Immune cell counting detection method

Abstract

The invention discloses an immune cell counting detection method, and belongs to the technical field of immune cell detection. According to the invention, the problem of one-sidedness of the existing detection method is solved, the immune assessment is divided into three levels, and the first level structure detects the proportion and absolute count of the basal lymphocyte subpopulation; the secondary structure deeply analyzes functional subtypes of T cells, including Th1 / Th2 / Th17, initial / memory / activated T cells and regulatory T cells; the tertiary structure is used for evaluating cytokine expression and immune checkpoint molecules; performing multi-parameter detection on the same biological sample through optimized multicolor flow cytometry, and generating a comprehensive immune assessment report; therefore, the whole state and potential problems of the immune system can be intuitively reflected, popularization and application in a clinical examination laboratory are facilitated, comprehensive and deep immune information from the number of cells to the functional state is clinically provided, and the method has important value for diagnosis, treatment monitoring and prognosis evaluation of clinical immune related diseases.

Description

Technical Field

[0001] This invention relates to the field of immune cell detection technology, specifically to a method for detecting immune cell counts. Background Technology

[0002] Immune cells play a crucial role in the human immune system, and changes in their quantity and function are closely related to the occurrence and development of various diseases. Traditional methods for detecting immune cells mainly focus on flow cytometry to detect lymphocyte subsets, but these methods suffer from limitations such as incomplete detection and imprecise interpretation of results.

[0003] As immunological research deepens, it has become clear that the complexity of the immune system goes far beyond what has been revealed so far. For example, CD4+ T cells can be further divided into subsets with different functions, such as Th1, Th2, Th17, and regulatory T cells (Tregs) with immunosuppressive functions. Imbalances in these functional subsets are closely related to various diseases such as autoimmune diseases, allergies, infections, and tumors; this level of analysis constitutes the secondary structure of immunity. The effector functions of immune cells, such as the cytokines they secrete (e.g., IFN-γ, IL-4, IL-17) and the immune checkpoint molecules they express on their surface (e.g., PD-1), are direct manifestations of the immune system's execution functions and regulation; this level can be considered the tertiary structure.

[0004] Currently, there is a lack of standardized, integrated testing protocols in clinical practice that can systematically cover the three levels mentioned above. Doctors and researchers usually need to conduct different experiments to obtain information at different levels, which not only increases the amount of samples used, testing time and cost, but may also make it difficult to correlate and integrate the data, thus failing to form a unified panoramic view of the immune status.

[0005] Therefore, to address the shortcomings of existing methods, a new method for detecting immune cell counts is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide an immune cell counting detection method. This method divides immune assessment into three levels: primary level, detecting the proportion and absolute count of basal lymphocyte subsets; secondary level, analyzing in-depth functional subtypes of T cells, including Th1 / Th2 / Th17, naive / memory / activated T cells, and regulatory T cells; and tertiary level, assessing cytokine expression and immune checkpoint molecules. Optimized multicolor flow cytometry is used to perform multi-parameter detection on the same biological sample, generating a comprehensive immune assessment report. This method can intuitively reflect the overall state and potential problems of the immune system, facilitating its widespread application in clinical testing laboratories. It provides comprehensive and in-depth immune information, from cell count to functional status, and is of significant value for the diagnosis, treatment monitoring, and prognostic assessment of clinical immune-related diseases, thus solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: An immune cell counting detection method, comprising: S1. Collect blood samples from the subjects and process them; S2. Perform primary structure detection on blood samples and detect basic lymphocyte subsets using the first antibody combination to obtain the first dataset, including the proportion and absolute count of T cells, B cells, and NK cells.

[0008] S3. Perform secondary structure detection on blood samples, and detect functional subtypes and regulatory T cells through a second antibody combination to obtain a second dataset including the proportions of Th1, Th2, Th17, naive T cells, memory T cells, activated T cells, and regulatory T cells. S4. Perform tertiary structure detection on blood samples, and analyze cytokine expression and / or immune checkpoint molecule expression using a third detection method to obtain a third dataset; S5. Integrate the first, second, and third datasets to generate a comprehensive immune cell analysis report.

[0009] Furthermore, the primary structure detection of the blood sample in S2 includes: 2-1. Obtain processed blood samples and perform analysis using flow cytometry; 2-2. Based on the expression of cell surface markers, lymphocyte subsets are classified and counted; 2-3. Calculate the test results of each indicator and compare them with the reference interval to determine whether there are any abnormalities.

[0010] Furthermore, the secondary structure detection of the blood sample in S3 includes: 3-1. Based on the primary structure detection, T cell subsets are further subdivided; 3-2. Different T cell subtypes are labeled with specific antibodies and detected by flow cytometry; 3-3. Analyze the changes in the proportion and number of T cells of different subtypes to assess the functional status of the immune system.

[0011] Furthermore, step S4 involves performing tertiary structure detection on the blood sample, including: 4-1. Flow cytometry was used to detect cytokines and analyze the secretion level of cytokines and the expression of cell surface markers. 4-2. Detect the expression level of PD-1 molecules on the surface of T cells to assess the activation status and immunosuppression of immune cells.

[0012] Furthermore, the third detection method in S4 includes: Cytokine secretion assay: The expression levels of cytokines in plasma were detected by flow cytometry using microspheres from a multi-cytokine assay kit (containing interleukin series, tumor necrosis factor series and interferon series). Immune checkpoint molecular detection: Cell surface staining was performed using antibodies against PD-1.

[0013] Furthermore, the primary structure detection in S2 includes the following indicators: average number of lymphocytes obtained, proportion of lymphocytes among nucleated cells, proportion of T cells (CD3+) among lymphocytes, proportion of CD4+ T cells among lymphocytes, proportion of CD8+ T cells among lymphocytes, CD4 / CD8 ratio, proportion of B cells (CD19+) among lymphocytes, proportion of NK cells (CD3-CD16+CD56+) among lymphocytes, absolute number of lymphocytes, absolute number of T cells (CD3+), absolute number of CD4+ T cells, absolute number of CD8+ T cells, absolute number of B cells (CD19+), and absolute number of NK cells (CD3-CD16+CD56+). The first antibody combination includes antibodies against CD45, CD3, CD4, CD8, CD19, CD16, and CD56; the basic lymphocyte subsets include total T cells (CD3+), helper / inducer T cells (CD3+CD4+), cytotoxic T cells (CD3+CD8+), B cells (CD19+), and NK cells (CD3-CD16+ and / or CD56+).

