Production method and production system

Abstract

To provide a production method that enables more accurate evaluation of a cell state and improvement of a metabolite yield.SOLUTION: A production method of the present invention is a production method for producing a metabolite by culturing cells, the method having a measurement step of individually measuring a physical quantity that changes due to at least one of metabolic activity and stress activity of the cells, an evaluation step of individually evaluating an activity state of the cells based on measurement results of the physical quantity, and a culturing step of culturing the cells.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a production method and a production system. [Background technology]

[0002] In biomanufacturing, genetic technology is used to produce target substances through the cells of microorganisms, animals, plants, etc. The yield of metabolic products produced in biomanufacturing is prone to fluctuate depending on various factors such as the state of the cells, the state of the culture medium, the environment during cultivation, and the techniques of the producer.

[0003] Many efforts have been made to improve the yield of metabolites, and methodologies have been reported, such as optimizing the components in the culture medium or the genes to be expressed, and modifying the design of the reactor itself. Other methods have been reported that maximize the activity of cells by providing appropriate feedback on the agitation speed, pH value, etc., through real-time monitoring of the culture environment. Examples of this method that have been reported include an increase in yield by several tens of percent to several times.

[0004] One example of a method for evaluating the maturity of cells is the following evaluation method disclosed in Patent Document 1. Cells are irradiated with light having a wavelength in the range of 460 to 500 nm or a part of that range, and the waveform of the resulting transmitted light is obtained as optical measurement information. The obtained optical measurement information and previously obtained information related to the maturity of the cells are used to evaluate the maturity of the cells. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2023-67851 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the above-mentioned evaluation method does not obtain information on a specific state within a cell, such as the metabolic activity or stress activity of the cell, but rather observes a mixed state in which multiple specific states are mixed within the cell and obtains information on maturity. Therefore, it has been found that cells that are not actually very active in producing a target substance may be evaluated as having a high level of maturity, and this is insufficient in terms of accuracy.

[0007] The present invention has been made in view of the above problems and circumstances, and aims to provide a production method and the like that can more accurately evaluate the state of cells and improve the yield of metabolic products. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems, the present inventors investigated the causes of the above-mentioned problems and found the following, which led to the present invention. The production method of the present invention comprises a measurement step, an evaluation step, and a culture step. The measurement step individually measures physical quantities that change due to at least one of metabolic activity and stress activity of cells. The evaluation step individually evaluates the activity state of the cells based on the measurement results of the physical quantities. The culture step cultures the cells. This allows for more accurate evaluation of the state of the cells and improves the yield of metabolic products. That is, the above-mentioned problems of the present invention are solved by the following means.

[0009] 1. A production method for producing a metabolite by culturing cells, comprising: a measuring step of individually measuring physical quantities of the cells that change due to at least one of metabolic activity and stress activity; an evaluation step of individually evaluating the activity state of the cells based on the measurement results of the physical quantities; A production method comprising a culturing step of culturing the cells.

[0010] 2. The production method according to item 1, wherein the measuring step comprises staining the cells with a fluorescent reagent and measuring the fluorescence intensity of the stained cells using a detector.

[0011] 3. The production method described in item 1, comprising a prediction step of predicting the yield of the metabolite based on the evaluation result of the activity state.

[0012] 4. The production method described in paragraph 1, wherein the evaluation step includes a step of extracting measurement data of a portion of the cells from a population of measurement data of the cells based on specific measurement parameters.

[0013] 5. The production method described in item 4, which comprises a sorting step of sorting a portion of the cells from the cell population based on the results of the extraction of the measurement data.

[0014] 6. The production method according to item 5, wherein the separated cells are returned to the reactor and cultured.

[0015] 7. The production method according to item 6, wherein the separated cells are automatically returned to the reactor.

[0016] 8. The prediction step constructing a machine-learned model based on the results of culturing the cells and the results of extracting the measurement data; 4. The production method described in paragraph 3, wherein the yield of the metabolite is predicted using the constructed machine-learned model.

[0017] 9. A production system for producing a metabolic product by culturing cells, comprising: The production system comprises a cell sorter that sorts the cells, a reactor that cultures the cells, and a transport device that transports the sorted cells from the cell sorter to the reactor. [Effects of the Invention]

[0018] The above-mentioned means of the present invention allow for more accurate evaluation of the state of cells and improve the yield of metabolites.

[0019] The mechanism by which the effects of the present invention are manifested or the mechanism of action is not clear, but is speculated as follows.

[0020] The yield of metabolites varies depending on the state of the cells. Therefore, it is necessary to evaluate the state of the cells in the series of production processes in which cells are cultured to obtain metabolites. In conventional techniques, the state of the cells is evaluated by separating a portion of a cell population for evaluation, disrupting the separated cells for evaluation, and analyzing the intracellular components. In this method, the evaluation results of the separated cells for evaluation are used as the evaluation results of the entire cell population, so the cell population is averaged for evaluation.

[0021] However, in reality, it is known that there is a large variation in the state of cells among cells in a cultured cell population. Therefore, it is believed that by evaluating the state of each cell individually rather than averaging the population, it is possible to accurately evaluate the cell population and the production status. In particular, it is believed that by evaluating the state of metabolic activity or stress activity of cells, which affect the amount of metabolic product production, as the cell status, it is possible to accurately evaluate the cell population and the production status. Furthermore, it is believed that the yield of metabolic products can be improved by appropriately changing the culture conditions (production conditions) depending on the results of cell evaluation. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a flowchart illustrating an example of a production method. [Figure 2] 1 is a graph showing the measurement results for a certain cell population, with the horizontal axis representing the intensity of forward scattered light (FS) and the vertical axis representing the intensity of side scattered light (SS). [Figure 3] FIG. 1 is a schematic diagram illustrating an example of a production system. [Figure 4] 1 is a graph showing the observation results of the 1-Negative Sample. [Figure 5] 1 is a graph showing the observation results of the 1-Positive Sample. [Figure 6] 1 is a graph showing the observation results of the 2-Negative Sample. [Figure 7] 2 is a graph showing the observation results of the 2-Positive Sample. [Figure 8] 1 is a graph showing the observation results of a gated sample. DETAILED DESCRIPTION OF THE INVENTION

[0023] The production method of the present invention is a production method for producing a metabolite by culturing cells. The production method comprises a measurement step, an evaluation step, and a culture step. The measurement step individually measures physical quantities that change due to at least one of metabolic activity and stress activity of the cells. The evaluation step individually evaluates the activity state of the cells based on the measurement results of the physical quantities. The culture step cultures the cells. The production method of the present invention is characterized as described above. The above features are technical features common to or corresponding to the following embodiments.

