A method and system for monitoring immune cell culture data based on activation analysis

Abstract

The present invention relates to the field of immune cell culture technology, and in particular to an immune cell culture data monitoring method and system based on activation analysis. When the immune cell culture equipment monitors that the signal protein is located on the abnormal standard signal pathway for conduction, the real-time metabolic network is restored based on the interference degree of the abnormal standard signal pathway on the metabolic pathway, and the metabolic enzyme concentration is calculated and analyzed to generate a metabolic activity monitoring benchmark; if the real-time monitoring detects that the metabolic data of the immune cell culture has an unreasonable metabolic condition, the peak integral deviation parameter of the cytokine compensation is constructed based on the metabolic enzyme concentration deviation of the metabolic activity monitoring benchmark to perform injection compensation regulation on the cytokines of the cultured immune cell samples. The present invention can perform rapid and accurate activation analysis on the abnormal proliferation data, the signal pathway data causing the abnormal proliferation, and the metabolic data in the immune cell culture process, which helps to better optimize the activation performance of immune cells and improve the culture quality.

Description

Technical Field

[0001] The present invention relates to the technical field of immune cell culture, and in particular to an immune cell culture data monitoring method and system based on activation analysis. Background Art

[0002] As an emerging precision medicine method, immune cell therapy has shown broad application prospects in the fields of cancer, autoimmune diseases and infectious diseases. However, the immune cell culture process is complex, and its metabolism, signal pathway conduction, and cytokine compensation regulation directly affect cell proliferation, activation and functional status. Therefore, it is particularly important to monitor and accurately analyze the data of the immune cell culture process in real time.

[0003] Traditional cell culture monitoring mainly relies on manual sampling and laboratory observation and testing. Although it can provide certain data information, its operation is complicated, time-consuming and difficult to achieve real-time performance, which increases a lot of manpower cost output and makes it difficult to ensure the timeliness of manual monitoring. At the same time, this culture monitoring method may cause large deviations in data monitoring such as metabolism, signal pathway conduction and cytokine demand during cell proliferation due to improper sample handling or operational errors, making it difficult to fully reflect the dynamic changes of cells during the culture process. Therefore, the development of a real-time monitoring method for immune cell culture data based on activation analysis is of great significance for improving culture efficiency, ensuring cell quality and achieving standardized production. Summary of the invention

[0004] The present invention overcomes the deficiencies of the prior art and provides an immune cell culture data monitoring method and system based on activation analysis.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The first aspect of the present invention provides a method for monitoring immune cell culture data based on activation analysis, comprising the following steps:

[0007] S102: obtaining a specific excitation wavelength and a standard specific emission wavelength of the fluorescent marker when the immune cell sample meets the culture requirements, constructing a rare fluorescence intensity domain coordinate reference map of the fluorescent marker based on the specific excitation wavelength and the standard specific emission wavelength, and performing abnormal proliferation analysis on the immune cell culture through the rare fluorescence intensity domain coordinate reference map to obtain an abnormal proliferation area;

[0008] S104: When the immune cell culture device monitors the presence of an abnormal proliferation area in the culture medium, a signal transduction localization model of the signal protein is constructed to analyze whether the actual phosphorylation activation index of each standard signal pathway is in an abnormal range that causes an unreasonable cell density, thereby obtaining a signal pathway activation analysis result;

[0009] S106: When the immune cell culture device monitors that the signal protein is located in the abnormal standard signal pathway, the real-time metabolic network of the immune cell sample signal protein conduction activation is obtained according to the real-time metabolite distribution, and the real-time metabolic network is restored based on the interference degree of the abnormal standard signal pathway on the metabolic pathway, and the concentration of metabolic enzymes is calculated and analyzed to generate a metabolic activity monitoring benchmark;

[0010] S108: If real-time monitoring detects that the metabolic data of the immune cell culture has unreasonable metabolism, an ideal spike model and an actual spike model for cytokine compensation are constructed based on the metabolic enzyme concentration deviation of the metabolic activity monitoring benchmark, and the cytokine injection compensation regulation of the cultured immune cell sample is performed based on the spike integral deviation parameters generated by the difference between the actual spike model and the ideal spike model.

[0011] More specifically, the step S102 includes the following steps:

[0012] Acquire the culture requirements of the immune cell sample, and import the culture requirements into the decision terminal of the immune cell culture device for reading and parsing, so as to obtain the preset culture decision of the immune cell culture device for the immune cell sample that meets the culture requirements;

[0013] Extract the immune cell sample through the preset culture decision, inject it into the preset culture area of ​​the culture medium, and the type of fluorescent marker marked on the immune cell sample, and retrieve the specific excitation wavelength required for the fluorescent marker to be irradiated in the big data network based on the type of fluorescent marker;

[0014] Based on big data, a biological cell knowledge graph is obtained, the culture requirements and specific excitation wavelengths are identified by traversing the biological cell knowledge graph, and a standard specific emission wavelength generated by the migration of signal proteins in a preset culture area when the immune cell culture device uses a specific excitation wavelength to irradiate the fluorescent marker to meet the culture requirements;

[0015] The difference between the wavelength of the specific emission plate of the combined standard and the specific excitation wavelength is used to calculate the Stokes shift generated by the signal protein in the process of migration in the preset culture area under the premise of meeting the culture requirements, which is defined as the accurate Stokes shift, and the rare fluorescence intensity coordinate interval of the abnormal Stokes shift is set according to the accurate Stokes shift;

[0016] Constructing a fluorescence intensity domain base coordinate system of the fluorescent marker of the signal protein in a preset culture region, using the fluorescence intensity coordinate corresponding to the accurate Stokes shift amount excluded in the rare fluorescence intensity coordinate interval as a coloring criterion, performing fluorescence coloring rendering processing on the fluorescence intensity domain base coordinate system based on the coloring criterion, and generating a rare fluorescence intensity domain coordinate reference map of the fluorescent marker when the signal protein migrates in the preset culture region;

[0017] A dynamic distribution model of fluorescent markers is constructed based on multi-frame microscopic image data during the culture process of immune cell samples, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell samples.

[0018] More specifically, in the step S102, a dynamic distribution model of fluorescent markers is constructed based on multiple frames of microscopic image data during the culture process of the immune cell sample, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell sample, which specifically includes the following steps:

[0019] After the fluorescent markers are injected into the signal proteins of the immune cell samples and then placed in the immune cell culture device for culture, the proliferation status of the culture medium during the whole cycle of the immune cell culture device is observed and captured in real time by using a confocal microscope, and multi-frame microscopic image data of the immune cell samples proliferating in the preset culture area at a predetermined proliferation time sequence is obtained;

[0020] Constructing a state space model of immune cells proliferating in a preset culture area, obtaining a first-order differential equation describing the spatial state of the state space model at a given proliferation time sequence, solving the state variables of the first-order differential equation using multiple frames of microscopic image data as input items, fitting the state variables through the state space model, and generating a dynamic distribution model of fluorescent markers in the preset culture area of ​​fluorescent markers marked on the immune cell sample at a given proliferation time sequence;

[0021] Obtaining the prescribed proliferation characteristics of the immune cell sample when it reaches the culture requirements, obtaining the proliferation interval at which the immune cell sample produces the prescribed proliferation characteristics on the given proliferation time sequence through a fluorescent marker dynamic distribution model, and constructing a proliferation step matrix of the given proliferation time sequence according to the proliferation interval;

[0022] The fluorescent marker migration slices when the signal protein migrates in the preset culture area on the established proliferation time sequence are separated by the fluorescent marker dynamic change model, and the real-time fluorescence intensity coordinate value detected by each fluorescent marker migration slice is obtained, and it is determined whether each of the real-time fluorescence intensity coordinate values ​​can be queried in the rare fluorescence intensity domain coordinate reference map;

[0023] If the real-time fluorescence intensity coordinate value can be found in the rare fluorescence intensity domain coordinate reference map, it means that the characteristic fluorescence of the fluorescent marker on the signal protein has an abnormal fluorescence wavelength, and the real-time fluorescence intensity coordinate value is calibrated as an abnormal fluorescence intensity coordinate value;

[0024] The abnormal proliferation node corresponding to the abnormal fluorescence intensity coordinate value is located by the proliferation step matrix coordinates to obtain the starting proliferation node and the ending proliferation node of the abnormal proliferation node. The proliferation area where the abnormal proliferation node appears is planned in the preset culture area according to the starting proliferation node and the ending proliferation node to obtain the abnormal proliferation area of ​​the cultured immune cell sample.

