Sample pretreatment automation method for flow detection

Abstract

The invention provides a sample pretreatment automation method for flow cytometry, and belongs to the technical field of sample pretreatment for flow cytometry. A biological sample to be detected is collected and placed in a collection tube containing an EDTA-K2 anticoagulant for pretreatment; an automatic pipetting system is used for carrying out incubation reaction on an anticoagulant whole blood sample and an intelligent matching fluorescence labeling antibody mixed solution, and a double-layer game optimization verification system is used for verifying the rationality of an antibody adding proportion and reaction condition parameters; carrying out fluorescence characteristic readability index prediction on the incubated cell suspension by adopting an intelligent dilution parameter regulator model, dynamically adjusting hemolysis parameters and suspension conditions, and adding 1 * hemolysin working solution by using an automatic liquid adding device to carry out hemolysis treatment so as to finish pretreatment; the technical problems that in the pretreatment process of flow cytometry detection samples, parameters are often based on artificial experience, and the reproducibility and standardization degree of detection results are insufficient are solved.

Description

Technical Field

[0001] This invention belongs to the field of sample pretreatment technology for flow cytometry detection, and more specifically, relates to an automated method for sample pretreatment for flow cytometry detection. Background Technology

[0002] Flow cytometry, as a crucial technique in modern biomedical testing, is widely used in fields such as immune cell analysis, disease diagnosis, and treatment monitoring. Its core lies in the specific labeling of cell surface antigens with fluorescently labeled antibodies, followed by high-throughput detection and analysis using a flow cytometer. Traditional sample pretreatment methods rely heavily on the experience and subjective judgment of senior technicians. Key operational conditions such as antibody ratios, incubation times, and hemolysis parameters are determined through accumulated experience. This method, when used by experienced laboratory technicians, can yield relatively stable results. However, in current flow cytometry practice, the sample pretreatment process involves multiple complex steps and parameter settings. Novice operators often struggle to accurately grasp the optimal combination of parameters, leading to problems such as inaccurate antibody dosage, improper incubation conditions, and misjudgments of hemolysis levels. This results in significant differences in test results obtained from the same sample by different operators. In other words, current technologies suffer from the technical problem that parameters in flow cytometry sample pretreatment are often based on human experience, leading to insufficient reproducibility and standardization of test results for novice operators. Summary of the Invention

[0003] In view of this, the present invention provides an automated sample pretreatment method for flow cytometry detection, which can solve the technical problems in the prior art where parameters in the sample pretreatment process for flow cytometry detection are often based on human experience, making it easy for novice operators to operate and resulting in insufficient reproducibility and standardization of the detection results.

[0004] This invention is implemented as follows: An automated sample pretreatment method for flow cytometry detection includes: collecting the biological sample to be tested and placing it in a collection tube containing EDTA-K2 anticoagulant, with a sample volume of at least 200 μL; storing the sample at room temperature without shaking after collection and performing subsequent processing within 24 hours; adding 50 μL of anticoagulated whole blood sample to the bottom of the flow cytometry tube using an automated pipetting system; adding 35 μL of intelligently proportioned fluorescently labeled antibody mixture to the bottom of the flow cytometry tube; gently vortexing to mix; and incubating at room temperature (25±5℃) in the dark for 15 minutes; establishing a two-layer game optimization verification system, and constructing an upper-layer game with the goal of maximizing label uniformity. The model and the lower-level game model aimed at optimizing the detection signal intensity were used to verify the rationality of the antibody addition ratio and reaction condition parameters through a two-level game optimization verification system. A pre-trained intelligent dilution parameter regulator model was used to predict the fluorescence feature readability index of the incubated cell suspension, and the subsequent hemolysis parameters and suspension conditions were dynamically adjusted based on the fluorescence feature readability index prediction results. 450 μL of 1× hemolysin working solution was added to the stained blood sample flow cytometry tube through an automated liquid addition device, and after gentle vortexing and incubation at room temperature (25±5℃) in the dark for 15 minutes. After hemolysis, the sample was vortexed and prepared for flow cytometry detection through an automated transfer system.

[0005] Specifically, the EDTA-K2 anticoagulant is used to prevent blood clotting and maintain cell morphological integrity by chelating calcium in the blood. 2+ Ion blocking of coagulation cascade reaction, input includes biological sample to be tested and buffer environment, output is stable cell suspension state.

[0006] Specifically, the intelligent ratio fluorescently labeled antibody mixture automatically calculates the optimal ratio of various fluorescently labeled antibodies through a preset antibody concentration ratio algorithm. The intelligent ratio fluorescently labeled antibody mixture contains CD3 / CD4 / CD8 / CD16 / CD56 / CD19 / CD45 reagents. The input includes anticoagulated whole blood samples and antibody concentration parameters, and the output is a fluorescently labeled antibody mixture with maximized labeling uniformity.

[0007] Specifically, the upper-level game model in the two-layer game optimization verification system has an objective function that includes a product term of the label uniformity coefficient and the antibody concentration parameter, a logarithmic function term of the fluorescence intensity calibration curve deviation parameter, an exponential function term of the antibody addition ratio parameter, a sine function term of the reaction temperature parameter, and a square root function term of the reaction time parameter. The constraints are the antibody concentration range and temperature stability requirements.

[0008] Specifically, the lower-level game model in the two-layer game optimization verification system is an objective function that includes a product term of the signal strength parameter and the detector gain parameter, a natural logarithm function term of the signal-to-noise ratio parameter, a cosine function term of the excitation light power parameter, an arctangent function term of the optical path alignment accuracy parameter, and a cube root function term of the sampling time interval parameter. The constraints are the dynamic range of the detection equipment and the cell activity maintenance standard.

[0009] The intelligent dilution parameter regulator model is specifically constructed based on the Vision Transformer architecture image classification network, which includes 16 encoder layers and 8 decoder units. Each layer contains a multi-head self-attention mechanism and a feedforward neural network module. The input includes the feature data of the incubated cell suspension, and the output is the fluorescence feature readability index prediction result.

[0010] Specifically, the fluorescence feature readability index is calculated by analyzing the signal separation, peak sharpness, and background noise level of each detection channel in the fluorescence data matrix. The higher the value, the better the identifiability of the fluorescence data matrix. The input includes the fluorescence data matrix and detection channel parameters, and the output is the fluorescence feature readability index value.

[0011] Specifically, the hemolysis parameters include the hemolysin dilution factor, incubation time, and final suspension volume. The inputs include the fluorescence feature readability index prediction results and the current sample features, and the output is the optimized combination of hemolysis parameters.

[0012] Specifically, the 1× hemolysin working solution is a hemolysin concentrate diluted with purified water at a ratio of 1:9 to form the 1× hemolysin working solution. It is an isotonic solution containing selective cell membrane disrupting components, used to selectively disrupt the red blood cell membrane structure while preserving the integrity of nucleated cells.

[0013] Before the automated transfer system is used to prepare the hemolyzed sample for flow cytometry testing, the sample must be stained and stored in a light-protected environment at 2-8°C, and the testing must be completed within 24 hours.

