Method and system for assessing co-linearity of multiple autofluorescence spectra of sample

Abstract

Aspects of the present disclosure include methods for assessing co-linearity between autofluorescence spectra of particles in a sample. A method according to certain embodiments comprises: irradiating a sample comprising particles in a flowing stream with a light source in a flow cytometer; detecting light from the irradiated particles using a light detection system having a photodetector; measuring an autofluorescence spectrum generated by the particles in the sample; and evaluating colinearity between autofluorescence spectra produced by two or more different particles in the sample. Systems and non-transitory computer-readable storage media configured to perform the subject methods are also provided.

Description

[0001] Cross-reference to related applications

[0002] Pursuant to 35 U.S.C., 119(e), this application claims priority to U.S. Provisional Patent Application Serial No. 63 / 712,319, filed October 25, 2024, the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] This application relates to methods and systems for evaluating collinearity in multiple autofluorescence spectra of samples. Background Technology

[0004] Characterization of analytes in biofluids has become an important component of biological research, medical diagnostics, and the assessment of overall patient health. Detection of analytes in biofluids (such as human blood or blood-derived products) can provide results that may play a role in determining treatment options for patients with various diseases.

[0005] Flow cytometry is a technique used to characterize biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample, and is often used to sort such biological materials. A flow cytometer typically includes a sample reservoir for receiving a fluid sample (e.g., a blood sample) and a sheath fluid reservoir containing sheath fluid. The flow cytometer delivers particles (including cells) from the fluid sample as a cell stream to a flow cell, while also guiding the sheath fluid into the flow cell. To characterize the components of the flow stream, it is illuminated. Changes in the material within the flow stream, such as the presence of morphological or fluorescent labels, can cause changes in the observed light, which allow for characterization and separation. To characterize the components in the flow stream, light must be incident on and collected from the flow stream. The light source in a flow cytometer can be varied and may include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. This light source is aligned with the flow stream and collects and quantifies the optical response from the illuminated particles.

[0006] The separation of biological particles has been achieved by adding sorting or collection capabilities to flow cytometers. Particles detected in the separation stream as having one or more desired properties are individually separated from the sample stream by mechanical or electrical removal. A common flow cytometry sorting technique utilizes droplet sorting, in which a fluid stream containing linearly separated particles breaks into droplets. The droplet containing the particle of interest becomes charged and is deflected into a collection tube by passing through an electric field. Typically, linearly separated particles in the stream are characterized as they pass through an observation point located directly below the nozzle tip. Once a particle is identified as meeting one or more desired criteria, the time it takes for it to reach the droplet break-off point and break into droplets from the stream can be predicted. Ideally, a brief charge is applied to the fluid stream before the droplet containing the selected particle breaks off from the stream, and then grounded immediately after the droplet breaks off. The droplet to be sorted retains its charge upon breaking off from the fluid stream, while all other droplets remain uncharged.

[0007] Biological cells contain endogenous compounds that emit fluorescence, thus producing autofluorescence without the addition of fluorochromes or dyes. The autofluorescence spectrum and intensity of a given cell will depend on the abundance of different autofluorescent compounds (e.g., metabolites) within the cell. Because different cell types may have different relative abundances of these endogenous autofluorescent compounds, the total autofluorescence of a cell has characteristic spectra for different cell types and can vary due to many biological and experimental factors, including cell treatment, cell death, cell activation, cell stimulation, etc. Therefore, heterogeneous samples containing multiple cell types, or comparisons of cell types under multiple treatment conditions, may contain a range of autofluorescence spectra from different cells. Summary of the Invention

[0008] The inventors recognized that the autofluorescence of particles irradiated in the flow stream of a flow cytometer can be modeled as an additional fluorophore or a set of fluorophores in spectral unmixing. If autofluorescence is not included in the spectral unmixing of the fluorescence of particles in a sample, the signal generated by autofluorescence is not directly considered, leading to artifacts in the unmixed data, such as artificially high background signals from fluorophores with spectra similar to autofluorescence. In some cases, autofluorescence can be a source of error in the accuracy of particle parameter measurements when it is not considered in the fluorescence measured from sample particles. For example, predictive measures such as similarity and condition number alone are insufficient to predict the correct set of autofluorescence spectra to be used in spectral unmixing. Specifically, autofluorescence can cause pathological spectral matrices due to overly similar autofluorescence spectra, negatively impacting the unmixing variance of other fluorophores in the panel. This can lead to inconsistent unmixed data, resulting in unreliable data analysis, inaccurate particle identification, and sorting. Furthermore, when autofluorescence is not adequately accounted for in the spectral unmixing of fluorescence data signals in flow cytometry experiments, one or more data signals may be overestimated, leading to inaccurate and inconsistent estimates of fluorophore abundance in the sample.

[0009] Embodiments of this disclosure address the aforementioned and other issues. In some embodiments, by including autofluorescence spectroscopy during unmixing, the contribution of autofluorescence (i.e., the contribution of autofluorescence to the unmixed signal of other fluorophores) is appropriately extracted when spectral unmixing is performed. Including autofluorescence spectroscopy, as described herein, provides accurate fluorescence measurements, parameter calculations, and imaging for multicolor (i.e., samples labeled with multiple fluorophores) spectral flow cytometry panels. This disclosure also provides accurate spectral unmixing for high autofluorescence sample types (e.g., digestive tissue or tumor samples) and samples with high autofluorescence heterogeneity.

[0010] This disclosure provides an accurate characterization of the autofluorescence contribution of spectral data signals without requiring repetitive, laborious iterations and empirical checks on spectral unmixing performance, which necessitates repeated user judgment and analysis. In some instances, the methods described herein provide methods for evaluating (and, if necessary, removing) the autofluorescence spectra in fluorophore panels with two or more fluorophores, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, fifteen or more, twenty-five or more, fifty or more, seventy or more, and 100 or more fluorophores, as well as fluorophore panels including those with 100 or more fluorophores. Furthermore, this disclosure provides for the accurate evaluation and optimization of the autofluorescence contribution in fluorophore panels exhibiting large autofluorescence heterogeneity, for example, wherein the autofluorescence heterogeneity varies by 1% or more, such as 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more, and includes variations of 99.9% or more.

[0011] This disclosure includes methods for assessing collinearity between autofluorescence spectra of particles in a sample. The method according to some embodiments includes: illuminating a sample containing particles in a flow stream with a light source in a flow cytometer; detecting the light from the illuminating particles using a light detection system with a photodetector; measuring the autofluorescence spectra generated by the particles in the sample; and assessing collinearity between the autofluorescence spectra generated by two or more different particles in the sample. A system and a non-transitory computer-readable storage medium configured to perform the subject method are also provided.

[0012] In some embodiments, the sample comprises a plurality of different particles, and the method includes measuring the autofluorescence spectrum generated by each different particle in the sample. In some instances, the particles comprise one or more added fluorophores, such as two or more and including three or more different added fluorophores, such as four or more fluorophores, such as five or more fluorophores, such as six or more fluorophores, such as seven or more fluorophores, such as eight or more fluorophores, such as nine or more fluorophores, such as ten or more fluorophores, such as fifteen or more fluorophores, such as twenty-five or more fluorophores, such as fifty or more fluorophores, such as seventy-five or more fluorophores, and include a fluorophore panel having one hundred or more fluorophores.

[0013] In some embodiments, the method includes measuring the autofluorescence of a sample from unlabeled particles. In some embodiments, the method includes measuring the autofluorescence of a sample from a single-stained control particle. In some embodiments, the method includes measuring the autofluorescence of a sample from particles having multiple fluorophores. In some embodiments, a population for evaluating collinear autofluorescence spectra is selected. In some instances, selecting a population of autofluorescence spectra includes generating a scatter plot of fluorescence parameters of the sample particles and gating one or more populations on the scatter plot based on the median fluorescence intensity measured for each particle.

[0014] In some instances, selecting populations based on autofluorescence spectra involves applying unsupervised clustering algorithms to identify particle populations. Unsupervised clustering algorithms include one or more of the following: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy C-means clustering, centroid-based clustering, and the Birch algorithm. In some cases, the unsupervised clustering algorithm is a self-organizing map algorithm (e.g., FlowSOM).

[0015] In some embodiments, selecting a population of autofluorescence spectra includes using statistical analysis algorithms. In some instances, the statistical analysis algorithms include one or more of the following: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical cluster analysis (HCA), linear discriminant analysis, and matrix factorization. In some cases, the statistical analysis algorithms include dimensionality reduction. In some cases, the autofluorescence spectra of the particles are determined based on the median fluorescence intensity (MFI) of each particle population.

[0016] In some instances, the method includes assessing collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores. In some cases, assessing collinearity between each autofluorescence spectrum produced by particles in the sample is performed between each fluorophore. In some instances, the data generated by flow cytometry is spectrally unmixed flow cytometry data. In some instances, the flow cytometry data is compensated flow cytometry data. In some instances, variance includes noise in the flow cytometry data.

[0017] In some embodiments, the method includes evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample. In some instances, evaluating the unmixing performance includes: generating a spectral matrix associated with fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; applying the spectral matrix to unmix fluorescence spectra generated by an unstained control, a monostained control, a stained sample, or any combination thereof; and calculating one or more of unmixing bias and unmixing variance. In some instances, calculating unmixing bias includes measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectra. In some instances, the method includes determining that no false-positive unmixed fluorophore signals associated with the autofluorescence spectra are present in the unmixed fluorophore channels across all generated particle populations in the sample. In some instances, calculating unmixing variance includes measuring unmixing-dependent spread in the unmixed fluorophore signals. In some embodiments, the method includes identifying autofluorescence spectra generated by particles in the sample that minimize the unmixing bias.

[0018] In some embodiments, assessing collinearity includes: generating a spectral matrix associated with autofluorescence generated by particles in the sample; calculating an inverse matrix based on the generated spectral matrix; and identifying autofluorescence spectra associated with variance in data generated by flow cytometry using these autofluorescence spectra. In some instances, the method includes identifying autofluorescence spectra that contribute to the variance in the flow cytometry data. In some instances, the method includes identifying autofluorescence spectra affected by variance in the flow cytometry data. In some instances, the inverse matrix is ​​a pseudo-inverse matrix. In some instances, the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix. In some instances, the inverse matrix is ​​a gramian inverse matrix. In some cases, the inverse matrix is ​​calculated according to the following equation:

[0019]

[0020] in:

[0021] G is the Gram inverse matrix;

[0022] M is the spectral matrix; and

[0023] M T It is the transpose of the spectral matrix.

[0024] In some embodiments, the analytically computed inverse matrix includes deriving a quantitative metric from the inverse matrix. In some instances, the quantitative metric is a matrix norm. In some instances, the quantitative metric is a vector norm.

[0025] In some embodiments, the method includes identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data. In some instances, identifying autofluorescence spectra that minimize the variance of the generated data includes identifying autofluorescence spectra exhibiting the greatest spectral matrix conditioning. In some instances, the method includes identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias and minimize the variance of the generated data.

[0026] In some instances, the method includes removing autofluorescence contributions from the generated flow cytometry data. In some instances, the method includes iteratively identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data. In some instances, a visual representation of the evaluated collinearity of the autofluorescence spectra in the generated data is produced, e.g., on a display. In some instances, the visualization highlights autofluorescence spectra associated with the variance in the generated data. In some instances, the visualization includes a panel hotspot matrix. In some instances, the visualization includes a diagonal visualization of the panel hotspot matrix. In some instances, the visualization includes a diffusion correlation matrix. In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as the diffusion inflation factor, SIF). In some instances, the method includes measuring the diffusion inflation factor (SIF) based on the hotspot matrix. In some instances, the measured diffusion inflation factors are evaluated to determine whether they are limited to autofluorescence spectra.

[0027] In some embodiments, assessing collinearity among autofluorescence spectra includes assessing the variance decomposition ratio (VDP). In some embodiments, the method includes assessing whether the measured diffusion expansion factor is limited to autofluorescence spectra based on the collinearity of the assessed autofluorescence spectra using the variance decomposition ratio. In some instances, assessing the variance decomposition ratio includes using singular value decomposition (SVD) to identify collinear groups of spectra. In some instances, a condition index is calculated for each singular value generated by the singular value decomposition. In some instances, the condition index is calculated as the ratio of the largest calculated singular value to each individually calculated singular value. In some instances, the method includes identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index. In some instances, the method includes identifying autofluorescence spectra with a condition index greater than 15. In some cases, the method includes identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index and a condition index greater than 15 as collinear.

[0028] In some embodiments, the method includes removing autofluorescence spectra determined to be collinear. In some instances, the method further includes removing autofluorescence spectra that contribute most to the variance of the flow cytometry data. In some instances, the method includes determining the optimal combination of autofluorescence spectra to use when analyzing flow cytometry data. In some instances, the method includes removing fluorescence spectra of fluorophores that contribute to the variance of the flow cytometry data based on the calculated collinearity of the fluorophore fluorescence spectrum with one or more autofluorescence spectra. In some instances, the method includes removing autofluorescence spectra that contribute most to the unmixing bias in the spectral unmixing of the flow cytometry data.

[0029] In some embodiments, light is detected in multiple photodetector channels. In some instances, the detected light is scattered light, such as forward-scattered light, side-scattered light, or a combination thereof. In some instances, the method includes illuminating a sample with a light source. In some instances, the light source includes a laser, such as multiple lasers.

[0030] This disclosure also includes flow cytometry systems for practicing subject-matter methods, such as evaluating collinearity in the autofluorescence spectra of samples. A system according to certain embodiments includes: a light source configured to illuminate a sample having particles in a flowing stream; a light detection system with a photodetector for detecting the light from the particles in the sample; and a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to measure the autofluorescence spectra generated by the particles in the sample and evaluate collinearity between the autofluorescence spectra generated by two or more different particles in the sample.

[0031] In some embodiments, the sample comprises a plurality of different particles, and the memory includes instructions for measuring the autofluorescence spectrum generated by each of the different particles in the sample. Where the particles comprise one or more fluorophores, the memory may include instructions for evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0032] In some embodiments, the memory includes instructions for measuring the autofluorescence of a sample from unlabeled particles. In some embodiments, the memory includes instructions for measuring the autofluorescence of a sample from a single-stained control particle. In some embodiments, the memory includes instructions for measuring the autofluorescence of a sample from particles having multiple fluorophores. In some embodiments, the memory includes instructions for selecting a population of autofluorescence spectra for evaluating collinearity. In some instances, the memory includes instructions for selecting a population of autofluorescence spectra by generating a scatter plot of fluorescence parameters of particles in the sample and gating one or more populations on the scatter plot based on the median fluorescence intensity measured for each particle in the sample. In some instances, the memory includes instructions for selecting a population of autofluorescence spectra by applying an unsupervised clustering algorithm to identify the particle population. In some instances, unsupervised clustering algorithms include one or more of the following: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy C-means clustering, centroid-based clustering, and the Birch algorithm. In some cases, unsupervised clustering algorithms are self-organizing map algorithms (e.g., FlowSOM).

[0033] In some embodiments, the memory includes instructions for selecting populations of autofluorescence spectra using statistical analysis algorithms. In some instances, the statistical analysis algorithms include one or more of the following: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical clustering analysis (HCA), linear discriminant analysis, and matrix factorization. In some cases, the statistical analysis algorithms include dimensionality reduction. In some cases, the memory includes instructions for determining the autofluorescence spectra of particles based on the median fluorescence intensity (MFI) of each particle population.

[0034] In some embodiments, the memory includes instructions for evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in the sample. In some instances, the memory includes instructions for evaluating the unmixing performance by: generating a spectral matrix associated with fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; applying the spectral matrix to unmix fluorescence spectra generated by an unstained control, a single-stained control, a stained sample, or any combination thereof; and calculating one or more of unmixing bias and unmixing variance. In some instances, the memory includes instructions for calculating the unmixing bias by measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectra. In some instances, the memory includes instructions for determining that no false-positive unmixed fluorophore signals associated with the autofluorescence spectra are present in the unmixed fluorophore channels across all generated particle populations in the sample. In some instances, the memory includes instructions for calculating the unmixing variance by measuring unmixing-dependent diffusion in the unmixed fluorophore signals. In some embodiments, the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize the unmixing bias.

[0035] In some instances, the memory includes instructions for assessing collinearity by: generating a spectral matrix associated with autofluorescence produced by particles in a sample, calculating an inverse matrix based on the generated spectral matrix, and identifying an autofluorescence spectrum associated with the variance in data generated by the flow cytometer using the autofluorescence spectrum.

[0036] In some embodiments, the memory includes instructions for identifying autofluorescence spectra that contribute to the variance in flow cytometry data. In some instances, the memory includes instructions for identifying autofluorescence spectra affected by the variance in flow cytometry data. In some instances, the inverse matrix is ​​a pseudo-inverse matrix. In some instances, the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix. In some instances, the inverse matrix is ​​a Gram inverse matrix. In some cases, the inverse matrix is ​​calculated according to the following equation:

[0037]

[0038] in:

[0039] G is the Gram inverse matrix;

[0040] M is the spectral matrix; and

[0041] M T It is the transpose of the spectral matrix.

[0042] In some embodiments, the memory includes instructions for analyzing the computed inverse matrix by deriving a quantitative metric from the inverse matrix. In some instances, the quantitative metric is a matrix norm. In some instances, the quantitative metric is a vector norm. In some instances, the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data. In some instances, the memory includes instructions for identifying autofluorescence spectra that minimize the variance of the generated data by identifying autofluorescence spectra exhibiting maximum spectral matrix modulation. In some instances, the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias and minimize the variance of the generated data.

[0043] In some instances, the memory includes instructions for removing autofluorescence contributions from the generated flow cytometry data. In some instances, the memory includes instructions for iteratively identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data.

[0044] In some embodiments, the flow cytometry system includes a display for visualizing the assessed collinearity of autofluorescence spectra. In some instances, the memory includes instructions for generating a visual representation on the display of the assessed collinearity of autofluorescence spectra in the generated data. In some instances, the visualization highlights autofluorescence spectra associated with the variance in the generated data. In some instances, the visualization includes a panel hotspot matrix. In some instances, the visualization includes a diagonal visualization of the panel hotspot matrix. In some instances, the visualization includes a diffusion correlation matrix. In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as the diffusion inflation factor, SIF). In some instances, the memory includes instructions for measuring the diffusion inflation factor (SIF) based on the hotspot matrix. In some instances, the memory includes instructions for evaluating the measured diffusion inflation factors to determine whether they are limited to autofluorescence spectra.

[0045] In some embodiments, the memory includes instructions that, when executed by a processor, cause the processor to evaluate collinearity among autofluorescence spectra by evaluating a variance decomposition ratio (VDP). In some embodiments, the memory includes instructions for evaluating whether a measured diffusion expansion factor is limited to autofluorescence spectra based on the collinearity of the evaluated autofluorescence spectra using a variance decomposition ratio. In some instances, the memory includes instructions for evaluating a variance decomposition ratio by identifying collinear groups of spectra using singular value decomposition (SVD). In some instances, the memory includes instructions for calculating a condition index for each singular value generated by the singular value decomposition. In some instances, the memory includes instructions for calculating the condition index as the ratio of the largest calculated singular value to each individually calculated singular value. In some instances, the memory includes instructions for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index. In some instances, the memory includes instructions for identifying autofluorescence spectra with a condition index greater than 15. In some instances, the memory includes instructions for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 as collinear.

[0046] In some instances, the memory includes instructions for removing autofluorescence spectra determined to be collinear. In some instances, the memory includes instructions for removing autofluorescence spectra that contribute most to the variance of flow cytometry data. In some instances, the memory includes instructions for determining the optimal combination of autofluorescence spectra to use when analyzing flow cytometry data. In some instances, the memory includes instructions for removing fluorophore fluorescence spectra that contribute to the variance of flow cytometry data based on the calculated collinearity of the fluorophore fluorescence spectrum with one or more autofluorescence spectra. In some instances, the memory includes instructions for removing autofluorescence spectra that contribute most to the unmixing bias in the spectral unmixing of flow cytometry data.

[0047] This disclosure also includes non-transitory computer-readable storage media, such as for practicing one or more computer-implemented methods described herein. In some embodiments, the non-transitory computer-readable storage medium includes: an algorithm for illuminating a sample comprising particles in a flow stream with a light source in a flow cytometer; an algorithm for detecting light from the illuminating particles using a light detection system including a photodetector; an algorithm for measuring the autofluorescence spectrum generated by particles in the sample; and an algorithm for evaluating collinearity between autofluorescence spectra generated by two or more different particles in the sample.

[0048] In some embodiments, the sample has multiple distinct particles, and the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence spectrum generated by each of the distinct particles in the sample. In some instances, the particles of the sample have one or more fluorophores, and the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0049] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample from unlabeled particles. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample from a single-stained control particle. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample from particles having multiple fluorophores. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra for evaluating collinearity. In some instances, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra by generating a scatter plot of fluorescence parameters of particles in the sample and gating one or more populations on the scatter plot based on the median fluorescence intensity measured for each particle. In some instances, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra by identifying a population of particles by applying an unsupervised clustering algorithm. In some instances, unsupervised clustering algorithms include one or more of the following: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy C-means clustering, centroid-based clustering, and the Birch algorithm. In some cases, unsupervised clustering algorithms are self-organizing map algorithms (e.g., FlowSOM).

[0050] In some embodiments, the non-transitory computer-readable storage medium includes algorithms for selecting populations of autofluorescence spectra using statistical analysis algorithms. In some instances, the statistical analysis algorithms include one or more of the following: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical clustering analysis (HCA), linear discriminant analysis, and matrix factorization. In some cases, the statistical analysis algorithms include dimensionality reduction. In some cases, the non-transitory computer-readable storage medium includes algorithms for determining the autofluorescence spectra of particles based on the median fluorescence intensity (MFI) of each particle population.

[0051] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the unmixing performance by: generating a spectral matrix associated with fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; applying the spectral matrix to unmix fluorescence spectra generated by an unstained control, a monostained control, a stained sample, or any combination thereof; and calculating one or more of an unmixing bias and an unmixing variance. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating the unmixing bias by measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes an algorithm for determining that no false-positive unmixed fluorophore signals associated with the autofluorescence spectra are present in the unmixed fluorophore channels across all generated particle populations in the sample. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating the unmixing variance by measuring unmixing-dependent diffusion in the unmixed fluorophore signals. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in a sample that minimize unmixing bias.

[0052] In some instances, the non-transitory computer-readable storage medium includes algorithms for evaluating collinearity, such as algorithms for generating a spectral matrix associated with autofluorescence generated by particles in a sample, algorithms for calculating an inverse matrix based on the generated spectral matrix, and algorithms for identifying autofluorescence spectra associated with the variance in data generated by flow cytometry using these autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes algorithms for identifying autofluorescence spectra that contribute to the variance in flow cytometry data.

[0053] In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra affected by variance in flow cytometry data. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra that contribute to the variance in flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra affected by variance in flow cytometry data. In some instances, the inverse matrix is ​​a pseudo-inverse matrix. In some instances, the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix. In some instances, the inverse matrix is ​​a Gram inverse matrix. In some cases, the inverse matrix is ​​calculated according to the following equation:

[0054]

[0055] in:

[0056] G is the Gram inverse matrix;

[0057] M is the spectral matrix; and

[0058] M T It is the transpose of the spectral matrix.

