Methods and compositions for generating induced pluripotent stems cells

Abstract

Reprogramming adult or somatic cells into induced pluripotent stem cells (iPSCs) is widely recognized as an inefficient process, with success rates typically below 1%, and is characterized by significant heterogeneity. Numerous strategies have been explored to enhance reprogramming efficiency, including chemical, biochemical, and biophysical manipulations. However, achieving high-efficiency iPSC production and generating cell lines with consistent molecular properties that enable reliable dow nstream differentiation into therapeutically or diagnostically relevant cell ty pes remain poorly understood. The compositions and methods described herein address these challenges by significantly improving the efficiency of iPSC reprogramming. Moreover, the resulting iPSCs exhibit molecular characteristics that enhance cellular responses and differentiation capabilities, allowing for optimization in specific therapeutic and diagnostic applications.

Description

METHODS AND COMPOSITIONS FOR GENERATING INDUCED PLURIPOTENT STEMS CELLS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Application No. 63 / 711,875 filed October 25, 2024, the specification of which is incorporated herein its entirety by reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Grant No. CA250382 awarded by National Institutes of Health. The government has certain rights in the invention.REFERENCE TO AN ELECTRONIC SEQUENCE LISTING

[0003] The contents of the electronic sequence listing (UCI_24_16_PCT.xml; Size: 4,096 bytes; and Date of Creation: October 24, 2025) is herein incorporated by reference in its entirety7.FIELD OF THE INVENTION

[0004] The present invention relates to cell engineering and cell therapies, providing methods and compositions for reprogramming cells, e.g., derived from human biopsy samples, into stem cells or other therapeutically relevant cell types.BACKGROUND OF THE INVENTION

[0005] Biomechanical cues are critical for proper embryonic development, including during stem cell fate commitment, limb formation, and organogenesis. Stem cell differentiation, e.g.. is mediated in part through physical interactions between cells and their surrounding extracellular environment, including matrix proteins and / or other neighboring cells. These physical interactions transmit mechanical cues into the cell through activation of membrane proteins localized at the cell surface (e.g., integrins and cadherins) and subsequent activation of their associated downstream (intracellular) effectors. These signals propagate into the nucleus to impact gene expression in ways that curb or bias fate selection in the presence of dynamic or sustained paracrine (or other soluble) cues.

[0006] Within adherent cells, activation of these membrane proteins (and their downstream targets) can be altered by compositional (molecular) or structural (elastic / mechanical or topographical) changes to cell adhesive substrates within both in vitro and in vivo contexts. Morerecently, these altered substrate properties have been shown to influence cell fate decisions during induced pluripotent stem cell (iPSC) reprogramming. While the ability of these substrate properties to greatly enhance the efficiency of iPSC reprogramming has been well demonstrated, an understanding of the specific adhesion-associated molecular factors involved remains largely unknown. Some contributions from integrin binding and cytoskeletal remodeling through kinase activities have been observed through mechanical and molecular screens. Furthermore, technological advances in bulk and single-cell transcriptomics have revealed that, during reprogramming, cells acquire sternness through stochastic processes whereby cells undergo fate transitions along inferred “developmental trajectories'’ -amenable to changes in paracrine signaling and transcriptional noise modulation. However, how these fate trajectories are impacted by altered biomechanical signaling has not been explored.BRIEF SUMMARY OF THE INVENTION

[0007] It is an objective of the present invention to provide compositions and methods that allow for the reprogramming of adult / somatic cells, e.g., derived from human biopsy samples, into stem cells or other therapeutically relevant cell types, as specified in the independent claims. Embodiments of the invention are given in the dependent claims. Embodiments of the present invention can be freely combined with each other if they are not mutually exclusive.

[0008] Reprogramming of adult / somatic cells into induced pluripotent stem cells (iPSCs) is known to be an inefficient (<1 %) and heterogeneous process. There have been many attempts to understand how to promote more efficient reprogramming (i.e.. via chemical, biochemical, and biophysical manipulations) into iPSCs. However, it is still poorly understood how to produce iPSCs during reprogramming at high efficiency or how to produce cell lines with reproducible molecular properties that support robust downstream differentiation towards desired cell types for therapeutic or diagnostic purposes.

