Method for quality scoring of flow cytometry and system therefor

Abstract

A measurement uncertainty associated with light detected from the sample is identified.SOLUTION: In one embodiment, a method includes introducing a sample into a flow cytometer, flowing the introduced sample in a flow stream, illuminating the sample in the flow stream using a light source, detecting light from particles in the sample flowing in the flow stream, and determining a measurement uncertainty associated with the detected light. In some embodiments, measurement uncertainties corresponding to individual particles in a sample are identified. In some embodiments, measurement uncertainty may be determined for individual parameters of light detected for particles in a sample. In some embodiments, the method further includes generating a per-particle quality score based on the per-particle measurement uncertainty. Further provided are systems, integrated circuit devices (e.g., field programmable gate arrays), and non-transitory computer-readable storage media for implementing the subject methods.SELECTED DRAWING: None

Description

[Background technology]

[0001] Characterization of analytes in biological fluids has become an important part of biological research, medical diagnostics, and the assessment of a patient's overall health and well-being. Detecting analytes in biological fluids, such as human blood or blood-derived products, can yield results that may be relevant to determining treatment protocols for patients with various medical conditions.

[0002] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir containing a sheath fluid. The flow cytometer carries particles (including cells) in the fluid sample as a cell stream to a flow cell, while the sheath fluid is directed toward the flow cell. To characterize components in the flow stream, light is irradiated onto the flow stream. Changes in the biological materials in the flow stream, such as morphology or the presence of fluorescent labels, can alter the observed light, enabling characterization and separation. To characterize components in a flow stream, light must be directed onto the flow stream and collected. The light source for a flow cytometer can be a variety of light sources, including one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream, and optical responses from the illuminated particles are collected and quantified.

[0003] Separation of biological particles has been achieved by adding a sorting or collection function to a flow cytometer. Particles present in the separated stream and detected as having one or more desired properties are individually separated from the sample stream by mechanical or electrical removal. A common flow sorting technique utilizes droplet sorting, in which a fluid stream containing linearly separated particles is split into droplets. Droplets containing particles of interest are electrically charged and deflected into a collection tube by passing through an electric field. Typically, linearly separated particles in a stream are characterized as they pass an observation point directly below the nozzle tip. Once a particle is identified as meeting one or more desired criteria, it is possible to predict the time at which the particle will reach the droplet breakoff point and separate from the stream into droplets. Ideally, the fluid stream is briefly charged just before droplets containing selected particles separate from the fluid stream, and then grounded immediately after the droplets separate. The droplets to be sorted maintain their charge as they separate from the fluid stream, while all other droplets remain uncharged. Summary of the Invention [Problem to be solved by the invention]

[0004] Flow cytometry is used to measure characteristics of single particles or cells based on optical signals. To effectively identify and / or separate populations of particles or cells based on the measured signals, a practitioner must be able to determine whether a difference in the measured signal between two particles is due to a true intrinsic difference between those particles, or whether the difference in the measured signal is due to random measurement error. Ultimately, the presence of measurement error affects the confidence in classification decisions that may be made for a given particle or cell.

[0005] Currently, flow cytometry data do not contain any information about measurement uncertainty at the event-, parameter-, or population-specific level, requiring practitioners to manually measure, calculate, or estimate sources of measurement uncertainty.

[0006] Accordingly, the inventors have recognized a need for automated reporting of measurement and classification uncertainties of flow cytometry data, e.g., through quantitative quality scores. In particular, there is a need to improve the ability to distinguish true biological variability from measurement error. Embodiments of the present disclosure address this need. Embodiments of the present disclosure address limitations of existing techniques, e.g., by associating event-specific measurement uncertainties with event data. In embodiments, methods are provided for estimating event-specific measurement uncertainties. Improvements to such existing techniques may, among other things, improve sorting purity and yield. [Means for solving the problem]

[0007] Aspects of the present disclosure include methods for determining a measurement uncertainty associated with light detected from a sample. In some embodiments, the method includes introducing the sample into a flow cytometer, flowing the introduced sample through a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing through the flow stream, and determining a measurement uncertainty associated with the detected light. In some embodiments, the method determines a measurement uncertainty corresponding to each particle in the sample. In some embodiments, the method determines a measurement uncertainty for each parameter of the light detected for the particles in the sample. In some embodiments, the method further generates a quality score for each particle based on the measurement uncertainty for each particle. Systems, integrated circuit devices (e.g., field programmable gate arrays), and non-transitory computer-readable storage media for implementing the subject methods are also provided. [Brief explanation of the drawings]

[0008] The present disclosure can be best understood from the following detailed description when read in conjunction with the accompanying drawing figures, including:

[0009] [Figure 1A] FIG. 1 shows an exemplary result 100 of a flow cytometry measurement of an event with associated biological and measurement uncertainties. [Figure 1B]FIG. 1 illustrates exemplary hierarchical gates applied to sample events. [Figure 1C] 1 is a flowchart illustrating a technique for dealing with measurement uncertainty according to the prior art. [Figure 1D] 1 is a flowchart illustrating a technique for determining measurement uncertainty per parameter, per event, per gate, per sample, or per record, according to an embodiment. [Figure 1E] FIG. 1 illustrates an exemplary process for estimating measurement uncertainty according to the “GLS unmixing approach.” [Figure 1F] FIG. 1 illustrates the storage of measurement data according to the prior art. [Figure 1G] FIG. 1 illustrates a technique for recording measurement data along with measurement uncertainty data, according to an embodiment. [Figure 1H] 1 is a flowchart for determining measurement uncertainty and classification uncertainty of flow cytometry data according to an embodiment. [Figure 2] FIG. 1 illustrates a flow cytometry system according to an embodiment. [Figure 3-1] FIG. 1 illustrates an image-enabled particle sorter according to an embodiment. [Figure 3-2] FIG. 1 illustrates an image-enabled particle sorter according to an embodiment. [Figure 4] FIG. 1 is a functional block diagram illustrating a particle analysis system according to an embodiment. [Figure 5] FIG. 1 is a functional block diagram illustrating an example of a control system according to an embodiment. [Figure 6A] FIG. 1 is a schematic diagram illustrating a particle sorting system according to an embodiment. [Figure 6B] FIG. 1 is a schematic diagram illustrating a particle sorting system according to an embodiment. [Figure 7] FIG. 1 illustrates aspects of a computerized control system according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Aspects of the present disclosure include methods for determining a measurement uncertainty associated with light detected from a sample. In some embodiments, the method includes introducing the sample into a flow cytometer, flowing the introduced sample through a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing through the flow stream, and determining a measurement uncertainty associated with the detected light. In some embodiments, the method determines a measurement uncertainty corresponding to each particle in the sample. In some embodiments, the method determines a measurement uncertainty for each parameter of the light detected for the particles in the sample. In some embodiments, the method further generates a quality score for each particle based on the measurement uncertainty for each particle. Systems, integrated circuit devices (e.g., field programmable gate arrays), and non-transitory computer-readable storage media for implementing the subject methods are also provided.

[0011] Before the present disclosure is described in more detail, it is to be understood that this disclosure is not limited to the particular embodiments described, as such may, of course, vary. The scope of the present disclosure will be limited only by the appended claims, and it is to be further understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0012] When a range of values ​​is given, it is understood that each intervening value between the upper and lower limits of that range, to the tenth of the unit of the lower limit unless the context clearly indicates otherwise, and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also encompassed within the disclosure.

[0013] In this specification, a range is presented with the term "about" before the numerical values. The term "about" is used herein to literally support the exact number that it precedes, as well as a number that is close to or approximately the number that it precedes. When determining whether a number is close to or approximately a specifically stated number, the unstated number that is close or approximately the number may be a number that, in the context in which the specifically stated number is presented, provides a substantial equivalent to the specifically stated number.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of this disclosure, representative illustrative methods and materials are described.

[0015] All publications and patents cited herein are incorporated by reference to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure 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.

[0016] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for use of exclusive terminology such as "solely," "only," and the like, or for use of a "negative" limitation in connection with the recitation of claim elements.

[0017] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein comprises separate components and features which may be readily separated from or combined with any of the features of the other multiple embodiments without departing from the scope or spirit of the disclosure. Any recited method may be carried out in the order of events recited or in any other order which is logically possible.

[0018] Although the systems and methods have been or will be described for grammatical fluidity with functional descriptions, it should be clearly understood that the claims, unless expressly recited under 35 U.S.C. 112, should not be construed as necessarily limited in any way by limitations of "means" or "step" construction, but should be accorded the full scope of the meaning and equivalents of the definition given by the claims under the judicial theory of equivalents, and that if a claim is expressly recited under 35 U.S.C. 112, it should be accorded the full legal equivalents under 35 U.S.C. 112.

[0019] As summarized above, the present disclosure provides methods for determining measurement uncertainties associated with light detected from a sample. In further describing embodiments of the present disclosure, the methods for determining measurement uncertainties associated with light detected from a sample include determining measurement uncertainties corresponding to individual particles in the sample, determining measurement uncertainties for individual parameters of the light detected for the particles in the sample, and generating a quality score for each particle based on the measurement uncertainties for each particle. Next, systems, integrated circuit devices, and non-transitory computer-readable storage media, in each case programmed to perform the subject methods, are described.

[0020] Background technology As described herein, flow cytometry is used to measure characteristics of single particles (also called microparticles) or cells based on optical signals (i.e., detected light). A common use of flow cytometry is to identify different cell subpopulations (or cell populations) or particle types based on differences in measured optical signals, such as the amount of fluorescence emission from a fluorochrome-labeled antibody in one or more spectral detection bands, or the amount of elastically scattered light (e.g., forward and side scattered light) measured at different angles. To effectively identify and separate populations of cells or particles based on these measured signals, a practitioner must be able to determine whether a difference in the measured signal between two particles is due to a true, intrinsic difference between the particles (e.g., differences in expression levels of biological markers or differences in cell size or morphology) or whether the difference in the measured signal is due to random measurement error (also referred to as measurement noise, measurement uncertainty, or measurement variability). Figure 1A shows an exemplary result 100 of a flow cytometry measurement of an event, illustrating how the overall variation 103 of such a measurement is composed of a combination of inherent biological variability alone 101 and uncertainty from measurement noise 102. Figure 1B shows an exemplary hierarchical gate applied to the sample events. Ultimately, the presence of measurement error affects the confidence in the classification decisions (i.e., gating) that may be made for a given microparticle as shown in Figure 1B.

[0021] Measurement errors are inevitable in flow cytometry and can arise from multiple sources, including electronic noise (e.g., Johnson-Nyquist noise), digital conversion errors, optical shot noise, or random variations in optical excitation and collection efficiency resulting from random variations in illumination intensity, fluid flow, optical alignment, photodetector gain, and other hardware components during experimental measurements. The magnitude of the error resulting from each of these sources depends on the specific instrument, sample, and measurement conditions (e.g., photodetector gain, flow rate, etc.) used for a given sample. The total uncertainty in a flow cytometry sample (i.e., the total uncertainty associated with measuring detected light corresponding to events within a flow cytometry sample) arises from a combination of the above-mentioned measurement uncertainties and the inherent variability of the sample itself, such as the randomly varying expression levels of biomolecules in a single biological cell population of interest or the randomly varying fluorescence intensity of synthetic fluorescent beads caused by uncontrolled variations in the manufacturing process.

[0022] An important source of measurement uncertainty in multicolor flow cytometry experiments is so-called "spillover spreading error." See Nguyen R, Perfetto S, Mahnke YD, Chattopadhyay P, Roederer M. Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A. 2013 Mar;83(3):306-15. doi:10.1002 / cyto.a.22251. Epub 2013 Feb 6. PMID: 23389989; PMCID: PMC3678531. This manifests as increased variance in the spectrally unmixed or corrected measurement parameter, usually the parameter of interest in a biological assay, as a result of optical shot noise caused by fluorescence emission from one or more fluorophores coexpressed with another fluorophore of interest.

[0023] The ability to distinguish true biological variability from measurement error is a key limiting factor in sensitivity and resolution in flow cytometry assays. Embodiments of the present invention address this limitation by providing novel and inventive techniques for distinguishing and considering measurement error or measurement uncertainty in the context of flow cytometry experiments.

[0024] In some existing techniques, flow cytometry measurement error can be quantified in one of two ways: (i) first, by measuring various sources of measurement noise a priori using controlled standard samples and protocols; or (ii) second, by applying post-hoc statistical analysis to the measurement data.

[0025] The first uncertainty measurement approach, according to existing techniques (item (i) above), uses a specific type of controlled standard sample to measure the measurement error of a given instrument. For example, calibration beads with low inherent variability and well-characterized intensities may be used to measure the robust standard deviation (rSD) and robust coefficient of variation (rCV) of a given optical measurement parameter. More complex protocols may enable the measurement of specific noise sources under controlled conditions. For example, techniques that may be employed include measuring signal-independent background noise ("B") under various system conditions, such as the presence or absence of constant optical noise due to the excitation light source, using an electronic event trigger in the absence of particles. In another example, in the absence of particle-dependent error sources such as inherent intensity fluctuations and fluid / optical fluctuations, a particle-free signal source such as a light-emitting diode (LED) may be used to characterize the measurement error due solely to constant background and photon counting error. Finally, in another example, a representative biological sample, such as a single-stain control or a fluorescence minus one (FMO) control, can be used to estimate the overall degree of uncertainty present in a fully stained sample of interest within a multicolor flow cytometry panel. The advantage or merit of this first uncertainty measurement approach (i.e., technique (i) above) is that it allows for the measurement of specific sources of measurement variance isolated from biological and sample-dependent sources of variation. However, the disadvantage or downside of this first measurement approach (i.e., technique (i) above) is that the measurement uncertainty is not measured directly on the sample of interest under the measurement conditions of the sample of interest, and furthermore, the measurement uncertainty cannot be associated with a specific single cell or particle within a given data set. Furthermore, in order to apply the statistical findings obtained from these individual uncertainty measurements to a given sample of interest, the measurements must be manually correlated and multiple empirical corrections must be made to account for differences in measurement conditions between the calibration sample and the sample of interest.

[0026] The second uncertainty measurement approach ((ii) above) according to existing techniques involves acquiring data from a sample of interest and then applying statistical techniques to such data. A typical workflow involves identifying a subpopulation of interest by gating based on a set of measurement parameters, and then applying an appropriate statistical metric, such as a robust standard deviation (rSD) or robust coefficient of variation (rCV), to events in that subpopulation. Additional metrics, such as a staining index or a separation index, may be used to define the statistical degree of separation between two subpopulations with respect to a given measurement parameter. Statistical confidence may also be obtained by performing technical replicates, such as measuring the same sample multiple times. The advantage of this second uncertainty measurement approach ((ii) above) is that it describes the uncertainty for a specific sample and population of interest under the precise measurement conditions used. However, a significant drawback of this approach ((ii) above) is that it cannot be used to distinguish between technical measurement uncertainty arising from the measurement process and biological variability arising from the inherent biological properties of the sample itself. These drawbacks or limitations hinder practitioners from distinguishing between measurement noise and biologically meaningful variation.

[0027] FIG. 1C illustrates an existing technique for addressing measurement uncertainty according to the prior art. Flowchart 130 begins with step 131, which involves recording raw data from a flow cytometer. Upon completion of step 131, flowchart 130 proceeds to step 132. In step 132, spectral unmixing is performed on the data acquired by the flow cytometer in step 131. Any convenient correction or spectral unmixing technique may be used for correction or spectral unmixing. Further details regarding spectral unmixing are provided in International Application No. PCT / US2021 / 026616, published as WO 2021 / 221884, and International Application No. PCT / US2021 / 046741, published as WO 2022 / 076088, the disclosures of each of which are incorporated herein by reference. Upon completion of step 132, flowchart 130 proceeds to step 133. Step 133 involves data analysis of the flow cytometry data acquired in step 131. Such analysis may involve, for example, applying one or more gating or clustering algorithms. Upon completion of step 133, flowchart 130 proceeds to step 134, which reports the results of processing the flow cytometry data in steps 132 and 133. Unlike embodiments of the present invention, flowchart 130 is unable to identify and report the resulting metrics of measurement uncertainty on a per-parameter, per-particle, per-population, and / or per-sample basis.

