Detection device and method for determining killing ability of in-vitro immune cells

Abstract

The invention relates to a detection device and method for determining the killing capacity of in-vitro immune cells. The device comprises an input module, a monitoring module, an image recognition module, a processor and an output module. According to the method, the killing ability of the immune cells is evaluated based on an artificial intelligence model, the artificial intelligence model is combined with real-time phenotypic signs and number changes of tumor targeted cells and immune cells to judge the killing ability of the immune cells, and specifically, the killing ability of the immune cells is judged according to the number changes of the tumor targeted cells. The evaluation method comprises the following steps: firstly, calculating the immune cell proliferation amount Mn, the tumor cell killing amount Qn, the cytokine release amount Fn and the immune cell toxicity grade Dn according to the weight in a processor, and then obtaining the evaluation result of each stage by matching with normal distribution data of the tumor targeted cell survival cycle tn and combining with the weight in the processor.

Description

Technical Field

[0001] The present application relates to the technical field, and in particular to a detection device and method for determining the killing ability of in vitro immune cells. Background Art

[0002] In vitro efficacy evaluation is an important step in testing the ability to cultivate in vitro immune cells. For therapeutic methods such as T cells and NK cells, the main indicators for evaluating their efficacy usually include cell killing activity, killing specificity, cell proliferation ability, cytokine secretion ability, etc. On the one hand, only in vitro cells with lethality can truly inhibit tumors in the human body; on the other hand, it is also necessary to consider the toxicity of cytokine release and the immunogenicity after entering the human system. In vitro cell killing ability usually refers to the killing effect of immune cells (such as T cells, NK cells, etc.) on tumor cells or virus-infected cells under in vitro experimental conditions. This ability can be evaluated through a variety of experimental methods, mainly including the following aspects:

[0003] 1. Cytotoxicity test: The killing ability of immune cells is evaluated by detecting their killing activity on target cells. Commonly used methods include radioactive isotope 51Cr release detection, fluorescent dye double staining flow cytometry, firefly luciferase method, etc. These methods use different labeling methods to detect the damage or death of target cells, thereby evaluating the killing effect of immune cells.

[0004] 2. Cytokine release: Activated immune cells release cytokines, such as interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), etc. These cytokines can affect the survival and function of target cells and are also an important indicator for evaluating cell killing ability.

[0005] 3. Cell apoptosis detection: The killing ability of immune cells is evaluated by detecting the apoptosis of target cells. Commonly used detection methods include Annexin V staining and terminal deoxyribonucleotidyl transferase (TUNEL) staining.

[0006] 4. Cell proliferation experiment: indirectly evaluate the killing ability of immune cells by detecting their own proliferation. Immune cells with strong proliferation ability usually have better killing effect.

[0007] 5. Gene editing technology: such as CAR-T cell technology, which uses genetic engineering technology to introduce specific chimeric antigen receptors (CARs) into T cells, enabling them to specifically recognize and kill tumor cells that express corresponding antigens.

[0008] 6. Phenotypic analysis of immune cells: Detect the molecular expression on the surface of immune cells, such as activation marker CD25 and exhaustion marker PD-1, by flow cytometry and other methods to evaluate the killing potential of immune cells.

[0009] In practical applications, these methods can be used alone or in combination to comprehensively evaluate the in vitro killing ability of immune cells. For example, the in vitro killing ability of CAR-T cells can be evaluated by co-culturing CAR-T cells with tumor cells and then detecting the apoptosis rate of target cells by flow cytometry; the killing ability of CAR-NK cells can also be verified by similar co-culture experiments.

[0010] The defect of the existing technology is that it is impossible to form a multi-dimensional, full-life cycle dynamic lethality detection. Summary of the invention

[0011] The purpose of this application is to overcome the shortcomings of the prior art and provide a detection device and method for measuring the killing ability of in vitro immune cells, so as to realize multi-dimensional and full-life cycle dynamic killing ability detection.

