Waveform restoration for flow cytometry

Abstract

A method of characterizing particles in flow cytometry is described. The method includes detecting a plurality of pulse waveforms resulting from particles passing through an interrogation zone in a flow cytometer. The plurality of pulse waveforms include gaussian pulse waveforms and non-gaussian pulse waveforms. The method includes discriminating the non-gaussian pulse waveforms from the gaussian pulse waveforms, selecting data points from the non-gaussian pulse waveforms, and extracting parameters based on the data points selected from the non-gaussian pulse waveforms. The method includes characterizing particles associated with the non-gaussian pulse waveforms based on the parameters.

Description

WAVEFORM RESTORATION FOR FLOW CYTOMETRY CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application is being filed on September 19, 2025, as a PCT International application and claims the benefit of and priority to U.S. Provisional Application No. 63 / 697,185, filed on September 20, 2024, titled WAVEFORM RESTORATION FOR FLOW CYTOMETRY, the disclosure of which is hereby incorporated by reference in its entirety. BACKGROUND

[0002] In flow cytometry, particles are arranged in a sample stream to pass in a single file line through one or more excitation light beams. Light that is scattered and / or emitted by the particles from interaction with the one or more excitation light beams is collected and analyzed to characterize and differentiate the particles. In a sorting flow cytometer, particles may be extracted out of the sample stream after having been characterized by their interaction with the one or more excitation beams, and thereby sorted into different groups.

[0003] In flow cytometry, gain is a unitless quantity that measures the amplification of a signal detected by a detector. The gain applies a voltage to accelerate electrons through the detector. Increasing the voltage increases the energy of the electrons, which amplifies the signal.

[0004] Flow cytometers can analyze particles that range in size from large particles to small particles but not typically together at the same time due to limitations associated with the gain settings of the flow cytometers. For example, when analyzing small particles, the gain is typically increased to increase resolution. Conversely, when analyzing large particles, the gain is typically reduced to avoid saturation of the detected signal. When the gain is set too high for analyzing large particles, a saturated pulse waveform is typically produced, which causes the pulse waveform to have a distorted, non-gaussian shape affecting data quality.

[0005] Another type of distorted pulse waveform shape occurs when two cells pass through the one or more excitation light beams at substantially the same time such that they have a doublet shape. Typically, doublet pulse waveforms and other types of concatenated pulse waveforms (e.g., triplets, quadruplets, and the like) are excluded from analysis in flow cytometry because they affect the quality of data which can cause false positives and / or false negatives.

[0006] Gating is typically performed to remove distorted pulse waveforms such as saturated pulse waveforms and concatenated pulse waveforms from a data set for analysis to ensure thatthe distorted pulse waveforms do not influence the accuracy of the analysis. However, it is also desirable to analyze as many of the pulse waveforms as possible to increase yield from a sample stream of particles passing through the interrogation zone of the flow cytometer. SUMMARY

[0007] In general terms, the present disclosure relates to flow cytometry. In one possible configuration, parameters are extracted from the non-gaussian pulse waveforms to increase a yield of events that can be used for flow cytometry analysis. Various aspects are described in this disclosure, which include, but are not limited to, the following aspects.

[0008] One aspect relates to a method of characterizing particles in flow cytometry, the method comprising: detecting a plurality of pulse waveforms resulting from particles passing through an interrogation zone in a flow cytometer, the plurality of pulse waveforms including gaussian pulse waveforms and non-gaussian pulse waveforms; discriminating the non-gaussian pulse waveforms from the gaussian pulse waveforms; selecting data points from the non- gaussian pulse waveforms; extracting parameters based on the data points selected from the non- gaussian pulse waveforms; and characterizing particles associated with the non-gaussian pulse waveforms based on the parameters.

[0009] Another aspect relates to a system for characterizing particles, the system comprising: one or more detectors generating pulse waveforms from scattered light resulting from the particles passing through one or more excitation light beams at an interrogation zone; and a processing circuitry having non-transitory computer readable storage media storing instructions which, when executed by the processing circuity, cause the processing circuitry to: detect a plurality of pulse waveforms resulting from the particles passing through the interrogation zone, the plurality of pulse waveforms including gaussian pulse waveforms and non-gaussian pulse waveforms; discriminate the non-gaussian pulse waveforms from the gaussian pulse waveforms; select data points from the non-gaussian pulse waveforms; extract parameters based on the data points selected from each of the non-gaussian pulse waveforms; and characterize particles associated with the non-gaussian pulse waveforms based on the parameters.