[0028] Embodiments of the present invention In embodiments of the present invention, an alternative approach is employed to directly estimate, measure, or predict the measurement uncertainty of some or all measurement parameters for each particle in a flow cytometry sample and associate the resulting measurement uncertainty indicators with the flow cytometry sample data. In embodiments, one or more indicators of the resulting measurement uncertainty may be determined, reported, or recorded on a per-parameter, per-particle, per-population, and / or per-sample basis, or any combination thereof. FIG. 1D illustrates a technique for determining measurement uncertainty on a per-parameter, per-event, per-gate, per-sample, or per-record basis, according to embodiments. Flowchart 140 begins with step 141, where raw flow cytometry data is recorded. Subsequent processing occurs in subsequent steps of flowchart 140, with the measurement data being processed in steps 141, 142, 146, and 149, and the measurement uncertainty associated with such measurement data being processed in steps 143, 144, 145, 147, and 148. Such raw cytometry data is further processed in steps 143 and 142. In step 142, correction or spectral unmixing may be applied to the raw cytometry data. Such correction or spectral unmixing results in the calculation or estimation of derived parameters. In step 143, measurement uncertainty is estimated on a per-parameter or per-event basis based on the raw parameter data obtained in step 141. In step 144, measurement uncertainty is estimated on a per-parameter or per-event basis based on the derived parameters. In step 145, gating uncertainty is estimated on a per-event or per-parameter basis. In step 147, a summary of measurement uncertainty, in this case including a per-event Q-score, is calculated as described herein. In step 147, such results are obtained on a per-record or per-sample basis. In step 146, analysis, such as application of a gating algorithm or clustering algorithm, is performed on the flow cytometry data, in this case the events using the derived parameters (i.e., the unmixed data). In step 148, the entire file of Q-score statistics is reported, eg, recorded or displayed.In step 149, the overall results of the analyzed flow cytometry data are reported, e.g., recorded or displayed, including a quality score, e.g., a Q-score. In embodiments, associated measurement uncertainty data, e.g., as calculated in steps 144, 145, and 147 of flowchart 140, may be used to, among other things, provide more accurate data analysis, increase statistical confidence in the analysis results, enable higher fidelity unsupervised clustering, and report overall measurement quality and assay performance.

[0029] Embodiments of the present disclosure may be applied to single-cell analysis methods other than flow cytometry, for example, in the context of sequencing-based single-cell proteomics such as CITE-Seq (BD AbSeq).

[0030] Conceptual similarities in next-generation sequencing Embodiments of the present invention are conceptually similar to the ubiquitous use of Phred quality scores ("Q-scores") in DNA sequencing. Q-scores in DNA sequencing are defined as the negative logarithm of the probability that a given base call is incorrect and are reported in logarithmic decibels (e.g., a Q-score of 30 indicates a 1 in 1,000 probability of a base call error, while a Q-score of 40 indicates a 1 in 10,000 probability). For every base call in every sequencing read in a DNA sequencing experiment, an associated Q-score records the probability that the base call is correct. Sequence data (base calls) and Q-score data are then correlated and most commonly stored in the widely used FASTQ data format, which combines sequence data and associated Q-scores into a single data file.

[0031] In flow cytometry, as in the present invention, a single flow cytometry measurement event (e.g., measurement of a single particle) is analogous to a single sequence read, a single measurement parameter associated with a measurement event is analogous to a single base call, a per-parameter, per-event flow cytometry uncertainty score is analogous to a Q-score, and a modified FCS format or a new "FCSQ" format is analogous to the FASTQ format. Several key properties of the Q-score exist in embodiments of the present invention, including, for example, the use of a compressed Q-score representation to conserve storage space and the use of the sum of the Q-score index to indicate the overall read quality of a given sequence (flow cytometry event) or sequence dataset (flow cytometry sample data).

[0032] Some important differences between sequencing Q scores and embodiments of the present invention include, for example, that sequencing data has a discrete value domain (A, C, G, T) compared to the continuous value domain (floating-point numbers or integers) of flow cytometry data, and that whereas sequencing errors are described as binary classification errors (whether a base call is accurate or incorrect), in embodiments, flow cytometry measurement errors for a given parameter may be defined continuously (measurement parameters have a degree of numerical uncertainty, described, for example, by standard deviation or standard error of the mean). In embodiments, flow cytometry gating classification is also a binary classification problem (an event either belongs to a gated population or not, depending on whether the corresponding event data falls within a range associated with the gated population), and in that sense exhibits results similar to sequencing Q scores. In embodiments, the term "binary classification" refers to dividing event data (e.g., flow cytometry events) into two distinct populations (i.e., determining whether an event is classified as belonging to a population or not belonging to such population). In some cases, binary classification of flow cytometry data is performed using classification trees. In some cases, binary classification of flow cytometry data is performed using hierarchical gates. As mentioned above, in some embodiments, binary classification in flow cytometry is a form of dichotomy in which a continuous function is transformed into a binary variable, and continuous values ​​can be made binary by defining a cutoff value, and such cutoff value is used to classify corresponding events as positive or negative based on whether the event data value is above or below the cutoff value.

[0033] Uncertainty estimation method In embodiments, there are many ways in which the measurement uncertainty metric used in connection with the embodiments may be estimated for a flow cytometry data set. In embodiments, any convenient technique capable of estimating measurement uncertainty may be used. Examples for use in embodiments are described herein. However, the present invention is not limited to such metrics, and in embodiments, any metric that quantifies measurement uncertainty may be used. In one embodiment, measurement uncertainty may be calculated from instrument calibration data, a physical noise model, and a semi-empirical noise model that combines measurements corresponding to a given event. Subpanels A, B, and C of FIG. 1E show an exemplary process for estimating measurement uncertainty according to the "GLS Unmixing Approach." Further details regarding such exemplary techniques may be found in U.S. Application No. 18 / 986,295, which claims priority to U.S. Provisional Application No. 63 / 622,370, the disclosure of which is incorporated herein in its entirety.

[0034] In another embodiment, measurement uncertainty is estimated a posteriori on an event-by-event basis using statistical algorithms configured to characterize the data distribution within the sample. Yet another embodiment may employ a combination of a priori noise modeling and posterior distribution fitting using techniques such as Bayesian estimation.

[0035] Uncertainty score index In embodiments, the specific numerical index used to report uncertainty may include any index of measurement uncertainty, and such indexes may vary. Furthermore, in embodiments, the type of index may vary by parameter. That is, for a given event where multiple parameter measurements are associated, in embodiments, a different measurement uncertainty index may be applied to each parameter. In embodiments, uncertainty scores may also be reported for binary classification results resulting from analyses such as gating, population membership, etc. (In embodiments, gating in flow cytometry refers to a process used to separate specific event populations from a larger sample based on characteristics such as size, granularity, and fluorescence. In embodiments, population membership or population gating refers to the process of classifying events into populations or subpopulations based on specific characteristics such as size, morphology, and protein expression.) These may be reported as uncertainty scores (i.e., higher values ​​indicate higher uncertainty) or quality scores (i.e., higher values ​​indicate higher confidence). In embodiments, for example, uncertainty scores for events with other binary classification results may be derived from the uncertainty scores for the other parameters. Examples of metrics of interest include direct quantification (e.g., standard deviation), confidence intervals, the likelihood that a measurement of an event is within a few percent of the true value, or residuals that describe the fit of unmixed data. In embodiments, a confidence interval is a range of values ​​that is likely to include the value of an unknown population parameter. Such an interval represents a plausible range for the parameter given the characteristics of the sample. Confidence intervals are derived from sample statistics and calculated with a specified confidence level. That is, estimates of statistical values ​​based on samples of a population contain uncertainty, and a confidence interval specifies the range of values ​​that such estimated statistical values ​​are expected to fall within a certain percentage of when an experiment is performed or when the population is resampled. The confidence level is the percentage of times an estimate is expected to be reproduced between the upper and lower limits of the confidence interval. For example, a confidence interval with a 95% confidence level would expect an estimate to be recovered 95 times out of 100 times between the upper and lower limits specified in the confidence interval.

[0036] Gating Quality Score Embodiments of the present invention may include the calculation of a gating quality score. In embodiments, a quality score may be calculated that estimates the likelihood that an event belongs to a given gate. This likelihood may be determined for each gate in the gating hierarchy, or may be determined hierarchically by considering the likelihood of membership of a gate to all of its parent gates. Figure 1B illustrates hierarchical gates and the associated likelihood of membership for an exemplary hierarchical gate.

[0037] Purpose The uncertainty scores calculated by embodiments of the present invention are used in a variety of different contexts, including, but not limited to, assessing statistical significance or confidence intervals for event classification, use in variance-stabilizing transformations for data visualization and analysis, use in data pre-processing to minimize intra-cluster variance in supervised or unsupervised clustering techniques, use in data standardization to normalize measurement uncertainty across data sets measured on different instruments or under different conditions, and use in probabilistic analysis (fuzzy logic) for cell classification and sorting.

[0038] Formatting and Data Storage In embodiments, the uncertainty scores, measurements, or results may be stored in association with the measurement data (e.g., raw flow cytometry data or corrected or unmixed cytometry data) in any convenient manner, and such manners may vary. Figure 1F illustrates the storage of measurement data according to existing techniques, where the measurement data is ultimately stored in an FCS file format 170, but measurement uncertainty on a per-parameter, per-event, per-gate, per-sample, or other scale is not estimated, calculated, or recorded.

[0039] FIG. 1G illustrates how measurement data may be recorded along with measurement uncertainty data in embodiments of the present invention. The multi-file model 171 according to the embodiment has a single shared data record, with uncertainty scores added as additional event-specific FCS parameters (e.g., for the measurement parameter "FITC-A," an additional measurement parameter "FITC-A-Uncertainty" is also stored). This multi-file approach 171 is a simple solution and shows certain similarities to the sequencing FASTQ implementation described herein. A drawback or downside of the multi-file model approach 171 is that it increases file size and data storage requirements. Because earlier FCS formats store all values ​​as 32-bit floating-point numbers, only the FCS3.2+ format offers the storage benefits of compressed uncertainty scores (e.g., 1 byte instead of 4 bytes).

[0040] In other embodiments, a split data record approach 172 is used, where a separate data file (e.g., referred to as .FCSQ) containing the uncertainty scores is generated along with the general flow data file (.FCS) (i.e., in accordance with existing techniques). In embodiments, the split data record approach may provide uncertainty scores for all or only some of the parameters in the .FCS file.

[0041] In an embodiment, the sum of the uncertainty scores at the population level or record level may be stored as an additional keyword in the .FCS file header.

[0042] In an embodiment, to aid in reproducibility and traceability, all metadata required to generate an uncertainty score for a given recording (including instrument calibration parameters and noise model information) may be stored in the .FCS file header.

[0043] method Aspects of the present disclosure include methods for determining a measurement uncertainty associated with light detected from a sample. In some embodiments, the method includes introducing the sample into a flow cytometer, flowing the introduced sample in a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing through the flow stream, and determining a measurement uncertainty associated with the detected light. In some embodiments, the method determines a measurement uncertainty corresponding to an individual particle in the sample. In some embodiments, the method determines a measurement uncertainty for an individual parameter of the light detected for the particles in the sample. In some embodiments, the method further generates a quality score for each particle based on the measurement uncertainty for each particle.

[0044] In embodiments, upon detecting light from the particles, multiple parameters of the detected light are measured for the particles in the sample. Some embodiments further generate event data based on the detected light, the event data including parameter measurements for the particles in the sample. Other embodiments further include spectrally resolving the detected light from the particles. In some cases, spectrally resolving the detected light from the particles generates multiple derived parameters. Method embodiments of the present disclosure further include spectrally resolving the detected light from each particle to generate multiple derived parameters for each particle. In some cases, the derived parameters include unmixed detected light. In other cases, a measurement uncertainty is determined for the derived parameters.

[0045] In embodiments, the method further comprises applying a first gate to the detected light to identify a first subset of particles in the sample. In embodiments, the measurement uncertainty corresponds to the identification of the first subset of particles. In embodiments, the method further comprises applying a corresponding plurality of gates to the detected light to identify multiple subsets of particles in the sample. In some cases, the measurement uncertainty corresponds to the identification of each of the multiple subsets of particles. In embodiments, the measurement uncertainty corresponds to the gate-level uncertainty.

[0046] Method embodiments of the present disclosure further include generating a quality score (Q-score) for each particle based on the measurement uncertainty for each particle. Other embodiments further include recording each event and its associated measurement uncertainty. Still other embodiments further include recording measurements for each particle and its associated measurement uncertainty. Certain other embodiments further include recording measurements of multiple parameters for each particle and its associated measurement uncertainty for each parameter. Embodiments further include recording a measurement uncertainty, where the measurement uncertainty corresponds to the measurement uncertainty for one or more of each recorded parameter, each recorded event, each gate or other classification, or sample.

[0047] In embodiments, the measurement uncertainties of different parameters measured for particles in a sample comprise different metrics. In some embodiments, the measurement uncertainty corresponds to a binary classification event. In such cases, the binary classification event may correspond to one or more of a gating decision or a population membership classification decision. In embodiments, the measurement uncertainty comprises a metric that quantifies the measurement uncertainty. Methods of interest further comprise generating an uncertainty score based on the measurement uncertainty. In embodiments, the measurement uncertainty is reflected in the uncertainty score, with higher values ​​of the uncertainty score indicating greater measurement uncertainty. Other methods of interest further comprise generating a quality score based on the measurement uncertainty. In embodiments, the measurement uncertainty is reflected in the quality score, with higher values ​​of the quality score indicating greater confidence.

[0048] In embodiments, the measurement uncertainty comprises one or more of a direct quantification optionally including a standard deviation, a confidence interval, a likelihood that a measurement is within a specified percentage of the true value, or a residual related to the goodness of fit of the unmixed data. In some embodiments, the measurement uncertainty is associated with a binary classification, which optionally includes one or more of a gating or population membership classification.

[0049] In some embodiments of the disclosed method, a quality score is calculated based on the measurement uncertainty, where the quality score reflects the likelihood of belonging to a gate. In some embodiments, the likelihood of belonging to a gate is calculated for each gate in a gate hierarchy. In other embodiments, the likelihood of belonging to a gate is calculated by considering each parent gate in the gate hierarchy. In other embodiments, determining the measurement uncertainty associated with the detected light includes calculating the measurement uncertainty based on a semi-empirical noise model, where the semi-empirical noise model includes one or more of instrument calibration data, a physical noise model, or measurements. In yet other embodiments, determining the measurement uncertainty associated with the detected light includes estimating the measurement uncertainty for each event based on a statistical algorithm, where the statistical algorithm measures characteristics of the data distribution within the sample.

[0050] In embodiments, when determining the measurement uncertainty associated with the detected light, a combination of noise modeling and distribution fitting is applied to estimate the measurement uncertainty. In some embodiments, when determining the measurement uncertainty associated with the detected light, Bayesian inference is applied to estimate the measurement uncertainty.

[0051] Featured embodiments further report a measurement uncertainty for each particle of the subset of particles of the sample. Other embodiments further report a measurement uncertainty for multiple parameters for each particle of the subset of particles of the sample. Still other embodiments further report a measurement uncertainty associated with membership in a subpopulation defined by a gate for each particle of the subset of particles.

[0052] In embodiments, the measurement uncertainty includes random measurement error. In some embodiments, the measurement uncertainty includes spillover diffusion error. In other embodiments, the measurement uncertainty reflects variations other than true intrinsic differences between particles of a sample. In still other embodiments, the measurement uncertainty reflects the confidence in a classification decision regarding particles of a sample. In some cases, the measurement uncertainty reflects the overall measurement uncertainty associated with multiple parameter measurements of light detected from a particle. In other cases, the measurement uncertainty reflects the overall measurement uncertainty associated with measurements of light detected from multiple particles. In still other cases, the measurement uncertainty reflects the overall measurement uncertainty associated with the sample.

[0053] In an embodiment, determining the measurement uncertainty associated with the detected light includes one or more of estimating the measurement uncertainty associated with the detected light, measuring the measurement uncertainty associated with the detected light, or predicting the measurement uncertainty associated with the detected light.

[0054] Methods of interest further include classifying particles based on the detected light and calculating a statistical significance of the particle classification based at least in part on the measurement uncertainty. Other methods of interest further include classifying particles based on the detected light and calculating a confidence interval for the particle classification based at least in part on the measurement uncertainty. Still other methods of interest further include applying a variance-stabilizing transform to data including measurements of detected light from particles in the sample based at least in part on the measurement uncertainty and visualizing aspects of the transformed data. Some embodiments further include preprocessing data including measurements of detected light from particles in the sample to minimize intra-cluster variance based at least in part on the measurement uncertainty and applying a clustering algorithm to the preprocessed data, where the clustering algorithm optionally includes one or more of a supervised clustering algorithm and an unsupervised clustering algorithm. Other embodiments further include standardizing data including measurements of detected light from particles in the sample to normalize the measurement uncertainty across different datasets based at least in part on the measurement uncertainty, where the different datasets optionally include one or more of datasets collected with different instruments or under different conditions. Optionally, the method further comprises using the measurement uncertainty in a probabilistic analysis of particle classification or sorting, the probabilistic classification optionally including the application of fuzzy logic techniques.