[0014] Further, the secondary structure detection in S3 includes the following indicators: the proportion of regulatory T cells (Tregs) CD3+CD4+CD25+CD127low to CD4+ T cells, the proportion of total T cells (CD3+) to lymphocytes, the proportion of CD4+ T cells to total T cells, the proportion of Th1 cells to CD4+ T cells, the proportion of Th2 cells to CD4+ T cells, the proportion of Th17 cells to CD4+ T cells, the proportion of CD28+ T cells to CD4+ T cells, the proportion of HLA-DR+ activated T cells to CD4+ T cells, and CD38... + Percentage of activated T cells in CD4+ T cells, percentage of dual activated T cells in CD4+ T cells, percentage of naive T cells in CD4+ T cells, percentage of memory T cells in CD4+ T cells, percentage of CD8+ T cells in total T cells, percentage of CD28+ T cells in CD8+ T cells, percentage of HLA-DR+ activated T cells in CD8+ T cells, percentage of CD38+ activated T cells in CD8+ T cells, percentage of dual activated T cells in CD8+ T cells, percentage of naive T cells in CD8+ T cells, and percentage of memory T cells in CD8+ T cells; The second antibody combination includes at least five antibodies targeting CD25, CD127, CD28, CD45RA, CD45RO, HLA-DR, and CD38. The second antibody combination also includes: antibodies for distinguishing Th1, Th2 and Th17 cells, achieved by surface staining with antibodies targeting chemokine receptors CXCR3, CCR4 and CCR6; The regulatory T cells (Tregs) have the phenotype of CD3+CD4+CD25+CD127low / -. The activated T cells include CD28+, HLA-DR+ and / or CD38+ CD4+ or CD8+ T cells.

[0015] Furthermore, the tertiary structure detection in S4 includes the following indicators: the expression level of cytokines, including but not limited to IL-2, IFN-γ, TNF-α, IL-4, IL-10, IL-17, and the expression of PD-1 molecules on the surface of T cells.

[0016] Furthermore, the step of integrating the first dataset, the second dataset, and the third dataset in S5 includes: 5-1. Based on the first and second datasets, calculate the normalized immune diversity index, which characterizes the breadth of the immune system response. ; 5-2. Based on the initial T cells in the second dataset and memory T cells The proportion of immune age shift factor, which characterizes the decline of immune reserve capacity, is calculated. ; 5-3. Based on the expression levels of cytokines and immune checkpoint molecules in the third dataset, calculate the comprehensive immune stress load value, which characterizes the systemic load. ; 5-4. Calculate the entropy-based immune homeostasis index according to the following formula. :

[0017] in, The normalized immune diversity index (dimensionless) was calculated based on Shannon entropy. This is a global adjustment parameter used to scale the exponent as a whole; The adjustment parameters for controlling the rate of decline determine how quickly immune elasticity declines with age. The adjustment parameters for controlling the degree of compression determine the intensity of the inhibitory effect of immune stress on elasticity. , and All are dimensionless constants determined based on clinical data; , It is an immune age shift factor. This represents the proportion of initial T cells in the second detection set. The proportion of memory T cells in the second detection group. This represents the ratio of naive T cells to memory T cells. This is the reference ratio for the healthy control group; The term is used to simulate the exponential decline dynamics of immune elasticity with increasing age shift; The comprehensive immune stress load value (dimensionless) is calculated by incorporating the ratio of pro-inflammatory / anti-inflammatory cytokines and the normalized expression level of key immune checkpoints (such as PD-1). The term is used to simulate the logarithmic compression effect of immune stress on the system's elasticity, i.e., saturation dynamics; 5-5. Add the entropy-based immune homeostasis index to the immune cell analysis report.

[0018] Furthermore, before analyzing the first, second, or third datasets, a data correction step based on non-linear data normalization according to distribution characteristics is included: Load a pre-trained probability distribution mapping model (Normalizing Flow model); this model has pre-learned the multidimensional data distribution characteristics (Reference Manifold) of a standard biological reference sample for a specific multicolor antibody combination under ideal experimental conditions. Input the raw high-dimensional flow cytometry data of the subject's blood sample to be analyzed; The invertible nonlinear transformation function required to calculate the multidimensional data distribution characteristics from the subject sample data distribution to the standard reference sample is used in the nonlinear distribution correction model. The reversible nonlinear transformation function is applied to perform nonlinear data standardization (Dynamic Manifold Alignment) on the subject sample data based on distribution characteristics. The output is aligned data; wherein the nonlinear data normalization based on distribution characteristics corrects the nonlinear data geometric distortion caused by spectral spillover error diffusion (SSE) and cellular autofluorescence heterogeneity.

[0019] Compared with the prior art, the beneficial effects of the present invention are: In this invention, immune cell analysis is clearly divided into three levels: the primary level detects the proportion and absolute count of basic lymphocyte subsets; the secondary level analyzes in depth the functional subtypes of T cells, including Th1 / Th2 / Th17, naive / memory / activated T cells, and regulatory T cells; and the tertiary level assesses cytokine expression and immune checkpoint molecules. This allows for multi-level analysis using the same blood sample to the maximum extent, improving sample utilization and reducing batch-to-batch errors. It also reveals more deeply the functional polarization, activation state, memory differentiation, and exhaustion degree of T cells, providing high-quality, in-depth information for understanding complex immune states. This allows for a direct reflection of the overall state and potential problems of the immune system, facilitating its application in clinical laboratories. It provides comprehensive and in-depth immune information, from cell count to functional state, and is of significant value for the diagnosis, treatment monitoring, and prognostic assessment of clinical immune-related diseases. Attached Figure Description

[0020] Figure 1 This is a diagram of the primary structure of the present invention. Figure 2 This is a diagram showing the secondary structure of the present invention. Figure 3 This is a diagram showing the secondary structure of the present invention. Figure 4 This is a diagram showing the secondary structure of the present invention. Figure 5 This is a diagram showing the secondary structure of the present invention. Figure 6 This is a diagram of the three-level structure detection of the present invention; Figure 7 This is a diagram of the three-level structure detection of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] To address the technical issues of existing immune cell detection methods, which primarily focus on flow cytometry for lymphocyte subset detection but suffer from incomplete testing and imprecise result interpretation, please refer to [link to relevant documentation]. Figures 1-7 This embodiment provides the following technical solution: An immune cell counting detection method, comprising: S1. Collect blood samples from the subject, specifically human peripheral blood samples, and process them.