[0010] A variety of additional aspects will be set forth in the description that follows. The aspects can relate to individual features and to combination of features. It is to be understood that both the foregoing general description and the following detailed description are exemplary andexplanatory only and are not restrictive of the broad inventive concepts upon which the embodiments disclosed herein are based. DESCRIPTION OF THE FIGURES

[0011] The following drawing figures, which form a part of this application, are illustrative of the described technology and are not meant to limit the scope of the disclosure in any manner.

[0012] FIG.1 schematically illustrates an example of a flow cytometry system that includes a flow cytometer and a waveform analysis device.

[0013] FIG.2A illustrates an example of a waveform pulse generated from a particle passing through an interrogation zone of the flow cytometer of FIG.1.

[0014] FIG.2B further illustrates the example of the waveform pulse generated from a particle passing through an interrogation zone of the flow cytometer of FIG.1.

[0015] FIG.2C further illustrates the example of the waveform pulse generated from a particle passing through an interrogation zone of the flow cytometer of FIG.1.

[0016] FIG.3 illustrates an example of a plot that can be displayed on a graphical user interface by the waveform analysis device of FIG.1.

[0017] FIG.4 illustrates a plurality of plots that provide an example of a gating strategy for pulse waveform data collected by a side scatter (SSC) detector of the flow cytometer of FIG.1.

[0018] FIG.5 schematically illustrates an example of a method of characterizing particles in flow cytometry that can be performed by the waveform analysis device of FIG.1.

[0019] FIG.6 illustrates an example of a pulse waveform detected by a detector of the flow cytometer of FIG.1, the pulse waveform having a gaussian shape.

[0020] FIG.7 illustrates another example of a pulse waveform detected by a detector of the flow cytometer of FIG.1, the pulse waveform having a distorted saturated shape.

[0021] FIG.8 illustrates another example of a pulse waveform detected by a detector of the flow cytometer of FIG.1, the pulse waveform having a distorted doublet shape.

[0022] FIG.9 illustrates an example of a plot showing events detected by a violet light channel of the SSC detector of the flow cytometer of FIG.1.

[0023] FIG.10 illustrates an example of a plot showing the events from the plot of FIG.9 after the method of FIG.5 is performed to restore gaussian profile shapes to a grouping of events associated with pulse waveforms having distorted profile shapes.

[0024] FIG.11 schematically illustrates an exemplary architecture of a computing device for implementing aspects of the flow cytometer system of FIG.1. DETAILED DESCRIPTION

[0025] Various embodiments will be described in detail with reference to the drawings, where like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.

[0026] FIG.1 schematically illustrates an example of a flow cytometry system 100. In general, flow cytometry is a technique for measuring and analyzing properties of particles or cells when flowing in a sample stream. Data from millions of particles or cells can be collected by the flow cytometry system 100 in a matter of minutes and displayed in a variety of formats. Illustrative example applications of flow cytometry include phenotyping to identify and count specific cell types within a population, analyzing DNA or RNA content within cells, determining presence of antigens on a surface or within cells, and assessing cell health status.

[0027] As shown in the illustrative example of FIG.1, the flow cytometry system 100 generally includes three main component subsystems: a fluidic system 110, an optical system 120, and an electronic system 130. The fluidic system 110 includes a nozzle 112 which receives a sample containing particles or cells suspended in a fluid. The nozzle 112 creates the sample stream 114 of the particles or cells arranged in a single file line. Each particle or cell passes through one or more light excitation beams produced by a light emitting unit 102. The particles or cells intersect with the one or more light excitation beams at an interrogation zone 116. In some examples, the light emitting unit 102 includes one or more light-emitting diodes (LEDs). In further examples, the light emitting unit 102 includes one or more lasers.

[0028] The optical system 120 includes the light emitting unit 102, optical elements 122, and detectors 124. At the interrogation zone 116, light from the light emitting unit 102 hits the particles or cells in the sample stream 114 and scatters. The optical elements 122 direct the scattered light toward the detectors 124. The detectors 124 can include a forward scatter (FSC) detector to measure scatter in the path of the light emitting unit 102, one or more side scatter (SSC) detectors to measure scatter at an angle relative to the light emitting unit 102, one or morefluorescence detectors (FL1, FL2, FL3 … FLn) to measure the emitted fluorescence intensity at different wavelengths of light, and additional types of detectors. In some examples, the optical system 120 includes a plurality of SSC detectors for measuring side scatter in a plurality of channels each associated with a light excitation beam produced by the light emitting unit 102.

