Information processing method and device for flow cytometer and flow cytometer

Abstract

The invention discloses an information processing method and device for a flow cytometer and the flow cytometer. The information processing method may include: acquiring a sample signal through a detection channel included in a flow cytometer when a sample fluid flows through the flow cytometer; and determining a number of peaks that the sample signal has for characterizing a sensitivity corresponding to the detection channel, where the sample fluid includes a plurality of types of particles each corresponding to a different signal strength, and the plurality of types of particles include unstained particles.

Description

Technical Field

[0001] The present disclosure relates to the field of flow cytometers, and in particular to an information processing method and device for flow cytometers and a flow cytometer. Background Art

[0002] Flow cytometers have a wide range of applications, including in various areas of life sciences. Sensitivity is an important parameter for evaluating flow cytometer performance. Summary of the Invention

[0003] A brief overview of the present disclosure is provided below to provide a basic understanding of certain aspects of the present disclosure. However, it should be understood that this overview is not an exhaustive overview of the present disclosure. It is not intended to identify key or important parts of the present disclosure, nor is it intended to limit the scope of the present disclosure. Its purpose is simply to present certain concepts of the present disclosure in a simplified form as a prelude to the more detailed description that will be given later.

[0004] The purpose of the present disclosure is to provide an improved information processing method and device for a flow cytometer and a flow cytometer, so as to characterize the sensitivity of the flow cytometer, etc.

[0005] According to one aspect of the present disclosure, there is provided an information processing method for a flow cytometer, comprising: acquiring a sample signal through a detection channel included in the flow cytometer when a sample fluid flows through the flow cytometer; and determining the number of peaks of the sample signal to characterize the sensitivity corresponding to the detection channel, wherein the sample fluid includes a plurality of types of particles each corresponding to a different signal intensity, and the plurality of types of particles include unstained particles.

[0006] According to another aspect of the present disclosure, there is provided an information processing device for a flow cytometer, comprising: a processing circuit configured to: acquire a sample signal through a detection channel included in the flow cytometer when a sample fluid flows through the flow cytometer; and determine the number of peaks of the sample signal to characterize the sensitivity corresponding to the detection channel, wherein the sample fluid comprises a plurality of types of particles each corresponding to a different signal intensities, and the plurality of types of particles comprise unstained particles.

[0007] According to yet another aspect of the present disclosure, a flow cytometer including the above-mentioned information processing device is provided.

[0008] According to other aspects of the present disclosure, a computer program code and a computer program product for implementing the above-mentioned method according to the present disclosure, as well as a computer-readable storage medium having the computer program code for implementing the above-mentioned method according to the present disclosure recorded thereon are also provided.

[0009] Other aspects of the embodiments of the present disclosure are given in the following description, wherein the detailed description is used to fully disclose the preferred embodiments of the embodiments of the present disclosure without imposing limitations thereon. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The present disclosure may be better understood by referring to the detailed description given below in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to represent the same or similar parts. The accompanying drawings, together with the following detailed description, are incorporated into and form a part of this specification and are used to further illustrate the preferred embodiments of the present disclosure and to explain the principles and advantages of the present disclosure. Among them:

[0011] Figure 1 is a flowchart illustrating an example of a flow of an information processing method for a flow cytometer according to an embodiment of the present disclosure;

[0012] Figure 2 is a schematic diagram illustrating a specific example of a sensitivity detection process according to an embodiment of the present disclosure.

[0013] Figures 3A to 3F Schematic diagrams each showing an example of sample signals acquired through six detection channels.

[0014] Figure 4 An example of a portion of a sensitivity detection report obtained using the information processing method according to an embodiment of the present disclosure is shown.

[0015] Figure 5 is a schematic diagram showing an example of a scattered signal acquired through a scattered channel.

[0016] Figure 6 is a block diagram showing a configuration example of an information processing apparatus for a flow cytometer according to an embodiment of the present disclosure; and

[0017] Figure 7 : is a block diagram showing an example structure of a personal computer that can be employed in the embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of an actual implementation are described in this specification. However, it should be understood that in the process of developing any such actual implementation, many implementation-specific decisions must be made in order to achieve the developer's specific goals, such as compliance with system and business-related constraints, which may vary from implementation to implementation. In addition, it should be understood that although the development work may be very complex and time-consuming, it is a routine task for those skilled in the art who benefit from the contents of this disclosure.

