Methods for reprogramming cells

Abstract

The present invention relates to a method for determining conditions required to reprogram a first cell type to a second cell type comprising the steps of: exposing a first plurality of test units each comprising cells to different TFs or combinations of TFs, respectively, and labeling the cells to indicate exposure to the TFs or combinations of TFs; pooling the plurality of test units and subdividing the pool of test units to form a second plurality of test units; exposing the second plurality of test units to different TFs or combinations of TFs, respectively; identifying the cell type of the cells in the test units and deconvoluting the labels to identify the identity, order and timing of the TFs to which the cells were exposed; optionally, using data from the preceding steps to guide selection of TFs used in the initial step and repeating as needed; and identifying the cells of the second cell type and deconvoluting the labels to thereby identify the identity and order of the TFs required to reprogram the cells of the first cell type to the cells of the second cell type.

Description

Background Technology

[0001] Cell therapy, which uses cells expanded and potentially modified in the laboratory to treat patients with serious diseases, is a promising technology that is yielding excellent results in oncology (for leukemia and solid tumors) (Ottaviano et al., 2022) and regenerative medicine (e.g., in the rehabilitation of burn patients) (Cossu et al., 2018). Cells can be safely derived from patients or donors, manipulated to expand, differentiate, and potentially correct genetic defects or enhance their therapeutic capabilities, and then administered back to the patient. However, the production of these advanced therapeutic medicine products (ATMPs) remains challenging. One of the main limitations is the lack of pathways to obtain therapeutically useful amounts of cells.

[0002] The promise of stem cells in regenerative medicine lies in the unprecedented possibility of deriving any type of cell from human pluripotent stem cells, such as induced pluripotent stem cells (iPSCs) or human embryonic stem cells (hESCs). These pluripotent stem cells have the potential to generate any human tissue in vitro because they are part of or highly similar to embryonic structures that produce all the primitive structures in our bodies. However, the main challenge lies in guiding these stem cells to develop into specific cell types that can be used for therapeutic purposes in a robust and scalable manner. Expanding and / or differentiating donor-derived stem cells into clinically useful cell products requires cell culture conditions tailored to specific applications and target cell types, and testing and optimizing these conditions is both expensive and time-consuming. Cell culture conditions must be formulated by carefully considering developmental cues from the human embryo, many of which are not yet fully understood. In any case, these conditions may be ineffective in the absence of a physiological microenvironment that provides additional stimuli that are difficult to simulate in cell culture, such as specific cell-cell interactions, extracellular matrix composition, and tissue stiffness. There is no prior method to determine which of these conditions will produce a highly reproducible population of highly homogeneous, correctly differentiated cells, and which is among the scalable methods with the potential to meet regulatory requirements.

[0003] Cell reprogramming, the process of transforming one cell type into another by manipulating gene regulatory networks that define cell identity, holds great promise in regenerative medicine but has not yet been widely applied. While it is now known that the phenotype of one cell type can be converted to that of another by manipulating gene circuits, the components required for cell fate conversion are difficult to identify and, in most cases, unknown. The identification of factors that directly reprogram cell type identity is currently limited (among other things) by the cost of exhaustive experimental testing on seemingly plausible sets of factors, making this approach inefficient and unscalable.

[0004] One of the most important examples of cell reprogramming is the use of exogenous transcription factors (TFs) to convert somatic cells into a pluripotent state. For example, Yamanaka (Yamanaka S. Cell 2006; 126:663-76; PMID:16904174) demonstrated that a small group of TFs, namely OCT4, KLF4, SOX2, and MYC (OKSM), can reprogram human fibroblasts into induced pluripotent stem cells (iPSCs) when introduced into cells. Subsequently, other research groups demonstrated that it is possible to convert fibroblasts into hepatocytes, cardiomyocytes, and various other cell types (Huang P, et al., Nature 2011; 475:386-9; Sekiya S, Suzuki A., Nature 2011; 475:390-3; Kogiso T, et al., Hepatol Int 2013; 7:937-44; Ieda M, et al., Cell 2010; 142:375-86; Song K, et al., Nature 2012; 485:599-604; Qian L, et al., Nature 2012; 485:593-8; Takahashi K, Yamanaka S., Nat Rev Mol Cell Biol 2016;17:183-93; Sadahiro T, et al., Circ. Res. 2015;). 116:1378-91. Tsunemoto RK, et al., EMBO J 2015; 34:1445-55; Heinaniemi M, et al., Nat Methods 2013; 10:577-83; LangAH, et al., PLoS Comput Biol 2014; 10:e1003734; Del Sol ICA, Stem Cells 2013; 31:2127-35; Davis FP, Eddy SR. PLoS One 2013; 8:1-8; D'Alessio et al., Stem Cell Reports 2015; 5:763–75).

[0005] In addition to reprogramming cells to obtain pluripotent cells (iPSCs), many related reprogramming strategies have been demonstrated, such as: (i) "forward programming," in which less differentiated cells, such as iPSCs, are reprogrammed into a specific cell type (Dalby et al., Stem Cell Reports 11:1462-1478); (ii) "direct conversion," in which one differentiated cell type is converted into another differentiated cell type without undergoing a pluripotent or progenitor state (Zhou et al., Nature 455:627-632); and combinations thereof, such as (iii) "indirect (or induced) reprogramming," in which ectopic OKSM is transiently expressed to achieve partial reprogramming into a less differentiated (but not fully pluripotent) intermediate, followed by forward programming to obtain a more differentiated cell type different from the starting cell type (Efe et al., Nat Cell Biol 13:215-222).

[0006] It should be noted that "cell reprogramming" differs qualitatively from traditional "cell differentiation" in both its biological mechanisms and the actual methods by which it is achieved. Cell differentiation occurs by progressively limiting developmental potential until a terminal state is reached; Waddington's analogy of a ball rolling down a mountain to a lower, less energetic resting place in a valley perfectly illustrates this point. The Strategy of the Genes (See Allen & Unwin, London, 1957), but this is not necessarily true in cell reprogramming (the opposite is true for iPSCs). In cell differentiation methods, cultured (stem) cells are exposed to developmental cues such as morphogenetics, growth factors, hormones, and small molecules, which are extracellular effector molecules added to the cell culture medium. These molecules typically function by binding to protein elements of cell signaling mechanisms (e.g., cell surface receptors) and cytoplasmic signaling mechanisms (e.g., mechanisms mediated by kinase cascades). In the case of cell reprogramming, cell fate switching is guided by the ectopic expression of transcription factors (TFs), which translocate to the nucleus and directly control gene expression, typically by binding to gene regulatory elements in enhancer and promoter DNA and stimulating (or inhibiting) mRNA transcription. These two different strategies for manipulating cell fate (the first is mediated by the control of the cellular environment (i.e., directed differentiation), and the second is mediated by the reconnection of gene circuits that determine cell fate (cell reprogramming)) are also referred to as the “outside-in” and “inside-out” approaches, respectively (Shakiba et al., Cell Syst. 12:561-592).

[0007] A carefully orchestrated cell reprogramming process (TF) itself, among other genes, can regulate one or more TFs, thereby inducing and / or stabilizing gene regulatory networks (GRNs) that specify cell fate. One of the best-understood GRNs is the pluripotency gene regulatory network (PGRN), which is induced and stabilized by adding the Yamanaka factor OKSM to (differentiated) cells. At the heart of the PGRN are the three musketeers of pluripotency-inducing TFs: OCT4, SOX2, and NANOG. These TFs regulate a larger network of interconnected secondary genes. The core TFs are regulated in a synergistic manner, forming self-regulating and feedforward gene loops that lead to network stability. It is known that NANOG and LIN28 can substitute for KLF4 and MYC in the Yamanaka cocktail, indicating that specific GRNs can be induced by substitution. Indeed, reprogramming is also known to be influenced by non-coding RNAs that regulate gene expression, such as LincRoR and Let7, which affect key nodes in the PGRN; therefore, in this application, non-coding RNAs that regulate gene expression are included in the definition of TFs.

[0008] GRNs are essentially composed of genomic components (such as genes at nodes and their cis-regulatory modules) and regulatory state components (i.e., TFs that provide regulatory input to these modules, including non-coding RNAs). The connections between different regulatory gene products (i.e., TFs) and homologous cis-regulatory elements that control other network nodes define network circuits that can explain developmental processes (such as the maintenance of pluripotency in blastocysts or the differentiation of pluripotent stem cells into pancreatic β cells).

[0009] Few GRNs are as well-defined as PGRNs, and the TFs that influence specific cell reprogramming transitions are largely unknown. Recently, numerous computational methods have been developed to predict TFs that can be used to facilitate reprogramming from one cell type to another (reviewed by Kamaraj et al. (Cell Cycle 15: 3343–3354)). These methods typically consider differential gene expression between the starting and target cell types and leverage knowledge of GRN architecture to rank TFs according to their influence within the core GRN that establishes the target cell. The availability of such methods to a priori assess the ability of TFs to influence cell fate is a significant advantage of “inside-out” approaches, which, in contrast, currently lack widely applicable strategies for predicting guiding components of cell culture media that drive cell differentiation.

[0010] While the computational methods cited above have been used to predict whether OKSM can induce PGRN, as well as a relatively small number of other cell fate transitions (TFs), these algorithms (and their required input data) are far from perfect and often result in lists of TF candidates with little or no overlap. Therefore, the literature suggests that cell reprogramming is difficult to achieve and, even when successful, is typically inefficient and / or has low reproducibility. Experimentally identifying useful transcription factor cocktails using methods used in the prior art is laborious and subject to two related major limitations.

[0011] First, the conventional approach to identifying the reprogramming cocktail is to screen candidate TFs in a large pool (or a set of smaller pools) and then identify the minimum core TF by sequentially eliminating each TF, as illustrated in the pioneering experiments of Takahashi and Yamanaka (Cell 126:663-676), where a pool containing 24 TFs was screened in a cell-based assay to identify the OKSM in reprogramming fibroblasts into iPSCs. In further demonstration of this strategy, Zhou et al. (Nature 455:627-633) introduced a pool of 9 TFs into the mouse pancreas in vivo and observed that pancreatic exocrine cells were reprogrammed into insulin-expressing β cells. In this case, Zhou et al. were also able to infer, through the elimination process, that the effective reprogramming cocktail contained PDX1, NGN3, and MAFA. In both instances, these pooled screening experiments failed to identify functional combinations of TFs beyond the minimum core cocktail because the presence of the core TF in the pool masked the effect of eliminating other effective TF cocktails from the pool. This limitation could be overcome if a set of TFs could be systematically screened to evaluate all combinations in individual experiments. However, the number of experiments required would be relatively large, as there are 10,626 different combinations of four TFs among a set of 24 candidate TFs in Yamanaka. Furthermore, the number of cells required for such large-scale screening would also be substantial, as experiments would need to be replicated, and the efficiency of reprogramming human cells with OKSM, for example, is only 0.01% to 0.02% (Takahashi et al., Cell 131:861–872). Therefore, scalable screening methods capable of systematically evaluating individual TF combinations are needed, as “large-scale screening of combination TF cocktails remains challenging” (Li and Hon, Frontiers in Bioengineering and Biotechnology (2021) vol 9 article 748942).

[0012] Secondly, pooled screening methods cannot uncover optimal approaches where the order of TF addition is crucial for certain types of reprogramming. Recent advances in single-cell omics technologies (such as RNA-Seq and ATAC-Seq) have enabled us to study cells during reprogramming at unprecedented resolution. These technologies reveal that cell reprogramming is a dynamic process in which cells transition through a continuum of cellular states characterized by different TF expression patterns accompanying GRN remodeling. For example, the optimal reprogramming of pancreatic progenitor cells into insulin-expressing β cells using TFs PDX1, NGN3, and MAFA can only be achieved through differential expression dynamics (Saxena et al., Nature Communications 7 Article number 11247). Therefore, screening methods capable of exploring TF combinations expressed in sequence are needed to discover optimal cell reprogramming approaches.

[0013] In our 2004 patent application WO2004 / 031369, we described a method for identifying factors required to differentiate pluripotent cells into more targeted cell types by using split and merge culture methods and labeling multicellular units in the form of beads, wherein the labeling identifies the conditions to which the beads are exposed.

[0014] One of the main drawbacks of this system is that the culture conditions to which the cells are exposed must be determined through careful literature review, leading to the formation of hypotheses, which are then screened according to WO2004 / 031369 to test these hypotheses. Furthermore, the system is only applicable to cell differentiation because, in 2004, only a few instances of reprogramming existed, and these instances were mediated by a single TF or pairs of TFs.

[0015] In this invention, we provide an improvement on our 2004 process, in which cells are optimally reprogrammed from one cell type to another. We combine computational methods for predicting transcription factors with experimental methods for determining the effects of TF administration to cultured cells and the timing of that administration. Summary of the Invention

[0016] In a first aspect, the present invention provides a method for determining the conditions required to reprogram a first cell type to a second cell type by screening combinations of TFs in which TFs are sequentially added to cells.

[0017] Therefore, the present invention provides a method for determining the conditions required to reprogram a first cell type to a second cell type, comprising the following steps: Exposing cells of the first cell type to the first TF; Remove the first TF and expose the cells to different TFs; Optionally, repeat the steps described above; Identify one or more cells displaying one or more markers of a second cell type and determine the identity and order of TFs to which the cells are exposed.

[0018] The splitting / merging technique described in WO2004 / 031369 is suitable for sequentially screening the effects of cell exposure to multiple individual TFs and combinations of TFs. The cell unit in WO2004 / 031369 is envisioned as a bead containing multiple cells that can be sorted together; in this invention, it is envisioned that cell units such as those employed in WO2004 / 031369 can be used, or single cells can be used and sorted individually. Therefore, the test unit can contain multiple cells or a single cell.

[0019] Therefore, in a further aspect, the present invention includes the following steps: a. Each of the first plurality of test units containing cells is exposed to different TFs or combinations of TFs, and the cells are labeled to indicate exposure to said TFs or combinations of TFs; b. Merge multiple test units and subdivide the test unit pool to form a second set of test units; c. Expose the second or multiple test units to different test cases or combinations of test cases; d. Optionally iterate and repeat steps (a) to (c) as needed; e. Identify the cell type of cells in the test unit and deconvolve the markers to identify the identity and order of the TFs to which the cells are exposed; f. Optionally, use the data from step (e) to guide the selection of the TF used in step (a), and repeat steps (a) to (e) as needed; g. Identify cells of the second cell type and deconvolve the markers to identify the identity and order of the TFs required to reprogram cells of the first cell type into cells of the second cell type.

