Method and system for characterizing spillover diffusion in flow cytometer data - Patents.com

Abstract

A method is provided for characterizing spillover diffusion from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome. In some embodiments, the method includes partitioning the fluorescent flow cytometer data according to the intensity of the data for the first fluorochrome. In embodiments, the method also includes estimating, for each partitioned quantile, a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome using a first linear regression, based on the assumption that the intensity of light collected from the first fluorochrome is zero, and obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation using a second linear regression. A system and computer-readable medium are also provided for characterizing spillover diffusion from a first fluorochrome in fluorescent flow cytometer data collected for the second fluorochrome.

Description

[Technical field]

[0001] Introduction Flow cytometry is a technique used to characterize and often sort biological materials such as cells in a blood sample or particles of interest in other types of biological or chemical samples. A flow cytometer typically includes a sample container for receiving a fluid sample, such as a blood sample, and a sheath container that contains a sheath fluid. The flow cytometer transports particles (including cells) in the fluid sample as a cell stream to a flow cell, while directing the sheath fluid to the flow cell. The flow stream is illuminated with light to characterize the components of the flow stream. Variations in the materials in the flow stream, such as morphology or the presence of fluorescent labels, can cause variations in the observed light, which allow for characterization and separation. Particles, such as molecules in a fluid suspension, analyte-bound beads, or individual cells, typically pass through a detection region where the particles are exposed to excitation light from two or more lasers, and the light scattering and fluorescence properties of the particles are measured. The particles or their components are typically labeled with fluorescent dyes to facilitate detection. By labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. In some embodiments, the analyzer includes multiple detectors, one detector for each scattering parameter being measured and one or more detectors for each different dye being detected. For example, some embodiments include a spectral configuration in which one or more sensors or detectors are used per dye. The resulting data includes the measured signal for each light scattering detector and the fluorescent emission.

[0002] The flow cytometer may further comprise means for recording the measured data and analyzing the data. For example, data storage and analysis can be performed using a computer connected to the detection electronics. For example, the data can be stored in a table format where each row corresponds to the data of one particle and the columns correspond to each of the measured features. Using a standard file format such as the "FCS" file format to store the data from the particle analyzer makes it easy to analyze the data using a separate program and / or machine. Using current analysis methods, the data is usually displayed in a one-dimensional histogram or two-dimensional (2D) plot for ease of visualization, although other methods can also be used to visualize multi-dimensional data.

[0003] Parameters measured using a flow cytometer typically include light at the excitation wavelength that is scattered by the particles primarily at a narrow angle along the front, called forward scatter (FSC), excitation light that is scattered by the particles in a direction perpendicular to the excitation laser, called side scatter (SSC), and light emitted from fluorescent molecules in one or more detectors that measure signals across a spectral wavelength range, or fluorescent dyes that are detected primarily at that particular detector or array of detectors. Different cell types can be distinguished by their light scattering properties and the fluorescence emissions that result from labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes.

[0004] Both flow cytometers and scanning cytometers are commercially available, for example, from BD Biosciences, San Jose, Calif. Flow cytometry is described, for example, in Landy et al., eds., Clinical Flow Cytometry, Annals of the New York Academy of Sciences Volume 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 ed., Wiley-Liss (2003), all of which are incorporated herein by reference. Fluorescence imaging microscopy is described, for example, in Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd Edition, Plenum Press (1989), incorporated herein by reference.

[0005] After flow cytometer data is received from one or more detectors, a data analysis process is often performed to allow a user to understand the data. However, analysis of flow cytometer data is often complicated by spillover, a phenomenon in which particle-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, light can "spillover" and be detected by off-target detectors. Spillover can be corrected by unmixing, where new intensity values ​​for each fluorochrome are calculated by solving simultaneous equations that relate the intensity values ​​of the fluorochromes and the detector measurements through the observed level of spillover. Unmixing is often referred to as "compensation" when the number of detectors is equal to the number of fluorochromes being unmixed. FIG. 1A is a flow chart illustrating a conventional spillover compensation process. Step 101 identifies populations of fluorescent flow cytometer data that are positive and negative for a particular fluorochrome. Step 102 calculates a fluorescent spillover matrix that includes spillover coefficients that quantify the degree to which spillover adds signal to the fluorescent flow cytometer data. In step 103, the fluorescent flow cytometer data is mathematically adjusted based on the fluorescent spillover matrix such that spillover is compensated for. Unmixing corrects for the intensity contribution from each fluorochrome to each of the other fluorochromes, but cannot correct for the noise contribution, i.e., the error contributed to the fluorescent flow cytometer data by spillover. This noise is referred to as "spillover diffusion." In some instances, the spillover diffusion noise is constructive, resulting in higher signal intensities than would otherwise be observed, while in other instances the noise is destructive, resulting in lower intensities.

[0006] Traditional methods of quantifying spillover spreading involve the calculation of spillover diffusion 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, one limitation of traditional spillover diffusion coefficients is the need to identify populations of flow cytometer data that represent samples that are positive for a particular parameter (i.e., emit light from the fluorochrome of interest) and populations of flow cytometer data that are negative for the same parameter (i.e., do not emit light from the fluorochrome of interest). For example, FIG. 1B illustrates the identification of the positive 100b and negative 100a populations required for the calculation of the spillover diffusion coefficient according to Nguyen et al. (2013). Similarly, FIG. 2 depicts a traditional workflow by which both spillover compensation and characterization of spillover diffusion are performed. After identifying the positive and negative populations of the fluorescence flow cytometer data (step 101), calculating the fluorescence spillover matrix (step 102), and spillover compensation (step 103), a spillover diffusion matrix including spillover diffusion coefficients can be calculated (step 201). However, similar to calculating the fluorescence spillover matrix 102, calculating the spillover diffusion matrix 201 requires identifying the positive and negative populations, which is often an error-prone and time-consuming task for the user. Summary of the Invention

[0007] Thus, the present inventors have discovered that an efficient solution for characterizing spillover diffusion in flow cytometer data analysis is desirable.

[0008] Aspects of the invention include methods for characterizing spillover diffusion from a first fluorochrome in flow cytometer data acquired for a second fluorochrome. In some embodiments, the method includes receiving flow cytometer data collected for each of a first fluorochrome and a second fluorochrome to assess the extent to which light emitted from the first fluorochrome causes error in the fluorescent flow cytometer data collected for the second fluorochrome. After receiving, the method embodiment further includes dividing the fluorescent flow cytometer data into a number of quantiles that correspond to the intensity of the data for the first fluorochrome. The method embodiment further includes estimating a zero-adjusted standard deviation of the intensity of the light collected from the second fluorochrome for each divided quantile based on the assumption that the intensity of the light collected from the first fluorochrome is zero. In an embodiment, estimating the zero-adjusted standard deviation includes calculating a standard deviation (σ) of the intensity of the light emitted from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero, and adjusting the standard deviation (σ) of the observed values ​​of the light emitted from the second fluorochrome based on σ. In an embodiment, estimating the zero-adjusted standard deviation involves calculating a first linear regression including calculating a linear fit between the square root of the median intensity of the light collected from the first fluorochrome and the standard deviation of the intensity of the light collected from the second fluorochrome. In an embodiment, σ is taken from the y-intercept of the linear fit calculated in the first linear regression. An embodiment of the method further includes obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation with a second linear regression. In some embodiments, calculating the second linear regression involves calculating a linear fit between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome for each divided quantile. In certain embodiments, the spillover diffusion coefficient is taken from the slope of a linear fit calculated between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome.In embodiments, the spillover diffusion coefficients thus obtained are calculated for each combination of the first and second fluorochromes (i.e., the spillover from each fluorochrome is characterized for each other fluorochrome) and incorporated into a spillover diffusion matrix. Embodiments of the method may further include adjusting the fluorescent flow cytometer data based on the spillover diffusion matrix.

[0009] Aspects of the invention further relate to a system, the system comprising a particle analyzer component configured to obtain fluorescent flow cytometer data, and a processor, the processor comprising a memory operatively coupled to the processor, the memory having instructions stored therein, which when executed by the processor, cause the processor to characterize spillover diffusion from a first fluorochrome in flow cytometer data obtained for a second fluorochrome. In some embodiments, the processor is configured to receive fluorescent flow cytometer data collected for each of the first and second fluorochromes to assess the extent to which light emitted from the first fluorochrome causes error in the fluorescent flow cytometer data collected for the second fluorochrome. After the data is received, the processor may be configured to divide the fluorescent flow cytometer data into a number of quantiles depending on the intensity of the data for the first fluorochrome. In embodiments, the processor is further configured to estimate a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each divided quantile based on the assumption that the intensity of light collected from the first fluorochrome is zero. In an embodiment, estimating the zero-adjusted standard deviation includes calculating a standard deviation (σ0) of the intensity of the light emitted from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero, and adjusting the standard deviation (σ) of the observed values ​​of the light emitted from the second fluorochrome based on σ0. In an embodiment, estimating the zero-adjusted standard deviation involves calculating a first linear regression including calculating a linear fit between the square root of the median intensity of the light collected from the first fluorochrome and the standard deviation of the intensity of the light collected from the second fluorochrome. In an embodiment, σ0 is taken from the y-intercept of the linear fit calculated in the first linear regression. The processor may be further configured to obtain a spillover diffusion coefficient from the zero-adjusted standard deviation in a second linear regression. In some embodiments, calculating the second linear regression involves calculating a linear fit between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome for each divided quantile.In certain embodiments, the spillover diffusion coefficient is taken from the slope of a linear fit calculated between the zero-adjusted standard deviation and the median intensity of light collected from the first fluorochrome. In embodiments, the spillover diffusion coefficient thus obtained is calculated for each combination of the first and second fluorochromes (i.e., the spillover from each fluorophore is characterized for each other fluorochrome) and incorporated into a spillover diffusion matrix. The processor may be further configured to adjust the fluorescence flow cytometer data based on the spillover diffusion matrix.