[0012] The purpose of this application is achieved through the following technical solutions:

[0013] The first aspect of the present application provides a detection device for determining the killing ability of in vitro immune cells, the device comprising:

[0014] At least one input module, used for inputting real-time experimental data;

[0015] A monitoring module for obtaining microscopic images of immune cell culture acting on tumor-targeted cells;

[0016] An image recognition module, processing the microscopic image to obtain clear phenotypic signs of immune cells and tumor-targeted cells;

[0017] A processor with a built-in artificial intelligence model determines the vital signs and survival number of the immune cells and tumor-targeted cells based on the phenotypic signs of the immune cells and tumor-targeted cells, and evaluates the killing ability of the immune cells at different stages based on the artificial intelligence model in combination with experimental data;

[0018] The output module is used to output the evaluation report of the killing ability of immune cells.

[0019] Furthermore, the input factors of the artificial intelligence model include the immune cell life cycle T, and the normal distribution data of the following at different stages within the immune cell life cycle T: immune cell proliferation amount Mn, survival number of tumor-targeted cells Qn, cytokine release amount Fn, immune cell toxicity level Dn, and tumor-targeted cell survival period tn, where n represents different stages;

[0020] The killing ability of immune cells is positively correlated with Mn, Qn, Fn, and Dn. The initial weights of Mn, Qn, Fn, and Dn are configured according to the types of immune cells and tumor-targeted cells;

[0021] The normal distribution data of the tumor-targeting cell survival period tn is used to characterize the coefficient Jn of the lethality in different time periods within the life cycle T of the immune cell, and the value thereof is (0, 1).

[0022] Furthermore, the artificial intelligence model continuously modifies the weights of Qn, Fn, and Dn based on input data and killing ability evaluation results, wherein the input data includes experimental data, phenotypic signs of immune cells and tumor-targeted cells, and the number of survivors.

[0023] The second aspect of the present application provides a detection method for determining the killing ability of in vitro immune cells, the method comprising:

[0024] S01: Different culture systems are set up according to the types of tumor-targeted cells to obtain the corresponding solid tumor-targeted cells;

[0025] S02: quantitatively input the immune cells to be tested into the culture system;

[0026] S03: Record the phenotypic signs and number of tumor-targeted cells and immune cells, number the tumor-targeted cells and immune cells through fluorescent labeling, and output experimental data;

[0027] S04: Regularly test, repeat the operation of step S03, and output the experimental data;

[0028] S05: Input the experimental data into the device as described above, and output an evaluation report on the killing ability of immune cells based on the device.

[0029] Furthermore, the culture density of the tumor-targeting cells is 0.3×10 6 cells / mL, 2.2×10 cells / mL for 100 mm culture dish 6 cells / mL.

[0030] Furthermore, the density of the immune cells is 0.6×10 5 cells / mL, 2.5×10 cells / mL for 100 mm culture dish 5 cells / mL.

[0031] Furthermore, the different culture systems include different single targeted tumor cells, and combinations of different tumor targeted cells.

[0032] Furthermore, the combination of different tumor-targeting cells has no more than 3 types of tumor-targeting cells.

[0033] Furthermore, among the different culture systems, each culture system is set into n groups according to the life stages of immune cells, that is, each stage corresponds to a group. When performing steps S03 and S04, the culture systems of the corresponding groups are treated by an elution method, which specifically includes:

[0034] For culture system A;

[0035] In the initial state, each group’s immune cells and tumor-targeting cells were fluorescently labeled;

[0036] In the first stage, the immune cells of the first group were eluted, and then the phenotypic signs and numbers of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated;

[0037] In the second stage, the immune cells of the second group were eluted, and the phenotypic signs and number of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated.

[0038] And so on, until the elution process of the nth group of immune cells is completed, and the phenotypic signs and quantity of tumor-targeted cells and immune cells are obtained.

[0039] Furthermore, the elution method adopts an acidic elution method, using a low pH glycine buffer to elute the immune cells to maintain the biological activity of the immune cells.

[0040] The present invention determines the killing ability of immune cells based on an artificial intelligence model combined with real-time phenotypic signs and quantity changes of tumor-targeted cells and immune cells. Specifically, changes in the number of tumor-targeted cells reflect the proliferation amount Mn of immune cells, the amount of tumor cells killed Qn, the cytokine release amount Fn, and the immune cell toxicity level Dn. Combined with the normal distribution data of the tumor-targeted cell survival period tn and the weights in the processor, the evaluation results of each stage can be obtained.

