Methods and systems for characterizing spillover spread in flow cytometry data

Abstract

Methods of characterizing spillover spread from a first fluorescent dye in fluorescence flow cytometry data collected for a second fluorescent dye are provided. In some embodiments, the methods include partitioning the fluorescence flow cytometry data according to intensity with respect to the first fluorescent dye. In embodiments, the methods further include estimating, by a first linear regression, a zeroed standard deviation of intensity of light collected from the second fluorescent dye for each of the partitioned quantiles based on an assumption that intensity of light collected from the first fluorescent dye is zero, and obtaining, by a second linear regression, a spillover spread coefficient from the zeroed standard deviation. Systems and computer-readable media for characterizing spillover spread from a first fluorescent dye in fluorescence flow cytometry data collected for a second fluorescent dye are also provided.

Description

[0001] Cross-referencing

[0002] Pursuant to 35 U.S.SC §119(e), this application claims priority to U.S. Provisional Patent Application No. 63 / 020,758, filed May 6, 2020, and U.S. Provisional Patent Application No. 63 / 076,611, filed September 10, 2020, the disclosures of which are incorporated herein by reference in their entirety.

[0003] Foreword

[0004] 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 typically to sort biological materials. A flow cytometer generally includes a sample reservoir for receiving a fluid sample (e.g., a blood sample) and a sheath reservoir containing sheath fluid. The flow cytometer transports particles (including cells) in the fluid sample as a flow stream to the flow cell, while simultaneously guiding the sheath fluid into the flow cell. To characterize the composition of the flow stream, the flow stream is illuminated. Changes in the material in the flow stream, such as morphology or the presence of fluorescent labels, can cause observed changes in light, and these changes allow for characterization and separation. For example, particles in a fluid suspension (e.g., molecules, analyte-bound beads, or single cells) pass through a detection region where they are exposed to excitation light (typically from one or more lasers), and the light scattering and fluorescence properties of the particles are measured. Particles or components thereof are typically labeled with fluorescent dyes for easy detection. By using fluorescent dyes with different spectral signatures to label different particles or components, multiple different particles and components can be detected simultaneously. In some implementations, the analyzer includes multiple detectors, one detector for each scattering parameter to be measured, and one or more detectors for each different dye to be detected. For example, some embodiments include spectral configurations where more than one sensor or detector is used for each dye. The acquired data includes signals measured for each light scattering detector and fluorescence emission.

[0005] Flow cytometers may also include devices for recording and analyzing measurement data. For example, data storage and analysis can be performed using a computer connected to the detection electronics. Data can be stored, for instance, in tabular form, with each row corresponding to the data for one particle and columns corresponding to the characteristics of each measurement. Storing data from the particle analyzer using a standard file format (such as the “FCS” file format) facilitates analysis of the data using separate programs and / or machines. Using current analytical methods, data is typically displayed as one-dimensional histograms or two-dimensional (2D) charts for easy visualization, but other methods can also be used to visualize multidimensional data.

[0006] Parameters measured using flow cytometry typically include light at the excitation wavelength, scattered at a narrow angle primarily in a forward direction by particles (called forward scattering (FSC)); excitation light scattered by particles in a direction orthogonal to the excitation laser (called side scattering (SSC)); and light emitted from fluorescent molecules in one or more detectors measuring signals within the spectral wavelength range, or from fluorescent dyes primarily detected in that particular detector or array of detectors. Different cell types can be identified by their light scattering characteristics and fluorescence emission, which is generated by labeling various cellular proteins or other components with antibodies or other fluorescent probes labeled with fluorescent dyes.

[0007] Flow cytometers and scanning cytometers are available from sources such as BD Biosciences (San Jose, California). Flow cytometry is described in, for example, Landy et al. (eds.), Clinical Flow Cytometry, Annals of the New York Academy of Sciences, Vol. 677 (1993); Bauer et al. (eds.), Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins (1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology, No. 91, Humana Press (1997); and Practical Shapiro, Flow Cytometry, 4th Edition, Wiley-Liss (2003); all of which are incorporated herein by reference. Fluorescence imaging microscopy is described in, for example, Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd Edition, Plenum. Press (1989), which is incorporated into this paper by reference.

[0008] After flow cytometry data is received from one or more detectors, it is typically processed through data analysis, allowing the user to understand the data. However, flow cytometry data analysis is often complicated by spillover, a phenomenon where light modulated by particles indicating a specific fluorescent dye is received by one or more detectors not configured to measure that parameter. Thus, light may "spill over" and be detected by detectors that are off-target. Spillover can be corrected by unmixing, where a set of equations is solved to calculate new intensity values ​​for each fluorescent dye, which correlates the observed spillover level with the measured detector values. Unmixing is often referred to as "compensation" when the number of detectors equals the number of unmixed fluorescent dyes. Figure 1A A flowchart illustrating a typical overflow compensation process is depicted. In step 101, populations of fluorescence flow cytometry data that are positive and negative for a specific fluorescent dye are identified. In step 102, a fluorescence overflow matrix containing overflow coefficients is calculated, quantifying the extent to which overflow adds signal to the fluorescence flow cytometry data. In step 103, the fluorescence flow cytometry data are mathematically adjusted based on the fluorescence overflow matrix to compensate for the overflow. While unmixing can correct for the intensity contribution of each fluorescent dye to each other, it cannot correct for the noise contribution, i.e., the error contributing to the fluorescence flow cytometry data through overflow. This noise is called "overflow diffusion." In some cases, overflow diffusion noise is constructive, resulting in signal strength higher than would otherwise be observed, while in others, the noise is destructive, resulting in reduced intensity.

[0009] Conventional methods for quantifying spillover spreading involve calculating spillover spreading coefficients, as described in Nguyen et al. (2013). Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry Part A, 83(3), 306-315; the disclosure of which is incorporated herein by reference. However, a limitation of conventional spillover spreading coefficients is that they require identification of a population of flow cytometry data that are positive for a specific parameter (i.e., emitting light from the fluorescent dye of interest) and a population of flow cytometry data that are negative for the same parameter (i.e., not emitting light from the fluorescent dye of interest). For example, Figure 1B The identification of the positive 100b and negative 100a populations required for calculating the spillover diffusion coefficient according to Nguyen et al. (2013) is shown. Similarly, Figure 2A routine workflow for jointly performing spillover compensation and spillover diffusion characterization is described. After identifying the positive and negative populations in the fluorescence flow cytometry data (step 101), calculating the fluorescence spillover matrix (step 102), and spillover compensation (step 103), a spillover diffusion matrix including the spillover diffusion coefficient can be calculated (step 201). However, as with the calculation of the fluorescence spillover matrix 102, the calculation of the spillover diffusion matrix 201 requires the identification of the positive and negative populations, which is typically an error-prone and time-consuming task for users. Summary of the Invention

[0010] Therefore, the inventors recognized the need for an effective solution for characterizing spillover diffusion in flow cytometry data analysis.

[0011] Aspects of the present invention include methods for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye. In some embodiments, the method includes receiving flow cytometry data collected for each of a first and a second fluorescent dye to assess the extent to which light emitted from the first fluorescent dye causes error in the flow cytometry data collected for the second fluorescent dye. After receiving the flow cytometry data, embodiments of the method further include dividing the flow cytometry data into a plurality of quantiles based on the intensity of the data relative to the first fluorescent dye. Embodiments of the method further include estimating a zeroing standard deviation of the intensity of light collected from the second fluorescent dye for each of the divided quantiles, based on the assumption that the intensity of light collected from the first fluorescent dye is zero. In embodiments, estimating the zeroing standard deviation includes calculating the standard deviation of the intensity of light emitted from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero (σ0), and adjusting the standard deviation (σ) of the observed light emitted from the second fluorescent dye based on σ0. In embodiments, estimating the standard deviation of zeroing involves calculating a first linear regression, which includes calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye. In embodiments, σ0 is taken from the y-intercept of the linear fit calculated in the first linear regression. Embodiments of the method further include obtaining an overflow diffusion coefficient from the standard deviation of zeroing via a second linear regression. In some embodiments, calculating the second linear regression involves calculating a linear fit between the standard deviation of zeroing and the median intensity of light collected from the first fluorescent dye for each divided quantile. In some embodiments, the overflow diffusion coefficient is taken from the slope of the linear fit calculated between the standard deviation of zeroing and the median intensity of light collected from the first fluorescent dye. In embodiments, the overflow diffusion coefficient obtained in this way is calculated for each combination of the first and second fluorescent dyes, i.e., such that overflows from each fluorescent dye are characterized for each other fluorescent dye and combined in the overflow diffusion matrix. Embodiments of the method may also include adjusting the fluorescence flow cytometry data based on the overflow diffusion matrix.

[0012] The invention also relates to a system comprising a particle analyzer component configured to acquire fluorescence flow cytometry data, and a processor including a memory operatively coupled thereto, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to characterize spillover diffusion originating from a first fluorescent dye in flow cytometry data acquired for a second fluorescent dye. In some embodiments, the processor is configured to receive fluorescence flow cytometry data collected for each of the first and second fluorescent dyes to assess the extent to which light emitted from the first fluorescent dye causes error in the fluorescence flow cytometry data collected for the second fluorescent dye. Upon receiving the data, the processor may be configured to divide the fluorescence flow cytometry data into a plurality of quantiles based on the intensity of the data relative to the first fluorescent dye. In embodiments, the processor is further configured to estimate a zero-adjustment standard deviation of the intensity of light collected from the second fluorescent dye for each of the divided quantiles, based on the assumption that the intensity of light collected from the first fluorescent dye is zero. In an embodiment, estimating the zero-calibration standard deviation includes calculating the standard deviation of the intensity of light emitted from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero (σ0); and adjusting the observed standard deviation (σ) of the light emitted by the second fluorescent dye based on σ0. In an embodiment, estimating the zero-calibration standard deviation involves calculating a first linear regression, which includes calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye. In an embodiment, σ0 is taken from the y-intercept of the linear fit calculated in the first linear regression. The processor may also be configured to obtain an overflow diffusion coefficient from the zero-calibration standard deviation via a second linear regression. In some embodiments, calculating the second linear regression involves calculating a linear fit between the zero-calibration standard deviation and the median intensity of light collected from the first fluorescent dye for each divided quantile. In some embodiments, the overflow diffusion coefficient is taken from the slope of the linear fit calculated between the zero-calibration standard deviation and the median intensity of light collected from the first fluorescent dye. In an embodiment, the spillover diffusion coefficient obtained in this manner is calculated for each combination of the first and second fluorescent dyes, i.e., such that spillovers originating from each fluorescent dye are characterized for each other fluorescent dye and combined in the spillover diffusion matrix. The processor can also be configured to adjust the fluorescence flow cytometry data based on the spillover diffusion matrix.

[0013] This disclosure also includes a non-transitory computer-readable storage medium having instructions for practicing the methods of this subject matter. In some embodiments, the non-transitory storage medium includes instructions for: receiving fluorescence flow cytometry data comprising intensity signals collected from at least a first fluorescent dye and a second fluorescent dye; partitioning the fluorescence flow cytometry data according to the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye; estimating a zeroing standard deviation of the intensity of the light collected from the second fluorescent dye for each partitioned quantile by a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; obtaining an overflow diffusion coefficient from the zeroing standard deviation by a second linear regression to characterize overflow diffusion originating from the first fluorescent dye in the flow cytometry data obtained for the second fluorescent dye; combining the overflow diffusion coefficients calculated for each pair of the first and second fluorescent dyes in an overflow diffusion matrix; and adjusting the fluorescence flow cytometry data based on the overflow diffusion matrix. Attached Figure Description

[0014] The invention will be best understood from the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures:

[0015] Figure 1A A flowchart illustrating the conventional process for overflow compensation is depicted.

[0016] Figure 1B A graphical representation of positive and negative populations in fluorescence flow cytometry data is depicted based on the routine procedures used for overflow compensation and overflow diffusion matrix calculations.

[0017] Figure 2 The general procedure for performing overflow compensation by incorporating the calculation of the overflow expansion matrix is ​​described.

[0018] Figure 3 A graphical representation of the first linear regression is depicted.

[0019] Figure 4 A graphical representation of the second linear regression is depicted.

[0020] Figure 5A The overflow expansion matrix calculated according to an embodiment of the method is depicted.

[0021] Figure 5B The overflow expansion matrix calculated according to the conventional process is depicted.

[0022] Figure 6 A flowchart illustrating the overflow compensation process based on the AutoSpill algorithm is provided.

[0023] Figure 7 A flowchart depicts an embodiment of the execution of an immediate method incorporating the AutoSpill algorithm.

[0024] Figure 8 A flowchart depicts an embodiment of the execution of an immediate method in conjunction with a conventional overflow compensation algorithm.

[0025] Figure 9 A flow cytometer according to certain embodiments is described.

[0026] Figure 10 A functional block diagram of an example processor according to certain embodiments is depicted.

[0027] Figure 11 A block diagram of a computing system according to certain embodiments is depicted.

[0028] Figure 12 Regression analysis performed on compensated and uncompensated data according to an embodiment of the real-time method is described.

[0029] Figure 13 The level of consistency between the overflow expansion matrix calculated using the instantaneous method and the regular overflow expansion matrix is ​​depicted.

[0030] Figure 14 The effect of performing a first linear regression on fluorescence flow cytometry data is described. Detailed Implementation

[0031] A method is provided for characterizing spillover diffusion originating from a first fluorescent dye in fluorescence flow cytometry data collected for a second fluorescent dye. In some embodiments, the method includes partitioning the fluorescence flow cytometry data according to the intensity of the data relative to the first fluorescent dye. In embodiments, the method further includes estimating a zero-adjusted standard deviation of the intensity of light collected from the second fluorescent dye for each partitioned quantile using a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; and obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation using a second linear regression. Systems and computer-readable media for characterizing spillover diffusion originating from a first fluorescent dye in fluorescence flow cytometry data collected for a second fluorescent dye are also provided.

[0032] Before describing the invention in more detail, it should be understood that the invention is not limited to the specific embodiments described, as embodiments may, of course, vary. It should also be understood that, since the scope of the invention 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.