[0059] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for analyzing the computed inverse matrix by deriving a quantitative metric from the inverse matrix. In some instances, the quantitative metric is a matrix norm. In some instances, the quantitative metric is a vector norm. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra that minimize the variance of the generated data by identifying autofluorescence spectra exhibiting maximum spectral matrix modulation. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias and minimize the variance of the generated data.

[0060] In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence contributions from the generated flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for iteratively identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data.

[0061] In some embodiments, the flow cytometry system includes a display for visualizing the assessed collinearity of autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes an algorithm for generating a visual representation of the assessed collinearity of autofluorescence spectra in the generated data on the display. In some instances, the visualization highlights autofluorescence spectra associated with variance in the generated data. In some instances, the visualization includes a panel hotspot matrix. In some instances, the visualization includes a diagonal visualization of the panel hotspot matrix. In some instances, the visualization includes a diffusion correlation matrix. In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as the diffusion inflation factor, SIF). In some instances, the non-transitory computer-readable storage medium includes an algorithm for measuring the diffusion inflation factor (SIF) based on the hotspot matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the measured diffusion inflation factors to determine whether they are limited to autofluorescence spectra.

[0062] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity among autofluorescence spectra by assessing the variance decomposition ratio (VDP). In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for assessing whether a measured diffusion expansion factor is limited to autofluorescence spectra based on the collinearity of the assessed autofluorescence spectra by using the variance decomposition ratio. In some instances, the non-transitory computer-readable storage medium includes an algorithm for assessing the variance decomposition ratio by identifying collinear groups of spectra using singular value decomposition (SVD). In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating a condition index for each singular value generated by the singular value decomposition. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating the condition index as the ratio of the largest calculated singular value to each individually calculated singular value. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a condition index greater than 15. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 for each condition index as collinear.

[0063] In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra determined to be collinear. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra that contribute most to the variance in flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining the optimal combination of autofluorescence spectra to use when analyzing flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing fluorophore fluorescence spectra that contribute to the variance in flow cytometry data based on the collinearity of calculated fluorophore fluorescence spectra with one or more autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra that contribute most to the unmixing bias in the spectral unmixing of flow cytometry data. Attached Figure Description

[0064] This disclosure can be best understood by reading in conjunction with the accompanying drawings, which include the following detailed description:

[0065] Figure 1A A flowchart illustrating collinearity of the autofluorescence spectra of particles in a sample in a flowing stream, according to certain embodiments, is described. Figure 1BA flowchart illustrating a workflow for determining an optimal combination of autofluorescence spectra to be used in a spectral unmixing matrix, according to certain embodiments, is provided. Figure 1C-1K An experiment for evaluating autofluorescence collinearity according to certain embodiments is described. Figure 1C The process of acquiring two unstained samples is described, including unstained, unstimulated PBMCs and unstained, stimulated PBMCs. Figure 1D Different autofluorescence (AF) populations were described. Figure 1E The unmixing performance of different spectral unmixing matrices for evaluating the absence of autofluorescence spectra is described. Figure 1F The demixing performance of different autofluorescence matrix options with a single autofluorescence spectrum but two different versions is described. Figure 1G The demixing performance of two autofluorescence spectral selections was described. Figure 1H The demixing performance of three autofluorescence spectral selections was described. Figure 1I The demixing performance of four autofluorescence spectral selections was described. Figure 1J The method of evaluating unmixing performance using a hotspot matrix is ​​described. Figure 1K The method for evaluating unmixing performance using variance decomposition ratio (VDP) analysis is described.

[0066] Figure 2 A flow cytometry system according to certain embodiments is shown.

[0067] Figure 3 An image-enabled particle sorter according to certain embodiments is described.

[0068] Figure 4 A functional block diagram of a particle analysis system according to certain embodiments is depicted.

[0069] Figure 5 A functional block diagram of an example control system according to certain embodiments is depicted.

[0070] Figures 6A-6B A schematic diagram of a particle sorting system according to certain embodiments is depicted.

[0071] Figure 7 Aspects of a computer control system according to certain embodiments are described. Detailed Implementation

[0072] This disclosure includes methods for assessing collinearity between autofluorescence spectra of particles in a sample. The method according to some embodiments includes: illuminating a sample containing particles in a flow stream with a light source in a flow cytometer; detecting the light from the illuminating particles using a light detection system with a photodetector; measuring the autofluorescence spectra generated by the particles in the sample; and assessing collinearity between the autofluorescence spectra generated by two or more different particles in the sample. A system and a non-transitory computer-readable storage medium configured to perform the subject method are also provided.

[0073] Before describing this disclosure in more detail, it should be understood that this disclosure is not limited to the specific embodiments described, which may vary. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of this disclosure will be limited only by the appended claims.

[0074] Where a range of values ​​is provided, it should be understood that every intermediate value between the upper and lower limits of the range (accurate to one-tenth of the lower limit unit unless otherwise expressly specified by the context), as well as any other specified value or intermediate value within that range, is included in this invention. The upper and lower limits of these smaller ranges can be independently included in the smaller range and also in this invention, subject to any specific exclusions within the range. When the range contains one or both limit values, the range excluding any or both of the included limit values ​​is also included in this invention.

[0075] Certain ranges are presented in this document with the term "approximately" preceding the numerical value. The term "approximately" is used to provide textual support for the exact numerical value preceding it, as well as for values ​​that are close to or approximate to the preceding term. In determining whether a numerical value is close to or approximate to a specifically listed numerical value, the close or approximate unlisted numerical value should be one that is substantially equivalent to the specifically listed numerical value in the context presented.

[0076] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of this invention, only representative exemplary methods and materials are described herein.

[0077] All publications and patents referenced in this specification are incorporated herein by reference as if each individual publication or patent were expressly and individually indicated to be incorporated herein by reference, and to disclose and describe technical methods and / or materials of interest in the referenced publications. Any reference to a publication is based on its publication date being earlier than the filing date of this application and should not be construed as an admission that the invention is not entitled to precede that publication by a prior disclosure. Furthermore, the publication dates provided may differ from the actual publication dates, which may require independent verification.

[0078] It should be noted that, unless the context clearly specifies otherwise, the use of an element whose quantity is not limited in this document and in the appended claims implies the presence of at least one such element. It should also be noted that the claims may be drafted to exclude any optional elements. Therefore, this statement is intended as a prior basis for the use of exclusive terms such as "unique," "only," etc., when referencing claim elements or using the "negative" limitation. It will be apparent to those skilled in the art, upon reading this invention, that each individual embodiment described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments, without departing from the scope or spirit of the invention. Any enumerated methods may be performed in the order of the enumerated events, or in any other logically feasible order.

[0079] Although the apparatus and method have been or will be described in conjunction with functional interpretation for grammatical fluency, it should be clearly understood that, unless expressly provided in 35 USC § 112, the claims should not be construed in any way as necessarily limited to “apparatus” or “step”, but should be given the full range of meaning and equivalents provided by the definition of the claims in accordance with the doctrine of judicial equivalence, and if the claims are expressly provided in 35 USC § 112, they should be given the full range of legal equivalents in accordance with 35 U.SC 112, etc.

[0080] A method for assessing collinearity between autofluorescence spectra of particles in a sample.

[0081] This disclosure includes methods for assessing collinearity between autofluorescence spectra of particles in a sample. In embodiments, assessing collinearity of the autofluorescence spectra of particles in a sample improves the sensitivity and accuracy of flow cytometry data. In some instances, the assessed collinearity provides more accurate spectral unmixing to separate the contributions between the spectra of different components of the detected light, for example, where the spectral unmixing efficiency is improved by 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, and including 95% or more. In some instances, autofluorescence can be modeled as additional fluorophores or a group of fluorophores in the spectral unmixing, allowing the autofluorescence of particles in the sample to be directly accounted for. In some instances, accounting for autofluorescence reduces or eliminates artifacts in the spectral unmixing data. In some instances, accounting for the contribution of autofluorescence can reduce or eliminate artificially high background signals from fluorophores with similar spectra to the autofluorescence. By including the autofluorescence spectrum in the unmixing process, the contribution of autofluorescence to the unmixing signal of other fluorophores is appropriately extracted. In some cases, correctly including autofluorescence spectra provides accurate results for multicolor flow cytometry panels, especially in high autofluorescence sample types (such as digestive tissue or tumor samples) and samples with high autofluorescence heterogeneity.

[0082] In some embodiments, the method includes minimizing the collinearity of autofluorescence spectra used in the spectral unmixing of the generated flow cytometry data. In some cases, minimizing this collinearity of autofluorescence spectra provides a reduction in the variance of the data generated in the spectral unmixed data, for example, a reduction of 5% or more, such as 10% or more, 25% or more, 50% or more, 75% or more, 90% or more, and including 95% or more.

[0083] In embodiments, the collinearity of the autofluorescence spectra of particles in the sample is evaluated. In some embodiments, collinearity describes an effect where the combination of spectra in the matrix (between two different autofluorescence spectra or between an autofluorescence spectrum and a fluorescence spectrum from a fluorophore) becomes nearly linearly correlated, leading to poor matrix conditioning and amplification of the variance of the subsequent unmixed data. In some instances, the methods described herein provide a way to evaluate and select the optimal set of autofluorescence spectra for unmixing based on quantitative analysis of spectral collinearity.

[0084] In some embodiments, the method includes using hotspot analysis (evaluating a hotspot matrix, which is computed as the inverse of the correlation matrix of the spectral matrix, as described in more detail below). In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as a diffusion inflation factor), which describes the degree to which the unmixing variance of the fluorescence spectra would be amplified due to collinearity in the context of a particular unmixing matrix. Large off-diagonal entries in the hotspot matrix indicate which spectral combinations are involved in collinear relationships. In some instances, off-diagonal entries can be used to predict the degree of covariance between unmixed fluorescence pairs. In some instances, it is the expected effect of a given selection of autofluorescence spectra on the variance of other fluorophores. In some instances, the method includes identifying a group of collinear autofluorescence spectra. In some instances, the method includes assessing a quantitative effect on the variance of the unmixed data in response to a group of collinear autofluorescence spectra. In some instances, the method includes identifying a set of autofluorescence spectra for spectral unmixing of flow cytometry data.

[0085] As described in more detail below, in some instances, the method involves using variance decomposition ratio (VDP) analysis and condition indexes (CI) to identify collinear autofluorescence spectral groups. In some instances, VDP analysis and condition indexes use singular value decomposition.

[0086] In some embodiments, methods for evaluating the collinearity of autofluorescence spectra (and the emission spectra of fluorophores as described below) include one or two of the following: hotspot analysis (evaluating the hotspot matrix, which is calculated as the inverse of the correlation matrix of the spectral matrix) and variance decomposition ratio (VDP) analysis and condition index (CI). In some instances, collinearity of autofluorescence spectra is evaluated by a combination of hotspot analysis (evaluating the hotspot matrix, which is calculated as the inverse of the correlation matrix of the spectral matrix) and variance decomposition ratio (VDP) analysis and condition index (CI).

[0087] In practicing the subject method, a sample containing particles is illuminated with light from a light source (e.g., in a flow stream of a flow cytometer). In some embodiments, the light source is a broadband light source that emits light with a wide range of wavelengths, such as across 50 nm or more, for example 100 nm or more, for example 150 nm or more, for example 200 nm or more, for example 250 nm or more, for example 300 nm or more, for example 350 nm or more, for example 400 nm or more, and including across 500 nm or more. For example, a suitable broadband light source emits light with wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light with wavelengths from 400 nm to 1000 nm. When the method involves illumination with a broadband light source, the broadband light source schemes of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stable fiber-coupled broadband light sources, broadband LEDs with a continuous spectrum, superluminescent diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, and other broadband light sources or any combination thereof.

[0088] In other embodiments, the method includes illumination with a narrowband light source emitting a specific wavelength or a narrow range of wavelengths, such as illumination with a light source emitting a narrow wavelength range (e.g., 50 nm or less, e.g., 40 nm or less, e.g., 30 nm or less, e.g., 25 nm or less, e.g., 20 nm or less, e.g., 15 nm or less, e.g., 10 nm or less, e.g., 5 nm or less, e.g., 2 nm or less), and also includes illumination with a light source emitting a specific wavelength of light (i.e., monochromatic light). When the method includes illumination with a narrowband light source, the narrowband light source scheme of interest may include, but is not limited to, narrow-wavelength LEDs, laser diodes, or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0089] In some embodiments, the method includes irradiating the sample with one or more lasers. As described above, the type and number of lasers will vary depending on the sample and the light to be collected, and can be gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other instances, the method includes irradiating the flow with a dye laser, such as a stilbene dye laser, a coumarin dye laser, or a rhodamine dye laser. In still other instances, the method includes irradiating the flow with a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, or combinations thereof. In other instances, the method involves irradiating the flow with a solid-state laser, such as a ruby ​​laser, an Nd:YAG laser, an NdCrYAG laser, an Er:YAG laser, an Nd:YLF laser, an Nd:YVO4 laser, an Nd:yCa4O(BO3)3 laser, an Nd:YCOB laser, a titanite sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a ytterbium₂O₃ laser, or a cerium-doped laser, or combinations thereof.

[0090] The sample can be illuminated with one or more of the aforementioned light sources, such as two or more light sources, three or more light sources, four or more light sources, five or more light sources, and including ten or more light sources. The light sources can include combinations of any type of light source. For example, in some embodiments, the method includes illuminating the sample in the flowing stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0091] The sample can be illuminated with light ranging from 200 nm to 1500 nm, for example from 250 nm to 1250 nm, for example from 300 nm to 1000 nm, for example from 350 nm to 900 nm, and including from 400 nm to 800 nm. For example, when the light source is a broadband light source, the sample can be illuminated with light ranging from 200 nm to 900 nm. In other instances, when the light source comprises multiple narrowband light sources, the sample can be illuminated with specific wavelengths in the range of 200 nm to 900 nm. For example, the light source can be multiple narrowband LEDs (1 nm – 25 nm), each independently emitting light in the range of 200 nm to 900 nm. In other embodiments, the narrowband light source comprises one or more lasers (e.g., a laser array) and illuminates the sample with specific wavelengths ranging from 200 nm to 700 nm, for example using a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers as described above.

[0092] When using more than one light source, the sample can be illuminated simultaneously or sequentially, or a combination thereof. For example, the sample can be illuminated simultaneously with each light source. In other embodiments, the flow stream is illuminated sequentially with each light source. When illuminating the sample sequentially with more than one light source, the duration of illumination for each light source can be independently 0.001 microseconds or longer, for example 0.01 microseconds or longer, for example 0.1 microseconds or longer, for example 1 microsecond or longer, for example 5 microseconds or longer, for example 10 microseconds or longer, for example 30 microseconds or longer, and includes 60 microseconds or longer. For example, the method can include illuminating the sample with a light source (e.g., a laser) for a duration from 0.001 microseconds to 100 microseconds, for example 0.01 microseconds to 75 microseconds, for example 0.1 microseconds to 50 microseconds, for example 1 microsecond to 25 microseconds, and includes 5 microseconds to 10 microseconds. In embodiments where the sample is illuminated sequentially with two or more light sources, the duration of illumination for each light source can be the same or different.

[0093] The time interval between each light source illumination can also vary as needed, independently spaced by a delay of 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 15 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the time interval between each light source illumination can range from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, such as from 0.1 microseconds to 35 microseconds, such as from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In some embodiments, the time interval between each light source illumination is 10 microseconds. In embodiments where the sample is sequentially illuminated by more than two (i.e., three or more) light sources, the delay between each light source illumination can be the same or different.

[0094] The sample can be illuminated continuously or at discrete intervals. In some instances, the method involves illuminating the sample continuously with a light source. In other instances, the sample is illuminated with a light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including once every 1000 milliseconds, or some other interval.

[0095] Depending on the light source, the sample can be illuminated from varying distances, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 2.5 mm or more, 5 mm or more, 10 mm or more, 15 mm or more, 25 mm or more, and including 50 mm or more. Furthermore, the illumination angle can also vary, ranging from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and including from 30° to 60°, such as at a 90° angle.

[0096] In some embodiments, the method includes illuminating a sample with two or more frequency-shifted beams of light. As described above, a beam generator assembly having a laser and an acousto-optic device for frequency-shifting the laser can be employed. In these embodiments, the method includes illuminating the acousto-optic device with a laser. Depending on the desired wavelength of the light generated in the output laser beam (e.g., for illuminating a sample in a flowing stream), the laser can have a specific wavelength varying from 200 nm to 1500 nm, for example from 250 nm to 1250 nm, for example from 300 nm to 1000 nm, for example from 350 nm to 900 nm, and including from 400 nm to 800 nm. The acousto-optic device can be illuminating with one or more lasers, for example two or more lasers, for example three or more lasers, for example four or more lasers, for example five or more lasers, and including ten or more lasers. The lasers can include any type of laser combination. For example, in some embodiments, the method includes illuminating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0097] When using more than one laser, the acousto-optic device can be irradiated simultaneously, sequentially, or in combination with the laser. For example, each laser can be used to irradiate the acousto-optic device simultaneously. In other embodiments, each laser is used to irradiate the acousto-optic device sequentially. When using more than one laser to sequentially irradiate the acousto-optic device, the duration of irradiation by each laser can be independently 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 30 microseconds or more, and including 60 microseconds or more. For example, the method can include irradiating the acousto-optic device with lasers for a duration from 0.001 microseconds to 100 microseconds, for example, from 0.01 microseconds to 75 microseconds, for example, from 0.1 microseconds to 50 microseconds, for example, from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In embodiments where the acousto-optic device is sequentially irradiated by two or more lasers, the duration of irradiation by each laser can be the same or different.

[0098] The time interval between each laser irradiation can also vary as needed, independently spaced by a delay of 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 15 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the time interval between each light source irradiation can range from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, such as from 0.1 microseconds to 35 microseconds, such as from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In some embodiments, the time interval between each laser irradiation is 10 microseconds. In embodiments where more than two (i.e., three or more) lasers are sequentially irradiated into the acousto-optic device, the delay between each laser irradiation can be the same or different.

[0099] The acousto-optic device can be irradiated continuously or at discrete intervals. In some instances, the method involves continuously irradiating the acousto-optic device with a laser. In other cases, the acousto-optic device is irradiated with a laser at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, and including once every 1000 milliseconds, or some other interval.

[0100] Depending on the laser, the acousto-optic device can be irradiated from varying distances, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 2.5 mm or more, 5 mm or more, 10 mm or more, 15 mm or more, 25 mm or more, and including 50 mm or more. Furthermore, the irradiation angle can also vary, ranging from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and including from 30° to 60°, such as at a 90° angle.

[0101] In some embodiments, the method includes applying radio frequency (RF) drive signals to an acousto-optic device to generate an angle-deflected laser beam. Two or more RF drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angle-deflected laser beams, such as three or more RF drive signals, four or more RF drive signals, five or more RF drive signals, six or more RF drive signals, seven or more RF drive signals, eight or more RF drive signals, nine or more RF drive signals, ten or more RF drive signals, fifteen or more RF drive signals, twenty-five or more RF drive signals, fifty or more RF drive signals, and including one hundred or more RF drive signals.

[0102] Each angle-deflecting laser beam generated by a radio frequency (RF) drive signal has an intensity based on the amplitude of the applied RF drive signal. In some embodiments, the method includes applying an RF drive signal with a sufficient amplitude to generate an angle-deflecting laser beam with a desired intensity. In some instances, each applied RF drive signal independently has an amplitude ranging from about 0.001V to about 500V, for example from about 0.005V to about 400V, for example from about 0.01V to about 300V, for example from about 0.05V to about 200V, for example from about 0.1V to about 100V, for example from about 0.5V to about 75V, for example from about 1V to 50V, for example from about 2V to 40V, for example from 3V to about 30V, and including from about 5V to about 25V. In some instances, each applied radio frequency drive signal independently has an amplitude from about 0.001V to 100V, for example from about 0.001V to 200V, for example from 0.001V to 300V, for example from 0.001V to 400V, and including from 0.001V to 500V. Each applied radio frequency drive signal in some embodiments has a frequency from about 0.001MHz to about 500MHz, for example from about 0.005MHz to about 400MHz, for example from about 0.01MHz to about 300MHz, for example from about 0.05MHz to about 200MHz, for example from about 0.1MHz to about 100MHz, for example from about 0.5MHz to about 90MHz, for example from about 1MHz to about 75MHz, for example from about 2MHz to about 70MHz, for example from about 3MHz to about 65MHz, for example from about 4MHz to about 60MHz, and including from about 5MHz to about 50MHz. Each applied radio frequency drive signal has a frequency of about 0.001 MHz to about 100 MHz in some embodiments, such as 0.001 MHz to 200 MHz, such as 0.001 MHz to 300 MHz, such as 0.001 MHz to 400 MHz, such as 0.001 MHz to 500 MHz.

[0103] In these embodiments, the angle-deflecting laser beams in the output laser beam are spatially separated. Depending on the applied RF drive signal and the desired illumination profile of the output laser beam, the angle-deflecting laser beams may be separated by 0.001 µm or more, for example, 0.005 µm or more, for example, 0.01 µm or more, for example, 0.05 µm or more, for example, 0.1 µm or more, for example, 0.5 µm or more, for example, 1 µm or more, for example, 5 µm or more, for example, 10 µm or more, for example, 100 µm or more, for example, 500 µm or more, for example, 1000 µm or more, and including 5000 µm or more. In some embodiments, the angle-deflecting laser beams overlap, for example, with adjacent angle-deflecting laser beams on the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., beam spot overlap) can be 0.001 µm or more, such as 0.005 µm or more, such as 0.01 µm or more, such as 0.05 µm or more, such as 0.1 µm or more, such as 0.5 µm or more, such as 1 µm or more, such as 5 µm or more, such as 10 µm or more, and includes 100 µm or more overlap.

[0104] In some cases, a flow can be illuminated with multiple frequency-shifted beams and the particles in the flow can be imaged, as described by Diebold et al. in Nature Photonics, Vol. 7 (10), and as described in U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; 10,684,21 The disclosures of these patents are described in 1; 10,845,295; 10,935,482; 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851; the disclosures of these patents are incorporated herein by reference.

[0105] In practicing the subject method, light from each particle is detected by a light detection system. In embodiments, the light detection system includes one or more photodetectors, such as two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more photodetectors. The photodetectors used in practicing the subject method can be any convenient light detection scheme, including but not limited to light sensors or photodetectors such as avalanche photodiodes (APDs), active pixel sensors (APS), quadrant photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or combinations thereof, and other photodetectors. In some embodiments, the photodetector is a photomultiplier tube, for example, having an effective detection surface area per region ranging from 0.01 cm². 2 up to 10cm 2 Photomultiplier tubes, for example, from 0.05 cm 2 up to 9 cm 2 For example, from 0.1 cm 2 up to 8 cm 2 For example, from 0.5 cm 2 up to 7cm 2 And including from 1 cm 2 up to 5 cm 2 Detects light from the illuminated sample in two or more photodetector channels, such as 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 12 or more, 16 or more, 24 or more, 32 or more, 64 or more, 128 or more, 256 or more, and includes 512 or more photodetector channels.

