Optogenetic-based adherent cell culture optimization method and system

Abstract

The invention belongs to the field of cell culture automation and intelligent regulation and control, and discloses an optogenetics-based adherent cell culture optimization method and system. The method comprises the following steps: inputting an adherent cell type and a culture target, and calling an adherent cell-optical genetic parameter joint database; performing targeted pretreatment on the target adherent cells; processing through a multi-parameter optimization algorithm hardware platform to generate an executable customized photostimulation scheme; performing accurate light stimulation on the positive adherent cells; comparing the obtained detection data with normal adherent cell parameters in a joint database, and evaluating the influence of a photostimulation scheme on the cells; updating the parameters of the multi-parameter optimization algorithm and the prediction model; and inputting the adherent cell type and the culture target to the updated prediction model, and outputting an optimal light stimulation scheme. The core problems of low photostimulation accuracy and poor targeting in the prior art are solved, and high-activity and high-consistency light-sensitive adherent cells are stably produced.

Description

An optimization method and system for adherent cell culture based on optogenetics Technical Field

[0001] This invention relates to the field of automated and intelligent control of cell culture, specifically to an optogenetic-based method and system for optimizing adherent cell culture, which is suitable for the precise culture, regulation, and parameter optimization of light-sensitive adherent cells, providing high-quality cell materials for biomedical research. Background Technology

[0002] Cell culture is one of the core supporting technologies for life science research and clinical medical development. Among them, adherent culture has become one of the most widely used methods in basic research and drug screening due to its simplicity, low cost, and ability to maintain cell function well. A typical photosensitive adherent cell culture process involves steps such as cell isolation and primary culture, transfection of photosensitive proteins, photostimulation activation, and functional detection.

[0003] However, existing adherent culture methods heavily rely on operator experience and have inherent defects in the following three key aspects, leading to low culture efficiency and unstable cell quality: 1. Insufficient precision of light stimulation: Existing light sources are mostly general-purpose, unable to achieve coordinated customization of "wavelength, driving voltage, driving current, luminescence mode, and temperature," making it difficult to adapt to the specific needs of different photosensitive adherent cells. This easily leads to insufficient or excessive activation of photosensitive proteins (such as CHR2), causing cell stress or functional damage; 2. Poor targeting: The lack of a specific photosensitive protein delivery system for adherent cells makes it impossible to accurately target and implant photosensitive proteins into target adherent cells, resulting in low light stimulation efficiency, numerous non-specific reactions, and poor consistency due to reliance on manual operation for positive cell screening; 3. Disconnect between monitoring and optimization: Only basic indicators such as cell viability and adhesion rate are monitored, without in-depth monitoring of cell electrophysiological signals (such as impedance and action potential), and there is a lack of a closed-loop optimization mechanism of "light stimulation parameters - cell response," making it impossible to dynamically adjust culture strategies. Relying on manual experience to adjust parameters leads to large batch-to-batch variations.

[0004] Furthermore, the correlation between light stimulation parameters and cell state is unclear during the transition from culture to subsequent functional assays, making it difficult to predict the impact of light stimulation on cell function and interfering with downstream experiments (such as patch-clamp recording). In summary, existing adherent culture methods suffer from low precision and poor targeting of light stimulation, as well as a lack of closed-loop optimization, making it difficult to stably and efficiently obtain high-quality, functionally complete, and consistently active light-sensitive adherent cells. This has become a major technical bottleneck for downstream applications such as optogenetics research. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method and system for adherent cell culture based on optogenetics, in order to solve the core problems in existing adherent cell culture technology, such as low precision of light stimulation, poor targeting, single monitoring dimensions, and lack of closed-loop optimization, which lead to low cell quality, large batch-to-batch differences, and difficulty in adapting to the needs of optogenetic research. This invention aims to achieve standardization, intelligence, and precision in adherent cell culture.