[0025] More specifically, the step S104 includes the following steps:

[0026] When the immune cell culture device monitors abnormal proliferation areas of immune cells in the culture medium, the immune cell type information is obtained, the immune cell type information is identified through the biological cell knowledge graph, a standard signal pathway map of the immune cells is obtained, and a standard signal pathway model of the immune cells is constructed based on the standard signal pathway map;

[0027] Based on the real-time fluorescence intensity coordinate values ​​of the fluorescent marker migration slices in the abnormal proliferation area, the terminal distribution position of the signal protein is fitted in the standard signal pathway model to generate a signal transduction localization model in which the signal protein is located in the signal pathway of the immune cell;

[0028] Using a kinase activity assay to detect the phosphorylation level of tissue proteins in each standard signaling pathway in the solid immune cells corresponding to the signal transduction localization model, and obtaining an actual phosphorylation activation index for each standard signaling pathway;

[0029] Acquiring real-time image data of the abnormal proliferation area at the termination proliferation node, and performing feature extraction on the real-time image data to obtain real-time proliferation density features of immune cells in the abnormal proliferation area at the termination proliferation node;

[0030] Based on the culture requirements, a baseline proliferation density feature showing normal fluorescence intensity of the fluorescent marker in the abnormal proliferation area under the condition of stable proliferation of the signal protein is obtained, and a feature misalignment algorithm is introduced to calculate the feature misalignment amount generated by the real-time proliferation density feature and the baseline proliferation density feature to obtain a density feature misalignment drift function;

[0031] If the density characteristic misalignment drift function is greater than the preset density characteristic misalignment drift function, a fluorescent reporter gene tree is established based on the characteristic misalignment function, which is a fluorescent reporter missing gene tree, where the fluorescent intensity of the fluorescent marker shows normal immune cell proliferation density compared to the fluorescent intensity of the fluorescent marker showing abnormality under stable proliferation conditions. The tree is defined as a fluorescent reporter missing gene tree, and an activation node array of the fluorescent reporter missing gene tree is extracted;

[0032] Obtaining the abnormal activation efficiency of each activation node in the activation node array, and combining the abnormal phosphorylation activation index interval of the activation node array with the abnormal activation efficiency of each activation node;

[0033] If the actual phosphorylation activation index is not within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as a normal standard signal pathway; if the actual phosphorylation activation index is within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as an abnormal standard signal pathway, and the signal pathway activation analysis result is obtained.

[0034] More specifically, the step S106 includes the following steps:

[0035] When the immune cell culture device monitors that the signal protein of the immune cell sample in the culture medium is located on the abnormal standard signal pathway for conduction, the detection is performed to obtain the real-time metabolite distribution of the signal protein located on the abnormal standard signal pathway to dynamically regulate the metabolism of the immune cells during the culture of the immune cell sample in the abnormal proliferation area;

[0036] Draw a real-time metabolite distribution pattern diagram of immune cell culture in the abnormal proliferation area according to the real-time metabolite distribution, identify the real-time metabolite distribution pattern diagram through the biological cell knowledge map, and obtain a real-time metabolic network in which the signal protein of the immune cell sample is located on the abnormal standard signal pathway to conduct activation;

[0037] Divide the real-time metabolite distribution pattern map into N sub-pattern maps based on one or more abnormal standard signal pathways, and obtain the actual relative concentration of oxidative phosphorylation converted to aerobic glycolysis and the regional distribution gradient of each actual relative concentration in each sub-pattern map during metabolic reprogramming;

[0038] Based on the correlation cosine similarity between the actual relative concentration and the regional distribution gradient of each actual relative concentration, each abnormal standard signal pathway and each metabolic pathway in the real-time metabolic network, only metabolic pathways corresponding to correlation cosine similarities greater than preset correlation cosine similarities are extracted and marked as suspected metabolic error pathways;

[0039] The Warburg effect is introduced to analyze the degree of interference of abnormal standard signal pathways with the actual metabolic reprogramming of suspected metabolic error pathways, and the real-time metabolic network is restored based on the actual degree of interference with metabolic reprogramming to obtain the original metabolic network. The metabolic enzyme concentrations between the original metabolic network and the real-time metabolic network are calculated and analyzed to generate a metabolic activity monitoring benchmark.

[0040] More specifically, in the step S106, the Warburg effect is introduced to analyze the degree of interference of the abnormal standard signal pathway with the actual metabolic reprogramming of the suspected metabolic error pathway, so as to restore the real-time metabolic network based on the actual degree of interference of metabolic reprogramming to obtain the original metabolic network, calculate and analyze the concentration of metabolic enzymes between the original metabolic network and the real-time metabolic network, and generate a metabolic activity monitoring benchmark, which specifically includes the following steps:

[0041] The Warburg effect was introduced to extract the actual relative concentration and regional distribution gradient of oxidative phosphorylation converted to aerobic glycolysis corresponding to the suspected metabolic error pathway, and bundled them into a metabolic reprogramming error package. Based on the metabolic reprogramming error package, the interference amplitude of abnormal standard signal pathways on the suspected metabolic error pathway was analyzed and calculated in the Warburg effect to obtain the actual degree of metabolic reprogramming interference;

[0042] Taking the actual degree of metabolic reprogramming interference as a constraint, searching the real-time metabolic network based on the constraint to generate several candidate solutions for the conversion of oxidative phosphorylation to aerobic glycolysis, updating and iterating the real-time metabolic network according to the several candidate solutions, and obtaining the original metabolic network in which the signal protein of the immune cell is located on the abnormal standard signal pathway to conduct activation;

[0043] Obtaining the concentration of metabolic enzymes involved in the activation of the real-time metabolic network, which is defined as the first metabolic enzyme concentration; and obtaining the concentration of metabolic enzymes involved in the activation of the original metabolic network, which is defined as the second metabolic enzyme concentration;

[0044] If the concentration of the first metabolic enzyme is greater than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as reasonable metabolism. If the concentration of the first metabolic enzyme is less than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as unreasonable metabolism, and a metabolic activity monitoring benchmark is generated.

[0045] More specifically, the step S108 includes the following steps:

[0046] The metabolic activity monitoring benchmark is configured at a monitoring terminal of an immune cell culture device to monitor the metabolic data of the immune cell sample culture process in real time based on the metabolic activity monitoring benchmark;

[0047] If the monitoring terminal of the immune cell culture device detects that the metabolic data in the immune cell culture process has an unreasonable metabolic condition, the difference between the first metabolic enzyme concentration and the second metabolic enzyme concentration at the preset proliferation time sequence is calculated to obtain the metabolic enzyme concentration deviation at the preset proliferation time sequence;

[0048] Based on the culture requirements, the ideal metabolic activity index is obtained. The necessary cytokine amount when the immune cell metabolic network produces a unit metabolic enzyme concentration to reach the ideal metabolic activity index is obtained through the biological cell knowledge map, and the benchmark peak intensity is set according to the necessary cytokine amount;

[0049] Constructing a spike model framework, presetting a predetermined spike amplitude according to an ideal metabolic activity index, cumulatively fitting the reference spike intensity corresponding to the cytokine compensation amount into the spike model framework based on the metabolic enzyme concentration deviation, and obtaining the current spike amplitude of the spike model framework;

[0050] If the current spike amplitude is less than the predetermined spike amplitude, the cumulative fitting operation continues; if the current spike amplitude is greater than or equal to the predetermined spike amplitude, the cumulative fitting operation is stopped, and the accumulated spike model is output, which is defined as the ideal spike model;

[0051] Acquire the actual cytokine amount of immune cells in the abnormal proliferation area, construct a spike model of the current cytokine amount, define it as the actual spike model, calculate the model deviation block of the actual spike model compared with the ideal spike model, and obtain the spike integral deviation parameter of the model deviation block;

[0052] Based on the peak integral deviation parameters, injection compensation regulation of cytokines during the culture of immune cell samples was performed to increase the expression activity of immune cell metabolic enzymes.

[0053] A second aspect of the present invention provides an immune cell culture data monitoring system based on activation analysis, the immune cell culture data monitoring system comprising a memory and a processor, the memory storing an immune cell culture data monitoring method program based on activation analysis, and when the immune cell culture data monitoring method program is executed by the processor, any one of the immune cell culture data monitoring method steps is implemented.