[0014] Furthermore, it includes establishing an automated quality control and monitoring system to assess cell viability and fluorescence signal stability in processed samples, ensuring that sample quality meets the requirements for flow cytometry detection. Specifically, this automated quality control and monitoring system includes a cell counting module, a viability detection module, and a fluorescence signal stability assessment module. Through multi-dimensional parameter monitoring, it ensures that sample quality meets the standard requirements for flow cytometry detection. Inputs include the processed sample, and outputs are the cell viability assessment results and the fluorescence signal stability detection results.

[0015] The upper-level game model takes into account the label uniformity coefficient and antibody concentration parameters as inputs, and outputs the rationality verification results of the antibody addition ratio and reaction condition parameters.

[0016] The lower-level game model's inputs include signal strength parameters and detector gain parameters, and its output is the optimized result of the detected signal strength.

[0017] The input of the 1×hemolysin working solution includes a whole blood sample and lysis time parameters, and the output is a target cell mixture with red blood cells removed.

[0018] In the process of automatically calculating the optimal ratio of various fluorescently labeled antibodies using a preset antibody concentration ratio algorithm, the antibody concentration ratio algorithm is trained using a machine learning model and can adaptively adjust the antibody ratio according to sample characteristics.

[0019] This invention addresses the technical problems of insufficient reproducibility and standardization in flow cytometry sample pretreatment by constructing an intelligent and automated sample pretreatment system. Through an automated pipetting system and intelligent mixing of fluorescently labeled antibody solutions, this invention eliminates the subjectivity and arbitrariness of manual operation, ensuring the accuracy and consistency of antibody addition and avoiding mixing errors and operational mistakes caused by inexperienced operators. By employing a two-layer game-theoretic optimization verification system and an intelligent dilution parameter regulator model, this invention establishes an objective parameter optimization mechanism, replacing the traditional method's reliance on manual experience and ensuring optimal processing parameter combinations for each test. Through an automated quality control and monitoring system, this invention achieves real-time monitoring and standardized evaluation of sample quality, providing unified quality standards and operational guidance for different operators. In summary, this invention solves the technical problems mentioned in the background art, where parameters in flow cytometry sample pretreatment are often based on manual experience, leading to insufficient reproducibility and standardization of test results for novice operators. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention.

[0021] Figure 2 This is a comparison diagram of the fluorescence channel signal distribution in Example 2.

[0022] Figure 3 This is a graph evaluating the cell treatment effect in Example 2. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0024] like Figure 1 The diagram shown is a flowchart of an automated sample pretreatment method for flow cytometry provided by this invention. This method includes the following steps:

[0025] S01. Collect the biological sample to be tested and place it in a collection tube containing EDTA-K2 anticoagulant. The sample volume should be at least 200 μL. After collection, store the sample at room temperature without shaking and perform subsequent processing within 24 hours.

[0026] S02. Using an automated pipetting system, take 50 μL of anticoagulated whole blood sample and add it to the bottom of a flow cytometry tube. Add 35 μL of a smart ratio fluorescently labeled antibody mixture to the bottom of the flow cytometry tube. The smart ratio fluorescently labeled antibody mixture contains CD3 / CD4 / CD8 / CD16 / CD56 / CD19 / CD45 reagents. Gently vortex to mix and then incubate at room temperature (25±5℃) in the dark for 15 minutes.

[0027] S03. Establish a two-layer game optimization verification system, construct an upper-layer game model with the goal of maximizing label uniformity and a lower-layer game model with the goal of optimizing detection signal intensity, and verify the rationality of antibody addition ratio and reaction condition parameters through the two-layer game optimization verification system.

[0028] S04. A pre-trained intelligent dilution parameter regulator model is used to predict the fluorescence feature readability index of the incubated cell suspension, and the subsequent hemolysis parameters and suspension conditions are dynamically adjusted based on the fluorescence feature readability index prediction results.

[0029] S05. Add 450 μL of 1× hemolysin working solution to the stained blood sample flow cytometer using an automated liquid addition device. The 1× hemolysin working solution is a concentrated hemolysin solution diluted with purified water at a ratio of 1:9. After gently vortexing and mixing, incubate at room temperature (25±5℃) in the dark for 15 minutes.

[0030] S06. After hemolysis, the sample is vortexed and mixed, and then prepared for flow cytometer testing via an automated transfer system. After staining, the sample is stored in the dark at 2-8°C and must be tested within 24 hours.

[0031] S07. Optionally, it also includes establishing an automated quality control and monitoring system to assess cell viability and detect fluorescence signal stability in processed samples, ensuring that sample quality meets the requirements for flow cytometry detection.

[0032] The EDTA-K2 anticoagulant is used to prevent blood clotting and maintain cell morphological integrity by chelating calcium in the blood. 2+ Ion blocking of coagulation cascade reaction, input includes biological sample to be tested and buffer environment, output is stable cell suspension state.

[0033] The intelligent ratio fluorescently labeled antibody mixture automatically calculates the optimal ratio of various fluorescently labeled antibodies through a preset antibody concentration ratio algorithm. The input includes anticoagulated whole blood samples and antibody concentration parameters, and the output is a fluorescently labeled antibody mixture with maximized labeling uniformity.

[0034] In the two-layer game optimization verification system, the objective function of the upper-layer game model includes a product term of the label uniformity coefficient and the antibody concentration parameter, a logarithmic function term of the fluorescence intensity calibration curve deviation parameter, an exponential function term of the antibody addition ratio parameter, a sine function term of the reaction temperature parameter, and a square root function term of the reaction time parameter. The constraints are the antibody concentration range and temperature stability requirements. The inputs include the label uniformity coefficient and the antibody concentration parameter, and the output is the verification result of the rationality of the antibody addition ratio and reaction condition parameters.

[0035] In the two-layer game optimization verification system, the objective function of the lower-layer game model includes a product term of the signal strength parameter and the detector gain parameter, a natural logarithm function term of the signal-to-noise ratio parameter, a cosine function term of the excitation light power parameter, an arctangent function term of the optical path alignment accuracy parameter, and a cube root function term of the sampling time interval parameter. The constraints are the dynamic range of the detection device and the cell activity maintenance standard. The inputs include the signal strength parameter and the detector gain parameter, and the output is the optimal result of the detection signal strength.

[0036] The intelligent dilution parameter regulator model is constructed based on the image classification network of the Vision Transformer architecture. It contains 16 encoder layers and 8 decoder units. Each layer contains a multi-head self-attention mechanism and a feedforward neural network module. The input includes the feature data of the incubated cell suspension, and the output is the prediction result of the fluorescence feature readability index.

[0037] The fluorescence feature readability index is calculated by analyzing the signal separation, peak sharpness, and background noise level of each detection channel in the fluorescence data matrix. The higher the value, the better the identifiability of the fluorescence data matrix. The input includes the fluorescence data matrix and detection channel parameters, and the output is the fluorescence feature readability index value.

[0038] The hemolysis parameters include hemolysin dilution factor, incubation time, and final suspension volume. The inputs include the fluorescence feature readability index prediction result and the current sample features. The output is the optimized combination of hemolysis parameters.

[0039] The 1×hemolysin working solution is an isotonic solution containing selective cell membrane disrupting components, used to selectively disrupt the red blood cell membrane structure while preserving the integrity of nucleated cells. The inputs include whole blood sample and lysis time parameters, and the output is a target cell mixture from which red blood cells have been removed.