[0055] In embodiments, the method is a method for distinguishing true variability of particles in a sample from measurement error. In other embodiments, the method is a method for distinguishing true biological variability from measurement error. In yet other embodiments, the method is a method for increasing the sensitivity of a flow cytometry assay. In some cases, the method is a method for increasing the resolution of a flow cytometry assay. In other cases, the method is a method for assessing the quality of particle analysis results by associating event-specific estimates of measurement uncertainty with flow cytometry measurements.

[0056] In embodiments, a quantitative measure of measurement uncertainty is associated with the flow cytometry measurement. In further embodiments, the measurement uncertainty is used in connection with analyzing or filtering the data. In some embodiments, a gate-associated confidence score is further identified for each event and gate, the gate-associated confidence score comprising the likelihood that the true biological expression level for a given event falls within the given gate.

[0057] In some cases, the method is a method for calculating a gate-belonging confidence score. In some embodiments, the gate-belonging confidence score based on measurement uncertainty is further used for particle classification and sorting, which includes probabilistic sorting with a configurable likelihood threshold to maximize purity and / or yield.

[0058] In embodiments, a light detection system is used to detect light. In some cases, the light detection system detects light in multiple light detector channels. In other cases, the light detection system has multiple light detectors.

[0059] 1H shows a flowchart 180 for determining measurement and classification uncertainties for flow cytometry data according to one embodiment. As described in more detail herein, a sample is introduced into a flow cytometer in step 181, the sample flows in a flow stream in step 182, a light source is used to illuminate the sample in the flow stream in step 183, light from particles in the sample flowing in the flow stream is detected in step 184, and a measurement uncertainty associated with the detected light is determined in step 185. Further details regarding each of steps 181, 182, 183, 184, and 185 are described herein.

[0060] In some cases, the sample analyzed in the present method is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, a plant, a fungus, or a subset of animal tissues, cells, or components, such as may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, or semen, as the case may be. Thus, "biological sample" refers to both a naturally occurring organism or a subset of its tissues, and homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, genitourinary tract, tears, saliva, milk, blood cells, tumors, and organs. Biological samples may be any type of organismal tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample, such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases, the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger stick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0061] In some embodiments, the sample source is a "mammal" or "mammalian," which terms are used broadly to describe organisms belonging to the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult); in some embodiments, the human subject is a juvenile, adolescent, or adult. While the present disclosure may be applied to samples from human subjects, it should be understood that the methods may also be performed on samples from other animal subjects (i.e., "non-human subjects"), including, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0062] Cells of interest may be subject to characterization according to various parameters, such as phenotypic characteristics identified by attaching specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analysis droplets determined to contain target cells. A variety of cells may be characterized using the subject methods. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells bearing convenient cell surface markers or antigens that may be internalized or labeled by convenient affinity agents or their conjugates. For example, target cells may comprise cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor alpha / beta, T cell receptor gamma / delta, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, the target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) from whole blood, bone marrow, or umbilical cord blood.

[0063] In practicing the subject methods, a volume of an initial fluid sample is injected into a flow cytometer. The volume of sample injected into the particle sorting module may vary, for example, within the range of 0.001 mL to 1000 mL of sample, e.g., 0.005 mL to 900 mL, e.g., 0.01 mL to 800 mL, e.g., 0.05 mL to 700 mL, e.g., 0.1 mL to 600 mL, e.g., 0.5 mL to 500 mL, e.g., 1 mL to 400 mL, e.g., 2 mL to 300 mL, e.g., 5 mL to 100 mL.

[0064] Methods according to embodiments of the present disclosure enumerate and optionally sort labeled particles (e.g., target cells) in a sample. In practicing the subject methods, a fluid sample containing particles is first introduced into a flow nozzle of the system. Upon exiting the flow nozzle, the particles pass substantially one at a time through a sample interrogation region where each particle is illuminated by a light source, and measurements of light scattering parameters, and optionally fluorescence emission measurements (e.g., two or more light scattering parameters and one or more fluorescence emission measurements), are recorded separately for each particle, as desired. Depending on the characteristics of the flow stream being interrogated, the light may illuminate a flow stream of 0.001 mm or greater, e.g., 0.005 mm or greater, e.g., 0.01 mm or greater, e.g., 0.05 mm or greater, e.g., 0.1 mm or greater, e.g., 0.5 mm or greater, e.g., 1 mm or greater. In certain embodiments, the methods illuminate a planar cross-section of the flow stream within the sample interrogation region, e.g., with a laser (as described above). In another embodiment, the method illuminates a predetermined length of the flow stream within the sample interrogation region to correspond to the illumination profile of a diffuse laser beam or lamp.

[0065] In some embodiments, the method irradiates the flow stream at or near the nozzle orifice of the flow cell. For example, the method may irradiate the flow stream at about 0.001 mm or more from the nozzle orifice, such as 0.005 mm or more, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, for example 1 mm or more from the nozzle orifice. In some embodiments, the method irradiates the flow stream directly adjacent to the nozzle orifice of the flow cell.

[0066] In embodiments of the method, detectors such as photomultiplier tubes (PMTs) are used to record the light passing through each particle (sometimes referred to as forward scattered light), the light reflected perpendicular to the direction of particle flow through the detection region (sometimes referred to as orthogonal or side scattered light), and, if the particles are labeled with one or more fluorescent markers, the fluorescence emitted by the particles as they pass through the detection region and are illuminated by an energy source. Forward scattered light (FSC), side scattered light (SSC), and fluorescent emission each have separate parameters per particle (or "event"). Thus, for example, two, three, or four parameters may be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle may be analyzed in real time or stored in a data storage and analysis means, such as a computer, as desired.

[0067] In some embodiments, particles are detected and uniquely identified by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels, as desired. The fluorescence emitted in the detection channels used to identify particles and their associated binding complexes may be measured after excitation by a single light source, or may be measured separately after excitation by different light sources. When separate excitation light sources are used to excite particle labels, the particle labels may be selected such that all particle labels are excitable by each of the excitation light sources used.

[0068] In some embodiments, the method further includes data acquisition, analysis, and recording, e.g., using a computer, where multiple data channels record data from each detector regarding light scattering and fluorescence emitted by each particle as it passes through the sample interrogation region of the particle sorting module. In these embodiments, when analyzed, particles are classified and counted so that each particle exists as a set of digitized parameter values. The subject system may be configured to trigger on selected parameters to distinguish particles of interest from background and noise. "Trigger" refers to a preset threshold for detecting a parameter and may be used as a means for detecting when a particle has passed through a light source. Detection of an event exceeding the selected parameter threshold triggers the collection of light scattering and fluorescence data for the particle. Data regarding particles or other components in the analysis medium that cause a response below the threshold is not acquired. The trigger parameter may be detection of forward scattered light resulting from a particle passing through a light beam. In this manner, the flow cytometer detects and collects light scattering and fluorescence data for particles.

[0069] Specific subpopulations of interest are then further analyzed by "gating" based on the data collected for the entire population. To select an appropriate gate, the data is plotted to separate the subpopulations as best as possible. This procedure may be performed by plotting forward scatter (FSC) versus side (i.e., orthogonal) scatter (SSC) on a two-dimensional dot plot. A subpopulation of particles (i.e., cells within the gate) is then selected, and particles not within the gate are excluded. If desired, a gate may be selected by drawing a line around the desired subpopulation using a cursor on the computer screen. Only those particles within the gate are then further analyzed by plotting other parameters of these particles, such as fluorescence. If desired, the above analysis may be configured to calculate the number of particles of interest in the sample.

[0070] Featured methods may also use the particles in research, clinical trials, or treatment. In some embodiments, the subject methods obtain individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods obtain cells from a fluid or tissue sample used as a research or diagnostic specimen for a disease such as cancer. Similarly, the subject methods obtain cells from a fluid or tissue sample used for treatment. Cell therapy protocols are protocols in which viable cellular material, including, for example, cells and tissue, may be prepared and introduced into a subject as a therapeutic treatment. Conditions that may be treated by administration of flow cytometry-sorted samples include, but are not limited to, blood disorders, immune system disorders, organ damage, and the like.

[0071] A typical cell therapy protocol may include the steps of sample collection, cell isolation, genetic modification, culture, in vitro expansion, cell harvesting, sample volume reduction, sample washing, biopreservation, storage, and cell introduction into a subject. A protocol may begin by collecting viable cells and tissue from a tissue source in a subject to generate a cell and / or tissue sample. The sample may be collected by any suitable procedure, including, for example, administering a cell mobilizing agent to the subject, drawing blood from the subject, or removing bone marrow from the subject. After sample collection, cell enrichment may be performed by multiple methods, including, for example, centrifugation-based methods, filter-based methods, elutriation, magnetic separation, fluorescence-activated cell sorting (FACS), and the like. In some cases, the enriched cells may be genetically modified by any convenient method, such as nuclease-mediated gene editing. Genetically modified cells may be cultured, activated, and expanded in vitro. In some cases, the cells are preserved, e.g., cryopreserved, and stored for future use. At the time of use, the cells are thawed and then administered to a patient, e.g., the cells may be infused into a patient.

[0072] System for determining measurement uncertainty associated with light detected from a sample - Patent Application 20070122997 As summarized above, aspects of the present disclosure include systems for determining measurement uncertainty associated with light detected from a sample. In system embodiments, the system is configured to perform the subject methods. Aspects of the present disclosure further include a flow cytometer. Flow cytometers of interest include a light source configured to illuminate particles in a flow stream at an interrogation point within a flow cell. Flow cytometers of interest also include a light detection system having a plurality of light detectors.

[0073] Flow cells of interest include cuvettes configured to transport particles in a flow stream. As used herein, the term "flow cell" is used in its conventional sense to refer to an element having a flow path for a liquid flow stream to transport particles in a sheath fluid. Cuvettes of interest include a passageway (i.e., a flow path) extending therethrough. The flow stream may include a liquid sample injected from a sample tube. In some cases, flow cells include optically transparent flow paths. The cuvette may be constructed of, for example, quartz, glass, or clear plastic. In some embodiments, the cuvette is formed from silica, such as fused silica. In some cases, flow cells are configured to be illuminated with light from a light source at one or more interrogation points. The term "interrogation point" used herein refers to an area within the flow cell where particles are illuminated by light from the light source, e.g., for analysis. The size of the interrogation point may vary as desired. For example, if 0 μm represents the axis of light emitted by the light source, the interrogation point may be within a range of -50 μm to 50 μm, e.g., -25 μm to 40 μm, or e.g., -15 μm to 30 μm. Depending on certain considerations (eg, number and placement of lasers), there may be multiple illumination points within the flow cell.

[0074] In some embodiments, the flow cell has or is configured for use with a sample injection port configured to deliver a sample to the flow cell, hi embodiments, the sample injection system is configured to deliver a suitable flow of sample to the internal chamber (i.e., flow path) of the flow cell. Depending on the desired characteristics of the flow stream, the flow rate of the sample delivered by the sample injection port to the chamber of the flow cell may be 1 μL / min or more, such as 2 μL / min or more, for example 3 μL / min or more, such as 5 μL / min or more, for example 10 μL / min or more, such as 15 μL / min or more, for example 25 μL / min or more, for example 50 μL / min or more, for example 100 μL / min or more, and in some cases the flow rate of the sample delivered by the sample injection port to the chamber of the flow cell is 1 μL / sec or more, such as 2 μL / sec or more, for example 3 μL / sec or more, for example 5 μL / sec or more, for example 10 μL / sec or more, for example 15 μL / sec or more, for example 25 μL / sec or more, for example 50 μL / sec or more, for example 100 μL / sec or more.

[0075] The sample injection port may be an orifice in the wall of the internal chamber or a tube located at the proximal end of the internal chamber. When the sample injection port is an orifice in the wall of the internal chamber, the orifice may have any suitable shape. Cross-sectional shapes of interest include, but are not limited to, rectilinear shapes, such as square, rectangular, trapezoidal, triangular, and hexagonal; curved shapes, such as circular and oval; and irregular shapes, such as a parabolic bottom joined to a flat top. In some embodiments, the sample injection port has a circular orifice. The size of the orifice of the sample injection port may vary depending on the shape, and in some cases may have an opening in the range of 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, e.g., 1.25 mm to 1.75 mm, e.g., a 1.5 mm opening.

[0076] In some cases, the sample injection port is a tube located at the proximal end of the internal chamber of the flow cell. For example, the sample injection port may be a tube aligned with the orifice of the flow cell. When the sample injection port is a tube aligned with the orifice of the flow cell, the cross-sectional shape of the sample injection tube may have any suitable shape. Cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, and hexagonal; curved cross-sectional shapes, such as circular and oval; and irregular shapes, such as a parabolic bottom joined to a flat top. The orifice of the tube may vary depending on the shape and may in some cases have an opening in the range of 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, e.g., 1.25 mm to 1.75 mm, e.g., 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may have a beveled tip with a bevel angle within a range of 1° to 10°, for example, 2° to 9°, for example, 3° to 8°, for example, 4° to 7°, for example, a bevel angle of 5°.

[0077] In some embodiments, the flow cell further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid, e.g., along with the sample, into the internal chamber of the flow cell to generate a laminated flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the flow rate of the sheath fluid delivered to the chamber of the flow cell may be 25 μL / sec or more, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, e.g., 2500 μL / sec or more.

[0078] In some embodiments, the sheath fluid injection port is an orifice in the wall of the internal chamber. The sheath fluid injection port orifice may have any suitable shape, and cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes, such as circular and oval, and irregular shapes, such as a parabolic bottom joined to a flat top. The size of the sheath fluid injection port orifice may vary depending on the shape, and in some cases may have an opening in the range of 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, e.g., 1.25 mm to 1.75 mm, e.g., a 1.5 mm opening.

[0079] The flow cytometer of the present disclosure includes a light source configured to illuminate particles in the flow stream at an interrogation point within the flow cell. The number of light sources in a flow cytometer may vary. In some embodiments, the flow cytometer includes a single light source. Alternatively, the flow cytometer may include multiple light sources in some cases. In some such cases, the number of light sources is in the range of 2 to 10, e.g., 2 to 5, e.g., 2 to 4. Any convenient light source may be used as the light source described herein. In some embodiments, the light source is a laser. In embodiments, the laser may be any convenient laser, such as a continuous wave laser. For example, the laser may be a diode laser, e.g., an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser may be a helium-neon (HeNe) laser. In some cases, the laser is a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO 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 cases, the subject flow cytometer is equipped with a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, the laser of interest includes 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 still other cases, the subject flow cytometers are equipped with solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0080] In some embodiments, the laser light source may further include one or more optical adjustment components. In some embodiments, the optical adjustment components are disposed between the light source and the flow cell and may include any device capable of changing the spatial width or other characteristics of the illumination from the light source, such as the illumination direction, wavelength, beam width, beam intensity, and focus. The optical adjustment protocol may include any convenient device for adjusting one or more characteristics of the light source, including, but not limited to, lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimation protocols, and combinations thereof. In some embodiments, the flow cytometer of interest includes one or more focusing lenses. In one example, the focusing lens may be a reduction lens. In yet other embodiments, the flow cytometer of interest includes optical fibers.

[0081] The light source may be positioned at any suitable distance from the flow cell, for example the light source and the flow cell are separated by 0.005 mm or more, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, such as 0.5 mm or more, for example 1 mm or more, such as 5 mm or more, for example 10 mm or more, for example 25 mm or more, such as 100 mm or more. Additionally, the light source may be positioned at any suitable angle relative to the flow cell, for example between 10 degrees and 90 degrees, for example between 15 degrees and 85 degrees, for example between 20 degrees and 80 degrees, for example between 25 degrees and 75 degrees, for example between 30 degrees and 60 degrees, for example at an angle of 90 degrees.

[0082] In some embodiments, the light source of interest includes multiple lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, e.g., ten or more lasers, e.g., fifteen or more lasers, configured to provide laser light for separate illumination of the flow stream. Depending on the desired wavelength of light for illuminating the flow stream, each laser may have a different specific wavelength within a range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, e.g., 400 nm to 800 nm. In certain embodiments, the lasers of interest may include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0083] In some embodiments, the light source is an optical beam generator configured to generate two or more beams of frequency-shifted light. In some cases, the optical beam generator comprises a laser, a radio frequency generator configured to apply a radio frequency drive signal to an 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. For example, optical beam generator lasers of interest include those listed above.