[0054] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0055] When the immune cell culture equipment monitors the presence of abnormal proliferation areas in the culture medium, a signal transduction localization model of the signal protein is constructed to analyze whether the actual phosphorylation activation index of each standard signal pathway is in an abnormal range that leads to an unreasonable cell density, and the signal pathway activation analysis results are obtained; when the immune cell culture equipment monitors that the signal protein is located on an abnormal standard signal pathway for conduction, the real-time metabolic network of the signal protein conduction activation of the immune cell sample is obtained based on the real-time metabolite distribution, and the real-time metabolic network is restored based on the degree of interference of the abnormal standard signal pathway on the metabolic pathway, and the metabolic enzyme concentration is analyzed to generate a metabolic activity monitoring benchmark; if the real-time monitoring detects that the metabolic data of the immune cell culture has an unreasonable metabolic condition, an ideal spike model and an actual spike model for cytokine compensation are constructed based on the metabolic enzyme concentration deviation of the metabolic activity monitoring benchmark, and the cytokine injection compensation regulation of the cultured immune cell sample is performed based on the spike integral deviation parameter generated by the difference between the actual spike model and the ideal spike model. The present invention can perform rapid and accurate activation analysis on abnormal proliferation data, signal pathway data leading to abnormal proliferation, and metabolic data during the immune cell culture process, so as to perform reasonable culture data monitoring of immune cells based on the results of the activation analysis, which helps to better optimize the activation performance of immune cells and improve the culture quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0057] Figure 1 The process of an immune cell culture data monitoring method based on activation analysis is shown Figure 1 ;

[0058] Figure 2 The process of an immune cell culture data monitoring method based on activation analysis is shown Figure 2 ;

[0059] Figure 3 A system framework diagram of an immune cell culture data monitoring system based on activation analysis is shown. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0062] The first aspect of the present invention provides a method for monitoring immune cell culture data based on activation analysis, such as Figure 1 As shown, the following steps are included:

[0063] S102: obtaining a specific excitation wavelength and a standard specific emission wavelength of the fluorescent marker when the immune cell sample meets the culture requirements, constructing a rare fluorescence intensity domain coordinate reference map of the fluorescent marker based on the specific excitation wavelength and the standard specific emission wavelength, and performing abnormal proliferation analysis on the immune cell culture through the rare fluorescence intensity domain coordinate reference map to obtain an abnormal proliferation area;

[0064] S104: When the immune cell culture device monitors the presence of an abnormal proliferation area in the culture medium, a signal transduction localization model of the signal protein is constructed to analyze whether the actual phosphorylation activation index of each standard signal pathway is in an abnormal range that causes an unreasonable cell density, thereby obtaining a signal pathway activation analysis result;

[0065] S106: When the immune cell culture device monitors that the signal protein is located in the abnormal standard signal pathway, the real-time metabolic network of the immune cell sample signal protein conduction activation is obtained according to the real-time metabolite distribution, and the real-time metabolic network is restored based on the interference degree of the abnormal standard signal pathway on the metabolic pathway, and the concentration of metabolic enzymes is calculated and analyzed to generate a metabolic activity monitoring benchmark;

[0066] S108: If real-time monitoring detects that the metabolic data of the immune cell culture has unreasonable metabolism, an ideal spike model and an actual spike model for cytokine compensation are constructed based on the metabolic enzyme concentration deviation of the metabolic activity monitoring benchmark, and the cytokine injection compensation regulation of the cultured immune cell sample is performed based on the spike integral deviation parameters generated by the difference between the actual spike model and the ideal spike model.

[0067] More specifically, the step S102 includes the following steps:

[0068] Acquire the culture requirements of the immune cell sample, and import the culture requirements into the decision terminal of the immune cell culture device for reading and parsing, so as to obtain the preset culture decision of the immune cell culture device for the immune cell sample that meets the culture requirements;

[0069] Extract the immune cell sample through the preset culture decision, inject it into the preset culture area of ​​the culture medium, and the type of fluorescent marker marked on the immune cell sample, and retrieve the specific excitation wavelength required for the fluorescent marker to be irradiated in the big data network based on the type of fluorescent marker;

[0070] Based on big data, a biological cell knowledge graph is obtained, the culture requirements and specific excitation wavelengths are identified by traversing the biological cell knowledge graph, and a standard specific emission wavelength generated by the migration of signal proteins in a preset culture area when the immune cell culture device uses a specific excitation wavelength to irradiate the fluorescent marker to meet the culture requirements;

[0071] The difference between the wavelength of the specific emission plate of the combined standard and the specific excitation wavelength is used to calculate the Stokes shift generated by the signal protein in the process of migration in the preset culture area under the premise of meeting the culture requirements, which is defined as the accurate Stokes shift, and the rare fluorescence intensity coordinate interval of the abnormal Stokes shift is set according to the accurate Stokes shift;

[0072] A fluorescence intensity domain base coordinate system of the fluorescent marker of the signal protein in a preset culture region is constructed, and the fluorescence intensity coordinate corresponding to the accurate Stokes shift is excluded in the rare fluorescence intensity coordinate interval as a coloring criterion. Based on the coloring criterion, a bright coloring rendering process is performed on the fluorescence intensity domain base coordinate system to generate a rare fluorescence intensity domain coordinate reference map of the fluorescent marker when the signal protein migrates in the preset culture region;

[0073] A dynamic distribution model of fluorescent markers is constructed based on multi-frame microscopic image data during the culture process of immune cell samples, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell samples.

[0074] It should be noted that the existing culture of immune cells usually uses fluorescent markers to mark them, and specifically bind to target molecules (such as proteins, nucleic acids or other biological molecules) to observe and detect the activation performance of the immune cells during the culture process; however, the existing fluorescent labeling detection during immune cell proliferation culture still relies on a large amount of manual collaborative monitoring, observation, recording and analysis of the fluorescence wavelength and fluorescence intensity to determine the degree of activation. The manpower consumption is large, which greatly reduces the efficiency of immune cell culture, and there may be human intervention errors. It is difficult to ensure the accuracy of the monitored immune cell culture data, resulting in greater deviations in the subsequent immune cell culture optimization. Fluorescent markers can usually be labeled on signal proteins to observe their spatial migration distribution during the proliferation process so as to judge the abnormal proliferation caused by signal protein transmission based on fluorescence. In view of this, the present method first determines a specific excitation wavelength for the fluorescent marker specified for the immune cell according to the culture requirements and a standard specific emission wavelength that corresponds to the requirements. The specific excitation wavelength and the standard specific emission wavelength are a criterion for judging when the spatial migration distribution of the signal protein in the immune cell culture meets the requirements, and the unreasonable transmission of the signal protein can reflect the abnormal proliferation of the immune cells. Therefore, the present method sets a rare fluorescence intensity coordinate interval expressing the abnormal Stokes shift according to the accurate Stokes shift between the specific excitation wavelength and the standard specific emission wavelength; wherein the Stokes shift is a function of the excitation wavelength and the emission wavelength. The quantified value of the difference between the two; the rare fluorescence intensity coordinate interval is the unreasonable wavelength light displayed by the fluorescent marker when the regional proliferation is abnormal. When the fluorescence in the signal protein migration area shows the unreasonable wavelength light corresponding to the rare fluorescence intensity coordinate interval, it can be quickly determined that the immune cell proliferation in the area is abnormal; therefore, the method relies on this criterion to construct the fluorescence marker distribution during signal protein migration in the form of a fluorescence intensity domain coordinate system as a rare fluorescence intensity domain coordinate reference map of rare fluorescence intensity coordinates that only appear when the immune cell proliferation is abnormal, thereby providing a reliable and accurate judgment basis for subsequent abnormal immune cell proliferation, achieving a rapid determination of abnormal proliferation, greatly reducing the intervention error and manpower output cost of manual observation and analysis, and improving the monitoring efficiency and accuracy of abnormal immune cell culture proliferation.