[0020] This invention employs the method described in WO2004031369 (see...) Figure 1 In this method, known as CombiCult®, the sequential splitting and merging of cells is used to expose multiple cells to many different combinations of reagents.

[0021] In one implementation, the TF used in steps (a) and / or (c) can be selected based on computational analysis of known cell reprogramming events. The data used for analysis can be based on a previous execution of the method of the present invention or derived from existing technology. This computational selection, combined with experimental selection provided by a split / merge selection methodology, achieves unprecedented precision in identifying the factors required for cell reprogramming and the ideal timing of their application.

[0022] The test unit in this invention may take the form of beads, capsules, aggregates, or the like containing one or more cells. In some embodiments, achieving an average cell allocation of one cell per unit may result in some units being cell-free. In some embodiments, the test unit is a single cell.

[0023] The labeling of test units can be performed in various ways, such as as described in WO2004 / 031369, including nucleic acid labeling, radio frequency coded tags, fluorescent or optical tags, and spatial encoding of test units on a surface or matrix. However, preferably, labeling is performed by modifying the nucleic acids of each cell, which allows for precise tracking of their exposure to TF. Nucleic acids can be modified using, for example, retroviral integration or genome editing techniques.

[0024] The merging and splitting of test units containing multiple cells or single cells allows cells to be exposed to a range of different TFs and combinations of TFs at different times, and the properties of TFs and the timing of exposure can be monitored.

[0025] The split / merge scheme includes the following steps: (a) Provide a first set of test unit clusters, each containing one or more cells, and expose the clusters to the desired TF or TF combination; (b) Merge two or more of the groups to form at least one pool; (c) Subdivide the pool to generate another group of test units; (d) Expose the additional group to the desired TF or TF combination; (e) Optionally, repeat steps (b) to (d) iteratively as needed; and (f) Evaluate the impact of the TFs that a given test unit is exposed to on the test unit.

[0026] Repeating TFs at different times during the split-merge sequence allows for the assessment of their effects at different points in cell development. For example, a pool can be exposed to a combination of four TFs in the first culture and then split. Different splits of the pool can be exposed to different combinations of TFs, which may or may not include any of the TFs used in the first culture. After merging and splitting again, further different splits can be exposed to further combinations of TFs, which may include TFs from the first or second culture as well as new TFs. Thus, cells in different splits are exposed to different TFs, as well as to the same TFs at different times.

[0027] In some implementations, TFs are added individually to the cell unit in successive method steps. For example, successive method steps in the procedure may include adding a single TF or adding a combination of TFs; alternatively, successive method steps in the procedure may only include adding a single TF. Thus, a procedure may combine method steps for adding multiple groups of TFs with steps for adding a single TF, or it may add only a single TF.

[0028] In this context, “procedure” refers to the execution of the entire method as represented by steps (a) through (f) above.

[0029] While repetitive cycles of splitting and merging can be used extremely efficiently, similar to combinatorial chemistry schemes, schemes involving at least two successive splitting steps (without re-merging) can be used if the necessary processing capacity is available. The disadvantage of such schemes is that they can rapidly produce a large number of individually treated samples. However, their advantage is that each sample does not require laborious deconvolution, as the cellular units within are exposed to only one set of conditions. Therefore, with suitable sample processing equipment, splitting methods can produce rapid results.

[0030] As explained above, another advantage of this invention is that TFs can be added sequentially and individually or in large combinations in predetermined groups. This prevents the effect of one TF from being masked by a dominant TF that may exist in a combined TF combination used in conventional experiments.

[0031] In a preferred embodiment, TFs are added to the cells sequentially and individually so that the effects of each TF can be assessed separately.

[0032] The method of the present invention enables the testing of thousands or millions of TFs and TF combinations in multiple high-throughput assays to determine the conditions necessary to achieve desired results in any cellular process.

[0033] In contrast to methods that combine multiple TFs together and then remove them individually to identify effective TFs, the method of this invention can test the sequential addition of TFs to determine the effect of each TF when added in multiple sequential combinations, as well as to test multiple combinations of TFs. This can elucidate the individual steps in the pathway from one cell type to a second cell type, rather than just the overall conversion.

[0034] In some embodiments, the present invention provides a method for identifying genes that influence cellular processes, comprising the steps of: a) determining the effect of one or more TFs or combinations of TFs on a test cell according to the foregoing aspects of the present invention; b) analyzing gene expression of the cell cell upon exposure to the TFs; and c) identifying genes differentially expressed under desired culture conditions.

[0035] Advantageously, the TFs used induce alterations in cellular processes; these TFs are selected for generating cells in which gene expression is analyzed. Gene expression can be conveniently analyzed using any comparative expression monitoring technique, including PCR-based techniques such as RT-PCR, gene expression serial analysis (SAGE), RNA sequencing (RNA seq) including single-cell RNA seq, in situ hybridization including single-cell hybridization, or array techniques, such as those widely available from vendors (e.g., Affymetrix). In another aspect, the invention provides a method for generating nucleic acids encoding gene products that influence cellular processes, comprising identifying the gene as described above and generating at least the coding region of said gene by nucleic acid synthesis or biological replication.

[0036] In a further aspect, a method for inducing cellular processes in cells is provided, comprising the steps of: a) identifying one or more genes differentially expressed in relation to cellular processes according to the invention; and b) regulating the expression of said one or more genes in the cells. The expression of a gene in a cell can be regulated, for example, by transfecting or otherwise transferring the gene into the cell to cause it to be overexpressed transiently or permanently. Alternatively, the expression of an endogenous gene can be altered, for example by targeting enhancer insertion or by administering an exogenous agent that causes an increase (e.g., a placebo inducer) or a decrease (e.g., an antisense, RNAi, a transcription factor) in gene expression. Furthermore, the gene product itself can be administered to or introduced into the cell to achieve an increase in its activity. Additionally, agents that increase or decrease the activity of gene products (e.g., competitive and non-competitive inhibitors, drugs, pharmaceuticals) can be administered to the cell. In a further aspect, the invention provides a method for identifying the state of cellular processes in cells, comprising the steps of: a) identifying one or more genes differentially expressed in relation to cellular processes as described above; and b) detecting the regulation of the expression of said one or more genes in the cell, thereby determining the state of the cellular processes in the cell. Advantageously, the gene used in this analysis encodes a cellular marker that can be detected, for example, by immunoassay. Alternatively, the gene product can be an enzyme whose activity can be determined using fluorescence, colorimetry, radiometry, or other methods.

[0037] The present invention further provides a method for regulating cellular processes, comprising the steps of: a) determining the effect of one or more TFs on cells according to the foregoing aspects of the present invention; b) exposing cells to TFs that cause changes in cellular processes; and c) isolating the desired cells.

[0038] Therefore, the present invention provides a method for generating differentiated cells from a second different differentiated cell type according to the foregoing aspects of the present invention.

[0039] A method for identifying TFs capable of inducing cellular processes is also provided, comprising the steps of: a) determining the effect of one or more TFs on cells according to the foregoing aspects of the invention; and b) identifying those TFs that induce desired cellular processes in cells. TFs identified according to the invention can be expressed or synthesized using conventional chemical, biochemical, or other techniques and used in methods for regulating specific cellular processes in cells, such as those described herein.

[0040] In a further aspect, the present invention relates to a method for developing and using a database of cellular gene expression data associated with TF exposure, which can provide improved TF selection in step (a) of the method of the first aspect of the invention. By using machine learning and inputting experimental data from the method of the present invention, the database can be improved, further optimizing TF selection and thus optimizing the performance of the method of the present invention. Attached Figure Description

[0041] Figure 1 This is a diagram illustrating the operation of the CombiCult® split / merge selection method.

[0042] Figure 2 is a schematic diagram of the workflow of the CombiCult® method of the present invention.

[0043] Figure 3 This is a schematic diagram of the genetic barcode marking of cells in the method of the present invention.

[0044] Figure 4 A screening matrix of 10 transcription factors for generating insulin-producing cells using the method of the present invention is described.

[0045] Figure 5 These are micrographs showing insulin and CD49a expression in differentiated cells obtained by the method of this invention.

[0046] Figure 6Overview of Negative and Positive Controls. In both the negative and positive control settings, the same culture media were used to place cPP cells onto beads. Medium A consisted of high-glucose DMEM supplemented with Asc (50 μg), 1% B27, EGF (50 ng), FGF-7 (50 ng), and RA (50 nM). The aim here was to guide the differentiation process towards pancreatic progenitor cells, particularly considering the early stages of cPP development. Subsequently, medium B was a combination of 5% KOSR, T3 (1 μM), and RA (25 nM), added to the high-glucose DMEM to promote the culture of endocrine progenitor cells. Finally, medium C contained low-glucose DMEM enriched with 10% FBS, T3 (1 μM), and Alk5i (10 μM). In the negative control, no transfection occurred, while the positive control involved sequential transfection starting with Pdx1, followed by Ngn3 and MafA.

[0047] Figure 7A This is a graphical illustration of the combination of transcription factors in computational analysis to determine the transcription factors added to... Figure 5 The frequency of hits identified in the analysis.

[0048] Figure 7B Displayed as a bar chart Figure 7A The results are explained in the text.

[0049] Figure 8 The invention describes the selection of optimal transcription factors using the method of the present invention, and also explains the derivation of time information for transcription factor addition. Detailed Implementation

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art, such as in peptide chemistry, cell culture and phage display, nucleic acid chemistry, and biochemistry. Molecular biology, genetics, and biochemistry methods used standard techniques (see Sambrook et al., Molecular Cloning: A Laboratory Manual, 3rd ed., 2001, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY; Ausubel et al., Short Protocols in Molecular Biology (1999) 4th ed., John Wiley & Sons, Inc.; Patil and Sivaram, A Complete Guide to Gene Cloning: From Basic to Advanced, Springer: ISBN 978-3-030-96853-3, 28 April 2023; Jaroszewicz et al., Phage display and other peptide display technologies, FEMS Microbiology Reviews, fuab052, 46, 2022, 1-25, and resources at Addgene.org), which are incorporated herein by reference.

[0051] As used herein, the term "culture conditions" refers to the environment in which cells are placed or exposed to promote the growth or differentiation of said cells. Therefore, the term refers to culture media, temperature, atmospheric conditions, substrate, agitation conditions, etc., that may affect cell growth and / or differentiation. More specifically, the term refers to specific reagents that can be incorporated into the culture medium and may affect cell growth and / or differentiation. In a preferred embodiment, culture conditions are the reagents or combinations of reagents to which cells are exposed.

[0052] The term "cell" as used herein refers to the smallest structural unit in an organism capable of independent function, or a single-celled organism composed of one or more nuclei, cytoplasm, and various organelles, all surrounded by a semi-permeable cell membrane or cell wall. Cells can be prokaryotic, eukaryotic, or archaea. For example, a cell can be a eukaryotic cell. Mammalian cells are preferred, especially human cells. Cells can be natural or modified, for example, through genetic manipulation or passage in culture to achieve desired characteristics. Stem cells, defined in more detail below, are totipotent, pluripotent, or multipotent cells capable of producing more than one differentiated cell type. Stem cells can differentiate in vitro to produce differentiated cells, which themselves can be multipotent or terminally differentiated. Cells differentiated in vitro are artificially produced cells by exposing stem cells to one or more agents that promote cell differentiation.

[0053] Cellular processes refer to any characteristic, function, process, event, cause, or result that occurs or is observed or can be attributed to a cell, whether intracellular or extracellular. Examples of cellular processes include, but are not limited to, vitality, aging, death, pluripotency, morphology, signal transduction, binding, recognition, molecular production or destruction (degradation), mutation, protein folding, transcription, translation, catalysis, synaptic transmission, vesicle transport, organelle function, cell cycle, metabolism, proliferation, division, differentiation, phenotype, genotype, gene expression, or the control of these processes.

[0054] A cell unit is a group of cells, which can be a collection of individual cells. Cell unit pools can be sorted, subdivided, and processed substantially without dissociating the cell units themselves, allowing the cell units to act as cell colonies and each cell within a cell unit to be exposed to the same culture conditions. For example, a cell unit can contain beads with attached cell populations.

[0055] A totipotent cell is a cell that has the potential to differentiate into any type of somatic cell or germ cell found in an organism. Therefore, any desired cell can be derived from a totipotent cell in some way.

[0056] Pluripotent cells are cells that can differentiate into more than one, but not all, cell types.

[0057] As used herein, a marker or tag is a means of identifying cell units and / or determining the culture conditions or sequence of culture conditions to which the cell units are exposed. Thus, a marker can be a set of markers, each added at a specific culture step; or a marker added at the start of an experiment and modified according to the culture steps to which the cell units are exposed, or a marker tracked within those steps; or simply a location reference that allows inference of the culture steps used. A marker or tag can also be a device for reporting or recording the location or identity of a cell unit at any given time, or for assigning a unique identifier to a cell unit. Examples of markers or tags are molecules with unique sequences, structures, or qualities; or fluorescent molecules or objects such as beads; or radio frequency and other transponders; or objects with unique markings or shapes. Preferably, the marker is a genetic barcode as further described herein.

[0058] Cells are exposed to culture conditions or reagents when they come into contact with a culture medium or grow under conditions that affect one or more cellular processes, such as cell growth, differentiation, or metabolic state. Therefore, if the culture conditions involve culturing cells in a medium in the presence of a reagent, the cells are placed in the medium containing the reagent for a sufficient duration to allow it to exert its effect. Similarly, if the conditions are temperature conditions, the cells are cultured at the desired temperature.

[0059] The merging of one or more groups of cell units involves mixing these groups to produce a single group or pool containing cell units from more than one background, i.e., cell units that have been exposed to more than one group of culture conditions. A pool may be further subdivided into multiple groups, randomly or non-randomly; for the purposes of this invention, such groups are not themselves “pools,” but they can be merged through combination, for example, after exposure to different groups of culture conditions.

[0060] The terms "proliferating cell growth" and "cell proliferation" are used interchangeably herein to refer to a multiple increase in the number of cells without differentiation into different cell types or lineages. In other words, these terms refer to an increase in the number of living cells. Preferably, proliferation is not accompanied by significant changes in phenotype or genotype.

[0061] Cell differentiation is the process by which a cell type develops into different cell types. For example, bispotent, pluripotent, or totipotent cells can differentiate into nerve cells. Differentiation can occur alongside proliferation or independently of it. The term "differentiation" generally refers to the phenotype of acquiring a mature cell type (e.g., neuron or lymphocyte) from a cell type with a less defined developmental stage, but it does not exclude transdifferentiation, i.e., the transformation of a mature cell type into another mature cell type, such as a neuron transforming into a lymphocyte.