[0023] S2. Perform primary structure detection on blood samples, and detect basic lymphocyte subsets through the first antibody combination to obtain the first dataset including the proportion and absolute count of T cells, B cells and NK cells; wherein, the absolute count is achieved by the absolute counting microsphere method or the volume method.

[0024] Primary structure analysis of blood samples includes: 2-1. Obtain processed blood samples and perform analysis using flow cytometry; 2-2. Based on the expression of cell surface markers, lymphocyte subsets are classified and counted; 2-3. Calculate the test results of each indicator and compare them with the reference interval to determine whether there are any abnormalities. The primary structure test includes the following indicators: average number of lymphocytes obtained, proportion of lymphocytes among nucleated cells, proportion of T cells (CD3+) among lymphocytes, proportion of CD4+ T cells among lymphocytes, proportion of CD8+ T cells among lymphocytes, CD4 / CD8 ratio, proportion of B cells (CD19+) among lymphocytes, proportion of NK cells (CD3-CD16+CD56+) among lymphocytes, absolute number of lymphocytes, absolute number of T cells (CD3+), absolute number of CD4+ T cells, and absolute number of CD8+ T cells. The absolute number of B cells (CD19+) and NK cells (CD3-CD16+CD56+) is measured. The first antibody combination includes antibodies against CD3, CD4, CD8, CD19, CD16, and CD56. The basic lymphocyte subsets include total T cells (CD3+), helper / inducer T cells (CD3+CD4+), cytotoxic T cells (CD3+CD8+), B cells (CD19+), and NK cells (CD3-CD16+ and / or CD56+). Specifically, primary structure detection includes flow cytometry lymphocyte subset detection and flow cytometry lymphocyte subset detection (absolute count). The indicators include:

[0025] S3. Perform secondary structure detection on blood samples, and detect functional subtypes and regulatory T cells using a second antibody combination to obtain a second dataset including the proportions of Th1, Th2, Th17, naive T cells, memory T cells, activated T cells, and regulatory T cells.

[0026] Secondary structure analysis of blood samples includes: 3-1. Based on the primary structure detection, T cell subsets are further subdivided; 3-2. Different T cell subtypes are labeled with specific antibodies and detected by flow cytometry; 3-3. Analyze the changes in the proportion and number of each subtype of T cells to assess the functional status of the immune system. The secondary structure detection included the following indicators: the proportion of regulatory T cells (Tregs) CD3+CD4+CD25+CD127low to CD4+ T cells; the proportion of total T cells (CD3+) to lymphocytes; the proportion of CD4+ T cells to total T cells; the proportion of Th1 cells to CD4+ T cells; the proportion of Th2 cells to CD4+ T cells; the proportion of Th17 cells to CD4+ T cells; the proportion of CD28+ T cells to CD4+ T cells; the proportion of HLA-DR+ activated T cells to CD4+ T cells; the proportion of CD38+ activated T cells to CD4+ T cells; the proportion of dual-activated T cells to CD4+ T cells; the proportion of naive T cells to CD4+ T cells; the proportion of memory T cells to CD4+ T cells; the proportion of CD8+ T cells to total T cells; the proportion of CD28+ T cells to CD8+ T cells; and the proportion of HLA-DR+ activated T cells to CD4+ T cells. The proportions of CD8+ T cells, CD38+ activated T cells, dual activated T cells, naive T cells, and memory T cells are included in the second antibody combination. The second antibody combination includes at least five antibodies targeting CD25, CD127, CD28, CD45RA, CD45RO, HLA-DR, and CD38. It also includes antibodies to differentiate Th1, Th2, and Th17 cells, achieved through surface staining against chemokine receptors CXCR3, CCR4, and CCR6. The regulatory T cell (Treg) phenotype is CD3+CD4+CD25+CD127low / -. Activated T cells include CD28+, HLA-DR+, and / or CD38+ CD4+ or CD8+ T cells. Specifically, secondary structure detection includes flow cytometry detection of regulatory T cells and flow cytometry detection of T lymphocyte immunophenotypic subtypes. Indicators include:

[0027] S4. Perform tertiary structure detection on blood samples and analyze cytokine expression and / or immune checkpoint molecule expression using a third detection method to obtain a third dataset; wherein, all or part of the detections in S2, S3, and S4 are performed independently on equal aliquots of the same sample, or sequentially on the same sample using a multi-round staining strategy.

[0028] The blood sample underwent tertiary structural analysis, including: 4-1. Flow cytometry was used to detect cytokines and analyze the secretion level of cytokines and the expression of cell surface markers. 4-2. Detect the expression level of PD-1 molecules on the surface of T cells to assess the activation status and immunosuppression of immune cells. The tertiary structure detection mentioned above includes the following indicators: the expression level of cytokines, including but not limited to IL-2, IFN-γ, TNF-α, IL-4, IL-10, IL-17, and the expression of PD-1 molecules on the surface of T cells. Specifically, the tertiary structure detection includes: flow cytometry detection of cytokines (factor 6) and (factor 12), and flow cytometry detection of PD-1 molecules on the surface of T cells. The indicators include:

[0029] The third detection method includes: Cytokine secretion assay: The expression levels of cytokines in plasma were detected by flow cytometry using microspheres from a multi-cytokine assay kit (containing interleukin series, tumor necrosis factor series and interferon series). Immune checkpoint molecular detection: Cell surface staining was performed using antibodies against PD-1.

[0030]

[0031] S5. Integrate the first, second, and third datasets to generate a comprehensive immune cell analysis report. The core platform for the detection method is multicolor flow cytometry. The immune cell analysis report lists the detection results, units, and reference ranges for each detection item in tabular form, and marks the results that exceed the reference range. The report also includes flow cytometry analysis atlases representing key data from the first, second, and third datasets.

[0032] The beneficial effects achieved by the above content are as follows: By clearly dividing immune cell analysis into three levels, the primary structure detects the proportion and absolute count of basic lymphocyte subsets; the secondary structure provides in-depth analysis of T cell functional subtypes, including Th1 / Th2 / Th17, naive / memory / activated T cells, and regulatory T cells; and the tertiary structure assesses cytokine expression and immune checkpoint molecules. This allows for multi-level analysis using the same blood sample to the greatest extent possible, improving sample utilization and reducing batch-to-batch errors. It also reveals more deeply the functional polarization, activation state, memory differentiation, and exhaustion level of T cells, providing high-quality, in-depth information for understanding complex immune states. This allows for a direct reflection of the overall state and potential problems of the immune system, facilitating its widespread application in clinical testing laboratories. It provides comprehensive and in-depth immune information, from cell count to functional status, and is of significant value for the diagnosis, treatment monitoring, and prognostic assessment of clinical immune-related diseases.

