Particle characterization in flow cytometry

Abstract

A method of characterizing a particle in flow cytometry includes determining a first pulse width value of the particle using a first technique. The method includes determining a second pulse width value of the particle using a second technique. The method further includes comparing the first pulse width value to the second pulse width value and characterizing the particle as a concatemer particle when a difference between the first pulse width value and the second pulse width value exceeds a threshold.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application was filed as a PCT international application on May 3, 2024, and claims the benefit and priority of U.S. Provisional Application No. 63 / 501,943, filed on May 12, 2023; the disclosure of which is incorporated herein by reference in its entirety. Background Art

[0003] In flow cytometry, particles are arranged in a sample stream, passing through one or more excitation beams in a single file. Light scattered and / or emitted by the particles' interaction with the one or more excitation beams is collected and analyzed to characterize and distinguish the particles. In classification flow cytometry, particles can be extracted from the sample stream after they have been characterized by their interaction with the one or more excitation beams, and thereby classified into different groups.

[0004] Doublets occur when two cells pass through one or more excitation beams at the same time. Typically, doublets and other types of concatenated cells (e.g., triplets, quartets, etc.) are excluded from analysis in flow cytometry because they can affect the quality of the data, for example by causing false positives and / or false negatives in the data. Summary of the invention

[0005] In general, the present disclosure relates to characterizing particles in flow cytometry. In one possible configuration, particles are characterized based on a difference between a first pulse width value and a second pulse width value calculated using different techniques to distinguish singlets from other types of particles. Various aspects are described in the present disclosure, including but not limited to the following aspects.

[0006] One aspect relates to a method for characterizing particles in flow cytometry, the method comprising: determining a first pulse width value for the particle using a first technique; determining a second pulse width value for the particle using a second technique; comparing the first pulse width value with the second pulse width value; and characterizing the particle as a concatemeric particle when the difference between the first pulse width value and the second pulse width value exceeds a threshold.

[0007] On the other hand, a system for characterizing particles is provided, the system comprising: a light emitting unit that generates one or more excitation light beams; a fluid system that causes particles to flow through the one or more excitation light beams at an interrogation zone; an optical element that directs scattered light from the interrogation zone; one or more detectors that measure characteristics of the scattered light; and a processing circuit system having a non-transitory computer-readable storage medium storing instructions, which instructions, when executed by the processing circuit system, cause the processing circuit system to: determine a first pulse width value for the particle using a first technique; determine a second pulse width value for the particle using a second technique; compare the first pulse width value to the second pulse width value; and characterize the particle as a concatemer particle when the difference between the first pulse width value and the second pulse width value exceeds a threshold.

[0008] Another aspect relates to a method for characterizing particles in flow cytometry, the method comprising: obtaining a waveform of the particle; determining an average of the baselines on both sides of the waveform of the particle; subtracting the average from the data points of the waveform; determining a maximum value of the waveform; calculating a half-maximum value by dividing the maximum value by two; subtracting the half-maximum value from the data points of the waveform; obtaining absolute values ​​of the data points to generate a folded waveform; determining a first minimum and a last minimum in the folded waveform; generating a first vector by performing a linear regression on a first set of data points close to the first minimum value; generating a second vector by performing a linear regression on a second set of data points close to the last minimum value; and measuring a direct pulse width value of the particle as the distance between the first vector and the second vector at the half-maximum value.

[0009] 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 are not limitations of the broad inventive concepts on which the embodiments disclosed herein are based. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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.

[0011] Figure 1 An example of a flow cytometer system is schematically shown.

[0012] Figure 2 It schematically shows that Figure 1 An example of the method of characterizing particles performed by a flow cytometer system.

[0013] Figure 3 It schematically shows that Figure 1 An example of a method of measuring a first pulse width value of a particle performed by a flow cytometer system.

[0014] Figure 4 The graph shows the Figure 3 An example of a first waveform and a second waveform obtained during operation of the method.

[0015] Figure 5 The graph shows the Figure 3 An example of a first waveform and a second waveform after operation of the method.

[0016] Figure 6 Shown in more detail Figure 3 An example of the operations in the method.

[0017] Figure 7 The graph shows the Figure 6 00143] An example of the first waveform and the second waveform after the operation in .

[0018] Figure 8 Shows Figure 3 An example of another operation in a method.

[0019] Fig. 9 The graph shows the Figure 8 An example of a first waveform and a second waveform after the steps in the operation.

[0020] Fig.10 The graph shows the Figure 8 An example of the first vector and the second vector generated for the first waveform and the second waveform after the operation of

[0021] Fig.11 The measurement is shown in a graph Figure 4 An example of a first waveform and a second pulse width value of a second waveform.

[0022] Fig.12 The curve diagram shows the Figure 2 The method for characterizing particles was previously Figure 1 Data acquired in a flow cytometer system.