[0106] Light can be measured using a photodetector at one or more wavelengths, such as at two or more wavelengths, at five or more different wavelengths, at ten or more different wavelengths, at 25 or more different wavelengths, at 50 or more different wavelengths, at 100 or more different wavelengths, at 200 or more different wavelengths, at 300 or more different wavelengths, and including at 400 or more different wavelengths. Light can be measured continuously or at discrete intervals. In some instances, the detector of interest is configured to measure light continuously. In other instances, the detector of interest is configured to perform measurements at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, and including every 1000 milliseconds, or some other interval.

[0107] In some embodiments, the light detected from the sample is scattered light. The term "scattered light" is used herein in its conventional sense to refer to light energy propagating from particles in the sample (e.g., flowing in a flow), which is deflected from the path of the incident beam, for example, by reflection, refraction, or deflection. In some instances, the scattered light detected from particles in the flow is forward scattered light (FSC). In other instances, the scattered light detected from particles in the flow is side scattered light (SSC). And in still other instances, the scattered light detected from particles in the flow is backscattered light (BSC).

[0108] In some embodiments, the light detected from each particle in the sample is transmitted light, such as light detected using a bright-field detector. In other embodiments, the light detected from each particle in the sample is emitted light, such as particle luminescence (i.e., fluorescence or phosphorescence), as described in more detail below.

[0109] In this embodiment, the autofluorescence produced by the irradiated particles is measured. The term “autofluorescence” is used herein in its conventional sense to refer to the light emitted by the structure or components of a particle (e.g., cellular components within a cell) when illuminated by a light source. Autofluorescence is not derived from and is distinct from added fluorescent markers. In some instances, the particles are cells, and cellular autofluorescence includes the light emission of autofluorescent molecules such as NADPH, flavins, proteins, and amino acids such as tryptophan, tyrosine, and phenylalanine. In some cases, cellular autofluorescence includes the light emission of intrinsic properties of collagen or elastin. The autofluorescence of particles may vary in certain cases depending on the state of the cell, such as whether the cell is unstimulated, stimulated, activated, or in some other biological or physical state. As described herein, the method includes assessing collinearity of the autofluorescence spectra from particles (e.g., cellular autofluorescence). In some embodiments, the contribution of the autofluorescence spectrum to the spectral unmixing of the fluorescence flow cytometry data is considered, reduced, and eliminated as needed.

[0110] In some instances, the sample comprises two or more particles of different types, such as three or more, four or more, five or more, ten or more, fifteen or more, twenty-five or more, fifty or more, seventy-five or more, and even one hundred or more particles of different types. In some instances, the particles are cells. In some instances, the cells are in two or more different states, such as one or more cells being in stimulated, unstimulated, activated, and inactivated states.

[0111] In some embodiments, the method includes measuring the autofluorescence of a sample from unlabeled particles. In some embodiments, the method includes measuring the autofluorescence of a sample from a single-stained control particle. In some embodiments, the method includes measuring the autofluorescence of a sample from particles having multiple fluorophores. In some embodiments, the particles in the sample include one or more fluorophores that emit fluorescence in response to illumination by a light source. In some embodiments, the method includes evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores. In the embodiments described below, the fluorescence spectra of the fluorophores may be combined with the autofluorescence spectra for data analysis.

[0112] For example, each particle may include two or more fluorophores, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, fifteen or more, twenty-five or more, fifty or more, seventy-five or more, and fluorophore panels including 100 or more fluorophores. The subject fluorophore panel may include any suitable group of fluorophores. According to some embodiments, the fluorophores of interest have an excitation maximum of 100 nm to 800 nm, such as 150 nm to 750 nm, 200 nm to 700 nm, 250 nm to 650 nm, 300 nm to 600 nm, and including 400 nm to 500 nm. According to some embodiments, the fluorophore of interest has an emission maximum of 400 nm to 1000 nm, such as 450 nm to 950 nm, 500 nm to 900 nm, 550 nm to 850 nm, and including 600 nm to 800 nm. In some cases, the fluorophore is a luminescent dye, such as a fluorescent dye with a peak emission wavelength of 200 nm or more, such as 250 nm or more, 300 nm or more, 350 nm or more, 400 nm or more, 450 nm or more, 500 nm or more, 550 nm or more, 600 nm or more, 650 nm or more, 700 nm or more, 750 nm or more, 800 nm or more, 850 nm or more, 900 nm or more, 950 nm or more, 1000 nm or more, and including 1050 nm or more. For example, the fluorophore can be a fluorescent dye with a peak emission wavelength of 200 nm to 1200 nm, such as 300 nm to 1100 nm, such as 400 nm to 1000 nm, such as 500 nm to 900 nm, and including fluorescent dyes with a peak emission wavelength of 600 nm to 800 nm. In some embodiments, the fluorophore of interest may include, but is not limited to, dyes suitable for analytical applications (e.g., flow cytometry, imaging, etc.).Examples include acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes (e.g., diphenylmethane dyes), chlorophyll-containing dyes, triarylmethane dyes (e.g., triphenylmethane dyes), azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinone imine dyes, acridine dyes, uronine dyes, saffron dyes, indamine, indophenol dyes, fluorane dyes, oxazine dyes, oxazolone dyes, thiazine dyes, thiazolium dyes, xanthan dyes, fluorene dyes, pyronine dyes, fluoroalkyl dyes, rhodamine dyes, phenanthridine dyes, and dyes combining two or more of the above dyes (e.g., tandem dyes), polymer dyes having one or more monomer dye units, and mixtures of two or more of the above dyes. A wide variety of dyes are commercially available from various sources, such as Molecular Probes (Eugene, OR), Dyomics GmbH (Jena, Germany), Sigma-Aldrich (St. Louis, MO), Sirigen, Inc. (Santa Barbara, CA), and Exciton (Dayton, OH). For example, fluorophores may include 4-acetamido-4'-isothiocyanostilbene-2,2'-disulfonic acid; acridine and its derivatives, such as acridine, acridine orange, acridine yellow, acridine red, and acridine isothiocyanate; allophycocyanin, phycoerythrin, and polydinocyanin-chlorophyll; 5-(2'-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-vinylsulfonyl)phenyl]naphthalenediamine-3,5-disulfonate (Lucifer Yellow VS); N-4-anilino-1-naphthylmaleimide; aminoanisinolamide; brilliant yellow; coumarins and their derivatives, such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), and 7-amino-4-trifluoromethylcoumarin (Coumaran 151); cyanine and its derivatives, such as cyanin, Cy3, Cy3.5, Cy5, Cy5.5 and Cy7; 4',6-diamidinyl-2-phenylindole (DAPI); 5',5"-dibromopyrogallol-sulfophthalein (Bromopyrogallol Red); 7-diethylamino-3-4'-isothiocyanophenyl-4-methylcoumarin; diethylaminocoumarin; diethylenetriaminepentaacetic acid ester; 4,4'-diisothiocyanodihydrostilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid; 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansyl) chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl-4'-isothiocyanate (DABITC); eosin and its derivatives, such as eosin and eosin isothiocyanate; erythrosine and its derivatives,Examples include erythrosine B and erythrosine isothiocyanate; ethidium bromide; fluorescein and its derivatives, such as 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazine-2-yl)aminofluorescein (DTAF), 2'7'-dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein isothiocyanate (FITC), fluorescein chlorotriazine, naphthofluorescein, and QFITC ​​(XRITC); fluorescein; IR144; IR1446; green fluorescent protein (GFP); reef coral fluorescent protein (RCFP); lissamine™; lissamine rhodamine, Lucifer Yellow); Malachite green isothiocyanate; 4-methylumbelliferone; o-cresolphthalein; nitrotyrosine; paracinon red; Nile red; Oregon green; phenol red; β-phycoerythrin; phthalaldehyde; pyrene and its derivatives, such as pyrene, pyrenebutyric acid and succinimide-1-pyrenebutyric acid; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A); Rhodamine and its derivatives, such as 6-carboxy-X-rhodamine (ROX), 6-carboxy-rhodamine (R6G), 4,7-dichlororhodamine sulfonylamine, rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulfonylrhodamine B, sulfonylrhodamine 101, sulfonylrhodamine 101 sulfonyl chloride derivatives (Texas) Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA), tetramethylrhodamine and tetramethylrhodamine isothiocyanate (TRITC); riboflavin; rosehip acid and terbium chelate derivatives; xanthones; dye conjugated polymers (i.e., polymer-attached dyes), such as fluorescein isothiocyanate-dextran and dyes combining two or more dyes (e.g., tandem dyes), polymer dyes having one or more monomer dye units, and mixtures or combinations of two or more of the above dyes.

[0113] In some instances, the fluorophore (i.e., the dye) is a fluorescent polymer dye. The fluorescent polymer dyes used in the subject methods and systems are diverse. In some instances of the method, polymer dyes include conjugated polymers. Conjugated polymers (CPs) are characterized by a delocalized electronic structure comprising a backbone of alternating unsaturated bonds (e.g., double and / or triple bonds) and saturated bonds (e.g., single bonds), where π electrons can move from one bond to another. Therefore, the conjugated backbone can endow polymer dyes with extended linear structures and finite bond angles between polymer repeating units. For example, proteins and nucleic acids, while also polymers, do not form extended rod-like structures in some cases, but rather fold into more advanced three-dimensional shapes. Furthermore, CPs may form “rigid rod” polymer backbones, and experience finite torsional (e.g., twist) angles between monomer repeating units along the polymer backbone. In some instances, polymer dyes include CPs with rigid rod structures. As mentioned above, the structural properties of polymer dyes can influence the fluorescence properties of the molecule.

[0114] Any readily available polymer dye can be used in the subject methods and systems. In some instances, the polymer dye is a polychromatic with a structure capable of collecting light to amplify the fluorescence output of a fluorophore. In some instances, the polymer dye is capable of collecting light and efficiently converting it into emitted light at longer wavelengths. In some cases, the polymer dye has a light-collecting polychromatic system that can efficiently transfer energy to nearby luminescent species (e.g., a “signal chromatophore”). Energy transfer mechanisms include, for example, resonant energy transfer (e.g., Forster (or fluorescent) resonant energy transfer, FRET), quantum charge exchange (Dexter energy transfer), etc. In some instances, these energy transfer mechanisms are relatively short-range; that is, the close proximity of the light-collecting polychromatic system to the signal chromatophore provides efficient energy transfer. Under conditions of efficient energy transfer, the emission of the signal chromatophore is amplified when the number of individual chromatophores in the light-collecting polychromatic system is large; that is, the emission of the signal chromatophore is stronger when the incident light (“excitation light”) is at a wavelength absorbed by the light-collecting polychromatic system than when the signal chromatophore is directly excited by pump light.

[0115] Polychromatic groups can be found in conjugated polymers. Conjugated polymers (CPs) are characterized by delocalized electronic structures and can be used as highly responsive optical reporters for chemical and biological targets. Because the effective conjugation length is significantly shorter than the polymer chain length, the backbone contains a large number of closely spaced conjugated segments. Therefore, conjugated polymers are efficient for light harvesting and can achieve optical amplification through energy transfer.

[0116] In some instances, polymers can be used as direct fluorescent reporters, such as fluorescent polymers with high extinction coefficients, high brightness, etc. In other instances, polymers can be used as strong chromophores, where color or optical density serves as an indicator.

[0117] Polymer dyes of interest include, but are not limited to, those described in U.S. Publications 20040142344, 20080293164, 20080064042, 20100136702, 20110256549, 20120028828, 20120252986, 20130190193, and 20160025735, the disclosures of which are incorporated herein by reference in their entirety; and Gaylord et al., J. Am. Chem. Soc., 2001, 123 (26), pp 6417–6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411–2419; and Traina et al., J. Am. Chem. Soc., 2011, 133 (32), pp The dyes described in 12600–12607 are incorporated herein by reference in their entirety.

[0118] In some embodiments, a population of autofluorescence spectra is selected for evaluating collinearity. In some instances, selecting a population of autofluorescence spectra involves generating a scatter plot of fluorescence parameters of particles in the sample and gating one or more populations on the scatter plot based on the median fluorescence intensity measured for each particle in the sample.

[0119] In some instances, selecting populations based on autofluorescence spectra involves applying unsupervised clustering algorithms to identify particle populations. Unsupervised clustering algorithms include one or more of the following: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy C-means clustering, centroid-based clustering, and the Birch algorithm. In some cases, the unsupervised clustering algorithm is a self-organizing map algorithm (e.g., FlowSOM).

[0120] In some embodiments, selecting a population of autofluorescence spectra includes using statistical analysis algorithms. In some instances, the statistical analysis algorithms include one or more of the following: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical cluster analysis (HCA), linear discriminant analysis, and matrix factorization. In some cases, the statistical analysis algorithms include dimensionality reduction. In some cases, the autofluorescence spectra of the particles are determined based on the median fluorescence intensity (MFI) of each particle population.

[0121] In some embodiments, evaluating collinearity between autofluorescence spectra generated by two or more different particles in a sample includes: generating a spectral matrix associated with the autofluorescence generated by the particles in the sample, calculating an inverse matrix based on the generated spectral matrix, and identifying autofluorescence spectra associated with the variance in data generated by flow cytometry using the autofluorescence spectra.

[0122] In some embodiments, autofluorescence spectral features are received from experimental data (i.e., the results of experiments performed on a particular instrument). In other cases, autofluorescence spectral features are received from simulated data. The input spectral matrix can be associated with any suitable number of autofluorescence identifiers. In some embodiments, the number of autofluorescence identifiers in the input spectral matrix ranges from 2 to 150, for example 2 to 140, 2 to 130, 2 to 120, 2 to 110, 2 to 100, 2 to 90, 2 to 80, 2 to 70, 2 to 60, and includes 2 to 50. The set of autofluorescence spectral identifiers used in a given embodiment of the invention may be referred to as an autofluorescence palette, which collectively refers to autofluorescence spectral identifiers.

[0123] In some cases, autofluorescence spectral features include one or more overflow values. The term "overflow value" refers to the relative amount of signal emitted by a given autofluorescence spectrum into each detector band. In some cases, the overflow value is normalized for the detector with the largest signal for that autofluorescence (i.e., the "peak" detector). In some instances, particle-modulated light indicating a particular autofluorescence is received by one or more detectors in a particle analyzer (e.g., a flow cytometer) that are not the peak detectors for that autofluorescence spectrum. Therefore, the light may "overflow" and be detected by the non-peak detectors. In other words, the specific autofluorescence spectrum used in an experiment and its associated autofluorescence emission bands can be selected to generally correspond to certain detectors. However, as more detectors are provided and more labels are used, a perfect correspondence between some detectors and autofluorescence emission spectra may not be possible. Often, while the peak of the emission spectrum of a particular fluorescent molecule may fall within the window of a particular detector, some emission spectra of that label may also overlap with the windows of one or more other detectors. This can be referred to as overflow.

[0124] In some embodiments, the spectral matrix described herein may include one or more autofluorescence spectral features. Autofluorescence is an inherent fluorescent signal produced by particles (e.g., cells) when measured in flow cytometry. It originates from endogenous molecules within the cell that are fluorescently active, such as metabolites. Different cells of the same type (e.g., lymphocytes) may have the same autofluorescence spectrum but different intensities; for example, larger cells generally tend to have a larger autofluorescence signal. In some cases, different types of particles are associated with different autofluorescence spectra. For example, different types of cells (e.g., lymphocytes and monocytes) may not only have different levels of autofluorescence but may also have different autofluorescence spectra (e.g., the spectral features of lymphocyte autofluorescence may differ from those of monocyte autofluorescence). In some instances, such as in spectrocytology, the spectral features of autofluorescence are measured by observing unstained cells, and if multiple autofluorescence spectra are included, they are included as additional "fluorophore" parameters during spectral unmixing.

[0125] In some embodiments, the obtained spectral matrix is ​​a submatrix of the input spectral matrix (i.e., the spectral matrix associated with the instrument identifier), which includes only autofluorescence spectral features and spectral features of fluorophores (if present). The term "submatrix" is discussed herein in its conventional sense to describe a matrix obtained by removing a combination of rows and / or columns from another matrix.

[0126] In some instances, the method involves calculating the inverse matrix from the obtained spectral matrix. As discussed herein, the term "inverse matrix" can be used to describe the inverse of a matrix in its conventional sense, i.e., the inverse of matrix A is A. -1 , when AA -1 =A -1 When A = I, where I is the identity matrix. However, for the purposes of this disclosure, the term "inverse matrix" may also include other types of inverses, such as the inverse of a non-square matrix. For example, in some embodiments, the inverse matrix is ​​a pseudo-inverse matrix. More broadly, a "pseudo-inverse matrix" is a matrix that extends the inverse of a square matrix or an invertible matrix to a non-square matrix. In some instances, the pseudo-inverse matrix is ​​the Moore-Penrose pseudo-inverse matrix. In these cases, the pseudo-inverse matrix can be calculated as follows:

[0127]

[0128] Where A is a Matrix, A T It is the transpose of A, A + It is a pseudo-inverse. A general discussion of pseudo-inverses (such as the Moore-Penrose pseudo-inverse) can be found, for example, in U.S. Patent Nos. 7,065,286 and 9,575,162.

[0129] The pseudo-inverse is applicable to evaluating autofluorescence spectroscopy because the pseudo-inverse of the spectral matrix determines the mapping from the original variance to the unmixed variance. For example, both spectroscopy and conventional flow cytometry can be described as linear mixture models:

[0130]

[0131] Where y is the [m x 1] vector of the detector signal, M is the [m x n] matrix of spectral features, and f is the [n x 1] vector of fluorophore abundance. Spectral unmixing involves solving a system of linear equations for "f" using the least squares method. For ordinary least squares, the solution can be described as:

[0132]

[0133] Where M † It is the Moore-Penrose pseudo-inverse of M (or the inverse if M is a square matrix with compensation).

[0134] In some embodiments, the inverse matrix is ​​the Gram inverse matrix (also known as the "inverse moment matrix" of the spectral matrix). The Gram matrix is ​​described, for example, in "Matrix Analysis" by Horn, RA, and Johnson, CR (2012), which is incorporated herein by reference. In some embodiments, the Gram inverse matrix is ​​calculated according to the following equation.

[0135]

[0136] Where G is the Gram inverse matrix, M is the spectral matrix, and M T It is the transpose of the spectral matrix.

[0137] Given the variance-covariance matrix V measured by the detector y In this case, linear estimator theory can also be used to calculate the variance-covariance matrix V of the mixed solutions. f :

[0138]

[0139] in T This represents the transpose operator. The diagonal elements of Vy are the variance or noise terms for each detector, V f The diagonal elements are the unmixing variance of each fluorophore.

[0140] From this relationship, it can be clearly seen that the pseudo-inverse of the spectral matrix M... † The original variance V is determined y To unmixed variance V f The mapping. This is proven by the following evidence: Let M denote the [n×m] spectral matrix, M †Let V denote the pseudo-inverse of the [n×m] spectral matrix, where m is the number of detectors and n is the number of autofluorescence spectra. y Let [n×m] be the detector covariance matrix. Let V represent the measurement variance of detector i. The unmixed spontaneous fluorescence covariance matrix V is of size [n×m]. f diagonal entries Represents the unmixing variance of autofluorescence j, and the off-diagonal entries. Let V represent the unmixing covariance of the autofluorescence spectra j and k. f The definition is as follows:

[0141]

[0142] As shown above, regardless of Regardless of its structure, the unmixing variance of the autofluorescence j depends on its inverse spectrum. The properties of the autofluorescence spectra. The unmixing covariance of the autofluorescence spectra j and k depends on the two inverse spectra. and .if It is diagonal (without covariance):

[0143]

[0144] If we make the simplifying assumption that the detector noise is uncorrelated, then the unmixing variance depends only on the magnitude of the inverse spectrum, which can be summarized by the vector norm of the inverse spectrum. If It is homoscedastic, which makes :

[0145]

[0146] If we further assume that the detector variances in all channels are equal, then Inverse matrix of Gram It is directly proportional. Therefore, this shows that the Gram inverse matrix approximately predicts the true covariance matrix of the unmixed data.

[0147] After computing the inverse matrix, embodiments of the method include deriving a quantitative metric from the inverse matrix. In some cases, the quantitative metric is a matrix norm. In some instances, the quantitative metric is a vector norm. In other cases, the quantitative metric is derived from some combination of matrix and vector norms, such as the sum of the vector norms of a subset of certain columns or rows in the inverse matrix. Suitable norms include, but are not limited to, L. 2 Norms, 1-norm, 2-norm, infinity norm, and Frobenius norm. In some instances, the norm is L. 2Norms. In some instances, the norm is the 1-norm. In some instances, the norm is the 2-norm. In some instances, the norm is the infinity norm. In some instances, the norm is the Frobenius norm. The Frobenius norm is described, for example, in Golub, GH and VanLoan, CF (1996), *Matrix Computation*, 3rd Edition, Baltimore, MD: Johns Hopkins, the entire contents of which are incorporated herein by reference. In some cases, the Frobenius norm is calculated as follows (adapted from Golub and VanLoan):

[0148]

[0149] Where A is matrix.

[0150] In some embodiments, the method includes evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample. In some instances, evaluating the unmixing performance includes: generating a spectral matrix associated with fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; applying the spectral matrix to unmix fluorescence spectra generated by an unstained control, a monostained control, a stained sample, or any combination thereof; and calculating one or more of unmixing bias and unmixing variance. In some instances, calculating the unmixing bias includes measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectra. In some instances, the method includes determining that no false-positive unmixed fluorophore signals associated with the autofluorescence spectra are present in the unmixed fluorophore channels across all generated particle populations in the sample. In some instances, calculating the unmixing variance includes measuring unmixing-dependent diffusion in the unmixed fluorophore signals. In some embodiments, the method includes identifying autofluorescence spectra generated by particles in the sample that minimize the unmixing bias.

[0151] In some embodiments, the method further includes generating a visual representation of the evaluated collinearity of the autofluorescence spectra in the generated data. Any suitable visualization can be employed. In some embodiments, the visualization includes a flow cytometry data plot based on a set of simulated autofluorescence spectra. In other words, the visualization will include exemplary flow cytometry data that would be generated if the sample was run on a specific instrument with a specific set of particles having a specific autofluorescence spectrum. In some embodiments, the visualization may highlight (e.g., by highlighting, color coding, grouping, pointing with arrows, etc.) the flow cytometry data that, if generated using certain fluorophores and autofluorescence, would be correlated with variance. In some embodiments, the visualization highlights flow cytometry data generated using particle autofluorescence and fluorophores that contribute to the data variance. In some cases, the visualization highlights flow cytometry data generated using autofluorescence affected by the data variance. In other versions, the visualization includes a table or matrix quantifying the degree to which autofluorescence (and / or fluorophores) is correlated with (e.g., causes and / or is affected by) variance. For example, the table or matrix may be populated with the quantitative measures discussed above. In some versions of this, the cells of a table or matrix are color-coded based on the degree to which autofluorescence (and / or fluorophores) is associated with (e.g., contributing to and / or being influenced by) variance. In selected cases, cells are color-coded with a lighter intensity color if the associated autofluorescence (and / or fluorophores) is less associated with variance, and with a darker intensity color if the associated autofluorescence (and / or fluorophores) is more associated with variance.

