Flow cytometry quality scoring method and system

Abstract

Aspects of the present disclosure include methods for identifying measurement uncertainty associated with light detected from a sample. A method according to certain embodiments includes introducing a sample into a flow cytometer, flowing the introduced sample in a flow stream, irradiating the sample in the flow stream with a light source, detecting light from particles in the sample flowing in the flow stream, and identifying measurement uncertainty associated with the detected light. In some embodiments, measurement uncertainty is identified corresponding to individual particles in a sample. In certain embodiments, measurement uncertainty is identified for various parameters of detection light of particles in a sample. The method according to some embodiments also includes generating a mass fraction for each particle based on the measured uncertainty for each particle. Systems, integrated circuit devices (e.g., field programmable gate arrays), and non-transitory computer-readable storage media for practicing the subject methods are also provided.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 667,408, filed July 3, 2024, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] This invention relates to a flow cytometry quality scoring method and system. Background Technology

[0004] Characterization of analytes in biofluids has become an important part of biological research, medical diagnostics, and the assessment of patients' overall health and well-being. Detection of analytes in biofluids (such as human blood or blood-derived products) can provide results that can play a role in determining treatment plans for patients with a variety of disease conditions.

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

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

[0007] Flow cytometry is used to measure the characteristics of individual particles or cells based on optical signals. In order to effectively identify and / or separate populations of particles or cells based on these measured signals, practitioners must be able to determine whether a measured difference in the signal between two particles is due to genuine intrinsic differences between the particles or to random measurement error. Ultimately, the presence of measurement error affects the confidence level for making a classification decision about a given particle or cell.

[0008] Currently, flow cytometry data do not include any information about measurement uncertainty at the event level, parameter level, or population level. Practitioners need to manually measure, calculate, or estimate the sources of measurement uncertainty. Summary of the Invention

[0009] Therefore, the inventors have recognized the need for automated reporting of measurement and classification uncertainties in flow cytometry data, such as through quantitative quality scoring. In particular, there is a need to improve the ability to distinguish between true biological variability and measurement errors. Embodiments of this disclosure address this need. Embodiments of this disclosure, for example, overcome the limitations of the prior art by correlating event-specific measurement uncertainties with event data. In the embodiments, methods for estimating event-specific measurement uncertainties are provided. Such improvements to the prior art can, among other things, improve sorting purity and yield.

[0010] This disclosure includes methods for identifying measurement uncertainties associated with light detected from a sample. The method according to some embodiments includes introducing a sample into a flow cytometer, allowing the introduced sample to flow in a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing in the flow stream, and identifying measurement uncertainties associated with the detected light. In some embodiments, measurement uncertainties are identified corresponding to individual particles in the sample. In some embodiments, measurement uncertainties are identified for individual parameters of the detected light on the particles in the sample. The method according to some embodiments further includes generating 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 are also provided for practicing the subject-matter methods. Attached Figure Description

[0011] This disclosure can be best understood by reading the following detailed description in conjunction with the accompanying drawings. The drawings include the following figures:

[0012] Figure 1A Exemplary results 100 of flow cytometry measurements of events with associated biological and measurement uncertainties are shown. Figure 1B An exemplary layered gate applied to a sample event is shown. Figure 1C A technique for resolving measurement uncertainty, based on existing technology, is shown. Figure 1D The present invention illustrates a technique for identifying measurement uncertainty on a per-parameter, per-event, per-gate, per-sample, or per-record basis, according to embodiments. Figure 1E An exemplary procedure for estimating measurement uncertainty according to the "GLS unmixing method" is shown. Figure 1F The measurement data storage according to the prior art is shown. Figure 1G A technique for recording measurement data together with measurement uncertainty data, according to an embodiment, is shown. Figure 1H A flowchart for identifying measurement uncertainty and classification uncertainty in flow cytometry data, according to an embodiment, is shown.

[0013] Figure 2 A flow cytometry system according to certain embodiments is presented.

[0014] Figure 3 An image-enabled particle sorter according to certain embodiments is described.

[0015] Figure 4 A functional block diagram of a particle analysis system according to certain embodiments is depicted.

[0016] Figure 5 A functional block diagram of an example control system according to certain embodiments is depicted.

[0017] Figures 6A-6B A schematic diagram of a particle sorting system according to certain embodiments is depicted.

[0018] Figure 7 Aspects of a computer control system according to certain embodiments are described. Detailed Implementation

[0019] This disclosure includes methods for identifying measurement uncertainties associated with light detected from a sample. The method according to some embodiments includes introducing a sample into a flow cytometer, allowing the introduced sample to flow in a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing in the flow stream, and identifying measurement uncertainties associated with the detected light. In some embodiments, measurement uncertainties are identified corresponding to individual particles in the sample. In some embodiments, measurement uncertainties are identified for individual parameters of the detected light on the particles in the sample. The method according to some embodiments further includes generating 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 are also provided for practicing the subject-matter methods.

[0020] Before describing this disclosure in more detail, it should be understood that this disclosure is not limited to the specific embodiments described, as such embodiments can certainly be varied. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of this disclosure will be defined only by the appended claims.

[0021] Where numerical ranges are provided, it should be understood that, unless the context explicitly states otherwise, every intermediate value (accurate to one-tenth of the lower limit unit) between the upper and lower limits of the range, as well as any other stated or intermediate value within the range, is included in this disclosure. The upper and lower limits of these smaller ranges may be independently included within the smaller ranges and also within this disclosure, but are subject to any specific exclusion boundaries within the ranges. Where the range includes one or both boundaries, any one or both included ranges excluding these included boundaries are also included in this disclosure.

[0022] Certain ranges presented in this article are preceded by the term "approximately". The term "approximately" as used herein is intended to provide textual support for the exact number preceding it, as well as for numbers that are close to or approximate to the number preceding it. In determining whether a number is close to or approximate to a specifically cited number, the close or approximate uncited number may be a number that provides a substantial equivalent to the specifically cited number in the context in which it is presented.

[0023] 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 pertains. Although any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of this disclosure, representative illustrative methods and materials are described hereafter.

[0024] All publications and patents referenced in this specification are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference to disclose and describe the methods and / or materials relating to the referenced publication. Any reference to a publication is for its disclosure prior to the filing date and should not be construed as an admission that this disclosure is not entitled to precede that publication by virtue of a prior disclosure. Furthermore, the publication dates provided may differ from the actual publication dates, which require independent verification.

[0025] It should be noted that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly specifies otherwise. It should also be noted that claims may be drafted to exclude any optional elements. Therefore, this statement is intended to serve as a basis for using exclusive terms such as “only,” “unique,” ​​or for using negative restrictions in the citation of elements of the claims.

[0026] As will be understood by those skilled in the art upon reading this disclosure, each individual embodiment described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any other plurality of embodiments without departing from the scope or spirit of this disclosure. Any cited method may be performed in the order of the cited events or in any other logically possible order.

[0027] Although the systems and methods have been or will be described in conjunction with functional interpretation for grammatical fluency, it should be clearly understood that, unless expressly stated in accordance with 35 U.SC §112, the claims should not be construed as necessarily being limited in any way to the construction of “apparatus” or “step”, but should be given the full meaning and equivalents of the claims as defined by the judicial principle of equivalents, and where the claims are expressly stated in accordance with 35 U.SC §112, they should be given all legal equivalents in accordance with 35 U.SC §112.

[0028] As described above, this disclosure provides a method for identifying measurement uncertainties associated with light detected from a sample. In further describing embodiments of this disclosure, a method for identifying measurement uncertainties associated with light detected from a sample is described, including identifying measurement uncertainties corresponding to individual particles in the sample, identifying measurement uncertainties for individual parameters of the detected light of the particles in the sample, and generating a quality score for each particle based on the measurement uncertainty of each particle. Next, systems, integrated circuit devices, and non-transitory computer-readable storage media programmed to practice the subject method are described.

[0029] background

[0030] As discussed in this article, flow cytometry is used to measure the characteristics of individual particles (also known as microparticles) or cells based on optical signals (i.e., detected light). A common use case for 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 emitted by a fluorescently labeled antibody in one or more spectral detection bands, or the amount of elastically scattered light measured at different angles (e.g., forward scattering and side scattering). In order to effectively identify and separate cell or particle populations based on these measured signals, practitioners must be able to determine whether the signal difference between two measured particles is due to genuine intrinsic differences between these particles (e.g., differences in biomarker expression, or differences in cell size or morphology), or whether the measured signal difference is due to random measurement errors (also known as measurement noise, measurement uncertainty, or measurement variability). Figure 1A An exemplary result 100 depicting a flow cytometry measurement of an event is shown, illustrating how the overall variation in such a measurement 103 is composed of the uncertainty of individual intrinsic biological variability 101 and measurement noise 102. Figure 1B An exemplary layered gating method applied to sample events is shown. Ultimately, the presence of measurement error affects the confidence in making a classification decision for a given particle (i.e., gating), as... Figure 1B As shown.

[0031] Measurement errors are unavoidable in flow cytometry and can be caused by a variety of sources, including, for example, electronic noise (e.g., Johnson-Nyquist noise), digitization errors, photon shot noise, or random variations in optical excitation and collection efficiency caused by random fluctuations in illumination intensity, fluid dynamics, optical alignment, photodetector gain, and other hardware components during experimental measurements. The magnitude of the error generated by each of these sources depends on the specific instrument, sample, and measurement conditions used for a given sample (e.g., photodetector gain, flow rate, etc.). The total uncertainty in a flow cytometry sample (i.e., the total uncertainty associated with the measurement of the detection light corresponding to an event in the flow cytometry sample) stems from a combination of the aforementioned measurement uncertainties and the inherent variability of the sample itself, such as the degree of random variation in the expression of biomolecules on a single population of cells of interest, or the degree of random variation in fluorescence intensity in synthetic fluorescent beads due to uncontrollable changes during manufacturing.

[0032] A significant source of measurement uncertainty in multicolor flow cytometry experiments is the 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 6:23389989; PMCID:PMC3678531. This manifests as an increase in the variance of the measured parameters (often the parameters of interest in bioanalysis) after spectral unmixing or compensation, due to photon shot noise caused by the fluorescence emission of one or more fluorophores co-expressed with another fluorophore of interest.

[0033] The ability to distinguish between real biological variability and measurement error is a significant limiting factor for sensitivity and resolution in flow cytometry assays. Embodiments of this disclosure address these limitations by providing novel and inventive techniques for distinguishing and taking into account measurement errors or measurement uncertainties, including within the context of flow cytometry experiments.

[0034] In some existing techniques, measurement errors in flow cytometry can be quantified in one of two ways: (i) first, by using controlled reference samples and protocols to a priori measure different sources of measurement noise; or (ii) second, by performing posterior statistical analysis on the measurement data.

[0035] In the first type of uncertainty measurement method in the prior art (item (i) above), a specific type of controlled reference sample is used to measure the measurement error of a given instrument. For example, a robust standard deviation (rSD) and robust coefficient of variation (rCV) of a given optical measurement parameter can be measured using calibration beads with low intrinsic variability and good characterization intensity. More sophisticated schemes can measure specific noise sources under controlled conditions. For example, one technique involves using electronic event triggering in a particle-free environment to measure signal-independent background noise (“B”) under different system conditions (e.g., with or without constant optical noise caused by the excitation source). In another example, a particle-free signal source (e.g., a light-emitting diode (LED)) can be used to characterize measurement errors consisting only of constant background and photon counting errors, without particle-dependent error sources (e.g., intrinsic intensity variations and fluid / optical fluctuations). Finally, in another example, representative biological samples, such as single-stain controls or fluorescence minus one (FMO) controls, can be used to infer the degree of total uncertainty that may exist in a fully stained sample of interest in a multicolor flow cytometry panel. The advantage or benefit of this first method of uncertainty measurement (i.e., the technique under section (i)) is that it allows for the measurement of specific sources of measurement variance that are separate from biological and sample-dependent sources of variation. However, the disadvantage or disadvantage of this first method (i.e., the technique under section (i)) is that the measurement uncertainty is not measured directly in the sample under the measurement conditions of the sample of interest itself, and, furthermore, the measurement uncertainty cannot be correlated with a specific individual cell or particle in a given dataset. Furthermore, in order to apply the statistical insights derived from these individual uncertainty measurements to a given sample of interest, the measurements must be manually correlated with each other, and some empirical corrections must be made to account for differences in measurement conditions between the calibration sample and the sample of interest.

[0036] In the second type of uncertainty measurement method in the prior art (section (ii) above), statistical techniques are applied to the data from the sample of interest after the data are obtained. A typical workflow would be to identify the subpopulation of interest by gating over a set of measurement parameters, and then apply appropriate statistical indices, such as robust standard deviation (rSD) or robust coefficient of variation (rCV), to the events in that subpopulation. Additional indices, such as staining indices or segregation indices, can be used to define the degree of statistical separation between two subpopulations relative to a given measurement parameter. Statistical confidence can be obtained by performing technical repetitions (e.g., measuring the same sample multiple times). The advantage of this second uncertainty measurement method (section (ii)) is that it describes the uncertainty of a particular sample and the population of interest under the exact measurement conditions used. However, a significant drawback of this method (section (ii)) 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 characteristics of the sample itself. This drawback or limitation prevents practitioners from distinguishing between measurement noise and biologically significant variability.

[0037] Figure 1C The diagram illustrates a prior art technique for resolving measurement uncertainties. Flowchart 130 begins with step 131: recording raw data from the flow cytometer. After completing 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 compensation or spectral unmixing technique can be used to perform compensation or spectral unmixing. Further details regarding spectral unmixing can be found in International Application No. PCT / US2021 / 026616, published under International Publication No. WO 2021 / 221884 A1, and International Application No. PCT / US2021 / 046741, published under International Publication No. WO 2022 / 076088 A1, the disclosures of which are each incorporated herein by reference. After completing step 132, flowchart 130 proceeds to step 133. In step 133, data analysis is performed on the flow cytometry data acquired in step 131. This analysis may include, for example, applying one or more gating or clustering algorithms. After completing step 133, flowchart 130 proceeds to step 134. In step 134, the results are reported, namely the results of processing the flow cytometry data in steps 132 and 133. Unlike embodiments of this disclosure, flowchart 130 does not identify and report a measure of measurement uncertainty on a per-parameter, per-particle, per-population, and / or per-sample basis.

[0038] Embodiments of this disclosure

[0039] Embodiments of this disclosure employ an alternative approach in which the measurement uncertainty of some or all measurement parameters of each particle in a flow cytometry sample is directly estimated, measured, or predicted, and the resulting measurement uncertainty metric is correlated with the flow cytometry sample data. In embodiments, the resulting metric of measurement uncertainty may be identified, reported, or recorded on a per-parameter, per-particle, per-population, and / or per-sample basis, or any combination thereof. Figure 1D The illustration depicts a technique for identifying measurement uncertainties on a per-parameter, per-event, per-gated, per-sample, or per-recording basis, according to one embodiment. Flowchart 140 begins with step 141: recording raw flow cytometry data. Subsequent processing occurs in subsequent steps of flowchart 140, where measurement data are processed in steps 141, 142, 146, and 149, and measurement uncertainties associated with these measurement data are processed in steps 143, 144, 145, 147, and 148. These raw cytometry data are further processed in steps 143 and 142. In step 142, compensation or spectral unmixing may be applied to the raw cytometry data. This compensation or spectral unmixing results in the calculation or estimation of derived parameters. In step 143, measurement uncertainties are estimated on a per-parameter or per-event basis based on the raw parameter data acquired in step 141. In step 144, measurement uncertainties are estimated on a per-parameter or per-event basis based on the derived parameters. In step 145, gating uncertainties are estimated on a per-event or per-parameter basis. In step 147, a summary of the measurement uncertainty is calculated, including the Q score for each event, as discussed herein. In step 147, such results are obtained on a per-record or per-sample basis. In step 146, analysis is performed on the flow cytometry data, such as applying gating or clustering algorithms, using derived parameters (i.e., unmixed data) to analyze the events. In step 148, a complete Q score statistics file is reported, such as recorded or displayed. In step 149, the complete results of the analyzed flow cytometry data are reported, including quality scores, such as Q scores, such as recorded or displayed. In this embodiment, the associated measurement uncertainty data, such as those calculated in steps 144, 145, and 147 of flowchart 140, can be used to provide more accurate data analysis, increase the statistical confidence of the analysis results, achieve higher fidelity unsupervised clustering, and report overall measurement quality and assay performance, etc.

[0040] The embodiments of this disclosure can be applied to single-cell analysis modalities other than flow cytometry. For example, the embodiments of this disclosure can be applied to sequencing-based single-cell proteomics, such as CITE-Seq (BD AbSeq).