[0040] The automated quality control and monitoring system includes a cell counting module, a viability detection module, and a fluorescence signal stability assessment module. It ensures that the sample quality meets the standard requirements for flow cytometry detection through multi-dimensional parameter monitoring. The input includes the processed sample, and the output is the cell viability assessment result and the fluorescence signal stability detection result.

[0041] The specific implementation methods of the above steps are described in detail below.

[0042] The specific implementation of step S01 involves collecting and pre-processing biological samples using an automated sampling system. The sampling system first extracts the biological sample from the collection site using a vacuum negative pressure device. The collection volume is set within the range of 200 μL to 1000 μL, preferably 500 μL to ensure sufficient sample volume for subsequent testing. An EDTA-K2 anticoagulant is pre-placed in the collection tube at a concentration of 1.5 to 2.0 mg / mL, which reacts with free Ca2+ in the blood through a chelation mechanism. 2+ Ions bind to form stable complexes, blocking the activation process of clotting factors, thereby preventing blood clotting and maintaining the integrity of cell membrane structure. After collection, samples are placed in a light-proof container, with the ambient temperature controlled between 20°C and 25°C. Shaking or violent vibration should be avoided to prevent cell damage. The upper limit of storage time is set at 24 hours to ensure the stability of cell viability and morphological characteristics.

[0043] The specific implementation of step S02 involves using a precision pipetting control system to achieve accurate proportioning and thorough mixing of anticoagulated whole blood samples and fluorescently labeled antibodies. The automated pipetting system precisely aspirates 50 μL of anticoagulated whole blood sample via an electric piston drive mechanism, controlling the pipetting accuracy within ±1 μL, and adds the sample to the preset position at the bottom of the flow cytometer. The intelligent proportioning of the fluorescently labeled antibody mixture is prepared based on a preset concentration ratio algorithm. This algorithm calculates the optimal ratio based on the binding affinity constant of each antibody and the target cell surface antigen density. Specifically, the CD3 antibody concentration is set to 2–4 μg / mL, the CD4 and CD8 antibody concentrations are each set to 1.5–3 μg / mL, the CD16 and CD56 antibody concentrations are each set to 1–2 μg / mL, the CD19 antibody concentration is set to 1.5–3 μg / mL, and the CD45 antibody concentration is set to 2–4 μg / mL. The total volume of the mixture was controlled at 35 μL. It was mixed for 5 seconds using a vortex mixer at 2000 rpm, and then incubated in a constant temperature environment in the dark for 15 minutes. The temperature was controlled within the range of 25±5℃ to ensure the full binding of the antibody to the cell surface antigen.

[0044] The specific implementation of step S03 involves establishing an optimization verification system based on two-layer game theory to evaluate and adjust antibody ratio parameters. The upper-layer game model uses maximizing label uniformity as the objective function. This function comprehensively considers the linear relationship between the label uniformity coefficient and antibody concentration, the logarithmic transformation of the fluorescence intensity calibration curve deviation, the exponential decay of the antibody addition ratio, the sinusoidal periodicity of the reaction temperature, and the square root growth of the reaction time. Model constraints include limiting the antibody concentration range to 0.5 to 10 μg / mL and requiring temperature stability deviations not exceeding ±2℃. The lower-layer game model aims to optimize the detection signal intensity. The objective function includes the product of signal intensity and detector gain, the natural logarithm of the signal-to-noise ratio, the cosine modulation of the excitation power, the arctangent correction of the optical path alignment accuracy, and the cube root optimization of the sampling time interval. Constraints include maintaining the dynamic range of the detection device at 10... 2 Up to 10 6 The standard for maintaining cell viability requires a survival rate of over 90%. The two-layer game solution employs a layer-by-layer iterative optimization algorithm, achieving convergence of the global optimal solution through repeated calls between the upper and lower layer models.

[0045] The specific implementation of step S04 involves using a pre-trained deep learning model to perform fluorescence feature analysis and parameter prediction on the cell suspension. The intelligent dilution parameter regulator model receives multidimensional feature data of the incubated cell suspension as input, including the fluorescence intensity distribution histogram, cell count density, and combinations of forward and side scattering parameters. The model performs dimensionality compression and feature mapping on the input data through a feature extraction layer, and then calculates the fluorescence feature readability index through a multi-layer neural network. This index reflects a comprehensive evaluation of the signal separation, peak sharpness, and background noise level of each detection channel in the fluorescence data matrix. The value range is set from 0 to 1, and the threshold is set to 0.7. A value higher than this threshold indicates that the fluorescence data has good identifiability. Based on the prediction results, the system automatically adjusts subsequent hemolysis parameters, including selecting the hemolysin dilution factor between 8 and 12 times, adjusting the incubation time between 10 and 20 minutes, and determining the final suspension volume between 400 and 600 μL.

[0046] The specific implementation of step S05 involves achieving precise addition and reaction control of the hemolysin working solution using an automated liquid addition device. The automated liquid addition device employs a peristaltic pump drive mechanism, adding 450 μL of 1× hemolysin working solution to the flow cytometry tube of the stained blood sample at a constant flow rate, with the addition accuracy controlled within ±5 μL. The 1× hemolysin working solution is prepared using a standard dilution protocol, diluting the hemolysin concentrate with purified water at a volume ratio of 1:9. The resistivity of the purified water must be greater than 18 MΩ·cm. The hemolysin concentrate contains selective membrane disruptors such as ammonium chloride and potassium bicarbonate. The osmotic pressure of the working solution is adjusted to the range of 280 to 320 mOsm / kg to maintain an isotonic state, and the pH value is controlled between 7.2 and 7.6 to maintain physiological conditions. After addition, the solution is thoroughly mixed using a vortex mixer for 3 to 5 seconds. The sample is then incubated at room temperature in the dark for 15 minutes. During this process, the hemolysin selectively disrupts the erythrocyte membrane structure while preserving the integrity of nucleated cells.

[0047] The specific implementation of step S06 involves using an automated transfer system to import the processed sample into the flow cytometer for detection preparation. After the hemolysis reaction, the sample undergoes a final vortex mixing process, with mixing parameters set at 1500 rpm for 3 seconds to ensure uniform distribution of the cell suspension. The automated transfer system includes a sample identification module, a positioning module, and a transfer control module. Barcode scanning technology is used to identify the sample tubes, with positioning accuracy controlled within ±0.1 mm. During transfer, the sample temperature is maintained at a low temperature of 2°C to 8°C, and the transfer time is controlled within 5 minutes to prevent cell sedimentation and fluorescence signal attenuation. Before being processed by the flow cytometer, the sample undergoes a final quality assessment, including a cell concentration requirement of 10... 5 Up to 10 7Within the range of cells / mL, the fluorescence signal intensity should be such that the ratio of signal to background in each channel is greater than 10, and the detection time window should be set to within 24 hours after sample staining to ensure the stability of the fluorescent label.

[0048] Step S07 is optional. Its specific implementation involves constructing a multi-dimensional automated quality control and monitoring system to comprehensively assess sample quality. The cell counting module uses image recognition algorithms to count cell suspensions in real time. After acquiring cell images through a microscopic imaging system, image preprocessing, feature extraction, and pattern recognition steps are performed to complete cell count, with a counting accuracy requirement of over 95%. The viability detection module, based on the principle of cell membrane integrity assessment, determines cell activity through fluorescent dye permeability testing, with a viability threshold set at 90% or higher as the acceptable standard. The fluorescence signal stability assessment module monitors the signal intensity changes of each fluorescence channel, analyzing the signal attenuation rate through continuous time-point sampling. The stability index requires a signal intensity change rate of less than 5% per hour. The quality control system also includes an abnormal alarm mechanism, automatically triggering an alarm and prompting operators to intervene when detection parameters exceed preset threshold ranges.