[0023] Fig.13 The curve diagram shows the Figure 2 After characterizing the particles by Figure 1 In the data acquired in the flow cytometer system, particles are characterized using a first threshold.

[0024] Fig.14 The curve diagram shows the Figure 2 After characterizing the particles by Figure 1 In the data acquired in the flow cytometer system, particles are characterized using a second threshold.

[0025] Fig.15The curve diagram shows the Figure 2 After characterizing the particles by Figure 1 In the data acquired in the flow cytometer system, particles are characterized using a third threshold.

[0026] Fig.16 It shows that it can be used to implement Figure 1 An exemplary architecture of a computing system for various aspects of a flow cytometer system. DETAILED DESCRIPTION

[0027] Various embodiments will be described in detail with reference to the accompanying drawings, throughout the several views, wherein like reference numerals represent like parts and components. Reference to various embodiments does not limit the scope of the appended claims. In addition, any examples set forth in this specification are not intended to be limiting, but merely set forth some of the many possible embodiments with respect to the appended claims.

[0028] Figure 1 An example of a flow cytometer system 100 is schematically shown. In general, flow cytometry is a technique for measuring and analyzing the properties of particles or cells as they flow in a fluid stream. The flow cytometer system 100 can collect data from millions of particles or cells in a few minutes and display it 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 the presence of antigens on surfaces or within cells, and assessing cell health status.

[0029] like Figure 1 As shown in the illustrative example of , the flow cytometer system 100 generally includes three major component subsystems: a fluid system 110, an optical system 120, and an electronic system 130. The fluid system 110 includes a nozzle 112, which receives a sample containing particles or cells suspended in a fluid. The nozzle 112 produces a fluid stream 114 of particles or cells arranged in a single line. Each particle or cell passes through one or more light excitation beams generated by the light emitting unit 102. The particles or cells intersect with the one or more light excitation beams at the interrogation zone 116. In some examples, the light emitting unit 102 includes one or more light emitting diodes (LEDs). In other examples, the light emitting unit 102 includes one or more lasers.

[0030] The optical system 120 includes the light emitting unit 102, the optical element 122, and the detector 124. At the interrogation zone 116, the light from the light emitting unit 102 strikes particles or cells in the fluid stream 114 and scatters. The optical element 122 directs the scattered light toward the detector 124. The detector 124 may include a forward scatter (FSC) detector for measuring scatter in the path of the light emitting unit 102, a side scatter (SSC) detector for measuring scatter at a 90 degree angle relative to the light emitting unit 102, one or more fluorescence detectors (FL1, FL2, FL3 ... FLn) for measuring the intensity of fluorescence emitted at different wavelengths of light, and additional types of detectors.

[0031] Typically, due to the diffraction of light around the particles, the FSC intensity is proportional to the size or diameter of the particles. Therefore, FSC can be used to distinguish particles by size. On the other hand, SSC is produced by light refracted or reflected by the internal structure of the particles, and can therefore provide information about the internal complexity or granularity of the particles. By adding fluorescent markers to the sample, different fluorescent signals / channels (e.g., green, orange, and red) can be analyzed for obtaining the functional characteristics of the cells. For example, since T cells have CD3 binding sites, samples containing T cells can be "stained" using anti-CD3 antibodies bound to fluorescent molecules. When these cells pass through the interrogation zone 116 in a single line, the light from the light source excites the fluorescent label 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 at the same time, and enables the particles to be classified according to the function of the particles based on the wavelength of the detected light.

[0032] 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.

[0033] The waveform analysis device 150 is configured to receive digital waveform data for processing and analysis. The waveform analysis device 150 can display the digital waveform data and the analysis of the digital waveform data for a user of the flow cytometer system 100 on a graphical user interface (GUI) 152. In some examples, the waveform analysis device 150 includes a computing device 1600 that is communicatively coupled to the flow cytometer 101, such as by a wired or wireless connection. In some examples, the computing device 1600 can be communicatively coupled to the flow cytometer 101 through a network, such as a local area network (LAN) using Ethernet or Wi-Fi, or through a wide area network (WAN) such as the Internet. The flow cytometer 101 may include a fluid system 110, an optical system 120, and a waveform acquisition device 140, but does not include a waveform analysis device 150. In an alternative embodiment, the waveform analysis device 150 is integrated with the flow cytometer 101.

[0034] The flow cytometer system 100 includes the elements shown and described for the purpose of discussion, and it will be understood that many variations in components and functions are possible. The optical element 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.

[0035] Figure 2 An example of a method 200 of characterizing particles that may be performed by the flow cytometer system 100 is schematically illustrated. In some examples, the method 200 is performed by the waveform analysis device 150.

[0036] Method 200 includes an operation 202 of determining a first pulse width value of a particle using a first technique. In some examples, the first technique includes directly measuring the pulse width of the particle. An example of the first technique is Figure 3 The method 300 shown in FIG. 1 is described in more detail below.

