Method for predicting gene introduction rate

Abstract

Disclosed is a method for predicting the gene introduction rate of an animal cell, which comprises (1) a step for measuring the cell complexity of an animal cell into which a gene has been introduced, and (2) a step for predicting the gene introduction rate on the basis of the value measured in step (1).

Description

Technical Field

[0001] The present invention relates to a method for predicting gene introduction rate. Background Art

[0002] In recent years, research and development of genetic recombinant cell drugs such as chimeric antigen receptor (CAR)-T cells (cell preparations into which genes have been introduced / genes have been introduced) are being actively carried out. For genetic recombinant cell drugs such as CAR-T cells, the amount of the prescribed product is defined by multiplying the overall number of cells by the ratio of the introduced gene. Therefore, for CAR-T cell products, the CAR introduction rate is the most important indicator in the manufacturing process, transportation and administration. However, flow cytometry has always been used to measure the method for measuring the gene introduction rate for cells, but there are problems such as long time from measurement to analysis (6 to 7 hours) and the need for complicated steps (the staining step of the cell is a manual operation). For example, Non-Patent Literature 1 has reported the measurement of CAR-T cells using flow cytometry.

[0003] Prior art literature

[0004] Non-patent literature

[0005] Non-patent literature 1: Cytometry Part B: Clinical Cytometry, Vol. 100, No. 2, pp. 218-224 Summary of the invention

[0006] Problems to be solved by the invention

[0007] An object of the present invention is to provide a method for predicting a gene introduction rate, which method can measure the gene introduction rate of cells more simply and efficiently than conventional methods.

[0008] Means for solving problems

[0009] The inventors of the present application have conducted intensive studies to achieve the above-mentioned purpose and have found that the gene introduction rate into animal cells can be simply and efficiently predicted by measuring the complexity within cells (for example, SSC measured by flow cytometry) and using it as an index.

[0010] The present invention has been completed as a result of further studies based on this finding, and provides the following method for predicting the gene introduction rate into animal cells.

[0011] [1] A method for predicting the gene transfer rate of animal cells, comprising:

[0012] (1) a step of measuring the cell complexity of the gene-transferred animal cell; and

[0013] (2) A step of predicting the gene introduction rate based on the value measured in step (1).

[0014] [2] The method of [1], wherein the cellular complexity is the complexity within the cell.

[0015] [3] The method according to [1] or [2], wherein in step (2), the distribution of cell complexity is used to predict the gene introduction rate of the cell population.

[0016] [4] The method according to any one of [1] to [3], wherein in step (2), a parameter of the distribution of cell complexity is used to predict the gene introduction rate of the cell population.

[0017] [5] The method according to [4], wherein the parameter of the distribution of cell complexity is at least one selected from the group consisting of: mean, median, mode, variance, kurtosis, skewness, maximum, minimum, quartiles, peak height and half width.

[0018] [6] The method of [4], wherein the parameter of the distribution of cell complexity is at least one selected from the group consisting of: mean, median, mode, variance, kurtosis and skewness.

[0019] [7] The method according to [3], wherein in step (2), the gene introduction rate of the cell population is predicted by calculating the distribution of the complexity of the animal cells into which the gene is introduced based on the distribution of the cell complexity.

[0020] [8] The method according to any one of [1] to [7], wherein the determination of cell complexity in step (1) is performed by measuring side scatter (SSC) or side fluorescence (SFL) using a flow cytometer.

[0021] [9] The method according to any one of [1] to [8], wherein the determination of cell complexity in step (1) is performed by measuring SSC or SFL using a flow cytometer, and in step (2), the pulse height, width and area of ​​SSC or SFL are used to predict the gene introduction rate of the cell population.

[0022]

[10] The method according to [1], [2], [8] or [9], wherein in step (2), the value of cell size is further used to predict the gene introduction rate of the cell population.

[0023]

[11] The method according to

[10] , wherein the value of the cell size is a forward scatter (FSC) measurement value obtained by flow cytometry.

[0024]

[12] The method according to

[11] , wherein in step (2), the pulse height, width and area of ​​FSC are used to predict the gene introduction rate of the cell population.

[0025]

[13] The method according to any one of [1] to [7], wherein the cell complexity is the complexity of the cell surface.

[0026]

[14] The method according to

[13] , wherein the complexity of the cell surface is the cell's circumference, area envelope (Solidity), unevenness, arithmetic mean roughness, maximum height, ten-point average roughness, average interval between unevenness, average interval between local peaks, load length ratio, fractal dimension, aspect ratio, circularity, roundness or density.

[0027]

[15] A method according to any one of [1] to [7], wherein the determination of cell complexity in step (1) is performed by measuring intracellular organelles, cell surface glycans, cell membranes, cellular lipids, intracellular metabolites, cellular fat droplets or cell surface shape.

[0028]

[16] The method according to any one of [1] to [7], wherein the determination of cell complexity in step (1) is performed by measuring the spontaneous fluorescence derived from the cells.

[0029]

[17] The method according to any one of [1] to

[16] , wherein the animal cell is an immune cell.

[0030]

[18] The method according to

[17] , wherein the immune cells are T cells or NK cells.

[0031]

[19] The method according to any one of [1] to

[16] , wherein the animal cell is an epithelial cell.

[0032] [19a] The method according to

[19] , wherein the animal cell is a liver cancer cell.

[0033] [19b] The method according to any one of [1] to [19a], wherein the animal cell is a human cell.

[0034]

[20] The method according to any one of [1] to [19b], wherein the nucleic acid to be gene introduced is a nucleic acid encoding a chimeric antigen receptor.

[0035]

[21] The method according to any one of [1] to

[12] and

[17] to

[20] , wherein the difference in cell complexity between gene-introduced cells and gene-unintroduced cells is 5% or more as measured by SSC or SFL obtained by flow cytometry.

[0036]

[22] A method for producing a gene-transferred cell preparation, comprising:

[0037] (I) A step of measuring the complexity of gene-transferred animal cells and determining the gene transfer rate based on the measured value.

[0038] Effects of the Invention

[0039] According to the gene introduction rate prediction method of the present invention, the gene introduction rate of cells can be measured by a simpler operation (for example, the required operation is only automatic measurement using a measurement device) compared to conventional methods, without complicated steps. In addition, according to the gene introduction rate prediction method of the present invention, the gene introduction rate of cells can be measured efficiently (for example, in a short period of time of less than 5 minutes). BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The figures show the results of Example 1. (A) is a figure showing the CAR expression rate in T cells as a control without gene introduction, (B) is a figure showing the CAR expression rate in CAR-T cells into which the CAR gene was introduced, (C) is a figure showing the SSC in T cells as a control without gene introduction, (D) is a figure showing the SSC in CAR-T cells into which the CAR gene was introduced, (E) is a figure showing the correlation between the CAR positive rate (%) and the high SSC value (the region obtained by visually cutting out the high SSC site from the histogram of the control and setting a gate) (N=5), (F) is a figure showing the correlation between the CAR positive rate (%) and the mean, variance, median, mode, skewness, and kurtosis of SSC (N=5).

[0041] Figure 2 The figures show the results of Example 2. (A) is a figure showing the CAR expression rate in SK-Hep-1 as a control without gene introduction, (B) is a figure showing the CAR expression rate in SK-Hep-1 into which the CAR gene was introduced, (C) is a figure showing the SSC in SK-Hep-1 as a control without gene introduction, (D) is a figure showing the SSC in SK-Hep-1 into which the CAR gene was introduced, and (E) is a figure showing the correlation between the CAR positive rate (%) and the SSC high value (N=4).

[0042] Figure 3The figures show the results of Example 3. (A) is a figure showing SFL in T cells as a control without gene introduction, (B) is a figure showing SFL in CAR-T cells into which CAR gene was introduced, (C) is a figure showing the correlation between CAR positive rate (%) and SFL high value (N=5). (D) is a figure showing the correlation between CAR positive rate (%) and the mean, variance, median, mode, skewness, and kurtosis of SFL (N=5).

[0043] Figure 4 This is a graph showing the results of Example 4. This graph shows the distribution of SSC of cells into which the CAR gene was introduced, obtained by a mixed log-normal distribution model, using the SSC distribution of cells into which the CAR gene was introduced measured by flow cytometry. The CAR positivity predicted by flow cytometry was 66.8%, while the CAR positivity obtained by the mixed log-normal distribution model was 67.5%.

[0044] Figure 5 Graphs showing the results of Example 5. (A) Graph showing the CAR gene introduction rate (%) predicted by the machine learning model using SSC-H, SSC-W and SSC-A and the CAR gene introduction rate (%) measured by flow cytometry (N = 18). (B) Graph showing the CAR gene introduction rate (%) predicted by the machine learning model using SSC-H, SSC-W, SSC-A, FSC-H, FSC-W and FSC-A and the CAR gene introduction rate (%) measured by flow cytometry (N = 18).