[0033] When a range of values ​​is provided, it should be understood that, unless the context explicitly specifies otherwise, every intermediate value between the upper and lower limits of the range up to one-tenth of the lower limit unit, as well as any other stated or intermediate value within the range, is included in this invention. The upper and lower limits of these smaller ranges may be independently included within the smaller ranges and also within this invention, subject to any specific exclusions imposed by the range. When the range includes one or both limits, ranges excluding one or both of these limits are also included in this invention.

[0034] Certain ranges appearing in this document are preceded by the term "approximately". The term "approximately" is used to provide textual support for the exact number that follows it, as well as 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 that is substantially equivalent to the specifically listed number in the context in which it is presented.

[0035] 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 invention pertains. Although any methods and materials similar to or equivalent to those described herein can also be used in the practice or testing of this invention, representative illustrative methods and materials are described hereafter.

[0036] All publications and patents referenced in this specification are incorporated herein by reference, as if each individual publication or patent were specifically and separately indicated to be incorporated herein by reference, and are included herein by reference to disclose and describe the methods and / or materials referencing those publications. References to any publication are for the purpose of disclosure prior to the submission date and should not be construed as an admission that the invention is not entitled to prior publication based on prior art. Furthermore, the publication dates provided may differ from the actual publication dates and may require independent verification.

[0037] It should be noted that, unless the context clearly specifies otherwise, the singular forms “a,” “an,” and “the” used herein and in the appended claims include plural references. It should also be noted that claims may be drafted to exclude any optional elements. Therefore, this statement is intended as a priori basis for the use of proprietary terms such as “alone,” “only,” etc., when stating claim elements or using the “negative” limitation.

[0038] Upon reading this disclosure, those skilled in the art will understand that 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 the invention. Any of the described methods can be performed in the order of the listed events or in any other logically possible order.

[0039] While systems and methods have been or will be described for the sake of grammatical fluidity and functional interpretation, it should be clearly understood that, unless expressly stated in 35 U.SC §112, claims should never be construed as necessarily being bound by “means” or “steps,” but should be given the full meaning and equivalents of the definitions provided by the claims under the doctrine of judicial equivalence, and if a claim is expressly stated in 35 U.SC §112, it should be given all its legal equivalents in 35 U.SC §112.

[0040] Methods for characterizing spillover diffusion in fluorescence flow cytometry data

[0041] As described above, aspects of this disclosure include methods for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye. In embodiments, the method includes receiving fluorescence flow cytometry data. "Fluorescence flow cytometry data" refers to information about parameters of a sample (e.g., cells, particles) collected in a flow cell by any number of fluorescence detectors in a particle analyzer. In embodiments, the fluorescence flow cytometry data includes signals from a variety of different fluorescent dyes, such as 2 to 40 different fluorescent dyes, including 3 to 30 different fluorescent dyes, such as 3 to 20 different fluorescent dyes, and in some instances including 3 to 5 different fluorescent dyes. In some embodiments, the variety of different fluorescent dyes includes two or more different fluorescent dyes, including three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, twenty or more, twenty-five or more, and thirty or more different fluorescent dyes. Fluorescence flow cytometry data can be obtained through any convenient protocol (including those described below).

[0042] In some embodiments, the method includes generating one or more population clusters based on parameters (e.g., fluorescence) of analytes (e.g., cells, particles) in a determined sample. As used herein, a “population” or “subpopulation” of analytes (e.g., cells or other particles) generally refers to a group of analytes having properties (e.g., optical properties, impedance properties, or temporal properties) associated with one or more measured fluorescence parameters, such that the measured parameter data form clusters in the data space. Thus, a population is considered a cluster in the data. Conversely, each data cluster is generally interpreted as a population corresponding to a specific type of cell or analyte, although clusters corresponding to noise or background are also commonly observed. Clusters may be defined by a subset of dimensions, such as a subset of the measured fluorescence parameters (i.e., fluorescence), corresponding to populations that differ only in a subset of the measured parameters or features extracted from the sample.

[0043] In some embodiments, the fluorescence flow cytometry data includes an intensity signal derived from the first fluorescent dye in the flow cytometry data obtained for a second fluorescent dye. In other words, light emitted from the first fluorescent dye is collected by a detector configured to collect light emitted from the second fluorescent dye. As described in the preamble, the fluorescence flow cytometry data at the collection point (i.e., the point where the fluorescence flow cytometry data is received by one or more fluorescence detectors) undergoes overflow diffusion. Overflow is a phenomenon in which particle-modulated light indicating a particular fluorescent dye is received by one or more detectors not configured to measure that parameter. Thus, light may “overflow” and be detected by detectors that are off-target. Therefore, overflow diffusion is noise present in the fluorescence flow cytometry data caused by overflow. Thus, in some embodiments, unadjusted flow cytometry data is erroneous because one or more detectors inadvertently detect light of certain wavelengths. In this case, light emitted from the first fluorescent dye adds a signal to the detector configured to detect light from the second fluorescent dye, i.e., the first fluorescent dye causes overflow. Therefore, the resulting flow cytometry data collected by the detector undergoes overflow diffusion due to the presence of light emitted from the first fluorescent dye.

[0044] Upon receiving flow cytometry data, embodiments of the present invention include partitioning the flow cytometry data. As used herein, “partitioning” means assigning data to multiple distinct groups. In some instances, partitioning fluorescence flow cytometry data includes assigning flow cytometry data to quantiles. A quantile, used in its conventional sense, describes each of a set of values ​​that divides a frequency distribution into equal groups, each group containing the same component of the total population. Thus, in embodiments, each quantile contains fluorescence flow cytometry data points that share the same component as each of the other groups. In some embodiments, fluorescence flow cytometry data is partitioned based on the intensity of the fluorescence flow cytometry data relative to a first fluorescent dye. In other words, the intensity of light emitted by the first fluorescent dye associated with a single fluorescence flow cytometry data point determines which quantile the data point is assigned to. In embodiments, partitioning fluorescence flow cytometry data based on the intensity of the data relative to the first fluorescent dye includes assigning data points within the same quantile that are associated with similar intensities of light received for the first fluorescent dye.

[0045] Fluorescence flow cytometry data can be assigned to any convenient number of distinct quantiles. In some embodiments, the number of quantiles to which fluorescence flow cytometry data is assigned can be scaled to the size of the fluorescence flow cytometry data, i.e., how many data points exist. In some embodiments, larger flow cytometry datasets are divided into more distinct quantiles, while smaller flow cytometry datasets are divided into fewer distinct quantiles. In other embodiments, fluorescence flow cytometry data is typically divided into a default number of quantiles. In these embodiments, the default number of quantiles can be changed to match different sizes of flow cytometry datasets. Changing the default number of quantiles may involve reducing the number of quantiles to which flow cytometry data is assigned to ensure that each quantile has a sufficient number of data points for estimating the standard deviation of data points within each quantile. In some embodiments, the default number of quantiles is 256. In some embodiments, when presenting smaller flow cytometry datasets, the number of quantiles may be reduced to as low as 8 quantiles. Therefore, in some embodiments, the number of quantiles ranges from 8 to 256.

[0046] After partitioning the fluorescence flow cytometry data, embodiments of the invention include zeroing the standard deviation for estimating the intensity of light collected from the second fluorescent dye for each of the partitioned quantiles. "Zeroing" refers to the standard deviation calculated for flow cytometry data points contained within the quantile, which has been adjusted to reflect the assumption that the intensity of light collected from the first fluorescent dye is zero. To estimate the zeroing standard deviation, embodiments of the invention include calculating the median intensity of light emitted from the first fluorescent dye for each quantile. Embodiments of the invention also include calculating the standard deviation (σ) of the intensity of light emitted from the second fluorescent dye. In some embodiments, the standard deviation of the intensity of light emitted from the second fluorescent dye is a robust standard deviation, i.e., it resists outlier effects. In some embodiments, based on the assumption that the intensity of light collected from the first fluorescent dye is zero (σ0), the median intensity of light emitted from the first fluorescent dye and the standard deviation of light emitted from the second fluorescent dye are then used to estimate the standard deviation of the intensity of light collected from the second fluorescent dye.

[0047] In one embodiment, estimating σ0 includes performing a first linear regression. In another embodiment, performing the first linear regression includes calculating a linear fit between the square root of the median intensity of light emitted from the first fluorescent dye and the standard deviation (σ) of the intensity of light emitted from the second fluorescent dye. In another embodiment, the square root of the median intensity of light emitted from the first fluorescent dye is plotted along the x-axis, and the standard deviation of the intensity of light emitted from the second fluorescent dye is plotted along the y-axis. In some embodiments, the first linear regression is performed using an ordinary least squares regression model. Ordinary least squares regression models are described, for example, in Hutcheson, GD (1999). Ordinary least-squares regression; Hutcheson, GD The multivariate social scientist (pp. 56-113), which is incorporated herein by reference. In other embodiments, the first linear regression is performed using a weighted least squares model. Weighted least squares models are discussed, for example, in Strutz, T. (2015). Data Fitting and Uncertainty: A practical introduction to weighted least squares and beyond, which is incorporated herein by reference. In other embodiments, a first linear regression is performed using a robust linear model. Robust linear models are described, for example, in Andersen, R. (2008). Modern methods for robust regression, which is incorporated herein by reference.

[0048] After calculating the linear fit, embodiments of the invention include calculating σ0 by determining the y-intercept of the linear fit, based on the assumption that the intensity of light collected from the first fluorescent dye is zero. In other words, the standard deviation of the intensity of light emitted from the second fluorescent dye when the median fluorescence of light emitted from the first fluorescent dye is zero (i.e., when the line intersects the y-axis) is used as σ0. For example, Figure 3 A first linear regression is described. The square root of the median intensity of light emitted from the first fluorescent dye is plotted along the x-axis 302, and the standard deviation of the intensity of light emitted from the second fluorescent dye is plotted along the y-axis 301. A linear fit 303 is calculated for flow cytometry data points 304. Based on the assumption that the intensity of light collected from the first fluorescent dye is zero (σ0), a value 305 (at which the linear fit 303 intercepts the y-axis 301) is used as an estimate of the standard deviation of the intensity of light collected from the second fluorescent dye. After estimating σ0 through the first linear regression, embodiments of the invention further include calculating the zeroing standard deviation based on the estimate of σ0. In such embodiments, the zeroing standard deviation is determined by σ0. 2 and The square root of the difference between them is Sure.

[0049] Various aspects of the invention also include obtaining an overflow diffusion coefficient. In some embodiments, obtaining the overflow diffusion coefficient includes quantifying the degree to which flow cytometry data collected by a detector against a second fluorescent dye is affected by light simultaneously collected by the same detector from a first fluorescent dye. In some instances, the flow cytometry data subjected to overflow diffusion is affected by signal intensity higher than that which would otherwise be observed (i.e., overflow diffusion noise is constructive). In other instances, the flow cytometry data subjected to overflow diffusion is affected by signal intensity lower than that which would otherwise be observed (i.e., overflow diffusion noise is destructive). In embodiments, obtaining the overflow diffusion coefficient involves performing a second linear regression. In such embodiments, performing a second linear regression includes calculating a linear fit between the zeroing standard deviation and the median intensity of light collected from the first fluorescent dye for each defined quantile. In embodiments, the zeroing standard deviation is plotted along the y-axis, and the median intensity of light collected from the first fluorescent dye is plotted along the x-axis. The overflow diffusion coefficient is then obtained from the slope of the linear fit calculated between the zeroing standard deviation and the median intensity of light collected from the first fluorescent dye. In some embodiments, a second linear regression is performed using an ordinary least squares regression model. In other embodiments, a weighted least squares model is used to perform the second linear regression. In other embodiments, the second linear regression is performed using a robust linear model. In some embodiments, both the first and second linear regressions are performed using a weighted least squares model. In other embodiments, both the first and second linear regressions are performed using robust linear models.

[0050] For example, Figure 4 A graphical representation of the second linear regression is depicted. The zeroed standard deviation is plotted along the y-axis 401, and the median intensity of light collected from the first fluorescent dye is plotted along the x-axis 402. The linear fit 403 is calculated based on the fluorescence flow cytometry data 404. The spillover diffusion coefficient is obtained from the slope 405 of the linear fit 403.

[0051] Therefore, in the embodiments, the spillover diffusion coefficient as described herein can be calculated according to Formula 1:

[0052]

[0053] As shown in Equation 1, SS is the spillover diffusion coefficient; σ is the standard deviation of the light collected from the second fluorescent dye; σ0 is an estimate of the standard deviation of the intensity of the light collected from the second fluorescent dye based on the assumption that the intensity of the light collected from the first fluorescent dye is zero; and F is the median intensity of the light collected from the first fluorescent dye. Therefore, the spillover diffusion coefficient measures the degree to which the presence of light associated with a specific fluorescent dye affects the fluorescence flow cytometry data collected by a given fluorescence detector. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed by the light emitted from the relevant fluorescent dye collected by a given detector to the fluorescence flow cytometry data. In this embodiment, for a given pair of first and second fluorescent dyes, a higher spillover diffusion coefficient corresponds to more spillover diffusion. In this embodiment, the spillover diffusion coefficient is obtained without identifying populations of fluorescence flow cytometry data that are positive (i.e., exhibiting relevant parameters) and negative (i.e., not exhibiting relevant parameters) relative to a specific fluorescent dye.

[0054] In some embodiments, the first linear regression and the second linear regression are combined in a combined linear regression. In such embodiments, the combined linear regression is configured to calculate the standard deviation of the intensity of light collected from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero (σ0), and simultaneously obtain the spillover diffusion coefficient. In embodiments, the combined linear regression is configured to calculate a linear fit between the square of the standard deviation of the intensity of light collected from the second fluorescent dye and the median intensity of light collected from the first fluorescent dye. In some embodiments, the combined linear regression is performed using a weighted least squares model. In other embodiments, the combined linear regression is performed using a robust linear model.