[0033] Working principle: By dividing immune assessment into three levels, the primary structure detects the proportion and absolute count of basic lymphocyte subsets; the secondary structure analyzes in depth the functional subtypes of T cells, including Th1 / Th2 / Th17, naive / memory / activated T cells, and regulatory T cells; the tertiary structure assesses cytokine expression and immune checkpoint molecules; and through optimized multicolor flow cytometry, multiple parameters are detected on the same biological sample, generating a comprehensive immune assessment report, providing clinicians with comprehensive and in-depth immune information from cell count to functional status.

[0034] The step of integrating the first dataset, the second dataset, and the third dataset in S5 includes: 5-1. Based on the first and second datasets, calculate the normalized immune diversity index, which characterizes the breadth of the immune system response. ; 5-2. Based on the initial T cells in the second dataset and memory T cells The proportion of immune age shift factor, which characterizes the decline of immune reserve capacity, is calculated. ; 5-3. Based on the expression levels of cytokines and immune checkpoint molecules in the third dataset, calculate the comprehensive immune stress load value, which characterizes the systemic load. ; 5-4. Calculate the entropy-based immune homeostasis index according to the following formula. :

[0035] in, The normalized immune diversity index (dimensionless) was calculated based on Shannon entropy. This is a global adjustment parameter used to scale the exponent as a whole; The adjustment parameters for controlling the rate of decline determine how quickly immune elasticity declines with age. The adjustment parameters for controlling the degree of compression determine the intensity of the inhibitory effect of immune stress on elasticity. , and All are dimensionless constants determined based on clinical data; , It is an immune age shift factor. This represents the proportion of initial T cells in the second detection set. The proportion of memory T cells in the second detection group. This represents the ratio of naive T cells to memory T cells. This is the reference ratio for the healthy control group; The term is used to simulate the exponential decline dynamics of immune elasticity with increasing age shift; The comprehensive immune stress load value (dimensionless) is calculated by incorporating the ratio of pro-inflammatory / anti-inflammatory cytokines and the normalized expression level of key immune checkpoints (such as PD-1). The term is used to simulate the logarithmic compression effect of immune stress on the system's elasticity, i.e., saturation dynamics; 5-5. Add the entropy-based immune homeostasis index to the immune cell analysis report.

[0036] In a preferred embodiment of the present invention, step S5, namely the step of integrating the first, second, and third datasets and generating a comprehensive report, is not a simple data aggregation or linear scoring, but rather includes a complex nonlinear dynamic calculation process executed by a dedicated immune resilience analysis controller. This process aims to calculate a novel index that quantifies the immune system's ability to maintain homeostasis and its resilience by deeply fusing multidimensional data; namely, an entropy-based immune homeostasis index.

[0037] The background to this technical solution lies in the fact that the immune system is a highly complex nonlinear adaptive system. Traditional immune detection methods, including the primary, secondary, and tertiary structure detection provided in the basic embodiments of this invention, while constructing detailed static immune maps, are insufficient in clinical practice to assess the system's "resilience" based solely on a static snapshot at a given moment. Immune resilience refers to a system's ability to maintain functional homeostasis or rapidly recover when subjected to external disturbances (such as infection, drug treatment, aging, or chronic inflammation). The immune system's response exhibits significant nonlinear characteristics. For example, when the system deviates from its optimal state, its functional decline may accelerate dramatically, exhibiting exponential decay; or, when faced with extremely high-intensity stimuli, its responsiveness may reach its upper limit, demonstrating a significant saturation effect or logarithmic compression effect. Existing linear analysis methods cannot capture these complex dynamic characteristics, leading to biased and limited assessments of the true health status of the immune system. The introduction of the immune resilience analysis controller aims to address the key technical problem of how to quantitatively assess the nonlinear dynamic resilience and robustness of the immune system.

[0038] The immunoelasticity analysis controller is a specially designed computing unit responsible for performing high-performance numerical computation and data analysis tasks. In terms of hardware implementation, this controller can take various forms to meet different application scenarios. It can be an application-specific integrated circuit (ASIC) or field-programmable gate array (FPGA) integrated within the flow cytometry analysis system, specifically designed for hardware acceleration of the nonlinear dynamics model calculations of this invention to achieve real-time analysis of high-throughput samples. Alternatively, it can be a standalone hardware module equipped with a high-performance central processing unit (CPU) and dedicated parallel computing units (such as graphics processing units). Utilizing parallel computing capabilities, it can efficiently process large-scale flow cytometry data and perform complex floating-point operations. In terms of software implementation, the controller runs a specially developed immunoelasticity analysis algorithm library, which includes modules for data preprocessing, feature extraction, nonlinear model calculation, and result visualization. The controller connects to the laboratory information management system through a standardized data interface, enabling seamless data transfer and automatic report generation.

[0039] The workflow of the immune resilience analysis controller begins with receiving the first, second, and third datasets obtained in the preceding steps. Its core computational process consists of four key stages: calculating the normalized immune diversity index, calculating the immune age shift factor, calculating the comprehensive immune stress load value, and finally, nonlinear dynamic fusion.

[0040] Phase 1: Calculation of Normalized Immune Diversity Index This index aims to quantify the breadth of the immune system's response, forming the fundamental structure of immune resilience. A healthy, resilient immune system typically possesses high diversity, meaning it has a variety of functional and specific cell subsets capable of addressing diverse challenges. The controller accesses a first dataset (basic lymphocyte subsets, such as T cells, B cells, and NK cells) and a second dataset (T cell functional subtypes, such as Th1, Th2, Th17, regulatory T cells, naive / memory / activated T cells, etc.) to extract precise proportion data for all relevant cell subsets.

[0041] To quantify this diversity, the concept of Shannon entropy is introduced. In information theory, entropy measures the uncertainty or information content of a system. In immunology, we can view the immune system as an information processing system; the higher its diversity, the greater the entropy, and the stronger the system's complexity and adaptability. The calculation of the controller involves more than just counting the number of cell types (richness); more importantly, it considers the evenness of distribution among these subpopulations (uniformity). If the system is dominated by a few cell types, even with a large total cell count, its entropy will be low, indicating a rigid system structure and insufficient resilience.

