Duplex analysis in flow cytometry

Abstract

A flow cytometry system for analyzing a fluid flow of particles. The system collects waveform data without thresholding radiation light detected from particles passing through the light beam in the interrogation zone. The system identifies a duplex from the waveform data. For each duplex identified from the waveform data, the system separates the duplex waveform into a single waveform. The system analyzes a single waveform separated for each duplex, and classifies the duplex based on the analysis of the single waveform.

Description

Technical Field

[0001] This application was filed on January 22, 2024 as a PCT International Application and claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 481,298, filed on January 24, 2023, the disclosure of which is hereby incorporated by reference in its entirety. Background Art

[0002] Flow cytometry is a technique used to detect and analyze the chemical and physical properties of cells or particles in a fluid sample. For example, a flow cytometer can be used to evaluate cells from blood, bone marrow, tumors, or other body fluids. Typically, the sample is passed through a fluid nozzle that arranges the particles in a sheath fluid into a single file line. As the particles pass through the single file, a laser beam illuminates the particles to generate radiation that includes forward scattered light, side scattered light, and fluorescence. The radiation can then be detected and analyzed to determine one or more characteristics of the particles. Summary of the Invention

[0003] In general, the present disclosure relates to analyzing particles using flow cytometry. In one possible configuration, waveform data is collected without thresholding, doublets are identified from the waveform data, and the doublets are separated into separate individual waveforms for analysis. Various aspects are described in the present disclosure, including but not limited to the following.

[0004] One aspect relates to a flow cytometer system for analyzing a fluid stream of particles, the flow cytometer system comprising: a light source for emitting a light beam toward an interrogation zone; an optical system comprising a detector for detecting radiated light from particles passing through the light beam in the interrogation zone; and processing circuitry having a non-transitory computer-readable storage medium storing instructions that, when executed by the processing circuitry, cause the processing circuitry to: collect waveform data without thresholding the radiated light detected from the particles passing through the light beam in the interrogation zone; identify doublets from the waveform data; for each doublet identified from the waveform data, separate the doublet waveform into individual waveforms; perform analysis of the individual waveforms separated for each doublet; and classify the doublets based on the analysis of the individual waveforms.

[0005] Another aspect relates to a method of analyzing particles flowing through a flow cytometer, the method comprising: collecting waveform data without thresholding radiated light detected from particles passing through a light beam in an interrogation zone; identifying doublets from the waveform data; for each doublet identified from the waveform data, separating the doublet waveform into individual waveforms; analyzing the individual waveforms separated for each doublet; and classifying the doublets based on the analysis of the individual waveforms.

[0006] Another aspect relates to a non-transitory computer-readable medium comprising program instructions that, when executed by a processor, cause the processor to: collect waveform data without thresholding radiated light detected from particles passing through a light beam in an interrogation zone; identify doublets from the waveform data; for each doublet identified from the waveform data, separate the doublet waveform into individual waveforms; perform analysis of the individual waveforms separated for each doublet; and classify the doublets based on the analysis of the individual waveforms.

[0007] Various additional aspects will be described in the following description. These aspects may relate to individual features as well as to combinations of features. It should be understood that both the foregoing general description and the following detailed description are exemplary and illustrative only and do not limit the broad inventive concepts on which the embodiments disclosed herein are based. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The following drawings, which form a part of this application, are illustrative of the described technology and are not meant to limit the scope of the present disclosure in any way.

[0009] Figure 1 An example of a flow cytometer system is schematically shown.

[0010] Figure 2A Shows the particles entering Figure 1 Example of the interrogation zone of a flow cytometer in a .

[0011] Figure 2B Shows the particles passing through Figure 2A Example of the central area of ​​the interrogation zone.

[0012] Figure 2C Shows the particles leaving Figure 2A Example of an inquiry area.

[0013] Figure 3 Shown from Figure 1 Example of waveform data acquired by a flow cytometer in a system plotted against a threshold.

[0014] Figure 4 Schematically shows Figure 1 Example of a waveform analysis device for a flow cytometer system.

[0015] Figure 5 Graphically shows the Figure 1 Example of a doublet waveform detected by the flow cytometry system.

[0016] Figure 6 Schematically shows the Figure 1 An example of a method for performing flow cytometric analysis using a flow cytometer system.

[0017] Figure 7 Graphically shows the Figure 1 Another example of a doublet waveform detected by the flow cytometry system.

[0018] Figure 8 The independent component analysis is used to illustrate the Figure 5 Example of separation of the doublet waveform into separate waveforms for each cell of the doublet.

[0019] Figure 9 The independent component analysis is used to illustrate the Figure 7 Example of separation of the doublet waveform into separate waveforms for each cell of the doublet.