[0062] The differentiation state of a cell is the level at which a cell differentiates along a specific pathway or lineage.

[0063] Cellular process state refers to whether a cellular process is occurring, and in complex cellular processes, it can represent a specific step or stage in that process. For example, a cell differentiation pathway may be inactive or may have been induced, and may involve multiple discrete steps or components, such as signal transduction events characterized by the presence of a particular set of enzymes or intermediates.

[0064] A gene is a nucleic acid that encodes a gene product (whether a polypeptide or an RNA gene product). As used herein, a gene includes at least a coding sequence that encodes the gene product; optionally, it may include one or more regulatory regions required for transcription and / or translation of that coding sequence.

[0065] Gene products are typically proteins encoded by genes in a conventional manner. However, the term also encompasses non-peptide gene products encoded by genes, such as ribonucleic acid (RNA).

[0066] Nucleic acid synthesis can be performed using any available technology. Preferably, nucleic acid synthesis is automated. Furthermore, nucleic acids can be produced through biological replication (e.g., by cloning and replicating in bacteria or eukaryotic cells) according to procedures known in the art.

[0067] Differentially expressed genes, which are expressed at different levels in response to cell culture conditions, can be identified by gene expression analysis (e.g., on a gene chip), sequencing (e.g., RNA-seq), or any method known in the art. Differentially expressed genes, relative to overall gene expression levels, show more or fewer mRNA or gene products in cells under the test conditions than under alternative conditions.

[0068] Transfected genes can be transferred into cells by any appropriate means. The term is used in this article to refer to routine transfection (e.g., using calcium phosphate), but also includes other techniques used to transfer nucleic acids into cells, including transformation, viral transduction, electroporation, etc.

[0069] The term regulation is used to describe the increase and / or decrease of a regulated parameter. Therefore, the regulation of gene expression includes both increasing and decreasing gene expression.

[0070] stem cells in Stem Cells: Scientific Progress and Future Research DirectionsThe Department of Health and Human Services, June 2001. http: / / www.nih.gov / news / stemcell / scireport.htm provides a detailed description of stem cells. The contents of that report are incorporated herein by reference.

[0071] Stem cells are cells capable of differentiating to form at least one, and sometimes many, specialized or differentiated cell types. The entire set of different cells that can be formed from stem cells is considered exhaustive; that is, it includes all the different cell types that make up an organism. Stem cells are present throughout the entire life cycle of an organism, from the early embryo where stem cells are relatively abundant to the adult where stem cells are relatively scarce. Stem cells, present in many tissues of adult animals, play an important role in normal tissue repair and homeostasis. The existence of these cells raises the possibility that they could provide a means of generating specialized functional cells that could be transplanted into the human body to replace dead or nonfunctional cells in diseased tissues. The list of diseases for which this could provide therapy includes Parkinson's disease, diabetes, spinal cord injury, stroke, chronic heart disease, end-stage renal disease, liver failure, and cancer.

[0072] Different stem cells possess varying potentials to form different cell types: spermatogonial stem cells are unipotent because they naturally produce only sperm, while hematopoietic stem cells are pluripotent, and embryonic stem cells are considered capable of generating all cell types and are referred to as totipotent or pluripotent. To date, three types of mammalian pluripotent stem cells have been isolated. These cells can generate cell types that are typically derived from all three germ layers of the embryo (endoderm, mesoderm, and ectoderm). These three types of stem cells are: embryonic carcinoma (EC) cells derived from testicular tumors; embryonic stem (ES) cells derived from preimplantation embryos (usually blastocysts); and embryonic germ (EG) cells derived from postimplantation embryos (usually fetal cells destined to become part of the gonads).

[0073] Differentiated cell types can alter their phenotype. This phenomenon, known as transdifferentiation, is the transformation of one differentiated cell type into another, with or without intervening cell division. Differentiation can sometimes be reversed or altered. In vitro protocols that can induce transdifferentiation in cell lines are now available. Furthermore, specialized cell types can be dedifferentiated to generate stem cell-like cells with the potential to differentiate into other cell types.

[0074] Many systems designed for the in vitro differentiation of stem cells are complex, multi-stage procedures in which the exact nature of each step and the timing of each step are important. For example, Lee et al. (2000, Nature Biotechnology, vol. 18, p 675-679) used a five-stage protocol to derive dopaminergic neurons from mouse ES cells: 1) Undifferentiated ES cells were expanded in ES cell culture medium on a gelatin-coated tissue culture surface in the presence of LIF; 2) Embryoids were generated in ES cell culture medium in suspension culture for 4 days; 3) Nestin-positive cells were screened from the embryoids in ITSFn medium for 8 days after being plated on a tissue culture surface; 4) Nestin-positive cells were expanded in N2 medium containing bFGF / laminoids for 6 days; 5) Finally, the expanded neuronal precursor cells were induced to differentiate by removing bFGF from the N2 medium containing laminoids. In a second example of continuous cell culture, Bonner-Weir et al. [Proc.Natl. Acad. Sci. (2000) 97: 7999-8004] derived insulin-producing cells from human pancreatic ductal cells by: 1) selecting ductal cells instead of islet cells for 2 to 4 days by selective adhesion to a solid surface in the presence of serum; 2) subsequently removing the serum and adding keratinocyte growth factor to select ductal epithelial cells instead of fibroblasts for 5 to 10 days; and 3) covering the cells with the extracellular matrix preparation “Matrigel” for 3 to 6 weeks. In a further example of continuous cell culture, Lumelsky et al. [Science (2001) 292: 1389-1394] derived insulin-secreting cells through directed differentiation of mouse embryonic stem (ES) cells by: 1) expanding ES cells in the presence of LIF for 2 to 3 days; 2) generating embryoid bodies in the absence of LIF for 4 days; 3) selecting nestin-positive cells using ITSFn medium for 6 to 7 days; 4) expanding pancreatic endocrine precursors in N2 medium containing 827 medium supplement and bFGF for 6 days; and 5) inducing differentiation into insulin-secreting cells by removing bFGF and adding nicotinamide.

[0075] However, in determining cell differentiation, it is important not only for the sequence and duration of individual steps, or the sequential addition of different factors. Since embryonic development is regulated by the action of a gradient of signaling factors that impart positional information, it is predictable that the concentration of a single signaling factor, as well as the relative concentrations of two or more factors, will be crucial for determining the fate of cell populations in vitro and in vivo. Factor concentrations change during development, and stem cells respond differently to different concentrations of the same molecule. For example, stem cells isolated from the CNS of late-stage embryos respond differently to different concentrations of EGF: low concentrations of EGF induce proliferative signals, while higher concentrations induce proliferation and differentiation into astrocytes. Many factors that influence stem cell self-renewal and differentiation in vitro have been found to be naturally occurring molecules. This is predictable because differentiation is induced and controlled by signaling molecules and receptors acting along signal transduction pathways. However, similarly, many synthetic compounds are likely to influence stem cell differentiation. Such synthetic compounds, which are likely to interact with cellular targets (so-called druggable targets) within signal transduction and signaling pathways, are routinely synthesized, for example, for drug screening by pharmaceutical companies. Once known, these compounds can be used to direct the differentiation of stem cells under in vitro conditions, or they can be administered in vivo, in which case they will act on resident stem cells in the patient's target organs.

[0076] Common variables in tissue culture: When developing conditions for successful culture of a specific cell type, or to achieve or regulate cellular processes, it is often important to consider various factors. One important factor is deciding whether to proliferate cells in suspension or as a monolayer attached to the matrix. Most cells tend to adhere to the matrix, although some cells (including transformed cells, hematopoietic cells, and cells derived from ascites) can proliferate in suspension.

[0077] Assuming adherent cells are being cultured, a crucial factor is the choice of adhesion matrix. Most laboratories use single-use plastics as tissue culture matrices. These plastics include polystyrene (the most common type), polyethylene, polycarbonate, Perspex, PVC, Teflon, cellophane, and cellulose acetate. While virtually any plastic may be used, many require processing to make them wettable and suitable for cell attachment. Furthermore, it is quite possible that any properly prepared solid matrix can be used to support cells; matrices used to date include glass (e.g., aluminoborosilicate glass and soda-lime glass), rubber, synthetic fibers, polymeric dextran, and metals (e.g., stainless steel and titanium). Certain cell types, such as bronchial epithelial cells, vascular endothelial cells, skeletal muscle cells, and neurons, require growth matrices coated with biological products (typically extracellular matrix materials such as fibronectin, collagen, laminin, polylysine, etc.). The growth matrix and application method (wet or dry coating, or gelation) can influence cellular processes such as cell growth and differentiation characteristics, which must be determined empirically as described above. Among the variables in cell culture, the choice of culture medium and supplements (such as serum) is perhaps the most obviously important. They provide the aqueous compartment for cell growth and contain nutrients and various factors, some of which are listed above, while others are less well-defined. Some of these factors are essential for adhesion, others are used for signal transduction (e.g., hormones, mitogens, cytokines), and still others act as detoxifiers. Commonly used media include RPMI 1640, MEM / Hank salt, MEM / Earle salt, F12, DMEM / F12, L15, MCDB 153, etc. The composition of various media can vary considerably; some common differences include sodium bicarbonate concentration, concentration of divalent ions (e.g., Ca and Mg), buffer composition, antibiotics, trace elements, nucleosides, peptides, synthetic compounds, drugs, etc. It is well known that different media are selective, meaning they promote the growth of only certain cell types. Culture medium supplements (such as serum, pituitary extracts, and other extracts) are often essential for cell growth in cultures. Furthermore, they frequently determine the phenotype of cells in the culture; that is, they can determine cell survival or guide differentiation. The role of supplements in cellular processes (such as differentiation) is complex and depends on their concentration, the timing of their addition to the culture, the cell type used, and the culture medium. The uncertainty surrounding the properties of these supplements and their potential to influence cell phenotype has driven the development of serum-free media. As with all media, their development has been largely achieved through trial and error, as discussed above. The gas phase in tissue culture is also important, and the composition and volume of gas used may depend on the type of culture medium used, the required buffer volume, whether the culture vessel is open or sealed, and whether specific cellular processes need to be modulated.Common variables include the concentrations of carbon dioxide and oxygen. Other conditions important for tissue culture include the choice of culture vessel, headspace size, seeding density, temperature, frequency of medium changes, enzyme treatment, and the rate and mode of agitation or stirring. Therefore, altering cell culture conditions is one way to achieve desired cellular processes. One aspect of the invention recognizes that altering cell culture conditions in a continuous manner can be a highly efficient method for achieving cellular effects. In various applications, such as in studies of cell differentiation, a specific series of different tissue culture conditions is often required to achieve cellular processes. Different conditions may include adding or removing substances from the culture medium at specific time points, or changing the culture medium. As mentioned above, such sets of conditions (examples of which are given below) are typically developed through trial and error.

[0078] Formation of Test Units One aspect of this invention is that, in some embodiments, cell populations (cell colonies) can grow in cell culture under various conditions and, when disturbed and mixed with other colonies, can largely maintain their integrity under these conditions. These populations or colonies are referred to herein as test units. In some cases, the test unit is a single cell, in which case it is not necessary to form cell colonies.

[0079] For example, test cell formation can be achieved by growing cells as adherent cultures on a solid substrate, such as a carrier. If cell proliferation occurs after seeding onto the carrier, the progeny cells will attach to the same carrier and form part of the same colony. Typically, live adherent cells do not readily detach from their growth substrate, so the integrity of the cell colony remains despite any mechanical manipulation of the carrier, agitation of the culture medium, or transfer to another tissue culture system.

[0080] Similarly, if multiple carriers are placed in the same container at any given time (e.g., merging beads), cells will not substantially transfer from one bead to another. One advantage of growing cells in units or colonies is that if units are placed sequentially in a set of different tissue culture media, all cells constituting the colony will be exposed to the same series of culture conditions in the same order and for the same duration. An advantage of growing cells in units that are not necessarily attached to the tissue culture container is that individual colonies can be freely removed and transferred to different culture containers. One advantage of this method is that it allows for the miniaturization of tissue culture: colonizing microcarrier beads (see below) requires relatively fewer cells compared to even the smallest tissue culture flasks. Another advantage of cell units formed on carriers is that cell culture can be scaled up. Growing stem cells on carriers provides a method for scaled-up production, thus providing sufficient material for stem cell therapy. Similarly, differentiating stem cells on carriers provides a method for scaled-up production as differentiation proceeds, ultimately providing sufficient material for cell replacement therapy. Scale-up of such cell cultures requires at least 50 g (dry weight) of microcarriers, preferably 100 g, 500 g, 1 kg, 10 kg, or more. Another important advantage of forming cell units on a solid matrix is ​​that the matrix (and therefore, based on association, also includes attached cells) can be labeled by a variety of means. 3 mm and 5 mm glass beads have been widely used as cell adhesion matrices, particularly in glass bead bioreactors used for scale-up cell culture (e.g., bioreactors manufactured by Meredos GmbH). These beads are typically used in packed beds rather than batch cultures to avoid mechanical damage to adherent cells. In contrast, when cells are grown on smaller carriers, they can be treated as suspension cultures. A common method for growing cells on small carriers is called microcarrier cell culture (see 'Microcarrier cell culture, Principles and Methods', Edition AA, available from Amersham Biosciences (18-1140-62); which is incorporated herein by reference in its entirety). Microcarrier cultures are commercially available for the production of antibodies and interferons in fermenters up to 4000 liters. A variety of microcarriers are available, varying in shape and size and made from different materials.Microcarrier beads made from polystyrene (Biosilon, Nunc), glass (Bioglass, Solohill Eng), collagen (Biospheres, Solohill Eng), DEAE Sephadex (Cytodex-1, Pharmacia), dextran (Dormacell, Pfeifer & Langen), cellulose (DE-53, Whatman), gelatin (Gelibead, Hazelton Lab), and DEAE dextran (Microdex, Dextran Prod.) are commercially available. These carriers have been well characterized in terms of specific gravity, diameter, and usable surface area for cell growth. Furthermore, many porous (micro)carriers that significantly increase the surface area available for cell growth are also available. Another characteristic of these porous carriers is that they are suitable for both the growth of adherent cells and the growth of suspended cells carried by trapping them in an open, interconnected network of pores. Porous carriers can be obtained from materials such as gelatin (Cultispher G, HyClone), cellulose (Cytocell, Pharmacia), polyethylene (Cytoline 1 and 2, Pharmacia), silicone rubber (lmmobasil, Ashby Scientific), collagen (Microsphere, Cellex Biosciences), and glass (Siran, Schott Glassware). These carriers are suitable to varying degrees for stirred bed, fluidized bed, or fixed bed culture systems.