[0042] In the specific calculation, the controller first processes the proportion of each identified cell subpopulation. For each subpopulation, the controller calculates the natural logarithm of the proportion, and then calculates the product of the proportion and its natural logarithm. Next, this product is summed for all cell subpopulations. Finally, the negative value of this sum is taken to obtain the original Shannon entropy value. This calculation process accurately reflects the uniformity and richness of the component distribution within the system. Subsequently, the controller needs to normalize this original entropy value. This is because the maximum number of detectable cell subpopulations may differ between different individuals or under different experimental conditions. The controller obtains the normalized immune diversity index by dividing the original entropy value by the theoretically maximum possible entropy value (i.e., the entropy value assuming all cell subpopulation proportions are completely equal). This index is a dimensionless parameter with a value range within the standardized interval, facilitating cross-sectional comparisons between different individuals. This index forms the cornerstone of the final entropy-based immune homeostasis index.

[0043] Phase Two: Calculation of the Immune Age Shift Factor This factor aims to quantify the degree of decline in immune reserve capacity, i.e., the process of immunosenescence. Immunosenescence is an important endogenous factor affecting immune elasticity. One of its core biological characteristics is the decline of thymic function, leading to a reduction in the output of naive T cells, while memory T cells in peripheral blood gradually accumulate due to repeated stimulation by antigens. Naive T cells represent the potential of the immune system to respond to neoantigens, and their number is directly related to the long-term adaptive capacity of the immune system.

[0044] The immune elasticity analysis controller specifically accesses the second dataset to precisely extract the ratio of naive T cells to memory T cells. The controller first calculates the ratio of naive T cells to memory T cells in the subject sample. This ratio is a key indicator of immune reserve status.

[0045] The controller then compares this ratio with a pre-defined reference ratio for a healthy control group. This reference ratio is established based on the analysis of large-scale clinical epidemiological data and represents the immune reserve level of healthy individuals under ideal conditions or within a specific age group. The controller internally stores a dynamic database of reference ratios, which can automatically select the most appropriate reference value based on the subject's chronological age. The immune age shift factor is defined as the absolute difference between the subject's current ratio and the reference ratio. The larger the value of this factor, the further the subject's immune reserve status deviates from the ideal state, the more significant the shift in immune age relative to chronological age, and the lower the long-term resilience of the system.

[0046] Phase 3: Calculation of Comprehensive Immune Stress Load This potential energy aims to quantify the current load on the immune system. The immune system enters a state of stress when responding to infection, inflammation, tumors, or autoimmune responses. Sustained stress depletes immune resources, suppresses immune function, and thus weakens immune resilience. The immune resilience analysis controller quantifies this stress potential energy by analyzing a third dataset. This third dataset includes the expression levels of key cytokines and immune checkpoint molecules.

[0047] When calculating stress potential, the controller needs to consider two aspects of information. The first is the inflammatory burden. The controller analyzes the levels of various pro-inflammatory cytokines (such as interferon-γ, tumor necrosis factor-α, and interleukin-17) and anti-inflammatory cytokines (such as interleukin-10). It focuses not only on the absolute values ​​of individual factors but also on the dynamic balance between pro-inflammatory and anti-inflammatory factors. The controller uses a pre-defined weighted algorithm to fuse the data of these cytokines into a comprehensive inflammatory burden score. For example, imbalances between helper T cells type 1 and 2 (Th1 / Th2) or helper T cells type 17 and regulatory T cells (Th17 / Treg) are given higher weights.

[0048] Secondly, the controller analyzes the expression levels of key immune checkpoint molecules (such as PD-1) on the surface of T cells. High expression of these molecules usually indicates that T cells are in a state of functional exhaustion or suppression, which is an important marker of chronic stress and immune escape. The controller normalizes the expression levels of immune checkpoint molecules and calculates a exhaustion score.

[0049] Finally, the controller integrates the inflammation burden score and exhaustion score into a final comprehensive immune stress burden value using a fusion algorithm. This potential value is also a dimensionless parameter; the higher the value, the greater the stress the immune system is currently under and the lower its available functional redundancy.

[0050] Phase Four: Nonlinear Dynamics Fusion and Generation of Entropy-Based Immune Homeostasis Index The controller integrates the core parameters calculated in the first three stages (normalized immune diversity index, immune age shift factor, and comprehensive immune stress load) through a specific nonlinear dynamic model. The core innovation of this invention lies in recognizing that the effects of these three parameters on immune resilience are not a simple linear superposition, but rather involve complex nonlinear interactions that determine the macroscopic behavior of the immune system. This model incorporates two key nonlinear regulatory mechanisms, respectively simulating the effects of immune aging and immune stress on system resilience.

[0051] The degradation of biological systems is rarely uniform. When a system deviates from its optimal state to a certain extent, the rate of functional decline accelerates dramatically, exhibiting exponential decay. For example, when immune reserves fall below a certain threshold, the system's resilience drops precipitously, demonstrating increased fragility. To accurately simulate this phenomenon, the immunoelasticity analysis controller incorporates an exponential decay function in its calculations to handle the immune age shift factor.

[0052] In practice, the controller first multiplies the immune age shift factor by a preset adjustment parameter (which determines the rate of decay). Then, it calculates the negative value of this product. Next, the controller calculates an exponential function with the negative value as the exponent and the base of the natural constant. This process ensures that when the immune age shift factor is small, its impact on immune resilience is relatively limited; however, as the shift factor increases, its inhibitory effect on the overall index rapidly strengthens exponentially. The controller multiplies this exponential decay term by the normalized immune diversity index to form the numerator of the computational model. This means that even with a high diversity index, significant immune aging can severely weaken the overall resilience of the system. This design accurately simulates the nonlinear functional decline of the immune system with aging, reflecting real biological reality far better than traditional linear scoring.

[0053] When the immune system is stimulated by external factors, its stress response intensifies, thereby inhibiting the overall resilience of the system. However, this inhibitory effect also exhibits a saturation phenomenon, a logarithmic compression effect prevalent in biological systems. When stress levels are low, an increase in stress significantly affects system resilience; but when stress levels are already very high, the system may have already activated various negative feedback mechanisms or reached its maximum inhibitory level. At this point, further increases in stress have a diminishing marginal impact on resilience, and the system enters a saturated or "numb" state.