[0010] Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for performing the subject methods. In some embodiments, the non-transitory storage medium includes instructions for receiving fluorescent flow cytometer data including intensity signals collected from at least a first and a second fluorochrome, instructions for partitioning the fluorescent flow cytometer data according to the intensity of the fluorescent flow cytometer data for the first fluorochrome, instructions for estimating a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each partitioned quantile based on the assumption that the intensity of light collected from the first fluorochrome is zero by a first linear regression, instructions for deriving a spillover diffusion coefficient from the zero-adjusted standard deviation by a second linear regression to characterize the spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome, instructions for incorporating the calculated spillover diffusion coefficients for each pair of the first and second fluorochromes into a spillover diffusion matrix, and instructions for adjusting the fluorescent flow cytometer data based on the spillover diffusion matrix. [Brief description of the drawings]

[0011] The invention is best understood from the following detailed description when read in conjunction with the accompanying drawing, in which: [Figure 1A] 1 is a flowchart illustrating a conventional process for spillover compensation. [Figure 1B]1 is a graphical representation showing discrimination of positive and negative populations of fluorescent flow cytometer data according to a conventional process for spillover compensation and spillover diffusion matrix calculation. [Diagram 2] FIG. 1 illustrates a conventional process for spillover compensation along with calculation of a spillover diffusion matrix. [Diagram 3] 1 is a graphical representation of the first linear regression. [Figure 4] 1 is a graphical representation of a second linear regression. [Figure 5A] FIG. 2 illustrates a spillover diffusion matrix calculated according to an embodiment of the method. [Figure 5B] FIG. 1 illustrates a spillover diffusion matrix calculated according to a conventional process. [Figure 6] 1 is a flowchart illustrating the process of spillover compensation by the AutoSpill algorithm. [Figure 7] 1 is a flowchart illustrating a process for carrying out an embodiment of the present method in conjunction with the AutoSpill algorithm. [Figure 8] 1 is a flow chart illustrating a process for carrying out an embodiment of the present method in conjunction with a conventional spillover compensation algorithm. [Figure 9] FIG. 1 illustrates a flow cytometer in accordance with certain embodiments. [Figure 10] FIG. 2 is a functional block diagram of an example processor in accordance with certain embodiments. [Figure 11] FIG. 1 is a block diagram of a computing system in accordance with certain embodiments. [Figure 12] FIG. 13 illustrates a regression analysis performed on compensated and uncompensated data according to an embodiment of the present method. [Figure 13] FIG. 13 is a diagram showing the degree of agreement between the spillover diffusion matrix calculated according to the present method and a conventional spillover diffusion matrix. [Figure 14] FIG. 13 shows the effect of performing a first linear regression on fluorescent flow cytometer data. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] A method is provided for characterizing spillover diffusion from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome. In some embodiments, the method includes dividing the fluorescent flow cytometer data according to the intensity of the data for the first fluorochrome. In embodiments, the method also includes estimating a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each divided quantile using a first linear regression based on the assumption that the intensity of light collected from the first fluorochrome is zero, and obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation using a second linear regression. A system and computer readable medium are also provided for characterizing spillover diffusion from a first fluorochrome in fluorescent flow cytometer data collected for a second fluorochrome.

[0013] Before the present invention is described in more detail, it should be understood that the invention is not limited to the particular embodiments described, which may, of course, vary. It should also be understood that the terminology used herein is for the purpose of describing only particular embodiments, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0014] Where a range of values ​​is provided, it is understood that each intermediate value between the upper and lower limit of that range, to the tenth of the unit of the lower limit, and any other stated or intermediate value within this stated range, is encompassed within the invention, unless the context clearly dictates otherwise. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the subject invention, subject to any specifically excluded limits in the stated ranges. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0015] Certain ranges are presented herein with numerical values ​​preceded by the term "about". The term "about" is used herein to provide literal support for the exact number it precedes, as well as the number that is close to or approximately the number it precedes. In determining whether a number is close to or approximately a specifically stated number, the number that is close to or approximately the unstated number may be a number that provides a substantial equivalent to the specifically stated number in the context in which it is presented.

[0016] 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 belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are now described.

[0017] All publications and patents cited herein are incorporated by reference herein to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates which may need to be independently confirmed.

[0018] It should be noted that, as used in this specification and the appended claims, the articles "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude optional elements. As such, this statement is intended to serve as a prior basis for using exclusive terminology, such as "solely," "solely," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0019] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other embodiments without departing from the scope or spirit of the invention. Any described method can be carried out in the order of events described or in any other order which is logically possible.

[0020] Although the systems and methods have been or will be described with functional descriptions for grammatical fluidity, it is expressly understood that unless expressly recited under 35 U.S.C. 35 U.S.C. 112, the claims should not necessarily be construed as limited by construction of "means" or "step" limitations, but should be accorded the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of legal equivalents, and if the claims are expressly recited under 35 U.S.C. 35 U.S.C. 112, they should be accorded the full legal equivalents under 35 U.S.C. 35 U.S.C.

[0021] A method for characterizing spillover diffusion in fluorescence flow cytometer data As discussed above, aspects of the present disclosure include methods for characterizing spillover diffusion from a first fluorochrome in flow cytometer data obtained for a second fluorochrome. In an embodiment, the method includes receiving fluorescent flow cytometer data. By "fluorescent flow cytometer data" is meant information about parameters of a sample (e.g., cells, particles) in a flow cell collected by any number of fluorescent detectors in a particle analyzer. In an embodiment, the fluorescent flow cytometer data includes signals from a plurality of different fluorochromes ranging from 2 to 40 different fluorochromes, for example, 3 to 30 different fluorochromes, for example, 3 to 20 different fluorochromes, and in some cases, 3 to 5 different fluorochromes. In some embodiments, the plurality of different fluorochromes includes 2 or more different fluorochromes, for example, 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, 20 or more, 25 or more, and 30 or more different fluorochromes. Fluorescence flow cytometer data can be obtained by any suitable protocol, including those described below.

[0022] In some embodiments, the method includes generating one or more population clusters based on determined parameters (e.g., fluorescence) of analytes (e.g., cells, particles) in the sample. As used herein, a "population," or "subpopulation" of analytes, such as cells or other particles, generally refers to a group of analytes having characteristics (e.g., optical properties, impedance, or time properties) with respect to one or more measured fluorescence parameters such that the measured parameter data forms a cluster in data space. In this way, populations are recognized as clusters in the data. Conversely, each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte, although clusters corresponding to noise or background are typically also observed. Clusters can be defined with respect to a subset of dimensions, for example, a subset of measured fluorescence parameters (i.e., fluorescent dyes), which correspond to populations that differ in only a subset of the measured parameters or features extracted from the measurement of the sample.

[0023] In some embodiments, the fluorescent flow cytometer data includes an intensity signal originating from a first fluorochrome in the flow cytometer data obtained for a second fluorochrome. In other words, the light emitted from the first fluorochrome is collected by a detector configured to collect the light emitted from the second fluorochrome. As explained in the introduction section, the fluorescent flow cytometer data at the time of collection (i.e., when received by one or more fluorescent detectors) is subject to spillover diffusion. Spillover is a phenomenon in which particle-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, the light can "spillover" and be detected by an off-target detector. Thus, spillover diffusion is a noise present in the fluorescent flow cytometer data caused by spillover. Thus, in some embodiments, the unadjusted flow cytometer data is erroneous due to unintended detection of light of a particular wavelength by one or more detectors. In this case, the light emitted from the first fluorochrome adds a signal to a detector configured to detect light from the second fluorochrome, i.e., the first fluorochrome causes spillover. Therefore, the resulting flow cytometer data collected by the detector is subject to spillover diffusion due to the presence of light emitted from the first fluorochrome.

[0024] After the flow cytometer data is received, an embodiment of the present invention includes splitting the flow cytometer data. As described herein, "splitting" refers to distributing the data into multiple, separate, groups. In some examples, splitting the fluorescent flow cytometer data includes distributing the flow cytometer data into quantiles. "Quantile" is referred to in its conventional sense to describe each of a series of values ​​that divide a frequency distribution into equal groups, each containing the same percentage of the entire population. Thus, in an embodiment, each quantile contains the same percentage of fluorescent flow cytometer data points as each of the other quantiles. In certain embodiments, the fluorescent flow cytometer data is split according to the intensity of the fluorescent flow cytometer data for a first fluorochrome. In other words, the intensity of light emitted from the first fluorochrome associated with an individual fluorescent flow cytometer data point determines the quantile into which that data point is split. In an embodiment, splitting the fluorescent flow cytometer data according to the intensity of the data for the first fluorochrome includes distributing data points associated with similar intensities of light received for the first fluorochrome into the same quantile.

[0025] The fluorescent flow cytometer data may be distributed into any suitable number of different quantiles. In some embodiments, the number of quantiles into which the fluorescent flow cytometer data is distributed may be scaled depending on the size of the fluorescent flow cytometer data, i.e., how many data points are present. In some embodiments, larger flow cytometer data sets are divided into more different quantiles, while smaller flow cytometer data sets are divided into fewer different quantiles. In other embodiments, the fluorescent flow cytometer data is generally divided into a predefined number of quantiles. In such embodiments, the predefined number of quantiles may be changed to suit different sizes of the flow cytometer data sets. Changing the predefined number of quantiles may involve reducing the number of quantiles into which the flow cytometer data is distributed to ensure that each quantile has a sufficient number of data points to estimate the standard deviation of the data points at each quantile. In certain embodiments, the predefined number of quantiles is 256. In some embodiments, when smaller flow cytometer data sets are presented, the number of quantiles may be reduced to as low as 8 quantiles. Thus, in some embodiments, the number of quantiles ranges from 8 to 256.

[0026] After the fluorescent flow cytometer data is partitioned, embodiments of the invention include estimating a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each partitioned quantile. By "zero-adjusted" we mean a standard deviation calculated for flow cytometer data points that fall within a quantile adjusted to reflect the assumption that the intensity of light collected from the first fluorochrome is zero. To estimate the zero-adjusted standard deviation, embodiments of the invention include calculating the median of the intensity of light emitted from the first fluorochrome for each quantile. Embodiments of the invention further include calculating the standard deviation (σ) of the intensity of light emitted from the second fluorochrome. In some embodiments, the standard deviation of the intensity of light emitted from the second fluorochrome is a robust standard deviation, i.e., the standard deviation is resistant to outlier effects. In certain embodiments, the median intensity of light emitted from the first fluorochrome and the standard deviation of the light emitted from the second fluorochrome are then used to estimate the standard deviation (σ0) of the intensity of light collected from the second fluorochrome based on the assumption that the intensity of light collected from the first fluorochrome is zero.

[0027] In an embodiment, estimating σ 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 of the 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 of the 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 with an ordinary least squares regression model. Ordinary least squares regression models are described, for example, in Hutcheson, GD (1999), The multivariate social scientist (pp. 56-113), which is incorporated herein by reference. In other embodiments, the first linear regression is performed with a weighted least squares model. Weighted least squares models are described, 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 yet other embodiments, the first linear regression is performed by 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.

[0028] After the linear fit is calculated, an embodiment of the invention includes calculating σ0 based on the assumption that the intensity of the light collected from the first fluorochrome is zero by determining the y-intercept of the linear fit. In other words, the standard deviation of the intensity of the light emitted from the second fluorochrome is taken as σ0 when the median fluorescence of the light emitted from the first fluorochrome is zero (i.e., when the line crosses the y-axis). For example, FIG. 3 is a graphical representation of a first linear regression. The square root of the median intensity of the light emitted from the first fluorochrome is plotted along the x-axis 302, and the standard deviation of the intensity of the light emitted from the second fluorochrome is plotted along the y-axis 301. For the flow cytometer data points 304, a linear fit 303 is calculated. The value 305 where the linear fit 303 crosses the y-axis 301 is taken as an estimate of the standard deviation σ0 of the intensity of the light collected from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero. After σ is estimated by the first linear regression, embodiments of the present invention further include calculating a zero-adjusted standard deviation based on the estimate of σ. In such embodiments, the zero-adjusted standard deviation is calculated as σ 2 and σ0 2 The square root of the difference between

number

[0029] Aspects of the invention further include obtaining a spillover diffusion coefficient. In certain embodiments, obtaining the spillover diffusion coefficient includes quantifying the extent to which fluorescent flow cytometer data collected for the second fluorochrome by a detector is affected by the simultaneous collection of light from the first fluorochrome by the same detector. In some examples, the fluorescent flow cytometer data affected by spillover diffusion is affected by a higher signal intensity than would otherwise be observed (i.e., the spillover diffusion noise is constructive). In other examples, the fluorescent flow cytometer data affected by spillover diffusion is affected by a lower signal intensity than would otherwise be observed (i.e., the spillover diffusion noise is destructive). In embodiments, obtaining the spillover diffusion coefficient involves performing a second linear regression. In such embodiments, performing the second linear regression includes calculating a linear fit between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome for each quantile divided. In embodiments, the zero-adjusted standard deviation is plotted along the y-axis and the median intensity of the light collected from the first fluorochrome is plotted along the x-axis. The slope of the linear fit calculated between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome then gives the spillover diffusion coefficient. In some embodiments, the second linear regression is performed with an ordinary least squares regression model. In other embodiments, the second linear regression is performed with a weighted least squares model. In still other embodiments, the second linear regression is performed with a robust linear model. In certain embodiments, both the first and second linear regressions are performed with a weighted least squares model. In other embodiments, both the first and second linear regressions are performed with a robust linear model.