[0045] Figure 6 Figures showing the results of Example 6. (A) is an immunostaining image of a control T cell without gene introduction, (B) is an immunostaining image of a CAR-T cell into which a CAR gene has been introduced, (C) is an electron microscope image of a control T cell without gene introduction, (D) is an electron microscope image of a CAR-T cell into which a CAR gene has been introduced, and (E) is an image showing the area (μm) of a control T cell without gene introduction and a CAR-T cell into which a CAR gene has been introduced. 2 ), diameter (μm), circumference (μm) and area envelope (μm).

[0046] Fig. 7A These are images showing the morphology of T cells and CAR-T cells in Example 7. (A): Transmission electron microscope image.

[0047] Figure 7B These are images showing the morphology of T cells and CAR-T cells in Example 7. (B): Scanning electron microscope image.

[0048] Fig. 8A This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0049] Figure 8B This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0050] Figure 8C This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0051] Fig.8D This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0052] Fig. 8E This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0053] Figure 8F This is a graph showing the results of metabolomic and lipidomic analysis of T cells and CAR-T cells in Example 8. The horizontal axis represents each component, and the vertical axis represents the difference in amount between T cells and CAR-T cells. A t-test was performed on each component. *: p<0.05, **: p<0.01, ***: p<0.001

[0054] Fig.9A It is a graph showing the results of metabolome and lipidome analysis of T cells and CAR-T cells over time in Example 8. For each component, a two-way ANOVA was performed based on culture days and cell types, and the changes in the amount of each component over time were plotted for the top 50 components with the lowest p-values.

[0055] Fig. 9B It is a graph showing the results of metabolome and lipidome analysis of T cells and CAR-T cells over time in Example 8. For each component, a two-way ANOVA was performed based on culture days and cell types, and the changes in the amount of each component over time were plotted for the top 50 components with the lowest p-values.

[0056] Fig.10 This is a diagram showing the results of principal component analysis for metabolome and lipidome analysis of T cells and CAR-T cells in Example 8. Principal component analysis was performed using each component, and a scatter plot was drawn with the first principal component as the horizontal axis and the second principal component as the vertical axis. Samples from each donor of UTD (T cells), CAR-T, and 7x19 CAR-T are drawn in close proximity and can be distinguished based on the presence or absence of the CAR gene and its type.

[0057] Fig.11 Graphs showing the results of metabolome and lipidome enrichment analysis in Example 9.

[0058] Fig.12 This is a diagram showing the results of quantitative analysis of mitochondria in T cells and CAR-T cells in Example 9.

[0059] Fig.13 This is a graph showing the difference in autofluorescence between T cells and CAR-T cells in Example 9.

[0060] Fig.14 This is the holographic microscope image of Example 10.

[0061] Fig.15 The figures show the results of Example 11. (A) is a figure showing the flow cytometry classification of mCherry gene introduced into γδT cells, (B) is a figure showing the SSC histogram of mCherry-negative cells and mCherry-positive cells, (C) is a figure showing the median SSC of mCherry-negative cells and mCherry-positive cells (t-test was performed on the median SSC) (N=4, error range is standard deviation).

[0062] Fig.16 Graphs showing the results of Example 12. (A) is a graph showing flow cytometry fractionation of CAR gene-introduced γδT cells, and (B) is a graph showing the median SSC of CAR-negative cells and CAR-positive cells.

[0063] Fig.17Figures showing the results of Example 13. (A) is a graph showing flow cytometry classification of CAR gene-introduced NK92 cells, and (B) is a graph showing the median SSC of CAR-negative cells and CAR-positive cells (t-test performed on the median SSC) (N=4, error range is standard deviation).

[0064] Fig.18 Figures showing the results of total glycomic analysis of T cells and CAR-T cells in Example 14. (A) shows the results of principal component analysis. (B) shows the results of heat map analysis. The heat map analysis shows the classification of CAR-T cells and T cells (UTD) by class, and each column uses a different color to represent the glycan expression level in each cell. DETAILED DESCRIPTION

[0065] Hereinafter, embodiments of the present invention will be described in detail.

[0066] "comprise(s) / comprising" means that although the elements after the statement are included, it is not limited to the elements. Therefore, it implies the inclusion of the elements after the statement, but does not imply the exclusion of any other elements. "Consist(s) of / consisting of" means that any element after the statement is included, and is limited to the element. Therefore, the statement "consist(s) of / consisting of" means that the listed elements are required or necessary, and there are substantially no other elements. "Consist(s) essentially of / consisting essentially of" means that any element after the statement is included, and the element is limited to other elements that do not affect the activity or effect specified in the present disclosure. Therefore, the expression “consist(s) essentially of / consisting essentially of” means that the listed elements are required or necessary, while other elements are optional, and other elements may or may not be present depending on whether they affect the activity or function of the listed elements.

[0067] In this specification, "pluripotent stem cell" refers to embryonic stem cells (ES cells) and cells with the same differentiation pluripotency, that is, cells that potentially have the ability to differentiate into various tissues (entoderm, mesoderm and ectoderm all) of an organism. As cells with the same pluripotency as ES cells, "induced pluripotent stem cells" (also referred to as "iPS cells" in this specification) can be cited. In the case where pluripotent stem cells are ES cells or any cells derived from human embryos, these cells can be cells prepared by destroying embryos, or can be cells prepared without destroying embryos, preferably cells prepared without destroying embryos.

[0068] In the present specification, the term "cell population" refers to two or more cells of the same or different types. The term "cell population" also refers to a mass of cells of the same or different types.

[0069] The method for predicting the gene introduction rate of animal cells of the present invention is characterized in that it comprises:

[0070] (1) a step of measuring the cell complexity of the gene-transferred animal cell; and

[0071] (2) A step of predicting the gene introduction rate based on the value measured in step (1).

[0072] The "gene introduction rate" in the present invention refers to the introduction rate of nucleic acid into cells, and is, for example, the ratio of cells into which nucleic acid is introduced among all cells, the nucleic acid uptake amount of a cell population, or the expression rate of the introduced gene in the entire cell population. When the introduced gene is CAR, it is also recorded as the CAR positive rate.

[0073] -Step (1)

[0074] In step (1), the cell complexity of the gene-transferred animal cells is measured.

[0075] The kind of animal cell is not particularly limited, and various animal cells can be widely used. As the kind of animal cell, splenocyte, neuron, glial cell, pancreatic beta cell, bone marrow cell, mesangial cell, Langerhans cell, epidermal cell, epithelial cell, endothelial cell, fibroblast, fibroblast, muscle cell (for example, skeletal muscle cell, cardiomyocyte, myoblast and muscle satellite cell), adipocyte, immune cell (for example, macrophage, T cell, B cell, natural killer cell (NK cell), mast cell, neutrophil, basophil, eosinophil, monocyte, megakaryocyte), synoviocyte, chondrocyte, osteocyte, osteoblast, osteoclast, mammary cell, hepatocyte, stromal cell, egg cell and sperm cell, and stem cell (including neural stem cell, hematopoietic stem cell, mesenchymal stem cell, dental pulp stem cell, iPS cell, ES cell etc. pluripotent stem cell), progenitor cell, blood cell, oocyte and fertilized egg that can be induced to differentiate into these cells. T cells include αβT cells, γδT cells, helper T cells, cytotoxic T cells, regulatory T cells, suppressor T cells, tumor infiltrating T cells, memory T cells, naive T cells, NK T cells, TCR-T cells, STAR receptor T cells, CAR-T cells, etc. In addition, animal cells also include primary cells, the above cells prepared by inducing the in vitro differentiation of the above stem cells (e.g., iPS cells), etc. In addition, animal cells also include various cancer cells. Animal cells can be only 1, or include more than 2.

[0076] Furthermore, the organism from which the animal cells are derived is not particularly limited, and examples of such organisms include mammals such as humans, mice, rats, cows, horses, pigs, rabbits, dogs, cats, goats, monkeys, and chimpanzees, among which humans are preferred.

[0077] Animal cells are animal cells into which foreign genes have been introduced. "Foreign genes" are genes or monomeric nucleotides introduced from the outside in order to make animal cells express desired proteins, and can be appropriately selected according to the purpose of the animal cells. The "animal cells into which genes have been introduced" in the present invention include animal cells into which gene introduction has been attempted and into which genes have actually been introduced, and animal cells into which genes have not been introduced. In addition, in the present invention, "(animal) cells into which genes have been introduced" refers to (animal) cells into which genes have actually been introduced. Depending on the context, "cells into which genes have been introduced" and "cells into which genes have been introduced" can be used interchangeably.

[0078] Exogenous genes can be, for example, genes for expressing chimeric antigen receptors (CAR). Exogenous genes can be, for example, genes for expressing CAR and genes for expressing cytokines and / or chemokines. As with general or known CAR, CAR expressed by animal cells is basically composed of peptides at the following sites connected via a spacer as needed: (i) antigen recognition sites (e.g., single-chain antibodies, ligands, and peptides, etc.) that recognize cell surface antigens of cancer cells, (ii) transmembrane regions, and (iii) signal transduction regions that induce T cell activation. As other exogenous genes, TCR (T cell receptor), STAR (synthetic T cell receptor and antigen receptor), chimeric TAC (T cell antigen conjugate) receptor, suicide gene (iCas9, HSV-TK, etc.), cytokines (interleukins, chemokines, etc.), etc. can be cited.

