CN116868045A

CN116868045A - Integrated flow cytometry data quality control

Info

Publication number: CN116868045A
Application number: CN202280016069.6A
Authority: CN
Inventors: 约瑟夫·斯佩德伦; 杰弗里·奥斯本; 托马斯·普莱斯勒; 保罗·巴克利·珀塞尔; 迈克尔·戈尔登; 米特·帕斯塔基亚
Original assignee: Becton Dickinson and Co
Current assignee: Becton Dickinson and Co
Priority date: 2021-11-24
Filing date: 2022-11-22
Publication date: 2023-10-10

Abstract

Aspects of the present disclosure include methods for integrating data quality assessment with data acquisition of a flow cytometer. Methods according to certain embodiments include detecting light from a sample in a flow stream illuminated by a light source with a light detection system, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal in a plurality of binning windows having overlapping data bins, and evaluating the plurality of binning windows for outlier events in the generated data signal. A system is also described having a processor with a memory having instructions for practicing the subject methods. A non-transitory computer readable storage medium is also provided.

Description

Integrated flow cytometry data quality control

Cross Reference to Related Applications

According to 35u.s.c. ≡119 (e), the present application claims priority from U.S. provisional patent application No. 63/282,904 filed on day 2021, 11 and 24 and U.S. provisional patent application No. 63/320,487 filed on day 2022, 3 and 16, the disclosures of which are incorporated herein by reference in their entirety.

Introduction to the application

The light detection can be used, for example, to characterize components of a sample (e.g., a biological sample) when the sample is used to diagnose a disease or medical condition. When the sample is illuminated, light may be scattered by the sample, transmitted through the sample, and emitted by the sample (e.g., by fluorescence). Variations in the composition of the sample, such as morphology, absorbance, and the presence of fluorescent markers, can result in variations in the light scattered, transmitted, or emitted by the sample. These changes can be used to characterize and identify the presence of components in a sample. To quantify these changes, light is collected and directed to the surface of the detector.

One technique for characterizing components in a sample using light detection is flow cytometry. Using the data generated from the detected light, the distribution of the components can be recorded and the desired material can be sorted. Flow cytometry measures the amount of different molecular analytes on or in a biological cell in part by recording the intensity of analyte-specific fluorescent markers attached to or in the biological cell. These fluorescent labels target specific molecular analytes by using antibodies that bind with high specificity to their molecular antigens, which may be surface markers on the cells, intracellular proteins and organelles, or molecules secreted from the cells. Fluorescent labels used in immunofluorescence flow cytometry are typically composed of two molecular components conjugated together: fluorescent reactive molecules (commonly referred to as "dyes," "tags," "fluorochromes," or "fluorophores") and antibodies that specifically bind to certain target molecules (commonly referred to as "antigens" or "markers") on, within, or around cells. The molecular linked construct consisting of a fluorophore and an antibody is often referred to as a "conjugate", "reagent" or "label". By labelling cells with a mixture of conjugates (known as a "panel"), wherein each conjugate type has an antibody targeting a different antigen and a fluorophore with unique spectral properties (emission and excitation) when measured on a flow cytometer, many different molecular analytes can be measured simultaneously on a single cell. This is known as "polychromatic", "multiparameter" or "polychromatic" flow cytometry. The biological properties of the different cell types in the sample can then be determined by examining the expression pattern of the molecular analyte on each cell, as revealed by measuring the antibody-fluorophore conjugate on the cell.

Flow cytometry data quality may be negatively affected by problems in sample quality or data collection, such as clogging or bubbles in the sample central stream. This may lead to erroneous conclusions from the data analysis.

Disclosure of Invention

Aspects of the present disclosure include methods for integrating data quality assessment with data acquisition of a flow cytometer. Methods according to certain embodiments include detecting light from a sample in a flow stream illuminated by a light source with a light detection system, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal in a plurality of binning windows having overlapping data bins, and evaluating the plurality of binning windows for outlier (outlier) events in the generated data signal. A system is also described having a processor with a memory having instructions for practicing the subject methods. A non-transitory computer readable storage medium is also provided.

In some embodiments, binning the generated event data signals includes binning a predetermined number of event data signals in each binning window, such as, for example, 500 event data signals in each binning window. In some examples, the event data signals are sequentially binned based on the time of event detection by the light detection system. In some instances, one or more event data signals are binned in two or more binning windows, such as where two or more binning windows overlap by a predetermined time frame. In some examples, the binning window overlaps a time frame of 0.001 μs to 100 μs. In some embodiments, the event data signal includes a fluorescence intensity of the event. In certain embodiments, the fluorescence intensity corresponds to the expression level of each event.

In some embodiments, a transformation is applied to each event data signal. In some examples, the transformation is one or more of a centered log-to-log transformation (additive log-ratio transformation), an equal length log-to-log transformation. In some examples, the method includes calculating a median parameter for each of the overlapping bins. In some examples, the median or average fluorescence intensity of events for each bin is calculated. In some examples, a median or average expression level of events for each data bin is calculated. In some embodiments, evaluating the plurality of binning windows for outlier events includes comparing a median parameter of each data bin to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some examples, the method includes dynamically calculating the outlier threshold, such as in real-time. For example, the outlier threshold may be dynamically adjusted in real-time in response to binning of each generated event data signal.

In some embodiments, the method includes classifying one or more bins as bins containing outliers. In some examples, when the median parameter is determined to be greater than the outlier threshold, the data bins are classified as data bins that include outliers. In some examples, the method further comprises identifying the event data signal of the data bin containing the outlier as the outlier. In some embodiments, when the plurality of bins are evaluated for an outlier event, an alert is generated that an outlier is detected. In some examples, the method includes generating an alert of a change in flow rate in a flow stream of the particle analyzer. In some examples, an alert is generated that a clog is present in the particle analyzer. In some examples, the flow rate of the flow stream is measured with a flow rate sensor. In certain examples, the method includes comparing the measured flow rate of the flow stream to the expected flow rate of the flow stream and re-evaluating the bins classified as bins containing outliers based on the compared flow rate of the flow stream. In some examples, the one or more bins are reclassified based on the compared flow rates of the flow streams. In some examples, no alert is generated when the measured flow rate is within a predetermined threshold of the expected flow rate, such as when the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the method includes comparing the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system and re-evaluating the bins classified as bins containing outliers based on the parameters of the light detection system, such as the photodetector voltage of one or more photodetectors of the light detection system. In certain embodiments, the calibration of the particle analyzer is performed in response to the bins being classified as containing outliers. In some examples, calibrating the particle analyzer includes detecting light from a standard composition in the flow stream illuminated by a light source with a light detection system, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal into a plurality of binning windows having overlapping bins, evaluating the data signals generated in the plurality of binning windows, and determining that no bins containing outliers are present.

Aspects of the disclosure also include systems (e.g., particle analyzers) having light detection systems. In an embodiment, a light detection system includes a detector component having a photodetector configured to detect light from a sample illuminated in a flow stream and to generate an event data signal in response to an event detected with the light detection system, and a modulator component configured to bin the generated event data signal in a plurality of bin windows including overlapping bins. In an embodiment, the detector component includes one or more of a light scattering photodetector, a fluorescent photodetector, and a light loss photodetector. In some examples, the detector assembly includes a forward scatter photodetector. In some examples, the detector component includes a side scatter photodetector. In some examples, the detector component includes a backscatter photodetector. In some examples, the detector assembly includes one or more fluorescent photodetectors. In some examples, the detector component includes a light loss photodetector. In some examples, the detector component includes a dark field photodetector. In some examples, the detector assembly includes a bright field photodetector. In some embodiments, the light detection system includes two or more photodetectors, such as 3 or more, such as 4 or more, such as 5 or more, and including 10 or more photodetectors. In some examples, the photodetectors form a photodetector array, such as a photodetector array having 4 or more photodetectors, such as 8 or more photodetectors, such as 12 or more photodetectors, such as 16 or more photodetectors, such as 24 or more photodetectors, and including a photodetector array having 48 or more photodetectors. In some embodiments, the detector component is configured to generate event data signals in 4 or more photodetector channels, such as 16 or more, such as 32 or more, and includes generating event data signals in 64 or more photodetector channels.

In some embodiments, the modulator component comprises an integrated circuit (e.g., field programmable gate array, FPGA) having programming for binning event data signals from two or more different photodetector channels. In some examples, the modulator component is configured to bin a predetermined number of event data signals into each binning window, such as, for example, 500 event data signals in each binning window. In some examples, the modulator component is configured to sequentially bin the generated event data signals based on event detection times of the light detection system. In some examples, the modulator component is configured to bin one or more of the event data signals into two or more bin windows, such as where two or more bin windows overlap by a predetermined time frame. In some examples, the binning window overlaps a time frame of 0.001 μs to 100 μs. In some embodiments, the event data signal includes a fluorescence intensity of the event. In certain embodiments, the fluorescence intensity corresponds to the expression level of each event. In some examples, the modulator component is configured to apply a transform to each event data signal. In some examples, the transform is one or more of a centered log ratio transform, an additive log ratio transform, and an equal length log ratio transform.

In an embodiment, a system includes a processor having a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to evaluate the plurality of binning windows for outlier events in a generated data signal. In some embodiments, the memory includes instructions for median or average fluorescence intensity for events for each data bin. In some embodiments, the memory includes instructions to calculate a median or average expression level for the events for each data bin. In some embodiments, the memory includes instructions to evaluate the plurality of binning windows for outlier events by comparing a median parameter of each of the bins to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some examples, the memory includes instructions for dynamically calculating the outlier threshold in real-time, such as dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the memory includes instructions for classifying the data bins as data bins containing outliers when the median parameter is determined to be greater than the predetermined outlier threshold. In some examples, the memory includes instructions to identify event data signals for each of the bins containing outliers as outliers. In some examples, the memory includes instructions for generating an alert that an outlier is detected when the plurality of bins are evaluated for the outlier event. In some embodiments, the memory includes instructions for generating an alert of a change in a flow rate in a flow stream of the particle analyzer. In some embodiments, the memory includes instructions to generate an alert that a jam is present in the particle analyzer.

In certain embodiments, the system of interest includes a flow sensor that measures the flow rate of the flow stream. In some embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to compare the measured flow rate of the flow stream with an expected flow rate of the flow stream, and re-evaluate a data bin classified as a data bin containing outliers based on the compared flow rate of the flow stream. In some examples, the memory includes instructions to reclassify the data bins based on the compared flow rates of the flow streams. In some embodiments, the memory includes instructions to not generate an alarm when the measured flow rate is within a predetermined threshold of the expected flow rate, such as not generating an alarm when the predetermined threshold is within 5% or less of the expected flow rate.

In some embodiments, the memory includes instructions to compare the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system and re-evaluate the bins classified as bins containing outliers based on the parameters of the light detection system. In some embodiments, the system includes a memory having instructions to perform calibration of the particle analyzer in response to one or more bins classified as bins containing outliers. In some examples, the instructions for calibrating the particle analyzer include instructions for detecting light from a standard composition in the flow stream illuminated by the light source with the light detection system, instructions for generating event data signals in response to events detected with the light detection system, instructions for binning the generated event data signals into a plurality of binning windows having overlapping bins, instructions for evaluating the generated data signals in the plurality of binning windows, and instructions for determining that there are no bins containing outliers.

Aspects of the present disclosure also include non-transitory computer-readable storage media for practicing the subject methods. A non-transitory computer-readable storage medium according to some embodiments includes instructions having: an algorithm for detecting light in a flow stream from a sample illuminated by a light source using a light detection system; an algorithm for generating an event data signal in response to an event detected with the light detection system; an algorithm for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and an algorithm for evaluating the plurality of binning windows for outlier events in the generated data signal. In some examples, the non-transitory computer-readable storage medium includes an algorithm for binning a predetermined number of event data signals in each window. In some examples, the non-transitory computer-readable storage medium includes an algorithm for sequentially binning event data signals based on event detection times of the light detection system. In some examples, the non-transitory computer-readable storage medium includes an algorithm for binning one or more event data signals into two or more binning windows, such as where the two or more binning windows overlap a predetermined time frame. For example, the binning windows may overlap time frames of 0.001 μs to 100 μs.

In some examples, the non-transitory computer-readable storage medium includes an algorithm for applying the transformation to each event data signal. In certain embodiments, the transformation is one or more of a centered logarithmic ratio transformation, an additive logarithmic ratio transformation, an equal length logarithmic ratio transformation, or a combination thereof. In some examples, the non-transitory computer-readable storage medium includes an algorithm for calculating a median parameter for each of the overlapping bins. In some examples, the non-transitory computer-readable storage medium includes an algorithm for calculating a median or average fluorescence intensity for events for each data bin. In some examples, the non-transitory computer-readable storage medium includes an algorithm for calculating a median or average expression level of events for each data bin.

In some examples, the non-transitory computer-readable storage medium includes an algorithm for evaluating a plurality of binning windows for outlier events by comparing a median parameter of each bin to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some examples, the non-transitory computer-readable storage medium includes an algorithm for dynamically calculating the outlier threshold in real-time, such as an algorithm for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying a data bin as a data bin containing outliers when the median parameter is determined to be greater than a predetermined outlier threshold. In some examples, the non-transitory computer-readable storage medium includes an algorithm for identifying event data signals for each data bin containing outliers as outliers.

In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for generating an alert that an outlier is detected when evaluating the plurality of bins for the outlier event. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating an alarm indicating that there is a change in a flow rate in the flow stream of the particle analyzer. In some examples, the non-transitory computer readable storage medium includes an algorithm for generating an alert that a jam is present in the particle analyzer.

In certain examples, the non-transitory computer-readable storage medium includes an algorithm for measuring a flow rate of the flow stream with a flow rate sensor. In some examples, the non-transitory computer-readable storage medium includes an algorithm for comparing the measured flow rate of the flow stream to the desired flow rate of the flow stream, and an algorithm for re-evaluating the bins classified as bins containing outliers based on the compared flow rates of the flow streams. In some examples, the non-transitory computer-readable storage medium includes an algorithm for reclassifying the data bins based on the compared flow rates of the flow streams. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for not generating an alarm when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. In some examples, the non-transitory computer-readable storage medium includes an algorithm for comparing median fluorescence intensity of a plurality of bins to one or more parameters of the light detection system, and an algorithm for re-evaluating bins classified as bins containing outliers based on parameters of the light detection system (e.g., photodetector voltages of one or more photodetectors of the light detection system). In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for performing calibration of the particle analyzer in response to one or more bins classified as containing outliers. In certain examples, the algorithm for calibrating the particle analyzer includes an algorithm for detecting light from a standard composition in a flow stream illuminated by a light source with a light detection system, an algorithm for generating an event data signal in response to an event detected with the light detection system, an algorithm for binning the generated event data signal into a plurality of binned windows having overlapping bins, an algorithm for evaluating the data signals generated in the plurality of binned windows, and an algorithm for determining that no bins containing outliers are present.

Drawings

The invention is best understood from the following detailed description when read in connection with the accompanying drawing figures. Included in the drawings are the following figures:

FIG. 1 depicts binning a generated event data signal into multiple binning windows with overlapping data bins in accordance with certain embodiments.

Fig. 2 depicts an evaluation of data signals from a particle analyzer, according to some embodiments.

FIG. 3 depicts a flow chart for dynamic real-time evaluation of data signals from a particle analyzer, in accordance with certain embodiments.

FIG. 4A depicts a functional block diagram of a particle analysis system according to some embodiments. Fig. 4B depicts a flow cytometer according to some embodiments.

FIG. 5 depicts a functional block diagram of one example of a particle analyzer control system, according to certain embodiments.

Fig. 6A depicts a schematic diagram of a particle sorter system, according to certain embodiments.

Fig. 6B depicts a schematic diagram of a particle sorter system, according to certain embodiments.

FIG. 7 depicts a block diagram of a computing system, according to some embodiments.

Detailed Description

Aspects of the present disclosure include methods for integrating data quality assessment with data acquisition of a flow cytometer. Methods according to certain embodiments include detecting light from a sample in a free-flowing stream illuminated by a light source with a light detection system, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal in a plurality of binning windows having overlapping data bins, and evaluating the plurality of binning windows for outlier events in the generated data signal. A system is also described having a processor with a memory having instructions for practicing the subject methods. A non-transitory computer readable storage medium is also provided.

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

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

Certain ranges of values are provided herein preceded by the term "about. The term "about" is used herein to provide literal support for the exact number following it, as well as numbers near or approximating the end of the term. In determining whether a number is near or near a specifically recited number, the near or near non-recited number may be a number that provides a substantial equivalent of the specifically recited number in the context in which it is presented.

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

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

It should be noted that, as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should also be noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as antecedent basis for use of exclusive terminology such as "solely," "only" and the like in connection with recitation of claim elements, or use of a "no" limitation.

It will be apparent to those of skill in the art upon reading this disclosure that each of the various embodiments described and illustrated herein have individual components and features that may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any of the enumerated methods may be performed in the order of enumerated events or in any other order that is logically possible.

Although the apparatus and method have been and will be described in terms of a functional explanation for grammatical fluidity, it is to be clearly understood that the claims are not to be construed as necessarily limited in any way to the construction of "devices" or "steps" unless explicitly set forth in 35U.S. c. -) 112, but are to be accorded the full scope of meaning and equivalents of the definitions provided by the claims under judicial doctrine of equivalents, and in the case of claims explicitly set forth in 35U.S. c. -) 112, are to be accorded full legal equivalents in 35U.s.c. -) 112.

As summarized above, the present disclosure provides methods for integrating data quality assessment with data acquisition of a flow cytometer, such as where data acquisition may be dynamically adjusted in response to data quality assessment. In further describing embodiments of the present disclosure, a method for detecting light with a light detection system is first described in more detail, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal in a plurality of binning windows having overlapping bins and evaluating the plurality of binning windows for outlier events. Next, a system is provided that includes a light detection system having a detector component and a modulator component configured to bin event data signals in a plurality of binning windows. A non-transitory computer readable storage medium is also described.

Method for integrating data quality assessment with data acquisition in particle analyzer

Aspects of the present disclosure include methods for dynamic data quality assessment, such as in connection with data acquisition in a particle analyzer (e.g., a flow cytometer as described in more detail below). In some embodiments, event data signal evaluation as described herein provides increased accuracy of data collection by a light detection system, such as where spurious data signals, event data signal irregularities (e.g., based on flow rate or voltage parameters of the light detection system), or photodetector noise are more effectively detected and in some instances excluded. In some examples, the methods described herein provide new options for using the particle analyzer, such as allowing data collection to be paused, alerting a user during collection, performing a cleaning or calibration process, and resuming data collection once it is determined to remove outliers from the collected event data signals (as described in more detail below). In certain embodiments, the methods described herein provide for generating an alert to a user due to a malfunction or unexpected change in a parameter of a particle analyzer, such as a flow rate of a flow stream due to a blockage or change in voltage or detector gain of one or more photodetectors of a light detection system. In certain embodiments, integrating the event data signal evaluation with the data acquisition provides predictive detection of parameter changes in one or more components of the particle analyzer, such as light illumination from a light source or light detection by one or more photodetectors in a flow stream. As described below, in some instances, the calibration protocol may be implemented prior to or in response to the acquisition of one or more event data signals.