[0075] More specifically, in the step S102, a dynamic distribution model of fluorescent markers is constructed based on multiple frames of microscopic image data during the culture process of the immune cell sample, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell sample, which specifically includes the following steps:

[0076] After the fluorescent markers are injected into the signal proteins of the immune cell samples and then placed in the immune cell culture device for culture, the proliferation status of the culture medium during the whole cycle of the immune cell culture device is observed and captured in real time by using a confocal microscope, and multi-frame microscopic image data of the immune cell samples proliferating in the preset culture area at a predetermined proliferation time sequence is obtained;

[0077] Constructing a state space model of immune cells proliferating in a preset culture area, obtaining a first-order differential equation describing the spatial state of the state space model at a given proliferation time sequence, solving the state variables of the first-order differential equation using multiple frames of microscopic image data as input items, fitting the state variables through the state space model, and generating a dynamic distribution model of fluorescent markers in the preset culture area of ​​fluorescent markers marked on the immune cell sample at a given proliferation time sequence;

[0078] Obtaining the prescribed proliferation characteristics of the immune cell sample when it reaches the culture requirements, obtaining the proliferation interval at which the immune cell sample produces the prescribed proliferation characteristics on the given proliferation time sequence through a fluorescent marker dynamic distribution model, and constructing a proliferation step matrix of the given proliferation time sequence according to the proliferation interval;

[0079] The fluorescent marker migration slices when the signal protein migrates in the preset culture area on the established proliferation time sequence are separated by the fluorescent marker dynamic change model, and the real-time fluorescence intensity coordinate value detected by each fluorescent marker migration slice is obtained, and it is determined whether each of the real-time fluorescence intensity coordinate values ​​can be queried in the rare fluorescence intensity domain coordinate reference map;

[0080] If the real-time fluorescence intensity coordinate value can be found in the rare fluorescence intensity domain coordinate reference map, it means that the characteristic fluorescence of the fluorescent marker on the signal protein has an abnormal fluorescence wavelength, and the real-time fluorescence intensity coordinate value is calibrated as an abnormal fluorescence intensity coordinate value;

[0081] The abnormal proliferation node corresponding to the abnormal fluorescence intensity coordinate value is located by the proliferation step matrix coordinates to obtain the starting proliferation node and the ending proliferation node of the abnormal proliferation node. The proliferation area where the abnormal proliferation node appears is planned in the preset culture area according to the starting proliferation node and the ending proliferation node to obtain the abnormal proliferation area of ​​the cultured immune cell sample.

[0082] It should be noted that, since the migration and distribution of signal proteins in the immune cell culture process is a time-series dynamic state, in order to ensure that the accurate brightness coordinate value of the fluorescent marker can be obtained, the method constructs a dynamic distribution model of fluorescent markers that describes the dynamic migration and distribution of fluorescent markers in a given proliferation time sequence by observing multiple frames of microscopic image data of immune cell fluorescent markers with a confocal microscope, thereby further improving the accuracy and reliability of abnormal monitoring of immune cell culture proliferation; if the real-time fluorescence intensity coordinate value cannot be queried in the rare fluorescence intensity domain coordinate reference map, it means that the fluorescence wavelength displayed in the cell proliferation area satisfies the accurate Stokes shift corresponding to the specific excitation wavelength and the standard specific emission wavelength, that is, the fluorescence wavelength is displayed normally, so the proliferation area corresponding to the fluorescent marker is a normal fluorescent marker; if the real-time fluorescence intensity coordinate value can be queried, it means that the fluorescence wavelength does not meet the culture requirements, so at this time, the characteristic fluorescence of the fluorescent marker on the signal protein has an abnormal fluorescence wavelength, that is, the proliferation area corresponding to the fluorescent marker is an abnormal fluorescent marker. For the proliferation and cultivation of immune cells, a standard proliferation reference is usually specified to determine whether it meets the cultivation requirements, that is, the proliferation characteristics are specified. Since the proliferation of immune cells follows a predetermined time sequence, if the immune cell culture meets the cultivation requirements, its signal protein will form a certain proliferation interval in the process of migration and distribution on the predetermined proliferation time sequence, and the prescribed proliferation characteristics will be generated in sequence, so that these proliferation intervals will constitute a proliferation step matrix, and the proliferation step matrix can provide an accurate and reliable timing planning basis for the area involved in the abnormal fluorescence intensity coordinate value, so that the definition of the abnormal proliferation area is more accurate, and the positioning precision of the abnormal proliferation range during the immune cell culture process can be ensured. Through this method, the coordinate analysis of the abnormal fluorescence wavelength can be performed according to the time-series dynamic migration distribution of the fluorescent marker, so that the area with abnormal proliferation during the immune cell culture can be accurately monitored, and the monitoring performance of abnormal immune cell culture can be improved.

[0083] More specifically, the step S104 includes the following steps:

[0084] When the immune cell culture device monitors abnormal proliferation areas of immune cells in the culture medium, the immune cell type information is obtained, the immune cell type information is identified through the biological cell knowledge graph, a standard signal pathway map of the immune cells is obtained, and a standard signal pathway model of the immune cells is constructed based on the standard signal pathway map;

[0085] Based on the real-time fluorescence intensity coordinate values ​​of the fluorescent marker migration slices in the abnormal proliferation area, the terminal distribution position of the signal protein is fitted in the standard signal pathway model to generate a signal transduction localization model in which the signal protein is located in the signal pathway of the immune cell;

[0086] Using a kinase activity assay to detect the phosphorylation level of tissue proteins in each standard signaling pathway in the solid immune cells corresponding to the signal transduction localization model, and obtaining an actual phosphorylation activation index for each standard signaling pathway;

[0087] Acquiring real-time image data of the abnormal proliferation area at the termination proliferation node, and performing feature extraction on the real-time image data to obtain real-time proliferation density features of immune cells in the abnormal proliferation area at the termination proliferation node;

[0088] Based on the culture requirements, a baseline proliferation density feature showing normal fluorescence intensity of the fluorescent marker in the abnormal proliferation area under the condition of stable proliferation of the signal protein is obtained, and a feature misalignment algorithm is introduced to calculate the feature misalignment amount generated by the real-time proliferation density feature and the baseline proliferation density feature to obtain a density feature misalignment drift function;

[0089] If the density characteristic misalignment drift function is greater than the preset density characteristic misalignment drift function, a fluorescent reporter gene tree is established based on the characteristic misalignment function, which is a fluorescent reporter missing gene tree, where the fluorescent intensity of the fluorescent marker shows normal immune cell proliferation density compared to the fluorescent intensity of the fluorescent marker showing abnormality under stable proliferation conditions. The tree is defined as a fluorescent reporter missing gene tree, and an activation node array of the fluorescent reporter missing gene tree is extracted;

[0090] Obtaining the abnormal activation efficiency of each activation node in the activation node array, and combining the abnormal phosphorylation activation index interval of the activation node array with the abnormal activation efficiency of each activation node;

[0091] If the actual phosphorylation activation index is not within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as a normal standard signal pathway; if the actual phosphorylation activation index is within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as an abnormal standard signal pathway, and the signal pathway activation analysis result is obtained.

[0092] It should be noted that the activation of immune cells depends on the conduction of intracellular signal pathways, such as PI3K / AKT, Ras / MAPK, etc. The expression level of the signal pathway can reflect the activation status of the intracellular signal pathway, and improper regulation of these signal pathways may lead to abnormal activation of signal proteins, and abnormal signal pathways may cause abnormal metabolic activity during subsequent immune cell culture, so it is crucial to monitor and activate abnormal signal pathways. In view of this, this method first constructs a signal transduction localization model that presents the final distribution form in the signal pathway within immune cells based on the fluorescent marker migration distribution of signal proteins, so as to restore the true state of signal transduction during immune cell culture to the maximum extent, and effectively improve the analysis accuracy of signal pathway activation. Since signal proteins regulate the activity of other proteins by adding or removing phosphate groups to achieve signal transmission, the phosphorylation activation index for each signal pathway transmission signal can measure the activation performance of its signal transmission, so the actual phosphorylation activation index of each standard signal pathway can be obtained by detecting solid cells through kinase activity assay. When the signal pathway of immune cell culture is improperly transmitted, resulting in abnormal proliferation, the proliferation density between cells may deviate greatly from the culture requirements, that is, there is a density feature dislocation phenomenon. The density feature dislocation drift function can be greater than the preset density feature dislocation drift function as a monitoring basis for monitoring the state of the signal pathway when the immune cell has abnormal proliferation. When density feature dislocation occurs, it may be caused by the fluorescence intensity of the fluorescent marker under the stable proliferation condition of the immune cell showing a normal proliferation density compared to the abnormality under the stable proliferation condition. The fluorescent genes of different pathways are missing. Therefore, this method obtains abnormal activation nodes of different pathways by constructing a fluorescent reporter gene tree, thereby further presetting an abnormal phosphorylation activation index interval that reflects abnormal activation of the signal pathway. Through this method, when an abnormal proliferation area is detected during the immune cell culture process, the activation performance of its signal pathway can be monitored and analyzed in a timely manner, thereby realizing efficient and accurate tracing and determination of different specific signal pathways that lead to the generation of abnormal proliferation areas, and improving the monitoring performance based on the reasonable degree of activation of the immune cell's own signal transmission.

