating the complexity index of the calculated spectral unmixing matrix.

[0225] 5. The method according to Clause 2, wherein the method includes evaluating a hotspot matrix of the calculated spectral unmixing matrix.

[0226] 6. The method according to any one of Clauses 1-5, wherein the evaluation matrix quality parameters include a data plot of the calculated spectral unmixing matrix.

[0227] 7. The method according to any one of Clauses 1-6, wherein the input includes single-stain positive control data.

[0228] 8. The method described in Clause 7, wherein the monochromatic positive control data are generated from monochromatic control beads.

[0229] 9. The method described in Clause 7, wherein the single-stain positive control data are generated from single-stain control cells.

[0230] 10. The method according to any one of clauses 1-9, wherein the input includes autofluorescence control data.

[0231] 11. The method according to any one of Clauses 1-10, wherein the input includes unstained control data.

[0232] 12. The method according to any one of Clauses 1-11, wherein the input includes negative control data.

[0233] 13. The method according to any one of Clauses 1-12, wherein the input includes raw control data.

[0234] 14. The method according to any one of Clauses 1-13, wherein the input includes calculations based on control data.

[0235] 15. The method according to any one of Clauses 1-14, wherein the input includes comparative data statistics.

[0236] 16. The method according to any one of Clauses 1-15, wherein the input includes calculated gate statistics.

[0237] 17. The method according to any one of Clauses 1-16, wherein the input is adjusted using a graphical user interface.

[0238] 18. The method according to any one of clauses 1-17, wherein the method comprises:

[0239] The input comparison data is stored in the computer cache; and

[0240] One or more of the following can be generated from stored input control data: input control data calculation and input control data statistics.

[0241] 19. The method of claim 18, wherein the method further comprises clearing stored input control data from a computer cache when adjusting input control data.

[0242] 20. The method according to any one of Clauses 18-19, wherein the method includes determining whether input comparison data is stored in a computer cache.

[0243] 21. The method according to Clause 20, wherein the method comprises:

[0244] When it is determined that the input control data does not exist in the computer cache, generate one or more control data calculations and control data statistics;

[0245] The generated control data calculations and statistics are stored in the computer cache.

[0246] 22. The method according to any one of clauses 18-21, wherein the method comprises:

[0247] Ensure that the input control data includes uncached negative controls;

[0248] Data calculations and statistics are generated for uncached negative controls; and

[0249] Calculation and statistics of data for cached and uncached negative controls.

[0250] 23. The method according to any one of clauses 18-22, wherein the method comprises:

[0251] Ensure that the input control data includes cached negative controls;

[0252] Retrieve cached negative control data for calculation and statistical analysis; and

[0253] Recalculate and statistically analyze the cached negative control data.

[0254] 24. The method according to any one of clauses 18-21, wherein the method comprises:

[0255] Ensure that the input control data includes uncached negative controls;

[0256] Retrieve input control data including cached negative controls; and

[0257] Generate data calculations and statistics for both uncached and cached negative controls.

[0258] 25. The method according to any one of clauses 18-24, wherein the method further comprises recalculating the spectral unmixing matrix using updated input control data stored in a computer cache.

[0259] 26. The method according to any one of Clauses 1-25, wherein the spectral unmixing matrix is ​​recalculated when the adjusted input includes an input variation equal to or below a predetermined threshold.

[0260] 27. The method according to Clauses 1-25, wherein the spectral unmixing matrix is ​​recalculated when the adjusted input includes an input variation above a predetermined threshold.

[0261] 28. The method according to any one of Clauses 1-27, wherein the method includes retrieving the calculated input from a computer cache before adjusting the input.

[0262] 29. The method according to any one of Clauses 1-28, wherein the method further comprises clearing the cached input when the input is adjusted.

[0263] 30. The method according to any one of clauses 1-28, wherein the method further comprises clearing the buffered input and applying the new input to the spectral unmixing matrix.