In an embodiment, light from particles in a sample illuminated by a light source in a flow stream is transmitted to a light detection system (e.g., a light detection system having a detector component comprising a photodetector configured to detect light and generate data signals in a plurality of photodetector channels and a modulator component configured to bin the generated event data signals in a plurality of bin windows, as described in more detail below). Light may be measured by the photodetector at one or more wavelengths, such as at 2 or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and including measuring light from particles in the flow stream at 400 or more different wavelengths. In certain examples, light from the sample is spectrally separated into 2 or more spectral ranges, and each spectral range is detected in one or more photodetector channels, such as 3 or more spectral ranges, such as 4 or more, such as 8 or more, such as 16 or more, such as 32 or more, such as 64 or more, such as 128 or more, such as 256 or more, and including 512 or more different spectral ranges. The light may be measured continuously or at discrete intervals. In some cases, the detector of interest is configured to continuously take measurements of light. In other cases, the detector of interest is configured to measure light at discrete intervals, such as every 0.001 ms, every 0.01 ms, every 0.1 ms, every 1 ms, every 10 ms, every 100 ms, and including every 1000 ms or some other interval.

During each discrete time interval, one or more measurements of light from particles in the sample may be made, such as 2 or more times, such as 3 or more times, such as 5 or more times, and including 10 or more times. In certain embodiments, the light from the light source is measured 2 or more times by the photodetector, in some cases the data is averaged.

In some examples, the light detected from the particles in the sample is scattered light. In some examples, the scattered light is forward scattered light. In some examples, the scattered light is back scattered light. In some examples, the scattered light is side scattered light. In some examples, the light transmitted from the illuminated particles is transmitted light. In certain embodiments, the light detected from each particle is emitted light, such as particle luminescence (i.e., fluorescence or phosphorescence). In these embodiments, each particle may include one or more fluorophores that emit fluorescence in response to illumination by the two or more light sources. For example, each particle may include 2 or more fluorophores, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, and 10 or more fluorophores. In some examples, each particle includes a fluorophore that emits fluorescence in response to illumination by a light source. In some embodiments, the event data signal includes a fluorescence intensity of the event. In certain embodiments, the fluorescence intensity corresponds to the expression level of each event.

In some embodiments, fluorophores of interest may include, but are not limited to, dyes suitable for analytical applications (e.g., flow cytometry, imaging, etc.), such as acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes (e.g., diphenylmethane dyes), chlorophyll-containing dyes, triarylmethane dyes (e.g., triphenylmethane dyes), azo dyes, diazo dyes, nitrodyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinonimine dyes, azine dyes, eurodhin dyes, safranine dyes, indamine, indophenol dyes, fluoro dyes, oxazine dyes, oxazolone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluoro dyes, rhodamine dyes, phenanthridine dyes, and mixtures of two or more of the foregoing (e.g., in tandem) dyes, polymer dyes having one or more monomer dye units, and the foregoing. A large number of dyes are commercially available from a variety of sources, such as Molecular Probes (Eugene, OR), dyomics GmbH (Yena, germany), sigma Aldrich (St.Louis, MO), siriben company (St.Barbara, calif.), bidi medical (Becton Dickinson, BD) and company (Fulan, g Lin Hu, NJ) and Exciton (St.OH). For example, the number of the cells to be processed, The fluorophore may comprise 4-acetamido-4 '-isothiocyanatestilbene-2, 2' -disulfonic acid; acridine and derivatives such as acridine, acridine orange, acridine yellow, acridine red and acridine isothiocyanate; allophycocyanin (APC), phycoerythrin (PE), polymethine-chlorophyll protein, 5- (2' -aminoethyl) aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N- [ 3-vinylsulfonyl) phenyl]Naphthalimide-3, 5 disulfonate (fluorescein Lucifer Yellow VS); n- (4-anilino-1-naphthyl) maleimide; anthranilamide; bright yellow; coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, coumarin 120), 7-amino-4-trifluoromethylcoumarin (coumarin 151); cyanines and derivatives such as cyanooctyl, cy3, cy3.5, cy5, cy5.5, and Cy7;4', 6-diamino-2-phenylindole (DAPI); 5',5 "-dibromo pyrogallol sulfophthalein (bromophthalic trimellitic acid red); 7-diethylamino-3- (4' -isothiocyanatophenyl) -4-methylcoumarin; diethylaminocoumarin; diethylenetriamine pentaacetic acid ester; 4,4 '-diisothiocyano dihydrostilbene-2, 2' -disulfonic acid; 4,4 '-diisothiocyano stilbene-2, 2' -disulfonic acid; 5- [ dimethylamino ]]Naphthalene-1-sulfonyl chloride (DNS, dansyl chloride); 4- (4' -dimethylaminophenylazo) benzoic acid (DABCYL); 4-dimethylaminophenyl azo phenyl-4' -isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosine and derivatives such as erythrosine B and erythrosine isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5- (4, 6-dichlorotriazin-2-yl) aminofluorescein (DTAF), 2',7' -dimethoxy-4 ',5' -dichloro-6-carboxy-nitrostyrene (JOE); fluorescein Isothiocyanate (FITC); fluorescein chlorotriazine, and QFITC (XRITC); fluorescent amine; IR144; IR1446; green Fluorescent Protein (GFP); coral reef fluorescent protein (RCFP); lizhi amine ^TM The method comprises the steps of carrying out a first treatment on the surface of the Lizheimine rhodamine; fluorescent yellow; malachite green isothiocyanate; 4-methylumbelliferone; o-cresolphthalein; nitrotyrosine; pararosaniline; nile red; oregon green; phenol red; b-phycoerythrin; o-phthalaldehyde; pyrene and its derivatives such as pyrene, pyrene butyrate and succinimidyl 1-pyrene butyrate; activated Red 4 (Cibacron) ^TM Bright red 3B-ase:Sub>A); rhodamine and derivatives, such as 6-carboxy-X-Rhodamine (ROX), 6-carboxyrhodamine(R6G), 4, 7-dichloro rhodamine lissamine, rhodamine B sulfonyl chloride, rhodamine (rhodi), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine (sulforhodamine 101), sulfonyl chloride derivatives of sulforhodamine 101 (Texas Red), N' -tetramethyl-6-carboxyrhodamine (TAMRA), tetramethyl rhodamine, and Tetramethyl Rhodamine Isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives; xanthenes; dye conjugated polymers (i.e., polymer-attached dyes) such as fluorescein isothiocyanate-dextran and dyes that bind two or more dyes (e.g., tandem dyes or protein complex tandem dyes) such as Phycoerythrin (PE) tandem dyes or Allophycocyanin (APC) tandem dyes, for example, phycoerythrin-CF 594 (PE-CF 594) tandem, phycoerythrin-cyanine 5 tandem (PE-Cy 5), phycoerythrin-cyanine 5.5 tandem (PE-Cy 5.5), phycoerythrin-cyanine 7 tandem (PE-Cy 7), allophycocyanin-R700 tandem (APC-R700), allophycocyanin-cyanine 7 (APC-Cy 7), polymeric dyes having one or more monomeric dye units, and mixtures of two or more of the foregoing dyes, or combinations thereof.

In some examples, the fluorophore is a polymeric dye. In some cases of this method, the polymeric dye comprises a conjugated polymer. Conjugated Polymers (CPs) are characterized by a delocalized electron structure comprising a backbone of alternating unsaturated bonds (e.g., double and/or triple bonds) and saturated bonds (e.g., single bonds), wherein pi electrons can move from one bond to another. Thus, the conjugated backbone may impart an extended linear structure on the polymer dye with limited bond angles between the repeat units of the polymer. For example, proteins and nucleic acids, while also polymeric, in some cases do not form elongated rod-like structures, but rather fold into higher order three-dimensional shapes. In addition, CPs may form a "rigid rod" polymer backbone and experience limited torsion (e.g., twist) angles between monomeric repeat units along the polymer backbone. In some examples, the polymeric dye includes CP having a rigid rod-like structure. The structural characteristics of the polymeric dye can affect the fluorescent properties of the molecule.

Polymeric dyes of interest include, but are not limited to, those described by Gaylord et al in U.S. publication nos. 20040142344, 20080293164, 20080064042, 20100136702, 2011025549, 20110257374, 20120028828, 20120252986, 20130190193, 20160264737, 20160266131, 20180231530, 20180009990, 20180009989, and 20180163054, the disclosures of which are incorporated herein by reference in their entirety; and Gaylord et al, J.Am.chem.Soc.,2001,123 (26), pages 6417-6418; feng et al chem.soc.rev.,2010,39,2411-2419; and Traina et al, J.Am.chem.Soc.,2011,133 (32), pages 12600-12607, the disclosures of which are incorporated herein by reference in their entirety.

The polymeric dye may have one or more desired spectral properties such as a particular maximum absorption wavelength, a particular maximum emission wavelength, an extinction coefficient, a quantum yield, etc. (see, e.g., chattopladhyay et al, "light purple fluorophores: a new class of superluminescent fluorescent compounds for immunofluorescence experiments: blood count portion A,81A (6), 456-466, 2012). In some embodiments, the polymeric dye has an absorption curve between 280nm and 475 nm. In certain embodiments, the polymeric dye has an absorbance maximum (excitation maximum) in the range of 280nm and 475 nm. In some embodiments, the polymeric dye absorbs incident light having a wavelength in a range between 280nm and 475 nm. In some embodiments, the polymeric dye has a maximum emission wavelength in the range of 400nm to 850nm, such as 415nm to 800nm, with specific examples of maximum emission of interest including, but not limited to: 421nm, 510nm, 570nm, 602nm, 650nm, 711nm, and 786nm. In some examples, the polymeric dye has an emission maximum wavelength within a range selected from the group consisting of: 410nm to 430nm, 500nm to 520nm, 560nm to 580nm, 590nm to 610nm, 640nm to 660nm, 700nm to 720nm, and 775nm to 795nm. In certain embodiments, the polymeric dye has a maximum emission wavelength of 421 nm. In some examples, the polymeric dye has a maximum emission wavelength of 510 nm. In some examples, the polymeric dye has a maximum emission wavelength of 570 nm. In certain embodiments, the polymeric dye has a maximum emission wavelength of 602 nm. In some examples, the polymeric dye has a maximum emission wavelength of 650 nm. In certain examples, the polymeric dye has a maximum emission wavelength of 711 nm. In some embodiments, the polymeric dye has a maximum emission wavelength of 786nm. In certain examples, the polymeric dye has a maximum emission wavelength of 421nm±5nm. In some embodiments, the polymeric dye has a maximum emission wavelength of 510nm±5nm. In certain examples, the polymeric dye has a maximum emission wavelength of 570nm±5nm. In some examples, the polymeric dye has a maximum emission wavelength of 602nm±5nm. In some embodiments, the polymeric dye has a maximum emission wavelength of 650nm±5nm. In certain examples, the polymeric dye has a maximum emission wavelength of 711nm±5nm. In some cases, the polymeric dye has a maximum emission wavelength of 786nm±5nm. In certain embodiments, the polymeric dye has an emission maximum selected from 421nm, 510nm, 570nm, 602nm, 650nm, 711nm, and 786nm.

Specific polymeric dyes that may be used include, but are not limited to BD Horizon Brilliant ^TM Dyes, e.g. BD Horizon Brilliant ^TM Violet dye (e.g., BV421, BV480, BV510, BV570, BV605, BV650, BV711, BV786, BV 829); BD Horizon Brilliantzxf ^TM Ultraviolet dyes (e.g., BUV395, BUV496, BUV563, BUV615, BUV661, BUV737, BUV 805); and BD Horizon Brilliantzxf ^TM Blue dyes (e.g., BB515, BB630, BB660, BB700, BB755, BB 790) (BD Biosciences, san jose, CA).

In some embodiments, light from particles in a sample is detected in two or more photodetector channels, such as 4 or more, such as 8 or more, such as 12 or more, such as 16 or more, such as 20 or more, such as 24 or more, such as 28 or more, such as 32 or more, such as 36 or more, such as 40 or more, such as 44 or more, such as 48 or more, such as 52 or more, such as 56 or more, such as 60 or more, and including 64 or more photodetector channels. Detecting light from one or more spectral ranges, such as 2 or more spectral ranges/such as 3 or more, in each photodetector channel, and including detecting 4 or more different spectral ranges in each photodetector channel, depending on the spectral width of each predetermined spectral range of the light and the number of photodetector channels employed. In an embodiment, generating one or more event data signals, such as 2 or more event data signals, such as 4 or more, such as 8 or more, such as 16 or more, in each photodetector channel in response to the detected light, and including generating 32 or more event data signals in each photodetector channel in response to the detected light.

In practicing the subject method, event data signals generated in response to events detected in a light detection system are binned in a plurality of binning windows having overlapping bins. The term binned data signal is used herein in its conventional sense to refer to non-consecutive groupings or bins of raw event data signals into predetermined intervals (i.e., bins) or combinations of event data signals from different photodetector channels into quantized versions of the data signal. In some embodiments, the binned event data signal includes a fluorescence intensity of light detected in response to an event. In some embodiments, the binned event data signal comprises a fluorescent representation of an event detected by a light detection system. In certain embodiments, the binned event data signal comprises a time value of an event detected by the light detection system.

In some embodiments, event data signals from two or more different photodetector channels are stored. The method includes binning together data signals from two or more different photodetector channels, such as 3 or more, such as 4 or more, and including data signals from 8 or more different photodetector channels. In some examples, the method includes binning data signals from non-adjacent photodetectors. In other cases, the method includes binning data signals from adjacent photodetectors. In certain examples, the method includes horizontal binning of adjacent photodetectors. In certain embodiments, the method includes dynamically binning data signals from two or more different photodetector channels in real time.

In some embodiments, each binning window comprises a predetermined time frame for data (i.e., generated event data signals) to be collected by the particle analyzer. In some cases, each binning window includes a data acquisition time frame of 0.001 μs or greater, such as 0.005 μs or greater, such as 0.01 μs or greater, such as 0.05 μs or greater, such as 0.1 μs or greater, such as 0.5 μs or greater, such as 1 μs or greater, such as 5 μs or greater, such as 10 μs or greater, such as 50 μs or greater, such as 100 μs or greater, such as 500 μs or greater, and includes a data acquisition time frame of 1000 μs or greater. In some cases, each binning window includes a data acquisition time frame from 0.001 μs to 5000 μs, such as 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as 0.1 μs to 3000 μs, such as 0.5 μs to 2500 μs, such as 1 μs to 2000 μs, such as 5 μs to 1000 μs, such as 10 μs to 500 μs, and a data acquisition time frame from 50 μs to 100 μs. Each binning window may be the same duration or may be a different duration.

In some embodiments, binning the generated event data signals includes binning a predetermined number of event data signals in each binning window, such as wherein 5 or more event data signals are binned in each binning window, such as 10 or more event data signals, such as 25 or more event data signals, such as 50 or more event data signals, such as 75 or more event data signals, such as 100 or more event data signals, such as 125 or more event data signals, such as 150 or more event data signals, such as 175 or more event data signals, such as 200 or more event data signals, such as 300 or more event data signals, such as 400 or more event data signals, such as 500 or more event data signals, such as 600 or more event data signals, such as 700 or more event data signals, such as 800 or more event data signals, such as 100 or more event data signals, and including 1000 or more event data signals in each binning window.

In some embodiments, two or more binning windows at least partially overlap, such as one or more event data signals binned in more than one binning window. For example, 3 or more binning windows may at least partially overlap, such as 4 or more and including 5 or more of the binning windows at least partially overlap. In certain examples, the binning windows according to the methods of the present disclosure form a sliding window configuration in which each binning window at least partially overlaps a previous binning window and a next binning window. For example, in a sliding window binning configuration with 3 different binning windows, the second binning window at least partially overlaps the first binning window (at the beginning of the second binning window) and at least partially overlaps the third binning window (at the end of the second binning window). In a sliding window binning configuration, each generated event data signal is binned in at least one binning window such that no event data signal is lost in data acquisition. FIG. 1 depicts event data signals generated by binning in a plurality of binning windows having overlapping data bins according to some embodiments. In non-consecutive binning, each predetermined set of generated event data signals (each portion of the data acquisition window 101, e.g., 500 or more data signals) is binned. As shown in fig. 1, the end of each binning window is the beginning of the subsequent binning window. Each binning window may be the same length or different lengths. In a sliding window binning configuration, each binning window at least partially overlaps with a previous binning window and a next binning window. As shown in fig. 1, one or more segments of the data acquisition window 102 are binned in each binning window. In an embodiment of the present disclosure, one or more portions of the data acquisition window 102 are binned in two or more different binning windows.

In some examples, one or more of the generated event data signals are binned in two or more binning windows (i.e., event data signals generated with the binning windows overlapping). In some examples, the binning windows overlap by 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more, and include the binning windows overlap by 1000 μs or more. In certain examples, the binning window overlaps from 0.001 μs to 5000 μs, such as 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as 0.1 μs to 3000 μs, such as 0.5 μs to 2500 μs, such as 1 μs to 2000 μs, such as 5 μs to 1000 μs, such as 10 μs to 500 μs, and includes a time frame from 50 μs to 100 μs.

In some examples, each binning window is adjusted in response to a change in the data acquisition rate of the particle analyzer, such as when the flow rate of the flow stream changes. In one example, the size of the one or more binning windows may be increased in response to a change in the flow rate of the flow stream (e.g., when the flow rate is increased), such as by 5% or more, such as by 10% or more, such as by 25% or more, and including 50% or more. In some examples, the increase in the size of each binning window is equal to the change in the flow rate of the flow stream (i.e., the size of the binning window increases by 10% when the flow rate of the flow stream increases by 10%). In other examples, the increase in the size of each binning window is 5% or more, such as 10% or more, such as 20% or more, such as 30% or more, such as 40% or more, such as 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, and including 95% or more of the change in the flow rate of the flow stream. In a second example, the size of the one or more binning windows may be reduced in response to a change in the flow rate of the flow stream (e.g., where the flow rate is reduced), such as by 5% or more, such as by 10% or more, such as by 25% or more, and including by 50% or more. In some examples, the decrease in the size of each binning window is equal to the change in the flow rate of the flow stream (i.e., the size of the binning window decreases by 10% when the flow rate of the flow stream decreases by 10%). In other cases, the decrease in the size of each binning window is 5% or more, such as 10% or more, such as 20% or more, such as 30% or more, such as 40% or more, such as 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, and including 95% or more of the change in the flow rate of the flow stream. In certain examples, one or more binning windows are automatically adjusted in response to a change in the data acquisition rate of the particle analyzer.

In some embodiments, a transformation is applied to each event data signal. In some examples, the transform is one or more of a centered log ratio transform, an additive log ratio transform, and an equal length log ratio transform. In some examples, the method includes calculating a median parameter for each of the overlapping bins. In some examples, the method includes calculating an average parameter for each of the overlapping bins. In some examples, the median or average fluorescence intensity of events for each bin is calculated. In some examples, the method includes calculating a median or average expression level of the events for each data bin.