[0093] It should be noted that if the actual phosphorylation activation index is not within the abnormal phosphorylation activation index range, it means that the activation performance of this signal pathway is normal when transmitting signal proteins, and it is not the main signal pathway that causes the formation of abnormal proliferation areas; if it is within the range, it means that the activation performance of this signal pathway is abnormal, and it is very likely to cause the formation of abnormal proliferation areas when transmitting signal proteins.

[0094] More specifically, the step S106 is as follows: Figure 2 As shown, the specific steps include:

[0095] S202: When the immune cell culture device monitors that the signal protein of the immune cell sample in the culture medium is located on the abnormal standard signal pathway for conduction, the real-time metabolite distribution of the signal protein located on the abnormal standard signal pathway for dynamically regulating the metabolism of the immune cells during the culture of the immune cell sample in the abnormal proliferation area is detected;

[0096] S204: drawing a real-time metabolite distribution pattern diagram of the immune cell culture in the abnormal proliferation area according to the real-time metabolite distribution, identifying the real-time metabolite distribution pattern diagram through the biological cell knowledge map, and obtaining a real-time metabolic network in which the signal protein of the immune cell sample is located on the abnormal standard signal pathway for conduction activation;

[0097] S206: dividing the real-time metabolite distribution pattern map into N sub-pattern maps based on one or more abnormal standard signal pathways, and obtaining the actual relative concentration of oxidative phosphorylation converted to aerobic glycolysis and the regional distribution gradient of each actual relative concentration in each sub-pattern map during the metabolic reprogramming process;

[0098] S208: calculating the correlation cosine similarity between each abnormal standard signal pathway and each metabolic pathway in the real-time metabolic network based on the actual relative concentration and the regional distribution gradient of each actual relative concentration, and only extracting the metabolic pathways corresponding to the correlation cosine similarity greater than the preset correlation cosine similarity, and marking them as suspected metabolic error pathways;

[0099] S210: The Warburg effect is introduced to analyze the degree of interference of abnormal standard signal pathways with the actual metabolic reprogramming of suspected metabolic error pathways, so as to restore the real-time metabolic network based on the actual degree of interference of metabolic reprogramming to obtain the original metabolic network, calculate and analyze the metabolic enzyme concentrations between the original metabolic network and the real-time metabolic network, and generate a metabolic activity monitoring benchmark.

[0100] It should be noted that when immune cells are activated, metabolic reprogramming from oxidative phosphorylation to aerobic glycolysis (also known as the Warburg effect) occurs to meet the energy and biosynthesis needs of immune cells, and abnormal regulation of signal transduction pathways may interfere with this process and affect the metabolic state of immune cells. Therefore, this method identifies and obtains the real-time metabolic network when the signal transduction of immune cell samples is activated based on the real-time metabolite distribution of immune cell signal proteins located on abnormal standard signal pathways that dynamically regulate immune cell metabolism in the abnormal proliferation area. Since improper signal pathway conduction affects the dynamic regulation of the metabolic network according to the state of the cell (such as differentiation, proliferation, and stress), the influence correlation of each abnormal standard signal pathway with each metabolic pathway in the real-time metabolic network can be calculated based on the actual relative concentration of oxidative phosphorylation converted to aerobic glycolysis during metabolic reprogramming and the regional distribution gradient of each actual relative concentration, that is, it is reflected by the correlation cosine similarity. If the correlation cosine similarity is greater than the preset correlation cosine similarity, it means that the abnormal standard signal pathway has a greater influence on the dynamic regulation of metabolic reprogramming of the metabolic pathway, so the pathway is marked as a suspected metabolic error pathway. Abnormal regulation of signal transduction pathways will interfere with the process of converting oxidative phosphorylation to aerobic glycolysis. Therefore, abnormal standard signal pathways have a certain degree of interference with the metabolic reprogramming of suspected metabolic error pathways. Therefore, the real-time metabolic network can be restored according to the degree of interference to restore it to a metabolic network state before it is not interfered with by abnormal standard signal pathways. The restored original metabolic network can be used as a criterion for judging whether the current real-time metabolic activity is reasonable or not, thereby constructing a monitoring benchmark for whether the metabolic activity of immune cells is abnormal or not. This method can accurately activate and analyze the impact of abnormal signal pathways on the metabolic network during the metabolic reprogramming of immune cells, so that the metabolic activity data of immune cell culture can be quickly monitored under abnormal signal pathways, improving the reliability and credibility of immune cell culture metabolic data, and avoiding the phenomenon of large activation analysis errors in metabolic activities.

[0101] More specifically, in the step S106, the Warburg effect is introduced to analyze the degree of interference of the abnormal standard signal pathway with the actual metabolic reprogramming of the suspected metabolic error pathway, so as to restore the real-time metabolic network based on the actual degree of interference of metabolic reprogramming to obtain the original metabolic network, calculate and analyze the concentration of metabolic enzymes between the original metabolic network and the real-time metabolic network, and generate a metabolic activity monitoring benchmark, which specifically includes the following steps:

[0102] The Warburg effect was introduced to extract the actual relative concentration and regional distribution gradient of oxidative phosphorylation converted to aerobic glycolysis corresponding to the suspected metabolic error pathway, and bundled them into a metabolic reprogramming error package. Based on the metabolic reprogramming error package, the interference amplitude of abnormal standard signal pathways on the suspected metabolic error pathway was analyzed and calculated in the Warburg effect to obtain the actual degree of metabolic reprogramming interference;

[0103] Taking the actual degree of metabolic reprogramming interference as a constraint, searching the real-time metabolic network based on the constraint to generate several candidate solutions for the conversion of oxidative phosphorylation to aerobic glycolysis, updating and iterating the real-time metabolic network according to the several candidate solutions, and obtaining the original metabolic network in which the signal protein of the immune cell is located on the abnormal standard signal pathway to conduct activation;

[0104] Obtaining the concentration of metabolic enzymes involved in the activation of the real-time metabolic network, which is defined as the first metabolic enzyme concentration; and obtaining the concentration of metabolic enzymes involved in the activation of the original metabolic network, which is defined as the second metabolic enzyme concentration;

[0105] If the concentration of the first metabolic enzyme is greater than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as reasonable metabolism. If the concentration of the first metabolic enzyme is less than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as unreasonable metabolism, and a metabolic activity monitoring benchmark is generated.

[0106] It should be noted that since abnormal signal pathways will cause certain interference to the metabolic reprogramming of suspected metabolic error pathways, that is, the conversion of oxidative phosphorylation to aerobic glycolysis will produce errors, this method uses the Warburg effect analysis to calculate the actual metabolic reprogramming interference degree of abnormal standard signal pathways on suspected metabolic error pathways; and the expression level of metabolic enzymes can reflect the degree of metabolic activity of immune cells. In other words, the increase in the concentration of metabolic enzymes may indicate that the metabolic activity of cells is enhanced. Therefore, it is necessary to restore the real-time metabolic network to the original metabolic network when it is not affected by the regulation of abnormal standard signal pathways to compare the actual metabolic enzyme concentrations generated by the real-time metabolic network, so that it can be timely known whether the metabolic activity of its immune cells is affected by the abnormal regulation of abnormal standard signal pathways. The actual degree of metabolic reprogramming interference is actually a process quantity of the original metabolic network changing into a real-time metabolic network after being affected by abnormal standard signal pathways. Therefore, this method uses the actual degree of metabolic reprogramming interference as a constraint condition to search for several candidate solutions for the conversion of oxidative phosphorylation to aerobic glycolysis generated by the real-time metabolic network, so as to continuously update and iterate to restore the real-time metabolic network. Among them, if the concentration of the first metabolic enzyme is greater than the concentration of the second metabolic enzyme, it means that the metabolic enzyme of the immune cells increased under the influence of the abnormal standard signal pathway, which means that the metabolic activity of the cells increased or maintained at a normal level; on the contrary, it means that the metabolic enzyme of the immune cells decreased under the influence of the abnormal standard signal pathway, which means that the metabolic activity of the cells decreased, and it is difficult to meet the high-quality cultivation of immune cells. This method can be used to activate and analyze the metabolic activity level of immune cells based on the influence of abnormal standard signal pathways on metabolic pathways, so as to further monitor and accurately analyze the metabolic activity data in the process of immune cell cultivation as a monitoring benchmark, timely discover abnormal metabolic activities of immune cell culture, and improve the quality of cell metabolism.