[0041] Taking period T as an example, the nth evaluation data in period T is characterized by function Ftn, and Ftn is obtained by time accumulation calculation using formula (1), namely:

[0042] Ftn=(Mn*Qn*Fn*Dn)*Jn (1)

[0043] The higher the peak value of the function Ftn is, the stronger the killing ability of the immune cells is during this period. When J1=0, Fn has no effect.

[0044] When J1=1, the killing power is the greatest, and J1 is the killing rate of immune cells to tumor target cells. In other words, the larger the J1, the greater the proportion of Fn.

[0045] Based on different tumor-targeting cells, the corresponding weights of immune cells are also different. For example, when immune cells kill tumor-targeting cells 1 and tumor-targeting cells 2, their corresponding immune cell proliferation amount Mn, tumor-targeting cell survival number Qn, cytokine release amount Fn, and immune cell toxicity level Dn are the same, but according to the weights, the lethality of each of the two tumor-targeting cells at different times can be obtained.

[0046] The Jn reflects the different periods of lethality, and the weights of Mn, Qn, Fn, and Dn can also be calculated through the different Jn values.

[0047] When immune cells kill tumor-targeted cells, they may kill multiple tumor-targeted cells. At this time, the evaluation formula for the above killing degree will change, but the evaluation function F becomes a multi-factor, and the above example is no longer applicable.

[0048] The present invention continuously modifies the artificial intelligence model in combination with input data to improve the fit.

[0049] The neural network includes a weight adjustment function so that the result is related to the input data, the weight is adjusted based on different inputs, and the accuracy of the artificial intelligence model is determined based on the quality of the fit.

[0050] The neural network includes a hidden layer for determining the influence of input data of different periods and cell types on the results, and maps the results with a sigmoid function as an output layer function to output the results.

[0051] The neural network uses an error back propagation algorithm to adjust weights so that input data of different periods and different cell types can be fitted with the minimum error.

[0052] Beneficial effects of the present invention: The present invention evaluates the killing ability of immune cells based on an artificial intelligence model. The artificial intelligence model combines the real-time tumor-targeted cells, the phenotypic signs of immune cells, and the changes in quantity to determine the killing ability of immune cells. Specifically, the changes in the number of tumor-targeted cells reflect the proliferation amount Mn of immune cells, the amount of tumor cells killed Qn, the amount of cytokine release Fn, and the level of immune cell toxicity Dn. Combined with the normal distribution data of the survival cycle tn of tumor-targeted cells, combined with the weights in the processor, the evaluation results of each stage can be obtained. In the evaluation results, the artificial intelligence model is continuously corrected in combination with experimental data to improve the fit.

[0053] The present invention uses a quantitative input method to evaluate the killing ability of immune cells, which can avoid the defects of strong randomness in traditional operations, realize standard quantitative analysis, and facilitate the evaluation of the immune function of immune cells; the present invention evaluates the killing ability of immune cells based on an artificial intelligence model, can achieve data acquisition, and can output corresponding results based on the input related data. The present invention uses automated control to detect tumor-targeted cells, and designs the experimental process to obtain quantitative data at different stages. In the test results of the present invention, the output is image analysis and numerical statistics, combined with the results of image recognition, and the table analyzed by each cell is output, which can provide researchers with more detailed evaluation results of the killing ability of immune cells.

[0054] The present invention uses a low-pH glycine buffer to elute the immune cells, so as to maintain the biological activity of the immune cells and improve the killing efficiency of the immune cells.

[0055] The present invention aims to cultivate different immune cells and evaluate the killing ability of immune cells from multiple sources to obtain more comprehensive data, improve the accuracy of the evaluation results, and provide a basis for accurately evaluating the quality of immune cell products. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a device in one embodiment of the present application;

[0057] Figure 2 This is a schematic diagram of a method flow of a certain embodiment of the present application;

[0058] Figure 3 This is a microscope observation image of immune cells and target cells at a certain stage in an embodiment of the present application. DETAILED DESCRIPTION

[0059] The technical solution of the present application is further described in detail below in conjunction with specific embodiments, but the protection scope of the present application is not limited to the following.