In some embodiments, evaluating the plurality of binning windows of outlier events includes comparing the median parameter of each data bin to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some examples, the method includes dynamically calculating the outlier threshold, such as in real-time. For example, the outlier threshold may be dynamically adjusted in real-time in response to binning of each generated event data signal. In some embodiments, the method includes implementing a dynamic algorithm, such as a machine learning algorithm for calculating or adjusting the calculated outlier threshold. In some examples, the outlier threshold may be calculated by a machine learning algorithm. In some examples, the change in outlier threshold is determined by a machine learning algorithm. For example, in some embodiments, the change in outlier threshold may be sufficient to increase the number of event data signals above or below a median absolute deviation or median margin percentage. In other embodiments, the change in outlier threshold is sufficient to reduce the number of event data signals above or below a median absolute deviation or median margin percentage. The term "machine learning" is used herein in its conventional sense to refer to the adjustment of an outlier threshold (e.g., a median absolute deviation outlier threshold or a median tolerance percentage outlier threshold) by a computational method that ascertains and implements information directly from data without relying on a predetermined equation as a model. In some embodiments, machine learning includes a learning algorithm that finds patterns in the data signal (e.g., from a plurality of event data signals from the illuminated sample). In these embodiments, the learning algorithm is configured to generate better and more accurate decisions and predictions from the number of data signals (i.e., as the number of characterized event data signals from the sample increases, the learning algorithm becomes more robust). Machine learning protocols of interest may include, but are not limited to, artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, bayesian networks, dynamic bayesian networks, genetic algorithms, and other machine learning protocols.

In some embodiments, the method includes classifying one or more data bins in the plurality of binning windows as data bins containing outliers, such as wherein 2 or more data bins are classified as data bins containing outliers, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, and including 10 or more. In some examples, when the median (or average) parameter is determined to be greater than the outlier threshold, the bins are classified as bins containing outliers. In some examples, the parameter is a fluorescence intensity of an event for each bin, and the bin is classified as a bin containing outliers when the median or average fluorescence intensity of the event data signal is greater than a calculated outlier threshold (e.g., a median absolute deviation outlier threshold or a median allowable percentage outlier threshold). In other examples, the parameter is an expression level of an event for each data bin, and the data bin is classified as a data bin containing outliers when the median or average expression level of the event is greater than the calculated outlier threshold. In some embodiments, when the median (or mean) parameter is determined to be within a predetermined percentage of (i.e., greater than or less than) the outlier threshold, the data bin is classified as a data bin containing an outlier, such as where the median parameter (e.g., fluorescence intensity or expression level of the event) is within 10% of the outlier threshold, such as within 9% of the outlier threshold, such as within 8% of the outlier threshold, such as within 7% of the outlier threshold, such as within 6% of the outlier threshold, such as within 5% of the outlier threshold, such as within 4% of the outlier threshold, such as within 3% of the outlier threshold, such as within 2% of the outlier threshold, and including where the parameter is within 1% of the outlier threshold. In some examples, the method further comprises identifying the event data signal of the data bin containing the outlier as the outlier.

In certain embodiments, one or more outliers in the event data signal are identified using a median absolute deviation algorithm having the steps of:

1. dividing the input data D into different or overlapping bins with equal or nearly equal number of events, using a preferred bin size B, but allowing minor adjustments to improve uniformity of bin size over D; if overlapping bins are used, these are all exactly the size of B.

2. The relevant characteristics of these bins, such as the time or median expression of the selected parameters, are calculated and stored in xr [ ].

3. The result xr is suitably rescaled to x (e.g., the start of acquisition, last bin shorter than the other bins, user adjustment speed during acquisition)

4. Calculating xm as the median (x)

5. Centered around xm with x and stored as xc [ ]

6. Calculating absxc [ ] as |xc|

7. Calculating mad as the median (absxc)

8. Calculate m [ ] as ((0.6745 x xc)/mad)

9. If |m [ i ] | > T, bin i is marked as an outlier

10. If the event is in at least one bin marked as outlier, the event in D is marked as outlier (in the case of overlapping bins, the event may belong to several bins)

Wherein: d: flow cytometry expression data with temporal channels

T: MAD outlier threshold

B: bin size

In certain embodiments, one or more outliers in the event data signal are identified using a median outlier threshold algorithm having the steps of:

4. Calculating xm as the median (x)

5. If |x [ i ] | > xm (1+T), bin i is marked as an outlier

6. If the event is in at least one bin marked as outlier, the event in D is marked as outlier (in the case of overlapping bins, the event may belong to several bins)

Wherein: d: flow cytometry expression data with temporal channels

T: median outlier threshold

B: bin size

In some embodiments, the algorithm is such that not all of the collected event data signals are required before calculating the median parameter characteristics. In some examples, once the data stream stabilizes during data acquisition, the median characteristics (i.e., xm) from each of the plurality of binning windows may be estimated from a subset of bins for which no remaining data is available. The rest of the algorithm may remain the same as the bins and event data signals are classified as data becomes available.

In some embodiments, when the plurality of bins are evaluated for an outlier event, an alert is generated that an outlier is detected. In some examples, the method includes generating an alert of a change in flow rate in a flow stream of the particle analyzer. In some examples, the flow rate of the flow stream is measured with a flow rate sensor. In one example, an alert may be generated that there is an increase in the flow rate of the flow stream, such as when there is an increase in the flow rate of the flow stream of 1% or more, such as 2% or more, such as 3% or more, such as 4% 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, and including the case of an increase in the flow rate of the flow stream of 95% or more. In one example, an alert of a decrease in the flow rate of the presence flow stream may be generated, such as when the presence flow rate decreases by 1% or more, such as 2% or more, such as 3% or more, such as 4% 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, and including a case where the flow rate of the presence flow stream decreases by 95% or more. In response to the generated alert, a change in flow rate in the flow stream may be correlated to one or more of the subject methods described herein, such as where the change in flow rate may be correlated to a rate at which the event data signal is generated, a binning rate of the generated event data signal, a size of each binning window (as described above), or an evaluation of multiple binning windows for outlier events in the generated data signal. In certain examples, the rate at which the event data signal is generated by the particle analyzer is adjusted (i.e., increased or decreased) in response to the generated alert (there is a change in the flow rate of the flow stream). In other examples, the binning rate of the generated event data signals is adjusted in response to the generated alarms (there is a change in the flow rate of the flow stream). In other examples, the size of each binning window is adjusted in response to a generated alert (there is a change in the flow rate of the flow stream). In other examples, in response to the generated alert (there is a change in the flow rate of the flow stream), the evaluation of the plurality of binning windows for outlier events in the generated data signal is adjusted.

Fig. 2 depicts an evaluation of data signals from a particle analyzer, according to some embodiments. Event data signals generated in response to light from a sample detected by the light detection system are binned in a plurality of binning windows (201). A data bin containing outliers is identified (202) by comparing median parameters of the data bin to outlier thresholds. The bins containing outliers may be re-evaluated, such as in response to changes or adjustments to parameters of the particle analyzer system (e.g., flow rate or photodetector voltage as described below). In some embodiments, event data signals from bins containing outliers may be excluded (203) from the acquired data.

In some examples, an alert is generated that a blockage, bubble, or entrainment is present in the flow stream in the particle analyzer. In certain examples, the method includes comparing the measured flow rate of the flow stream to the expected flow rate of the flow stream and re-evaluating the bins classified as bins containing outliers based on the compared flow rate of the flow stream. In some examples, the one or more bins are reclassified based on the compared flow rates of the flow streams. In some examples, no alert is generated when the measured flow rate is within a predetermined threshold of the expected flow rate, e.g., when the measured flow rate is within 20% or less of the expected flow rate, such as within 15% or less, such as within 10% or less, such as within 9% or less, such as within 8% or less, such as within 7% or less, such as within 6% or less, such as within 5% or less, such as within 4% or less, such as within 3% or less, such as within 2% or less, such as within 1% or less, and including not generating an alert when the measured flow rate is within 0.1% or less of the expected flow rate.

In some embodiments, the alert is generated due to an irregular parameter in the light detection system or due to a change in a parameter of the light detection system. In some examples, an alert is generated when there is a change in photodetector voltage (or detected irregularity) of one or more photodetectors of the light detection system. In some examples, the method includes comparing a median or average fluorescence intensity of the plurality of bins to one or more parameters of the light detection system and re-evaluating the bins classified as containing outliers based on changes in the parameters of the light detection system (or detected irregularities).

In some embodiments, when a change, anomaly, or irregularity is detected in a flow stream (e.g., flow rate) or a parameter of a light detection system (e.g., light detector voltage), the method includes removing one or more of the re-estimated bins classified as containing outliers. One or more bins containing outliers may be removed from the collected sample data, such as all bins containing outliers when generating an alert related to a flow stream or parameter of the light detection system. In other embodiments, when a change, anomaly, or irregularity is detected in a parameter of the flow stream or the light detection system, the method includes reloading the sample by the particle analyzer and rerun the sample collection.

In certain embodiments, the calibration of the particle analyzer is performed in response to the bins being classified as containing outliers. In some examples, calibration of the particle analyzer includes detecting light from a standard composition in the flow stream illuminated by a light source with a light detection system, generating an event data signal in response to an event detected with the light detection system, binning the generated event data signal into a plurality of binning windows having overlapping bins, evaluating the data signals generated in the plurality of binning windows, and determining that no bins containing outliers are present. In some examples, the standard composition includes particles (e.g., beads) having one or more fluorophores. In certain embodiments, the standard composition comprises multispectral beads, such as those described in U.S. provisional patent application No. 63/221,227 filed on day 2021, 7, 13, the disclosure of which is incorporated herein by reference.

Particles of interest according to certain embodiments may include unimodal multi-fluorophore beads that provide bright photodetector signals at all light source wavelengths (e.g., at all LEDs or lasers of the system) and at the detection wavelengths of the photodetectors. Each particle may have one or more different types of fluorophores, such as 2 or more, or 3 or more, or 4 or more, or 5 or more, or 6 or more, or 7 or more, or 8 or more, or 9 or more, or 10 or more, or 11 or more, or 12 or more, or 13 or more, or 14 or more, or 15 or more, 16 or more, or 17 or more, or 18 or more, or 19 or more, or 20 or more, or 25 or more, or 30 or more, or 35 or more, or 40 or more, or 45 or more, 50 or more different types of fluorophores. For example, each particle may include 2, or 3, or 4, or 5, or 6, or 7, or 8, or 9, or 10, or 11, or 12, or 13, or 14, or 15, or 16, or 17, or 18, or 19, or 20 different types of fluorophores. In an embodiment, each fluorophore is stably associated with the particle. Stable association means that the fluorophore is not readily dissociated from the particle for contact with a liquid medium, such as an aqueous medium. In some embodiments, one or more fluorophores are covalently conjugated to the particle. In other embodiments, one or more fluorophores are physically associated with (i.e., non-covalently coupled to) the particle. In other embodiments, one or more fluorophores are covalently conjugated to the particle, and one or more fluorophores are physically associated with the particle.

The particles may be of any convenient shape for irradiation by a light source as described above. In some examples, the particles are solid supports that are shaped or configured as discs, spheres, ovals, cubes, blocks, cones, and the like, as well as irregular shapes. The mass of the particles may in some examples vary in the range of 0.01mg to 20mg, such as 0.05mg to 19.5mg, such as 0.1mg to 19mg, such as 0.5mg to 18.5mg, such as 1mg to 18mg, such as 1.5mg to 17.5mg, such as 2mg to 15mg, and including 3mg to 10 mg. The particles may have, for example, 0.01mm as determined using a vertex system or equivalent ² Or greater, such as 0.05mm ² Or greater, such as 0.1mm ² Or greater, such as 0.5mm ² Or larger, or smallerSuch as 1mm ² Or greater, such as 1.5mm ² Or greater, such as 2mm ² Or greater, such as 2.5mm ² Or greater, such as 3mm ² Or greater, such as 3.5mm ² Or greater, such as 4mm ² Or greater, such as 4.5mm ² Or greater and including 5mm ² Or a larger surface area.

The size of the particles may vary as desired, with in some examples the particles having a maximum size of 0.01mm to 10mm, such as 0.05mm to 9.5mm, such as 0.1mm to 9mm, such as 0.5mm to 8.5mm, such as 1mm to 8mm, such as 1.5mm to 7.5mm, such as 2mm to 7mm, such as 2.5mm to 6.5mm, and including 3mm to 6mm. In some cases, the particles have a shortest dimension in the range of 0.01mm to 5mm, such as 0.05mm to 4.5mm, such as 0.1mm to 4mm, such as 0.5mm to 3.5mm, and including from 1mm to 3 mm.

In certain cases, the particles of interest are porous, such as where the particles have a porosity in the range of 5 μ to 100 μ, such as 10 μ to 90 μ, such as 15 μ to 85 μ, such as 20 μ to 80 μ, such as 25 μ to 75 μ, and including from 30 μ to 70 μ, such as 50 μ, as determined using a capillary flow porosimeter or equivalent.

The particles may be formed of any convenient material. Of interest in some embodiments are particles, such as beads, that have low or no autofluorescence. Suitable materials include, but are not limited to, glass materials (e.g., silicate), ceramic materials (e.g., calcium phosphate), metallic materials, polymeric materials, and the like, such as polyethylene, polypropylene, polytetrafluoroethylene, polyvinylidene fluoride, and the like. In some examples, the particles are formed from a solid support, such as the porous matrix described in U.S. patent No. 9,797,899, the disclosure of which is incorporated herein by reference. Thus, the surface area of the particles may be any suitable macroporous or microporous substrate, where suitable macroporous and microporous substrates include, but are not limited to, ceramic substrates, frits such as sintered glass, polymeric substrates, and metal-organic polymeric substrates. In some embodiments, the porous matrix is a frit. The term "frit" is used herein in its conventional sense to refer to a porous composition formed from a sintered particulate solid such as glass. The frit may have a chemical composition that varies depending on the type of sintered particles used to prepare the frit, wherein usable frits include, but are not limited to, frits composed of alumino silicate, boron trioxide, borophosphosilicate glass, borosilicate glass, ceramic glaze, cobalt glass, brown glass (cranberry glass), fluorophosphate glass, fluorosilicate glass, fused silica, germanium dioxide, metal and sulfide intercalated borosilicate, leaded glass, phosphate glass, phosphorus pentoxide glass, phosphosilicate glass, potassium silicate, soda lime glass, sodium hexametaphosphate glass, sodium silicate, tellurite glass, uranium glass, glass stones, and combinations thereof. In some embodiments, the porous matrix is a frit, such as borosilicate, aluminosilicate, fluorosilicate, potassium silicate, or borophosphosilicate frit. In some embodiments, the particles are formed from a porous organic polymer.

FIG. 3 depicts a flow chart of dynamic real-time evaluation of data signals from a particle analyzer, according to some embodiments. In step 301, light from a flow stream that is illuminated by a light source is detected by a light detection system. In response to an event detected by the light detection system, an event data signal is generated at step 302. In step 303, the generated event data signals are binned in a plurality of binning windows having overlapping data bins. A binning window is evaluated for outlier events in the generated event data signal in step 304, wherein bins containing outliers are classified when compared to an outlier threshold. In certain embodiments, the method includes determining a change in a parameter of the particle analyzer, such as a change in a flow rate of the flow stream (306 a) or a change in a parameter of the light detection system, such as a change in a photodetector voltage (306 b), at step 305. In step 307, each bin containing outliers is re-evaluated, such as in response to a change in the particle analyzer parameter. In certain embodiments, the bins containing outliers are removed (308 a). In other embodiments, one or more data acquisition parameters (e.g., the rate of event data signal binning) are adjusted (308 b) in response to the re-evaluated binning window and parameter changes of the particle analyzer. In other embodiments, the method includes calibrating a particle analyzer (308 c) in response to the evaluated plurality of binning windows in which one or more bins containing outliers are identified.

Methods according to certain embodiments include illuminating a sample (e.g., in a flow stream of a flow cytometer) with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide range of wavelengths, such as spanning 50nm or more, such as 100nm or more, such as 150nm or more, such as 200nm or more, such as 250nm or more, such as 300nm or more, such as 350nm or more, such as 400nm or more, and including spanning 500nm or more. For example, one suitable broadband light source emits light having a wavelength from 200nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength from 400nm to 1000 nm. Where the method includes illumination with a broadband light source, the broadband light source protocol of interest may include, but is not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stable fiber coupled broadband light sources, broadband LEDs with continuous spectrum, superluminescent emitting diodes, semiconductor light emitting diodes, broad spectrum LED white light sources, multi-LED integrated white light sources, and other broadband light sources or any combination thereof.

Depending on the light source, the sample may be illuminated from a distance such as 0.01mm or more, such as 0.05mm or more, such as 0.1mm or more, such as 0.5mm or more, such as 1mm or more, such as 2.5mm or more, such as 5mm or more, such as 10mm or more, such as 15mm or more, such as 25mm or more, and distance variations including 50mm or more. Furthermore, the angle or illumination may also vary, ranging from 10 ° to 90 °, such as 15 ° to 85 °, such as 20 ° to 80 °, such as 25 ° to 75 °, and including angles from 30 ° to 60 °, for example 90 °.

In some embodiments, the method includes irradiating the sample in the flow stream with a continuous wave light source, such as where the light source provides uninterrupted light flux and maintains irradiation of particles in the flow stream with little or no unwanted change in light intensity. In some embodiments, the continuous light source emits non-pulsed or non-stroboscopic illumination. In certain embodiments, the continuous light source provides a substantially constant intensity of emitted light. For example, the method may include illuminating the sample in the flow stream with a continuous light source that provides a light emission intensity during a time interval of illumination that varies by 10% or less, such as 9% or less, such as 8% or less, such as 7% or less, such as 6% or less, such as 5% or less, such as 4% or less, such as 3% or less, such as 2% or less, such as 1% or less, such as 0.5% or less, such as 0.1% or less, such as 0.01% or less, such as 0.001% or less, such as 0.0001% or less, such as 0.00001% or less, and including a light emission intensity variation during a time interval wherein illumination is 0.000001% or less. The intensity of the light output may be measured using any convenient protocol, including but not limited to scanning slit profilers, charge Coupled Devices (CCDs), such as enhanced charge coupled devices (ICCDs), position sensors, power sensors (e.g., thermopile power sensors), optical power sensors, energy meters, digital laser photometers, laser diode detectors, and other types of photodetectors.

In other embodiments, the method includes illuminating a sample propagating through the flow stream with a pulsed light source, such as wherein light is emitted at predetermined time intervals, each time interval having a predetermined illumination duration (i.e., pulse width). In certain embodiments, the method includes illuminating the particles with a pulsed light source in each of the probe regions of the flow stream having periodic flashes of light. For example, the frequency of each light pulse may be 0.0001kHz or higher, such as 0.0005kHz or higher, such as 0.001kHz or higher, such as 0.005kHz or higher, such as 0.01kHz or higher, such as 0.05kHz or higher, such as 0.1kHz or higher, such as 0.5kHz or higher, such as 1kHz or higher, such as 2.5kHz or higher, such as 5kHz or higher, such as 10kHz or higher, such as 25kHz or higher, such as 50kHz or higher, and including 100kHz or higher. In certain examples, the pulsed illumination of the light source has a frequency in the range of 0.00001kHz to 1000kHz, such as 0.00005kHz to 900kHz, such as 0.0001kHz to 800kHz, such as 0.0005kHz to 700kHz, such as 0.001kHz to 600kHz, such as 0.005kHz to 500kHz, such as 0.01kHz to 400kHz, such as 0.05kHz to 300kHz, such as 0.1kHz to 200kHz, and including 1kHz to 100kHz. The duration of the light irradiation (i.e., pulse width) of each light pulse may vary, and may be 0.000001ms or more, such as 0.000005ms or more, such as 0.00001ms or more, such as 0.00005ms or more, such as 0.0001ms or more, such as 0.0005ms or more, such as 0.001ms or more, such as 0.005ms or more, such as 0.01ms or more, such as 0.05ms or more, such as 0.1ms or more, such as 0.5ms or more, such as 1ms or more, such as 2ms or more, such as 3ms or more, such as 4ms or more, such as 5ms or more, such as 10ms or more, such as 25ms or more, such as 50ms or more, such as 100ms or more, and including 500ms or more. For example, the duration of the light irradiation may be in the range from 0.000001ms to 1000ms, such as 0.000005ms to 950ms, such as 0.00001ms to 900ms, such as 0.00005ms to 850ms, such as 0.0001ms to 800ms, such as 0.0005ms to 750ms, such as 0.001ms to 700ms, such as 0.005ms to 650ms, such as 0.01ms to 600ms, such as 0.05ms to 550ms, such as 0.1ms to 500ms, such as 0.5ms to 450ms, such as 1ms to 400ms, such as 5ms to 350ms, and include from 10ms to 300ms.