[0107] More specifically, the step S108 includes the following steps:

[0108] The metabolic activity monitoring benchmark is configured at a monitoring terminal of an immune cell culture device to monitor the metabolic data of the immune cell sample culture process in real time based on the metabolic activity monitoring benchmark;

[0109] If the monitoring terminal of the immune cell culture device detects that the metabolic data in the immune cell culture process has an unreasonable metabolic condition, the difference between the first metabolic enzyme concentration and the second metabolic enzyme concentration at the preset proliferation time sequence is calculated to obtain the metabolic enzyme concentration deviation at the preset proliferation time sequence;

[0110] Based on the culture requirements, the ideal metabolic activity index is obtained. The necessary cytokine amount when the immune cell metabolic network produces a unit metabolic enzyme concentration to reach the ideal metabolic activity index is obtained through the biological cell knowledge map, and the benchmark peak intensity is set according to the necessary cytokine amount;

[0111] Constructing a spike model framework, presetting a predetermined spike amplitude according to an ideal metabolic activity index, cumulatively fitting the reference spike intensity corresponding to the cytokine compensation amount into the spike model framework based on the metabolic enzyme concentration deviation, and obtaining the current spike amplitude of the spike model framework;

[0112] If the current spike amplitude is less than the predetermined spike amplitude, the cumulative fitting operation continues; if the current spike amplitude is greater than or equal to the predetermined spike amplitude, the cumulative fitting operation is stopped, and the accumulated spike model is output, which is defined as the ideal spike model;

[0113] Acquire the actual cytokine amount of immune cells in the abnormal proliferation area, construct a spike model of the current cytokine amount, define it as the actual spike model, calculate the model deviation block of the actual spike model compared with the ideal spike model, and obtain the spike integral deviation parameter of the model deviation block;

[0114] Based on the peak integral deviation parameters, injection compensation regulation of cytokines during the culture of immune cell samples was performed to increase the expression activity of immune cell metabolic enzymes.

[0115] It should be noted that cytokines are a class of small molecule proteins with powerful regulatory effects, which can play a key regulatory role in immune response and cell metabolism to promote the absorption and metabolism of nutrients in immune cell culture and repair metabolic abnormalities. However, when abnormal cell proliferation occurs, its own cytokine secretion may be difficult to optimize metabolic activity, so some cytokines can be injected from the outside to reasonably regulate it. In this regard, this method uses the ideal metabolic activity index as the standard to perform a cumulative fitting of the spike model for the total amount of cytokines required for the current cell metabolic activity to meet the culture requirements, so that an ideal spike model can be obtained, and then the actual spike model of the actual cytokine amount in the current cell metabolic activity process is cumulatively fitted. The model deviation formed by the two is the amount of cytokines that need to be compensated, that is, the peak integral deviation parameter. This method can more quickly accumulate and quantify the number of cytokine adjustments that need to be compensated for unreasonable metabolic activities during the immune cell culture process. Compared with traditional manual analysis and statistics, it can greatly improve the calculation efficiency, reduce unnecessary calculation steps, and avoid the calculation and statistical errors of artificial cytokine compensation. It can improve the accuracy and regulation performance of cytokine compensation for immune cell metabolic activities, and effectively improve the activation quality of immune cell culture.

[0116] In addition, the immune cell culture data monitoring method based on activation analysis also includes the following steps:

[0117] Obtaining target culture medium and culture regulation information of immune cell samples, and extracting abnormal activation criteria in the immune cell culture process according to the culture regulation information;

[0118] By constructing a simulation model of the target culture medium, the immune cell sample is simulated and cultured. During the simulation culture process, the residence area of ​​all nutrients in the culture medium is obtained, and the concentration parameters and accurate residence points of each nutrient in the residence area are determined;

[0119] Taking the residence area as the base of the distribution map, the concentration parameters of each nutrient are fitted and drawn within the base of the distribution map based on the accurate residence points to obtain the concentration distribution pattern map of various nutrients;

[0120] Acquire a culture log of the immune cell sample, and extract a historical hotspot distribution map of a series of abnormal cell division regions that meet the abnormal activation criteria when the immune cell sample generates a concentration distribution pattern map in the target culture medium through the culture log, to obtain a number of historical abnormal division hotspot distribution maps;

[0121] The maximum likelihood algorithm is introduced to divide the concentration distribution pattern map into K sub-pattern maps. Based on the concentration parameters and the accurate residence points, the probability of each hot spot in each historical abnormal splitting hot spot distribution map residing in each sub-pattern map is calculated in the maximum likelihood algorithm to obtain the abnormal residence probability set.

[0122] Preset future split nodes and build a prediction model, perform training learning and verification on the abnormal residence probability set through a dynamic prediction model, output the trained prediction model, use the trained prediction model to predict the split hotspots of future split nodes, and obtain a future abnormal split hotspot distribution map;

[0123] Obtain the hotspot density coefficient of each sub-pattern map in the future abnormal division hotspot distribution map. If the hotspot density coefficient is greater than the preset hotspot density coefficient, calibrate the sub-pattern map corresponding to the hotspot density coefficient as a division risk sub-pattern map to optimize and increase the monitoring strategy and monitoring intensity of the immune cell culture equipment for the division risk sub-pattern map.

[0124] It should be noted that the cultivation of immune cells requires a variety of different nutrients, and these nutrients are distributed in the immune cell culture medium. However, if the concentration of the nutrients is not distributed properly, it may lead to an unreasonable gradient distribution of the concentration of each nutrient. This may cause the immune cells to absorb nutrients of different concentration gradients during culture, resulting in excessive division or uneven division, and this phenomenon may be distributed in multiple areas in the culture medium, greatly reducing the culture quality of the immune cells and increasing the rate of immune cell division and apoptosis. In this regard, this method can obtain a series of hotspot distributions of abnormal division areas according to the nutrient concentration distribution that occurs during the current immune cell simulation culture. Since these hotspot distributions may be caused by abnormal concentration distribution, it is necessary to calculate the abnormal residence probability of each hotspot in each historical abnormal division hotspot distribution map that resides in each sub-pattern map. Then, these abnormal residence probabilities are trained to obtain a prediction model to predict the areas where abnormal cell division may occur in the future under the current nutrient concentration distribution conditions. This can increase and optimize the monitoring intensity and monitoring strategy of abnormal division areas that will occur in the future, thereby avoiding the probability of abnormal division under unreasonable concentration gradient distribution conditions, improving the division survival rate of immune cell culture, and improving the rationality of monitoring the culture process.

[0125] In addition, the immune cell culture data monitoring method based on activation analysis also includes the following steps:

[0126] Acquire multiple dissolved oxygen concentration monitoring values ​​of immune cells at preset time nodes when the immune cell culture device executes a preset culture decision, and calculate the dissolved oxygen concentration monitoring change rate by combining the multiple dissolved oxygen concentration monitoring values;

[0127] If the dissolved oxygen concentration monitoring change rate is greater than the preset dissolved oxygen concentration monitoring change rate, the model specification parameters of the immune cell culture equipment are obtained;

[0128] Based on the model specification parameters, several regulatory factors that can cause the immune cell culture equipment to have an increasing dissolved oxygen concentration in the immune cells are obtained from the big data, which are defined as the first type of regulatory factors; and several regulatory factors that can cause the immune cells to have a decreasing dissolved oxygen concentration are obtained, which are defined as the second type of regulatory factors;

[0129] The factor association method is introduced to calculate the correlation between each of the first-class control factors and each of the second-class control factors and the dissolved oxygen concentration monitoring change rate, and multiple factor correlations are obtained. Only the first-class control factors whose factor correlations are greater than the preset factor correlations are extracted, and defined as the first-class abnormal control factors; only the second-class control factors whose factor correlations are greater than the preset factor correlations are extracted, and defined as the second-class abnormal control factors;

[0130] Obtain the expected dissolved oxygen concentration value and the minimum survival critical index required to maintain the survival of immune cells during the culture process, identify the expected dissolved oxygen concentration value in the biological cell knowledge graph based on the minimum survival critical index, obtain the activation state change characteristics of the expected dissolved oxygen concentration value when the immune cells reach the minimum survival critical index, and simultaneously obtain the actual regulatory factor set of the rice dumpling production line equipment;

[0131] When the expected dissolved oxygen concentration under the activation state change characteristic changes gradually, it is determined whether the actual control factor set has at least one type of abnormal control factor. If so, monitoring optimization is performed on the control mechanism corresponding to the immune cell culture device in the type of abnormal control factor to obtain a first monitoring plan;

[0132] When the expected dissolved oxygen concentration under the activation state change characteristic shows a decreasing change, it is determined whether there is at least one Class II abnormal regulatory factor in the actual regulatory factor set. If so, monitoring optimization is performed on the regulatory mechanism corresponding to the immune cell culture equipment in the Class II abnormal regulatory factor to obtain a second monitoring plan.