[0055] Embodiments of the invention also include calculating spillover diffusion coefficients for each possible combination of the first and second fluorescent dyes, thereby enabling the determination of how the fluorescence flow cytometry data collected at each detector is affected by the presence of light associated with each fluorescent dye. In other words, aspects of the invention include calculating multiple spillover diffusion coefficients (e.g., as described above) such that a spillover diffusion coefficient is provided for each possible pair of first and second fluorescent dyes. In embodiments, the spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes are combined in a spillover diffusion matrix. In some embodiments, the spillover diffusion matrix demonstrates how the detection of a particular fluorescent dye by its corresponding detector is affected by spillover from other fluorescent dyes. In embodiments, the spillover diffusion matrix, as described herein, including spillover diffusion coefficients, characterizes the spillover diffusion effect originating from each fluorescent dye in the fluorescence flow cytometry data collected for each other fluorescent dye without identifying the population of fluorescence flow cytometry data that are positive and negative relative to said fluorescent dye. For example, Figure 5AAn embodiment of an overflow diffusion matrix is ​​presented, which provides overflow diffusion coefficients (e.g., obtained as described above) for 23 different fluorescent dyes. Each column of the matrix corresponds to a detector configured to detect one of the 23 different fluorescent dyes, and each row of the matrix corresponds to parameters of the detected flow cytometry data. The cells where the columns and rows intersect are filled with overflow diffusion coefficients calculated for the pair of first and second fluorescent dyes, indicating the extent to which the fluorescent dye in question (i.e., the first fluorescent dye) contributes error to the associated detector (i.e., the detection of light emitted from the second fluorescent dye). The total extent of overflow diffusion caused by the fluorescent dye can be approximated by summing all values ​​in its row, while the total extent to which the detector is affected by overflow diffusion can be calculated by summing all values ​​in its column. In some embodiments, the overflow diffusion coefficients are summed to calculate the total diffusion effect (i.e., the cumulative effect of overflow diffusion on a specific subset of the fluorescence flow cytometry data).

[0056] As described above, in embodiments, the spillover diffusion matrix as described herein is filled with spillover diffusion coefficients calculated without identifying positive and negative populations of the fluorescence flow cytometry data relative to each relevant fluorescent dye. However, in some embodiments, the spillover diffusion matrix as described herein is filled with spillover diffusion coefficients that approximate spillover diffusion coefficients calculated with identification of positive and negative populations of the fluorescence flow cytometry data relative to each relevant fluorescent dye; that is, they approximate spillover diffusion coefficients calculated as taught by Nguyen et al. (2013). For example, Figure 5B Depicting based on computation Figure 5A The spillover diffusion matrix shown is a conventional spillover diffusion matrix calculated from the same dataset (i.e., the spillover diffusion matrix that needs to be identified for positive and negative populations of fluorescence flow cytometry data relative to each associated fluorescent dye). When the spillover diffusion matrix calculated as described herein ( Figure 5A (as shown) and the conventionally calculated spillover diffusion matrix ( Figure 5B When comparing the two matrices (as shown), a high degree of consistency in the magnitude of spillover diffusion was observed between the two matrices for each fluorescent dye-fluorescent dye pair.

[0057] Various aspects of this disclosure also include adjusting fluorescence flow cytometry data to account for spillover diffusion. "Adjusting" means modifying the data to more accurately quantify the presence of fluorescent dyes in the irradiated sample (e.g., cells, particles) in the flow cell. In some embodiments, the fluorescence flow cytometry data is adjusted so that it no longer includes errors caused by spillover diffusion. In embodiments, adjusting the fluorescence flow cytometry data includes generating a spillover diffusion-adjusted population. In some embodiments, generating a spillover diffusion-adjusted population includes subtracting the magnitude of spillover diffusion from a relevant population of the flow cytometry data, i.e., offsetting the effect of the signal affected by spillover diffusion. In some embodiments, the magnitude of spillover diffusion is determined by a spillover diffusion matrix. In some embodiments, adjusting the flow cytometry data includes subtracting the total diffusion effect from a relevant portion of the flow cytometry data.

[0058] Some embodiments of the present invention also include compensation for spillover in fluorescence flow cytometry data. As discussed above in the introduction, spillover is the phenomenon in which particle-modulated light indicating a particular fluorescent dye is received by one or more detectors not configured to measure that parameter. Therefore, compensation mathematically eliminates this overlap from the fluorescence flow cytometry data. Any convenient method can be used to compensate for spillover in fluorescence flow cytometry data. In some embodiments, unmixing can be performed. Unmixing uses a single-staining reference control to separate the fluorescence population and identify the spectrum associated with each fluorescent dye. In other embodiments, spillover compensation is performed via the AutoSpill algorithm. AutoSpill is an algorithm developed by FlowJo LLC (a subsidiary of Becton Dickinson) for calculating spillover and generating a fluorescence spillover matrix consisting of spillover coefficients, which mathematically characterize the extent to which light emitted from one fluorescent dye adds a signal to flow cytometry data collected for another fluorescent dye. AutoSpill is described in Roca et al. (2020). AutoSpill: a method for calculating spillover coefficients in high-parameter flow cytometry.bioRxiv, which is incorporated herein by reference. AutoSpill combines automatic cell gating, robust linear regression-based initial spillover matrix calculation, and iterative refinement to reduce errors. AutoSpill determines the spillover coefficients based on the slope of a linear regression that uses fluorescence in the primary channel (the channel assigned to the dye in the monochromatic control) as the dependent variable and fluorescence in the secondary channels (i.e., light collected by another detector) as the independent variable. No spillover corresponds to a zero slope in this regression. Furthermore, AutoSpill iteratively refines the spillover matrix and recalculates the compensation, thereby reducing errors in the spillover matrix and compensation to negligible levels. For example, Figure 6 A sample workflow representing the AutoSpill algorithm 600 is described. In step 601, a fluorescence spillover matrix is ​​calculated by obtaining spillover coefficients using linear regression (e.g., as described above). In step 602, fluorescence flow cytometry data is compensated based on the fluorescence spillover matrix calculated in step 601.

[0059] In some embodiments of the invention, an overflow diffusion matrix consisting of overflow diffusion coefficients (obtained as described above) is calculated in conjunction with compensation for overflow of fluorescence flow cytometry data. This can be performed with or without identifying positive and negative populations of the fluorescence flow cytometry data for each relevant fluorescent dye. In some embodiments, without identifying positive and negative populations, the calculation of the overflow diffusion matrix is ​​performed in conjunction with overflow compensation. In such embodiments, the calculation of the overflow matrix and overflow compensation is performed via AutoSpill. For example, Figure 7 The workflow involving AutoSpill is described. Because AutoSpill performs linear regression, which eliminates the need to identify positive and negative groups, step 101 (as described above) is skipped. Figures 1A to 1B and Figure 2 The above is not mandatory. Thus, AutoSpill performs the calculation of the fluorescence spillover matrix (step 601) and compensates the fluorescence flow cytometry data based on the calculated fluorescence spillover matrix (step 602). After the samples are compensated, the spillover expansion matrix is ​​calculated using the spillover expansion coefficients described herein (step 701). In some embodiments, if necessary, the fluorescence flow cytometry data can be further adjusted to account for errors present in the data due to spillover.

[0060] In other embodiments, when identifying positive and negative populations, overflow compensation is incorporated into the calculation of the overflow diffusion matrix. In such embodiments, overflow compensation can be performed by algorithms other than AutoSpill. In one embodiment, compensation is performed by demixing. For example, Figure 8 The workflow for identifying positive and negative populations in the flow cytometry data is described (step 101). After the populations are identified, the fluorescence spillover matrix can be calculated in a conventional manner (step 102), and the flow cytometry data can be compensated based on the fluorescence spillover matrix (step 103). After compensation, the spillover expansion matrix is ​​calculated as described herein (step 701). In some embodiments, the flow cytometry data can be further adjusted, if necessary, to account for errors present in the data due to spillover. Although step 701 does not require the identification of positive and negative populations in the flow cytometry data, this identification is performed for spillover compensation.

[0061] As described above, fluorescence flow cytometry data used in the methods of this invention can be obtained using any convenient protocol. In some embodiments, a sample containing particles is illuminated with a light source, and the light from the sample is detected to generate a population of relevant particles based at least in part on measurements of the detected light. In some instances, the sample is a biological sample. The term “biological sample” is used in its conventional sense to refer to a whole organism, a plant, fungus, or animal tissue, a subset of cells, or, in some instances, components found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic sac blood, urine, vaginal fluid, and semen. Thus, a “biological sample” refers to a natural organism or a subset of its tissues, and homogenates, lysates, or extracts prepared from such organism or subsets, including but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular and genitourinary tract, tears, saliva, milk, blood cells, tumors, and organs. Biological samples can be any type of biological tissue, including healthy tissue and diseased tissue (e.g., cancerous tissue, malignant tissue, 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., wherein in some instances, the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger prick (wherein the blood may or may not be bound with any reagents (e.g., preservatives, anticoagulants, etc.) before testing).

[0062] In some embodiments, the source of the sample is "mammal" or "milk," terms that are widely used to describe organisms within the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (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 the invention 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).

[0063] In implementing the methods of this subject matter, a sample containing particles (e.g., in a flow stream of a flow cytometer) is illuminated by 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 50 nm or more, for example 100 nm or more, for example 150 nm or more, for example 200 nm or more, for example 250 nm or more, for example 300 nm or more, for example 350 nm or more, for example 400 nm or more, and including spanning 500 nm or more. 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 with a broadband light source, the broadband light source protocol of interest may include, but is 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.

[0064] In other embodiments, the method includes illumination with a narrowband light source emitting a specific wavelength or a narrow wavelength range, such as illumination with a light source emitting light within a narrow wavelength range (e.g., 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, 2 nm or less), and illumination with a light source emitting light of a specific wavelength (i.e., monochromatic light). When the method includes illumination with a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to: narrow-wavelength LEDs, laser diodes or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0065] Aspects of the present invention include collecting fluorescence using a fluorescence detector. In some instances, the fluorescence detector may be configured to detect fluorescence emission from fluorescent molecules, such as specific binding components of a label associated with particles in a flow cell (e.g., labeled antibodies that specifically bind to a label of interest). In some embodiments, the method includes detecting fluorescence from a sample using one or more fluorescence detectors, 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, ten or more, fifteen or more, and including 25 or more fluorescence detectors. In embodiments, each fluorescence detector is configured to generate a fluorescence data signal. Fluorescence from the sample may be detected independently by one or more fluorescence detectors in a wavelength range of 200 nm to 1200 nm. In some instances, the method includes detecting fluorescence from a sample within a wavelength range, such as 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and including 600 nm to 800 nm. In other instances, the method includes detecting fluorescence at one or more specific wavelengths using each fluorescence detector. For example, fluorescence can be detected at one or more of the following wavelengths: 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, and any combination thereof, depending on the number of different fluorescence detectors in the subject matter optical detection system. In some embodiments, the method includes detecting the wavelength of light corresponding to the fluorescence peak wavelength of certain fluorescent dyes present in the sample. In an embodiment, fluorescence flow cytometry data is received from one or more fluorescence detectors (e.g., one or more detection channels), said one or more fluorescence detectors being, for example, two or more, three or more, four or more, five or more, six or more, and including eight or more fluorescence detectors (e.g., eight or more detection channels).

[0066] A system for characterizing spillover diffusion in fluorescence flow cytometry data.

[0067] Various aspects of this disclosure include systems for classifying fluorescence flow cytometry data. In embodiments, fluorescence flow cytometry data is clustered, adjusted for spillover diffusion, and partitioned such that individual populations are classified differently. In some embodiments, the system includes a particle analyzer configured to generate fluorescence flow cytometry data and a processor configured to analyze fluorescence flow cytometry data.

[0068] In some embodiments, the particle analyzer of this subject includes a flow cell and a laser configured to irradiate particles in the flow cell. In embodiments, the laser can be any convenient laser, such as a continuous-wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some instances, the laser is 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. In other instances, the flow cytometer of this subject includes dye lasers, such as stilbene, coumarin, or rhodamine lasers. In other examples, 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, and combinations thereof. In other examples, the flow cytometers of this subject include 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, ytterbium oxide lasers, or cerium-doped lasers, and combinations thereof.

[0069] Various aspects of the invention also include a forward-scattering detector configured to detect forward-scattered light. The number of forward-scattering detectors in the flow cytometer of this invention can vary as needed. For example, the particle analyzer of this invention may include one or more forward-scattering detectors, such as two or more, three or more, four or more, and including five or more. In some embodiments, the flow cytometer includes one forward-scattering detector. In other embodiments, the flow cytometer includes two forward-scattering detectors.

[0070] Any convenient detector for detecting the collected light can be used for the forward scattering detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as: active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. 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, for example, with an effective detection surface area of ​​0.01 cm² per region. 2 Up to 10cm 2 For example, 0.05cm 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 Photomultiplier tubes.

[0071] If the particle analyzer of this subject includes multiple forward-scattering detectors, each detector can be identical, or the detector set can be a combination of detectors of different types. For example, in the case where the particle analyzer of this subject includes two forward-scattering detectors, in some embodiments, the first forward-scattering detector is a CCD-type device, and the second forward-scattering detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first and second forward-scattering detectors are CCD-type devices. In yet another embodiment, both the first and second forward-scattering detectors are CMOS-type devices. Still in other embodiments, the first forward-scattering detector is a CCD-type device, and the second forward-scattering detector is a photomultiplier tube (PMT). In other embodiments, the first forward-scattering detector is a CMOS-type device, and the second forward-scattering detector is a photomultiplier tube. In yet another embodiment, both the first and second forward-scattering detectors are photomultiplier tubes.

[0072] In embodiments, the forward scattering detector is configured to measure light continuously or at discrete intervals. In some instances, the detector of interest is configured to measure the collected light continuously. In other instances, the detector of interest is configured to measure light at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, and every 1000 milliseconds, or at other intervals.

[0073] Embodiments of the present invention also include an optical dispersion / splitter module located between the flow cell and the forward scattering detector. Optical dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators and combinations thereof, and other wavelength separation devices. In some embodiments, the bandpass filter is located between the flow cell and the forward scattering detector. In other embodiments, more than one bandpass filter is located between the flow cell and the forward scattering detector; the more than one bandpass filter may be, for example, two or more, three or more, four or more, and including five or more. In embodiments, the minimum bandwidth range of the bandpass filter is 2 nm to 100 nm, for example, 3 nm to 95 nm, for example, 5 nm to 95 nm, for example, 10 nm to 90 nm, for example, 12 nm to 85 nm, for example, 15 nm to 80 nm, and includes bandpass filters with a minimum bandwidth range of 20 nm to 50 nm wavelengths that reflect light with other wavelengths to the forward scattering detector.