[0029] Generally, FSC intensity is proportional to the size or diameter of a particle due to light diffraction around the particle. FSC may therefore be used for the discrimination of particles by size. SSC, on the other hand, is produced from light refracted or reflected by internal structures of the particle and may therefore provide information about the internal complexity or granularity of the particle. By adding fluorescent labelling to a sample, different fluorescent signals / channels (e.g., green, orange, and red) can be analyzed for functional characteristics of a cell. For example, since T-cells present CD3 binding sites, a sample containing T-cells may be “stained” with anti-CD3 antibodies conjugated with a fluorescent molecule. As these cells pass through the interrogation zone 116 in a single file line, the light from the source light excites the fluorescent tag, or fluorochrome, to emit photons at a wavelength detectable by a fluorescence detector. The detectors 124 may therefore simultaneously measure several parameters and enable categorization of particles by their function based on detected wavelengths of light.

[0030] The electronic system 130 includes a waveform acquisition device 140 and a waveform analysis device 150. The waveform acquisition device 140 is communicatively coupled with the detectors 124 to receive analog waveform data 126 generated by the detectors 124. The waveform acquisition device 140 includes an analog-to-digital converter (ADC) 142 that is configured to digitize the analog waveform data 126 received from the detectors 124.

[0031] The waveform analysis device 150 is configured to receive the digital waveform data for processing and analysis. The waveform analysis device 150 can display the digital waveform data and analyses thereof on a graphical user interface (GUI) 152 for a user of the flow cytometry system 100. In some examples, the waveform analysis device 150 includes a computing device 1100 (see FIG.11) communicatively coupled with a flow cytometer 101 such as through a wired or wireless connection. In some examples, the computing device 1100 can be communicatively coupled with the flow cytometer 101 over a network such as a local area network (LAN) using Ethernet or Wi-Fi, or over a wide area network (WAN) such as the Internet. The flow cytometer 101 may include the fluidic system 110, optical system 120, and thewaveform acquisition device 140, but not the waveform analysis device 150. In alternative embodiments, the waveform analysis device 150 is integrated with the flow cytometer 101.

[0032] The flow cytometry system 100 includes elements which are shown and described for purposes of discussion, and it will be appreciated that numerous variations in components and functions are possible. For example, the optical elements 122 may include filters, dichroic mirrors, and / or beam splitters to select different wavelengths of light and provide the wavelength to the appropriate detector. The detectors 124 may comprise, for example, photomultiplier tubes (PMTs) or avalanche photodiodes (APDs) or single photon counting devices.

[0033] FIGS.2A-2C illustrate an example of a waveform pulse 202 generated from a particle 200 passing through the interrogation zone 116 of the flow cytometer 101. As the particle 200 passes through the interrogation zone 116, the waveform pulses are detected by one or more of the detectors 124. In the example of FIGS.2A-2C, the waveform pulse 202 is a forward scatter signal detected by the FSC detector (see FIG.1) to measure scatter in the path of the light emitting unit 102. Waveform pulses representing the side scatter signals of the particles passing through the interrogation zone 116 may be similarly generated by the SSC detector.

[0034] FIG.2A shows an example of the waveform pulse 202 generated as the particle 200 starts to intersect with the interrogation zone 116. When the particle enters the interrogation zone 116, the particle 200 begins to scatter light photons from the one or more light excitation beams. The detector 124 produces a current or voltage as an output that is proportional to the scattered light photons. As shown in FIG.2A, as the particle 200 begins to intersect the interrogation zone 116, the output of the detector 124 begins to rise due to current flowing in the detector 124. The amount of current flowing in the detector 124 is based on the gain that is set for the detector 124.

[0035] FIG.2B shows an example of the waveform pulse 202 generated as the particle 200 passes through a central area of the interrogation zone 116. The density of the light photons from the one or more light excitation beams is highest in the central portion of the interrogation zone 116. As shown in FIG.2B, this causes the output of the detector 124 to peak when the particle 200 is in the central area of the interrogation zone 116 due to the increased light scatter.

[0036] FIG.2C shows an example of the waveform pulse 202 generated as the particle 200 exits the interrogation zone 116. As the particle 200 exits the interrogation zone 116, the current or voltage output of the detector 124 returns to the baseline. Once the pulse waveform 202 isgenerated, this is called an event. In FIG.2C, the pulse waveform 202 has a gaussian shape, which is a symmetric bell curve shape that includes several parameters including a height H, a width W, and an area A. These parameters can be used to characterize the particle 202.

[0037] The height H of the pulse waveform 234 is a difference between a maximum FSC signal generated by the particle crossing the interrogation zone 116 and the baseline noise between pulses. The height H represents a maximum current / voltage output by the detector 124 which can be proportional to the FSC signal intensity and size of the particle.

[0038] The width W of the pulse waveform 234 represents a measure of a duration of the pulse waveform (e.g., time of flight) in the interrogation zone 116. For a sample stream having a constant flow velocity and a laser beam spot having fixed dimensions at the interrogation zone 116, the width W is a function of the a length and / or a size of the particle.