[0030] For example, Figure 4 is a graphical representation of a second linear regression. The zero-adjusted standard deviation is plotted along the y-axis 401, and the median intensity of light collected from the first fluorochrome is plotted along the x-axis 402. A linear fit 403 is calculated based on the fluorescent flow cytometer data 404. The spillover diffusion coefficient is obtained from the slope 405 of the linear fit 403.

[0031] As a result, in embodiments, the spillover diffusion coefficients described herein may be calculated according to Equation 1.

[0032]

number

[0033] As shown in Equation 1, SS is the spillover diffusion coefficient, σ is the standard deviation of the light collected from the second fluorochrome, σ is an estimate of the standard deviation of the intensity of the light collected from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero, and F is the median intensity of the light collected from the first fluorochrome. Thus, the spillover diffusion coefficient measures the extent to which the fluorescence flow cytometer data collected by a given fluorescence detector is affected by the presence of light associated with a particular fluorochrome. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed to the fluorescence flow cytometer data by the light emitted from the associated fluorochrome collected by a given detector. In an embodiment, for a given pair of first and second fluorochromes, the higher the spillover diffusion coefficient, the greater the spillover diffusion. In an embodiment, the spillover diffusion coefficient is obtained without distinguishing between populations of fluorescence flow cytometer data that are positive (i.e., exhibiting the associated parameter) and negative (i.e., not exhibiting the associated parameter) for a particular fluorochrome.

[0034] In some embodiments, the first and second linear regressions are combined into a combined linear regression. In such embodiments, the combined linear regression is configured to calculate the standard deviation (σ0) 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 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 the light collected from the second fluorescent dye and the median intensity of the light collected from the first fluorescent dye. In some embodiments, the combined linear regression is performed by a weighted least squares model. In other embodiments, the combined linear regression is performed by a robust linear model.

[0035] An embodiment of the invention also includes calculating a spillover diffusion coefficient for each possible combination of a first and a second fluorochrome, such that it can be determined how the fluorescent flow cytometer data collected at each detector is affected by the presence of light associated with each fluorochrome. Stated differently, an aspect of the invention includes calculating a plurality of spillover diffusion coefficients (e.g., as described above) such that a spillover diffusion coefficient is provided for each possible pair of a first and a second fluorochrome. In an embodiment, the calculated spillover diffusion coefficients for each pair of a first and a second fluorochrome are combined into a spillover diffusion matrix. In certain embodiments, the spillover diffusion matrix demonstrates how detection of a particular fluorochrome by a corresponding detector is affected by spillover from other fluorochromes. In an embodiment, the spillover diffusion matrix including the spillover diffusion coefficients described herein characterizes the spillover diffusion effect from each fluorochrome in the fluorescent flow cytometer data collected for each other fluorochrome, without distinguishing between populations of fluorescent flow cytometer data that are positive or negative for the aforementioned fluorochrome. For example, FIG. 5A illustrates one embodiment of a spillover diffusion matrix providing spillover diffusion coefficients (e.g., obtained as described above) for 23 different fluorochromes. Each column of the matrix corresponds to a detector configured to detect one of the 23 different fluorochromes, and each row of the matrix corresponds to a parameter of the detected flow cytometer data. At the intersection of a column and a row is located a calculated spillover diffusion coefficient for a pair of a first and a second fluorochrome that indicates the extent to which the fluorochrome under discussion (i.e., the first fluorochrome) contributes error to the associated detector (i.e., detection of light emitted from the second fluorochrome). The total extent to which a fluorochrome causes spillover diffusion can be estimated by summing all the values ​​in that row, and the total extent to which a detector is affected by spillover diffusion can be calculated by summing all the values ​​in that 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 particular subset of the fluorescent flow cytometer data).

[0036] As mentioned above, in embodiments, the spillover diffusion matrices described herein are populated with spillover diffusion coefficients calculated without distinguishing between positive and negative populations of the fluorescent flow cytometer data for each of the associated fluorochromes. However, in some embodiments, the spillover diffusion matrices described herein are populated with spillover diffusion coefficients that approximate the spillover diffusion coefficients calculated with distinguishing between positive and negative populations of the fluorescent flow cytometer data for each of the associated fluorochromes, i.e., they approximate the spillover diffusion coefficients calculated as taught by Nguyen et al. (2013). For example, FIG. 5B shows a conventional spillover diffusion matrix (i.e., requiring distinguishing between positive and negative populations of the fluorescent flow cytometer data for each of the associated fluorochromes) calculated based on the same data set used to calculate the spillover diffusion matrix shown in FIG. 5A. When comparing the spillover diffusion matrix calculated as described herein (shown in FIG. 5A) with a conventionally calculated spillover diffusion matrix (shown in FIG. 5B), a high level of agreement between the two matrices is observed with respect to the magnitude of spillover diffusion for each fluorochrome-fluorochrome pair.

[0037] Aspects of the present disclosure further include adjusting the fluorescent flow cytometer data to account for spillover diffusion. By "adjusting" it is meant modifying the data to more accurately quantify the presence of fluorochromes in the illuminated sample (e.g., cells, particles) in the flow cell. In some embodiments, the fluorescent flow cytometer data is adjusted so that it no longer includes errors due to spillover diffusion. In embodiments, adjusting the fluorescent flow cytometer data includes generating a spillover diffusion adjusted population. In certain embodiments, generating a spillover diffusion adjusted population includes subtracting a magnitude of spillover diffusion from a relevant population(s) of the flow cytometer data, i.e., counteracting the effects of signals affected by spillover diffusion. In certain embodiments, the magnitude of spillover diffusion is determined from a spillover diffusion matrix. In some embodiments, adjusting the flow cytometer data includes subtracting a total diffusion effect from a relevant portion of the flow cytometer data.

[0038] Some embodiments of the present invention further include compensating the fluorescent flow cytometer data for spillover. As described in the introduction, spillover is a phenomenon in which particle-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, compensation mathematically removes this overlap from the fluorescent flow cytometer data. Any suitable method can be used to compensate the fluorescent flow cytometer data for spillover. In some embodiments, unmixing may be performed. Unmixing uses single-stained reference controls to separate fluorescent populations and identify the spectra associated with each fluorochrome. In other embodiments, spillover compensation is performed by the AutoSpill algorithm. AutoSpill is an algorithm developed by FlowJo LLC (a subsidiary of Becton Dickinson) that calculates spillover and generates a fluorescent spillover matrix composed of spillover coefficients that mathematically characterize the degree to which light emitted from one fluorochrome adds signal to flow cytometer data collected for other fluorochromes. AutoSpill is described in Roca et al. (2020), AutoSpill: a method for calculating spillover coefficients in high-parameter flow cytometry. bioRxiv, incorporated herein by reference. AutoSpill combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. AutoSpill determines spillover coefficients from the slope of a linear regression with the fluorescence of the primary channel (the channel assigned to the single-color control dye) as the dependent variable and the fluorescence of the secondary channel (i.e., the light collected by the other detector) as the independent variable. No spillover corresponds to a slope of this regression of zero. Furthermore, AutoSpill iteratively refines the spillover matrix and recalculates the compensation, thereby reducing the error of the spillover matrix and the error of the compensation to negligible magnitude.For example, Figure 6 shows a sample workflow illustrating the AutoSpill algorithm 600. In step 601, a fluorescence spillover matrix is ​​calculated by using linear regression (e.g., as described above) to obtain spillover coefficients. In step 602, the fluorescence flow cytometer data is compensated based on the fluorescence spillover matrix calculated in step 601.

[0039] In some embodiments of the present invention, a spillover diffusion matrix composed of spillover diffusion coefficients (obtained as described above) is calculated together with compensating the fluorescence flow cytometer data for spillover. The calculation may or may not be performed with the identification of positive and negative populations of the fluorescence flow cytometer data for each of the relevant fluorochromes. In some embodiments, the calculation of the spillover diffusion matrix together with the spillover compensation is performed without the identification of positive and negative populations. In such an embodiment, the calculation of the spillover matrix and the spillover compensation are performed by AutoSpill. For example, FIG. 7 shows a workflow for AutoSpill. Step 101 (described in FIG. 1A, FIG. 1B and FIG. 2 above) is not required since AutoSpill performs a linear regression that eliminates the need for identification of positive and negative populations. Therefore, AutoSpill performs the calculation of the fluorescence spillover matrix (step 601) and the compensation of the fluorescence flow cytometer data based on the calculated fluorescence spillover matrix (step 602). After the sample has been compensated, the spillover diffusion matrix is ​​calculated using the spillover diffusion coefficients described herein (step 701). In some embodiments, the fluorescence flow cytometer data may be additionally adjusted, if necessary, to account for errors present in the data caused by spillover.

[0040] In other embodiments, calculating the spillover diffusion matrix together with spillover compensation is performed by identifying positive and negative populations. In such embodiments, spillover compensation may be performed by an algorithm other than AutoSpill. In embodiments, compensation is performed via unmixing. For example, FIG. 8 shows a workflow in which positive and negative populations of flow cytometer data are identified (step 101). After the populations are identified, calculation of the fluorescence spillover matrix (step 102) and compensation of the fluorescence flow cytometer data based on the fluorescence spillover matrix (step 103) may be performed in a conventional manner. Following compensation, a spillover diffusion matrix is ​​calculated (step 701) as described herein. In some embodiments, the fluorescence flow cytometer data may be additionally adjusted, if necessary, to account for errors present in the data caused by spillover. Step 701 does not require identifying positive and negative populations of the flow cytometer data, although such identification is performed for spillover compensation.