[0019] It is also necessary to explain here that, in order to avoid obscuring the present disclosure due to unnecessary details, the accompanying drawings only show the device structure and / or processing steps closely related to the scheme according to the present disclosure, while other details that are not closely related to the present disclosure are omitted.

[0020] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0021] First, refer to Figures 1 to 5 An implementation example of an information processing method for a flow cytometer according to an embodiment of the present disclosure is described. Figure 1 1 is a flowchart illustrating an example of the flow of an information processing method 100 for a flow cytometer according to an embodiment of the present disclosure. Figure 2 is a schematic diagram illustrating a specific example of a sensitivity detection process according to an embodiment of the present disclosure. Figures 3A to 3F Schematic diagrams each showing an example of sample signals acquired through six detection channels. Figure 4 An example of a portion of a sensitivity detection report obtained using the information processing method 100 is shown. Figure 5 is a schematic diagram showing an example of a scattered signal acquired through a scattered channel.

[0022] like Figure 1 As shown, the information processing method 100 according to an embodiment of the present disclosure may start at step S102 and end at step S108. The information processing method 100 may include a sample signal acquisition step S104 and a peak number determination step S106.

[0023] In the sample signal acquisition step S104, when the sample fluid (also referred to as "reagent") flows through the flow cytometer, a sample signal (e.g., a fluorescence signal) is acquired through a detection channel (e.g., a fluorescence channel) included in the flow cytometer. For example, the sample fluid includes multiple types of particles, each corresponding to a different signal intensity. The multiple types of particles may include unstained particles (which may be referred to as a "blank control" or a "negative control"). That is, one type of particle among the multiple types of particles is an unstained particle. In addition, the other types of particles are stained particles, which can emit a fluorescence signal under the excitation of a laser included in the flow cytometer. As can be understood, among the multiple types of particles, the signal intensity corresponding to the unstained particles is the lowest.

[0024] Particles of the same type can correspond to the same signal intensity. Note that two particles corresponding to the same signal intensity do not necessarily mean that the intensities of the signals excited from the two particles under the same conditions are exactly the same. Instead, it can mean that the intensities of the signals excited from the two particles under the same conditions are both within a predetermined range corresponding to the types of the two particles. Accordingly, the intensities of signals emitted from multiple types of particles corresponding to different signal intensities can have different ranges.

[0025] For example, the type of particles can be controlled by controlling the amount of dye. For example, different types of particles can be obtained by dyeing the particles with different amounts of the same dye.

[0026] For example, the type of particles can be controlled by controlling the type of dye. For example, different types of particles can be obtained by dyeing the particles with the same amount of different dyes.

[0027] For example, the type of particles can be controlled by controlling the type and amount of dye. For example, different types of particles can be obtained by dyeing the particles with different dyes in different amounts.

[0028] In the peak number determination step S106 , the number of peaks (also referred to as “peak number”) of the sample signal acquired in the sample signal acquisition step S104 may be determined to characterize the sensitivity corresponding to the detection channel (also referred to as “fluorescence sensitivity”).

[0029] Sensitivity is an important parameter for characterizing the performance of a flow cytometer. In some technologies, the coefficient of variation (CV) or robust coefficient of variation (rCV) is used to characterize sensitivity. The inventors of the present application have found through a large number of experiments that if the sample fluid includes a blank control, in the sample signal obtained by the detection channel with lower sensitivity, the peak with lower signal intensity is mixed with the peak corresponding to the blank control and is difficult to distinguish. Based on this, the inventors of the present application proposed the above-described method of characterizing sensitivity based on the number of peaks in the sample signal obtained by detecting a sample fluid including a blank control. Compared to the method of characterizing sensitivity based on CV or rCV of the sample signal, the method based on the number of peaks of the sample signal can better characterize the sensitivity of the detection channel.