[0049] The intelligent dilution parameter regulator model employs a deep learning network structure based on a visual transformer architecture. This model includes an input embedding layer, a position encoding layer, 16 transformer encoder layers, and 8 decoder units. The input embedding layer converts multi-dimensional fluorescence feature data into a 512-dimensional vector representation, achieving a unified mapping of the feature space through linear transformation and normalization operations. The position encoding layer uses a combination of sine and cosine functions to generate position embedding vectors, providing spatial location information for the sequence data. Each transformer encoder layer includes a multi-head self-attention mechanism and a feedforward neural network module. The multi-head attention mechanism is configured with 8 attention heads, each with a dimension of 64, achieving global feature association modeling by parallel computation of attention weights in different subspaces. The feedforward neural network module uses a two-layer fully connected structure with a hidden layer dimension of 2048, and uses the GELU activation function to provide a smooth non-linear transformation. The decoder unit adopts a similar multi-head self-attention structure, while adding an encoder-decoder attention mechanism to achieve feature fusion. The model output layer maps the decoder output to a numerical prediction result of the fluorescence feature readability index through linear projection.

[0050] The training dataset was built using a multi-stage data collection and annotation strategy. First, raw fluorescence detection data from different biological sample types were collected, including normal samples, abnormal samples, and boundary samples, accounting for 40%, 35%, and 25% of the total dataset, respectively. In the data preprocessing stage, the raw fluorescence data underwent noise reduction, normalization, and feature engineering. Noise reduction employed wavelet transform filtering to remove high-frequency noise components, and normalization used Z-score standardization to unify data dimensions. In the feature engineering stage, 128 feature dimensions were extracted, including statistical features, distribution features, and frequency domain features of fluorescence intensity. Data annotation employed a semi-supervised learning method, combining human expert annotation and automatic annotation algorithms. The number of manually annotated samples was 5000, and the number of automatically annotated samples was 20000. The training, validation, and test sets were divided in a 7:2:1 ratio, and a 5-fold cross-validation method was used to evaluate the model's generalization performance. Model training used the Adam optimizer with a learning rate of 0.0001, a batch size of 32, and 100 training epochs, employing an early stopping mechanism to prevent overfitting.

[0051] The key technical concepts of this invention include a two-layer game-theoretic optimization verification system, an intelligent dilution parameter regulator model, an automated quality control and monitoring system, and a multi-dimensional feature fusion mechanism. The two-layer game-theoretic optimization verification system achieves global optimization of antibody ratios and detection parameters by constructing upper and lower layer game models. Compared to traditional single-objective optimization methods, this system can simultaneously consider two key indicators: label uniformity and signal intensity. Through the equilibrium solution mechanism of game theory, it avoids the trap of local optima, significantly improving the comprehensiveness and stability of parameter optimization. The intelligent dilution parameter regulator model is based on a deep learning-based visual transformer architecture. It captures the global dependencies of fluorescence feature data through a multi-head self-attention mechanism. Compared to traditional rule-based parameter adjustment methods, this model can adaptively learn the optimal processing parameters for different sample types, achieving intelligent and personalized parameter adjustment and significantly reducing the need for manual intervention. The automated quality control and monitoring system integrates monitoring functions across multiple dimensions, including cell counting, viability detection, and fluorescence stability assessment. Compared to traditional single-indicator quality control methods, this system can comprehensively evaluate sample quality status and ensure the reliability and reproducibility of detection results through real-time monitoring and anomaly warning mechanisms.

[0052] The synergistic effect of these key technological approaches offers significant overall advantages over existing technologies. The two-layer game-theoretic optimization system provides the theoretically optimal initial parameter configuration for subsequent intelligent parameter adjustment. The intelligent dilution parameter regulator model performs fine-tuning based on the optimized baseline parameters, while the automated quality control system monitors and ensures quality throughout the entire processing flow. This multi-level, multi-dimensional collaborative optimization mechanism achieves a seamless transition from theoretical optimization to practical application. Compared to traditional independent module processing methods, the collaborative system fully leverages the strengths of each module and compensates for the shortcomings of individual modules, creating a complementary overall effect. Ultimately, this significantly improves the automation level of sample preprocessing and the accuracy of detection results.

[0053] It should be noted that this invention also solves the following technical problems: Existing technologies suffer from a lack of quantitative standards for evaluating antibody labeling efficiency during sample pretreatment in flow cytometry, leading to difficulties in accurately controlling labeling quality. Traditional antibody labeling effectiveness evaluation relies primarily on the subjective judgment and experience of technicians, assessing labeling effectiveness by observing cell morphology changes and fluorescence signal intensity. This lack of objective quantitative evaluation standards results in significant differences in labeling quality assessments of the same sample by different operators, affecting the accuracy and comparability of subsequent detection results. This invention establishes a quantitative antibody labeling effectiveness evaluation standard by optimizing the labeling uniformity coefficient and fluorescence intensity calibration curve deviation parameters in the validation system through a two-layer game approach. This standard objectively assesses the uniformity and accuracy of antibody labeling, providing reliable quantitative indicators for labeling quality control, thereby ensuring the standardization and consistency of the labeling process. Furthermore, existing technologies also suffer from the problem of inaccurate control of hemolysis levels during sample pretreatment in flow cytometry, where the assessment of hemolysis effects relies on manual observation. Traditional hemolysis assessment primarily relies on technicians visually observing sample color changes and transparency to determine complete hemolysis. This subjective approach is susceptible to individual experience and differences in visual perception, leading to inaccurate control of hemolysis levels and resulting in incomplete or excessive hemolysis, affecting the integrity of target cells and the stability of detection signals. This invention, through the fluorescence feature readability index prediction function of an intelligent dilution parameter adjuster model, objectively assesses the quality of sample fluorescence characteristics and automatically adjusts hemolysis parameters accordingly. This achieves precise control of hemolysis effects, eliminates the subjectivity and uncertainty of manual judgment, ensures standardized and consistent hemolysis processing, and thus improves the accuracy and reliability of flow cytometry detection.