[0152] In some embodiments, the method includes generating a panel hotspot matrix. As described herein, a “panel hotspot matrix” is a mechanism for mathematically describing and / or visualizing the effect of diffusion on autofluorescence (and / or fluorophores). In selected cases, the panel hotspot matrix serves as the visualization described above. In some embodiments, the panel hotspot matrix is ​​a diagonal matrix. The panel hotspot matrix can be calculated in some cases by taking the square root of the absolute value of the inverse matrix (e.g., the Gram inverse). In some cases, the panel hotspot matrix can be calculated as follows:

[0153]

[0154] The calculation of the panel hotspot matrix can result in two different metrics: the pseudo-inverse matrix row norm and off-diagonal entries. The pseudo-inverse matrix row norm (i.e., the diagonal of the panel hotspot matrix) indicates which autofluorescence (and / or fluorophores) are most affected by unmixing-dependent diffusion. In some cases, the diagonal entries are the 2-norm of the pseudo-inverse for each autofluorescence (and / or fluorophore). In some versions, the pseudo-inverse matrix row norm can be represented on a scale corresponding to a factor by which the standard deviation of the unmixed data in that autofluorescence (and / or fluorophore) will be amplified by the unmixing in that panel. For example, 1 corresponds to no effect, while 2 corresponds to a 2x diffusion, and so on. Examining the off-diagonal entries in the full panel hotspot matrix reveals problematic combinations of autofluorescence (and / or fluorophores) in the panel. The off-diagonal entries are the dot product of the corresponding row and column pseudo-inverses of the autofluorescence (and / or fluorophore) spectra. The off-diagonal values ​​represent the magnitude of the covariance between two pseudo-inverse matrix entries for the autofluorescence (and / or fluorophore) spectra. For example, in some embodiments, an off-diagonal value of 0 indicates no covariance, while higher values ​​indicate a correspondingly higher level of covariance.

[0155] In some embodiments, the method includes separately analyzing the row norm (i.e., diagonal) of the pseudo-inverse matrix of the panel hotspot matrix. In some such embodiments, the method includes generating a diagonal visualization. The diagonal visualization can be any representation (e.g., a graphical representation) of categorical data configured to evaluate and / or compare factors by which the standard deviation of unmixed data in autofluorescence spectra (and / or fluorophores) will be amplified by unmixing in a particular panel. In some embodiments, the diagonal visualization is a bar chart, wherein each bar represents a factor by which the standard deviation of unmixed data in each autofluorescence spectrum (and / or fluorophore) is amplified by unmixing in the panel.

[0156] In some cases, the method includes generating a visualization of exemplary flow cytometry data based on a panel hotspot matrix, which will be generated using specific autofluorescence spectra (and / or fluorophores). The exemplary flow cytometry data can be actual flow cytometry data, i.e., data generated from a flow cytometry experiment. Alternatively, the data can be simulated. Thematic visualizations of exemplary flow cytometry data demonstrate the effect of using certain autofluorescence spectra (and / or fluorophores) in an experiment. In some embodiments, the visualization shows exemplary flow cytometry data generated using a specific pair of autofluorescence spectra (and / or fluorophores), for example, to show how the covariance associated with those autofluorescence spectra (and / or fluorophores) affects data quality. Alternatively or additionally, exemplary flow cytometry data can be simulated using each autofluorescence spectrum (and / or fluorophore) in the sample instead of just a pair of autofluorescence spectra (and / or fluorophores). Examining such exemplary flow cytometry data can reveal problematic combinations of autofluorescence spectra (and / or fluorophores) in the panel.

[0157] In some cases, the method includes generating a spread correlation matrix. As described herein, a “spread correlation matrix” is a mechanism for mathematically describing and / or visualizing the effect of a particular autofluorescence spectrum (and / or fluorophore) on certain data populations (e.g., double-negative populations). In embodiments, the spread correlation matrix can be used to predict skewness in double-negative populations. “Skewness” herein refers to a measure describing the degree to which a population (e.g., a double-negative population) shifts in a particular direction (e.g., in a direction corresponding to positive or negative correlation) due to the manner of data collection and / or preparation. In some embodiments, preparing the spread correlation matrix includes treating the Gram inverse as a covariance matrix and normalizing each row and each column to the square root of its diagonal elements to compute the correlation matrix. In some cases, the spread correlation matrix is ​​computed as follows:

[0158]

[0159] in This represents taking the diagonal of a two-dimensional matrix, or forming a diagonal matrix from a one-dimensional vector. This operation is equivalent to dividing each row by the square root of its diagonal, and each column by the square root of its diagonal. The entries [i,j] of the diffusion correlation matrix correspond to the correlation between the rows of the pseudo-inverse matrix corresponding to autofluorescence spectra (and / or fluorophores) i and j. In some cases, the method includes generating a visualization of exemplary flow cytometry data generated using a specific autofluorescence spectrum (and / or fluorophore) based on the diffusion correlation matrix. Similar to visualizations related to panel hotspot matrices, visualizations of exemplary flow cytometry data created with the diffusion correlation matrix can be real or simulated. In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as the diffusion inflation factor, SIF). In some instances, the method includes measuring the diffusion inflation factor (SIF) based on the hotspot matrix. In some instances, the measured diffusion inflation factors are evaluated to determine whether they are limited to autofluorescence spectra.

[0160] In embodiments, the method includes optimizing the autofluorescence spectra to be used to generate flow cytometry data based on an assessment of autofluorescence collinearity. A set of autofluorescence spectra (and / or fluorophores) can be described as “suitable” for a flow cytometry protocol when it produces interpretable flow cytometry data that reliably provides insight into features of interest in the studied sample. In some embodiments, a set of autofluorescence spectra is suitable for a flow cytometry protocol when the panel provides increased biological resolution. “Biological resolution” refers to the ability to distinguish different entities (e.g., cells, molecules, antigens, parts, epitopes, etc.) in a biological specimen. In some cases, the autofluorescence spectra identified herein still produce the highest biological resolution despite the presence of measurement variance and variance in the flow cytometry data space (e.g., flow cytometry data after fluorescence compensation or spectral unmixing). In some versions, the “maximum” biological resolution is evaluated using a set of or more other sets of autofluorescence (and / or fluorophores) that are different from the autofluorescence spectrum (and / or fluorophore) evaluated and / or identified herein (i.e., containing one or more autofluorescence spectra (and / or fluorophores) that are different from that autofluorescence spectrum (and / or fluorophore)).

[0161] In some embodiments, optimizing the autofluorescence spectra set to be used for flow cytometry data analysis includes using an optimization algorithm. In some instances, the optimization algorithm is a constrained optimization algorithm. “Constrained optimization” herein refers to optimization in the conventional sense to describe the process of optimizing variables given constraints on these variables. Any suitable constrained optimization method can be employed. In some cases, the constrained optimization method is a minimization algorithm. “Minimization algorithm” refers to a constrained optimization method in which the method seeks to minimize a particular variable. Examples of constrained optimization techniques that may be employed include, but are not limited to, local search, local repair, backtracking, and constraint propagation. In some cases, these can be combined with minimization techniques such as simulated annealing and genetic (evolutionary) algorithms. In some instances, the autofluorescence spectra set to be used for flow cytometry data analysis described herein can be optimized in conjunction with the optimization scheme described in U.S. Patent Publication No. 2023 / 0243735, published December 19, 2022, which is incorporated herein by reference.

[0162] In some embodiments, optimizing an autofluorescence spectrum set for flow cytometry data analysis includes adjusting one or more autofluorescence spectra in the set and evaluating the suitability of the adjusted set for generating flow cytometry data. "Adjusting" an autofluorescence spectrum in the set refers to switching one autofluorescence spectrum (or fluorophore) to a different autofluorescence spectrum within the spectral matrix. One or more autofluorescence spectra in the set can be adjusted at any given time. In some instances, the method includes switching out a single autofluorescence spectrum in the set at a given time. In some cases, optimizing an autofluorescence spectrum set involves maintaining an autofluorescence spectrum set with a constant size. In other words, even if one or more autofluorescence spectra are adjusted, the number of autofluorescence spectra in the resulting set remains unchanged. For example, an evaluated set of autofluorescence spectra with N autofluorescence spectra will continue to have N autofluorescence spectra after adjustment. In some cases, the autofluorescence spectra in the set are not exchanged for autofluorescence spectra already present in the set. After generating the adjusted autofluorescence spectrum set, methods of interest also include evaluating the adjusted autofluorescence spectrum set, i.e., calculating the inverse matrix based on the obtained spectral matrix and evaluating the applicability of the autofluorescence spectrum set for generating flow cytometry data by analyzing the calculated inverse matrix, thereby identifying autofluorescence spectra in the autofluorescence spectrum set that are associated with the variance in the flow cytometry data generated using the autofluorescence spectrum set.

[0163] In some embodiments, the method of interest further includes comparing the evaluation of an initial set of autofluorescence spectra with the evaluation of adjusted fluorescence spectra. For example, the method may include determining which set of first and adjusted autofluorescence spectra is associated with a smaller variance in the flow cytometry data. If the first or adjusted set of autofluorescence spectra, compared to the other set, includes autofluorescence spectra with less association with variance in the flow cytometry data, and / or has autofluorescence spectra with a smaller association with variance (e.g., determined by a quantitative measure), then that set of autofluorescence spectra may be identified as more suitable for analyzing the flow cytometry data. In some instances, the method includes discarding sets of autofluorescence spectra with autofluorescence spectra that have a larger association with variance.

[0164] In some cases, the method involves iteratively adjusting the autofluorescence spectra and evaluating the suitability of each iteratively adjusted set of autofluorescence spectra. In embodiments, any set of autofluorescence spectra from the first set and the adjusted sets that has been evaluated as having a small correlation with the variance in the flow cytometry data can serve as a seed for the next part of the iterative process. A “seed” refers to a set of autofluorescence spectra identified in one iteration of the method that correlates with a smaller variance in the flow cytometry data compared to one or more slightly modified sets of autofluorescence spectra. In some embodiments, the iterative process is repeated until a certain condition is met. Any suitable condition can be used to terminate the iterative process. In some instances, the iterative process terminates when a certain runtime has elapsed. In other cases, the iterative process terminates when the evaluations produced for each iteratively adjusted set of autofluorescence spectra converge. In other words, the iterative process terminates when only small variance differences are observed between subsequent sets of autofluorescence spectra.

[0165] As described above, in some instances, the method includes using variance decomposition ratio (VDP) analysis and condition index (CI) to identify collinear groups of autofluorescence spectra. In some instances, VDP analysis and condition index use singular value decomposition. In some embodiments, the method includes assessing whether the measured diffusion expansion factor (as described above) is limited to autofluorescence spectra based on the collinearity of the assessed autofluorescence spectra using variance decomposition ratio.

[0166] In some instances, evaluating the variance decomposition scale involves using singular value decomposition (SVD) to identify collinear spectral groups. In some instances, the method involves identifying problematic combinations of autofluorescence spectra (and / or fluorophores) that lead to high variance in unmixed spectral data. These problematic combinations of autofluorescence spectra (and / or fluorophores) can be identified based on their collinearity (near-linear correlation) across all particle and fluorophore backgrounds. This collinearity leads to ill-conditioned spectral matrices and causes high unmixing variance. In some instances, determining collinear combinations of autofluorescence spectra (and / or fluorophores) involves calculating the singular value decomposition (SVD) of the spectral matrix of the autofluorescence spectrum (and / or fluorophore) of interest. This represents the spectral matrix X as the product of three matrices X = UDV^T. The diagonal matrix D contains the singular values, and V is a matrix containing the right singular vectors of the matrix decomposition. In some embodiments, the method includes variance decomposition of three or more different autofluorescence spectra measured from the irradiated sample, such as four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more different autofluorescence spectra.

[0167] In some instances, a condition index is calculated for each singular value generated by the singular value decomposition. In some instances, the condition index is calculated as the ratio of the largest computed singular value to each individually computed singular value. For example, the condition index of the k-th singular value is the ratio of the largest singular value to the k-th singular value.

[0168] In some embodiments, a matrix of variance decomposition proportions (VDPs) for each autofluorescence spectrum (and / or fluorophore) relative to each singular value is calculated. This matrix describes the proportion of the unmixing variance of each autofluorescence spectrum (and / or fluorophore) that can be attributed to each component in the singular value decomposition. In some embodiments, the method includes analyzing the calculated conditional index and the corresponding variance decomposition proportion matrix to identify collinear (e.g., nearly correlated) combinations of autofluorescence spectra (and / or fluorophores).

[0169] In some instances, the method includes identifying variance decomposition proportions corresponding to a condition index of 10 or greater, such as condition indices of 11 or greater, 12 or greater, 13 or greater, 14 or greater, 15 or greater, 16 or greater, 17 or greater, 18 or greater, 19 or greater, 20 or greater, 21 or greater, 22 or greater, 23 or greater, 24 or greater, 25 or greater, 26 or greater, 27 or greater, 28 or greater, 29 or greater, 30 or greater, and also includes identifying variance decomposition proportions corresponding to a condition index of 35 or greater.

[0170] In some instances, the method includes identifying autofluorescence spectra and / or fluorophores whose variance decomposition ratio of the condition index exceeds a certain large proportion, such as 0.3 or greater, 0.4 or greater, 0.5 or greater, 0.6 or greater, and including 0.7 or greater. In some cases, the method includes identifying autofluorescence spectra (and / or fluorophores) with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 as collinear.

[0171] In some embodiments, the method includes comparing the identification of collinear autofluorescence spectra (and / or fluorophores) determined by calculated variance decomposition ratios and condition indices with the aforementioned hotspot matrix analysis. This can be performed on a sample or reference sample. In some instances, the method includes confirming that two or more autofluorescence spectra (and / or fluorophores) are collinear and represent a problematic combination of spectral unmixing.

[0172] In some embodiments, the method includes removing autofluorescence spectra determined to be collinear. In some instances, the method further includes removing autofluorescence spectra that contribute most to the variance of the flow cytometry data. In some instances, the method includes determining the optimal combination of autofluorescence spectra to use when analyzing flow cytometry data. In some instances, the method includes removing fluorescence spectra of fluorophores that contribute to the variance of the flow cytometry data based on the calculated collinearity of the fluorophore fluorescence spectrum with one or more autofluorescence spectra. In some instances, the method includes removing autofluorescence spectra that contribute most to the unmixing bias in the spectral unmixing of the flow cytometry data.

[0173] In some cases, the method involves selecting the autofluorescence spectrum that will provide the best spectral unmixing for flow cytometry data. In some embodiments, the method includes spectrally unmixing flow cytometry data using the selected autofluorescence spectrum. In some instances, the method includes selecting an appropriate subset of autofluorescence spectra to extract autofluorescence signals from heterogeneous samples through unmixing without negatively impacting other unmixing parameters. In some instances, collinear combinations of identified autofluorescence spectra (and / or fluorophores) facilitate flow cytometry experimental design and data analysis to ensure the use of collinear autofluorescence (and / or fluorophores) on particles (e.g., bioanalytes) with sufficiently high signal-to-noise ratios to overcome large unmixing variances, or to adjust data visualization or data scaling schemes to account for large unmixing variances in identified collinear autofluorescence (and / or fluorophores).

[0174] Figure 1AA flowchart illustrating collinearity of autofluorescence spectra of particles in a sample in a flow stream, according to certain embodiments, is described. In step 101, a sample containing particles in the flow stream is illuminated with a light source. In step 102, light from the illuminated particles is detected using a photodetector of a light detection system. In step 103, autofluorescence spectra are generated based on the measured light from different particles in the sample. In some instances, where the particles also include fluorophores, one or more fluorescence spectra are generated based on the measured light. In step 104, collinearity between autofluorescence spectra is evaluated among two or more different particles in the sample. This collinearity can be evaluated using one or both of hotspot matrix analysis and variance decomposition ratio (VDP) analysis, as depicted in steps 104a and 104b, respectively. For hotspot matrix analysis (step 104a), the method includes generating a spectral matrix associated with the autofluorescence spectra, calculating an inverse matrix based on the generated spectral matrix, and identifying autofluorescence spectra associated with the variance in data generated by flow cytometry using the autofluorescence spectra. For variance decomposition proportional analysis (step 104b), the singular value decomposition of the autofluorescence spectra is calculated, a condition index is calculated for each autofluorescence spectrum, and a variance decomposition proportional fraction is calculated for each condition index. Any autofluorescence spectrum or fluorophore whose VDP at the condition index exceeds a large fraction, such as 0.3 or 0.5 (the specific threshold for a large VDP fraction is empirically determined and may vary). Any set of two or more autofluorescence spectra or fluorophores with large VDPs at the same large condition index are identified as collinear. In step 105, collinear autofluorescence spectra or those that contribute to variance in the spectral unmixing of flow cytometry data are identified. In some cases, the set of autofluorescence spectra providing the lowest collinearity is selected for spectral unmixing of the flow cytometry data.

[0175] In some embodiments, the method includes determining an optimal combination of autofluorescence spectra for use in a spectral unmixing matrix to unmix spectral data signals from irradiated samples in a flow stream from a flow cytometer. Figure 1BThis document outlines an example workflow for determining an optimized combination of autofluorescence spectra according to certain embodiments. In step 111, flow cytometry data (real-time or recorded data) of unstained samples containing cell types of interest are acquired. In some instances, data from monostained controls and fully stained samples are also acquired. In step 112, a population of autofluorescence spectra is defined based on the analysis of the unstained samples. In some instances, candidate autofluorescence spectrum groups are defined manually (e.g., on a scatter plot). In some instances, candidate autofluorescence spectrum groups are defined by applying an unsupervised clustering algorithm (e.g., FlowSOM). In some instances, candidate autofluorescence spectrum groups are defined by applying statistical analysis (e.g., principal component analysis, PCA). In step 113, the unmixing performance of spectral matrices generated based on different combinations of autofluorescence spectra is evaluated. For this purpose, a spectral unmixing matrix containing a fluorophore spectral panel and a subset of autofluorescence spectra is generated (step 114). In step 115, each generated unmixing matrix is ​​evaluated by unmixing the unstained controls, monostained controls, and fully stained samples using the unmixing matrix. In step 116, metrics associated with unmixing bias and unmixing-dependent diffusion (variance) are evaluated. In step 117, a combination of autofluorescence spectra balancing unmixing bias and unmixing-dependent diffusion is identified. Based on the identified optimized autofluorescence spectra, a set of autofluorescence spectra is selected to be included in the spectral unmixing matrix used for unmixing flow cytometry data (step 118).

[0176] Figure 1C-1K Experiments for evaluating autofluorescence collinearity according to certain embodiments are depicted. In this experiment, T cell activation is being investigated. These experiments involve observing PBMCs treated with a specific stimulus mixture that triggers T cell activation. The same 13-color panel is compared on stimulated and unstimulated samples to understand which biomarkers in the panel change expression upon stimulation. When a single-stain control is set up, stimulated PBMCs appear different from unstimulated PBMCs in terms of scattering spectra. Unstained stimulated cells appear to have a very broad autofluorescence range and are much brighter than unstained unstimulated cells. Stimulated and unstimulated PBMCs may have different autofluorescence spectra because their biology is very different. To ensure that the 13-color panel has clear resolution for positive and negative expression of each biomarker and to ensure that the background signal caused by autofluorescence in the unmixed data is minimized, this will allow for a more accurate comparison of the expression spectra between stimulated and unstimulated samples. Figure 1C The acquisition of two unstained samples was depicted, including unstained, unstimulated PBMCs and unstained, stimulated PBMCs. For each unstained record, a scatter plot was observed. Gates were drawn around the different visible populations. There were three discrete populations in each sample (six populations in total). Figure 1DDifferent autofluorescence (AF) populations were depicted and compared. All six unstained populations were added as candidate AF spectra. The AF spectra of the identified unstained populations were visualized. Similarity scores were analyzed and subsets of the spectra were selected. Figure 1E The unmixing performance of different spectral unmixing matrices for evaluating the absence of autofluorescence spectra is depicted. Autofluorescence is not selected in this option. All xn values ​​for one of the monostained controls on unstimulated cells were analyzed. Unmixing with the matrix without AF selection was initially selected. Many unmixing errors were found because AF in the samples was not considered. Figure 1F The unmixing performance of different autofluorescence matrix options with single autofluorescence spectra but two different versions was described. A new matrix with a single AF selection was created. Different single autofluorescence was selected where problem regions were still found. Figure 1G The unmixing performance of two autofluorescence spectra was evaluated. Since there were several different AF populations in the sample, two unstimulated AF spectra were selected simultaneously. This resolved CD3 false positives and did not introduce many new problems. This did indeed result in a much higher complexity score, but examination of the hotspot matrix revealed that the unmixing hotspots were concentrated in the AF spectra themselves, without affecting other fluorophores. This was confirmed by examining all other parameters in -xn; the fluorophores were not significantly affected, but note that the hotspot value for BUV395 was slightly higher. Figure 1H The unmixing performance of three selected autofluorescence spectra was assessed. The stimulated records were examined to assess unmixing. If properly unmixed, unstained samples should show a population with all markers centered at zero. Significant false-positive expression was observed in the unstained stimulated samples due to AF. A third AF spectrum was added. This improved the results, but BV421-A now appears less than ideal. Figure 1I The unmixing performance of four selected autofluorescence spectra was assessed. A fourth autofluorescence spectrum was added to the matrix. All populations were centered at zero, although BV421 now showed significant diffusion. To check, all xn were switched back to the unstimulated BV421 monostained control. Positive expression was observed and unmixing appeared good. The final 13-color and 4-autofluorescence matrix was determined and obtained. Figure 1J This paper describes the evaluation of unmixing performance using a hotspot matrix for matrices containing only fluorophores from particles in the sample, as well as matrices applying one, two, three, and four autofluorescence spectra. Hotspot analysis reveals where additional diffusion is expected due to unmixing. Like the complexity score, the values ​​in the hotspot matrix will vary depending on the fluorophore and autofluorescence spectrum selected for the spectral matrix. Unlike the complexity score, the hotspot matrix predicts which fluorophores in the panel will experience unmixing problems and how severe these problems will be. Figure 1KThe method for evaluating unmixing performance using variance decomposition ratio (VDP) analysis is described. The VDP matrix includes singular value decomposition calculations for each fluorophore and autofluorescence spectrum in the sample cells. Additionally, the conditional exponent for each is calculated. Collinearity of autofluorescence (and fluorophores) can be assessed using hotspot matrix analysis, VDP analysis, or both. Using the assessed collinearity, autofluorescence spectra and fluorophores can be identified and selected for evaluating spectral unmixing, where, in some cases, the optimal spectral unmixing performance is determined by the autofluorescence spectra and fluorophores that minimize collinearity.

[0177] In some instances, the samples analyzed in this method are biological samples. The term "biological sample," used in its conventional sense, refers to a subset of tissues, cells, or components of a whole organism, plant, fungus, or animal, which in some cases may be present in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Therefore, "biological sample" refers both to a natural organism or a subset of its tissues and to homogenates, lysates, or extracts prepared from such organisms or subsets of its tissues, including but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular and genitourinary tract, tears, saliva, breast milk, blood cells, tumors, and organs. Biological samples can be any type of biological tissue, including healthy and diseased tissues (e.g., cancerous, malignant, necrotic, etc.). In some embodiments, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc., wherein in some instances the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger prick (where the blood may or may not be bound to any reagents, such as preservatives, anticoagulants, etc., before testing).