[0030] For example, the number of peaks can be determined as needed using appropriate data processing methods. For example, a histogram drawn based on the sample signal can be smoothed, and individual peaks can be obtained based on peak tops and peak valleys. Interference peaks can then be filtered out based on predetermined parameters (e.g., peak value, half-width, etc.), and the number of remaining peaks can be calculated as the peak number.

[0031] For example, the information processing method 100 can be used to characterize the sensitivity at the level of several fluorescent molecules, and thus can be applicable to a nano-flow cytometer capable of detecting nanoparticles or extracellular vesicles, but is not limited thereto.

[0032] For example, a smaller difference between the number of peaks and the number of types of particles possessed by the sample fluid may indicate a higher sensitivity.

[0033] In the case where the flow cytometer includes multiple detection channels, for each detection channel, the sensitivity of the detection channel can be characterized based on the number of peaks of the sample signal obtained through the detection channel in the manner described above. Figures 3A to 3F In the case where the sample fluid has 8 types of particles, the first to sixth sample signals obtained through the first to sixth detection channels CH1 to CH6 can be used to determine the sensitivity of the first to sixth detection channels CH1 to CH6 based on the number of peaks in the first to sixth sample signals. Figures 3A to 3FIt can be seen that the first sample signal, the second sample signal, and the third sample signal have 8 peaks, the fourth sample signal has 6 peaks, the fifth sample signal has 5 peaks, and the sixth sample signal has 4 peaks. This indicates that the sensitivity of each of the first detection channel CH1, the second detection channel CH2, and the third detection channel CH3 is higher than the sensitivity of each of the fourth detection channel CH4, the fifth detection channel CH5, and the sixth detection channel CH6, the sensitivity of the fourth detection channel CH4 is higher than the sensitivity of each of the fifth detection channel CH5 and the sixth detection channel CH6, and the sensitivity of the fifth detection channel CH5 is higher than the sensitivity of the sixth detection channel CH6.

[0034] For example, the information processing method 100 can be executed independently of a daily quality control (QC) process, thereby saving the running time of the daily QC process and reducing the cost of daily consumables.

[0035] For example, the information processing method 100 can be automatically executed in response to user input, so that sensitivity can be more conveniently characterized. Figure 2 As shown, the information processing method 100 can start in response to a user inputting a reagent number. The reagent number is used to select the sample fluid used in the sensitivity detection process. In this article, the sensitivity detection process refers to the process of characterizing or detecting sensitivity using the information processing method 100.

[0036] In addition, after the number of peaks is determined in the peak number determination step S106 , the determined number of peaks may be presented to the user, for example, by displaying the determined number of peaks on a user interface, thereby facilitating the user to intuitively determine the sensitivity based on the number of peaks.

[0037] For example, the information processing method 100 may further include quantifying the sensitivity. In this case, the quantified sensitivity or both the quantified sensitivity and the number of peaks may be presented to the user. For example, the coefficient corresponding to the number of peaks may be determined as the sensitivity. When the number of peaks is equal to the number of types of particles in the sample fluid, the corresponding coefficient is the largest (i.e., the sensitivity is the highest), and as the difference between the number of peaks and the number of types of particles in the sample fluid increases, the coefficient decreases (i.e., the sensitivity decreases). For example, the coefficient corresponding to the number of peaks may be set based on experience or a limited number of experiments.

[0038] For example, Figure 2As shown, a notification indicating that the sensitivity test passed or failed can also be sent to the user, thereby further facilitating the user's determination of whether the sensitivity meets predetermined requirements. As an example, the number of peaks determined in step S106 can be compared with a target number of peaks. If the determined number of peaks is less than the target number of peaks, a notification indicating that the sensitivity test failed is sent to the user. If the determined number of peaks is not less than the target number of peaks, a notification indicating that the sensitivity test passed is sent to the user. For different detection channels, the target number of peaks can be set to be the same or different according to actual needs.

[0039] As another example, the quantized sensitivity can be compared with a first predetermined threshold. If the quantized sensitivity is less than the first predetermined threshold, a notification is sent to the user indicating that the sensitivity test failed. If the quantized sensitivity is not less than the first predetermined threshold, a notification is sent to the user indicating that the sensitivity test passed. The first predetermined threshold can be set to the same or different for different detection channels as needed.