[0006] To achieve the above objectives, this invention provides an optogenetic-based method for optimizing adherent cell culture, comprising the following steps: Step S1, inputting the adherent cell type and culture objective, and calling the "Adherent Cell - The "Optogenetic Parameter Joint Database" provides targeted data support for precise culture. Step S2: Based on the cell type and culture target data from Step S1, targeted pretreatment is performed on adherent cells. Step S3: Based on the data from Step S1 and the positive cell characteristics from Step S2, physiological tolerance boundaries of the light source parameters are set, and a light stimulation scheme is generated using an algorithm. Step S4: The light source is activated to precisely stimulate the adherent cells while simultaneously maintaining environmental stability, and the stability of the light source parameters is ensured by circuitry. Step S5: While Step S4 is in progress, the basal state of the cells is monitored, and electrophysiological parameters are collected using electrodes. The data is then integrated. Step S6: The detection data obtained in Step S5 is compared with the standard parameters for normal adherent cells in the database, and the impact of the stimulation scheme on the cells is determined using cell biology theory to identify state deviations. Step S7: The detection data from Step S5 is categorized and stored in the joint database. The detection data is compared with the optimal data to identify state deviations and their causes, and the stimulation model is updated using an algorithm. Step S8: The trained "Light Source Parameter-" database is used to further refine the data. The "Adherent Cell Status" prediction model takes the adherent cell type and culture objective as input and directly outputs the optimal light stimulation scheme to guide the subsequent culture process and avoid repeated trial and error.

[0007] Preferably, in step S2, the targeted pretreatment unit purifies the non-toxic vector carrying the CHR2 photosensitive protein through the AAV vector preparation submodule, transfects the target adherent cells according to the preset multiplicity of infection (MOI), and selects positive cell populations through the fluorescence screening submodule to ensure photoresponsive targeting; the incubation time after transfection is set to 24-48h according to the cell type to ensure efficient expression of the photosensitive protein.

[0008] Preferably, in step S3, the light source parameter optimization unit uses an FPGA or STM32 chip as the control core, solves the objective function through a multi-parameter collaborative optimization algorithm, sets weight coefficients based on cell viability, photoresponse rate, and action potential amplitude, and generates a customized photostimulation scheme with light source wavelength, driving voltage, current, and temperature as constraint variables.

[0009] Preferably, in step S4, the light stimulation and culture control unit switches between point light source and array light source (single point aperture is about 10μm) through the Micro LED light source submodule, and the constant voltage / constant current drive submodule ensures that the light source parameter fluctuation is ≤±2%; the environmental control submodule maintains the temperature at 37℃±0.1℃, humidity at 95%±1%, and CO2 concentration at 5.0%±0.2% to ensure a stable culture environment.

[0010] Preferably, the photostimulation and culture control unit includes a self-developed Micro LED light source, a constant voltage / constant current drive circuit, an FPGA / STM32 control chip, temperature, humidity and CO2 concentration sensors, and a light-shielding device (including a narrow band filter).

[0011] Preferably, in step S5, the monitoring unit detects cell viability, adhesion rate, and purity through the basic state detection submodule (cell counter, fluorescence microscope); the electrophysiological monitoring submodule collects cell impedance, action potential amplitude, light stimulation response delay, and response rate through platinum-iridium alloy electrodes, and forms a complete data chain after data preprocessing (standard normalization).

[0012] Preferably, the monitoring unit includes a cell counter, a fluorescence microscope, a platinum-iridium alloy electrode, an electrophysiological signal acquisition instrument, and a data preprocessing module.

[0013] Preferably, step S7 includes the following steps: Step S71, storing the detection data in a joint database according to the categories of "basic parameters - electrophysiological parameters - photostimulation parameters"; Step S72, comparing the detection data with historical best data to identify state deviations and their causes (e.g., low photoresponse rate due to insufficient driving voltage); Step S73, adjusting the regression coefficients of the prediction model and the weights of the multi-parameter optimization algorithm using the detection data through an incremental update formula; Step S74, adjusting the photostimulation scheme optimization strategy based on the updated parameters; Step S75, applying the optimization strategy to subsequent culture to achieve closed-loop iteration.