[0020] Figure 10 An exemplary architecture of a computing device that can be used to implement aspects of the present disclosure is shown. DETAILED DESCRIPTION

[0021] Various embodiments will be described in detail with reference to the accompanying drawings, wherein like reference numerals represent like parts and components throughout the several views. Reference to various embodiments does not limit the scope of the appended claims. Furthermore, any examples set forth in this specification are not intended to be limiting, but merely set forth some of the many possible embodiments of the appended claims.

[0022] Figure 1Schematically illustrated is an example of a flow cytometer system 100. In some cases, the flow cytometer system 100 can include aspects and features described in U.S. Provisional Patent Application No. 63 / 410,984, filed on September 28, 2022, entitled “Flow Cytometry Waveform Processing,” U.S. Provisional Patent Application No. 63 / 481,289, filed on January 24, 2023, entitled “Threshold Logic for Flow Cytometry Waveform Analysis,” and U.S. Provisional Patent Application No. 63 / 481,293, filed on January 24, 2023, entitled “Control Variable Adjustment for Flow Cytometry Waveform Acquisition,” which are incorporated herein by reference in their entireties.

[0023] In general, flow cytometry is a technique for measuring and analyzing the properties of particles or cells as they flow in a fluid stream. Data from millions of particles or cells can be collected by the flow cytometer system 100 in a matter of minutes and displayed in a variety of formats. Illustrative applications of flow cytometry include: phenotyping to identify and count specific cell types within a population; analyzing the DNA or RNA content within a cell; determining the presence of antigens on or within a cell; and assessing cell health.

[0024] like Figure 1 As shown in the illustrative example of FIG, a flow cytometer system 100 generally includes three major component subsystems: a fluidics system 110, an optics system 120, and an electronics system 130. The fluidics system 110 includes a nozzle 112 that receives a sample containing particles or cells suspended in a fluid. The nozzle 112 creates and ejects a fluid stream 114 of particles or cells arranged in a single file. Each particle or cell passes through one or more light beams generated by a light source 102. The point where a particle or cell intersects the light beam is referred to as an interrogation zone 116. In some examples, the light source 102 includes one or more lasers.

[0025] Optical system 120 includes light source 102, optical element 122, and detector 124. At interrogation zone 116, light from light source 102 strikes particles or cells in fluid stream 114 and scatters. Optical element 122 directs the scattered light toward detector 124. Detector 124 may include a forward scatter (FSC) detector for measuring scattering in the path of light source 102; a side scatter (SSC) detector for measuring scattering at a ninety-degree angle relative to light source 102; and one or more fluorescence detectors (FL1, FL2, FL3, ..., FLn) for measuring emitted fluorescence intensity at different wavelengths of light.

[0026] Typically, the FSC intensity is proportional to the size or diameter of the particle due to the diffraction of light around the particle. Therefore, FSC can be used to distinguish particles by size, and on the other hand, it is generated from the light refracted or reflected by the internal structure of the particle, so it can provide information about the internal complexity or granularity of the particle. By adding fluorescent markers to the sample, different fluorescent signals / channels (e.g., green, orange, and red) can be analyzed for the functional properties of the cell. For example, since T cells have CD3 binding sites, samples containing T cells can be "stained" with anti-CD3 antibodies conjugated to fluorescent molecules. When these cells pass through the interrogation zone 116, the light from the light source excites the fluorescent tag or fluorescent dye to emit photons at a wavelength that can be detected by the fluorescence detector. Therefore, the detector 124 can measure several parameters simultaneously, as well as enable the particles to be classified by the function of the particles based on the wavelength of the detected light.

[0027] The electronic system 130 includes a waveform acquisition device 140 and a waveform analysis device 150. The waveform acquisition device 140 is communicatively coupled to the detector 124 to receive the analog waveform data 126 generated by the detector 124. The waveform acquisition device 140 includes an analog-to-digital converter (ADC) 142 configured to digitize the analog waveform data 126 received from the detector 124.

[0028] The waveform analysis device 150 is configured to receive digital waveform data and display it to a user of the flow cytometer system 100. In some embodiments, the waveform analysis device 150 comprises a computing device communicatively coupled to the flow cytometer 101, such as via a network. The flow cytometer 101 can include a fluidics system 110, an optical system 120, and a waveform acquisition device 140. In other embodiments, the waveform analysis device 150 is integrated with the flow cytometer 101.