[0039] The area A of the pulse waveform 234 approximates the total scatter signal (e.g., number of photons) gathered during the laser interrogation of the particle at the interrogation zone 116. The area A represents the FSC signal intensity and size of the particle.

[0040] FIG.3 illustrates an example of a plot 300 that can be displayed on the GUI 152 by the waveform analysis device 150. The plot 300 includes a plurality of events detected by the FSC detector of the flow cytometer 101. Each event is associated with a pulse waveform, and the events are plotted based on the height H versus the area A of the pulse waveforms. As shown in FIG.3, a majority of the events are arranged on a diagonal. The events arranged on the diagonal generally have pulse waveforms with gaussian profiles. Events distributed off the diagonal are events that typically have pulse waveforms with non-gaussian (i.e., distorted) shapes.

[0041] As shown in the example of FIG.3, a gate 302 is applied to the events arranged on the diagonal. In this illustrative example, the gate 302 selects about 93.7% of a total of about 487,000 events detected by the FSC detector of the flow cytometer 101. The gate 302 causes about 30,700 events to be excluded from analysis because these events are not along the diagonal inside the gate. It is desirable to increase the yield of events such that it is higher than 93.7% because this would provide a more complete and thorough analysis of a sample since the pulse waveforms that are excluded from the analysis based on the gate 302 may include valuable information that is lost when the data is excluded from the analysis.

[0042] FIG.4 illustrates a plurality of plots 402-408 that provide an example of a gating strategy 400 for pulse waveform data collected by the SSC detector of the flow cytometer 101. The plots 402-408 can be displayed on the GUI 152 by the waveform analysis device 150.

[0043] The plot 402 shows a total number of events detected by the violet light channel of the SSC detector. Like in the example shown in FIG.3, each event is associated with a pulse waveform, and the events are plotted based on the height H versus the area A of the pulse waveforms. A majority of the events are arranged on a diagonal in the plot 402, while other events are distributed off the diagonal. As discussed above, the events arranged on the diagonal generally have pulse waveforms with gaussian profiles, while the events distributed off the diagonal are events that typically have pulse waveforms with non-gaussian shapes.

[0044] The plot 406 shows events selected for further analysis after the gating strategy 400 has been applied to the total number of events displayed in the plot 402. As shown in the plot 406, the events selected for analysis are arranged along the diagonal.

[0045] The plot 408 shows events that have been discarded after the gating strategy 400 has been applied to the total number of events displayed in the plot 402. As shown in the plot 408, the events that are discarded from analysis are distributed off the diagonal.

[0046] The plot 404 shows a quantity of events (51.82%) selected from the total number of events displayed in the plot 402 for analysis (these events are shown in the plot 406). The plot 404 further shows a quantity of events (45.30%) discarded from the total number of events displayed in the plot 402 (these events are shown in the plot 408).

[0047] The gating strategy 400 applied in the plots 402-408 selects only events that are on the diagonal in the plot 402 because the other events that are not on the diagonal are distorted such that these events are excluded from the analysis because they affect the quality of the data. The events that are not on the diagonal can include saturated pulse waveforms, doublet and other types of concatenated pulse waveforms, and other distorted types of pulse waveforms.

[0048] As shown in FIG.4, 45.30% of the total events detected by the SSC detector are excluded from further analysis. This significantly lowers the yield of events that can be used for analyzing the sample from the total number of events detected from the sample. As described above, it is desirable to increase the yield of events such that it is higher than 51.82% because this would provide a more complete and thorough analysis of the sample since the pulsewaveforms that are excluded from the analysis, based on gating strategy 400, may include valuable information that is lost when the data is excluded from the analysis.

[0049] FIG.5 schematically illustrates an example of a method 500 of characterizing particles in flow cytometry that can be performed by the waveform analysis device 150. As described above, the waveform analysis device 150 is configured to receive the digital waveform data from the waveform acquisition device 140 for processing and analysis. In some examples, the method 500 is a software application that is stored on a memory of the waveform analysis device 150, and that is executed by a processing device of the waveform analysis device 150.

[0050] As will be described in more detail, the method 500 restores pulse waveforms that have non-gaussian shapes that would otherwise be excluded from analysis to increase an amount of quality data for analysis. The method 500 increases the yield of events detected by the flow cytometer 101 that can be used for analyzing a sample of particles or cells.