[0041] Conceptual similarity in the context of next-generation sequencing

[0042] The embodiments of this disclosure are conceptually similar to the Phred quality score (“PhED”) commonly used in DNA sequencing. Q points number In the context of DNA sequencing, the Q score is defined as the negative logarithm of the probability of a given base call error, reported in logarithmic decibels (e.g., a Q score of 30 indicates a probability of 0.1% error, a Q score of 40 indicates a probability of 0.01% error, and so on). For each base call in each sequencing read in a DNA sequencing experiment, an associated Q score records the probability that the base call was correct. The sequence data (base calls) and Q score data are then correlated and stored, most commonly through the widely used FASTQ data format, which combines sequencing data and associated Q scores in a single data file.

[0043] In the context of flow cytometry, as in this disclosure, a single flow cytometry measurement event (e.g., measurement of a single particle) is analogous to a single sequencing read; a single measurement parameter associated with a measurement event is analogous to a single base determination; the flow cytometry uncertainty score per parameter, per event, is analogous to a Q score; and the modified FCS or the new “new SQ” format is analogous to the FASTQ format. Several important characteristics of the Q score exist in embodiments of this disclosure, including, for example, using a compressed Q score representation to save storage space, and using aggregated Q score metrics to indicate the overall read quality of a given sequence (flow cytometry event) or sequencing dataset (flow cytometry sample data).

[0044] Some key differences between sequencing Q scores and embodiments of this disclosure include, for example: sequencing data has a discrete value range (A, C, G, or T), while flow cytometry data has a continuous value range (floating-point or integer); and sequencing errors are described as binary classification errors (base determination is either correct or incorrect), whereas in embodiments, flow cytometry measurement errors for a given parameter can be continuously defined (e.g., the measurement parameter has some degree of uncertainty described by standard deviation or mean standard error). In embodiments, flow cytometry gating classification is also a binary classification problem (an event belongs to or outside a gating population depending on whether the corresponding event data falls within the range associated with the gating population), and in this sense, it exhibits behavior 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 particular population). In some cases, classification trees are used for binary classification of flow cytometry data. In other cases, hierarchical gating is used for binary classification of flow cytometry data. As described above, in some embodiments, binary classification in the context of flow cytometry is a form of dichotomy in which a continuous function is transformed into a binary variable; a continuous value can be transformed into a binary value by defining a cutoff value; and then the cutoff value is used to classify the corresponding event as positive or negative based on whether the event data value is higher or lower than the cutoff value.

[0045] Uncertainty estimation methods

[0046] In the embodiments, numerous methods exist for estimating measurement uncertainty metrics for flow cytometry datasets. Any convenient technique for estimating measurement uncertainty can be employed in the embodiments. Examples used in the embodiments are described herein. However, this disclosure is not limited to such metrics, and any metric for quantifying measurement uncertainty can be employed in the embodiments. In one embodiment, measurement uncertainty can be calculated from a semi-empirical noise model that combines instrument calibration data, a physical noise model, and measurements corresponding to a given event. Figure 1E Subfigures A, B, and C illustrate an exemplary process for estimating measurement uncertainty according to the “GLS unmixing method.” Further details of this exemplary technique can 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 by reference in its entirety.

[0047] In another embodiment, a statistical algorithm configured to measure the distribution characteristics of data within a sample is used to posteriorly estimate the measurement uncertainty on a per-event basis. In yet another embodiment, a combination of prior noise modeling and posterior distribution fitting can be employed, using methods such as Bayesian inference.

[0048] Uncertainty fractional measure

[0049] In embodiments, the specific numerical measure used to report uncertainty may include any measurement uncertainty measure, and these measures may vary. Furthermore, in embodiments, the type of measure may vary depending on the parameter. That is, for a given event, where multiple parameter measurements are associated, in embodiments, a different measurement uncertainty measure may be applied to each parameter. In embodiments, uncertainty scores for binary classification results generated by analyses (e.g., gating, population membership, etc.) may also be reported. (In embodiments, gating in the context of flow cytometry refers to the process of separating a specific population of events from a larger sample based on features such as size, granularity, and fluorescence. In embodiments, population membership or population gating refers to the process of classifying events into a population or subpopulation based on specific features 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, uncertainty scores for events in other binary classification results may, for example, be derived from other per-parameter uncertainty scores. Examples of metrics of interest include, for example: direct quantification (e.g., standard deviation); confidence intervals; the probability that an event measurement falls within a certain percentage of its true value; or residuals describing the goodness of fit of unmixed data. In an embodiment, a confidence interval is a range of values ​​that may contain unknown population parameter values. Such intervals represent a reasonable range for that parameter given a sample characteristic. Confidence intervals are derived from sample statistics and calculated using a specified confidence level. That is, estimations of statistics based on a population sample involve uncertainty, and a confidence interval specifies a range of values ​​within which the estimated statistic is expected to fall within a certain percentage of the time during experimental runs or population resampling. The confidence level is the percentage of times the estimated value is expected to recur between the upper and lower limits of the confidence interval. For example, for a confidence interval with a 95% confidence level, it can be expected that the estimated value will fall between the upper and lower limits of the confidence interval 95 times out of 100.

[0050] Gating quality score

[0051] Embodiments of this disclosure may include calculating a gating quality score. In an embodiment, a quality score may be calculated that estimates the probability that an event belongs to a member of a given gate. This probability may be defined for each gate in the gating hierarchy, or it may be hierarchically defined considering the probabilities of members of the parent gates of all gates. Figure 1B The illustration depicts a layered door and the possible members associated with an exemplary layered door.

[0052] application

[0053] The uncertainty scores calculated by embodiments of this disclosure have found applications in a variety of different contexts, including but not limited to: evaluating the statistical significance or confidence intervals of event classifications; variance-stabilizing transformations for data visualization and analysis; minimizing intra-cluster variance in supervised or unsupervised clustering techniques in data preprocessing; normalizing measurement uncertainty between datasets measured under different instruments or conditions in data standardization; and probabilistic analysis (“fuzzy logic”) for cell classification and sorting.

[0054] Format and data storage

[0055] In embodiments, uncertainty scores or measurements or results can be stored and associated with measurement data (e.g., raw flow cytometry data or compensated or unmixed flow cytometry data) in any convenient manner, and this manner can vary. Figure 1F The illustration shows a measurement data storage method according to the prior art, in which the measurement data is ultimately stored in FCS file format 170, but no measure of uncertainty for each parameter, event, gate, or sample or other measurement is estimated, calculated, or recorded.

[0056] Figure 1G This disclosure illustrates how embodiments of the present disclosure can record measurement data along with measurement uncertainty data. The multi-file model 171 according to an embodiment includes a single shared data record: the uncertainty score is added as an additional event-specific FCS parameter (e.g., for measurement parameter "number", an additional measurement parameter "number" is added (e.g., "number", and an additional uncertainty is also saved"). This multi-file approach 171 is a simple solution and exhibits some similarities to the sequencing FASTQ implementation described herein. A disadvantage or disadvantage of the multi-file model approach 171 is the increased file size and data storage requirements. Only the FCS 3.2+ format can achieve the storage space advantage of compressing the uncertainty score (e.g., 1 byte instead of 4 bytes), because earlier FCS formats store all values ​​as 32-bit floating-point numbers.

[0057] Other embodiments use a separate data recording method 172: generating a separate data file (e.g., named .FCSQ) containing uncertainty scores along with a typical streaming data file (.FCS) (i.e., according to the prior art). In embodiments, the separate data recording method allows uncertainty scores to be provided for all or only some parameters in the .FCS file.

[0058] In this embodiment, the aggregate uncertainty score at the group or record level can be stored as an additional keyword in the .FCS file header.

[0059] In this embodiment, to support repeatability and traceability, all metadata (including instrument calibration parameters and noise model information) required to generate an uncertainty score for a given record can be stored in the .FCS file header.

[0060] method

[0061] This disclosure includes methods for identifying measurement uncertainties associated with light detected from a sample. The method according to some embodiments includes introducing a sample into a flow cytometer, allowing the introduced sample to flow in a flow stream, illuminating the sample in the flow stream with a light source, detecting light from particles in the sample flowing in the flow stream, and identifying measurement uncertainties associated with the detected light. In some embodiments, measurement uncertainties are identified corresponding to individual particles in the sample. In some embodiments, measurement uncertainties are identified for individual parameters of the detected light from particles in the sample. The method according to some embodiments further includes generating a mass fraction for each particle based on the measurement uncertainty for each particle.

[0062] In embodiments, detecting light from particles includes measuring multiple parameters of the detected light from the particles in the sample. Some embodiments further include generating event data based on the detected light, wherein the event data includes parameter measurements of the particles in the sample. Other embodiments further include performing spectral analysis on the light detected from the particles. In some cases, performing spectral analysis on the light detected from the particles yields multiple derived parameters. Embodiments of the method disclosed herein further include generating multiple derived parameters for each particle by performing spectral analysis on the light detected from each particle. In some cases, the derived parameters include unmixed detected light. In other cases, measurement uncertainties of the derived parameters are identified.

[0063] The embodiments further include: identifying a first subset of particles in the sample by applying a first gate to the detected light. In the embodiments, the measurement uncertainty corresponds to the identification of the first subset of particles. Methods of interest also include: identifying multiple subsets of particles in the sample by applying corresponding multiple gates to the detected light. In some cases, the measurement uncertainty corresponds to the identification of each of the multiple subsets of particles. In the embodiments, the measurement uncertainty corresponds to gate-level uncertainty.

[0064] Embodiments of the method disclosed herein further include: generating a mass fraction (Q-score) for each particle based on the measurement uncertainty of each particle. Other embodiments include: recording each event and the associated measurement uncertainty for each event. Still other embodiments include: recording the measurement value for each particle and the associated measurement uncertainty for each particle. Certain other embodiments include: recording the measurement values ​​of multiple parameters for each particle and the associated measurement uncertainty for each parameter for each particle. Embodiments also include: recording measurement uncertainties, wherein the measurement uncertainty corresponds to measurement uncertainties of one or more of the following: each recorded parameter; each recorded event; each phylum or other classification; or the sample.

[0065] In embodiments, the measurement uncertainty of different parameters for particle measurements in a sample comprises different measures. In some embodiments, the measurement uncertainty corresponds to a binary classification event. In this case, the binary classification event may correspond to one or more of the following: gating decisions or group membership classification decisions. In embodiments, the measurement uncertainty includes measures that quantify the measurement uncertainty. Methods of interest further include generating an uncertainty score based on the measurement uncertainty. In embodiments, the measurement uncertainty is reflected in the uncertainty score, where a higher uncertainty score indicates greater measurement uncertainty. Other methods of interest include generating a quality score based on the measurement uncertainty. In embodiments, the measurement uncertainty is reflected in the quality score, where a higher quality score indicates higher confidence.

[0066] In embodiments, measurement uncertainty includes one or more of the following: direct quantification, optionally including standard deviation; confidence interval; the probability that the measured value falls within a specified percentage range of the true value; or residuals related to the goodness of fit of the unmixed data. In some embodiments, measurement uncertainty is related to binary classification, wherein binary classification optionally includes one or more of gating or group membership classification.

[0067] Embodiments of the method disclosed herein further include: calculating a quality score based on measurement uncertainty, wherein the quality score reflects the probability of a member in a door. In one embodiment, the probability of a member in a door is calculated for each door in a door hierarchy. In other embodiments, the probability of a member in a door is calculated considering each hierarchical parent door of the door. In other embodiments, identifying measurement uncertainty associated with detected light includes: calculating the measurement uncertainty based on a semi-empirical noise model, wherein the semi-empirical noise model includes one or more of the following: instrument calibration data, a physical noise model, or a measurement value. In still other embodiments, identifying measurement uncertainty associated with detected light includes: estimating the measurement uncertainty on a per-event basis based on a statistical algorithm, wherein the statistical algorithm includes properties of the distribution of data within the measurement sample.

[0068] In some embodiments, identifying measurement uncertainties associated with detected light includes estimating the measurement uncertainty by applying a combination of noise modeling and distribution fitting. In some embodiments, identifying measurement uncertainties associated with detected light includes estimating the measurement uncertainty by applying Bayesian inference.

[0069] Examples of interest also include: reporting measurement uncertainties for each particle in a subset of particles in a sample. Other embodiments include: reporting measurement uncertainties for multiple parameters for each particle in a subset of particles in a sample. Still other embodiments include: reporting measurement uncertainties associated with membership in a subgroup defined by a phylogenetic tree for each particle in the subset of particles.

[0070] In some embodiments, measurement uncertainty includes random measurement error. In some embodiments, measurement uncertainty includes spillover diffusion error. In other embodiments, measurement uncertainty reflects variations between sample particles other than true intrinsic differences. In still other embodiments, measurement uncertainty reflects confidence in a particle classification decision regarding the sample. In some cases, measurement uncertainty reflects aggregate measurement uncertainty related to multiple parameter measurements of light detected from particles. In other cases, measurement uncertainty reflects aggregate measurement uncertainty related to measurements of light detected from multiple particles. In still other cases, measurement uncertainty reflects aggregate measurement uncertainty related to the sample.

[0071] In an embodiment, identifying measurement uncertainty associated with the detected light includes one or more of the following: estimating measurement uncertainty associated with the detected light, measuring measurement uncertainty associated with the detected light, or predicting measurement uncertainty associated with the detected light.

[0072] Methods of interest also include: classifying particles based on detected light; and calculating the statistical significance of particle classification based at least in part on measurement uncertainty. Other methods of interest include: classifying particles based on detected light; and calculating confidence intervals for particle classification based at least in part on measurement uncertainty. Still other methods of interest include: applying a variance-stabilizing transformation to data including measurements of light detected from particles in a sample, based at least in part on measurement uncertainty; and visualizing aspects of the transformed data. Some embodiments also include: 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; and applying a clustering algorithm to the preprocessed data, wherein the clustering algorithm optionally includes one or more of supervised and unsupervised clustering algorithms. Other embodiments also include: standardizing data including measurements of light detected from particles in a sample, based at least in part on measurement uncertainty, to normalize measurement uncertainty across different datasets, wherein the different datasets optionally include one or more datasets collected on different instruments or datasets collected under different conditions. In some cases, the method also includes: using measurement uncertainty for probabilistic analysis of particle classification or sorting, wherein probabilistic classification may optionally include the application of fuzzy logic techniques.

[0073] In one embodiment, the method is a method for distinguishing between true variability of sample particles and measurement error. In other embodiments, the method is a method for distinguishing between true biological variability and measurement error. In still other embodiments, the method is a method for improving the sensitivity of flow cytometry assays. In some cases, the method is a method for improving the resolution of flow cytometry assays. In other cases, the method is a method for evaluating the quality of particle analysis results by correlating event-specific measurement uncertainty estimates with flow cytometry measurements.

[0074] In embodiments, a quantitative measure of measurement uncertainty is associated with a flow cytometry measurement. Embodiments also include using measurement uncertainty in data analysis or sorting. Some embodiments further include identifying a gate member confidence score for each event and each gate, wherein the gate member confidence score includes the probability that the true biological expression level of a given event falls within a given gate.

[0075] In some cases, this method is a method for calculating gate member confidence scores. Some embodiments also include using gate member confidence scores based on measurement uncertainty in particle classification and sorting, wherein particle classification and sorting includes probabilistic sorting with configurable probability thresholds to maximize purity and / or yield.

[0076] In this embodiment, a light detection system is used to detect light. In some cases, the light detection system detects light in multiple photodetector channels. In other cases, the light detection system includes multiple photodetectors.

[0077] Figure 1H A flowchart 180 is depicted according to certain embodiments for identifying measurement and classification uncertainties in flow cytometry data. As discussed in more detail herein, in step 181, a sample is introduced into the flow cytometer; in step 182, the sample is allowed to flow in a flow stream; in step 183, the sample in the flow stream is illuminated with a light source; in step 184, light from particles in the sample flowing in the flow stream is detected; and in step 185, measurement uncertainties associated with the detected light are identified. Further details regarding each of steps 181, 182, 183, 184, and 185 are described herein.

[0078] In some cases, the samples analyzed in this method are biological samples. The term "biological sample," used in its conventional sense, refers to a subset of tissues, cells, or components of a whole organism, plant, fungus, or animal, which in some cases may be present in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Therefore, "biological sample" refers both to the original organism or a subset of its tissues and to homogenates, lysates, or extracts prepared from such organisms or subsets of their tissues, including but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory system, gastrointestinal tract, cardiovascular system and genitourinary tract, tears, saliva, breast milk, blood cells, tumors, and organs. Biological samples can be any type of biological tissue, including healthy and diseased tissues (e.g., cancerous, malignant, necrotic, etc.). In some embodiments, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc., wherein in some cases the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger prick (where the blood may or may not be mixed with any reagents such as preservatives, anticoagulants, etc. before testing).