In other embodiments, the method includes illuminating with a narrow-band light source that emits light of a particular wavelength or narrow wavelength range, such as, for example, with a light source that emits light of a narrow wavelength range, such as 50nm or less, such as 40nm or less, such as 30nm or less, such as 25nm or less, such as 20nm or less, such as 15nm or less, such as 10nm or less, such as 5nm or less, such as 2nm or less, and including a light source that emits light of a particular wavelength (i.e., monochromatic light). Where the method includes illumination with a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, a narrow wavelength LED, a laser diode coupled to one or more optical bandpass filters or a broadband light source, a diffraction grating, a monochromator, or any combination thereof.

In certain embodiments, the method comprises 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 desired light collected, and may be a gas laser, such as heliumNeon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO ₂ A laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other examples, the method includes irradiating the flow stream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In other cases, the method includes irradiating the flow stream 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, and combinations thereof. In other cases, the method includes irradiating the flow stream with a solid state laser, such as a ruby laser, nd: YAG laser, ndCrYAG laser, er YAG laser, nd YLF laser, nd YVO ₄ Laser, nd YCa ₄ O(BO ₃ ) ₃ Nd: YCOB laser, tisapphire laser, thulium YAG laser, ytterbium YAG laser, yb ₂ O ₃ A laser or a cerium doped laser, and combinations thereof.

In certain embodiments, the method comprises illuminating the sample with two or more frequency-shifted beams. As described above, a beam generator component having a laser and an acousto-optic device for frequency shifting the laser may be employed. In these embodiments, the method includes irradiating the acousto-optic device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for illuminating a sample in a flowing stream), the laser light may have a specific wavelength ranging from 200nm to 1500nm, such as 250nm to 1250nm, such as 300nm to 1000nm, such as 350nm to 900nm, and including from 400nm to 800 nm. The acousto-optic device may be illuminated with one or more lasers, for example 2 or more lasers, such as 3 or more lasers, such as 4 or more lasers, such as 5 or more lasers, and including 10 or more lasers. The lasers may comprise any type of combination of lasers. For example, in some embodiments, the method includes illuminating an 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.

In the case of more than one laser, the acousto-optic device may be irradiated with the lasers simultaneously or sequentially or in combination thereof. For example, an acousto-optic device may be illuminated with each of the lasers simultaneously. In other embodiments, the acousto-optic device is sequentially illuminated with each laser. Where more than one laser is used to sequentially illuminate the acousto-optic devices, the time for each laser to illuminate the acousto-optic devices may independently be 0.001 microsecond or greater, for example 0.01 microsecond or greater, such as 0.1 microsecond or greater, such as 1 microsecond or greater, such as 5 microsecond or greater, such as 10 microsecond or greater, such as 30 microsecond or greater, and including 60 microsecond or greater. For example, the method may include illuminating the acousto-optic device with the laser for a duration of 0.001 microsecond to 100 microseconds, such as 0.01 microsecond to 75 microseconds, such as 0.1 microsecond to 50 microseconds, such as 1 microsecond to 25 microseconds, and including a duration of 5 microseconds to 10 microseconds. In embodiments where the acousto-optic device is sequentially illuminated with two or more lasers, the duration of illumination of the acousto-optic device by each laser may be the same or different.

In an embodiment, the method comprises applying a radio frequency drive signal to the acousto-optic device to generate an angularly deflected laser beam. Two or more radio frequency drive signals may be applied to the acousto-optic device to produce an output laser beam having a desired number of angularly deflected laser beams, such as 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 including 100 or more radio frequency drive signals.

The angularly deflected laser beams generated by the radio frequency drive signals each have an intensity based on the amplitude of the applied radio frequency drive signal. In some embodiments, the method includes applying a radio frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam having a desired intensity. In some cases, each applied radio frequency drive signal independently has an amplitude of from about 0.001V to about 500V, such as from about 0.005V to about 400V, such as from about 0.01V to about 300V, such as from about 0.05V to about 200V, such as from about 0.1V to about 100V, such as from about 0.5V to about 75V, such as from about 1V to 50V, such as from about 2V to 40V, such as from 3V to about 30V, and including from about 5V to about 25V. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001MHz to about 500MHz, such as about 0.005MHz to about 400MHz, such as about 0.01MHz to about 300MHz, such as about 0.05MHz to about 200MHz, such as about 0.1MHz to about 100MHz, such as about 0.5MHz to about 90MHz, such as about 1MHz to about 75MHz, such as about 2MHz to about 70MHz, such as about 3MHz to about 65MHz, such as about 4MHz to about 60MHz, and includes about 5MHz to about 50MHz.

In these embodiments, the angularly deflected ones of the output laser beams are spatially separated. Depending on the applied radio frequency drive signal and the desired irradiance distribution of the output laser beam, the angularly deflected laser beam may be separated by 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, such as 100 μm or more, such as 500 μm or more, such as 1000 μm or more, and including 5000 μm or more. In some embodiments, the angularly deflected laser beam overlaps, such as with an adjacent angularly deflected laser beam along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (such as the overlap of beam spots) may be an overlap of 0.001 μm or more, an overlap such as 0.005 μm or more, an overlap such as 0.01 μm or more, an overlap such as 0.05 μm or more, an overlap such as 0.1 μm or more, an overlap such as 0.5 μm or more, an overlap such as 1 μm or more, an overlap such as 5 μm or more, an overlap such as 10 μm or more, and an overlap including 100 μm or more.

In some examples, the flow stream is illuminated with a plurality of frequency-shifted beams and cells in the flow stream are imaged by fluorescence imaging using radio frequency marker emission (FIRE) to generate frequency-encoded images, such as those described by Diebold et al. Nature Photonics, volume 7 (10); 806-810 (2013), U.S. patent No. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; and U.S. patent publication No. 2017/0133857; 2017/032886; 2017/0350803; 2018/0275042; the disclosures of 2019/0376895 and 2019/0376894 are incorporated herein by reference.

The sample may be illuminated with one or more of the above-described light sources, such as 2 or more light sources, such as 3 or more light sources, such as 4 or more light sources, such as 5 or more light sources, and including 10 or more light sources. The light source may comprise any combination of light source types. For example, in some embodiments, the method includes illuminating a sample in a flow stream with a laser array, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers. In some examples, the light source includes a plurality of lasers, such as where the plurality of lasers are configured to illuminate the flow stream at locations spaced apart from one another. For example, the light source may include 2 or more lasers spaced apart from each other along the longitudinal axis of the flow stream, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, and 10 or more lasers spaced apart from each other along the longitudinal axis of the flow stream. In certain embodiments, each of the plurality of lasers is spaced apart from each other (e.g., such that each laser irradiates a different portion of the flow stream) by 0.0001mm or more, such as 0.0005mm or more, such as 0.001mm or more, such as 0.005mm or more, such as 0.01mm or more, such as 0.05mm or more, such as 0.1mm or more, such as 0.5mm or more, such as 1mm or more, such as 2mm or more, such as 3mm or more, such as 4mm or more, such as 5mm or more, such as 10mm or more, such as 20mm or more, such as 30mm or more, such as 40mm or more, such as 50mm or more, and including 100mm or more. The time interval between irradiation of particles with each laser may be 0.001 to 500 μs, such as 0.005 to 400 μs, such as 0.01 to 300 μs, such as 0.05 to 200 μs, such as 0.1 to 100 μs, such as 0.5 to 75 μs, and including 1 to 50 μs, depending on the flow rate of the particles through the flow stream.

The sample may be irradiated with wavelengths from 200nm to 1500nm, such as 250nm to 1250nm, such as 300nm to 1000nm, such as 350nm to 900nm, and including 400nm to 800 nm. For example, when the light source is a broadband light source, the sample may be illuminated with a wavelength of 200nm to 900 nm. In other examples, where the light source comprises a plurality of narrowband light sources, the sample may be illuminated with a particular wavelength in the range from 200nm to 900 nm. For example, the light source may be a plurality of narrow-band LEDs (1 nm-25 nm), each independently emitting light in the wavelength range between 200nm and 900 nm. In other embodiments, the narrowband light source comprises one or more lasers (such as a laser array) and the sample is irradiated with a specific wavelength in the range of 200nm to 700nm, for example with a laser array having a gas laser, an excimer laser, a dye laser, a metal vapor laser, and a solid state laser as described above.

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

The time period between each light source illumination may also vary as desired, independently separated by a delay of 0.001 microsecond or more, such as 0.01 microsecond or more, such as 0.1 microsecond or more, such as 1 microsecond or more, such as 5 microsecond or more, such as 10 microsecond or more, such as 15 microsecond or more, such as 30 microsecond or more, and including 60 microsecond or more. For example, the time period between each light source illumination may be in the range of 0.001 microsecond to 60 microsecond, such as 0.01 microsecond to 50 microsecond, such as 0.1 microsecond to 35 microsecond, such as 1 microsecond to 25 microsecond, and include from 5 microsecond to 10 microsecond. In some embodiments, the time period between illumination of each light source is 10 microseconds. In embodiments where the sample is sequentially illuminated by more than two (i.e., 3 or more) light sources, the delay between illumination of each light source may be the same or different.

The sample may be irradiated continuously or at discrete intervals. In some examples, the method includes continuously illuminating a sample in the sample with a light source. In other examples, the sample is irradiated with the light source at discrete intervals, such as every 0.001 ms, every 0.01 ms, every 0.1 ms, every 1 ms, every 10 ms, every 100 ms, and including every 1000 ms, or some other interval.

Particle analyzer system

Aspects of the disclosure also include systems (e.g., particle analyzers) having light detection systems for practicing the subject methods. In an embodiment, a light detection system includes a detector assembly having a photodetector configured to detect light from a sample illuminated in a flow stream and to generate an event data signal in response to an event detected with the light detection system, and a modulator assembly configured to bin the generated event data signal in a plurality of bin windows including overlapping bins. In some embodiments, the detector component includes one or more of a light scattering photodetector, a fluorescent photodetector, and a light loss photodetector. In some examples, the detector assembly includes a forward scatter photodetector. In some examples, the detector component includes a side scatter photodetector. In some examples, the detector component includes a backscatter photodetector. In some examples, the detector assembly includes one or more fluorescent photodetectors. In some examples, the detector component includes a light loss photodetector. In some examples, the detector component includes a dark field photodetector. In some examples, the detector assembly includes a bright field photodetector.

The photodetectors of the subject photodetection system may be any photosensor, such as an Active Pixel Sensor (APS), an Avalanche Photodiode (APD), an image sensor, a Charge Coupled Device (CCD), an enhanced charge coupled device (ICCD), a Complementary Metal Oxide Semiconductor (CMOS) image sensor or an N-type metal oxide semiconductor (NMOS) image sensor, a light emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube, a phototransistor, a quantum dot photoconductor or photodiode, and combinations thereof, as well as other types of photodetectors.

In some embodiments, the light detection system includes two or more photodetectors, such as 3 or more, such as 4 or more, such as 5 or more, and includes 10 or more photodetectors. In some examples, the photodetectors form a photodetector array. The term "photodetector array" is used in its conventional sense to refer to an arrangement or series of two or more photodetectors configured to detect light. In embodiments, the photodetector array may include 2 or more photodetectors, such as 3 or more photodetectors, such as 4 or more photodetectors, such as 5 or more photodetectors, such as 6 or more photodetectors, such as 7 or more photodetectors, such as 8 or more photodetectors, such as 9 or more photodetectors, such as 10 or more photodetectors, such as 12 or more photodetectors, and 15 or more photodetectors. In certain embodiments, the photodetector array comprises 5 photodetectors. The photodetectors may be arranged in any geometric configuration as desired, with the arrangement of interest including, but not limited to, square configurations, rectangular configurations, trapezoidal configurations, triangular configurations, hexagonal configurations, heptagonal configurations, octagonal configurations, nonagonal configurations, decagonal configurations, dodecagonal configurations, circular configurations, elliptical configurations, and irregularly shaped configurations. The photodetectors in the photodetector array may be oriented at an angle ranging from 10 ° to 180 °, such as 15 ° to 170 °, such as 20 ° to 160 °, such as 25 ° to 150 °, such as 30 ° to 120 °, and including from 45 ° to 90 °, relative to another photodetector (as referenced in the X-Z plane). In an embodiment, the detector component is configured to generate data signals in 4 or more photodetector channels, such as 16 or more, such as 32 or more, and included in 64 or more photodetector channels. In some embodiments, the detector component is configured to generate a data signal in one or more photodetector channels for each separate spectral component of the light. In some examples, adjacent spectral components of light generate data signals in adjacent photodetector channels. In some examples, adjacent spectral components of light generate data signals in non-adjacent photodetector channels.

In certain embodiments, the light detection system of interest includes a photodiode array having more than one photodiode, such as two or more photodiodes, such as three or more, such as five or more, and 10 or more photodiodes, where each photodiode may have an effective detection surface area per region ranging from 0.01cm ² To 10cm ² Such as 0.05cm ² To 9cm ² Such as 0.1cm ² To 8cm ² Such as 0.5cm ² To 7cm ² And comprises from 1cm ² To 5cm ² 。

In embodiments of the present disclosure, the photodetector of interest is configured to measure light collected at one or more wavelengths, such as at 2 or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and includes measuring light from particles in the flow stream at 400 or more different wavelengths. In an embodiment, the photodetector is configured to measure light continuously or at discrete intervals. In some examples, the detector of interest is configured to continuously make measurements of the collected light. In other cases, the detector of interest is configured to measure light at discrete intervals, such as every 0.001 ms, every 0.01 ms, every 0.1 ms, every 1 ms, every 10 ms, every 100 ms, and including every 1000 ms or some other interval.

In some embodiments, the light detection system includes a wavelength separator configured to spectrally separate light from the sample into light of multiple spectral ranges. In certain examples, the wavelength separator is an optical component configured to separate light collected from the sample into a predetermined spectral range. In some embodiments, the wavelength separator is configured to spatially diffract light collected from the sample into a predetermined spectral range. The wavelength separator may be any convenient light separation protocol such as a spectrometer, prism, diffraction grating, one or more dichroic mirrors, bandpass filters, or beam splitters. In certain examples, the wavelength separator comprises a spectrometer.

In some examples, the wavelength separator produces light of two or more predetermined spectral ranges, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, such as 25 or more, such as 50 or more, such as 75 or more, and light comprising 100 or more predetermined spectral ranges. In certain examples, the wavelength separator is configured to spectrally separate light having a plurality of ranges of wavelengths from 200nm to 1200nm, such as 300nm to 1100nm, such as 400nm to 1000nm, such as 500nm to 900nm, such as 600nm to 800 nm. In some embodiments, each predetermined spectral range may have a spectral width of 0.0001nm or greater, such as 0.0005nm or greater, such as 0.001nm or greater, such as 0.005nm or greater, such as 0.01nm or greater, such as 0.05nm or greater, such as 0.1nm or greater, such as 0.5nm or greater, such as 1nm or greater, such as 5nm or greater, and including 10nm or greater. For example, in one example, each predetermined spectral range has a spectral width of 5nm, such as where one predetermined spectral separation range of light is from 210nm to 215nm.

In certain embodiments, the wavelength separator is configured to separate light collected from the sample into predetermined spectral ranges by passing light having the predetermined spectral ranges and reflecting light of one or more remaining spectral ranges. In some embodiments, the wavelength separator is configured to separate light collected from the sample into predetermined spectral ranges by passing light having the predetermined spectral ranges and absorbing one or more remaining spectral ranges of the light. In some embodiments, the wavelength separator comprises a linearly variable filter. In some examples, the wavelength separator includes one or more linearly variable bandpass filters. For example, the wavelength separator may comprise a long-pass linear variable bandpass filter, a short-pass linear variable bandpass filter, or a combination of a long-pass linear variable bandpass filter and a short-pass linear variable bandpass filter. In other embodiments, the wavelength separator includes one or more linearly variable edge filters. For example, the wavelength separator may comprise a long-pass linear variable edge filter, a short-pass linear variable edge filter, or a combination of a long-pass linear variable edge filter and a short-pass linear variable edge filter.

In certain embodiments, the long-pass linear variable bandpass filter is a linear variable bandpass filter that passes light of wavelengths greater than 400nm, greater than 450nm, greater than 500nm, greater than 550nm, greater than 600nm, greater than 650nm, greater than 700nm, greater than 750nm, greater than 800nm through the bandpass filter. In some examples, the wavelength separator includes a short-pass linear variable bandpass filter. In certain embodiments, the short-pass linear variable bandpass filter is a linear variable bandpass filter that passes wavelengths of light of 600nm or less, 550nm or less, 500nm or less, 450nm or less, 400nm or less, 350nm or less, 300nm or less, 250nm or less, 200nm or less through the bandpass filter. In some embodiments, the wavelength separator component includes one or more long-pass linear variable bandpass filters and one or more short-pass linear variable bandpass filters. In certain embodiments, the wavelength separator component includes a long-pass linear variable bandpass filter and a short-pass linear variable bandpass filter.

In some embodiments, the wavelength separator includes one or more linearly variable edge filters, such as 2 or more, such as 3 or more, such as 4 or more, and 5 or more linearly variable edge filters. In certain embodiments, the long-pass linearly-variable edge filter is a linearly-variable edge filter that passes light of wavelengths greater than 400nm, greater than 450nm, greater than 500nm, greater than 550nm, greater than 600nm, greater than 650nm, greater than 700nm, greater than 750nm, greater than 800nm through the edge filter. In some examples, the wavelength separator includes a short-pass linear variable edge filter. In certain embodiments, the short-pass linearly-variable edge filter is a linearly-variable edge filter that passes wavelengths of light of 600nm or less, 550nm or less, 500nm or less, 450nm or less, 400nm or less, 350nm or less, 300nm or less, 250nm or less, 200nm or less through the edge filter. In some embodiments, the wavelength separator component includes one or more long-pass linearly-variable edge filters and one or more short-pass linearly-variable edge filters. In certain embodiments, the wavelength separator component includes a long-pass linearly-variable edge filter and a short-pass linearly-variable edge filter.

In some embodiments, the detector component comprises an optical adjustment component that is capable of changing the spatial width of the light from the illuminated sample or some other characteristic of the light, such as propagation direction, wavelength, beam width, beam intensity, and focal spot. The optical adjustment protocol may be any convenient device that adjusts one or more characteristics of the light source, including but not limited to lenses, mirrors, filters, optical fibers, wavelength splitters, pinholes, slits, collimation protocols, and combinations thereof.