[0009] Thus, the present invention features a composition that is based on the ability^ of the cell to make adhesions with other neighboring cells or their extracellular matrix (ECM) and the known role that these extracellular signals and forces contribute in cell differentiation during normal development. The gene expression of 347 genes related to cell-cell and cell-ECM adhesion and signaling was assessed, collectively referred to as the integrin and cadherin adhesomes, during iPSC reprogramming and adhesome genes that were dynamically regulated (based on mRNA levels), and therefore potentially plays an impactful role on the process of reprogramming a somatic cell back into an iPSC. Repression of the majority of these adhesomegenes (via shRNA delivery) led to enhanced reprogramming efficiency (measured by TRA-1-60 immunostaining and gene expression). Single-cell transcriptome analysis during adhesome gene knockdown (specifically Shroom3) was performed, which increased reprogramming efficiency by 27-fold. This analysis showed that manipulation of adhesome signaling through gene disruption mediates sternness acquisition through altered cell fate trajectories. The Inventors found that by manipulating cell-environment interaction through gene knockdown or altered ECM, iPSC formation was encouraged from alternative cell types in the population as cells take new' trajectories towards reprogramming. Furthermore, certain cell types that are on the trajectory' to reprogramming become more stable when adhesome gene expression is knocked down. Given that the developmental trajectory of a cell shapes its molecular memory and behavior in response to environmental cues, the present invention enables the creation of iPSCs derived from distinct developmental traj ectories (mediated by adhesome / cell-environment manipulation) that will also impact iPSC cell response to extracellular cues, which can be optimized for specific downstream cell applications (i.e., cell therapy or diagnostic). In summary, adhesome gene expression can be modulated to manipulate the cell types during reprogramming and acquire more desired cell fates, such as iPSCs or other intermediary cell fates during the reprogramming process.

[0010] In some embodiments, the present invention features a composition for generating induced pluripotent stem cells (iPSCs) from a biological sample. In some embodiments, the composition comprises one or more reprogramming factors (e.g., Oct4 (Pou5fl), Sox2, Klf4, and cMyc) and a factor configured to modulate an adhesome component. In some embodiments, the factor is configured to modulate gene expression of the adhesome component. For example, the factor configured to modulate gene expression of the adhesome component (e.g., SHROOM3) may be an RNA-based inhibitory agent, including but not limited to, a short hairpin RNA (shRNA), an antisense oligonucleotide (ASO), or a small interfering RNA (siRNA). Alternatively, in some embodiments, the factor configured to modulate the function of the adhesome component (e.g., SHROOM3) is a small molecule. In other embodiments, the factor configured to modulate the function of the adhesome component (e.g., SHROOM3) comprises a dominant negative mutant of the adhesome component or a functional portion thereof.

[0011] In some embodiments, the present invention features a method of generating induced pluripotent stem cells (iPSCs) from a biological sample. In some embodiments, the method comprises a) obtaining or having obtained a biological sample, b) contacting the biological sample with an RNA-based inhibitory' agent (e.g., shRNA, ASO, or siRNA), wherein theRNA-based inhibitory- agent is configured to modulate gene expression of an adhesome component, c) reprogramming the biological sample to generate iPSCs, and d) isolating and expanding the iPSCs. In other embodiments, the method comprises a) obtaining or having obtained a biological sample, b) contacting the biological sample with a small molecule, wherein the molecule is configured to module the function of an adhesome component to generate iPSCs, c) reprogramming cells in the biological sample, and d) isolating and expanding the iPSCs. In alternative embodiments, the method comprises a) obtaining or having obtained a biological sample, b) contacting the biological sample with a dominant negative mutant of an adhesome component (e.g., SHR00M3) or a functional portion thereof, c) reprogramming cells in the biological sample to generate iPSCs, and d) isolating and expanding the iPSCs.

[0012] In other embodiments, the present invention features a method for reducing mutagenesis in induced pluripotent stem cells (iPSCs). The method may comprise obtaining or having obtained a biological sample and contacting the biological sample with a composition as described herein (e.g., a composition comprising one or more reprogramming factors and a factor configured to modulate an adhesome component). In some embodiments, contact of the composition with the somatic cell reduces mutagenesis during reprogramming.

[0013] One of the unique and inventive technical features of the present invention is the targeting of adhesome genes. Without wishing to limit the invention to any theory or mechanism, it is believed that the technical feature of the present invention advantageously provides for the modulation of cell reprogramming into iPSCs. Additionally, the present invention uses single-cell transcriptome analysis to elucidate how adhesome gene perturbations (and different ECM environments) alter cell fate / developmental trajectories during reprogramming. None of the presently known prior references or works have the unique inventive technical feature of the present invention.