[0133] It should be noted that the concentration of dissolved oxygen is crucial for immune cell culture. The suitability of dissolved oxygen concentration can directly affect the metabolism and growth of cells to ensure that the cells are in the best growth and functional state. However, when the immune cell culture equipment is improperly regulated, it may lead to large errors in the regulation of dissolved oxygen concentration, which makes the dissolved oxygen concentration always in an unsuitable state for immune cell culture, which is not conducive to the high-quality culture of immune cells. Therefore, this method can locate some abnormal regulatory factors that cause the immune cell culture equipment to fail to ensure that the immune cells are in the minimum survival critical index requirement to reach the expected dissolved oxygen concentration, and then monitor and optimize the regulatory mechanism that affects the dissolved oxygen regulation error of the immune cell culture equipment based on these abnormal regulatory factors, so as to eliminate the dissolved oxygen concentration regulation error of each regulatory mechanism in the immune cell culture equipment to the greatest extent, reduce the phenomenon that the dissolved oxygen concentration is not suitable for culturing immune cells, improve the culture and living environment of immune cells, ensure that the survival rate meets the requirements, and improve the activation performance and culture quality of immune cells.

[0134] The second aspect of the present invention provides an immune cell culture data monitoring system based on activation analysis, such as Figure 3 As shown, the immune cell culture data monitoring system includes a memory 31 and a processor 32. The memory 31 stores an immune cell culture data monitoring method program based on activation analysis. When the immune cell culture data monitoring method program is executed by the processor 32, any one of the immune cell culture data monitoring method steps is implemented.

[0135] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for monitoring immune cell culture data based on activation analysis, characterized in that: The following steps are involved: S102: obtaining a specific excitation wavelength and a standard specific emission wavelength of the fluorescent marker when the immune cell sample meets the culture requirements, constructing a rare fluorescence intensity domain coordinate reference map of the fluorescent marker based on the specific excitation wavelength and the standard specific emission wavelength, and performing abnormal proliferation analysis on the immune cell culture through the rare fluorescence intensity domain coordinate reference map to obtain an abnormal proliferation area; S104: When the immune cell culture device monitors the presence of an abnormal proliferation area in the culture medium, a signal transduction localization model of the signal protein is constructed to analyze whether the actual phosphorylation activation index of each standard signal pathway is in an abnormal range that causes an unreasonable cell density, thereby obtaining a signal pathway activation analysis result; S106: When the immune cell culture device monitors that the signal protein is located in the abnormal standard signal pathway, the real-time metabolic network of the immune cell sample signal protein conduction activation is obtained according to the real-time metabolite distribution, and the real-time metabolic network is restored based on the interference degree of the abnormal standard signal pathway on the metabolic pathway, and the concentration of metabolic enzymes is calculated and analyzed to generate a metabolic activity monitoring benchmark; S108: If real-time monitoring detects that the metabolic data of the immune cell culture has an unreasonable metabolic condition, an ideal spike model and an actual spike model for cytokine compensation are constructed based on the metabolic enzyme concentration deviation of the metabolic activity monitoring benchmark, and the injection compensation regulation of the cytokines of the cultured immune cell sample is performed in combination with the spike integral deviation parameter generated by the difference between the actual spike model and the ideal spike model; The step S108 of constructing an ideal spike model and an actual spike model for cytokine compensation specifically includes the following steps: Constructing a spike model framework, presetting a predetermined spike amplitude according to an ideal metabolic activity index, cumulatively fitting the reference spike intensity corresponding to the cytokine compensation amount into the spike model framework based on the metabolic enzyme concentration deviation, and obtaining the current spike amplitude of the spike model framework; If the current spike amplitude is less than the predetermined spike amplitude, the cumulative fitting operation continues; if the current spike amplitude is greater than or equal to the predetermined spike amplitude, the cumulative fitting operation is stopped, and the accumulated spike model is output, which is defined as the ideal spike model; The actual cytokine amount of immune cells in the abnormal proliferation area is obtained, and a spike model of the current cytokine amount is constructed, which is defined as the actual spike model.

2. The immune cell culture data monitoring method based on activation analysis according to claim 1, characterized in that: The step S102 specifically includes the following steps: Acquire the culture requirements of the immune cell sample, and import the culture requirements into the decision terminal of the immune cell culture device for reading and parsing, so as to obtain the preset culture decision of the immune cell culture device for the immune cell sample that meets the culture requirements; Extract the immune cell sample through the preset culture decision, inject it into the preset culture area of ​​the culture medium, and the type of fluorescent marker marked on the immune cell sample, and retrieve the specific excitation wavelength required for the fluorescent marker to be irradiated in the big data network based on the type of fluorescent marker; Based on big data, a biological cell knowledge graph is obtained, the culture requirements and specific excitation wavelengths are identified by traversing the biological cell knowledge graph, and a standard specific emission wavelength generated by the migration of signal proteins in a preset culture area when the immune cell culture device uses a specific excitation wavelength to irradiate the fluorescent marker to meet the culture requirements; The difference between the specific emission wavelength and the specific excitation wavelength of the combined standard is used to calculate the Stokes shift generated by the signal protein in the migration process in the preset culture area under the premise of meeting the culture requirements, which is defined as the accurate Stokes shift, and the rare fluorescence intensity coordinate interval of the abnormal Stokes shift is set according to the accurate Stokes shift; Constructing a fluorescence intensity domain base coordinate system of the fluorescent marker of the signal protein in a preset culture region, using the fluorescence intensity coordinate corresponding to the accurate Stokes shift amount excluded in the rare fluorescence intensity coordinate interval as a coloring criterion, performing fluorescence coloring rendering processing on the fluorescence intensity domain base coordinate system based on the coloring criterion, and generating a rare fluorescence intensity domain coordinate reference map of the fluorescent marker when the signal protein migrates in the preset culture region; A dynamic distribution model of fluorescent markers is constructed based on multi-frame microscopic image data during the culture process of immune cell samples, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell samples.

3. The method for monitoring immune cell culture data based on activation analysis according to claim 2, characterized in that: In the step S102, a dynamic distribution model of fluorescent markers is constructed based on the multi-frame microscopic image data during the culture process of the immune cell sample, and abnormal proliferation analysis is performed on the dynamic distribution model of fluorescent markers based on the rare fluorescence intensity domain coordinate reference map to obtain the abnormal proliferation area of ​​the cultured immune cell sample, which specifically includes the following steps: After the fluorescent markers are injected into the signal proteins of the immune cell samples and then placed in the immune cell culture device for culture, the proliferation status of the culture medium during the whole cycle of the immune cell culture device is observed and captured in real time by using a confocal microscope, and multi-frame microscopic image data of the immune cell samples proliferating in the preset culture area at a predetermined proliferation time sequence is obtained; Constructing a state space model of immune cells proliferating in a preset culture area, obtaining a first-order differential equation describing the spatial state of the state space model at a given proliferation time sequence, solving the state variables of the first-order differential equation using multiple frames of microscopic image data as input items, fitting the state variables through the state space model, and generating a dynamic distribution model of fluorescent markers in the preset culture area of ​​fluorescent markers marked on the immune cell sample at a given proliferation time sequence; Obtaining the prescribed proliferation characteristics of the immune cell sample when it reaches the culture requirements, obtaining the proliferation interval at which the immune cell sample produces the prescribed proliferation characteristics on the given proliferation time sequence through a fluorescent marker dynamic distribution model, and constructing a proliferation step matrix of the given proliferation time sequence according to the proliferation interval; The fluorescent marker migration slices when the signal protein migrates in the preset culture area on the established proliferation time sequence are separated by the fluorescent marker dynamic change model, and the real-time fluorescence intensity coordinate value detected by each fluorescent marker migration slice is obtained, and it is determined whether each of the real-time fluorescence intensity coordinate values ​​can be queried in the rare fluorescence intensity domain coordinate reference map; If the real-time fluorescence intensity coordinate value can be found in the rare fluorescence intensity domain coordinate reference map, it means that the characteristic fluorescence of the fluorescent marker on the signal protein has an abnormal fluorescence wavelength, and the real-time fluorescence intensity coordinate value is calibrated as an abnormal fluorescence intensity coordinate value; The abnormal proliferation node corresponding to the abnormal fluorescence intensity coordinate value is located by the proliferation step matrix coordinates to obtain the starting proliferation node and the ending proliferation node of the abnormal proliferation node. The proliferation area where the abnormal proliferation node appears is planned in the preset culture area according to the starting proliferation node and the ending proliferation node to obtain the abnormal proliferation area of ​​the cultured immune cell sample.