In an embodiment, the light detection system comprises a modulator component configured to bin the generated event data signal in a plurality of binning windows comprising overlapping data bins. In some examples, the modulator component comprises an integrated circuit. In an embodiment, the integrated circuit device may be a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or a Complex Programmable Logic Device (CPLD), or some other integrated circuit device. In some embodiments, the modulator component comprises an integrated circuit (e.g., field programmable gate array, FPGA) having programming for binning event data signals from two or more different photodetector channels. In some embodiments, the binned event data signal includes a fluorescence intensity of light detected in response to an event. In some embodiments, the binned event data signal comprises a fluorescent representation of an event detected by a light detection system. In certain embodiments, the binned event data signal comprises a time value of an event detected by the light detection system.

In some embodiments, the modulator component includes one or more amplifiers. In some embodiments, the amplifier component includes a plurality of amplifiers, such as transimpedance amplifiers, summing amplifiers, differential amplifiers, or combinations thereof. In some examples, the modulator component includes an amplifier for each photodetector channel, such as where the modulator component includes 2 or more amplifiers, such as 4 or more, such as 8 or more, such as 12 or more, such as 16 or more, such as 20 or more, such as 24 or more, such as 28 or more, such as 32 or more, such as 36 or more, such as 40 or more, such as 44 or more, such as 48 or more, such as 52 or more, such as 56 or more, such as 60 or more, and including 64 or more amplifiers.

In some embodiments, the modulator means comprises a first amplifier means configured to amplify the data signal from each photodetector channel and a second amplifier means configured to amplify the data signal from the electronic switch means. In some examples, the first amplifier component comprises a plurality of transimpedance amplifiers and the second amplifier component comprises a plurality of differential amplifiers.

In some examples, the modulator component includes an electronic switch (e.g., a digital switching circuit) configured to data signals from two or more different photodetector channels. In some examples, the switch is configured to multiplex or demultiplex the output data signal from each photodetector channel. Depending on the number of photodetector channels and amplifiers employed in the light detection system (as described above), the electronic switch components may include 2 or more electronic switches, such as 3 or more electronic switches, such as 4 or more electronic switches, such as 5 or more electronic switches, such as 6 or more electronic switches, such as 7 or more electronic switches, such as 8 or more electronic switches, such as 9 or more electronic switches, such as 10 or more electronic switches, such as 15 or more electronic switches, such as 25 or more electronic switches, such as 50 or more electronic switches, such as 100 or more electronic switches, such as 250 or more electronic switches, such as 500 or more electronic switches, such as 750 or more electronic switches, and 1000 or more electronic switches.

In some embodiments, the light detection system of interest includes a photodetector array having N photodetector channels and an amplifier component having N transimpedance amplifiers, where N is an integer from 4 to 1000. In some examples, the light detection system includes a photodetector array having N photodetector channels and a modulator component having N transimpedance amplifiers and an electronic switch component having an N x N switch array. In some examples, the photodetector array may be a photodiode array having N photodiodes. The modulator components in these embodiments may include an array of N transimpedance amplifiers and n×n switches. In some embodiments, N is 8. In other embodiments, N is 16. In other embodiments, N is 32. In other embodiments, N is 64. In other embodiments, N is 128.

In some examples, the modulator component is configured to bin a predetermined time frame of data acquisition (i.e., generated event data signals) of the particle analyzer into each binning window. In some cases, each binning window includes a data acquisition time frame of 0.001 μs or greater, such as 0.005 μs or greater, such as 0.01 μs or greater, such as 0.05 μs or greater, such as 0.1 μs or greater, such as 0.5 μs or greater, such as 1 μs or greater, such as 5 μs or greater, such as 10 μs or greater, such as 50 μs or greater, such as 100 μs or greater, such as 500 μs or greater, and includes a data acquisition time frame of 1000 μs or greater. In some cases, each binning window includes a data acquisition time frame from 0.001 μs to 5000 μs, such as 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as 0.1 μs to 3000 μs, such as 0.5 μs to 2500 μs, such as 1 μs to 2000 μs, such as 5 μs to 1000 μs, such as 10 μs to 500 μs, and a data acquisition time frame from 50 μs to 100 μs. Each binning window may be the same duration or may be a different duration. In some embodiments, the modulator component is configured to bin a predetermined number of event data signals into each bin window, such as wherein 5 or more event data signals are binned in each bin window, such as 10 or more event data signals, such as 25 or more event data signals, such as 50 or more event data signals, such as 75 or more event data signals, such as 100 or more event data signals, such as 125 or more event data signals, such as 150 or more event data signals, such as 175 or more event data signals, such as 200 or more event data signals, such as 300 or more event data signals, such as 400 or more event data signals, such as 500 or more event data signals, such as 600 or more event data signals, such as 700 or more event data signals, such as 900 or more event data signals, and including 1000 or more event data signals in each bin.

In some embodiments, the modulator component is configured to bin one or more of the generated event data signals into two or more binning windows (i.e., event data signals generated where the binning windows overlap). In some examples, the modulator component is configured to bin one or more generated event data signals in a time frame overlapping 0.001 μs or more, such as 0.005 μs or more, such as 0.01 μs or more, such as 0.05 μs or more, such as 0.1 μs or more, such as 0.5 μs or more, such as 1 μs or more, such as 5 μs or more, such as 10 μs or more, such as 50 μs or more, such as 100 μs or more, such as 500 μs or more, and two or more binning windows including a binning window overlapping 1000 μs or more. In certain examples, the binning window overlaps from 0.001 μs to 5000 μs, such as 0.005 μs to 4500 μs, such as 0.01 μs to 4000 μs, such as 0.05 μs to 3500 μs, such as 0.1 μs to 3000 μs, such as 0.5 μs to 2500 μs, such as 1 μs to 2000 μs, such as 5 μs to 1000 μs, such as 10 μs to 500 μs, and includes a time frame from 50 μs to 100 μs.

In some embodiments, the modulator component is configured to adjust the binning window in response to a change in the data acquisition rate of the particle analyzer (such as in the case where the flow rate of the flow stream is changed). In one example, the modulator component is configured to increase the size of one or more binning windows, such as by 5% or more, such as by 10% or more, such as by 25% or more, and including by 50% or more, in response to a change in the flow rate of the flow stream. In another example, the modulator component is configured to reduce the size of the one or more binning windows, such as by 5% or more, such as by 10% or more, such as by 25% or more, and including by 50% or more, in response to a change in the flow rate of the flow stream.

In some embodiments, the modulator component is configured to apply a transform to each event data signal. In some examples, the transform is one or more of a centered log ratio transform, an additive log ratio transform, and an equal length log ratio transform.

In some embodiments, a system includes a processor having a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate a median parameter for each of the overlapping bins. In some examples, the memory includes instructions for calculating an average parameter for each of the overlapping bins. In some examples, the memory includes instructions to calculate a median or average fluorescence intensity for the events for each bin. In some examples, the memory includes instructions for calculating a median or average expression level of events for each data bin.

In some embodiments, the memory includes instructions for evaluating a plurality of binning windows of outlier events, including comparing a median parameter of each of the bins to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the memory includes instructions for dynamically calculating outlier thresholds, such as in real-time. In some examples, the memory includes instructions to dynamically adjust the outlier threshold in real-time in response to binning of each of the generated event data signals. In certain embodiments, the system includes a processor having a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to implement a dynamic algorithm, such as a machine learning algorithm for calculating or adjusting the calculated outlier threshold. In some examples, the memory includes instructions for calculating the outlier threshold by a machine learning algorithm. In some examples, the memory includes instructions for determining an outlier threshold using a machine learning algorithm. Machine learning protocols of interest that may be programmed into the memory of the system may include, but are not limited to, artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, bayesian networks, dynamic bayesian networks, genetic algorithms, and other machine learning protocols.

In some embodiments, a system includes a processor having a memory operably coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to classify one or more bins of a plurality of binning windows as containing outliers, such as bins in which 2 or more bins are classified as containing outliers, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, and including 10 or more. In some examples, the memory includes instructions to classify the data bin as a data bin including an outlier when the median (or average) parameter is determined to be greater than the outlier threshold. In some examples, the parameter is a fluorescence intensity of an event for each bin, and the memory includes instructions for classifying the bin as a bin containing an outlier when a median or average fluorescence intensity of the event data signal is greater than a calculated outlier threshold (e.g., a median absolute deviation outlier threshold or a median allowable percentage outlier threshold). In other examples, the parameter is an expression level of an event for each data bin, and the memory includes instructions for classifying the data bin as a data bin containing an outlier when a median or average expression level of the event is greater than a calculated outlier threshold. In some embodiments, the memory includes instructions to: when the median (or mean) parameter is determined to be within a predetermined percentage of (i.e., greater than or less than) the outlier threshold, the data bin is classified as containing an outlier, such as wherein the median parameter (e.g., fluorescence intensity or expression level of the event) is within 10% of the outlier threshold, such as within 9% of the outlier threshold, such as within 8% of the outlier threshold, such as within 7% of the outlier threshold, such as within 6% of the outlier threshold, such as within 5% of the outlier threshold, such as within 4% of the outlier threshold, such as within 3% of the outlier threshold, such as within 2% of the outlier threshold, and including wherein the median parameter is within 1% of the outlier threshold. In some examples, the memory includes instructions for identifying an event data signal of a data bin including an outlier as the outlier.

In certain embodiments, the system of interest includes a flow sensor that measures the flow rate of the flow stream. The flow rate sensor may be any convenient protocol for measuring the rate of fluid flow (such as through a flow cell) and includes, but is not limited to, volumetric flow sensors, mass flow sensors, velocity flow sensors, and sensors including feedback monitoring with optical scatter sensors, laser diffraction sensors, optical absorption sensors, and emission sensors. In certain embodiments, the flow rate sensor is configured to measure a rate of sample flow in the flow cell, such as wherein the flow rate of the sample in the flow stream is 1 μL/min (microliter/min) or greater, such as 2 μL/min or greater, such as 3 μL/min or greater, such as 5 μL/min or greater, such as 10 μL/min or greater, such as 25 μL/min or greater, such as 50 μL/min or greater, such as 75 μL/min or greater, such as 100 μL/min or greater, such as 250 μL/min or greater, such as 500 μL/min or greater, such as 750 μL/min or greater, and including 1000 μL/min or greater. In some embodiments, the memory includes instructions for generating an alert that an outlier is detected when the plurality of bins are evaluated for the outlier event. In some examples, the memory includes instructions for generating an alert of a change in a flow rate in a flow stream of the particle analyzer. In one example, the memory includes instructions for generating an alert that there is an increase in the flow rate of the flow stream, such as when there is an increase in the flow rate of the flow stream of 1% or more, such as 2% or more, such as 3% or more, such as 4% 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, and including the case of an increase in the flow rate of the flow stream of 95% or more. In one example, an alert of a decrease in the flow rate of the presence flow stream may be generated, such as when the presence flow rate decreases by 1% or more, such as 2% or more, such as 3% or more, such as 4% 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, and including a case where the flow rate of the presence flow stream decreases by 95% or more. In some examples, the memory includes instructions for correlating one or more subject methods in response to a change in flow rate in the stream, such as where the change in flow rate can be related to a rate at which the event data signal is generated, a binning rate of the generated event data signal, a size of each binning window (as described above), or an evaluation of a plurality of binning windows for outlier events in the generated data signal. In certain examples, the rate at which the event data signal is generated by the particle analyzer is adjusted (i.e., increased or decreased) in response to the generated alert (there is a change in the flow rate of the flow stream). In other examples, the binning rate of the generated event data signals is adjusted in response to the generated alarms (there is a change in the flow rate of the flow stream). In other examples, the size of each binning window is adjusted in response to the generated alarm (there is a change in the flow rate of the flow stream). In other examples, in response to the generated alert (there is a change in the flow rate of the flow stream), the evaluation of the plurality of binning windows for outlier events in the generated data signal is adjusted.

In some embodiments, the memory includes instructions for generating an alert that a blockage, bubble, or entrainment is present in the flow stream in the particle analyzer. In certain examples, the memory includes instructions for comparing the measured flow rate of the flow stream to a desired flow rate of the flow stream, and re-evaluating the bins classified as bins containing outliers based on the compared flow rate of the flow stream. In some examples, the memory includes instructions for reclassifying the one or more bins based on the compared flow rates of the flow streams. In some examples, no alert is generated when the measured flow rate is within a predetermined threshold of the expected flow rate, e.g., when the measured flow rate is within 20% or less of the expected flow rate, such as within 15% or less, such as within 10% or less, such as within 9% or less, such as within 8% or less, such as within 7% or less, such as within 6% or less, such as within 5% or less, such as within 4% or less, such as within 3% or less, such as within 2% or less, such as within 1% or less, and including not generating an alert when the measured flow rate is within 0.1% or less of the expected flow rate.

In some embodiments, the memory includes instructions for generating an alert due to an irregular parameter in the light detection system or due to a change in a parameter of the light detection system. In some examples, the memory includes instructions for generating an alert when a photodetector voltage of one or more photodetectors of the light detection system changes (or a detected irregularity). In some examples, the memory includes instructions for comparing a median or average fluorescence intensity of the plurality of bins to one or more parameters of the light detection system and re-evaluating the bins classified as containing outliers based on a change in a parameter (or detected irregularity) of the light detection system.

In some embodiments, the memory includes instructions for removing one or more re-evaluated bins classified as bins containing outliers when a change, anomaly, or irregularity is detected in a flow stream (e.g., flow rate) or a parameter of the light detection system (e.g., photodetector voltage). One or more bins containing outliers may be removed from the collected sample data, such as all bins containing outliers when generating an alert related to a flow stream or parameter of the light detection system. In other embodiments, the memory includes instructions for reloading the sample by the particle analyzer and re-running the sample collection when a change, abnormality, or irregularity is detected in a parameter of the flow stream or the light detection system.

In some embodiments, the system includes a memory having instructions to perform calibration of the particle analyzer in response to one or more bins classified as bins containing outliers. In some examples, the instructions for calibrating the particle analyzer include instructions for detecting light from a standard composition in the flow stream illuminated by the light source with the light detection system, instructions for generating event data signals in response to events detected with the light detection system, instructions for binning the generated event data signals into a plurality of binning windows having overlapping bins, instructions for evaluating the generated data signals in the plurality of binning windows, and instructions for determining that there are no bins containing outliers.

In some embodiments, the system of the present disclosure further comprises a light source for illuminating the sample with particles in the flow stream. The system of interest includes a light source configured to illuminate a sample in a flow stream. In embodiments, the light source may be any suitable broadband or narrowband light source. Depending on the components (e.g., cells, beads, non-cellular particles, etc.) in the sample, the light source may be configured to emit light wavelengths ranging from 200nm to 1500nm, such as 250nm to 1250nm, such as 300nm to 1000nm, such as 350nm to 900nm, and including ranges from 400nm to 800 nm. For example, the light source may comprise a broadband light source emitting light having a wavelength from 200nm to 900 nm. In other examples, the light source comprises a narrowband light source emitting in the wavelength range from 200nm to 900 nm. For example, the light source may be a narrow band LED (1 nm-25 nm) that emits light having a wavelength range between 200nm and 900 nm.

In some embodiments, the light source is a laser. The laser of interest may comprise a pulsed laser or a continuous wave laser. The laser may be, for example, a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO ₂ A 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; dye lasers, such as stilbene, coumarin, or rhodamine lasers; metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; solid state lasers, such as ruby lasers, nd: YAG laser, ndCrYAG laser, Er: YAG laser, nd: YLF laser, nd: YVO ₄ Laser, nd: YCa ₄ O(BO ₃ ) ₃ Laser, nd: YCOB laser, ti sapphire laser, thulium YAG laser, yb ₂ O ₃ A laser or cerium doped laser and combinations thereof; a semiconductor diode laser, an Optically Pumped Semiconductor Laser (OPSL), or a frequency or frequency tripled implementation of any of the above lasers.

In other embodiments, the light source is a non-laser light source such as a lamp, including but not limited to a halogen lamp, deuterium arc lamp, xenon arc lamp, light emitting diode, such as a broadband LED with continuous spectrum, super luminescent light emitting diode, semiconductor light emitting diode, broad spectrum LED white light source, integrated multiple LEDs. In some examples, the non-laser light source is a stable fiber coupled broadband light source, a white light source, and other light sources, or any combination thereof.

In certain embodiments, the light source is a beam generator configured to generate two or more frequency shifted beams. In some examples, the beam generator includes a laser, the radio frequency generator configured to apply a radio frequency drive signal to the acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous wave laser. The acousto-optic device may be any convenient acousto-optic protocol configured to frequency shift the laser using an applied acoustic wave. In certain embodiments, the acousto-optic device is an optical deflector. The acousto-optic device in the present system is configured to generate an angularly deflected laser beam from light from a laser and an applied radio frequency drive signal. The radio frequency drive signal may 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.

In an embodiment, the controller is configured to apply the radio frequency drive signal to the acousto-optic device to produce a desired number of angularly deflected laser beams in the output laser beams, such as configured 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 including being configured to apply 100 or more radio frequency drive signals.

In some examples, to generate an intensity distribution of the angularly deflected laser beam in the output laser beam, the controller is configured to apply a radio frequency drive signal having a varying amplitude, such as about 0.001V to about 500V, such as about 0.005V to about 400V, such as about 0.01V to about 300V, such as about 0.05V to about 200V, such as about 0.1V to about 100V, such as about 0.5V to about 75V, such as about 1V to 50V, such as about 2V to 40V, such as 3V to about 30V, and including from about 5V to about 25V. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001MHz to about 500MHz, such as about 0.005MHz to about 400MHz, such as about 0.01MHz to about 300MHz, such as about 0.05MHz to about 200MHz, such as about 0.1MHz to about 100MHz, such as about 0.5MHz to about 90MHz, such as about 1MHz to about 75MHz, such as about 2MHz to about 70MHz, such as about 3MHz to about 65MHz, such as about 4MHz to about 60MHz, and includes about 5MHz to about 50MHz.

In certain embodiments, the controller has a processor with a memory operably coupled to the processor such that the memory contains instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, and the memory including the memory may include instructions for generating 100 or more angularly deflected laser beams having the same intensity. In other embodiments, the controller may include instructions for generating two or more angularly deflected laser beams having different intensities, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, and the controller may include instructions for generating 100 or more angularly deflected laser beams having different intensities.

In certain examples, a beam generator configured to generate two or more frequency shifted beams of light includes, for example, U.S. patent No. 9,423,353; 9,784,661 and 10,006,852 and U.S. patent publication nos. 2017/0133857 and 2017/0350803, the disclosures of which are incorporated herein by reference.

In certain embodiments, the system further comprises a flow cell configured to propagate the sample in the flow stream. Any convenient flow cell for propagating a fluid sample to a sample interrogation zone may be employed, wherein in some embodiments the flow cell comprises a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a planar surface having an orifice transverse to the longitudinal axis. The length of the proximal cylindrical portion (as measured along the longitudinal axis) may vary in the range of 1mm to 15mm, such as 1.5mm to 12.5mm, such as 2mm to 10mm, such as 3mm to 9mm, and including from 4mm to 8mm. The length of the distal frustoconical portion (as measured along the longitudinal axis) may also vary, ranging from 1mm to 10mm, such as 2mm to 9mm, such as 3mm to 8mm, and including from 4mm to 7mm. In some embodiments, the diameter of the flow cell nozzle chamber may vary from 1mm to 10mm, such as 2mm to 9mm, such as 3mm to 8mm, and including 4mm to 7mm.