[0264] 31. The method according to any one of clauses 1-30, wherein adjusting the cache input includes excluding one or more controls when recalculating the spectral unmixing matrix.

[0265] 32. The method according to any one of clauses 1-31, wherein adjusting the cache input includes applying new control data when recalculating the spectral unmixing matrix.

[0266] 33. The method according to any one of clauses 1-32, wherein adjusting the buffer input includes changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0267] 34. The method according to any one of clauses 1-33, wherein the method includes displaying a plurality of different matrix quality parameters on a graphical user interface.

[0268] 35. The method according to any one of clauses 1-34, wherein the updated matrix quality parameters of the spectral unmixing matrix are displayed in real time in response to the adjusted input.

[0269] 36. A system for adjusting parameters of a spectral unmixing matrix, the system comprising a processor, the processor including a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon, the instructions, when executed by the processor, causing the processor to:

[0270] The calculated spectral unmixing matrix is ​​received in the computer cache of the system processor;

[0271] Evaluate the matrix quality parameters of the calculated spectral unmixing matrix;

[0272] The buffer input to the spectral unmixing matrix is ​​adjusted based on the evaluated matrix quality parameters;

[0273] The spectral unmixing matrix is ​​recalculated using the adjusted input; and

[0274] In response to the adjusted input, the updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface on the display.

[0275] 37. The system according to Clause 36, wherein the memory includes instructions for evaluating matrix quality parameters of a computed spectral unmixing matrix, the matrix quality parameters being selected from a group consisting of a complexity index, a similarity index, a hotspot matrix, a conditional index, a variance decomposition proportional matrix (VDP), an extended correlation matrix, collinearity, a variance inflation factor, and a covariance matrix.

[0276] 38. The system according to Clause 37, wherein the memory includes instructions for evaluating a similarity index of the calculated spectral unmixing matrix.

[0277] 39. The system according to Clause 37, wherein the memory includes instructions for evaluating the complexity index of the computed spectral unmixing matrix.

[0278] 40. The system according to Clause 37, wherein the memory includes instructions for evaluating a hotspot matrix of a calculated spectral unmixing matrix.

[0279] 41. The system according to any one of clauses 36-40, wherein the memory includes instructions for evaluating matrix quality parameters by visualizing a data plot of the computed spectral unmixing matrix.

[0280] 42. The system according to clauses 36-41, wherein the input includes monochromatic positive control data.

[0281] 43. The system according to Clause 42, wherein monochromatic positive control data are generated from monochromatic control beads.

[0282] 44. The system according to Clause 42, wherein single-stain positive control data are generated from single-stain control cells.

[0283] 45. The system according to any one of clauses 36-44, wherein the input includes autofluorescence control data.

[0284] 46. ​​The system according to any one of clauses 36-45, wherein the input includes unstained control data.

[0285] 47. The system according to any one of clauses 36-46, wherein the input includes negative control data.

[0286] 48. The system according to any one of clauses 36-47, wherein the input includes raw control data.

[0287] 49. The system according to any one of clauses 36-48, wherein the input includes comparison data calculations.

[0288] 50. The system according to any one of clauses 36-49, wherein the input includes comparative data statistics.

[0289] 51. The system according to any one of clauses 36-50, wherein the inputs include calculated gate statistics.

[0290] 52. The system according to any one of clauses 36-51, wherein the memory includes instructions for adjusting input using a graphical user interface.

[0291] 53. The system according to any one of clauses 28-52, wherein said memory includes instructions for operating:

[0292] The input comparison data is stored in the computer cache;

[0293] One or more of the following can be generated from stored input control data: input control data calculation and input control data statistics.

[0294] 54. The system according to Clause 53, wherein the memory further includes instructions for clearing stored input reference data from the computer cache when the input reference data is adjusted.

[0295] 55. The system according to any one of clauses 53-54, wherein the memory includes instructions for determining whether input comparison data is stored in a computer cache.