[0178] In some embodiments, the source of the sample is "mammal" or "milk," terms that are widely used to describe organisms within the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some instances, the subject is a human. The method can be applied to samples obtained from human subjects of both sexes and at any developmental stage (i.e., newborns, infants, toddlers, adolescents, and adults), wherein in some embodiments the human subject is a toddler, adolescent, or adult. While this disclosure is applicable to samples from human subjects, it should be understood that the method can also be implemented on other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0179] Cells of interest can be targeted and characterized based on various parameters, such as phenotypic features identified by attaching specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analytical droplets determined to contain target cells. A variety of cells can be characterized using a subject-specific approach. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells with readily available cell surface markers or antigens that can be captured or labeled by readily available affinity agents or conjugates thereof. For example, target cells may include cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialotetrahexosylganglioside GD2, and CD71. In some embodiments, target cells are selected from HIV-infected cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) derived from whole blood, bone marrow, or umbilical cord blood.

[0180] When practicing the subject method according to certain embodiments, a certain amount of initial fluid sample is injected into the flow cytometer. The amount of sample injected into the particle sorting module can vary, for example, ranging from 0.001 mL to 1000 mL, for example from 0.005 mL to 900 mL, for example from 0.01 mL to 800 mL, for example from 0.05 mL to 700 mL, for example from 0.1 mL to 600 mL, for example from 0.5 mL to 500 mL, for example from 1 mL to 400 mL, for example from 2 mL to 300 mL, and including samples from 5 mL to 100 mL.

[0181] In some embodiments, the method includes counting and optionally sorting labeled particles (e.g., target cells) in a sample. In practicing the subject method, a fluid sample comprising particles is first introduced into a flow nozzle of the system. After exiting the flow nozzle, the particles pass through a sample detection zone substantially one at a time, where each particle is illuminated by a light source, and measurements of light scattering parameters for each particle are recorded, and in some instances, fluorescence emission (e.g., measurements of two or more light scattering parameters and one or more fluorescence emissions) is recorded as needed. Depending on the characteristics of the detected flow, flow streams of 0.001 mm or more can be illuminated, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, and flow streams of 1 mm or more can be illuminated. In some embodiments, the method includes illuminating a planar cross-section of the flow stream in the sample detection zone, for example, with a laser (as described above). In other embodiments, the method includes illuminating a predetermined length of flow stream in the sample detection zone, such as corresponding to an illumination profile of a diffuse laser beam or lamp.

[0182] In some embodiments, the method includes atomizing the flow at or near the flow cell nozzle orifice. For example, the method may include atomizing the flow at a distance of approximately 0.001 mm or more from the nozzle orifice, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, and even at a distance of 1 mm or more from the nozzle orifice. In some embodiments, the method includes atomizing the flow immediately adjacent to the flow cell nozzle orifice.

[0183] In embodiments of this method, detectors, such as photomultiplier tubes (PMTs), are used to record light passing through each particle (in some cases referred to as forward scattering), light reflected orthogonally to the flow direction of the particle through the detection zone (in some cases referred to as orthogonal or lateral scattering), and fluorescence emitted from the particle as it passes through the detection zone and is illuminated by energy, if the particle is labeled with a fluorescent marker. Each of forward scattering (FSC), lateral scattering (SSC), and fluorescence emission includes a separate parameter for each particle (or each "event"). Thus, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle is analyzed in real time as needed or stored in a data storage and analysis device, such as a computer.

[0184] In some embodiments, particles are detected and uniquely identified by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels. The fluorescence emitted in the detection channels used to identify the particles and their associated binding complexes can be measured after excitation with a single light source, or individually after excitation with different light sources. If separate excitation sources are used to excite the particle tags, the tags can be selected such that all tags can be excited by each excitation source used.

[0185] In some embodiments, the method further includes data acquisition, analysis, and recording, for example using a computer, where multiple data channels record data from each detector for light scattering and fluorescence emitted as each particle passes through the sample detection area of ​​the particle sorting module. In these embodiments, the analysis includes classifying and counting particles such that each particle exists as a set of digitized parameter values. The subject system can be configured to trigger on selected parameters to distinguish particles of interest from background and noise. "Trigger" refers to a preset threshold for the detection parameter and can be used as a means of detecting particles passing through the light source. Detecting an event exceeding the selected parameter threshold triggers the acquisition of light scattering and fluorescence data for that particle. For particles or other components that elicit a response below the threshold in the medium being measured, no data is acquired. The trigger parameter could be the detection of forward scattered light caused by a particle passing through a light beam. Flow cytometry then detects and collects the light scattering and fluorescence data for that particle.

[0186] The specific subpopulations of interest are then further analyzed by "gating" based on data collected for the entire population. To select an appropriate gate, the data is plotted to obtain the best possible subpopulation separation. This process can be performed by plotting forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC) on a two-dimensional dot plot. The granular subpopulations (i.e., those cells within the gate) are then selected, and granules not within the gate are excluded. If needed, gates can be selected by drawing lines around the desired subpopulation on the computer screen using a cursor. Those granules within the gate are then further analyzed only by plotting other parameters of these granules (e.g., fluorescence). If necessary, the above analysis can be configured to generate counts of granules of interest in the sample.

[0187] Methods of interest may also include the use of particles in research, laboratory testing, or treatment. In some embodiments, the subject method includes obtaining single cells prepared from a target fluid or tissue biological sample. For example, the subject method includes obtaining cells from a fluid or tissue sample for use as a research or diagnostic specimen for diseases such as cancer. Similarly, the subject method includes obtaining cells from a fluid or tissue sample for treatment. Cell therapy protocols are those that can prepare living cellular materials, including, for example, cells and tissues, and introduce them into a subject for therapeutic treatment. Situations where treatment can be performed by applying samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system disorders, organ damage, etc.

[0188] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, genetic modification, in vitro culture and expansion, cell harvesting, sample volume reduction and washing, biopreservation, storage, and introduction of cells into a subject. The protocol can begin with the collection of live cells and tissues from a subject's tissue source to produce cell and / or tissue samples. Samples can be collected using any suitable procedure, including, for example, administration of cell mobilizing agents to the subject, blood collection from the subject, or bone marrow extraction from the subject. After sample collection, cells can be enriched using several methods, including, for example, centrifugation-based methods, filter-based methods, elutriation, magnetic separation methods, fluorescence-activated cell sorting (FACS), etc. In some instances, the enriched cells can be genetically modified using any convenient method, such as nuclease-mediated gene editing. Genetically modified cells can then be cultured, activated, and expanded in vitro. In some instances, cells are preserved, such as cryopreserved, and stored for future use, where the cells are thawed and then administered to the patient, for example, cells can be infused into the patient.

[0189] system

[0190] This disclosure also includes a flow cytometry system for implementing subject-matter methods, such as evaluating collinearity of autofluorescence spectra of samples. In some embodiments, the system is configured to identify and select an optimal combination of autofluorescence spectra for characterizing a sample by flow cytometry. A system according to some embodiments includes: a light source configured to illuminate a sample having particles in a flowing stream; a light detection system having a photodetector for detecting light from particles in the sample; and a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to measure autofluorescence spectra generated by particles in the sample and evaluate collinearity between autofluorescence spectra generated by two or more different particles in the sample.

[0191] The programmable logic can be implemented in any of a variety of devices, such as specially programmed event processing computers, wireless communication devices, integrated circuit devices, etc. In some embodiments, the programmable logic can be executed by a specially programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Combinations of computing devices, such as a combination of DSPs and microprocessors, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration in at least partial data connectivity, can implement one or more features described herein.

[0192] In some cases, the system is a particle analyzer or includes a particle analyzer. A particle analyzer of interest may include: a flow cell for conveying particles in a flow stream; a light source for illuminating particles in the flow stream at a detection point; and a particle-modulated light detector for detecting particle-modulated light. In some embodiments, the particle analyzer is a flow cytometer. In some cases where the particle analyzer is a flow cytometer, the flow cytometer is a full-spectrum flow cytometer.

[0193] As discussed herein, "flow cell" is described in its conventional sense as a component, such as a cuvette, containing a flow channel for a liquid flow stream used to transport particles in a sheath fluid. A cuvette of interest includes a container having a channel extending therethrough. The flow stream may include a liquid sample injected from a sample tube. A flow cell of interest includes a light-accessible flow channel. In some instances, the flow cell contains a transparent material (e.g., quartz) that allows light transmission. In some embodiments, the flow cell is a flow cell in an airflow, wherein optical detection of particles occurs outside the flow cell (i.e., in free space).

[0194] In some instances, the flow stream is configured to illuminate the detection point with light from a light source. The flow stream configured in the flow channel may include a liquid sample injected from a sample tube. In some embodiments, the flow stream may include a narrow, fast-flowing liquid stream arranged such that linearly separated particles transported therein are separated from each other in a single file. The term "detection point" as discussed herein refers to an area within the flow cell where particles are illuminated by light from a light source, for example, for analysis. The size of the detection point can vary as needed. For example, with 0 μm representing the optical axis emitted by the light source, the detection point can range from -100 μm to 100 μm, for example from -50 μm to 50 μm, for example from -25 μm to 40 μm, and includes a range from -15 μm to 30 μm.

[0195] After particles are irradiated in a flow cell, particle-modulated light can be observed. "Particle-modulated light" refers to the light received from a particle in a flow stream after it has been irradiated with light from a light source. In some instances, the particle-modulated light is side-scattered light. As discussed herein, side-scattered light refers to light refracted and reflected from the surface and internal structure of the particle. In other embodiments, the particle-modulated light includes forward-scattered light (i.e., light that propagates primarily forward through or around the particle). In other cases, the particle-modulated light includes fluorescence (i.e., light emitted by a fluorophore after irradiation with light of an excitation wavelength).

[0196] The system according to some embodiments includes a light source configured to irradiate particles of a sample. In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit light with varying wavelengths, ranging from 200 nm to 1500 nm, for example from 250 nm to 1250 nm, for example from 300 nm to 1000 nm, for example from 350 nm to 900 nm, and including from 400 nm to 800 nm. For example, the light source may include a broadband light source emitting light with wavelengths from 200 nm to 900 nm. In other cases, the light source includes a narrowband light source emitting wavelengths ranging from 200 nm to 900 nm. For example, the light source may be a narrowband LED (1 nm–25 nm) emitting wavelengths between 200 nm and 900 nm. In some embodiments, the light source is a laser. In some instances, the subject system includes gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, carbon dioxide lasers, carbon monoxide lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other instances, the subject system includes dye lasers, such as stilbene lasers, coumarin lasers, or rhodamine lasers. In still other instances, lasers of interest include metal vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, or combinations thereof. In other cases, the subject system includes solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium₂O₃ lasers, or cerium-doped lasers and combinations thereof.

[0197] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, and light-emitting diodes (LEDs), such as broadband LEDs with a continuous spectrum, superluminescent LEDs, semiconductor LEDs, broadband LED white light sources, and multi-LED integrated light sources. In some instances, the non-laser light source is a stabilized fiber-coupled broadband light source, a white light source, and other light sources or any combination thereof.

[0198] The light source can be positioned at any suitable distance from the sample (e.g., the flow stream in a flow cytometer), such as 0.001 mm or more, 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, and including distances of 100 mm or more. Furthermore, the light source can illuminate the sample at any suitable angle (e.g., relative to the vertical axis of the flow stream), such as angles ranging from 10° to 90°, from 15° to 85°, from 20° to 80°, from 25° to 75°, and including angles from 30° to 60°, such as at a 90° angle.

[0199] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some instances, the system includes a light source configured to continuously illuminate the sample, such as using a continuous-wave laser that continuously illuminates the flow stream at a detection point in a flow cytometer. In other cases, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds, or some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to provide intermittent illumination of the sample with the light source. For example, the subject system in these embodiments may include one or more laser beam choppers, manually or computer-controlled beam stoppers, for blocking the sample and exposing it to the light source.

[0200] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous-wave lasers. For example, the laser may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a carbon dioxide laser, a carbon monoxide laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser; or a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, or a strontium laser. Lasers, including neon-copper (NeCu) lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium₂O₃ lasers, or cerium-doped lasers and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSL), or frequency doubling or third harmonics of any of the above lasers.

[0201] In some embodiments, the light source is a beam generator configured to generate two or more beams of frequency-shifted light. In some instances, the beam generator includes a laser, a radio frequency (RF) generator configured to apply an RF drive signal to an acousto-optic device to generate two or more angle-deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, the laser of interest in the beam generator of interest can be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser; or a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, or a helium-cadmium (HeCd) laser. Selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, titania-sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium₂O₃ lasers, or cerium-doped lasers, and combinations thereof.

[0202] The acousto-optic device can be any convenient acousto-optic solution, configured to frequency-shift the laser using applied acoustic waves. In some embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate an angle-deflected laser beam based on light from the laser and an applied radio frequency drive signal. The radio frequency drive signal can be applied to the acousto-optic device using any suitable radio frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0203] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angle-deflected laser beams in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0204] In some instances, in order to generate an intensity distribution of an angle-deflected laser beam in the output laser beam, the controller is configured to apply an amplitude-varying radio frequency drive signal, for example from about 0.001V to about 500V, for example from about 0.005V to about 400V, for example from about 0.01V to about 300V, for example from about 0.05V to about 200V, for example from about 0.1V to about 100V, for example from about 0.5V to about 75V, for example from about 1V to 50V, for example from about 2V to 40V, for example from 3V to about 30V, and including from about 5V to about 25V. Each applied radio frequency drive signal has, in some embodiments, a frequency ranging from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and including frequencies ranging from about 5 MHz to about 50 MHz.

[0205] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with an angle-deflected laser beam having a desired intensity distribution. For example, the memory may include instructions to generate two or more angle-deflected laser beams with the same intensity, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and including the memory may include instructions to generate 100 or more angle-deflected laser beams with the same intensity. In other embodiments, they may include instructions to generate two or more angle-deflected laser beams with different intensities, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and including the memory may include instructions to generate 100 or more angle-deflected laser beams with different intensities.

[0206] In some embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from edge to center. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam can be from 0.1% to about 99%, for example 0.5% to about 95%, for example 1% to about 90%, for example about 2% to about 85%, for example about 3% to about 80%, for example about 4% to about 75%, for example about 5% to about 70%, for example about 6% to about 65%, for example about 7% to about 60%, for example about 8% to about 55%, and includes about 10% to about 50%. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from edge to center. In these examples, the intensity of the angle-deflected laser beam at the edge of the output beam can be from 0.1% to about 99% of the intensity of the angle-deflected laser beam at the center of the output laser beam along the horizontal axis, for example, 0.5% to about 95%, for example, 1% to about 90%, for example, about 2% to about 85%, for example, about 3% to about 80%, for example, about 4% to about 75%, for example, about 5% to about 70%, for example, about 6% to about 65%, for example, about 7% to about 60%, for example, about 8% to about 55%, and includes about 10% to about 50%. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to produce an output laser beam with a Gaussian intensity distribution along the horizontal axis. Still in other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to produce an output laser beam with a top-hat intensity distribution along the horizontal axis.

[0207] In embodiments, the beam generator of interest may be configured to generate spatially separated angle-deflected laser beams within the output laser beam. Depending on the applied radio frequency drive signal and the desired illumination profile of the output laser beam, the angle-deflected laser beams may be separated by 0.001 μm or more, for example, 0.005 μm or more, 0.01 μm or more, 0.05 μm or more, 0.1 μm or more, 0.5 μm or more, 1 μm or more, 5 μm or more, 10 μm or more, 100 μm or more, 500 μm or more, 1000 μm or more, and include separations of 5000 μm or more. In some embodiments, the system is configured to generate overlapping angle-deflected laser beams within the output laser beam, for example, overlapping with adjacent angle-deflected laser beams on the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., beam spot overlap) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and includes 100 μm or more.

[0208] In some instances, beam generators configured to generate two or more frequency-shifted beams include laser excitation modules such as those described by Diebold et al. in Nature Photonics, Vol. 7 (10); 806-810 (2013), and U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; 10,684,211; 10,845,295; 10,935,482; The laser excitation modules described in patents 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851; the disclosures of these patents are incorporated herein by reference.

[0209] In some embodiments, the system includes a light detection system with photodetectors configured to detect light emitted by irradiated particles. In some embodiments, the light detection system is configured to detect scattered light. In some instances, the light detection system includes a side-scattering light detector. In some instances, the light detection system includes a forward-scattering light detector. In other embodiments, the light detection system includes a plurality of scattered light detectors, such as two or more, three or more, four or more, and including five or more. In some embodiments, the light detection system further includes a fluorescence detector configured to detect light at one or more fluorescence wavelengths. In other embodiments, the light detection system includes a plurality of fluorescence detectors, such as two or more, three or more, four or more, five or more, ten or more, fifteen or more, and including twenty or more.

[0210] Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. In some embodiments, the collected light is measured using 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 some embodiments, the detector is a photomultiplier tube, for example, having an effective detection surface area per region ranging from 0.01 cm². 2 up to 10cm 2 Photomultiplier tubes, for example, from 0.05 cm 2 up to 9cm 2 For example, from 0.1cm 2 up to 8cm 2 For example, from 0.5cm 2 up to 7cm 2 And including from 1cm 2 up to 5cm 2 .

[0211] In cases where the subject system includes multiple fluorescence detectors, each fluorescence detector may be identical, or the set of fluorescence detectors may be a combination of different types of detectors. For example, in cases where the subject system includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first and second fluorescence detectors are CCD-type devices. In other embodiments, both the first and second fluorescence detectors are CMOS-type devices. In other embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector is a photomultiplier tube (PMT). In other embodiments, the first fluorescence detector is a CMOS-type device and the second fluorescence detector is a photomultiplier tube. In other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0212] In embodiments of this disclosure, the fluorescence detector of interest is configured to measure collected light at one or more wavelengths, such as two or more wavelengths, five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths, and includes measuring light emitted by a sample in the flow stream at 400 or more different wavelengths. In some embodiments, two or more detectors in the module described herein are configured to measure collected light at the same or overlapping wavelengths.

[0213] In some embodiments, the fluorescence detector of interest is configured to measure collected light within a wavelength range (e.g., 200 nm–1000 nm). In some embodiments, the detector of interest is configured to collect a spectrum within a wavelength range. For example, a flow cytometer may include one or more detectors configured to collect a spectrum within one or more wavelength ranges from 200 nm to 1000 nm. In other embodiments, the detector of interest is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a module may include one or more detectors configured to measure light at one or more wavelengths 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 some embodiments, one or more detectors may be configured to pair with specific fluorophores, such as those used with the sample in a fluorescence assay.

[0214] In some embodiments, the optical detection system described herein is part of a flow cytometer. The flow cytometer may include any suitable mechanism for supplying sheath fluid and sample fluid to a sample fluid input coupler and a sheath fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., a conduit) that is fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line that is fluidly connected to a sheath fluid reservoir. Similarly, the flow cytometer may include any suitable mechanism for managing waste from the flow stream. The fluid output coupler may be fluidly connected to a waste line that is fluidly connected to a waste reservoir. A fluid management system applicable to the subject flow cytometer is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0215] In some embodiments, the flow cytometer includes a flow cell. The flow cell of interest includes a cuvette configured to deliver particles in a flow stream. As discussed herein, "flow cell" is described in its conventional sense as a component containing a flow channel for delivering particles in a sheath fluid. The cuvette of interest has a channel (i.e., a flow channel) extending therethrough. The flow stream configured in the flow channel may include a liquid sample injected from a sample tube. In some cases, the flow cell includes a light-accessible flow channel. The cuvette may be made of, for example, quartz, glass, transparent plastic, etc. In some embodiments, the cuvette is formed of silica, such as fused silica. In some instances, the flow cell is configured to be illuminated with light from a light source at one or more detection points. The "detection point" discussed herein refers to an area within the flow cell in which particles are illuminated by light from a light source, for example, for analysis. The size of the detection point may vary as needed. For example, where 0 μm represents the optical axis emitted by the light source, the range of the detection point can be from -50 μm to 50 μm, for example from -25 μm to 40 μm, and includes from -15 μm to 30 μm. Depending on certain considerations (e.g., the number and arrangement of lasers), multiple irradiation points may exist within the flow cell.

[0216] In some embodiments, the flow cell includes or is configured for use with a sample injection port configured to provide a sample to the flow cell. In another embodiment, the sample injection system is configured to provide a suitable sample flow to the flow cell chamber (i.e., the flow channel). Depending on the required characteristics of the flow, the sample rate delivered to the flow cell via the sample injection port can be 1 μL / min or more, for example 2 μL / min or more, for example 3 μL / min or more, for example 5 μL / min or more, for example 10 μL / min or more, for example 15 μL / min or more, for example 25 μL / min or more, for example 50 μL / min or more, and includes 100 μL / min or more. In some instances, the sample rate delivered to the flow cell via the sample injection port is 1 μL / sec or more, for example 2 μL / sec or more, for example 3 μL / sec or more, for example 5 μL / sec or more, for example 10 μL / sec or more, for example 15 μL / sec or more, for example 25 μL / sec or more, for example 50 μL / sec or more, and includes 100 μL / sec or more.

[0217] The sample injection port can be an orifice located in the inner chamber wall or a conduit located proximal to the inner chamber. When the sample injection port is an orifice located in the inner chamber wall, the orifice can be of any suitable shape, wherein the cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses, etc.; and irregular shapes, such as a parabolic lower portion coupled to a flat top portion. In some embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice can vary depending on the shape, and in some cases has an opening ranging from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes an opening from 1.25 mm to 1.75 mm, for example 1.5 mm.

[0218] In some cases, the sample injection port is a conduit located proximal to the flow cell chamber. For example, the sample injection port may be a conduit positioned so that its orifice is aligned with the flow cell orifice. When the sample injection port is a conduit aligned with the flow cell orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, including but not limited to: linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes such as circles, ellipses; and irregular shapes, such as a parabolic lower portion coupled to a flat top portion. The orifice of the conduit may vary depending on its shape, and in some cases has an opening ranging from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and including 1.25 mm to 1.75 mm, for example 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip with a bevel angle ranging from 1° to 10°, such as from 2° to 9°, such as from 3° to 8°, such as from 4° to 7°, and including a bevel angle of 5°.

[0219] In some embodiments, the flow cell further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid into the flow cell chamber, for example, in combination with a sample to generate a laminar sheath fluid flow around the sample flow. Depending on the desired characteristics of the flow flow, the rate of sheath fluid delivered to the flow cell chamber through the sheath fluid injection port can be 25 μL / sec or more, for example 50 μL / sec or more, for example 75 μL / sec or more, for example 100 μL / sec or more, for example 250 μL / sec or more, for example 500 μL / sec or more, for example 750 μL / sec or more, for example 1000 μL / sec or more, and includes 2500 μL / sec or more.