[0079] The means for introducing foreign genes into animal cells are not particularly limited, and various known or commonly used means can be used. Typically, the foreign genes are introduced into animal cells using expression vectors and expressed. The expression vectors can be linear or circular, and can be non-viral vectors (such as plasmids), viral vectors, or transposon vectors.

[0080] The method for introducing the expression vector into the animal cell can be a suitable method in accordance with the embodiment. For example, the expression vector can be introduced into the animal cell by known methods such as a virus infection method, a calcium phosphate method, a lipofection method, a microinjection method or an electroporation method. The expression vector can be prepared into a form suitable for use in each method by known means or appropriately using a commercially available kit (according to its instructions).

[0081] The expression vector can be introduced into animal cells by a viral infection method. As viral vectors, for example, retroviral vectors, lentiviral vectors, adenoviral vectors, and adeno-associated viral vectors can be cited. When using these viral vectors, the vector containing the desired foreign gene and the packaging vector (plasmid) of each virus are transfected into the host cell using the corresponding commercially available kit to produce the recombinant virus, and then the animal cell is infected with the obtained recombinant virus.

[0082] When there are multiple exogenous genes, all of them may be contained in one expression vector, all of them may be contained in different expression vectors, or some of the multiple exogenous genes may be contained in one expression vector, while the other exogenous genes may be contained in different expression vectors. When one expression vector contains multiple exogenous genes, the order in which these exogenous genes are arranged from the upstream side to the downstream side is not particularly limited.

[0083] Exogenous genes can be composed of nucleic acids (polynucleotides) having base sequences encoding the desired proteins or polypeptides according to their amino acid sequences. Those skilled in the art can design and prepare expression vectors that can express the desired proteins (polypeptides) in animal cells. The nucleic acids contained in the expression vectors can be prepared by chemically synthesizing DNA or as cDNA (cloning).

[0084] The expression vector may contain, in addition to the foreign gene, sequences such as a promoter, a terminator, an enhancer, a start codon, a stop codon, a polyadenylation signal, a nuclear localization signal (NLS) and a multiple cloning site (MCS) if necessary. The expression vector may further contain nucleic acids (base sequences) encoding "functional genes" such as reporter genes (e.g., genes encoding fluorescent proteins of various colors), drug selection genes (e.g., kanamycin resistance gene, ampicillin resistance gene and puromycin resistance gene), and suicide genes (e.g., genes encoding diphtheria A toxin, herpes simplex virus thymidine kinase (HSV-TK), carboxypeptidase G2 (CPG2), carboxylesterase (CA), cytosine deaminase (CD), cytochrome P450 (cyt-450), deoxycytidine kinase (dCK), nitroreductase (NR), purine nucleoside phosphorylase (PNP), thymidine phosphorylase (TP), varicella zoster virus thymidine kinase (VZV-TK), xanthine-guanine phosphoribosyltransferase (XGPRT), inducible caspase 9, etc.).

[0085] "Nucleic acid" may be any monomeric nucleotide, or any molecule obtained by polymerizing a nucleotide and a molecule having the same function as the nucleotide, for example, RNA as a ribonucleotide polymer, DNA as a deoxyribonucleotide polymer, a polymer as a mixture of ribonucleotides and deoxyribonucleotides, and a nucleotide polymer containing nucleotide analogs, and may also be a nucleotide polymer containing a nucleic acid derivative. Nucleic acid may be a single-stranded nucleic acid or a double-stranded nucleic acid. Double-stranded nucleic acids also include double-stranded nucleic acids in which one strand hybridizes with the other strand under stringent conditions.

[0086] Nucleotide analogs may be any molecules that are modified ribonucleotides, deoxyribonucleotides, RNA or DNA for the purpose of improving nuclease resistance or stabilizing it compared to RNA or DNA, increasing affinity for complementary nucleic acid, enhancing cell permeability, or visualization. Nucleotide analogs may be naturally occurring molecules or non-natural molecules, and examples thereof include sugar moiety modified nucleotide analogs (e.g., 2'-O-methylribose substituted nucleotide analogs, 2'-O-propylribose substituted nucleotide analogs, 2'-methoxyethoxyribose substituted nucleotide analogs, 2'-O-methoxyethyl ribose substituted nucleotide analogs, 2'-O-[2-(guanidinium)ethyl]ribose substituted nucleotide analogs, 2'-fluororibose substituted nucleotide analogs, bridged nucleic acid (BNA: Bridged Nucleic Acid), locked artificial nucleic acid (LNA: Locked Nucleic Acid), ethylene bridged nucleic acid (ENA: Ethylene bridgednucleic acid), peptide nucleic acid (PNA), oxygen peptide nucleic acid (OPNA) and peptide ribonucleic acid (PRNA)), phosphodiester bond modified nucleotide analogs (e.g., phosphorothioate bond substituted nucleotide analogs and N3'-P5' phosphoramidate bond substituted nucleotide analogs), and the like.

[0087] Nucleic acid derivatives may be any molecules as long as they are molecules obtained by adding other chemical substances to nucleic acid for the purpose of improving nuclease resistance, stabilization, increasing affinity with complementary nucleic acid, enhancing cell permeability or visualization compared to nucleic acid. Specific examples thereof include 5'-polyamine addition derivatives, cholesterol addition derivatives, steroid addition derivatives, bile acid addition derivatives, vitamin addition derivatives, Cy addition derivatives, Cy3 addition derivatives, 6-FAM addition derivatives, biotin addition derivatives, etc.

[0088] In another embodiment of the present invention, proteins, siRNA, shRNA, dsRNA, miRNA, antisense nucleic acids, etc. can be introduced into animal cells instead of exogenous genes. Even if these substances other than exogenous genes are introduced into animal cells, the gene introduction rate of the cells can be measured by measuring the cell complexity.

[0089] The "cell complexity" is not particularly limited as long as it can characterize the complexity of the cell, and examples thereof include the complexity inside the cell and the complexity on the cell surface, and the complexity inside the cell is preferred.

[0090] There is no particular limitation on the index used to represent "intracellular complexity", as long as it can characterize the complexity of the cell. Examples of intracellular complexity include intracellular granular properties, the degree of nuclear lobation of the cell nucleus, the cell nucleus, intracellular organelles (e.g., mitochondria), membrane structures, chromosomes, chromatin, nucleosomes, oil droplets, etc. In addition, examples include the complexity of cellular lipids, intracellular metabolites, intracellular polysaccharides, etc.

[0091] There is no particular limitation on the index used to represent the "complexity of the cell surface", as long as it can represent the complexity of the cell surface. As an index used to represent the complexity of the cell surface, for example, the circumference of the cell, the area envelope, the degree of concavity and convexity, the arithmetic mean roughness, the maximum height, the ten-point average roughness, the average interval of concavity and convexity, the average interval of local peaks, the load length ratio, the fractal dimension, the aspect ratio, the circularity, the roundness, the density, etc. can be cited. In addition, as the complexity of the cell surface, for example, the complexity of the shape of the cell surface, the polysaccharide on the cell surface, etc. can be cited.

[0092] An example of the definition of each of the above-mentioned indicators is shown below.

[0093] - Solidity = an area wrapped around the actual perimeter / an area wrapped around the envelope perimeter

[0094] - Unevenness: The degree of unevenness on the cell surface

[0095] - Arithmetic mean roughness, maximum height, ten-point average roughness, average interval between asperities, average interval between local peaks, load length ratio: specified in JIS B 0601:1994 and JIS B 0031:1994.

[0096] -Fractal dimension: In fractal geometry, a statistic that indicates how completely a fractal appears to fill space when it is magnified to a finer scale.

[0097] -Aspect ratio = short diameter / maximum length

[0098] - Circularity = Circular area circumference / actual particle circumference (or 4π (area) / (circumference) 2 )

[0099] -Roundness: A geometric tolerance that indicates the roundness of a circle. JIS defines roundness as the deviation of a circular object from a geometrically correct circle.