[0014] This invention also provides an optogenetic-based adherent cell culture optimization system, comprising: an input and data unit, including an input module for receiving adherent cell types and culture targets, and a joint database of adherent cell-opogenetic parameters for storing basic culture parameters, optogenetic correlation parameters, and historical experimental data; a light source parameter optimization unit, including a multi-parameter optimization algorithm hardware platform (FPGA / STM32 control chip) and a light source parameter adjustment submodule, for generating customized light stimulation schemes; a light stimulation and culture control unit, including a Micro LED light source submodule, a driving submodule, and an environmental control submodule, for executing light stimulation and maintaining a stable culture environment; a monitoring unit, including a basic state detection submodule and an electrophysiological monitoring submodule, for collecting cell state and electrophysiological signals; a comparative analysis unit, including a data comparison submodule and an effect evaluation submodule, for evaluating the effect of light stimulation; a prediction unit, including a data preprocessing submodule and an algorithm model training submodule, for constructing a "light source parameter-cell state" prediction model; and a closed-loop feedback unit, including a data feedback submodule and a model update submodule, for feeding back data and updating the database and model parameters.

[0015] Preferably, the closed-loop feedback unit includes: a data classification and storage submodule for storing detection data by type; a deviation analysis submodule for identifying cultivation deviations and their causes; a model update submodule for adjusting algorithm and model parameters through incremental learning; and a strategy iteration submodule for applying optimized strategies to subsequent cultivation.

[0016] Therefore, the present invention adopts the above-mentioned method and system for optimizing adherent cell culture based on optogenetics, and the beneficial technical effects are as follows: (1) The present invention achieves precise delivery and positive screening of CHR2 protein through the targeted pretreatment unit, and generates a customized photostimulation scheme for suitable cells by combining the multi-parameter synergistic optimization of the light source parameter optimization unit; the photostimulation and culture control unit ensures that the parameter fluctuation is stable ≤±2%, effectively avoids abnormal activation of photosensitive protein, solves the core problem of low precision and poor targeting of photostimulation in the prior art, and stably produces highly active and highly consistent photosensitive adherent cells, which are especially suitable for optogenetic research.

[0017] (2) This invention integrates intelligent decision-making (light source optimization + prediction model), automated execution (light stimulation + environmental control), multi-dimensional monitoring (basal state + electrophysiological signal) and closed-loop feedback. By dynamically updating the model and algorithm through detection data, it gets rid of dependence on human experience, avoids repeated trial and error, shortens the optimization cycle by more than 30%, realizes the standardization and intelligence of the culture process, and reduces reagent and time costs.

[0018] (3) The optimization strategy of the present invention can be adapted to the needs of different types of cells such as neural adherent cells and light-sensitive epithelial cells, and generate personalized solutions. At the same time, it takes into account the needs of downstream functional detection (such as patch clamp) in the decision-making stage. By precisely controlling the light stimulation and culture environment parameters, it ensures the smooth transition of cells from culture to detection, avoids interference with electrophysiological signals, and provides reliable cell materials for downstream applications such as drug screening and disease mechanism research, significantly expanding the scope and value of technology applications. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 is the overall framework diagram of the optogenetic-based adherent cell culture optimization system; Figure 2 is the flowchart of the targeted preprocessing unit; Figure 3 is the flowchart of the light source parameter optimization unit; Figure 4 is the flowchart of the closed-loop feedback unit (including the model incremental update logic); Figure 5 is the flowchart of the prediction model training and update process. Detailed Implementation

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

[0022] Existing methods for culturing photosensitive adherent cells suffer from several key defects, including insufficient precision in light stimulation (general light sources cannot be used to customize parameters, easily leading to abnormal activation of photosensitive proteins), poor targeting (lack of specific delivery systems, resulting in poor consistency in positive screening), and a disconnect between monitoring and optimization (monitoring only basic indicators without a closed-loop optimization mechanism, relying on manual experience and causing large batch-to-batch variations). Furthermore, the relationship between light stimulation parameters and cell state is unclear during the transition from culture to subsequent functional testing, easily interfering with downstream experiments. Ultimately, it is difficult to stably and efficiently obtain high-quality photosensitive adherent cells with consistent activity and complete function, becoming a major technical bottleneck for downstream applications such as optogenetics research.