[0220] In some embodiments, the sheath fluid injection port is an orifice located in the inner chamber wall. The sheath fluid injection port orifice can be of any suitable shape, wherein the cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses; and irregular shapes, such as a parabolic lower portion coupled to a flat top portion. The size of the sheath fluid injection port orifice can vary depending on the shape, and in some cases has an opening ranging from 0.1 mm to 5.0 mm, for example 0.2 mm to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes an opening ranging from 1.25 mm to 1.75 mm, for example 1.5 mm.

[0221] In some embodiments, the system includes or is operatively coupled to a flow cytometer. Suitable flow cytometry systems may include, but are not limited to, those in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo et al. (2012), Ann Clin Biochem, January; 49(pt 1):17-28; Linden et al., Seminar on Thrombosis and Hemostasis, October 2004; 30(5):502-11; Alison et al., Journal of Pathology (J Pathol, December 2010; 222(4):335-344; and Herbig et al. (2007) Crit Rev TherDrug CarrierSyst. 24(3):203-255; the contents of these documents are incorporated herein by reference.In some cases, flow cytometry systems of interest include the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, and BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, and BD Biosciences... Influx™ Cell Sorter, BD Biosciences Jazz™ Cell Sorter, BD Biosciences Aria™ Cell Sorter, BD Biosciences FACSAria™ II Cell Sorter, BD Biosciences FACSAria™ III Cell Sorter, BD Biosciences FACSAria™ Fusion Cell Sorter, BD Biosciences FACSMelody™ Cell Sorter, BD Biosciences FACSymphony™ S6 Cell Sorter, BD Biosciences FACSDiscover™ Cell Sorter, etc.

[0222] In some embodiments, the subject system is a flow cytometry system, such as U.S. Patent Nos. 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074; 10,302,545; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,0 Those described in 95,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; 4,498,766; the disclosures of these patents are incorporated herein by reference in their entirety.

[0223] In some embodiments, the flow cytometer is configured as an imaging flow cytometer. For example, in some cases, the subject system is a flow cytometry system configured to image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), such as those described in Diebold et al., Nature Photonics, Vol. 7 (10); 806-810 (2013), and U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; Those described in 10,408,758; 10,451,538; 10,620,111; 10,684,211; 10,845,295; 10,935,482; 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851; the disclosures of these patents are incorporated herein by reference.

[0224] Figure 2A system 200 for flow cytometry according to an illustrative embodiment of this disclosure is shown. System 200 includes a laser 201 configured to irradiate particles 211 in a flow stream 214 at a detection point 215 within a flow cell 210. Although Figure 2 The example shows a single laser, but it should be understood that multiple lasers can also be used. The laser beam from laser 201 is directed to focusing lens 202, which focuses the beam onto the portion of the fluid flow within flow cell 210 where the sample particles 211 reside. Flow cell 210 is part of a fluid system that guides particles (typically one at a time) in the flow to the focused laser beam for detection. Alternatively, in the case of a flow cytometer that is an airflow cytometer, a nozzle top can be used.

[0225] like Figure 2 As shown, flow cell 210 is fluidly connected to a sheath fluid reservoir 203 containing sheath fluid and a sample fluid reservoir 204 containing sample fluid. Sheath fluid from sheath fluid reservoir 203 is supplied via conduit (i.e., sheath fluid line) 207 to at least one sheath fluid injection port 208. Furthermore, sample fluid containing particles 211 from sample fluid reservoir 204 is supplied via conduit (i.e., sample fluid line) 205 to sample injection port 206. Sample injection port 206 is fluidly connected to a sample syringe 213 (e.g., sample injection needle) configured to introduce particles 211 into the interior of flow cell 210. Particles 211 are hydrodynamically focused by the sheath fluid entering from sheath fluid injection port 208, such that a flow stream 214 is formed downstream of the conical portion 212 of flow cell 210. Particles emitted at the distal end of flow cell 210 can be processed and / or collected by any suitable method. For example, depending on the type of flow cytometry performed, particles can be collected at the distal end of flow cell 210, for example, via a waste line. Alternatively, the particles can be sorted.

[0226] Light from one or more laser beams interacts with particles 211 in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various wavelengths depending on the characteristics of the particles, such as their size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or inside the particles. The fluorescence emission, as well as the diffracted, refracted, reflected, and scattered light, can be routed to one or more detectors. Specifically, forward scattered light (FSC) is routed to a forward scattered light detector 223. The forward scattered light detector 223 is positioned slightly off-axis from the direct beam passing through the flow cell 210 and is configured to detect diffracted light, i.e., excitation light that propagates primarily forward through or around the particles. The intensity of the light detected by the forward scattered light detector 223 depends on the overall size of the particles. The forward scattered light detector may include, for example, a photodiode. Positioned between the forward scattered light detectors 223 are an optical filter 221a and a scattering baffle 222. Optical filter 221a can be configured to filter out non-FSC light of at least one wavelength, while scattering baffle 222 can be configured to prevent the incident beam (i.e., non-scattered light) from laser 201 from being detected by forward scattering detector 223.

[0227] Furthermore, side-scattered light (SSC) is detected by side-scattered light detector 224. In other words, side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structure of particle 211, which increases with the complexity of the particle structure. Figure 2 In the example, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to a side-scatter light detector 224 while transmitting non-SSC (e.g., fluorescence) light. An optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by the side-scatter light detector 224. Fluorescence detectors 225a-225c are also shown, each configured to detect fluorescence of different wavelengths. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or wavelength range) to the fluorescence detector 225a while transmitting light of other wavelengths. The optical filter 221c may be configured to prevent light of at least one wavelength not corresponding to the first wavelength (or wavelength range) from being detected by the fluorescence detector 225a. Similarly, the dichroic mirror 220c is configured to reflect FL light corresponding to a second wavelength (or wavelength range) to the fluorescence detector 225b while transmitting light of a third wavelength (or wavelength range) for detection by the fluorescence detector 225c. Optical filter 221d is configured to prevent light of at least one wavelength that does not correspond to the second wavelength (or wavelength range) from being detected by fluorescence detector 225b. Furthermore, optical filter 221e is configured to prevent light of at least one wavelength that does not correspond to the third wavelength (or wavelength range) from being detected by fluorescence detector 225c.

[0228] Those skilled in the art will recognize that the flow cytometer according to embodiments of this disclosure is not limited to... Figure 2 The flow cytometer described herein 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. For example, although Figure 2 The example shown is for illustrative purposes and features three fluorescence detectors, but it should be understood that any suitable number of fluorescence detectors may be used.

[0229] During operation, the cytometer is controlled by controller / processor 290, and measurement data from the detector can be stored in memory 295 and processed by controller / processor 290. Although not explicitly shown, controller / processor 290 is coupled to the detector to receive output signals from the detector and can also be coupled to the electrical and electromechanical components of the flow cytometer to control laser 201, fluid flow parameters, etc. Input / output (I / O) capability 297 may also be provided in the system. Memory 295, controller / processor 290, and I / O 297 may be provided entirely as part of the flow cytometer. In such an embodiment, a display may also form part of I / O capability 297 for presenting experimental data to the user of cytometer 200. Alternatively, memory 295 and controller / processor 290, along with some or all of the I / O capability, may be part of one or more external devices (e.g., a general-purpose computer). In some embodiments, some or all of memory 295 and controller / processor 290 may communicate wirelessly or wired with cytometer 210. The controller / processor 290, combined with memory 295 and I / O 297, can be configured to perform various functions related to the preparation and analysis of flow cytometry experiments.

[0230] Different fluorescent molecules in the fluorophore panel used for flow cytometry experiments will emit light in their respective characteristic wavelength bands. Specific fluorescent labels used in the experiment and their associated fluorescence emission bands can be selected to generally coincide with the filter window of the detector. I / O 297 can be configured to receive data on a flow cytometry experiment having a fluorescently labeled panel and multiple cell populations with multiple markers, each cell population having a subset of multiple markers. I / O 297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, label density data, emission spectral data, data on label assignment to one or more markers, and flow cytometry configuration data. Flow cytometry experiment data, such as label spectral characteristics and flow cytometry configuration data, can also be stored in memory 295. Controller / processor 290 can be configured to evaluate one or more label-to-marker assignments.

[0231] In some embodiments, the subject system is a particle sorting system configured to sort particles using enclosed particle sorting modules, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference. In some embodiments, a sorting decision module having multiple sorting decision units is used to sort particles (e.g., cells) of a sample, such as those described in U.S. Patent Publication No. 2020 / 0256781 (filed December 23, 2019), the disclosure of which is incorporated herein by reference. In some embodiments, the system for sorting components of a sample includes a particle sorting module with deflection plates, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference.

[0232] In some embodiments, the system is an image-enabled particle sorter that uses radio frequency marker emission imaging, for example... Figure 3As depicted, the particle sorter 300 includes an illumination assembly 300a comprising a light source 301 (e.g., a 488nm laser) that generates an output beam 301a, which is split into beams 302a and 302b by a beam splitter 302. Beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a with one or more angle deflections. In some instances, the output beam 303a generated from the acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. Beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a with one or more angle deflections. In some instances, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 303a and 304a generated from acousto-optic devices 303 and 304, respectively, are combined with beam splitter 305 to generate output beam 305a, which is transmitted through optical component 306 (e.g., objective lens) to illuminate particles in flow cell 307. In some embodiments, acousto-optic device 303 (AOD) splits a single laser beam into an array of beams, each beam having a different optical frequency and angle. A second AOD 304 tunes the optical frequency of a reference beam, which is then overlapped with the beam array at beam combiner 305. In some embodiments, the light illumination system having a light source and acousto-optic devices may also include those described in Schraivogel et al. ("High-speed fluorescence image-enabled cellsorting," Science (2022), 375(6578):315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosure of which is incorporated herein by reference.

[0233] Output beam 305a irradiates sample particles 308 propagating through flow cell 307 (e.g., together with sheath fluid 309) at irradiation region 310. As shown in irradiation region 310, multiple beams (e.g., angle-deflected RF-shifted beams, depicted as points on irradiation region 310) overlap with a reference local oscillator beam (depicted as shaded lines on irradiation region 310). Due to their different optical frequencies, the overlapping beams exhibit beat frequency behavior, resulting in each small beam carrying a different frequency f. 1-n Sine modulation.

[0234] Light from the illuminated sample is transmitted to a light detection system 300b, which includes multiple photodetectors. The light detection system 300b includes a forward-scattering photodetector 311 for generating a forward-scattering image 311a and a side-scattering photodetector 312 for generating a side-scattering image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattering detector 311 and the side-scattering detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some instances, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence photodetectors 314-317. In some instances, photodetectors 314-317 are photomultiplier tubes. Light from the illuminated sample is directed by a beamsplitter 320 to the side-scattering detection channel 312 and the fluorescence detection channels 314-317. The optical detection system 300b includes bandpass optics 321, 322, 323, and 324 (e.g., dichroic mirrors) for propagating light of a predetermined wavelength to photodetectors 314-317. In some examples, optics 321 is a 534 nm / 40 nm bandpass filter. In some examples, optics 322 is a 586 nm / 42 nm bandpass filter. In some examples, optics 323 is a 700 nm / 54 nm bandpass filter. In some examples, optics 324 is a 783 nm / 56 nm bandpass 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 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm.

[0235] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright field light detection channel 313, and fluorescence detection channels 314-317 are digitally processed in real time by processors 350 and 351. Images 311a-317a can be generated in each light detection channel based on the data signals generated in processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated in sorting trigger 352. Sorting assembly 300c includes deflection plate 331 for deflecting particles into sample container 332 or into waste stream 333. In some instances, sorting assembly 300c is configured to sort particles using enclosed particle sorting modules, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference. In some embodiments, the sorting component 300c includes a sorting decision module having multiple sorting decision units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0236] In some embodiments, the system is a particle analyzer, wherein the particle analysis system 401 ( Figure 4 It can be used to analyze and characterize particles, whether or not the particles are physically sorted into a collection container. Figure 4 A functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization is shown. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 includes a fluid system 402. The fluid system 402 may include or be coupled to a sample tube 405 and a moving fluid column within the sample tube, wherein sample particles 403 (e.g., cells) move along a common sample path 409.

[0237] The particle analysis system 401 includes a detection system 404 configured to collect signals from each particle as it passes through one or more detection stations along a common sample path. Detection station 408 typically refers to a monitored area 407 of the common sample path. In some embodiments, detection may include detecting light or one or more other properties of particle 403 as it passes through the monitored area 407. Figure 4 The image shows a detection station 408 with a monitored area 407. Some embodiments of the particle analysis system 401 may include multiple detection stations. Furthermore, some detection stations can monitor multiple areas.

[0238] Each signal is assigned a signal value to form a data point for each particle. As mentioned above, this data can be referred to as event data. The data points can be multi-dimensional data points, including values ​​of various attributes measured for the particle. The detection system 404 is configured to collect a series of such data points within a first time interval.

[0239] The particle analysis system 401 may further include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The control system may be operationally associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the number of data points collected by the detection system 404 during the first time interval. The control system 406 may be further configured to generate an experimental signal frequency based on the number of data points in that portion of the first time interval. The control system 406 may additionally compare the experimental signal frequency with the calculated signal frequency or a predetermined signal frequency.

[0240] Figure 5An example functional block diagram of a particle analyzer control system (e.g., an analysis controller (i.e., a processor) 500) is shown for analyzing and displaying biological events. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0241] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to an analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel. The analysis controller 500 can be a processor configured to perform the method of the present invention, for example, by applying a distance-based classification model to determine a density discrimination threshold in a size-based analyte feature space, applying a density-based clustering algorithm to separate the analyte data into high-density clusters and low-density clusters based on the density threshold, and classifying the analyte data based on the high-density clusters and low-density clusters in the size-based analyte feature space.

[0242] The analysis controller 500 can be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 may include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display of a first graph including the biological event data to a display device 506. The analysis controller 500 can be further configured to render regions of interest as gates around the population of biological event data displayed on the display device 506, for example, overlaid on the first graph. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest plotted on a single-parameter histogram or bivariate graph. In some embodiments, the display may be used to display particle parameters or saturation detector data.

[0243] The analysis controller 500 may be further configured to display the biological event data inside the door on the display device 506 in a manner different from other events in the biological event data outside the door. For example, the analysis controller 500 may be configured to render the colors of the biological event data inside the door differently from the colors of the biological event data outside the door. The display device 506 may be a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0244] The analysis controller 500 can be configured to receive a door selection signal from a first input device. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a door selection signal to the analysis controller 500, identifying a door to be displayed on or operated via the display device 506 (e.g., clicking when the cursor is positioned on or inside the desired door). In some embodiments, the first device can be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, stylus, optical detector, or voice recognition system. Some input devices may include multiple input functions. In such embodiments, each input function can be considered an input device. For example, as... Figure 5 As shown, the mouse 510 can include a right button and a left button, and each button can generate a trigger event.

[0245] The triggering event may cause the analysis controller 500 to change the way the data is displayed, the portion of the data actually displayed on the display device 506, and / or provide input for further processing, such as selecting a group of interest for particle sorting.

[0246] In some embodiments, the analysis controller 500 may be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 may be further configured to automatically modify the graph visualization to facilitate the gate setting process. Modifications may be based on a specific distribution of the biological event data received by the analysis controller 500.

[0247] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

[0248] Display device 506 can be configured to receive display data from analysis controller 500. The display data may include gates for portions of graphs and delineations of biological event data. Display device 506 can be further configured to change the presented information based on input received from analysis controller 500, combined with input from particle analyzer 502, storage device 504, keyboard 508, and / or mouse 510.

[0249] In some implementations, the analysis controller 500 may generate a user interface to receive example events for sorting. For example, the user interface may include controls for receiving example events or example images. The example events, images, or example gates may be provided before collecting event data for the samples, or based on a partial initial set of events from the samples.

[0250] Figure 6A This is a schematic diagram of a particle sorting system 600 (e.g., a particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorting system 600 is a cell sorting system. Figure 6A As shown, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, include, or be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a flow). Within the moving fluid column 608, particles 609 (e.g., cells) align in a single file across a monitored area 611 (e.g., where laser streams intersect) and are irradiated by an irradiation source 612 (e.g., a laser). Vibration of the droplet-forming transducer 602 causes the moving fluid column 608 to break into multiple droplets 610, some of which contain particles 609.

[0251] In operation, a detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitored area 611. The detection station 614 feeds into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At the droplet disconnection point, notified by a timing droplet delay (Δt), a flash charge can be applied to a moving fluid column 608, causing the droplet of interest to carry a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplet can then be sorted by activating a deflection plate (not shown) to deflect it into a container (e.g., a collection tube or a porous or microporous sample plate), where a pore or micropore can be associated with a specific droplet of interest. Figure 6A As shown, the droplets can be collected in the discharge container 638.

[0252] A detection system 616 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as a particle of interest passes through a monitored region 611. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, the entire contents of which are incorporated herein by reference. The detection system 616 allows the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 616 may be fed an amplitude signal 620 and / or a phase signal 618, which are then fed through an amplifier 622 to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624, in turn, control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be included in a control system.

[0253] In some embodiments, sorting electronics (e.g., detection system 616, detection station 614, and processor 640) may be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decisions may be included in the event data of the particles. In some embodiments, detection system 616 and detection station 614 may be implemented as a single detection unit or communication coupling, such that event measurements can be collected by one of detection system 616 or detection station 614 and provided to non-collecting elements.

[0254] Figure 6B This is a schematic diagram of a particle sorting system according to an embodiment of the present document. Figure 6B The particle sorting system 600 shown includes deflection plates 652 and 654. Charge can be applied via a current-charged wire in the barbs. This generates a droplet stream containing particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to produce light scattering and fluorescence information. The particle information is analyzed, for example by sorting electronics or other detection systems. Figure 6B (Not shown in the analysis). Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, thereby guiding the droplets toward a target collection container (e.g., one of 672, 674, 676, or 678). Figure 6B As shown, deflection plates 652 and 654 can be controlled to guide particles along a first path 662 toward receiver 674 or along a second path 668 toward receiver 678. If the particles are of no interest (e.g., do not exhibit scattering or irradiation information within a specified sorting range), the deflection plates can allow the particles to continue moving along flow path 664. Such uncharged droplets may enter the waste receiver, for example, via suction device 670.

[0255] It can include sorting electronics to initiate the collection of measurement data, receive the fluorescence signal of particles, and determine how to adjust the deflection plate to sort the particles. Figure 6BThe example implementation of the illustrated embodiment includes the BD FACSAria™ series flow cytometer, commercially available from Becton Dickinson, Inc. (Franklin Lake, New Jersey).

[0256] Computer control system

[0257] The system may include a display and operator input devices. Operator input devices may be, for example, a keyboard, mouse, etc. The processing module includes a processor that can access memory having instructions stored thereon for executing the subject method steps. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or other existing or future-available processors. The processor executes an operating system that interfaces with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of various computer programs written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically collaborates with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services according to known techniques. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0258] System memory can be any known or future memory storage device. Examples include any common random access memory (RAM), magnetic media (such as resident hard drives or magnetic tape), optical media (such as optical discs), flash memory devices, or other memory storage devices. Memory storage devices can be any known or future devices, including optical disc drives, magnetic tape drives, or floppy disk drives. This type of memory storage device is typically read from and / or written to program storage media (not shown) (such as optical discs). Any of these program storage media, or other media currently in use or that may be developed in the future, can be considered a computer program product. It is understood that these program storage media typically store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0259] In some embodiments, a computer program product is described, including a computer-usable medium in which control logic (computer software program, including program code) is stored. When executed by a computer's processor, the control logic causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, such as using a hardware state machine. The implementation of a hardware state machine to perform the functions described herein will be apparent to those skilled in the art.

[0260] The memory can be any suitable device in which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks, optical discs, magnetic tapes, RAM, or any other suitable device, whether fixed or portable). The processor may include a general-purpose digital microprocessor, appropriately programmed via a computer-readable medium carrying the necessary program code. The programming can be provided to the processor remotely via a communication channel or pre-stored in a computer program product using any of these devices connected to the memory, such as memory or other portable or fixed computer-readable storage media. For example, a disk or optical disc may carry the programming and can be read by a disk writer / reader. The systems disclosed herein also include programming, such as in the form of a computer program product, algorithms for practicing the methods described above. The programming according to this disclosure can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tapes; optical storage media such as CD-ROMs; electronic storage media such as RAM and ROMs; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0261] The processor can also access communication channels to communicate with users at remote locations. A remote location refers to a user who does not directly interact with the system and relays input information from external devices, such as computers connected to a wide area network (WAN), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones), to the input manager.

[0262] In some embodiments, the system according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile Communications (GSM).

[0263] In one embodiment, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces, such as USB ports, USB-C ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices, such as computer terminals configured for similar complementary data communication (e.g., in a doctor's office or hospital environment).

[0264] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user can use in conjunction with them.

[0265] In one embodiment, the communication interface is configured to provide connectivity for data transmission using the Internet Protocol (IP) via a cellular telephone network, a short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0266] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communication interface, such as using common standards like 802.11 or Bluetooth. ® The protocol is RF, or IrDA infrared protocol. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop computer; or a larger device, such as a desktop computer, 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 touchscreen.

[0267] In some embodiments, the communication interface is configured to automatically or semi-automatically transmit data stored in the subject system (e.g., in an optional data storage unit) to a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0268] The output controller may include controllers for any of the various known display devices used to present information to a user (whether human or machine, local or remote). If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. The graphical user interface (GUI) controller may include any of the various known or future software programs used to provide a graphical input and output interface between the system and the user, and to process user input. Functional elements of the computer may communicate with each other via a system bus. Some such communication may be accomplished using a network or other types of remote communication in alternative embodiments. The output manager may also provide information generated by the processing module to a user at a remote location, such as via the Internet, telephone, or satellite networks, according to known technologies. Data presented by the output manager may be implemented according to a variety of known technologies. 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 a remote source. One or more platforms present in the subject system may be any type of known computer platform or type to be developed in the future, although they will generally fall into the category of computers commonly referred to as servers. However, they may also be mainframes, workstations, or other computer types. They can be connected via any known or future type of wired or other communication system, including wireless systems, whether networked or otherwise. They can be located in the same location or they can be physically separated. Various operating systems can be deployed on any computer platform, possibly depending on the type and / or brand of the chosen computer platform. Suitable operating systems include Windows. ® NT ® Windows ® XP, Windows ® 7. Windows ® 8. Windows ® 10. iOS ® macOS ® Linux ® Ubuntu ® Fedora ® OS / 400 ® i5 / OS ® IBMi ® Android™, SGIIRIX ® Oracle Solaris ® And others.

[0269] Figure 7A general architecture of an example computing device 700 according to certain embodiments is depicted. Figure 7 The general architecture of the computing device 700 depicted includes the arrangement of computer hardware and software components. However, it is not necessary to show all these generally conventional components to provide a disclosure of what can be achieved. As shown, the computing device 700 includes a processing unit 710, a network interface 720, a computer-readable media drive 730, an input / output device interface 740, a display 750, and an input device 760, all of which can communicate with each other via a communication bus. The network interface 720 can provide connectivity to one or more networks or computing systems. Thus, the processing unit 710 can receive information and instructions from other computing systems or services via the network. The processing unit 710 can also communicate with a memory 770 and further provide output information to an optional display 750 via the input / output device interface 740. For example, analysis software (e.g., data analysis software or programs such as FlowJo®) stored in the non-transitory memory of an analysis system can display flow cytometry event data to a user. The input / output device interface 740 can also accept input from optional input devices 760, such as keyboards, mice, digital pens, microphones, touch screens, gesture recognition systems, voice recognition systems, game controllers, accelerometers, gyroscopes, or other input devices.