[0041] As summarized above, the fluorescence flow cytometer data used in the methods of the present invention can be obtained using any suitable protocol. In some embodiments, a sample having particles is illuminated with a light source, light from the sample is detected, and a population of related particles is generated based at least in part on measurements of the detected light. In some examples, the sample is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, a plant, a fungus, or a subset of animal tissues, cells, or components that may be found in, in certain examples, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. The biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some examples, the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0042] In certain embodiments, the source of the sample is "mammal" or "mammalian," which terms are used broadly to refer to organisms within the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., neonate, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. It is understood that while the invention may be applied to samples from human subjects, it may also be practiced on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0043] In practicing the subject methods, a sample having particles (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source, emitting light having a wide range of wavelengths, e.g., ranging from 50 nm or more, 100 nm or more, 150 nm or more, 200 nm or more, 250 nm or more, 300 nm or more, 350 nm or more, 400 nm or more, and 500 nm or more. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes illuminating with a broadband light source, the broadband light source protocol of interest may include, but is not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, an ultra-bright light emitting diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0044] In other embodiments, the method includes illuminating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, such as a light source that emits light in a narrow wavelength range, such as a range of 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, including a light source that emits light of a specific wavelength (i.e., monochromatic light). When the method includes illuminating with a narrowband light source, the subject narrowband light source protocol may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0045] Aspects of the invention include collecting the fluorescence using a fluorescence detector. The fluorescence detector may, in some examples, be configured to detect fluorescent emissions from fluorescent molecules associated with the particles in the flow cell, such as labeled specific binding members (such as labeled antibodies that specifically bind to a marker of interest). In certain embodiments, the method includes detecting fluorescence from the sample using one or more fluorescence detectors, where the number of fluorescence detectors may be, for example, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, or 25 or more. In embodiments, each fluorescence detector is configured to generate a fluorescence data signal. The fluorescence from the sample may be detected independently by each fluorescence detector over one or more of a wavelength range of 200 nm to 1200 nm. In some examples, the method includes detecting the fluorescence from the sample over a wavelength range, where the wavelength range may be, for example, 200 nm to 1200 nm, for example, 300 nm to 1100 nm, for example, 400 nm to 1000 nm, for example, 500 nm to 900 nm, or may be 600 nm to 800 nm. In other examples, the method includes detecting fluorescence at each fluorescence detector at one or more specific wavelengths. For example, the fluorescence may be detected at one or more of 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 optical detection system. In certain embodiments, the method includes detecting wavelengths of light corresponding to the fluorescence peak wavelengths of specific fluorescent dyes present in the sample. In embodiments, the fluorescence flow cytometer data is received from one or more fluorescence detectors (e.g., one or more detection channels), and the number of the fluorescence detectors (detection channels) may be, for example, 2 or more, for example, 3 or more, for example, 4 or more, for example, 5 or more, for example, 6 or more, and may be 8 or more.

[0046] System for characterizing spillover diffusion in fluorescence flow cytometer data Aspects of the present disclosure include a system for classifying fluorescent flow cytometer data. In embodiments, the fluorescent flow cytometer data is clustered, adjusted for spillover diffusion, and partitioned such that distinct populations are classified differently. In some embodiments, the system includes a particle analyzer configured to generate the fluorescent flow cytometer data and a processor configured to analyze the fluorescent flow cytometer data.

[0047] In some embodiments, a subject particle analyzer includes a flow cell and a laser configured to illuminate particles within the flow cell. In embodiments, the laser may be any suitable laser, such as a continuous wave laser. For example, the laser may 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 may be a helium neon (HeNe) laser. In some examples, 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 CO2 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 combinations thereof. In other examples, a subject flow cytometer includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the subject lasers 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 yet another example, the subject flow cytometers include solid state lasers, such as, for example, ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, or cerium doped lasers, and combinations thereof.

[0048] Aspects of the invention also include a forward scatter detector configured to detect forward scattered light. The number of forward scatter detectors in a subject flow cytometer may vary as needed. For example, a subject particle analyzer may include one or more forward scatter detectors, and the number of forward scatter detectors may be, for example, two or more, such as three or more, such as four or more, or may be five or more. In certain embodiments, the flow cytometer includes one forward scatter detector. In other embodiments, the flow cytometer includes two forward scatter detectors.

[0049] Any suitable detector for detecting collected light may be used in the forward scatter 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), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a photomultiplier tube, e.g., with an active detection surface area of ​​each region of 0.01 cm2 or less. 2 ~10cm 2 and the surface area ranges from 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 It may be 1 cm 2 ~5cm 2 may be also possible.

[0050] Where a subject particle analyzer includes multiple forward scatter detectors, each detector may be the same, or the set of detectors may be a combination of different types of detectors. For example, where a subject particle analyzer includes two forward scatter detectors, in some embodiments, the first forward scatter detector is a CCD type device and the second forward scatter detector (or image sensor) is a CMOS type device. In other embodiments, both the first and second forward scatter detectors are CCD type devices. In yet other embodiments, both the first and second forward scatter detectors are CMOS type devices. In yet other embodiments, the first forward scatter detector is a CCD type device and the second forward scatter detector is a photomultiplier tube (PMT). In yet other embodiments, the first forward scatter detector is a CMOS type device and the second forward scatter detector is a photomultiplier tube. In yet other embodiments, both the first and second forward scatter detectors are photomultiplier tubes.

[0051] In embodiments, the forward scatter detector is configured to measure light continuously or at discrete intervals. In some examples, the target detector is configured to make continuous measurements of the collected light. In other examples, the target detector is configured to measure at discrete intervals and may measure light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, every 1000 milliseconds, or some other interval.

[0052] Embodiments of the invention also include a light dispersion / separator module disposed between the flow cell and the forward scatter detector. Light dispersion devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices. In some embodiments, a bandpass filter is disposed between the flow cell and the forward scatter detector. In other embodiments, more than one bandpass filter is disposed between the flow cell and the forward scatter detector, and the number of bandpass filters may be, for example, 2 or more, 3 or more, 4 or more, or 5 or more. In embodiments, the bandpass filter has a minimum bandwidth in the range of 2 nm to 100 nm, and the minimum bandwidth may be in the range of, 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, or 20 nm to 50 nm. Light of a certain wavelength reaches the forward scatter detector, and light of other wavelengths is reflected.

[0053] Certain embodiments of the invention include a side scatter detector configured to detect side scattered wavelengths of light (e.g., light refracted and reflected from the surface and internal structures of a particle. In other embodiments, the flow cytometer includes multiple side scatter detectors, such as two or more, such as three or more, such as four or more, and even five or more.

[0054] Any suitable detector for detecting collected light may be used in the side scatter detector described herein. The subject detector may include, but is not limited to, an optical sensor or detector, such as, for example, an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge-coupled device (CCD), an intensified charge-coupled device (ICCD), a light-emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photocell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor, or a photodiode, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a photomultiplier tube, for example, with an active detection surface area of ​​each region of 0.01 cm. 2 ~10cm 2 and the surface area ranges from 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 It may be 1 cm 2 ~5cm 2 may be also possible.

[0055] Where a subject particle analyzer includes multiple side scatter detectors, each side scatter detector may be the same, or the set of side scatter detectors may be a combination of different types of detectors. For example, where a subject particle analyzer includes two side scatter detectors, in some embodiments, the first side scatter detector is a CCD type device and the second side scatter detector (or image sensor) is a CMOS type device. In other embodiments, both the first and second side scatter detectors are CCD type devices. In yet other embodiments, both the first and second side scatter detectors are CMOS type devices. In yet other embodiments, the first side scatter detector is a CCD type device and the second side scatter detector is a photomultiplier tube (PMT). In yet other embodiments, the first side scatter detector is a CMOS type device and the second side scatter detector is a photomultiplier tube. In yet other embodiments, both the first and second side scatter detectors are photomultiplier tubes.

[0056] Embodiments of the invention also include a light dispersing / separating module disposed between the flow cell and the side scatter detector. Light dispersing devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices.

[0057] In embodiments, the subject particle analyzer also includes a fluorescence detector configured to detect one or more fluorescence wavelengths, hi other embodiments, the particle analyzer includes multiple fluorescence detectors, such as 2 or more, such as 3 or more, such as 4 or more, 5 or more, 10 or more, 15 or more, or even 20 or more.

[0058] Any suitable detector for detecting collected light may 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), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a photomultiplier tube, e.g., with an active detection surface area of ​​each region of 0.01 cm2 or less. 2 ~10cm 2 and the surface area ranges from 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 It may be 1 cm 2 ~5cm 2 may be also possible.

[0059] Where a subject particle analyzer includes multiple fluorescence detectors, each fluorescence detector may be the same, or the collection of fluorescence detectors may be a combination of different types of detectors. For example, where a subject particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector (or image sensor) is a CMOS type device. In other embodiments, both the first and second fluorescence detectors are CCD type devices. In yet other embodiments, both the first and second fluorescence detectors are CMOS type devices. In yet other embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector is a photomultiplier tube (PMT). In yet other embodiments, the first fluorescence detector is a CMOS type device and the second fluorescence detector is a photomultiplier tube. In yet other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0060] Embodiments of the invention also include a light dispersing / separating module disposed between the flow cell and the fluorescence detector. Light dispersing devices of interest include, but are not limited to, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices.

[0061] In embodiments of the present disclosure, the subject fluorescence detectors are configured to measure collected light at one or more wavelengths, such as two or more wavelengths, such as five or more different wavelengths, such as ten or more different wavelengths, such as 25 or more different wavelengths, such as 50 or more different wavelengths, such as 100 or more different wavelengths, such as 200 or more different wavelengths, such as 300 or more different wavelengths, including 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 described herein are configured to measure the same or overlapping wavelengths of collected light.

[0062] In some embodiments, the subject's fluorescence detector is configured to measure light collected over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the subject's detector is configured to collect a spectrum of light over a wavelength range. For example, the particle analyzer may include one or more detectors configured to collect a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the subject's detector is configured to measure light emitted by the sample in the flow stream at one or more specific wavelengths. For example, the particle analyzer may include one or more detectors configured to measure light at one or more of 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 certain embodiments, one or more detectors may be configured to be paired with a particular fluorophore, such as one that is used with a sample in a fluorescence assay.

[0063] Suitable flow cytometry systems include those described in 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 ed., Wiley-Liss (1995); Virgo et al., (2012) Ann Clin Biochem. Jan; 49(pt 1): 17-28; Linden et al., Semin Thromb Hemost. 2004 Oct; 30(5): 502-11; Alison et al., J Pathol, 2010 Dec; 222(4): 335-344; and Herbig et al., (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular examples, flow cytometry systems of interest include BD Biosciences FACSCanto™ flow cytometers, BD Biosciences FACSCanto™ II flow cytometers, BD Accuri™ flow cytometers, BD Accuri™ C6 Plus flow cytometers, BD Biosciences FACSCelesta™ flow cytometers, BD Biosciences FACSLyric™ flow cytometers, BD Biosciences FACSVerse™ flow cytometers, BD Biosciences FACSymphony™ flow cytometers, BD Biosciences LSRFortessa™ flow cytometers, BD Biosciences LSRFortessa™ X-20 flow cytometers, BD Biosciences FACSPresto™ flow cytometers, BD Biosciences FACSVia™ flow cytometers, as well as BD Biosciences FACSCalibur™ cell sorters, BD Biosciences FACSCount™ cell sorters, BD Biosciences FACSLyric™ cell sorters, BD Examples of cell sorters include BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter.

[0064] In some embodiments, the subject system is configured as disclosed in U.S. Pat. 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, 10,302,5 No. 45, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. No. 9,726,527, No. 9,453,789, No. 9,200,334, No. 9,097,640, No. 9,095,494, No. 9,092, No. 034, No. 8,975,595, No. 8,753,573; No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,2 No. 01,875, No. 7,129,505, No. 6,821,740, No. 6,813,017, No. 6,809,804, No. 6,372,506 and the flow cytometry systems described in US Pat. Nos. 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 entireties.