[0040] In some examples, in addition to considering the number of peaks or the sensitivity of the quantification, other parameters are considered when determining whether the sensitivity test is passed, which will be described in more detail later.

[0041] like Figure 2 As shown, in the subsequent stage of the sensitivity detection process, the sample fluid can be unloaded and the sample line can be cleaned.

[0042] For example, the information processing method 100 may further include determining the distance between the first subset and the second subset of the sample signal, which correspond to the first peak and the second peak, respectively (not shown). For example, the first peak may be the peak with the smallest signal intensity among the peaks of the sample signal. That is, the first peak corresponds to the unstained particles. The signal intensity of the second peak may be greater than the signal intensity of the first peak and less than the signal intensity of other peaks except the first peak and the second peak. For example, for Figure 3A In the first sample signal shown, the first peak and the second peak may be peaks P1 and P2, respectively, and the first subset and the second subset may be envelopes corresponding to the first peak P1 and the second peak P2, respectively. Figures 3A to 3F In , the subset of sample signals corresponding to each peak is shown by a horizontal line with vertical lines at both ends. Figures 3A to 3F In the figures, reference numerals P1, P2, P3, P4, P5, P6, P7, and P8 are used to represent peaks with successively increasing signal intensities; however, the same reference numerals do not necessarily represent the same peaks in different figures.

[0043] For example, sensitivity can be characterized based on the number of peaks in the sample signal and the distance between the first subset and the second subset of the sample signal, thereby better characterizing the sensitivity of the detection channel. For example, for a given sample fluid, with a fixed number of peaks, a larger distance indicates a higher sensitivity.

[0044] For example, the sensitivity may be quantified. Specifically, the distance may be normalized to a value less than or equal to 1, and the value obtained by multiplying the coefficient corresponding to the number of peaks by the normalized distance may be used as the sensitivity.

[0045] As an example, the distance between the first subset and the second subset can be represented by Fisher distance FD. In this case, the above distance can be obtained by the following formula (1):

[0046]

[0047] In formula (1), MFI1 and MFI2 represent the medians of the first subset and the second subset, respectively, and δ1 and δ2 represent the standard deviations of the first subset and the second subset, respectively.

[0048] As another example, the distance between the first subset and the second subset can be represented by a stain index (SI). In this case, the distance can be obtained by the following formula (2):

[0049]

[0050] For example, the information processing method 100 may also include obtaining a scattering signal (not shown) through a scattering channel included in the flow cytometer when the sample fluid flows through the flow cytometer. The inventors of the present application have found through experiments that when the sample fluid flows through the flow cytometer, the scattering signal has two peaks, and the number of peaks of the scattering signal does not change with the number of types of particles in the sample fluid. The inventors have found through analysis that the peak with a larger signal intensity of the two peaks of the scattering signal corresponds to the fluorescent signal emitted by the sample fluid, while the peak with a smaller signal intensity (i.e., the peak with the smallest signal intensity) corresponds to the background noise. Therefore, the background noise can be determined based on the subset of the scattering signal corresponding to the peak with the smallest signal intensity. For example, for Figure 5 As an example of the scattered signal shown, background noise may be determined based on a subset of the scattered signal corresponding to peak P1.

[0051] For example, the information processing method 100 may further include determining the resolution of the detection channel (not shown). As an example, the resolution of the detection channel may be characterized based on the CV or rCV of one or more peaks (in some examples, the peak with the maximum signal intensity) of the sample signal. For example, the smaller the CV or rCV, the higher the resolution. Since the methods for solving CV and rCV are known in the art, they will not be described in detail.

[0052] As another example, the resolution of a detection channel can be characterized based on the distance between a subset of sample signals corresponding to two adjacent peaks among the remaining peaks of the sample signal, excluding the first peak, thereby more accurately characterizing the resolution. For example, for a given sample fluid, a larger distance indicates a higher resolution of the corresponding detection channel.

[0053] For example, the distance between the subsets of sample signals corresponding to the two adjacent peaks can be represented by Fisher distance or staining index.