[0060] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0061] In the description of the present application, it should be understood that if the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. appear, the orientation or position relationship indicated by these terms is based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0062] In addition, if the terms "first" or "second" appear, these terms are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, if the term "plurality" appears, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0063] In this application, unless otherwise clearly specified and limited, if the terms "installed", "connected", "connected", "fixed" and the like appear, these terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integrated connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to the specific circumstances.

[0064] In the present application, unless otherwise clearly specified and limited, if there is a description that a first feature is "above" or "below" a second feature, etc., or similar descriptions appear, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "above" and "above" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. The first feature being "below", "below" and "below" the second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0065] It should be noted that if an element is referred to as being "fixed to" or "disposed on" another element, it may be directly on the other element or there may be a central element. If an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. If any, the terms "vertical", "horizontal", "upper", "lower", "left", "right" and similar expressions used in this application are for illustrative purposes only and do not represent the only implementation method.

[0066] Example 1

[0067] The artificial intelligence model described in the present invention is a computing model based on tensorflow in the deep learning framework implemented in C++.

[0068] like Figure 1 As shown, an embodiment of the present invention provides a detection device 1 for determining the killing ability of in vitro immune cells, and the device 1 comprises:

[0069] At least one input module 3, used to input real-time experimental data;

[0070] Monitoring module 2, used to obtain microscopic images of immune cell culture acting on tumor target cells;

[0071] Image recognition module 4, processes the microscopic image (image content such as Figure 3 to obtain clear phenotypic signs of immune cells and tumor-targeting cells;

[0072] The processor 5 has a built-in artificial intelligence model 6, which determines the vital signs and the number of survival of the immune cells and tumor-targeting cells based on the phenotypic signs of the immune cells and tumor-targeting cells, and evaluates the killing ability of the immune cells at different stages based on the artificial intelligence model 6 in combination with the experimental data;

[0073] The output module 7 is used to output an evaluation report on the killing ability of immune cells.

[0074] Specifically, the artificial intelligence model 6 includes:

[0075] Input layer, used to input real-time experimental data;

[0076] A feature extraction layer, used to obtain images of phenotypic signs of immune cells during their culture process;

[0077] The output layer is used to output the data processed by the artificial intelligence model 6;

[0078] The artificial intelligence model performs real-time monitoring, extraction, comparison, and analysis based on the feature extraction layer to obtain phenotypic signs of immune cells and tumor-targeted cells.

[0079] The input factors of the artificial intelligence model 6 include the life cycle T of immune cells, and different stages of the life cycle T of immune cells:

[0080] Normal distribution data of immune cell proliferation Mn, tumor-targeted cell survival number Qn, cytokine release Fn, and immune cell toxicity level Dn; the killing ability of immune cells is positively correlated with Mn, Qn, Fn, and Dn, and the initial weights of Mn, Qn, Fn, and Dn are configured according to the types of immune cells and tumor-targeted cells;

[0081] The normal distribution data of the tumor-targeting cell survival period tn is used to characterize the coefficient Jn of the lethality in different time periods within the life cycle T of the immune cell, and the value thereof is (0, 1).

[0082] The artificial intelligence model continuously modifies the weights of Qn, Fn, and Dn based on input data and killing ability evaluation results, where the input data includes experimental data, phenotypic signs of immune cells and tumor-targeted cells, and the number of survivors.

[0083] refer to Figure 2 As shown;

[0084] The present invention also provides a detection method for determining the killing ability of in vitro immune cells, which specifically comprises:

[0085] S01: Different culture systems are set up according to the types of tumor-targeted cells to obtain the corresponding solid tumor-targeted cells;

[0086] S02: quantitatively input the immune cells to be tested into the culture system;

[0087] S03: Record the phenotypic signs and number of tumor-targeted cells and immune cells, number the tumor-targeted cells and immune cells through fluorescent labeling, and output experimental data;

[0088] S04: Regularly test, repeat the operation of step S03, and output the experimental data;

[0089] S05: Input the experimental data into the device 1, and output an evaluation report on the killing ability of immune cells based on the device 1.