[0100] -Compactness = (circumference) 2 / area

[0101] There are no particular restrictions on the method for measuring the complexity within the cell, as long as it can measure the complexity within the cell, and examples include methods using various commercially available measuring devices, visual measurement methods using a microscope, image analysis using a microscope, and the like. Examples of such devices for measuring the complexity within the cell include, for example, flow cytometers, holographic microscopes, high DNA content measurement devices, spectrophotometers, and electrophoresis devices. By measuring side scatter (SSC (side scatter)) with a flow cytometer, information about the complexity within the cell can be obtained from the SSC, and thus the SSC can be used as an indicator of the complexity within the cell. In addition, by measuring side fluorescence (SFL (side fluorescence light)) with a flow cytometer, information about the amount of intracellular nucleic acids, etc. stained with fluorescent substances can be obtained from the SFL; therefore, the SFL can be used as an indicator of the complexity within the cell. After staining the cells with fluorescent substances that can stain nucleic acids (e.g., polymethine dyes, DAPI, PI, actinomycin dyes (e.g., 7-AAD), and Hoechst dyes (e.g., Hoechst 33342)), the SFL is measured. The SFL can also be measured after staining the cells with various known fluorescent substances that can stain cytoplasm, organelles, cell membranes, etc. In addition, a holographic microscope can be used to specifically quantify the intracellular organelles. By quantifying the amount of DNA using a high-quality DNA measurement device, a spectrophotometer, an electrophoresis system, etc., the amount of DNA can be used as an indicator of the complexity within the cell.

[0102] Examples of flow cytometers include those manufactured by Beckman Coulter, Inc. (e.g., Gallios, Navios, Navios EX, CytoFLEX, CytoFLEX S, CytoFLEX LX, Cytomics FC 500, and DxH500), BD Biosciences (e.g., BD FACSCalibur TM Flow cytometer, BD FACSCanto TM II flow cytometer, BDFACSVerse TM Flow cytometer, BD FACSLyric TM Flow cytometer, BD LSRFortessaTM Flow cytometer, BDLSRFortessa TM X-20 flow cytometer and BD FACSymphony TM flow cytometers), Thermo Fisher Scientific (e.g., Attune flow cytometer), Agilent Technologies (e.g., Novocyte flow cytometer), Sartorius (e.g., iQue3 flow cytometer), Sony Corporation (e.g., SA3800 and SP6800Z), Sysmex Corporation (e.g., Multi-item automated hematology analyzer XN-550, Multi-item automated hematology analyzer XN-450, Multi-item automated hematology analyzer XN-350, Multi-item automated hematology analyzer XN-330, Multi-item automated hematology analyzer pocH TM -80i and multi-item automatic blood cell counter XQ-320). Flow cytometry can use SSC as a benchmark to measure the complexity of cells and forward scatter (FSC) as a benchmark to measure cell size. Using a flow cytometer to measure cell complexity and size can be implemented according to known methods, such as according to the manufacturer's manual of the flow cytometer.

[0103] There is no particular limitation on the method for measuring the complexity of the cell surface, as long as it can measure the complexity of the cell surface. For example, there can be mentioned methods of measuring using various commercially available measuring devices, methods of visually measuring using a microscope, and image analysis of images obtained using a measuring device.

[0104] In addition, cell complexity can be measured by measuring, for example, intracellular organelles, cell surface glycans, cell membranes, cell lipids, intracellular metabolites, cell fat droplets, cell surface shape, etc. Cell complexity can also be measured by measuring autofluorescence from cells (e.g., by flow cytometry, microscope or microplate reader). Here, autofluorescence is the natural luminescence (photoluminescence) that occurs when biological structures such as mitochondria and lysosomes absorb light, and is used in distinction from light originating from artificially added fluorescent labels (fluorophores).

[0105] An example of the definition of each of the above-mentioned indicators is shown below.

[0106] -Intracellular organelles refer to membrane-delimited structures present in eukaryotic cells (e.g., mitochondria, endoplasmic reticulum, Golgi apparatus, lysosomes, vacuoles, endosomes, and peroxisomes). Gene introduction can increase the amount of, for example, mitochondria.

[0107] -Cell surface glycans include N-linked glycans, O-linked glycans, glycolipid glycans, glycosaminoglycans and free oligosaccharides. Although most glycans exist outside cells, they can basically be intracellular glycans or extracellular glycans. Due to gene introduction, for example, glycolipid glycans, glycosaminoglycans and free oligosaccharides increase, while N-linked glycans and O-linked glycans decrease.

[0108] -Cell membrane refers to the biological membrane that separates cells from the external environment. For example, gene introduction can change the composition of cell membranes and increase the amount of cell membranes.

[0109] -Cellular lipids refer to lipid components contained in cells, especially lipids contained in cell membranes. For example, gene introduction will change the composition of cellular lipids and increase phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol, sphingomyelin, triglycerides, hexosylceramide, dihexosylceramide, lysophosphatidylethanolamine, etc.

[0110] -Intracellular metabolites refer to metabolic intermediates present in cells. Gene introduction may change, for example, the composition of cellular metabolism.

[0111] -Cellular fat droplets are intracellular organelles that exist in cells and store lipids. Gene introduction can increase the number of, for example, fat droplets.

[0112] - The cell surface shape can be expressed by, for example, the degree of concavity and convexity. For example, gene introduction may change, for example, the cell surface shape, making it more concavity and convexity, and more complex (the circumference becomes longer, and the area envelope becomes smaller).

[0113] -Step (2)

[0114] In step (2), the gene introduction rate is predicted based on the value measured in step (1).

[0115] In the examples described below, it was confirmed that the cell complexity is correlated with the gene introduction rate, so the gene introduction rate can be predicted by using the cell complexity value obtained by the measurement. In the present invention, the term "predict" is used in the same meaning as "measurement", and these terms can be used interchangeably.

[0116] In step (2), the distribution of cell complexity can be used to predict the gene introduction rate of the cell group. As an embodiment, the proportion of the area with high complexity of the cell compared to the distribution of the complexity of the control cells that have not been gene introduced is used to predict the gene introduction rate of the cell group. In addition, as another embodiment, the parameters of the distribution of cell complexity are used to predict the gene introduction rate of the cell group. Such parameters as the distribution of cell complexity are not particularly limited, for example, the mean, median, mode, variance (standard deviation), skewness, kurtosis, maximum value, minimum value, quartile, peak height and half width (especially mean, median, mode, variance (standard deviation), skewness and kurtosis) of the distribution measured in step (1) can be cited. The mean, median, mode, variance (standard deviation) are positively correlated with the gene introduction rate, while skewness and kurtosis are negatively correlated with the gene introduction rate.

[0117] As a method for using the distribution parameters of cell complexity to predict the gene introduction rate of a cell population, for example, a calibration curve is created based on the gene introduction rate of cells with known gene introduction rates and the distribution parameters of cell complexity, and the gene introduction rate is determined using the calibration curve based on the parameters obtained from the values ​​measured in step (1). The calibration curve can be created by a common method such as the least squares method using software or the like.

[0118] As another embodiment of step (2), the distribution of the complexity of the animal cells into which the gene is introduced is obtained according to the cell complexity, thereby predicting the gene introduction rate of the cell group. For example, by using a mixed log-normal distribution model, the ratio of the two groups of cell groups into which the gene is introduced and the cell group into which the gene is not introduced relative to the entire cell group is obtained, thereby predicting the gene introduction rate of the cell group. Specifically, for the data obtained by logarithmic transformation of the values ​​measured in step (1), the mixed normal distribution is fitted using the maximum likelihood method, thereby inferring each parameter of the mixed log-normal distribution model, and the gene introduction rate of the cell group is obtained (for details, see the embodiments described later). Such a calculation process can be performed by using known software such as R, SAS, SPSS or JMP.

[0119] As another embodiment of step (1) and step (2), the cell complexity determination in step (1) is performed by measuring SSC or SFL using a flow cytometer, whereby the pulse height, width and area of ​​SSC or SFL are used to predict the gene introduction rate of the cell group in step (2), thereby predicting the gene introduction rate of the cell group. That is, the pulse height, width and area of ​​SSC or SFL measured in each cell are used to predict the gene introduction rate. Using multiple values ​​in this way enables robust analysis. In addition, in addition to the value of cell complexity, the value of cell size (for example, FSC measured by flow cytometry, in particular the pulse height, width and area of ​​FSC) can also be used to predict the gene introduction rate. Combining the value of cell complexity and the value of cell size in this way can achieve a more robust analysis. The gene introduction rate of the cell group can be predicted by, for example, using a machine learning model. That is, learning data with known gene introduction rate is provided to the prediction model to make it learn, a prediction model that has completed learning is created, and then the value of animal cell complexity is input into the prediction model that has completed learning to predict the gene introduction rate. As a specific example, the data of determining whether a single cell has gene introduction is used as the correct answer label, and the measured values ​​of the pulse height, width and area of ​​SSC or SFL are used as learning data to create a prediction model that has completed learning, and then the measured values ​​of the pulse height, width and area of ​​SSC or SFL of a single cell are input into the prediction model that has completed learning to predict the gene introduction rate. As learning data, in addition to the measured values ​​of the pulse height, width and area of ​​SSC or SFL of a single cell, the distribution parameters of SSC or SFL of a cell group can also be used. By predicting, the probability of being classified as a cell into which a gene is introduced can be obtained, so it is possible to determine whether a single cell has gene introduction based on a certain threshold (for example, 50%, etc.), and the gene introduction rate of a cell group can be obtained, or the average value of the probability of a single cell being classified as a cell into which a gene is introduced can be used as the gene introduction rate of a cell group. As a machine learning model, any model that can perform regression analysis can be used, for example, lasso regression, ridge regression, elastic network regression, principal component regression, partial least squares regression, random forest, gradient boosting decision tree, neural network, deep learning, support vector machine, etc.