[0054] Specifically, the principle of this invention is as follows: This invention solves the technical problem that parameters in the sample pretreatment process for flow cytometry detection are often based on human experience, leading to insufficient reproducibility and standardization of test results, especially for novice operators. Its fundamental principle lies in establishing a standardized operating procedure and an objective parameter optimization mechanism through intelligent algorithms and an automated control system. First, the intelligent ratio of fluorescently labeled antibody mixtures, through a preset antibody concentration ratio algorithm, can automatically calculate the optimal ratio of various fluorescently labeled antibodies based on sample characteristics, eliminating the subjectivity and uncertainty of human experience-based judgment and ensuring the standardization and consistency of the antibody labeling process. Second, the two-layer game optimization verification system adopts mathematical optimization theory. The upper-level game model maximizes label uniformity, while the lower-level game model optimizes the detection signal intensity. This parameter optimization method based on mathematical models is objective and reproducible, providing a unified parameter setting standard for different operators. Third, the intelligent dilution parameter regulator model, based on the Vision Transformer architecture, uses deep learning algorithms to intelligently analyze the fluorescence characteristics of samples. This allows for an objective assessment of the sample's fluorescence characteristic readability index and automatic adjustment of hemolysis parameters and suspension conditions, avoiding the subjectivity of traditional methods that rely on human experience to judge the degree of hemolysis. Fourth, the automated quality control and monitoring system provides objective evaluation standards for sample quality through standardized detection modules and evaluation algorithms, ensuring consistent quality assessment results for different operators. Finally, the entire system reduces manual operation steps and lowers the skill requirements through automated transmission and control mechanisms, enabling novice operators to obtain test results comparable to those of experienced technicians. Therefore, the technical solution of this invention, through intelligent algorithms and automated control systems, establishes standardized operating procedures and objective parameter optimization mechanisms, significantly improving the reproducibility and standardization of flow cytometry test results.

[0055] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0056] In this embodiment, the specific implementation of step S01 is the same as described above, and will not be repeated in detail here.

[0057] The specific implementation of step S02 involves using an intelligent ratio fluorescently labeled antibody mixture preparation algorithm to achieve the optimal concentration ratio of each antibody. The concentration calculation formula for the intelligent ratio fluorescently labeled antibody mixture is as follows:

[0058]

[0059] In the formula, C mix , i ρ represents the final concentration of the i-th antibody in the mixture, in μg / mL;target，i N represents the density of the i-th antigen on the surface of the target cell, expressed in units of cells / cell; cell This refers to the cell concentration per unit volume, expressed as cells / mL; M ab，i K represents the molecular weight of the i-th antibody, expressed in g / mol. sat，i The saturation bonding coefficient is determined by K. sat，i =1+K d，i / C antigen Calculations show that it is dimensionless; K d，i C is the dissociation constant between the i-th antibody and its corresponding antigen, expressed in mol / L; antigen This refers to the antigen concentration, expressed in mol / L; N avogadro is Avogadro's constant, with a value of 6.022 × 10⁻⁶. 23 pcs / mol; η binding，i Let be the binding efficiency coefficient of the i-th antibody, ranging from 0.6 to 0.9; 10 9 Unit conversion factor.

[0060] The parameter acquisition method is as follows: ρ target，i The antigen was obtained using flow cytometry quantitative analysis, including step 1: preparing a standardized cell suspension; and step 2: labeling with saturated concentration antibodies and calculating the number of antigens on the surface of each cell by comparing the fluorescence intensity with that of standard microspheres. cell The blood cell counter was used for determination, including step 1: appropriate dilution of the blood sample; step 2: counting the number of cells per unit volume using an automated blood cell analyzer. ab，i The antibody was obtained using mass spectrometry, including step 1: protein digestion of the antibody sample; and step 2: determination of the antibody molecular weight by matrix-assisted laser desorption / ionization time-of-flight mass spectrometry. d，i The determination was performed using surface plasmon resonance (SPR) technology, including step 1: immobilizing the antibody on the surface of the sensor chip; and step 2: obtaining the dissociation constant by fitting binding and dissociation kinetic curves of antigen solutions at different concentrations. antigen The antigen was obtained using an enzyme-linked immunosorbent assay (ELISA), which included the following steps: Step 1: preparing an antigen standard curve; Step 2: measuring the absorbance of the antigen in the sample; and Step 3: calculating the antigen concentration based on the standard curve.

[0061] The specific implementation of step S03 involves establishing a two-layer game-theoretic optimization verification system to evaluate the rationality of the antibody ratio and detection parameters. The objective function expression of the upper-layer game model is as follows:

[0062]

[0063] In the formula, F upper μ represents the objective function value of the upper-level game model. uniformTo label the uniformity coefficient, ranging from 0 to 1; C mix，i σ represents the concentration parameter of the i-th antibody, in μg / mL; n is the number of antibody types, set to 7; calib R is the deviation parameter of the fluorescence intensity calibration curve, in relative fluorescence units; ratio Add a dimensionless proportional parameter to the antibody; T react τ is the reaction temperature parameter, in °C. incub α is the reaction time parameter, in min; α1, α2, α3, α4, α5 are weighting coefficients, ranging from 0.3 to 0.5, 0.1 to 0.3, 0.1 to 0.2, 0.05 to 0.15, 0.05 to 0.15 respectively; ∈ is a small positive number to prevent the singularity of the logarithmic function, set to 0.001; λ is the exponential decay coefficient, set to 0.5; ω is the angular frequency parameter, set to π / 12; φ is the phase parameter, set to π / 4.

[0064] The objective function expression of the lower-level game model is as follows:

[0065]

[0066] In the formula, F lower S represents the objective function value of the lower-level game model. signal This is a signal intensity parameter, expressed in relative fluorescence units; G detector The detector gain parameter is dimensionless; SNR ratio P is a signal-to-noise ratio parameter, dimensionless; laser Excitation power parameter, unit is mW; A align This refers to the optical path alignment accuracy parameter, in μm; t sampling β1, β2, β3, β4, and β5 are weighting coefficients, ranging from 0.4 to 0.6, 0.2 to 0.3, 0.1 to 0.2, 0.05 to 0.15, and 0.05 to 0.15, respectively; δ is a small positive number to prevent the singularity of the logarithmic function, set to 0.001; ψ is the cosine function modulation coefficient, set to π / 100; and χ is the arctangent function amplification coefficient, set to 10.

[0067] The parameter acquisition method is as follows: μ uniform The coefficient of variation was calculated using the following steps: Step 1: measuring the fluorescence intensity of each cell in the same cell population; Step 2: calculating the ratio of the standard deviation to the mean of the fluorescence intensity to obtain the coefficient of variation, which was then expressed as μ. uniform =1-CV fluor Calculate the label uniformity coefficient (SNR). ratioThe signal-to-noise power ratio is calculated using the following steps: Step 1: Measure the root mean square value of the target signal; Step 2: Measure the root mean square value of the background noise; Step 3: Calculate the logarithmic form of the ratio.

[0068] The specific implementation of step S04 involves using a smart dilution parameter adjuster model to calculate the fluorescence feature readability index and dynamically adjust subsequent processing parameters. The formula for calculating the fluorescence feature readability index is as follows:

[0069] R index =w1·S separation +w2·C clarity +w3·(1-N noise );

[0070] In the formula, R index The fluorescence characteristic readability index ranges from 0 to 1; S separation C represents the signal separation of each detection channel, dimensionless; clarity N is the peak sharpness parameter, dimensionless; noise w1 represents the background noise level, which is dimensionless; w2, w3 are weighting coefficients with values ​​of 0.5, 0.3, and 0.2 respectively, satisfying w1 + w2 + w3 = 1.

[0071] The formula for calculating signal separation is:

[0072]

[0073] In the formula, m is the number of detection channels; μ k and μ j σ represents the mean fluorescence intensity of the k-th and j-th channels, respectively; k and σ j , respectively, are the standard deviations of fluorescence intensity for channels k and j.

[0074] The formula for calculating peak resolution is:

[0075]

[0076] In the formula, H peak，k W represents the peak height of the fluorescence peak in the k-th channel. base，k denoted as the baseline width of the fluorescence peak in the k-th channel.

