A method, system, terminal, and storage medium for calibrating mitochondrial quality indicators.

Abstract

This application relates to a method, system, terminal, and storage medium for calibrating mitochondrial quality indicators, involving bioanalytical detection technology. The method includes: acquiring sample data in response to a signal indicating completion of sample preparation; performing a first target cell gate treatment on the sample data according to preset gate parameters to obtain an initial target cell population; extracting a dataset from the initial target cell population and generating a feature value matrix based on the dataset; calibrating the feature value matrix using a set of calibration coefficients to obtain calibration gate parameters, wherein the set of calibration coefficients is related to the storage time of the sample; and performing a second target cell gate treatment on the sample data according to the calibration gate parameters to obtain a calibrated target cell population. This application has the effect of correcting changes in mitochondrial quality indicators due to calibration time.

Description

Technical Field

[0001] This application relates to the field of bioanalytical detection, and in particular to a method, system, terminal, and storage medium for calibrating mitochondrial quality indicators. Background Technology

[0002] Mitochondria are semi-autonomous organelles in eukaryotic cells, enclosed by a double-layered, highly specialized unit membrane. They produce energy to maintain normal cellular physiological activities. Numerous studies have shown that mitochondria participate in activities such as growth, development, metabolism, and biological evolution, and also participate in intracellular calcium metabolism. 2+ Mitochondria are responsible for maintaining homeostasis, producing reactive oxygen species, and releasing cytochrome C. Under various pathogenic stimuli, mitochondria are highly susceptible to structural and functional damage, which directly affects the normal function of other cells and tissues in the body. When mitochondria are damaged, their membrane potential decreases, and their morphology and function change.

[0003] The relevant technology involves detecting intracellular mitochondrial quality using flow cytometry. Flow cytometry can rapidly and accurately perform multi-parameter analysis on single cells, and quantitatively assess the functional status of mitochondria using fluorescent probes.

[0004] Regarding the aforementioned technologies, when using this type of probe, membrane potential collapse may cause changes in cell morphology and structure, leading to changes in probe content. Consequently, the fluorescence intensity of the probe obtained in the detection will vary significantly over time, failing to meet the requirements for use. Summary of the Invention

[0005] To correct for changes in mitochondrial quality indicators caused by time, this application provides a method, system, terminal, and storage medium for correcting mitochondrial quality indicators.

[0006] Firstly, this application provides a method for correcting mitochondrial quality indicators, employing the following technical solution:

[0007] A method for correcting mitochondrial quality indicators, comprising:

[0008] In response to the signal indicating that the preparation of the sample to be tested is complete, the sample data is acquired;

[0009] According to the preset gate parameters, the sample data to be tested is subjected to the first target cell gate processing to obtain the initial target cell population;

[0010] A dataset is extracted from the initial target cell population, and a feature value matrix is ​​generated based on the dataset;

[0011] The eigenvalue matrix is ​​corrected by a set of correction coefficients to obtain the correction gate parameters. The set of correction coefficients is related to the storage time of the sample to be tested.

[0012] According to the calibration gate parameters, the sample data to be tested is subjected to a second target cell gate processing to obtain a calibration target cell population.

[0013] By adopting the above technical solution, by extracting the data characteristics of the initial target cell population and performing correction processing in combination with a set of correction coefficients related to the storage time, the fluorescence intensity drift caused by the extended sample storage time can be effectively eliminated, making the mitochondrial quality indicators detected at different time points comparable and correcting the changes in mitochondrial quality indicators caused by the correction time.

[0014] Optionally, the product of the set of correction coefficients and the eigenvalue matrix is ​​calculated to obtain the correction gate parameters.

[0015] By adopting the above technical solution, the distribution characteristics of fluorescence intensity of target cell population can be accurately reflected, ensuring the accuracy and stability of the calibration results.

[0016] Optionally, the eigenvalue matrix includes at least one of the mean, median, and standard deviation.

[0017] By adopting the above technical solution, the distribution characteristics of the fluorescence intensity of the mitochondrial probe in the target cell population can be comprehensively characterized, providing rich statistical information for the construction of the correction coefficient and improving the robustness and adaptability of the correction model.