[0079] In some embodiments, the sample source is "mammal" or "mammal," terms that are broadly used to describe organisms within the class Mammalia, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method can be applied to samples obtained from both sexes and human subjects at any developmental stage (i.e., newborns, infants, adolescents, teenagers, and adults), wherein in some embodiments, the human subject is an adolescent, teenager, or adult. While the invention can be applied to samples from human subjects, it should be understood that the method can also be implemented for samples from other animal subjects (i.e., in "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0080] Cells of interest can be targeted and characterized based on various parameters, such as phenotypic features identified by attaching specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analytical droplets determined to contain target cells. A variety of cells can be characterized using the methods described in this subject matter. Target cells include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells with readily available cell surface markers or antigens that can be captured or labeled by readily available affinity reagents or conjugates thereof. For example, target cells may include cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminal), SSEA-3, TRA-1-60 antigen, disialotetrahexosylganglioside GD2, and CD71. In some embodiments, target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) derived from whole blood, bone marrow, or umbilical cord blood.

[0081] In practicing the methods described herein, a certain amount of initial fluid sample is injected into the flow cytometer. The amount of sample injected into the particle sorting module can vary, for example, ranging from 0.001 mL to 1000 mL, for example from 0.005 mL to 900 mL, for example from 0.01 mL to 800 mL, for example from 0.05 mL to 700 mL, for example from 0.1 mL to 600 mL, for example from 0.5 mL to 500 mL, for example from 1 mL to 400 mL, for example from 2 mL to 300 mL, and includes samples ranging from 5 mL to 100 mL.

[0082] The method according to embodiments of this disclosure includes counting and optionally sorting labeled particles (e.g., target cells) in a sample. In practicing this subject matter method, a fluid sample containing particles is first introduced into a flow nozzle of the system. After exiting the flow nozzle, the particles pass through a sample detection region substantially one at a time, where each particle is illuminated by a light source, and the light scattering parameters and (in some cases) the desired fluorescence emission (e.g., measurements of two or more light scattering parameters and one or more fluorescence emissions) for each particle are recorded. Depending on the characteristics of the detected flow, a flow of 0.001 mm or more, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, and including a flow of 1 mm or more, can be illuminated. In some embodiments, the method includes illuminating a planar cross-section of the flow in the sample detection region, for example, with a laser (as described above). In other embodiments, the method includes illuminating a predetermined length of flow in the sample detection region, such as corresponding to an illumination profile of a diffuse laser beam or lamp.

[0083] In some embodiments, the method includes atomizing the flow at or near the flow cell nozzle orifice. For example, the method may include atomizing the flow at a distance of approximately 0.001 mm or more from the nozzle orifice, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, and includes atomizing the flow at a distance of 1 mm or more from the nozzle orifice. In some embodiments, the method includes atomizing the flow immediately adjacent to the flow cell nozzle orifice.

[0084] In embodiments of this method, a detector, such as a photomultiplier tube (PMT), is used to record light passing through each particle (in some cases referred to as forward scattered light), light reflected perpendicularly in the direction the particle flows through the detection area (in some cases referred to as vertical or side scattered light), and, if the particle is labeled with a fluorescent marker, fluorescence emitted from the particle as it passes through the detection area and is illuminated by energy. Forward scattered light (FSC), side scattered light (SSC), and fluorescence emission each include individual parameters for each particle (or each "event"). Therefore, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle can be analyzed in real time or stored as needed in a data storage and analysis device (e.g., a computer).

[0085] 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 needed. The fluorescence in the detection channels used to identify the particles and their associated binding complexes can be measured after excitation with a single light source or individually after excitation with different light sources. If separate excitation sources are used to excite the particle markers, the markers can be selected such that all markers can be excited by each excitation source available.

[0086] In some embodiments, the method further includes data acquisition, analysis, and recording, for example using a computer, where multiple data channels record data from each detector, as well as fluorescence scattered and emitted by each particle as it passes through the sample detection area of ​​the particle sorting module. In these embodiments, the analysis includes classifying and counting particles such that each particle represents a set of digitized parameter values. The subject system can be configured to trigger on selected parameters to distinguish particles of interest from background and noise. "Trigger" refers to a preset threshold for the detection parameter and can be used as a means of detecting particles passing through the light source. Detecting an event exceeding the selected parameter threshold triggers the acquisition of light scattering and fluorescence data for that particle. Data for particles or other components that elicit a response below the threshold in the test medium will not be acquired. The trigger parameter could be the detection of positively scattered light caused by a particle passing through a light beam. Flow cytometry then detects and collects the light scattering and fluorescence data for that particle.

[0087] Then, specific target subpopulations are further analyzed by “gating” based on data collected for the entire population. To select an appropriate gate, the data is plotted to obtain the best possible subpopulation separation. This process can be performed by plotting forward scattered light (FSC) and side-scattered light (SSC) on a two-dimensional dot plot. Particle subpopulations (i.e., those cells within the gate) are then selected, and particles not within the gate are excluded. If needed, gates can be selected by drawing lines around the desired subpopulation using a cursor on the computer screen. Other parameters (e.g., fluorescence) of only these particles within the gate are then plotted and analyzed. If necessary, the above analysis can be configured to determine the count of particles of interest in the sample.

[0088] Methods of interest may also include the use of particles in research, laboratory testing, or treatment. In some embodiments, the methods of this subject matter include obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the methods of this subject matter include obtaining cells from a fluid or tissue sample for use as research or diagnostic specimens for diseases such as cancer. Similarly, the methods of this subject matter include obtaining cells from a fluid or tissue sample for treatment. A cell therapy protocol is a protocol in which viable cellular materials, including, for example, cells and tissues, are prepared and introduced into a subject as a therapeutic treatment. Conditions that can be treated by administering samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system disorders, organ damage, etc.

[0089] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, genetic modification, in vitro culture and expansion, cell harvesting, sample volume reduction and washing, biopreservation, storage, and introduction of cells into a subject. The protocol can begin by collecting viable cells and tissues from a subject's tissue to generate cell and / or tissue samples. Samples can be collected using any suitable procedure, such as administering cell mobilizing agents to the subject, drawing blood from the subject, or extracting bone marrow from the subject. After sample collection, cells can be enriched using various methods, including, for example, centrifugation-based methods, filtration-based methods, washing, magnetic separation methods, fluorescence-activated cell sorting (FACS), etc. In some cases, the enriched cells can be genetically modified using any convenient method, such as nuclease-mediated gene editing. Genetically modified cells can then be cultured, activated, and expanded in vitro. In some cases, cells are preserved, such as cryopreserved, and stored for future use, where the cells are thawed and then administered to the patient, for example, by infusion into the patient.

[0090] A system for identifying measurement uncertainties associated with light detected from a sample.

[0091] As described above, aspects of this disclosure include systems for identifying measurement uncertainties associated with light detected from a sample. In embodiments, the systems are configured to practice the methods of this subject matter. Aspects of this disclosure also include flow cytometers. Flow cytometers of interest include a light source configured to illuminate particles in the flow stream at a detection point within a flow cell. Flow cytometers of interest also include a light detection system comprising a plurality of photodetectors.

[0092] A flow cell of interest includes a cuvette configured for delivering particles in a flow stream. As described herein, a “flow cell” is described in its conventional sense as an assembly containing a flow channel for a liquid flow stream to deliver particles in a sheath fluid. The cuvette of interest has a channel (i.e., a flow channel) extending therethrough. The flow stream configured by the flow channel may include a liquid sample injected from a sample tube. In some cases, the flow cell includes a light-accessible flow channel. The cuvette may be made of, for example, quartz, glass, transparent plastic, etc. In some embodiments, the cuvette is formed of silica, such as fused silica. In some cases, the flow cell is configured to be illuminated by light from a light source at one or more detection points. A “detection point” discussed herein refers to an area within the flow cell in which particles are illuminated by light from a light source, for example, for analysis. The size of the detection point can vary as needed. For example, where 0 μm represents the axis of light emitted by the light source, the range of detection points can be from -50 μm to 50 μm, for example -25 μm to 40 μm, and includes -15 μm to 30 μm. Depending on certain factors (such as the number and arrangement of lasers), there may be multiple irradiation points within the flow cell.

[0093] In some embodiments, the flow cell includes or is configured for use with a sample injection port, which is configured to provide a sample to the flow cell. In embodiments, the sample injection system is configured to provide a suitable sample flow into the flow cell chamber (i.e., the flow channel). Depending on the desired characteristics of the flow, the sample rate delivered from the sample injection port to the flow cell chamber can be 1 μL / min or higher, for example 2 μL / min or higher, for example 3 μL / min or higher, for example 5 μL / min or higher, for example 10 μL / min or higher, for example 15 μL / min or higher, for example 25 μL / min or higher, for example 50 μL / min or higher, and includes 100 μL / min or higher. In some cases, the sample rate delivered from the sample injection port to the flow cell chamber is 1 μL / sec or higher, for example 2 μL / sec or higher, for example 3 μL / sec or higher, for example 5 μL / sec or higher, for example 10 μL / sec or higher, for example 15 μL / sec or higher, for example 25 μL / sec or higher, for example 50 μL / sec or higher, and includes 100 μL / sec or higher.

[0094] The sample injection port can be an orifice located in the inner chamber wall or a conduit located near the proximal end of the inner chamber. When the sample injection port is an orifice located in the inner chamber wall, the orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to: linear cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses, etc.; and irregular shapes, such as a parabolic bottom portion connecting to a planar top portion. In some embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice can vary depending on the shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0095] In some cases, the sample injection port is a conduit located near the proximal end of the flow cell chamber. For example, the sample injection port may be a conduit positioned to align its orifice with the flow cell orifice. When the sample injection port is a conduit aligned with the flow cell orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, including but not limited to: straight cross-sectional shapes such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes such as circles, ellipses; and irregular shapes, such as a parabolic bottom portion connecting to a planar top portion. The size of the conduit orifice can vary depending on the shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip with a bevel angle ranging from 1° to 10°, such as from 2° to 9°, such as from 3° to 8°, such as from 4° to 7°, and including a 5° bevel.

[0096] In some embodiments, the flow cell further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid into the flow cell chamber, for example, in combination with a sample to generate a laminar flow around the sample. Depending on the desired characteristics of the flow flow, the sheath fluid rate delivered to the flow cell chamber can be 25 μL / sec or higher, for example 50 μL / sec or higher, for example 75 μL / sec or higher, for example 100 μL / sec or higher, for example 250 μL / sec or higher, for example 500 μL / sec or higher, for example 750 μL / sec or higher, for example 1000 μL / sec or higher, and includes 2500 μL / sec or higher.

[0097] In some embodiments, the sheath fluid injection port is an orifice located in the inner chamber wall. The sheath fluid injection port orifice can be of any suitable shape, wherein the cross-sectional shapes of interest include, but are not limited to: straight cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses; and irregular shapes, such as a parabolic bottom portion connecting to a planar top portion. The size of the sheath fluid injection port orifice can vary depending on the shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 mm to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0098] The flow cytometer disclosed herein includes a light source configured to illuminate particles in a flow stream at a detection point within a flow cell. The number of light sources in the flow cytometer can vary. In some embodiments, the flow cytometer includes a single light source. Alternatively, the flow cytometer may, in certain cases, include multiple light sources. In some such cases, the number of light sources ranges from 2 to 10, for example 2 to 5, and includes 2 to 4. Any convenient light source can be used as the light source described herein. In some embodiments, the light source is a laser. In embodiments, the laser can be any convenient laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some 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 CO2 laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or combinations thereof. In other cases, the system of interest includes dye lasers, such as stilbene, coumarin, or rhodamine lasers. However, in still other cases, lasers of interest include metal vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, or combinations thereof. In other cases, the subject system includes solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, N7:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium trioxide lasers, or cerium-doped lasers and combinations thereof.

[0099] The laser source according to some embodiments may also include one or more optical adjustment components. In some embodiments, the optical adjustment components are located between the source and the flow cell and may include any means capable of changing the spatial width of the illumination or certain other characteristics of the light source illumination, such as the illumination direction, wavelength, beam width, beam intensity, and focal spot. The optical adjustment scheme may include any convenient means for adjusting one or more characteristics of the source, including but not limited to lenses, mirrors, filters, optical fibers, wavelength splitters, pinholes, slits, collimation schemes, 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 reducing lens. In other embodiments, the flow cytometer of interest includes optical fibers.

[0100] The light source can be positioned at any suitable distance from the flow cell, for example, 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, and at a distance of 100 mm or more. Additionally, the light source can be positioned relative to the flow cell at any suitable angle, for example, an angle ranging from 10 degrees to 90 degrees, from 15 degrees to 85 degrees, from 20 degrees to 80 degrees, from 25 degrees to 75 degrees, and including angles from 30 degrees to 60 degrees, such as a 90-degree angle.

[0101] In some embodiments, the light source of interest includes a plurality of lasers configured to provide laser light for discrete illumination of the flowing stream. The plurality of lasers may be, for example, two or more lasers, three or more lasers, four or more lasers, five or more lasers, ten or more lasers, and may include 15 or more lasers configured to provide laser light for discrete illumination of the flowing stream. Depending on the wavelength required to illuminate the flowing stream, each laser may have a specific wavelength varying from 200 nm to 1500 nm, for example from 250 nm to 1250 nm, for example from 300 nm to 1000 nm, for example from 350 nm to 900 nm, and including wavelengths from 400 nm to 800 nm. In some embodiments, the lasers of interest may include one or more of 405 nm, 488 nm, 561 nm, and 635 nm lasers.

[0102] In some embodiments, the light source is a beam generator configured to generate two or more frequency-shifted beams. In some cases, the beam generator includes a laser or a radio frequency (RF) generator configured to apply an RF drive signal to the acousto-optic device to generate two or more angle-deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, lasers in the beam generator of interest include those listed above.

[0103] The acousto-optic device can be any convenient acousto-optic scheme configured to frequency-shift a laser using applied acoustic waves. In some embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate an angle-deflected laser beam from the light from the laser and an applied radio frequency (RF) drive signal. Any suitable RF drive signal source can be used to apply the RF drive signal to the acousto-optic device, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0104] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angle-deflected laser beams in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0105] In some cases, in order to generate the intensity distribution of the angle-deflected laser beam in the output laser beam, the controller is configured to apply an RF drive signal with varying amplitude, for example from about 0.001V to about 500V, for example from about 0.005V to about 400V, for example from about 0.01V to about 300V, for example from about 0.05V to about 200V, for example from about 0.1V to about 100V, for example from about 0.5V to about 75V, for example from about 1V to 50V, for example from about 2V to 40V, for example from 3V to about 30V, and including from about 5V to about 25V. Each applied radio frequency drive signal has, in some embodiments, a frequency ranging from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and including frequencies ranging from about 5 MHz to about 50 MHz.

[0106] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with angle-deflected laser beams having a desired intensity distribution. For example, the memory may include instructions to generate two or more angle-deflected laser beams with the same intensity, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and the memory may include instructions to generate 100 or more angle-deflected laser beams with the same intensity. In other embodiments, the memory may include instructions to generate two or more angle-deflected laser beams with different intensities, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and the memory may include instructions to generate 100 or more angle-deflected laser beams with different intensities.

[0107] In some embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from the edge to the center. In these cases, the intensity of the angle-deflected laser beam at the center of the output beam along the horizontal axis can be from 0.1% to about 99%, for example from 0.5% to about 95%, for example from 1% to about 90%, for example from about 2% to about 85%, for example from about 3% to about 80%, for example from about 4% to about 75%, for example from about 5% to about 70%, for example from about 6% to about 65%, for example from about 7% to about 60%, for example from about 8% to about 55%, and includes about 10% to about 50%. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from the edge to the center. In these cases, the intensity of the angle-deflected laser beam at the edge of the output laser beam along the horizontal axis can be from 0.1% to about 99%, for example from 0.5% to about 95%, for example from 1% to about 90%, for example from about 2% to about 85%, for example from about 3% to about 80%, for example from about 4% to about 75%, for example from about 5% to about 70%, for example from about 6% to about 65%, for example from about 7% to about 60%, for example from about 8% to about 55%, and including about 10% to about 50%. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with a Gaussian intensity distribution along the horizontal axis. In other embodiments, the controller has a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with a top hat intensity distribution along the horizontal axis.