[0084] The acousto-optic device may be any convenient acousto-optic protocol configured to frequency-shift laser light using applied acoustic waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device of the subject system is configured to generate an angularly deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optic device using any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0085] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams of the output laser beam, for example configured to apply 3 or more high frequency drive signals, for example 4 or more high frequency drive signals, for example 5 or more high frequency drive signals, for example 6 or more high frequency drive signals, for example 7 or more high frequency drive signals, for example 8 or more high frequency drive signals, for example 9 or more high frequency drive signals, for example 10 or more high frequency drive signals, for example 15 or more high frequency drive signals, for example 25 or more high frequency drive signals, for example 50 or more high frequency drive signals, for example configured to apply 100 or more high frequency drive signals.

[0086] In some cases, to generate an intensity profile of the angularly deflected laser beam of the output laser beam, the controller is configured to apply a high frequency drive signal having a varying amplitude within a range, for example, from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from about 3 V to about 30 V, or for example, from about 5 V to about 25 V. The applied high frequency drive signal in some embodiments has a frequency within the range of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, for example, about 5 MHz to about 50 MHz.

[0087] In some embodiments, the controller includes a processor to which a memory is operatively coupled, the memory storing instructions that, when executed by the processor, cause the processor to generate an output laser beam including an angularly deflected laser beam having a desired intensity profile. For example, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more angularly deflected laser beams of the same intensity, e.g., the memory may include instructions for generating one hundred or more angularly deflected laser beams of the same intensity. In other embodiments, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more angularly deflected laser beams of different intensities, e.g., the memory may include instructions for generating one hundred or more angularly deflected laser beams of different intensities.

[0088] In some embodiments, the controller has a processor to which the memory is operatively coupled, such that the memory stores instructions that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the center to the edges of the output laser beam along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99%, for example, 0.5% to about 95%, for example, 1% to about 90%, for example, about 2% to about 85%, for example, about 3% to about 80%, for example, about 4% to about 75%, for example, about 5% to about 70%, for example, about 6% to about 65%, for example, about 7% to about 60%, for example, about 8% to about 55%, or may be in a range of about 10% to about 50% of the intensity of the angularly deflected laser beam at the edges of the output laser beam along the horizontal axis. In other embodiments, the controller has a processor to which the memory is operatively coupled, such that the memory stores instructions that, when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99%, for example, 0.5% to about 95%, for example, 1% to about 90%, for example, about 2% to about 85%, for example, about 3% to about 80%, for example, about 4% to about 75%, for example, about 5% to about 70%, for example, about 6% to about 65%, for example, about 7% to about 60%, for example, about 8% to about 55%, or may be in a range of about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller has a processor to which a memory is operatively coupled, such that the memory stores instructions that, when executed by the processor, cause the processor to generate an output laser beam having a Gaussian intensity profile along a horizontal axis.In yet another embodiment, the controller has a processor to which the memory is operatively coupled, such that the memory stores instructions that, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along a horizontal axis.

[0089] In embodiments, the optical beam generator of interest may be configured to generate spatially separated angularly deflected laser beams of the output laser beam. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly deflected laser beams 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, such as 5000 μm or more. In some embodiments, the system is configured to generate angularly deflected laser beams of the output laser beam that overlap, for example, adjacent angularly deflected laser beams along a horizontal axis of the output laser beam. The overlap of adjacent angularly deflected laser beams (e.g., overlap of beam spots) may be 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more.

[0090] In some cases, the light beam generator configured to generate two or more beams of frequency-shifted light may be any of the light beam generators described in U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, 10,684,211, 10,845,295, 10,935,482, U.S. Pat. Nos. 5,623,353, 5,623,353, 5,784,661, 5,983,132, 5,006,852, 5,036,699, 5,078,045, 5,222,316, 5,288,546, 5,324,019, 5,408,758, 5,451,538, 5,620,111, 5,684,211, 5,845,295, 5,935,482 ... and a laser excitation module such as those described in U.S. Pat. No. 10,935,485, U.S. Pat. No. 1,105,728, U.S. Pat. No. 1,128,0718, U.S. Pat. No. 1,132,7016, U.S. Pat. No. 1,366,052, U.S. Pat. No. 1,371,937, U.S. Pat. No. 1,169,2926, U.S. Pat. No. 1,163,0053, U.S. Pat. No. 1,774,343, U.S. Pat. No. 1,1940,369, and U.S. Pat. No. 1,1946,851.

[0091] Additionally, the flow cytometer includes a photodetector configured to collect light emitted from the illuminated particles. The photodetector is configured to detect the particle-modulated light transmitted by the fiber optic collection element and generate a signal based on a characteristic (e.g., intensity) of the light. For example, the one or more particle-modulated light detectors may include one or more side scatter detectors for detecting light of a side-scattered wavelength (i.e., light refracted and reflected by the surface and internal structure of the particle). In some embodiments, the flow cytometer includes one side scatter detector. In other embodiments, the flow cytometer includes multiple side scatter detectors, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more side scatter detectors.

[0092] Any convenient detector for detecting collected light may be used in the side scatter light detectors described herein. Detectors of interest may include, but are not limited to, optical sensors or photodetectors, such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, among other detectors. In some embodiments, collected light is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, e.g., a 0.01 cm 2 ~10cm 2 , e.g. 0.05cm 2 ~9cm 2 , e.g., 0.1 cm 2 ~8cm 2 , e.g., 0.5 cm 2 ~7cm 2 , e.g. 1 cm 2 ~5cm 2 The photomultiplier tube has an active detection surface area of ​​each region within the range of .times. ...

[0093] In embodiments, the subject flow cytometer further comprises a fluorescence detector configured to detect light at one or more fluorescent wavelengths, hi other embodiments, the flow cytometer comprises a plurality of fluorescence detectors, e.g., 2 or more, e.g., 3 or more, e.g., 4 or more, e.g., 5 or more, e.g., 10 or more, e.g., 15 or more, e.g., 20 or more fluorescence detectors.

[0094] Any convenient detector for detecting collected light may be used in the fluorescence detectors described herein. Detectors of interest may include, but are not limited to, optical sensors or photodetectors, such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, among other detectors. In some embodiments, collected light is measured by a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, e.g., a 0.01 cm 2 ~10 cm 2 , e.g., 0.05 cm 2 ~9cm 2 , e.g., 0.1 cm 2 ~8cm 2 , e.g., 0.5 cm 2 ~7cm 2 , e.g. 1 cm 2 ~5cm 2 The photomultiplier tube has an active detection surface area of ​​each region within the range of .times. ...

[0095] When a subject flow cytometer includes multiple fluorescence detectors, each fluorescence detector may be identical, or the collection of fluorescence detectors may be a combination of different types of detectors. For example, when a subject flow cytometer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-based device and the second fluorescence detector (or image sensor) is a CMOS-based device. In other embodiments, both the first and second fluorescence detectors are CCD-based devices. In still other embodiments, both the first and second fluorescence detectors are CMOS-based devices. In still other embodiments, the first fluorescence detector is a CCD-based device and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS-based device and the second fluorescence detector is a photomultiplier tube. In still other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0096] In embodiments of the present disclosure, a fluorescence detector of interest is configured to measure collected light at one or more wavelengths, e.g., two or more wavelengths, e.g., five or more different wavelengths, e.g., ten or more different wavelengths, e.g., twenty-five or more different wavelengths, e.g., fifty or more different wavelengths, e.g., one hundred or more different wavelengths, e.g., two or more different wavelengths, e.g., three hundred or more different wavelengths, e.g., four hundred or more different wavelengths, e.g., two or more detectors of a module as described herein are configured to measure collected light at the same wavelength or overlapping wavelengths.

[0097] In some embodiments, the fluorescence detector of interest is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the detector of interest is configured to collect a spectrum of light over a range of wavelengths. For example, a flow cytometer may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In yet other embodiments, the detector of interest is configured to measure light emitted from a sample in the flow stream at one or more specific wavelengths. For example, a module may have one or more detectors configured to measure light at one or more of: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the one or more detectors may be configured to pair with a particular fluorophore, such as a fluorophore used with a sample in a fluorescence assay.

[0098] A flow cytometer may have any suitable mechanism or mechanisms for supplying sheath fluid and sample fluid to the sheath fluid input coupler and sample fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., tubing) that is fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line that is fluidly connected to a sheath fluid reservoir. Similarly, a flow cytometer may have any suitable mechanism or mechanisms for managing waste from the flow stream. The fluid output coupler may be fluidly connected to a waste line that is fluidly connected to a waste reservoir. A fluid management system that may be adapted for use in the subject flow cytometer is described in U.S. Patent Application Publication No. 2022 / 0341838, the entire disclosure of which is incorporated herein by reference.

[0099] Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan;49(pt 1):17-28; Linden, et al., Semin Thromb Hemost. 2004 Oct;30(5):502-11; Alison, et al. J Pathol, 2010 Dec;222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255.In some cases, flow cytometry systems of interest include the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, and the BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences These include the FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, and BD Biosciences FACSDiscover™ cell sorter.

[0100] In some embodiments, the subject system may be configured to perform a variety of tasks, including but not limited to, the following: U.S. Pat. Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 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, the disclosures of which are incorporated herein by reference in their entireties. Nos. 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,140,300, 7,544,326, 7,201,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766.

[0101] In some embodiments, the flow cytometer is configured as an imaging flow cytometer. For example, in some cases, the subject system may be configured as described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Pat. Nos. 9,423,353, 9,784,661, and 9,983,132. Nos., U.S. Patent Nos. 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, 10,684,211, 10,845,295, 10,935,482, 10,935,485, 11,105,728, 11,280 and U.S. Patent No. 1,946,851 (the disclosures of which are incorporated herein by reference). In some embodiments, the flow cytometry system is configured to image particles in a flow stream by fluorescence imaging using radio frequency tag emission (FIRE), as described in U.S. Patent Nos. 718, 1,327,016, 1,366,052, 1,371,937, 1,692,926, 1,1630,053, 1,774,343, 1,940,369, and 1,946,851. In some embodiments, the flow cytometer is a particle sorter, and the particle sorter is an image-enabled particle sorter. Image-enabled particle sorters are described in U.S. Pat. Nos. 10,324,019, 10,620,111, 11,105,728, and 11,774,343, as well as U.S. patent application Ser. Nos. 18 / 537,103, 18 / 657,618, 18 / 657,623, and 18 / 657,633, the entire disclosures of which are incorporated herein by reference.

[0102] FIG. 2 illustrates a system 200 for flow cytometry according to an exemplary embodiment of the present disclosure. The system 200 includes a laser 201 configured to illuminate particles 211 in a flow stream 214 at an interrogation point 215 within a flow cell 210. While one laser is shown in the example of FIG. 2, it is understood that multiple lasers may also be used. The laser beam from the laser 201 is directed to a focusing lens 202, which focuses the laser beam onto a portion of the fluid stream within the flow cell 210 where the sample particles 211 reside. The flow cell 210 is part of a fluid system that directs particles in the stream, typically one at a time, into the focused laser beam for interrogation. Alternatively, if the flow cytometer is a stream-in-air cytometer, a nozzle top may be used.

[0103] As shown in FIG. 2 , flow cell 210 is fluidly connected to sheath fluid reservoir 203 containing sheath fluid and sample fluid reservoir 204 containing sample fluid. Sheath fluid from sheath fluid reservoir 203 is supplied to at least one sheath fluid injection port 208 via tubing (i.e., sheath fluid line) 207. Additionally, sample fluid containing particles 211 from sample fluid reservoir 204 is supplied to sample injection port 206 via tubing (i.e., sample fluid line) 205. Sample injection port 206 is fluidly connected to sample injector 213 (e.g., a sample injection needle) configured to introduce particles 211 into the interior of flow cell 210. Particles 211 are hydrodynamically focused via sheath fluid flowing from sheath fluid injection port 208 such that flow stream 214 is formed downstream of tapered section 212 of flow cell 210. Particles emitted at the distal end of flow cell 210 may be discarded and / or collected via any suitable protocol. For example, depending on the type of flow cytometry being performed, particles may be collected at the distal end of flow cell 210, for example, via a waste line. Alternatively, particles may be sorted.

[0104] Light from one or more laser beams interacts with particles 211 in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at a variety of different wavelengths depending on the particle's characteristics, such as its size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent radiation, as well as the diffracted, refracted, reflected, and scattered light, may be sent to one or more detectors. In particular, forward-scattered light (FSC) is sent to a forward-scattered light detector 223. The forward-scattered light detector 223 is positioned slightly off-axis from the direct beam passing through the flow cell 210 and is configured to detect diffracted light, i.e., excitation light traveling primarily in a forward direction through or around the particle. The intensity of the light detected by the forward-scattered light detector 223 depends on the overall size of the particle. The forward-scattered light detector may include, for example, a photodiode. A scattering bar 222 is positioned between the forward-scattered light detector 223 and the optical filter 221a. The optical filter 221a may be configured to remove non-FSC light of at least one wavelength, while the scattering bar 222 may be configured to prevent the incident beam from the laser 201 (i.e., non-scattered light) from being detected by the forward scattered light detector 223.

[0105] Additionally, side-scattered light (SSC) is detected by side-scattered light detector 224. In other words, side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structures of particle 211, which tends to increase as the structural complexity of the particle increases. In the example of FIG. 2, flow cytometer 200 includes dichroic mirror 220a configured to reflect SSC light to side-scattered light detector 224 while passing non-SSC light (e.g., fluorescent light). Optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by side-scattered light detector 224. Fluorescence detectors 225a-225c are also shown, each configured to detect fluorescent light of a different wavelength. For example, dichroic mirror 220b may be configured to reflect fluorescent light (FL) corresponding to a first wavelength (or wavelength range) to fluorescence detector 225a while passing light of other wavelengths. Optical filter 221c may be configured to prevent light of at least one wavelength that does not correspond to the first wavelength (or wavelength range) from being detected by fluorescence detector 225a. Similarly, dichroic mirror 220c is configured to reflect FL light corresponding to the second wavelength (or wavelength range) to fluorescence detector 225b, while passing light of a third wavelength (or wavelength range) for detection by fluorescence detector 225c. Optical filter 221d is configured to prevent light of at least one wavelength that does not correspond to the second wavelength (or wavelength range) from being detected by fluorescence detector 225b. Additionally, optical filter 221e is configured to prevent light of at least one wavelength that does not correspond to the third wavelength (or wavelength range) from being detected by fluorescence detector 225c.

[0106] Those skilled in the art will recognize that flow cytometers according to embodiments of the present disclosure are not limited to the flow cytometer shown in FIG. 2 , but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors of various wavelengths and in a variety of different configurations. For example, while three fluorescence detectors are shown in the embodiment of FIG. 2 for illustrative purposes, it will be understood that any suitable number of fluorescence detectors may be used.

[0107] During operation, the operation of the flow cytometer is controlled by the controller / processor 290, and measurement data from the detectors may be stored in memory 295 and processed by the controller / processor 290. Although not explicitly shown, the controller / processor 290 is coupled to the detectors to receive output signals from the detectors, and may further be coupled to the electrical and electromechanical components of the flow cytometer to control the laser 201, fluid flow parameters, etc. An input / output (I / O) function 297 may also be provided in the system. The memory 295, controller / processor 290, and I / O function 297 may be provided entirely as an integral part of the flow cytometer. In such an embodiment, a display may also form part of the I / O function 297 to present experimental data to a user of the flow cytometer 200. Alternatively, some or all of the memory 295 and the controller / processor 290 and I / O function 297 may be part of one or more external devices, such as a general-purpose computer. In some embodiments, memory 295 and some or all of controller / processor 290 may be in wireless or wired communication with the flow cytometer. Controller / processor 290 in conjunction with memory 295 and I / O functionality 297 may be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0108] The various fluorescent molecules of a panel of fluorescent dyes used in a flow cytometer experiment each emit light in a unique wavelength band. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to generally match the filter windows of the detector. I / O function 297 may be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of multiple markers. I / O function 297 may be further configured to receive biological data assigning one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data, may further be stored in memory 295. Controller / processor 290 may be configured to evaluate one or more assignments of labels to markers.