[0037] Method 200 includes an operation 204 of determining a second pulse width value of the particle using a second technique. In some examples, the second technique includes indirectly measuring the pulse width. An example of the second technique is Fig. 9 , which will be described in more detail below.

[0038] Method 200 includes operation 206 of comparing the first pulse width value to the second pulse width value. In some examples, operation 206 includes determining a difference between the first pulse width value and the second pulse width value. In some examples, the difference between the first pulse width value and the second pulse width value is determined by Equation 1,

[0039] (1) Wherein W1 is the first pulse width value, and W2 is the second pulse width value. Given Equation 1, the difference between the first pulse width value and the second pulse width value is determined by subtracting the second pulse width value from the first pulse width value, dividing by the sum of the first pulse width value and the second pulse width value, taking the absolute value of the result, and multiplying by a coefficient C. In some examples, the coefficient C is 2. The coefficient C may vary depending on the hardware of the flow cytometer system 100. As described above, the first pulse width value W1 may include a pulse width that directly measures a particle, such as by performing Figure 3 The method 300 shown in FIG. 3 and the second pulse width value W2 may include indirectly measuring the pulse width of the particle.

[0040] The method 200 also includes determining whether the difference between the first pulse width value and the second pulse width value exceeds a threshold value at operation 208. When the difference is equal to or less than the threshold value (i.e., "No" in operation 208), the method 200 proceeds to characterize the particle as a singlet particle at operation 210. Otherwise, when the difference exceeds the threshold value (i.e., "Yes" in operation 208), the method 200 proceeds to characterize the particle as a concatenated particle at operation 212. As used herein, a concatenated particle includes two or more particles that pass through the interrogation zone 116 substantially simultaneously, such as a doublet, a triplet, a quartet, etc.

[0041] In some additional examples, the method 200 may include characterizing pulse measurements of any shape, including pulse measurements distorted by saturation, noise, improper baseline correction, etc., from pulse measurements of a Gaussian shape typical for a single isolated particle (i.e., a "singlet") passing through the interrogation zone 116. In some examples, the method 200 may also include classifying the particles into one or more groups based on a difference between the first pulse width value and the second pulse width value. In such examples, the flow cytometer system 100 is a classification flow cytometer that extracts particles from a sample stream based on the difference between the first pulse width value and the second pulse width value calculated for the particles.

[0042] Figure 3 An example of a method 300 of determining a direct pulse width value of a particle is schematically shown. In some examples, the method 300 is performed as operation 202 of the method 200. The method 300 may be performed by the flow cytometer system 100. In some examples, the method is performed by the waveform analysis device 150.

[0043] The method 300 includes an operation 302 of obtaining a waveform of a particle. For example, the operation 302 may include the waveform analysis device 150 obtaining a waveform from the waveform acquisition device 140 after the analog waveform data 126 received from the detector 124 has been digitized by the ADC 142 of the waveform acquisition device 140 .

[0044] Figure 4 An example of a first waveform 12 and a second waveform 14 obtained in operation 302 of method 300 is shown in a graph. In this example, the first waveform 12 and the second waveform 14 are from HEK293 cells analyzed by the flow cytometer system 100. As described above, the particles are constrained to pass through the interrogation zone 116 in a single file line, where the particles intersect one or more light excitation beams from the light emitting unit 102. This causes the particles to emit and / or scatter light, which is collected by the detector 124 and converted into waveforms, such as Figure 4 . In this example, the first waveform 12 and the second waveform 14 are shown as solid lines each having experimental data points and a Gaussian fit to the experimental data points.

[0045] exist Figure 4 In the example of FIG. 1 , the first waveform 12 has a Gaussian shape with a single peak, which represents a single isolated particle (i.e., a "singlet") passing through the interrogation zone 116. The second waveform 14 has a non-Gaussian shape with two peaks, which represents the simultaneous illumination of two particles (i.e., a "doublet") passing through the interrogation zone 116. Higher particle concentrations and / or increasing the cross-sectional size of the fluid stream 114 increase the probability of simultaneously illuminating two or more particles passing through the interrogation zone 116. Although the following disclosure relates to doublets, the techniques described herein can be similarly applied to detect other types of n-concatemer cells including triplets, quartets, etc.

[0046] Return to reference Figure 3 , method 300 includes an operation 304 of setting the baseline of the waveform obtained in operation 302 to zero. Operation 304 may include determining an average of the baselines on both sides of the waveform of the particle and subtracting the average from the data points of the waveform to set the baseline to zero.