[0296] 56. The system according to Clause 55, wherein the memory includes instructions for the following operations:

[0297] When it is determined that the input control data does not exist in the computer cache, generate one or more control data calculations and control data statistics; and

[0298] The generated control data calculations and statistics are stored in the computer cache.

[0299] 57. The system according to any one of clauses 53-56, wherein said memory includes instructions for operating:

[0300] Ensure that the input control data includes uncached negative controls;

[0301] Data calculations and statistics are generated for uncached negative controls; and

[0302] Calculation and statistics of data for cached and uncached negative controls.

[0303] 58. The system according to any one of clauses 53-57, wherein said memory includes instructions for the following operations:

[0304] Ensure that the input control data includes cached negative controls;

[0305] Retrieve cached negative control data for calculation and statistical analysis; and

[0306] Recalculate and statistically analyze the cached negative control data.

[0307] 59. The system according to any one of clauses 53-58, wherein said memory includes instructions for operating:

[0308] Ensure that the input control data includes uncached negative controls;

[0309] Retrieve input control data including cached negative controls; and

[0310] Generate data calculations and statistics for both uncached and cached negative controls.

[0311] 60. The system according to any one of clauses 53-59, wherein the memory further includes instructions for recalculating the spectral unmixing matrix with updated input control data stored in a computer cache.

[0312] 61. The system according to any one of clauses 36-60, wherein the memory includes instructions for recalculating the spectral unmixing matrix when the adjusted input includes an input variation equal to or below a predetermined threshold.

[0313] 62. The system according to any one of clauses 36-60, wherein the memory includes instructions for recalculating the spectral unmixing matrix when the adjusted input includes an input variation above a predetermined threshold.

[0314] 63. The system according to any one of clauses 36-62, wherein the memory includes instructions for retrieving computed input from a computer cache before adjusting the input.

[0315] 64. The system according to any one of clauses 36-63, wherein the memory includes instructions for clearing cached inputs when inputs are adjusted.

[0316] 65. The system according to any one of clauses 36-64, wherein the memory includes instructions for clearing cached inputs and applying new inputs to the spectral unmixing matrix.

[0317] 66. The system according to any one of clauses 36-65, wherein the memory includes instructions for excluding one or more controls when recalculating the spectral unmixing matrix.

[0318] 67. The system according to any one of clauses 36-66, wherein the memory includes instructions for adjusting the cached input by applying new control data when recalculating the spectral unmixing matrix.

[0319] 68. The system according to any one of clauses 36-67, wherein the memory includes instructions for adjusting the buffer input by changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0320] 69. The system according to any one of clauses 36-68, wherein the memory includes instructions for displaying a plurality of different matrix quality parameters on a graphical user interface.

[0321] 70. The system according to any one of clauses 36-69, wherein the memory includes instructions for displaying updated matrix quality parameters of the spectral unmixing matrix in real time in response to adjusted input.

[0322] 71. The system according to any one of clauses 36-70, wherein the system comprises a flow cytometer.

[0323] 72. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions including an algorithm for adjusting parameters of a spectral unmixing matrix, comprising:

[0324] An algorithm for receiving the computed spectral unmixing matrix in a computer buffer;

[0325] An algorithm for evaluating the matrix quality parameters of the calculated spectral unmixing matrix;

[0326] An algorithm for adjusting the buffered input to the spectral unmixing matrix based on the evaluated matrix quality parameters;

[0327] An algorithm for recalculating the spectral unmixing matrix using the adjusted input; and

[0328] An algorithm for displaying updated matrix quality parameters of the spectral unmixing matrix on a graphical user interface on a display in response to adjusted input.

[0329] 73. The non-transitory computer-readable storage medium as described in Clause 72, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating matrix quality parameters of a computed spectral unmixing matrix, the matrix quality parameters being selected from the group consisting of a complexity index, a similarity index, a hotspot matrix, a conditional index, a variance decomposition proportional matrix (VDP), an extended correlation matrix, collinearity, a variance inflation factor, and a covariance matrix.