[0051] The method 500 includes an operation 502 of detecting a plurality of pulse waveforms resulting from particles passing through the interrogation zone 116 in the flow cytometer 101. The plurality of pulse waveforms includes both gaussian pulse waveforms and non-gaussian pulse waveforms. As discussed above, a gaussian pulse waveform has a symmetric bell curve shape. A non-gaussian pulse waveform is asymmetrical due to one or more distortions.

[0052] In some examples, the plurality of pulse waveforms detected in operation 502 are of particles ranging in size from about 40 nanometers to about 1,000 nanometers, or more. In such examples, the plurality of pulse waveforms are detected in operation 502 without adjusting a gain setting of the flow cytometer 101 such that both large particles (i.e., 1,000 nanometers or more) and small particles (i.e., 40 nanometers) are detected under the same gain settings.

[0053] FIG.6 illustrates an example of a pulse waveform 600 detected by a detector 124 of the flow cytometer 101. The pulse waveform 600 can be displayed on the GUI 152 by the waveform analysis device 150. As shown in FIG.6, the pulse waveform 600 has a gaussian shape. The pulse waveform 600 is selected within the encircled area 410 along the diagonal in the plot 406 of FIG.4 which is where gaussian pulse waveforms typically occur.

[0054] FIG.7 illustrates another example of a pulse waveform 700 detected by a detector 124 of the flow cytometer 101. The pulse waveform 700 can be displayed on the GUI 152 by the waveform analysis device 150. As shown in FIG.7, the pulse waveform 700 has a shape that isnot gaussian. Instead, the pulse waveform 700 has a distorted saturated shape because a top portion 704 of the pulse waveform 700 is truncated. This is typical of a saturated pulse waveform that is generated when the gain setting of the flow cytometer 101 is set too high for detecting particles having a relatively large size. The pulse waveform 700 is selected within the encircled area 412 in the plot 408 of FIG.4 which is where saturated pulse waveforms typically occur.

[0055] FIG.8 illustrates another example of a pulse waveform 800 detected by a detector 124 of the flow cytometer 101. The pulse waveform 800 can be displayed on the GUI 152 by the waveform analysis device 150. As shown in FIG.8, the pulse waveform 800 has a profile shape that is not gaussian. Instead, the pulse waveform 800 has a distorted doublet shape because a right side portion 804 of the pulse waveform 700 includes a second peak. This is typical of a doublet pulse waveform that is generated when two particles pass through the interrogation zone 116 at substantially the same time which can occur when the sample has a high concentration of particles causing them to stick together. The pulse waveform 800 is selected within the encircled area 414 in the plot 408 of FIG.4 which is where doublet pulse waveforms typically occur.

[0056] The method 500 includes an operation 504 of discriminating non-gaussian pulse waveforms from the gaussian pulse waveforms. For example, operation 504 can include selecting pulse waveforms that have a truncated shape such as the saturated pulse waveform shown in FIG.7. Alternatively, operation 504 can include selecting pulse waveforms that have two or more peaks such as the doublet pulse waveform shown in FIG.8.

[0057] In some examples, the non-gaussian pulse waveforms are automatically selected in operation 504 based on identification of one or more groupings of events distributed off a diagonal of events and that are in areas known to have distorted pulse waveforms such as truncated pulse waveforms or concatenated pulse waveforms. In other examples, operation 504 can included manually selecting the non-gaussian pulse waveforms such as by manually applying a gate to a grouping of events in an area off the diagonal of events.

[0058] As further shown in FIG.5, the method 500 includes an operation 506 of selecting data points from the non-gaussian pulse waveforms discriminated in operation 504. Operation 506 includes selecting three or more data points from the non-gaussian pulse waveforms.

[0059] In FIG.6, the pulse waveform 600 includes a plurality of raw data points detected by the detector 124 of the flow cytometer 101. The pulse waveform 600 includes data points 602a,602b, and 602c that are selected among the plurality of raw data points. The data points 602a, 602b, and 602c include coordinates , respectively.

[0060] As shown in the 702a are selected on the pulse waveform 700 in FIG. 7.three data points. In some examples, additional data points can be selected on the pulse waveform 700 such as a second set of data points 702b, which includes three additional data points.

[0061] As shown in the example of FIG. 8, a first set of data points 802a are selected on the pulse waveform 800. The first set of data points 802a includes three data points. In some examples, additional data points can be selected on the pulse waveform 800 such as a second set of data points 802b, which includes three additional data points.

[0062] In both examples shown in FIGS. 7 and 8, operation 506 includes selecting the data points from undistorted portions of the non-gaussian pulse waveforms. In the example shown in FIG. 7, the first and second sets of data points 702a, 702b are selected from a bottom portion 706 where the pulse waveform 700 is not distorted. A top portion 704 of the pulse waveform 700 is distorted due to the truncated shape that results from saturation of the signal such that none of the first and second sets of data points 702a, 702b are selected from the top portion 704.