[0074] Some embodiments of the invention include a side-scattering detector configured to detect the side-scattered wavelength of light (e.g., light refracted and reflected from the surface and internal structure of a particle). In other embodiments, the flow cytometer includes a plurality of side-scattering detectors, such as two or more, three or more, four or more, and including five or more.

[0075] Any convenient detector for detecting the collected light can be used for the side-scattering detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as: active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. 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, for example, with an effective detection surface area of ​​0.01 cm² per region. 2 Up to 10cm 2 For example, 0.05cm 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 Photomultiplier tubes.

[0076] When the particle analyzer of this subject includes multiple side-scattering detectors, each side-scattering detector can be identical, or the set of side-scattering detectors can be a combination of different types of detectors. For example, in the case where the particle analyzer of this subject includes two side-scattering detectors, in some embodiments, the first side-scattering detector is a CCD-type device, and the second side-scattering detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first and second side-scattering detectors are CCD-type devices. In yet another embodiment, both the first and second side-scattering detectors are CMOS-type devices. In other embodiments, the first side-scattering detector is a CCD-type device, and the second side-scattering detector is a photomultiplier tube (PMT). In other embodiments, the first side-scattering detector is a CMOS-type device, and the second side-scattering detector is a photomultiplier tube. In yet another embodiment, both the first and second side-scattering detectors are photomultiplier tubes.

[0077] Embodiments of the present invention also include an optical dispersion / splitter module located between the flow cell and the side-scattering detector. Optical dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators and combinations thereof, and other wavelength separation devices.

[0078] In one embodiment, the particle analyzer further includes a fluorescence detector configured to detect light at one or more fluorescence wavelengths. In other embodiments, the particle analyzer 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.

[0079] Any convenient detector for detecting the collected light can be used in the fluorescence detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as: active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. 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, for example, with an effective detection surface area of ​​0.01 cm² per region. 2 Up to 10cm 2 For example, 0.05cm 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 Photomultiplier tubes.

[0080] If the particle analyzer of this subject includes multiple fluorescence detectors, each fluorescence detector can be identical, or the set of fluorescence detectors can be a combination of different types of detectors. For example, in the case where the particle analyzer of this subject 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 still 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.

[0081] Embodiments of the present invention also include an optical dispersion / splitter module located between the flow cell and the fluorescence detector. Optical dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators and combinations thereof, and other wavelength separation devices.

[0082] In embodiments of this disclosure, the fluorescence detector of interest is configured to measure light collected at one or more wavelengths, such as at two or more wavelengths, such as at five or more different wavelengths, such as at ten or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and includes measuring light emitted by a sample in a flow stream at 400 or more different wavelengths. In some embodiments, two or more detectors in a flow cytometer as described herein are configured to measure collected light at the same or overlapping wavelengths.

[0083] In some embodiments, the detector of interest (FOO) is configured to measure collected light within a wavelength range (e.g., 200 nm to 1000 nm). In some embodiments, the FOC is configured to collect a spectrum within a wavelength range. For example, a particle analyzer may include one or more detectors configured to collect a spectrum within one or more wavelength ranges from 200 nm to 1000 nm. In other embodiments, the FOC is configured to measure light emitted by a sample in a flowing stream at one or more specific wavelengths. For example, a particle analyzer may include one or more detectors configured to measure light at one or more of the following wavelengths: 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, and any combination thereof. In some embodiments, one or more detectors may be configured to pair with a specific fluorophore, such as a fluorophore used with the sample in a fluorescence assay.

[0084] Suitable flow cytometry systems may include, but are not limited to, Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd Edition, Wiley-Liss (1995); Virgo et al. (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden et al., Semin Throm Hemost. Oct 2004; 30(5):502-11; Alison et al. J Pathol, Dec 2010; 222(4):335-344; and Herbig et al. (2007) CritRev Ther Drug Carrier The contents of Syst. 24(3):203-255, as described herein, are incorporated herein by reference. In some cases, flow cytometry systems of interest include BD Biosciences FACSCanto. TM Flow cytometer, BD Biosciences FACSCanto TMII flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSL Fortessa TM X-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BDBiosciences FACSVia TM Flow cytometer and BD Biosciences FACSCalibur TM Cell sorting instrument, BDBiosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorting instrument, BDBiosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorting instrument, BD Biosciences FACSAria TM II Cell Sorter, BD Biosciences FACSAria TM III Cell Sorter, BD Biosciences FACSAria TM Fusion cell sorter and BD Biosciences FACSMelody TM Cell sorter, BD Biosciences FACSymphony TM S6 cell sorter, etc.

[0085] In some embodiments, the subject system is a flow cytometer 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 9,095. The flow cytometry systems described in ,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, the disclosures of which are incorporated herein by reference in their entirety.

[0086] In some cases, the flow cytometry system of the present invention is configured to image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), as described, for example, in Diebold et al., Nature Photonics, Vol. 7(10), 806-810 (2013), and in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019. The disclosures described in U.S. Patent Publications 10,408,758, 10,451,538, 10,620,111 and 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895 and 2019 / 0376894 are incorporated herein by reference.

[0087] In some embodiments, the system further 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 characterize spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for a second fluorescent dye. In embodiments, the processor is configured to receive fluorescence flow cytometry data. In embodiments, the fluorescence flow cytometry data includes signals from a plurality of different fluorescent dyes, such as, for example, 2 to 20 different fluorescent dyes, and includes 3 to 5 different fluorescent dyes. In some embodiments, the plurality of different fluorescent dyes includes 2 or more different fluorescent dyes, including 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, and 20 or more different fluorescent dyes. The fluorescence flow cytometry data can be obtained through any convenient protocol.

[0088] In some embodiments, fluorescence flow cytometry data includes an intensity signal derived from the first fluorescent dye in the flow cytometry data obtained for a second fluorescent dye. In other words, light emitted from the first fluorescent dye is collected by a detector configured to collect light emitted from the second fluorescent dye. As described in the preamble, the fluorescence flow cytometry data at the collection point (i.e., the point where it is received by one or more fluorescence detectors) undergoes overflow diffusion. Overflow is the phenomenon in which particle-modulated light indicating a particular fluorescent dye is received by one or more detectors not configured to measure that parameter. Thus, light may “overflow” and be detected by detectors that are off-target. Therefore, overflow diffusion is noise present in the fluorescence flow cytometry data caused by overflow. Thus, in some embodiments, unadjusted flow cytometry data is erroneous because one or more detectors unintentionally detect light of certain wavelengths. In this case, light emitted from the first fluorescent dye adds a signal to the detector configured to detect light from the second fluorescent dye, i.e., the first fluorescent dye causes overflow. Therefore, the flow cytometry data collected by the detector thus undergoes overflow diffusion due to the presence of light emitted from the first fluorescent dye.

[0089] Upon receiving fluorescence flow cytometry data, the processor can be configured to segment the data. In some cases, segmenting the data involves assigning the data to quantiles. In embodiments, each quantile contains fluorescence flow cytometry data points of the same component as each other component. In some embodiments, the data is segmented based on the intensity of the data relative to a first fluorescent dye. In other words, the intensity of light emitted by the first fluorescent dye associated with a single data point determines which quantile the data point is assigned to. In embodiments, segmenting the data based on the intensity of the data relative to the first fluorescent dye includes assigning data points within the same quantile that are associated with similar intensities of light received for the first fluorescent dye.

[0090] In embodiments, the processor is configured to distribute fluorescence flow cytometry data into any convenient number of distinct quantiles. In some embodiments, the number of quantiles into which the fluorescence flow cytometry data is distributed can be scaled to the size of the fluorescence flow cytometry data, i.e., how many data points exist. In some embodiments, larger flow cytometry datasets are divided into more distinct quantiles, while smaller flow cytometry datasets are divided into fewer distinct quantiles. In other embodiments, the fluorescence flow cytometry data is typically divided into a default number of quantiles. In these embodiments, the default number of quantiles can be changed to match different sizes of the flow cytometry dataset. Changing the default number of quantiles may involve reducing the number of quantiles into which the flow cytometry data is distributed to ensure that each quantile has a sufficient number of data points for estimating the standard deviation of data points within each quantile. In some embodiments, the default number of quantiles is 256. In some embodiments, when presenting smaller flow cytometry datasets, the number of quantiles may be reduced to as low as 8 quantiles. Therefore, in some embodiments, the number of quantiles ranges from 8 to 256.

[0091] After the fluorescence flow cytometry data has been partitioned, the processor can be configured to zero the standard deviation of the estimated intensity of light collected from the second fluorescent dye for each of the partitioned quantiles. To estimate the zero standard deviation, embodiments of the invention include calculating the median intensity of light emitted from the first fluorescent dye for each quantile. Embodiments of the invention also include calculating the standard deviation (σ) of the intensity of light emitted from the second fluorescent dye. In some embodiments, the standard deviation of the intensity of light emitted from the second fluorescent dye is a robust standard deviation, i.e., the standard deviation resists outlier effects. In some embodiments, based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the median intensity of light emitted from the first fluorescent dye and the standard deviation of light emitted from the second fluorescent dye are then used to estimate the standard deviation (σ0) of the intensity of light collected from the second fluorescent dye.

[0092] In an embodiment, estimating σ0 includes performing a first linear regression. In an embodiment, performing the first linear regression includes calculating a linear fit between the square root of the median intensity of light emitted from the first fluorescent dye and the standard deviation (σ) of the intensity of light emitted from the second fluorescent dye. In an embodiment, the square root of the median intensity of light emitted from the first fluorescent dye is plotted along the x-axis, and the standard deviation of the intensity of light emitted from the second fluorescent dye is plotted along the y-axis. In some embodiments, the first linear regression is performed using an ordinary least squares regression model. In other embodiments, the first linear regression is performed using a weighted least squares model. In other embodiments, the first linear regression is performed using a robust linear model.

[0093] After calculating the linear fit, the processor can be configured to calculate σ0 by determining the y-intercept of the linear fit based on the assumption that the intensity of light collected from the first fluorescent dye is zero. In other words, the standard deviation of the intensity of light emitted from the second fluorescent dye when the median fluorescence of light emitted from the first fluorescent dye is zero (i.e., when the line intersects the y-axis) is taken as σ0. After estimating σ0 through the first linear regression, embodiments of the invention further include calculating the zeroing standard deviation based on the estimated value of σ0. In such embodiments, the zeroing standard deviation is determined by σ0. 2 and The square root of the difference between them is Sure.

[0094] The processor can also be configured to obtain an overflow diffusion coefficient. In some embodiments, obtaining the overflow diffusion coefficient includes quantifying the degree to which flow cytometry data collected by the detector against the second fluorescent dye is affected by light simultaneously collected by the same detector from the first fluorescent dye. In some instances, the flow cytometry data subjected to overflow diffusion is affected by signal intensity higher than that which would otherwise be observed (i.e., overflow diffusion noise is constructive). In other instances, the flow cytometry data subjected to overflow diffusion is affected by signal intensity lower than that which would otherwise be observed (i.e., overflow diffusion noise is destructive). In embodiments, obtaining the overflow diffusion coefficient involves performing a second linear regression. In such embodiments, performing a second linear regression includes calculating a linear fit between the zeroing standard deviation and the median intensity of light collected from the first fluorescent dye for each defined quantile. In embodiments, the zeroing standard deviation (i.e., the dependent variable) is plotted along the y-axis, and the median intensity of light collected from the first fluorescent dye (i.e., the independent variable) is plotted along the x-axis. The spillover diffusion coefficient is then obtained from the slope of the linear fit calculated between the zeroing standard deviation and the median intensity of the light collected from the first fluorescent dye. In some embodiments, a second linear regression is performed using an ordinary least squares regression model. In other embodiments, a weighted least squares model is used to perform the second linear regression. In other embodiments, the second linear regression is performed using a robust linear model. In some embodiments, both the first and second linear regressions are performed using a weighted least squares model. In other embodiments, both the first and second linear regressions are performed using a robust linear model.

[0095] Therefore, in the embodiments, the spillover diffusion coefficient as described herein can be calculated according to Formula 1:

[0096]

[0097] As shown in Equation 1, SS is the spillover diffusion coefficient; σ is the standard deviation of the light collected from the second fluorescent dye; σ0 is an estimate of the standard deviation of the intensity of the light collected from the second fluorescent dye based on the assumption that the intensity of the light collected from the first fluorescent dye is zero; and F is the median intensity of the light collected from the first fluorescent dye. Therefore, the spillover diffusion coefficient measures the degree to which the presence of light associated with a specific fluorescent dye affects the fluorescence flow cytometry data collected by a given fluorescence detector. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed by the light emitted from the relevant fluorescent dye collected by a given detector to the fluorescence flow cytometry data. In this embodiment, for a given pair of first and second fluorescent dyes, a higher spillover diffusion coefficient corresponds to more spillover diffusion. In this embodiment, the spillover diffusion coefficient is obtained without identifying groups of fluorescence flow cytometry data that are positive (i.e., do exhibit relevant parameters) and negative (i.e., do not exhibit relevant parameters) relative to a specific fluorescent dye.

[0098] In some embodiments, the first linear regression and the second linear regression are combined in a combined linear regression. In such an embodiment, the combined linear regression is configured to calculate the standard deviation (σ0) of the intensity of light collected from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero, and simultaneously obtain the spillover diffusion coefficient. In an embodiment, the combined linear regression is configured to calculate a linear fit between the square of the standard deviation of the intensity of light collected from the second fluorescent dye and the median intensity of light collected from the first fluorescent dye. In some embodiments, the combined linear regression is performed using a weighted least squares model. In other embodiments, the combined linear regression is performed using a robust linear model.