[0023] In view of this, according to an embodiment of the present invention, an embodiment of an optimization method for adherent cell culture based on optogenetics is provided.

[0024] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments. Unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by those skilled in the art.

[0025] Example 1 is shown in Figures 1-5. This example provides a method for optimizing adherent cell culture based on optogenetics, which can be used on servers, terminals, and mobile terminals, such as mobile phones and tablets. The method includes the following steps: Step S1, input the adherent cell type and culture target, and call the historical data in the adherent cell-optogenetic parameter joint database. The historical data includes basic culture parameters, optogenetic association parameters, and historical experimental data.

[0026] Step S2: Based on the input data of step S1, the photosensitive modification and positive screening of adherent cells are completed through the targeted pretreatment unit. Referring to Figure 2, the processing flow diagram of the targeted pretreatment unit is shown. The specific process is as follows: (1) AAV vector preparation: Purify the non-toxic AAV vector carrying CHR2 photosensitive protein to ensure that the vector purity is ≥98% and is adapted to the serum type requirements of the target adherent cells; (2) Precise transfection: Set the infection multiple (MOI=100) according to the cell type, transfect the AAV-CHR2 vector into the target adherent cells, control the transfection environment temperature at 37℃ and the incubation time at 24-48h to ensure efficient expression of photosensitive protein; (3) Positive screening: Detect the fluorescence intensity through a fluorescence microscope, set the screening threshold to ≥0.8, screen the positive cell population, ensure that the positive cell ratio is ≥85%, and output the pretreated positive adherent cells for subsequent result evaluation.

[0027] Step S3: Based on the preprocessing results of step S2 and the historical data in the database, a customized photostimulation scheme is generated through the light source parameter optimization unit. Referring to Figure 3, which is a schematic diagram of the processing flow of the light source parameter optimization unit, the specific process is as follows: (1) Set optimization goals and constraints: The optimization goals are cell viability ≥95%, photostimulation response rate ≥90%, and action potential amplitude ≥50mV. The constraints include wavelength 450-500nm, driving voltage 2.0-3.0V, driving current 10-50mA, and temperature 36-38℃; (2) Multi-parameter collaborative optimization: Based on the FPGA / STM32 control chip, a multi-parameter optimization algorithm is run to construct the objective function (weighted fusion of viability, response rate, and action potential index), and optimization is performed. The objective function formula is: In the formula This is a vector of light source parameters (wavelength, voltage, current, temperature). The effect rate of light stimulation; Cell viability; This is the action potential; , , These are the weighting coefficients.

[0028] (3) Light emission stimulation scheme: Determine the type of light source (point light source / array light source) and aperture size (approximately 10 μm). Analyze and output the results for cell indicator evaluation.

[0029] Step S4: The light stimulation scheme and basic culture strategy generated in step S3 are executed through the light stimulation and culture control unit. The specific process is as follows: (1) Light stimulation execution: Start the Micro LED light source, switch the light emission mode according to the optimized scheme, maintain the stability of the light source parameters through the constant voltage / constant current drive submodule, and shield harmful wavelengths with the light shielding device (including 470nm narrow band filter); (2) Environmental stability control: Adjust the culture environment in real time, maintain the temperature at 37℃±0.1℃, humidity at 95%±1%, and CO2 concentration at 5.0%±0.2%, and ensure the stability of the culture microenvironment; Add reagents: Simultaneously execute the basic culture reagent ratio scheme, weigh the digestive enzyme (such as pancreatic enzyme concentration 0.21%) through a high-precision electronic balance, add FBS (concentration 10.5%) quantitatively through a peristaltic pump, mix evenly with magnetic stirring for 5 minutes, and control the pH value at 7.2-7.4. The reagent concentration is standardized by standard normalization treatment to ensure parameter standardization. The normalization formula is: In the formula The original data, Uncharacteristic historical values The characteristic is the historical standard deviation. -6 (To avoid a denominator of 0) This is the normalized data.