[0081] Since the physical properties of the carriers are well known, the number of carriers used in the experiment can be easily calculated. Some of the carriers described, as well as many others, are available as dried products that can be precisely weighed and then prepared by swelling in a liquid culture medium. Furthermore, the number of cells used to inoculate the microcarrier culture can be calculated and varied. For example, a Cytodex 3 culture (2 g / L) inoculated with 6 cells per bead will yield a culture containing 8 million 42 microcarriers, growing at a density of 5 x 10⁴ cells / cm² on these microcarriers to produce 48 million cells / L.

[0082] Harvesting of cells grown on microcarriers, or release of labels from microcarriers (see below), can be achieved by enzymatic detachment of cells and / or, where applicable, by digestion of the carrier: gelatin carriers can be dissolved with trypsin and / or EDTA, collagen carriers can be dissolved with collagenase, and dextran carriers can be dissolved with dextranase.

[0083] Besides solid or porous microcarriers, cells can be grouped by confinement, i.e., restricted within a barrier of permeable culture medium. Membrane culture systems have been developed in which permeable dialysis membranes retain cell populations but allow free exchange of culture medium and its components with internal and external compartments. Cell culture in hollow fiber columns has also been developed, and a variety of fibers and even one-stop systems are commercially available (e.g., from Amicon, Cellex Biosciences). Cell encapsulation in semi-solid matrices has also been developed, where cells are immobilized by adsorption, covalent bonding, cross-linking, or retention in polymer matrices. Materials used include gelatin, polylysine, alginate, and agarose. A typical protocol involves mixing 5% agarose with a cell suspension in its normal growth medium at 40°C, emulsifying the mixture with an equal volume of paraffin oil, and cooling in an ice bath to produce spheres with a diameter of 80 to 200 μm. These spheres can then be separated from the oil and transferred to culture medium in tissue culture containers.

[0084] Cell trapping is a simple method for immobilizing cell populations, similar to using microcarriers or porous matrices. A simple technique involves entangling cells in cellulose fibers such as DEAE, TLC, QAE, and TEAE (all available from Sigma). More sophisticated devices are ceramic columns suitable for suspension cells, such as those in the Opticel culture system (Cellex Biosciences).

[0085] Those skilled in the art will envision that, in addition to the methods described above for creating cell units, other methods for creating cell groups include forming 3D cultures of cells (e.g., neurospheres or embryoids), or using tissues, or even entire organisms (e.g., fruit flies or Caenorhabditis elegans).

[0086] Test units, or the matrices that compose them, can bind to specific factors, including but not limited to proteins, nucleic acids, or other chemicals such as drugs. Pretreatment of the matrix can be achieved in various ways, such as simply incubating the matrix with the target factor, or covalently or non-covalently attaching the factor to the matrix. Soluble factors can be incorporated into dried materials through impregnation. This technique relies on the rapid infusion of a liquid carrying the soluble factor into a porous, dried material, causing the material to swell and become ready for use. Solid factors can be incorporated, for example, by mixing factors from fibrinogen with a thrombin solution, forming a fibrin clot containing the factor. Besides impregnating, trapping, or encapsulating factors with cells, a variety of other methods for binding one or more factors to a cell population are conceivable.

[0087] One method for combining cell populations with a variety of different factors is to pre-form a cocktail of factors and then combine it with a specific cell population. A second method involves subjecting the cell population to a series of factors sequentially. Taking a dried formulation of a cell population growth matrix as an example, this method would involve first partially swelling the matrix in a solution containing a first factor, and then further swelling it in a solution containing a second factor, resulting in a matrix that has been combined with both factors. By designing systematic schemes for combining cell populations with different combinations of factors, it becomes possible to sample the effects of any combination of a set of factors on the cell population. Regardless of the method used to treat the cell unit with factors, these factors are taken up by the cells that make up that cell unit. Factors leaking into the growth medium are diluted to such an extent that their concentrations fall below physiologically relevant limits and they have no effect on any other cell populations exposed therein. The diffusion of factors from, for example, the matrix that constitutes part of the cell unit is controlled by parameters such as the properties and size of the material, the average pore size, and the molecular weight and concentration of the factors. If it is necessary to calibrate the process, the release of the factor can be measured by physical assays (e.g., HPLC analysis or releasing the labeled factor into the culture medium) or by bioassays (e.g., extraganglionic growth bioassay for neurotrophic factors).

[0088] The combination of continuous cell culture split-and-merge cell cultures to form test units (especially microscopic test units) is more useful for sampling under a variety of tissue culture conditions because each cell unit constitutes an easily handled unit that can be exposed to a variety of cell culture conditions. For simplicity, in this discussion, we will assume that cell grouping is generated by growing cells in microcarrier cultures, and the terms test unit, cell unit, cell population, single cell, colony, and bead are used interchangeably. However, the methods described are equally applicable to any test unit, such as those mentioned above.

[0089] A particularly efficient method for sampling large numbers of cell culture conditions is called combinatorial cell culture or split-and-combination cell culture. Figure 1In one embodiment, the method involves sequentially subdividing and combining multiple groups of cell units to sample various combinations of cell culture conditions. In one aspect of the invention, the method operates by taking an initial starting culture (or different starting cultures) of cell units, dividing it into X aliquots, each aliquot containing multiple beads (groups / colonies / carriers / cells) grown individually under different culture conditions. After a given time of cell culture, cell units can be merged by combining and mixing the beads from the different aliquots. This pool can then be further subdivided into X² aliquots, each cultured under different conditions for a period of time, and subsequently merged. This iterative procedure of splitting, culturing, and merging cell units (or merging, splitting, and culturing; depending on where the cycle begins) enables systematic sampling of many different combinations of cell culture conditions.

[0090] The complexity of the experiment, or in other words, the number of different combinations of cell culture conditions tested, is equal to the product of the number of different conditions sampled in each round (X1 x X2 x ... Xn). It should be noted that the step of merging all cell units before subsequent splitting can be optional, and a step of merging a finite number of cell units can have the same effect.

[0091] Therefore, this invention embodies many related methods for systematically sampling multiple combinations of cell culture conditions, wherein multiple test units are processed in batches. Regardless of the exact manner in which multiple cell culture conditions are sampled, the procedure is efficient because multiple cell units can share a single container in which they are cultured under the same conditions, and it can be performed at any one time using only a few culture containers (the number of culture containers in use equals the number of samples split).

[0092] In many respects, the procedure is analogous to the split-synthesis of large chemical libraries (known as combinatorial chemistry), which samples all possible combinations of bonds between chemical structural unit groups (see, for example: Combinatorial Chemistry, Oxford University Press (2000), Hicham Fenniri (Editor)). Split-and-merge cell cultures can be repeated an arbitrary number of rounds, and an arbitrary number of conditions can be sampled in each round. As long as the number of test units (cells or colonization beads in this example) is greater than or equal to the number of different conditions sampled across all rounds, and assuming that the splitting of cell units occurs completely randomly, it can be expected that at least one cell unit has been cultured according to every possible combination of the experimentally sampled culture conditions. The procedure can be used to sample growth or differentiation conditions for any cell type, or to sample the efficiency of any cell type in producing biomolecules (e.g., erythropoietin or interferon). Because the procedure is iterative, it is well-suited for testing multi-step tissue culture protocols, such as those described above in conjunction with stem cell differentiation. Variables that can be sampled using this technique include cell type, cell grouping (e.g., microcarrier culture, cell encapsulation, whole organism), growth substrate (e.g., fibronectin on microcarriers), duration of cell culture cycles, temperature, different culture media (including different concentrations of components), growth factors, conditioned media, co-culture with various cell types (e.g., feeder cells), animal or plant extracts, drugs, other synthetic chemicals, viral infection (including transgenic viruses), addition of transgenes, addition of antisense or antigene molecules (e.g., RNAi, triple helix), sensory input (for organisms), electrical stimulation, light stimulation, or redox stimulation.

[0093] The purpose of performing a split-and-merge process on cell units in a split-and-merge cell culture is to systematically expose them to predefined combinations of conditions. Those skilled in the art will devise many different means to achieve this result. In addition to the split-and-merge process and its variations, it is worthwhile to briefly discuss the split-and-merge process. A split-and-merge process involves subdividing a group of cell units at least twice, without merging them in between. If the split-and-merge process is used in a large number of rounds, the number of individual samples produced increases exponentially. In this case, a certain level of automation is essential, such as using robotic platforms and sophisticated sample tracking systems. The advantage of the split-and-merge step is that (because the cell units are not merged) the lineages of various cell units can be separated based on their cell culture history. Therefore, the split-and-merge step can be used to infer whether a particular cell culture condition is responsible for any given cell process, and thus to infer the culture history of the cell units.

[0094] The splitting and / or merging of cell units according to a predetermined protocol can be performed completely randomly or can follow a predetermined protocol. When cell units are randomly split and / or merged, the separation of a given cell unit into any group is not predetermined or biased in any way. To induce exposure of at least one cell unit to each possible combination of cell culture conditions with a high probability, it is advantageous to use a larger number of cell units than the total number of cell culture condition combinations being tested. Thus, in some cases, it is advantageous to split and / or merge cell units according to a predetermined protocol, with the overall effect of preventing accidental duplication or omission of combinations. The predetermined treatment of cell units can optionally be planned in advance and recorded on a spreadsheet or computer program, and the splitting and / or merging operations can be performed using automated methods such as robotics. The labeling of cell units (see below) can be done by any of a number of means, such as RFID tagging, optical tagging, or spatial coding. Robotic devices capable of identifying samples and thus distributing samples according to a predetermined protocol have been described (see 'Combinatorial Chemistry, A practical Approach', Oxford University Press (2000), Ed H. Fenniri). Alternatively, standard laboratory liquid handling and / or tissue culture robots (such as those manufactured by Beckman Coulter Inc, Fullerton, CA; The Automation Partnership, Royston, UK) are capable of spatially encoding the identities of multiple samples and adding, removing, or transferring these samples according to pre-programmed protocols.

[0095] Cell units can be analyzed and / or isolated after each round of cell culture, or after a specified number of rounds, to observe any given cellular processes that may be affected by tissue culture conditions. The following examples are illustrative and are not intended to limit the scope of the invention.

[0096] After each round of cell culture, or after a specified number of rounds, cell units can be assayed to determine the presence of members exhibiting increased cell proliferation. This can be achieved through various techniques, such as visual examination of cell units under a microscope, or by quantifying cell-specific marker products. These can be endogenous markers, such as specific DNA sequences, or cellular proteins that can be detected by ligands or antibodies. Alternatively, exogenous markers, such as green fluorescent protein (GFP), can be introduced into the cell units being assayed to provide cell-specific readings. Live cells can be visualized using various live dyes, or conversely, dead cells can be labeled using various methods, such as propidium iodide. Furthermore, labeled cell units can be separated from unlabeled cell units using various techniques, both manual and automated, including affinity purification (“panning”), or by fluorescence-activated cell sorting (FACS) or substantially similar techniques (Figure 17). Depending on the application, standard laboratory equipment may be used, or the use of specialized instruments may be advantageous. For example, some analytical and sorting instruments (see, for example, Union Biometrica Inc., Somerville MA, USA) have flow cell diameters up to one millimeter, enabling flow sorting of beads up to 500 micrometers in diameter. These instruments provide readings of bead size and optical density, as well as two fluorescence emission wavelengths from tags such as GFP, YFP, or OS-red. Sorting speeds of up to 180,000 beads per hour, and dispensing into multi-well plates or batch receivers, can be achieved. After each round of cell culture, or after a specified number of rounds, cell units can be assayed to determine the presence of members displaying a specific genotype or phenotype. Genotyping can be performed using well-known techniques such as polymerase chain reaction (PCR), fluorescence in situ hybridization (FISH), DNA sequencing, etc. Phenotyping can be performed using a variety of techniques, such as visual examination of cell units under a microscope or by detecting cell-specific biomarker products. This could be endogenous biomarkers such as specific DNA or RNA sequences, or cellular proteins that can be detected through ligands, enzyme substrate conversion, or antibodies that recognize specific phenotypic biomarkers (see Appendix E of...). Stem Cells: Scientific Progress and Future Research DirectionsDepartment of Health and Human Services. June 2001; the appendix mentioned is incorporated herein by reference. Genetic markers can also be exogenous, i.e., markers introduced into a cell population, for example, through transfection or viral transduction. Examples of exogenous markers are fluorescent proteins (e.g., GFP) or cell surface antigens that are not typically expressed in a particular cell lineage, are epitope-modified, or are from a different species. Transgenic or exogenous marker genes with associated transcriptional control elements can be expressed in a manner that reflects a pattern representing an endogenous gene. This can be achieved by associating the gene with a minimal cell type-specific promoter or by integrating the transgene into a specific locus (see, for example, European Patent No. EP 0695351). Labeled cell units can be separated from unlabeled cell units using various techniques, whether manual or automated, including affinity purification (“panning”) or by fluorescence-activated cell sorting (FACS). Nishikawa et al. (1998, Development vol 125, p1747-1757) used cell surface markers recognized by antibodies to track the differentiation of pluripotent mouse ES cells. Using FACS, they were able to identify and purify cells of hematopoietic lineages at various stages of differentiation.

[0097] Alternative or complementary techniques for enriching cell units with specific genotypes or phenotypes are essential components of genetic selection. This can be achieved, for example, by introducing selective markers into cell units and measuring viability under selective conditions, see, for example, Soria et al. (2000, Diabetes vol 49, p1-6), who used such a system to select insulin-secreting cells from differentiated ES cells. Li et al. (1998, Curr Biol vol 8, p 971-974) identified neural progenitor cells by integrating the bifunctional selection marker / reporter gene f3geo (which provides f3-galactosidase activity and G418 resistance) into the Sox2 locus in mouse ES cells via homologous recombination. Since one of the characteristics of neural progenitor cells is the expression of Sox2, and therefore the expression of the integrated marker gene, these cells can be selected from non-neuronal lineages by adding G418 after differentiation induced with retinoic acid. Cell viability can be determined by microscopic examination or by monitoring f3-gal activity. Unlike phenotype-based selection methods, which may be limited by the availability of suitable ligands or antibodies, genetic selection can be applied to any differentially expressed gene.