[0065] In particular examples, the flow cytometry systems of the invention are configured to image particles in a flow stream by fluorescence imaging using radio frequency tagged emission (FIRE), and are described in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013), as well as in U.S. Pat. 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; 10,408,758; and US Pat. Nos. 10,451,538, 10,620,111, and U.S. Patent Application Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0066] In certain embodiments, the subject system further includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to characterize spillover diffusion from a first fluorochrome in flow cytometer data obtained for a second fluorochrome. In embodiments, the processor is configured to receive the fluorescent flow cytometer data. In embodiments, the fluorescent flow cytometer data includes signals from a plurality of different fluorochromes, the number of different fluorochromes may range, for example, from 2 to 20, and may be from 3 to 5. In some embodiments, the plurality of different fluorochromes includes 2 or more different fluorochromes, 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 fluorochromes. The fluorescent flow cytometer data may be obtained with any suitable protocol.

[0067] In some embodiments, the fluorescent flow cytometer data includes an intensity signal originating from a first fluorochrome in the flow cytometer data obtained for a second fluorochrome. In other words, the light emitted from the first fluorochrome is collected by a detector configured to collect the light emitted from the second fluorochrome. As explained in the introduction section, the fluorescent flow cytometer data at the time of collection (i.e., when received by one or more fluorescent detectors) is subject to spillover diffusion. Spillover is a phenomenon in which particle-modulated light indicative of a particular fluorochrome is received by one or more detectors that are not configured to measure that parameter. Thus, the light can "spillover" and be detected by off-target detectors. Thus, spillover diffusion is noise present in the fluorescent flow cytometer data caused by spillover. Thus, in some embodiments, the unadjusted flow cytometer data is erroneous due to unintended detection of light of a particular wavelength by one or more detectors. In this case, the light emitted from the first fluorochrome adds a signal to the detector configured to detect the light from the second fluorochrome, i.e., the first fluorochrome causes spillover. Therefore, the resulting flow cytometer data collected by the detector is subject to spillover diffusion due to the presence of light emitted from the first fluorochrome.

[0068] After the fluorescent flow cytometer data is received, the processor may be configured to divide the fluorescent flow cytometer data. In some embodiments, dividing the fluorescent flow cytometer data includes distributing the flow cytometer data into quantiles. In embodiments, each quantile includes the same proportion of fluorescent flow cytometer data points as each other quantile. In certain embodiments, the fluorescent flow cytometer data is divided according to an intensity of the fluorescent flow cytometer data for a first fluorochrome. In other words, the intensity of light emitted from the first fluorochrome associated with an individual fluorescent flow cytometer data point determines the quantile into which that data point is divided. In embodiments, dividing the fluorescent flow cytometer data according to the intensity of the data for the first fluorochrome includes distributing data points associated with a similar intensity of light received for the first fluorochrome into the same quantile.

[0069] In embodiments, the processor is configured to distribute the fluorescent flow cytometer data into any suitable number of different quantiles. In some embodiments, the number of quantiles into which the fluorescent flow cytometer data is distributed may be scaled depending on the size of the fluorescent flow cytometer data, i.e., how many data points are present. In some embodiments, larger flow cytometer data sets are divided into more different quantiles, while smaller flow cytometer data sets are divided into fewer different quantiles. In other embodiments, the fluorescent flow cytometer data is generally divided into a predefined number of quantiles. In such embodiments, the predefined number of quantiles may be changed to suit different sizes of the flow cytometer data sets. Changing the predefined number of quantiles may involve reducing the number of quantiles into which the flow cytometer data is distributed to ensure that each quantile has a sufficient number of data points to estimate the standard deviation of the data points at each quantile. In certain embodiments, the predefined number of quantiles is 256. In some embodiments, when smaller flow cytometer data sets are presented, the number of quantiles may be reduced to as low as 8 quantiles. Thus, in some embodiments, the number of quantiles ranges from 8 to 256.

[0070] After the fluorescent flow cytometer data is partitioned, the processor may be configured to estimate a zero-adjusted standard deviation of the intensity of the light collected from the second fluorochrome for each partitioned quantile. To estimate the zero-adjusted standard deviation, an embodiment of the invention includes calculating a median of the intensity of the light emitted from the first fluorochrome for each quantile. An embodiment of the invention further includes calculating a standard deviation (σ) of the intensity of the light emitted from the second fluorochrome. In some embodiments, the standard deviation of the intensity of the light emitted from the second fluorochrome is a robust standard deviation, i.e., the standard deviation is resistant to outlier effects. In certain embodiments, the median of the intensity of the light emitted from the first fluorochrome and the standard deviation of the light emitted from the second fluorochrome are then used to estimate a standard deviation (σ0) of the intensity of the light collected from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero.

[0071] In embodiments, estimating σ includes performing a first linear regression. In embodiments, performing the first linear regression includes calculating a linear fit between the square root of the median of the 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 embodiments, the square root of the median of the 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 with an ordinary least squares regression model. In other embodiments, the first linear regression is performed with a weighted least squares model. In still other embodiments, the first linear regression is performed with a robust linear model.

[0072] After the linear fit is calculated, the processor may be configured to calculate σ based on the assumption that the intensity of the light collected from the first fluorochrome is zero by determining the y-intercept of the linear fit. In other words, the standard deviation of the intensity of the light emitted from the second fluorochrome is taken as σ when the median fluorescence of the light emitted from the first fluorochrome is zero (i.e., when the line crosses the y-axis). After σ is estimated by the first linear regression, embodiments of the invention further include calculating a zero-adjusted standard deviation based on the estimate of σ. In such embodiments, the zero-adjusted standard deviation is calculated as σ. 2 and σ0 2 The square root of the difference between

number

[0073] The processor may be further configured to obtain a spillover diffusion coefficient. In certain embodiments, obtaining the spillover diffusion coefficient includes quantifying the extent to which the fluorescent flow cytometer data collected for the second fluorochrome by the detector is affected by the simultaneous collection of light from the first fluorochrome by the same detector. In some examples, the fluorescent flow cytometer data affected by spillover diffusion is affected by a higher signal intensity than would otherwise be observed (i.e., the spillover diffusion noise is constructive). In other examples, the fluorescent flow cytometer data affected by spillover diffusion is affected by a lower signal intensity than would otherwise be observed (i.e., the spillover diffusion noise is destructive). In embodiments, obtaining the spillover diffusion coefficient involves performing a second linear regression. In such embodiments, performing the second linear regression includes calculating a linear fit between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome for each quantile divided. In embodiments, the zero-adjusted standard deviation is plotted along the Y-axis (i.e., the dependent variable) and the median intensity of the light collected from the first fluorochrome is plotted along the X-axis (i.e., the independent variable). The spillover diffusion coefficient is then obtained from the slope of a linear fit calculated between the zero-adjusted standard deviation and the median intensity of the light collected from the first fluorochrome. In some embodiments, the second linear regression is performed with an ordinary least squares regression model. In other embodiments, the second linear regression is performed with a weighted least squares model. In still other embodiments, the second linear regression is performed with a robust linear model. In certain embodiments, both the first and second linear regressions are performed with a weighted least squares model. In other embodiments, both the first and second linear regressions are performed with a robust linear model.

[0074] As a result, in embodiments, the spillover diffusion coefficients described herein may be calculated according to Equation 1.

[0075]

number

[0076] As shown in Equation 1, SS is the spillover diffusion coefficient, σ is the standard deviation of the light collected from the second fluorochrome, σ is an estimate of the standard deviation of the intensity of the light collected from the second fluorochrome based on the assumption that the intensity of the light collected from the first fluorochrome is zero, and F is the median intensity of the light collected from the first fluorochrome. Thus, the spillover diffusion coefficient measures the extent to which the fluorescence flow cytometer data collected by a given fluorescence detector is affected by the presence of light associated with a particular fluorochrome. In other words, the spillover diffusion coefficient estimates the error (i.e., noise) contributed to the fluorescence flow cytometer data by the light emitted from the associated fluorochrome collected by a given detector. In an embodiment, for a given pair of first and second fluorochromes, the higher the spillover diffusion coefficient, the greater the spillover diffusion. In an embodiment, the spillover diffusion coefficient is obtained without distinguishing between populations of fluorescence flow cytometer data that are positive (i.e., exhibiting the associated parameter) and negative (i.e., not exhibiting the associated parameter) for a particular fluorochrome.

[0077] In some embodiments, the first and second linear regressions are combined into a combined linear regression. In such embodiments, the combined linear regression is configured to calculate a standard deviation (σ0) 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 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 the light collected from the second fluorescent dye and the median intensity of the light collected from the first fluorescent dye. In some embodiments, the combined linear regression is performed by a weighted least squares model. In other embodiments, the combined linear regression is performed by a robust linear model.

[0078] The processor may also be configured to calculate a spillover diffusion coefficient for each possible combination of the first and second fluorochromes such that it can be determined how the fluorescent flow cytometer data collected at each detector is affected by the presence of light associated with each fluorochrome. Stated differently, aspects of the invention include calculating a plurality of spillover diffusion coefficients (e.g., as described above) such that a spillover diffusion coefficient is provided for each possible pair of first and second fluorochromes. In embodiments, the calculated spillover diffusion coefficients for each pair of first and second fluorochromes are combined into a spillover diffusion matrix. In certain embodiments, the spillover diffusion matrix demonstrates how detection of a particular fluorochrome by a corresponding detector is affected by spillover from other fluorochromes. In embodiments, the spillover diffusion matrix including the spillover diffusion coefficients described herein characterizes the spillover diffusion effects from each fluorochrome in the fluorescent flow cytometer data collected for each other fluorochrome without distinguishing between populations of fluorescent flow cytometer data that are positive or negative for the aforementioned fluorochrome. Each column of the matrix corresponds to a detector configured to detect one of the different fluorochromes, and each row of the matrix corresponds to a parameter of the detected flow cytometer data. In the cell where the column and row intersect, a calculated spillover diffusion coefficient for the pair of first and second fluorochromes is placed, which indicates the extent to which the fluorochrome under discussion (i.e., the first fluorochrome) contributes error to the associated detector (i.e., detection of light emitted from the second fluorochrome). The total extent to which a fluorochrome causes spillover diffusion can be estimated by summing all the values ​​in that row, and the total extent to which a detector is affected by spillover diffusion can be calculated by summing all the values ​​in that 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 particular subset of the fluorescent flow cytometer data).

[0079] As noted above, in embodiments, the spillover diffusion matrices described herein are populated with spillover diffusion coefficients calculated without distinguishing between positive and negative populations of the fluorescent flow cytometer data for each of the associated fluorochromes. However, in some embodiments, the spillover diffusion matrices described herein are populated with spillover diffusion coefficients that approximate the spillover diffusion coefficients calculated with distinguishing between positive and negative populations of the fluorescent flow cytometer data for each of the associated fluorochromes, i.e., they approximate the spillover diffusion coefficients calculated as taught by Nguyen et al. (2013).

[0080] The processor may be further configured to adjust the fluorescent flow cytometer data to account for spillover diffusion. In some embodiments, the fluorescent flow cytometer data is adjusted such that it no longer includes errors due to spillover diffusion. In embodiments, adjusting the fluorescent flow cytometer data includes generating a spillover diffusion adjusted population. In certain embodiments, generating the spillover diffusion adjusted population includes subtracting a magnitude of the spillover diffusion from a relevant population(s) of the flow cytometer data, i.e., counteracting the effects of signals affected by spillover diffusion. In certain embodiments, the magnitude of the spillover diffusion is determined from a spillover diffusion matrix. In some embodiments, adjusting the flow cytometer data includes subtracting a total diffusion effect from a relevant portion of the flow cytometer data.