[0054] To simulate this phenomenon, the immune elasticity analysis controller employs a logarithmic function to process the total immune stress load value. Specifically, the controller first increments the total immune stress load value by one (to ensure the input value is positive and avoid taking the logarithm of zero). Then, it calculates the natural logarithm of this sum. Next, it multiplies this logarithmic value by a preset adjustment parameter (which determines the degree of compression). Finally, it increments this product by one again, forming the denominator in the calculation model.

[0055] This design causes the entropy-based immune homeostasis index to decrease with increasing stress potential, but at a gradually slower rate. This logarithmic compression effect reflects the adaptive mechanisms and response thresholds of the immune system under extreme stress, and improves the robustness of the model, preventing the overall resilience index from being excessively suppressed due to an abnormally high single stress indicator.

[0056] The immunoelasticity analysis controller performs the final integrated calculation. It divides the numerator (the product of the normalized immunodiversity index and the exponential decay function value) by the denominator (the stress potential influence term processed by the logarithmic function). The final result is then multiplied by a global adjustment parameter (global scaling factor) to obtain the final entropy-based immunohomeostasis index.

[0057] It is important to emphasize that all adjustment parameters involved in this calculation process (including parameters controlling the decay rate, parameters controlling the degree of compression, and the global scaling factor) are dimensionless constants obtained through machine learning regression analysis using large-scale clinical cohort data. The determination of these parameters involved collecting a large amount of immune data from patients of different disease states and age groups, along with their clinical prognostic information. By using machine learning algorithms such as support vector machines, random forests, or neural networks, the model parameters were optimized to ensure that the calculated entropy-based immune homeostasis index has the strongest correlation with clinical outcomes. These parameters are embedded in the controller's firmware, ensuring the universality and accuracy of the calculation model and guaranteeing dimensional consistency throughout the calculation process. The final entropy-based immune homeostasis index is also a dimensionless relative index.

[0058] Through this series of complex intelligent calculation steps, this invention successfully applies the principles of systems biology, nonlinear dynamics, and information theory to immune assessment, providing a novel indicator that can quantify the dynamic resilience of the immune system, greatly enhancing the depth of immune assessment and its clinical predictive value.

[0059] Before analyzing the first, second, or third dataset, a data correction step based on distribution characteristics and non-linear data normalization is included: Load a pre-trained probability distribution mapping model (Normalizing Flow model); this model has pre-learned the multidimensional data distribution characteristics (Reference Manifold) of a standard biological reference sample for a specific multicolor antibody combination under ideal experimental conditions. Input the raw high-dimensional flow cytometry data of the subject's blood sample to be analyzed; The invertible nonlinear transformation function required to calculate the multidimensional data distribution characteristics from the subject sample data distribution to the standard reference sample is used in the nonlinear distribution correction model. The reversible nonlinear transformation function is applied to perform nonlinear data standardization (Dynamic Manifold Alignment) on the subject sample data based on distribution characteristics. The output is aligned data; wherein the nonlinear data normalization based on distribution characteristics corrects the nonlinear data geometric distortion caused by spectral spillover error diffusion (SSE) and cellular autofluorescence heterogeneity.

[0060] In another preferred embodiment of the present invention, to ensure the accuracy and reliability of the aforementioned tertiary structure detection and immunoelasticity analysis, the present invention provides an intelligent data preprocessing method performed before analyzing the first, second, or third datasets. This method is executed by an independent data geometry correction controller and employs advanced computational techniques based on nonlinear data normalization of distribution characteristics. This technique aims to address a long-standing and difficult-to-solve technical bottleneck in high-dimensional flow cytometry data analysis: nonlinear distortion of data geometry.

[0061] In multicolor flow cytometry detection, especially in the secondary and tertiary structure detection of this invention, multiple (dozens or even tens of) fluorescent antibodies need to be used simultaneously. This inevitably leads to spectral overlap, meaning that photons emitted by one fluorophore may leak into other channels. Traditionally, linear compensation matrices or spectral splitting techniques have been used to address this problem. However, these linear methods have serious limitations and cannot solve the serious problems caused by statistical fluctuations in photon counting (Poisson noise characteristics) and detector response nonlinearity in practical applications.

[0062] Specifically, there are two main factors contributing to nonlinear distortion. The first is spectral spillover error diffusion. This is due to the Poisson noise characteristics of photon counting and the nonlinearity of the detector response. It causes the compensated data points to diffuse around their true locations. Crucially, this diffusion is nonlinear and heterogeneous: the higher the signal intensity, the more severe the diffusion. In high-dimensional space, spectral spillover error diffusion leads to blurred cell population boundaries and distorted shapes, particularly exhibiting a "fan-shaped" diffusion effect in high-expression regions.

[0063] Second, there is the heterogeneity of cellular autofluorescence. Different types of cells have different levels of autofluorescence and spectral characteristics, and the autofluorescence also behaves differently in different channels. This heterogeneity can cause nonlinear shifts and rotations in the overall position of cell populations in high-dimensional space, especially when analyzing rare cell populations or biomarkers with low expression levels.

[0064] These nonlinear distortions severely affect the true geometric structure of high-dimensional data, i.e., the data distribution characteristics. Data distribution characteristics refer to the geometric structure formed by the aggregation of data points in high-dimensional space. Ideally, cells of the same species should cluster in a compact, regular region. Nonlinear distortions cause the data distribution characteristics to bend, stretch, or fold, thus severely affecting the reliability of subsequent cell subpopulation identification and analysis. This implementation aims to solve the key technical problem of how to correct the geometric structure distortion of high-dimensional data that traditional linear methods cannot handle.

[0065] To overcome the limitations of traditional linear methods, this invention introduces a data geometry correction controller. This controller is typically configured with high-performance parallel computing units to support the advanced deep learning models it runs on, namely nonlinear distribution correction models.

[0066] Nonlinear distribution correction models are powerful deep generative models. Their core idea is to map a complex probability distribution (such as a high-dimensional streaming data distribution) to a simple, known prior distribution (such as a standard Gaussian distribution) and vice versa through a series of invertible, parameterized nonlinear transformation functions. Compared to other deep learning models, the key advantage of nonlinear distribution correction models lies in the complete reversibility of their transformation process and the ability to accurately calculate the probability density of the target data. This makes them ideal for data correction and normalization tasks, as they can reconstruct the true structure of the data without losing information.