[0018] Optional, obtain the calibration dataset;

[0019] Construct a linear fitting equation;

[0020] Using the calibration dataset as the fitting target, the coefficients in the linear fitting equation are solved to obtain the correction coefficient set.

[0021] By adopting the above technical solution, a calibration dataset is obtained by training based on actual sample data, which has strong generalization ability and is applicable to samples from different batches and sources, thus ensuring the universality and reliability of the calibration method.

[0022] Optionally, the fluorescence intensity values ​​in the sample data to be tested are converted into relative intensity values;

[0023] Take the target relative intensity value that is greater than the correction gate parameter from the relative intensity values;

[0024] The target cells corresponding to the relative intensity value of the target are identified in the sample data to be tested, and the corrected target cell population is obtained.

[0025] By adopting the above technical solution, the range of positive cells can be dynamically determined by correcting the gate parameters. This allows for adaptive adjustment of the gate position, avoiding the deviation caused by a fixed threshold gate, and improving the accuracy and repeatability of mitochondrial quality index detection.

[0026] Optionally, biomarkers may be collected from the initial target cell population;

[0027] Based on the markers, the initial target cell population is divided into several target cell subpopulations.

[0028] By employing the above-mentioned technical approach, it is possible to separately assess the mitochondrial quality of different immune cell subsets. This refined analysis facilitates in-depth research into changes in mitochondrial function in different cell subsets under disease states, thereby enhancing the clinical diagnostic value of the detection.

[0029] Optionally, the sample data to be tested is obtained based on flow cytometry.

[0030] Secondly, this application provides a calibration system for mitochondrial quality indicators, employing the following technical solution:

[0031] A calibration system for mitochondrial quality indicators, comprising:

[0032] The acquisition module is used to acquire data of the sample to be tested;

[0033] A memory for storing the program of the calibration method for the mitochondrial quality indicators;

[0034] The processor and the program in the memory can be loaded and executed by the processor to implement the method for correcting the mitochondrial quality indicators.

[0035] By adopting the above technical solution, by extracting the data characteristics of the initial target cell population and performing correction processing in combination with a set of correction coefficients related to the storage time, the fluorescence intensity drift caused by the extended sample storage time can be effectively eliminated, making the mitochondrial quality indicators detected at different time points comparable and correcting the changes in mitochondrial quality indicators caused by the correction time.

[0036] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0037] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described in any of the above-mentioned embodiments.

[0038] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates the implementation of changes in mitochondrial quality indicators caused by calibration time, and adopts the following technical solution:

[0039] A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing any of the aforementioned methods for correcting mitochondrial quality indicators.

[0040] In summary, this application includes at least one of the following beneficial technical effects:

[0041] By extracting the data features of the initial target cell population and combining them with a set of correction coefficients related to the storage time for correction processing, the fluorescence intensity drift caused by the extended sample storage time can be effectively eliminated, making the mitochondrial quality indicators detected at different time points comparable and correcting the changes in mitochondrial quality indicators caused by the correction time.

[0042] The calibration dataset is trained based on real sample data, giving it strong generalization ability and making it applicable to samples from different batches and sources, thus ensuring the universality and reliability of the calibration method.

[0043] By dynamically determining the range of positive cells by calibrating the gate parameters, the gate position can be adaptively adjusted, avoiding the bias caused by a fixed threshold gate and improving the accuracy and repeatability of mitochondrial quality index detection. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a method for correcting mitochondrial quality indicators disclosed in an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of a signal gating result based on the first 90% of fluorescence intensity, as disclosed in an embodiment of this application.

[0046] Figure 3 This is a graph showing the target cell quality results disclosed in an embodiment of this application.

[0047] Figure 4 This is a schematic diagram of a mitochondrial quality indicator calibration system disclosed in an embodiment of this application. Detailed Implementation

[0048] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1 To be continued Figure 4 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0049] This application discloses a method for correcting mitochondrial quality indicators. (Refer to...) Figure 1 The method includes:

[0050] Step S101: In response to the signal indicating that the preparation of the sample to be tested is complete, acquire the sample data.