[0081] In some embodiments, the processor is configured to compensate the fluorescent flow cytometer data for spillover. Any suitable method can be used to compensate the fluorescent flow cytometer data for spillover. In some embodiments, unmixing may be performed. Unmixing uses single-stained reference controls to separate fluorescent populations and identify the spectra associated with each fluorochrome. In other embodiments, spillover compensation is performed by the AutoSpill algorithm.

[0082] In some embodiments of the invention, a spillover diffusion matrix composed of spillover diffusion coefficients (obtained as described above) is calculated together with compensating the fluorescent flow cytometer data for spillover. The calculation may be performed with or without identifying positive and negative populations of the fluorescent flow cytometer data for each of the relevant fluorochromes. In some embodiments, calculating the spillover diffusion matrix together with spillover compensation is performed without identifying positive and negative populations. In such embodiments, the calculation of the spillover matrix and the spillover compensation are performed by AutoSpill. In some embodiments, the fluorescent flow cytometer data may be additionally adjusted, if necessary, to account for errors present in the data caused by spillover.

[0083] In other embodiments, calculating the spillover diffusion matrix along with spillover compensation is performed by identifying positive and negative populations. In such embodiments, spillover compensation may be performed by an algorithm other than AutoSpill. In embodiments, compensation is performed via unmixing. In some embodiments, the fluorescence flow cytometer data may be additionally adjusted, if necessary, to account for errors present in the data caused by spillover.

[0084] 9 shows a system 900 for flow cytometry in accordance with an exemplary embodiment of the invention. System 900 includes a flow cytometer 910, a controller / processor 990, and a memory 995. Flow cytometer 910 includes one or more excitation lasers 915a-915c, a focusing lens 920, a flow chamber 925, a forward scatter detector 930, a side scatter detector 935, a fluorescence collection lens 940, one or more beam splitters 945a-945g, one or more bandpass filters 950a-950e, one or more longpass ("LP") filters 955a-955b, and one or more fluorescence detectors 960a-960f.

[0085] The pump lasers 915a-915c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the pump lasers 915a-915c are 488 nm, 633 nm, and 325 nm, respectively, in the example system of FIG. 9. The laser beams are first guided through one or more of the beam splitters 945a and 945b. The beam splitter 945a transmits 488 nm light and reflects 633 nm light. The beam splitter 945b transmits ultraviolet light (light having a wavelength in the range of 10-400 nm) and reflects 488 nm and 633 nm light.

[0086] The laser beam is then directed to a focusing lens 920, which focuses the beam onto the portion of the fluid stream where the sample particles are located in a flow chamber 925. The flow chamber is the part of the fluid system that directs particles in the stream, usually one at a time, towards 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.

[0087] Light from the laser beam(s) interacts with particles in the sample by diffraction, refraction, reflection, scattering and absorption with re-emission at a variety of different wavelengths depending on the particle's properties such as size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent emission, as well as the diffracted, refracted, reflected, and scattered light, may be routed through one or more of beam splitters 945a-945g, bandpass filters 950a-950e, longpass filters 955a-955b, and fluorescence collection lens 940 to one or more of forward scatter detector 930, side scatter detector 935, and one or more fluorescence detectors 960a-960f.

[0088] The fluorescence collection lens 940 collects light emitted from the particle and laser beam interaction and routes the light towards one or more beam splitters 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 represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends from the center of the spectral band by 10 nm on either side, i.e., from 500 nm to 520 nm. Shortpass filters transmit light with wavelengths equal to or less than a specified wavelength. Longpass filters, such as longpass filters 955a-955b, transmit light with wavelengths equal to or greater than a given wavelength. For example, longpass filter 955a, a 670 nm longpass filter, transmits light with wavelengths equal to or greater than 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorescent dye. The filters can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0089] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short pass and long pass. For example, beam splitter 945g is a 620SP beam splitter, meaning that beam splitter 945g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 945a-945g can comprise optical mirrors, such as dichroic mirrors.

[0090] The forward scatter detector 930 is positioned off-axis from the direct beam through the flow cell and is configured to detect diffracted light, excitation light traveling through or around the particle in a primarily forward direction. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 935 is configured to detect refracted and reflected light from the surface and internal structure of the particle, which tends to increase as the complexity of the particle's structure increases. Fluorescence emission from fluorescent molecules associated with the particle may be detected by one or more fluorescence detectors 960a-960f. The side scatter detector 935 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 930, the side scatter detector 935 and the fluorescence detector may be converted to electronic signals (voltage) by these detectors. This data may provide information about the sample.

[0091] During operation, the operation of the cytometer is controlled by the controller / processor 990, and measurement data from the detectors may be stored in the memory 995 and processed by the controller / processor 990. Although not explicitly shown, the controller / processor 990 may be coupled to the detectors to receive output signals therefrom, and may also be coupled to electrical and electromechanical components of the flow cytometer 910 to control lasers, fluid flow parameters, and the like. 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 an integral 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 a user of the system 900. Alternatively, the memory 995 and the controller / processor 990 and some or all of the I / O functionality may be part of one or more external devices, such as a general purpose computer. In some embodiments, some or all of the memory 995 and the controller / processor 990 may be in wireless or wired communication with the flow cytometer 910. The controller / processor 990 in combination with memory 995 and I / O 997 can be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0092] The system shown in FIG. 9 includes six different detectors that detect fluorescence in six different wavelength bands (sometimes referred to herein as the “filter window” of a given detector) defined by the configuration of filters and / or splitters in the beam path from the flow cell 925 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their respective characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to generally match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescence emission spectra becomes impossible. In general, the peak of the emission spectrum of a particular fluorescent molecule may be within the filter window of one particular detector, but a portion of the emission spectrum of that label will also overlap the filter window of one or more other detectors. This is sometimes referred to as spillover. The I / O 997 may be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations having multiple markers, each cell population having a subset of the multiple markers. The I / O 997 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 995. The controller / processor 990 may be configured to evaluate one or more assignments of labels to markers.

[0093] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer depicted in Figure 9, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0094] FIG. 10 is a functional block diagram of an example of a processor 1000 for analyzing and displaying data. The processor 1000 may be configured to perform various processes for controlling a graphical display of biological events. The flow cytometer 1002 may be configured to obtain fluorescence flow cytometer data by analyzing a biological sample (e.g., as described above). The apparatus may be configured to provide the biological event data to the processor 1000. A data communication channel may be included between the flow cytometer 1002 and the processor 1000. The data may be provided to the processor 1000 via the data communication channel. The processor 1000 may be configured to provide a graphical display including a plot (e.g., as described above) to the display device 1006. The processor 1000 may be further configured to render a gate over the plot, for example, around a population of fluorescence flow cytometer data shown by the display device 1006. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest depicted on a single parameter histogram or bivariate plot. In some embodiments, the display may be used to display analyte parameters or saturation detector data.

[0095] The processor 1000 may be further configured to display the fluorescent flow cytometer data on the display device 1006 within the gate differently than other events in the fluorescent flow cytometer data outside the gate. For example, the processor 1000 may be configured to render the color of the fluorescent flow cytometer data contained within the gate differently than the color of the fluorescent flow cytometer data outside the gate. In this manner, the processor 1000 may be configured to render a different color to represent each unique population of data. The display device 1006 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0096] The processor 1000 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device may be implemented as a mouse 1010. The mouse 1010 may initiate a gate selection signal to the processor 1000 that identifies a population to be displayed on or manipulated via the display device 1006 (e.g., by clicking on or within the desired gate when a cursor is positioned there). In some implementations, the first input device may be implemented as a keyboard 1008 or other means for providing an input signal to the processor 1000, such as a touch screen, a stylus, an optical detector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in FIG. 10, the mouse 1010 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0097] The trigger event can cause the processor 1000 to change the way the fluorescent flow cytometer data is displayed, what portions of the data are actually displayed on the display device 1006, and / or provide input for further processing, such as selection of a subject population for analysis.

[0098] In some embodiments, the processor 1000 may be configured to detect when a gate selection is initiated by the mouse 1010. The processor 1000 may be further configured to automatically modify the plot visualization to facilitate gating. The modification may be based on a particular distribution of the data received by the processor 1000.

[0099] The processor 1000 may be connected to a storage device 1004. The storage device 1004 may be configured to receive and store data from the processor 1000. The storage device 1004 may be further configured to enable retrieval of data, such as fluorescent flow cytometer data, by the processor 1000.

[0100] The display device 1006 may be configured to receive display data from the processor 1000. The display data may consist of a plot of the fluorescent flow cytometer data and a gate that delineates a section of the plot. The display device 1006 may be further configured in conjunction with input from the flow cytometer 1002, the storage device 1004, the keyboard 1008, and / or the mouse 1010 to modify the information presented according to input received from the processor 1000.

[0101] In some implementations, the processor 1000 can generate a user interface for receiving the exemplary events for sorting. For example, the user interface can include a control for receiving the exemplary events or the exemplary images. The exemplary events or images or the exemplary gates can be provided prior to collection of event data for the sample or based on an initial set of events for a portion of the sample.

[0102] Computer Control System Aspects of the present disclosure further include a computer control system, the system further comprising one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer readable storage medium having a computer program stored thereon, the computer program, when loaded into the computer, includes instructions for receiving fluorescent flow cytometer data including intensity signals collected from at least a first and a second fluorochrome, instructions for dividing the fluorescent flow cytometer data according to the intensity of the fluorescent flow cytometer data for the first fluorochrome, instructions for estimating a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each divided quantile based on the assumption that the intensity of light collected from the first fluorochrome is zero by a first linear regression, instructions for obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation by a second linear regression to characterize the spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome, instructions for incorporating the calculated spillover diffusion coefficient for each pair of the first and second fluorochromes into a spillover diffusion matrix, and instructions for adjusting the fluorescent flow cytometer data based on the spillover diffusion matrix.

[0103] In embodiments, the system is configured to analyze the data within software or analysis tools for analyzing flow cytometer data, such as FlowJo® (Ashland, OR). FlowJo® is a software package for analyzing flow cytometer data developed by FlowJo LLC (a subsidiary of Becton Dickinson). The software is configured to manage and generate graphical reports regarding flow cytometer data (https: / / www.flowjo.com / learn / flowjo-university / flowjo). The initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®) by appropriate means, such as manual gating, cluster analysis, or other computational techniques. The system, or portions thereof, can be implemented as a software component of software for data analysis, such as FlowJo®. In these embodiments, a computerized control system according to the present disclosure can function as a software "plug-in" to an existing software package, such as FlowJo®.

[0104] In an embodiment, the system includes an input module, a processing module, and an output module. The subject systems may include both hardware and software components, where the hardware components may take the form of one or more platforms, e.g., servers, such that the functional elements, i.e., those elements of the system that perform specific tasks of the system (managing the input and output of information, processing information, etc.), may be performed by executing software applications on and across one or more computer platforms representing the system.

[0105] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor that accesses a memory having instructions stored therein to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. 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 the light source with the flow stream based on the first and second optical signals, hi some embodiments, the processor includes analog electronics that provide feedback control, such as, for example, negative feedback control.

[0106] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write compact disk, flash memory devices, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or disk drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or floppy disk, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in conjunction with the memory storage devices.

[0107] In some embodiments, a computer program product is described that includes a computer usable medium having control logic (a computer software program including program code) stored thereon. The control logic, when executed by a processor, causes the computer to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example using hardware state machines. The implementation of hardware state machines to perform the functions described herein will be apparent to one of ordinary skill in the relevant art.