[0108] In embodiments, the beam generator of interest can be configured to generate spatially separated angle-deflected laser beams within the output laser beam. Depending on the applied radio frequency drive signal and the desired illumination profile of the output laser beam, the angle-deflected laser beams can be spaced 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and include a distance of 5000 μm or more. In some embodiments, the system is configured to generate overlapping angle-deflected laser beams within the output laser beam, for example, overlapping with adjacent angle-deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., spot overlap) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and includes 100 μm or more.

[0109] In some cases, beam generators configured to generate two or more frequency-shifted beams include, for example, U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; and 10,684. The laser excitation modules described in ,211; 10,845,295; 10,935,482; 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851 are disclosed herein by reference.

[0110] Furthermore, the flow cytometer includes detectors configured to collect light emitted by irradiated particles. The photodetectors are configured to detect particle-modulated light transmitted by the fiber optic light collecting element and generate a signal based on the characteristics of that light, such as intensity. For example, one or more particle-modulated photodetectors may include one or more side-scatter photodetectors for detecting side-scattered light wavelengths (i.e., light refracted and reflected from the particle surface and internal structure). In some embodiments, the flow cytometer includes a single side-scatter photodetector. In other embodiments, the flow cytometer includes multiple side-scatter photodetectors, such as two or more, three or more, four or more, and including five or more.

[0111] Any convenient detector for detecting the collected light can be used for the side-scattered light detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof. In some embodiments, the collected light is measured using a charge-coupled device (CCD), semiconductor charge-coupled device (CCD), active pixel sensor (APS), complementary metal-oxide-semiconductor (CMOS) image sensor, or N-type metal-oxide-semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, for example, with an effective detection surface of 0.01 cm² per region. 2 up to 10cm 2 For example, 0.05cm 2 up to 9cm 2 For example, 0.1cm 2 up to 8cm 2 For example, 0.5cm 2 up to 7cm 2 and 1cm 2 up to 5cm 2 Photomultiplier tubes within the range.

[0112] In one embodiment, the flow cytometer further includes a fluorescence detector configured to detect light at one or more fluorescence wavelengths. In other embodiments, the flow cytometer includes multiple fluorescence detectors, such as two or more, three or more, four or more, five or more, ten or more, fifteen or more, and including twenty or more.

[0113] Any convenient detector for detecting the collected light can be used for the fluorescence detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof. In some embodiments, the collected light is measured using a charge-coupled device (CCD), semiconductor charge-coupled device (CCD), active pixel sensor (APS), complementary metal-oxide-semiconductor (CMOS) image sensor, or N-type metal-oxide-semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, for example, with an effective detection surface of 0.01 cm² per region. 2 up to 10cm 2 For example, 0.05cm 2 up to 9cm 2 For example, 0.1cm 2 up to 8cm 2 For example, 0.5cm 2 up to 7cm 2 and 1cm 2 up to 5cm 2 Photomultiplier tubes within the range.

[0114] In the case of a flow cytometer that includes multiple fluorescence detectors, each fluorescence detector may be identical, or the set of fluorescence detectors may be a combination of detectors of different types. For example, in the case of a flow cytometer that includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first and second fluorescence detectors are CCD-type devices. In still other embodiments, both the first and second fluorescence detectors are CMOS-type devices. In still other embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS-type device and the second fluorescence detector is a photomultiplier tube. In still other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0115] In embodiments of this disclosure, the fluorescence detector of interest is configured to measure collected light at one or more wavelengths, such as at two or more wavelengths, such as at five or more different wavelengths, such as at ten or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and includes measuring light emitted from a sample in the flow stream at 400 or more different wavelengths. In some embodiments, two or more detectors in the module described herein are configured to measure collected light at the same or overlapping wavelengths.

[0116] In some embodiments, the fluorescence detector of interest is configured to measure collected light within a wavelength range (e.g., 200 nm–1000 nm). In some embodiments, the detector of interest is configured to collect a spectrum within a wavelength range. For example, a flow cytometer may include one or more detectors configured to collect a spectrum in one or more wavelength ranges from 200 nm to 1000 nm. In other embodiments, the detector of interest is configured to measure light emitted by a sample in a flowing stream at one or more specific wavelengths. For example, the module may include one or more detectors configured to measure light at or above one of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In some embodiments, one or more detectors may be configured to pair with a specific fluorophore, such as a fluorophore used with the sample in a fluorescence assay.

[0117] Flow cytometers may include any suitable mechanisms for supplying sheath fluid and sample fluid to the sample fluid input coupler and the sheath fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., tubing) fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line fluidly connected to a sheath fluid reservoir. Similarly, flow cytometers may include any suitable mechanisms for managing waste from the flow stream. A fluid output coupler may be fluidly connected to a waste stream line fluidly connected to a waste reservoir. A fluid management system suitable for the subject flow cytometer is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0118] Suitable flow cytometry systems may include, but are not limited to, those in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo et al. (2012), Ann Clin Biochem, January; 49(pt 1):17-28; Linden et al., Semin Thrombosis and Hemostasis, October 2004; 30(5):502-11; Alison et al., Journal of Pathology (J Pathol, December 2010; 222(4):335-344; and Herbig et al. (2007) Crit Rev Ther Drug Carrier System 24(3):203-255; the contents of which are hereby incorporated herein by reference. In some instances, flow cytometry systems of interest include BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II flow cytometer, BDAccuri TM Flow cytometer, BD Accuri TM C6 Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TMX-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TM Flow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter, BD Biosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BDBiosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, BD Biosciences FACSAria TM II Cell Sorter, BD Biosciences FACSAria TM III Cell Sorter, BD Biosciences FACSAria TM Fusion Cell Sorter and BD Biosciences FACSMelody TM Cell sorter, BDBiosciences FACSymphony TM S6 cell sorter, etc.

[0119] In some embodiments, the subject system is a flow cytometry system, such as U.S. Patent Nos. 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074; 10,302,545; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9 The following are listed in ,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; 4,498,766; their disclosures are incorporated herein by reference in their entirety.

[0120] In some embodiments, the flow cytometer is configured as an imaging flow cytometer. For example, in some cases, the subject system is a flow cytometry system configured to image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), such as those described by Diebold et al. in Nature Photonics, Vol. 7 (10); 806-810 (2013); and U.S. Patent Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,036,699; 10,078,045; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,111; Those described in 10,684,211; 10,845,295; 10,935,482; 10,935,485; 11,105,728; 11,280,718; 11,327,016; 11,366,052; 11,371,937; 11,692,926; 11,630,053; 11,774,343; 11,940,369; and 11,946,851; the disclosures of which are incorporated herein by reference. In some embodiments, the flow cytometer is a particle sorter, which is an image-supporting particle sorter. Image-supporting particle sorters are described in U.S. Patent Nos. 10,324,019; 10,620,111; 11,105,728; and 11,774,343; and U.S. Patent Application Nos. 18 / 537,103; 18 / 657,618; 18,657,623 and 18 / 657,633, the disclosures of which are incorporated herein by reference in their entirety.

[0121] Figure 2 A system 200 for flow cytometry according to an illustrative embodiment of this disclosure is shown. System 200 includes a laser 201 configured to irradiate particles 211 in a flow stream 214 at a detection point 215 within a flow cell 210. Although Figure 2 The example shows a single laser, but it should be understood that multiple lasers can also be used. The laser beam from laser 201 is directed to focusing lens 202, which focuses the beam onto the portion of the fluid flow containing sample particles 211 within flow cell 210. Flow cell 210 is part of a fluid system that directs particles (typically one at a time) in the flow to the focused laser beam for detection. Alternatively, in the case of a flow cytometer that is a flow air cytometer, the nozzle top can be used.

[0122] like Figure 2As shown, flow cell 210 is fluidly connected to a sheath fluid reservoir 203 containing sheath fluid and a sample fluid reservoir 204 containing sample fluid. Sheath fluid in sheath fluid reservoir 203 is supplied via conduit (i.e., sheath fluid line) 207 to at least one sheath fluid injection port 208. Furthermore, sample fluid containing particles 211 from sample fluid reservoir 204 is supplied via conduit (i.e., sample fluid line) 205 to sample injection port 206. Sample injection port 206 is fluidly connected to a sample 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 by the sheath fluid entering from sheath fluid injection port 208, such that a flow stream 214 is formed downstream of the conical portion 212 of flow cell 210. Particles emitted at the distal end of flow cell 210 can be disposed of and / or collected by any suitable method. For example, depending on the type of flow cytometry performed, particles can be collected at the distal end of flow cell 210, for example, via a waste line. Alternatively, particles can be sorted.

[0123] Light from one or more laser beams interacts with particles 211 in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various wavelengths depending on the characteristics of the particles (e.g., their size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on the particles). Fluorescence emission, as well as diffracted, refracted, reflected, and scattered light, can be routed to one or more detectors. Specifically, forward scattered light (FSC) is routed to forward scattered light detector 223. Forward scattered light detector 223 is positioned slightly off-center from the beam axis that passes directly through flow cell 210 and is configured to detect diffracted light, i.e., excitation light that passes through or around the particles primarily in the forward direction. The intensity of the light detected by forward scattered light detector 223 depends on the overall size of the particles. The forward scattered detector may include, for example, a photodiode. Positioned between forward scattered light detectors 223 are optical filters 221a and scattering baffles 222. Optical filter 221a can be configured to filter out non-FSC light of at least one wavelength, while scattering baffle 222 can be configured to prevent the incident beam (i.e. non-scattered light) from laser 201 from being detected by forward scattering detector 223.

[0124] Furthermore, side-scattered light (SSC) is detected by side-scattered light detector 224. In other words, side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structure of particle 211, which tends to increase with increasing particle structure complexity. Figure 2In the example, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to a side-scatter light detector 224 while transmitting non-SSC (e.g., fluorescence) light. An optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by the side-scatter light detector 224. Fluorescence detectors 225a-225c are also shown, each configured to detect fluorescence of a different wavelength. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or wavelength range) to the fluorescence detector 225a while transmitting light of other wavelengths. The optical filter 221c may be configured to prevent the fluorescence detector 225a from detecting light of at least one wavelength that does not correspond to the first wavelength (or wavelength range). Similarly, the dichroic mirror 220c is configured to reflect FL light corresponding to a second wavelength (or wavelength range) to the fluorescence detector 225b while transmitting light of a third wavelength (or wavelength range) for detection by the fluorescence detector 225c. Optical filter 221d is configured to prevent fluorescence detector 225b from detecting light of at least one wavelength that does not correspond to the second wavelength (or wavelength range). Furthermore, optical filter 221e is configured to prevent fluorescence detector 225c from detecting light of at least one wavelength that does not correspond to the third wavelength (or wavelength range).

[0125] Those skilled in the art will recognize that the flow cytometer according to embodiments of this disclosure is not limited to... Figure 2 The flow cytometer described herein may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors, and may operate at various wavelengths and in various different configurations. For example, although Figure 2 The example shown is for illustrative purposes and features three fluorescence detectors, but it should be understood that any suitable number of fluorescence detectors may be used.

[0126] During operation, the cytometer is controlled by a controller / processor 290, and measurement data from the detectors can 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 them and can also be coupled to the electrical and electromechanical components of the flow cytometer to control the laser 201, fluid flow parameters, etc. Input / output (I / O) capability 297 may also be provided in the system. Memory 295, controller / processor 290, and I / O 297 may be provided entirely as integral components of the flow cytometer. In such an embodiment, a display may also form part of the I / O capability 297 for presenting experimental data to the user of the cytometer 200. Alternatively, memory 295 and controller / processor 290, along with some or all of the I / O capability, may be part of one or more external devices (e.g., a general-purpose computer). In some embodiments, memory 295 and controller / processor 290 may communicate wirelessly or wiredly with the cytometer 210. The controller / processor 290, together with memory 295 and I / O 297, can be configured to perform various functions related to the preparation and analysis of flow cytometry experiments.

[0127] Different fluorescent molecules in the fluorophore panel used for flow cytometry experiments will emit light in their respective characteristic wavelength bands. Specific fluorescent markers used in the experiment and their associated fluorescence emission bands can be selected to typically coincide with the filter window of the detector. I / O 297 can be configured to receive data on flow cytometry experiments with a fluorescent marker panel and multiple cell populations having multiple markers, each cell population having a subset of multiple markers. I / O 297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectral data, data on label assignment to one or more markers, and flow cytometry configuration data. Flow cytometry experiment data, such as label spectral characteristics and flow cytometry configuration data, can also be stored in memory 295. Controller / processor 290 can be configured to evaluate one or more label assignments to markers.

[0128] In some embodiments, the subject system is a particle sorting system configured to sort particles using enclosed particle sorting modules, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference. In some embodiments, a sorting decision module having multiple sorting decision units is used to sort particles (e.g., cells) in a sample, such as those described in U.S. Patent Publication No. 2020 / 0256781 (filed December 23, 2019), the disclosure of which is incorporated herein by reference. In some embodiments, the system for sorting sample components includes a particle sorting module with deflection plates, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference.

[0129] In some embodiments, the system is an image-enabled particle sorter that uses fluorescence imaging emitted by radio frequency tags, such as... Figure 3 As depicted, the particle sorter 300 includes an illumination assembly 300a comprising a light source 301 (e.g., a 488nm laser) that generates an output beam 301a, which is split into beams 302a and 302b by a beam splitter 302. Beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a with one or more angled deflections. In some instances, the output beam 303a generated by the acousto-optic device 303 includes a locally oscillating beam and multiple radio frequency comb beams. Beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a with one or more angled deflections. In some instances, the output beam 304a generated by the acousto-optic device 304 includes a locally oscillating beam and multiple radio frequency comb beams. Output beams 303a and 304a generated by acousto-optic devices 303 and 304, respectively, are combined by beam splitter 305 to produce output beam 305a, which is transmitted through optical component 306 (e.g., objective lens) to illuminate particles in flow cell 307. In some embodiments, acousto-optic device 303 (AOD) splits a single laser beam into an array of small beams, each with a different optical frequency and angle. A second AOD 304 tunes the optical frequency of a reference beam, which is then overlapped with the array of small beams at beam combiner 305. In some embodiments, the light illumination system having a light source and acousto-optic devices may also include those described in Schraivogel et al. (“High-speed fluorescence image-enabled cellsorting” Science (2022), 375(6578):315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosure of which is incorporated herein by reference.

[0130] Output beam 305a irradiates sample particles 308 (e.g., together with sheath fluid 309) propagating through flow cell 307 at irradiation region 310. As shown in irradiation region 310, multiple beams (e.g., angle-deflected radio frequency shift beams, depicted as dots in irradiation region 310) overlap with a reference local oscillation beam (depicted as shaded lines in irradiation region 310). Due to their different optical frequencies, the overlapping beams exhibit beat frequency behavior, resulting in each small beam carrying a different frequency f. 1-n Sine modulation.

[0131] Light from the irradiated sample is transmitted to a light detection system 300b, which includes multiple photodetectors. The light detection system 300b includes a forward-scattering photodetector 311 for generating a forward-scattering image 311a and a side-scattering photodetector 312 for generating a side-scattering image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattering detector 311 and the side-scattering detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the irradiated sample is also detected by fluorescence photodetectors 314-317. In some cases, photodetectors 314-317 are photomultiplier tubes. Light from the irradiated sample is directed by a beamsplitter 320 to the side-scattering detection channel 312 and the fluorescence detection channels 314-317. The optical detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for propagating light of a predetermined wavelength to photodetectors 314-317. In some cases, optical component 321 has a 534nm / 40nm bandpass. In some cases, optical component 322 has a 586nm / 42nm bandpass. In some cases, optical component 323 has a 700nm / 54nm bandpass. In some cases, optical component 324 has a 783nm / 56nm bandpass. The first number indicates the center of the spectral band. The second number provides the range of the spectral band. Therefore, the 510 / 20 filter extends 10nm on each side of the center of the spectral band, or from 500nm to 520nm.

[0132] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright field light detection channel 313, and fluorescence detection channels 314-317 are digitally processed in real time by processors 350 and 351. Images 311a-317a can be generated in each light detection channel based on the data signals generated in processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated in sorting trigger 352. Sorting assembly 300c includes a deflection plate 331 for deflecting particles into sample container 332 or waste stream 333. In some cases, sorting assembly 300c is configured to sort particles using enclosed particle sorting modules, such as those described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017), the disclosure of which is incorporated herein by reference. In some embodiments, the sorting component 300c includes a sorting decision module having a plurality of sorting decision units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0133] In some embodiments, the system is a particle analyzer, wherein the particle analysis system 401 ( Figure 4 It can be used to analyze and characterize particles, whether or not the particles are physically sorted into a collection container. Figure 4 A functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization is shown. In some embodiments, the particle analysis system 401 is a flow cytometry system. The particle analysis system 401 includes a fluid system 402. The fluid system 402 may include or be coupled to a sample tube 405 and a fluid column moving within the sample tube, wherein particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0134] The particle analysis system 401 includes a detection system 404 configured to collect signals from each particle as it passes through one or more detection stations along a common sample path. Detection stations 408 typically refer to a monitored area 407 of the common sample path. Detection may, in some embodiments, include detecting light or other properties of particles 403 as they pass through the monitored area 407. Figure 4 The diagram shows a detection station 408 with a monitored area 407. Some implementations of the particle analysis system 401 may include multiple detection stations. Furthermore, some detection stations can monitor more than one area.