[0090] The culture density of the tumor-targeting cells is 0.3×10 6 cells / mL, 2.2×10 cells / mL for 100 mm culture dish 6 cells / mL.

[0091] The density of the immune cells is 0.6×10 5cells / mL, 2.5×10 cells / mL for 100 mm culture dish 5 cells / mL.

[0092] The different culture systems include different single targeted tumor cells and combinations of different tumor targeted cells.

[0093] The types of tumor-targeting cells in the combination of different tumor-targeting cells do not exceed 3.

[0094] In the different culture systems, each culture system is set to n groups according to the life stages of immune cells, that is, each stage corresponds to a group. When performing steps S03 and S04, the culture systems of the corresponding groups are treated by an elution method, which specifically includes:

[0095] For culture system A;

[0096] In the initial state, each group’s immune cells and tumor-targeting cells were fluorescently labeled;

[0097] In the first stage, the immune cells of the first group were eluted, and then the phenotypic signs and numbers of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated;

[0098] In the second stage, the immune cells of the second group were eluted, and the phenotypic signs and number of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated.

[0099] And so on, until the elution process of the nth group of immune cells is completed, and the phenotypic signs and quantity of tumor-targeted cells and immune cells are obtained.

[0100] The elution method adopts an acidic elution method and uses a low-pH glycine buffer to elute the immune cells to maintain the biological activity of the immune cells.

[0101] For the implementation of the detection of the killing ability of immune cells in vitro, fluorescent markers of the phenotypic signs of immune cells and immune cells and tumor-targeting cells can be collected for differentiation. In this embodiment, the immune cells used are T effector cells, and the tumor-targeting cells are liver cancer cells.

[0102] The liver cancer cells HL-7702 were digested, counted and mixed, and 3×10 6The cells / mL were inoculated in a 96-well plate at a density of 100 μL per well, and cultured overnight at 37°C in CO2. T effector cells were then added in the same way, and the final concentration of T cells was set to three concentration gradients: 800 cells / well, 400 cells / well, and 200 cells / well. The blank control group was added with the same amount of saline. After incubation for 24 hours, the non-adherent cells were washed away, and the fluorescent antibody of MDSC was added at a final concentration of 1 mg / mL per well, incubated at 4°C for 30 minutes, washed twice with PBS, and then 4% paraformaldehyde was added to fix the cells for 10 minutes; then 100 μL of Hoechst at a concentration of 25 μg / mL was added to each well, stained for 15 minutes, and the phenotypic signs of T cells were observed under a fluorescence microscope.

[0103] Among them, the detection time t is counted in hours as the segmentation of the killing ability detection of T effector cells (i.e. human immune cells), and each 24 hours is a stage. The killing effect of T effector cells is closely related to their own characteristics. Their proliferation amounts are M1, M2, and M3, the number of surviving liver cancer cells is Q1, Q2, and Q3 respectively, the cytokine release amounts are F1, F2, and F3 respectively, and the toxicity levels of T effector cells are D1, D2, and D3 respectively.

[0104] Then its comprehensive killing ability is M1*Q1*F1*D1+M2*Q2*F2*D2+M3*Q3*F3*D3, which is the comprehensive effect result of the killing ability of T effector cells in different time periods.

[0105] The above data are input into the artificial intelligence model through the input layer, and the corresponding weight factors are determined based on the output results, that is, the weight factors of the T effector cell life cycle T and its different stages of Mn, Qn, Fn, and Dn, and the corresponding number of T effector cells and liver cancer cells are Q1, Q2, Q3 and M1, M2, M3.

[0106] The comprehensive calculation result of its lethality is:

[0107] T=J1*M1*Q1*F1*D1+J2*M2*Q2*F2*D2+J3*M3*Q3*F3*D3,

[0108] Among them, J1+J2+J3=1, and it will be updated in real time as the experimental data increases.