4. The method for monitoring immune cell culture data based on activation analysis according to claim 1, characterized in that: The step S104 specifically includes the following steps: When the immune cell culture device monitors abnormal proliferation areas of immune cells in the culture medium, the immune cell type information is obtained, the immune cell type information is identified through the biological cell knowledge graph, a standard signal pathway map of the immune cells is obtained, and a standard signal pathway model of the immune cells is constructed based on the standard signal pathway map; Based on the real-time fluorescence intensity coordinate values ​​of the fluorescent marker migration slices in the abnormal proliferation area, the terminal distribution position of the signal protein is fitted in the standard signal pathway model to generate a signal transduction localization model in which the signal protein is located in the signal pathway of the immune cell; Using a kinase activity assay to detect the phosphorylation level of tissue proteins in each standard signaling pathway in the solid immune cells corresponding to the signal transduction localization model, and obtaining an actual phosphorylation activation index for each standard signaling pathway; Acquiring real-time image data of the abnormal proliferation area at the termination proliferation node, and performing feature extraction on the real-time image data to obtain real-time proliferation density features of immune cells in the abnormal proliferation area at the termination proliferation node; Based on the culture requirements, a baseline proliferation density feature showing normal fluorescence intensity of the fluorescent marker in the abnormal proliferation area under the condition of stable proliferation of the signal protein is obtained, and a feature misalignment algorithm is introduced to calculate the feature misalignment amount generated by the real-time proliferation density feature and the baseline proliferation density feature to obtain a density feature misalignment drift function; If the density characteristic misalignment drift function is greater than the preset density characteristic misalignment drift function, a fluorescent reporter gene tree is established based on the characteristic misalignment function, which is a fluorescent reporter missing gene tree, where the fluorescent intensity of the fluorescent marker shows normal immune cell proliferation density compared to the fluorescent intensity of the fluorescent marker showing abnormality under stable proliferation conditions. The tree is defined as a fluorescent reporter missing gene tree, and an activation node array of the fluorescent reporter missing gene tree is extracted; Obtaining the abnormal activation efficiency of each activation node in the activation node array, and combining the abnormal phosphorylation activation index interval of the activation node array with the abnormal activation efficiency of each activation node; If the actual phosphorylation activation index is not within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as a normal standard signal pathway; if the actual phosphorylation activation index is within the abnormal phosphorylation activation index interval, the standard signal pathway corresponding to the actual phosphorylation activation index is marked as an abnormal standard signal pathway, and the signal pathway activation analysis result is obtained.

5. The method for monitoring immune cell culture data based on activation analysis according to claim 1, characterized in that: The step S106 specifically includes the following steps: When the immune cell culture device monitors that the signal protein of the immune cell sample in the culture medium is located on the abnormal standard signal pathway for conduction, the detection is performed to obtain the real-time metabolite distribution of the signal protein located on the abnormal standard signal pathway to dynamically regulate the metabolism of the immune cells during the culture of the immune cell sample in the abnormal proliferation area; Draw a real-time metabolite distribution pattern diagram of immune cell culture in the abnormal proliferation area according to the real-time metabolite distribution, identify the real-time metabolite distribution pattern diagram through the biological cell knowledge map, and obtain a real-time metabolic network in which the signal protein of the immune cell sample is located on the abnormal standard signal pathway to conduct activation; Divide the real-time metabolite distribution pattern map into N sub-pattern maps based on one or more abnormal standard signal pathways, and obtain the actual relative concentration of oxidative phosphorylation converted to aerobic glycolysis and the regional distribution gradient of each actual relative concentration in each sub-pattern map during metabolic reprogramming; Based on the correlation cosine similarity between the actual relative concentration and the regional distribution gradient of each actual relative concentration, each abnormal standard signal pathway and each metabolic pathway in the real-time metabolic network, only metabolic pathways corresponding to correlation cosine similarities greater than preset correlation cosine similarities are extracted and marked as suspected metabolic error pathways; The Warburg effect is introduced to analyze the degree of interference of abnormal standard signal pathways with the actual metabolic reprogramming of suspected metabolic error pathways, and the real-time metabolic network is restored based on the actual degree of interference with metabolic reprogramming to obtain the original metabolic network. The metabolic enzyme concentrations between the original metabolic network and the real-time metabolic network are calculated and analyzed to generate a metabolic activity monitoring benchmark.

6. The method for monitoring immune cell culture data based on activation analysis according to claim 5, characterized in that: In the step S106, the Warburg effect is introduced to analyze the degree of interference of abnormal standard signal pathways with actual metabolic reprogramming of suspected metabolic error pathways, so as to restore the real-time metabolic network based on the actual degree of interference of metabolic reprogramming to obtain the original metabolic network, calculate and analyze the concentration of metabolic enzymes between the original metabolic network and the real-time metabolic network, and generate a metabolic activity monitoring benchmark, which specifically includes the following steps: The Warburg effect was introduced to extract the actual relative concentration and regional distribution gradient of oxidative phosphorylation converted to aerobic glycolysis corresponding to the suspected metabolic error pathway, and bundled them into a metabolic reprogramming error package. Based on the metabolic reprogramming error package, the interference amplitude of abnormal standard signal pathways on the suspected metabolic error pathway was analyzed and calculated in the Warburg effect to obtain the actual degree of metabolic reprogramming interference; Taking the actual degree of metabolic reprogramming interference as a constraint, searching the real-time metabolic network based on the constraint to generate several candidate solutions for the conversion of oxidative phosphorylation to aerobic glycolysis, updating and iterating the real-time metabolic network according to the several candidate solutions, and obtaining the original metabolic network in which the signal protein of the immune cell is located on the abnormal standard signal pathway to conduct activation; Obtaining the concentration of metabolic enzymes involved in the activation of the real-time metabolic network, which is defined as the first metabolic enzyme concentration; and obtaining the concentration of metabolic enzymes involved in the activation of the original metabolic network, which is defined as the second metabolic enzyme concentration; If the concentration of the first metabolic enzyme is greater than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as reasonable metabolism. If the concentration of the first metabolic enzyme is less than the concentration of the second metabolic enzyme, the metabolic activity of the cultured immune cells in the abnormal proliferation area is marked as unreasonable metabolism, and a metabolic activity monitoring benchmark is generated.

7. The method for monitoring immune cell culture data based on activation analysis according to claim 1, characterized in that: The step S108 specifically includes the following steps: The metabolic activity monitoring benchmark is configured at a monitoring terminal of an immune cell culture device to monitor the metabolic data of the immune cell sample culture process in real time based on the metabolic activity monitoring benchmark; If the monitoring terminal of the immune cell culture device detects that the metabolic data in the immune cell culture process has an unreasonable metabolic condition, the difference between the first metabolic enzyme concentration and the second metabolic enzyme concentration at the preset proliferation time sequence is calculated to obtain the metabolic enzyme concentration deviation at the preset proliferation time sequence; Based on the culture requirements, the ideal metabolic activity index is obtained. The necessary cytokine amount when the immune cell metabolic network produces a unit metabolic enzyme concentration to reach the ideal metabolic activity index is obtained through the biological cell knowledge map, and the benchmark peak intensity is set according to the necessary cytokine amount; Calculating a model deviation block of the actual spike model compared to the ideal spike model, and obtaining a spike integral deviation parameter of the model deviation block; Based on the peak integral deviation parameters, injection compensation regulation of cytokines during the culture of immune cell samples was performed to increase the expression activity of immune cell metabolic enzymes.

8. An immune cell culture data monitoring system based on activation analysis, characterized in that: The immune cell culture data monitoring system includes a memory and a processor. The memory stores an immune cell culture data monitoring method program based on activation analysis. When the immune cell culture data monitoring method program is executed by the processor, the immune cell culture data monitoring method steps as described in any one of claims 1 to 7 are implemented.

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