[0024] In this embodiment, from the viewpoint of being able to evaluate the state of the cells more accurately, it is preferable that the measurement step includes a step of staining the cells with a fluorescent reagent and measuring the fluorescence intensity of the stained cells using a detector.

[0025] In this embodiment, from the viewpoint of improving the yield of the metabolite at the end of the culture, it is preferable to have a prediction step of predicting the yield of the metabolite based on the evaluation result of the activity state.

[0026] In this embodiment, from the viewpoint of being able to evaluate the state of the cells more accurately, it is preferable that the evaluation step includes a step of extracting measurement data of a portion of cells from a population of measurement data of cells based on specific measurement parameters.

[0027] In this embodiment, from the viewpoint of improving the yield of metabolites, it is preferable to have a separation step of separating a portion of cells from the cell population based on the results of extraction of measurement data.

[0028] In this embodiment, it is preferable to return the separated cells to the reactor and culture them, from the viewpoint of improving the yield of metabolites.

[0029] In this embodiment, from the viewpoint of optimizing the number of work steps and improving the yield of metabolites, it is preferable to automatically return the separated cells to the reactor.

[0030] In this embodiment, from the viewpoint of improving the yield of metabolites, the prediction step preferably includes a step of constructing a machine-learned model based on the cell culture results and the measurement data extraction results, and preferably predicting the yield of metabolites using the constructed machine-learned model.

[0031] The production system of this embodiment is a production system that produces metabolites by culturing cells, and includes a cell sorter that sorts cells, a reactor that cultures the cells, and a transport device that transports the sorted cells from the cell sorter to the reactor.

[0032] Hereinafter, one or more embodiments of the present invention will be described with reference to the drawings. However, the scope of the present invention is not limited to the disclosed embodiments. In this application, the symbol "to" is used to mean that the numerical values ​​before and after it are included as the lower limit and upper limit.

[0033] 1. Production method "Biomanufacturing" refers to the entire process or part of the process of producing a target substance through cells of microorganisms, animals, plants, etc. using genetic technology. The present invention is applied to biomanufacturing. The target substance is included in the metabolic products obtained by culturing the cells.

[0034] FIG. 1 is a flowchart showing an example of a production method according to this embodiment.

[0035] The production method of this embodiment includes a measurement step (step S1), an evaluation step (step S2), and a culture step (step S5). In the measurement step S1, physical quantities that change due to at least one of metabolic activity and stress activity of cells are individually measured. In the evaluation step S2, the activity state of the cells is individually evaluated based on the measurement results of the physical quantities. In the culture step S5, the cells are cultured.

[0036] The timing at which the incubation step S5 is performed is not limited. The incubation step S5 may be performed before the measurement step S1. Furthermore, the incubation step S5 may be performed both before the measurement step S1 and after the evaluation step S2.

[0037] The production method of this embodiment may further include a prediction step (step S3), in which the yield of the metabolite is predicted based on the evaluation result of the activity state.

[0038] The production method of this embodiment may further include a sorting step (step S4). The evaluation step S2 includes a step of extracting measurement data of a portion of cells from the population of cell measurement data based on specific measurement parameters. In this case, in the sorting step S4, a portion of cells are sorted from the population of cells based on the measurement data extraction results.

[0039] In the production method of this embodiment, the timing of performing the series of steps is not limited. Typically, cell culture is performed by performing pre-culture and main culture, and scaling up from a small scale to a large scale. The series of steps according to this embodiment may be performed in the pre-culture or the main culture. Furthermore, the series of steps according to this embodiment may be performed repeatedly.

[0040] For example, the series of steps according to this embodiment may be performed intermittently several times in the pre-culture, and continuously in the main culture until the culture is completed. In this case, the pre-culture is performed for the purpose of concentrating highly productive cells, and the main culture is performed for the purpose of increasing highly productive cells and discarding less productive cells. By performing the main culture continuously, the productivity of the cells can be observed in real time, and the culture conditions (production conditions) can be adjusted in real time. In this embodiment, "highly productive cells" refer to cells that produce a large amount of metabolic products.

[0041] In this embodiment, chemical reactions that occur within cells and involve energy changes are referred to as "metabolic reactions" or "metabolism." Metabolic reactions include reactions that use energy to synthesize high molecular weight compounds such as proteins (anabolic reactions), and reactions that decompose high molecular weight compounds to extract energy (catabolic reactions). In this embodiment, both of these reactions are included in the term "metabolic reactions."

[0042] In this embodiment, "metabolites" refer to substances produced by metabolic reactions of cells, and include target substances of interest that are the targets of the production process. Metabolic products are obtained by disrupting cells after the production process. Components obtained by disrupting cells after the production process include DNA, RNA, proteins, etc. contained in the cells, as well as substances produced by metabolic reactions of the cells, i.e., metabolites. Furthermore, cells after the production process may be used in their intact state without being disrupted.

[0043] Metabolites include primary metabolites and secondary metabolites. Primary metabolites are components produced by biochemical reactions essential for sustaining life. Secondary metabolites are components that are not essential for sustaining life but are produced specifically by each species. Metabolites also include intermediates produced during metabolic reactions.