[0109] For example, in the experiment, the number of T effector cells was 35, and their different stages were T1 cells, T2 cells, and T3 cells, with corresponding numbers of 15, 10, and 10, and the number of liver cancer cells were Q1, Q2, and Q3, respectively.

[0110] The killing capacity of T effector cells is:

[0111] J1*M1*Q1*F1*D1+J2*M2*Q2*F2*D2+J3*M3*Q3*F3*D3, where the life cycle of T1 cells is 24h, the life cycle of T2 cells is 38h, and the life cycle of T3 cells is 72h. The immune cell proliferation index Mn of immune cells in this process is M1, M2, and M3, respectively, and its weight is a0. The weight factors of the number of surviving immune cells Qn, Dn, and Fn are a1, a2, and a3, respectively. Finally, the killing ability of T effector cells T1 cells, T2 cells, and T3 cells at different stages is:

[0112] T1=J1*(M1*a0)*(Q1*a1)*(F1*a2)*(D1*a3)

[0113] T2=J2*(M2*a0)*(Q2*a1)*(F2*a2)*(D2*a3)

[0114] T2=J3*(M3*a0)*(Q3*a1)*(F3*a2)*(D3*a3)

[0115] Comprehensive lethality T=T1+T2+T3, and the weights of T1, T2, and T3 can also be set.

[0116] After going through different life cycles of T effector cells, the killing ability of T effector cells can be reflected in real time, so as to provide a reference for the later clinical application of T effector cells. By using monitoring modules, image recognition modules, processors, and output modules, the markers of T effector cells and liver cancer cells at different stages can be collected in real time to output an evaluation report on the killing ability of T effector cells in real time. The artificial intelligence model in the processor contains an evaluation algorithm for killing ability, which is continuously corrected and learned to improve the accuracy of the killing ability of T effector cells.

[0117] Example 2

[0118] This embodiment detects the killing ability of T effector cells to obtain the killing efficiency of T effector cells at different stages, and corrects the weight factor of the killing ability of T effector cells in the artificial intelligence model to more effectively reflect the accurate clinical status of T effector cells and more effectively evaluate the killing ability of T effector cells.

[0119] Experimental Materials:

[0120] Breast cancer cell line (MCF-7);

[0121] T cells (isolated and activated from the peripheral blood of healthy volunteers);

[0122] 35mm and 100mm culture dishes;

[0123] Low pH glycine buffer (for elution method);

[0124] Fluorescent labeling reagents;

[0125] Microscope and image acquisition system;

[0126] Artificial intelligence model (configured according to the characteristics of T cells and MCF-7 cells);

[0127] Experimental steps:

[0128] Prepare the training system:

[0129] MCF-7 cells were cultured at 0.3×10 6 cells / mL were inoculated in a 35 mm culture dish at a density of 2.2×10 6 The cells were inoculated at a density of 1.5 cells / mL in a 100 mm culture dish.

[0130] The culture dish was placed under appropriate culture conditions (37° C., 5% CO 2 ) for culture until the cells reached the desired density.

[0131] Invest in immune cells:

[0132] The activated T cells were cultured at 0.6×10 5 cells / mL density (35 mm culture dish) and 2.5×10 5 The density of cells / mL (100 mm culture dish) was quantitatively added into the culture system containing MCF-7 cells.

[0133] Initial Record:

[0134] MCF-7 cells and T cells were numbered using fluorescent labeling reagents, and their phenotypic signs and numbers were recorded.

[0135] Input the initial data into the input module of the detection device.

[0136] Regular testing:

[0137] At regular intervals (24 hours), microscopic images of T cells acting on MCF-7 cells during the culture process were obtained using a microscope.

[0138] The microscopic images are input into the monitoring module and processed by the image recognition module to obtain clear cell phenotype signs.

[0139] Repeat this step to record data at multiple time points.

[0140] Data Analysis:

[0141] All experimental data are input into the processor of the detection device. The built-in artificial intelligence model in the processor determines the vital signs and survival number of immune cells and tumor-targeted cells based on their phenotypic signs.