[0014] Moreover, the prior references teach away from the present invention. For example, current technologies for generating induced pluripotent stem cells (iPSCs), such as the reprogramming cocktails comprising Yamanaka factors and / or inhibitors, have inherent limitations. These approaches often result in the generation of iPSCs that must then undergo extensive screening to ensure their usability and to identify any potential mutations. Additionally, these methods address technical challenges related to low efficiency. In contrast, the present invention offers a more advanced and effective solution for producing iPSCs, significantly-improving both efficiency and reliability.

[0015] Furthermore, the inventive technical features of the present invention contributed to a surprising result. For example, the present invention leads to more efficient iPSC reprogramming. In addition, the present invention leads to iPSCs with molecular characteristics that favor cellular responses and differentiation properties that can be optimized for specific cellular applications (therapy or diagnostic).

[0016] Any feature or combination of features described herein are included within the scope of the present invention provided that the features included in any such combination are not mutually inconsistent as will be apparent from the context, this specification, and the knowledge of one of ordinary skills in the art. Additional advantages and aspects of the present invention are apparent in the following detailed description and claims.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

[0017] The features and advantages of the present invention will become apparent from a consideration of the following detailed description presented in connection with the accompanying drawings in which:

[0018] FIG. 1A-1F shows dynamic adhesome expression is refractory to successful reprogramming. FIG. 1A shows a heatmap of row-normalized transcriptomic expression of dynamic adhesome genes (FPKM>5 and fold change>6) in hiF-Ts during iPSC reprogramming (values represent percent of max gene expression during timeline). Temporal gene expression patterns were clustered into 4 patterns with hierarchical clustering. Average expression pattern shown as a bolded line. FIG. IB shows number of adhesome genes in total or that are dynamic or non-dynamic. Proportions of cadherin and / or integrin genes in the entire adhesome and the dynamic adhesome, respectively. A two-sided Fisher's exact test was performed for overrepresentation / underrepresentation of integrin / cadherin genes in the dynamic adhesome compared to the entire adhesome (two-sided Fisher's exact test. P=0.0001065, P=0.00892, respectively). FIG. 1C shows a schematic representation of RNAi screening of the 103 dynamic adhesome genes. Three unique shRNA containing lenti viral constructs were separately transduced into hiF-Ts before reprogramming. FIG. ID shows the number of colonies relative to controls for each dynamic adhesome gene in the RNAi screen. Values shown are the logarithm base 2 of the average of up to 3 shRNA targets per gene divided by the average of the non-targeting control conditions which are represented by y=0. Representative images of TRA-1-60+ iPSC colonies (black) for selected knockdowns. FIG. IE shows fold change of colony count per gene knockdown of reprogramming with or without inhibitors. Gene namesdepicted represent top 10 knockdown genes and controls, respectively (robust linear regression M-estimation, R2 = .70, PO.OOOl). FIG. IF shows a protein-protein interaction network of dynamic adhesome genes (nodes). Edge thickness represents confidence in prediction of interaction between proteins. Nodes are colored by log of fold change of gene knockdown reprogramming efficiency compared to the average of the controls. Permutation tests were performed between individual functional categories and the rest of the categories with 10,000 permutations (permutation test; *P < 0.05).

[0019] FIG. 2A-2I shows scRNA-seq reveals novel cell t pes during hiPSC reprogramming. FIG. 2A shows a schematic of scRNA-seq time point collection and sequencing during iPSC reprogramming. FIG. 2B shows UMAP plots of populations of reprogramming cells from LacZ (left) and SHROOM3 (right) knockdown conditions. Each point represents a cell profiled by scRNA-seq and the color represents the time point of iPSC reprogramming that the cell was harvested and sequenced. FIG. 2C show row-normalized expression heatmap of representative marker genes from each cell type. Representative marker genes were selected from the top 35 differentially expressed genes per cluster. FIG. 2D shows UMAP plot colored by Leiden cluster assignment and labeled cell type. FIG. 2E shows a bar plot of subpopulation proportions for each time point (row) per condition. Colors represent different cell types at each time point from the LacZ (left) and SHROOM3 (right) kd conditions. FIG. 2F-2I shows violin plots of SHROOM3 and LacZ kd conditions comparing SHROOM3 expression (FIG. 2F), PCP signature expression (FIG. 2G), EMT signature expression (FIG. 2H), and pluripotency signature expression (FIG. 21) (two-sided Student’s t-test: ****P < 0.0001; ***P < 0.001; dotted gray lines represent mean).

