Production method for automatically preparing cells in cell factory

Abstract

The invention discloses a production method for automatically preparing cells in a cell factory, which comprises the following steps: S100, extracting target cells in a standardized manner, pre-culturing the target cells, and dividing a culture cavity into a plurality of culture adherent sub-cavities and a plurality of culture suspension sub-cavities; s200, inoculating original cells into the culture adherent sub-cavity or the culture suspension sub-cavity according to culture requirements; s300, the environment control module adjusts parameters in the culture adherent sub-cavity or the culture suspension sub-cavity in real time in a communication mode; s400, executing a passage decision to form progeny cells; s500, rejecting progeny cells of too early generations and too late generations; and S600, packaging the progeny cells in the culture box and outputting the progeny cells, dividing the culture sub-cavities according to the types of the cultured target cells, not only improving the space utilization rate of the culture box, but also improving the iteration success rate of the target cells, and replacing manual intervention with an image recognition learning module, thereby improving the efficiency of the cell culture. The cell production efficiency can be improved, and invalid cells generated during culture can be accurately removed.

Description

Technical Field

[0001] The present invention belongs to the technical field of cell culture, and specifically refers to a production method for automatically preparing cells in a cell factory. Background Art

[0002] In the field of cell therapy and regenerative medicine, cell production is the process of amplifying and processing initial cells with certain functions to obtain batches of cell products industrially. However, existing technologies still rely on manual operations and the costs remain high, which is in great conflict with the increasingly urgent demand for large-scale cell production.

[0003] Existing cell production methods have poor culture space planning and fail to intervene in time during the cell iteration process, resulting in technical problems such as low space utilization and low proportion of effective cells, resulting in a small number of output cell products and a low ratio of active cells.

[0004] Therefore, those skilled in the art need a production method that can effectively utilize culture space and has a high cell qualification rate to overcome the above problems. Summary of the Invention

[0005] In response to the above situation, in order to overcome the defects of the existing technology, the present invention proposes a production method for automated cell preparation in a cell factory. In order to solve the technical defects of low identification culture space utilization and low effective cell yield, the present invention creatively divides the culture chamber according to the target cell types to be cultured, which not only improves the space utilization of the culture box, but also improves the iteration success rate of the target cells. It also replaces manual intervention through an image recognition learning module, which can improve the efficiency of cell production and accurately eliminate invalid cells generated during culture.

[0006] The technical solution adopted by the present invention is as follows: A production method for automatically preparing cells in a cell factory according to an embodiment of the present invention is suitable for the production of stem cells, immune cells, and adult cells, comprising the following steps: S100, the automation module of the cell factory determines the type of target cells, extracts the target cells in a standardized manner, and pre-cultures the target cells to obtain primary cells with culture value, and uses the environmental control module to establish a reasonable environment in the culture chamber of the culture box, dividing the culture chamber into multiple adherent culture sub-chambers and multiple suspended culture sub-chambers; S200, the automation module trains the image recognition learning module on the culture box using images of cells at various stages in the database, the image recognition learning module identifies the original cells, and inoculates the original cells into the adherent culture chamber or the suspension culture chamber according to culture requirements; S300, the image recognition learning module communicates with the environmental control module to adjust the temperature, pH value and nutrient concentration parameters in the culture adherent sub-chamber or the culture suspension sub-chamber according to the real-time image of the primitive cells; S400, the image recognition learning module continuously observes whether the original cells meet the subculture standard, executes subculture decision for the original cells that meet the requirements, forms progeny cells and transfers them to other adherent culture chambers or suspension culture chambers; S500, the image recognition learning module observes and evaluates the activity of the progeny cells of different generations, and eliminates the progeny cells of early generations with viral characteristics and the aging progeny cells of late generations; S600: The culture box encapsulates the progeny cells and outputs them, preparing to inoculate the subsequent original cells.

[0007] According to a production method for automated cell preparation in a cell factory according to the present invention, by dividing the culture chamber according to the target cell type to be cultured, not only the space utilization rate of the culture box can be improved, but also the iteration success rate of the target cells can be improved. By replacing manual intervention with an image recognition learning module, the efficiency of cell production can be improved and invalid cells generated during culture can be accurately eliminated.

[0008] According to some embodiments of the present invention, the image recognition learning module in S200 identifies the contour integrity, cell nucleus ratio, adherent area and activity of the original cell, and determines whether the original cell is an adherent cell suitable for inoculation into the culture adherent sub-chamber or a suspension cell suitable for inoculation into the culture suspension sub-chamber.

[0009] According to some embodiments of the present invention, when the image recognition learning module determines that the original cells are the adherent cells, collagen coating or hydrophilization treatment is performed on the inner wall of the culture adherent sub-chamber; when the image recognition learning module determines that the original cells are the suspended cells, continuous aggregate rotation is applied to the culture suspension sub-chamber.