[0067] Architecturally, the nonlinear distribution correction model consists of multiple stacked "flow" layers. Each layer performs a reversible nonlinear transformation. These layers are cleverly designed to ensure both the reversibility of the transformation and computational efficiency. By stacking a sufficient number of flow layers, the model can fit arbitrarily complex probability distributions.

[0068] The workflow of the data geometry correction controller consists of two main phases: a pre-training phase (learning the distribution characteristics of reference data) and an application phase (nonlinear data normalization based on the distribution characteristics).

[0069] Phase 1: Pre-training Phase and Learning the Distribution Characteristics of Reference Data In the pre-training phase (corresponding to step D1), the goal is to enable the nonlinear distribution correction model to learn the "gold standard" data distribution that a standard biological reference sample should present under ideal experimental conditions for a specific multicolor antibody combination, i.e., the multidimensional data distribution characteristics of the standard reference sample (reference data distribution characteristics).

[0070] First, high-quality standard biological reference samples need to be prepared. This typically includes peripheral blood mononuclear cells from multiple healthy donors or dedicated calibrators. During data acquisition, experimental conditions need to be strictly controlled, instrument settings optimized, and noise and operational errors minimized to obtain data that is as close as possible to the actual biological state.

[0071] Then, this high-quality reference data is fed into the nonlinear distribution correction model for training. The goal of training is to maximize the likelihood probability of the model generating this data. The model progressively adjusts the parameters of its internal nonlinear transformation function using optimization algorithms such as backpropagation and stochastic gradient descent. During training, the model learns how to gradually transform complex reference data distributions into simpler prior distributions.

[0072] Once trained, the model contains all the geometric information about the distribution characteristics of the reference data. It understands the ideal distribution morphology, density, and relative positions of various immune cell populations in high-dimensional space under a specific experimental system. This pre-trained model is loaded and stored in the storage unit of the data geometry correction controller as a benchmark for subsequent corrections.

[0073] Phase Two: Application Phase and Nonlinear Data Standardization Based on Distribution Characteristics When actually testing subject samples, the data geometric correction controller begins to perform a nonlinear data normalization step based on distribution characteristics.

[0074] (1) Data input: The controller inputs the raw high-dimensional flow cytometry data of the subject's blood sample to be analyzed. These raw data are nonlinearly distorted due to spectral overflow error diffusion, autofluorescence heterogeneity, and small fluctuations in experimental operation, and their distribution data distribution characteristics deviate from the reference data distribution characteristics.

[0075] (2) Calculation of the reversible nonlinear transformation function: This is the most crucial step. The controller's task is to use the loaded nonlinear distribution correction model to calculate the reversible nonlinear transformation function required to transform the subject sample data distribution into the reference data distribution characteristics. The model evaluates the position of the subject data points on the data distribution characteristics and compares it with the geometric features of the corresponding positions on the reference data distribution characteristics. Because the nonlinear distribution correction model has powerful nonlinear transformation capabilities, it can capture various local and global geometric distortions present in the data. The model generates a specific transformation function that defines how to move each data point in high-dimensional space to conform to the geometric features of the reference data distribution characteristics.

[0076] This transformation is not a globally uniform linear operation, but a complex "twisting" process. It can dynamically adjust according to the local data density and geometric features around each data point, and can accurately correct local nonlinear distortions.

[0077] (3) Execution of nonlinear data standardization based on distribution characteristics: The controller applies the calculated reversible nonlinear transformation function to perform nonlinear data standardization based on distribution characteristics for each data point in the subject sample data. This process can be figuratively understood as a fine "shaping" and "resetting" of distorted data distribution characteristics.

[0078] Specifically, for diffusion distortion caused by spectral spillover error diffusion: in high fluorescence intensity regions, diffusion leads to an increase in data variance. Nonlinear distribution correction models can identify this intensity-dependent variance change pattern and, through nonlinear transformation, "compress" the data in severely diffused regions, restoring the compact structure and clear boundaries that the cell population should have.

[0079] Regarding the offset distortion caused by cell autofluorescence heterogeneity: the model can identify the nonlinear offset pattern caused by autofluorescence and reposition each cell population to its correct position in the distribution characteristics of the reference data through a transformation function, thus eliminating the interference of background noise.

[0080] (4) Output of aligned data: After alignment, the controller outputs the corrected data. This data presents a clearer and more accurate geometric structure in high-dimensional space. The boundaries between cell subpopulations are distinct, the signal-to-noise ratio of the data is significantly improved, and the resolution is greatly enhanced.

[0081] By introducing a distribution-based nonlinear data normalization technique using a nonlinear distribution correction model, this invention significantly improves the analytical accuracy and reliability of high-dimensional flow cytometry data. It represents a leap from traditional linear correction to modern nonlinear geometric reconstruction. This method not only solves the nonlinear distortion problem that traditional methods cannot address, but also improves the sensitivity and specificity of detection, especially when analyzing rare cell subpopulations. This intelligent data geometric correction method provides a solid data foundation for comprehensive and in-depth immunological analysis.

[0082] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting immune cell counts, characterized in that, include: S1. Collect blood samples from the subjects and process them; S2. Perform primary structure detection on blood samples, and detect basic lymphocyte subsets through the first antibody combination to obtain the first dataset including the proportion and absolute count of T cells, B cells and NK cells. S3. Perform secondary structure detection on blood samples, and detect functional subtypes and regulatory T cells through a second antibody combination to obtain a second dataset including the proportions of Th1, Th2, Th17, naive T cells, memory T cells, activated T cells, and regulatory T cells. S4. Perform tertiary structure detection on blood samples, and analyze cytokine expression and / or immune checkpoint molecule expression using a third detection method to obtain a third dataset; S5. Integrate the first, second, and third datasets to generate a comprehensive immune cell analysis report.

2. The method for detecting immune cell counts according to claim 1, characterized in that, The primary structure detection of the blood sample in S2 includes: 2-1. Obtain processed blood samples and perform analysis using flow cytometry; 2-2. Based on the expression of cell surface markers, lymphocyte subsets are classified and counted; 2-3. Calculate the test results of each indicator and compare them with the reference interval to determine whether there are any abnormalities.

3. The method for detecting immune cell counts according to claim 2, characterized in that, The secondary structure detection of the blood sample in S3 includes: 3-1. Based on the primary structure detection, T cell subsets are further subdivided; 3-2. Different T cell subtypes are labeled with specific antibodies and detected by flow cytometry; 3-3. Analyze the changes in the proportion and number of T cells of different subtypes to assess the functional status of the immune system.