[0051] The sample used for testing is anticoagulated human whole blood. The preparation completion signal is generated by the acquisition software in the flow cytometer or by offline analysis software. In some implementations, the sample data is recorded as an FCS (Flow Cytometry Standard) file.

[0052] For example, the process of preparing a sample for testing using flow cytometry is as follows:

[0053] (1) At room temperature, take 10 μL of multicolor antibody detection reagent (CD8 / CD19 / CD3 / CD16+56 / CD45 / CD4) into a pre-labeled flow cytometer tube;

[0054] (2) Add 100 μL of well-mixed anticoagulated human whole blood sample to the bottom of the test tube; avoid touching the flow cytometer wall during operation, as the blood sample on the tube wall cannot be stained, which will affect the experimental results; and reverse sample addition is required when transferring human whole blood sample.

[0055] (3) Gently shake on a vortex mixer for 5 seconds, and incubate at room temperature in the dark for 15 minutes;

[0056] (4) Add 2 mL of hemolysin working solution that has been left at room temperature;

[0057] (5) Gently shake on a vortex mixer for 5 seconds, and incubate at room temperature in the dark for 15 minutes;

[0058] (6) Place the flow cytometer in a centrifuge and centrifuge at 300g for 5 minutes at room temperature to remove the supernatant;

[0059] (7) Add 250 μL of phosphate buffer, gently vortex for 5 seconds, add the reconstituted mitochondrial probe (MitoDye) reconstituted solution, and vortex to mix.

[0060] (8) Place in a 37℃ constant temperature incubator and incubate in the dark for 30 minutes;

[0061] (9) After incubation, vortex to mix and then perform detection on a flow cytometer;

[0062] (10) The flow cytometer outputs a preparation completion signal.

[0063] Step S102: According to the preset gate parameters, perform the first target cell gate processing on the sample data to be tested to obtain the initial target cell population.

[0064] The preset gate parameter (coef) is a predefined gate threshold used to control the fluorescence intensity of the mitochondrial probe. For example, if the preset gate parameter is 0.90, it means that the signal proportion within the first 90% of the fluorescence intensity range needs to be delineated.

[0065] In one implementation, the preset gate parameter is related to the storage time of the sample to be tested. For example, the preset gate parameter is set to 0.90 for a sample that has just been prepared; and to 0.96 for a sample that has been prepared for 2 hours.

[0066] For example, taking the delineation of CD3+CD4+ and CD3+CD8+ cell populations as an example, it is necessary to delineate the desired target cells in the sample to be tested. Delineating target cells is a gradual process. After removing cell debris, dead cells, or non-target impurities and identifying the core population of live cells, cells comprising 90.7% of the sample are delineated as target cells, and the sample is updated accordingly. Then, after removing cell adhesions, cells comprising 97.1% of the sample are delineated as target cells, and the sample is updated accordingly. After confirming single cells again, delineating them in the sample... 99.8% of the cells were identified as target cells, and the sample was updated accordingly. Next, white blood cells were identified from the single cells, and 9.57% of the cells in the sample were identified as target cells, and the sample was updated accordingly. Lymphocytes were then identified from the white blood cells, and 81.4% of the cells in the sample were identified as target cells, and the sample was updated accordingly. Finally, specific lymphocytes were identified from the lymphocytes, and 34.5% of the CD3+CD4+ cell population and 40.2% of the CD3+CD8+ cell population were identified as target cells. It should be noted that, taking the CD3+CD4+ cell population as an example, its actual percentage is obtained by multiplying the above percentages together (i.e., 90.7% × 97.1% × 99.8% × 9.57% × 81.4% × 40.2%).

[0067] Furthermore, biomarkers are collected from the initial target cell population. Based on these biomarkers, the initial target cell population is divided into several target cell subpopulations. For example, the initial target cell population can be divided into phyla such as leukocytes, lymphocytes, CD3+ cells, CD3+CD4+ cells, and CD3+CD8+ cells.