[0044] In this embodiment, the cells to be used are not particularly limited as long as they are capable of producing a target substance. Examples of the cells include bacteria (Escherichia coli, Bacillus, Streptomyces, Pseudomonas, Rhodococcus, Streptococcus, Staphylococcus, etc.), fungi (e.g., yeasts (Saccharomyces, Schizosaccharomyces, Pichia, Lipomyces, etc.)), filamentous fungi (Aspergillus, etc.)), and the like. genera, etc.), algae (microalgae, including organisms belonging to the cyanobacteria, rhodophyta, glaucophyta, chlorophyta, charophyta, heterokontophyta, dinoflagellates, cryptophyta, haptophyta, euglenopphyta, and chlorarachniophyta phyla), insect cells (Drosophila S2, Spodoptera SF, etc.), plant cells (tobacco-derived (BY-2 cells, etc.), Arabidopsis-derived, rice-derived, soybean-derived, tomato-derived, sesame-derived, and Catharanthus roseus-derived cells, etc.), and animal cells (hybridoma, COS, CHO, HEK293, 3T3 cells, etc.).

[0045] The cells to be used may be natural cells, or may be mutants that have been treated with drugs such as NTG, ultraviolet light, radiation, or the like. Examples of such mutants include those that have improved productivity of a target substance.

[0046] The cells to be used may also be cells into which a nucleic acid for producing a target substance has been introduced. For example, if the target substance is a protein, such a nucleic acid may be a nucleic acid encoding the protein. Methods for introducing a nucleic acid into a host cell include known methods, such as using an expression vector containing the nucleic acid. The expression vector used for introducing the nucleic acid is not particularly limited, and a wide variety of known expression vectors can be used. An appropriate expression vector may be selected, taking into consideration the type of host into which the nucleic acid is introduced. In addition to the nucleic acid, the expression vector may also contain a promoter, enhancer, terminator, polyadenylation signal, selection marker, origin of replication, etc. Both autonomously replicating vectors and vectors that are integrated into the genome of the host cell upon introduction and replicated together with the chromosome into which they are integrated can be used as expression vectors.

[0047] Methods for constructing expression vectors and introducing the expression vectors into cells are well known and can be carried out with reference to, for example, the descriptions in Sambrook and Russell, Molecular Cloning, A Laboratory Manual 3rd edition, Cold Spring Harbor Laboratory Press (2001), etc. Methods for introducing expression vectors into hosts include, for example, the competent cell method, the protoplast method, the spheroplast method, electroporation, the calcium phosphate method, the lipofection method, the Agrobacterium method, the polyethylene glycol method, the liposome method, the microinjection method, and the lithium acetate method.

[0048] The target substance of the production process is not particularly limited, and examples thereof include fats and oils, fatty acids, vitamins, antibiotics, nucleic acids, amino acids, polysaccharides, organic acids, alcohols, sugars, sugar alcohols, foods, antibodies, enzymes, vaccines, lectins, cytokines, hormones, receptors, ligands, peptides, and proteins. Fatty acids include propionic acid, hydroxypropionic acid, EPA (eicosapentaenoic acid), and DHA (docosahexaenoic acid). Vitamins include riboflavin, thiamine, and ascorbic acid. Organic acids include acetic acid, lactic acid, and succinic acid. Alcohols include ethanol. Sugars include xylose and mannose. Sugar alcohols include xylitol and mannitol.

[0049] Examples of cells that can be used without disruption after producing a target substance include lactic acid bacteria, embryonic stem (ES) cells, pluripotent stem cells such as induced pluripotent stem (iPS) cells, etc. Lactic acid bacteria include not only Lactobacillus and Lactococcus, but also Bifidobacteria, which are considered lactic acid bacteria in a broad sense. Specific examples of lactic acid bacteria include lactic acid bacteria belonging to the genera Lactobacillus, Bifidobacterium, Leuconostoc, Lactococcus, Pediococcus, Enterococcus, Streptococcus, and Weissella.

[0050] Each step will be explained in order below.

[0051] (1) Measurement process In the measuring step, physical quantities that change due to at least one of metabolic activity and stress activity of cells are individually measured.

[0052] In this embodiment, "metabolic activity" refers to activity related to metabolic reactions, and "stress activity" refers to activity related to cellular stress responses.

[0053] Generally, cells with high metabolic activity tend to be more susceptible to stress, and when cells respond to stress, their stress activity increases. Therefore, cell productivity can be evaluated based on either the state of metabolic activity of the cells alone or the state of stress activity alone. However, because metabolic reactions tend to be suppressed when cells respond to stress, it is preferable to evaluate the cell activity state, i.e., productivity, from the perspective of accurately evaluating the cell activity state.

[0054] One method for evaluating cellular productivity is to directly measure the production amount of a target substance contained in a metabolite. A method for measuring a specific compound among metabolites containing thousands of compounds is, for example, the fluorescent-labeled antibody method. In this method, an antibody against the target compound (antigen) is labeled with a fluorescent dye, and an antigen-antibody reaction is induced, thereby labeling the target compound with the fluorescent dye. The location, amount, etc. of the target compound can be measured by irradiating a measurement sample containing the labeled compound with excitation light and observing the resulting fluorescent image.

[0055] In measurement methods using antibodies, such as fluorescently labeled antibody methods, it is necessary to directly bind the antibody to the compound to be measured. Generally, it is difficult to introduce an antibody into cells without disrupting the cells. Therefore, in measurement methods using antibodies, the antibody is bound to the compound to be measured in the components extracted by disrupting the cells. However, disrupting the cells makes it impossible to continue culturing them. Therefore, in this embodiment, it is necessary to individually evaluate the state of the cells without disrupting them.

[0056] That is, in this embodiment, a physical quantity that changes due to at least one of metabolic activity and stress activity is measured from outside the cell without disrupting the cell.

[0057] The activating components shown below are components that are activated by cell growth activity and undergo quantitative changes due to at least one of metabolic activity and stress activity. Therefore, physical quantities that change in accordance with the quantitative changes of the activating components may be measured.