[0109] In some embodiments, the subject system is a particle sorting system configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having a plurality of sorting determination units, such as that described in U.S. Patent Application Publication No. 2020 / 0256781, filed December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, a system for sorting components of a sample includes a particle sorting module with deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0110] In one embodiment, the system is a fluorescence imaging system using a radio frequency tag emission imaging particle sorter, as shown in FIG. 3. Particle sorter 300 includes an optical illumination unit 300a including a light source 301 (e.g., a 488 nm laser) that generates an output beam of light 301a, which is split into beams 302a and 302b by beam splitter 302. Light beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector (AOD)) 303 to generate output beam 303a having one or more angularly deflected beams of light. Optionally, output beam 303a from acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. Light beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector (AOD)) 304 to generate output beam 304a having one or more angularly deflected beams of light. In some cases, output beam 304a from acousto-optic device 304 includes a local oscillator beam and multiple high-frequency comb beams. Output beams 303a and 304a from acousto-optic device 303 and acousto-optic device 304, respectively, are combined in beam combiner 305 to generate output beam 305a, which is transmitted through optics 306 (e.g., an objective lens) to illuminate particles in flow cell 307. In some embodiments, acousto-optic device 303 (AOD) splits a single laser beam into an array of beamlets, each with a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam, which is then superimposed with the array of beamlets in beam combiner 305. In some embodiments, the light source and the light irradiation system having the acousto-optical device may further include those described in Schraivogel, et al. (“High-speed fluorescence image-enabled cell sorting” Science (2022), 375 (6578): 315-320) and U.S. Patent Application Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0111] Output beam 305a illuminates sample particles 308 propagating through flow cell 307 (e.g., with sheath fluid 309) at illumination region 310. As shown in illumination region 310, multiple beams (e.g., angularly polarized, high-frequency shifted beams of light shown as dots across illumination region 310) overlap with a reference local oscillator beam (shown as a cross-hatched line across illumination region 310). The overlapping beams exhibit beat behavior due to their different optical frequencies, with each beamlet emitting at a different frequency f 1-n transmits sinusoidal modulation.

[0112] Light from the illuminated sample is transmitted to a light detection system 300b having multiple light detectors. The light detection system 300b includes a forward scattered light detector 311 for generating a forward scattered light image 311a and a side scattered light detector 312 for generating a side scattered light image 312a. The light detection system 300b further includes a bright-field light detector 313 for generating a light loss image 313a. In some embodiments, the forward scattered light detector 311 and the side scattered light detector 312 are photodiodes (e.g., avalanche photodiodes (APDs)). In some cases, the bright-field light detector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is further detected by fluorescence detectors 314-317. In some cases, the light detectors 314-317 are photomultiplier tubes. Light from the illuminated sample is directed via beam splitter 320 to side-scattered light detection channel 312 and fluorescence detection channels 314-317. Light detection system 300b includes bandpass optics 321-324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to photodetectors 314-317, respectively. In some cases, optic 321 is a 534 nm / 40 nm bandpass. In some cases, optic 322 is a 586 nm / 42 nm bandpass. In some cases, optic 323 is a 700 nm / 54 nm bandpass. In some cases, optic 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number represents the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, from 500 nm to 520 nm.

[0113] Data signals generated in response to light detected in forward-scattered light detection channel 311, side-scattered light detection channel 312, bright-field light detection channel 313, and fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Based on the data signals generated by processors 350 and 351, images 311a-317a can be generated in each light detection channel. Image-corresponding sorting is performed in response to a sorting signal generated by sorting trigger 352. Sorting unit 300c includes deflection plates 331 for deflecting particles into a sample container 332 or a waste stream 333. In some cases, sorting unit 300c is configured to sort particles using a sealed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In one embodiment, the sorting section 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0114] In some embodiments, the system is a particle analyzer, and particles can be analyzed and characterized using a particle analysis system 401 (FIG. 4), with or without physical sorting of particles into a collection vessel. FIG. 4 is a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 includes a fluid system 402. The fluid system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube, through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0115] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. A detection station 408 generally refers to a monitoring region 407 of the common sample path. Detection, in some embodiments, may involve detecting light or one or more other properties of the particle 403 as it passes through the monitoring region 407. In FIG. 4, one detection station 408 is shown with one monitoring region 407. In some embodiments of the particle analysis system 401, multiple detection stations may be provided. Additionally, some detection stations may monitor more than one region.

[0116] Each signal is assigned a signal value, generating a data point for each particle. As described above, this data may be referred to as event data. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect a series of such data points over a first time interval.

[0117] The particle analysis system 401 may further include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further 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 further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0118] 5 is a functional block diagram of an example particle analysis control system, such as an analysis controller (i.e., processor) 500 for analyzing and displaying biological events. Analysis controller 500 can be configured to perform various processes for controlling the graphical display of biological events.

[0119] A particle analyzer or particle sorting system 502 may be configured to acquire the biological event data. For example, a flow cytometer may generate flow cytometry event data. The particle analyzer 502 may be configured to provide the biological event data to the analysis controller 500. A data communication channel may be included between the particle analyzer or particle sorting system 502 and the analysis controller 500. The biological event data may be provided to the analysis controller 500 via the data communication channel.

[0120] The analysis controller 500 may be configured to receive biological event data from a particle analyzer or particle sorting system 502. The biological event data received from the particle analyzer or particle sorting system 502 may include flow cytometry event data. The analysis controller 500 may be configured to provide a graphical display including a first plot of the biological event data on a display device 506. The analysis controller 500 may further be configured to render a region of interest as a gate around a population of the biological event data displayed by the display device 506, e.g., overlaid on the first plot. In some embodiments, the gate may be a logical combination of one or more illustrated regions of interest plotted on a histogram or bivariate plot of a parameter. In some embodiments, the display may be used to display particle parameters or saturation detector data.

[0121] Analysis controller 500 may further be configured to display the in-gate biological event data on display device 506 differently from other events in the outside-gate biological event data. For example, analysis controller 500 may be configured to render the color of the biological event data included within the gate differently from the color of the outside-gate biological event data. Display device 506 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0122] The analysis controller 500 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device may be implemented as a mouse 510. The mouse 510 may initiate a gate selection signal to the analysis controller 500 identifying a gate to be displayed or manipulated via the display device 506 (e.g., by clicking on or within the desired gate when the cursor is over the desired gate). In some embodiments, the first device may be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a pen, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such embodiments, each input function may 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, and the right mouse button and the left mouse button may each generate a trigger event.

[0123] A trigger event can cause the analysis controller 500 to change how the data is displayed, which portions of the data are actually displayed on the display device 506, and / or provide input to further processing, such as selecting a population of interest for particle sorting.

[0124] In some embodiments, the analysis controller 500 may be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 may further be configured to automatically modify the visualization of the plot to facilitate gating. This modification may be made based on a particular distribution of the biological event data received by the analysis controller 500.

[0125] 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 be further configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 may be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0126] A display device 506 may be configured to receive display data from the analysis controller 500. The display data may include plots of the biological event data and gates outlining sections of the plots. The display device 506 may be further configured to modify the information displayed in response to input received from the analysis controller 500 in conjunction with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0127] In some embodiments, the analysis controller 500 can generate a user interface for receiving example events for filtering. For example, the user interface can include controls for receiving example events or example images. The example events or images, or example gates, can be provided prior to collection of event data for a sample, or can be provided based on an initial set of events for a portion of the sample.

[0128] FIG. 6A is a schematic diagram illustrating a particle sorting system 600 (e.g., particle analyzer or particle sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorting system 600 is a cell sorting system. As shown in FIG. 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, include, or be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in a single file and traverse a monitoring region 611 (e.g., where the laser and the stream intersect) that is illuminated by an illumination source 612 (e.g., a laser). Vibration of droplet-forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0129] During operation, a detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitoring region 611. The detection station 614 feeds a timing circuit 628, which in turn feeds a flash charge circuit 630. At a droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 608 so that the droplet of interest carries a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the charged droplets into a receptacle, such as a collection tube or a multi-well or microwell sample plate, where a well or microwell can be specifically associated with the droplet of interest. As shown in FIG. 6A, the droplets can be collected in a drain receptacle 638.

[0130] A detection system 616 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitoring region 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, the entire contents of which are incorporated herein by reference. The detection system 616 enables the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 616 may provide an amplitude signal 620 and / or a phase signal 618, which are then provided (via an amplifier 622) to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624 then control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be provided within the control system.

[0131] In some embodiments, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled to a memory configured to store detected events and sorting decisions based on the detected events. The sorting decisions can be included in the event data for the particles. In some embodiments, detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by either detection system 616 or detection station 614 and provided to a non-collection element.

[0132] FIG. 6B is a schematic diagram illustrating a particle sorting system according to one embodiment presented herein. The particle sorting system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream of charging wires within the barbs, generating a stream of droplets 610 containing particles 609 for analysis. The particles can be illuminated using one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed by sorting electronics or other detection systems (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel the charged droplets and direct them toward a desired collection vessel (e.g., one of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a container 674 or along a second path 668 toward a container 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector plates may allow the particle to continue along path 664. Such uncharged droplets may be directed into a waste container, such as via an aspirator 670.

[0133] Sorting electronics can be included to initiate measurement collection, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to sort the particles. Exemplary implementations of the embodiment shown in Figure 6B include the BD FACSAria™ system of flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0134] Computer Control System Aspects of the present disclosure further include a computerized control system, the computerized control system comprising one or more computers for full or partial automation. In some embodiments, the system comprises a computer having a non-transitory computer-readable storage medium having a computer program stored thereon, the computer program comprising instructions for performing the methods of the present disclosure when loaded into the computer. For example, the computer may be configured to determine measurement uncertainties corresponding to individual particles in the sample, or to determine measurement uncertainties for individual parameters of light detected for particles in the sample, or to generate a quality score for each particle based on the measurement uncertainties for each particle.

[0135] 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 that can access a memory in which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, an input / output controller, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system that interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system typically cooperates with the processor to coordinate and execute functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0136] 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 a read-write compact disk, flash memory device, or other memory storage device. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, tape drive, or diskette drive. These types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk. Any of these program storage media, or other program storage media now in use or that may be developed in the future, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or on program storage devices used in conjunction with the memory storage devices.

[0137] In some embodiments, a computer program product is described that includes a computer-usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor, a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementation of hardware state machines to perform the functions described herein will be apparent to one skilled in the relevant art.

[0138] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, tape, RAM, or any other suitable device, fixed or portable). The processor may include a general-purpose digital microprocessor appropriately programmed from a computer-readable medium storing the necessary program code. The program may be provided to the processor remotely via a communication channel or pre-recorded on a computer program product, such as a memory, or on other portable or fixed computer-readable storage media using one of these devices connected to the memory. For example, a magnetic or optical disk may store the program and be read by a disk writer / reader. The system of the present disclosure may further include a program, e.g., in the form of a computer program product, an algorithm for use in implementing the method as described above. The program according to the present disclosure may be recorded on a computer-readable medium, e.g., 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 magnetic disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and hybrids of these categories, such as magnetic / optical storage media.

[0139] The processor may also access a communication channel to communicate with a user at a remote location, meaning that the user does not have direct contact with the system but instead relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), telephone network, satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0140] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications such as radio frequency identification (RFID), ZigBee communications protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0141] 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, a USB-C port, an RS-232 port, or any other suitable electrical connection port, to enable data communication between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment) configured for similar complementary data communication.

[0142] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, allowing the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that a user may use in conjunction with the device.

[0143] In one embodiment, the communication interface is configured to provide connectivity for data transfer using Internet Protocol (IP) via a cellular network, short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0144] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communications interface using a common standard, such as 802.11, Bluetooth® RF protocol, or IrDA infrared protocol. The server device may be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer; or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0145] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored in the subject system, e.g., any data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0146] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input / output interface between the system and the user and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved using a network or other type of remote communication in alternative embodiments. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be performed according to various known techniques. In some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can obtain additional SQL, HTML, XML, or other documents or data from remote sources. The platform or platforms present in the subject system are typically a class of computers commonly referred to as servers, but may be any type of computer platform now known or later developed. However, the platforms may also be mainframe computers, workstations, or other computer types. The platforms may be networked or not, and may be connected via any type of cabling, now known or later, or other communication systems, including wireless systems. The platforms may be co-located or physically separated.Various operating systems may be used on any of the computer platforms, depending in some cases on the type and / or configuration of the computer platform selected. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, Windows 10, iOS, macOS, Linux, Ubuntu, Fedora, OS / 400, i5 / OS, IBM i, Android™, SGI IRIX, Oracle Solaris, etc.

[0147] FIG. 7 illustrates a general configuration of an exemplary computing device 700 according to one embodiment. The general configuration of computing device 700 illustrated in FIG. 7 includes an arrangement of computer hardware and software components. However, not all of these typical conventional elements need be shown to provide a useful disclosure. As illustrated, 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 input devices 760, all of which may communicate with each other via a communications bus. Network interface 720 may provide connectivity to one or more networks or computing systems. Thus, processing unit 710 may receive information and instructions from other computing systems or services via a network. Processing unit 710 may further communicate with memory 770 and may further provide output information for an optional display 750 via input / output device interface 740. For example, analysis software (e.g., data analysis software or program, e.g., FlowJo®) stored as executable instructions in non-transitory memory of the analysis system can display flow cytometry event data to a user. The input / output device interface 740 may further accept input from any input device 760, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0148] Memory 770 may include computer program instructions (grouped in some embodiments as modules or components) that processing unit 710 executes to implement one or more embodiments. Memory 770 generally includes RAM, ROM, and / or other persistent, secondary, 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. Data may be stored in data storage device 790. Memory 770 may further include computer program instructions and other information for implementing aspects of the present disclosure, such as a module 773 for determining a measurement uncertainty associated with the detected light or a module 774 for recording the measurement uncertainty associated with the detected light.

[0149] Integrated Circuit Devices Aspects of the present disclosure further include an integrated circuit device that is programmed to determine a measurement uncertainty associated with light detected from a sample, as described in the methods detailed above. In some embodiments, the integrated circuit device of interest comprises a field programmable gate array (FPGA). In other embodiments, the integrated circuit device comprises an application specific integrated circuit (ASIC). In yet other embodiments, the integrated circuit device comprises a complex programmable logic device (CPLD). In embodiments, the integrated circuit device is configured to perform the subject methods, as described herein.

[0150] kit Aspects of the present disclosure further include kits, the kits including one or more of the integrated circuits described herein, the systems described herein, or the non-transitory computer-readable recording media described herein. In some embodiments, the kits may further include a program for the subject systems, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, a compact disc, a DVD, a Blu-ray disc, etc.), or instructions for downloading the program from an Internet web protocol or cloud server.

[0151] In addition to the above components, the subject kits may (in some embodiments) further include instructions for use. These instructions may be present in the subject kits in a variety of forms, and one or more of these instructions may be present in the kit. One form in which these instructions may be provided is as information printed on a suitable medium or substrate, such as one or more pieces of paper on which the information is printed, kit packaging, a package insert, etc. Another form in which these instructions may be present is as a computer-readable medium on which the information is recorded, such as a diskette, a compact disc (CD), a portable flash drive, etc. Another form in which these instructions may be present is a website address that may be used via the Internet to access the information at a remote location.

[0152] usefulness Embodiments of the present disclosure find use in a variety of applications where it is desirable to analyze and separate particle components in a sample in a fluid medium, such as a biological sample. In some embodiments, the systems and methods described herein are used for flow cytometric characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods are used for spectroscopy of emitted light. Additionally, the subject systems and methods find use in enhancing signals obtained from light collected from a sample (e.g., in a flow stream). In some cases, the present disclosure finds use in enhancing measurements of light collected from a sample illuminated in a flow stream within a flow cytometer. Embodiments of the present disclosure find use where it is desirable to provide a flow cytometer with increased cell sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0153] Embodiments of the present disclosure find use in applications where cells prepared from a biological sample may be desirable for use in research, clinical trials, or therapy. In some embodiments, the subject methods and devices may facilitate obtaining and / or analyzing individual cells prepared from a targeted fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used for therapy.

[0154] Regardless of the scope of the appended claims, the present disclosure is further defined by the following notes.

[0155] Appendix 1. Introduce the sample into the flow cytometer. The introduced sample is passed through a flow stream. illuminating the sample in the flow stream with a light source; detecting light from particles in the sample flowing through the flow stream; A method for determining a measurement uncertainty associated with detected light.

[0156] Appendix 2. The method of appendix 1 for determining the measurement uncertainty corresponding to each particle in a sample.

[0157] Clause 3. The method of clause 1 or clause 2, wherein detecting light from the particles involves measuring a plurality of parameters of the detected light for the particles in the sample.

[0158] Appendix 4. The method of any one of appendices 1 to 3, wherein measurement uncertainties are determined for individual parameters of light detected for particles in a sample.

[0159] Appendix 5. The method of any one of Appendixes 1-4, generating event data based on the detected light, the event data including parameter measurements of particles in the sample.

[0160] Appendix 6. The method of any one of appendices 1-5, wherein the light detected from the particles is spectrally resolved.

[0161] Clause 7. The method of any one of clauses 1-6, wherein, upon spectrally decomposing the light detected from the particle, a plurality of derived parameters are generated.

[0162] Appendix 8. The method of any one of Appendixes 1-7, wherein the light detected from each particle is spectrally resolved to generate a plurality of derived parameters for each particle.

[0163] Appendix 9. The method of any one of appendices 1 to 8, wherein the derived parameters include unmixed detected light.

[0164] Clause 10. The method of any one of clauses 1 to 9, specifying measurement uncertainties for derived parameters.