[0030] Step S5: Perform full-dimensional detection of cell status during the culture process using the monitoring unit. The specific process is as follows: (1) Basic status detection: Use a cell counter and fluorescence microscope to detect cell viability, adhesion rate, and purity, ensuring data acquisition accuracy ≤ ±1%. The cell viability calculation formula is: The formula for calculating the photostimulation response rate is: In the formula, The number of living cells. The number of cells responding to light stimulation. To detect the total number of cells; (2) Electrophysiological signal detection: Cell impedance, action potential amplitude, light stimulation response delay and response rate were collected by platinum-iridium alloy electrodes. The formula for calculating the action potential amplitude is: In the formula, This represents the peak value of the action potential. This is the resting potential. After data collection, outliers were removed using the mean-standard deviation screening rule (formula: x is determined to be an outlier. The mean of the data. The standard deviation is used to form a standardized detection data chain after standard normalization; (3) Output detection report: classify and organize the basic state data and electrophysiological data, and clarify whether each indicator meets the culture target.

[0031] Step S6: Evaluate the cultivation effect through comparative analysis unit. The specific process is as follows: (1) Data comparison: Compare the detection data from step S5 with the historical best data and preset cultivation targets in the joint database, and identify the differences through the index deviation formula: In the formula The deviation of the j-th indicator, For the detection value, (2) Effect evaluation: If the core indicators (survival rate, response rate, action potential) meet the target requirements, the culture is deemed qualified; if there is a deviation, identify the cause of the deviation (e.g., low light response rate is due to insufficient driving voltage).

[0032] Step S7: Model updates and strategy iterations are achieved through a closed-loop feedback unit, forming a closed-loop optimization. Referring to Figure 4, this specifically includes the following steps: Step S71: The detection data from Step S5 are categorized into "basic parameters - electrophysiological parameters - light stimulation parameters" and stored in the adherent cell-opgenetic parameter joint database; Step S72: The detection data is compared with historical data and culture targets using the deviation formula. Identify the state deviation and its causes (such as low adhesion rate due to temperature and humidity fluctuations); Step S73: Use the detection data to adjust the regression coefficients of the prediction model and the weights of the multi-parameter optimization algorithm through the incremental update formula to optimize model performance. The process of training and updating the prediction model can be seen in Figure 5. The incremental update formula is: In the formula, , These are the regression coefficients before and after the update, respectively. For learning rate, This is the actual detection value. These are the model's predicted values. Step S74: Based on the updated model parameters, adjust the light source parameter optimization strategy (e.g., appropriately increase the driving voltage, adjust the illumination duration); Step S75: Apply the optimized strategy to the subsequent cell culture process to achieve closed-loop iterative optimization.

[0033] The technical process in this embodiment is as follows: After inputting the cell type and culture requirements, the system retrieves data from the cell characteristic-light response parameter database; cells are transduced using the REDMAP photosensitizing system carried by the AAV vector, and positive cell populations are screened by fluorescence; culture medium ratios and light-environment control strategies are generated through deep learning and multi-objective optimization algorithms, clarifying parameters such as red / far-red light timing and cytokine concentration; the reagent preparation and dynamic light control modules work together to implement chemical supply and light regulation to maintain microenvironment homeostasis; the verification module collects indicators such as cell activity, and the feedback unit feeds back data to drive model iteration.