[0077] The parameter acquisition method is as follows: μ k and σ k The method employed is statistical analysis, including step 1: acquiring the fluorescence intensity data sequence of the k-th channel; and step 2: calculating the arithmetic mean of the data sequence as μ. k Step 3: Calculate the standard deviation of the data sequence as σ. k H peak，k and Wbase，k The peak detection algorithm is used to obtain the peak height, which includes the following steps: Step 1: smoothing and filtering the fluorescence intensity distribution curve; Step 2: identifying the local maximum value as the peak height; Step 3: calculating the width at half the peak height as the baseline width.

[0078] The specific implementation methods for steps S05-S06 are the same as those described above, and will not be repeated in detail here.

[0079] The specific implementation of optional step S07 is to construct an automated quality control and monitoring system, wherein the calculation formula for the cell viability assessment index is as follows:

[0080]

[0081] In the formula, V cell Cell viability percentage; N live N represents the number of living cells. total This represents the total number of cells.

[0082] The formula for calculating the fluorescence signal stability evaluation index is as follows:

[0083]

[0084] In the formula, S stability σ is the fluorescence signal stability coefficient, ranging from 0 to 1; time The standard deviation of fluorescence intensity at different time points; μ time The value represents the average fluorescence intensity at different time points.

[0085] The parameter acquisition method is as follows: N live and N total The cells were counted using fluorescence microscopy, including step 1: double-labeling the cells using a live-dead cell staining reagent; step 2: counting the number of live cells and dead cells separately under a fluorescence microscope; and step 3: calculating the ratio of live cells to total cells.

[0086] The objective function F of the upper-level game model upper The principle is based on multi-objective optimization theory. By integrating multiple factors such as label uniformity, calibration accuracy, ratio optimization, temperature regulation, and time control into a unified objective function, the global optimization of antibody ratio parameters is achieved. The product term in this function... This reflects the linear relationship between label uniformity and antibody concentration, logarithmic term α2·ln(σ calib +∈) embodies the nonlinear penalty mechanism for calibration deviation, and the exponential term ɑ3·exp(-λ·R ratio This achieves adaptive adjustment of the proportioning parameters, with the sine term α4·sin(ω·T) react +φ) provides periodic optimization of the temperature parameter, square root term This ensures a gradual increase in the time parameter. Compared to traditional single-objective optimization methods, this function can simultaneously consider multiple mutually constraining optimization objectives, avoiding the trap of local optima and significantly improving the accuracy of antibody matching and the reproducibility of detection results.

[0087] The objective function F of the lower-level game model lower The principle is based on signal processing optimization theory. By integrating detection parameters such as signal strength, signal-to-noise ratio, excitation power, alignment accuracy, and sampling frequency into a comprehensive optimization objective, the overall performance of the detection system is improved. The product term β1·S in this function... signal ·G detector This ensures the coordinated optimization of signal strength and detector gain, for several terms β2·ln(SNR) ratio +δ) achieves nonlinear enhancement of the signal-to-noise ratio, and the cosine term β3·cos(ψ·P) laser The periodic modulation of the excitation power is provided by the arctangent term β4·arctan(χ·A) align This ensures asymptotic convergence of alignment accuracy, with the cube root term... This function achieves smooth adjustment of the sampling frequency. Compared with traditional independent parameter adjustment methods, it establishes the intrinsic relationship between various detection parameters, maximizes the overall detection performance through a collaborative optimization mechanism, and effectively reduces detection errors and system noise.

[0088] Formula for calculating the readability index of fluorescence features R index The principle is based on multidimensional feature fusion theory. It constructs a comprehensive evaluation system for fluorescence data quality by weighting and combining three key indicators: signal separation, peak sharpness, and noise level. The separation term quantifies the distinguishing ability between different fluorescence channels, the sharpness term reflects the sharpness characteristics of the fluorescence peak, and the noise term assesses the degree of influence of background interference. Compared to traditional single-indicator evaluation methods, this index can comprehensively reflect the identifiability and reliability of fluorescence data, providing a scientific basis for subsequent parameter adjustment and significantly improving the adaptability of sample processing and the accuracy of detection results.

[0089] Signal separation degree calculation formula S separation The principle is based on statistical distance measurement theory, which quantifies the separability of signals by calculating the statistical distance between signal distributions in different fluorescence channels. The formula uses standardized Euclidean distance; the numerator calculates the absolute difference between the means of different channels, while the denominator considers the variance contribution of each channel. Finally, the overall separation degree is obtained by averaging the values ​​of multiple channel pairs. Compared to traditional simple difference comparison methods, this formula fully considers the statistical characteristics of signal distribution, accurately reflects the separation quality of multi-channel fluorescence signals, and provides a reliable quantitative evaluation standard for signal optimization.

[0090] Cell viability assessment formula V cell The principle is based on fundamental theories of cell biology, reflecting the biological activity of a sample by statistically analyzing the ratio of live cells to total cells. This formula uses a simple ratio calculation to intuitively express the survival status of the cell population, providing a direct quantitative indicator for sample quality assessment. Compared to traditional qualitative observation methods, this formula achieves quantitative assessment of cell viability, providing a standardized basis for automated quality control and ensuring effective monitoring of cell activity during sample processing.

[0091] To better understand and implement this invention, the following is a specific application scenario of Example 2: The technical team first performed sample collection and pretreatment. Peripheral blood samples were collected from acute leukemia patients, with a collection volume of 800 μL, and placed in collection tubes containing EDTA-K2 anticoagulant at a concentration of 1.8 mg / mL. After collection, the samples were stored at room temperature (22°C) protected from light, with the shaking amplitude strictly controlled to be less than 5 mm, ensuring that subsequent processing steps were completed within 18 hours. The technical team effectively blocked the formation of calcium in the blood through a chelation mechanism. 2+ Ion-mediated coagulation cascades maintain the integrity of cell membrane structure and the stability of cell morphology.

[0092] The technical team then prepared a smart-proportioned fluorescently labeled antibody mixture. Using an automated pipetting system, 50 μL of anticoagulated whole blood sample was precisely aspirated, with pipetting accuracy controlled within ±0.8 μL. The smart-proportioned fluorescently labeled antibody mixture contained CD3 / CD4 / CD8 / CD16 / CD56 / CD19 / CD45 reagents. Specifically, CD3 antibody labeled the total T lymphocyte population, CD4 antibody labeled helper T lymphocytes, CD8 antibody labeled cytotoxic T lymphocytes, CD16 and CD56 antibodies labeled natural killer cells, CD19 antibody labeled B lymphocytes, and CD45 antibody served as a universal marker for leukocytes for gating analysis. The optimal concentration ratio calculated by the intelligent mixing algorithm is as follows: CD3 antibody 3.2 μg / mL, CD4 antibody 2.8 μg / mL, CD8 antibody 2.5 μg / mL, CD16 antibody 1.6 μg / mL, CD56 antibody 1.8 μg / mL, CD19 antibody 2.9 μg / mL, and CD45 antibody 3.5 μg / mL. The total volume of the mixture is 35 μL. The molecular weights of the antibodies are 150,000 g / mol, 145,000 g / mol, 140,000 g / mol, 155,000 g / mol, 138,000 g / mol, 142,000 g / mol, and 148,000 g / mol, respectively, with binding efficiency coefficients of 0.82, 0.78, 0.85, 0.75, 0.80, 0.83, and 0.87, respectively.