[0270] The memory 770 may contain computer program instructions (grouped into modules or components in some embodiments) that the processing unit 710 executes to implement one or more embodiments. The memory 770 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. The memory 770 may store an operating system 772 that provides computer program instructions for use by the processing unit 710 in the general management and operation of the computing device 700. Data may be stored in a data storage device 790. The memory 770 may also include computer program instructions and other information for implementing aspects of this disclosure.

[0271] Non-transitory computer-readable storage medium

[0272] This disclosure also includes non-transitory computer-readable storage media having instructions for practicing the subject methods, such as for practicing one or more computer-implemented methods described herein. The computer-readable storage media can be deployed on one or more computers for fully or partially automating systems used to practice the methods described herein. In some embodiments, instructions according to the methods described herein can be encoded in a “programmed” form onto a computer-readable medium, wherein the term “computer-readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network-attached storage (NAS), whether such devices are internal or external to a computer. Files containing information can be “stored” on a computer-readable medium, where “stored” means recording the information so that it can be accessed and retrieved by a computer at a later time. The computer-implemented methods described herein can be executed using a program that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, and many other languages.

[0273] In some embodiments, the non-transitory computer-readable storage medium includes algorithms for illuminating a sample comprising particles in a flow stream with a light source in a flow cytometer, algorithms for detecting light from the illuminating particles using a light detection system including a photodetector, algorithms for measuring autofluorescence spectra generated by particles in the sample, and algorithms for evaluating collinearity between autofluorescence spectra generated by two or more different particles in the sample.

[0274] In some embodiments, the sample has a variety of different particles, and the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence spectrum produced by each of the different particles in the sample. In some instances, the particles in the sample have one or more fluorophores, and the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0275] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample of unlabeled particles. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample of single-stained control particles. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for measuring the autofluorescence of a sample of particles having multiple fluorophores. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra for evaluating collinearity. In some instances, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra by generating a scatter plot of fluorescence parameters of particles in a sample and gating one or more populations on the scatter plot based on the median fluorescence intensity measured for each particle. In some instances, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra by identifying particle populations by applying an unsupervised clustering algorithm. In some instances, the unsupervised clustering algorithm includes one or more of the following: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy c-means clustering, centroid-based clustering, and the Birch algorithm. In some cases, the unsupervised clustering algorithm is a self-organizing map algorithm (e.g., FlowSOM).

[0276] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for selecting a population of autofluorescence spectra using a statistical analysis algorithm. In some instances, the statistical analysis algorithm includes one or more of principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical clustering analysis (HCA), linear discriminant analysis, and matrix factorization. In some cases, the statistical analysis algorithm includes dimensionality reduction. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining the autofluorescence spectra of particles based on the median fluorescence intensity (MFI) of each particle population.

[0277] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the unmixing performance by: generating a spectral matrix associated with fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; applying the spectral matrix to unmix fluorescence spectra generated by an unstained control, a single-stained control, a stained sample, or any combination thereof; and calculating one or more of an unmixing bias and an unmixing variance. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating the unmixing bias by measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes an algorithm for determining that, in all particle populations generated in the sample, there are no false-positive unmixed fluorophore signals associated with the autofluorescence spectra in the unmixed fluorophore channels. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating unmixing variance by measuring unmixing-dependent diffusion in the unmixed fluorophore signal. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in a sample that minimize unmixing bias.

[0278] In some instances, the non-transitory computer-readable storage medium includes algorithms for evaluating collinearity, such as algorithms for generating a spectral matrix associated with autofluorescence generated by particles in the sample, algorithms for calculating an inverse matrix based on the generated spectral matrix, and algorithms for identifying autofluorescence spectra associated with the variance in data generated by flow cytometry using the autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes algorithms for identifying autofluorescence spectra that contribute to the variance in flow cytometry data.

[0279] In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra affected by variance in flow cytometry data. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra that contribute to variance in flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra affected by variance in flow cytometry data. In some instances, the inverse matrix is ​​a pseudo-inverse matrix. In some instances, the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix. In some instances, the inverse matrix is ​​a Gram inverse matrix. In some cases, the inverse matrix is ​​calculated according to the following equation:

[0280]

[0281] in:

[0282] G is the Gram inverse matrix;

[0283] M is the spectral matrix; and

[0284] M T It is the transpose of the spectral matrix.

[0285] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for analyzing the computed inverse matrix by deriving a quantitative metric from the inverse matrix. In some instances, the quantitative metric is a matrix norm. In some instances, the quantitative metric is a vector norm. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra that minimize the variance of the generated data by identifying autofluorescence spectra exhibiting maximum spectral matrix modulation. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias and minimize the variance of the generated data.

[0286] In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence contributions from generated flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for iteratively identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data.

[0287] In some embodiments, the flow cytometry system includes a display for visualizing the collinearity of the evaluated autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes an algorithm for visualizing the evaluated collinearity of the autofluorescence spectra in the generated data on the display. In some instances, the visualization highlights the autofluorescence spectra associated with the variance in the generated data. In some instances, the visualization includes a panel hotspot matrix. In some instances, the visualization includes a diagonal visualization of the panel hotspot matrix. In some instances, the visualization includes a diffusion correlation matrix. In some instances, the diagonal values ​​of the correlation matrix of the spectral matrix include a variance inflation factor (also referred to herein as the diffusion inflation factor, SIF). In some instances, the non-transitory computer-readable storage medium includes an algorithm for measuring the diffusion inflation factor (SIF) based on the hotspot matrix. In some instances, the non-transitory computer-readable storage medium includes an algorithm for evaluating the measured diffusion inflation factors to determine whether they are limited to autofluorescence spectra.

[0288] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity among autofluorescence spectra by assessing the variance decomposition ratio (VDP). In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for assessing whether a measured diffusion expansion factor is limited to autofluorescence spectra based on the collinearity of the assessed autofluorescence spectra by means of the variance decomposition ratio. In some instances, the non-transitory computer-readable storage medium includes an algorithm for assessing the variance decomposition ratio by using singular value decomposition (SVD) to identify collinear spectral groups. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating a condition index for each singular value generated by the singular value decomposition. In some instances, the non-transitory computer-readable storage medium includes an algorithm for calculating the condition index as the ratio of the largest calculated singular value to each individually calculated singular value. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a condition index greater than 15. In some instances, the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index and a condition index greater than 15 as collinear.

[0289] In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra determined to be collinear. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra that contribute most to the variance in flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for determining the optimal combination of autofluorescence spectra used when analyzing flow cytometry data. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing fluorophore fluorescence spectra that contribute to the variance in flow cytometry data based on calculated collinearity of fluorophore fluorescence spectra with one or more autofluorescence spectra. In some instances, the non-transitory computer-readable storage medium includes algorithms for removing autofluorescence spectra that contribute most to unmixing bias in the spectral unmixing of flow cytometry data.

[0290] The non-transitory computer-readable storage medium can be deployed on one or more computer systems having a display and operator input devices. Operator input devices can be, for example, a keyboard, mouse, etc. The processing module includes a processor that can access memory having instructions stored thereon for performing the subject method steps. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor, or it may be other currently available or future available processors. The processor executes the operating system, and the operating system interacts with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of the functions of various computer programs written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services according to known techniques.

[0291] kit

[0292] Various aspects of this disclosure further include kits, wherein the kits include storage media such as magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network attached storage (NAS). Any of these program storage media, or other media now in use or that may be developed in the future, may be included in the subject kit. In embodiments, the program storage media includes instructions for analyzing flow cytometry data, as described herein, and instructions for use with the systems described herein. In embodiments, instructions contained on a computer-readable medium provided in the subject kit, or portions thereof, may be implemented as software components of data analysis software. In these embodiments, a computer control system according to this disclosure may act as a software “plug-in” for existing software packages such as FlowJo®.

[0293] In addition to the components described above, the theme kit may further include (in some embodiments) instructions. These instructions may exist in various forms within the theme kit, with one or more of them present in the kit. One form of these instructions may be printed information on a suitable medium or substrate, such as printed paper, kit packaging, inserts, etc. Another form of these instructions is a computer-readable medium, such as a disk, optical disc (CD), portable flash drive, etc., on which information is recorded. Yet another form of these instructions may be a website address, accessible via the Internet for accessing information at a remote site.

[0294] practicality

[0295] The methods, systems, and computer systems described herein are useful in a variety of applications where calibration or optimization of optical detection systems (e.g., those with photodetectors) is required, for example, in particle analyzers. The methods and systems described herein are also used for optical detection systems used to analyze and sort particulate components of samples in fluid media (e.g., biological samples). This disclosure is also applicable to flow cytometry, wherein it is desirable to provide a flow cytometer with improved cell sorting accuracy, enhanced particle collection, reduced energy consumption, particle charging efficiency, more accurate particle charging, and enhanced particle deflection during cell sorting. In embodiments, this disclosure reduces the need for user input or manual adjustments during sample analysis using a flow cytometer. In some embodiments, the methods and systems described herein provide a fully automated scheme so that adjustments to the flow cytometer during use require little, if any, manual input.

[0296] experiment

[0297] The following is presented in an example rather than a limitation:

[0298] Example workflow for determining the optimal combination of autofluorescence spectra to unmix the spectral matrix.

[0299] Step 1: Acquire data from an unstained sample containing one or more cell types of interest using flow cytometry. In some instances, data from a single-color control and full-body stained plates are also acquired.

[0300] Step 2: Define a set of candidate autofluorescence spectra based on the analysis of one or more unstained samples.

[0301] a. Option 1: Manual Gating: The user visually inspects the scattering and fluorescence parameter plots and draws gates around different populations that may have different autofluorescence. The median fluorescence intensity (MFI) for each population is calculated in each detector, and these MFIs are used to determine the different autofluorescence (AF) spectra.

[0302] Option b: Unsupervised clustering: Use clustering techniques (e.g., FlowSOM) to identify different populations without manual gating. Calculate the median fluorescence intensity (MFI) for each population in each detector and use these MFIs to determine the distinct autofluorescence spectra.

[0303] c. Option 3: Statistical analysis (PCA, SVD, other matrix decomposition or component analysis techniques): Using statistical techniques to identify the spectral components that explain most of the variance in the total measurement data.

[0304] d. Option 4: A combination of gating, clustering, and statistical analysis (e.g., coarsely segmenting the data by manual gating and using component analysis to identify spectra within each manually gated subset).

[0305] Step 3: Evaluate the unmixing performance of different combinations of the autofluorescence spectra determined in Step 2 above.

[0306] a. Create a spectral matrix containing panel fluorophore spectra and subsets of autofluorescence spectra.

[0307] b. Use the generated spectral unmixing matrix to unmix unstained controls, monostained controls, and / or fully stained samples.

[0308] c. Analyze the unmixing performance of these samples based on two types of metrics:

[0309] i. Measures for assessing unmixing bias: Does the population exhibit false-positive unmixed fluorophore signals due to autofluorescence? If autofluorescence is properly unmixed, there should be minimal false-positive signals in the unmixed fluorophore channels of all populations. This can be observed, for example, by confirming that the unmixed fluorophore MFI of the population in the unstained record is close to zero.

[0310] ii. Assessing the measure of unmixing variance (diffusion): Is the unmixing-dependent diffusion, measured by hotspot (SIF) or VDP, large in the unmixed fluorophore signal? If so, the selected subset of AFs may be too collinear with the fluorophore spectrum, resulting in high fluorophore variance. In some cases, high SIF in the unmixing matrix is ​​acceptable as long as it is limited to the autofluorescence spectrum itself. This can be confirmed by VDP analysis.

[0311] Step 4: Based on the above metrics, find a combination of autofluorescence spectra that simultaneously balances bias and unmixing-dependent diffusion.

[0312] Once the optimal combination of desired autofluorescence spectra is determined, the subset determined in the spectral matrix is ​​used when unmixing the sample of interest in flow cytometry experiments.

[0313] Notwithstanding the appended claims, this disclosure is also defined by the following provisions:

[0314] 1. A method comprising:

[0315] Illuminate a sample containing particles in a flowing stream using a light source in a flow cytometer;

[0316] A light detection system, including a photodetector, is used to detect light from the irradiated particles;

[0317] Measure the autofluorescence spectra generated by particles in the sample; and

[0318] Evaluate collinearity between autofluorescence spectra generated by two or more different particles in a sample.

[0319] 2. The method according to Clause 1, wherein the sample comprises a plurality of different particles, and the method comprises measuring the autofluorescence spectrum generated by each of the different particles in the sample.

[0320] 3. The method according to any one of Clauses 1-2, wherein the method comprises measuring autofluorescence from one or more of the following: a sample of unlabeled particles, a sample of monostained control particles, and a sample of particles containing multiple fluorophores.

[0321] 4. The method according to any one of Clauses 1-3, wherein the method includes selecting a population for evaluating collinear autofluorescence spectra.

[0322] 5. The method according to Clause 4, wherein the population selected for the autofluorescence spectrum includes:

[0323] Generate a scatter plot including the fluorescence parameters of particles in the sample; and

[0324] Gating one or more populations on a scatter plot based on the median fluorescence intensity measured for each of the particles.

[0325] 6. The method according to any one of Clauses 4-5, wherein selecting the population of said autofluorescence spectra includes applying an unsupervised clustering algorithm to identify the particle population.

[0326] 7. The method according to Clause 6, wherein the unsupervised clustering algorithm is selected from the group consisting of: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy c-means clustering, centroid-based clustering, and Birch algorithm.

[0327] 8. The method according to Clause 7, wherein the unsupervised clustering algorithm includes a self-organizing map algorithm.

[0328] 9. The method according to any one of Clauses 4-8, wherein selecting the population of said autofluorescence spectra includes a statistical analysis algorithm.

[0329] 10. The method according to Clause 9, wherein the statistical analysis algorithm is selected from the group consisting of: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical cluster analysis (HCA), linear discriminant analysis, and matrix factorization.

[0330] 11. The method according to Clause 10, wherein the statistical analysis algorithm includes dimensionality reduction.

[0331] 12. The method according to any one of clauses 4-11, wherein the autofluorescence spectrum of the particles is determined based on the median fluorescence intensity of each particle population.

[0332] 13. The method according to any one of clauses 1-12, wherein the particle further comprises one or more fluorophores.

[0333] 14. The method according to any one of clauses 1-13, wherein the method includes evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0334] 15. The method according to any one of clauses 1-14, wherein the method includes evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample.

[0335] 16. The method according to Clause 15, wherein evaluating demixing performance includes:

[0336] Generate a spectral matrix associated with the fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; and

[0337] The spectral matrix is ​​used to unmix fluorescence spectra generated from unstained controls, monostained controls, stained samples, or any combination thereof; and

[0338] Calculate one or more of the unmixing bias and unmixing variance.

[0339] 17. The method according to Clause 16, wherein calculating the unmixing bias includes measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectrum.

[0340] 18. The method of claim 17, wherein the method includes determining that, in all generated particle populations of the sample, there is no false-positive unmixed fluorophore signal associated with autofluorescence spectroscopy in the unmixed fluorophore channel.

[0341] 19. The method according to any one of clauses 16-18, wherein calculating the unmixing variance comprises measuring unmixing-dependent diffusion in the unmixed fluorophore signal.

[0342] 20. The method according to any one of clauses 1-19, wherein evaluating said collinearity comprises:

[0343] Generate a spectral matrix associated with the autofluorescence produced by particles in the sample;

[0344] Calculate the inverse matrix based on the generated spectral matrix; and

[0345] Identify autofluorescence spectra that are correlated with the variance in the data generated by flow cytometry using the autofluorescence spectra.

[0346] 21. The method of claim 20, wherein the method includes identifying autofluorescence spectra that contribute to the variance in flow cytometry data.

[0347] 22. The method according to any one of clauses 20-21, wherein the method includes identifying autofluorescence spectra affected by variance in flow cytometry data.

[0348] 23. The method according to any one of clauses 20-22, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0349] 24. The method according to Clause 23, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0350] 25. The method according to any one of clauses 20-24, wherein the inverse matrix is ​​a Gram inverse matrix.

[0351] 26. The method according to Clause 25, wherein the inverse matrix is ​​calculated according to the following equation:

[0352]

[0353] in:

[0354] G is the Gram inverse matrix;

[0355] M is the spectral matrix; and

[0356] M T It is the transpose of the spectral matrix.

[0357] 27. The method according to any one of clauses 20-26, wherein analyzing the calculated inverse matrix includes deriving a quantitative measure from the inverse matrix.

[0358] 28. The method according to Clause 27, wherein the quantitative measure is a matrix norm.

[0359] 29. The method according to Clause 27, wherein the quantitative measure is a vector norm.

[0360] 30. The method according to any one of clauses 1-29, wherein the method further comprises identifying an autofluorescence spectrum generated by particles in the sample that minimizes unmixing bias.

[0361] 31. The method according to any one of clauses 1-29, wherein the method further comprises identifying an autofluorescence spectrum generated by particles in the sample that minimizes the variance of the generated data.

[0362] 32. The method according to clause 31, wherein identifying the autofluorescence spectrum that minimizes the variance of the generated data includes identifying the autofluorescence spectrum exhibiting maximum spectral matrix modulation.

[0363] 33. The method according to any one of clauses 30-32, wherein the method includes identifying an autofluorescence spectrum generated by particles in the sample that minimizes unmixing bias and minimizes the variance of the generated data.

[0364] 34. The method according to any one of clauses 30-33, wherein the method includes removing autofluorescence contributions from the generated flow cytometry data.

[0365] 35. The method according to any one of clauses 30-34, wherein the method comprises iteratively identifying autofluorescence spectra generated by particles in a sample that minimize the variance of the generated data.

[0366] 36. The method according to any one of clauses 20-35, wherein the flow cytometry data is spectrally unmixed flow cytometry data.

[0367] 37. The method according to any one of clauses 20-36, wherein the flow cytometry data is compensated flow cytometry data.

[0368] 38. The method according to any one of clauses 20-37, wherein the variance includes noise in the flow cytometry data.

[0369] 39. The method according to any one of clauses 1-38 further includes generating an evaluated collinearity visualization of the autofluorescence spectra in the generated data.

[0370] 40. The method according to Clause 39, wherein the visualization highlights the autofluorescence spectrum associated with the variance in the generated data.

[0371] 41. The method according to any one of clauses 39-40, wherein the visualization includes a panel hotspot matrix.

[0372] 42. The method according to Clause 41, wherein the visualization includes a diagonal visualization of a panel hotspot matrix.

[0373] 43. The method according to any one of clauses 39-42, wherein the visualization includes a diffusion correlation matrix.

[0374] 44. The method according to any one of clauses 41-43, wherein the method includes measuring the diffusion expansion factor (SIF) based on a hotspot matrix.

[0375] 45. The method according to Clause 44, wherein the method further comprises assessing whether the measured diffusion expansion factor is limited to autofluorescence spectroscopy.

[0376] 46. ​​The method according to any one of clauses 1-45, wherein assessing the collinearity between the autofluorescence spectra includes assessing the variance decomposition ratio (VDP).

[0377] 47. The method according to Clause 46, wherein the method includes assessing whether the measured diffusion expansion factor is limited to the autofluorescence spectrum by means of variance decomposition ratio based on the collinearity of the assessed autofluorescence spectrum.

[0378] 48. The method according to any one of clauses 46-47, wherein evaluating the variance decomposition ratio includes singular value decomposition (SVD) to identify collinear spectral groups.

[0379] 49. The method according to Clause 48, wherein the method further comprises calculating a condition index for each singular value generated for the singular value decomposition.

[0380] 50. The method according to Clause 49, wherein the condition index is calculated as the ratio of the largest computed singular value to each individually computed singular value.

[0381] 51. The method according to any one of clauses 46-50, wherein the method includes identifying autofluorescence spectra in which the variance decomposition ratio of each condition index is greater than 0.3.

[0382] 52. The method according to any one of clauses 46-51, wherein the method includes identifying autofluorescence spectra with a condition index greater than 15.

[0383] 53. The method according to any one of clauses 51-52, wherein the method includes identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 for each condition index as collinear.

[0384] 54. The method according to any one of clauses 1-53, wherein the method further comprises removing the autofluorescence spectrum determined to be collinear.

[0385] 55. The method according to any one of clauses 1-53, wherein the method further comprises removing the autofluorescence spectrum that contributes the most to the variance in the flow cytometry data.

[0386] 56. The method according to any one of clauses 1-53, wherein the method further comprises removing the autofluorescence spectrum that contributes the most to the unmixing bias in the spectral unmixing of the flow cytometry data.

[0387] 57. A flow cytometer system, comprising:

[0388] A light source used to illuminate a sample, including particles in a flowing stream;

[0389] A photodetector-based optical detection system for detecting light from irradiated particles; and

[0390] A processor, including memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0391] Measure the autofluorescence spectra generated by particles in the sample; and

[0392] Evaluate collinearity between autofluorescence spectra generated by two or more different particles in a sample.

[0393] 58. The system according to Clause 57, wherein the sample comprises a plurality of different particles, and the memory includes instructions for measuring the autofluorescence spectrum generated by each of the different particles in the sample.

[0394] 59. The system according to any one of clauses 57-58, wherein the memory includes instructions for measuring autofluorescence from one or more of the following: a sample of unlabeled particles, a sample of monostained control particles, and a sample of particles comprising multiple fluorophores.

[0395] 60. The system according to any one of clauses 57-59, wherein the memory includes instructions for selecting a population of autofluorescence spectra for evaluating collinearity.

[0396] 61. The system according to Clause 60, wherein the memory includes instructions for selecting a population of the autofluorescence spectrum in such a way as:

[0397] Generate a scatter plot including the fluorescence parameters of particles in the sample; and

[0398] Gating one or more populations on a scatter plot based on the median fluorescence intensity measured for each of the particles.

[0399] 62. The system according to any one of clauses 60-61, wherein the memory includes instructions for identifying a population of particles by applying an unsupervised clustering algorithm to select a population of autofluorescence spectra.

[0400] 63. The system according to Clause 62, wherein the unsupervised clustering algorithm is selected from the group consisting of: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy c-means clustering, centroid-based clustering, and Birch algorithm.

[0401] 64. The system according to Clause 63, wherein the unsupervised clustering algorithm includes a self-organizing map algorithm.

[0402] 65. The system according to any one of clauses 60-64, wherein the memory includes instructions for selecting a population of said autofluorescence spectra by applying a statistical analysis algorithm.

[0403] 66. The system according to Clause 65, wherein the statistical analysis algorithm is selected from the group consisting of: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical cluster analysis (HCA), linear discriminant analysis, and matrix factorization.

[0404] 67. The system according to Clause 66, wherein the statistical analysis algorithm includes dimensionality reduction.

[0405] 68. The system according to any one of clauses 60-67, wherein the memory includes instructions for determining the autofluorescence spectrum of the particles based on the median fluorescence intensity of each particle population.