[0063] In the example shown in FIG. 8, the first and second sets of data points 802a, 802b are selected from a left side portion 806 on the pulse waveform 800 where the first peak is present. None of the first and second sets of data points 802a, 802b are selected from a right side portion 804 where the pulse waveform 800 is distorted due to the second peak.

[0064] In some examples, operation 506 includes automatically selecting the data points on the undistorted portions of the non-gaussian pulse waveforms. For example, when the non- gaussian pulse waveforms are selected in operation 504 in an area of a plot known to have truncated pulse waveforms, operation 506 can include selecting the data points only from the bottom portion of the non-gaussian pulse waveforms. When the non-gaussian pulse waveforms are selected in operation 504 in an area of a plot known to have concatenated pulse waveforms, operation 506 can include selecting the data points only from a right side portion or a left side portion where a primary peak is present. In some examples, operation 506 can include manually selecting the data points on the undistorted portions of the non-gaussian pulse waveforms.

[0065] The method 500 includes an operation 508 of extracting parameters based on the data points selected in operation 506 from the non-gaussian pulse waveforms. Operation 508 can include extracting a height, an area, and a full width at half maximum (FWHM) based on the three or more data points selected on the non-gaussian pulse waveform in operation 506. By extracting the parameters from the non-gaussian pulse waveforms, a yield of events used for analysis from a total number of events detected by the flow cytometer is increased.

[0066] Operation 508 can include a first step of restoring a gaussian profile to a distorted pulse waveform based on the data points selected from the undistorted portion of the pulse waveform, and a second step of determining the parameters based on the restored gaussian profile. With regards to the first step, the restoration of the gaussian profile is based on thegeneral expression for a gaussian profile, which is represented by equation (1)మ^^^^^^ = ^^^^ି^^ష^್ ^ (1)where parameter a is the height of thethe width of the curve. The natural logarithm of equation (1) results in equations (2) and (3). ln൫^^^^^^൯ = ln^^^^− ^௫ି^ ଶ^ ^ (2)(3)

[0067] Equations (4) are derived^^ = ln൫^^^^^^൯^^ = − 1(4)

[0068] When three data points are selected from the undistorted portion of the non-gaussianpulse waveform, there are three pairs of coordinates represented by equation (5).^^^^, ^^^^, ^^^ଶ, ^^ଶ^, ^^^ଷ,^^ଷ^ (5)

[0069] This provides a system of linear equations (6) with three unknowns: ^^1 = ^^^^21 + ^^^^1 + ^^^^ (6)^^3 = ^^^^23 + ^^^^3 + ^^

[0070] The solution of the system of linear equations (6) is presented by equations (7).^^ = ^^ି^^^^ଷ(^^ଶ − ^^^) + ^^ଶ(^^^ − ^^ଷ) + ^^^(^^ଷ − ^^ଶ)^^(^^^ − ^^ଶ)^ (7)^^ = (^^^ − ^^ଶ)(^^^ − ^^ଷ)(^^ଶ − ^^ଷ)

[0071] Equations (1) – (7) illustrate how the three or more raw data points selected in operation 506 are used to restore a gaussian shape for a non-gaussian pulse waveform. In the example illustrated in FIG.6, a gaussian profile 604 that connects the raw data points is calculated using equations (1) – (7) based on the data points 602a, 602b, and 602c.

[0072] As shown in FIG.7, a gaussian profile 708 is restored for the saturated pulse waveform using equations (1) – (7) based on the first set of data points 702a, or the second set of data points 702b, or both the first and second sets of the data points 702a, 702b. Increasing the number of data points selected in operation 506 can provide better resolution and accuracy for restoring the saturated pulse waveform into a gaussian pulse waveform.

[0073] As shown in FIG.8, a gaussian profile 808 is restored for the doublet pulse waveform using equations (1) – (7) based on the first set of data points 802a, or the second set ofdata points 802b, or both the first and second sets of data points 802a, 802b. Increasing the number of data points selected in operation 506 can provide better resolution and accuracy for restoring the doublet pulse waveform into a gaussian pulse waveform.

[0074] Once the pulse waveform is restored to have the gaussian shape, equations 8-10 areused in operation 508 for determining the parameters from the restored pulse waveform.^^^^^^^^ℎ^^ = ^^ (8)^^^^^^^^ = √^^^^^^ (9)^^^^^^^^ = 2√^^^^2^^ (10)

[0075] In the example shown in FIG.6, a height H, an area A, and a full width at half maximum (FWHM) are calculated from the gaussian profile 604 using equations (8), (9), and (10), respectively. In the example shown in FIG.7, the height H, the area A, and the FWHM are calculated from the gaussian profile 708 using equations (8), (9), and (10), respectively. In the example shown in FIG.8, the height H, the area A, and the FWHM are calculated from the gaussian profile 808 using equations (8), (9), and (10), respectively.