[0093] The technical team established a two-layer game-theoretic optimization and verification system to verify and optimize antibody ratio parameters. In the upper-layer game model, the label uniformity coefficient was calculated to be 0.92 using the coefficient of variation, the number of antibody types was set to 7, the fluorescence intensity calibration curve deviation parameter was measured to be 2.4 relative fluorescence units, the optimized antibody addition ratio parameter was 0.68, the reaction temperature parameter was set to 25℃, and the reaction time parameter was set to 15 minutes. The weight coefficients of the upper-layer game model were set to 0.42, 0.18, 0.15, 0.12, and 0.13, respectively. In the lower-layer game model, the signal intensity parameter was measured to be 1850 relative fluorescence units, the detector gain parameter was set to 2.5, the calculated signal-to-noise ratio parameter was 45.6, the excitation light power parameter was set to 25mW, the optical path alignment accuracy parameter was measured to be 0.8μm, and the sampling time interval parameter was set to 0.5ms. The weight coefficients of the lower-layer game model were set to 0.52, 0.24, 0.14, 0.08, and 0.02, respectively. The optimization results obtained by the two-layer game solving algorithm show that the current parameter configuration has reached the theoretical optimal state.

[0094] The technical team used an intelligent dilution parameter adjuster model to analyze the fluorescence characteristics of the incubated cell suspension. Seven detection channels were set, corresponding to seven fluorescently labeled antibodies. The mean fluorescence intensities of each channel were 1240, 980, 1150, 620, 750, 1080, and 1450 relative fluorescence units (RFUs), with standard deviations of 85, 72, 94, 48, 61, 79, and 98 RFUs, respectively. The signal separation was calculated to be 0.78, the peak sharpness to be 0.85, and the background noise level to be 0.12. The fluorescence feature readability index weighting coefficients were set to 0.5, 0.3, and 0.2, respectively, resulting in a calculated readability index of 0.826, higher than the set threshold of 0.7, indicating good identifiability of the fluorescence data. Figure 2 As shown, the signal distribution of each fluorescence channel exhibits good separation, with clear peak shapes and low background noise. Based on the prediction results, the system automatically adjusted the hemolysis parameters to a hemolysin dilution factor of 10-fold, an incubation time of 15 minutes, and a final suspension volume of 500 μL.

[0095] The technical team performed hemolysis and sample preparation. Using 10× hemolysin concentrate produced by Qingdao Ruiscare Biotechnology Co., Ltd., a 1× hemolysin working solution was prepared by diluting the concentrate with purified water at a volume ratio of 1:9. The osmotic pressure of the working solution was adjusted to 295 mOsm / kg, and the pH was controlled at 7.4. 450 μL of the working solution was precisely added to the stained blood sample flow cytometry tube using an automated dispensing device. The hemolysis reaction was incubated at room temperature (25°C) in the dark for 15 minutes. During this incubation, the ammonium chloride and potassium bicarbonate components in the hemolysin selectively disrupted the erythrocyte membrane structure while completely preserving the morphology and fluorescent labeling characteristics of nucleated cells. Figure 3As shown, the red blood cell removal rate in the hemolyzed sample reached 99.2%, the nucleated cell recovery rate remained at 94.8%, and the cell viability remained at 92.5%.

[0096] The technical team completed the automated transfer and flow cytometry analysis. After hemolysis, the sample was introduced into the flow cytometer via an automated transfer system. The sample temperature was maintained at 4°C during transfer, and the transfer time was controlled within 3 minutes. The final cell concentration of the sample was adjusted to 2.5 × 10⁻⁶. 6 The cell count / mL was [number] cells / mL, and the signal-to-background ratio for each fluorescence channel was greater than 15, meeting the technical requirements for flow cytometry detection. The detection process employed 488nm laser excitation, and the percentage data for each cell subpopulation were obtained through multi-parameter analysis.

[0097] The technical team established a comprehensive quality control and monitoring system to ensure the reliability of the test results. Cell counting results are shown in Table 1, displaying the count data and percentage information for each cell subpopulation. Fluorescence signal stability monitoring results are shown in Table 2, recording the changes in signal intensity of each fluorescence channel at different time points. Quality control parameters are shown in Table 3, including the evaluation results of key indicators such as cell viability, signal stability, and detection accuracy.

[0098] Table 1. Cell subset count results

[0099] Cell subsets Cell Count percentage <![CDATA[CD3 + T lymphocytes 1250 68.5％ <![CDATA[CD4 + Helper T cells 720 39.4％ <![CDATA[CD8 + Cytotoxic T cells 480 26.3％ <![CDATA[CD16 + CD56 + NK cells 180 9.9％ <![CDATA[CD19 + B lymphocytes 390 21.4％

[0100] Table 2 Fluorescence signal stability monitoring

[0101] Time point CD3 channel CD4 channel CD8 channel CD19 channel 0 minutes 1240 980 1150 1080 30 minutes 1235 976 1148 1077 60 minutes 1232 972 1145 1074 90 minutes 1228 968 1142 1071

[0102] Table 3 Quality Control Parameter Evaluation

[0103] Control parameters Measured values Qualification Standard Evaluation results Cell vitality 92.5％ >90％ qualified Signal stability coefficient 0.976 >0.95 qualified Detection accuracy 98.2％ >95％ qualified Reproducibility coefficient of variation 3.1％ <5％ qualified

[0104] Through automated quality control monitoring, all indicators met the preset standards, ensuring the accuracy and reliability of the test results. The fluorescence signal stability coefficient was calculated to be 0.976, indicating that the signal remained highly stable within 90 minutes. Cell viability assessment results showed that the proportion of live cells in the treated sample was 92.5%, meeting the cell viability requirements for flow cytometry detection. Detection accuracy was verified by comparison with standard reference materials, achieving an accuracy rate of 98.2%, with a reproducibility coefficient of variation controlled at 3.1%, significantly better than the 10-15% variation range of traditional manual methods.

[0105] Among the reagents mentioned above, the CD3 / CD4 / CD8 / CD16 / CD56 / CD19 / CD45 monoclonal antibodies can specifically bind to the target antigen. CD45 antibody is reactive to granulocytes, monocytes, CD3+ T lymphocytes, CD4+ T lymphocytes, CD8+ T lymphocytes, CD19+ B lymphocytes, and natural killer (NK) lymphocytes in blood samples; CD3 antibody is reactive only to CD3+ T lymphocytes, CD4+ T lymphocytes, and CD8+ T lymphocytes, and not to granulocytes, monocytes, CD19+ B lymphocytes, or natural killer (NK) lymphocytes; CD4 antibody is reactive only to monocytes, CD3+ T lymphocytes, and CD4+ T lymphocytes, and not to granulocytes, CD8+ T lymphocytes, CD19+ B lymphocytes, or natural killer (NK) lymphocytes; CD8 antibody is reactive only to CD3+ T lymphocytes, CD8+ T lymphocytes, and natural killer (NK) lymphocytes. The CD16 antibody is reactive only to granulocytes and natural killer (NK) lymphocytes, but not to monocytes, CD3+ T lymphocytes, CD4+ T lymphocytes, CD8+ T lymphocytes, or CD19+ B lymphocytes. The CD56 antibody is reactive only to natural killer (NK) lymphocytes, but not to granulocytes, monocytes, CD3+ T lymphocytes, CD4+ T lymphocytes, CD8+ T lymphocytes, or CD19+ B lymphocytes. The CD19 antibody is reactive only to CD19+ B lymphocytes, but not to granulocytes, monocytes, CD3+ T lymphocytes, CD4+ T lymphocytes, CD8+ T lymphocytes, or natural killer (NK) lymphocytes. It should be noted that when the bilirubin concentration is ≤80mg / dL, the hemoglobin concentration is ≤4000mg / dL, the rheumatoid factor is ≤2000IU / mL, and the triglyceride concentration is ≤120mmol / L in the test sample, it will not interfere with the test results of CD3+T lymphocytes%, CD3+CD4+T lymphocytes%, CD3+CD8+T lymphocytes%, CD3-CD19+B lymphocytes%, and CD3-CD16+CD56+NK lymphocytes.