[0047] like Figure 4 As shown in the example of , the first waveform 12 has a baseline having a mean value of about 250 units, and operation 304 includes subtracting the mean value of about 250 units from all data points of the data points of the first waveform 12. As another example, the second waveform 14 has a baseline having a mean value of about -500 units, and operation 304 includes subtracting the mean value from all data points of the data points of the second waveform 14. As used herein, a unit represents a voltage value from the detector 124, which is digitized by the ADC 142 of the waveform acquisition device 140 using a predefined sampling rate and bit resolution.

[0048] Figure 5An example of the first waveform 12 and the second waveform 14 after completing operation 304 in method 300 is shown in a graph. Figure 5 As shown in , the first waveform 12 and the second waveform 14 each have their baselines set to zero. Figure 4 and Figure 5 As shown in FIG. 1 , the maximum (M) value of the first waveform 12 is from Figure 4 The approximately 3,250 units were reduced to Figure 5 The maximum (M) value of the second waveform 14 is reduced from about 3000 units in Figure 4 The number of units increased from about 4,100 to Figure 5 There are about 4,600 units in the market, an increase of about 500 units.

[0049] Return to reference Figure 3 , method 300 includes an operation 306 of folding the waveform in half. Figure 6 An example of operation 306 is schematically shown in more detail. Operation 306 may include a step 602 of determining a maximum (M) value of a waveform. Figure 5 In the example of , the maximum (M) value of the first waveform 12 is approximately 3000 units, and the maximum (M) value of the second waveform 14 is approximately 4600 units.

[0050] Operation 306 may also include step 604 of calculating a half maximum (HM) value by dividing the maximum (M) value by two. Figure 5 In the illustrative example shown in , the half maximum (HM) value of the first waveform 12 is approximately 1500 units. Figure 5 As further shown in , the half maximum (HM) value of the second waveform 14 is approximately 2300 units.

[0051] Operation 306 may also include a step 606 of subtracting half maximum (HM) values ​​from the data points of the waveform, and a step 608 of obtaining absolute values ​​of the data points to generate a folded waveform. Figure 7 An example of the first waveform 12 and the second waveform 14 after completing operation 306 in the method 300 is graphically illustrated.

[0052] Return to reference Figure 3 , method 300 includes an operation 308 of determining a first vector and a second vector. Figure 8 An example of operation 308 is schematically shown in more detail. Operation 308 may include determining a first minimum value (l f ) data point, and determining the last minimum value (l l ) step 804.

[0053] exist Figure 7In the example shown in FIG. 1 , the first minimum value (l f ) data point appears at about 91 bin units, and the last minimum value (l l ) data point occurs at approximately 110 bin units. The first minimum value (l f ) data point appears at about 70 bin units, and the last minimum value (l l ) data point occurs at approximately 124 bin units. As used herein, 1 bin unit is equal to 0.15 (μs).

[0054] Operation 308 may also include step 806 of expanding the waveform, around the first minimum value (l f ) data points step 808 of selecting the first set of data points, and the step of surrounding the last minimum value (l l ) data points to select a second set of data points in step 810. As an example, step 808 may include surrounding the first minimum value (l f ) data points, and step 810 may include selecting about 5 data points around the last minimum value (l l )Data Points Select approximately 5 data points.

[0055] Fig. 9 An example of the first waveform 12 and the second waveform 14 after completing step 810 in operation 308 is shown in a graph. Fig. 9 As shown in FIG. 1 , the first waveform 12 includes a first minimum value (l ) on a first side around a maximum value (M) of the first waveform 12. f ) data points, and the last minimum value (l on the second side of the maximum (M) value surrounding the first waveform 12. l ) data points.

[0056] Similarly, if Fig. 9 As further shown in the example of FIG. 1 , the second waveform 14 includes a first minimum value (l ) on a first side around a maximum value (M) of the second waveform 14. f ) data points, and the last minimum value (l on the second side of the maximum (M) value surrounding the second waveform 14. l ) data points.

[0057] Operation 308 may also include a step 812 of generating a first vector by performing a linear regression on the first set of data points, and a step 814 of generating a second vector by performing a linear regression on the second set of data points. Fig.10 Examples of first vectors V1 and second vectors V2 generated for the first waveform 12 and the second waveform 14 after completing operation 308 in the method 300 are graphically illustrated.

[0058] Return to reference Figure 3 , method 300 includes an operation 310 of determining a first pulse width value as a distance between the first vector V1 and the second vector V2 at half maximum. In some instances, the first pulse width value is a full width at half maximum (FWHM). In this illustrative example, the first pulse width value of the first waveform 12 calculated according to method 300 is 19.00, and the first pulse width value of the second waveform 14 calculated according to the operation of method 300 is 54.38.

[0059] Fig.11 An example of measuring the second pulse width values ​​of the first waveform 12 and the second waveform 14 is shown in a graph. Fig.11 As shown in , measuring the second pulse width value may also include setting the baseline to zero by determining an average of the baselines on both sides of the waveform of the particle and subtracting the average from the data points of the waveform (ie, operation 304 of method 300 ).