[0165] Clause 11. The method of any one of clauses 1-10, wherein a first subset of particles in the sample is identified by applying a first gate to the detected light.

[0166] Clause 12. The method of clause 11, wherein the measurement uncertainty corresponds to the identification of a first subset of particles.

[0167] Appendix 13. The method of any one of appendices 1-12, wherein a plurality of subsets of particles in the sample are identified by applying a corresponding plurality of gates to the detected light.

[0168] Clause 14. The method of clause 13, wherein the measurement uncertainty corresponds to the identification of each of a plurality of subsets of particles.

[0169] Appendix 15. The method according to any one of appendices 1 to 14, wherein the measurement uncertainty corresponds to the gate level uncertainty.

[0170] Appendix 16. The method of any one of appendices 1 to 15, generating a quality score (Q score) for each particle based on the measurement uncertainty for each particle.

[0171] Clause 17. The method of any one of clauses 1 to 16, recording each event and the associated measurement uncertainty for each event.

[0172] Clause 18. The method of any one of clauses 1 to 17, wherein the measurement value for each particle and the associated measurement uncertainty for each particle are recorded.

[0173] Clause 19. The method of any one of clauses 1 to 18, recording measurements of multiple parameters for each particle and associated measurement uncertainties for each parameter for each particle.

[0174] Clause 20. The method of any one of clauses 1 to 19, recording a measurement uncertainty, the measurement uncertainty corresponding to the measurement uncertainty for one or more of each recorded parameter, each recorded event, each gate or other classification, or sample.

[0175] Clause 21. The method of any one of clauses 1 to 20, wherein the measurement uncertainties of different parameters measured for particles in a sample include different metrics.

[0176] Attachment 22. The method of any one of attachments 1 to 21, wherein the measurement uncertainty corresponds to a binary classification event.

[0177] Appendix 23. The method of Appendix 22, wherein the binary classification event corresponds to one or more of a gating decision or a population membership classification decision.

[0178] Clause 24. The method of any one of clauses 1 to 23, wherein the measurement uncertainty includes an index that quantifies the measurement uncertainty.

[0179] Clause 25. The method of any one of clauses 1 to 24, generating an uncertainty score based on measurement uncertainty.

[0180] Appendix 26. The method of any one of appendices 1 to 25, wherein measurement uncertainty is reflected in an uncertainty score, with a higher uncertainty score indicating a larger measurement uncertainty.

[0181] Addendum 27. The method of any one of Addendums 1 to 26, generating a quality score based on measurement uncertainty.

[0182] Appendix 28. The method of any one of appendices 1 to 27, wherein measurement uncertainty is reflected in a quality score, with a higher quality score indicating a higher degree of confidence.

[0183] Clause 29. The method of any one of clauses 1 to 28, wherein the measurement uncertainty comprises one or more of a direct quantification optionally including standard deviation, a confidence interval, the likelihood that the measurement is within a specified percentage of the true value, or a residual related to the goodness of fit of the unmixed data.

[0184] Appendix 30. The method of any one of appendices 1 to 29, wherein the measurement uncertainty is associated with a binary classification, the binary classification optionally including one or more of a gating classification or a population affiliation classification.

[0185] Appendix 31. The method of any one of appendices 1 to 30, wherein a quality score is calculated based on measurement uncertainty, the quality score reflecting the likelihood of belonging to a gate.

[0186] Appendix 32. The method of Appendix 31, in which the likelihood of belonging to a gate is calculated for each gate in the gate hierarchy.

[0187] Appendix 33. The method of Appendix 31, in which the likelihood of belonging to a gate is calculated taking into account each hierarchical parent gate of the gate.

[0188] Clause 34. The method of any one of clauses 1 to 33, wherein determining the measurement uncertainty associated with the detected light comprises calculating the measurement uncertainty based on a semi-empirical noise model, the semi-empirical noise model including one or more of instrument calibration data, a physical noise model, or measurements.

[0189] Attachment 35. The method of any one of attachments 1 to 34, wherein determining the measurement uncertainty associated with the detected light comprises estimating the measurement uncertainty on an event-by-event basis based on a statistical algorithm, the statistical algorithm measuring characteristics of the data distribution within the sample.

[0190] Addendum 36. The method of any one of Addendums 1 to 35, wherein determining the measurement uncertainty associated with the detected light applies a combination of noise modeling and distribution fitting to estimate the measurement uncertainty.

[0191] Addendum 37. The method of any one of Addendums 1 to 36, wherein determining the measurement uncertainty associated with the detected light involves applying Bayesian inference to estimate the measurement uncertainty.

[0192] Item 38. The method of any one of Items 1 to 37, reporting measurement uncertainty for each particle of a subset of particles of a sample.

[0193] Clause 39. The method of any one of clauses 1 to 38, reporting measurement uncertainties for a plurality of parameters for each particle of a subset of particles of a sample.

[0194] Clause 40. The method of any one of clauses 1 to 39, reporting a measurement uncertainty associated with membership in a subpopulation defined by the gate for each particle of the subset of particles.

[0195] Clause 41. The method of any one of clauses 1 to 40, wherein the measurement uncertainty includes random measurement error.

[0196] Attachment 42. The method of any one of attachments 1 to 41, wherein the measurement uncertainty includes spillover diffusion error.

[0197] Attachment 43. The method of any one of attachments 1 to 42, wherein the measurement uncertainty reflects variations other than true intrinsic differences between particles of the sample.

[0198] Attachment 44. The method of any one of attachments 1 to 43, wherein the measurement uncertainty reflects the confidence in the classification decision for the particles of the sample.

[0199] Clause 45. The method of any one of clauses 1 to 44, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with measurements of multiple parameters of the light detected from the particle.

[0200] Clause 46. The method of any one of clauses 1 to 45, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with the measurement of light detected from multiple particles.

[0201] Addendum 47. The method of any one of Addendums 1 to 46, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with the sample.

[0202] Appendix 48. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light 48. The method according to any one of appendices 1 to 47, comprising performing one or more of the following:

[0203] Clause 49. Classifying particles based on detected light; 49. The method of any one of claims 1-48, wherein the statistical significance of the particle classification is calculated based at least in part on the measurement uncertainty.

[0204] Clause 50. Classifying particles based on detected light; 50. The method of any one of claims 1-49, wherein a confidence interval for the classification of the particle is calculated based at least in part on the measurement uncertainty.

[0205] Clause 51. Applying a variance stabilizing transformation to data including measurements of light detected from particles in a sample based at least in part on the measurement uncertainty; 51. The method of any one of appendices 1 to 50, wherein the transformed data is visualized.

[0206] Clause 52. Preprocessing data including measurements of light detected from particles in a sample to minimize intra-cluster variance based at least in part on measurement uncertainty; 52. The method of any one of appendices 1-51, wherein a clustering algorithm is applied to the pre-processed data, the clustering algorithm optionally including one or more of a supervised clustering algorithm and an unsupervised clustering algorithm.

[0207] Clause 53. The method of any one of clauses 1-52, standardizing data including measurements of light detected from particles in a sample and normalizing measurement uncertainty across different data sets based at least in part on measurement uncertainty, where the different data sets optionally include one or more of data sets collected with different instruments or under different conditions.

[0208] Clause 54. The method of any one of clauses 1 to 53, wherein measurement uncertainty is used for probabilistic analysis of particle classification or sorting, and the probabilistic classification optionally includes the application of fuzzy logic techniques.

[0209] Addendum 55. The method of any one of Addendums 1 to 54, which is a method for distinguishing true variability in the particles of a sample from measurement error.

[0210] Appendix 56. The method of any one of appendices 1 to 55, which is a method for distinguishing true biological variability from measurement error.

[0211] Appendix 57. The method of any one of appendices 1 to 56, which is a method for increasing the sensitivity of a flow cytometry assay.

[0212] Appendix 58. The method of any one of appendices 1 to 57, which is a method for increasing the resolution of a flow cytometry assay.

[0213] Addendum 59. The method of any one of Addendums 1 to 58, which is a method for assessing the quality of particle analysis results by associating event-specific estimates of measurement uncertainty with flow cytometry measurements.

[0214] Item 60. The method of any one of items 1 to 59, wherein a quantitative measure of measurement uncertainty is associated with the flow cytometry measurements.

[0215] Appendix 61. The method of any one of appendices 1 to 60, using measurement uncertainty in connection with analyzing or filtering data.

[0216] Appendix 62. The method of any one of appendices 1-61, wherein a gate-belonging confidence score is identified for each event and for each gate, the gate-belonging confidence score comprising the likelihood that the true biological expression level for a given event falls within the given gate.

[0217] Appendix 63. The method of any one of appendices 1 to 62, which is a method for calculating a gate affiliation confidence score.

[0218] Addendum 64. The method of any one of Addendums 1 to 63, wherein particle classification and sorting uses gate-affiliation confidence scores based on measurement uncertainty, and the particle classification and sorting includes probabilistic sorting using configurable likelihood thresholds to maximize purity and / or yield.

[0219] Addendum 65. The method of any one of Addendums 1 to 64, wherein the light is detected using a light detection system.

[0220] Clause 66. The method of clause 65, wherein the light detection system detects light in multiple photodetector channels.

[0221] Addendum 67. The method of Addendum 65 or 66, wherein the optical detection system comprises a plurality of optical detectors.

[0222] Addendum 68. A light source configured to illuminate a sample including a plurality of particles; a light detection system having a plurality of light detectors; a processor to which the memory is operatively coupled; It is equipped with The memory stores instructions that, when executed by the processor, cause the processor to determine a measurement uncertainty associated with light detected by the light detection system.

[0223] Clause 69. The system of clause 68, wherein the light detection system detects light in a plurality of light detector channels.

[0224] Addendum 70. The system of any one of Addendums 68 to 69, wherein the optical detection system includes a plurality of optical detectors.

[0225] Addendum 71. The system of any one of Addendums 68-70, which determines measurement uncertainties corresponding to individual particles in a sample.

[0226] Addendum 72. The system of any one of Addendums 68-71, wherein upon detecting light from the particles, a plurality of parameters of the detected light for the particles in the sample are measured.

[0227] Addendum 73. The system of any one of Addendums 68-72, wherein the system determines measurement uncertainties for individual parameters of the light detected for particles in the sample.

[0228] Addendum 74. The system of any one of Addendums 68-73, further configured to generate event data based on the detected light, the event data including parameter measurements of particles in the sample.

[0229] Addendum 75. The system of any one of Addendums 68-74, wherein the memory stores instructions that, when executed by the processor, cause the processor to spectrally resolve light detected from the particles.

[0230] Addendum 76. The system of any one of Addendums 68-75, wherein the system generates a plurality of derived parameters when spectrally resolving light detected from the particle.

[0231] Addendum 77. The system of any one of Addendums 68-76, wherein the memory stores instructions that, when executed by the processor, cause the processor to generate a plurality of derived parameters for each particle by spectrally decomposing the light detected from each particle.

[0232] Addendum 78. The system of any one of Addendums 68-77, wherein the derived parameters include unmixed detected light.

[0233] Addendum 79. The system of any one of Addendums 68-78, wherein the memory stores instructions that, when executed by the processor, cause the processor to determine a measurement uncertainty related to the derived parameter.

[0234] Addendum 80. The system of any one of Addendums 68-79, wherein the memory stores instructions that, when executed by the processor, cause the processor to identify a first subset of particles in the sample by applying a first gate to the detected light.

[0235] Clause 81. The system of clause 80, wherein the measurement uncertainty corresponds to the identification of the first subset of particles.

[0236] Addendum 82. The system of any one of Addendums 68-81, wherein the memory stores instructions that, when executed by the processor, cause the processor to identify multiple subsets of particles in the sample by applying corresponding multiple gates to the detected light.

[0237] Addendum 83. The system of Addendum 82, wherein the measurement uncertainty corresponds to the identification of each of a plurality of subsets of particles.

[0238] Addendum 84. The system of any one of Addendums 68 to 83, wherein the measurement uncertainty corresponds to the gate level uncertainty.

[0239] Addendum 85. The system of any one of Addendums 68-84, wherein the memory stores instructions that, when executed by the processor, cause the processor to generate a quality score (Q-score) for each particle based on the measurement uncertainty for each particle.

[0240] Addendum 86. The system of any one of Addendums 68-85, wherein the memory stores instructions that, when executed by the processor, cause the processor to record each event and the associated measurement uncertainty for each event.

[0241] Addendum 87. The system of any one of Addendums 68-86, wherein the memory stores instructions that, when executed by the processor, cause the processor to record the measurement value for each particle and the associated measurement uncertainty for each particle.

[0242] Addendum 88. The system of any one of Addendums 68-87, wherein the memory stores instructions that, when executed by the processor, cause the processor to record measurements for a plurality of parameters for each particle and an associated measurement uncertainty for each parameter for each particle.

[0243] Addendum 89. The system of any one of Addendums 68-88, wherein the memory stores instructions that, when executed by the processor, cause the processor to record a measurement uncertainty, the measurement uncertainty corresponding to the measurement uncertainty for one or more of each recorded parameter, each recorded event, each gate or other classification, or sample.

[0244] Addendum 90. The system of any one of Addendums 68 to 89, wherein the measurement uncertainties of different parameters measured for particles in a sample include different metrics.

[0245] Addendum 91. The system of any one of Addendums 68 to 90, wherein the measurement uncertainty corresponds to a binary classification event.

[0246] Addendum 92. The system of Addendum 91, wherein the binary classification event corresponds to one or more of a gating decision or a population membership classification decision.

[0247] Addendum 93. The system of any one of Addendums 68-92, wherein the measurement uncertainty includes an index that quantifies the measurement uncertainty.

[0248] Addendum 94. The system of any one of Addendums 68-93, wherein the memory stores instructions that, when executed by the processor, cause the processor to generate an uncertainty score based on the measurement uncertainty.

[0249] Addendum 95. The system of any one of Addendums 68-94, wherein measurement uncertainty is reflected in an uncertainty score, with a higher uncertainty score indicating a larger measurement uncertainty.

[0250] Addendum 96. The system of any one of Addendums 68-95, wherein the memory stores instructions that, when executed by the processor, cause the processor to generate a quality score based on the measurement uncertainty.

[0251] Addendum 97. The system of any one of Addendums 68-96, wherein measurement uncertainty is reflected in a quality score, with a higher quality score indicating a higher level of confidence.

[0252] Addendum 98. The system of any one of Addendums 68-97, wherein the measurement uncertainty includes one or more of a direct quantification optionally including standard deviation, a confidence interval, a likelihood that the measurement is within a specified percentage of the true value, or a residual related to the goodness of fit of the unmixed data.

[0253] Addendum 99. The system of any one of Addendums 68 to 98, wherein the measurement uncertainty is associated with a binary classification, and the binary classification optionally includes one or more of a gating classification or a population affiliation classification.

[0254] Addendum 100. The system of any one of Addendums 68-99, wherein the memory stores instructions that, when executed by the processor, cause the processor to calculate a quality score based on the measurement uncertainty, the quality score reflecting the likelihood of belonging to the gate.

[0255] Addendum 101. The system of Addendum 100, wherein the likelihood of belonging to a gate is calculated for each gate in the gate hierarchy.

[0256] Addendum 102. The system of Addendum 100, wherein the likelihood of belonging to a gate is calculated taking into account each hierarchical parent gate of the gate.

[0257] Addendum 103. The system of any one of Addendums 68-102, wherein when determining a measurement uncertainty associated with the detected light, the system calculates the measurement uncertainty based on a semi-empirical noise model, the semi-empirical noise model including one or more of instrument calibration data, a physical noise model, or measurements.

[0258] Addendum 104. The system of any one of Addendums 68-103, wherein determining the measurement uncertainty associated with the detected light comprises estimating the measurement uncertainty for each event based on a statistical algorithm, the statistical algorithm measuring characteristics of the data distribution within the sample.

[0259] Addendum 105. The system of any one of Addendums 68 to 104, wherein when determining the measurement uncertainty associated with the detected light, a combination of noise modeling and distribution fitting is applied to estimate the measurement uncertainty.

[0260] Addendum 106. The system of any one of Addendums 68 to 105, wherein when determining the measurement uncertainty associated with the detected light, Bayesian estimation is applied to estimate the measurement uncertainty.

[0261] Addendum 107. The system of any one of Addendums 68-106, wherein the memory stores instructions that, when executed by the processor, cause the processor to report a measurement uncertainty for each particle of a subset of particles of the sample.

[0262] Addendum 108. The system of any one of Addendums 68-107, wherein the memory stores instructions that, when executed by the processor, cause the processor to report measurement uncertainties for a plurality of parameters for each particle of a subset of particles of the sample.