[0029] Current flow cytometers use a field programmable gate array (FPGA) in the waveform acquisition device to obtain information about each particle passing through the light beam. The waveform acquisition device uses a single threshold to determine when the output of the detector begins to be converted from analog to digital. Only a single threshold can be used for a single run of a sample through the flow cytometer. The threshold is a constant value and can be referred to as a voltage threshold. Therefore, if the detector output exceeds the voltage value of the threshold or when the detector output exceeds the voltage value of the threshold, digitization begins and the digital value is sent to the FPGA. When the waveform data is digitized, the FPGA calculates the height, width and area of ​​each pulse. In addition to the height, width and area of ​​each pulse, other data related to the waveform (including data that does not exceed the voltage threshold) will not be captured, stored or otherwise available for analysis. In addition, if the user wants to adjust the threshold, the experiment must be rerun with the new threshold, which results in resource and time costs.

[0030] To solve the above problems, the flow cytometer system 100 is improved with a graphics processing unit (GPU) 152. Figure 1 In the example shown in FIG, a GPU 152 is shown as being included as a component of the waveform analysis device 150. The GPU 152 processes the continuous digital stream generated by the waveform acquisition device 140. The digital stream is continuous because the waveform acquisition device 140 does not threshold the waveform data generated by the detector 124. In contrast to current flow cytometry technology, during an experiment, the waveform acquisition device 140 continuously digitizes the analog waveform data 126 at a high rate (e.g., 1 GHz) without thresholding. In some cases, the GPU 152 enables the removal of the FPGA from the waveform acquisition device 140.

[0031] In view of the foregoing description, the waveform analysis device 150 receives a digitized version of the waveform data with added data points, and the waveform data for the experiment is displayed and made available to the GPU 152 for processing as a whole. In addition to having the ability to process large waveform data streams or waveform data files, the GPU 152 enables thresholding of the waveform at a post-processing step rather than at the waveform acquisition step. This in turn provides several technical benefits, including the ability to dynamically adjust thresholds and update graphical charts in real time without rerunning the experiment. The GPU 152 can also measure and extract biologically relevant information other than the three parameters of height, width, and area presented in the waveform data. Further details of operation and advantages are discussed below.

[0032] The flow cytometer system 100 includes the elements shown and described for the purposes of discussion, and it should be understood that there are many variations in the components and functions. The optical elements 122 may include a series of filters, dichroic mirrors, and / or beam splitters to select different wavelengths of light and provide the wavelengths to appropriate detectors. The detector 124 may include, for example, a photomultiplier tube (PMT) or an avalanche photodiode (APD) or a single photon counting device.

[0033] Figures 2A to 2C An example of waveform data generated by particle 201 as particle 201 passes through interrogation zone 116 is shown. As particle 201 passes through interrogation zone 116, pulses are detected by one or more of detectors 124.

[0034] Figure 2A An example of particle 201 entering interrogation zone 116 is shown. When particle 201 begins to intersect interrogation zone 116, particle 201 begins to generate scattered light and a fluorescent signal. Detector 124 generates a current or voltage proportional to the scattered light and fluorescent signal. As current flows in detector 124, the output of detector 124 begins to rise, as shown in graph 212.

[0035] Figure 2B An example of particle 201 passing through the central region of interrogation zone 116 is shown. As particle 201 continues to move through interrogation zone 116, particle 201 becomes fully illuminated. Since the photon density is highest in the central portion of interrogation zone 116, the maximum amount of light signal is generated in this example. As shown in graph 232, the current or voltage of detector 124 reaches a peak when particle 201 passes through the central region of interrogation zone 116.

[0036] Figure 2C 252. An example of a particle 201 leaving interrogation zone 116 is shown. When particle 201 leaves interrogation zone 116, the current or voltage output of detector 124 returns to baseline. The generation of a pulse shown in graph 252 is referred to as an event. The height of graph 252 represents the maximum current / voltage output by detector 124, which can be proportional to the signal strength and size of the particle. The width of graph 252 represents the time it takes for the particle to pass through interrogation zone 116, and the area under graph 252 can represent the signal strength and size of the particle. Therefore, the height, width, and area of ​​graph 252 can be used to characterize the particle.

[0037] Figure 3An example of waveform data 300 plotted against a threshold value 310 is shown. In this illustrative example, threshold value 310 represents a single constant threshold voltage. As previously described, in conventional polychromatic and spectral flow cytometry, threshold value 310 is used to specify when digitization of the detector output (e.g., analog waveform data 126) begins. That is, when waveform data 300 passes through threshold value 310, the waveform acquisition device begins calculating the height, width, and area of ​​each pulse 301 to 303 above threshold value 310. In the prior art, waveform data 300 below threshold value 310 is discarded during waveform acquisition.

[0038] A problem with the above approach is that threshold 310 cannot be appropriately set for the entire voltage waveform for the purpose of extracting event data. For example, threshold 310 in this example may be set too high to accurately analyze cells generating pulses similar to pulse 301 of waveform data 300. On the other hand, if threshold 310 is set too low, the overall signal-to-noise ratio of waveform data 300 is compromised. Furthermore, in conventional flow cytometers, a single threshold must be set prior to data acquisition, irreversibly discarding potentially relevant events.