[0054] The inventors of this application discovered through experiments that the resolution can be better characterized by the distance between the third subset and the fourth subset of the sample signals corresponding to the peak with the largest signal intensity among the peaks of the sample signal (which can be called the "third peak") and the peak with the second largest signal intensity after the third peak (which can be called the "fourth peak").

[0055] For example, the distance between any two peaks can be measured according to actual needs to characterize relevant properties of the sample fluid. For example, the distance between each peak other than the first peak and the first peak can be measured.

[0056] For example, Figure 2 As shown, the sensitivity detection process may include: determining the concentration of the sample fluid before acquiring the sample signal. In the case that the determined concentration of the sample fluid is not greater than the second predetermined threshold, a notification about the concentration of the sample fluid may be issued, so that the user may be notified to place the correct sample fluid. For example, the second predetermined threshold is related to whether the sample fluid exists, and the determined concentration of the sample fluid is less than the second predetermined threshold, which may indicate that no sample fluid is flowing through the flow cytometer. For example, the concentration of the sample fluid may be represented by events detected per second (events per second), in which case the second predetermined threshold may be, for example, 50, but is not limited thereto. Rather, those skilled in the art may set the second predetermined threshold according to actual conditions.

[0057] For example, Figure 2As shown, the sensitivity detection process may include determining the performance of the detection channel before acquiring the sample signal. For example, determining the performance of the detection channel may include: when all lasers included in the flow cytometer are turned on, adjusting the gain of the signal of the detection channel so that the median of a predetermined subset of the sample signal corresponding to a predetermined peak is a first target median, and determining that the performance of the detection channel meets the predetermined requirements when the following three conditions are met: 1) the median of the predetermined subset of the sample signal after gain adjustment is within a first predetermined range; 2) the CV or rCV of the predetermined subset is less than a first predetermined value; and 3) the deviation of the gain after adjustment from the gain before adjustment is within a second predetermined range. In some examples, when at least one of the above conditions 1) to 3) is met, it is determined that the performance of the detection channel meets the predetermined requirements. In addition, in some examples, when the performance of the detection channel does not meet the predetermined requirements, a notification may be issued to the user to prompt the user to adjust the corresponding parameters of the flow cytometer.

[0058] For example, the predetermined peaks used in determining the performance of the detection channels may be the same for different detection channels. For example, for each detection channel, the peak with the strongest signal intensity may be used as the predetermined peak when determining the performance of the detection channel. Of course, other peaks may also be selected as the predetermined peaks based on actual needs. In some examples, the predetermined peaks used in determining the performance of the detection channels may be different for different detection channels.

[0059] like Figure 2 As shown, a sensitivity detection report can be presented to the user. For example, the sensitivity detection report can include the acquired sample signal, such as Figures 3A to 3F The obtained sample signal is presented in the form of a logarithmic graph as shown. Figure 4 As shown, the sensitivity test report may include multiple parameters obtained during the sensitivity test process, such as gain, target gain, deviation from target gain, median (i.e., median of a predetermined subset corresponding to a predetermined peak), target median, deviation from target median, rCV (i.e., rCV of a predetermined subset), target rCV, distance between the first subset and the second subset (e.g., FD or SI), number of peaks, target number of peaks and / or sensitivity test results (i.e., pass or fail). Figure 4 In addition, according to actual needs, the sensitivity test report can include Figure 4More or fewer parameters may be included. For example, in some examples, the sensitivity test report may further include the concentration of the sample fluid, the target concentration (e.g., the second predetermined threshold value described above), and a determination result of whether the concentration of the sample fluid is less than the target concentration. In addition, in some examples, the sensitivity test report may further include a determination criterion for the sensitivity test result, such as one or more of the first to fourth determination criteria described below.