[0099] The processor can also be configured to calculate spillover diffusion coefficients for each possible combination of the first and second fluorescent dyes, thereby enabling the determination of how the fluorescence flow cytometry data collected at each detector is affected by the presence of light associated with each fluorescent dye. In other words, aspects of the invention include calculating multiple spillover diffusion coefficients (e.g., as described above) such that a spillover diffusion coefficient is provided for each possible pair of first and second fluorescent dyes. In embodiments, the spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes are combined in a spillover diffusion matrix. In some embodiments, the spillover diffusion matrix demonstrates how the detection of a particular fluorescent dye by its corresponding detector is affected by spillover from other fluorescent dyes. In embodiments, the spillover diffusion matrix, as described herein, including spillover diffusion coefficients, characterizes the spillover diffusion effect originating from each fluorescent dye in the fluorescence flow cytometry data collected for each other fluorescent dye without identifying populations of positive and negative fluorescence flow cytometry data relative to the fluorescent dye. Each column in the matrix corresponds to a detector configured to detect one of the different fluorescent dyes, and each row in the matrix corresponds to a parameter of the detected flow cytometry data. The cells where columns and rows intersect are filled with spillover diffusion coefficients calculated for the pair of first and second fluorescent dyes. These spillover diffusion coefficients indicate the extent to which the fluorescent dye in question (i.e., the first fluorescent dye) contributes error to the associated detector (i.e., the detection of light emitted from the second fluorescent dye). The total extent of spillover diffusion caused by the fluorescent dye can be approximated by summing all values ​​in its row, while the total extent of the detector's effect by spillover diffusion can be calculated by summing all values ​​in its column. In some embodiments, the spillover diffusion coefficients are summed to calculate the total diffusion effect (i.e., the cumulative effect of spillover diffusion on a specific subset of the fluorescence flow cytometry data).

[0100] As described above, in embodiments, the spillover diffusion matrix as described herein is filled with spillover diffusion coefficients calculated without identifying positive and negative populations of the fluorescence flow cytometry data relative to each associated fluorescent dye. However, in some embodiments, the spillover diffusion matrix as described herein is filled with spillover diffusion coefficients that approximate spillover diffusion coefficients calculated with identification of positive and negative populations of the fluorescence flow cytometry data relative to each associated fluorescent dye; that is, they approximate spillover diffusion coefficients calculated as taught by Nguyen et al. (2013).

[0101] The processor can also be configured to adjust fluorescence flow cytometry data to account for spillover diffusion. In some embodiments, the fluorescence flow cytometry data is adjusted such that it no longer includes errors caused by spillover diffusion. In embodiments, adjusting the fluorescence flow cytometry data includes generating a spillover diffusion-adjusted population. In some embodiments, generating a spillover diffusion-adjusted population includes subtracting the magnitude of spillover diffusion from a relevant population of the flow cytometry data, i.e., offsetting the effect of the signal affected by spillover diffusion. In some embodiments, the magnitude of spillover diffusion is determined by a spillover diffusion matrix. In some embodiments, adjusting the flow cytometry data includes subtracting the total diffusion effect from a relevant portion of the flow cytometry data.

[0102] In some embodiments, the processor is configured to compensate for overflow of fluorescence flow cytometry data. Any convenient method can be used to compensate for overflow of fluorescence flow cytometry data. In some embodiments, unmixing can be performed. Unmixing uses a single-stained reference control to separate fluorophores and identify the spectra associated with each fluorescent dye. In other embodiments, overflow compensation is performed via the AutoSpill algorithm.

[0103] In some embodiments of the invention, an overflow diffusion matrix consisting of overflow diffusion coefficients (obtained as described above) is calculated in conjunction with compensation for overflow in the fluorescence flow cytometry data. This can be performed with or without identifying positive and negative populations in the fluorescence flow cytometry data for each relevant fluorescent dye. In some embodiments, without identifying positive and negative populations, the calculation of the overflow diffusion matrix is ​​performed in conjunction with overflow compensation. In such embodiments, the calculation of the overflow matrix and overflow compensation is performed via AutoSpill. In some embodiments, if desired, the fluorescence flow cytometry data can be additionally adjusted to account for errors present in the data due to overflow.

[0104] In other embodiments, when identifying positive and negative populations, overflow compensation is incorporated into the calculation of the overflow diffusion matrix. In such embodiments, overflow compensation can be performed using algorithms other than AutoSpill. In some embodiments, compensation is performed by unmixing. In some embodiments, if desired, the fluorescence flow cytometry data can be further adjusted to account for errors present in the data due to overflow.

[0105] Figure 9A system 900 for flow cytometry according to an illustrative embodiment of the present invention is shown. System 900 includes a flow cytometer 910, a controller / processor 990, and a memory 995. The flow cytometer 910 includes one or more excitation lasers 915a-915c, a focusing lens 920, a flow chamber 925, a forward scattering detector 930, a side scattering detector 935, a fluorescence collecting lens 940, one or more beam splitters 945a-945g, one or more bandpass filters 950a-950e, one or more long-pass (“LP”) filters 955a-955b, and one or more fluorescence detectors 960a-960f.

[0106] The 915a-c laser is excited to emit light in the form of a laser beam. Figure 9 In the example system, the laser beams emitted from excitation lasers 915a-915c have wavelengths of 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first guided through one or more beamsplitters 945a and 945b. Beamsplitter 945a transmits 488 nm light and reflects 633 nm light. Beamsplitter 945b transmits UV light (light with wavelengths from 10 nm to 400 nm) and reflects both 488 nm and 633 nm light.

[0107] The laser beam is then directed to a focusing lens 920, which focuses the laser beam onto the portion of the sample containing particles within the liquid flow chamber 925. The flow chamber is part of a flow system that directs particles (typically one at a time) in the flow to the focused laser beam for interrogation. The flow chamber can comprise a flow cell in a benchtop cytometer or a nozzle tip in a stream-in-air cytometer.

[0108] Light from the laser beam interacts with particles in the sample through diffraction, refraction, reflection, scattering, and absorption, as well as re-emission at different wavelengths depending on the characteristics of the particles (e.g., their size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particles). The fluorescence emission, along with the diffracted, refracted, reflected, and scattered light, can be routed via one or more of beam splitters 945a-945g, bandpass filters 950a-950e, longpass filters 955a-955b, and fluorescence collecting lens 940 to one or more of forward scattering detector 930, side scattering detector 935, and one or more fluorescence detectors 960a-960f.

[0109] A fluorescence collecting lens 940 collects light emitted from particle-laser beam interactions and routes that light toward one or more beamsplitters and filters. Bandpass filters, such as bandpass filters 950a-950e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 950a is a 510 / 20 filter. The first number indicates the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm. Short-pass filters transmit light with wavelengths equal to or shorter than a specified wavelength. Long-pass filters, such as long-pass filters 955a-955b, transmit light with wavelengths equal to or longer than a specified wavelength. For example, long-pass filter 955a, as a 670 nm long-pass filter, transmits light with wavelengths equal to or longer than 670 nm. Filters are typically selected to optimize the detector's specificity for a particular fluorescent dye. Filters can be configured such that the spectral band of the light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0110] Beam splitters guide light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass characteristics. For example, beam splitter 905g is a 620SP beam splitter, meaning that beam splitter 945g transmits light wavelengths of 620 nm or less and reflects light wavelengths greater than 620 nm in different directions. In one embodiment, beam splitters 945a-945g can include optical mirrors, such as dichroic mirrors.

[0111] A forward scattering detector 930 is positioned offset from the axis of the direct beam passing through the flow cell and is configured to detect diffracted light, which is the excitation light that propagates primarily in the forward direction through or around the particle. The intensity of the light detected by the forward scattering detector depends on the overall size of the particle. The forward scattering detector can include a photodiode. A side scattering detector 935 is configured to detect refracted and reflected light from the particle surface and internal structure, and is designed to increase with increasing particle structure complexity. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 960a-960f. The side scattering detector 935 and the fluorescence detector can include photomultiplier tubes. The signals detected at the forward scattering detector 930, the side scattering detector 935, and the fluorescence detector can be converted into electrical signals (voltages) by the detectors. This data can provide information about the sample.

[0112] During operation, the cytometer is controlled by a controller / processor 990, and measurement data from the detector can be stored in a memory 995 and processed by the controller / processor 990. Although not explicitly shown, the controller / processor 990 is coupled to the detector to receive output signals from it, and may also be coupled to the electrical and electromechanical components of the flow cytometer 900 to control the laser, fluid flow parameters, etc. Input / output (I / O) functionality 997 may also be provided in the system. The memory 995, controller / processor 990, and I / O 997 may be provided entirely as part of the flow cytometer 910. In such an embodiment, a display may also form part of the I / O functionality 997 for presenting experimental data to the user of the cytometer 900. Optionally, part or all of the memory 995 and controller / processor 990, as well as the I / O functionality, may be part of one or more external devices, such as a general-purpose computer. In some embodiments, part or all of the memory 995 and controller / processor 990 may be able to communicate wirelessly or wiredly with the cytometer 910. Combined with memory 995 and I / O 997, controller / processor 990 can be configured to perform various functions related to the preparation and analysis of flow cytometry experiments.

[0113] Figure 9 The system shown comprises six different detectors that detect fluorescence in six different bands (which may be referred to herein as the “filter window” of a given detector) as defined by the configuration of filters and / or fractionators in the beam path from flow cell 925 to each detector. Different fluorescent molecules used in flow cytometry experiments emit light in their own characteristic bands. Specific fluorescent labels for experiments and their associated fluorescence emission bands can be selected to substantially coincide with the filter windows of the detectors. However, as more detectors are provided and more labels are used, a perfect correspondence between filter windows and fluorescence emission spectra is not possible. Generally, although the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of a particular detector, some emission spectra of that label may also overlap with the filter windows of one or more other detectors. This can be referred to as overflow. I / O 997 can be configured to receive data on flow cytometry experiments having fluorescently labeled panels and multiple cell populations with multiple labels, each cell population having a subset of multiple labels. I / O 997 can also be configured to receive biological data assigning one or more tags to one or more cell populations; receive tag density data; receive emission spectral data; receive data assigning tags to one or more tags; and cytometer configuration data. Flow cytometry experimental data, such as tag spectral characteristics and flow cytometry configuration data, can also be stored in memory 995. Controller / processor 990 can be configured to evaluate tag-to-tag assignments of one or more.

[0114] Those skilled in the art will recognize that the flow cytometer according to embodiments of the present invention is not limited to Figure 9 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 at various wavelengths and in various different configurations.

[0115] Figure 10 A functional block diagram of an example processor 1000 for analyzing and displaying data is shown. The processor 1000 can be configured to implement various processes for controlling graphical displays of biological events. A flow cytometer 1002 can be configured to acquire fluorescence flow cytometry data by analyzing biological samples (e.g., as described above). The device can be configured to provide biological event data to the processor 1000. A data communication channel can be included between the flow cytometer 1002 and the processor 1000. Data can be provided to the processor 1000 via the data communication channel. The processor 1000 can be configured to provide a graphical display, including plotting (e.g., as described above), to a display 1006. The processor 1000 can also be configured to present gates, for example, overlays, around the population of fluorescence flow cytometry data displayed by the display device 1006. In some embodiments, a gate can be a logical combination of one or more regions of interest plotted on a single-parameter histogram or bivariate plot. In some embodiments, the display can be used to display analyte parameters or saturation detector data.

[0116] The processor 1000 can also be configured to display fluorescence flow cytometry data inside the gate on the display device 1006, distinct from other events in the fluorescence flow cytometry data outside the gate. For example, the processor 1000 can be configured to render the colors of the fluorescence flow cytometry data contained inside the gate differently from the colors of the fluorescence flow cytometry data outside the gate. In this way, the processor 1000 can be configured to render different colors to represent each unique data cluster. The display device 1006 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0117] Processor 1000 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 1010. Mouse 1010 can initiate a door selection signal to processor 1000 to identify a group to be displayed on display device 1006 or manipulated by display device 1006 (e.g., by clicking a door when the cursor is on or inside the desired door). In some implementations, the first device can be implemented as a keyboard 1008 or other means for providing input signals to processor 1000, such as a touchscreen, stylus, optical detector, or voice recognition system. Some input devices can include multiple input functions. In such embodiments, each of the input functions can be considered an input device. For example, such as... Figure 10 As shown, mouse 1010 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0118] Triggering events enable the processor 1000 to change how fluorescence flow cytometry data is displayed, which parts of the data are actually displayed on the display device 1006, and / or to provide input for further processing, such as selecting populations of interest for analysis.

[0119] In some embodiments, the processor 1000 can be configured to detect when a door selection is initiated by the mouse 1010. The processor 1000 can also be configured to automatically modify the drawing visualization to facilitate the selection process. This modification can be based on a specific distribution of data received by the processor 1000.

[0120] The processor 1000 can be connected to the storage device 1004. The storage device 1004 can be configured to receive and store data from the processor 1000. The storage device 1004 can also be configured to allow the processor 1000 to retrieve data, such as fluorescence flow cytometry data.

[0121] Display device 1006 can be configured to receive display data from processor 1000. The display data can include plotting and outlining of fluorescence flow cytometry data. Display device 1006 can also be configured to change the presented information based on input received from processor 1000 and input from device 1002, storage device 1004, keyboard 1008 and / or mouse 1010.

[0122] In some implementations, the processor 1000 is capable of generating a user interface to receive sample events for sorting. For example, the user interface may include controls for receiving sample events or sample images. Sample events, images, or sample gates may be provided before the event data of the samples is acquired or based on an initial set of events for a portion of the samples.

[0123] Computer control system

[0124] This disclosure also includes a computer control system, wherein the system further includes one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer-readable storage medium thereon storing a computer program, wherein the computer program, when loaded onto the computer, includes instructions for: receiving fluorescence flow cytometry data comprising intensity signals collected from at least a first fluorescent dye and a second fluorescent dye; partitioning the fluorescence flow cytometry data according to the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye; estimating the zeroing standard deviation of the brightness of the light collected from the second fluorescent dye for each of the partitioned quantiles by a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; obtaining an overflow diffusion coefficient from the zeroing standard deviation by a second linear regression to characterize overflow diffusion originating from the first fluorescent dye in the flow cytometry data obtained for the second fluorescent dye; combining the overflow diffusion coefficients calculated for each pair of first and second fluorescent dyes in an overflow diffusion matrix; and adjusting the fluorescence flow cytometry data based on the overflow diffusion matrix.

[0125] In an embodiment, the system is configured to be used in software or analytical tools for analyzing flow cytometry data (e.g., (Ashland, OR) data analysis. FlowJo is a software package developed by FlowJo LLC (a subsidiary of Becton Dickinson) for analyzing flow cytometry data. The software is configured to manage flow cytometry data and generate graphical reports on it (https: / / www.flowjo.com / learn / flowjo-university / flowjo). It can be used with data analysis software or tools (such as...) The initial data is analyzed using appropriate means (e.g., manual gating, cluster analysis, or other computing techniques). The real-time system, or a portion thereof, can be implemented as a software component for analyzing the data, for example... In these embodiments, the computer control system according to this disclosure can be used as an existing software package (e.g. ) software "plugins".