[0039] Figure 4 An example of a waveform analysis device 150 is schematically shown. The waveform analysis device 150 receives, stores, and displays waveform data that has been continuously sampled but not thresholded upstream at the waveform acquisition device 140. The waveform analysis device 150 includes an interface 410 for receiving digitized raw waveform data 432, a persistent storage device 430 for storing the digitized raw waveform data 432, and may include a graphical user interface (GUI) 420 for displaying the digitized raw waveform data 432. The persistent storage device 430 may also store a plurality of dynamic threshold values ​​434 that allow for nonlinear thresholding and real-time updating and display of applied threshold values, as further described below. The persistent storage device 430 may include system memory such as random access memory (RAM) and / or long-term non-volatile storage such as a hard drive.

[0040] The waveform analysis device 150 may also include a cytometry analysis application 450, which includes a software application or a set of related software applications configured to instruct the GPU 152 to process the digitized raw waveform data 432. The cytometry analysis application 450 may be executed on one or more processors to provide the functionality described herein in conjunction with the GPU 152, such as receiving user input via the GUI 420. One or more components of the waveform analysis device 150 may reside in a cloud computing application in a network distributed system. In this regard, the waveform analysis device 150 may be any of a variety of computing devices, including but not limited to a personal computing device, a server computing device, or a distributed computing device.

[0041] Doublets occur when two cells are linked together. Typically, doublets are excluded from analysis in flow cytometry because they can affect the quality of the data, such as by causing false positives and / or false negatives to be included in the data. Although the following disclosure relates to doublets, it is contemplated that the methods and techniques described herein can be similarly applied to other types of n-linked cells, such as when more than two cells (such as three cells, four cells, etc.) are linked together.

[0042] Figure 5 An example of a waveform 500 representing a doublet detected by the flow cytometer system 100 is graphically shown. In this example, the waveform 500 is detected by one of the fluorescence channels (i.e., detectors FL1 to FLn) of the flow cytometer 101. Figure 5 As shown in FIG, waveform 500 includes a first peak 502 representing cells that are positive for a characteristic, such as the presence of a fluorescent dye. Waveform 500 also includes a second peak 504 representing cells that are not positive for the characteristic. The presence or absence of a characteristic is indicated by first peak 502 having a higher fluorescence voltage value than second peak 504.

[0043] In this example, if waveform 500 is not excluded from the flow cytometric analysis or is otherwise interpreted or perceived as a single cell, a false positive will be introduced into the data set because the second cell (i.e., second peak 504) is negative for the characteristic. Furthermore, if waveform 500 is excluded from the flow cytometric analysis, relevant information will be lost because the first cell (i.e., first peak 502) is positive for the characteristic. In view of the foregoing, it would be advantageous to include waveform 500 in the flow cytometric analysis without introducing false positives or false negatives into the data set.

[0044] Figure 6Schematically illustrated is an example of a method 600 of performing flow cytometric analysis by the flow cytometer system 100. As will be described in greater detail, the method 600 can improve the accuracy of the flow cytometric analysis by including analysis of doublets that are typically discarded during flow cytometry.

[0045] The method 600 includes an operation 602 of collecting waveform data. As described above, the waveform data is collected without thresholding doublets and without discarding doublets, so that the waveform data includes all events detected by the detector 124 of the optical system 120. Collecting waveform data without thresholding is advantageous compared to conventional flow cytometry systems that use thresholding because, in some cases, doublets may include connected events below the threshold, causing the doublet to be perceived as a single cell. For example, referring to Figure 5 If threshold 506 is applied to waveform 500, the second cell (ie, second peak 504) will not be detected because the entire second cell is below threshold 506, such that waveform 500 will be characterized as a single cell.

[0046] Reference Figure 6 , method 600 includes an operation 604 of identifying doublets in the waveform data collected in operation 602. Doublets are identified in operation 604 after acquisition because the waveform data collected in operation 602 includes all events detected by detector 124 without thresholding doublets and without discarding doublets.

[0047] Several different techniques may be used to identify doublets in operation 604. These techniques may be performed individually or in combination with one another to improve the accuracy of doublet identification in operation 604. The present disclosure is not limited to any one of the doublet identification techniques described below, and it is contemplated that new doublet identification techniques may be developed in the future.

[0048] In some examples, in operation 604, the area under the pulse and the waveform pulse (see Figure 2C ) are used to identify doublets. Due to their shape, which consists of two cells joined together, doublets typically have the same height as a single cell but twice the area.