[0330] 74. The non-transitory computer-readable storage medium as described in Clause 73, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating a similarity index of a computed spectral unmixing matrix.

[0331] 75. The non-transitory computer-readable storage medium as described in Clause 73, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating a complexity index of a computed spectral unmixing matrix.

[0332] 76. The non-transitory computer-readable storage medium as described in Clause 73, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating a hotspot matrix of a computed spectral unmixing matrix.

[0333] 77. A non-transitory computer-readable storage medium according to any one of clauses 72-76, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating matrix quality parameters by visualizing a data plot of a computed spectral unmixing matrix.

[0334] 78. A non-transitory computer-readable storage medium according to any one of Clauses 72-77, wherein the input includes monochromatic positive control data.

[0335] 79. The non-transitory computer-readable storage medium as described in Clause 78, wherein monochromatic positive control data are generated from monochromatic control beads.

[0336] 80. The non-transitory computer-readable storage medium as described in Clause 78, wherein single-stain positive control data are generated from single-stain control cells.

[0337] 81. A non-transitory computer-readable storage medium according to any one of Clauses 72-80, wherein the input includes autofluorescence control data.

[0338] 82. A non-transitory computer-readable storage medium according to any one of clauses 72-81, wherein the input includes unstained control data.

[0339] 83. A non-transitory computer-readable storage medium according to any one of Clauses 72-82, wherein the input includes negative control data.

[0340] 84. A non-transitory computer-readable storage medium according to any one of clauses 72-83, wherein the input includes original comparative data.

[0341] 85. A non-transitory computer-readable storage medium according to any one of clauses 72-84, wherein the input includes comparative data calculations.

[0342] 86. A non-transitory computer-readable storage medium according to any one of Clauses 72-85, wherein the input includes comparative data statistics.

[0343] 87. A non-transitory computer-readable storage medium according to any one of Clauses 72-86, wherein the input includes calculated gate statistics.

[0344] 88. A non-transitory computer-readable storage medium according to any one of clauses 72-87, wherein the non-transitory computer-readable storage medium includes an algorithm for adjusting input using a graphical user interface.

[0345] 89. The non-transitory computer-readable storage medium according to any one of clauses 72-88, wherein the non-transitory computer-readable storage medium comprises:

[0346] An algorithm used to store input comparison data in a computer cache;

[0347] An algorithm for generating one or more input control data calculations and input control data statistics from stored input control data.

[0348] 90. The non-transitory computer-readable storage medium as described in Clause 89, wherein the non-transitory computer-readable storage medium includes an algorithm for clearing stored input reference data from a computer cache when input reference data is adjusted.

[0349] 91. A non-transitory computer-readable storage medium according to any one of Clauses 89-90, wherein the non-transitory computer-readable storage medium includes an algorithm for determining whether input comparison data is stored in a computer cache.

[0350] 92. The non-transitory computer-readable storage medium as described in Clause 91, wherein the non-transitory computer-readable storage medium comprises:

[0351] Algorithms for generating one or more of the control data calculations and control data statistics when it is determined that the input control data does not exist in the computer cache; and

[0352] An algorithm used to calculate and store the generated control data statistics in a computer cache.

[0353] 93. The non-transitory computer-readable storage medium according to any one of clauses 89-92, wherein the non-transitory computer-readable storage medium comprises:

[0354] An algorithm used to determine whether input control data includes uncached negative controls;

[0355] Algorithms for generating data calculations and statistics for uncached negative controls; and

[0356] Algorithms for calculating and statistically analyzing data from cached negative controls.

[0357] 94. The non-transitory computer-readable storage medium according to any one of clauses 89-93, wherein the non-transitory computer-readable storage medium comprises:

[0358] An algorithm used to determine whether the input control data includes cached negative controls;

[0359] Algorithms for retrieving cached negative control data and for data calculation and statistics; and

[0360] Algorithms for recalculating cached negative control data and performing data statistics.