[0406] 69. The system according to any one of clauses 57-68, wherein the particle further comprises one or more fluorophores.

[0407] 70. The system according to any one of claims 57-69, wherein the memory includes instructions for evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0408] 71. The system according to any one of clauses 57-70, wherein the memory includes instructions for evaluating the demixing performance of two or more autofluorescence spectra generated by particles in a sample.

[0409] 72. The system according to Clause 71, wherein the memory includes instructions for evaluating demixing performance in such a manner as:

[0410] Generate a spectral matrix associated with the fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; and

[0411] The spectral matrix is ​​used to unmix fluorescence spectra generated from unstained controls, monostained controls, stained samples, or any combination thereof; and

[0412] Calculate one or more of the unmixing bias and unmixing variance.

[0413] 73. The system according to Clause 72, wherein the memory includes instructions for calculating unmixing bias by measuring the presence of a false-positive unmixed fluorophore signal associated with an autofluorescence spectrum.

[0414] 74. The system according to Clause 72, wherein the memory includes instructions for determining that, in all generated particle populations of the sample, there is no false-positive unmixed fluorophore signal associated with autofluorescence spectroscopy in the unmixed fluorophore channel.

[0415] 75. The system according to any one of clauses 72-74, wherein the memory includes instructions for calculating unmixing variance by measuring unmixing-dependent diffusion in the unmixed fluorophore signal.

[0416] 76. The system according to any one of clauses 57-75, wherein said memory includes instructions for evaluating said collinearity in such a way as:

[0417] Generate a spectral matrix associated with the autofluorescence produced by particles in the sample;

[0418] Calculate the inverse matrix based on the generated spectral matrix; and

[0419] Identify autofluorescence spectra that are correlated with the variance in the data generated by flow cytometry using the autofluorescence spectra.

[0420] 77. The system according to Clause 76, wherein the memory includes instructions for identifying autofluorescence spectra that contribute to the variance in flow cytometry data.

[0421] 78. The system according to any one of clauses 76-77, wherein the memory includes instructions for identifying autofluorescence spectra affected by variance in flow cytometry data.

[0422] 79. The system according to any one of clauses 76-78, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0423] 80. The system according to Clause 79, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0424] 81. The system according to any one of clauses 76-80, wherein the inverse matrix is ​​a Gram inverse matrix.

[0425] 82. The system according to clause 81, wherein the memory includes instructions for calculating the inverse matrix according to the following equation:

[0426]

[0427] in:

[0428] G is the Gram inverse matrix;

[0429] M is the spectral matrix; and

[0430] M T It is the transpose of the spectral matrix.

[0431] 83. The system according to any one of clauses 76-82, wherein the memory includes instructions for analyzing the calculated inverse matrix by deriving quantitative measures from the inverse matrix.

[0432] 84. The system according to Clause 83, wherein the quantitative measure is a matrix norm.

[0433] 85. The system according to Clause 83, wherein the quantitative measure is a vector norm.

[0434] 86. The system according to any one of clauses 57-85, wherein the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias.

[0435] 87. The system according to any one of clauses 57-86, wherein the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize the variance of the generated data.

[0436] 88. The system according to Clause 87, wherein the memory includes instructions for identifying an autofluorescence spectrum that minimizes the variance of the generated data by identifying an autofluorescence spectrum exhibiting maximum spectral matrix modulation.

[0437] 89. The system according to any one of clauses 86-88, wherein the memory includes instructions for identifying autofluorescence spectra generated by particles in the sample that minimize unmixing bias and minimize the variance of the generated data.

[0438] 90. The system according to any one of clauses 86-89, wherein the memory includes instructions for removing autofluorescence contributions from the generated flow cytometry data.

[0439] 91. The system according to any one of clauses 86-90, wherein the memory includes instructions for iteratively identifying autofluorescence spectra generated by particles in a sample that minimize the variance of the generated data.

[0440] 92. The system according to any one of clauses 57-91, wherein the flow cytometry data is spectrally unmixed flow cytometry data.

[0441] 93. The system according to any one of clauses 57-91, wherein the flow cytometry data is compensated flow cytometry data.

[0442] 94. The system according to any one of clauses 57-93, wherein the variance includes noise in the flow cytometry data.

[0443] 95. The system according to any one of clauses 57-94, wherein the memory includes instructions for generating, on a display, an evaluation of collinearity of the autofluorescence spectra in the generated data.

[0444] 96. The system according to Clause 95, wherein the visualization highlights the autofluorescence spectrum associated with the variance in the generated data.

[0445] 97. The system according to any one of clauses 95-96, wherein the visualization includes a panel hotspot matrix.

[0446] 98. The system according to Clause 59, wherein the visualization includes a diagonal visualization of a panel hotspot matrix.

[0447] 99. The system according to any one of clauses 95-98, wherein the visualization includes a diffusion correlation matrix.

[0448] 100. The system according to any one of clauses 95-99, wherein the memory includes instructions for measuring the diffusion expansion factor (SIF) based on the hotspot matrix.

[0449] 101. The system according to Clause 100, wherein the memory includes instructions for evaluating whether the measured diffusion expansion factor is limited to autofluorescence spectra.

[0450] 102. The system according to any one of clauses 57-101, wherein the memory includes instructions for evaluating collinearity between the autofluorescence spectra by evaluating the variance decomposition ratio (VDP).

[0451] 103. The system according to Clause 102, wherein the memory includes instructions for evaluating whether the measured diffusion expansion factor is limited to the autofluorescence spectrum based on the collinearity of the evaluated autofluorescence spectrum by means of variance decomposition ratio.

[0452] 104. The system according to Clause 102, wherein the memory includes instructions for evaluating the variance decomposition ratio by identifying collinear spectral groups through singular value decomposition (SVD).

[0453] 105. The system according to Clause 63, wherein the memory includes instructions for calculating a conditional index for each singular value generated for singular value decomposition.

[0454] 106. The system according to Clause 105, wherein the memory includes instructions for calculating the condition index as the ratio of the maximum computed singular value to each individually computed singular value.

[0455] 107. The system according to any one of clauses 102-106, wherein the memory includes instructions for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index.

[0456] 108. The system according to any one of clauses 102-107, wherein the memory includes instructions for identifying autofluorescence spectra with a condition index greater than 15.

[0457] 109. The system according to any one of clauses 107-108, wherein the memory includes instructions for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 as collinear.

[0458] 110. The system according to any one of clauses 57-109, wherein the memory includes instructions for removing autofluorescence spectra determined to be collinear.

[0459] 111. The system according to any one of clauses 57-109, wherein the memory includes instructions for removing autofluorescence spectra that contribute the most to the variance in flow cytometry data.

[0460] 112. The system according to any one of clauses 57-111, wherein the memory includes instructions for removing autofluorescence spectra that contribute the most to the unmixing bias in the spectral unmixing of flow cytometry data.

[0461] 113. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions comprising:

[0462] An algorithm for illuminating a sample containing particles in a flowing stream with a light source in a flow cytometer;

[0463] An algorithm for detecting light from irradiated particles using a light detection system that includes a photodetector;

[0464] An algorithm for measuring the autofluorescence spectra generated by particles in a sample; and

[0465] An algorithm for evaluating collinearity between autofluorescence spectra generated by two or more different particles in a sample.

[0466] 114. The non-transitory computer-readable storage medium of claim 113, wherein the sample comprises a plurality of different particles, and the non-transitory computer-readable storage medium comprises an algorithm for measuring the autofluorescence spectrum generated by each of the different particles in the sample.

[0467] 115. A non-transitory computer-readable storage medium according to any one of clauses 113-114, wherein the non-transitory computer-readable storage medium includes an algorithm for measuring autofluorescence from one or more of the following: a sample of unlabeled particles, a sample of monostained control particles, and a sample of particles comprising a plurality of fluorophores.

[0468] 116. A non-transitory computer-readable storage medium according to any one of clauses 113-115, wherein the non-transitory computer-readable storage medium includes an algorithm for selecting a population for evaluating collinear autofluorescence spectra.

[0469] 117. The non-transitory computer-readable storage medium according to Clause 116, wherein the non-transitory computer-readable storage medium includes an algorithm for selecting a population of the autofluorescence spectrum in such a way as:

[0470] Generate a scatter plot including the fluorescence parameters of particles in the sample; and

[0471] Gating one or more populations on a scatter plot based on the median fluorescence intensity measured for each of the particles.

[0472] 118. A non-transitory computer-readable storage medium according to any one of clauses 116-117, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying a population of particles by applying an unsupervised clustering algorithm to select a population of autofluorescence spectra.

[0473] 119. The non-transitory computer-readable storage medium as described in Clause 118, wherein the unsupervised clustering algorithm is selected from the group consisting of: self-organizing map clustering, K-means clustering, hierarchical clustering, density-based noisy applied spatial clustering (DBSCAN), Gaussian mixture clustering, spectral clustering, MeanShift clustering, hierarchical and density-based clustering, prior algorithms, fuzzy c-means clustering, centroid-based clustering, and Birch algorithm.

[0474] 120. The non-transitory computer-readable storage medium as described in Clause 119, wherein the unsupervised clustering algorithm includes a self-organizing map algorithm.

[0475] 121. A non-transitory computer-readable storage medium according to any one of clauses 116-120, wherein the non-transitory computer-readable storage medium includes an algorithm for selecting a population of said autofluorescence spectra by applying a statistical analysis algorithm.

[0476] 122. The non-transitory computer-readable storage medium as described in Clause 121, wherein the statistical analysis algorithm is selected from the group consisting of: principal component analysis (PCA), singular value decomposition (SVD), factor analysis (FA), partial least squares (PLS), correspondence analysis (CA), multiple correspondence analysis (MCA), hierarchical cluster analysis (HCA), linear discriminant analysis, and matrix factorization.

[0477] 123. The non-transitory computer-readable storage medium as described in Clause 122, wherein the statistical analysis algorithm includes dimensionality reduction.

[0478] 124. A non-transitory computer-readable storage medium according to any one of clauses 60-67, wherein the non-transitory computer-readable storage medium includes an algorithm for determining the autofluorescence spectrum of the particles based on the median fluorescence intensity of each particle population.

[0479] 125. The non-transitory computer-readable storage medium according to any one of clauses 113-124, wherein the particles further comprise one or more fluorophores.

[0480] 126. The non-transitory computer-readable storage medium of claim 125, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity between the autofluorescence spectra of one or more particles and the fluorescence spectra of one or more fluorophores.

[0481] 127. A non-transitory computer-readable storage medium according to any one of clauses 113-126, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample.

[0482] 128. The non-transitory computer-readable storage medium as described in Clause 127, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating demixing performance in such a manner as:

[0483] Generate a spectral matrix associated with the fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; and

[0484] The spectral matrix is ​​used to unmix fluorescence spectra generated from unstained controls, monostained controls, stained samples, or any combination thereof; and

[0485] Calculate one or more of the unmixing bias and unmixing variance.

[0486] 129. The non-transitory computer-readable storage medium of claim 128, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating unmixing bias by measuring the presence of a false-positive unmixed fluorophore signal associated with an autofluorescence spectrum.

[0487] 130. The non-transitory computer-readable storage medium of claim 128, wherein the non-transitory computer-readable storage medium includes an algorithm for determining that, in all generated particle populations of a sample, there is no false-positive unmixed fluorophore signal associated with an autofluorescence spectrum in the unmixed fluorophore channel.

[0488] 131. The non-transitory computer-readable storage medium according to any one of clauses 128-130, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating unmixing variance by measuring unmixing-dependent diffusion in unmixed fluorophore signals.

[0489] 132. A non-transitory computer-readable storage medium according to any one of clauses 113-132, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating the collinearity, comprising:

[0490] An algorithm for generating a spectral matrix associated with the autofluorescence produced by particles in a sample;

[0491] An algorithm for calculating the inverse matrix from the generated spectral matrix; and

[0492] An algorithm for identifying autofluorescence spectra that are correlated with the variance in data generated by flow cytometry using the autofluorescence spectra.

[0493] 133. The non-transitory computer-readable storage medium as described in Clause 132, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra that contribute to the variance in flow cytometry data.

[0494] 134. The non-transitory computer-readable storage medium according to any one of clauses 132-133, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra affected by variance in flow cytometry data.

[0495] 135. A non-transitory computer-readable storage medium according to any one of clauses 132-134, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0496] 135. The non-transitory computer-readable storage medium as described in Clause 135, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0497] 136. The non-transitory computer-readable storage medium according to any one of clauses 132-135, wherein the inverse matrix is ​​a Gram inverse matrix.

[0498] 137. The non-transitory computer-readable storage medium according to Clause 136, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating the inverse matrix according to the following equation:

[0499]

[0500] in:

[0501] G is the Gram inverse matrix;

[0502] M is the spectral matrix; and

[0503] M T It is the transpose of the spectral matrix.

[0504] 138. A non-transitory computer-readable storage medium according to any one of clauses 135-137, wherein the non-transitory computer-readable storage medium includes an algorithm for analyzing the computed inverse matrix by deriving a quantitative measure from the inverse matrix.

[0505] 139. The non-transitory computer-readable storage medium as described in Clause 138, wherein the quantitative measure is a matrix norm.

[0506] 140. The non-transitory computer-readable storage medium as described in Clause 138, wherein the quantitative measure is a vector norm.

[0507] 141. A non-transitory computer-readable storage medium according to any one of clauses 113-140, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in a sample that minimize unmixing bias.

[0508] 142. The non-transitory computer-readable storage medium according to any one of clauses 113-141, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in a sample that minimize the variance of the generated data.

[0509] 143. The non-transitory computer-readable storage medium as described in Clause 142, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying an autofluorescence spectrum that minimizes the variance of the generated data by identifying an autofluorescence spectrum exhibiting maximum spectral matrix modulation.

[0510] 144. A non-transitory computer-readable storage medium according to any one of clauses 141-143, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra generated by particles in a sample that minimize unmixing bias and the variance of the generated data.

[0511] 145. A non-transitory computer-readable storage medium according to any one of clauses 141-144, wherein the non-transitory computer-readable storage medium includes an algorithm for removing autofluorescence contributions from generated flow cytometry data.

[0512] 146. A non-transitory computer-readable storage medium according to any one of clauses 141-145, wherein the non-transitory computer-readable storage medium includes an algorithm for iteratively identifying autofluorescence spectra generated by particles in a sample that minimize the variance of the generated data.

[0513] 147. The non-transitory computer-readable storage medium according to any one of clauses 113-146, wherein the flow cytometry data is spectrally unmixed flow cytometry data.

[0514] 148. The non-transitory computer-readable storage medium according to any one of clauses 113-146, wherein the flow cytometry data is compensated flow cytometry data.

[0515] 149. The non-transitory computer-readable storage medium according to any one of clauses 113-146, wherein the variance includes noise in flow cytometry data.

[0516] 150. A non-transitory computer-readable storage medium according to any one of clauses 113-149, wherein the non-transitory computer-readable storage medium includes an algorithm for generating an evaluation of collinearity visualization of autofluorescence spectra in the generated data.

[0517] 151. The non-transitory computer-readable storage medium as described in Clause 150, wherein the visualization highlights the autofluorescence spectrum associated with the variance in the generated data.

[0518] 152. The non-transitory computer-readable storage medium according to any one of clauses 150-151, wherein the visualization comprises a panel hotspot matrix.

[0519] 153. The non-transitory computer-readable storage medium as described in Clause 152, wherein the visualization includes a diagonal visualization of a panel hotspot matrix.

[0520] 154. The non-transitory computer-readable storage medium according to any one of clauses 150-153, wherein the visualization includes a diffusion correlation matrix.

[0521] 155. A non-transitory computer-readable storage medium according to any one of clauses 152-154, wherein the memory includes instructions for measuring the diffusion expansion factor (SIF) based on a hotspot matrix.

[0522] 156. The system according to Clause 155, wherein the memory includes instructions for evaluating whether the measured diffusion expansion factor is limited to autofluorescence spectra.

[0523] 157. A non-transitory computer-readable storage medium according to any one of clauses 113-156, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating collinearity between the autofluorescence spectra by evaluating the variance decomposition ratio (VDP).

[0524] 158. The non-transitory computer-readable storage medium as described in Clause 157, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating whether the measured diffusion expansion factor is limited to the autofluorescence spectrum based on the collinearity of the evaluated autofluorescence spectrum by means of variance decomposition ratio.

[0525] 159. The non-transitory computer-readable storage medium as described in Clause 157, wherein the non-transitory computer-readable storage medium includes an algorithm for evaluating the variance decomposition ratio by identifying collinear spectral groups through singular value decomposition (SVD).

[0526] 160. The non-transitory computer-readable storage medium according to Clause 159, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating a conditional index for each singular value generated for singular value decomposition.

[0527] 161. The non-transitory computer-readable storage medium as described in Clause 160, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating the condition index as the ratio of the largest computed singular value to each individually computed singular value.

[0528] 162. The non-transitory computer-readable storage medium according to any one of clauses 157-161, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 for each condition index.

[0529] 163. The non-transitory computer-readable storage medium according to any one of clauses 157-162, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a condition index greater than 15.

[0530] 164. The non-transitory computer-readable storage medium according to any one of clauses 162-163, wherein the non-transitory computer-readable storage medium includes an algorithm for identifying autofluorescence spectra with a variance decomposition ratio greater than 0.3 and a condition index greater than 15 as collinear.

[0531] 165. A non-transitory computer-readable storage medium according to any one of clauses 113-164, wherein the non-transitory computer-readable storage medium includes an algorithm for removing autofluorescence spectra determined to be collinear.

[0532] 166. A non-transitory computer-readable storage medium according to any one of clauses 113-164, wherein the non-transitory computer-readable storage medium includes an algorithm for removing autofluorescence spectra that contribute the most to the variance in flow cytometry data.

[0533] 167. A non-transitory computer-readable storage medium according to any one of clauses 113-166, wherein the non-transitory computer-readable storage medium includes an algorithm for removing autofluorescence spectra that contribute the most to the unmixing bias in the spectral unmixing of flow cytometry data.

[0534] Although the foregoing disclosure has been described in detail by way of illustration and example in order to make it clear, it will be apparent to those skilled in the art, based on the teachings of this disclosure, that some changes and modifications may be made without departing from the spirit or scope of the appended claims.

[0535] Therefore, the foregoing only illustrates the principles of this disclosure. It should be understood that those skilled in the art will be able to design various arrangements that, while not explicitly described or shown herein, embody the principles of this disclosure and are included within its spirit and scope. Furthermore, all examples and conditional language cited herein are primarily intended to assist the reader in understanding the principles of this disclosure and the concepts it contributes to advancing the art, and should be construed as not being limited to these specifically cited examples and conditions. Additionally, all statements herein recounting the principles, aspects, and embodiments of this disclosure, as well as specific examples thereof, are intended to cover their structural and functional equivalents. Furthermore, it is intended that such equivalents include both currently known equivalents and future-developed equivalents, i.e., any element developed to perform the same function, regardless of its structure. Moreover, nothing disclosed herein is intended to be exclusive to the public, whether or not such disclosure is expressly stated in the claims.

[0536] Therefore, the scope of this disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the spirit and scope of this disclosure are embodied in the appended claims. In the claims, 35 USC §112(f) or 35 USC §112(6) expressly provides that the limitation is invoked only if the exact phrase “means for…” or the exact phrase “step for…” is expressly recited at the beginning of the limitation in the claim; if such an exact phrase is not used in the limitation in the claim, 35 USC §112(f) or 35 USC §112(6) is not invoked.

Claims

1. A method comprising: Illuminate a sample containing particles in a flowing stream using a light source in a flow cytometer; A light detection system, including a photodetector, is used to detect light from the irradiated particles; Measure the autofluorescence spectrum generated by particles in the sample; as well as Evaluate collinearity between autofluorescence spectra generated by two or more different particles in a sample.

2. The method of claim 1, wherein the method comprises selecting a population for evaluating collinear autofluorescence spectra.

3. The method of claim 2, wherein selecting the population of the autofluorescence spectra comprises: a) Generate a scatter plot that includes the fluorescence parameters of particles in the sample; as well as Gating one or more populations on a scatter plot based on the median fluorescence intensity measured for each particle in the particles; or b) Apply unsupervised clustering algorithms to identify particle populations; or c) Apply statistical analysis algorithms.

4. The method according to any one of claims 1-3, wherein the method includes evaluating the unmixing performance of two or more autofluorescence spectra generated by particles in a sample, wherein evaluating the unmixing performance includes: Generate a spectral matrix associated with the fluorescence spectra of one or more fluorophores and one or more autofluorescence spectra generated by particles in the sample; as well as The spectral matrix is ​​used to unmix fluorescence spectra generated from unstained controls, monostained controls, stained samples, or any combination thereof; and Calculate one or more of the unmixing bias and unmixing variance.

5. The method of claim 4, wherein calculating the unmixing bias includes measuring the presence of false-positive unmixed fluorophore signals associated with the autofluorescence spectrum.

6. The method according to any one of claims 1-5, wherein evaluating the collinearity comprises: Generate a spectral matrix associated with the autofluorescence produced by particles in the sample; Calculate the inverse matrix based on the generated spectral matrix; as well as Identify autofluorescence spectra that are correlated with the variance in data generated by flow cytometry using autofluorescence spectroscopy.

7. The method according to any one of claims 1-6, wherein the method further comprises identifying an autofluorescence spectrum generated by particles in the sample that minimizes unmixing bias and minimizes the variance of the generated data.

8. The method according to any one of claims 1-7, further comprising generating a visualization of the evaluated collinearity of the autofluorescence spectra in the generated data.

9. The method of claim 8, wherein the visualization comprises a panel hotspot matrix.

10. The method according to any one of claims 1-9, wherein evaluating the collinearity between the autofluorescence spectra includes evaluating the variance decomposition ratio (VDP).

11. The method according to any one of claims 1-10, wherein the method further comprises removing autofluorescence spectra determined to be collinear.

12. The method according to any one of claims 1-11, wherein the method further comprises removing the autofluorescence spectra that contribute the most to the variance of the flow cytometry data and removing the autofluorescence spectra that contribute the most to the unmixing bias in the spectral unmixing of the flow cytometry data.

13. A flow cytometer system, comprising: A light source used to illuminate a sample, including particles in a flowing stream; A light detection system including a photodetector for detecting light from irradiated particles; as well as A processor, including memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: Measure the autofluorescence spectra generated by particles in the sample; and Evaluate collinearity between autofluorescence spectra generated by two or more different particles in a sample.

14. The system of claim 13, wherein the memory includes instructions for removing the following autofluorescence spectrum: The autofluorescence spectra determined to be collinear; or The autofluorescence spectrum that contributes the most to the variance in flow cytometry data; or The autofluorescence spectrum has the largest contribution to the unmixing bias in the spectral unmixing of flow cytometry data.

15. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions comprising: An algorithm for illuminating a sample containing particles in a flowing stream with a light source in a flow cytometer; An algorithm for detecting light from irradiated particles using a light detection system that includes a photodetector; An algorithm for measuring the autofluorescence spectra generated by particles in a sample; as well as An algorithm for evaluating collinearity between autofluorescence spectra generated by two or more different particles in a sample.

Images