[0098] Determining the identity or culture history of cell units: When dealing with large numbers of cell units, their identity and / or culture history (e.g., the temporal sequence and exact nature of a series of culture conditions that any group or unit may have been exposed to) can become convoluted. For example, split-and-merge schemes for cell culture necessarily involve mixing cell units in each round, making it difficult to track individual units. Determining the culture history of a particular cell unit in a mixture of cell units that have undergone multiple culture conditions is sometimes referred to as “deconvolution” of the cell culture history. One way to perform this operation is to label the cell units, and therefore labeling cell units is advantageous. Labeling can be done at the beginning of the experiment or in each round of the experiment, and can involve unique tags (which may be modified or not modified during the experiment) or a series of tags containing a unique set. Similarly, the tags can be read during each round or only at the end of the experiment. Preferably, unique tags (e.g., RFID tags) are read during each round, while the tags added consecutively in each round are read at the end of the experiment. The labeling of cell units can be achieved by a variety of means, such as labeling the cells themselves or any material to which the cells are attached or otherwise bound. Any chemical and non-chemical method used to encode synthetic combinatorial libraries can be modified for this purpose, some of which are described in Methods in Enzymology Vol 267 (1996), 'Combinatorial Chemistry', John N. Abelson (Editor); Combinatorial Chemistry, Oxford University Press (2000), Hicham Fenniri (Editor); K. Braeckmans et al., 'Scanning the code', Modern Drug Discovery (Feb. 2003); K. Braeckmans et al., 'Encoding microcarriers: Present and Future Technologies'; Nature Reviews Drug Discovery, vol. 1, p. 447-456 (2002), all of which are incorporated herein by reference. Below are some examples of labeling methods. One method of labeling cell units involves binding the cell unit to a tag that is modified sequentially when placed under different culture conditions. This could involve, for example, adding or subtracting additional units from the tag to change its stereochemistry, sequence, or quality; or altering the electronic memory as in a read / write RF transponder (see below).Another approach to labeling cell units involves sequentially associating a unique tag with the cell unit each time it is cultured under different conditions. This allows subsequent detection and identification of the tag to provide a clear record of the temporal sequence and identity of the cell culture conditions to which the cell unit was exposed. The tag can be taken up by the cell, attached to the cell surface via adsorption, suitable ligands or antibodies, or conjugated to a cell-associated matrix (e.g., a carrier) via adsorption, colloidal repulsion, or various bonds (e.g., covalent or non-covalent bonds, such as biotin-streptavidin bonds). For example, a simple tag that can be introduced into cells or attached to a cell-associated matrix is ​​an oligonucleotide of a specified length and / or sequence. The oligonucleotide can contain any class of nucleic acids (e.g., RNA, DNA, PNA, linear, circular, or viral) and can contain specific sequences for amplification (e.g., primer sequences for PCR) or markers for detection (e.g., fluorophores or quenchers, or isotope tags). The detection of these tags can be direct, such as by sequencing oligonucleotides or hybridizing them with complementary sequences (e.g., on microarrays or chips), or indirect, such as by monitoring the gene product encoded by the oligonucleotides, or the interference of nucleotides with cellular activity (e.g., antisense repression of specific genes). A favorable method for amplifying nucleic acids is rolling circle amplification (RCA; 2002, V. Demidov). Expert Rev. Mol. Diagn2(6), pp. 89-95), where the nucleic acid tag may contain an RCA template, an extension primer, or a strut to assist in the cyclization of the microloop template. Any molecular or macromolecular tag can be used, as long as it can be detected, including peptide tags, colored or fluorescent compounds, secondary amines, halogenated hydrocarbons, stable isotope mixtures, etc. The tag can be attached to the cell unit directly or via an intermediate (e.g., an antibody generated against a cell unit component) or via an interaction pair (e.g., biotin-streptavidin). In addition, the tag can be protected from degradation by components of the cell culture, for example by chemical or other modifications or by encapsulation. Encapsulation of the tag can be performed in many different media, such as in beads. Many types of beads are available from suppliers such as Bangs Laboratories Inc. (Fishers IN, USA), and encapsulation can be used to standardize tag dosage in addition to providing components for tag amplification and / or detection (e.g., by providing PCR primers for use with the DNA tag). A preferred method for labeling cell units is to use fluorescent beads, such as those manufactured by Luminex Corporation (Austin, TX, USA). The Luminex system comprises polystyrene beads and a reader capable of characterizing the spectral features of each bead. The polystyrene beads may be externally derivatized (e.g., using avidin or antibodies) or not, and internally stained with two distinctly different fluorophores in varying proportions. Another preferred method employs beads manufactured, for example, by Bangs Laboratories Inc. (Fishers IN, USA). The Bangs system comprises groups of beads that can be distinguished based on different sizes (e.g., groups of beads with diameters of 4.4 μm and 5.5 μm). Beads within each group can be further distinguished from each other based on different fluorescence intensities due to differentiated loading with a single fluorescent dye. Many different dyes with different absorption or emission characteristics can be used, which can be internally loaded or externally attached to a carrier by various means. Furthermore, "quantum dots" can be used to obtain a vast number of different fluorescent labels that can be easily read.

[0099] Preferred labeling techniques advantageously used when the test unit is a single cell include nucleic acid modification of the cell by inserting a nucleic acid “barcode”, by viral infection (e.g., by lentivirus) or by gene editing (e.g., by CRISPR).

[0100] Cell growth matrices, such as those described in the context of cell unit formation, can be derivatized or coated with substances that promote labeling without interfering with cell growth. A preferred method for derivatizing a carrier is to covalently or nonvalently modify it with biotin, to which the tag can be attached via streptavidin or avidin. Generally, it is important to use tags that do not induce cellular effects on their own (i.e., inert tags), and that are distinguishable from molecules present in the cell unit or culture medium, and capable of attaching to their target and subsequently being detected against the background of these molecules. To facilitate detection, it may be advantageous to selectively elute the tag from the cell unit or peel the cell off the cell unit using selective conditions. More sophisticated molecular labeling strategies are also conceivable, including “binary encoding” strategies, where information is recorded by a set of binary codes assigned to a set of molecular tags and mixtures thereof.

[0101] Tag detection can be performed using a variety of methods familiar to those skilled in the art. These methods include mass spectrometry, nuclear magnetic resonance, sequencing, hybridization, antigen detection, electrophoresis, spectroscopy, microscopy, image analysis, and fluorescence detection.

[0102] Of particular interest are tagging or encoding strategies where marking is performed only once, or where marking and / or detection are non-physical and therefore non-invasive. Radio Frequency Identification (RFID) is an example of a system exhibiting these characteristics. RFID employs a transponder (RF tag), an antenna, and a reader. An RF tag is a small electronic circuit, typically encapsulated in glass or plastic, which in its simplest form provides access to a unique identifier that can be “read” without contact or line of sight by suitable electronic equipment. Tags can also store user-generated information, again without contact or line of sight. A “reader” is an electronic unit that transmits information between itself and one or more tags (it should be noted that the term reader is used interchangeably to refer to both read-only units and read / write units). Readers can vary considerably in size and characteristics, and can operate independently or be connected to a remote computer system. An antenna is used to transmit information from the reader to the tag and to receive information transmitted by the RF tag. The size and form of the antenna will reflect the specific application and can range from small circular coils to large planar structures. RFID systems can operate independently or be connected to a remote computer for a more comprehensive interpretation and manipulation of the identification and associated data from the tags. Nicolaou et al. (1995, Angew Chem Intl Ed Engl, vol. 34, p. 2289) described an RFID strategy for combinatorial chemistry comprising: (i) a porous shell containing a synthetic matrix and a semiconductor tag; (ii) a solid-phase synthetic resin; and (iii) a single-addressable or multi-addressable RFID tag semiconductor unit encapsulated in glass and capable of receiving, storing, and transmitting radio frequency signals. Similar devices can be adapted for growing and tracking cell units by simply replacing the solid-phase synthetic resin with tissue culture microcarriers or suitable cell units. Further variations of this tag are conceivable, including, but not limited to (coated or uncoated) RF tags on which cells are directly grown, or RF tags implanted in cell units or organisms.

[0103] Therefore, labels do not necessarily need to be distinguished primarily by their chemical or molecular structure. Various variations of non-chemical labeling strategies can be designed to identify a given cellular unit in a mixture or to infer the identity of different cellular units constituting the mixture. For example, optical or visual labeling methods have been described where objects of different shapes, graphic-coded objects, or different colors indicate the identity of a sample (e.g., see 1998, Guiles et al., Angew. Chem. Intl EdEngl, vol. 37, p926; Luminex Corp, Austin TX, USA; BD Biosciences; Memobead Technologies, Ghent, Belgium), or where patterns or barcodes are etched onto a substrate such as a ceramic strip and identified using pattern recognition techniques (e.g., see 1997, Xiao et al., Angew. Chem. Intl EdEngl, vol. 36, p780; SmartBead Technologies, Babraham, UK). Another approach to tracking or labeling cellular units is to encode their identity spatially (i.e., by their location in space). In this method, different cell units are isolated in defined relative positions, and these positions represent or encode the identity of the unit. For example, cell units can be cultured in an array, whereby the identity and / or culture history of each unit is known and associated with a specific position in the array. In its simplest form, such an array can contain a collection of tissue culture flasks, wells of multiwell plates, or positions on glass slides or other surfaces. Examples of position encoding strategies can be found in Geysen et al. (1984, Proc Natl Acad Sci USA vol. 81, p. 3998-4002), Fodor et al. (1991, Science vol. 251, p. 767-773), Ziaddin and Sabatini (2001, Nature, Vol. 411, p. 107-110), and Wu et al. (2002, Trends Cell Biol. Vol. 12(10), p. 485-8). This invention has many aspects, and each aspect can have many arrangements that can be combined to form the invention.

[0104] It is evident that labeling all cell units is not necessary to infer information about the results produced by combinations of cell culture protocols. Therefore, even without cell unit labeling, it is still possible, according to the present invention, to measure a large number of combinations of cell culture conditions and determine whether one or more of them can induce a specific cellular effect. However, cell units are preferably labeled. Labeling of cell units allows useful information to be derived from experiments regarding the results of specific conditions sampled from the labeled cell units, rather than from all cell units.

[0105] Alternatively, it is sometimes advantageous to label one or a few groups of cell units that have been fully exposed to a certain culture protocol (e.g., a group of cell units isolated into the same culture medium during a specific splitting or merging step). It is also obvious that labeling certain cell units makes it possible to infer the identity of other (potentially unlabeled) cell units.

[0106] Similarly, it is clear that performing cell culture experiments in which various conditions are omitted can provide information about the utility of these conditions relative to a particular experimental outcome. Therefore, it will be possible to evaluate each condition sampled in the manner according to the invention by repeating the experiment multiple times, each time omitting different sets of conditions. The split-split cell culture step can also be used to determine the effect of a particular set of conditions on experimental results. In fact, the split-split step results in the formation of lineages of specific cell units, each exposed to unique cell culture conditions at the branching point. By studying the different lineages, it is possible to determine the utility of the tissue culture conditions studied at the branching point relative to a particular experimental outcome.

[0107] Cell reprogramming involves altering the identity or function of a cell. This may involve transforming a cell from one type to another, or restoring a mature cell to a more pluripotent or stem cell-like state before it redifferentiates into a more directed state.

[0108] In the context of this invention, reprogramming refers to the transformation of a differentiated, directed cell type into another differentiated, directed cell type. Reprogramming does not include the simple conversion of pluripotent cells into differentiated cell types.

[0109] Many techniques for reprogramming are known to those skilled in the art; in one implementation, reprogramming as referred to herein may encompass any one or more of these techniques.

[0110] Induced pluripotent stem cells (iPSCs): This method, discovered by Shinya Yamanaka and colleagues in 2006, involves introducing a specific set of transcription factors (often referred to as Yamanaka factors: Oct4, Sox2, Klf4, and c-Myc) into mature cells.

[0111] These factors induce mature cells to revert to a pluripotent state, similar to embryonic stem cells. These pluripotent cells can then be further differentiated into the desired cell type.

[0112] Yamanaka uses retroviral vectors to deliver TF, but there are many other available methods, including non-integrative viral vectors, mRNA delivery, plasmids and direct addition of TF protein, as well as the use of non-coding RNA.

[0113] Various types of ncRNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), can participate in the regulation of transcription factors. These ncRNAs can be used to replace TF in the assay of this invention.

[0114] MicroRNAs (miRNAs) are short (approximately 22 nucleotides) ncRNAs that typically bind to the 3' untranslated region (UTR) of target mRNAs, leading to their degradation or translational repression. Through this mechanism, miRNAs can negatively regulate the levels of specific transcription factors (Bartel DP. (2009) MicroRNAs: target recognition and regulatory functions. *Cell*. 136(2):215-33).

[0115] Long noncoding RNAs (lncRNAs) are longer than 200 nucleotides and have multiple functions. Some lncRNAs can directly interact with transcription factors, regulating their activity, and therefore can be used to regulate TF activity in the assays according to the present invention. See Rinn JL, Chang HY. (2012) Genome regulation by long noncoding RNAs. Annual Review of Biochemistry. 81:145-66.

[0116] Non-coding RNAs have been shown to bind to or act as alternatives to classical Yamanaka factors: 1. miR-302 / 367 cluster: This microRNA cluster has been shown to improve reprogramming efficiency when used in combination with Yamanaka factors (Oct4, Sox2, Klf4, and c-Myc). See Anokye-Danso F et al. (2011) Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells topluripotency. Cell Stem Cell. 8(4):376-88.

[0117] 2. **lncRNA-RoR**: This lncRNA is involved in the regulation of reprogramming and the maintenance of pluripotency. lncRNA-RoR has been shown to regulate the core transcriptional network of pluripotency, including influencing the level of the Yamanaka factor. See Loewer S et al. (2013) Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells. *Nature Genetics*. 45(12):1504-9.

[0118] Direct lineage transformation (transdifferentiation): This method bypasses the pluripotency state. Instead, it uses a combination of lineage-specific transcription factors to directly transform cells from one mature cell type to another.

[0119] For example, fibroblasts can directly differentiate into neurons, cardiomyocytes, or other cell types, depending on the factors used. Currently, there are many techniques available for transdifferentiation: 1. Viral vector-mediated introduction of transcription factors: Viral vectors are used to deliver lineage-specific transcription factors into cells. For example: Fibroblasts are converted into neurons using factors such as Ascl1, Brn2 and Myt1l (Vierbuchen et al., (2010). Nature, 463(7284), 1035-1041).