[0060] The second pulse width value is an indirect measurement of the pulse width of the first waveform 12 and the second waveform 14 derived from the maximum (M) value and the pulse area (A). The second pulse width value is calculated according to Equation 2.

[0061] (2) Pulse width = 1.6651 × σ

[0062]

[0063] A=sum(Y)

[0064] In this illustrative example, the second pulse width value of the first waveform 12 is 19.14 bin units, and the second pulse width value of the second waveform 14 is 47.70 bin units. Table 1, as provided below, summarizes the first pulse width values ​​and the second pulse width values ​​of the first waveform 12 and the second waveform 14, and the difference between these values ​​calculated by Equation 1.

[0065] First pulse width value Second pulse width value Difference First Waveform 19.00 19.14 0.0073 Second Waveform 54.38 47.70 0.1309

[0066] Table 1

[0067] In view of the foregoing, the difference between the first pulse width value and the second pulse width value of the first waveform 12 (0.0073) is small, which is desirable because the first waveform 12 has a Gaussian shape typical for a single isolated particle (i.e., a "singlet") passing through the interrogation zone 116. In contrast, the difference between the first pulse width value and the second pulse width value of the second waveform 14 (0.1309) is large, which is desirable because the second waveform 14 has a non-Gaussian shape with two peaks, which is typical for two particles (i.e., a "doublet") simultaneously irradiated through the interrogation zone 116. The calculated difference between the first pulse width value and the second pulse width value for a given waveform can be compared to a threshold value used to characterize the waveform as a singlet or other type of particle.

[0068] Fig.12 Data acquired from the flow cytometer system 100 prior to characterizing particles according to the method 200 is shown in a graph. Fig.12 In the illustrative example of , 3000 pulse measurements from a HEK293 cell sample are obtained. A first plot 1202 includes pulse area (A) versus maximum (M) values. A second plot 1204 includes second pulse width values ​​determined according to Equation 2. A third plot 1206 includes first pulse width values ​​determined according to method 300.

[0069] Fig.13 Data acquired from the flow cytometer system 100 after characterizing particles according to the method 200 is shown graphically. Fig.13 Includes 3000 pulse measurements from HEK293 cell samples Fig.12 1302, second plot 1304, and third plot 1306 of the same data shown in FIG. Fig.13 In the example, the particles are characterized by using a first threshold value of 0.1, so that when the difference between the first pulse width value and the second pulse width value exceeds the first threshold value, the cell is characterized as a multimeric cell (i.e., a doublet, a triplet, etc.). In this illustrative example, the first waveform 12 will be characterized as a singlet because the difference between the first pulse width value and the second pulse width value of the first waveform 12 is less than 0.1 (0.0073<0.1), and the second waveform 14 will be characterized as a doublet (or other multimeric particle) because the difference between the first pulse width value and the second pulse width value of the second waveform 14 is greater than 0.1 (0.1309>0.1).

[0070] Fig.14 Data acquired from the flow cytometer system 100 after characterizing particles according to the method 200 is shown graphically. Fig.14 Includes 3000 pulse measurements from HEK293 cell samples Fig.121402, second plot 1404, and third plot 1406 of the same data shown in FIG. Fig.14 In the example, the particles are characterized by using a second threshold value of 0.07, so that when the difference between the first pulse width value and the second pulse width value exceeds the second threshold value, the cell is characterized as a concatemer cell (i.e., a doublet, a triplet, etc.). In this example, the first waveform 12 will be characterized as a singlet because the difference between the first pulse width value and the second pulse width value of the first waveform 12 is less than 0.07 (0.0073<0.07). The second waveform 14 will be characterized as a doublet (or other type of particle) because the difference between the first pulse width value and the second pulse width value of the second waveform 14 is greater than 0.07 (0.1309>0.07).

[0071] Fig.15 Data acquired from the flow cytometer system 100 after characterizing particles according to the method 200 is shown graphically. Fig.15 Includes 3000 pulse measurements from HEK293 cell samples Fig.12 1502, second plot 1504, and third plot 1506 of the same data shown in FIG. Fig.15 In the example, the particles are characterized by using a third threshold value of 0.03, so that when the difference between the first pulse width value and the second pulse width value exceeds the third threshold value, the cell is characterized as a concatenated cell (i.e., a doublet, a triplet, etc.). In this example, the first waveform 12 will be characterized as a singlet because the difference between the first pulse width value and the second pulse width value of the first waveform 12 is less than 0.03 (0.0073<0.03). The second waveform 14 will be characterized as a doublet (or other type of particle) because the difference between the first pulse width value and the second pulse width value of the second waveform 14 is greater than 0.03 (0.1309>0.03).