[0135] Each signal is assigned a signal value to form a data point for each particle. As mentioned above, this data can be referred to as event data. The data points can be multi-dimensional data points, including values ​​of corresponding attributes measured for the particle. The detection system 404 is configured to collect a series of such data points within a first time interval.

[0136] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The control system may be operationally associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the number of data points collected by the detection system 404 during the first time interval. The control system 406 may also be configured to generate an experimental signal frequency based on the number of data points within that portion of the first time interval. The control system 406 may also compare the experimental signal frequency with a calculated signal frequency or a predetermined signal frequency.

[0137] Figure 5 A functional block diagram of an example particle analyzer control system for analyzing and displaying biological events is shown, such as an analysis controller (i.e., processor) 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0138] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel may be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel.

[0139] Analysis controller 500 can be configured to receive biological event data from particle analyzer or sorting system 502. The biological event data received from particle analyzer or sorting system 502 may include flow cytometry event data. Analysis controller 500 can be configured to provide a graphical display of a first graph including the biological event data to display device 506. Analysis controller 500 can also be configured to render regions of interest as gates around the population of biological event data displayed by display device 506, for example, overlaying them on the first graph. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest plotted on a single-parameter histogram or bivariate graph. In some embodiments, the display may be used to display particle parameters or saturation detector data.

[0140] The analysis controller 500 can also be configured to display bio-event data on the display device 506 in a manner different from other bio-event data outside the door, inside the door. For example, the analysis controller 500 can be configured to render the colors of bio-event data included inside the door differently from the colors of bio-event data outside the door. The display device 506 can be a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0141] The analysis controller 500 can be configured to receive a door selection signal from a first input device that identifies the door. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a door selection signal to the analysis controller 500, identifying a door to be displayed on or operated via the display device 506 (e.g., by clicking on or inside the desired door when the cursor is over it). In some implementations, the first device can be implemented as a keyboard 508 or other means that provides input signals to the analysis controller 500, such as a touchscreen, stylus, optical detector, or voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function can be considered an input device. For example, as... Figure 5 As shown, the mouse 510 can include a right button and a left button, and each button can generate a trigger event.

[0142] Triggering events may cause the analysis controller 500 to change the way data is displayed, the portion of data actually displayed on the display device 506, and / or provide input for further processing such as selecting groups of interest for particle sorting.

[0143] 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 also be configured to automatically modify the plotting visualization to facilitate the gating process. This modification may be based on a specific distribution of the biological event data received by the analysis controller 500.

[0144] The analysis controller 500 can be connected to the storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can also be configured to allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

[0145] Display device 506 can be configured to receive display data from analysis controller 500. The display data may include graphs of biological event data and gates outlining portions of the graphs. Display device 506 can also be configured to change the presented information based on input received from analysis controller 500 and input from particle analyzer 502, storage device 504, keyboard 508, and / or mouse 510.

[0146] In some implementations, the analysis controller 500 may generate a user interface to receive sample events for sorting. For example, the user interface may include controls for receiving sample events or sample images. Sample events, images, or sample gates may be provided before collecting event data for the sample, or based on an initial set of events for a portion of the sample.

[0147] Figure 6A This is a schematic diagram of a particle sorting system 600 (e.g., a particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorting system 600 is a cell sorting system. Figure 6A As shown, a droplet formation sensor 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, including particles 609, into a moving fluid column 608 (e.g., a flow). Within the moving fluid column 608, particles 609 (e.g., cells) are arranged in a single file to pass through a monitored area 611 (e.g., a laser-flow intersection) and are irradiated by an irradiation source 612 (e.g., a laser). Vibration of the droplet formation sensor 602 causes the moving fluid column 608 to break into multiple droplets 610, some of which include particles 609.

[0148] In operation, a detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitored area 611. The detection station 614 is fed into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At the droplet breakpoint, a flash charge can be applied to the moving fluid column 608 based on a timed droplet delay (Δt), causing the droplet of interest to carry a charge. The droplet of interest may include one or more particles or cells to be sorted. The charged droplet can then be sorted by activating a deflection plate (not shown) to deflect the droplet into a container (e.g., a collection tube or a porous or microporous sample plate), where a pore or micropore can be associated with a specific droplet of interest. Figure 6A As shown, the droplets can be collected in the discharge container 638.

[0149] A detection system 616 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitored region 611. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 616 allows the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 616 may be fed an amplitude signal 620 and / or a phase signal 618, which are then fed (via amplifier 622) to amplitude control circuitry 626 and / or frequency control circuitry 624. Amplitude control circuitry 626 and / or frequency control circuitry 624, in turn, control droplet formation sensor 602. Amplitude control circuitry 626 and / or frequency control circuitry 624 may be included in a control system.

[0150] In some implementations, sorting electronics (e.g., detection system 616, detection station 614, and processor 640) may be coupled to a memory configured to store detected events and sorting decisions based thereon. These sorting decisions may be included in the event data of the particles. In some implementations, detection system 616 and detection station 614 may be implemented as a single detection unit or communication coupling, such that event measurement results can be collected by one of detection system 616 or detection station 614 and provided to non-collection elements.

[0151] Figure 6B This is a schematic diagram of a particle sorting system according to an embodiment of the present document. Figure 6B The particle sorting system 600 shown includes deflection plates 652 and 654. Charge can be applied via a current-charged wire in the barbs. This generates a droplet stream 610 comprising particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to produce light scattering and fluorescence information. Particle information is obtained, for example, through sorting electronics or other detection systems (not shown in the diagram). Figure 6B (As shown in the diagram) is being analyzed. Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, thereby guiding the droplets toward a target collection container (e.g., one of 672, 674, 676, or 678). Figure 6B As shown, deflector plates 652 and 654 can be controlled to guide particles along a first path 662 to container 674 or along a second path 668 to container 678. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particles to continue flowing along flow path 664. Such uncharged droplets may enter the waste receiver, for example, via suction device 670.

[0152] It may include sorting electronics for initiating the collection of measurement results, receiving the fluorescence signal of particles, and determining how to adjust the deflection plate to induce particle sorting. Figure 6B The example implementation of the illustrated embodiment includes BD FACSAria, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ). TM A series of flow cytometers.

[0153] Computer control system

[0154] This disclosure further includes a computer control system, wherein the system includes one or more computers for full or partial automation. In some embodiments, the system includes a computer having a non-transitory computer-readable storage medium on which a computer program is stored, which, when loaded onto the computer, includes instructions for performing the methods of this disclosure. For example, the computer may be configured to identify measurement uncertainties corresponding to individual particles in a sample, or to identify measurement uncertainties for individual parameters of the detection light of particles in the sample, or may be configured to generate a quality score for each particle based on the measurement uncertainty of each particle.

[0155] The system may include a display and operator input devices. Operator input devices may be, for example, a keyboard, mouse, etc. The processing module includes a processor that can access memory with stored instructions for performing the steps of the methods described herein. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or other processors available now or in the future. The processor executes the operating system, which interacts with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of various computer programs written in a variety of programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically collaborates with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services according to known techniques. In some embodiments, the processor includes analog electronics that provide feedback control (e.g., negative feedback control).

[0156] System memory can be any of a variety of known or future memory storage devices. Examples include any common random access memory (RAM), magnetic media (such as resident hard disks or magnetic tapes), optical media (such as optical discs), flash memory devices, or other memory storage devices. Memory storage devices can be any of a variety of known or future devices, including optical disc drives, magnetic tape drives, or floppy disk drives. Such memory storage devices are typically read from and / or written to program storage media (not shown) (such as optical discs). Any of these program storage media, or any other media currently in use or that may be developed in the future, can be considered a computer program product. As will be understood, these program storage media typically store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in system memory and / or program storage devices used with memory storage devices.

[0157] In some embodiments, a computer program product is described, comprising a computer-usable medium having control logic (computer software program, including program code) stored therein. When the control logic is executed by a computer's processor, it causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, such as using a hardware state machine. Those skilled in the art will understand how a hardware state machine can be implemented to perform the functions described herein.

[0158] The memory can be any suitable device in which a processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks, optical discs, magnetic tapes, RAM, or any other suitable device, whether fixed or portable). The processor can include a general-purpose digital microprocessor that is appropriately programmed from a computer-readable medium carrying the necessary program code. Programming can be provided to the processor remotely via a communication channel or previously stored in a computer program product (e.g., memory or some other portable or fixed computer-readable storage medium) using any of those devices connected to the memory. For example, a disk or optical disc can carry the programming and can be read by a disk writer / reader. The systems of this disclosure also include programming, for example in the form of a computer program product, an algorithm for practicing the methods described above. Programming according to this disclosure can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0159] The processor can also access communication channels to communicate with users at remote locations. Remote locations refer to situations where users do not directly interact with the system but instead relay input information to the input manager via external devices such as computers connected to a wide area network (“WAN”), telephone networks, satellite networks, or any other suitable communication channel, including mobile phones (i.e., smartphones)

[0160] In some embodiments, the system according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB)). Communication protocols and cellular communications, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0161] In one embodiment, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces like USB ports, USB-C ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices, such as computer terminals configured for similar complementary data communication (e.g., in a doctor's office or hospital environment).

[0162] In one embodiment, the communication interface is configured for infrared communication. Communication or any other suitable wireless communication protocol to enable the subject system to communicate with other devices (such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user may use in conjunction with).

[0163] In one embodiment, the communication interface is configured to provide connectivity for data transmission via a mobile network, short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) (which is connected to the Internet), or a Wi-Fi connection to the Internet at a Wi-Fi hotspot, utilizing the Internet Protocol (IP).

[0164] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communication interface, for example, using a common standard such as 802.11 or... The protocol is RF, or IrDA infrared protocol. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop; or a larger device, such as a desktop computer, facility, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, keyboard, mouse, or touchscreen.

[0165] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject system (e.g., in an optional data storage unit) with a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0166] The output controller may include controllers for various known display devices used to present information to a user (whether human or machine, local or remote). If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of image elements. The graphical user interface (GUI) controller may include any of various known or future software programs used to provide a graphical input and output interface between the system and the user, and to process user input. Functional elements of the computer may communicate with each other via a system bus. Some of this communication may be accomplished using a network or other types of remote communication in alternative embodiments. According to known technologies, the output manager may also provide information generated by the processing module to a user at a remote location, such as via the Internet, telephone, or satellite networks. The way the output manager presents data may be implemented according to various known technologies. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the subject system may be any type of known computer platform or type to be developed in the future, although they will typically be a class of computers commonly referred to as servers. However, they may also be mainframes, workstations, or other computer types. They can be connected via any known or future type of wired or other communication system (including wireless systems), whether networked or otherwise. They can be located in the same location or physically separated. Various operating systems can be deployed on any computer platform, possibly depending on the type and / or brand of the chosen platform. Suitable operating systems include... XP 7. 8. 10. Android TM , Oracle wait.

[0167] Figure 7 A general architecture of an example computing device 700 according to certain embodiments is described. Figure 7The general architecture of the computing device 700 depicted includes the arrangement of computer hardware and software components. However, it is not necessary to show all these generally conventional components in order to provide publicly available information. As shown, the computing device 700 includes a processing unit 710, a network interface 720, a computer-readable media drive 730, an input / output device interface 740, a display 750, and an input device 760, all of which communicate with each other via a communication bus. The network interface 720 can provide connectivity to one or more networks or computing systems. Therefore, the processing unit 710 can receive information and instructions from other computing systems or services via the network. The processing unit 710 can also communicate with and from memory 770, and further provide output information to the optional display 750 via the input / output device interface 740. For example, analysis software (e.g., data analysis software or programs, such as...) stored as executable instructions in the non-transitory memory of an analysis system... It can display flow cytometry event data to the user. The input / output device interface 740 can also accept input from optional input devices 760, such as keyboards, mice, digital pens, microphones, touch screens, gesture recognition systems, voice recognition systems, game controllers, accelerometers, gyroscopes, or other input devices.

[0168] Memory 770 may include computer program instructions (grouped into modules or components in some embodiments) that processing unit 710 executes to implement one or more embodiments. Memory 770 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 770 may store an operating system 772 that provides computer program instructions used by processing unit 710 in the routine management and operation of computing device 700. Data may be stored in data storage device 790. Memory 770 may further include computer program instructions for implementing aspects of this disclosure and other information, such as a module 773 for identifying measurement uncertainties associated with detected light or a module 774 for recording measurement uncertainties associated with detected light.

[0169] Integrated circuit devices

[0170] This disclosure also includes an integrated circuit device programmed to identify measurement uncertainties associated with light detected from a sample, as described in detail in the methods described above. In some embodiments, the integrated circuit device of interest includes a field-programmable gate array (FPGA). In other embodiments, the integrated circuit device includes an application-specific integrated circuit (ASIC). In still other embodiments, the integrated circuit device includes a complex programmable logic device (CPLD). In embodiments, the integrated circuit device is configured to practice the methods of this subject matter as described herein.

[0171] kit

[0172] This disclosure also includes kits, wherein the kit includes one or more integrated circuits described herein, or the system described herein, or the non-transitory computer-readable recording medium described herein. In some embodiments, the kit may further include instructions for programming the subject system, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, optical disc, DVD, Blu-ray disc, etc.) or for downloading programming instructions from an Internet network protocol or cloud server.

[0173] In addition to the components described above, the theme kit may (in some embodiments) further include instructions. These instructions may exist in a variety of forms within the theme kit, with one or more forms possible. One form of these instructions may be as printed information on a suitable medium or substrate, such as one or more sheets of paper with the information printed on them, in the kit's packaging, in a packaging insert, etc. Another form of these instructions is as a computer-readable medium, such as a disk, optical disc (CD), portable flash drive, etc., on which the information is recorded. Yet another possible form of these instructions is a website address, which can be used via the Internet to access information at a remote site.

[0174] practicality

[0175] Embodiments of this disclosure are useful in a variety of applications where it is desirable to analyze and sort particulate components in samples (e.g., biological samples) in a fluid medium. In some embodiments, the systems and methods described herein are useful in flow cytometry characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods are useful in the spectroscopy of emitted light. Furthermore, the subject systems and methods are useful in increasing the signal available from light collected from a sample (e.g., in a flow stream). In some cases, this disclosure is useful in enhancing the measurement of light collected from a sample irradiated in a flow stream of a flow cytometer. Embodiments of this disclosure are useful in providing flow cytometers that offer improved cell sorting accuracy, enhanced particle collection, particle charging efficiency, more accurate particle charging, and enhanced particle deflection during cell sorting.

[0176] The embodiments of this disclosure are useful in applications where it may be desirable to use cells prepared from biological samples for research, laboratory testing, or therapeutic purposes. In some embodiments, the subject methods and apparatus may facilitate the acquisition and / or analysis of single cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate the acquisition of cells from fluid or tissue samples for use as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate the acquisition of cells from fluid or tissue samples for therapeutic purposes.

[0177] Notwithstanding the appended claims, this disclosure is also limited by the following provisions.

[0178] Clause 1. A method comprising:

[0179] The sample is introduced into the flow cytometer;

[0180] The introduced sample flows in the flow stream;

[0181] Illuminate the sample in the flowing stream with a light source;

[0182] Light used to detect particles in a sample flowing in a flow stream; and

[0183] Identify measurement uncertainties associated with the detected light.

[0184] Clause 2. The method according to Clause 1, wherein measurement uncertainty is identified corresponding to a single particle in the sample.

[0185] Clause 3. The method according to any of the preceding clauses, wherein detecting light from particles includes measuring multiple parameters of the detected light for particles in the sample.

[0186] Clause 4. The method according to any of the preceding clauses, wherein measurement uncertainty is identified for each parameter of the detected light in relation to particles in the sample.

[0187] Clause 5. The method described pursuant to any of the preceding clauses further includes:

[0188] Based on detected light-generating event data, which includes parameter measurements of particles in the sample.

[0189] Clause 6. The method described pursuant to any of the preceding clauses further comprises:

[0190] Spectral analysis is performed on the light detected from the particles.