[0049] In some examples, operation 604 includes executing one or more detection algorithms that may include detecting multimodality of the waveform. For example, a multimodal waveform with two modes in the forward scatter channel and the side scatter channel is desirable, where the difference between the modes is a function of the amount of overlap. In combination with thresholding and other adjustable parameters, a waveform is identified as a doublet when it is multimodal above a certain threshold and within the constraints of the other parameters.

[0050] Figure 7 Another example of a waveform 700 representing a doublet detected by the flow cytometer system 100 is shown graphically. In this example, the waveform 700 is detected from the forward scatter channel (FSC) detector 124 (see FIG. Figure 1 ) is generated. A similar waveform can be generated from the side scatter channel (SSC) detector 124 of the flow cytometer 101. Figure 7 As shown in FIG, waveform 700 includes a first peak 702 and a second peak 704. A predetermined threshold 706 is applied to waveform 700 to measure a separation parameter 708 between first peak 702 and second peak 704. According to the above example, the predetermined threshold 706 is applied after acquisition.

[0051] The separation parameter 708 between the first peak 702 and the second peak 704 is a function of the amount of overlap between the two connected cells. For example, a greater overlap between the two connected cells results in a smaller value for the separation parameter 708. In contrast, a smaller overlap between the two connected cells results in a larger value for the separation parameter 708. Thus, the separation parameter 708 can be used to determine the amount of overlap between two cells passing through the interrogation zone 116.

[0052] The detection algorithm executed in operation 604 identifies doublets by comparing the separation parameter 708 to a threshold distance value. For example, when the separation parameter 708 is less than the threshold distance value, this indicates that the two cells substantially overlap each other, such that they are connected together in a doublet. When the separation parameter 708 is greater than the threshold distance value, this may indicate that the two cells do not overlap each other, such that they are not connected together and are therefore single cells.

[0053] In some examples, the total length L of the waveform 700 is measured. The total length L is then compared to a default threshold, and doublets are identified based on the comparison. In some further examples, the ratio between the separation parameter 708 and the total length L of the waveform 700 is used to identify whether the waveform 700 is a single cell or a doublet.

[0054] Operation 604 may also include transforming the domain of the waveform data collected in operation 602 (which includes time series data). In such an example, the time series data may be transformed into a different domain (such as the frequency domain). Thereafter, a heuristic threshold may be applied to identify doublets. For example, due to periodicity, Figure 7 The separation parameter 708 shown in FIG is a blip in the spectrum. In a single cell waveform, this blip is typically not present. Thus, operation 604 can include identifying the separation parameter 708 and thresholding it (e.g., by comparing the separation parameter 708 to a threshold distance value) to distinguish doublets from single cells.

[0055] In another example, operation 604 may include executing a supervised classification machine learning algorithm to identify doublets. Supervised learning (SL) is a machine learning technique that uses training data that includes labeled samples so that each data point contains a feature (covariate) and an associated label. The supervised learning algorithm analyzes the training data to generate an inference function that can be used to map new samples.

[0056] In another example, operation 604 may include executing an unsupervised clustering machine learning algorithm to identify doublets. Unsupervised learning is a type of machine learning algorithm that identifies patterns from unlabeled data points. Such techniques can be used to identify waveform clusters corresponding to doublets. As described above, operation 604 may include combining several of the algorithms described above for identifying doublets.

[0057] like Figure 6 As further shown in FIG, method 600 includes separating the doublet waveforms into individual waveforms at operation 606. The doublet waveforms are separated at operation 606 after acquisition because the waveform data collected at operation 602 includes all events detected by detector 124 without thresholding the doublets and without discarding the doublets.

[0058] Several different techniques may be used to separate the doublet waveforms in operation 606. These techniques may be performed individually or in combination with one another to improve the doublet waveform separation in operation 606. The present disclosure is not limited to any one of the doublet waveform separation techniques described below, and it is contemplated that new doublet waveform separation techniques may be developed in the future.

[0059] In some examples, the doublet waveforms are separated in operation 606 by executing one or more blind source separation algorithms. Blind source separation involves separating one or more source signals from a set of mixed signals, typically without the aid of information (or very little information) about the source signals or the mixing process.

[0060] At least one example of a blind source separation algorithm that may be performed in operation 606 includes independent component analysis (ICA) to separate the doublet waveform into separate waveforms for each cell of the doublet. ICA may be particularly useful when the waveform of each cell in the doublet is non-Gaussian.

[0061] In some examples, a preprocessing step is performed to first identify doublets, and then the doublets are windowed to a fixed size before performing ICA. For example, ICA can use the time series data collected in operation 602, but the length of the data should ideally be fixed. A window can be applied around the doublet before performing ICA. For each channel of the flow cytometer system 100 (e.g., a scatter channel and a fluorescence channel), the window covering the doublet should ideally be the same size. As an illustrative example, when there are k channels and the window is predefined to include 1000 data points, this will produce a k×1000 matrix.