[0263] Addendum 109. The system of any one of Addendums 68-108, wherein the memory stores instructions that, when executed by the processor, cause the processor to report, for each particle of the subset of particles, a measurement uncertainty associated with membership in a subpopulation defined by the gate.

[0264] Addendum 110. The system of any one of Addendums 68-109, wherein the measurement uncertainty includes random measurement error.

[0265] Addendum 111. The system of any one of Addendums 68-110, wherein the measurement uncertainty includes spillover diffusion error.

[0266] Addendum 112. The system of any one of Addendums 68-111, wherein the measurement uncertainty reflects variations other than true intrinsic differences between particles of the sample.

[0267] Addendum 113. The system of any one of Addendums 68-112, wherein the measurement uncertainty reflects the confidence in the classification decision for the particles of the sample.

[0268] Addendum 114. The system of any one of Addendums 68-113, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with measurements of multiple parameters of the light detected from the particle.

[0269] Addendum 115. The system of any one of Addendums 68-114, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with measurements of light detected from multiple particles.

[0270] Addendum 116. The system of any one of Addendums 68 to 115, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with the sample.

[0271] Annex 117. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light The system of any one of appendices 68 to 116, performing one or more of the following:

[0272] Addendum 118. The memory, when executed by the processor, causes the processor to: Classifying particles based on the detected light, Calculating the statistical significance of particle classifications based at least in part on measurement uncertainties 118. The system of any one of claims 68 to 117, wherein the system stores instructions.

[0273] Addendum 119. The memory, when executed by the processor, causes the processor to: Classifying particles based on the detected light, Calculating confidence intervals for particle classifications based at least in part on measurement uncertainties 19. The system of any one of clauses 68-118, storing instructions.

[0274] Addendum 120. The memory, when executed by the processor, causes the processor to: applying a variance stabilizing transform to data including measurements of light detected from particles in the sample based at least in part on the measurement uncertainty; Visualize the state of the converted data 119. The system of claim 68, wherein the system stores instructions.

[0275] Addendum 121. The memory, when executed by the processor, causes the processor to: preprocessing data including measurements of light detected from particles in the sample to minimize intra-cluster variance based at least in part on measurement uncertainty; Applying a clustering algorithm to the preprocessed data It remembers the commands, 121. The system of any one of Clauses 68-120, wherein the clustering algorithm optionally includes one or more of a supervised clustering algorithm and an unsupervised clustering algorithm.

[0276] Addendum 122. The system of any one of Addendums 68-121, wherein the memory stores instructions that, when executed by the processor, cause the processor to standardize data including measurements of light detected from particles in a sample and normalize the measurement uncertainty across different data sets based at least in part on the measurement uncertainty, the different data sets optionally including one or more of data sets collected with different instruments or under different conditions.

[0277] Addendum 123. The system of any one of Addendums 68-122, wherein the memory stores instructions that, when executed by the processor, cause the processor to use measurement uncertainty in a probabilistic analysis of particle classification or sorting, the probabilistic classification optionally including the application of fuzzy logic techniques.

[0278] Addendum 124. The system of any one of Addendums 68-123, configured to distinguish true variability in the particles of the sample from measurement error.

[0279] Addendum 125. The system of any one of Addendums 68-124, configured to distinguish true biological variability from measurement error.

[0280] Addendum 126. A system described in any one of Addendums 68 to 125, configured to distinguish improved sensitivity of a flow cytometry assay.

[0281] Addendum 127. A system described in any one of Addendums 68 to 126, configured to distinguish improved resolution in flow cytometry assays.

[0282] Addendum 128. A system described in any one of Addendums 68 to 127, configured to assess the quality of particle analysis results by associating an event-specific estimate of measurement uncertainty with flow cytometry measurements.

[0283] Addendum 129. A system described in any one of Addendums 68 to 128, configured to associate a quantitative indicator of measurement uncertainty with a flow cytometry measurement.

[0284] Addendum 130. The system of any one of Addendums 68 to 129, configured to use measurement uncertainty in connection with analyzing or filtering data.

[0285] Addendum 131. The system of any one of Addendums 68-130, wherein the memory stores instructions that, when executed by the processor, cause the processor to determine a gate-associated confidence score for each event and for each gate, the gate-associated confidence score comprising a likelihood that a true biological expression level for a given event falls within the given gate.

[0286] Addendum 132. The system of any one of Addendums 68 to 131, configured to calculate a gate affiliation confidence score.

[0287] Addendum 133. The system of any one of Addendums 68-132, wherein the memory stores instructions that, when executed by the processor, cause the processor to use gate-assigned confidence scores based on measurement uncertainty for particle classification and sorting, and the particle classification and sorting includes probabilistic sorting using configurable likelihood thresholds to maximize purity and / or yield.

[0288] Addendum 134. An integrated circuit programmed to determine a measurement uncertainty associated with light detected from particles in a sample by an optical detection system.

[0289] Clause 135. The integrated circuit of clause 134, wherein the integrated circuit is a field programmable gate array (FPGA).

[0290] Item 136. The integrated circuit of item 134, which is an application specific integrated circuit (ASIC).

[0291] Attachment 137. The integrated circuit of attachment 134, which is a complex programmable logic device (CPLD).

[0292] Addendum 138. The integrated circuit of any one of Addendums 134-137, which determines measurement uncertainties corresponding to individual particles in a sample.

[0293] Clause 139. The integrated circuit of any one of clauses 134-138, wherein upon detecting light from the particles, the integrated circuit measures a plurality of parameters of the detected light for the particles in the sample.

[0294] Clause 140. The integrated circuit of any one of clauses 134-139, which determines measurement uncertainties for individual parameters of light detected for particles in a sample.

[0295] Addendum 141. The integrated circuit of any one of Addendums 134-140, programmed to generate event data based on the detected light, the event data including parameter measurements of particles in the sample.

[0296] Clause 142. The integrated circuit of any one of clauses 134-141, programmed to spectrally resolve light detected from particles.

[0297] Clause 143. The integrated circuit of any one of clauses 134-142, which generates a plurality of derived parameters when spectrally decomposing light detected from a particle.

[0298] Addendum 144. The integrated circuit of any one of Addendums 134-143, programmed to generate a plurality of derived parameters for each particle by spectrally decomposing the light detected from each particle.

[0299] Addendum 145. The integrated circuit of any one of Addendums 134 to 144, wherein the derived parameters include unmixed detected light.

[0300] Clause 146. The integrated circuit of any one of clauses 134-145, specifying a measurement uncertainty for the derived parameter.

[0301] Addendum 147. The integrated circuit of any one of Addendums 134-146, programmed for an algorithm to identify a first subset of particles in the sample by applying a first gate to the detected light.

[0302] Clause 148. The integrated circuit of clause 147, wherein the measurement uncertainty corresponds to the identification of the first subset of particles.

[0303] Addendum 149. The integrated circuit of any one of Addendums 134-148, programmed to identify a plurality of subsets of particles in the sample by applying a corresponding plurality of gates to the detected light.

[0304] Clause 150. The integrated circuit of clause 149, wherein the measurement uncertainty corresponds to the identification of each of a plurality of subsets of particles.

[0305] Addendum 151. The integrated circuit of any one of Addendums 134 to 150, wherein the measurement uncertainty corresponds to gate-level uncertainty.

[0306] Clause 152. The integrated circuit of any one of clauses 134-151, programmed to generate a quality score (Q-score) for each particle based on the measurement uncertainty for each particle.

[0307] Clause 153. The integrated circuit of any one of clauses 134-152, programmed to record each event and the associated measurement uncertainty for each event.

[0308] Clause 154. The integrated circuit of any one of clauses 134-153, programmed to record the measurement value for each particle and the associated measurement uncertainty for each particle.

[0309] Clause 155. The integrated circuit of any one of Clauses 134-154, programmed to record measurements of a plurality of parameters for each particle and an associated measurement uncertainty for each parameter for each particle.

[0310] Clause 156. The integrated circuit of any one of Clauses 134-155, programmed to record a measurement uncertainty, the measurement uncertainty corresponding to the measurement uncertainty for one or more of each recorded parameter, each recorded event, each gate or other classification, or sample.

[0311] Addendum 157. The integrated circuit of any one of Addendums 134-156, wherein the measurement uncertainties of different parameters measured for particles in a sample include different metrics.

[0312] Addendum 158. The integrated circuit of any one of Addendums 134 to 157, wherein the measurement uncertainty corresponds to a binary classification event.

[0313] Clause 159. The integrated circuit of clause 158, wherein the binary classification event corresponds to one or more of a gating decision or a population membership classification decision.

[0314] Addendum 160. The integrated circuit of any one of Addendums 134-159, wherein the measurement uncertainty includes an index that quantifies the measurement uncertainty.

[0315] Clause 161. The integrated circuit of any one of Clauses 134-160, programmed to generate an uncertainty score based on the measurement uncertainty.

[0316] Addendum 162. The integrated circuit of any one of Addendums 134-161, wherein measurement uncertainty is reflected in an uncertainty score, with a higher uncertainty score indicating greater measurement uncertainty.

[0317] Clause 163. The integrated circuit of any one of Clauses 134-162, programmed to generate a quality score based on measurement uncertainty.

[0318] Addendum 164. The integrated circuit of any one of Addendums 134 to 163, wherein measurement uncertainty is reflected in a quality score, with a higher quality score indicating a higher degree of confidence.

[0319] Addendum 165. The integrated circuit of any one of Addendums 134-164, wherein the measurement uncertainty includes one or more of a direct quantification optionally including standard deviation, a confidence interval, a likelihood that the measurement is within a specified percentage of the true value, or a residual related to the goodness of fit of the unmixed data.

[0320] Addendum 166. The integrated circuit of any one of Addendums 134-165, wherein the measurement uncertainty is associated with a binary classification, the binary classification optionally including one or more of a gating classification or a population membership classification.

[0321] Annex 167. Programmed for an algorithm to calculate a quality score based on measurement uncertainty, 167. The integrated circuit of any one of appendices 134 to 166, wherein the quality score reflects the likelihood of belonging to the gate.

[0322] Addendum 168. The integrated circuit of Addendum 167, wherein the likelihood of belonging to a gate is calculated for each gate in the gate hierarchy.

[0323] Addendum 169. The integrated circuit of Addendum 167, wherein the likelihood of belonging to a gate is calculated taking into account each hierarchical parent gate of the gate.

[0324] Clause 170. The integrated circuit of any one of Clauses 134-169, wherein determining a measurement uncertainty associated with the detected light calculates the measurement uncertainty based on a semi-empirical noise model, the semi-empirical noise model including one or more of instrument calibration data, a physical noise model, or measurements.

[0325] Addendum 171. The integrated circuit of any one of Addendums 134-170, wherein determining the measurement uncertainty associated with the detected light comprises estimating the measurement uncertainty on an event-by-event basis based on a statistical algorithm, the statistical algorithm measuring a characteristic of the data distribution within the sample.

[0326] Addendum 172. The integrated circuit of any one of Addendums 134 to 171, wherein determining a measurement uncertainty associated with the detected light applies a combination of noise modeling and distribution fitting to estimate the measurement uncertainty.

[0327] Addendum 173. The integrated circuit of any one of Addendums 134 to 172, wherein determining a measurement uncertainty associated with the detected light applies Bayesian inference to estimate the measurement uncertainty.

[0328] Clause 174. The integrated circuit of any one of clauses 134-173, reporting measurement uncertainty for each particle of a subset of particles of a sample.

[0329] Clause 175. The integrated circuit of any one of clauses 134-174, reporting measurement uncertainty for a plurality of parameters for each particle of a subset of particles of a sample.

[0330] Clause 176. The integrated circuit of any one of clauses 134-175, reporting a measurement uncertainty associated with membership in a subpopulation defined by a gate for each particle of a subset of particles.

[0331] Addendum 177. The integrated circuit of any one of Addendums 134 to 176, wherein the measurement uncertainty includes random measurement error.

[0332] Addendum 178. The integrated circuit of any one of Addendums 134 to 177, wherein the measurement uncertainty includes spillover diffusion error.

[0333] Addendum 179. The integrated circuit of any one of Addendums 134-178, wherein the measurement uncertainty reflects variations other than true intrinsic differences between particles of the sample.

[0334] Addendum 180. The integrated circuit of any one of Addendums 134-179, wherein the measurement uncertainty reflects the confidence in a classification decision regarding particles of the sample.

[0335] Addendum 181. The integrated circuit of any one of Addendums 134-180, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with measurements of multiple parameters of the light detected from the particle.

[0336] Addendum 182. The integrated circuit of any one of Addendums 134-181, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with a measurement of light detected from multiple particles.

[0337] Addendum 183. The integrated circuit of any one of Addendums 134 to 182, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with the sample.

[0338] Annex 184. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light 184. The integrated circuit according to any one of appendices 134 to 183, which performs one or more of the following:

[0339] Addendum 185. Classifying particles based on detected light; Calculating the statistical significance of particle classifications based at least in part on measurement uncertainties 185. The integrated circuit of any one of appendices 134 to 184, programmed to:

[0340] Addendum 186. Classifying particles based on detected light; Calculating confidence intervals for particle classifications based at least in part on measurement uncertainties 186. The integrated circuit of any one of appendices 134 to 185, programmed as follows:

[0341] Clause 187. Applying a variance stabilizing transformation to data including measurements of light detected from particles in a sample based at least in part on the measurement uncertainty; Visualize the state of the transformed data 187. The integrated circuit of any one of appendices 134 to 186, programmed to:

[0342] Appendix 188. Preprocessing data including measurements of light detected from particles in a sample to minimize intra-cluster variance based at least in part on measurement uncertainty; Applying a clustering algorithm to the preprocessed data It is programmed so that 188. The integrated circuit of any one of appendices 134 to 187, wherein the clustering algorithm optionally includes one or more of a supervised clustering algorithm and an unsupervised clustering algorithm.

[0343] Clause 189. The integrated circuit of any one of Clauses 134-188, programmed to standardize data including measurements of light detected from particles in a sample and normalize measurement uncertainty across different data sets based at least in part on measurement uncertainty, where the different data sets optionally include one or more of data sets collected with different instruments or under different conditions.

[0344] Clause 190. The integrated circuit of any one of clauses 134-189, programmed to use measurement uncertainty in a probabilistic analysis of particle classification or sorting, the probabilistic classification optionally including the application of fuzzy logic techniques.

[0345] Addendum 191. The integrated circuit of any one of Addendums 134-190, configured to distinguish true variability in the particles of a sample from measurement error.

[0346] Clause 192. The integrated circuit of any one of clauses 134-191, configured to distinguish true biological variability from measurement error.

[0347] Clause 193. The integrated circuit of any one of clauses 134-192, configured to differentiate for increased sensitivity in a flow cytometry assay.

[0348] Clause 194. The integrated circuit of any one of clauses 134-193, configured to differentiate for improved resolution in flow cytometry assays.

[0349] Addendum 195. The integrated circuit of any one of Addendums 134-194, configured to assess the quality of particle analysis results by associating an event-specific estimate of measurement uncertainty with flow cytometry measurements.

[0350] Clause 196. The integrated circuit of any one of clauses 134-195, wherein a quantitative measure of measurement uncertainty is associated with the flow cytometry measurement.

[0351] Clause 197. The integrated circuit of any one of clauses 134-196, programmed to use measurement uncertainty in connection with analyzing or filtering data.

[0352] Addendum 198. The integrated circuit of any one of Addendums 134-197, programmed to determine a gate-belonging confidence score for each event and for each gate, the gate-belonging confidence score comprising the likelihood that the true biological expression level for a given event is contained within the given gate.

[0353] Addendum 199. The integrated circuit of any one of Addendums 134 to 198, configured to calculate a gate-affiliated confidence score.

[0354] Addendum 200. An integrated circuit according to any one of Addendums 134-199, programmed to use gate-association confidence scores based on measurement uncertainty for particle classification and sorting, including probabilistic sorting using configurable likelihood thresholds to maximize purity and / or yield.

[0355] Addendum 201. A non-transitory computer-readable storage medium having stored thereon instructions having an algorithm for determining a measurement uncertainty associated with light detected from particles in a sample by an optical detection system.

[0356] Clause 202. The non-transitory computer-readable storage medium of Clause 201, which identifies measurement uncertainties corresponding to individual particles in a sample.

[0357] Clause 203. The non-transitory computer-readable storage medium of clause 201 or 202, wherein upon detecting light from the particles, a plurality of parameters of the detected light for the particles in the sample are measured.

[0358] Clause 204. The non-transitory computer-readable storage medium of any one of Clauses 201-203, specifying measurement uncertainties for individual parameters of light detected for particles in a sample.

[0359] Addendum 205. The non-transitory computer-readable storage medium of any one of Addendums 201-204, further comprising an algorithm for generating event data based on the detected light, the event data including parameter measurements of particles in the sample.