[0068] Step S103: Extract the dataset from the initial target cell population and generate an eigenvalue matrix based on the dataset.

[0069] The dataset includes, but is not limited to, at least one of the mean, median, and standard deviation. For example, taking a dataset that includes the mean, median, and standard deviation, the eigenvalue matrix would be:

[0070] ;

[0071] Where T4 represents the CD3+CD4+ cell population phylum, T8 represents the CD3+CD8+ cell population phylum; Mean represents the mean, Median represents the median, and SD represents the standard deviation.

[0072] Furthermore, the eigenvalue matrix can be split to facilitate the subsequent construction of gating parameters for different target cell populations. For example, the eigenvalue matrix A can be represented as... , , .

[0073] Step S104: The eigenvalue matrix is ​​corrected using the correction coefficient set to obtain the correction gate parameters. The correction coefficient set is related to the storage time of the sample to be tested.

[0074] As cell samples change over time, apoptosis or death can occur, causing mitochondrial probes to become free again. This leads to the redistribution and binding of remaining mitochondrial probes within the cells, resulting in bias in the detection results generated by the gating strategy. Therefore, it is necessary to regenerate the gating parameters.

[0075] Optionally, the product of the set of correction coefficients and the eigenvalue matrix can be calculated to obtain the correction gate parameters.

[0076] Let the set of correction coefficients be denoted as Calibration_INDEX_coef_T#, then Calibration_INDEX_coef_T# = , where n is the total number of data types in the dataset.

[0077] Taking T4 cells as an example, let AT4 = logAT4 = (5.02, 5.03, 4.95), and the calibration coefficient set Calibration_INDEX_coef_T# = Then the correction gate parameter is logAT4×Calibration_INDEX_coef_T#=5.02*(-2.95)+5.03*1.85+4.95*1.30=0.9315.

[0078] Furthermore, the eigenvalue matrix is ​​corrected using a set of correction coefficients to obtain the correction gate parameters. Specifically, this involves: performing a logarithmic transformation on each component of the eigenvalue matrix to obtain a logarithmic eigenvalue matrix; and calculating a linear combination of the logarithmic eigenvalue matrix and the set of correction coefficients to obtain the correction gate parameters. These correction gate parameters represent the threshold signal within a certain proportion of the mitochondrial probe fluorescence intensity in the target cell population.

[0079] Optionally, obtain the calibration dataset. Construct a linear fitting equation. Using the calibration dataset as the fitting target, solve for the coefficients in the linear fitting equation to obtain the correction coefficient set.

[0080] In one optional implementation, the calibration coefficient set is pre-constructed as follows: Flow cytometry data of multiple calibration samples at different storage durations are acquired; for each calibration sample, a first target cell gate is performed according to preset gate parameters, and the mitochondrial probe fluorescence intensity dataset of the target cell population is extracted; eigenvalues ​​are calculated from the dataset to form an eigenvalue matrix; a fitting equation is constructed with the eigenvalue matrix as the independent variable and the calibration gate parameters as the dependent variable; the coefficients in the linear fitting equation are solved with the goal of minimizing the deviation of the mitochondrial quality index of the calibration sample from the baseline duration (e.g., 0 hours, 2 hours, 4 hours, 6 hours, etc.) at different storage durations, to obtain the calibration coefficient set corresponding to the storage duration.

[0081] Optionally, the linear fitting equation can be a generalized linear model or a Poisson regression model.

[0082] Optionally, separate sets of calibration coefficients can be constructed for different target cell subpopulations.

[0083] Step S105: According to the calibration gate parameters, perform a second target cell gate processing on the sample data to be tested to obtain the calibration target cell population.

[0084] For example, the fluorescence intensity values ​​in the sample data are converted into relative intensity values. A target relative intensity value greater than the calibration gate parameter is selected from the relative intensity values. Target cells corresponding to the target relative intensity value are then identified in the sample data to obtain the calibration target cell population.