[0120] Fibroblasts were converted into cardiomyocytes using factors such as Gata4, Mef2c and Tbx5 (Ieda et al., (2010) Cell, 142(3), 375-386).

[0121] 2. RNA-based transcription factor delivery: This method uses synthetic modified mRNA to transiently express essential transcription factors. RNA-based approaches can alleviate concerns associated with potential DNA integration into the host genome. Warren et al., (2010) Cell Stem Cell, 7(5), 618-630.

[0122] 3. Small molecules and chemicals: Although transcription factors are most commonly used for direct lineage conversion, some studies have successfully used combinations of small molecules to improve the efficiency of the process, or in rare cases, transdifferentiation can be achieved without genetic factors.

[0123] For example: Hou et al. (2013). Science, 341(6146), 651-654 successfully reprogrammed mouse somatic cells into pluripotent stem cells using only small molecules, highlighting the potential to generate iPSCs without introducing exogenous genes.

[0124] Shi et al. (2008). Cell stem cell, 2(6), 525-528 showed how combinations of small molecules can significantly improve the efficiency of iPSC generation from mouse fibroblasts.

[0125] Fu et al. (2015). Cell Research, 25(9), 1013-1024 used a set of small molecules to directly convert mouse fibroblasts into cardiomyocytes.

[0126] 4. CRISPR / Cas9-based activation of endogenous genes: The CRISPR / Cas9 system, initially known for its gene-editing capabilities, can be modified to achieve gene activation by using inactivated Cas9 (dCas9) fused with a transcription activator. This can be used to activate endogenous lineage-specific genes, thereby promoting transdifferentiation. Gilbert et al., (2013) Cell, 154(2), 442-451.

[0127] 5. microRNA (miRNA): Some miRNAs have been found to play a role in cell fate determination and can be used in conjunction with transcription factors or alone to drive direct lineage conversion. Jayawardena et al., (2012) Circulation research, 110(11), 1465-1473.

[0128] 6. Epigenetic regulators: Direct lineage transformation involves significant changes in the cellular epigenetic landscape. Incorporation of molecules that regulate epigenetic markers can improve the efficiency of transdifferentiation. Polo, J et al., (2012) Cell, 151(7), 1617-1632.

[0129] 7. Extracellular cues and the microenvironment: Although transcription factor-mediated transformation is the main driving factor, the efficiency and success rate of the transformation process may be affected by the microenvironment, including extracellular matrix composition, neighboring cells, and culture conditions. Engler, AJ et al., (2006). Cell, 126(4), 677-689.

[0130] In the context of this invention, reprogramming is preferably transdifferentiation, through which cells directly transform from one differentiation state to another without going through a pluripotent state.

[0131] The term "cell type" as used herein refers to a differentiated cell belonging to a specific lineage. A cell type is not a totipotent cell. In the implementation scheme, the cell type is not a pluripotent cell capable of differentiating into more than one lineage. A cell lineage can be any of the various cell types capable of developing during normal embryogenesis. Examples include: Hematopoietic lineage: This lineage produces all the different blood cells. Hematopoietic stem cells (HSCs) in the bone marrow differentiate into various types of blood cells, such as red blood cells (erythrocytes), various white blood cells (neutrophils, lymphocytes, and monocytes), and platelets.

[0132] Neuronal lineages: Neural stem cells differentiate into various types of neurons and glial cells. These include motor neurons, sensory neurons, astrocytes, oligodendrocytes, and microglia.

[0133] Epidermal lineage: Epidermal stem cells in the skin differentiate into various skin cells, including keratinocytes, melanocytes, and hair follicle cells.

[0134] Muscle lineage: Myogenic stem cells (or satellite cells) can differentiate into myofibrils or myocytes.

[0135] Mesenchymal stem cell (MSC) lineages: MSCs are pluripotent and can generate various cell types, including osteoblasts, chondrocytes, fibroblasts, and adipocytes.

[0136] Intestinal lineage: Stem cells in the intestinal crypts differentiate into various types of intestinal cells, such as absorptive intestinal epithelial cells, goblet cells, Paneth cells, and intestinal endocrine cells.

[0137] Liver lineage: Liver stem cells can differentiate into hepatocytes (the main functional cells of the liver) and bile duct cells (arranged in the bile ducts).

[0138] Cardiac lineage: Cardiac progenitor cells can differentiate into various cell types of the heart, such as cardiomyocytes, endothelial cells, and smooth muscle cells.

[0139] Germ cell lineage: This lineage causes the formation of eggs in females and sperm in males.

[0140] Retinal lineage: Retinal progenitor cells can differentiate into various types of retinal cells, such as photoreceptors, bipolar cells, and ganglion cells.

[0141] Reprogramming can involve the transformation of cells from one lineage to another, or the transformation of one cell type within a lineage to another cell type within the same lineage, such as the transformation from fibroblasts to osteoblasts.

[0142] As used herein, a "test unit" is a cell or cell population that can be treated as a unit. It is synonymous with a cell unit. For example, a test unit can be a cell unit as referred to in WO2004031369, which contains cells arranged on a microcarrier. However, in a preferred embodiment, the test unit is a single cell.

[0143] When a test unit contains a vector, and the vector further contains one or more cells, the vector can be colonized with cells in a random manner. When the expected number of cells is low (e.g., one or two cells per unit), the probability of cells colonizing the test unit may decrease, resulting in some units not containing cells. Therefore, in an embodiment, not all test units contain cells. However, preferably, the test unit is a single cell and does not contain any vector; in such an embodiment, all test units contain cells.

[0144] The term "reagent" as used herein can refer to any chemical, small molecule, transcription factor, nucleic acid, protein, compound, or ion capable of interacting with cells. In a preferred embodiment, the reagent is a transcription factor, small molecule, or protein compound, or nucleic acid. In the most preferred embodiment, the reagent is a transcription factor (TF) or an agent that affects the activity of transcription factors, such as ncRNA.

[0145] As cited in the references cited herein and described in more detail below, certain reagents are known in the art to promote reprogramming. Transcription factors are commonly used to influence reprogramming.

[0146] “Tag” can include any technology capable of individually tagging cells. For example, refer to the technologies described above. In a preferred embodiment, as described below, cells are tagged by genetic modification, such as by introducing retroviral markers or by using genome editing technologies.

[0147] Label deconvolution refers to the identification of labels on cells and, optionally, the order in which cells acquired these labels, thereby determining which reagents or combinations of reagents the cells have been exposed to. Since cells are labeled whenever they are exposed to a given reagent, the reagent leaves an "imprint" on the cell that can be used to map the history of the cell's exposure to the reagent and the timing of that exposure.

[0148] Predicting the most effective transdifferentiation induction reagents requires a combination of experimental data, computational modeling, and optimization techniques. Algorithms can be used to investigate the outcomes of cell exposure to a given reagent, thereby predicting which reagents can be used to induce the desired transdifferentiation in a given cell type. The results of reagent exposure should be presented to the algorithm as a consistent metric; for example, the results could be in the form of gene expression data, cell morphology scores, specific gene activation events, etc. The output from the algorithm can be used to select the initial reagent for performing the method of this invention. In the case of multiple executions of the method, the output data can be used to inform further execution instances of the method.

[0149] Machine learning (ML, AI) can be used to better predict the input reagents used for the desired transdifferentiation outcome. A database of gene expression data related to the reagents can be provided, and a gene regulatory network can be constructed that further includes machine learning models configured to output gene regulatory maps.

[0150] The merging, splitting, or subdivision of cell populations are performed as described in WO2004013369. Iterative repetition of the process steps of this invention can also be performed in a manner similar to WO2004013369 and as described above.

[0151] Cell type identification can be performed by any suitable means, including genetic, morphological, immunochemical, or other characteristics.

[0152] Gene expression analysis includes analyzing one or more reporter genes, as well as analysis based on multiple cellular genes, including the use of arrays that analyze expression across the entire genome. In one embodiment, gene expression analysis focuses on one or more genes as markers of a desired cell type. The method of the present invention can be used to identify these genes by analyzing changes in gene expression in cells exposed to reagents that, according to analysis of alternative cell differentiation markers (e.g., immunochemical markers or cell morphology), lead to a desired transdifferentiation outcome.

[0153] Genes that influence cell transdifferentiation can then be purposefully modulated to induce transdifferentiation; for example, information about gene regulation in response to exposure to a reagent can be used to select gene regulation techniques (such as genome editing) to further regulate the gene in place of the reagent application.

[0154] CombiCult® Procedures for splitting and merging cell populations to determine the effect of reagents on cell fate are described in WO2004031369. This combinatorial screening platform is capable of testing thousands of time-resolved combinations of culture conditions simultaneously. It is based on combinatorial science, which has been successfully used in the chemical synthesis of new compounds. In short, stem cells are grown in or on beads that can be labeled with condition-specific fluorescent markers. At each step of the differentiation process, the beads are merged and split, and subjected to different conditions under which they are simultaneously labeled in a condition-specific manner.

[0155] This process repeats multiple steps to ensure that each bead is exposed to a specific combination of conditions, and that multiple beads undergo the same combination. Cells within the beads are then screened using antibodies against markers and selected using a large-particle sorter. Positive beads are isolated and dissociated, and their tags are read using FACS, thus detecting all tag combinations enriched in positive beads. A computational deconvolution strategy identifies and quantifies the most frequent tag combinations, which correspond to molecules added to a specific set and sequence in the cell culture (also known as a “scheme”). Highly representative schemes are then independently validated in vitro and compared to gold-standard differentiation or expansion procedures. CombiCult® has been successfully deployed in multiple cases, enabling the discovery of novel schemes, such as those for hematopoietic stem cell expansion, macrophage, neutrophil, natural killer cell, megakaryocyte, smooth muscle cell, and oligodendrocyte progenitor differentiation.

[0156] The decision of which molecules to include in the CombiCult® screening comes from a careful reading of the literature, meaning its ability to discover new combinations is somewhat limited by the hypotheses already put forward. By leveraging the power of modern machine learning models combined with a large number of high-resolution single-cell transcriptomics datasets, this invention is able to generate new data-driven hypotheses with an unprecedented breadth.

[0157] Furthermore, CombiCult®'s multiplex analysis potential is limited by the use of beads and the fact that each screening selects one positive cell fate while discarding the remaining material.

[0158] This invention provides: 1. Define a custom computational analysis of the molecular inputs to be tested, including a machine learning algorithm to prioritize molecular drivers of cell fate acquisition and prevent contradictory or conflicting pathways from being tested together; 2. Single-cell labeling method, which completely bypasses the need for sorting beads and large particles, and obtains single-cell clarity and obtains differentiation results monitoring based on gene expression through low-cost single-cell sequencing (i.e., Oxford Nanopore benchtop sequencing); 3. The second computational pipeline analyzes the results of combinatorial screening by identifying viral barcodes and their representativeness in obtaining target cell fates.

[0159] These three elements create a virtuous cycle where information from one screening can be used to better optimize the initial predictions, as each round generates increasing experimental evidence linking molecules to their transcriptional functions. Ultimately, once a critical amount of evidence is generated from multiple experiments, a novel AI-based approach can be implemented where, for a given spectrum, an ordered set of molecular drivers can be efficiently predicted, effectively reducing the need for combinatorial testing.

[0160] Computational analysis The results of a single CombiCult screening can be used to select input TFs for further screening, or alternatively, to determine the optimal TF for the desired transdifferentiation protocol.

[0161] A large amount of publicly available single-cell RNA sequencing (scRNA-seq) and single-cell chromatin accessibility data (scATAC-seq) from actively differentiating cells reveal a continuum of gene expression changes.

[0162] Typically, the algorithm used to select TFs can process the filtering results as follows: enter: 1. A list of cells with their respective identities: cell_list 2. Reagent list: reagent_list 3. Data on the effect of each reagent on each cell: effect_data (this can be gene expression profiles, changes in cell phenotype, or any measurable measure after exposure) 4. Required cell type profile: desired_profile Output: Selected reagent list: selected_reagents step: 1. Initialize an empty list: selected_reagents = [] 2. For each cell in cell_list: a. For each reagent in reagent_list: i. Obtain the effect of the reagent on the cell from effect_data.

[0163] ii. Calculate the similarity score between this influence and the desired_profile. (This can be a relevance score, a frequency measure, or any relevant similarity metric.) b. Rank the reagents used to process the cells based on similarity scores.

[0164] c. Store the reagent that ranks highest for this cell.

[0165] 3. Compile a list of the reagents that rank highest across all cells.

[0166] 4. For each reagent in the summary list: a. Calculate the frequency at which it is selected in all cells.

[0167] b. If the frequency is higher than a certain threshold (indicating consistent efficacy): i. Add the reagent to selected_reagents.

[0168] 5. Returns selected_reagents.

[0169] By further sorting cells into cell types, the algorithm can return selected_reagents associated with transdifferentiation events of a specific cell type.

[0170] Machine learning can be applied to algorithms to further optimize reagent selection. For example, machine learning algorithms can be applied in the following ways: enter: 1. Training dataset: contains cell identity, time and identity of reagent exposure, and the observed effects after exposure.

[0171] 2. Required cell type profile: desired_profile Output: The optimal reagents for transdifferentiation: optimal_reagents step: 1. Data preprocessing: a. Normalize and standardize the data (e.g., z-score normalization for gene expression levels).

[0172] b. Split the dataset into a training set and a validation set.

[0173] 2. Feature selection: a. Select relevant features (e.g., specific gene expression levels, cell markers).

[0174] b. If the dataset is high-dimensional, consider dimensionality reduction methods such as PCA.

[0175] 3. Model selection: a. Choose an appropriate ML model. Regression models (such as random forests or gradient boosting machines (GBM)) are examples.

[0176] b. Train the model on the training set.

[0177] c. Validate the model on the validation set.

[0178] 4. Hyperparameter tuning: a. Use techniques such as grid search or random search to find the optimal hyperparameters for the selected model.

[0179] b. Retrain the model using the optimal hyperparameters. 5. Prediction: a. Use trained models to predict the effect of each reagent on transdifferentiation toward the desired cell type spectrum.

[0180] b. Rank the reagents based on the effectiveness of the prediction.

[0181] 6. Model Explanation (Optional but Recommended): a. Use techniques such as SHAP (SHapley Additive exPlanations) or feature importance scoring to explain which features (e.g., time-specific, cellular markers) are most influential in prediction.

[0182] 7. Return: a. Return the highest-ranked reagent as optimal_reagents.