[0034] The invention will be further illustrated below with specific examples: S101, Information entry and data retrieval; The cell type is entered as CHR2-expressing neural adherent cells (number CELL-CHR2-NEU-002), and the culture target is 48-hour viability ≥95%, light response rate ≥90%, and action potential amplitude ≥50mV; 50 sets of relevant historical data from the adherent cell-optogenetic parameter joint database are retrieved to obtain the basic parameter ranges: trypsin concentration 0.20%-0.22%, FBS concentration 10%-11%, light wavelength 468-472nm, and driving voltage 2.3-2.7V. Numerical parameters (such as trypsin concentration) in the historical data are normalized using Min-Max, and categorical data (such as cell type) are encoded using one-hot encoding. In the formula, For the target cell type, ensure that the data is compatible with subsequent algorithm calculations.

[0035] S102, Targeted Pretreatment and Parameter Optimization; AAV vector preparation (purity 98.6%) → transfection (MOI=100, incubation 36h) → fluorescence screening, obtaining a positive cell rate of 89.2%; The objective function was solved using the light source parameter optimization unit: The output light stimulation scheme includes: wavelength 470nm, driving voltage 2.5V, driving current 32mA, temperature 37.0℃, and an array light source (aperture 10). ).

[0036] S103, Light Stimulation and Culture Execution; Light Stimulation Unit Startup: Loading a 470nm narrowband filter, constant current drive (fluctuation 1.2%), array light source irradiation; Environmental Control: Maintaining temperature 37.0℃, CO2 5.0%, humidity 95%; (normalized) Add 10.5% FBS using a peristaltic pump, stir and mix, then adjust the pH to 7.3.

[0037] S104, Effect detection and closed-loop optimization; Detection after 48 hours: Cell viability Photoresponsivity Action potential amplitude ( The adhesion rate was 95.7%, and all core indicators met the standards. Data feedback: The data was stored in the joint database and updated incrementally using the formula. ( The weights of the prediction model were adjusted (for the regression coefficients of voltage on response rate); iterative optimization: the updated model was applied to the next batch of culture, and the light response rate was further improved to 93.8%, with a batch consistency error of ≤2%.

[0038] Example 2: An optogenetic-based adherent cell culture optimization system, comprising: an input and data unit, including an input module for receiving adherent cell types (e.g., CHR2 neural adherent cells, light-sensitive HeLa cells) and culture objectives (e.g., viability, light response rate, action potential requirements), and an adherent cell-opogenetic parameter joint database for storing basic culture parameters (digestive enzyme concentration, serum ratio, etc.), optogenetic correlation parameters (light-sensitive protein type, vector characteristics, light source parameter range), and historical experimental data ("light source parameter-cell response" correlation data); a targeted pretreatment unit, connected to the input and data unit, including an AAV vector preparation submodule, a CHR2 protein transfection submodule, and a positive cell screening submodule, used to complete the light-sensitive modification of adherent cells, achieving precise delivery of light-sensitive proteins and positive cell screening; and a light source parameter optimization unit, connected to the input and data unit and the targeted pretreatment unit, including a multi-parameter optimization algorithm hardware platform (FPGA / STM32 control chip) and a light source parameter adjustment submodule, used to optimize the objective function based on the culture objectives through multi-parameter optimization. A customized photostimulation protocol is generated based on historical data. The photostimulation and culture control unit, connected to the light source parameter optimization unit, includes a Micro LED light source submodule, a constant voltage / constant current drive submodule, a light-shielding device (including a narrow-band filter), and an environmental control submodule (temperature, humidity, and CO2 concentration sensors) to execute the photostimulation protocol and maintain a stable culture environment. A monitoring unit, connected to the photostimulation and culture control unit, includes a basic state detection submodule (cell counter, fluorescence microscope) and an electrophysiological monitoring submodule (platinum-iridium alloy electrodes, electrophysiological signal acquisition instrument) to monitor the viability using the viability formula. Response rate formula Action potential amplitude formula It detects basal cellular state and electrophysiological function indicators, and processes the data through outlier removal and standardization. The comparative analysis unit, connected to the monitoring unit and input / data unit, includes a data comparison submodule and an effect evaluation submodule, used to evaluate data using a deviation formula. The system compares test data with historical data and training objectives to evaluate training effectiveness and identify causes of deviations. The prediction unit, connected to the comparative analysis unit and input / data unit, includes a data preprocessing submodule (outlier removal and normalization) and an algorithm model training submodule, used for training based on a linear regression model. A "light source parameter - cell state" prediction model is constructed to support parameter optimization. A closed-loop feedback unit, connected to the prediction unit, light source parameter optimization unit, and input and data unit, includes a data classification and storage submodule, a deviation analysis submodule, a model update submodule, and a strategy iteration submodule. This unit feeds the detection data back to the joint database via an incremental update formula. Update the model parameters and adjust the optimization strategy to achieve closed-loop iteration.