[0058] Activating components include reactive oxygen species (ROS), specifically superoxide, hydrogen peroxide, hydroxyl radicals, etc. Activating components include citric acid, isocitrate, iron-sulfur clusters, cytochrome c, aconitase, thioredoxin, peroxiredoxin, thioredoxin reductase, glutathione reductase, glutathione peroxidase, glutathione, glutathione reductase, electron transport system, superoxide dismutase, oxaloacetate, acetyl-CoA, CoA, cis-aconitic acid, oxalosuccinic acid, NADH, and NAD. + , succinyl-CoA, α-ketoglutarate, succinate, GDP, ATP, ADP, GTP, CoA-SH, fumarate, ubiquinone, ubiquinol, L-malate, citrate synthase, aconitate hydratase, isocitrate dehydrogenase, oxoglutarate dehydrogenase complex, succinyl-CoA synthase, succinate dehydrogenase, fumarase, malate dehydrogenase, lactate, glucose, glucokinase, glucose-6-phosphate, glucose phosphate isomerase, fulvestrin Examples of enzymes that can be used include fructose-6-phosphate, phosphofructokinase, fructose 1,6-bisphosphate, aldolase, hydroxyacetone phosphate, glyceraldehyde 3-phosphate, triosephosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, 1,3-bisphosphoglycerate kinase, 3-phosphoglycerate, 2-phosphoglycerate, phosphoglycerate mutase, enolase, phosphoenolpyruvate, pyruvate kinase, pyruvate, and lactate dehydrogenase.

[0059] Furthermore, when the intracellular pH value changes due to cell growth activity, a physical quantity that changes in accordance with the change in pH value may be measured.

[0060] The physical quantity to be measured is not particularly limited as long as it changes due to at least one of metabolic activity and stress activity. The physical quantity to be measured may be one type or two or more types. For example, a method may be used in which cells are fluorescently stained using a reagent that emits fluorescence under specific conditions, and the intensity of the fluorescence emitted by irradiating the cells with excitation light is measured. Examples of the reagent include a mitochondrial membrane potential detection kit and an intracellular pH measurement reagent. These reagents can be introduced into cells without disrupting the cells. In addition to these reagents, any reagent that can be introduced into cells without disrupting the cells can be applied to this embodiment.

[0061] (1.1) Mitochondrial membrane potential measurement Some of the metabolic reactions occurring within cells are carried out by mitochondria. Therefore, physical quantities that change due to mitochondrial activity may be measured. Examples of indicators of mitochondrial activity include intracellular mitochondrial dehydrogenase activity, intracellular mitochondrial membrane potential, intracellular oxidative stress level, mitochondrial respiratory activity, ATP synthase activity, and intracellular mitochondrial content. Among these, measuring the mitochondrial membrane potential is preferred, as it allows introducing reagents into cells without disrupting the cells.

[0062] Mitochondrial membrane potential can be quantified by labeling mitochondria in cells with a specific reagent. The absolute value of the signal amount derived from the reagent reflecting the mitochondrial membrane potential varies depending on the type of cell used, the type of reagent, and exposure conditions such as exposure time. Therefore, in the evaluation step described below, the signal amount derived from the reagent between the cells being compared may be evaluated as a relative value rather than an absolute value.

[0063] The reagent used may be any substance that can specifically label mitochondria in living cells and measure the mitochondrial membrane potential, such as a substance that has fluorescent properties and is selectively taken up into mitochondria by the mitochondrial membrane potential.

[0064] (1.2)pH measurement The intracellular pH value may change due to metabolic reactions and stress responses occurring within the cell. In such cases, the physical quantity that changes due to the intracellular pH value may be measured.

[0065] The intracellular pH value can be measured by introducing into the cells a reagent that emits fluorescence within a specific pH range. The absolute value of the signal amount derived from the reagent, which reflects the pH value, varies depending on the type of cell used, the type of reagent, and exposure conditions such as exposure time. Therefore, in the evaluation step described below, the signal amount derived from the reagent between the cells being compared may be evaluated as a relative value rather than an absolute value.

[0066] The reagent used may be any substance that emits fluorescence in the cytoplasm of living cells within a specific pH range.

[0067] (2) Evaluation process In the evaluation step, the activation state of the cells is evaluated individually based on the measurement results of the physical quantities.

[0068] As described above, when a portion of a cell population is isolated for evaluation and the evaluation results of the isolated evaluation cells are used as the evaluation results for the entire cell population, the cell population is averaged for evaluation. When the cell population is averaged for evaluation, it is impossible to determine from the evaluation results whether the evaluated cell state applies to all cells or only to some of the cells. For example, when low productivity is evaluated based on the activity state of cells, if the productivity is low for all cells, it is necessary to change the culture conditions, etc., but if the productivity is low only for some cells, it is not necessarily necessary to make major changes to the culture conditions, etc. Therefore, by individually evaluating the activity state of cells, the cell state can be evaluated more accurately and the yield of metabolic products can be improved.

[0069] (2.1) Gating The evaluation step may include a step of extracting measurement data of a portion of cells from a population of measurement data of cells based on a specific measurement parameter. The boundary for extracting data is called a "gate," and the extraction step is called "gating."

[0070] The types of measurement parameters are not limited and may be one type or two or more types. At least one of the measurement parameters includes a physical quantity that changes due to at least one of metabolic activity and stress activity. The other measurement parameters do not necessarily have to be physical quantities that change due to at least one of metabolic activity and stress activity.

[0071] When there is only one measurement parameter, gating is performed based on that one measurement parameter. When there are two or more measurement parameters, gating is first performed on the entire sample based on the first parameter to extract a portion of the sample. Next, gating is performed on the extracted portion of the sample based on the second parameter to further extract a portion of the sample. By performing multistage extraction using multiple measurement parameters, the activity state of cells can be evaluated more accurately.

[0072] An example of multi-stage extraction using multiple types of measurement parameters will be described.