[0126] In an embodiment, the system includes an input module, a processing module, and an output module. This system may include hardware and software components, wherein the hardware components may take the form of one or more platforms, such as servers, so that the functional elements of the system—that is, those elements that perform specific tasks of the system (e.g., managing the input and output of information, processing information, etc.)—can be executed by software applications on or across one or more computer platforms representing the system.

[0127] The system may include a display and operator input devices. Operator input devices may be, for example, a keyboard, mouse, etc. The processing module includes a processor that can access memory on which instructions for performing steps of the methods of this subject are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or one of other processors that are already available or will be available in the future. The processor executes the operating system and the interface between the operating system and firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs written in various programming languages ​​well known in the art, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof. The operating system typically cooperates with the processor to coordinate and execute the functions of other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, communication control, and related services according to known technologies. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that allow a user to manually align a light source with a flow based on a first optical signal and a second optical signal. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0128] System memory can be any of a variety of known or future memory storage devices. Examples include any common random access memory (RAM), magnetic media such as resident hard disks or magnetic tapes, optical media such as read / write compact disks, flash memory devices, or other memory storage devices. Memory storage devices can be any of a variety of known or future devices, including compact disk drives, magnetic tape drives, removable hard disk drives, or floppy disk drives. This type of memory storage device typically reads from and / or writes to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or floppy disk. Any of these program storage media, or other media currently in use or that may be developed in the future, can be considered a computer program product. It should be understood that 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 devices used in conjunction with memory storage devices.

[0129] In some embodiments, a computer program product is described, the computer program including a computer-usable medium in which control logic (computer software program, including program code) is stored. When the control logic is executed by a processor or computer, it causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementing a hardware state machine to perform the functions described herein will be apparent to those skilled in the art.

[0130] The memory can be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic disks or optical discs or magnetic tapes or RAM, or any other suitable fixed or portable device). The processor can include a general-purpose digital microprocessor, which is appropriately programmed from 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 (such as memory or some other portable or fixed computer-readable storage medium) using any device connected to the memory. For example, a magnetic disk or optical disc can carry the programming and can be read by a disk writer / reader. The system of the present invention also includes programming, for example in the form of a computer program product, or in the form of an algorithm for practicing the methods described above. The programming according to the present invention 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 ROM; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0131] The processor can also access communication channels to communicate with users in remote locations. A remote location refers to a location where the user does not have direct contact with the system and relays input information from 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)) to the input manager.

[0132] In some embodiments, the system according to this disclosure 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 another device. The communication interface can be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB)). Communication protocols and cellular communications such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0133] In one embodiment, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces such as USB ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices such as computer terminals configured for similar complementary data communication (e.g., in a doctor's office or hospital environment).

[0134] In one embodiment, the communication interface is configured for infrared communication. Communication or any other suitable wireless communication protocol that enables the system of this subject 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.

[0135] In one embodiment, the communication interface is configured to provide connectivity for data transmission via Internet Protocol (IP) through a cellular telephone network, Short Message Service (SMS), a wireless connection of a personal computer (PC) connected to the Internet on a local area network (LAN), or a WiFi connection connected to the Internet at a WiFi hotspot.

[0136] In one embodiment, the system is configured to communicate wirelessly with a server device via a communication interface, for example, using 802.11 or... The protocol or IrDA infrared protocol is a common standard for wireless communication with server equipment. The server equipment can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop; or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server equipment has a display (e.g., a liquid crystal display (LCD)) and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0137] In some embodiments, the communication interface is configured to automatically or semi-automatically transmit data stored in the subject system, such as data stored in an optional data storage unit, to a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0138] The output controller may include a controller for any of a variety of known display devices for presenting information to a user (whether human or machine, local or remote). If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of image elements. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. Functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished using networks or other types of remote communication in alternative embodiments. According to known techniques, the output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite networks. The presentation of data by the output manager may be implemented according to a variety of known techniques. As an example, the data may include SQL, HTML, or XML documents, emails, or other files, or other forms of data. The data may include an Internet URL address so that the user can retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the system of this subject may be any type of known computer platform or type to be developed in the future, although they are typically computer classes referred to as servers. However, they can also be host computers, workstations, or other computer types. They can be connected via any known or future type of cable or other communication system, including wireless systems (whether networked or otherwise). They may be located in the same place or physically separated. Depending on the type and / or manufacture of the computer platform chosen, a variety of operating systems can be used on any computer platform. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, and others.

[0139] Figure 11 A general architecture of an example computing device 1100 according to certain embodiments is described. Figure 11The general architecture of the computing device 1100 depicted includes the arrangement of computer hardware and software components. However, it is not necessary to show all these generally conventional components for the purpose of providing an enabling disclosure. As shown, the computing device 1100 includes a processing unit 1110, a network interface 1120, a computer-readable media drive 1130, an input / output device interface 1140, a display 1150, and an input device 1160, all of which can communicate with each other via a communication bus. The network interface 1120 can provide a connection to one or more networks or computing systems. Therefore, the processing unit 1110 can receive information and instructions from other computing systems or services via a network. The processing unit 1110 can also communicate with a memory 1170 and further provide output information to an optional display 1150 via the input / output device interface 1140. For example, analysis software (e.g., such as executable instructions stored in the non-transitory memory of the analysis system) can be used as such. The data analysis software or program can display flow cytometry event data to the user. The input / output device interface 1140 can also accept input from optional input devices 1160, such as keyboards, mice, digital pens, microphones, touch screens, gesture recognition systems, voice recognition systems, game joysticks, accelerometers, gyroscopes, or other input devices.

[0140] Memory 1170 may contain computer program instructions (grouped into modules or components in some embodiments) that are executed by processing unit 1110 to implement one or more embodiments. Memory 1170 typically includes RAM, ROM, and / or other permanent, auxiliary, or non-transitory computer-readable media. Memory 1170 may store operating system 1172, which provides computer program instructions for use by processing unit 1110 in the general management and operation of computing device 1100. Data may be stored in data storage device 1190. Memory 1170 may also include computer program instructions and other information for implementing aspects of this disclosure.

[0141] Computer-readable storage media

[0142] This disclosure also includes non-transitory computer-readable storage media having instructions for practicing the methods of this subject matter. Computer-readable storage media can be used on one or more computers to achieve full or partial automation of the system for practicing the methods described herein. In some embodiments, instructions according to the methods described herein can be encoded in a “programmable” form onto 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. Examples of suitable 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 inside or outside a computer. In some instances, instructions can be provided on an integrated circuit device. In some instances, the integrated circuit device of interest may include a reconfigurable field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or a complex programmable logic device (CPLD). Files containing information can be “stored” on a computer-readable medium, where “stored” means recording the information so that a computer can access and retrieve it later. The computer-implemented methods described in this paper can be executed using programming, which can be written in one or more of any number of computer programming languages. These languages ​​include, for example, Java (Sun Microsystems, Inc., Santa Clara, CA), Visual Basic (Microsoft Corp., Redmond, WA), and C++ (AT&T Corp., Bedminster, NJ), as well as any other language.

[0143] In some embodiments, the computer-readable storage medium of interest includes a computer program stored thereon, wherein the computer program, when loaded onto a computer, includes the following instructions: the instructions are configured to receive fluorescence flow cytometry data comprising intensity signals collected from at least a first fluorescent dye and a second fluorescent dye; to partition the fluorescence flow cytometry data according to the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye; to estimate the zeroing standard deviation of the intensity of light collected from the second fluorescent dye for each of the partitioned quantiles by a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; to obtain an overflow diffusion coefficient from the zeroing standard deviation by a second linear regression to characterize the overflow diffusion originating from the first fluorescent dye in the flow cytometry data obtained for the second fluorescent dye; to combine the overflow diffusion coefficients calculated for each pair of first and second fluorescent dyes in an overflow diffusion matrix; and to adjust the fluorescence flow cytometry data based on the overflow diffusion matrix.

[0144] In this embodiment, the system is configured to run on software or analytical tools used for analyzing flow cytometry data (e.g., (Ashland, OR)) Analyzes data within data analysis software or tools (e.g., Ashland, OR). Capable of analyzing data within data analysis software or tools (e.g., The initial data is analyzed using appropriate methods (e.g., manual gating, cluster analysis, or other computational techniques). This system, or a portion thereof, can be implemented as a software component for data analysis, for example... In these embodiments, the computer control system according to this disclosure can be used as an existing software package (e.g. ) software "plugins".

[0145] Computer-readable storage media can be used in one or more computer systems having a display and operator input devices. Operator input devices can be, for example, a keyboard, a mouse, etc. The processing module includes a processor that can access memory on which instructions for performing the steps of the methods of this subject are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or one of other processors that are already available or will be available in the future. The processor executes the operating system and the interface between the operating system and firmware and hardware in a well-known manner, and facilitates the processor in coordinating and executing the functions of various computer programs written in various programming languages ​​well known in the art, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof. The operating system also provides scheduling, input / output control, file and data management, memory management, communication control, and related services according to known technologies.

[0146] practicality

[0147] The apparatus, methods, and computer systems of this subject matter can be used in a variety of applications where it is desirable to improve the resolution and accuracy of parameter determination for analytes (e.g., cells, particles) in biological samples. For example, this disclosure can be used to analyze data affected by spillover diffusion. Because flow cytometry typically involves collecting multiple fluorescence parameters by multiple detectors, the detected fluorescence intensity may be erroneously increased due to the same light being detected by multiple detectors. Therefore, this disclosure is used during the analysis of flow cytometry data containing signals from multiple fluorescent dyes. The apparatus, methods, and computer systems of this subject matter are particularly suitable for characterizing spillover diffusion in flow cytometry data having undefined positive and negative populations. In some embodiments, the methods and systems of this subject matter provide fully automated protocols, such that adjustments to the data require only minimal (if any) manual input.

[0148] This disclosure can be used to characterize a variety of analytes, particularly those related to medical diagnosis or patient care protocols, including but not limited to: proteins (including free proteins and proteins bound to the surface of structures such as cells), nucleic acids, viral particles, etc. Furthermore, samples can be from in vitro or in vivo sources, and the samples can be diagnostic samples.

[0149] kit

[0150] This disclosure also includes kits, which include storage media such as 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). Any of these program storage media, or other media currently in use or potentially developed in the future, may be included in this subject matter kit. In embodiments, the program storage media includes instructions for clustering fluorescence flow cytometry data into populations, determining spillover diffusion of the populations, adjusting the flow cytometry data based on spillover diffusion, and determining partitions between the adjusted flow cytometry data (e.g., as described above). In embodiments, the instructions on a computer-readable medium included in or in part of this subject matter kit can be implemented as software components for analyzing data, such as… In these embodiments, the computer control system according to this disclosure can be used as an existing software package (e.g. ) software "plugins".

[0151] In addition to the components described above, this topic suite may also include (in some embodiments) instructions, such as those for installing plugins into existing software packages (e.g., These instructions may exist in various forms within this topic suite, with one or more potentially present throughout the suite. One form of these instructions could be printed information on a suitable medium or substrate, such as one or more sheets of paper printed with information in the suite packaging, inserts, etc. Another form of these instructions could be a computer-readable medium on which information is recorded, such as a floppy disk, CD, portable flash drive, etc. Yet another possible form of these instructions could be a website address that allows access to information about the removed site via the internet.

[0152] experimental

[0153] Dataset

[0154] MM1 dataset (mouse spleen cells)For the single-stain control, spleen cells from C57Bl / 6 mice were disrupted using a glass slide, filtered through a 100 μm sieve, and erythrocytes were lysed. Cells were fixed and infiltrated with Foxp3 transcription factor staining buffer (eBioscience) according to the manufacturer's instructions, and stained overnight at 4°C with the immobilizable active dye eFluor780 (eBioscience) or the following antibodies: anti-CD4-BV421, anti-CD24-BV510, anti-CD3-BV570, anti-CD4-BV605, anti-CD3-BV650, anti-CD4-BV711, anti-CD4-BV785, anti-CD3-AF488 / anti-CD4-AF488 / anti-TCRβ-AF488, anti-CD4-PerCP-Cy5.5, anti-CD4-PE-594, and anti-CD8-PE-Cy7. Anti-MHC-II-AF700 (all Biolegend), Anti-CD19-BV750, Anti-CD3-BB630-P / Anti-Thy1.2-BB630-P, Anti-CD45.2-BB660-P2 / Anti-CD3-BB660-P2, Anti-TCRb-BB790-P / Anti-CD45-BB790-P, Anti-CD4-BUV395, Anti-CD4-BUV496, Anti-CD3-BUV563, Anti-CD3-BUV615-P, Anti-CD19-BUV661, Anti-CD21-BUV737, Anti-CD8-BUV805 (all BD) Biosciences), anti-CD4-PE / anti-CD3-PE / anti-CD8-PE, anti-IgM-PE-Cy5, anti-CD3-PE-Cy5.5, or anti-CD4-APC / anti-CD8-APC (all from eBioscience). For some fluorophores, multiple antibodies were used in the same compensation control, indicated by slashes. Samples were collected on a Symphony flow cytometer (BD Biosciences).

[0155] HS1 dataset (human PBMC): Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized blood samples from healthy human donors using Ficoll-Paque density centrifugation (MP Biomedical). The PBMCs were frozen and then stored in liquid nitrogen. The frozen PBMCs were thawed and counted, and the cell concentration was adjusted to 1 × 10⁻⁶ cells / mL. 6For each monochrome control, cells were seeded in V-bottom 96-well plates, washed once with PBS (Fisher Scientific), and stained with live / dead labeled antibodies and fluorescent dye conjugated to surface labels: anti-CD8-BUV805, anti-CD4-BUV496, anti-CD95-BUV737, anti-CD4-BUV615-P, anti-CD28-BB660-P, anti-CD4-BB630, anti-CD4-BV750-P, anti-CD31-BV480, anti-CXCR5-BV650, anti-CD4-PE, anti-CD4-PE-Cy5 (all from BD Biosciences); anti-CD3-PerCP-Vio700 (Miltenyi Biotec); Anti-CD3-FITC, Anti-CD4-PE-Cy5.5, Anti-CCR7-PE-Cy7, Anti-CD4-eFluor780 (all from eBioscience); Anti-CD4-BV786, Anti-CD4-BV711, Anti-CD4-BV605, Anti-HLA-DR-BV570, Anti-CD127-BV421, Anti-CD4-PE / Dazzle 594, Anti-CD4-AF647 (all from BioLegend).