[0010] According to some optional embodiments of the present invention, S400 includes the following steps: S401, the image recognition learning module determines whether the density of the original cells reaches a threshold; S402, when the density of the protocells reaches a threshold, injecting a digestion solution containing trypsin and monitoring the detachment state of the protocells; S403, determining the digestion degree of the original cells through the image recognition learning module and injecting a stop solution capable of neutralizing trypsin to form the progeny cells; S404, transferring the progeny cells to another of the adherent culture chambers or the suspended culture chambers.

[0011] According to some optional embodiments of the present invention, the upper limit of the generation of the offspring cells with viral characteristics eliminated by the image recognition learning module in S500 is in the range of 2-5, and the lower limit of the generation of the aged offspring cells eliminated by the image recognition learning module is in the range of 6-10.

[0012] According to some optional embodiments of the present invention, when encapsulating the progeny cells in S600, for the progeny cells that need to maintain activity, the progeny cells are transferred to a sodium alginate solution and dripped into a solution containing calcium ions through microfluidic technology to form microcapsules encapsulating the progeny cells. For the progeny cells that do not need to be used immediately, the progeny cells are preserved in a freezing bag.

[0013] The beneficial effects achieved by the present invention using the above structure are as follows: By dividing the culture chambers according to the target cell types, not only can the space utilization of the culture box be improved, but also the iteration success rate of the target cells can be increased. By replacing manual intervention with an image recognition learning module, the efficiency of cell production can be improved and invalid cells generated during culture can be accurately eliminated. Collagen coating or hydrophilization treatment is performed on the inner wall of the culture adherent sub-chamber to make it easier for the adherent cells to adhere to the inner wall of the culture adherent sub-chamber; Suspended cells can maintain a high degree of uniformity, and the formation time of suspended cell aggregates can be shortened by adjusting the direction and speed of the aggregate rotation; To avoid damage to the original cells caused by over-digestion, the image recognition learning module can accurately control the concentration of trypsin, digestion time, and the composition and volume of the culture medium according to the cell type and growth status, ensuring the activity and proliferation of the offspring cells; Effectively eliminate progeny cells with high viral risk and poor activity, thereby accurately retaining high-quality progeny cells and further improving the quality of cell products.

[0014] Additional aspects and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the steps of a production method for automatically preparing cells in a cell factory according to some embodiments of the present invention; Figure 2 1 is a schematic diagram of some steps of a production method for automatically preparing cells in a cell factory according to some embodiments of the present invention.

[0016] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] In the description of the present invention, it should be understood that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0019] refer to Figure 1 A method for automatically preparing cells in a cell factory according to an embodiment of the present invention is suitable for the production of stem cells, immune cells, and adult cells, for example, for producing induced functional stem cells or lymphocytes, comprising the following steps: S100. The automation module of the cell factory determines the type of target cells, extracts the target cells in a standardized manner and pre-cultures the target cells to obtain original cells with culture value, and uses the environmental control module to build a reasonable environment in the culture chamber of the culture box. The culture chamber is divided into multiple adherent culture sub-chambers and multiple suspended culture sub-chambers. The automation module is used to control the operation of the cell factory. When preparing cells, it determines which type the target cells belong to and calls the corresponding culture box. For example, the automation module can divide the target cells into adherent culture cells and spherical 3D culture cells, and then uses the environmental control module to distribute cell culture fluid in the culture chamber of the culture box. In this process, the environmental control module uses liquid level detection technology and volume calibration technology to control the cell culture fluid in multiple culture chambers to have the same concentration and volume.

[0020] S200, the automation module trains the image recognition learning module on the culture box through images of cells at each stage in the database. The image recognition learning module identifies the original cells and inoculates the original cells into the culture adherent sub-chamber or the culture suspension sub-chamber according to the culture requirements. The image recognition learning module can identify whether the original cells belong to the adherent culture cells or the spherical 3D culture cells, and inoculate the original cells belonging to the adherent culture cells into the culture adherent sub-chamber, and the original cells belonging to the spherical 3D culture cells into the culture suspension sub-chamber.

[0021] S300, the image recognition learning module communicates with the environmental control module, and adjusts the parameters of temperature, pH value and nutrient concentration in the culture adherent sub-chamber or culture suspension sub-chamber according to the real-time image of the primitive cells. During the culture of the primitive cells, the image recognition learning module can monitor the immediate status, growth rate, differentiation tendency and other information of the primitive cells. When the primitive cell activity is reduced, the growth rate is slowed down, etc., the image recognition learning module can judge whether the primitive cells are nutrient deficient or whether the temperature and pH values ​​are appropriate based on the characteristics exhibited by the primitive cells. The environmental control module can adjust the microfluidic nutrient delivery strategy of the culture adherent sub-chamber or culture suspension sub-chamber and fine-tune the temperature and pH values.