[0085] By adopting the above technical solution, by extracting the data characteristics of the initial target cell population and performing correction processing in combination with a set of correction coefficients related to the storage time, the fluorescence intensity drift caused by the extended sample storage time can be effectively eliminated, making the mitochondrial quality indicators detected at different time points comparable and correcting the changes in mitochondrial quality indicators caused by the correction time.

[0086] Please refer to Figure 2 and Figure 3 The method provided in this application, compared to existing technologies, offers different gating parameters for samples with varying storage durations, enabling the most accurate gating of target cells. This effectively eliminates fluorescence intensity drift caused by prolonged sample storage time, ensuring comparability of mitochondrial quality indicators detected at different time points and correcting for changes in mitochondrial quality indicators due to time constraints. Figure 2 71.4% of the cells were selected as target cells.

[0087] Table 1. Schematic diagram of T4 mitochondrial mass obtained by related technologies

[0088]

[0089] Table 1 shows the T4 cell mitochondrial mass (T4MM_%) obtained by detecting the same sample at different time points (0h, 2h, 4h, 6h) using the existing technology with a fixed gate parameter (coef = 0.9). As can be seen from the table, the detection results show a significant downward trend with increasing sample storage time: the average value at 0 hours is 71.35%, decreasing to 61.53% at 2 hours, further decreasing to 58.89% at 4 hours, and slightly recovering to 62.13% at 6 hours. Compared with the baseline (0 hours), the relative deviations at 2 hours, 4 hours, and 6 hours are -13.8%, -17.5%, and -12.9%, respectively. This result indicates that, without correction, sample storage time significantly affects the detection results of mitochondrial mass indicators, leading to a lack of comparability between detection results at different time points. This phenomenon suggests that membrane potential collapse causes changes in cell morphology and structure, resulting in changes in probe content and significant differences in the fluorescence intensity of the probes obtained during detection over time.

[0090] Table 2. Schematic diagram of mitochondrial quality according to the method provided in this application.

[0091]

[0092] Table 2 compares the T4 cell mitochondrial quality detection results obtained from the same sample at different time points using the uncalibrated method (fixed coef = 0.9) and the calibrated method disclosed in this application. The results show that the uncalibrated method yielded detection rates of 62.78%, 58.82%, and 60.82% at 2 hours, 4 hours, and 6 hours, respectively, significantly different from the 0-hour result (71.49%). After calibration using the method disclosed in this application, the detection rates at 2 hours, 4 hours, and 6 hours improved to 67.93%, 73.94%, and 66.57%, respectively, with mean values ​​of 71.17%, 64.56%, and 71.51%, respectively. The relative deviations from the 0-hour mean (70.78%) were improved to 0.56%, -8.79%, and 1.03%, respectively. Compared with the uncalibrated results, the deviations from the baseline results at each time point after calibration were significantly reduced, especially the results at 2 hours and 6 hours, which were essentially consistent with the 0-hour results. The results show that the correction method proposed in this application can effectively eliminate the detection bias caused by the extended sample storage time, making the mitochondrial quality indicators at different time points comparable.

[0093] Table 3. Schematic diagram of mitochondrial MM in T4 cells at different detection times.

[0094]

[0095] Table 3 shows the T4 cell mitochondrial quality index results obtained from two independent samples (Sample 1 and Sample 2) at 0 hours and 6 hours after calibration using the correction method disclosed in this application. The mean value of Sample 1 at 0 hours was 72.61%, and the mean value after calibration at 6 hours was 74.56%, with a relative deviation of 2.67%. The mean value of Sample 2 at 0 hours was 61.19%, and the mean value after calibration at 6 hours was 63.29%, with a relative deviation of 3.43%. The relative deviations between the calibrated results at the 6-hour time point and the respective 0-hour baseline results for both samples were within 4%, indicating that the method disclosed in this application has good stability and reproducibility. This validation result further demonstrates that this application is applicable to detection scenarios with different sample sources, and can achieve time-dependent calibration of mitochondrial quality indexes without the need for an additional negative control group or reliance on absolute counting equipment, meeting the requirements for result stability and comparability in scientific research and clinical testing.