[0191] Clause 7. The method according to any of the preceding clauses, wherein spectral analysis of the light detected from the particles produces a plurality of derived parameters.

[0192] Clause 8. The method described pursuant to any of the preceding clauses further comprises:

[0193] Multiple derived parameters are generated for each particle by performing spectral analysis on the light detected from each particle.

[0194] Clause 9. The method according to any of the preceding clauses, wherein the derived parameters include unmixed detected light.

[0195] Clause 10. The method according to any of the preceding clauses, wherein measurement uncertainty is identified for the derived parameters.

[0196] Clause 11. The method described pursuant to any of the preceding clauses further comprises:

[0197] The first subset of particles in the sample is identified by applying a first gate to the detected light.

[0198] Clause 12. The method according to Clause 11, wherein the measurement uncertainty corresponds to identifying the first subset of particles.

[0199] Clause 13. The method described pursuant to any of the preceding clauses further comprises:

[0200] Multiple subsets of particles in a sample are identified by applying appropriate gates to the detected light.

[0201] Clause 14. The method according to Clause 13, wherein the measurement uncertainty corresponds to identifying each of the plurality of particle subsets.

[0202] Clause 15. The method described in any of the preceding clauses, wherein the measurement uncertainty corresponds to gate-level uncertainty.

[0203] Clause 16. The method described pursuant to any of the preceding clauses further comprises:

[0204] A mass fraction (Q fraction) is generated for each particle based on the measurement uncertainty of each particle.

[0205] Clause 17. The method described pursuant to any of the preceding clauses further comprises:

[0206] Record each event and the associated measurement uncertainty for each event.

[0207] Clause 18. The method described pursuant to any of the preceding clauses further comprises:

[0208] Record the measurement results for each particle and the associated measurement uncertainty for each particle.

[0209] Clause 19. The method described pursuant to any of the preceding clauses further comprises:

[0210] Record the measurement results for multiple parameters for each particle, as well as the associated measurement uncertainty for each parameter of each particle.

[0211] Clause 20. The method described pursuant to any of the preceding clauses further comprises:

[0212] Record measurement uncertainties, wherein the measurement uncertainties correspond to measurement uncertainties for one or more of the following: parameters for each record; events for each record; each gate or other classification; or for the sample.

[0213] Clause 21. The method according to any of the preceding clauses, wherein the measurement uncertainty of different parameters for measuring particles in the sample includes different measures.

[0214] Clause 22. The method described in any of the preceding clauses, wherein the measurement uncertainty corresponds to a binary classification event.

[0215] Clause 23. The method according to Clause 22, wherein the binary classification event corresponds to one or more of the following: gate determination or group member classification decision.

[0216] Clause 24. The method according to any of the preceding clauses, wherein the measurement uncertainty includes a measure that quantifies the measurement uncertainty.

[0217] Clause 25. The method described pursuant to any of the preceding clauses further comprises:

[0218] An uncertainty score is generated based on the measurement uncertainty.

[0219] Clause 26. The method according to any of the preceding clauses, wherein the measurement uncertainty is reflected in an uncertainty score, wherein a higher uncertainty score indicates a greater measurement uncertainty.

[0220] Clause 27. The method described pursuant to any of the preceding clauses further comprises:

[0221] A quality score is generated based on the measurement uncertainty.

[0222] Clause 28. The method according to any of the preceding clauses, wherein the measurement uncertainty is reflected in the quality score, wherein a higher quality score represents a higher confidence level.

[0223] Clause 29. The method according to any of the preceding clauses, wherein the measurement uncertainty includes one or more of the following: direct quantification, optionally including standard deviation; confidence interval; probability that the measurement value falls within a specified percentage range of the true value; or residuals related to the goodness of fit of unmixed data.

[0224] Clause 30. The method according to any of the preceding clauses, wherein the measurement uncertainty is related to a binary classification, wherein the binary classification may optionally include one or more gating or group membership classifications.

[0225] Clause 31. The method described pursuant to any of the preceding clauses further comprises:

[0226] Calculate the quality score based on measurement uncertainty.

[0227] The quality score mentioned above reflects the likelihood of a member inside the door.

[0228] Clause 32. The method described in Clause 31, wherein the probability of membership within a door is calculated for each door in the door hierarchy.

[0229] Clause 33. The method described in Clause 31, wherein the probability of membership within a door is calculated by considering each level of the parent door of the door.

[0230] Clause 34. The method according to any of the preceding clauses, wherein identifying measurement uncertainties associated with the detected light includes:

[0231] Measurement uncertainty is calculated based on a semi-empirical noise model, wherein the semi-empirical noise model includes one or more of the following: instrument calibration data, physical noise model, or measured value.

[0232] Clause 35. The method according to any of the preceding clauses, wherein identifying measurement uncertainties associated with the detected light includes:

[0233] Measurement uncertainty is estimated on a per-event basis using statistical algorithms, wherein the statistical algorithms include characteristics of the distribution of data within the measurement sample.

[0234] Clause 36. The method according to any of the preceding clauses, wherein identifying measurement uncertainties associated with the detected light includes:

[0235] Estimating measurement uncertainty involves a combination of applying noise modeling and distribution fitting.

[0236] Clause 37. The method according to any of the preceding clauses, wherein identifying measurement uncertainties associated with the detected light includes:

[0237] Estimating measurement uncertainty involves applying Bayesian inference.

[0238] Clause 38. The method described pursuant to any of the preceding clauses further comprises:

[0239] Report measurement uncertainty for each particle in a subset of the sample.

[0240] Clause 39. The method described pursuant to any of the preceding clauses further comprises:

[0241] For each particle in a subset of the sample, the measurement uncertainty is reported for multiple parameters.

[0242] Clause 40. The method described pursuant to any of the preceding clauses further comprises:

[0243] For each particle in the particle subset, report the measurement uncertainty associated with its membership in the subgroup defined by the gate.

[0244] Clause 41. The method described in any of the preceding clauses, wherein measurement uncertainty includes random measurement error.

[0245] Clause 42. The method described in any of the preceding clauses, wherein the measurement uncertainty includes spillover diffusion error.

[0246] Clause 43. The method described in any of the preceding clauses, wherein the measurement uncertainty reflects variations other than the true inherent differences between sample particles.

[0247] Clause 44. The method according to any of the preceding clauses, wherein the measurement uncertainty reflects the confidence level of the classification decision regarding the sample particles.

[0248] Clause 45. The method according to any of the preceding clauses, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of multiple parameters of the light detected from the particle.

[0249] Clause 46. The method according to any of the preceding clauses, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of light detected from multiple particles.

[0250] Clause 47. The method according to any of the preceding clauses, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the sample.

[0251] Clause 48. The method according to any of the preceding clauses, wherein identifying measurement uncertainties associated with the detected light includes one or more of the following:

[0252] Estimate the measurement uncertainty associated with the detected light.

[0253] Measurement uncertainty associated with the detected light, or

[0254] Predict the measurement uncertainty associated with the detected light.

[0255] Clause 49. The method described pursuant to any of the preceding clauses further comprises:

[0256] Particles are classified based on the detected light; and

[0257] The statistical significance of particle classification is calculated at least in part based on the measurement uncertainty.

[0258] Clause 50. The method described pursuant to any of the preceding clauses further comprises:

[0259] Particles are classified based on the detected light; and

[0260] The confidence interval for particle classification is calculated at least in part based on the measurement uncertainty.

[0261] Clause 51. The method described pursuant to any of the preceding clauses further comprises:

[0262] Based at least in part on the measurement uncertainty, a variance-stabilizing transformation is applied to the data, including measurements of light detected from particles in the sample; and

[0263] Aspects of visualizing the transformed data.

[0264] Clause 52. The method described pursuant to any of the preceding clauses further comprises:

[0265] Based at least in part on the aforementioned measurement uncertainty, data including measurements of light detected from particles in the sample are preprocessed to minimize intra-cluster variance; and

[0266] Clustering algorithms are applied to preprocessed data, wherein the clustering algorithms may optionally include one or more of supervised clustering algorithms and unsupervised clustering algorithms.

[0267] Clause 53. The method described pursuant to any of the preceding clauses further comprises:

[0268] Based at least in part on the measurement uncertainty, the data including the measurement results of light detected from particles in the sample are standardized to normalize the measurement uncertainty across different datasets, wherein the different datasets may optionally include one or more datasets collected on different instruments or under different conditions.

[0269] Clause 54. The method described pursuant to any of the preceding clauses further comprises:

[0270] Probabilistic analysis of particle classification or sorting using the measurement uncertainty, wherein probabilistic classification may optionally include the application of fuzzy logic techniques.

[0271] Clause 55. The method described in any of the preceding clauses, wherein the method is a method for distinguishing between true variation in sample particles and measurement error.

[0272] Clause 56. The method described in any of the preceding clauses, wherein the method is a method for distinguishing between true biological variation and measurement error.

[0273] Clause 57. The method according to any of the preceding clauses, wherein the method is a method for improving the sensitivity of flow cytometry assays.

[0274] Clause 58. The method according to any of the preceding clauses, wherein the method is a method for improving the resolution of flow cytometry assays.

[0275] Clause 59. The method according to any of the preceding clauses, wherein the method is a method for assessing the quality of particle analysis results by correlating event-specific measurement uncertainty estimates with flow cytometry measurements.

[0276] Clause 60. The method according to any of the preceding clauses, wherein a quantitative measure of measurement uncertainty is associated with the flow cytometry measurement result.

[0277] Clause 61. The method described pursuant to any of the preceding clauses further comprises:

[0278] Measurement uncertainty is used in relation to data analysis or sorting.

[0279] Clause 62. The method described pursuant to any of the preceding clauses further comprises:

[0280] Identify a gate member confidence score for each event and each gate, wherein the gate member confidence score includes the probability that the true biological expression level of a given event falls within a given gate.

[0281] Clause 63. The method of any of the preceding clauses, wherein the method is a method for calculating the confidence score of gate members.

[0282] Clause 64. The method described pursuant to any of the preceding clauses further comprises:

[0283] Gate member confidence scores are used in particle classification and sorting based on measurement uncertainty, where particle classification and sorting includes probabilistic sorting with configurable probability thresholds to maximize purity and / or yield.

[0284] Clause 65. The method according to any of the preceding clauses, wherein a light detection system is used to detect the light.

[0285] Clause 66. The method according to Clause 65, wherein light is detected by a light detection system in multiple photodetector channels.

[0286] Clause 67. The method according to any one of Clauses 65 to 66, wherein the light detection system comprises a plurality of photodetectors.

[0287] Article 68. A system comprising:

[0288] A light source configured to irradiate a sample comprising multiple particles;

[0289] The optical detection system includes multiple photodetectors; and

[0290] A processor includes memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to identify measurement uncertainties associated with light detected from a light detection system.

[0291] Clause 69. The system according to Clause 68, wherein light is detected by a light detection system in multiple photodetector channels.

[0292] Clause 70. The system according to any one of Clauses 68 to 69, wherein the light detection system comprises a plurality of photodetectors.

[0293] Clause 71. The system according to any one of Clauses 68 to 70, wherein measurement uncertainty is identified corresponding to a single particle in the sample.

[0294] Clause 72. The system according to any one of Clauses 68 to 71, wherein detecting light from particles includes measuring multiple parameters of the detected light for particles in a sample.

[0295] Clause 73. The system according to any one of Clauses 68 to 72, wherein measurement uncertainty is identified for each parameter of the detected light in relation to particles in the sample.

[0296] Clause 74. The system according to any one of Clauses 68 to 73, wherein the system is further configured to be based on detected light generation event data, wherein the event data includes parameter measurements of particles in the sample.

[0297] Clause 75. The system according to any one of Clauses 68 to 74, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to perform spectral analysis on light detected from particles.

[0298] Clause 76. The system according to any one of Clauses 68 to 75, wherein spectral analysis of the light detected from the particles produces a plurality of derived parameters.

[0299] Clause 77. The system according to any one of Clauses 68 to 76, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to generate a plurality of derived parameters for each particle by performing spectral analysis on the light detected from each particle.

[0300] Clause 78. The system according to any one of Clauses 68 to 77, wherein the derived parameters include unmixed detected light.

[0301] Clause 79. The system according to any one of Clauses 68 to 78, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to identify measurement uncertainties for derived parameters.

[0302] Clause 80. The system according to any one of Clauses 68 to 79, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to identify a first subset of particles in a sample by applying a first gate to the detected light.

[0303] Clause 81. The system according to Clause 80, wherein the measurement uncertainty corresponds to the identification of the first subset of particles.

[0304] Clause 82. The system according to any one of Clauses 68 to 81, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to identify a plurality of particle subsets in a sample by applying corresponding plurality of gates to the detected light.

[0305] Clause 83. The system according to Clause 82, wherein the measurement uncertainty corresponds to identifying each of the plurality of particle subsets.

[0306] Clause 84. The system according to any one of Clauses 68 to 83, wherein the measurement uncertainty corresponds to the gate-level uncertainty.

[0307] Clause 85. The system according to any one of Clauses 68 to 84, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to generate a quality score (Q score) for each particle based on the measurement uncertainty of each particle.

[0308] Clause 86. The system according to any one of Clauses 68 to 85, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to record each event and the associated measurement uncertainty for each event.

[0309] Clause 87. The system according to any one of Clauses 68 to 86, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to record the measurement results for each particle and the associated measurement uncertainty for each particle.

[0310] Clause 88. The system according to any one of Clauses 68 to 87, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to record measurement results of a plurality of parameters for each particle and the associated measurement uncertainty for each parameter for each particle.

[0311] Clause 89. The system according to any one of Clauses 68 to 88, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to record measurement uncertainties, wherein the measurement uncertainties correspond to measurement uncertainties for one or more of the following: parameters for each record; events for each record; each gate or other classification; or for the sample.

[0312] Clause 90. The system according to any one of Clauses 68 to 89, wherein the measurement uncertainty of different parameters for measuring particles in a sample includes different measures.

[0313] Clause 91. The system according to any one of Clauses 68 to 90, wherein the measurement uncertainty corresponds to a binary classification event.

[0314] Clause 92. The system according to Clause 91, wherein the binary classification event corresponds to one or more of the following: gate determination or group member classification decision.

[0315] Clause 93. The system according to any one of Clauses 68 to 92, wherein the measurement uncertainty includes a measure that quantifies the measurement uncertainty.

[0316] Clause 94. The system according to any one of Clauses 68 to 93, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to generate an uncertainty score based on the measurement uncertainty.

[0317] Clause 95. The system according to any one of Clauses 68 to 94, wherein the measurement uncertainty is reflected in an uncertainty score, wherein a higher uncertainty score indicates a greater measurement uncertainty.

[0318] Clause 96. The system according to any one of Clauses 68 to 95, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to generate a quality score based on the measurement uncertainty.

[0319] Clause 97. The system according to any one of Clauses 68 to 96, wherein measurement uncertainty is reflected in the quality score, wherein a higher quality score indicates a higher confidence level.

[0320] Clause 98. The system according to any one of Clauses 68 to 97, wherein the measurement uncertainty includes one or more of the following: direct quantification, optionally including standard deviation; confidence interval; probability that the measured value falls within a specified percentage range of the true value; or residuals related to the goodness of fit of unmixed data.

[0321] Clause 99. The system according to any one of Clauses 68 to 98, wherein the measurement uncertainty is related to a binary classification, wherein the binary classification may optionally include one or more gating or group membership classifications.

[0322] Clause 100. A system according to any one of Clauses 68 to 99, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to calculate a quality score based on measurement uncertainty, wherein the quality score reflects the probability of membership within the gate.

[0323] Clause 101. The system as described in Clause 100, wherein the probability of members within a door is calculated for each door in a door hierarchy.

[0324] Clause 102. The system described in Clause 100, wherein the probability of a member within a door is calculated by considering each level of the parent door of that door.

[0325] Clause 103. The system according to any one of Clauses 68 to 102, wherein the measurement uncertainty associated with the detected light is identified as including:

[0326] Measurement uncertainty is calculated based on a semi-empirical noise model, wherein the semi-empirical noise model includes one or more of the following: instrument calibration data, physical noise model, or measured value.

[0327] Clause 104. The system according to any one of Clauses 68 to 103, wherein the identification of measurement uncertainties associated with the detected light includes:

[0328] Measurement uncertainty is estimated on a per-event basis using statistical algorithms, wherein the statistical algorithms include characteristics of the distribution of data within the measurement sample.

[0329] Clause 105. The system according to any one of Clauses 68 to 104, wherein the identification of measurement uncertainties associated with the detected light includes:

[0330] Estimating measurement uncertainty involves a combination of applying noise modeling and distribution fitting.