[0183] The algorithm of this invention applies gold-standard data preprocessing steps and machine learning methods to identify temporal (or pseudo-temporal) trajectories and their associated gene expression changes. These trajectories connect stem cells to terminally differentiated cells through a continuum of gene expression changes. This continuum can be viewed as a high-resolution sequence of molecular events specific to cell fate acquisition (Figure 2). Within this continuum, a set of transcription factors (TFs) driving gene expression changes can be detected. The algorithm of this invention prioritizes and sorts these TFs based on four main features: 1. Their trends along the differentiation trajectory (differential expression) 2. Their gene regulatory networks were inferred through multiple regression and externally validated TF-target networks and protein-protein interaction networks (PPIN). 3. Their specificity, that is, their preferential activity within a particular trajectory compared to all other trajectories in similar physiological processes (e.g., selection of T cell-specific driver groups compared to other lymphoid lineages during hematopoiesis). 4. Their dynamics in terms of switching time and / or instantaneous expression.

[0184] Furthermore, if matching public scATAC data are available, they can be supplemented by TF activity analysis in open chromatin regions, thereby providing orthogonal evidence for gene regulatory networks and complementing other datasets.

[0185] Once the TFs are identified, the target networks from each of them are screened for annotated interactions. If the interactors of two TFs show a significant degree of overlap but involve opposite interactions (e.g., one TF is reported to inhibit these interactors, while the other is reported to activate them), they will be marked as incompatible in the same screening, thus providing a more reasonable way to arrange the combinations.

[0186] The algorithm of this invention can utilize regression-based gene-gene association networks and existing PPINs to identify molecules that act as transducers and receptors in signal transduction pathways upstream of the TF and also change along the trajectory. Once candidate receptors are identified, their ligands can be inferred and included in combinatorial screening.

[0187] Finally, the small molecule-protein interaction network overlaid on PPIN, and the drug-transcriptome correlation database whose features can be explored along the trajectory, can also be included in Daedalus for prioritization and inclusion in the screening.

[0188] With the emergence of new hypotheses, improvements can be made by significantly expanding the multi-analysis and resolution capabilities of the CombiCult platform. The implementation in WO2004031369 is limited to identifying one cell differentiation at a time, meaning that many other possibilities that may arise during culture (e.g., alternative cell fates) remain unexplored. In this proposal, I will design and implement a system to use viral transduction markers (“barcoding”) for single cells. To avoid using beads (whose encapsulation represents a time-consuming and resource-intensive step) and to track all single cells during screening, barcoding is achieved through a unique, concise, expressed DNA sequence that acts as a unique conditional tag (UCT) delivered via a lentiviral vector, which is stably integrated into the cultured cells. Figure 3When cells undergo different splits and are treated with different molecules / TFs / growth factors (hereinafter referred to as "driver factors"), they are tagged with lentiviral vectors carrying barcodes specific to the particular conditions. By using an integrated viral tag at low doses (so that cells do not become saturated in each transduction cycle), cells can be uniquely barcoded for each condition, and UCT is retained within each cell, tracking the conditions under which they were inserted. In the first split, candidate driver factors and their corresponding vectors are added to the culture medium, which can be replaced after transduction to remove the viral vectors, depending on the protocol requirements.

[0189] The cells were then separated, resuspended, merged together, and split in different plates for a second split.

[0190] The merged and split cells underwent a second round of transduction and driving factor treatment; the culture medium was changed and the driving factors were re-added until the third merge and split process.

[0191] Repeat this process for as many splits as needed until the final split is achieved and the cells are isolated, optionally sorted via FACS and ready for single-cell RNA sequencing. The fundamental difference between this method and the method in WO2004031369 is that, in this invention, barcoding occurs at the single-cell level, and the data consists of the transcriptome of each cell and its corresponding UCT combination.

[0192] Develop new analytical pipelines for selecting options. Single-cell RNA analysis combined with UCT detection resolved the impact of different cell fate choices (including successful and unsuccessful ones) in a single snapshot. Analysis of transcriptome data annotated cells through cell similarity and the expression of cell type-specific traits. By reconstructing the UCT “lineage” tree and matching it with traits, it was possible to map the sequences of variables that cause cell fate-specific transcriptional profiles. Using single-cell data combined with UCT has three fundamental advantages: first, it can resolve even minute differences in cell fate acquisition; second, if the entire experiment is sequenced and analyzed, it can identify multiple cell fates at once; and third, it produces reusable and integrable data.

[0193] Regarding the first two aspects, scRNA-seq has been deployed in a similar manner to track guide RNA constructs in CRISPR screening (Dixit et al. 2016. Cell 167, 1853–1866) or lentivirus-mediated TF transduction (Joung et al. 2023. Cell 186, 209–229). Furthermore, complex studies using sequential genome editing (“trace”) at the single-cell level have been successfully conducted (Spanjaard et al. Nat Biotechnol. 2018 June; 36(5):469–473), providing the first analytical framework for connecting different events in a single experiment.

[0194] These pioneering methods have been used to study transcriptional responses to perturbations at the system level, including cell fate acquisition, demonstrating in principle the feasibility of tracking experimental and / or physiological conditions based on genetic tags at the single-cell level. The innovative and groundbreaking aspect of this proposal lies in the application of the CombiCult® principle, which can track combinations of thousands of molecules or TFs added sequentially in a culture, thereby resolving a much larger experimental space in a single assay. A third advantage greatly enhances predictive power and links the three elements of the platform together: the generation of datasets matching transcriptional changes at the single-cell level with specific cell culture conditions provides an excellent opportunity to re-input experimentally validated driver-target interaction data into the algorithm. This virtuous cycle allows the algorithm of this invention to become increasingly accurate, learning from its own experimental results and paving the way for supervised learning frameworks that, in the long term, can predict the molecules required for any type of differentiation with high precision. In fact, given the large amount of real-world data (i.e., data generated by known interventions), a language model (LM) can, in principle, be created that can predict the steps required to generate a specific cell type. Different types of LMs have been successfully deployed in studies of viral and antibody evolution (Hie et al. 2023. Nat biotechnol doi:10.1038 / s41587-023-01763-2) and chemical modification (Kosonocky et al. arXiv 2023 doi:10.48550 / arXiv.2305.16330).

[0195] The successful application of the sequencing-based platform of this invention will rapidly generate many such data points, thereby greatly accelerating the engineering and production of ATMP.

[0196] transcription factors The most widely accepted agents for promoting transdifferentiation are transcription factors. Transdifferentiation can be driven by overexpressing specific transcription factors, as observed in various transdifferentiation experiments: 1. Neuron: - Ascl1, Brn2, and Myt1l can reprogram fibroblasts into induced neurons (iNs).

[0197] NeuroD1 alone, or in combination with other factors, can also convert fibroblasts into neurons.

[0198] 2. Cardiac cardiomyocytes: Gata4, Mef2c, and Tbx5 can transform fibroblasts into induced cardiomyocytes (iCM).

[0199] 3. Endoderm cells: FoxA2 and Hnf1α can convert fibroblasts into hepatocyte types.

[0200] 4. Pancreatic β cells: Pdx1, Ngn3, and MafA, sometimes along with other factors, can transform various cell types into insulin-producing β-like cells.

[0201] 5. Endothelial cells: - Fli1, FoxC2, and Etv2 can transform fibroblasts into endothelial-like cells.

[0202] 6. Muscle cells: MyoD is a classic example of the ability to convert fibroblasts into skeletal muscle cells.

[0203] Other transcription factors that can be used for transdifferentiation can be obtained from the literature.

[0204] The foregoing are examples of agents known in the art that promote cell reprogramming.

[0205] Improved reagent database The method of the present invention can generate a database that can improve the starting parameters of experiments designed to identify suitable TFs for reprogrammed cells. Therefore, the present invention provides a method for selecting starting TFs in a method according to the foregoing aspects of the present invention, comprising the following steps: A database of TFs and cell transdifferentiation results is compiled; and a set of TFs or combinations of TFs are selected from this database to determine the conditions required for reprogramming cells.

[0206] The present invention also provides a method for improving a database of TFs and cell transdifferentiation results, comprising using the database to select TFs as in the foregoing embodiments, and inputting the results of a method for determining the conditions required for reprogramming cells into the database, thereby providing further data points.

[0207] The algorithms and machine learning methods described above can also be used to further improve the database.

[0208] In another aspect, the method according to the invention provides: a database of useful TFs (e.g., transcription factors) for reprogramming cells; at least one computer processor; and a memory operatively communicating with the processor, the memory containing instructions for configuring the processor to: (a) Gene regulatory networks (GRNs) are generated from a database of transcription factors. (b) Identify candidate transcription factors for the desired reprogramming event; (c) Analyze the effects of candidate transcription factors on cell reprogramming; (d) Optionally, iterate through steps (a) to (c); and (e) Output a set of optimal transcription factors.

[0209] The database preferably contains gene expression data, such as RNA-seq data.

[0210] Identifying candidate transcription factors involves querying a gene regulatory map to identify an optimal set of transcription factors for differentiating any given cell type from any given starting cell. Machine learning models can be used to generate metric calculations that function as a function of the gene regulatory map.

[0211] Examples of metric computation include critical algorithms, which can be configured for time-series RNA-seq data; see Oh et al., Biomed Res Int. 2013;2013:203681. doi: 10.1155 / 2013 / 203681. Epub 2013Mar 24.

[0212] The present invention further provides a method for determining the optimal transcription factors for cell reprogramming, comprising: using a computing device to organize a gene expression database related to the use of TFs in cell culture; generating a gene regulatory network from multiple gene expression datasets; determining candidate optimal TFs; analyzing the effect of the optimal TFs on cell differentiation; and outputting a set of optimal TFs.

[0213] As described in Kamaraj, Cell Cycle 2016, vol. 15, no. 24, 3343-3354, gene regulatory networks can be predicted computationally.

[0214] Methods included CellNet ( et al., Cell 2014; 158:903-15), which used microarray gene expression data and correlation-based network scoring; D'Alessio et al.'s method (D'Alessio et al., Stem Cell Reports 2015; 5:763–75), which used microarray gene expression data and information theory methods (Jensen-Shannon divergence); and Mogrify (Rackham et al., Nat Genet 2016; 48(3):331–5; PMID:26711105), which used cap analysis (CAGE) data of gene expression and network-based scoring.

[0215] Regardless of the computational method chosen, the iterative experimental procedures and computational analysis offer novel advantages over either the computational or experimental methods used individually.

[0216] To better understand the present invention and to illustrate how to implement embodiments of the invention, reference will now be made to embodiments which are not intended to limit the invention in any way.

[0217] Example Materials and methods mRNA production and transfection DNA template preparation and mRNA synthesis are based on HiScribe. TM The T7 ARCA mRNA Kit (with tail) (NED, #E2060S) protocol was used.

[0218] For transfection using jetMESSENGER® (Axil Scientific, #150-07), dilute 2 µg of mRNA in 200 µl of mRNA buffer (provided in the kit) and mix thoroughly for 10 seconds. Then, add 4 µl of jetMESSENGER® reagent and gently bind it to the mixture. After incubating at RT for 10 minutes, transfect by adding the transfection mixture to cells in antibiotic-free medium. For serial transfection, add the transfection mixture to the culture every 24 hours.

[0219] Immunofluorescence For immunostaining, cells were fixed with 4% paraformaldehyde (PFA) (Sigma Aldrich, #78775) at RT for 30 min and washed three times with DPBS for 5 min each time. For soluble proteins, the blocking solution was 10% bovine serum albumin (BSA) (Sigma Aldrich, #05470) with 0.5% Triton X-100 (Sigma Aldrich, #T8787) or 10% fetal bovine serum (FBS) (Thermo Scientific, #26140079); for membrane proteins, Triton X-100 was removed. Cells were incubated with the blocking solution at RT for one hour. The primary antibody diluted in the blocking solution was added to the cells and incubated overnight at 4°C. The next day, cells were washed three times with DPBS for 5 min each time. The secondary antibody was diluted 1:500 in DPBS and incubated at RT in the dark for 2 hours. Subsequently, cells were washed three times with DPBS for 5 min each time. For visualization of the cell nuclei, Hoechst 3342 dye (Thermo Fisher Scientific, #62249) was used at a dilution of 1:10,000 (in DPBS). The dye was then incubated in the dark for 5–10 minutes, followed by three washes in DPBS. Finally, staining was detected using a fluorescence microscope (Inverted Live Cel Confocal Microscope-LSM800 Airy, Clinical Science Building (CSB)) (Inverted Fluorescence Live Cell Microscope-A07, CSB).

[0220] Flow cytometry immunostaining To quantify the efficiency of serial transfection, FACS was performed. cPP was dissociated from the beads and fixed with 100 µl of 4% PFA at RT for 15 min. Cells were washed twice with staining buffer (BD, #554657) and then permeabilized with blocking buffer at RT for 30 min. After centrifugation, the conjugated antibody was diluted 1:20 in blocking buffer and incubated on ice in the dark for 30 min. After incubation, cells were washed twice with DPBS. Finally, cells were resuspended in DPBS and analyzed by FACS.

[0221] To quantify C-peptide using primary and secondary antibodies +Cells were processed using a standardized protocol. Initially, cells were dissociated and fixed on ice for 30 minutes with 100 µl of 4% PFA. They were then washed once with 200 µl of DPBS to remove excess fixative and cell debris, followed by blocking. Blocking was performed on ice for 30 minutes to minimize non-specific antibody binding. Subsequently, cells were incubated overnight with primary antibody at low temperature to promote specific binding of the primary antibody to its target antigen. The next day, cells were washed twice with blocking solution to remove unbound primary antibody, followed by blocking with secondary antibody on ice for 2 hours to ensure detectability of the primary antibody. After two additional washes with DPBS to remove excess secondary antibody, cells were carefully collected in FACS tubes for subsequent flow cytometry analysis to accurately quantify C-peptide. + cell.

[0222] Table 1. The table below summarizes the antibodies used and their respective dilutions. Fluorescence-activated cell sorting (FACS) FACS (LSRFortessa X-20, CSB) was performed using an LSRFortessa X-20 to assess and quantify transfected cells within the population. Side-scattered (SSC) versus forward-scattered (FSC) dot plots were used to identify live cells and separate them from diploids and dead cells. For live cells, GFP / APC / PerCP versus FSC dot plots were created for additional analysis. All FACS data were evaluated using FlowJo software.

[0223] a) CombiCult® screening.