[0108] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor over a communication channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices together with the memory. For example, a magnetic or optical disk may 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, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, for example 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 hybrids of these categories such as magnetic / optical storage media.

[0109] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a cellular phone (i.e., a smartphone).

[0110] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0111] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment) configured for similar complementary data communications.

[0112] In one embodiment, the communications interface is configured for infrared communications, Bluetooth® communications, or any other suitable wireless communications protocol to enable the subject system to communicate with other devices, such as a computer terminal and / or network, a communications-enabled mobile phone, a personal digital assistant, or any other communications device that a user may use in conjunction with.

[0113] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing the Internet Protocol (IP) via a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0114] In one embodiment, the subject system is configured to communicate wirelessly with the server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth RF protocols, or IrDA infrared protocol. The server device may be another portable device, such as a smartphone, a personal digital assistant (PDA) or a notebook computer, or a larger device, such as a desktop computer, an appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0115] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., within the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0116] 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. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. A 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. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished in alternative embodiments using a network or other type of remote communication. 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 network, in accordance with known techniques. Presentation of data by the output manager may be performed in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system will typically be of a class of computers commonly referred to as servers, but may be any type of known or future developed computer platform. However, they may be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication systems, including wireless systems, either networked or not. They may be co-located or physically separated.In some cases, depending on the type and / or configuration of the computer platform selected, various operating systems may be employed on any of the computer platforms. 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, etc.

[0117] FIG. 11 illustrates a general architecture of an exemplary computing device 1100 according to certain embodiments. The general architecture of the computing device 1100 illustrated in FIG. 11 includes an arrangement of computer hardware and software components. However, it is not necessary to illustrate all of these generally conventional elements to provide an enabling disclosure. As illustrated, the computing device 1100 includes a processing unit 1110, a network interface 1120, a computer-readable medium drive 1130, an input / output device interface 1140, a display 1150, and an input device 1160, all of which may communicate with each other via a communication bus. The network interface 1120 may provide connectivity to one or more networks or computing systems. Thus, the processing unit 1110 may receive information and instructions from other computing systems or services over a network. The processing unit 1110 may 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., data analysis software or programs such as FlowJo®) stored as executable instructions in non-transitory memory of the analysis system can display the flow cytometry event data to a user. The input / output device interface 1140 can also accept input from optional input devices 1160, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input device.

[0118] The memory 1170 may include computer program instructions (grouped as modules or components in some embodiments) that the processing unit 1110 executes to implement one or more embodiments. The memory 1170 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. The memory 1170 may store an operating system 1172 that provides computer program instructions used by the processing unit 1110 in the general management and operation of the computing device 1100. Data may be stored in the data storage device 1190. The memory 1170 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0119] Computer-readable storage medium Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for implementing the subject methods. The computer-readable storage medium may be employed on one or more computers for full or partial automation of a system for practicing the methods described herein. In some embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved 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 disks, solid-state disks, and network-attached storage (NAS), whether such devices are internal or external to the computer. In some examples, the instructions may be provided on an integrated circuit device. The target integrated circuit device may include a reconfigurable field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a complex programmable logic device (CPLD), in certain examples. Files containing information may be "stored" on a computer-readable medium, where "storing" means recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be performed using programming that may be written in one or more of any number of computer programming languages. Such 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 of a number of other languages.

[0120] In some embodiments, a subject computer readable storage medium includes a computer program stored thereon, which, when loaded into a computer, includes instructions for receiving fluorescent flow cytometer data including intensity signals collected from at least a first and a second fluorochrome; dividing the fluorescent flow cytometer data according to an intensity of the fluorescent flow cytometer data for the first fluorochrome; performing a first linear regression to estimate a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each divided quantile based on an assumption that the intensity of light collected from the first fluorochrome is zero; performing a second linear regression to obtain a spillover diffusion coefficient from the zero-adjusted standard deviation to characterize spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome; instructions for incorporating the calculated spillover diffusion coefficients for each pair of the first and second fluorochromes into a spillover diffusion matrix; and instructions for adjusting the fluorescent flow cytometer data based on the spillover diffusion matrix.

[0121] In embodiments, the system is configured to analyze the data within software or analysis tools for analyzing flow cytometer data, such as FlowJo®. The initial data can be analyzed within the data analysis software or tool (e.g., FlowJo®) by appropriate means, such as manual gating, cluster analysis, or other computational techniques. The system, or portions thereof, can be implemented as a software component of software for data analysis, such as FlowJo®. In these embodiments, a computerized system according to the present disclosure can function as a software "plug-in" to an existing software package, such as FlowJo®.

[0122] The computer readable storage medium may be employed on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor that accesses a memory having stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, Python, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0123] Utilities The subject devices, methods, and computer systems find use in a variety of applications where it is desirable to increase resolution and accuracy in the determination of parameters of analytes (e.g., cells, particles) in biological samples. For example, the present disclosure finds use in the analysis of data affected by spillover diffusion. Because flow cytometry often involves the collection of multiple fluorescence parameters by multiple detectors, the same light being detected by multiple detectors can spuriously increase the detected fluorescence intensity. As such, the present disclosure finds use during the analysis of flow cytometer data that includes signals from multiple fluorochromes. The subject devices, methods, and computer systems find use in characterizing spillover diffusion, particularly in flow cytometer data where positive and negative populations are poorly defined. In some embodiments, the subject methods and systems provide fully automated protocols such that adjustments to the data require little or no human input.

[0124] The present disclosure can be used to characterize many types of analytes, particularly those relevant to protocols for medical diagnosis or patient care, including, but not limited to, proteins (including both free proteins and proteins bound to the surfaces of structures such as cells), nucleic acids, viral particles, etc. Furthermore, the sample can be from an in vitro or in vivo source, and the sample can be a diagnostic sample.

[0125] kit Aspects of the present disclosure further include kits, including 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 disks, solid state disks, and network attached storage (NAS). Any of these program storage media, or others now in use or that may later be developed, may be included in the subject kits. In embodiments, the program storage media includes instructions for clustering fluorescent flow cytometer data into populations, determining spillover spread of the populations, adjusting the flow cytometer data based on the spillover spread, and determining a split between the adjusted flow cytometer data (e.g., as described above). In embodiments, the instructions included on the computer readable media provided in the subject kits, or portions thereof, may be implemented as software components of software for data analysis, such as FlowJo®. In these embodiments, a computer control system according to the present disclosure may function as a software "plug-in" to an existing software package, such as FlowJo®.

[0126] In addition to the above components, the subject kits may further include (in some embodiments) instructions for installing the plug-in into an existing software package, such as, for example, FlowJo®. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as a suitable medium or substrate, such as a sheet or sheets of paper on which the information is printed, in the kit packaging, in a package insert, etc. Yet another form in which these instructions may be present is a computer readable medium on which the information is recorded, such as a diskette, compact disc (CD), portable flash drive, etc. Yet another form in which these instructions may be present is a website address that may be used via the Internet to access the information at a remote site.

[0127] experiment Dataset MM1 dataset (mouse spleen cells): For single staining controls, spleen cells from C57Bl / 6 mice were disrupted on a slide and filtered through a 100 μm mesh to lyse red blood cells. Cells were fixed and permeabilized with Foxp3 Transcription Factor Staining Buffer Set (eBioscience) according to the manufacturer's instructions, and Fixable Viability Dye was applied. Staining was performed overnight at 4°C with 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-RF488 / anti-CD4-RF488 / anti-TCRβ-RF488, anti-CD4-PerCP-Cy5.5, anti-CD4-PE-594, anti-CD8-PE-Cy7, and anti-MHC-II-AF700. (all from 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 from 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 with the same compensation controls, indicated by slashes. Samples were acquired on a Symphony flow cytometer (BD Biosciences).

[0128] HS1 dataset (human PBMCs): Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized blood samples from healthy human donors using Ficoll-Paque density centrifugation (MP biomedicals), frozen, and stored in liquid nitrogen. Frozen PBMCs were thawed and counted, and cell concentrations of each single-color control were adjusted to 1 × 10 6 Cells were plated in V-bottom 96-well plates, washed once with PBS (Fisher Scientific), and stained with fluorochrome-conjugated antibodies against live / dead and surface markers: 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). Antibodies against CD4-PE / Dazzle 594 and CD4-AF647 (all from BioLegend, Inc.); anti-CD3-FITC, anti-CD4-PE-Cy5.5, anti-CCR7-PE-Cy7, and anti-CD4-eFluor780 (all from eBioscience, Inc.); anti-CD4-BV786, anti-CD4-BV711, anti-CD4-BV605, anti-HLA-DR-BV570, anti-CD127-BV421, anti-CD4-PE / Dazzle 594, and anti-CD4-AF647 (all from BioLegend, Inc.).

[0129] Samples were stained for 60 min at 4°C, washed twice with PBS / 1% FBS (Tico Europe), and then fixed and permeabilized with Foxp3 Transcription Factor Staining Buffer Set (eBioscience) according to the manufacturer's instructions. Cells were stored overnight at 4°C and then acquired on a Symphony flow cytometer equipped with Diva software (BD Biosciences). A minimum of 5 × 10 4 Event was acquired.

[0130] HS2 dataset (human PBMCs): Frozen PBMCs from human healthy donors were treated as in the HS1 set and stained with fluorochrome-conjugated antibodies against live / dead markers and the following surface markers: anti-CD8-BUV805, anti-CD4-BUV496, anti-CD95-BUV737, anti-CD28-BB660-P, anti-ICOS-BB630, anti-CXCR3-BV785, anti-PD-1-BV750-P, anti-CXCR5-BV650, anti-CCR2-BV605 (all from BD Biosciences); anti-CD3-PerCP-Vio700 (Miltenyi Biotec); anti-CD45RA-FITC, anti-CD14-PE-Cy5.5, Fixable Viability Dye. eFluor780 (all from eBioscience); anti-CD25-BV711, anti-CD31-BV480, anti-HLA-DR{BV570, anti-CD127{BV421, anti-CCR4-PE / Dazzle 594, anti-CCR7-PE-Cy7 (all from BioLegend).

[0131] Samples were stained for 60 min at 4°C, washed twice with PBS / 1% FBS (Tico Europe), and then fixed and permeabilized with Foxp3 transcription factor staining buffer set (eBioscience) according to the manufacturer's instructions. 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 acquired on a Symphony flow cytometer (BD Biosciences).

[0132] Be1 dataset (Beads): UltraComp eBeads.Compensation Beads (ThermoFisher) were used to optimize fluorescence compensation settings for multicolor flow cytometry analysis on a Symphony flow cytometer. UltraComp eBeads were stained with the following fluorochrome-conjugated anti-human antibodies: 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, anti-CD40-APC-R700 (all from BD Bioscience); anti-CD3-PerCP-Vio700 (Milteny Biotec); anti-CD57-FITC, anti-CD14-PE-Cy5.5, and Fixable Viability Dye. eFluor780 (all from eBioscience); anti-CD24-BV711, anti-CD19-BV480, anti-HLA-DR-BV570, anti-IgM-BV421, anti-CD11c-APC, anti-CD38-PE / Dazzle 594, anti-CD10-PE-Cy5, anti-IgD-PE-Cy7 (all from Biolegend).