[0120] As another embodiment of step (2), in addition to the value measured in step (1), the value measured at at least one stage before and after gene introduction is used to predict the gene introduction rate of the cell group, and the gene introduction rate of the cell group is predicted. That is, the complexity of the animal cells is measured in multiple steps for preparing animal cells that have been gene introduced, and the value of the complexity of the animal cells at multiple time points, rather than only one time point, is used to predict the gene introduction rate. By using the cell complexity at multiple time points in this way, a robust analysis can be performed. When the value of the complexity of the animal cells at multiple time points is used to predict the gene introduction rate, as the number of the multiple, for example, 2, 3, 4, 5, 6, 7, 8, 9 or 10 can be cited. As specific examples of multiple time points, raw materials, after cell activation, after gene introduction into cells, after amplification culture after gene introduction, etc. can be cited. As the value of the complexity of the animal cells used here, it is preferably a parameter of the distribution of the above-mentioned cell complexity. In addition, in addition to the value of the cell complexity, the value of the cell size (for example, FSC measured by flow cytometry) can also be used to predict the gene introduction rate. Combining the value of cell complexity and the value of cell size in this way can achieve a more robust analysis. The gene introduction rate of a cell population can be predicted by, for example, using a machine learning model. That is, the learning data of the known gene introduction rate is provided to the prediction model to make it learn, and a prediction model that has completed the learning is created, and then the value of the animal cell complexity measured at multiple time points is input into the prediction model that has completed the learning to predict the gene introduction rate. As a machine learning model, as long as it is a model that can perform regression analysis, for example, lasso regression, ridge regression, elastic network regression, principal component regression, partial least squares regression, random forest, gradient enhanced decision tree, neural network, deep learning, support vector machine, etc. can be cited.

[0121] Regarding the difference in complexity (particularly diameter and SSC or SFL value obtained by flow cytometry) between gene-transferred cells (animal cells into which genes are actually introduced) and gene-untransferred cells (animal cells into which genes are not introduced), in particular, one or more of the mean, median and mode (particularly median) of the distribution parameters of the complexity of the gene-transferred cell population and the gene-untransferred cell population is, for example, 5% or more, 6% or more, 7% or more, 8% or more, 9% or more, or 10% or more. Here, the complexity of the gene-transferred cell is greater than that of the gene-untransferred cell.

[0122] The method for producing a gene-transferred cell preparation of the present invention (also referred to as "the production method of the present invention" in this specification) is characterized in that it comprises:

[0123] (I) A step of measuring the complexity of gene-transferred animal cells and determining the gene transfer rate based on the measured value.

[0124] The manufacturing method of the present invention may further include:

[0125] (II) Step of introducing the gene into animal cells.

[0126] Step (I) and step (II) of the manufacturing method of the present invention can be implemented by the aforementioned method. The cell preparation manufactured by the manufacturing method of the present invention (hereinafter also referred to as "cell preparation of the present invention") is preferably prepared by mixing an effective amount of gene-transferred animal cells with a pharmaceutically acceptable carrier according to known means (for example, the method described in the Japanese Pharmacopoeia) to prepare a parenteral preparation. The cell preparation of the present invention is preferably prepared into parenteral preparations such as injections, suspensions, and infusions. As parenteral administration methods, intravenous, intra-arterial, intramuscular, intraperitoneal or subcutaneous administration can be cited. As pharmaceutically acceptable carriers, solvents, substrates, diluents, excipients, analgesics, buffers, preservatives, stabilizers, suspending agents, isotonic agents, surfactants, solubilizing agents, etc. can be cited.

[0127] The dosage of the cell preparation of the present invention can be appropriately determined according to various conditions such as the patient's weight, age, sex and symptoms. In addition, it can be administered once or multiple times. The cell preparation of the present invention can have a known form suitable for parenteral administration, for example, an injection or an infusion can be made. In addition, the cell preparation of the present invention may contain physiological saline, phosphate buffered saline (PBS), culture medium, etc. to stably maintain the cells. As culture medium, culture medium such as RPMI, AIM-V, X-VIVO10 can be cited, but is not limited to this. In addition, for the purpose of stabilization, a pharmaceutically acceptable carrier (e.g., human serum albumin), a preservative, etc. can be added to the cell preparation. The cell preparation of the present invention is applied to mammals including humans.

[0128] In the past, when a flow cytometer was used to measure the gene introduction rate, manual operation was required in order to perform antibody staining on the introduced gene, and the operation was complicated. In contrast, according to the method for predicting the gene introduction rate of the present invention, such complicated steps are not required. For example, the sample is only put into a medical instrument such as a flow cytometer, especially a blood cell analyzer, and the necessary operation is only the automatic determination using the measuring device, and the gene introduction rate of the cell can be determined by simple operation. In addition, according to the method for predicting the gene introduction rate of the present invention, for example, the gene introduction rate of the cell can be efficiently determined in a short time of less than 5 minutes, while the previous method requires 6 to 7 hours. In addition, the method of the present invention is not particularly limited to the cell type and can be applied to various cells. It is expected that the method of the present invention will be applicable to gene therapy such as CAR-T cell therapy.

[0129] When used in this specification and claims, unless the context requires otherwise, singular terms include plural terms and plural terms include singular terms. Therefore, unless otherwise clearly stated, singular articles (e.g., "a / an", "the", etc. in English) should also be understood to include the concept of their plural forms.

[0130] Example

[0131] Examples are provided below to explain the present invention in more detail. However, the present invention is not limited to these examples.

[0132] method

[0133] Culture medium

[0134] CAR-T cell culture medium: 2.6% OpTmizer expansion basal supplement (Thermo Fisher Scientific), 1% L-glutamine (Thermo Fisher Scientific), 1% streptomycin, and 2% CTS immune cell SR (Thermo Fisher Scientific) were added to OpTmizer CTS T cell expansion basal medium (ThermoFisher Scientific) to prepare a basal medium. MACS GMP IL-2 (Miltenyi Biotec) was added thereto at 20 IU / mL or 40 IU / mL.

[0135] SK-HEP-1 cell culture medium: prepared by adding 10% FBS (Biosera), 1% non-essential amino acids (FUJIFILM WakoPure Chemical Corporation), 1% penicillin-streptomycin solution (FUJIFILM Wako Pure Chemical Corporation), and 1 mM sodium pyruvate (FUJIFILM Wako Pure Chemical Corporation) to MEM, L-Gln (+) (Thermo Fisher Scientific).

[0136] NK92 cell culture medium: 20% FBS (Biosera) was added to RPMI 1640 culture medium (ThermoFisher Scientific), and MACS GMP IL-2 (Miltenyi Biotec) was added to give a concentration of 200 IU / mL.

[0137] <Introduction of genes into T cells>

[0138] After thawing Leukopak (HemaCare) or γδ T cells (Hemacare), the cells are diluted with CAR-T cell medium to 2.0×10 6 cells / mL or less (raw materials before manufacturing). The cells are inoculated into a culture bag such that the ratio of the cell suspension to MACS GMP T cell TransACT (Miltenyi Biotec) is 17.5:1, and cultured for more than 36 hours to less than 48 hours (cell activation step). The activated cells are diluted with medium using a LOVO cell processing system (Fresenius Kabi) or a centrifuge, and inoculated at 6.07×10 5 cells / cm 2 or less into a culture bag pre-coated with Retronectin (trademark) (Takara Bio Inc.) and retrovirus into which the CAR gene, the CAR gene, the IL-7 gene and the CCL19 gene, or the mCherry gene have been introduced, and cultured until the next day (gene introduction step). The cells are inoculated at 2.2×10 6 cells / cm 2 or less into a culture flask (G-REX, Wilson Wolf) and cultured for 3 - 7 days to produce CAR-T cells (final product). It should be noted that the CAR gene used has the base sequence shown in SEQ ID NO: 1, the CAR gene, the IL-7 gene and the CCL19 gene used have the base sequence shown in SEQ ID NO: 2, and the mCherry gene used has the base sequence shown in SEQ ID NO: 3 (this also applies to the following case of SK-HEP-1 cells).

[0139] <Introduction of the CAR gene into SK-HEP-1 cells>

[0140] The SK-HEP-1 cells (ATCC) are diluted with SK-HEP-1 cell medium to 2.0×10 6 cells / mL or less, and inoculated at 6.07×10 5 cells / cm 2 or less onto a culture plate pre-coated with Retronectin (Takara Bio Inc.) and retrovirus into which the CAR gene has been introduced, and cultured for 4 days, thereby introducing the CAR gene into the SK-HEP-1 cells.