[0060] It is described above that whether the sensitivity test is passed is determined based on the number of peaks or the sensitivity of quantification. In some examples, in addition to the number of peaks or the sensitivity of quantification, other parameters may be considered, such as one or more of the deviation from the target median, rCV, and the distance between the first subset and the second subset. For example, in some examples, the sensitivity test is determined to have been passed when the following first determination standard and one or more of the second to fourth determination standards are met. The first determination standard may be that the number of peaks is not less than the target number of peaks, the second determination standard may be that the deviation from the target gain is within a predetermined range R1 (for example, -20% ≤ deviation from the target gain ≤ 20%), the third determination standard may be that rCV is not greater than the target rCV, and the fourth determination standard may be that the deviation from the target median is within a predetermined range R2 (for example, -5% ≤ deviation from the target median ≤ 5%). For example, in some examples, the sensitivity test is determined to have been passed when all of the above and the first to fourth determination standards are met.

[0061] The above has described an information processing method 100 for a flow cytometer according to an embodiment of the present disclosure. Corresponding to the embodiment of the above-mentioned information processing method 100 for a flow cytometer, the present disclosure also provides the following embodiment of an information processing device for a flow cytometer. Figure 6 : is a block diagram showing a configuration example of an information processing apparatus 600 for flow cytometer according to an embodiment of the present disclosure.

[0062] For example, Figure 6 As shown, the information processing device 600 according to an embodiment of the present disclosure may include a processing circuit 602 .

[0063] Processing circuit 602 can be configured to: acquire a sample signal through a detection channel of the flow cytometer while a sample fluid flows through the flow cytometer; and determine the number of peaks in the sample signal to characterize the sensitivity of the detection channel. For example, the sample fluid may include multiple types of particles, each corresponding to a different signal intensity, and the multiple types of particles may include unstained particles. Compared to characterizing sensitivity based on the coefficient of variation or robust coefficient of variation of the sample signal, characterizing the sensitivity of the detection channel based on the number of peaks in the sample signal can better characterize the sensitivity of the detection channel.

[0064] For example, the processing circuit 602 may be configured to execute the above-mentioned information processing method 100, so the specific details may be referred to above in conjunction with Figures 1 to 5 The information processing method 100 is described briefly below.

[0065] For example, the processing circuit 602 may also be configured to send a notification to the user indicating that the sensitivity test has passed or a notification indicating that the sensitivity test has failed, thereby further facilitating the user's determination of whether the sensitivity meets predetermined requirements.

[0066] For example, the processing circuit 602 may also be configured to determine the distances between the first subset and the second subset of the sample signal corresponding to the first peak and the second peak, respectively, and characterize the sensitivity based on the number of peaks in the sample signal and the distances, thereby better characterizing the sensitivity of the detection channel. For example, the first peak may be the peak with the smallest signal intensity among the peaks in the sample signal. The signal intensity of the second peak may be greater than the signal intensity of the first peak and less than the signal intensity of other peaks other than the first peak and the second peak.

[0067] As an example, the distance between the first subset and the second subset can be represented by Fisher distance FD. In this case, the above distance can be obtained by the above formula (1).

[0068] As another example, the distance between the first subset and the second subset may be represented by a color index (SI). In this case, the distance may be obtained by the above formula (2).

[0069] For example, the processing circuit 602 may also be configured to acquire a scattered signal through a scattering channel included in the flow cytometer when the sample fluid flows through the flow cytometer, and determine background noise based on a subset of the scattered signal corresponding to a peak with the smallest signal intensity.

[0070] For example, the processing circuit 602 may also be configured to determine the resolution of the detection channel.

[0071] For example, the processing circuit 602 may be further configured to characterize the resolution of the detection channel based on the distance between subsets of sample signals corresponding to two adjacent peaks among the remaining peaks of the sample signal except the first peak.

[0072] For example, the distance between the subsets of sample signals corresponding to the two adjacent peaks can be represented by Fisher distance or staining index.

[0073] For example, the processing circuit 602 can also be configured to characterize the resolution of the detection channel based on the distance between the third subset and the fourth subset of the sample signals corresponding to the peak with the largest signal intensity among the peaks of the sample signal (which can be called the "third peak") and the peak with the second largest signal intensity after the third peak (which can be called the "fourth peak").

[0074] For example, the processing circuit 602 may be further configured to measure the distance between any two peaks to characterize relevant properties of the sample fluid.

[0075] For example, the processing circuit 602 may also be configured to determine the concentration of the sample fluid before acquiring the sample signal, and if the determined concentration of the sample fluid is not greater than a second predetermined threshold, a notification regarding the concentration of the sample fluid may be issued, thereby notifying the user to place the correct sample fluid.