[0039] In addition, the system also includes four major modules: information interaction and data management, optogenetic targeted modification, intelligent regulation and decision-making, and execution and feedback closed loop. Through the synergistic regulation of chemical and light environments, it overcomes the problems of fragmentation of traditional culture environments and unstable cell function, stably produces highly active light-responsive cells, adapts to optogenetic research, promotes the standardization and intelligentization of culture processes, and provides high-quality cell materials for downstream industries.

[0040] It is worth noting that the hardware connection methods and conventional circuit designs not described in detail in this invention are all existing technologies and are well known to those skilled in the art.

[0041] Therefore, the present invention employs the above-mentioned method and system for optimizing adherent cell culture based on optogenetics. By using a targeted pretreatment unit, it solves the problem of poor targeting of light-sensitive protein delivery. By optimizing light source parameters and implementing closed-loop control, it ensures the accuracy of light stimulation and environmental stability. By implementing closed-loop iteration throughout the entire process, it eliminates the reliance on human experience, effectively improving the culture quality and batch consistency of light-sensitive adherent cells. It can stably produce highly active and functionally complete cell materials, providing reliable technical support for downstream applications such as optogenetic research, drug screening, and disease mechanism research.

[0042] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing adherent cell culture based on optogenetics, characterized in that, Includes the following steps: Step S1: Input the adherent cell type and culture target, and call historical data from the adherent cell-optogenetic parameter joint database. The historical data includes basic culture parameters, optogenetic association parameters, and historical experimental data. Step S2: Perform targeted pretreatment on the target adherent cells. This includes: loading the CHR2 photosensitizing protein onto a non-toxic AAV vector, transfecting it into the target adherent cells, and obtaining a positive adherent cell population through fluorescence screening. Step S3: Based on the historical data from Step S1 and the positive cell characteristics of the positive adherent cell population from Step S2, process the data using a multi-parameter optimization algorithm hardware platform to generate an executable customized photostimulation scheme. The photostimulation scheme includes light source type, emission mode, wavelength, driving voltage, driving current, light source temperature, and pore size parameters. Step S4: Receive and execute the photostimulation scheme generated in Step S3 through the photostimulation and culture control unit, activating the Micro LED customized light source to precisely stimulate the positive adherent cells while maintaining stable temperature, humidity, and CO2 concentration in the culture environment. Step S5: Detect the changes observed in Step S4 through the monitoring unit. After processing, the adherent cells are in a certain state, and basic state parameters and electrophysiological signal parameters are obtained to form a complete detection data chain. Step S6: The detection data obtained in step S5 is compared with the parameters of normal adherent cells in the adherent cell-opiogenetic parameter joint database through the comparative analysis unit to evaluate the effect of the light stimulation protocol on the cells. Step S7: The detection data and analysis results of step S6 are fed back to the adherent cell-opiogenetic parameter joint database and the optimization unit to update the parameters of the multi-parameter optimization algorithm and the prediction model, forming a closed-loop iterative optimization. Step S8: The updated prediction model is called through the prediction unit, the adherent cell type and culture target are input, and the optimal light stimulation protocol is output to guide the subsequent culture process.

2. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, In step S2, the specific process of targeted pretreatment is as follows: determine the adhesion and incubation time of the target adherent cells, transfect the AAV-CHR2 vector into the cells according to the preset infection multiple, screen positive cells expressing CHR2 protein by fluorescence microscopy, and remove untransfected cells.

3. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, In step S3, the multi-parameter optimization algorithm hardware platform uses an FPGA or STM32 chip as the control core and solves the objective function through a multi-parameter collaborative optimization algorithm. The objective function takes cell viability, photoresponse rate, and action potential amplitude as optimization targets, and light source wavelength, driving voltage, driving current, and temperature as constraint variables.

4. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, In step S4, the Micro LED custom light source supports point light source or light source array emission, with a single point emission aperture of 10μm±1μm. The driving circuit adopts a constant voltage or constant current driving circuit to ensure that the light source parameters have a stable fluctuation range of ≤±2%.

5. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, In step S5, the basic state parameters include cell viability, adhesion rate, and purity; the electrophysiological signal parameters include cell impedance, action potential amplitude, photostimulation response delay, and response rate, which are collected in combination with conventional detection instruments using platinum-iridium alloy electrodes.

6. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, Step S7 includes the following steps: Step S71, storing the detection data obtained in Step S5 into a joint database according to the categories of "basic parameters - electrophysiological parameters - light stimulation parameters"; Step S72, comparing the detection data with historical best data to identify state deviations and their causes during the culture process; Step S73, updating the weight coefficients of the multi-parameter optimization algorithm and the regression coefficients of the prediction model using the detection data; Step S74, adjusting the optimization strategy of the light stimulation protocol based on the updated parameters; Step S75, applying the optimized strategy to subsequent adherent cell culture to achieve closed-loop iteration.

7. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, In step S8, the prediction model is built based on machine learning and statistical algorithms, including a linear regression basic model and a random forest high-precision model, and the model parameters are dynamically updated through incremental learning.

8. The method for optimizing adherent cell culture based on optogenetics according to claim 1, characterized in that, It also includes time-domain and frequency-domain analysis steps for the control unit's I / O signals, identifying harmonic interference in the driving signal through discrete Fourier transform, optimizing the parameters of the light source driving circuit, and improving the stability of light stimulation.

9. An optogenetic-based optimized system for adherent cell culture, characterized in that, include: The input and data unit includes an input module for receiving adherent cell types and culture targets, and a joint database of adherent cell-optogenetic parameters for storing basic culture parameters, optogenetic association parameters, and historical experimental data; the targeted pretreatment unit includes an AAV vector preparation submodule, a CHR2 protein transfection submodule, and a positive cell screening submodule, used to complete the photosensitivity modification and screening of target adherent cells; The light source parameter optimization unit includes a multi-parameter optimization algorithm hardware platform and a light source parameter adjustment submodule, used to generate customized photostimulation schemes; the photostimulation and culture control unit includes a Micro LED light source submodule, a driving circuit submodule, and an environmental control submodule, used to execute the photostimulation scheme and maintain the stability of the culture environment; the monitoring unit includes a basal state detection submodule and an electrophysiological monitoring submodule, used to collect the basal state and electrophysiological signals of adherent cells; the comparative analysis unit includes a data comparison submodule and an effect evaluation submodule, used to compare the detection data with standard parameters and evaluate the photostimulation effect; the prediction unit includes a data preprocessing submodule and an algorithm model training submodule, used to construct and train a "light source parameter - adherent cell state" prediction model; and the closed-loop feedback unit includes a data feedback submodule and a model update submodule, used to feed back the detection data and analysis results to the input and data unit, and update the database and algorithm model parameters.

10. The optogenetic-based adherent cell culture optimization system according to claim 9, characterized in that, The system also includes an integrated circuit module, which includes a control circuit based on an FPGA or STM32 chip, a power supply module circuit, a drive module circuit, a light source circuit, and the integrated design of each module to realize the coordinated operation of the hardware system.

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