[0331] Clause 106. The system according to any one of Clauses 68 to 105, wherein identifying measurement uncertainties associated with the detected light includes:

[0332] Estimating measurement uncertainty involves applying Bayesian inference.

[0333] Clause 107. The system according to any one of Clauses 68 to 106, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to report measurement uncertainty for each particle of a subset of particles in the sample.

[0334] Clause 108. The system according to any one of Clauses 68 to 107, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to report measurement uncertainty for each particle of a subset of particles of the sample, for a plurality of parameters.

[0335] Clause 109. The system according to any one of Clauses 68 to 108, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to report, for each particle in a subset of particles, a measurement uncertainty associated with membership in a subgroup defined by a gate.

[0336] Clause 110. The system according to any one of Clauses 68 to 109, wherein measurement uncertainty includes random measurement error.

[0337] Clause 111. The system according to any one of Clauses 68 to 110, wherein the measurement uncertainty includes spillover propagation error.

[0338] Clause 112. The system according to any one of Clauses 68 to 111, wherein the measurement uncertainty reflects variations other than the true inherent differences between sample particles.

[0339] Clause 113. The system according to any one of Clauses 68 to 112, wherein the measurement uncertainty reflects the confidence level of a classification decision regarding sample particles.

[0340] Clause 114. The system according to any one of Clauses 68 to 113, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of multiple parameters of light detected from the particle.

[0341] Clause 115. The system according to any one of Clauses 68 to 114, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of light detected from multiple particles.

[0342] Clause 116. The system according to any one of Clauses 68 to 115, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the sample.

[0343] Clause 117. The system according to any one of Clauses 68 to 116, wherein the measurement uncertainty associated with the detected light is identified includes one or more of the following:

[0344] Estimate the measurement uncertainty associated with the detected light.

[0345] Measurement uncertainty associated with the detected light, or

[0346] Predict the measurement uncertainty associated with the detected light.

[0347] Clause 118. A system according to any one of Clauses 68 to 117, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0348] Particles are classified based on the detected light; and

[0349] The statistical significance of particle classification is calculated at least in part based on the measurement uncertainty.

[0350] Clause 119. A system according to any one of Clauses 68 to 118, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0351] Particles are classified based on the detected light; and

[0352] The confidence interval for particle classification is calculated at least in part based on the measurement uncertainty.

[0353] Clause 120. A system according to any one of Clauses 68 to 119, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0354] Based at least in part on the measurement uncertainty, a variance-stabilizing transformation is applied to the data, including measurements of light detected from particles in the sample; and

[0355] Aspects of visualizing the transformed data.

[0356] Clause 121. A system according to any one of Clauses 68 to 120, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0357] Based at least in part on the aforementioned measurement uncertainty, data including measurements of light detected from particles in the sample are preprocessed to minimize intra-cluster variance; and

[0358] Clustering algorithms are applied to preprocessed data, wherein the clustering algorithms may optionally include one or more of supervised clustering algorithms and unsupervised clustering algorithms.

[0359] Clause 122. A system according to any one of Clauses 68 to 121, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0360] Based at least in part on the measurement uncertainty, the data including the measurement results of light detected from particles in the sample are standardized to normalize the measurement uncertainty across different datasets, wherein the different datasets may optionally include one or more datasets collected on different instruments or under different conditions.

[0361] Clause 123. A system according to any one of Clauses 68 to 122, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0362] Probabilistic analysis of particle classification or sorting using the measurement uncertainty, wherein probabilistic classification optionally includes the application of fuzzy logic techniques.

[0363] Clause 124. The system according to any one of Clauses 68 to 123, wherein the system is configured to distinguish between true variation of sample particles and measurement error.

[0364] Clause 125. The system according to any one of Clauses 68 to 124, wherein the system is configured to distinguish between real biological variation and measurement error.

[0365] Clause 126. The system according to any one of Clauses 68 to 125, wherein the system is configured to improve the sensitivity of flow cytometry assays.

[0366] Clause 127. The system according to any one of Clauses 68 to 126, wherein the system is configured to improve the resolution of flow cytometry assays.

[0367] Clause 128. The system according to any one of Clauses 68 to 127, wherein the system is configured to assess the quality of particle analysis results by correlating event-specific measurement uncertainty estimates with flow cytometry measurements.

[0368] Clause 129. The system according to any one of Clauses 68 to 128, wherein the system is configured to correlate a quantitative measure of measurement uncertainty with flow cytometry measurement results.

[0369] Clause 130. The system according to any one of Clauses 68 to 129, wherein the system is configured to use measurement uncertainty in relation to data analysis or sorting.

[0370] Clause 131. A system according to any one of Clauses 68 to 130, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0371] Identify a gate member confidence score for each event and each gate, wherein the gate member confidence score includes the probability that the true biological expression level of a given event falls within a given gate.

[0372] Clause 132. The system according to any one of Clauses 68 to 131, wherein the system is configured to calculate gate member confidence scores.

[0373] Clause 133. A system according to any one of Clauses 68 to 132, wherein the memory includes instructions stored thereon that, when executed by a processor, cause the processor to:

[0374] Gate member confidence scores are used in particle classification and sorting based on measurement uncertainty, where particle classification and sorting includes probabilistic sorting with configurable probability thresholds to maximize purity and / or yield.

[0375] Clause 134. An integrated circuit programmed to identify measurement uncertainties associated with light detected by a photodetector system from particles in a sample.

[0376] Clause 135. The integrated circuit as described in Clause 134, wherein the integrated circuit is a field-programmable gate array (FPGA).

[0377] Clause 136. The integrated circuit as described in Clause 134, wherein the integrated circuit is an application-specific integrated circuit (ASIC).

[0378] Clause 137. The integrated circuit as described in Clause 134, wherein the integrated circuit is a complex programmable logic device (CPLD).

[0379] Clause 138. An integrated circuit according to any one of Clauses 134 to 137, wherein measurement uncertainty is identified corresponding to a single particle in the sample.

[0380] Clause 139. The integrated circuit according to any one of Clauses 134 to 138, wherein detecting light from particles includes measuring multiple parameters of the detected light against particles in a sample.

[0381] Clause 140. An integrated circuit according to any one of Clauses 134 to 139, wherein measurement uncertainty is identified for each parameter of the detected light in relation to particles in the sample.

[0382] Clause 141. An integrated circuit according to any one of Clauses 134 to 140, wherein the integrated circuit is programmed to:

[0383] Based on detected light-generating event data, which includes parameter measurements of particles in the sample.

[0384] Clause 142. The integrated circuit according to any one of Clauses 134 to 141, wherein the integrated circuit is programmed to:

[0385] Spectral analysis is performed on the light detected from the particles.

[0386] Clause 143. An integrated circuit according to any one of Clauses 134 to 142, wherein spectral analysis of light detected from particles produces a plurality of derived parameters.

[0387] Clause 144. An integrated circuit according to any one of Clauses 134 to 143, wherein the integrated circuit is programmed to:

[0388] Multiple derived parameters are generated for each particle by performing spectral analysis on the light detected from each particle.

[0389] Clause 145. An integrated circuit according to any one of Clauses 134 to 144, wherein the derived parameters include unmixed detected light.

[0390] Clause 146. An integrated circuit according to any one of Clauses 134 to 145, wherein measurement uncertainties are identified for derived parameters.

[0391] Clause 147. The integrated circuit according to any one of Clauses 134 to 146, wherein the integrated circuit is programmed to:

[0392] An algorithm for identifying a first subset of particles in a sample by applying a first gate to the detected light.

[0393] Clause 148. The integrated circuit as described in Clause 147, wherein the measurement uncertainty corresponds to identifying the first subset of particles.

[0394] Clause 149. An integrated circuit according to any one of Clauses 134 to 148, wherein the integrated circuit is programmed to:

[0395] An algorithm for identifying multiple subsets of particles in a sample by applying corresponding gates to the detected light.

[0396] Clause 150. The integrated circuit as described in Clause 149, wherein the measurement uncertainty corresponds to identifying each of the plurality of particle subsets.

[0397] Clause 151. An integrated circuit according to any one of Clauses 134 to 150, wherein the measurement uncertainty corresponds to the gate-level uncertainty.

[0398] Clause 152. An integrated circuit according to any one of Clauses 134 to 151, wherein the integrated circuit is programmed to:

[0399] A quality score (Q score) is generated for each particle based on the measurement uncertainty of each particle.

[0400] Clause 153. An integrated circuit according to any one of Clauses 134 to 152, wherein the integrated circuit is programmed to:

[0401] Record each event and the associated measurement uncertainty for each event.

[0402] Clause 154. An integrated circuit according to any one of Clauses 134 to 153, wherein the integrated circuit is programmed to:

[0403] Record the measurement results for each particle and the associated measurement uncertainty for each particle.

[0404] Clause 155. An integrated circuit according to any one of Clauses 134 to 154, wherein the integrated circuit is programmed to:

[0405] Record the measurement results for multiple parameters for each particle, as well as the associated measurement uncertainty for each parameter of each particle.

[0406] Clause 156. An integrated circuit according to any one of Clauses 134 to 155, wherein said integrated circuit is programmed to:

[0407] Record measurement uncertainties, wherein the measurement uncertainties correspond to measurement uncertainties for one or more of the following: parameters for each record; events for each record; each gate or other classification; or for the sample.

[0408] Clause 157. An integrated circuit according to any one of Clauses 134 to 156, wherein the measurement uncertainty of different parameters measured for particles in a sample includes different measures.

[0409] Clause 158. An integrated circuit according to any one of Clauses 134 to 157, wherein the measurement uncertainty corresponds to a binary classification event.

[0410] Clause 159. The integrated circuit as described in Clause 158, wherein the binary classification event corresponds to one or more of the following: gate determination or group membership classification decision.

[0411] Clause 160. An integrated circuit according to any one of Clauses 134 to 159, wherein the measurement uncertainty includes a measure that quantifies the measurement uncertainty.

[0412] Clause 161. An integrated circuit according to any one of Clauses 134 to 160, wherein the integrated circuit is programmed to:

[0413] An uncertainty score is generated based on the measurement uncertainty.

[0414] Clause 162. An integrated circuit according to any one of Clauses 134 to 161, wherein measurement uncertainty is reflected in an uncertainty score, wherein a higher uncertainty score indicates greater measurement uncertainty.

[0415] Clause 163. An integrated circuit according to any one of Clauses 134 to 162, wherein the integrated circuit is programmed to:

[0416] A quality score is generated based on the measurement uncertainty.

[0417] Clause 164. An integrated circuit according to any one of Clauses 134 to 163, wherein measurement uncertainty is reflected in a quality score, wherein a higher quality score indicates a higher level of confidence.

[0418] Clause 165. An integrated circuit according to any one of Clauses 134 to 164, wherein the measurement uncertainty includes one or more of the following: direct quantification, optionally including standard deviation; confidence interval; probability that the measured value is within a specified percentage range of the true value; or residuals related to the goodness of fit of unmixed data.

[0419] Clause 166. An integrated circuit according to any one of Clauses 134 to 165, wherein the measurement uncertainty is related to a binary classification, wherein the binary classification may optionally include one or more gating or group membership classifications.

[0420] Clause 167. The integrated circuit according to any one of Clauses 134 to 166, wherein the integrated circuit is programmed to:

[0421] An algorithm for calculating quality scores based on measurement uncertainty.

[0422] The quality score mentioned above reflects the likelihood of membership within the gate.

[0423] Clause 168. The integrated circuit as described in Clause 167, wherein the probability of members within a gate is calculated for each gate in a gate hierarchy.

[0424] Clause 169. An integrated circuit as described in Clause 167, wherein the probability of a member within a gate is calculated by considering each level of the gate's parent gate.

[0425] Clause 170. The integrated circuit according to any one of Clauses 134 to 169, wherein the measurement uncertainty associated with the detected light includes:

[0426] Measurement uncertainty is calculated based on a semi-empirical noise model, wherein the semi-empirical noise model includes one or more of the following: instrument calibration data, physical noise model, or measured value.

[0427] Clause 171. The integrated circuit according to any one of Clauses 134 to 170, wherein the measurement uncertainty associated with the detected light includes:

[0428] Measurement uncertainty is estimated on a per-event basis using statistical algorithms, wherein the statistical algorithms include characteristics of the distribution of data within the measurement sample.

[0429] Clause 172. The integrated circuit according to any one of Clauses 134 to 171, wherein the measurement uncertainty associated with the detected light includes:

[0430] Estimating measurement uncertainty involves a combination of applying noise modeling and distribution fitting.

[0431] Clause 173. The integrated circuit according to any one of Clauses 134 to 172, wherein the measurement uncertainty associated with the detected light includes:

[0432] Estimating measurement uncertainty involves applying Bayesian inference.

[0433] Clause 174. The integrated circuit according to any one of Clauses 134 to 173 further comprises:

[0434] Report measurement uncertainty for each particle in a subset of the sample.

[0435] Clause 175. The integrated circuit according to any one of Clauses 134 to 174 further comprises:

[0436] For each particle in a subset of the sample, measurement uncertainty is reported for multiple parameters.

[0437] Clause 176. The integrated circuit according to any one of Clauses 134 to 175 further comprises:

[0438] For each particle in the particle subset, report the measurement uncertainty associated with its membership in the subgroup defined by the gate.

[0439] Clause 177. An integrated circuit according to any one of Clauses 134 to 176, wherein measurement uncertainty includes random measurement error.

[0440] Clause 178. An integrated circuit according to any one of Clauses 134 to 177, wherein the measurement uncertainty includes spillover propagation error.

[0441] Clause 179. An integrated circuit according to any one of Clauses 134 to 178, wherein the measurement uncertainty reflects variations other than the true inherent differences between sample particles.

[0442] Clause 180. An integrated circuit according to any one of Clauses 134 to 179, wherein the measurement uncertainty reflects the confidence level of a classification decision regarding sample particles.

[0443] Clause 181. An integrated circuit according to any one of Clauses 134 to 180, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of multiple parameters of light detected from the particle.

[0444] Clause 182. An integrated circuit according to any one of Clauses 134 to 181, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of light detected from a plurality of particles.

[0445] Clause 183. An integrated circuit according to any one of Clauses 134 to 182, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the sample.

[0446] Clause 184. The integrated circuit according to any one of Clauses 134 to 183, wherein the measurement uncertainty associated with the detected light includes one or more of the following:

[0447] Estimate the measurement uncertainty associated with the detected light.

[0448] Measurement uncertainty associated with the detected light, or

[0449] Predict the measurement uncertainty associated with the detected light.

[0450] Clause 185. An integrated circuit according to any one of Clauses 134 to 184, wherein the integrated circuit is programmed to:

[0451] Particles are classified based on the detected light; and

[0452] The statistical significance of particle classification is calculated at least in part based on the measurement uncertainty.

[0453] Clause 186. An integrated circuit according to any one of Clauses 134 to 185, wherein the integrated circuit is programmed to:

[0454] Particles are classified based on the detected light; and

[0455] The confidence interval for particle classification is calculated at least in part based on the measurement uncertainty.

[0456] Clause 187. An integrated circuit according to any one of Clauses 134 to 186, wherein the integrated circuit is programmed to:

[0457] Based at least in part on the measurement uncertainty, a variance-stabilizing transformation is applied to the data, including measurements of light detected from particles in the sample; and

[0458] Aspects of visualizing the transformed data.

[0459] Clause 188. An integrated circuit according to any one of Clauses 134 to 188, wherein said integrated circuit is programmed to:

[0460] Based at least in part on the measurement uncertainty, the data, including the measurement results of light detected from particles in the sample, are preprocessed to minimize intra-cluster variance;

[0461] Clustering algorithms are applied to preprocessed data, wherein the clustering algorithms may optionally include one or more of supervised clustering algorithms and unsupervised clustering algorithms.

[0462] Clause 189. An integrated circuit according to any one of Clauses 134 to 188, wherein the integrated circuit is programmed to:

[0463] Based at least in part on the measurement uncertainty, the data including the measurement results of light detected from particles in the sample are standardized to normalize the measurement uncertainty across different datasets, wherein the different datasets may optionally include one or more datasets collected on different instruments or under different conditions.

[0464] Clause 190. An integrated circuit according to any one of Clauses 134 to 189, wherein said integrated circuit is programmed to:

[0465] Probabilistic analysis of particle classification or sorting using the measurement uncertainty, wherein probabilistic classification may optionally include the application of fuzzy logic techniques.

[0466] Clause 191. An integrated circuit according to any one of Clauses 134 to 190, wherein the integrated circuit is configured to distinguish between true variation of sample particles and measurement error.