[0073] (forward and side scatter gating) When light is irradiated onto a cell, the irradiated light is refracted by the cell, generating forward scattered light (FS) and side scattered light (SS). The intensity of forward scattered light can be used to evaluate the size of the cell, and the intensity of side scattered light can be used to evaluate the granularity and complexity of the cell.

[0074] Figure 2 is a graph showing the measurement results for a certain cell population, with the horizontal axis representing the intensity of forward scattered light (FS) and the vertical axis representing the intensity of side scattered light (SS). In Figure 2, there are two main areas (areas PA and PB) where the plots are concentrated, and it can be determined that this cell population contains two or more cell types with different shapes. By selecting the areas thought to correspond to the cells to be measured and extracting the plots present in those areas, it is possible to eliminate the measurement data for cells not to be measured.

[0075] In other words, when a sample contains a mixture of multiple cell types or foreign bodies with significantly different shapes, forward and side scatter gating can be used to isolate the target cell type, resulting in more accurate assessment of cell status and improved metabolic yield.

[0076] (Labeled population gating) After forward and side scatter gating is performed to extract a portion of the cell population of the sample, labeled population gating is performed on the extracted cell population of the sample. Here, "labeled population gating" refers to gating performed based on the physical quantities measured in the measurement step.

[0077] (3) Prediction process In the prediction step, the yield of the metabolic product is predicted based on the evaluation result of the activity state. If the predicted value is lower than the desired yield, it is preferable to change the culture conditions (production conditions), etc. By appropriately changing the culture conditions according to the predicted value, the yield of the metabolic product at the end of the culture can be further improved.

[0078] The prediction step is not particularly limited. For example, cells with known expression levels of metabolites are separately prepared, and physical quantities that change due to at least one of metabolic activity and stress activity are measured for these cells. A calibration curve is created by plotting the expression levels of metabolites and the measured values ​​of the physical quantities, and the yield is predicted from the calibration curve. Alternatively, prediction may be performed using a machine learning model.

[0079] (3.1) Machine Learning The prediction step may include constructing a machine-learned model based on the cell culture results and the measurement data extraction results, and predicting the yield of the metabolites using the constructed machine-learned model. This allows for accurate prediction of the yield of the metabolites and further improvement of the yield of the metabolites.

[0080] Machine learning can be either supervised or unsupervised. "Supervised learning" refers to a learning method that learns the "relationship between input and output" from training data with correct answer labels. "Unsupervised learning" refers to a learning method that learns the "structure of a data group" from training data without correct answer labels.

[0081] Machine learning may be reinforcement learning, deep learning, or deep reinforcement learning. "Reinforcement learning" refers to a learning method that learns "optimal action sequences" through trial and error. "Deep learning" refers to a learning method that learns the features contained in data in a stepwise and deeper manner (at a deeper level) from a large amount of data. "Deep reinforcement learning" refers to a learning method that combines reinforcement learning and deep learning.

[0082] Common analytical methods (algorithms) can be applied to machine learning. For example, predictive models constructed using analytical methods such as linear regression, random forest, decision tree, support vector machine (SVM), support vector regression (SVR), neural network, and discriminant analysis can be applied to machine learning. Examples of linear regression include multiple regression analysis, partial least squares (PLS) regression, LASSO regression, Ridge regression, and principal component regression (PCR). A trained model is constructed by combining the results of multiple predictive models.

[0083] The prediction model is constructed based on data linking the culture results and the extracted measurement data. The prediction model can be constructed by performing machine learning using the features in the extracted measurement data as explanatory variables and the culture results as the target variable.

[0084] Here, "culture results" refers to the state of cells during the culture step described below or after the culture step has been completed. Specifically, examples include the amount (yield) of metabolic products produced in the cells, the amount (yield) of a target substance contained in the metabolic products, the amount (yield) of by-products other than the target substance contained in the metabolic products, and, if the target substance is a protein, the folding state of the protein.

[0085] When referring to a trained model, the data obtained in the measurement step is applied to the trained model. The prediction result may be obtained as, for example, classification, regression, clustering, anomaly detection (outlier detection), etc.

[0086] (4) Preparation process The evaluation step includes a step of extracting measurement data of a portion of cells from a population of cell measurement data based on specific measurement parameters. In this case, the sorting step involves sorting a portion of cells from the population of cells based on the measurement data extraction results. That is, if the evaluation step includes a gating step, the sorting step involves sorting a portion of cells from the population of cells based on the gating results.

[0087] Alternatively, after the yield of the metabolite is predicted in the prediction step, a portion of cells may be separated from the cell population in the separation step.

[0088] In a culture tank such as a reactor described below, there is an upper limit to the number of cells that can be cultured, so it is preferable to culture only highly productive cells and remove as many less productive cells as possible.

[0089] Cell sorting can be performed using, for example, a cell sorter, the details of which will be described later.

[0090] (5)Culture process In the culturing step, cells are cultured, and then the metabolites produced by the culture are collected as the products of the production step.

[0091] The medium used to culture cells is not particularly limited as long as it allows the cells to grow and produce the target substance. When the cells are microorganisms, synthetic or natural media containing nutrient sources such as carbon sources, nitrogen sources, and inorganic salts can be used. Examples of carbon sources include glucose, sucrose, fructose, maltose, glycerin, dextrin, starch, oligosaccharides, molasses, malt extract, and organic acids. Nitrogen sources include organic nitrogen sources such as various peptones, yeast extract, corn steep liquor, soybean flour, bran extract, meat extract, casein, amino acids, and urea. Nitrogen sources include inorganic nitrogen sources such as nitrates and ammonium salts. Inorganic salts include sodium salts, potassium salts, magnesium salts, iron salts, and other metal salts. Other nutrient sources include vitamins, amino acids, and nucleic acids.