[0141] <Manufacture of CAR-NK cells>

[0142] After thawing NK-92 (ATCC), which is a cell line of NK cells, the cells were cultured in NK92 cell culture medium. After culture, the cell suspension was incubated at 6.07×10 5 cells / cm 2 The cells were then inoculated into a culture bag pre-coated with Retronectin (trademark) (TakaraBio Inc.) and a retrovirus into which the CAR gene had been introduced, thereby producing CAR-NK cells.

[0143] <Measurement of intracellular complexity, cell size, and gene transfer efficiency using flow cytometry>

[0144] The gene transfer efficiency was determined using the fluorescence of CAR antigen or mCherry using a BD FACS Canto II flow cytometer (BD Biosciences). The complexity of the cells was quantified by SSC, and the cell size was quantified by FSC.

[0145] <Measurement of intracellular complexity using a blood cell analysis device>

[0146] Intracellular complexity was quantified by SFL using a multi-analyzer XN-330 (Sysmex Corporation).

[0147] <Cell distribution analysis using mixed lognormal distribution model>

[0148] Using the statistical analysis software R, the data obtained by logarithmic transformation of the SSC of single cells measured by flow cytometry were fitted with a mixed normal distribution using the maximum likelihood method to estimate the parameters of the mixed log-normal distribution model. The probability density function of the two-group mixed normal distribution is expressed as follows:

[0149] p(x)=λ1N(x|μ1,σ1)+λ2N(x|μ2,σ2)

[0150] where x is the natural logarithm of each measured value of the cell SSC,

[0151] N(x|μ,σ)

[0152] represents a normal distribution with mean μ and variance σ, λ is a weighting factor in the mixed distribution, and λ1+λ2=1. The EM algorithm is used to estimate each parameter by maximum likelihood, so that the log-likelihood function of the following probability density function p(x) is maximized:

[0153] ln L(λ, μ, σ|x) Since the ratios of these two groups to the total cell population are λ1 and λ2 respectively, the weighting factor λ of the larger group between μ1 and μ2 is estimated as the proportion of the target gene-introduced cells.

[0154] <Using machine learning models to predict gene transfer efficiency>

[0155] Using Python, a programming language in Jupyter notebooks, an integrated development environment running on a web browser, a machine learning model that predicts the presence or absence of CAR gene introduction based on the SSC value of a single cell was constructed using a random forest algorithm. A certain sample was measured by flow cytometry using CAR antigen, and the data for which CAR positive / negative was determined based on the CAR antigen for a single cell was used as the correct answer label, and the measured values ​​of the height, width, and area of ​​the measured SSC (SSC-H, SSC-W, SSC-A) were used as training data to construct a prediction model. The probability of a single cell being classified as CAR positive can be obtained by prediction, and the average value of the CAR positive probability of all the cells tested is used as the predicted value of the CAR positive rate in the determination, and the SSC measurement values ​​of 16 samples other than the samples used for prediction are used as testing data and fitted to the model to predict the CAR positive rate of each sample.

[0156] In addition, a prediction model was constructed from one sample using all measured values ​​of FSC and SSC, which were parameters obtained by flow cytometry without staining, and the CAR positivity rates of 16 samples other than the sample used for prediction were predicted.

[0157] <Cell membrane structure analysis based on antibody staining>

[0158] After the cells were fixed, they were stained with anti-CD3 antibody (Abcam) and anti-CAR antibody, and then analyzed with Nanozoomer (Hamamatsu Photonics KK). Using the image analysis software Image J, the area, diameter, circumference, and area envelope (which indicates the smoothness of the particles) of each cell were calculated based on the captured images.

[0159] <Cell membrane structure analysis based on immunoelectron microscopy>

[0160] After the cells were fixed, they were stained with anti-CAR antibody and then double-stained with uranyl acetate and lead citrate staining solution, followed by analysis using an electron microscope (H-7600, Hitachi High-Tech Corporation).

[0161] <Isolation of CAR-T cells using MACS>

[0162] The cells were labeled with CAR antibody and Anti-PE MicroBeads UltraPure (Miltenyi Biotec) and then analyzed using MACS MultiStand (Miltenyi Biotec) and QuadroMACS TM Separator (Miltenyi Biotec).

[0163] <Imaging with a transmission electron microscope>

[0164] The cells were solidified into a gel using iPGell (GenoStaff Co., Ltd.). After fixation with 2.5% glutaraldehyde phosphate buffer, post-fixation was performed with 1% osmium acid. Subsequently, resin-embedded blocks were prepared according to the prescribed method and cut into 80 μm ultrathin sections. The thinly sliced ​​sections were double-stained with uranyl acetate and lead citrate staining solutions and observed under an electron microscope (H-7600, Hitachi High-Tech Corporation).

[0165] <Imaging by Scanning Electron Microscope>

[0166] Unfrozen cells were seeded onto poly-L-lysine coated coverslips, left to stand, and fixed with 1% glutaraldehyde phosphate buffer. After post-fixation with 1% osmium acid, dehydration, drying, and gold evaporation were performed according to the prescribed method. The prepared specimens were observed under a scanning electron microscope (VE8800, Keyence Corporation).

[0167] <Metabolome and lipidome analysis>

[0168] The cell pellet was washed with 10 6 Each cell was dissolved in 1 mL of methanol, vortexed and homogenized. After homogenization, hydrophilic interaction chromatography-tandem mass spectrometry (HILIC / MS / MS), lipidomics, and gas chromatography-tandem mass spectrometry (GC / MS / MS) were used for analysis. Three batches of T cells and CAR-T cells with gene introduction from each T cell were used as samples, and samples were taken on days 1, 2, 3, and 4 after gene introduction.

[0169] In HILIC / MS / MS, 100 μL of homogenate was centrifuged at 21,500 g for 5 minutes at 4 ° C. Then, 76 μL of the obtained supernatant was mixed with 4 μL of 400 mM ammonium formate. The mixture was vortexed and centrifuged at 21,500 g for 5 minutes at 4 ° C. The supernatant (2 μL) was injected into a liquid chromatography-tandem mass spectrometry (LC / MS / MS) system consisting of a UHPLC Nexera liquid chromatography system (Shimadzu Corporation) and a 5500QTRAP mass spectrometer (B Sciex Pte. Ltd.). Chromatographic separation was performed on a ZIC-cHILIC column (2.1×100 mm, 3 μm, Merck Millipore). Metabolites were detected using selected reaction monitoring (SRM), and the presence of each metabolite was evaluated by the peak area on the SRM chromatogram.

[0170] For lipidomics, 2 μL of the supernatant obtained by centrifugation was analyzed using an LC / MS / MS system consisting of an Ultimate 3000RSLC system (Thermo Fisher Scientific) and a QExactive HF-X mass spectrometer (Thermo Fisher Scientific). Waters) for separation based on liquid chromatography.

[0171] GC / MS / MS analysis was performed as follows: 10 μL of the supernatant obtained by centrifugal homogenization was dried in a nitrogen stream, and then the derivative was formed by a two-step reaction of oximation and trimethylsilylation. Then, 1 μL of the reaction mixture was injected into an Agilent 7890B series gas chromatography system using a GC Injector 80 automatic sampler (Agilent Technologies). Gas chromatography-based separation was performed on a J&W Scientific DB-5MS-DG column (30 m × 0.25 mm inner diameter, df = 0.25 μm, Agilent Technologies). Each target molecule was detected by SRM, and the peak area was evaluated using MassHunter software (Agilent Technologies).

[0172] <Lipid enrichment and network analysis>

[0173] The results of metabolome and lipidome analyses were analyzed using the MetENP R package (doi:https: / / doi.org / 10.1101 / 2020.11.20.391912).

[0174] Using MitoTracker TM Quantify mitochondria>

[0175] The assay was performed using MitoTracker Deep Red (Thermo Fisher Scientific) according to the manual.

[0176] <Quantification of Autofluorescence>

[0177] Using flow cytometry, unstained cells were excited with lasers at wavelengths of 488 nm and 633 nm, and autofluorescence was measured in fluorescence channels with central wavelengths of 530 nm, 585 nm, and 660 nm.

[0178] <Imaging by holographic microscopy>

[0179] Cells were imaged using 3D Cell Explorer (Nanolive SA) according to the manual.

[0180] <Total Glycomics>

[0181] After pretreatment of the cells, N-linked glycans were determined by cleaving the target glycans with trypsin PNGaseF (FUJIFILM Wako Pure Chemical Corporation), capturing the glycans by glycoblotting, and labeling with aoWR (H) (Sumitomo Bakelite Co., Ltd.), followed by MALDI-TOF MS. Glycosphingolipid (GSL) glycans and free oligosaccharides (FOS) were determined by cleaving the target glycans with EGCaseI (New England Biolabs), capturing the glycans by glycoblotting, and labeling with aoWR (H), followed by MALDI-TOF MS. Glycosaminoglycans (GAGs) were degraded into Δ2 sugars using chondroitinase ABC (Sigma-Aldrich) or heparinase I, II, III (Sigma-Aldrich), and then captured to beads by glycoblotting, 2AB labeled, and determined by UPLC. O-linked glycans were labeled by the BEP method, where 3-methyl-1-phenyl-5-pyrazolone was added in the presence of pyrazolone via a β-elimination reaction (with simultaneous Michael addition to the glycan binding site of the deglycosylated peptide) followed by MALDI-TOF MS.