[0072] In view of the foregoing, method 200 is an objective method for distinguishing single isolated particles passing through interrogation zone 116 from other types of particles such as concatenated particles (e.g., doublets, triplets, etc.), and from pulse measurements that are distorted due to saturation, noise, incorrect baseline correction, etc. In addition, the thresholds (e.g., first threshold 0.1, second threshold 0.07, and third threshold 0.03) can be adjusted as needed to achieve different levels of pulse shape discrimination. In addition, pulse shape discrimination by method 200 is automatic, so that it does not require a previous population to set the gating for further discrimination, but can instead be performed on the fly. This can result in higher yields. In addition, pulse shape discrimination by method 200 can be performed on a field programmable gate array (FPGA), which is an integrated circuit designed to be configured by a customer or designer after manufacturing.

[0073] Fig.16 Schematically illustrates an exemplary architecture of a computing device 1600 for implementing aspects of the flow cytometer system 100, including aspects performed by the waveform analysis device 150. The computing device 1600 includes one or more processing devices 1602, a memory storage device 1604, and a system bus 1606 that couples the memory storage device 1604 to the one or more processing devices 1602.

[0074] One or more processing devices 1602 may include a processor, such as a central processing unit (CPU). One or more processing devices 1602 may include a microcontroller having one or more digital signal processors, field programmable gate arrays (FPGAs), and / or other types of electronic circuits. In some additional examples, one or more processing devices 1602 are part of a processing circuit system having a non-transitory computer-readable storage medium storing instructions that, when executed by the processing circuit system, cause the processing circuit system to perform the aspects and functions described herein.

[0075] The memory storage device 1604 may include a random access memory ("RAM") 1608 and a read only memory ("ROM") 1610. Basic input and output logic having basic routines for transferring information between elements in the flow cytometer system 100 may be stored in the ROM 1610. The flow cytometer system 100 may additionally include a mass storage device 1612 which may store an operating system 1614 and software instructions 1616. The mass storage device 1612 is connected to the one or more processing devices 1602 via the system bus 1606. The mass storage device 1612 and the computer readable data storage media provide non-volatile, non-transitory computer memory storage.

[0076] Although the description of computer-readable data storage media contained herein refers to mass storage device 1612, it should be understood by those skilled in the art that computer-readable data storage media can be any available non-transitory physical device or article from which flow cytometer system 100 can read data and / or instructions. Computer-readable storage media can consist entirely of non-transitory media. Mass storage device 1612 is an example of a computer-readable storage device.

[0077] Computer-readable data storage media include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing 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 that can be used to store information and can be accessed by a device.

[0078] The flow cytometer system 100 can operate in a networked environment using logical connections to other devices through a communication network 1620. For example, the computing device 1600 is connected to the communication network 1620 through a network interface unit 1618 connected to the system bus 1606. The network interface unit 1618 can be connected to other types of communication networks and devices, including through Bluetooth, Wi-Fi, Ethernet, and cellular telecommunication networks. The network interface unit 1618 can connect the computing device 1600 to additional networks, systems, and devices. The computing device 1600 also includes an input / output unit 1622 for receiving and processing input and output from one or more peripheral devices and a user interface 152.

[0079] The mass storage device 1612 and the RAM 1608 store software instructions and data. The software instructions may include an operating system 1614 suitable for controlling the operation of the computing device 1600. The mass storage device 1612 and / or the RAM 1608 may also store software instructions 1616 that, when executed by the one or more processing devices 1602, provide the functionality and aspects described herein.

[0080] The various embodiments described above are provided by way of illustration only and should not be understood to be limiting in any way. Various modifications may be made to the embodiments described above without departing from the true spirit and scope of the present disclosure.

[0081] Embodiments of the present disclosure may be described with reference to the following numbered clauses, with preferred features listed in the dependent clauses:

[0082] 1. A method for characterizing particles in flow cytometry, the method comprising:

[0083] determining a first pulse width value for the particle using a first technique;

[0084] determining a second pulse width value for the particle using a second technique;

[0085] comparing the first pulse width value to the second pulse width value; and

[0086] When the difference between the first pulse width value and the second pulse width value exceeds a threshold value, the particle is characterized as a concatemer particle.

[0087] 2. The method of clause 1, wherein the first technique comprises directly measuring a pulse width of the particle and the second technique comprises indirectly measuring the pulse width.

[0088] 3. The method of clause 2, wherein the first technique and the second technique each comprise:

[0089] determining an average of the baselines on both sides of the waveform of the particle; and

[0090] The mean is subtracted from the data points of the waveform to set the baseline to zero.

[0091] 4. The method of clause 3, wherein the first technique further comprises:

[0092] determining a maximum value of the waveform;

[0093] The half maximum value is calculated by dividing the maximum value by two;

[0094] subtracting the half maximum value from data points of the waveform; and

[0095] The absolute values ​​of the data points are obtained to generate a folded waveform.