[0076] The method 500 includes an operation 510 of characterizing particles associated with the non-gaussian pulse waveforms based on the parameters extracted in operation 508. For example, operation 510 can include characterizing a size and / or shape of the particles. As discussed above, the height H, the FWHM, and the area A can each be a function of particle size.

[0077] FIG.9 illustrates an example of a plot 900 showing events detected by the violet light channel of the SSC detector of the flow cytometer 101. The plot 900 can be displayed on the GUI 152 by the waveform analysis device 150. The plot 900 includes events that are arranged along a diagonal D. These events are typically selected for analysis because they are associated with pulse waveforms having a gaussian profile shape. In the example shown in FIG.9, the diagonal contains about 54.26% of the total events detected by the SSC detector.

[0078] The plot 900 includes a grouping of events 902 that are not positioned along the diagonal D. As described above, the grouping of events 902 is where saturated pulse waveforms are located. The grouping of events 902 is typically discarded and ignored when analyzing the data shown in the plot 900 because the pulse waveforms associated with the grouping of events902 are likely distorted (e.g., they have the truncated pulse waveform profile shape shown in FIG.7) such that these events can affect the quality of the data used for the analysis.

[0079] FIG.10 illustrates an example of a plot 1000 showing the events from the plot 900 after the method 500 is performed to restore gaussian profile shapes to the grouping of events 902 associated with pulse waveforms having distorted profile shapes. The plot 1000 can be displayed on the GUI 152 by the waveform analysis device 150. As shown in FIGS.9 and 10, the grouping of events 902 in the plot 900 are restored in FIG.10 to follow along the diagonal D of events such that these events can be used for analysis. In the example shown in FIG.10, the diagonal contains about 64.55% of the total events detected by the SSC detector.

[0080] Given the foregoing, the method 500 increases the yield of events that can be used for analysis from the total number of events detected by the flow cytometer 101. In the example illustrated in FIGS.9 and 10, the method 500 increased the yield of the events that can be used for analysis by about 10.29%. Advantageously, this can provide a more complete and thorough analysis of a sample of particles analyzed by the flow cytometry system 100.

[0081] Further the method 500 can be performed to overcome challenges related to gain setting that may be set too high for detecting larger particles or set too low for detecting smaller particles. By allowing restoration of saturated pulse wavelengths, the method 500 allows both small and large particles to be detected under a common gain setting without requiring hardware modifications to the flow cytometer 101 such as addition of channels to the detectors 124 of the flow cytometer 101 where one channel provides a larger gain for detecting smaller particles, and another channel provides a smaller gain for detecting larger particles. Instead, the method 500 enables detection for a larger range of particle sizes for a given gain under a single channel.

[0082] The method 500 can be performed on the fly as the particles in the sample stream 114 are passing through the interrogation zone 116. In some instances, the method 500 can be used to characterize particles that would otherwise be discarded by a sorting flow cytometer. For example, once a gaussian profile shape for a distorted pulse waveform associated with a particle is restored, the particle can be sorted by the sorting flow cytometer based on the parameters derived from the restored gaussian profile shape in accordance with the method 500.

[0083] FIG.11 schematically illustrates an exemplary architecture of a computing device 1100 for implementing aspects of the flow cytometry system 100 such as execution of themethod 500 performed by the waveform analysis device 150. The computing device 1100 includes one or more processing devices 1102, a memory storage device 1104, and a system bus 1106 coupling the memory storage device 1104 to the one or more processing devices 1102.

[0084] The one or more processing devices 1102 can include a processor such as a central processing unit (CPU). The one or more processing devices 1102 can include a microcontroller having one or more digital signal processors, field-programmable gate arrays (FPGA), and / or other types of electronic circuits. In some further examples, the one or more processing devices 1102 are part of a processing circuitry having non-transitory computer readable storage media storing instructions which, when executed by the processing circuity, cause the processing circuitry to perform the aspects and functionalities described herein.

[0085] The memory storage device 1104 can include a random-access memory (“RAM”) 1108 and a read-only memory (“ROM”) 1110. Basic input and output logic having basic routines transferring information between elements in the flow cytometry system 100 can be stored in the ROM 1110. The flow cytometry system 100 can additionally include a mass storage device 1112 that can store an operating system 1114 and software instructions 1116. The mass storage device 1112 is connected to the one or more processing devices 1102 through the system bus 1106. The mass storage device 1112 and computer-readable data storage media provide non-volatile, non- transitory computer memory storage.