[0092] The culture is preferably liquid culture, and various general cell culture methods can be used. The liquid culture is preferably aerobic culture, and the medium is preferably agitated. An example of such a culture is aerobic agitation culture. In aerobic agitation culture, the medium may be agitated only by aeration, such as by airlift.

[0093] The oxygen concentration in aerobic culture is preferably within the range of, for example, 5 to 50% of the saturation concentration. The liquid culture method may be any of batch culture, fed-batch culture, and continuous culture. Culture conditions (temperature, pH, culture time, etc.) can be appropriately set depending on the growth characteristics of the cells to be cultured. The culture temperature is usually within the range of 10 to 40°C, preferably within the range of 30 to 37°C. The pH is usually within the range of 4 to 8, preferably within the range of 5 to 7. The culture time is usually within the range of 1 to 500 hours.

[0094] 2. Production System The production system of this embodiment is a production system that produces metabolites by culturing cells, and includes a cell sorter that sorts the cells, a reactor that cultures the cells, and a transport device that transports the sorted cells from the cell sorter to the reactor. In other words, the production system of this embodiment is a production system that implements the above-mentioned production method.

[0095] 3 is a schematic diagram showing an example of a production system 100 according to this embodiment. The production system 100 includes a cell sorter 10, a reactor 20, and a first transfer device 30. The production system 100 may further include a second transfer device 40, an information processing device 50, etc.

[0096] As an example, the following describes a case where a reagent is introduced into sample cells and the fluorescence intensity is measured as a physical quantity. The cell sorter used in this case is also called a FACS (Fluorescence Activated Cell Sorting) device. The cell sorter 10 includes a flow cell 11, a light source 12, a first detector 13, a second detector 14, a controller 15, a deflection electrode plate 16, a collection container 17, a waste container 18, etc.

[0097] The cells of the sample into which the reagents etc. have been introduced are discharged from the flow cell 11. The discharged cells are individually irradiated with light from a light source 12, and the scattered light is detected by a first detector 13, and the fluorescent light is detected by a second detector 14, and the scattered light intensity and fluorescent intensity are measured. Based on the scattered light intensity, cells that are not considered to be the measurement target are excluded, and the measurement target cells are extracted. Furthermore, based on the fluorescent intensity, highly productive cells are extracted.

[0098] When measuring fluorescence intensity, examples of the wavelength (excitation wavelength) of the excitation light emitted from light source 12 include 355 nm, 405 nm, 488 nm, 561 nm, and 633 nm. Second detector 14 that detects fluorescence may be equipped with a bandpass filter so that it can detect only specific wavelengths. Second detector 14 that detects fluorescence may also be equipped with a filter that transmits light of wavelengths equal to or longer than the excitation wavelength.

[0099] Examples of bandpass filters include the following filters. The numbers in the following filters indicate "(center wavelength of transmitted wavelength range) / (transmitted wavelength range)", so for example, "530 / 30" indicates that light in the wavelength range of 515 to 545 nm is transmitted. Examples of bandpass filters include 379 / 28, 450 / 40, 525 / 50, 530 / 30, 575 / 25, 610 / 20, 670 / 30, 695 / 40, 710 / 50, 730 / 45, 740 / 35, and 780 / 60.

[0100] Thereafter, an electric charge is applied to the cells using a controller 15. Based on the extraction results, the path of the cells is controlled using a deflection electrode plate 16 so that highly productive cells are sorted. The cells to be sorted are placed in a sorting container 17, and the cells to be discarded are placed in a waste container 18.

[0101] The highly productive cells sorted into the sorting container 17 are then transported to the reactor 20 by the first transport device 30. The transported cells are cultured in the reactor 20. The configuration of the first transport device 30 is not particularly limited, and any known device for transporting cells can be applied. Furthermore, by including the first transport device 30, the production system 100 can automatically transport the sorted cells to the reactor. Automatic transport can reduce the number of work steps and time, improving work efficiency. Furthermore, while cells are prone to be stressed when sorted and transported manually, automatic transport can reduce the stress on the cells.

[0102] The productivity of the cells cultured in the reactor 20 may be evaluated and then returned to the reactor 20. By evaluating the productivity of the cells during culture, it is possible to remove low-productivity cells from the reactor early, thereby improving the final yield of metabolic products. When evaluating the productivity of the cells cultured in the reactor 20, the cells removed from the reactor 20 are transported to the cell sorter 10 by the second transport device 40, and only the highly productive cells are sorted out. The configuration of the second transport device 40 is not particularly limited, and any known device for transporting cells can be applied.

[0103] The reactor 20 may be equipped with a device for aerating the culture medium (e.g., a gas supply device). It is preferable that an oxygen-containing gas is supplied by the gas supply device during cultivation. The oxygen concentration of the oxygen-containing gas used for aeration is preferably within a range of 5 to 50% of the saturation concentration. The reactor 20 may be equipped with a device for agitating the culture medium, such as an agitator blade. The reactor 20 may be equipped with devices for controlling the temperature, pH, dissolved oxygen concentration, etc. (e.g., a heater, a supply device for a pH adjusting reagent, etc.).

[0104] The information processing device 50 is a device that predicts the yield of metabolic products based on the evaluation results of the activity state of the cells, and is connected to the cell sorter 10. The information processing device 50 may be a computer that uses a CPU to execute a program that predicts the yield of metabolic products based on the measurement results of scattered light intensity and fluorescence intensity.

[0105] The information processing device 50 may be connected to the reactor 20. The information processing device 50 may predict the yield of metabolic products and, based on the prediction results, appropriately change the culture conditions (production conditions). Specifically, the information processing device 50 may, based on the prediction results, appropriately change the settings of various devices provided in the reactor 20. Examples of the settings of various devices include the gas supply amount, the rotation speed of the stirring blades, the heater temperature, and the supply amount of the pH adjusting reagent. This allows the culture conditions in the reactor 20 to be optimized in real time. [Example]

[0106] The present invention will be specifically described below with reference to examples, but the present invention is not limited to these. In the examples, the terms "parts" and "%" are used, but unless otherwise specified, they represent "parts by mass" or "% by mass." In the following examples, unless otherwise specified, the experiments were carried out at room temperature (25°C).