[0076] The processing circuit 602 may also be configured to determine the performance of the detection channel before acquiring the sample signal.

[0077] In addition, according to an embodiment of the present disclosure, a flow cytometer including the above-mentioned information processing device 600 may be provided. For example, the flow cytometer may include a nano-flow cytometer for detecting nanoparticles or extracellular vesicles, but is not limited thereto.

[0078] It should be noted that although the above describes the information processing device and method for a flow cytometer and the functional configuration and operation of the flow cytometer according to the embodiments of the present disclosure, this is only an example and not a limitation, and those skilled in the art may modify the above embodiments according to the principles of the present disclosure, for example, the functional modules and operations in each embodiment may be added, deleted or combined, and such modifications shall fall within the scope of the present disclosure.

[0079] In addition, it should be pointed out that the device embodiment here corresponds to the above-mentioned method embodiment. Therefore, for the content not described in detail in the device embodiment, please refer to the description of the corresponding part of the method embodiment, and will not be repeated here.

[0080] In addition, the present disclosure also provides a storage medium and a program product. It should be understood that the machine-executable instructions in the storage medium and the program product according to the embodiments of the present disclosure can also be configured to perform the above-mentioned information processing method. Therefore, for the contents not described in detail herein, reference can be made to the description of the corresponding parts above and will not be repeated here.

[0081] Accordingly, the storage medium for carrying the program product including the machine-executable instructions is also included in the disclosure of the present invention, including but not limited to a floppy disk, an optical disk, a magneto-optical disk, a memory card, a memory stick, and the like.

[0082] In addition, it should be noted that the above series of processes and devices can also be implemented by software and / or firmware. In the case of implementation by software and / or firmware, the data is transmitted from a storage medium or a network to a computer with a dedicated hardware structure, such as Figure 7 The general-purpose personal computer 700 shown is installed with programs constituting the software, and when various programs are installed, the computer can execute various functions and the like.

[0083] exist Figure 7 In the embodiment of the present invention, a central processing unit (CPU) 701 executes various processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage section 708 to a random access memory (RAM) 703. In the RAM 703, data required when the CPU 701 executes various processes and the like is also stored as needed.

[0084] The CPU 701, the ROM 702, and the RAM 703 are connected to one another via a bus 704. An input / output interface 705 is also connected to the bus 704.

[0085] The following components are connected to the input / output interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet.

[0086] A drive 710 is also connected to the input / output interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 710 as needed so that a computer program read therefrom is installed in the storage section 708 as needed.

[0087] In the case of realizing the above-described series of processing by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 711 .

[0088] It should be understood by those skilled in the art that such storage media is not limited to Figure 7 The removable medium 711 shown has a program stored therein and is distributed separately from the device to provide the program to the user. Examples of the removable medium 711 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), magneto-optical disks (including minidiscs (MDs) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be ROM 702, a hard disk included in storage section 708, or the like, in which the program is stored and distributed to the user along with the device containing it.

[0089] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is of course not limited to the above examples. Those skilled in the art may obtain various changes and modifications within the scope of the appended claims, and it should be understood that these changes and modifications will naturally fall within the technical scope of the present disclosure.

[0090] For example, a plurality of functions included in one unit in the above embodiments may be implemented by separate devices. Alternatively, a plurality of functions implemented by a plurality of units in the above embodiments may be implemented by separate devices, respectively. In addition, one of the above functions may be implemented by a plurality of units. Needless to say, such a configuration is included in the technical scope of the present disclosure.

[0091] In this specification, the steps described in the flowchart include not only processing executed in time series in the order described, but also processing executed in parallel or individually rather than necessarily in time series. In addition, even in the steps processed in time series, it goes without saying that the order can be changed as appropriate.

Claims

1. An information processing method for a flow cytometer, comprising: When the sample fluid flows through the flow cytometer, a sample signal is acquired through a detection channel included in the flow cytometer; as well as Determining the number of peaks of the sample signal to characterize the sensitivity corresponding to the detection channel, The sample fluid includes multiple types of particles each corresponding to a different signal intensity, and the multiple types of particles include undyed particles.