[0142] Combined with experimental data, the artificial intelligence model evaluates the killing ability of T cells at different stages and outputs an evaluation report.

[0143] Conclusion: This example successfully demonstrates the effectiveness and feasibility of the detection device and method proposed in this application in practical applications. Through multi-dimensional, full-life cycle dynamic detection, the killing ability of in vitro immune cells can be more accurately evaluated, providing strong support for the research and clinical application of immunotherapy.

[0144] Example 3

[0145] This embodiment also provides specific experimental data analysis.

[0146] Experimental purpose: To evaluate the in vitro killing ability of a certain T cell against specific tumor target cells (lung cancer cells).

[0147] Experimental conditions:

[0148] The experiment was performed using a 35 mm culture dish, and the seeding density of tumor-targeting cells was 0.3 × 10 6 cells / mL, and the seeding density of T cells was 0.6×10 5 cells / mL.

[0149] The life cycle T of immune cells was set to 72 hours and divided into three stages, each stage lasting 24 hours.

[0150] Immune cells were treated with acid elution using low pH glycine buffer.

[0151] Experimental steps and data recording:

[0152] S01: Preparation of training system

[0153] Lung cancer cells were placed in a 35 mm culture dish at a seeding density of 0.3 × 10 6 cells / mL.

[0154] S02: Invest in immune cells

[0155] T cells were added to the culture system at a seeding density of 0.6 × 10 5 cells / mL.

[0156] S03: Initial Log

[0157] Time: 0 hours

[0158] Fluorescent labeling: All lung cancer cells and T cells are numbered and labeled.

[0159] Phenotypic signs and quantities:

[0160] Total number of lung cancer cells: 300,000

[0161] Total T cells: 60,000

[0162] Microscopic images were recorded for subsequent analysis.

[0163] S04: Regular testing (Phase 1)

[0164] Time: 24 hours

[0165] Treatment: T cells in the first group (time 0-24 hours) were eluted, and the other groups remained unchanged.

[0166] Record:

[0167] Number of lung cancer cells alive: 250,000

[0168] T cell survival number: 55,000

[0169] Cytokine release (IFN-γ): 500pg / mL

[0170] Lung cancer cell apoptosis rate: 16.7%

[0171] Record microscopic images.

[0172] S04: Regular testing (second phase)

[0173] Time: 48 hours

[0174] Treatment: T cells in the second group (24-48 hours) were eluted, and the other groups remained unchanged.

[0175] Record:

[0176] Number of lung cancer cells alive: 180,000

[0177] T cell survival number: 50,000

[0178] Cytokine release (IFN-γ): 800pg / mL

[0179] Lung cancer cell apoptosis rate: 40%

[0180] Record microscopic images.

[0181] S04: Regular testing (Phase 3)

[0182] Time: 72 hours

[0183] Treatment: The T cells of the third group (48-72 hours) were washed and the final data were recorded.

[0184] Record:

[0185] Number of lung cancer cells alive: 100,000

[0186] T cell survival number: 45,000

[0187] Cytokine release (IFN-γ): 1200pg / mL

[0188] Lung cancer cell apoptosis rate: 66.7%

[0189] Record microscopic images.

[0190] S05: Data analysis and report output

[0191] Input all experimental data into the detection device.

[0192] Based on the built-in artificial intelligence model, the processor combines experimental data and the phenotypic signs of immune cells and tumor-targeted cells to evaluate the killing ability of T cells at different stages.

[0193] Output report, including:

[0194] The killing effect of T cells within 24 hours, 48 ​​hours, and 72 hours.

[0195] Changes in cytokine release.

[0196] Changes in the survival number and apoptosis rate of lung cancer cells.

[0197] Comprehensively evaluate the killing ability of T cells on lung cancer cells and provide a specific evaluation report.

[0198] The above is only a preferred embodiment of the present application. It should be understood that the present application is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge in the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present application, and shall be within the scope of protection of the claims attached to the present application.