[0107] Example 1: Evaluation of mitochondrial activity 1-1. Gene transfection CHO-K1 cells were placed in a 6-well dish at 0.25 × 10 6 The cells were seeded at 100 cells / well. Host: CHO-K1 cells (cell registration number: RCB0403) provided by the RIKEN Cell Engineering Division

[0108] Next, the transfection reagent for introducing the plasmid was diluted with the medium to make 150 μL of medium for 15 μL of the transfection reagent for introducing the plasmid. Transfection reagents for plasmid delivery: Thermo Fisher Scientific, product number 15338100 Culture medium: Opti-MEM Medium (Thermo Fisher Scientific, product number 31985062)

[0109] Next, a plasmid containing the ECFP (blue fluorescent protein) gene was diluted with medium. 14 μg of the plasmid (0.5-5 μg / μL) was diluted with 700 μL of medium. Then, 14 μL of PLUS reagent was added to the diluted solution. Culture medium: Opti-MEM Medium (Thermo Fisher Scientific, product number 31985062)

[0110] The diluted solution of the plasmid was added to the diluted solution of the transfection reagent for introducing the plasmid at a ratio of 1:1.

[0111] The resulting solution was incubated at room temperature for 5 minutes.

[0112] The incubated solution was added in an amount of 250 μL to each of the 6-well dishes seeded with the above cells.

[0113] This resulted in transfected CHO-K1 cells carrying the ECFP (blue fluorescent protein) gene. The transfected cells were then cultured for 16 hours or more in an incubator set at 37°C under 5% CO2.

[0114] 1-2. Introduction of reagents We prepared a sample of CHO-K1 cells without the ECFP gene (1-Negative Sample) and a sample of transfected cells with the ECFP gene (1-Positive Sample).The reagent was introduced into the cells of both samples using the following procedure.

[0115] For the transfected cells into which the ECFP gene had been introduced, the cells attached to the 6-well dish were washed twice with phosphate-buffered saline (PBS).

[0116] Fresh PBS was prepared. MT-1 Dye included in the MT-1 mitochondrial membrane potential detection kit "MT13 MT-1 MitoMP Detection Kit" (Dojindo Laboratories, Inc.) was added to this PBS and suspended to obtain a mixture. This mixture was then sprinkled onto the dish containing the cells, and the cells in the dish were cultured for 30 minutes in an incubator set to 37°C in the presence of 5% CO2.

[0117] The PBS in the dish was removed, and the cells were washed with fresh PBS. Then, the cells were detached from each other using trypsin.

[0118] The detached cells were washed with freshly prepared PBS and passed through a cell strainer to loosen the cell clumps.

[0119] The loosened cells were observed using a FACS (Fluorescence Activated Cell Sorting) device.

[0120] 1-3. Observation results using FACS equipment Because the ECFP gene was introduced into the 1-Positive Sample, it is believed that highly productive cells produce large amounts of blue fluorescent protein due to mitochondrial function. In other words, when irradiated with excitation light corresponding to the MT-1 reagent, which evaluates mitochondrial activity, the cells will emit fluorescence, and it is believed that the more active the cells are in mitochondria, the higher the fluorescence intensity. In addition, when irradiated with excitation light corresponding to blue fluorescent protein, the cells will emit fluorescence, and it is believed that the more productive the cells are in mitochondria, the higher the fluorescence intensity.

[0121] Each sample was irradiated with excitation light corresponding to the MT-1 reagent, and the intensity of the emitted fluorescence was measured. The excitation wavelength (Ex) was 488 nm, and the fluorescence wavelength (Em) was 530 nm. Each sample was also irradiated with excitation light corresponding to the blue fluorescent protein, and the intensity of the emitted fluorescence was measured. The excitation wavelength (Ex) was 405 nm, and the fluorescence wavelength (Em) was 450 nm.

[0122] Figure 4 is a graph showing the observation results of the 1-Negative Sample. Figure 5 is a graph showing the observation results of the 1-Positive Sample. The vertical axis of the graph shows the fluorescence intensity corresponding to the MT-1 reagent, and the horizontal axis shows the fluorescence intensity corresponding to the blue fluorescent protein.

[0123] As shown in Figure 5, for the 1-Positive Sample, plots with large values ​​on the horizontal axis of the graph also have large values ​​on the vertical axis of the graph. This indicates that there is a correlation between mitochondrial activity and the amount of blue fluorescent protein produced. This demonstrates that cell productivity can be evaluated by assessing mitochondrial activity in cells, without having to disrupt the cells to extract metabolic products.

[0124] Example 2: pH evaluation 2-1. Gene transfection Using the same procedure as in "1-1. Gene transfection" in Example 1, transfected cells were obtained in which the ECFP (blue fluorescent protein) gene was introduced into CHO-K1 cells.

[0125] 2-2. Introduction of reagents A sample of CHO-K1 cells without the ECFP gene (2-Negative Sample) and a sample of transfected cells with the ECFP gene (2-Positive Sample) were prepared. The BCECF-AM special packaging reagent was introduced into the cells of both samples using the same procedure as in "1-2. Introduction of Reagent" in Example 1.

[0126] The PBS in the dish was removed, and the cells were washed with fresh PBS. Then, the cells were detached from each other using trypsin.

[0127] The detached cells were washed with freshly prepared PBS and passed through a FACS filter to loosen cell clumps.

[0128] The loosened cells were observed using a FACS (Fluorescence Activated Cell Sorting) device.

[0129] 2-3. Observation results using FACS equipment Because the 2-Positive Sample contained the ECFP gene, it is believed that in highly productive cells, greater amounts of blue fluorescent protein are produced through mitochondrial function, and that this production leads to changes in the intracellular pH value. The intracellular pH measurement reagent exhibits a linear change in fluorescence intensity relative to the pH value in the range of 6.4 to 7.6. Therefore, it is believed that when irradiated with excitation light corresponding to the intracellular pH measurement reagent, the cells will emit fluorescence, and the fluorescence intensity will change in response to the pH value.