[0361] 95. The non-transitory computer-readable storage medium according to any one of clauses 89-94, wherein the non-transitory computer-readable storage medium comprises:

[0362] An algorithm used to determine whether input control data includes uncached negative controls;

[0363] An algorithm for retrieving input control data, including cached negative controls; and

[0364] Algorithms for generating data calculations and statistics for both uncached and cached negative controls.

[0365] 96. A non-transitory computer-readable storage medium according to any one of clauses 89-95, wherein the non-transitory computer-readable storage medium includes an algorithm for recalculating the spectral unmixing matrix with updated input control data stored in a computer cache.

[0366] 97. A non-transitory computer-readable storage medium according to any one of Clauses 72-96, wherein the non-transitory computer-readable storage medium includes an algorithm for recalculating the spectral unmixing matrix when the adjusted input includes an input variation equal to or below a predetermined threshold.

[0367] 98. A non-transitory computer-readable storage medium according to any one of clauses 72-97, wherein the non-transitory computer-readable storage medium includes an algorithm for recalculating the spectral unmixing matrix when the adjusted input includes an input variation above a predetermined threshold.

[0368] 99. A non-transitory computer-readable storage medium according to any one of Clauses 72-98, wherein the non-transitory computer-readable storage medium includes an algorithm for retrieving computed input from a computer cache before adjusting the input.

[0369] 100. A non-transitory computer-readable storage medium according to any one of Clauses 72-99, wherein the non-transitory computer-readable storage medium includes an algorithm for clearing cached input when input is adjusted.

[0370] 101. A non-transitory computer-readable storage medium according to any one of Clauses 72-100, wherein the non-transitory computer-readable storage medium includes an algorithm for clearing cached inputs and applying new inputs to the spectral unmixing matrix.

[0371] 102. A non-transitory computer-readable storage medium according to any one of clauses 72-101, wherein the non-transitory computer-readable storage medium includes an algorithm for excluding one or more controls when recalculating the spectral unmixing matrix.

[0372] 103. The non-transitory computer-readable storage medium according to any one of clauses 72-102, wherein the non-transitory computer-readable storage medium includes an algorithm for adjusting the cached input by applying new control data when recalculating the spectral unmixing matrix.

[0373] 104. The non-transitory computer-readable storage medium according to any one of clauses 72-103, wherein the non-transitory computer-readable storage medium includes an algorithm for adjusting the cached input by changing the autofluorescence gate of the autofluorescence control data when recalculating the spectral unmixing matrix.

[0374] 105. A non-transitory computer-readable storage medium according to any one of clauses 72-104, wherein the non-transitory computer-readable storage medium includes an algorithm for displaying a plurality of different matrix quality parameters on a graphical user interface.

[0375] 106. A non-transitory computer-readable storage medium according to any one of clauses 72-105, wherein the non-transitory computer-readable storage medium includes an algorithm for displaying updated matrix quality parameters of a spectral unmixing matrix in real time in response to an adjusted input.

[0376] Although the foregoing disclosure has been described in detail for purposes of clarity by way of illustration and examples, it will be apparent to those skilled in the art, in view of the teachings of this disclosure, that some changes and modifications may be made to this disclosure without departing from the spirit or scope of the appended claims.

[0377] Therefore, the foregoing only illustrates the principles of this disclosure. It should be understood that those skilled in the art can design various devices that, while not explicitly described or shown in this disclosure, embody the principles of this disclosure and are included within its spirit and scope. Furthermore, all examples and conditional language used herein are primarily intended to assist the reader in understanding the principles of this disclosure and its concept of contribution to the advanced technology, and should not be construed as limiting oneself to such specific examples and conditions. Moreover, the statements herein regarding the principles, aspects, embodiments, and specific examples of this disclosure are intended to cover their structural and functional equivalents. Additionally, it is intended that such equivalents include both currently known equivalents and future-developed equivalents, i.e., any element developed that performs the same function, regardless of its structure. Furthermore, nothing disclosed in this disclosure is intended for public disclosure, whether or not such disclosure is explicitly stated in the claims.