Claims

1. A detection device for determining the killing ability of in vitro immune cells, characterized in that: The device includes: At least one input module, used for inputting real-time experimental data; A monitoring module for obtaining microscopic images of immune cell culture acting on tumor-targeted cells; An image recognition module, processing the microscopic image to obtain clear phenotypic signs of immune cells and tumor-targeted cells; A processor with a built-in artificial intelligence model determines the vital signs and survival number of the immune cells and tumor-targeted cells based on the phenotypic signs of the immune cells and tumor-targeted cells, and evaluates the killing ability of the immune cells at different stages based on the artificial intelligence model in combination with experimental data; The output module is used to output the evaluation report of the killing ability of immune cells.

2. A detection device for determining the killing ability of in vitro immune cells according to claim 1, characterized in that: The input factors of the artificial intelligence model include the life cycle T of immune cells, and the normal distribution data of immune cell proliferation Mn, survival number Qn of tumor-targeting cells, cytokine release Fn, immune cell toxicity level Dn, and tumor-targeting cell survival period tn at different stages within the life cycle T of immune cells, where n represents different stages; the killing ability of immune cells is positively correlated with Mn, Qn, Fn, and Dn, and the initial weights of Mn, Qn, Fn, and Dn are configured according to the types of immune cells and tumor-targeting cells; The normal distribution data of the tumor-targeting cell survival period tn is used to characterize the coefficient Jn of the lethality in different time periods within the life cycle T of the immune cell, and the value thereof is (0, 1).

3. A detection device for determining the killing ability of in vitro immune cells according to claim 2, characterized in that: The artificial intelligence model continuously modifies the weights of Qn, Fn, and Dn based on input data and killing ability evaluation results, where the input data includes experimental data, phenotypic signs of immune cells and tumor-targeted cells, and the number of survivors.

4. A detection method for determining the killing ability of in vitro immune cells, characterized in that: The method includes: S01: Different culture systems are set up according to the types of tumor-targeted cells to obtain the corresponding solid tumor-targeted cells; S02: quantitatively input the immune cells to be tested into the culture system; S03: Record the phenotypic signs and number of tumor-targeted cells and immune cells, number the tumor-targeted cells and immune cells through fluorescent labeling, and output experimental data; S04: Regularly test, repeat the operation of step S03, and output the experimental data; S05: Inputting the experimental data into the device as described in any one of claims 1 to 3, and outputting an evaluation report on the killing ability of immune cells based on the device.

5. A detection method for determining the killing ability of in vitro immune cells according to claim 4, characterized in that: The culture density of the tumor-targeting cells is 0.3×10 6 cells / mL, 2.2×10 cells / mL for 100 mm culture dish 6 cells / mL.

6. A detection method for determining the killing ability of in vitro immune cells according to claim 4, characterized in that: The density of the immune cells is 0.6×10 5 cells / mL, 2.5×10 cells / mL for 100 mm culture dish 5 cells / mL.

7. A detection method for determining the killing ability of in vitro immune cells according to claim 4, characterized in that: The different culture systems include different single targeted tumor cells and combinations of different tumor targeted cells.

8. A detection method for determining the killing ability of in vitro immune cells according to claim 7, characterized in that: The types of tumor-targeting cells in the combination of different tumor-targeting cells do not exceed 3.

9. A detection method for determining the killing ability of in vitro immune cells according to claim 4, characterized in that: In the different culture systems, each culture system is set to n groups according to the life stages of immune cells, that is, each stage corresponds to a group. When performing steps S03 and S04, the culture systems of the corresponding groups are treated by an elution method, which specifically includes: For culture system A; In the initial state, each group’s immune cells and tumor-targeting cells were fluorescently labeled; In the first stage, the immune cells of the first group were eluted, and then the phenotypic signs and numbers of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated; In the second stage, the immune cells of the second group were eluted, and the phenotypic signs and number of tumor-targeted cells and immune cells were recorded. The remaining groups were not treated. And so on, until the elution process of the nth group of immune cells is completed, and the phenotypic signs and quantity of tumor-targeted cells and immune cells are obtained.

10. A detection method for determining the killing ability of in vitro immune cells according to claim 9, characterized in that: The elution method adopts an acidic elution method and uses a low-pH glycine buffer to elute the immune cells to maintain the biological activity of the immune cells.

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