2. The information processing method according to claim 1, wherein: characterizing the sensitivity based on the number of peaks of the sample signal and the distances between a first subset and a second subset of the sample signal corresponding to the first peak and the second peak, respectively; The first peak is the peak with the smallest signal intensity among the peaks, and the signal intensity of the second peak is greater than the signal intensity of the first peak and less than the signal intensity of other peaks except the first peak and the second peak.

3. The information processing method according to claim 2, wherein: The distance between the first subset and the second subset is represented by Fisher distance or staining index.

4. The information processing method according to any one of claims 1 to 3, further comprising: When the sample fluid flows through the flow cytometer, a scattering signal is acquired through a scattering channel included in the flow cytometer, and determining background noise based on the first subset of the scattered signals, The first subset of the scattered signals corresponds to a peak with the smallest signal intensity among the peaks of the scattered signals.

5. The information processing method according to any one of claims 1 to 3, further comprising: The resolution of the detection channel is characterized based on the distance between subsets of sample signals corresponding to two adjacent peaks among the remaining peaks except the first peak. The information processing method according to claim 5 , wherein: The two adjacent peaks are the third peak and the fourth peak, The third peak is a peak having the largest signal intensity among the peaks, and the fourth peak is a peak having a signal intensity second only to the third peak among the peaks.

7. The information processing method according to any one of claims 1 to 3, further comprising: The concentration of the sample fluid is determined before acquiring the sample signal, and a notification regarding the concentration of the sample fluid is issued if the concentration of the sample fluid is not greater than a predetermined threshold.

8. The information processing method according to claim 7, further comprising: The performance of the detection channel is determined before acquiring the sample signal.

9. An information processing device for a flow cytometer, comprising a processing circuit configured to: When the sample fluid flows through the flow cytometer, acquiring a sample signal through a detection channel included in the flow cytometer; and Determining the number of peaks of the sample signal to characterize the sensitivity corresponding to the detection channel, in, The sample fluid includes a plurality of types of particles each corresponding to a different signal intensity, and the plurality of types of particles include unstained particles.

10. The information processing apparatus according to claim 9, wherein: characterizing the sensitivity based on the number of peaks of the sample signal and the distances between a first subset and a second subset of the sample signal corresponding to the first peak and the second peak, respectively; The first peak is the peak with the smallest signal intensity among the peaks, and the signal intensity of the second peak is greater than the signal intensity of the first peak and less than the signal intensity of other peaks except the first peak and the second peak. The information processing apparatus according to claim 10 , wherein: The distance between the first subset and the second subset is represented by Fisher distance or staining index.

12. The information processing device according to any one of claims 9 to 11, wherein the processing circuit is further configured to: When the sample fluid flows through the flow cytometer, a scattering signal is acquired through a scattering channel included in the flow cytometer, and determining background noise based on the first subset of the scattered signals, in, The first subset of the scattered signals corresponds to peaks having the smallest signal intensity among the peaks of the scattered signals.

13. The information processing device according to any one of claims 9 to 11, wherein the processing circuit is further configured to: The resolution of the detection channel is characterized based on the distance between subsets of sample signals corresponding to two adjacent peaks among the remaining peaks except the first peak. The information processing apparatus according to claim 13 , wherein: The two adjacent peaks are the third peak and the fourth peak, The third peak is a peak having the largest signal intensity among the peaks, and the fourth peak is a peak having a signal intensity second only to the third peak among the peaks.

15. The information processing device according to any one of claims 9 to 11, wherein the processing circuit is further configured to: The concentration of the sample fluid is determined before acquiring the sample signal, and a notification regarding the concentration of the sample fluid is issued if the concentration of the sample fluid is not greater than a predetermined threshold.

16. The information processing device according to claim 15, wherein the processing circuit is further configured to: The performance of the detection channel is determined before acquiring the sample signal. 17 . A flow cytometer comprising the information processing device according to claim 9 . 18 . A computer-readable storage medium storing instructions, which, when executed by a processor, cause the processor to perform the information processing method according to claim 1 .

Images