[0224] i. Seed cells onto beads. Seed cPP onto CombiCult® beads. For effective seeding, coat beads with matrigel (1:100 dilution; BD, #354277) at 37°C for at least 2 hours. Generate single cells from confluent cPP and incubate with GCDR at 37°C for 10 minutes. Count cells to achieve the desired seeding density, and after centrifugation, resuspend cPP in their basal medium with supplements and the rock inhibitor Y27632 (15). Approximately 4.0 x 10⁻⁶ cells were seeded onto CombiCult® beads. 7 One cPP (250 cells / bead) was seeded onto 160,000 beads (40,000 / condition), and the mixture (cells and beads) was incubated overnight at 37°C.

[0225] ii. Merging / Splitting Process. Based on the designed matrix, a splitting-merging process was used to distribute cells onto beads under predetermined conditions (10 conditions expressing specific TFs) for four consecutive days, enabling the testing of 10,000 different TF combinations.

[0226] iii. mRNA transfection and addition of fluorescent tags. Over the following days, mRNA-TF transfection was performed according to the matrix, followed by the addition of a CombiCult® specific fluorescent tag at each cleavage (Figure 2). cPP was propelled into β cells via endocrine progenitor cells using a simple medium with a small amount of growth factors and known molecules. For the first two days, high-glucose DMEM was supplemented with Asc (50 μg), 1% B27, EGF (50 ng), FGF-7 (50 ng), and RA (50 nM). For the next two days, 5% KOSR, T3 (1 μM), and RA (25 nM) were added to the high-glucose DMEM. From day 5 to day 11, low-glucose DMEM was supplemented with 10% FBS, T3 (1 μM), and Alk5i (10 μM).

[0227] iv. Screening assay. Following the merging / splitting process, the beads were stained with an antibody against C-peptide (a functional marker of pancreatic β-cells) on day 11.

[0228] During CombiCult® screening, negative and positive controls were implemented to fine-tune the C-peptide staining protocol and establish standards and parameters for isolating positive beads. In both cases, the same culture medium as in the CombiCult® experiment was used. However, positive reprogrammed samples underwent… Pdx1 (Day 1) Ngn3 (Day 2) and MafA(Day 3) Serial mRNA transfection was performed using these three factors, which are known triple factors for inducing reprogramming into β cells, whereas no mRNA transfection was performed in the negative control. Beads from the selection were transferred to Eppendorf tubes. Once the beads settled, the culture medium was removed, and the beads were washed with DPBS to discard any remaining medium. Next, the beads were fixed with 4% PFA (in DPBS) at RT for 30 minutes and washed three times with DPBS for 5 minutes each time. Subsequently, the beads were incubated for one hour with blocking solution (10% BSA or FBS) containing 0.5% Triton X-100. The primary antibody (mouse anti-proinsulin C-peptide antibody (Millipore, #05-1109)) diluted in the blocking solution (1:1000) was added to the beads and incubated overnight at 4°C. On the second day, the beads were washed three times with DPBS for 5 minutes each time. The secondary antibody (donkey anti-mouse antibody, Alexa Fluor 488 (Thermo Fisher Scientific, #A-21202)) was diluted 1:500 in DPBS and incubated in the dark at RT for 2 hours. Finally, the sample was washed three times with DPBS for 5 minutes each time before detection.

[0229] b) COPAS TM Sorting. Using unstained and stained positive and negative controls ( Figure 3 A gating parameter was defined to facilitate the differentiation between negative and positive bead populations. Subsequently, all stained beads from the CombiCult® experiment were screened using a particle flow sorter called COPAS (Complex Object Parametric Analyzer and Sorter, Union Biometrica FP 2000 PRO, CSB). The COPAS system analyzed the fluorescence intensity of the beads using a gating threshold established by positive and negative controls. Through this analysis, the COPAS system identified positive hits containing reprogrammed β cells, particularly C-peptides. + Beads.

[0230] c) Bead digestion and tag separation. C-peptides are digested separately according to Plasticell's proprietary protocol. + Beads are used to release the fluorescent tags that accumulate in the beads during the experiment.

[0231] d) Tag analysis. The released tags are analyzed using FACS, and the resulting data is submitted to the proprietary CombiCult® bioinformatics program Ariadne® for deconvolution.

[0232] e) Ariadne® Bioinformatics. Ariadne® can identify specific fluorescence signal intensities as different tags, thereby elucidating the pathways responsible for reprogramming cPPs into β cells or combinations of the four TFs. Furthermore, through statistical analysis, it can rank the most effective reprogramming combinations, which can then be validated.

[0233] f) Validation. 1) Validation of the top-ranked protocols began with a replication of the bead-based CombiCult® results. cPP was seeded onto Matrigel-coated beads, followed by sequential TF delivery for four consecutive days, or simultaneous TF delivery on a single day, using the same culture medium as the screening process. After an 11-day incubation period, the beads were stained with an antibody against the C-peptide and screened using COPAS to identify those beads exhibiting the C-peptide signal. The most effective combination was determined by comparing the distribution of C-peptide fluorescence signals between negative and positive controls and between sequential and simultaneous TF delivery methods. 2) These ranked protocols were then validated in spheroids, a common and natural method for obtaining β-cells. In this method, cPP was seeded as single cells in 10 cm Matrigel-coated culture dishes (12 million cells per dish, two dishes per sample). TF was then delivered sequentially for four consecutive days, followed by spheroid formation using 24 million cells (200 cells per spheroid). Transfection was performed as described previously; for each 10 cm culture dish, 10 μg of mRNA was diluted in 1000 μl of mRNA buffer, and 20 µl of jetMESSENGER® was added to the mixture. The medium was changed periodically until day 11, as in the reprogramming experiment. At this point, some spheres were stained with an antibody against C-peptide for immunofluorescence analysis, while others were dissociated into single cells and stained with C-peptide to quantify the reprogramming efficiency as a measure of C-peptide expression via FACS. Additionally, another subset of spheres was used to measure C-peptide secretion in response to elevated glucose levels.

[0234] Glucose-stimulated insulin secretion Glucose-stimulated insulin secretion (GSIS) assays are frequently used in studies to evaluate the functional capacity of differentiated and reprogrammed β cells. These assays involve exposing these cells to different glucose concentrations, typically low glucose (2 mM) followed by high glucose (20 mM), to assess their insulin secretion response to glucose stimulation. C-peptides secreted under low and high glucose conditions were collected and quantified using an ELISA assay (Ultrasensitive C-peptide ELISA, Mercodia #10-1141-01).

[0235] Example 1: Adult pancreatic cells reprogrammed into β cells Zhou et al. (2008) demonstrated the reprogramming of adult pancreatic exocrine cells into β cells by testing a cocktail of nine transcription factors, including Pdx1, Ngn3, MafA, Nkx6.1, Nkx2.2, Isl1, NeuroD1, Pax4, and Pax6. The most effective combination was found to be Pdx1, Ngn3, and MafA.

[0236] To test the method of the present invention, a series of transcription factors were used in the split / merge experiments, and cells were labeled according to the identity, sequence, and timing of exposure to different transcription factors (see [link to original text]). Figure 4 ).

[0237] Therefore, using a split-and-merge approach, adult pancreatic exocrine cells were exposed to different combinations of transcription factors Pdx1, Ngn3, MafA, Nkx6.1, Nkx2.2, Isl1, NeuroD1, Rfx6, Pax4, and Pax6. Cells were screened for expression of CD49a and C-peptide or insulin. CD49a is a marker for cells expressing C-peptide. See also Figure 5 .

[0238] Computational analysis was performed on combinations of transcription factors that lead to reprogramming, and the data were tabulated. Figure 6 (A and B).

[0239] Pdx1, MafA, and Ngn3 were identified as the most effective transcription factors, reproducing the results of Zhou et al. (Figure 7). Rfx6 was also shown to be an important factor.

[0240] Furthermore, the optimal sequence of exposure to transcription factors was determined to be Pdx1, followed by Ngn3 at 4 hours, and finally a combination of Pdx1 and MafA between 6 and 11 hours (Figure 7).

[0241] Example 2: T lymphocyte generation This invention explores the potential of multiple cell fate acquisition experiments simultaneously, enabling us to investigate new questions: How do we generate an entire initial T lymphocyte pool? T lymphocytes are fundamental participants in cellular adaptive immunity because they utilize their T cell receptors (TCRs) to recognize foreign / pathological antigens and initiate robust responses. T lymphocyte maturation occurs in the thymus, where they upregulate various molecules associated with their function. CD8+ T cells exhibit cytotoxic activity, aggressively killing cells that positively interact with their TCRs via their antigens; CD4+ cells secrete molecules that regulate the activity of other immune cells in the context of diseased tissues; and regulatory T cells fine-tune responses from either population to avoid potentially harmful over-proliferating T cell-mediated immune responses. While CD8+ T cells have been successfully used in cancer immunotherapy, utilizing their cytotoxic capabilities via immune checkpoint inhibitors or adoptive cell therapy using chimeric antigen receptor (CAR-T) cells, the field of cell therapy has not yet managed to consistently differentiate CD4+ and regulatory T lymphocytes for therapeutic purposes (Montel-Hagen et al., CellStem Cell 2019 – doi:10.1016). This presents an excellent opportunity to create ATMPs, as they not only play a crucial role in the body's defense against cancer, but also, particularly, in the regulation (or emergence) of autoimmune diseases and immunodeficiency. In fact, the use of CD4+ and regulatory T lymphocytes for therapy remains far from a reality due to difficulties in expanding CD4+ and regulatory T lymphocytes or generating them from stem cell precursors (Hippen et al., Front Immunol 2022 - doi:10.3389).

Claims

1. A method for determining the conditions required to reprogram a first cell type to a second cell type by screening combinations of transcription factors (TFs) in which TFs are sequentially added to the cell.

2. The method according to claim 1, comprising the following steps: a. Expose cells of the first cell type to the first TF; b. Remove the first TF and expose the cells to different TFs; c. Optionally, repeat step (b) above; and d. Identify one or more cells displaying one or more markers of a second cell type, and determine the identity and order of the TFs to which the cells are exposed.

3. The method according to claim 1 or claim 2, comprising the following steps: a. Each of the first plurality of test units containing cells is exposed to different TFs or combinations of TFs, and the cells are labeled to indicate exposure to said TFs or combinations of TFs; b. Merge multiple test units and subdivide the test unit pool to form a second set of test units; c. Expose the second or multiple test units to different test cases or combinations of test cases; d. Optionally iterate and repeat steps (a) to (c) as needed; e. Identify the cell type of cells in the test unit and deconvolve the markers to identify the identity, order, and timing of the TFs that the cells were exposed to; f. Optionally, use the data from step (e) to guide the selection of the TF used in step (a), and repeat steps (a) to (e) as needed; g. Identify cells of the second cell type and deconvolve the markers to identify the identity and order of the TFs required to reprogram cells of the first cell type into cells of the second cell type.

4. The method of claim 3, wherein the TF is added individually to the cell unit in successive method steps.

5. The method of claim 3, wherein successive method steps in the procedure include adding a single TF or adding a combination of TFs.

6. The method of claim 3, wherein the successive method steps in the program consist of adding only a single TF.

7. The method of claim 1, wherein at least one plurality of test units comprises a test unit containing at least one cell and a test unit not containing a cell.

8. The method according to any of the preceding claims, wherein the TF or combination of TFs used in the first iteration of step (a) is selected from TFs known in the art for promoting cell reprogramming.

9. The method of claim 8, wherein the TF or combination of TFs used in the first iteration of step (c) is selected from TFs known in the art for promoting cell reprogramming.

10. The method according to any of the preceding claims, wherein the TF or combination of TFs used in the second and further iterations of steps (a) and (c) is selected by analyzing the data generated by step (e).

11. The method of claim 10, wherein the data generated in step (e) is processed by an algorithm that analyzes the impact of the identity and application time of each TF on the reprogramming of the first cell type.

12. The method of claim 3, wherein data generated by step (e) in a previous execution of the method is used to select the TF or combination of TFs used in the first iteration of step (a).

13. The method according to any of the preceding claims, wherein each test unit is a single cell.

14. The method of claim 13, wherein the cells are labeled by modifying the nucleic acids of the cells.

15. The method of claim 14, wherein the nucleic acids of the cell are modified by genome editing or viral integration.

16. The method of claim 15, wherein genome editing comprises CRISPR.

17. The method of claim 12, wherein the test unit is a single cell, and data derived from cell fates exposed to different TFs are combined to correlate transcriptional changes at the single-cell level with specific cell culture conditions, thereby generating a model that predicts the culture conditions required to generate a specific cell type.

18. The method of claim 17, wherein the model is used to generate initial TF selections for steps (a) to (c), and iterative execution of these steps results in an improved model.

19. A method for identifying genes that influence cellular processes, comprising the steps of: a) Determine the effect of one or more TFs or combinations of TFs on cells according to any one of the preceding claims; b) Analyze gene expression in the cells when exposed to the TF or a combination of TF; and c) Identify genes that are differentially expressed upon exposure to said TF or TF combination.

20. A method for producing a nucleic acid encoding a gene product that affects cellular processes, comprising identifying a gene according to claim 19 and producing at least the coding region of said gene by nucleic acid synthesis or biological replication.

21. A method for inducing a cellular process, comprising the following steps: a) Identify one or more genes that are differentially expressed in relation to cellular processes, as claimed in claim 19; and b) Regulate the expression of one or more of the genes mentioned above in the cell.

22. The method of claim 21, wherein regulation of gene expression in the cell comprises transfecting the one or more genes into the cell.

23. A method for selecting an initial TF in the method of claim 3, comprising the steps of: A database compiling TF and cell transdifferentiation results; and Select a set of TFs or a combination of TFs from the database for use in the method according to claim 1.

24. A method for improving a database of TF and cell transdifferentiation results, comprising using the database to select TF as described in claim 23, and inputting the results of the method as described in claim 1 into the database to provide further data points.

25. A system comprising a database of useful TFs for reprogramming cells, at least one computer processor, and a memory operatively communicative to said processor, said memory containing instructions for configuring said processor to: (a) Gene regulatory networks (GRNs) are generated from a database of transcription factors. (b) Identify candidate transcription factors for the desired reprogramming event; (c) Analyze the effects of candidate transcription factors on cell reprogramming; (d) Optionally, iterate through steps (a) to (c); and Output a set of optimal transcription factors.

26. The system of claim 25, wherein the database contains gene expression data.

27. A method for determining the optimal transcription factor for cell reprogramming, comprising: Using computing devices, we compiled a gene expression database related to the use of TF in cell culture; Gene regulatory networks were generated from multiple gene expression datasets. Identify candidate optimal TFs; analyze the impact of optimal TFs on cell differentiation; and output a set of optimal TFs.

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