[0133] Initial Gating Tessellation was performed using package tripack v.1.3-9, density estimation and spatial operations were performed using packages fields v.10.3 and sp v.1.4-1. Initial gates were calculated for each control independently on the two-dimensional density of forward and side-scattered events (FSC-A and SSC-A parameters). To robustly detect the target population, a two-stage tessellation was performed to isolate the desired density peaks. First, the data were trimmed at the extreme values ​​(1% and 99%). Second, the maxima were located numerically by a moving average (window size 3) on a soft estimate of the two-dimensional density. A first tessellation was performed on these density maxima, selecting the tile corresponding to the highest maximum, ignoring peaks close to the low values ​​of both FSC-A and SSC-A. Rectangular regions on the FSC-A / SSC-A plane were selected using the median and three times the mean absolute deviation of the events included in the selected tile. For this region, we performed a finer 2D density estimation, then again performed numerical detection of maxima (with a window size of 2) and tessellation by maxima. Finally, we defined a gate as the convex hull enclosing the points whose density is greater than a threshold (default value 33% of the maximum) and which belong to the tile containing the highest maximum.

[0134] method Characterization of spillover diffusion was performed in R v.3.6.3 using the packages 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 standard deviation of the fluorescence intensity in one parameter caused by an increase in the fluorescence intensity of the other parameter. The spillover diffusion coefficient can be calculated by comparing the fluorescence intensity of the primary detector with the standard deviation of the fluorescence of the secondary detector for paired positive and negative populations of single-color controls corresponding to the primary detector (Nguyen et al., 2013).

[0135] SSM Coefficients

number

[0136]

number

[0137] Here, σ positive and σ negative are the standard deviations of C fluorescence in the positive and negative populations, respectively, and F positive -F negative is the difference in P fluorescence intensity between the two. Conventional algorithms estimate the above quantities using the median and robust standard deviation of fluorescence in the positive and negative populations, but for linear regression, we take the theoretical quantity when P fluorescence (F) is equal to zero as negative, and the standard deviation is an unknown quantity (σ0). As a result, the equation relating F to σ is as follows, which is suitable for estimating σ0 by linear regression:

[0138]

number

[0139] The slope β is the spillover diffusion coefficient

number

[0140] To provide data for regression, events from the single-color control for parameter P were divided by quantiles. For controls with a large number of events, 256 quantiles were used, but sometimes as few as 8 quantiles were used to ensure that there were enough events in each quantile to reliably estimate the standard deviation. For each of the other parameters C, a robust standard deviation of fluorescence (84 thPercentile minus median) was calculated as an estimate of σ, and median fluorescence was calculated as an estimate of F. Because F values ​​can be negative and / or close to zero, instead of a simple square root function, a square root function was used prior to regression:

number

[0141] Using the estimate of σ0,

number

number

[0142] result Here, we used quantile partitioning and linear regression to estimate the linear relationship observed by Nguyen et al. This allows us to include events above, below, or in between the positive and negative populations of the original approach. We partitioned the events for each single-color control into quantiles at the primary detector and estimated the standard deviation of the autofluorescence levels for each quantile bin at all secondary detectors. We then used two linear regressions to first estimate the standard deviation at zero fluorescence and then estimate the spillover diffusion coefficients. Coefficients that were determined to be insignificant by the F-test were replaced with 0, and negative coefficients were replaced with 0 as well. Although the majority of the quantiles were in fact subsamples of the traditional positive and negative populations, the inclusion of additional quantiles improved our precision in estimating the spillover diffusion effect because all of these events fit the same linear relationship, assuming they are on-scale and in the linear range of the flow cytometer (Figure 12). As depicted in Figure 12, for control sets MM1 (left) and HS1 (right), regression analysis was performed across the gated events of one single color control from each set, with no well-defined positive and negative populations, and the major and minor channels are shown on the y- and x-axes, respectively. Uncompensated data points are shown in blue and compensated data points in black. Regressions from uncompensated data are shown as dashed lines and regressions from compensated data are shown as solid lines.

[0143] As a result, the spillover diffusion for the data sets in which the compensation matrix successfully orthogonalized the fluorescence signals present in the single-color controls was accurately estimated (Figure 13). As shown in Figure 13, the results compare the present method with the regular spillover diffusion matrix algorithm for the control sets MM1 (left) and HS1 (right) and show that the difference between both calculations is small. Values ​​are displayed on the logarithmic scale of the absolute difference, divided into positive (solid line) and negative (dashed line) values.

[0144] The adjustment step (first regression) removed the small quadratic effect due to σ0 in the initial estimates, allowing for more accurate estimation of the SS coefficients. If this adjustment step were omitted, i.e., if β were taken as the spillover diffusion coefficient, the diffusion effect would always be underestimated. In that case, a clear negative bias would be observed in comparison with the traditional SSM algorithm (Figure 14). Including the adjustment step eliminated the bias.

[0145] As shown in Fig. 14, we compare the results obtained with the conventional SSM algorithm for the control sets MM1 (left) and HS1 (right), where the first linear regression in our method was omitted, which leads to a systematic downward bias in the resulting coefficients. The scale and line codes are the same as in Fig. 13.

[0146] Notwithstanding the appended claims, the present disclosure is also defined by the following appended notes. 1. A method for characterizing spillover diffusion from a first fluorochrome in flow cytometer data acquired for a second fluorochrome, comprising: Dividing fluorescent flow cytometer data, the collected intensity signals from the light emitted by the first fluorochrome and the second fluorochrome, into a number of quantiles corresponding to the intensity of the fluorescent flow cytometer data for the first fluorochrome; estimating with a first linear regression a zero-adjusted standard deviation of the intensity of light collected from the second fluorochrome for each quantile based on the assumption that the intensity of light collected from the first fluorochrome is zero; and Obtaining a spillover diffusion coefficient from the zero-adjusted standard deviation in a second linear regression to characterize the spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome. The method includes: 2. The method of claim 1, wherein the first linear regression 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. 3. The method of claim 2, wherein estimating the zero-adjusted standard deviation further comprises 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 by determining the y-intercept of a linear fit calculated 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 of any one of claims 1 to 3, wherein the second linear regression includes calculating, for each divided quantile, a linear fit between the zero-adjusted standard deviation and the median intensity of light collected from the first fluorescent dye. 5. The method of claim 4, wherein calculating the spillover diffusion coefficient comprises obtaining the slope of a linear fit calculated between the zero-adjusted standard deviation and the median of the intensity of light collected from the first fluorescent dye. 6. The method of any one of the preceding claims, wherein the spillover diffusion coefficient is calculated according to Equation 1.

number

number

number

[0147] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those skilled in the art that, in light of the teachings of this invention, certain changes and modifications can be made without departing from the spirit or scope of the appended claims.

[0148] Thus, the above merely illustrates the principles of the invention. It will be understood that those skilled in the art can devise various arrangements that embody the principles of the invention and are included within its spirit and scope, although not expressly described or shown herein. Furthermore, all examples and conditional language described herein are intended primarily to aid the reader in understanding the principles of the invention and the concepts that the inventors contribute to furthering the art, and should not be construed as being limited to such specifically described examples and conditions. Furthermore, all statements herein that describe the principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, regardless of structure, i.e., any elements developed to perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly set forth in the claims.

[0149] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is expressly defined as being invoked for a limitation in a claim only if the precise phrase "means for" or the precise phrase "step for" appears at the beginning of such limitation in the claim, and if such precise phrase is not used in the limitation in the claim, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0150] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. §119(e), this application claims priority to the filing dates of 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.

Claims

1. 1. A method for characterizing spillover diffusion from a first fluorochrome in flow cytometer data acquired for a second fluorochrome, comprising: Dividing fluorescent flow cytometer data, the fluorescent flow cytometer data including intensity signals collected from light emitted by a first fluorescent dye and a second fluorescent dye, into a number of quantiles corresponding to the intensity of the fluorescent flow cytometer data for the first fluorescent dye; estimating, for each quantile, a zero-adjusted standard deviation of the intensity of light collected from the second fluorescent dye by a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; performing a second linear regression to obtain a spillover diffusion coefficient from the zero-adjusted standard deviation to characterize spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome; The method includes:

2. 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 a first fluorescent dye and the standard deviation of the intensity of light collected from the second fluorescent dye.

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

4. 4. The method of claim 1, wherein the second linear regression comprises calculating a linear fit between the zero-adjusted standard deviation and the median intensity of light collected from the first fluorescent dye for each quantile.

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

6. The method according to any one of claims 1 to 5, wherein the spillover diffusion coefficient is calculated according to Equation 1. [0010] Where: SS is the spillover diffusion coefficient; σ is the standard deviation of the light collected from the second fluorochrome; σ ０ is an estimate of the standard deviation of the intensity of light collected from the second fluorochrome, based on the assumption that the intensity of light collected from the first fluorochrome is zero; F is the median intensity of light collected from the first fluorochrome.

7. The method according to any one of claims 1 to 6, wherein the first linear regression is selected from an ordinary least squares model, a weighted least squares model, and a robust linear model.

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

9. The method of any one of claims 1 to 8, wherein the number of quantiles is determined based on a size of the fluorescent flow cytometer data.

10. The method of any one of claims 1 to 9, further comprising receiving fluorescence flow cytometer data comprising intensity signals collected from light emitted by the first fluorescent dye and the second fluorescent dye.

11. The method of any one of claims 1 to 10, wherein the fluorescence flow cytometer data is collected from light emitted from a number of different fluorescent dyes.

12. The method of claim 11 , further comprising calculating a spillover diffusion coefficient for each pair of a first and a second fluorescent dye in the plurality of fluorescent dyes.

13. 13. The method of claim 11 or 12, wherein the calculated spillover diffusion coefficients for each pair of a first and a second fluorescent dye in the plurality of fluorescent dyes are combined into a spillover diffusion matrix.

14. 1. A system comprising: a particle analyzer component configured to obtain fluorescence flow cytometer data; a processor comprising a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to: Dividing fluorescent flow cytometer data, the fluorescent flow cytometer data including intensity signals collected from light emitted by a first fluorescent dye and a second fluorescent dye, into a number of quantiles corresponding to the intensity of the fluorescent flow cytometer data for the first fluorescent dye; estimating, for each quantile, a zero-adjusted standard deviation of the intensity of light collected from the second fluorescent dye by a first linear regression based on the assumption that the intensity of light collected from the first fluorescent dye is zero; performing a second linear regression to obtain a spillover diffusion coefficient from the zero-adjusted standard deviation to characterize spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome; A system that allows this.

15. 1. A non-transitory computer readable storage medium having stored thereon instructions for characterizing spillover diffusion from a first fluorochrome in flow cytometer data acquired for a second fluorochrome by a method comprising: Dividing fluorescent flow cytometer data, the fluorescent flow cytometer data including intensity signals collected from light emitted by a first fluorescent dye and a second fluorescent dye, into a number of quantiles corresponding to the intensity of the fluorescent flow cytometer data for the first fluorescent dye; estimating by a first linear regression a zero-adjusted standard deviation of the intensity of light collected from the second fluorescent dye for each quantile based on the assumption that the intensity of light collected from the first fluorescent dye is zero; performing a second linear regression to obtain a spillover diffusion coefficient from the zero-adjusted standard deviation to characterize spillover diffusion from the first fluorochrome in the flow cytometer data obtained for the second fluorochrome; A non-transitory computer readable storage medium comprising:

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