[0062] Figure 8 Graphically shows the Figure 5 In this example, ICA is used to separate the doublet waveform into the waveforms of each cell of the doublet. Figure 8 As shown in FIG, a doublet waveform is separated into a first waveform 802 of a first cell of the doublet, and into a second waveform 804 of a second cell of the doublet.

[0063] Figure 9 Graphically shows the Figure 7 In this example, ICA is used to separate the doublet waveform into the waveforms of each cell of the doublet. Figure 9 As shown in FIG, a doublet waveform is separated into a first waveform 902 of a first cell of the doublet, and into a second waveform 904 of a second cell of the doublet.

[0064] In another example, a Gaussian mixture model is used in operation 606 to distinguish the individual waveforms of each cell in a doublet. In such an example, the doublet is viewed as a non-normalized probability density function. More specifically, the doublet can be characterized as a mixture of two Gaussian waveforms. The mode and variance are determined to identify the components of the mixture. A non-normalized Gaussian waveform is determined for each component waveform in the mixture. Using this technique, the waveform of a first cell in a doublet can be distinguished from the waveform of a second cell, such as when the first cell is positive for a characteristic (e.g., a fluorescent dye) and the second cell is negative.

[0065] An example of a Gaussian mixture model includes preprocessing (as in the ICA algorithm described above). In such an example, a time series of data is collected for each channel, and the doublets are windowed (for example, measurement record j is taken as measurement j+1000). The value at each measurement point is treated as the weighted number of samples at that point. This can be done for each waveform channel to obtain a sample for each measurement point. Expectation maximization or some other type of sampling method is performed to generate parameters for each component of the doublet in the Gaussian mixture model.

[0066] Another example involves windowing doublets and training via backpropagation to identify the parameters of a Gaussian curve that fits time series data. This is similar to curve fitting, where there is a fixed number of points and the parametric form of the curve is known.

[0067] In another example, heuristics and predictions are performed in operation 606 to separately identify the waveform of each cell in the doublet. This technique may include extracting information from the beginning and end of the doublet waveform to determine the characteristics of the waveform of each cell in the doublet. Using this information, each waveform may be predicted separately, starting from the front and back of the waveform.

[0068] As an illustrative example, the doublets are windowed to have a fixed (and identical) number of data points for each channel. A model can be trained on supervised data from non-doublets to predict the remainder of the waveform given a certain initial percentage. Example models can include neural networks, probabilistic time series models, etc. The trained model can be used to predict the individual waveforms of connected cells in the doublet.

[0069] In another example, a clustering technique may be performed in operation 606 to separately identify the waveform of each cell in the doublet. Such a technique may include maintaining a database of waveforms and using the waveforms in the database to separate and / or predict the individual waveform of each cell via a minimization scheme.

[0070] Method 600 also includes an operation 608 of analyzing the waveform separated in operation 606 for each cell in the doublet. For example, operation 608 can include analyzing each waveform in the doublet as if it were a single-cell waveform. In this manner, the first cell and the second cell of each doublet are each analyzed separately.

[0071] Next, method 600 includes an operation 610 of characterizing the doublets based on an analysis of each individual waveform in each doublet. Operation 610 may include characterizing the doublets based on whether each individual waveform includes a characteristic. As illustrative examples, operation 610 may include characterizing or classifying the doublets based on whether both cells in the doublet include the characteristic, whether one cell in the doublet includes the characteristic and the other cell in the doublet does not include the characteristic, or whether neither cell in the doublet includes the characteristic.

[0072] In some examples, method 600 may include operation 612 of providing an analysis of the waveform data collected in operation 602 based at least in part on the characterization of the doublets performed in operation 610. In some examples, operation 612 may include displaying the analysis on GUI 420 of waveform analysis device 150.

[0073] In some examples, operation 612 includes presenting a classification of doublets independent of single cells. In such examples, a user of the flow cytometer system 100 can view analysis specific to doublets. Such analysis can include information related to the magnitude of doublets, doublets having at least one cell with a characteristic, doublets having at least one cell without a characteristic, doublets having two cells with a characteristic, and / or doublets having two cells without a characteristic. In this manner, relevant information specific to doublets identified in the fluid stream 114 is presented to the user of the flow cytometer system 100. As discussed above, conventional flow cytometers typically discard doublets during waveform acquisition, causing this information to be lost and permanently unavailable.

[0074] In another example, operation 612 includes presenting the classification of doublets along with the single cells. Figure 8 As shown in the example of , the first waveform 802 can be included in the classification as a positive event, and the second waveform 804 can be included as a negative event. In this way, false positives are excluded from the classification, so that the feature provides a more accurate analysis of the sample as a whole.