[0467] Clause 192. An integrated circuit according to any one of Clauses 134 to 191, wherein the integrated circuit is configured to distinguish between real biological variation and measurement error.

[0468] Clause 193. An integrated circuit according to any one of Clauses 134 to 192, wherein the integrated circuit is configured to improve the sensitivity of flow cytometry assays.

[0469] Clause 194. An integrated circuit according to any one of Clauses 134 to 193, wherein the integrated circuit is configured to improve the resolution of flow cytometry assays.

[0470] Clause 195. An integrated circuit according to any one of Clauses 134 to 194, wherein the integrated circuit is configured to evaluate the quality of particle analysis results by correlating event-specific measurement uncertainty estimates with flow cytometry measurements.

[0471] Clause 196. An integrated circuit according to any one of Clauses 134 to 195, wherein a quantitative measure of measurement uncertainty is associated with flow cytometry measurement results.

[0472] Clause 197. An integrated circuit according to any one of Clauses 134 to 196, wherein the integrated circuit is programmed to:

[0473] Measurement uncertainty is used in relation to data analysis or sorting.

[0474] Clause 198. An integrated circuit according to any one of Clauses 134 to 197, wherein said integrated circuit is programmed to:

[0475] Identify a gate member confidence score for each event and each gate, wherein the gate member confidence score includes the probability that the true biological expression level of a given event falls within a given gate.

[0476] Clause 199. An integrated circuit according to any one of Clauses 134 to 198, wherein the integrated circuit is configured to calculate gate member confidence scores.

[0477] Clause 200. An integrated circuit according to any one of Clauses 134 to 199, wherein said integrated circuit is programmed to:

[0478] Gate member confidence scores are used in particle classification and sorting based on measurement uncertainty, where particle classification and sorting includes probabilistic sorting with configurable probability thresholds to maximize purity and / or yield.

[0479] Clause 201. A non-transitory computer-readable storage medium comprising instructions stored thereon, the instructions comprising:

[0480] An algorithm for identifying measurement uncertainties associated with light detected by a photodetector system from particles in a sample.

[0481] Clause 202. A non-transitory computer-readable storage medium as described in Clause 201, wherein measurement uncertainties are identified corresponding to individual particles in a sample.

[0482] Clause 203. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 202, wherein detecting light from particles includes measuring multiple parameters of the detected light for particles in a sample.

[0483] Clause 204. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 203, wherein measurement uncertainties are identified for each parameter of the detected light in relation to particles in the sample.

[0484] Clause 205. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 204 further comprises:

[0485] An algorithm for generating events based on detected light, where the event data includes parameter measurements of particles in the sample.

[0486] Clause 206. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 205 further comprises:

[0487] An algorithm for spectral analysis of light detected from particles.

[0488] Clause 207. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 206, wherein spectral analysis of light detected from particles produces a plurality of derived parameters.

[0489] Clause 208. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 207 further comprises:

[0490] An algorithm for generating multiple derived parameters for each particle by performing spectral analysis on the light detected from each particle.

[0491] Clause 209. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 208, wherein the derived parameters include unmixed detected light.

[0492] Clause 210. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 209, wherein measurement uncertainties are identified for derived parameters.

[0493] Clause 211. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 210 further comprises:

[0494] An algorithm for identifying a first subset of particles in a sample by applying a first gate to the detected light.

[0495] Clause 212. The non-transitory computer-readable storage medium as described in Clause 211, wherein the measurement uncertainty corresponds to the identification of the first subset of particles.

[0496] Clause 213. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 212 further comprises:

[0497] An algorithm for identifying multiple subsets of particles in a sample by applying corresponding gates to the detected light.

[0498] Clause 214. The non-transitory computer-readable storage medium as described in Clause 213, wherein the measurement uncertainty corresponds to identifying each of the plurality of particle subsets.

[0499] Clause 215. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 214, wherein the measurement uncertainty corresponds to a gate-level uncertainty.

[0500] Clause 216. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 215 further comprises:

[0501] An algorithm for generating a quality score (Q score) for each particle based on the measurement uncertainty of each particle.

[0502] Clause 217. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 216 further comprises:

[0503] An algorithm for recording the measurement uncertainty of each event and the associations for each event.

[0504] Clause 218. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 217 further comprises:

[0505] An algorithm for recording the measurement results for each particle and the associated measurement uncertainty for each particle.

[0506] Clause 219. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 218 further comprises:

[0507] An algorithm for recording the measurement results of multiple parameters for each particle and the associated measurement uncertainty for each parameter of each particle.

[0508] Clause 220. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 219 further comprises:

[0509] An algorithm for recording measurement uncertainty, wherein the measurement uncertainty corresponds to one or more of the following: parameters for each record; events for each record; each gate or other classification; or for the sample.

[0510] Clause 221. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 220, wherein the measurement uncertainty of different parameters for measuring particles in a sample includes different measures.

[0511] Clause 222. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 221, wherein measurement uncertainty corresponds to a binary classification event.

[0512] Clause 223. The non-transitory computer-readable storage medium as described in Clause 222, wherein the binary classification event corresponds to one or more of the following: gate determination or group membership classification decision.

[0513] Clause 224. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 223, wherein the measurement uncertainty includes a measure that quantifies the measurement uncertainty.

[0514] Clause 225. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 224 further comprises:

[0515] An algorithm for generating an uncertainty score based on the measurement uncertainty.

[0516] Clause 226. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 225, wherein measurement uncertainty is reflected in an uncertainty score, wherein a higher uncertainty score indicates greater measurement uncertainty.

[0517] Clause 227. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 226 further comprises:

[0518] An algorithm for generating quality scores based on the measurement uncertainty.

[0519] Clause 228. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 227, wherein measurement uncertainty is reflected in a quality score, wherein a higher quality score indicates a higher level of confidence.

[0520] Clause 229. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 228, wherein the measurement uncertainty comprises one or more of the following: direct quantification, optionally including standard deviation; confidence interval; probability that the measured value falls within a specified percentage range of the true value; or residuals related to the goodness of fit of unmixed data.

[0521] Clause 230. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 229, wherein measurement uncertainty is related to a binary classification, wherein the binary classification may optionally include one or more gating or group membership classifications.

[0522] Clause 231. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 230 further comprises:

[0523] An algorithm for calculating quality scores based on measurement uncertainty.

[0524] The quality score mentioned above reflects the likelihood of a member inside the door.

[0525] Clause 232. The non-transitory computer-readable storage medium as described in Clause 231, wherein the probability of members within a door is calculated for each door in a door hierarchy.

[0526] Clause 233. A non-transitory computer-readable storage medium as described in Clause 231, wherein the probability of members within a gate is calculated by considering each level of the parent gate of the gate.

[0527] Clause 234. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 233, wherein the measurement uncertainty associated with the detected light is identified as including:

[0528] An algorithm for calculating measurement uncertainty based on a semi-empirical noise model, wherein the semi-empirical noise model includes one or more of the following: instrument calibration data, physical noise model, or measurement value.

[0529] Clause 235. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 234, wherein the measurement uncertainty associated with the detected light is identified as including:

[0530] An algorithm for estimating measurement uncertainty on an event-by-event basis using a statistical algorithm, wherein the statistical algorithm includes characteristics of the distribution of data within the measurement sample.

[0531] Clause 236. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 235, wherein the measurement uncertainty associated with the detected light is identified as including:

[0532] Algorithms for estimating measurement uncertainty include a combination of noise modeling and distribution fitting.

[0533] Clause 237. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 236, wherein the measurement uncertainty associated with the detected light is identified as including:

[0534] Algorithms used to estimate measurement uncertainty include those that apply Bayesian inference.

[0535] Clause 238. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 237 further comprises:

[0536] An algorithm for reporting measurement uncertainty for each particle in a subset of particles for a sample.

[0537] Clause 239. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 238 further comprises:

[0538] An algorithm for reporting measurement uncertainty for each particle in a subset of particles of a sample, for multiple parameters.

[0539] Clause 240. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 239 further comprises:

[0540] An algorithm for reporting measurement uncertainties associated with membership in a subgroup defined by a gate for each particle in a particle subset.

[0541] Clause 241. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 240, wherein measurement uncertainty includes random measurement error.

[0542] Clause 242. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 241, wherein measurement uncertainty includes spillover propagation error.

[0543] Clause 243. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 242, wherein measurement uncertainty reflects variations other than the true inherent differences between sample particles.

[0544] Clause 244. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 243, wherein the measurement uncertainty reflects the confidence level of a classification decision regarding sample particles.

[0545] Clause 245. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 244, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of multiple parameters of light detected from the particles.

[0546] Clause 246. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 245, wherein the measurement uncertainty reflects the overall measurement uncertainty related to the measurement results of light detected from a plurality of particles.

[0547] Clause 247. A non-transitory computer-readable storage medium pursuant to any one of Clauses 201 to 246, wherein the measurement uncertainty reflects the overall measurement uncertainty relating to the sample.

[0548] Clause 248. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 247, wherein the measurement uncertainty associated with the detected light is identified includes one or more of the following:

[0549] Estimate the measurement uncertainty associated with the detected light.

[0550] Measurement uncertainty associated with the detected light, or

[0551] Predict the measurement uncertainty associated with the detected light.

[0552] Clause 249. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 248 further comprises:

[0553] An algorithm for classifying particles based on detected light; and

[0554] An algorithm for calculating the statistical significance of particle classification based at least in part on the measurement uncertainty.

[0555] Clause 250. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 249 further comprises:

[0556] An algorithm for classifying particles based on detected light; and

[0557] An algorithm for calculating confidence intervals for particle classification based at least in part on the measurement uncertainty.

[0558] Clause 251. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 250 further comprises:

[0559] An algorithm for applying a variance-stabilizing transformation to data including light detected from particles in a sample, based at least in part on the measurement uncertainty; and

[0560] Algorithms for visualizing transformed data.

[0561] Clause 252. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 251 further comprises:

[0562] An algorithm for preprocessing data, including measurements of light detected from particles in a sample, based at least in part on the measurement uncertainty, to minimize intra-cluster variance;

[0563] An algorithm for applying a clustering algorithm to preprocessed data, wherein the clustering algorithm may optionally include one or more of supervised clustering algorithms and unsupervised clustering algorithms.

[0564] Clause 253. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 252 further comprises:

[0565] An algorithm for normalizing measurement uncertainty across different datasets, at least in part, based on the measurement uncertainty, including data of light detected from particles in a sample, wherein the different datasets may optionally include one or more datasets collected on different instruments or under different conditions.

[0566] Clause 254. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 253 further comprises:

[0567] An algorithm for probabilistic analysis of particle classification or sorting using the measurement uncertainty, wherein probabilistic classification may optionally include the application of fuzzy logic techniques.

[0568] Clause 255. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 254, wherein a quantitative measure of measurement uncertainty is associated with flow cytometry measurement results.

[0569] Clause 256. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 255 further comprises:

[0570] Algorithms used in relation to measurement uncertainty in data analysis or sorting.

[0571] Clause 257. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 256 further comprises:

[0572] An algorithm for identifying the gate member confidence score for each event and each gate, wherein the gate member confidence score includes the probability that the true biological expression level of a given event falls within a given gate.

[0573] Clause 258. A non-transitory computer-readable storage medium according to any one of Clauses 201 to 257, wherein the method is a method for calculating gate member confidence scores.

[0574] Clause 259. The non-transitory computer-readable storage medium according to any one of Clauses 201 to 258 further comprises:

[0575] An algorithm for using gate member confidence scores in particle classification and sorting based on measurement uncertainty, wherein particle classification and sorting includes probabilistic sorting with configurable probability thresholds to maximize purity and / or yield.

[0576] Although the foregoing disclosure has been described in detail for the purpose of clarity by way of illustration and example, it will be readily apparent to those skilled in the art, based on the teachings of this disclosure, that some changes and modifications may be made without departing from the spirit or scope of the appended claims.

[0577] Therefore, the foregoing only illustrates the principles of this disclosure. It should be understood that those skilled in the art will be able to devise various arrangements, which, although not explicitly described or shown herein, embody the principles of this disclosure and are included within its spirit and scope. Furthermore, all examples and conditional language cited herein are primarily intended to aid the reader in understanding the principles of this disclosure and the concepts contributed by the inventors to advance the field, and should be construed as not being limited by the examples and conditions of such specific citations. Moreover, all statements and specific examples of the principles, aspects, and embodiments of this disclosure cited herein are intended to cover both structural and functional equivalents. Furthermore, it is intended that such equivalents include both currently known equivalents and future-developed equivalents, i.e., any developed element performing the same function, regardless of its structure. Furthermore, nothing disclosed herein is intended to be exclusive to the public, whether or not such disclosure is expressly cited in the claims.

[0578] Therefore, the scope of this disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of this disclosure are embodied in the appended claims. In the claims, 35U.SC§112(f) or 35U.SC§112(6) is explicitly defined as being invoked only if the exact phrase “means for…” or the exact phrase “steps for…” is explicitly referenced at the beginning of the limitation in the claim; if the exact phrase is not used in the limitation in the claim, then 35U.SC§112(f) or 35U.SC§112(6) is not invoked.

Claims

1. A method comprising: introducing a 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 a particle in the sample flowing in the flow stream; and identifying a measurement uncertainty associated with the detected light. The measurement uncertainty relates to a binary classification, where the binary classification optionally includes one or more of gating or population member classification.

2. The method of claim 1, wherein, 3. The method of any of the preceding claims, further comprising: calculating a quality score based on the measurement uncertainty, where the quality score reflects a likelihood of being in a gate. calculating the likelihood of being in a gate for each gate in a gate hierarchy, and / or where the likelihood of being in a gate is calculated considering each hierarchical parent gate of the gate.

4. The method of claim 3, wherein, Identifying a measurement uncertainty associated with the detected light includes one or more of:

5. The method according to any of the preceding claims, wherein, 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.

6. The method of any of the preceding claims, further comprising: classifying a particle based on the detected light; and calculating a confidence interval for the particle classification based at least in part on the measurement uncertainty.

7. The method of any of the preceding claims, further comprising: using the measurement uncertainty for probabilistic analysis of particle classification or sorting, where probabilistic classification optionally includes applying fuzzy logic techniques.

8. The method of any of the preceding claims, further comprising: identifying a gate member confidence score for each event and each gate, where the gate member confidence score includes a likelihood that a true biological expression level of a given event falls within a given gate.

9. The method of any of the preceding claims, where the method is a method for calculating gate member confidence scores.

10. The method of any of the preceding claims, further comprising: using the gate member confidence scores based on measurement uncertainty in particle classification and sorting, where particle classification and sorting includes probabilistic sorting with configurable likelihood thresholds to maximize purity and / or yield.

11. A system comprising: a light source configured to illuminate a sample comprising a plurality of particles; a light detection system comprising a plurality of photodetectors; and a processor comprising a memory operably coupled to the processor, where the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to identify a measurement uncertainty associated with light detected from the light detection system. The measurement uncertainty relates to a binary classification, where the binary classification optionally includes one or more of gating or population member classification. The quality score reflects a likelihood of being in a gate.

12. The system of claim 11, wherein, calculating the likelihood of being in a gate for each gate in a gate hierarchy, and / or where, 13. The system of any one of claims 11-12, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to calculate a quality score based on the measurement uncertainty, wherein, the likelihood of being in a gate is calculated considering each hierarchical parent gate of the gate.

14. The system of claim 13, wherein, Identifying a measurement uncertainty associated with the detected light includes one or more of: estimating the measurement uncertainty associated with the detected light, 15. The system of any one of claims 11 to 14, wherein, ​ ​ measure the measurement uncertainty associated with the detected light, or predict the measurement uncertainty associated with the detected light.

16. The system of any one of claims 11 to 15, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to: classify a particle based on the detected light; and calculate a confidence interval for the particle classification based at least in part on the measurement uncertainty.

17. The system of any one of claims 11 to 16, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to: use the measurement uncertainty for probabilistic analysis of particle classification or sorting, wherein probabilistic classification optionally includes applying fuzzy logic techniques.

18. The system of any one of claims 11 to 17, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to: identify a gate membership confidence score for each event and each gate, wherein the gate membership confidence score comprises a likelihood that a true biological expression level of a given event falls within a given gate.

19. The system of any one of claims 11 to 18, wherein the system is configured to calculate gate membership confidence scores.

20. The system of any one of claims 11 to 19, wherein the memory comprises instructions stored thereon that, when executed by the processor, cause the processor to: use measurement uncertainty-based gate membership confidence scores in particle classification and sorting, wherein particle classification and sorting includes probabilistic sorting with configurable likelihood thresholds to maximize purity and / or yield.

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