In some examples, the flow cell does not include a cylindrical portion, and the entire flow cell internal chamber is frustoconical. In these embodiments, the length of the frustoconical inner chamber (measured transverse to the longitudinal axis of the nozzle orifice) may be in the range of 1mm to 15mm, such as 1.5mm to 12.5mm, such as 2mm to 10mm, such as 3mm to 9mm, and including 4mm to 8mm. The diameter of the proximal portion of the frustoconical lumen may be in the range of 1mm to 10mm, such as 2mm to 9mm, such as 3mm to 8mm, and including 4mm to 7mm.

In some embodiments, the sample flow stream emanates from an orifice at the distal end of the flow cell. The flow cell orifice may be any suitable shape depending on the desired characteristics of the flow stream, with cross-sectional shapes of interest including, but not limited to: linear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear cross-sectional shapes, such as circular, oval, and irregular shapes, such as parabolic bottoms connected to a planar top. In certain embodiments, the flow cell of interest has a circular orifice. The size of the nozzle orifice may vary, in some embodiments, ranging from 1 μm to 20000 μm, such as 2 μm to 17500 μm, such as 5 μm to 15000 μm, such as 10 μm to 12500 μm, such as 15 μm to 10000 μm, such as 25 μm to 7500 μm, such as 50 μm to 5000 μm, such as 75 μm to 1000 μm, such as 100 μm to 750 μm, and including from 150 μm to 500 μm. In certain embodiments, the nozzle orifice is 100 μm.

In some embodiments, the flow cell includes a sample injection port configured to provide a sample to the flow cell. In an embodiment, the sample injection system is configured to provide a suitable sample flow to the flow cell interior chamber. Depending on the desired characteristics of the flow stream, the rate of sample delivery to the flow cell chamber through the sample injection port may be 1 μl/min or greater, such as 2 μl/min or greater, such as 3 μl/min or greater, such as 5 μl/min or greater, such as 10 μl/min or greater, such as 15 μl/min or greater, such as 25 μl/min or greater, such as 50 μl/min or greater, and including 100 μl/min or greater, wherein in some cases the rate of sample delivery to the flow cell chamber through the sample injection port is 1 μl/sec or greater, such as 2 μl/sec or greater, such as 3 μl/sec or greater, such as 5 μl/sec or greater, such as 10 μl/sec or greater, such as 15 μl/sec or greater, such as 25 μl/sec or greater, such as 50 μl/sec or greater, and including 100 μl/sec or greater.

The sample injection port may be an orifice located on the wall of the lumen or may be a conduit located at the proximal end of the lumen. Where the sample injection port is an orifice positioned in a wall of the inner chamber, the sample injection port orifice may be of any suitable shape, where cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear cross-sectional shapes, such as circular, oval, etc., and irregular shapes, such as parabolic bottoms connected to a planar top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, in some examples, having an opening ranging from 0.1mm to 5.0mm, e.g., 0.2 to 3.0mm, e.g., 0.5mm to 2.5mm, such as 0.75mm to 2.25mm, such as 1mm to 2mm, and including 1.25mm to 1.75mm, e.g., 1.5 mm.

In some examples, the sample injection port is a conduit located at the proximal end of the flow cell lumen. For example, the sample injection port may be a conduit positioned such that the orifice of the sample injection port is in line with the flow cell orifice. Where the sample injection port is a conduit positioned in line with the flow cell orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, where cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear cross-sectional shapes, such as circular, oval, and irregular shapes, such as parabolic bottoms connected to a planar top. The orifice of the catheter may vary depending on the shape, in some examples having an opening in the range of 0.1mm to 5.0mm, such as 0.2 to 3.0mm, such as 0.5mm to 2.5mm, such as 0.75mm to 2.25mm, such as 1mm to 2mm, and including 1.25mm to 1.75mm, for example 1.5mm. 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 having a beveled angle ranging from 1 ° to 10 °, such as 2 ° to 9 °, such as 3 ° to 8 °, such as 4 ° to 7 °, and including a beveled angle of 5 °.

In some embodiments, the flow cell further comprises a sheath fluid injection port configured to provide sheath fluid to the flow cell. In an embodiment, the sheath fluid injection system is configured to provide a sheath fluid flow to the flow cell interior chamber, e.g., in combination with the sample to produce a laminar flow stream of sheath fluid around the sample flow stream. Depending on the desired characteristics of the flow stream, the rate of sheath fluid delivered therethrough to the flow cell chamber may be 25 μL/sec or greater, such as 50 μL/sec or greater, such as 75 μL/sec or greater, such as 100 μL/sec or greater, such as 250 μL/sec or greater, such as 500 μL/sec or greater, such as 750 μL/sec or greater, such as 1000 μL/sec or greater, and including 2500 μL/sec or greater.

In some embodiments, the sheath fluid injection port is an orifice positioned in a wall of the lumen. The sheath fluid injection port orifice may be of any suitable shape, with cross-sectional shapes of interest including, but not limited to: linear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear cross-sectional shapes, such as circular, oval, and irregular shapes, such as parabolic bottoms connected to a planar top. The size of the sample injection port orifice may vary depending on the shape, in some examples, having an opening ranging from 0.1mm to 5.0mm, e.g., 0.2 to 3.0mm, e.g., 0.5mm to 2.5mm, such as 0.75mm to 2.25mm, such as 1mm to 2mm, and including 1.25mm to 1.75mm, e.g., 1.5 mm.

In some embodiments, the system further comprises a pump in fluid communication with the flow cell to pass the flow stream through the flow cell. Any convenient fluid pump protocol may be employed to control the flow of the flow stream through the flow cell. In certain examples, the system includes a peristaltic pump, such as a peristaltic pump with a pulse dampener. The pump in the present system is configured to deliver fluid through the flow cell at a rate suitable for detecting light from the sample in the flow stream. In some examples, the sample flow rate in the flow cell is 1 μL/min (microliter/min) or greater, such as 2 μL/min or greater, such as 3 μL/min or greater, such as 5 μL/min or greater, such as 10 μL/min or greater, such as 25 μL/min or greater, such as 50 μL/min or greater, such as 75 μL/min or greater, such as 100 μL/min or greater, such as 250 μL/min or greater, such as 500 μL/min or greater, such as 750 μL/min or greater, and including 1000 μL/min or greater. For example, the system may include a pump configured to flow the sample through the flow cell at a rate ranging from 1 μL/min to 500 μL/min, such as 1 μL/min to 250 μL/min, such as 1 μL/min to 100 μL/min, such as 2 μL/min to 90 μL/min, such as 3 μL/min to 80 μL/min, such as 4 μL/min to 70 μL/min, such as 5 μL/min to 60 μL/min, and including from 10 μL/min to 50 μL/min. In certain embodiments, the flow rate of the flow stream is from 5 μL/min to 6 μL/min.

In certain embodiments, a light detection system having a plurality of photodetectors as described above is part of or located in a particle analyzer, such as a particle sorter. In certain embodiments, the subject system is a flow cytometry system that includes a photodiode and an amplifier component as part of a light detection system for detecting light emitted by a sample in a flow stream. Suitable Flow Cytometry systems may include, but are not limited to, those described in Ormerod (ed.), flow Cytometry A Practical Approach, oxford Univ. Press (1997); jaroszeski et al (eds.), flow Cytometry Protocols, methods in Molecular Biology No.91, humana Press (1997); practical Flow Cytometry,3rd ed., wiley-Lists (1995); virgo, et al (2012) Ann Clin biochem. 49 17-28; linden et al, seminThrom thermo.2004 Oct;30 (5) 502-11; alison, et al J Pathol,2010Dec;222 (4) 335-344; herbig et al (2007) Crit Rev Ther Drug Carrier Syst.24 (3): 203-255; the disclosure of which is incorporated herein by reference. In certain examples, the flow cytometry system of interest includes BD Biosciences FACSCanto ^TM Flow cytometer, BD Biosciences FACSCanto ^TM II flow cytometer, BD Accuri ^TM Flow cytometer, BD Accuri ^TM C6 Plus flow cytometer, BD Biosciences FACSCelesta ^TM Flow cytometer, BD Biosciences FACSLyric ^TM Flow cytometer, BD Biosciences FACSVerse ^TM Flow cytometer, BD Biosciences FACSymphony ^TM Flow cytometer, BD Biosciences LSRFortessa ^TM Flow cytometer, BD Biosciences LSRFortessa ^TM X-20 flow cytometer, BD Biosciences FACSPresto ^TM Flow cytometer, BD Biosciences FACSVia ^TM Flow cytometer and BD Biosciences FACSCalibur ^TM Cell sorter, BD Biosciences FACSCount ^TM Cell sorter, BD Biosciences FACSLyric ^TM Cell sorter, BD Biosciences Via ^TM Cell sorter, BD Biosciences Influx ^TM Cell sorter, BD Biosciences Jazz ^TM Cell sorter, BD Biosciences Aria ^TM Cell sorter, BD Biosciences FACSAria ^TM II cell sorter, BD Biosciences FACSAria ^TM III cell sorter, BD Biosciences FACSAria ^TM Fusion cell sorter and BD Biosciences FACSMelody ^TM Cell sorter, BD Biosciences FACSymphony ^TM S6 cell sorter, etc.

In some embodiments, the subject system is a flow cytometry system, such as that described in U.S. patent No. 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; 9,095,494; 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 disclosure of which is incorporated herein by reference in its entirety.

In some embodiments, the subject system is a particle sorting system configured to sort particles with a closed particle sorting module, such as those described in U.S. patent publication No. 2017/0299493, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sort decision module having a plurality of sort decision units, such as those described in U.S. patent publication No. 2020/0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having a deflector plate, such as described in U.S. patent publication No. 2017/0299493 filed on day 3/28, the disclosure of which is incorporated herein by reference.

In certain examples, the flow cytometry systems of the present invention are configured to image particles in a flow stream by fluorescence imaging using radio frequency marker emission (FIRE), such as those described by Diebold et al. Nature Photonics, volume 7 (10); 806-810 (2013), U.S. patent No. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; and U.S. patent publication No. 2017/0133857; 2017/032886; 2017/0350803; 2018/0275042; the disclosures of 2019/0376895 and 2019/0376894 are incorporated herein by reference.

In some embodiments, the system is a particle analyzer, wherein the particle analysis system 401 (fig. 4A) can be used to analyze and characterize particles, wherein the particles are physically sorted into the collection container or not sorted into the collection container. FIG. 4A shows a functional block diagram of a particle analysis system for calculation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. The particle analysis system 401 shown in fig. 4A may be configured to perform the methods described herein, such as in whole or in part. Particle analysis system 401 includes a fluidics system 402. The fluidics system 402 may comprise or be coupled with a sample tube 405 and a moving fluid column within the sample tube, wherein particles 403 (e.g., cells) of a sample move along a common sampling path 409.

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 sampling path. The detection station 408 generally refers to the monitoring region 407 of the common sampling path. In some implementations, detecting may include detecting light or one or more other characteristics of particles 403 as they pass through monitoring region 407. In fig. 4A, a probe station 408 having a monitoring area 407 is shown. Some implementations of particle analysis system 401 may include multiple detection stations. In addition, some detection stations may monitor more than one area.

A signal value is assigned to each signal to form a data point for each particle. As described above, this data may be referred to as event data. The data points may be multi-dimensional data points comprising values for respective properties measured for the particles. The detection system 404 is configured to collect a series of such data points over a first time interval.

The particle analysis system 401 may also include a control system 306. The control system 406 may include one or more processors, amplitude control circuitry, and/or frequency control circuitry. The control system shown is operably associated with a fluidics 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 poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may also be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may also compare the experimental signal frequency to a calculated signal frequency or a predetermined signal frequency.

Fig. 4B shows a system 400 for flow cytometry according to an illustrative embodiment of the invention. The system 400 includes a flow cytometer 410, a controller/processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more long pass ("LP") filters 455a-455b, and one or more fluorescence photodetectors 460a-460f.

Excitation lasers 115a-c emit light in the form of a laser beam. In the exemplary system of FIG. 4B, the wavelengths of the laser beams emitted from excitation lasers 415a-415c are 488nm, 633nm, and 325nm, respectively. The laser beam is first directed through one or more beam splitters 445a and 445b. The beam splitter 445a transmits 488nm light and reflects 633nm light. The beam splitter 445b transmits UV light (light having a wavelength in the range of 10 to 400 nm) and reflects 488nm and 633nm light.

The laser beam is then directed to a focusing lens 420, which focusing lens 420 focuses the beam onto the portion of the fluid flow within flow chamber 425 where the sample particles are located. The flow cell is part of a fluidic system that directs particles in the stream, typically one at a time, to a focused laser beam for probing. The flow cell may comprise a flow cell in a bench top cytometer or a nozzle head in an air flow cytometer.

Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption and re-emits at a variety of different 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 in the particles. Fluorescence emission, as well as diffracted, refracted, reflected, and scattered light, may be routed through one or more of beam splitters 445a-445g, bandpass filters 450a-450e, long pass filters 455a-455b, and fluorescence collection lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence photodetectors 460a-460 f.

The fluorescence collection lens 440 collects light emitted from the particle-laser beam interactions and directs the light to one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510/20 filter. The first number represents the center of the band. The second number provides a range of spectral bands. Thus, the 510/20 filter extends 10nm on each side of the spectral band center, or from 500nm to 520nm. The short-pass filter transmits light wavelengths equal to or shorter than the specified wavelength. A long pass filter such as long pass filters 455a-455b transmits wavelengths of light equal to or longer than the specified wavelengths of light. For example, the long-pass filter 455a, which is a 670nm long-pass filter, transmits light equal to or longer than 670 nm. The filters are typically selected to optimize the specificity of the detector for a particular fluorescent dye. The filter may be configured such that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

The beam splitter directs light of different wavelengths in different directions. The beam splitter may be characterized by filter characteristics such as short pass and long pass. For example, the beam splitter 445g is a 620Sp beam splitter, meaning that the beam splitter 445g transmits light wavelengths of 620nm or less and reflects light wavelengths longer than 620nm in different directions. In one embodiment, beam splitters 445a-445g may include an optical mirror, such as a dichroic mirror.

The forward scatter detector 430 is positioned slightly off-axis from the direct light beam passing through the flow cell and is configured to detect diffracted light, i.e., excitation light traveling primarily in the forward direction through or around the particles. The intensity of the light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may comprise a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the surface and internal structures of the particle and tends to increase as the structural complexity of the particle increases. Fluorescence emission from fluorescent molecules associated with the particles may be detected by one or more fluorescence photodetectors 460a-460 f. The side scatter detector 435 and the fluorescent photodetector may include photomultiplier tubes. The signals detected at the forward scatter detector 430, the side scatter detector 435, and the fluorescent photodetectors may be converted to electrical signals (voltages) by the detectors. This data may provide information about the sample.

Those skilled in the art will recognize that a flow cytometer according to embodiments of the present invention is not limited to the flow cytometer depicted in fig. 4B, but may include any flow cytometer known in the art. For example, the flow cytometer may have any number of lasers, beam splitters, filters, and detectors of various wavelengths and various configurations.

In operation, flow cytometer operation is controlled by the controller/processor 490, and measurement data from the detector can be stored in the memory 495 and processed by the controller/processor 490. Although not explicitly shown, the controller/processor 190 is coupled to the detector to receive output signals therefrom and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, and the like. Input/output (I/O) capability 497 may also be provided in the system. Memory 495, controller/processor 490, and I/O497 may be provided entirely as an integral part of flow cytometer 410. In such embodiments, the display may also form part of the I/O capability 497 for presenting experimental data to a user of the flow cytometer 400. Alternatively, some or all of the memory 495 and controller/processor 490 and I/O capabilities may be part of one or more external devices, such as a general purpose computer. In some embodiments, some or all of memory 495 and controller/processor 490 may be in wireless or wired communication with flow cytometer 410. The controller/processor 490, along with the memory 495 and I/O497, may be configured to perform various functions related to the preparation and analysis of flow cytometer experiments.

The system shown in fig. 4B includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for a given detector), as defined by the configuration of filters and/or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in flow cytometer experiments will emit light in their own characteristic wavelength band. The particular fluorescent label and its associated fluorescence emission band used in the experiment may be selected to generally coincide with the filter window of the detector. However, a perfect correspondence between the filter window and the fluorescence emission spectrum is not possible, since more detectors are provided and more labels are used. Typically, although the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, some of the emission spectrum of the label will also overlap with the filter window of one or more other detectors. This may be referred to as overflow. The I/O497 may be configured to receive data regarding a flow cytometer experiment having a fluorescent marker panel and a plurality of cell populations having a plurality of markers, each cell population having a subset of the plurality of markers. The I/O497 may also be configured to receive biological data, marker density data, emission spectrum data, data specifying one or more markers to one or more cell populations, and cytometer configuration data. Flow cytometer experimental data, such as marker spectral characteristics and flow cytometer configuration data, may also be stored in the memory 495. The controller/processor 490 may be configured to evaluate one or more assignments of tags to tags.

Fig. 5 illustrates a functional block diagram of one example of a particle analyzer control system, such as an analysis controller 500, for analyzing and displaying biological events. The analysis controller 500 may be configured to implement various processes for controlling the graphical display of biological events.

The particle analyzer or sorting system 502 may be configured to collect biological event data. For example, a flow cytometer may generate flow cytometry event data. The particle analyzer 502 may be configured to provide biological event data to the analysis controller 500. A data communication channel may be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data may be provided to the analysis controller 500 via a data communication channel.

Analysis controller 500 may be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 may include flow cytometry event data. The analysis controller 500 may be configured to provide a graphical display comprising a first graph of biological event data to the display device 506. The analysis controller 500 may also be configured to present the region of interest as a gate overlaid on the first graph around the population of biological event data, for example, displayed by the display device 506. In some embodiments, the gating may be a logical combination of one or more graphical regions of interest plotted on a single parameter histogram or a bivariate graph. In some embodiments, a display may be used to display particle parameters or saturation detector data.

The analysis controller 500 may also be configured to display on the display device 506 within the strobe biological event data that is different from other events in the biological event data outside the strobe. For example, the analysis controller 500 may be configured to cause the color of the biological event data contained within the strobe to be different from the color of the biological event data outside the strobe. Display device 506 may be implemented as a monitor, tablet computer, smart phone, or other electronic device configured to present a graphical interface.

The analysis controller 500 may be configured to receive a strobe selection signal from the first input device identifying the strobe. For example, the first input device may be implemented as a mouse 510. The mouse 510 may initiate a gate selection signal to the analysis controller 500 that identifies a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking on or in a desired gate when a cursor is positioned over the desired gate). In some embodiments, the first device may be implemented as a keyboard 508 or other device for providing input signals to the analysis controller 500, such as a touch screen, stylus, optical detector, or voice recognition system. Some input devices may include multiple input functions. In such implementations, the input functions may each be considered an input device. For example, as shown in fig. 5, the mouse 510 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

The trigger event may cause the analysis controller 500 to change the manner in which the data is displayed, which portions of the data are actually displayed on the display device 506, and/or provide input for further processing, such as selecting a population of interest for particle sorting.

In some embodiments, the analysis controller 500 may be configured to detect when a gating selection is initiated by the mouse 510. The analysis controller 500 may also be configured to automatically modify the plot visualization to facilitate the gating process. The modification may be based on a particular distribution of biological event data received by the analysis controller 500.

The analysis controller 500 may be connected to a storage device 504. The storage device 504 may be configured to receive and store biological event data from the analysis controller 500. The storage device 504 may also be configured to receive and store streaming cellular event data from the analysis controller 500. The storage device 504 may also be configured to allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

The display device 506 may be configured to receive display data from the analysis controller 500. The display data may include a map of biological event data and a strobe that outlines a portion of the map. Display device 506 may also be configured to change the information presented based on input received from analysis controller 500 and input from particle analyzer 502, storage device 504, keyboard 508, and/or mouse 510.