[0075] Figure 10 An exemplary architecture of a computing device 1000 that may be used to implement aspects of the present disclosure, including a waveform analysis device 150 , is shown. Figure 10 The computing device shown in FIG can be used to execute the operating systems, applications, and software modules (including software engines) described herein.

[0076] The computing device 1000 includes at least one processing device 1002, such as a central processing unit (CPU). In this example, the computing device 1000 also includes a system memory 1004 and a system bus 1006 that couples various system components, including the system memory 1004, to the at least one processing device 1002. The system bus 1006 is one of any number of types of bus structures, including a memory bus or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0077] The system memory 1004 includes read-only memory (ROM) 1008 and random access memory (RAM) 1010. A basic input / output system 1012, containing the basic routines used to transfer information within the computing device 1000, such as during startup, is typically stored in the read-only memory 1008. In some examples, the system memory 1004 has a large memory capacity, such as equal to or greater than 1 terabyte of RAM. The RAM can be used to load and subsequently analyze waveform data (e.g., raw waveform data such as stored in a raw waveform data file, which may include digitized waveform data).

[0078] In some embodiments, the computing device 1000 also includes a secondary storage device 1014, such as a hard drive, for storing digital data. The secondary storage device 1014 is connected to the system bus 1006 via a secondary storage interface 1016. In some examples, the secondary storage device 1014 and its associated computer-readable media provide non-volatile storage of computer-readable instructions (including applications and program modules), data structures, and other data for the computing device 1000.

[0079] While the exemplary environment described herein utilizes a hard drive as a secondary storage device, other types of computer-readable storage media may be used in other embodiments. Examples of these other types of computer-readable storage media include magnetic tape cassettes, flash memory cards, digital video disks, Bernoulli cassettes, compact disc read-only memories, digital versatile disc read-only memories, random access memories, or read-only memories. Some embodiments include non-transitory media. Additionally, such computer-readable storage media may include local storage or cloud-based storage.

[0080] Several program modules may be stored in the secondary storage device 1014 or the system memory 1004, including an operating system 1018, one or more application programs 1020, other program modules 1022 (e.g., software engines described herein), and program data 1024. The computing device 1000 may use any suitable operating system such as Microsoft Windows TM, Google Chrome TM , Apple OS, and any other operating system for a computing device.

[0081] In some examples, a user provides input to the computing device 1000 through one or more input devices 1026. Examples of input devices 1026 include a keyboard 1028, a mouse 1030, a microphone 1032, and a touch sensor 1034 (such as a touchpad or touch-sensitive display). Additional examples include additional types of input devices 1026 or fewer types of input devices 1026. The input devices 1026 are connected to the at least one processing device 1002 via an input / output interface 1036 coupled to the system bus 1006. The input / output interface 1036 may include any number of input / output interfaces, such as a parallel port, a serial port, a game port, or a universal serial bus. In some possible implementations, wireless coupling between the input device 1026 and the input / output interface 1036 is also possible, such as via infrared, 802.11a / b / g / n, cellular, or other RF communication systems.

[0082] In this example embodiment, a display device 1042 (such as a monitor, liquid crystal display device, projector, or touch-sensitive display device) is also connected to the system bus 1006 via the video adapter 1040. In addition to the display device 1042, the computing device 1000 may also include various other peripheral devices (not shown), such as speakers or a printer.

[0083] When used in a local area networking environment or a wide area networking environment (such as the Internet), the computing device 1000 is typically connected to the network, such as through a network interface 1038 (such as an Ethernet interface). Other possible implementations use other communication devices. For example, some implementations of the computing device 1000 include a modem for communicating across the network.

[0084] The computing device 1000 typically includes at least some form of computer-readable media. Computer-readable media includes any available media that can be accessed by the computing device 1000. By way of example, computer-readable media includes computer-readable storage media and computer-readable communication media.

[0085] Computer-readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media includes, but is not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory, compact disc read-only memory, digital versatile disks or other optical storage devices, magnetic cassettes, magnetic tape, magnetic disk storage devices or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computing device. Computer-readable storage media does not include computer-readable communication media.

[0086] Computer-readable communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" refers to a signal that has one or more of its characteristics set or changed in such a way as to encode information in the signal. By way of example, computer-readable communication media include: wired media such as a wired network or direct-wired connection; and wireless media such as acoustic, radio frequency, infrared, and other wireless media. Any combination of the above is also included within the scope of computer-readable media.

[0087] Computing device 1000 is also an example of a programmable electronic device, which may include one or more such computing devices, and when multiple computing devices are included, such computing devices may be coupled together with a suitable data communication network to jointly perform various functions, methods, or operations disclosed herein.