[0096] 5. The method of clause 4, wherein the first technique further comprises:

[0097] determining a first minimum value and a last minimum value in the folded waveform;

[0098] selecting a first set of data points around the first minimum value; and

[0099] A second set of data points is selected around the last minimum.

[0100] 6. The method of clause 5, wherein the first technique further comprises:

[0101] generating a first vector by performing linear regression on the first set of data points;

[0102] generating a second vector by performing linear regression on the second set of data points; and

[0103] The first pulse width value is determined as the distance between the first vector and the second vector at the half maximum value.

[0104] 7. A method according to any of the preceding clauses, wherein the second technique further comprises:

[0105] The second pulse width value is measured as a function of a maximum value of the waveform and an area of ​​the waveform.

[0106] 8. A method according to any of the preceding clauses, wherein the difference between the first pulse width value and the second pulse width value is calculated by:

[0107] subtracting the second pulse width value from the first pulse width value;

[0108] divided by the sum of the first pulse width value and the second pulse width value;

[0109] Obtaining absolute values; and

[0110] The absolute value is multiplied by a coefficient.

[0111] 9. A method according to any of the preceding clauses, wherein the threshold value is 0.1.

[0112] 10. A method according to any of the preceding clauses, wherein the threshold value is 0.07.

[0113] 11. A method according to any of the preceding clauses, wherein the threshold value is 0.03.

[0114] 12. A method according to any of the preceding clauses, further comprising:

[0115] When the difference between the first pulse width value and the second pulse width value is equal to or less than the threshold value, the particle is characterized as a singlet particle.

[0116] 13. A method according to any of the preceding clauses, further comprising:

[0117] When the difference between the first pulse width value and the second pulse width value is equal to or less than the threshold value, the particle is characterized as having a Gaussian shape.

[0118] 14. A method according to any of the preceding clauses, further comprising:

[0119] When the difference between the first pulse width value and the second pulse width value exceeds the threshold, the particle is characterized as having a non-Gaussian shape.

[0120] 15. A method according to any of the preceding clauses, further comprising:

[0121] The particles are classified into one or more groups based on a difference between the first pulse width value and the second pulse width value.

[0122] 16. A system for characterizing particles, the system comprising:

[0123] a light emitting unit generating one or more excitation light beams;

[0124] a fluid system for flowing the particles through the one or more excitation light beams at an interrogation zone;

[0125] an optical element for directing scattered light from the interrogation zone;

[0126] one or more detectors to measure a characteristic of the scattered light; and

[0127] A processing circuit system having a non-transitory computer-readable storage medium storing instructions that, when executed by the processing circuit system, cause the processing circuit system to:

[0128] determining a first pulse width value for the particle using a first technique;

[0129] determining a second pulse width value for the particle using a second technique;

[0130] comparing the first pulse width value with the second pulse width value; and

[0131] When the difference between the first pulse width value and the second pulse width value exceeds a threshold value, the particle is characterized as a concatemer particle.

[0132] 17. The system of clause 16, wherein the first technique comprises directly measuring a pulse width of the particle and the second technique comprises indirectly measuring the pulse width.

[0133] 18. The system of clause 17, wherein the first technique and the second technique each comprise:

[0134] determining an average of the baselines on both sides of the waveform of the particle; and

[0135] The mean is subtracted from the data points of the waveform to set the baseline to zero.

[0136] 19. The system of clause 18, wherein the first technique further comprises:

[0137] determining a maximum value of the waveform;

[0138] The half maximum value is calculated by dividing the maximum value by two;

[0139] subtracting the half maximum value from data points of the waveform; and

[0140] The absolute values ​​of the data points are obtained to generate a folded waveform.

[0141] 20. The system of clause 19, wherein the first technique further comprises:

[0142] determining a first minimum value and a last minimum value in the folded waveform;

[0143] selecting a first set of data points around the first minimum value; and

[0144] A second set of data points is selected around the last minimum.

[0145] 21. The system of clause 20, wherein the first technique further comprises:

[0146] generating a first vector by performing linear regression on the first set of data points;

[0147] generating a second vector by performing linear regression on the second set of data points; and

[0148] The first pulse width value is measured as a distance between the first vector and the second vector at the half maximum value.

[0149] 22. The system of any one of clauses 16 to 21, wherein the second technique further comprises:

[0150] The second pulse width value is measured as a function of a maximum value of the waveform and an area of ​​the waveform.

[0151] 23. A system according to any of clauses 16 to 22, wherein the difference between the first pulse width value and the second pulse width value is calculated by:

[0152] subtracting the second pulse width value from the first pulse width value;

[0153] divided by the sum of the first pulse width value and the second pulse width value;

[0154] Obtaining absolute values; and

[0155] The absolute value is multiplied by a coefficient.

[0156] 24. A system according to any one of clauses 16 to 23, wherein the threshold value is 0.1.