[0022] S400, the image recognition learning module continuously observes whether the original cells meet the passaging standards, executes passaging decisions for the original cells that meet the requirements, forms progeny cells and transfers them to other culture adherent sub-chambers or culture suspension sub-chambers. Especially when the growth cycle of the original cells in the culture adherent sub-chamber or culture suspension sub-chamber reaches the logarithmic phase of growth, the image recognition learning module analyzes the cell density, activity, morphology and other parameters of the original cells. If the set parameters are met, the passaging decision is executed for the original cells.

[0023] S500, the image recognition learning module observes and evaluates the activity of offspring cells of different generations, eliminates offspring cells with viral characteristics of early generations and aged offspring cells of late generations, thereby increasing the ratio of effective cells in offspring cells and ensuring the quality of cell products.

[0024] S600: The culture box encapsulates the progeny cells and outputs them, ready for inoculation of subsequent original cells.

[0025] According to a production method for automated cell preparation in a cell factory according to the present invention, by dividing the culture chamber according to the target cell type to be cultured, not only the space utilization rate of the culture box can be improved, but also the iteration success rate of the target cells can be improved. By replacing manual intervention with an image recognition learning module, the efficiency of cell production can be improved and invalid cells generated during culture can be accurately eliminated.

[0026] refer to Figure 1According to some embodiments of the present invention, the image recognition learning module in S200 identifies the contour integrity, cell nucleus ratio, adherent area and activity of the original cells, and determines whether the original cells are adherent cells suitable for inoculation into the culture adherent sub-chamber or suspended cells suitable for inoculation into the culture suspension sub-chamber. For example, when the image recognition learning module identifies adherent cells such as fibroblasts and epithelial cells that need to attach to a solid surface for growth, the image recognition learning module instructs the adherent cells to be inoculated into the culture adherent sub-chamber. For example, when the image recognition learning module identifies suspended neural stem cells, pancreatic beta cells and other suspended cells, the image recognition learning module instructs the suspended cells to be inoculated into the culture suspension sub-chamber. The environmental control module can construct a stable environment suitable for the culture of suspended cell aggregates in the culture suspension sub-chamber through hanging drop technology or hydrogel technology. The image recognition learning module can also assign culture adherent sub-cavities and culture suspension sub-cavities to primitive cells based on cell types and production goals. For production needs that require single-layer cell observation and rely on matrix interaction, the relevant primitive cells can be inoculated into the culture adherent sub-cavity. For production needs such as building tissue models or toxicity testing, the relevant primitive cells can be inoculated into the culture suspension sub-cavity. The two culture modes can also be combined. The primitive cells are first cultured in the culture adherent sub-cavity. After that, the environmental control module applies rotation in the culture adherent sub-cavity, so that the primitive cells fall off the inner wall of the culture adherent sub-cavity and form spheres. Finally, the primitive cells are transferred to the culture suspension sub-cavity, thereby combining the advantages of different culture technologies to achieve optimization of cell production.

[0027] refer to Figure 1 According to some embodiments of the present invention, when the image recognition learning module determines that the original cells are adherent cells, collagen coating or hydrophilization treatment is performed on the inner wall of the culture adherent sub-chamber, so that the adherent cells are more easily attached to the inner wall of the culture adherent sub-chamber. When the image recognition learning module determines that the original cells are suspended cells, continuous aggregation rotation is applied to the culture suspension sub-chamber, so that the suspended cells can maintain a high uniformity. The direction and speed of the aggregation rotation can be adjusted to shorten the formation time of the suspended cell aggregates.

[0028] refer to Figure 2 According to some optional embodiments of the present invention, S400 includes the following steps: S401. The image recognition learning module determines whether the density of the original cells reaches a threshold. The original cells that reach the density threshold have good product quality and can be subcultured. The original cells that do not reach the density threshold are discarded or cultured for an extended period of time based on their characteristics.

[0029] S402. When the original cell density reaches a threshold, the cell culture medium can be drained using a microfluidic channel, and a digestion solution containing trypsin can be injected to monitor the detachment state of the original cells. Trypsin can act on the connecting proteins on the surface of the original cells, causing the original cells to fall off from the inner wall of the cavity and separate the original cells individually.

[0030] S403. The image recognition learning module is used to determine the degree of digestion of the original cells and inject a stop solution that can neutralize trypsin to form progeny cells. When a single-cell suspension is formed in the cavity, the image recognition learning module determines that the digestion of the original cells is complete. At this time, a stop solution containing serum can be injected to neutralize the activity of trypsin and avoid damage to the original cells caused by excessive digestion. At this time, the independent original cells are regarded as progeny cells that have completed the passage. The image recognition learning module can accurately control the concentration of trypsin, digestion time, and the composition and volume of the culture medium according to the cell type and growth status to ensure the activity and proliferation ability of the progeny cells.