In some implementations, the analysis controller 500 may generate a user interface to receive an exemplary event for sorting. For example, the user interface may include a controller for receiving an exemplary event or an exemplary image. An exemplary event or image or exemplary gating may be provided prior to collecting event data for a sample, or based on an initial set of events for a portion of the sample.

Fig. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, particle sorter system 600 is a cell sorter system. As shown in fig. 6A, a drop forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which fluid conduit 601 may be coupled to a nozzle 603, which may include or may be the nozzle 603. Within the fluid conduit 601, the sheath fluid 604 hydrodynamically concentrates a sample fluid 606 including particles 609 into a moving fluid column 608 (e.g., stream). Within the moving fluid column 608, particles 609 (e.g., cells) are arranged in a single row to pass through a monitoring region 611 (e.g., laser-stream intersection) illuminated by an illumination source 612 (e.g., laser). The vibration of the drop forming transducer 602 causes the moving fluid column 608 to break up into more drops 610, some of which contain particles 609.

In operation, the detection station 614 (e.g., event detector) identifies when particles of interest (or cells of interest) pass through the monitoring region 611. The detection station 614 feeds a timing circuit 628, which timing circuit 628 in turn feeds a flash (flash) charging circuit 630. At the drop break-off point, which is notified by the timed drop delay (Δt), flash charge can be applied to the moving fluid column 608 such that the drop of interest carries an electrical charge. The droplets of interest may include one or more particles or cells to be sorted. The charged droplets may then be sorted by activating a deflection plate (not shown) to deflect the droplets into a receptacle such as a collection tube or a porous or microporous sample plate, where the pores or micropores may be associated with droplets of particular interest. As shown in fig. 6A, the droplets may be collected in a discharge container 638.

The detection system 616 (e.g., a drop boundary detector) is used to automatically determine the phase of the drop drive signal as the particle of interest passes through the monitoring region 611. An exemplary drop boundary detector is described in U.S. patent No. 7,679,039, which is incorporated by reference herein in its entirety. The detection system 616 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 616 may feed an amplitude signal 620 and/or a phase signal 618, which in turn feeds (via an amplifier 622) 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 drop forming transducer 602. Amplitude control circuit 626 and/or frequency control circuit 624 may be included in a control system.

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

Fig. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in fig. 6B includes deflection plates 652 and 654. The charge may be applied via a flow charging line in the barb. This produces a droplet stream 610 containing particles 610 for analysis. The particles may be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. Such as by sorting electronics or other detection systems (not shown in fig. 6B). The deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets to direct the droplets to a target collection vessel (e.g., one of 672, 674, 676, or 678). As shown in fig. 6B, the deflector plates 652 and 654 may be controlled to direct particles along the first path 662 to the vessel 674 or along the second path 668 to the vessel 678. If the particles are of no interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflector plate may allow the particles to continue along the flow path 664. Such uncharged droplets may enter the waste container, such as via aspirator 670.

Sorting electronics may be included to begin collecting measurements, receiving fluorescent signals of the particles, and determining how to adjust the deflection plates to cause sorting of the particles. An exemplary implementation of the embodiment shown in FIG. 6B includes BD FACSaria commercially available from Becton, dickinson and Company (Franklin Lakes, NJ) ^TM Serial flow cytometry.

Computer controlled system

Aspects of the disclosure also include a computer-controlled system, wherein the system further comprises one or more computers for fully or partially automating the methods described herein. In some embodiments, a system includes a computer having a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when loaded onto the computer, includes instructions for practicing the subject method. The computer program according to some embodiments comprises: instructions for detecting light from a sample in the flow stream illuminated by the light source with a light detection system; instructions for generating an event data signal in response to an event detected with the light detection system; instructions for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and instructions for evaluating the plurality of binning windows for outlier events in the generated data signal.

In some embodiments, the computer program includes instructions for binning a predetermined number of event data signals in each window. In some embodiments, the computer program includes instructions for binning the event data signals sequentially based on time of event detection by the light detection system. In some embodiments, the computer program includes instructions for binning one or more event data signals into two or more binning windows, such as wherein the two or more binning windows overlap by a predetermined time frame. For example, the binning windows may overlap time frames of 0.001 μs to 100 μs.

In some embodiments, the computer program includes instructions for applying a transformation to each event data signal. In certain embodiments, the transformation is one or more of a centered logarithmic ratio transformation, an additive logarithmic ratio transformation, an equal length logarithmic ratio transformation, or a combination thereof. In some embodiments, the computer program includes instructions for calculating a median parameter for each of the overlapping bins. In some embodiments, the computer program includes instructions for calculating a median or average fluorescence intensity for the events for each bin. In some embodiments, the computer program includes instructions for calculating a median or average expression level of the events for each of the bins.

In some embodiments, the computer program includes instructions for evaluating the plurality of binning windows for outlier events by comparing the median parameter of each bin to an outlier threshold. In some examples, the outlier threshold is a Median Absolute Deviation (MAD) outlier threshold. In some examples, the outlier threshold is a median tolerance percentage outlier threshold. In some embodiments, the computer program includes instructions for dynamically calculating the outlier threshold in real-time, such as instructions for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals. In some embodiments, the computer program includes instructions for classifying the data bin as a data bin containing an outlier when the median parameter is determined to be greater than the predetermined outlier threshold. In some examples, the computer program includes instructions for identifying as outliers event data signals for each of the bins containing outliers.

In some embodiments, the computer program includes instructions for generating an alert that an outlier is detected when evaluating the plurality of bins for the outlier event. In some examples, the computer program includes instructions for generating an alert of a change in flow rate in a flow stream of the particle analyzer. In some examples, the computer program includes instructions for generating an alert that a jam is present in the particle analyzer.

In some examples, the computer program includes instructions to measure a flow rate of the flow stream with a flow rate sensor. In some examples, the computer program includes instructions for comparing the measured flow rate of the flow stream with the desired flow rate of the flow stream, and an algorithm for re-evaluating the bins classified as bins containing outliers based on the compared flow rates of the flow streams. In some embodiments, the computer program includes instructions for reclassifying the data bins based on the compared flow rates of the flow streams. In some embodiments, a computer program includes instructions to: for not generating an alarm when the measured flow rate is within a predetermined threshold of the expected flow rate, such as when the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the computer program includes instructions for comparing the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system, and an algorithm for re-evaluating the bins classified as bins containing outliers based on the parameters of the light detection system (e.g., the photodetector voltage of one or more photodetectors of the light detection system). In certain embodiments, the computer program comprises instructions for performing calibration of the particle analyzer in response to one or more bins classified as bins containing outliers. In certain examples, the computer program instructions for calibrating the particle analyzer comprise instructions for detecting light from a standard composition illuminated by a light source in a flow stream with a light detection system, instructions for generating an event data signal in response to an event detected with the light detection system, instructions for binning the generated event data signal into a plurality of binning windows having overlapping bins, instructions for evaluating the generated data signal in the plurality of binning windows, and instructions for determining that there is no bin containing outliers.

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

The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor having access to a memory with instructions stored thereon for performing the steps of the subject method. The processing modules may include an operating system, a Graphical User Interface (GUI) controller, a system memory, a memory storage device, an input output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of the other processors that are or will become available. The processor executes an operating system in a well known manner and interfaces with firmware and hardware, and facilitates coordination and execution of the functions of the various computer programs that may be written in various programming languages, such as Java, perl, C ++, other high-level or low-level languages, and combinations thereof, as are known in the art. An operating system typically cooperates with the processor, coordinates and performs functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, and communication control and related services, all according to known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that allow a user to manually align the light source with the flow stream based on the first light signal and the second light signal. In some embodiments, the processor includes analog electronics that provide feedback control (such as negative feedback control).

The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly available Random Access Memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as read and write optical discs, flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including an optical disk drive, a tape drive, a removable hard disk drive, or a floppy disk drive. Memory storage devices of this type typically read from and/or write to, respectively, a program storage medium (not shown), such as an optical disk, magnetic tape, removable hard disk, or floppy disk. Any of these program storage media, or other media now in use or later possibly developed, may be considered a computer program product. It will be appreciated that such program storage media typically store computer software programs and/or data. Computer software programs (also called computer control logic) are typically stored in system memory and/or program storage devices used in conjunction with memory storage devices.

In some embodiments, a computer program product comprising a computer useable medium having control logic (computer software program, including program code) stored therein is described. The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some of the functions are implemented primarily in hardware using, for example, a hardware state machine. It will be apparent to one of ordinary skill in the relevant art that implementing a hardware state machine to perform the functions described herein.

The memory may be any suitable device in which the processor may store and retrieve data, such as a magnetic, optical, or solid state storage device (including magnetic or optical disks or tape or RAM, or any other suitable fixed or portable device). The processor may comprise a general-purpose digital microprocessor adapted to be programmed from a computer readable medium carrying the necessary program code. The programming may be provided to the processor remotely over a communications channel, or may be pre-stored in a computer program product such as a memory or some other portable or fixed computer readable storage medium using any of those devices associated with the memory. For example, a magnetic disk or optical disk may carry the programming and be readable by a disk writer/reader. The system of the present invention also includes programming, for example in the form of a computer program product, algorithms for implementing the methods described above. The programming in accordance with the present invention may be recorded on a computer readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; an optical storage medium such as a CD-ROM; an electrical storage medium such as RAM and ROM; a portable flash memory drive; and mixtures of these types, such as magnetic/optical storage media.

The processor may also access a communication channel to communicate with a user at a remote location. By remote location is meant that the user does not directly contact the system and relays incoming information from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a mobile telephone (i.e., a smart phone), to the input manager.

In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and/or transmitter for communicating with the network and/or another device. The communication interface may be configured for wired or wireless communication including, but not limited to, radio Frequency (RF) communication (e.g., radio Frequency Identification (RFID), zigbee communication protocol, wiFi, infrared, wireless Universal Serial Bus (USB), ultra Wideband (UWB), bluetooth communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or global system for mobile communications (GSM).

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

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 device that a user may use in conjunction with.

In one embodiment, the communication interface is configured to provide a connection for data transfer over a cellular telephone network using Internet Protocol (IP), short Message Service (SMS), wireless connection to a Personal Computer (PC) on a Local Area Network (LAN) connected to the internet, or WiFi connection to the internet at a WiFi hotspot.

In one embodiment, the subject system is configured, for example, using a system such as 802.11 orThe common standard of the RF protocol or the IrDA infrared protocol communicates wirelessly with the server device via a communication interface. The server device may be another portable device such as a smart phone, a Personal Digital Assistant (PDA), or a notebook computer; or larger devices such as desktop computers, appliances, etc. In some embodiments, the server device has a display, such as a Liquid Crystal Display (LCD), and an input device, such as a button, keyboard, mouse, or touch screen.

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

The output controller may include a controller for any of a variety of known display devices for presenting information to a user (whether human or machine, whether local or remote). If one of the display devices provides visual information, the information may typically be logically and/or physically organized as an array of picture elements. A Graphical User Interface (GUI) controller may include any of a variety of known or future software programs for providing graphical input and output interfaces between a system and a user, as well as for processing user inputs. The functional elements of the computer may communicate with each other via a system bus. In alternative embodiments, some of these communications may be implemented using a network or other type of remote communications. The output manager may also provide the information generated by the processing module to a user at a remote location, for example, over the internet, telephone or satellite network, according to known techniques. The presentation of data by the output manager may be implemented according to various known techniques. As some examples, the data may include SQL, HTML or XML documents, emails or other files, or other forms of data. The data may include an internet URL address so that a user may retrieve additional SQL, HTML, XML or other documents or data from a remote source. The one or more platforms present in the subject system may be any type of known computer platform or type of future development, although they are typically a type of computer commonly referred to as servers. However, they may also be host computers, workstations, or other computer types. They may be connected via any known or future type of cable or other communication system (networked or otherwise) including wireless systems. They may be co-located or they may be physically separate. Various operating systems may be employed on any computer platform, possibly depending on the type and/or configuration of computer platform selected. Suitable operating systems include Windows, iOS, oracle Solaris, linux, IBM i, unix, etc.

Fig. 7 depicts a general architecture of an example computing device 700, according to some embodiments. The general architecture of the computing device 700 depicted in fig. 7 includes an arrangement of computer hardware and software components. Computing device 700 may include more (or fewer) elements than those shown in fig. 7. However, not all of these generally conventional elements need be shown to provide a disclosure that can be implemented. As shown, computing device 700 includes a processing unit 710, a network interface 720, a computer-readable medium drive 730, an input/output device interface 740, a display 750, and an input device 760, all of which may communicate with each other via a communication bus. Network interface 720 may provide a connection to one or more networks or computing systems. The processing unit 710 may thus receive information and instructions from other computing systems or services via a network. The processing unit 710 may also be in communication with memory 770, and also provide output information for optional display 750 via input/output device interface 740. Input/output device interface 740 may also accept input from an optional input device 760, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, joystick, accelerometer, gyroscope, or other input device.

Memory 770 may contain computer program instructions (grouped into modules or components in some embodiments) that are executed by processing unit 710 to implement one or more embodiments. Memory 770 typically includes RAM, ROM, and/or other permanent, auxiliary, or non-transitory computer-readable media. Memory 770 may store an operating system 772 that provides computer program instructions for use by processing unit 710 in the general management and operation of computing device 700. Memory 770 may also include computer program instructions and other information for implementing aspects of the present disclosure.

Non-transitory computer readable storage medium

Aspects of the present disclosure also include a non-transitory computer-readable storage medium having instructions for practicing the subject methods. The computer-readable storage medium can be used on one or more computers to implement a fully or partially automated system for practicing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in "programmed" form, where 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, nonvolatile memory cards, ROMs, DVD-ROMs, blu-ray disks, solid state disks, and network-attached storage (NAS), whether such devices are internal or external to a computer. The file containing the information may be "stored" on a computer readable medium, where "storing" means recording the information so that it can be accessed and retrieved by the computer at a later date. The computer-implemented methods described herein may be performed using programming, which may be written in one or more of any number of computer programming languages. Such languages include, for example, python, java, java script, C, C #, c++, go, R, swift, PHP, and any of many others.

A non-transitory computer-readable storage medium according to some embodiments includes instructions having: an algorithm for detecting light from a sample illuminated by a light source in a flow stream with a light detection system; an algorithm for generating an event data signal in response to an event detected with the light detection system; an algorithm for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and an algorithm for evaluating the plurality of binning windows for outlier events in the generated data signal. In some examples, the non-transitory computer-readable storage medium includes an algorithm for binning a predetermined number of event data signals in each window. In some examples, the non-transitory computer-readable storage medium includes an algorithm for sequentially binning event data signals based on event detection times of the light detection system. In some examples, the non-transitory computer-readable storage medium includes an algorithm for binning one or more event data signals into two or more binning windows, such as where the two or more binning windows overlap a predetermined time frame. For example, the binning windows may overlap time frames of 0.001 μs to 100 μs.

In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for generating an alert that an outlier is detected when evaluating the plurality of bins for the outlier event. In some examples, the non-transitory computer readable storage medium includes an algorithm for generating an alert indicating that there is a change in a flow rate in a flow stream of the particle analyzer. In some examples, the non-transitory computer readable storage medium includes an algorithm for generating an alert that a jam is present in the particle analyzer.

In certain examples, the non-transitory computer-readable storage medium includes an algorithm for measuring a flow rate of the flow stream with a flow rate sensor. In some examples, the non-transitory computer-readable storage medium includes an algorithm for comparing the measured flow rate of the flow stream to the desired flow rate of the flow stream, and an algorithm for re-evaluating the bins classified as bins containing outliers based on the compared flow rates of the flow streams. In some examples, the non-transitory computer-readable storage medium includes an algorithm for reclassifying the data bins based on the compared flow rates of the flow streams. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for not generating an alarm when the measured flow rate is within a predetermined threshold of the expected flow rate, such as where the predetermined threshold is within 5% or less of the expected flow rate. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for comparing the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system, and an algorithm for re-evaluating the bins classified as bins containing outliers based on the parameters of the light detection system (e.g., the photodetector voltage of one or more photodetectors of the light detection system). In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for performing calibration of the particle analyzer in response to one or more bins classified as containing outliers. In certain examples, the algorithm for calibrating the particle analyzer includes an algorithm for detecting light from a standard composition in a flow stream illuminated by a light source with a light detection system, an algorithm for generating event data signals in response to events detected with the light detection system, an algorithm for binning the generated event data signals into a plurality of binned windows having overlapping bins, an algorithm for evaluating data signals generated in the plurality of binned windows, and an algorithm for determining that no bins containing outliers are present.

The non-transitory computer readable storage medium may be used on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor having access to a memory with instructions stored thereon for performing the steps of the subject method. The processing modules may include an operating system, a Graphical User Interface (GUI) controller, a system memory, a memory storage device, an input output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or it may be one of the other processors that are or will become available. The processor executes an operating system in a well known manner, and interfaces with firmware and hardware, and facilitates coordination and execution of the functions of the various computer programs that may be written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as is well known in the art. An operating system typically cooperates with the processor, coordinates and performs functions of the other components of the computer. The operating system also provides scheduling, input-output control, file and data management, memory management, communication control and related services, all according to known techniques.

Kit of parts

Aspects of the disclosure also include kits, wherein the kits comprise one or more components of the light detection systems described herein. In some embodiments, the kit includes an integrated circuit device (e.g., FPGA), a wavelength separator (e.g., spectrometer), a plurality of photodetectors, one or more electronic components, such as transimpedance amplifiers, differential amplifiers, electronic switching components, and programs for the subject system, such as in the form of a computer-readable medium (e.g., flash drive, USB memory, optical disk, DVD, blu-ray disk, etc.), or instructions for downloading programs from an internet network protocol or cloud server. The kit may also include optical conditioning components such as lenses, mirrors, filters, optical fibers, pinholes, slits, collimation protocols, and combinations thereof.

The kit may also include instructions for implementing the subject methods. These instructions may be present in the kit of the present invention in a variety of forms, one or more of which may be present in the subject kit. One form in which these instructions may exist is as printed information on a suitable medium or substrate, for example, a sheet or sheets of paper printed with information, in the packaging of a kit, in a package insert, and so forth. Another form of such instructions is a computer-readable medium, such as a magnetic disk, compact Disk (CD), portable flash drive, etc., having information recorded thereon. Another form of these descriptions that may exist is a website address that can be used via the internet to access information at the removed site.

Practicality of use

The methods, systems, and computer systems of the present invention may be used in a variety of applications where optimized particle identification, characterization, and sorting are desired. The methods, systems, and computer readable storage media of the present invention may also be used in a variety of different applications where accurate and convenient sorting of droplets using a flow cytometer is desired. The present invention can also be used to automate flow cytometry to provide a fast, reliable system for characterizing and sorting cells from a biological sample. Embodiments of the present disclosure may be used in applications where it is desirable to minimize the amount of reliance on human input and regulation of the system, such as in research and high-throughput laboratory testing. The present disclosure also finds use in which it is desirable to provide a flow cytometer having improved cell sorting accuracy, enhanced particle collection, particle charging efficiency, more accurate particle charging, and enhanced particle deflection during cell sorting. In embodiments, the present disclosure reduces the need for user input or manual adjustment of the flow cytometer, or between sample analyses of the flow cytometer.