[0156] According to the manufacturer's instructions, samples were stained at 4°C for 60 minutes, washed twice in PBS / 1% FBS (Tico Europe), and then fixed and infiltrated with Foxp3 transcription factor staining buffer (eBioscience). Cells were stored overnight at 4°C and then collected using a Symphony flow cytometer with Diva software (BD Biosciences). A minimum of 5 × 10⁶ cells were collected for each sample. 4 One event.

[0157] HS2 dataset (human PBMC):Similar to group HS1, frozen PBMCs from healthy human donors were processed and stained with live / dead markers and fluorescently conjugated antibodies against the following surface markers: anti-CD8-BUV805, anti-CD4-BUV496, anti-CD-95-BUV737, anti-CD28-BB660-P, anti-ICOS-BB630, anti-CXCR3-BV785, anti-PD-1-BV750-P, anti-CXCR5-BV650, and anti-CCR2-BV605 (all BD). Biosciences); Anti-CD3-PerCP-Vio700 (Miltenyi Biotec); Anti-CD45RA-FITC, Anti-CD14-PE-Cy5.5, and the active dye immobilizable eFluor780 (all from eBioscience); Anti-CD25-BV711, Anti-CD31-BV480, Anti-HLA-DR{BV570, Anti-CD127{BV421, Anti-CCR4-PE / Dazzle 594, and Anti-CCR-7-PE-Cy7 (all from BioLegend).

[0158] According to the manufacturer's instructions, samples were stained at 4°C for 60 minutes, washed twice in PBS / 1% FBS (Tico Europe), and then fixed and infiltrated with Foxp3 transcription factor staining buffer (eBioscience). Cells were stained overnight at 4°C with anti-Ki67-BUV615-P, anti-CTLA-4-PE-Cy5, anti-RORt-PE (BD Biosciences), and anti-FOXP3-AF647 (BioLegend) anti-human intracellular antibodies. Samples were collected using a Symphony flow cytometer (BD Biosciences).

[0159] Be1 dataset (Beads):Ultra-compressed beads (UltraComp eBead). ThermoFisher beads are used to optimize fluorescence compensation settings for multicolor flow cytometry analysis in the Symphony flow cytometer. The ultra-compressed beads are stained with anti-human antibodies labeled with the following fluorescent dyes: anti-CD8-BUV805, anti-CD4-BUV496, anti-CD86-BUV737, anti-CD141-BUV615-P, anti-CD56-BUV563, anti-CD16-BUV395, anti-CD123-BB660-P, anti-CD80-BB630, anti-CD21-BV785, anti-CD27-BV750-P, anti-BAFF-R-BV650, anti-CD94-BV605, and anti-CD40-APC-R700 (all from BDBiosciences). Anti-CD3-PerCP-Vio700 (Miltenyi Biotec); Anti-CD57-FITC, Anti-CD14-PE-Cy5.5, and can fix reactive dye eFluor780 (all from eBioscience); Anti-CD24-BV711, Anti-CD19-BV480, Anti-HLA-DR-BV570, Anti-IgM-BV421, Anti-CD11c-APC, Anti-CD38-PE / Dazzle594, Anti-CD10-PE-Cy5, and Anti-IgD-PE-Cy7 (all from BioLegend).

[0160] Initial gating

[0161] Subdivision was performed using Packed Color Slice v.1.3-9, while density estimation and spatial computation were performed using Packed Fields v.10.3 and SP v.1.4-1. Initial gates were calculated independently for each control group based on the two-dimensional density of events according to forward and side scattering (FSC-A and SSC-A parameters). To reliably detect the population of interest, a two-step subdivision was performed to separate the desired density peaks. First, the data were adjusted based on extreme values ​​(1% and 99%). Then, the maximum values ​​were numerically located on the soft estimate of the two-dimensional density using a moving average (window size 3). A first subdivision was performed on these density maximums, selecting slices corresponding to the highest maximum values, ignoring peaks close to the lower values ​​of FSC-A and SSC-A. A rectangular region in the FSC-A / SSC-A plane was selected using the median of the events contained in the selected slices and 3 times the mean absolute deviation. Next, a finer two-dimensional density estimate was performed on this region, followed by numerical detection again for the maximum values ​​(window size 2), and subdivision based on the maximum values. Finally, a gate is defined as the convex hull that encloses points whose density is greater than a threshold (default is 33% of the maximum value) and which belong to the slice containing the highest maximum value.

[0162] method

[0163] The characterization of spillover diffusion was implemented in R v.3.6.3 using the wrappers flowCore v.1.50.0, flow-Workspace v.3.32.0, and ggplot2 v.3.3.0. Spillover diffusion is defined as the increase in the standard deviation of fluorescence intensity in one parameter as the fluorescence intensity of another parameter increases. For a pair of positive and negative populations in a monochromatic control corresponding to the primary detector, the spillover diffusion coefficient can be calculated by comparing the standard deviation of fluorescence intensity in the primary detector with that in the secondary detector (Nguyen et al., 2013).

[0164] The conventional formula for SSM coefficients This characterizes the increment of the standard deviation in parameter C caused by the spillover from parameter P (Nguyen et al., 2013).

[0165]

[0166] Where σ positive and σ negative These are the standard deviations of C-fluorescence in the positive and negative populations, respectively, and F... positive -F negative This refers to the difference in P-fluorescence intensity between them. While conventional algorithms use the median and robust standard deviation of fluorescence in the positive and negative populations to estimate the quantity, for linear regression, when P-fluorescence (F) equals zero, it is assumed that negative is the theoretical quantity, and the standard deviation is the unknown quantity (σ0). This leads to the following formula relating F to σ, which is applicable to estimating σ0 via linear regression:

[0167]

[0168] The slope β is not equal to the spillover diffusion coefficient. Except for the only case where σ0 equals zero.

[0169] To provide data for regression, events for the single-color-color control of parameter P are partitioned by quantile. For controls with a large number of events, 256 quantiles are used, but as few as 8 quantiles can be used to ensure sufficient events at each quantile to reliably estimate the standard deviation. For each other parameter C, the robust standard deviation of fluorescence (84th percentile minus median) is calculated as an estimate of σ, and the median fluorescence is calculated as an estimate of F. F values ​​may be negative and / or close to zero, therefore they are processed by [the relevant authority] before regression. A square root-like transformation is defined, rather than a simple square root function. The resulting regression provides an estimate of σ0.

[0170] Using the estimate of σ0, calculate the result for each quantile by... The estimated standard deviation σ′ of the defined zeroing is used, and these adjusted standard deviations provide data for the second regression. The regression calculation does not require an intercept term because the adjustment of σ0 forces it to zero.

[0171] result

[0172] Here, quantile partitioning and linear regression are used to estimate the linear relationships observed by Nguyen et al., allowing events to be included above, below, or within the positive and negative populations of the original method. Events for each monochromatic control are quantified in the primary detector, and the standard deviation of autofluorescence levels is estimated for each quantile window in each secondary detector. Next, two linear regressions are used to estimate the standard deviation at zero fluorescence and the spillover diffusion coefficient. Coefficients deemed insignificant using the F-test are replaced with zero, as are any negative coefficients. In fact, most quantiles are subsamples of the regular positive and negative populations, but including additional quantiles improves the accuracy of the estimated spillover diffusion effect because all these events conform to the same linear relationship, assuming all events are within the proportion and linear range of flow cytometry (…). Figure 12 ).like Figure 12 As shown, regression analysis was performed on gated events for a single monochromatic control in the control groups MM1 (left) and HS1 (right) without explicitly defining positive and negative populations. The primary and secondary channels are indicated on the y-axis and x-axis, respectively. Uncompensated data points are shown in blue, and compensated data points are shown in black. Regression of uncompensated data is shown as a dashed line, while regression of compensated data is shown as a solid line.

[0173] As a result, the compensation matrix successfully orthogonalized the spillover diffusion of the fluorescence signal dataset present in the monochromatic control, and the spillover diffusion was accurately estimated. Figure 13 ).like Figure 13 As shown, a comparison of results between this method and the usual overflow expansion matrix algorithm was performed for the control groups MM1 (left) and HS1 (right), revealing small differences between the two calculations. Values ​​are displayed on a logarithmic scale of the absolute value of the difference, separated by positive values ​​(solid lines) and negative values ​​(dashed lines).

[0174] The adjustment step (first regression) eliminates the small quadratic effect caused by σ0 in the initial estimate, thus allowing for a more accurate estimation of the SS coefficients. If this adjustment step is skipped, that is, if β' is used as the spillover diffusion coefficient, the diffusion effect will continue to be underestimated. In this case, a comparison with the conventional SSM algorithm will show a significant negative bias. Figure 14The adjustment steps eliminated this deviation.

[0175] like Figure 14 As shown, the results obtained by this method and the usual SSM algorithm for the control groups MM1 (left) and HS1 (right) are compared, but the first linear regression in this method is ignored, which leads to a systematic downward bias in the coefficients obtained using it. Scale bar and line code are also shown. Figure 13 The same as in.

[0176] Notwithstanding the appended claims, this disclosure is also defined by the following terms:

[0177] 1. A method for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye, the method comprising:

[0178] The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes.

[0179] Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the estimated intensity of light collected from the second fluorescent dye is zeroed out using a first linear regression for each of the divided quantiles; and

[0180] The spillover diffusion coefficient is obtained from the standard deviation of the zeroing using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

[0181] 2. The method according to Clause 1, wherein the first linear regression comprises calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye.

[0182] 3. The method according to Clause 2, wherein estimating the standard deviation of the zeroing further comprises, based on the assumption that the intensity of light collected from the first fluorescent dye is zero, calculating the standard deviation of the intensity of light emitted from the second fluorescent dye by determining the y-intercept of a calculated linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye, and adjusting the standard deviation of the intensity of light collected from the second fluorescent dye based on the determined y-intercept.

[0183] 4. The method according to any one of clauses 1 to 3, wherein the second linear regression comprises calculating a linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye for each divided quantile.

[0184] 5. The method according to Clause 4, wherein calculating the spillover diffusion coefficient includes obtaining the slope of a calculated linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye.

[0185] 6. The method according to any one of the preceding clauses, wherein the spillover diffusion coefficient is calculated according to Formula 1:

[0186]

[0187] in:

[0188] SS is the spillover diffusion coefficient;

[0189] σ is the standard deviation of the light collected from the second fluorescent dye;

[0190] σ0 is an estimate of the standard deviation of the intensity of the light collected from the second fluorescent dye, based on the assumption that the intensity of the light collected from the first fluorescent dye is zero; and

[0191] F is the median intensity of the light collected from the first fluorescent dye.

[0192] 7. The method according to any one of the preceding clauses, wherein the first linear regression is selected from the ordinary least squares model, the weighted least squares model, and the robust linear model.

[0193] 8. The method according to any one of the preceding clauses, wherein the second linear regression is selected from the ordinary least squares model, the weighted least squares model, and the robust linear model.

[0194] 9. The method according to any one of clauses 1 to 6, wherein the first linear regression and the second linear regression are weighted least squares models.

[0195] 10. The method according to any one of clauses 1 to 6, wherein the first linear regression and the second linear regression are robust linear models.

[0196] 11. The method according to Clause 1 further includes combining a first linear regression and a second linear regression in a combined linear regression model, the combined linear regression model being configured to calculate a linear fit between the square of the standard deviation of the intensity of light collected from the second fluorescent dye and the median intensity of light collected from the first fluorescent dye.

[0197] 12. The method according to Clause 11, wherein the combined linear regression model is configured to calculate the standard deviation of the intensity of light collected from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero, and simultaneously obtain the spillover diffusion coefficient.

[0198] 13. The method according to Clause 11 or 12, wherein the combined linear regression model is selected from weighted least squares model and robust linear model.

[0199] 14. The method according to any one of the preceding clauses, wherein the number of quantitation sites is determined based on the magnitude of the fluorescence flow cytometry data.

[0200] 15. The method according to Clause 14, wherein the number of quantiles is 8 to 256.

[0201] 16. The method according to any one of the preceding clauses further includes receiving fluorescence flow cytometry data comprising intensity signals collected from light emitted by the first fluorescent dye and the second fluorescent dye.

[0202] 17. The method according to any one of the preceding clauses, wherein the fluorescence flow cytometry data is collected from light emitted by a variety of different fluorescent dyes.

[0203] 18. The method according to Clause 17, wherein the plurality of different fluorescent dyes are 3 to 30 different fluorescent dyes.

[0204] 19. The method according to Clause 17 or 18 further includes calculating an overflow diffusion coefficient for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes.

[0205] 20. The method according to any one of clauses 17 to 19, wherein the spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes are combined in an spillover diffusion matrix.

[0206] 21. The method according to Clause 20 further includes adjusting flow cytometry data based on the spillover diffusion matrix.

[0207] 22. The method according to any one of the preceding clauses further includes calculating the fluorescence overflow matrix.

[0208] 23. The method according to Clause 22, wherein the fluorescence spillover matrix is ​​determined without the need to identify flow cytometry data populations that are positive for a particular fluorescent dye and flow cytometry data populations that are negative for a particular fluorescent dye.

[0209] 24. The method according to Clause 22 further includes identifying flow cytometry data populations that are positive for a specific fluorescent dye and flow cytometry data populations that are negative for a specific fluorescent dye.

[0210] 25. The method according to any one of clauses 22 to 24 further includes compensating for the overflow of flow cytometry data based on a fluorescence overflow matrix.

[0211] 26. A system comprising:

[0212] Particle analyzer components configured to acquire fluorescence flow cytometry data; and

[0213] A processor, comprising memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0214] The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes.

[0215] Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the estimated intensity of light collected from the second fluorescent dye is zeroed out using a first linear regression for each of the divided quantiles; and

[0216] The spillover diffusion coefficient is obtained from the standard deviation of the zeroing using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

[0217] 27. The system according to Clause 26, wherein the first linear regression comprises calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye.