[0360] Clause 206. The non-transitory computer-readable storage medium of any one of Clauses 201-205, further comprising an algorithm for spectrally decomposing light detected from the particles.

[0361] Clause 207. The non-transitory computer-readable storage medium of any one of Clauses 201-206, wherein when spectrally decomposing light detected from a particle, a plurality of derived parameters is generated.

[0362] Addendum 208. The non-transitory computer-readable storage medium of any one of Addendums 201-207, further comprising an algorithm for generating a plurality of derived parameters for each particle by spectrally decomposing the light detected from each particle.

[0363] Addendum 209. The non-transitory computer-readable storage medium of any one of Addendums 201-208, wherein the derived parameters include unmixed detected light.

[0364] Clause 210. The non-transitory computer-readable storage medium of any one of Clauses 201-209, specifying a measurement uncertainty for a derived parameter.

[0365] Addendum 211. The non-transitory computer-readable storage medium of any one of Addendums 201-210, further comprising an algorithm for identifying a first subset of particles in the sample by applying a first gate to the detected light.

[0366] Clause 212. The non-transitory computer-readable storage medium of Clause 211, wherein the measurement uncertainty corresponds to the identification of the first subset of particles.

[0367] Addendum 213. The non-transitory computer-readable storage medium of any one of Addendums 201-212, further comprising an algorithm for identifying multiple subsets of particles in the sample by applying corresponding multiple gates to the detected light.

[0368] Clause 214. The non-transitory computer-readable storage medium of Clause 213, wherein the measurement uncertainty corresponds to the identification of each of the plurality of subsets of particles.

[0369] Addendum 215. The non-transitory computer-readable storage medium of any one of Addendums 201-214, wherein the measurement uncertainty corresponds to the gate level uncertainty.

[0370] Clause 216. The non-transitory computer-readable storage medium of any one of Clauses 201-215, further comprising an algorithm for generating a quality score (Q-score) for each particle based on the measurement uncertainty for each particle.

[0371] Clause 217. The non-transitory computer-readable storage medium of any one of Clauses 201-216, further comprising an algorithm for recording each event and the associated measurement uncertainty for each event.

[0372] Clause 218. The non-transitory computer-readable storage medium of any one of Clauses 201-217, further comprising an algorithm for recording measurements for each particle and associated measurement uncertainty for each particle.

[0373] Addendum 219. The non-transitory computer-readable storage medium of any one of Addendums 201-218, further comprising an algorithm for recording measurements of multiple parameters for each particle and associated measurement uncertainties for each parameter for each particle.

[0374] Addendum 220. The non-transitory computer-readable storage medium of any one of Addendums 201-219, further comprising an algorithm for recording measurement uncertainties, the measurement uncertainties corresponding to the measurement uncertainties for one or more of each recorded parameter, each recorded event, each gate or other classification, or sample.

[0375] Addendum 221. The non-transitory computer-readable storage medium of any one of Addendums 201-220, wherein the measurement uncertainties of different parameters measured for particles in a sample include different metrics.

[0376] Addendum 222. The non-transitory computer-readable storage medium of any one of Addendums 201-221, wherein the measurement uncertainty corresponds to a binary classification event.

[0377] Clause 223. The non-transitory computer-readable storage medium of Clause 222, wherein the binary classification event corresponds to one or more of a gating decision or a population membership classification decision.

[0378] Addendum 224. The non-transitory computer-readable storage medium of any one of Addendums 201-223, wherein the measurement uncertainty includes an index that quantifies the measurement uncertainty.

[0379] Clause 225. The non-transitory computer-readable storage medium of any one of Clauses 201-224, further comprising an algorithm for generating an uncertainty score based on the measurement uncertainty.

[0380] Addendum 226. The non-transitory computer-readable storage medium of any one of Addendums 201-225, wherein measurement uncertainty is reflected in an uncertainty score, with a higher uncertainty score indicating a larger measurement uncertainty.

[0381] Clause 227. The non-transitory computer-readable storage medium of any one of Clauses 201-226, further comprising an algorithm for generating a quality score based on measurement uncertainty.

[0382] Addendum 228. The non-transitory computer-readable storage medium of any one of Addendums 201-227, wherein measurement uncertainty is reflected in a quality score, with a higher quality score indicating a higher level of confidence.

[0383] Addendum 229. The non-transitory computer-readable storage medium of any one of Addendums 201-228, wherein the measurement uncertainty includes one or more of a direct quantification optionally including standard deviation, a confidence interval, a likelihood that the measurement is within a specified percentage of the true value, or a residual related to the goodness of fit of the unmixed data.

[0384] Addendum 230. The non-transitory computer-readable storage medium of any one of Addendums 201-229, wherein the measurement uncertainty is associated with a binary classification, and the binary classification optionally includes one or more of a gating classification or a population membership classification.

[0385] Clause 231. further comprising an algorithm for calculating a quality score based on the measurement uncertainty; 231. The non-transitory computer-readable storage medium of any one of appendices 201-230, wherein the quality score reflects the likelihood of belonging to a gate.

[0386] Clause 232. The non-transitory computer-readable storage medium of Clause 231, wherein the likelihood of membership in a gate is calculated for each gate in a gate hierarchy.

[0387] Addendum 233. The non-transitory computer-readable storage medium of Addendum 231, wherein the likelihood of belonging to a gate is calculated taking into account each hierarchical parent gate of the gate.

[0388] Clause 234. The non-transitory computer-readable storage medium of any one of Clauses 201-233, wherein determining a measurement uncertainty associated with the detected light applies an algorithm for calculating the measurement uncertainty based on a semi-empirical noise model, the semi-empirical noise model including one or more of instrument calibration data, a physical noise model, or measurements.

[0389] Addendum 235. The non-transitory computer-readable storage medium of any one of Addendums 201-234, wherein determining the measurement uncertainty associated with the detected light comprises applying an algorithm for estimating the measurement uncertainty on an event-by-event basis based on a statistical algorithm, the statistical algorithm measuring a characteristic of the data distribution within the sample.

[0390] Clause 236. The non-transitory computer-readable storage medium of any one of Clauses 201-235, wherein determining a measurement uncertainty associated with the detected light applies an algorithm to estimate the measurement uncertainty by applying a combination of noise modeling and distribution fitting.

[0391] Addendum 237. The non-transitory computer-readable storage medium of any one of Addendums 201-236, wherein determining a measurement uncertainty associated with the detected light applies an algorithm for estimating the measurement uncertainty using Bayesian inference.

[0392] Clause 238. The non-transitory computer-readable storage medium of any one of Clauses 201-237, further comprising an algorithm for reporting a measurement uncertainty for each particle of a subset of particles of a sample.

[0393] Clause 239. The non-transitory computer-readable storage medium of any one of Clauses 201-238, further comprising an algorithm for reporting measurement uncertainty for a plurality of parameters for each particle of a subset of particles of a sample.

[0394] Addendum 240. The non-transitory computer-readable storage medium of any one of Addendums 201-239, further comprising an algorithm for reporting a measurement uncertainty associated with membership in a subpopulation defined by the gate for each particle of the subset of particles.

[0395] Clause 241. The non-transitory computer-readable storage medium of any one of Clauses 201-240, wherein the measurement uncertainty includes random measurement error.

[0396] Clause 242. The non-transitory computer-readable storage medium of any one of Clauses 201-241, wherein the measurement uncertainty includes spillover diffusion error.

[0397] Addendum 243. The non-transitory computer-readable storage medium of any one of Addendums 201-242, wherein the measurement uncertainty reflects variations other than true intrinsic differences between particles of the sample.

[0398] Addendum 244. The non-transitory computer-readable storage medium of any one of Addendums 201-243, wherein the measurement uncertainty reflects the confidence in a classification decision for particles of a sample.

[0399] Addendum 245. The non-transitory computer-readable storage medium of any one of Addendums 201-244, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with measurements of multiple parameters of light detected from the particle.

[0400] Addendum 246. The non-transitory computer-readable storage medium of any one of Addendums 201-245, wherein the measurement uncertainty reflects an overall measurement uncertainty associated with a measurement of light detected from multiple particles.

[0401] Addendum 247. The non-transitory computer-readable storage medium of any one of Addendums 201-246, wherein the measurement uncertainty reflects the overall measurement uncertainty associated with the sample.

[0402] Annex 248. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light A non-transitory computer-readable storage medium according to any one of appendices 201 to 247, which performs one or more of the following.

[0403] Appendix 249. Algorithms for classifying particles based on detected light; and Algorithm for calculating statistical significance of particle classifications based at least in part on measurement uncertainty 249. The non-transitory computer-readable storage medium of any one of Clauses 201-248, further comprising:

[0404] Appendix 250. Algorithms for classifying particles based on detected light; and Algorithm for calculating confidence intervals for particle classifications based at least in part on measurement uncertainty 249. The non-transitory computer-readable storage medium of any one of claims 201 to 249, further comprising:

[0405] Announcement 251. An algorithm for applying a variance stabilizing transform to data including measurements of light detected from particles in a sample, based at least in part on measurement uncertainty; and Algorithms for visualizing aspects of transformed data 251. The non-transitory computer-readable storage medium of any one of claims 201 to 250, further comprising:

[0406] Clause 252. An algorithm for preprocessing data including measurements of light detected from particles in a sample to minimize within-cluster variance based at least in part on measurement uncertainty; and Algorithms for applying clustering algorithms to preprocessed data and 252. The non-transitory computer-readable storage medium of any one of Clauses 201-251, wherein the clustering algorithm optionally includes one or more of a supervised clustering algorithm and an unsupervised clustering algorithm.

[0407] Addendum 253. The non-transitory computer-readable storage medium of any one of Addendums 201-252, further comprising an algorithm for standardizing data including measurements of light detected from particles in a sample and normalizing measurement uncertainty across different data sets based at least in part on the measurement uncertainty, where the different data sets optionally include one or more of data sets collected with different instruments or under different conditions.

[0408] Clause 254. The non-transitory computer-readable storage medium of any one of Clauses 201-253, further comprising an algorithm for using measurement uncertainty in probabilistic analysis of particle classification or sorting, the probabilistic classification optionally including the application of fuzzy logic techniques.

[0409] Clause 255. The non-transitory computer-readable storage medium of any one of clauses 201-254, wherein a quantitative measure of measurement uncertainty is associated with a flow cytometry measurement.

[0410] Clause 256. The non-transitory computer-readable storage medium of any one of Clauses 201-255, further comprising an algorithm for using measurement uncertainty in connection with analyzing or filtering data.

[0411] Addendum 257. The non-transitory computer-readable storage medium of any one of Addendums 201-256, further comprising an algorithm for identifying a gate-belonging confidence score for each event and for each gate, the gate-belonging confidence score comprising a likelihood that a true biological expression level for a given event falls within the given gate.

[0412] Appendix 258. The non-transitory computer-readable storage medium of any one of Appendixes 201 to 257, wherein the method is a method for calculating a confidence score of gate affiliation.

[0413] Addendum 259. The non-transitory computer-readable storage medium of any one of Addendums 201-258, further comprising an algorithm for using gate-affiliation confidence scores based on measurement uncertainty for particle classification and sorting, wherein the particle classification and sorting includes probabilistic sorting using configurable likelihood thresholds to maximize purity and / or yield.

[0414] Although the foregoing disclosure has been described in some detail by way of illustration and example for ease of understanding, it will be readily apparent to those skilled in the art, in view of the teachings of the present disclosure, that certain changes and modifications may be made without departing from the spirit or scope of the appended claims.

[0415] Thus, the foregoing merely illustrates the essence of the present disclosure. It is clear that those skilled in the art will be able to devise various configurations that embody the essence of the present disclosure and are within the spirit and scope of the present disclosure, even though not explicitly described or shown herein. Furthermore, all examples and conditional language set forth herein are intended essentially to aid the reader in understanding the essence of the disclosure and the concepts provided by the inventors to advance the art, and should not be construed as limiting the examples and conditions specifically set forth. Furthermore, all statements herein that describe the essence, aspects, and embodiments of the present disclosure, as well as specific examples of the present disclosure, are intended to encompass both structural and functional equivalents of the present disclosure. Additionally, such equivalents are intended to include both currently known equivalents and future-developed equivalents, i.e., all elements developed that perform the same function, regardless of structure. Furthermore, the descriptions disclosed herein are not intended to be publicly disclosed, regardless of whether such disclosure is explicitly recited in the claims.

[0416] Accordingly, it is not intended that the scope of the present disclosure be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present disclosure are embodied by the appended claims. With respect to claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) are expressly provided to be invoked with respect to a limitation in a claim only when the precise phrase "means for" or "step for" appears at the beginning of such limitation in the claim; if such precise phrase is not used in a claim limitation, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0417] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 667,408, filed July 3, 2024, the entire disclosure of which is incorporated herein by reference.

Claims

1. Introduce the sample into the flow cytometer. The introduced sample is passed through a flow stream. illuminating the sample in the flowstream with a light source; detecting light from particles in a sample flowing through the flow stream; A method for determining a measurement uncertainty associated with detected light.

2. The method of claim 1 , wherein the measurement uncertainty is associated with a binary classification, the binary classification optionally comprising one or more of a gating classification or a population membership classification.

3. calculating a quality score based on the measurement uncertainty; The method of claim 1 or 2, wherein the quality score reflects the likelihood of belonging to a gate.

4. The method of claim 3 , wherein the likelihood of belonging to the gate is calculated for each gate in a gate hierarchy, and / or the likelihood of belonging to the gate is calculated taking into account each hierarchical parent gate of the gate.

5. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light The method according to any one of claims 1 to 4, further comprising performing one or more of the following:

6. Classifying particles based on the detected light The method of any one of claims 1 to 5, further comprising calculating a confidence interval for the classification of a particle based at least in part on said measurement uncertainty.

7. A method according to any one of claims 1 to 6, wherein the measurement uncertainty is used for probabilistic analysis of particle classification or sorting, the probabilistic classification optionally including the application of fuzzy logic techniques.

8. 8. The method of claim 1, wherein a gate-belonging confidence score is identified for each event and for each gate, the gate-belonging confidence score comprising the likelihood that a true biological expression level for a given event falls within the given gate.

9. The method according to any one of claims 1 to 8, which is a method for calculating a gate-belonging confidence score.

10. 10. The method of claim 1, wherein particle classification and sorting uses gate-associated confidence scores based on measurement uncertainty, and the particle classification and sorting comprises probabilistic sorting with configurable likelihood thresholds to maximize purity and / or yield.

11. a light source configured to illuminate a sample including a plurality of particles; a light detection system having a plurality of light detectors; a processor to which the memory is operatively coupled; It is equipped with The memory storing instructions that, when executed by the processor, cause the processor to determine a measurement uncertainty associated with light detected by the light detection system.

12. 12. The system of claim 11, wherein the measurement uncertainty is associated with a binary classification, the binary classification optionally comprising one or more of a gating classification or a population membership classification.

13. 13. The system of claim 11 or 12, wherein the memory stores instructions that, when executed by the processor, cause the processor to calculate a quality score based on the measurement uncertainty, the quality score reflecting a likelihood of belonging to a gate.

14. The system of claim 13 , wherein the likelihood of belonging to the gate is calculated for each gate in a gate hierarchy, and / or the likelihood of belonging to the gate is calculated taking into account each hierarchical parent gate of the gate.

15. When determining the measurement uncertainty associated with the detected light, an estimate of the measurement uncertainty associated with the detected light; Measurement of the measurement uncertainty associated with the detected light, or Estimating the measurement uncertainty associated with the detected light The system according to any one of claims 11 to 14, further comprising one or more of:

16. The memory, when executed by the processor, causes the processor to: Classifying particles based on the detected light, calculating a confidence interval for the particle classification based at least in part on the measurement uncertainty; A system according to any one of claims 11 to 15, storing instructions.

17. 17. The system of any one of claims 11 to 16, wherein the memory stores instructions that, when executed by the processor, cause the processor to use the measurement uncertainty in a probabilistic analysis of particle classification or sorting, optionally including the application of fuzzy logic techniques.

18. 18. The system of claim 11, wherein the memory stores instructions that, when executed by the processor, cause the processor to determine, for each event and for each gate, a gate-associated confidence score, the gate-associated confidence score comprising a likelihood that a true biological expression level for a given event falls within the given gate.

19. The system according to any one of claims 11 to 18, configured to calculate a gate-affiliated confidence score.

20. 20. The system of claim 11, wherein the memory stores instructions that, when executed by the processor, cause the processor to use gate-associated confidence scores based on measurement uncertainty for particle classification and sorting, the particle classification and sorting comprising probabilistic sorting with configurable likelihood thresholds to maximize purity and / or yield.

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