[0378] Therefore, the scope of this disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of this disclosure are embodied in the appended claims. In the claims, 35 USC § 112(f) or 35 USC § 112(6) is expressly invoked only when the limitation in the claim begins with the explicit phrase “method for…” or “step for…”; if the limitation in the claim does not use the explicit phrase, then 35 USC § 112(f) or 35 USC § 112(6) is not invoked.

Claims

1. A system for adjusting parameters of a spectral unmixing matrix, the system comprising a processor, the processor including a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon, the instructions, when executed by the processor, causing the processor to: The calculated spectral unmixing matrix is ​​received in the computer cache of the system processor; Evaluate the matrix quality parameters of the calculated spectral unmixing matrix; The buffer input to the spectral unmixing matrix is ​​adjusted based on the evaluated matrix quality parameters; Recalculate the spectral unmixing matrix using the adjusted input; as well as In response to the adjusted input, the updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface on the display.

2. The system according to claim 1, wherein, The memory includes instructions for evaluating matrix quality parameters of the computed spectral unmixing matrix, the matrix quality parameters being selected from a group consisting of complexity index, similarity index, hotspot matrix, condition index, variance decomposition proportional matrix (VDP), extended correlation matrix, collinearity, variance inflation factor, and covariance matrix.

3. The system according to claim 37, wherein, The memory includes instructions for evaluating the following: The similarity index of the calculated spectral unmixing matrix; The complexity exponent of the calculated spectral unmixing matrix; or The hotspot matrix of the calculated spectral unmixing matrix.

4. The system according to any one of claims 1-3, wherein, The memory includes instructions for evaluating matrix quality parameters by visualizing a data graph of the computed spectral unmixing matrix.

5. The system according to claims 1-4, wherein, The input includes one or more of the following: Data from single-stain positive controls; Autofluorescence control data; Unstained control data; Negative control data; Original control data; Calculate based on the data; Comparative data statistics; and Calculation of gate statistics.

6. The system according to any one of claims 1-5, wherein, The memory includes instructions for adjusting input using the graphical user interface.

7. The system according to any one of claims 1-6, wherein, The memory includes instructions for the following operations: The input comparison data is stored in the computer's cache; One or more of the following can be generated from stored input control data: input control data calculation and input control data statistics.

8. The system according to any one of claims 1-7, wherein, The memory includes instructions for retrieving computed input from the computer cache before adjusting the input.

9. The system according to any one of claims 1-8, wherein, The memory includes instructions for clearing cached inputs when the input is adjusted.

10. The system according to any one of claims 1-9, wherein, The memory includes instructions for clearing cached inputs and applying new inputs to the spectral unmixing matrix.

11. The system according to any one of claims 1-10, wherein, The memory includes instructions for excluding one or more controls when recalculating the spectral unmixing matrix.

12. The system according to any one of claims 1-11, wherein, The memory includes instructions for displaying multiple different matrix quality parameters on the graphical user interface.

13. The system according to any one of claims 1-12, wherein, The memory includes instructions for displaying updated matrix quality parameters of the spectral unmixing matrix in real time in response to adjusted input.

14. The system according to any one of claims 1-14, wherein, The system includes a flow cytometer.

15. A computer-implemented method, comprising: The calculated spectral unmixing matrix is ​​received in the computer's cache; Evaluate the matrix quality parameters of the calculated spectral unmixing matrix; The buffer input to the spectral unmixing matrix is ​​adjusted based on the evaluated matrix quality parameters; Recalculate the spectral unmixing matrix using the adjusted input; as well as In response to the adjusted input, the updated matrix quality parameters of the spectral unmixing matrix are displayed on the graphical user interface on the display.

Images