[0096] Based on the same inventive concept, embodiments of this application provide a calibration system for mitochondrial quality indicators. Please refer to... Figure 4 ,include:

[0097] The acquisition module 401 is used to acquire the data of the sample to be tested;

[0098] Memory 402 is used to store the program for the above-mentioned correction method of mitochondrial quality indicators;

[0099] Processor 403, the program in memory can be loaded and executed by the processor to implement the above-mentioned method for correcting mitochondrial quality indicators.

[0100] By adopting the above technical solution, by extracting the data characteristics of the initial target cell population and performing correction processing in combination with a set of correction coefficients related to the storage time, the fluorescence intensity drift caused by the extended sample storage time can be effectively eliminated, making the mitochondrial quality indicators detected at different time points comparable and correcting the changes in mitochondrial quality indicators caused by the correction time.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0102] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed to correct a method for mitochondrial quality indicators.

[0103] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0104] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed to correct a method for mitochondrial quality indicators.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0106] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for calibrating mitochondrial quality indicators, characterized in that, include: In response to the signal indicating that the preparation of the sample to be tested is complete, acquire the sample data obtained based on flow cytometry; According to the preset gate parameters, the sample data to be tested is subjected to the first target cell gate processing to obtain the initial target cell population; Extracting a dataset from the initial target cell population and generating an eigenvalue matrix based on the dataset, the eigenvalue matrix including at least one of mean, median and standard deviation, includes: performing a logarithmic transformation on each component of the eigenvalue matrix to obtain a logarithmic eigenvalue matrix; The product of the correction coefficient set and the eigenvalue matrix is ​​calculated to obtain the correction gate parameter, wherein the correction coefficient set is related to the storage time of the sample to be tested; wherein, a calibration dataset is obtained; a linear fitting equation is constructed; the coefficients in the linear fitting equation are solved using the calibration dataset as the fitting target to obtain the correction coefficient set; According to the calibration gate parameters, the sample data to be tested is subjected to a second target cell gate processing to obtain a calibration target cell population.

2. The method for correcting mitochondrial quality indicators according to claim 1, characterized in that, The set of correction coefficients is pre-constructed in the following manner: Obtain flow cytometry data of multiple calibrated samples at different storage durations; For each calibrated sample, the first target cell gate is processed according to the preset gate parameters, and the mitochondrial probe fluorescence intensity dataset of the target cell population is extracted. Feature values ​​are calculated from the mitochondrial probe fluorescence intensity dataset to form the feature value matrix; Construct the linear fitting equation with the eigenvalue matrix as the independent variable and the correction gate parameter as the dependent variable; The goal is to minimize the deviation of the mitochondrial quality index of the calibrated sample from the baseline at different storage durations. The coefficients in the linear fitting equation are then calculated to obtain the set of correction coefficients corresponding to the storage duration.

3. The method for correcting mitochondrial quality indicators according to claim 1, characterized in that, The step of performing a second target cell gate processing on the sample data to be tested according to the corrected gate parameters to obtain a corrected target cell population includes: Convert the fluorescence intensity values ​​in the sample data to relative intensity values; Take the target relative intensity value that is greater than the correction gate parameter from the relative intensity values; The target cells corresponding to the relative intensity value of the target are identified in the sample data to be tested, and the corrected target cell population is obtained.

4. The method for correcting mitochondrial quality indicators according to claim 1, characterized in that, After performing a first target cell gate processing on the sample data to be tested according to preset gate parameters to obtain an initial target cell population, the process further includes: Collect biomarkers from the initial target cell population; Based on the markers, the initial target cell population is divided into several target cell subpopulations.

5. A calibration system for mitochondrial quality indicators, characterized in that, The system is used to perform the method for correcting mitochondrial quality indicators as described in any one of claims 1 to 4, including: The acquisition module is used to acquire data of the sample to be tested; A memory for storing the program of the calibration method for the mitochondrial quality indicators; The processor and the program in the memory can be loaded and executed by the processor to implement the method for correcting the mitochondrial quality indicators.

6. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 4.

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