[0182] Example 1: Correlation between CAR gene introduction rate of CAR-T cells and SSC of cells

[0183] CAR-T cells were manufactured and the complexity of the cells as the final product after manufacturing was determined by flow cytometry. The results are shown in Figure 1 It was confirmed that SSC increased due to CAR gene introduction ( Figure 1 AD). As a result, it was confirmed that SSC was correlated with CAR introduction rate ( Figure 1 E,N=5,y=0.0429x+0.6086,R 2 =0.0998). In addition, we evaluated whether the CAR introduction rate can be predicted from the mean, variance, median, mode, skewness, and kurtosis of the SSC distribution. The results showed that as the CAR introduction rate increased, the mean, variance, median, and mode increased, while the skewness and kurtosis decreased ( Figure 1 F). Therefore, it is suggested that all of these indicators can predict the CAR introduction rate.

[0184] Example 2: Prediction of gene transfer rate using SK-Hep-1 cells

[0185] Using the liver cancer cell line SK-HEP-1, we confirmed whether the CAR gene introduction used in Example 1 changes the cell complexity regardless of the introduced cells (i.e., other than T cells). The results are shown in Figure 2 It was confirmed that, as in Example 1, CAR gene introduction increased SSC ( Figure 2 AD). As a result, it was confirmed that SSC was correlated with the CAR introduction rate ( Figure 2 E,N=4,y=0.0088x+0.8339,R 2 =0.09912).

[0186] Example 3: Correlation between CAR gene introduction rate of CAR-T cells and SFL of cells

[0187] In Examples 1 and 2, cell complexity was measured using SSC; in order to confirm whether cell complexity increases with the CAR gene introduction rate even when cell complexity is based on the amount of nucleic acid stained with polymethine dye, the complexity of CAR-T cells was measured using XN-330. The results are shown in Figure 3 It was confirmed that CAR gene introduction increased SFL ( Figure 3 AB). As a result, it was confirmed that SFL was correlated with the CAR introduction rate ( Figure 3 C,N=5,y=4.1959e 0.0166x ,R 2 =0.09942). In addition, we evaluated whether the CAR introduction rate can be predicted from the mean, variance, median, mode, skewness, and kurtosis of the SFL distribution. The results showed that as the CAR introduction rate increased, the mean, variance, median, and mode increased, while the skewness and kurtosis decreased ( Figure 3D) These results indicate that all of these indicators can predict the CAR introduction rate.

[0188] Example 4: Prediction of gene introduction rate based on SSC distribution

[0189] The CAR introduction rate and SSC of CAR-T cells were determined by flow cytometry, and a mixed log-normal distribution model consisting of two populations of large and small was fitted to the SSC distribution by maximum likelihood estimation, and the ratio of each population was calculated. The results are shown in Figure 4 The ratio of high SSC cell population calculated by mixed log-normal distribution model (67.5%) was very close to the value of CAR gene introduction rate (66.8%) determined by flow cytometry.

[0190] Example 5: Predicting gene introduction rate using machine learning models

[0191] The SSC and FSC of CAR-T cells and CAR-T cells co-expressing IL-7 and CCL19 (the T cells into which the CAR gene, IL-7 gene, and CCL19 gene were introduced) were measured by flow cytometry, and the CAR gene introduction rate was predicted by a machine learning algorithm based on the height, width, and area (SSC-H, SSC-W, SSC-A, FSC-H, FSC-W, and FSC-A) of the measured SSC and FSC. The results are shown in Figure 5 It was confirmed that the CAR gene introduction rate predicted by the machine learning algorithm based on SSC-H, SSC-W and SSC-A was correlated with the CAR gene introduction rate measured by flow cytometry, which is a conventional method ( Figure 5 A, R 2 =0.59). In addition, the CAR gene introduction rate can be predicted with better accuracy by using FSC-H, FSC-W and FSC-A in addition to SSC-H, SSC-W and SSC-A using a machine learning algorithm ( Figure 5 B, R 2 =0.72).

[0192] Example 6: Comparison of cell membrane structure between CAR-T cells and T cells without gene introduction

[0193] By antibody staining ( Figure 6 AB) and electron microscopy ( Figure 6 CD) analyzed the cell membrane structure of CAR-T cells into which the CAR gene was introduced and T cells into which the gene was not introduced as a control. As a result, it was confirmed that the cell membrane structure of CAR-T cells was more complex than that of the control ( Figure 6 In addition, image analysis was performed to quantify the area, diameter, circumference, and area envelope of the cell membrane. Figure 6E). Image analysis confirmed that CAR-T cells had a longer circumference and a smaller area envelope than the control, and the cell membrane structure became more concave and complex.

[0194] Example 7: Comparison of cell membrane structure complexity between T cells and CAR-T cells

[0195] To investigate the complexity of the cell membrane, the morphology of the cell membrane was observed using transmission electron microscopy and scanning electron microscopy. After manufacturing CAR-T cells or CAR-T cells expressing IL-7 and CCL19, only the cells expressing CAR were concentrated using MACS. Subsequently, transmission electron microscopy ( Fig. 7A ) or scanning electron microscopy ( Figure 7B The results showed that CAR-T cells or CAR-T cells expressing IL-7 and CCL19 had a more complex cell membrane structure, showing more protrusions than T cells.

[0196] Example 8: Changes in lipid composition of cell membranes in T cells and CAR-T cells

[0197] In order to quantitatively observe the complexity of the cell membrane, metabolome and lipidome analysis were performed to quantify the membrane components. Three batches of T cells and CAR-T cells with gene introduction from each T cell were sampled on days 1, 2, 3, and 4. The results are shown in Figures 8-10. The abbreviations of the lipid components in the figure are as follows: PA: phosphatidic acid, PC: phosphatidylcholine, LPC: lysophosphatidylcholine, PE: phosphatidylethanolamine, LPE: lysophosphatidylethanolamine, PG: phosphatidylglycerol, PI: phosphatidylinositol, PS: phosphatidylserine, SM: sphingomyelin, MG: monoacylglycerol, TG: triacylglycerol, Cer: ceramide, CL: cardiolipin, DHSM: dihydrosphingomyelin, FFA: free fatty acid, GM3: ganglioside M3, Hex-Cer: hexosylceramide and Hex2-Cer: dihexosylceramide. As a more detailed classification, "a" represents an ester bond between glycerol and fatty acid, "e" represents an ether bond between glycerol and fatty acid, and the number of "a" or "e" represents the number of fatty acid residues bound to the glycerol portion. In addition, the total carbon number (x) and the double bond number (y) of the bound fatty acid residue are expressed as x:y. For example, "PC (aa-34:1)" means that two fatty acids are bound to the glycerol portion of phosphatidylcholine in the form of an ester group, the total carbon number of the fatty acid residue is 34, and there is one double bond therein.

[0198] Each target molecule was calculated as a value relative to the mean value of the QC sample obtained by mixing all samples. A t-test was performed on the values ​​of each component on the 4th day of the amplification culture, and components with significant differences were extracted (Figure 8). A two-way ANOVA was performed on the differences in the amounts of components in T cells and in CAR-T cells made by each T cell according to the number of days of culture and cell type, and components with significant differences were extracted (Figure 9). In addition, principal component analysis was performed on the values ​​of each component on the 4th day of the amplification culture, and a scatter plot was drawn with the first principal component as the horizontal axis and the second principal component as the vertical axis ( Fig.10 ). This analysis showed that the composition of membrane components in T cells and CAR-T cells changed. It was also found that the amount of components per cell increased in CAR-T cells, indicating an increase in the amount of cell membrane per cell.

[0199] Example 9: Quantification of gene transfer rate using mitochondrial differences

[0200] Enrichment analysis was performed using the metabolome and lipidome analysis results obtained in Example 8 ( Fig.11 ) and network analysis. In the figure, "TAG" means triglyceride, and "O-PC" means etherphosphatidylcholine. Enrichment analysis shows that in CAR-T cells, the expression of lipid subclasses of PG and PE in particular increases. Network analysis shows that the pathways involved in glycerophospholipid metabolism, glycosylphosphatidylinositol (GPI) anchor biosynthesis and glycerolipid metabolism have changed. It is reported that these pathways are involved in cell cycle initiation and T cell activation (Journal of Lipid Research, Vol. 54, No. 10, pp. 2665-2677 (2013), Biochemistry (Moscow) 81, 636-650 (2016)). Since PG and PE are known to be lipids abundant in mitochondria (Journal of Japanese Biochemical Society, Vol. 83, No. 6, pp. 462-474, 2011, Biochimica et Biophysica Acta 1859 (2017) 1558-1572), it is speculated that the amount of mitochondria has increased due to gene introduction. Therefore, MitoTracker was used to quantify the capacity of mitochondria. As a result, it was confirmed that gene introduction increased the capacity of mitochondria. These results show that the complexity of cells is different ( Fig.12 ). In addition, it is known that mitochondria emit autofluorescence when exposed to fluorescent wavelengths. Therefore, it was confirmed whether the CAR gene introduction rate can be predicted using autofluorescence. The results confirmed that the CAR gene introduction rate is correlated with autofluorescence ( Fig.13). These results suggest that mitochondrial complexity, including autofluorescence, can be used to predict gene transfer rates.