[0088] Although specific embodiments are described herein, the scope of the present disclosure is not limited to those specific embodiments.The scope of the present disclosure is defined by the following claims and any equivalents thereof.

Claims

1. A flow cytometer system for analyzing a fluid stream of particles, the flow cytometer system comprising: a light source for emitting a light beam toward the interrogation zone; an optical system comprising a detector for detecting radiated light from particles passing through the light beam in the interrogation zone; as well as processing circuitry having a non-transitory computer-readable storage medium storing instructions that, when executed by the processing circuitry, cause the processing circuitry to: collecting waveform data without thresholding radiated light detected from particles passing through the light beam in the interrogation zone; identifying doublets from the waveform data; for each doublet identified from the waveform data, separating the doublet waveform into individual waveforms; Analysis of individual waveforms separated for each doublet was performed; as well as Based on analysis of the individual waveforms, the doublets are classified.

2. The flow cytometry system according to claim 1, wherein The non-transitory computer-readable storage medium stores additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: The doublets are classified independently of the single cells in the waveform data.

3. The flow cytometry system according to claim 1, wherein The non-transitory computer-readable storage medium stores additional instructions that, when executed by the processing circuitry, further cause the processing circuitry to: The doublets are classified together with the single cells in the waveform data.

4. The flow cytometry system according to any one of claims 1 to 3, wherein: The doublets are classified based on whether the characteristic is detected in both cells, whether the characteristic is detected in one cell but not the other, or whether the characteristic is absent in both cells.

5. A flow cytometry system according to any one of the preceding claims, wherein The doublets are identified by calculating the ratio of the area to the height of the doublet waveform.

6. The flow cytometry system according to any one of claims 1 to 4, wherein The doublets are identified by a detection algorithm that determines whether the single waveform is within a predetermined distance threshold.

7. The flow cytometry system according to any one of claims 1 to 4, wherein: The doublets are identified by one or more machine learning algorithms.

8. The flow cytometry system according to any one of claims 1 to 4, wherein The doublet waveform is separated into the individual waveforms by performing independent component analysis.

9. A method for analyzing particles flowing through a flow cytometer, the method comprising: collecting waveform data without thresholding radiated light detected from particles passing through the light beam in the interrogation zone; identifying doublets from the waveform data; for each doublet identified from the waveform data, separating the doublet waveform into individual waveforms; Analysis was performed on individual waveforms isolated for each doublet; as well as Based on analysis of the individual waveforms, the doublets are classified.

10. The method according to claim 9, further comprising: The doublets are classified independently of the single cells in the waveform data, or the doublets are classified together with the single cells in the waveform data.

11. The method according to claim 9 or 10, further comprising: The doublets are classified based on whether the characteristic is detected in both cells, whether the characteristic is detected in one cell but not the other, or whether the characteristic is absent in both cells.

12. The method according to any one of claims 9 to 11, wherein The doublets are identified by calculating the ratio of the area to the height of the doublet waveform.

13. The method according to any one of claims 9 to 12, wherein: The doublets are identified by a detection algorithm that determines whether the single waveform is within a predetermined distance threshold.

14. The method according to any one of claims 9 to 12, wherein: The doublets are identified by one or more machine learning algorithms.

15. The method according to any one of claims 9 to 14, wherein The doublet waveform is separated into the individual waveforms by performing independent component analysis.

16. A non-transitory computer-readable medium comprising program instructions that, when executed by a processor, cause the processor to: collecting waveform data without thresholding radiated light detected from particles passing through the light beam in the interrogation zone; identifying doublets from the waveform data; for each doublet identified from the waveform data, separating the doublet waveform into individual waveforms; Analysis of individual waveforms separated for each doublet was performed; as well as Based on analysis of the individual waveforms, the doublets are classified.

17. The non-transitory computer readable medium of claim 16, further comprising additional program instructions that, when executed by a processor, further cause the processor to: The doublets are classified independently of the single cells in the waveform data, or the doublets are classified together with the single cells in the waveform data.

18. The non-transitory computer readable medium according to claim 16 or 17, wherein: The doublets are classified based on whether the characteristic is detected in both cells, whether the characteristic is detected in one cell but not the other, or whether the characteristic is absent in both cells.

19. The non-transitory computer readable medium according to any one of claims 16 to 18, wherein: The doublets are identified by calculating the ratio of the area to the height of the doublet waveform.

20. The non-transitory computer readable medium of claims 16 to 19, wherein: The doublet is identified by a detection algorithm that determines whether the single waveform is within a predetermined distance threshold or by one or more machine learning algorithms; as well as wherein the doublet waveform is separated into the single waveforms by performing independent component analysis.

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