[0218] 28. The system according to Clause 27, wherein estimating the standard deviation of the zeroing further comprises, based on the assumption that the intensity of light collected from the first fluorescent dye is zero, calculating the standard deviation of the intensity of light emitted from the second fluorescent dye by determining the y-intercept of a calculated linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye, and adjusting the standard deviation of the intensity of light collected from the second fluorescent dye based on the determined y-intercept.

[0219] 29. The system according to any one of clauses 26 to 28, wherein the second linear regression comprises calculating a linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye for each divided quantile.

[0220] 30. The system according to Clause 29, wherein calculating the spillover diffusion coefficient includes obtaining the slope of a calculated linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye.

[0221] 31. The system according to any one of clauses 26 to 30, wherein the spillover diffusion coefficient is calculated according to formula 1:

[0222]

[0223] in:

[0224] SS is the spillover diffusion coefficient;

[0225] σ is the standard deviation of the light collected from the second fluorescent dye;

[0226] σ0 is an estimate of the standard deviation of the intensity of light collected from the second fluorescent dye, based on the assumption that the light intensity collected from the first fluorescent dye is zero; and

[0227] F is the median intensity of the light collected from the first fluorescent dye.

[0228] 32. The system according to any one of Clauses 26 to 31, wherein the first linear regression is selected from ordinary least squares model, weighted least squares model and robust linear model.

[0229] 33. The system according to any one of clauses 26 to 32, wherein the second linear regression is selected from ordinary least squares model, weighted least squares model and robust linear model.

[0230] 34. A system according to any one of clauses 26 to 31, wherein the first linear regression and the second linear regression are weighted least squares models.

[0231] 35. A system according to any one of clauses 26 to 31, wherein the first linear regression and the second linear regression are robust linear models.

[0232] 36. The system according to Clause 26 further includes combining a first linear regression and a second linear regression in a combined linear regression model, the combined linear regression model being configured to calculate a linear fit between the square of the standard deviation of the intensity of light collected from the second fluorescent dye and the median intensity of light collected from the first fluorescent dye.

[0233] 37. The system according to Clause 36, wherein the combined linear regression model is configured to calculate the standard deviation of the intensity of light collected from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero, and simultaneously obtain the spillover diffusion coefficient.

[0234] 38. The system described in Clause 36 or 37, wherein the combined linear regression model is selected from weighted least squares model and robust linear model.

[0235] 39. The system according to any one of clauses 26 to 38, wherein the number of quantitation sites is determined based on the magnitude of the fluorescence flow cytometry data.

[0236] 40. The system described in Clause 39, wherein the number of quantiles is 8 to 256.

[0237] 41. The system according to any one of clauses 26 to 40 further includes receiving fluorescence flow cytometry data comprising intensity signals collected from light emitted by the first fluorescent dye and the second fluorescent dye.

[0238] 42. The system according to any one of clauses 26 to 41, wherein the fluorescence flow cytometry data is collected from light emitted by a variety of different fluorescent dyes.

[0239] 43. The system according to Clause 42, wherein the plurality of different fluorescent dyes are 3 to 30 different fluorescent dyes.

[0240] 44. The system according to clause 42 or 43 further includes calculating the spillover diffusion coefficient for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes.

[0241] 45. The system according to any one of clauses 42 to 44, wherein the spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes are combined in an spillover diffusion matrix.

[0242] 46. ​​The system according to Clause 45 further includes adjusting flow cytometry data based on the spillover diffusion matrix.

[0243] 47. The system according to any one of clauses 26 to 46 further includes calculating a fluorescence overflow matrix.

[0244] 48. The system according to Clause 47, wherein the fluorescence spillover matrix is ​​determined without the need to identify flow cytometry data populations that are positive for a particular fluorescent dye and flow cytometry data populations that are negative for a particular fluorescent dye.

[0245] 49. The system described in Clause 47 further includes the ability to identify flow cytometry data populations that are positive for a specific fluorescent dye and flow cytometry data populations that are negative for a specific fluorescent dye.

[0246] 50. The system according to any one of clauses 47 to 49 further includes compensation for flow cytometry data overflow based on a fluorescence overflow matrix.

[0247] 51. A non-transitory computer-readable storage medium comprising instructions stored thereon for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye by including the following method:

[0248] The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes.

[0249] Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the estimated intensity of light collected from the second fluorescent dye is zeroed out using a first linear regression for each of the divided quantiles; and

[0250] The spillover diffusion coefficient was obtained from the zeroed standard deviation using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

[0251] 52. The non-transitory computer-readable storage medium as described in Clause 51, wherein the first linear regression comprises calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye.

[0252] 53. The non-transitory computer-readable storage medium according to Clause 52, wherein estimating the standard deviation of the zeroing further comprises, based on the assumption that the intensity of light collected from the first fluorescent dye is zero, calculating the standard deviation of the intensity of light emitted from the second fluorescent dye by determining the y-intercept of a calculated linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye, and adjusting the standard deviation of the intensity of light collected from the second fluorescent dye based on the determined y-intercept.

[0253] 54. A non-transitory computer-readable storage medium according to any one of clauses 51 to 53, wherein the second linear regression comprises calculating a linear fit between the standard deviation of the zeroing and the median intensity of light collected from the first fluorescent dye for each divided quantile.

[0254] 55. The non-transitory computer-readable storage medium as described in Clause 54, wherein calculating the spillover diffusion coefficient includes obtaining the slope of a calculated linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye.

[0255] 56. A non-transitory computer-readable storage medium according to any one of clauses 51 to 55, wherein the spillover diffusion coefficient is calculated according to Formula 1:

[0256]

[0257] in:

[0258] SS is the spillover diffusion coefficient;

[0259] σ is the standard deviation of the light collected from the second fluorescent dye;

[0260] σ0 is an estimate of the standard deviation of the intensity of light collected from the second fluorescent dye, based on the assumption that the light intensity collected from the first fluorescent dye is zero; and

[0261] F is the median intensity of the light collected from the first fluorescent dye.

[0262] 57. A non-transitory computer-readable storage medium according to any one of clauses 51 to 56, wherein the first linear regression is selected from ordinary least squares model, weighted least squares model and robust linear model.

[0263] 58. A non-transitory computer-readable storage medium pursuant to any one of clauses 51 to 57, wherein the second linear regression is selected from ordinary least squares models, weighted least squares models, and robust linear models.

[0264] 59. A non-transitory computer-readable storage medium pursuant to any one of clauses 51 to 56, wherein the first linear regression and the second linear regression are weighted least squares models.

[0265] 60. A non-transitory computer-readable storage medium according to any one of clauses 51 to 56, wherein the first linear regression and the second linear regression are robust linear models.

[0266] 61. The non-transitory computer-readable storage medium according to Clause 51 further includes combining a first linear regression and a second linear regression in a combined linear regression model, the combined linear regression model being configured to calculate a linear fit between the square of the standard deviation of the intensity of light collected from the second fluorescent dye and the median intensity of light collected from the first fluorescent dye.

[0267] 62. The non-transitory computer-readable storage medium according to Clause 61, wherein the combined linear regression model is configured to calculate the standard deviation of the intensity of light collected from the second fluorescent dye based on the assumption that the intensity of light collected from the first fluorescent dye is zero, and simultaneously obtain the spillover diffusion coefficient.

[0268] 63. The non-transitory computer-readable storage medium as described in Clause 61 or 62, wherein the combined linear regression model is selected from weighted least squares models and robust linear models.

[0269] 64. A non-transitory computer-readable storage medium according to any one of clauses 51 to 63, wherein the number of quantiles is determined based on the size of the fluorescence flow cytometry data.

[0270] 65. The non-transitory computer-readable storage medium as described in Clause 64, wherein the number of quantiles is 8 to 256.

[0271] 66. The non-transitory computer-readable storage medium according to any one of clauses 51 to 65 further includes receiving fluorescence flow cytometry data comprising intensity signals collected from light emitted by the first fluorescent dye and the second fluorescent dye.

[0272] 67. A non-transitory computer-readable storage medium according to any one of clauses 51 to 66, wherein the fluorescence flow cytometry data is collected from light emitted by a variety of different fluorescent dyes.

[0273] 68. The non-transitory computer-readable storage medium as described in Clause 67, wherein the various fluorescent dyes are 3 to 30 different fluorescent dyes.

[0274] 69. The non-transitory computer-readable storage medium according to clause 67 or 68 further includes calculating the spillover diffusion coefficient for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes.

[0275] 70. A non-transitory computer-readable storage medium according to any one of clauses 67 to 69, wherein spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes are combined in an spillover diffusion matrix.

[0276] 71. The non-transitory computer-readable storage medium as described in Clause 70 further includes adjusting flow cytometry data based on the spillover diffusion matrix.

[0277] 72. The non-transitory computer-readable storage medium according to any one of clauses 51 to 71 further includes a computational fluorescence overflow matrix.

[0278] 73. The non-transitory computer-readable storage medium as described in Clause 72, wherein the fluorescence overflow matrix is ​​determined without the need to identify flow cytometry data populations that are positive for a particular fluorescent dye and flow cytometry data populations that are negative for a particular fluorescent dye.

[0279] 74. The non-transitory computer-readable storage medium as described in Clause 72 further includes the ability to identify flow cytometry data populations that are positive for a particular fluorescent dye and flow cytometry data populations that are negative for a particular fluorescent dye.

[0280] 75. The non-transitory computer-readable storage medium according to any one of clauses 72 to 74 further includes flow cytometry data overflow compensation based on a fluorescence overflow matrix.

[0281] Although the invention has been described in detail by way of illustrations and examples for the purpose of clarity, it will be readily understood by those skilled in the art, based on the teachings of the invention, that some changes and modifications may be made without departing from the spirit or scope of the appended claims.

[0282] Therefore, the above description is merely illustrative of the principles of the invention. It should be understood that those skilled in the art will be able to design various arrangements, although not explicitly described or shown herein, that embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language used herein are primarily intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to further developments in the field, and should be understood as not being limited to these specifically enumerated examples and conditions. Moreover, all statements herein describing the principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to include their structural and functional equivalents. Furthermore, such equivalents are contemplated to include both currently known equivalents and future-developed equivalents, i.e., any elements developed that perform the same function, regardless of their structure. Furthermore, nothing disclosed herein is intended to be exclusively disclosed to the public, whether or not such disclosure is expressly described in the claims.

[0283] Therefore, the scope of the invention is not limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the invention are embodied in the appended claims. In the claims, 35U.SC §112(f) or 35U.SC §112(f) is expressly defined as referring to a claim only if the limitation in the claim begins with the exact phrase “means for…” or the exact phrase “steps for…”; if the exact phrase is not used in the limitation of the claim, then 35U.SC §112(f) or 35U.SC §112(6) is not referred to.

Claims

1. A method for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye, the method comprising: The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes. Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the intensity of light collected from the second fluorescent dye is estimated to be zeroed out for each of the divided quantiles by a first linear regression. as well as The spillover diffusion coefficient was obtained from the zeroed standard deviation using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

2. The method of claim 1, wherein the first linear regression comprises calculating a linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye.

3. The method of claim 2, wherein estimating the standard deviation of the zeroing further comprises, based on the assumption that the intensity of light collected from the first fluorescent dye is zero, calculating the standard deviation of the intensity of light emitted from the second fluorescent dye by determining the y-intercept of a calculated linear fit between the square root of the median intensity of light collected from the first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye, and adjusting the standard deviation of the intensity of light collected from the second fluorescent dye based on the determined y-intercept.

4. The method according to any one of claims 1 to 3, wherein the second linear regression comprises calculating a linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye for each divided quantile.

5. The method of claim 4, wherein calculating the spillover diffusion coefficient comprises obtaining the slope of a calculated linear fit between the standard deviation of the zeroing and the median intensity of the light collected from the first fluorescent dye.

6. The method according to any one of claims 1 to 3, wherein the spillover diffusion coefficient is calculated according to formula 1: ， in: SS is the spillover diffusion coefficient; σ is the standard deviation of the light collected from the second fluorescent dye; σ0 is an estimate of the standard deviation of the intensity of the light collected from the second fluorescent dye, based on the assumption that the intensity of the light collected from the first fluorescent dye is zero; and F It is the median intensity of light collected from the first fluorescent dye.

7. The method according to any one of claims 1 to 3, wherein the first linear regression is selected from ordinary least squares model, weighted least squares model and robust linear model.

8. The method according to any one of claims 1 to 3, wherein the second linear regression is selected from ordinary least squares model, weighted least squares model and robust linear model.

9. The method according to any one of claims 1 to 3, wherein the number of quantitation sites is determined based on the number of data points in the fluorescence flow cytometry data.

10. The method according to any one of claims 1 to 3, further comprising receiving fluorescence flow cytometry data including intensity signals collected from light emitted by the first fluorescent dye and the second fluorescent dye.

11. The method according to any one of claims 1 to 3, wherein the fluorescence flow cytometry data is collected from light emitted by a variety of different fluorescent dyes.

12. The method of claim 11, further comprising calculating an overflow diffusion coefficient for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes.

13. The method of claim 11, wherein the spillover diffusion coefficients calculated for each pair of first and second fluorescent dyes among the plurality of fluorescent dyes are combined in an spillover diffusion matrix.

14. A system for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained against a second fluorescent dye, comprising: A particle analyzer component configured to acquire fluorescence flow cytometry data; as well as A processor, comprising memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes. Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the intensity of light collected from the second fluorescent dye is estimated to be zeroed out for each of the divided quantiles by a first linear regression. as well as The spillover diffusion coefficient is obtained from the standard deviation of the zeroing using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

15. A non-transitory computer-readable storage medium comprising instructions stored thereon for characterizing spillover diffusion originating from a first fluorescent dye in flow cytometry data obtained for a second fluorescent dye by including the following methods: The fluorescence flow cytometry data is divided into multiple subsites based on the intensity of the fluorescence flow cytometry data relative to the first fluorescent dye, and the fluorescence flow cytometry data includes intensity signals collected from light emitted by the first and second fluorescent dyes. Based on the assumption that the intensity of light collected from the first fluorescent dye is zero, the standard deviation of the intensity of light collected from the second fluorescent dye is estimated to be zeroed out for each of the divided quantiles by a first linear regression. as well as The spillover diffusion coefficient is obtained from the standard deviation of the zeroing using a second linear regression to characterize the spillover diffusion originating from the first fluorescent dye in flow cytometry data obtained for the second fluorescent dye.

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