[0201] Example 10: Comparison of intracellular structural complexity in T cells and CAR-T cells

[0202] The complexity of the intracellular structure of CAR-T cells into which the CAR gene was introduced and T cells into which the gene was not introduced as a control were compared under a holographic microscope. The results are shown in Fig.14 As a result, more vacuoles and oil droplet-like structures were confirmed in CAR-T cells than in T cells. This suggests that CAR-T cells have become more complex.

[0203] Example 11: Changes in the complexity of mCherry gene introduction into γδT cells

[0204] After the mCherry gene was introduced into γδT cells, the mCherry-negative cells and mCherry-positive cells were compared in terms of SSC ( Fig.15 A). As a result, SSC increased in mCherry-positive cells, indicating that mCherry introduction made γδT cells complex ( Fig.15 B, 15C).

[0205] Example 12: Changes in the complexity of γδT cells into which the CAR gene was introduced

[0206] After the CAR gene-transferred γδT cells were produced, the SSC of CAR-negative cells and CAR-positive cells were compared ( Fig.16 A). As a result, SSC increased in CAR-positive cells, indicating that CAR gene introduction made γδT cells complex ( Fig.16 B).

[0207] Example 13: Changes in the complexity of CAR-NK cells

[0208] After CAR-NK cells into which the CAR gene was introduced were produced from NK92 cells, the SSC of CAR-negative cells and CAR-positive cells were compared ( Fig.17 A). As a result, SSC in CAR-positive cells increased, indicating that CAR gene introduction made NK92 cells complex ( Fig.17 B).

[0209] Example 14: Total glycomic analysis using T cells and CAR-T cells

[0210] Glycans exist inside and outside cells. To study the complexity of cells, CAR-T cells into which the CAR gene was introduced and T cells without the gene were used as controls to analyze a total of 171 glycans, including N-linked glycans, O-linked glycans, glycosphingolipid (GSL) glycans, glycosaminoglycans (GAGs), and free oligosaccharides (FOS). As a result, T cells and CAR-T cells showed different glycan profiles ( Fig.18 A). In particular, N-linked glycans and O-linked glycans increased in T cells, while GSL, GAG, and FOS increased in CAR-T cells ( Fig.18 B) These results indicate that the complexity of cells varies. The following are the glycans that show differences in expression levels.

[0211] GLS-27; (Hex)3(HexNAc)2(NeuAc)1[a2,6],FOS-14; (Hex)5(HexNAc)2,FOS-15; (Hex)6(HexNAc)2,GLS-34; (Hex)4(HexNAc)2(NeuAc)1[a2,3],GLS-35; (Hex)3(HexNAc)2(deoxyhexose)1(NeuGc 26)1,FOS-05; (Hex)5(HexNAc)1,FOS-04; (Hex)4(HexNAc)1,GAG-03; ΔUA-GalNAc,4S,FOS-06; (Hex)6(HexNAc)1,GAG -01;ΔUA-GalNAc,GLS-01;(Hex)2,FOS-13;(Hex)4(HexNAc)2,N-40;(Hex)2(HexNAc)3(deoxyhexose)1+(Man)3(GlcNAc)2,N- 70; (Hex)3(HexNAc)4(deoxyhexose)1(NeuAc)1[a2,6]+(Man)3(GlcNAc)2,N-54; (Hex)2(HexNAc)3(deoxyhexose)1(NeuAc)1[a2,6]+(Man)3(GlcNAc)2,O-08; (Hex)1(HexNAc)1(NeuAc)2,N-11; (Hex)1(HexNAc)1+(Man)3(GlcNAc)2,N-50; (Hex)2(HexNAc c)3(NeuAc)1[a2,6]+(Man)3(GlcNAc)2,N-68;(Hex)2(HexNAc)3(deoxyhexose)1(NeuAc)2[a2,6 / a2,6]+(Man)3(GlcNAc)2,N-71;(Hex)4(HexNAc)4(NeuAc)1[a2,6]+(Man)3(GlcNAc)2,N-77;(Hex)4(HexNAc)4(deoxyhexose)1(NeuAc)1[a2,6]+(Man)3( GlcNAc)2,N-32; (Hex)2(HexNAc)3+(Man)3(GlcNAc)2,N-02; (Man)2(GlcNAc)2,N-33; (HexNAc)4(deoxyhexose)1+(Man)3(GlcNAc)2,N-31; (Hex)1(HexNAc)3(deoxyhexose)1+(Man)3(GlcNAc)2,N72; (Hex)4(HexNAc)5(deoxyhexose)1+(Man)3(GlcNAc)2,N-82;(Hex)3(HexNAc)4(deoxyhexose)1(NeuAc)2[a2,6 / a2,6]+(Man)3(GlcNAc)2,N-23;(Hex)1(HexNAc)2(deoxyhexose)1+(Man)3(GlcNAc)2,N-04;(Man)3(GlcNAc)2,N-06;(Hex)1+(Man)3(GlcNAc)2;

[0212] This application is based on Japanese Patent Application No. 2022-59600 filed on March 31, 2022, and Japanese Patent Application No. 2022-158218 filed on September 30, 2022, the entire contents of which are incorporated herein.

Claims

1. A method for predicting gene transfer rate in animal cells, comprising: (1) a step of measuring the cell complexity of the gene-transferred animal cell; as well as (2) A step of predicting the gene introduction rate based on the value measured in step (1).

2. The method according to claim 1, wherein: Cellular complexity is the complexity within a cell.

3. The method according to claim 1, wherein: In step (2), the distribution of cell complexity is used to predict the gene introduction rate of the cell population.

4. The method according to claim 1, wherein: In step (2), the parameters of the distribution of cell complexity are used to predict the gene introduction rate of the cell population.

5. The method according to claim 4, wherein: The parameter of the distribution of cellular complexity is at least one selected from the group consisting of mean, median, mode, variance, kurtosis, skewness, maximum, minimum, quartiles, peak height and half width.

6. The method according to claim 4, wherein: The parameter of the distribution of cellular complexity is at least one selected from the group consisting of: mean, median, mode, variance, kurtosis and skewness.

7. The method according to claim 3, wherein: In step (2), the gene introduction rate of the cell population is predicted by determining the distribution of the complexity of the animal cells into which the gene is introduced based on the distribution of the complexity of the cells.

8. The method according to claim 1, wherein: The cell complexity in step (1) is determined by measuring side scatter (SSC) or side fluorescence (SFL) using flow cytometry.

9. The method according to claim 1, wherein: In step (1), the cell complexity is determined by measuring SSC or SFL using a flow cytometer, and in step (2), the pulse height, width and area of ​​SSC or SFL are used to predict the gene introduction rate of the cell population.

10. The method according to claim 1, wherein: In step (2), the value of cell size is further used to predict the gene transfer rate of the cell population.

11. The method according to claim 10, wherein: As the value of the cell size, the forward scatter (FSC) measurement value obtained by flow cytometry was used.

12. The method according to claim 11, wherein: In step (2), the pulse height, width and area of ​​FSC are used to predict the gene transfer rate of the cell population.

13. The method according to claim 1, wherein: Cellular complexity is the complexity of the cell surface.

14. The method according to claim 13, wherein: The complexity of the cell surface is the cell's circumference, area envelope, concavity, arithmetic mean roughness, maximum height, ten-point mean roughness, mean interval between concavities and convexities, mean interval between local peaks, load-to-length ratio, fractal dimension, aspect ratio, circularity, roundness, or density.

15. The method according to claim 1, wherein: The determination of cell complexity in step (1) is carried out by measuring intracellular organelles, cell surface glycans, cell membranes, cell lipids, intracellular metabolites, cell fat droplets or cell surface shape.

16. The method according to claim 1, wherein: The determination of cell complexity in step (1) is performed by measuring the autofluorescence originating from the cells.

17. The method according to claim 1, wherein: The animal cells are immune cells.

18. The method according to claim 17, wherein: The immune cells are T cells or NK cells.

19. The method according to claim 1, wherein: The animal cells are epithelial cells.

20. The method according to claim 1, wherein: The nucleic acid to be introduced is a nucleic acid encoding a chimeric antigen receptor.

21. The method according to claim 1, wherein: The difference in cell complexity between the gene-transferred cells and the gene-untransferred cells was 5% or more as measured by SSC or SFL values ​​obtained by flow cytometry.

22. A method for producing a gene-transferred cell preparation, comprising: (I) A step of measuring the complexity of gene-transferred animal cells and determining the gene transfer rate based on the measured value.

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