[0086] Although the description of computer-readable data storage media contained herein refers to the mass storage device 1112, it should be appreciated by those skilled in the art that computer-readable data storage media can be any available non-transitory, physical device or article of manufacture from which the flow cytometry system 100 can read data and / or instructions. The computer-readable storage media can be comprised of entirely non-transitory media. The mass storage device 1112 is an example of a computer-readable storage device.

[0087] Computer-readable data storage media include volatile and non-volatile, removable, and non-removable, media implemented in any method or technology for storage of information such as computer-readable software instructions, data structures, program modules or other data. Example types of computer-readable data storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technology, or any other medium which can be used to store information, and which can be accessed by the device.

[0088] The flow cytometry system 100 can operate in a networked environment using logical connections to the other devices through a communications network 1120. For example, the computing device 1100 connects to the communications network 1120 through a network interface unit 1118 connected to the system bus 1106. The network interface unit 1118 can connect to other types of communications networks and devices, including through Bluetooth, Wi-Fi, Ethernet, and cellular telecommunications networks. The network interface unit 1118 can connect the computing device 1100 to additional networks, systems, and devices. The computing device 1100 also includes an input / output unit 1122 for receiving and processing inputs and outputs from one or more peripheral devices, and the graphical user interface 152.

[0089] The mass storage device 1112 and the RAM 1108 store software instructions and data. The software instructions can include an operating system 1114 suitable for controlling the operation of the computing device 1100. The mass storage device 1112 and / or the RAM 1108 can also store the software instructions 1116, which when executed by the one or more processing devices 1102, provide the functionalities and aspects described herein.

[0090] The various embodiments described above are provided by way of illustration only and should not be construed to be limiting in any way. Various modifications can be made to the embodiments described above without departing from the true spirit and scope of the disclosure.

[0091] The various embodiments described above are provided by way of illustration only and should not be construed to be limiting in any way. Various modifications can be made to the embodiments described above without departing from the true spirit and scope of the disclosure.

Claims

WHAT IS CLAIMED IS:

1. A method of characterizing particles in flow cytometry, the method comprising: detecting a plurality of pulse waveforms resulting from particles passing through an interrogation zone in a flow cytometer, the plurality of pulse waveforms including gaussian pulse waveforms and non-gaussian pulse waveforms; discriminating the non-gaussian pulse waveforms from the gaussian pulse waveforms; selecting data points from the non-gaussian pulse waveforms; extracting parameters based on the data points selected from the non-gaussian pulse waveforms; and characterizing particles associated with the non-gaussian pulse waveforms based on the parameters.

2. The method of claim 1, wherein three or more data points are selected from the non- gaussian pulse waveforms.

3. The method of claim 1 or 2, wherein the parameters extracted from the non-gaussian pulse waveforms include height, area, and full width at half maximum.

4. The method of claim 1, wherein the data points are selected from undistorted portions of the non-gaussian pulse waveforms.

5. The method of claim 4, wherein the data points are selected from a bottom portion of saturated pulse waveforms.

6. The method of claim 4, wherein the data points are selected from a left side portion or a right side portion of doublet pulse waveforms.

7. The method of any one of claims 1-6, wherein the plurality of pulse waveforms are detected for particles ranging in size from about 40 nanometers to about 1,000 nanometers.

8. The method of claim 7, wherein the plurality of pulse waveforms are detected without adjusting a gain setting of the flow cytometer.

9. The method of any one of claims 1-8, further comprising: using the parameters to increase a yield of events used for analysis from a total number of events detected by the flow cytometer.

10. The method of any one of claims 1-9, further comprising: restoring a gaussian profile to a non-gaussian pulse waveform based on the data points selected from an undistorted portion of the non-gaussian pulse waveform; and determining the parameters based on the restored gaussian profile.

11. The method of claim 10, further comprising: sorting particles associated with the restored non-gaussian pulse waveforms using a sorting flow cytometer based on the extracted parameters.

12. The method of any one of claims 1-11, wherein the discriminating comprises automatically selecting the non-gaussian pulse waveforms based on identification of one or more groupings of events distributed off a diagonal of events.

13. The method of any one of claims 1-12, wherein the non-gaussian pulse waveforms include saturated pulse waveforms and doublet pulse waveforms.

14. The method of any one of claims 1-13, wherein the method is performed in real-time as the particles pass through the interrogation zone.

15. The method of any one of claims 1-14, wherein characterizing the particles includes characterizing a size and shape of the particles associated with the non-gaussian pulse waveforms.

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