4. The method for detecting immune cell counts according to claim 3, characterized in that, The S4 step involves performing tertiary structure detection on the blood sample, including: 4-1. Flow cytometry was used to detect cytokines and analyze the secretion level of cytokines and the expression of cell surface markers. 4-2. Detect the expression level of PD-1 molecules on the surface of T cells to assess the activation status and immunosuppression of immune cells.

5. The method for detecting immune cell counts according to claim 4, characterized in that, The third detection method in S4 includes: Cytokine secretion assay: The expression levels of cytokines in plasma were detected by flow cytometry using microspheres from a multi-cytokine assay kit. Immune checkpoint molecular detection: Cell surface staining was performed using antibodies against PD-1.

6. The method for detecting immune cell counts according to claim 2, characterized in that, The primary structure detection in S2 includes the following indicators: average number of lymphocytes obtained, proportion of lymphocytes among nucleated cells, proportion of T cells (CD3+) among lymphocytes, proportion of CD4+ T cells among lymphocytes, proportion of CD8+ T cells among lymphocytes, CD4 / CD8 ratio, proportion of B cells (CD19+) among lymphocytes, proportion of NK cells (CD3-CD16+CD56+) among lymphocytes, absolute number of lymphocytes, absolute number of T cells (CD3+), absolute number of CD4+ T cells, absolute number of CD8+ T cells, absolute number of B cells (CD19+), and absolute number of NK cells (CD3-CD16+CD56+). The first antibody combination includes antibodies against CD45, CD3, CD4, CD8, CD19, CD16, and CD56; the basic lymphocyte subsets include total T cells (CD3+), helper / inducer T cells (CD3+CD4+), cytotoxic T cells (CD3+CD8+), B cells (CD19+), and NK cells (CD3-CD16+ and / or CD56+).

7. The method for detecting immune cell counts according to claim 3, characterized in that, The secondary structure detection in S3 includes the following indicators: the proportion of regulatory T cells (Tregs) with CD3+CD4+CD25+CD127low to CD4+ T cells, the proportion of total T cells (CD3+) to lymphocytes, the proportion of CD4+ T cells to total T cells, the proportion of Th1 cells to CD4+ T cells, the proportion of Th2 cells to CD4+ T cells, the proportion of Th17 cells to CD4+ T cells, the proportion of CD28+ T cells to CD4+ T cells, the proportion of HLA-DR+ activated T cells to CD4+ T cells, and CD38+ activated T cells. The percentages of T cells among CD4+ T cells, the percentages of dual activated T cells among CD4+ T cells, the percentages of naive T cells among CD4+ T cells, the percentages of memory T cells among CD4+ T cells, the percentages of CD8+ T cells among total T cells, the percentages of CD28+ T cells among CD8+ T cells, the percentages of HLA-DR+ activated T cells among CD8+ T cells, the percentages of CD38+ activated T cells among CD8+ T cells, the percentages of dual activated T cells among CD8+ T cells, the percentages of naive T cells among CD8+ T cells, and the percentages of memory T cells among CD8+ T cells; The second antibody combination includes at least five antibodies targeting CD25, CD127, CD28, CD45RA, CD45RO, HLA-DR, and CD38. The second antibody combination also includes antibodies for distinguishing Th1, Th2 and Th17 cells, which are distinguished by surface staining with antibodies targeting chemokine receptors CXCR3, CCR4 and CCR6; The regulatory T cells (Tregs) have the phenotype of CD3+CD4+CD25+CD127low / -. The activated T cells include CD28+, HLA-DR+ and / or CD38+ CD4+ or CD8+ T cells.

8. The method for detecting immune cell counts according to claim 4, characterized in that, The tertiary structure detection in S4 includes the following indicators: the expression level of cytokines, including but not limited to IL-2, IFN-γ, TNF-α, IL-4, IL-10, IL-17, and the expression of PD-1 molecules on the surface of T cells.

9. A method for detecting immune cell counts according to any one of claims 1-8, characterized in that, The step of integrating the first dataset, the second dataset, and the third dataset in S5 includes: 5-1. Based on the first and second datasets, calculate the normalized immune diversity index, which characterizes the breadth of the immune system response. ; 5-2. Based on the initial T cells in the second dataset and memory T cells The proportion of immune age shift factor, which characterizes the decline of immune reserve capacity, is calculated. ; 5-3. Based on the expression levels of cytokines and immune checkpoint molecules in the third dataset, calculate the comprehensive immune stress load value, which characterizes the systemic load. ; 5-4. Calculate the entropy-based immune homeostasis index according to the following formula. : ； in, The normalized immune diversity index was calculated based on Shannon entropy. This is a global adjustment parameter used to scale the exponent as a whole; The adjustment parameters for controlling the rate of decline determine how quickly immune elasticity declines with age. The adjustment parameters for controlling the degree of compression determine the intensity of the inhibitory effect of immune stress on elasticity. , It is an immune age shift factor. This represents the proportion of initial T cells in the second detection set. The proportion of memory T cells in the second detection set. This represents the ratio of naive T cells to memory T cells. This is the reference ratio for the healthy control group; The term is used to simulate the exponential decline dynamics of immune elasticity with increasing age shift; To synthesize the immune stress load value, its calculation incorporates the ratio of pro-inflammatory / anti-inflammatory cytokines and the normalized expression levels of key immune checkpoints; The term is used to simulate the logarithmic compression effect of immune stress on system elasticity; 5-5. Add the entropy-based immune homeostasis index to the immune cell analysis report.

10. A method for detecting immune cell counts according to any one of claims 1-8, characterized in that, Before analyzing the first, second, or third dataset, a data correction step based on distribution characteristics and non-linear data normalization is included: Load a pre-trained nonlinear distribution correction model; this model has been pre-learned to have multidimensional data distribution characteristics of a standard biological reference sample for a specific multicolor antibody combination under ideal experimental conditions. Input the raw high-dimensional flow cytometry data of the subject's blood sample to be analyzed; The invertible nonlinear transformation function required to calculate the multidimensional data distribution characteristics from the subject sample data distribution to the standard reference sample is used in the nonlinear distribution correction model. The reversible nonlinear transformation function is applied to perform nonlinear data standardization of the subject sample data based on distribution characteristics; The output is aligned data; wherein the nonlinear data normalization based on distribution characteristics corrects the geometric distortion of the nonlinear data caused by the diffusion of spectral overflow error and the heterogeneity of cell autofluorescence.

Images