[0130] Here, we attempted to observe the relationship between the fluorescence intensity corresponding to the MT-1 reagent and the fluorescence intensity corresponding to the intracellular pH measurement reagent. However, the excitation wavelength (Ex) of the intracellular pH measurement reagent is 490 nm, and the fluorescence wavelength (Em) is 526 nm, which are very close to the excitation wavelength and fluorescence wavelength of the MT-1 reagent, making it impossible to separate the fluorescence corresponding to each reagent. Therefore, we used the following method to observe the relationship between the fluorescence intensity corresponding to the MT-1 reagent and the fluorescence intensity corresponding to the intracellular pH measurement reagent.

[0131] Each sample was irradiated with excitation light corresponding to the MT-1 reagent, and the intensity of the emitted fluorescence was measured. The excitation wavelength (Ex) was 488 nm, and the emission wavelength (Em) was 520 nm. The intracellular pH measurement reagent has an isosbestic point at 440 nm, and it is known that fluorescence intensity does not depend on pH when excitation light has a wavelength of 439 nm or less. Therefore, each sample was irradiated with excitation light that does not correspond to the intracellular pH measurement reagent, and the intensity of the emitted fluorescence was measured. The excitation wavelength (Ex) was 405 nm, and the emission wavelength (Em) was 510 nm.

[0132] Figure 6 is a graph showing the observation results for the 2-negative sample. Figure 7 is a graph showing the observation results for the 2-positive sample. The vertical axis of the graph shows the fluorescence intensity corresponding to the MT-1 reagent, and the horizontal axis shows the fluorescence intensity at Ex 405 nm and Em 510 nm. However, as mentioned above, since the corresponding excitation and emission wavelengths of the MT-1 reagent and the intracellular pH measurement reagent are similar, the intracellular pH measurement reagent is excited and emits fluorescence simultaneously with the MT-1 reagent, and the fluorescence intensity is measured together with that of the MT-1 reagent. Therefore, the vertical axis of the graph is essentially the sum of the fluorescence intensity corresponding to the MT-1 reagent and the intracellular pH measurement reagent. Furthermore, the fluorescence shown on the horizontal axis of the graph is thought to be derived not from the fluorescence of the MT-1 reagent or the intracellular pH measurement reagent, but from other components.

[0133] As shown in Figure 6, the plots for the 2-Negative Sample are aligned in a straight line. On the other hand, as shown in Figure 7, for the 2-Positive Sample, many plots are seen in the P2 region, which deviates from the linear plot of the 2-Negative Sample. It is known that when metabolic reactions occur within cells and blue fluorescent protein is produced, the intracellular pH value decreases, and as the pH value decreases, the value on the vertical axis of the graph decreases. Therefore, the plots seen in the P2 region are thought to represent highly productive cells.

[0134] Therefore, gating was performed to extract plots present in the P2 region for the 2-Positive Sample. Figure 8 is a graph showing the observation results of the Gated Sample. For the Gated Sample, the same sample as the 2-Positive Sample was irradiated with excitation light corresponding to blue fluorescent protein, and the emitted fluorescence intensity was measured. The excitation wavelength (Ex) and fluorescence wavelength (Em) for the measurements were 434 nm and 477 nm, respectively. The vertical axis of the graph shows the fluorescence intensity corresponding to the MT-1 reagent, and the horizontal axis shows the fluorescence intensity corresponding to the blue fluorescent protein. Note that the vertical axis of the graph is essentially the sum of the fluorescence intensity corresponding to the MT-1 reagent and the fluorescence intensity corresponding to the intracellular pH measurement reagent.

[0135] As shown in Figure 8, in the gated sample, the P2 region has a higher value on the horizontal axis of the graph compared to the other plot regions. This indicates that there is a correlation between the intracellular pH value and the amount of blue fluorescent protein produced. This shows that cell productivity can be evaluated by assessing the cellular pH value without having to disrupt the cells to extract metabolic products. [Explanation of symbols]

[0136] 10 Cell sorter 11 Flow Cell 12 light source 13 First detector 14 Second detector 15 Controller 16 Deflection electrode plate 17 Preparative container 18 Disposal containers 20 Reactor 30 First conveying device 40 Second conveying device 50 Information processing equipment 100 Production System

Claims

1. A method for producing a metabolite by culturing cells, comprising: a measuring step of individually measuring physical quantities of the cells that change due to at least one of metabolic activity and stress activity; an evaluation step of individually evaluating the activity state of the cells based on the measurement results of the physical quantities; A production method comprising a culturing step of culturing the cells.

2. The production method according to claim 1 , wherein the measuring step comprises staining the cells with a fluorescent reagent and measuring the fluorescence intensity of the stained cells using a detector.

3. The production method according to claim 1 , further comprising a prediction step of predicting the yield of the metabolite based on the evaluation result of the activity state.

4. The production method according to claim 1 , wherein the evaluation step comprises extracting measurement data of a portion of the cells from a population of measurement data of the cells based on a specific measurement parameter.

5. The production method according to claim 4 , further comprising a sorting step of sorting a portion of the cells from the cell population based on the results of the extraction of the measurement data.

6. The production method according to claim 5, wherein the separated cells are returned to the reactor and cultured.

7. The production method according to claim 6, wherein the separated cells are automatically returned to the reactor.

8. The prediction step constructing a machine-learned model based on the results of culturing the cells and the results of extracting the measurement data; The production method according to claim 3 , wherein the yield of the metabolite is predicted using the constructed machine-learned model.

9. A production system for producing a metabolic product by culturing cells, comprising: The production system comprises a cell sorter that sorts the cells, a reactor that cultures the cells, and a transport device that transports the sorted cells from the cell sorter to the reactor.

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