[0031] S404. Transfer the progeny cells to another adherent culture chamber or suspension culture chamber. The single cell suspension containing the progeny cells can be transferred to the new adherent culture chamber or suspension culture chamber by a microfluidic pump.

[0032] refer to Figure 1 According to some optional embodiments of the present invention, the upper limit of the generation of progeny cells with viral characteristics eliminated by the image recognition learning module in S500 is in the range of 2-5, which can effectively eliminate progeny cells with high viral risks. The lower limit of the generation of aging progeny cells eliminated by the image recognition learning module is in the range of 6-10, which can effectively eliminate progeny cells with poor activity, thereby accurately retaining high-quality progeny cells and further improving the quality of cell products.

[0033] refer to Figure 1 According to some optional embodiments of the present invention, when encapsulating progeny cells in S600, for progeny cells that need to remain active, living cell encapsulation technology can be used to transfer the progeny cells into a sodium alginate solution and then drip into a solution containing calcium ions through microfluidic technology. Sodium alginate and calcium ions can undergo a cross-linking reaction to form microcapsules that encapsulate the progeny cells, which can both protect the progeny cells and maintain their normal metabolism. For progeny cells that do not need to be used immediately, cryopreservation bags are used to preserve the progeny cells, so that the progeny cells remain active at low temperatures and can be revived by warming for subsequent use.

[0034] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0035] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0036] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A production method for automated cell production in a cell factory, characterized in that: Suitable for the production of stem cells, immune cells and adult cells, including the following steps: S100, the automation module of the cell factory determines the type of target cells, extracts the target cells in a standardized manner, and pre-cultures the target cells to obtain primary cells with culture value, and uses the environmental control module to establish a reasonable environment in the culture chamber of the culture box, dividing the culture chamber into multiple adherent culture sub-chambers and multiple suspended culture sub-chambers; S200, the automation module trains the image recognition learning module on the culture box using images of cells at each iteration stage in the database, the image recognition learning module identifies the original cells, and inoculates the original cells into the adherent culture chamber or the suspension culture chamber according to culture requirements; S300, the image recognition learning module communicates with the environmental control module to adjust the temperature, pH value and nutrient concentration parameters in the culture adherent sub-chamber or the culture suspension sub-chamber according to the real-time image of the primitive cells; S400, the image recognition learning module continuously observes whether the original cells meet the subculture standard, executes subculture decision for the original cells that meet the requirements, forms progeny cells and transfers them to other adherent culture chambers or suspension culture chambers; S500, the image recognition learning module observes and evaluates the activity of the progeny cells of different generations, and eliminates the progeny cells of early generations with viral characteristics and the aging progeny cells of late generations; S600: The culture box encapsulates the progeny cells and outputs them, preparing to inoculate the subsequent original cells.

2. A production method for automated cell preparation in a cell factory according to claim 1, characterized in that: The image recognition learning module in S200 identifies the contour integrity, cell nucleus ratio, adherent area and activity of the original cell, and determines whether the original cell is an adherent cell suitable for inoculation into the culture adherent sub-chamber or a suspension cell suitable for inoculation into the culture suspension sub-chamber.

3. The method for automatically preparing cells in a cell factory according to claim 2, wherein: When the image recognition learning module determines that the original cells are the adherent cells, the automation module performs collagen coating or hydrophilization treatment on the inner wall of the culture adherent sub-chamber; when the image recognition learning module determines that the original cells are the suspension cells, the automation module applies continuous aggregate rotation to the culture suspension sub-chamber.

4. The method for automatically preparing cells in a cell factory according to claim 1, wherein: The S400 includes the following steps: S401, the image recognition learning module determines whether the density of the original cells reaches a threshold; S402, when the density of the protocells reaches a threshold, injecting a digestion solution containing trypsin and monitoring the detachment state of the protocells; S403, determining the digestion degree of the original cells through the image recognition learning module and injecting a stop solution capable of neutralizing trypsin to form the progeny cells; S404, transferring the progeny cells to another of the adherent culture chambers or the suspended culture chambers.

5. The method for automatically preparing cells in a cell factory according to claim 1, wherein: The upper limit of the generation of the progeny cells with viral characteristics eliminated by the image recognition learning module in S500 is in the range of 2-5, and the lower limit of the generation of the aged progeny cells eliminated by the image recognition learning module is in the range of 6-10.

6. The method for automatically preparing cells in a cell factory according to claim 1, wherein: When encapsulating the progeny cells in S600, for the progeny cells that need to maintain activity, the progeny cells are transferred to a sodium alginate solution and then dripped into a solution containing calcium ions using microfluidic technology to form microcapsules encapsulating the progeny cells. For the progeny cells that do not need to be used immediately, the progeny cells are stored in a freezing bag.

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