The present disclosure also finds use in applications where cells prepared from biological samples may be used in research, laboratory testing, or for desired applications in therapy. In some embodiments, the methods and devices of the present invention may be useful for obtaining single cells prepared from a fluid of interest or a tissue biological sample. For example, the methods and systems of the present invention facilitate obtaining cells from a fluid or tissue sample for use as a research or diagnostic sample for a disease, such as cancer. Also, the methods and systems of the present invention facilitate obtaining cells from a fluid or tissue sample for treatment. The methods and devices of the present disclosure allow for the separation and collection of cells from biological samples (e.g., organs, tissue fragments, fluids) with enhanced efficiency and low cost compared to traditional flow cytometry systems.

The disclosure is also defined by the following clauses, notwithstanding the appended claims:

1. a method for dynamically assessing in real-time the presence of outlier events in a data signal from a particle analyzer, the method comprising:

detecting light from a sample in the flow stream illuminated by the light source with a light detection system;

generating an event data signal in response to an event detected by the light detection system;

binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and

a plurality of binning windows are evaluated for outlier events in the generated data signal.

2. The method of clause 1, wherein the method comprises binning a predetermined number of event data signals in each binning window.

3. The method according to clause 2, wherein the method comprises binning 500 event data signals in each binning window.

4. The method of any of clauses 1-3, wherein the event data signals are sequentially binned based on a time of event detection by the light detection system.

5. The method of any of clauses 1-4, wherein the one or more event data signals are binned in two or more binning windows.

6. The method of clause 5, wherein the two or more binning windows overlap a predetermined time frame.

7. The method according to clause 6, wherein the binning window overlaps a time frame of 0.001 μs to 100 μs.

8. The method according to any of clauses 1-7, wherein the event data signal comprises a fluorescence intensity of the event.

9. The method according to clause 8, wherein the fluorescence intensity corresponds to the expression level of each event.

10. The method according to any of clauses 1-9, wherein a transformation is applied to each event data signal.

11. The method of clause 10, wherein the transforming comprises a centered log ratio transformation, an additive log ratio transformation, an equal length log ratio transformation, or a combination thereof.

12. The method of clause 11, wherein the transforming comprises a centered logarithmic ratio transformation.

13. The method of any of clauses 1-12, wherein the method further comprises calculating a median parameter for each of the overlapping bins.

14. The method according to clause 13, wherein the method comprises calculating a median or average fluorescence intensity for the events for each bin.

15. The method of clause 13, wherein the method further comprises calculating a median or average expression level of the events for each of the bins.

16. The method of any of clauses 13-15, wherein evaluating the plurality of binning windows for outlier events comprises comparing a median parameter of each bin to an outlier threshold.

17. The method of clause 16, wherein the outlier threshold comprises a Median Absolute Deviation (MAD) outlier threshold.

18. The method of clause 16, wherein the outlier threshold comprises a median tolerance percentage outlier threshold.

19. The method according to any of clauses 16-18, wherein the method comprises dynamically calculating the outlier threshold in real-time.

20. The method of any of clauses 16-19, wherein the outlier threshold is dynamically adjusted in real-time in response to binning of the generated event data signals.

21. The method of any of clauses 16-18, wherein the method comprises classifying the data bin as a data bin containing an outlier when the median parameter is determined to be greater than the predetermined outlier threshold.

22. The method of clause 21, wherein the method further comprises identifying the event data signals of each of the bins containing outliers as outliers.

23. The method of any of clauses 1-22, wherein the method further comprises generating an alert that an outlier is detected when evaluating the plurality of bins for the outlier event.

24. The method of clause 23, wherein the method comprises generating an alert that there is a change in flow rate in the flow stream of the particle analyzer.

25. The method of any of clauses 23-24, wherein the method comprises generating an alert that a clog is present in the particle analyzer.

26. The method of any of clauses 1-25, wherein the method further comprises measuring the flow rate of the flow stream with a flow rate sensor.

27. The method according to clause 26, wherein the method comprises:

comparing the measured flow rate of the flow stream to an expected flow rate of the flow stream; and

the bins classified as bins containing outliers are re-evaluated based on the compared flow rates of the flow streams.

28. The method of clause 27, wherein the method further comprises reclassifying the data bin based on the compared flow rates of the flow streams.

29. The method of any of clauses 27-28, wherein no alarm is generated when the measured flow rate is within a predetermined threshold of the expected flow rate.

30. The method of clause 29, wherein the predetermined threshold is in the range of 5% or less of the expected flow rate.

31. The method according to clause 23, wherein the method comprises:

comparing the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system; and

a data bin classified as a data bin containing outliers based on the reevaluation of the light detection system.

32. The method of clause 31, wherein the parameter of the light detection system comprises a photodetector voltage.

33. The method of clauses 21-32, wherein the method further comprises performing calibration of the particle analyzer in response to one or more bins classified as bins containing outliers.

34. The method according to clause 29, wherein the calibrating of the particle analyzer comprises:

detecting light from a standard component in the flow stream illuminated by the light source with a light detection system;

binning the generated event data signals into a plurality of binning windows comprising overlapping data bins;

evaluating the generated data signals in the plurality of binning windows; and

it is determined that there is no data bin containing outliers.

35. A particle analyzer, comprising:

a light detection system, comprising:

a detector component comprising a photodetector configured to detect light from a sample illuminated in the flow stream and to generate an event data signal in response to an event detected with the light detection system; and

a modulator component configured for binning the generated event data signals in a plurality of binning windows comprising a plurality of overlapping data bins; and

A processor comprising a memory operably coupled to the processor, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to evaluate the plurality of binning windows for outlier events in a generated data signal.

36. The particle analyzer of clause 35, wherein the detector component comprises a plurality of photodetectors.

37. The particle analyzer of clause 26, wherein the detector component comprises a photodetector array.

38. The particle analyzer of any of clauses 35-37, wherein the event data signals are generated in a plurality of photodetector channels.

39. The particle analyzer of clause 38, wherein the detector component includes 32 or more photodetector channels.

40. The particle analyzer of clause 38, wherein the detector component includes 64 or more photodetector channels.

41. The particle analyzer of any of clauses 38-40, wherein the modulator component comprises an integrated circuit comprising programming for binning event data signals from two or more different photodetector channels.

42. The particle analyzer of clause 41, wherein the integrated circuit is a field programmable gate array.

43. The particle analyzer of any of clauses 35-42, wherein the modulator component is configured to bin a predetermined number of event data signals into each bin window.

44. The particle analyzer of clause 43, wherein the modulator component is configured to bin 500 the event data signal in each bin window.

45. The particle analyzer of any of clauses 35-44, wherein the modulator component is configured to sequentially bin the generated event data signals based on an event detection time of the light detection system.

46. The particle analyzer of any of clauses 35-44, wherein the modulator component is configured to bin one or more of the event data signals into two or more binning windows.

47. The particle analyzer of clause 46, wherein the binning window overlaps a predetermined time frame.

48. The particle analyzer of clause 47, wherein the binning window overlaps a time frame of 0.001 μs to 100 μs.

49. The particle analyzer of any of clauses 35-48, wherein the event data signal comprises a fluorescence intensity of the event.

50. The particle analyzer of clause 49, wherein the fluorescence intensity corresponds to an expression level of each event.

51. The particle analyzer of any of clauses 35-50, wherein the modulator component is configured to apply a transformation to each event data signal.

52. The particle analyzer of clause 51, wherein the transformation comprises a centered log ratio transformation, an additive log ratio transformation, an isometric log ratio transformation, or a combination thereof.

53. The particle analyzer of clause 51, wherein the transformation comprises a centered log ratio transformation.

54. The particle analyzer of any of clauses 35-53, wherein the memory includes instructions to calculate a median parameter for each of the overlapping bins.

55. The particle analyzer of clause 54, wherein the memory includes instructions to calculate a median or average fluorescence intensity for each bin of events.

56. The particle analyzer of clause 54, wherein the memory includes instructions to calculate a median or average expression level of the events for each of the bins.

57. The particle analyzer of any of clauses 35-56, wherein the memory includes instructions for evaluating the plurality of binning windows for outlier events by comparing the median parameter of each bin to an outlier threshold.

58. The particle analyzer of clause 57, wherein the outlier threshold comprises a Median Absolute Deviation (MAD) outlier threshold.

59. The particle analyzer of clause 57, wherein the outlier threshold comprises a median tolerance percentage outlier threshold.

60. The particle analyzer of any of clauses 57-59, wherein the memory includes instructions to dynamically calculate the outlier threshold in real-time.

61. The particle analyzer of any of clauses 57-59, wherein the memory includes instructions to dynamically adjust the outlier threshold in real-time in response to binning of the generated event data signals.

62. The particle analyzer of any of clauses 57-61, wherein the memory includes instructions to classify the data bin as a data bin containing an outlier when the median parameter is determined to be greater than the predetermined outlier threshold.

63. The particle analyzer of clause 62, wherein the memory includes instructions to identify the event data signals of each of the bins containing outliers as outliers.

64. The particle analyzer of any of clauses 35-63, wherein the memory includes instructions for generating an alert that an outlier is detected when evaluating the plurality of bins for an outlier event.

65. The particle analyzer of clause 64, wherein the memory includes instructions to generate an alarm indicating that there is a flow rate change in the flow stream of the particle analyzer.

66. The particle analyzer of clause 64, wherein the memory includes instructions to generate an alert that a jam is present in the particle analyzer.

67. The particle analyzer of any of clauses 35-66, further comprising a flow rate sensor configured to measure a flow rate of the flow stream.

68. The particle analyzer of clause 67, wherein the memory includes instructions to:

69. The particle analyzer of clause 68, wherein the memory includes instructions to reclassify the data bin based on the comparative flow rates of the flow streams.

70. The particle analyzer of any of clauses 64-69, wherein the memory includes instructions to not generate an alarm when the measured flow rate is within a predetermined threshold of the expected flow rate.

71. The particle analyzer of clause 70, wherein the predetermined threshold is in the range of 5% or less of the expected flow rate.

72. The particle analyzer of any of clauses 64-71, wherein the memory includes instructions to:

the bins classified as bins containing outliers are re-evaluated based on the parameters of the light detection system.

73. The particle analyzer of clause 72, wherein the parameter of the light detection system comprises a photodetector voltage.

74. The particle analyzer of clauses 62-73, wherein the memory includes instructions to perform calibration of the particle analyzer in response to one or more bins classified as bins containing outliers.

75. A particle analyzer according to clause 74 wherein the calibrating of the particle analyzer comprises:

evaluating the generated data signals in the plurality of binning windows; and

it is determined that there is no data bin containing outliers.

76. The particle analyzer of any of clauses 35-75, wherein the particle analyzer is a flow cytometer.

77. A non-transitory computer-readable storage medium for dynamically evaluating a data signal from a particle analyzer in real time for the presence of an outlier event, comprising instructions stored thereon, the instructions comprising:

an algorithm for detecting light from a sample in the flow stream illuminated by the light source using the light detection system;

an algorithm for generating an event data signal in response to an event detected with the light detection system;

an algorithm for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and

an algorithm for evaluating a plurality of binning windows for outlier events in a generated data signal.

78. The non-transitory computer readable storage medium of clause 77, wherein the non-transitory computer readable storage medium comprises an algorithm for binning a predetermined number of event data signals into each window.

79. The non-transitory computer readable storage medium of clause 78, wherein the non-transitory computer readable storage medium comprises an algorithm for binning 500 event data signals in each window.

80. The non-transitory computer readable storage medium of any one of clauses 77-79, wherein the non-transitory computer readable storage medium comprises an algorithm for sequentially binning the event data signals based on event detection time of the light detection system.

81. The non-transitory computer readable storage medium of any one of clauses 77-80, wherein the non-transitory computer readable storage medium comprises an algorithm for binning one or more event data signals into two or more binning windows.

82. The non-transitory computer-readable storage medium of clause 81, wherein the two or more binning windows overlap by a predetermined time frame.

83. The non-transitory computer-readable storage medium of clause 82, wherein the binning window overlaps a time frame of 0.001 μs to 100 μs.

84. The non-transitory computer readable storage medium of any one of clauses 77-83, wherein the event data signal comprises a fluorescence intensity of the event.

85. The non-transitory computer-readable storage medium of clause 84, wherein the fluorescence intensity corresponds to an expression level of each event.

86. The non-transitory computer readable storage medium of any one of clauses 77-85, wherein the non-transitory computer readable storage medium comprises an algorithm for applying the transformation to each event data signal.

87. The non-transitory computer-readable storage medium of clause 86, wherein the transforming comprises a centered log-ratio transformation, an additive log-ratio transformation, an equal-length log-ratio transformation, or a combination thereof.

88. The non-transitory computer-readable storage medium of clause 87, wherein the transforming comprises a centered log-ratio transforming.

89. The non-transitory computer readable storage medium of any one of clauses 77-88, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a median parameter for each of the overlapping bins.

90. The non-transitory computer readable storage medium of clause 89, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a median or average fluorescence intensity of the events for each data bin.

91. The non-transitory computer readable storage medium of clause 89, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a median or average expression level of the events for each data bin.

92. The non-transitory computer readable storage medium of any one of clauses 89-91, comprising an algorithm for evaluating the plurality of binning windows for outlier events by comparing a median parameter of each bin to an outlier threshold.

93. The non-transitory computer-readable storage medium of clause 92, wherein the outlier threshold comprises a Median Absolute Deviation (MAD) outlier threshold.

94. The non-transitory computer-readable storage medium of clause 92, wherein the outlier threshold comprises a median tolerance percentage outlier threshold.

95. The non-transitory computer-readable storage medium of any one of clauses 92-94, wherein the non-transitory computer-readable storage medium comprises an algorithm for dynamically calculating the outlier threshold in real-time.

96. The non-transitory computer readable storage medium of any one of clauses 92-95, wherein the non-transitory computer readable storage medium comprises an algorithm for dynamically adjusting the outlier threshold in real-time in response to binning of the generated event data signals.

97. The non-transitory computer readable storage medium of any one of clauses 92-96, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying a data bin as containing an outlier when the median parameter is determined to be greater than the predetermined outlier threshold.

98. The non-transitory computer-readable storage medium of clause 97, wherein the non-transitory computer-readable storage medium comprises an algorithm for identifying as outliers event data signals for each of the bins containing outliers.

99. The non-transitory computer-readable storage medium of any one of clauses 77-98, wherein the non-transitory computer-readable storage medium comprises an algorithm for generating an alert that an outlier is detected when evaluating a plurality of bins for the outlier event.

100. The non-transitory computer readable storage medium of clause 99, wherein the non-transitory computer readable storage medium comprises an algorithm for generating an alert that there is a change in a flow rate in a flow stream of the particle analyzer.

101. The non-transitory computer readable storage medium of any one of clauses 99-100, wherein the non-transitory computer readable storage medium comprises an algorithm for generating an alert that a jam is present in the particle analyzer.

102. The non-transitory computer readable storage medium of any one of clauses 77-101, wherein the non-transitory computer readable storage medium comprises an algorithm for measuring a flow rate of the flow stream using a flow rate sensor.

103. The non-transitory computer-readable storage medium according to clause 102, wherein the non-transitory computer-readable storage medium comprises:

an algorithm for comparing the measured flow rate of the flow stream with a desired flow rate of the flow stream; and

An algorithm for re-evaluating bins classified as bins containing outliers based on the compared flow rates of the flow streams.

104. The non-transitory computer readable storage medium of clause 103, wherein the non-transitory computer readable storage medium comprises an algorithm for reclassifying the data bin based on the compared flow rates of the flow streams.

105. The non-transitory computer readable storage medium of any one of clauses 103-104, wherein the non-transitory computer readable storage medium comprises an algorithm for not generating an alarm when the measured flow rate is within a predetermined threshold of the desired flow rate.

106. The non-transitory computer readable storage medium of clause 105, wherein the predetermined threshold is within less than 5% of the expected flow rate.

107. The non-transitory computer-readable storage medium according to clause 106, wherein the non-transitory computer-readable storage medium comprises:

an algorithm for comparing the median fluorescence intensity of the plurality of bins to one or more parameters of the light detection system; and

an algorithm for re-evaluating bins classified as bins containing outliers based on parameters of the light detection system.

108. The non-transitory computer readable storage medium of clause 107, wherein the parameter of the light detection system comprises a photodetector voltage.

109. The non-transitory computer-readable storage medium of clauses 98-108, wherein the non-transitory computer-readable storage medium comprises an algorithm for performing calibration of the particle analyzer in response to one or more bins classified as bins containing outliers.

110. The non-transitory computer-readable storage medium according to clause 109, wherein the non-transitory computer-readable storage medium comprises:

an algorithm for detecting light from standard components in the flow stream illuminated by the light source using a light detection system;

an algorithm for binning the generated event data signals into a plurality of binning windows comprising overlapping data bins;

an algorithm for evaluating the generated data signals in the plurality of binning windows; and

an algorithm for determining that there is no data bin containing outliers.

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

Thus, the foregoing merely illustrates the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Thus, the scope of the invention is not limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the invention are embodied by the appended claims. In the claims, 35u.s.c. ≡112 (f) or 35u.s.c. ≡112 (6) are expressly defined as applicable for limitation in the claims only when the exact phrase "apparatus for …" or the exact phrase "step for …" is stated at the beginning of such limitation in the claims; if such exact phrases are not used to limit the claims, 35u.s.c. ≡112 (f) or 35u.s.c. ≡112 (6) are not applicable.

Claims

1. A method for dynamically evaluating in real-time a data signal from a particle analyzer for the presence of outlier events, the method comprising:

the plurality of binning windows are evaluated for outlier events in the generated data signal.

2. The method of claim 1, wherein the method comprises binning a predetermined number of event data signals in each binning window.

3. The method of any of claims 1-2, wherein the event data signals are sequentially binned based on event detection times of the light detection system.

4. A method according to any of claims 1-3, wherein one or more event data signals are binned in two or more binning windows.

5. The method of claim 4, wherein the two or more binning windows overlap by a predetermined time frame.

6. The method of any one of claims 1-5, wherein the event data signal comprises a fluorescence intensity of the event.

7. The method of any of claims 1-6, wherein a transformation is applied to each event data signal.

8. The method of any of claims 1-7, wherein the method further comprises calculating a median parameter for each of the overlapping bins.

9. The method of any of claims 1-8, wherein the method further comprises generating an alert that an outlier is detected when evaluating the plurality of bins for an outlier event.

10. The method of any one of claims 1-9, wherein the method further comprises measuring a flow rate of the flow stream with a flow rate sensor.

11. The method of claim 10, wherein the method comprises:

12. The method of claim 11, wherein the method further comprises reclassifying the bins based on the compared flow rates of the flow streams.

13. A particle analyzer, comprising:

a light detection system, comprising:

A detector component comprising a photodetector configured to detect light from a sample illuminated in a flow stream and to generate an event data signal in response to an event detected with the light detection system; and

a modulator component configured for binning the generated event data signals in a plurality of binning windows comprising overlapping data bins; and

a processor comprising a memory operably coupled to the processor, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to evaluate the plurality of binning windows for outlier events in the generated data signal.

14. A non-transitory computer-readable storage medium for dynamically evaluating in real-time a data signal from a particle analyzer for the presence of an outlier event, comprising instructions stored thereon, the instructions comprising:

an algorithm for generating event data signals in response to events detected with the light detection system;

An algorithm for evaluating the plurality of binning windows for outlier events in the generated data signal.