[0157] 25. A system according to any one of clauses 16 to 23, wherein the threshold value is 0.07.

[0158] 26. A system according to any of clauses 16 to 23, wherein the threshold value is 0.03.

[0159] 27. A method for characterizing particles in flow cytometry, the method comprising:

[0160] obtaining a waveform of the particle;

[0161] determining an average of the baselines on both sides of the waveform of the particle;

[0162] subtracting the mean value from the data points of the waveform;

[0163] determining a maximum value of the waveform;

[0164] The half maximum value is calculated by dividing the maximum value by two;

[0165] subtracting the half maximum value from data points of the waveform;

[0166] obtaining absolute values ​​of the data points to generate a folded waveform;

[0167] determining a first minimum value and a last minimum value in the folded waveform;

[0168] generating a first vector by performing linear regression on a first set of data points close to the first minimum value;

[0169] generating a second vector by performing linear regression on the second set of data points close to the last minimum; and

[0170] A direct pulse width value of the particle is measured as the distance between the first vector and the second vector at the half maximum value.

[0171] 28. The method according to clause 27, further comprising:

[0172] An indirect pulse width value is measured as a function of the maximum value of the waveform and the area of ​​the waveform.

[0173] 29. The method according to clause 28, further comprising:

[0174] When the difference between the direct pulse width value and the indirect pulse width value exceeds a threshold value, the particle is characterized as a concatemer particle.

[0175] 30. The method according to clause 29, further comprising:

[0176] The difference between the direct pulse width value and the indirect pulse width value is calculated by:

[0177] subtracting the indirect pulse width value from the direct pulse width value;

[0178] divided by the sum of the direct pulse width value and the indirect pulse width value;

[0179] Obtaining absolute values; and

[0180] The absolute value is multiplied by a coefficient.

[0181] 31. A method according to clause 29 or 30, wherein the threshold value is 0.1.

[0182] 32. A method according to clause 29 or 30, wherein the threshold value is 0.07.

[0183] 33. A method according to clause 29 or 30, wherein the threshold value is 0.03.

Claims

1. A method for characterizing particles in flow cytometry, the method comprising: determining a first pulse width value for the particle using a first technique; determining a second pulse width value for the particle using a second technique; comparing the first pulse width value with the second pulse width value; as well as When the difference between the first pulse width value and the second pulse width value exceeds a threshold value, the particle is characterized as a concatemer particle.

2. The method according to claim 1, wherein: The first technique involves directly measuring the pulse width of the particles, and the second technique involves indirectly measuring the pulse width.

3. The method according to claim 2, wherein: The first technique and the second technique each include: determining an average of the baselines on both sides of the waveform of the particle; and The mean is subtracted from the data points of the waveform to set the baseline to zero.

4. The method according to claim 3, wherein: The first technology also includes: determining a maximum value of the waveform; The half maximum value is calculated by dividing the maximum value by two; subtracting the half maximum value from the data points of the waveform; and The absolute values ​​of the data points are obtained to generate a folded waveform.

5. The method according to claim 4, wherein: The first technology also includes: determining a first minimum value and a last minimum value in the folded waveform; selecting a first set of data points around the first minimum value; and A second set of data points is selected around the last minimum.

6. The method according to claim 5, wherein: The first technology also includes: generating a first vector by performing linear regression on the first set of data points; generating a second vector by performing linear regression on the second set of data points; and The first pulse width value is determined as the distance between the first vector and the second vector at the half maximum value.

7. A method according to any one of the preceding claims, wherein: The second technology also includes: The second pulse width value is measured as a function of a maximum value of the waveform and an area of ​​the waveform.

8. A method according to any one of the preceding claims, wherein: The difference between the first pulse width value and the second pulse width value is calculated by: subtracting the second pulse width value from the first pulse width value; divided by the sum of the first pulse width value and the second pulse width value; Get the absolute value; as well as The absolute value is multiplied by a coefficient.

9. A method according to any one of the preceding claims, wherein: The threshold is 0.

1.

10. A method according to any one of the preceding claims, wherein: The threshold is 0.

07.

11. A method according to any one of the preceding claims, wherein: The threshold is 0.

03.

12. The method according to any one of the preceding claims, further comprising: When the difference between the first pulse width value and the second pulse width value is equal to or less than the threshold value, the particle is characterized as a singlet particle.

13. The method according to any one of the preceding claims, further comprising: When the difference between the first pulse width value and the second pulse width value is equal to or less than the threshold value, the particle is characterized as having a Gaussian shape.

14. The method according to any one of the preceding claims, further comprising: When the difference between the first pulse width value and the second pulse width value exceeds the threshold, the particle is characterized as having a non-Gaussian shape.

15. The method according to any one of the preceding claims, further comprising: The particles are classified into one or more groups based on a difference between the first pulse width value and the second pulse width value.

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