[0106] This invention represents a significant technological advancement over traditional manual methods. Traditional methods rely on operator experience for parameter adjustment, which is prone to batch-to-batch variations and human error. This invention, however, establishes a parameter optimization mechanism guided by a mathematical model through a two-layer game-theoretic optimization verification system, eliminating the influence of subjective factors on the results. Traditional methods use fixed-ratio antibody mixtures, unable to adaptively adjust according to different sample characteristics. This invention, through an intelligent ratio algorithm, achieves personalized antibody concentration optimization, improving label specificity and signal quality. Traditional methods lack real-time quality monitoring, making it difficult to promptly detect and correct operational deviations. This invention establishes a multi-dimensional automated quality control and monitoring system, ensuring controllable quality throughout the entire processing through real-time parameter monitoring and anomaly warning mechanisms. The reproducibility of traditional methods largely depends on operator proficiency. This invention, through automated process control and intelligent parameter adjustment, significantly improves the standardization of the method and the consistency of test results.

[0107] It should be noted that the variables involved in this invention are explained in detail in Table 4.

[0108] Table 4. Variable Explanation Table

[0109]

[0110] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. An automated sample pretreatment method for flow cytometry detection, characterized in that, include: Biological samples to be tested were collected and placed in collection tubes containing EDTA-K2 anticoagulant. The sample volume was at least 200 μL. After collection, the samples were stored at room temperature without shaking and processed within 24 hours. 50 μL of anticoagulated whole blood sample was added to the bottom of a flow cytometry tube using an automated pipetting system. 35 μL of a smart-proportioned fluorescently labeled antibody mixture was added to the bottom of the flow cytometry tube, gently vortexed to mix, and then incubated at room temperature (25 ± 5 °C) in the dark for 15 minutes. A two-layer game-theoretic optimization and validation system was established, constructing an upper-layer game model aimed at maximizing label uniformity and a lower-layer game model aimed at optimizing detection signal intensity. The lower-level game model was used to optimize and verify the rationality of the antibody addition ratio and reaction condition parameters through a two-level game optimization system. A pre-trained intelligent dilution parameter regulator model was used to predict the fluorescence characteristic readability index of the incubated cell suspension. Based on the fluorescence characteristic readability index prediction results, the subsequent hemolysis parameters and suspension conditions were dynamically adjusted. 450 μL of 1× hemolysin working solution was added to the stained blood sample flow cytometry tube through an automated liquid addition device. After gentle vortexing and mixing, the sample was incubated at room temperature (25±5℃) in the dark for 15 minutes. After hemolysis, the sample was vortexed and mixed, and then prepared for flow cytometry analysis through an automated transfer system.

2. The automated sample pretreatment method for flow cytometry detection according to claim 1, characterized in that, The EDTA-K2 anticoagulant is specifically used to prevent blood clotting and maintain cell morphology integrity by chelating calcium in the blood. 2+ Ion blocking of coagulation cascade reaction, input includes biological sample to be tested and buffer environment, output is stable cell suspension state.

3. The automated sample pretreatment method for flow cytometry detection according to claim 2, characterized in that, The intelligent ratio fluorescently labeled antibody mixture specifically calculates the optimal ratio of various fluorescently labeled antibodies using a preset antibody concentration ratio algorithm. The intelligent ratio fluorescently labeled antibody mixture contains... CD3 / CD4 / CD8 / CD16 / CD56 / CD19 / CD45 reagents, inputs include anticoagulated whole blood samples and antibody concentration parameters, output is a fluorescently labeled antibody mixture with maximized labeling uniformity.

4. The automated sample pretreatment method for flow cytometry detection according to claim 3, characterized in that, The upper-level game model in the two-layer game optimization verification system specifically includes an objective function comprising a product term of the label uniformity coefficient and the antibody concentration parameter, a logarithmic function term of the fluorescence intensity calibration curve deviation parameter, an exponential function term of the antibody addition ratio parameter, a sine function term of the reaction temperature parameter, and a square root function term of the reaction time parameter. The constraints are the antibody concentration range and temperature stability requirements.

5. The automated sample pretreatment method for flow cytometry detection according to claim 4, characterized in that, The lower-level game model in the two-layer game optimization verification system specifically includes an objective function comprising the product of signal strength parameter and detector gain parameter, the natural logarithm function of signal-to-noise ratio parameter, the cosine function of excitation light power parameter, the arctangent function of optical path alignment accuracy parameter, and the cube root function of sampling time interval parameter. The constraints are the dynamic range of the detection device and the cell activity maintenance standard.

6. The automated sample pretreatment method for flow cytometry detection according to claim 5, characterized in that, The intelligent dilution parameter regulator model is specifically constructed based on the Vision Transformer architecture image classification network, which includes 16 encoder layers and 8 decoder units. Each layer contains a multi-head self-attention mechanism and a feedforward neural network module. The input includes the feature data of the incubated cell suspension, and the output is the fluorescence feature readability index prediction result.

7. The automated sample pretreatment method for flow cytometry detection according to claim 6, characterized in that, The fluorescence feature readability index is specifically calculated by analyzing the signal separation, peak sharpness, and background noise level of each detection channel in the fluorescence data matrix. The input includes the fluorescence data matrix and detection channel parameters, and the output is the fluorescence feature readability index value.

8. The automated sample pretreatment method for flow cytometry detection according to claim 7, characterized in that, The hemolysis parameters specifically include the hemolysin dilution factor, incubation time, and final suspension volume. The inputs include the fluorescence feature readability index prediction results and the current sample features, and the output is the optimized combination of hemolysis parameters.

9. The automated sample pretreatment method for flow cytometry detection according to claim 8, characterized in that, The 1× hemolysin working solution is specifically a hemolysin concentrate diluted with purified water at a ratio of 1:9 to form a 1× hemolysin working solution. It is an isotonic solution containing selective cell membrane disrupting components, used to selectively disrupt the red blood cell membrane structure while preserving the integrity of nucleated cells.

10. The automated sample pretreatment method for flow cytometry detection according to claim 9, characterized in that, Before the flow cytometer testing step, after the hemolyzed sample is vortexed and prepared for testing via an automated transfer system, the sample is stained and stored in a light-protected environment at 2-8°C. The sample must be tested within 24 hours.

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