[0020] FIG. 3A-3H shows CellChat reveals differential subpopulation interactions during iPSC reprogramming. FIG. 3A show s a riverplot of signaling patterns conserved betw een different cell types in the LacZ (left) and SHROOM3 (right) kd conditions. FIG. 3B shows a heatmap of the differential number of interactions between the cell types in the LacZ and SHROOM3 kd conditions. FIG. 3C shows a heatmap of the Euclidean distances of the most differentially regulated signaling pathways between the LacZ and SHROOM3 kd conditions. Greater values for Euclidean distance represent lower functional similarity for the represented signaling pathway between the LacZ and SHROOM3 kd conditions. FIG. 3D shows violin plots of SHROOM3 and LacZ kd conditions comparing the module scores for EphA, fibronectin, and notch signaling for the NCC, SMC, and iPSC-like populations between LacZ and SHROOM3 kd conditions (Student’s t-test; ****P<0.0001; ***P<0.001; NS=not significant; dotted gray lines representmean). FIG. 3E shows hierarchical plots of EPHA, FN, and Notch signaling that show inferred interactions between cell types in the LacZ (left) and SHR00M3 (right) kd conditions. Solid and open circles represent source and target, respectively. Circle sizes are proportional to the number of cells in each cell group and edge width represents the communication probability. Edge colors are consistent with the signaling source. FIG. 3F shows a bar plot of normalized functional similarity between LacZ kd and SHR00M3 kd conditions at days 6, 9, 12, and 15 of iPSC reprogramming for EPHA, FN, and Notch signaling pathways. FIG. 3G shows heatmaps of the importance of cell types as senders, receivers, mediators, and influencers for Notch, FN, and EPHA signaling. FIG. 3H shows signaling pathways clustered by functional similarity per condition. LacZ and SHR00M3 kd conditions and communication probability denoted by shape and size of points, respectively.

[0021] FIG 4A-4J shows Fibronectin, Geltrex, and Matrigel inhibit iPSC reprogramming and promote focal adhesion maturity’. FIG. 4 A shows bar plot of the total count of TRA-1-60+ iPSC colonies from hiF-Ts seeded on various ECMs (bars represent mean ± standard error; two-sided Student’s t-test; ****P < 0.0001; ***P < 0.001; *P < 0.05; NS=not significant; n=6). FIG. 4B shows a schematic representation of ECM coating in a 10 cm polystyrene dish blocked with a PDMS cut-out and subsequent iPSC reprogramming. A representative image of colony formation after reprogramming is shown below. FIG. 4C shows representative fluorescent images of nuclear, focal adhesion, and actin cytoskeleton staining of hiF-Ts on selected ECMs after 2 days of culture. Scale bars represent 10 pm. FIG. 4D-4E shows quantification of circularity (FIG. 4D) or area (FIG. 4E) of focal adhesions of cells on selected ECMs. Dotted lines represent mean. Circularity of gelatin, Geltrex, Matrigel, and fibronectin were significantly lower than the uncoated control condition. Areas of focal adhesion from Geltrex, Matrigel, and Fibronectin w ere significantly higher than the control. For figures FIG. 4D and FIG. 4E, two-sided Student's t-test, ****p<0.0001: NS=not significant; number of analyzed iPSC colonies >1100 for each condition; dotted red lines represent mean). FIG. 4F shows a schematic of traction force microscopy time course experiment. FIG. 4G shows traction forces of representative cells for selected time points. FIG. 4H shows line plots of total elastic energy’ exerted by reprogramming cells at selected time points. Number of cells per time point labeled in plot. Shaded region represents mean ± 95% confidence interval. FIG. 41 shows the bar plot of ncWNT signaling strength for each cell type at days 6, 9, 12, and 15. Signaling is classified by incoming (solid bars) and outgoing (striped bars). FIG. 4J shows bar plot of number of TRA-1-60+ iPSC colonies generated in the LacZ or SHROOM3 kd condition with or without fibronectin coating normalized to the SHROOM3 kdwithout fibronectin coating condition (bars represent mean ± standard error; two-sided Student’s t-test; **P < 0.01 ; n=8).

[0022] FIG. 5A-5J shows SHROOM3 kd redirects trajectory of iPSC reprogramming. FIG. 5 A shows UMAP plots of iPSC reprogramming cells in (left) LacZ kd or (right) SHROOM3 kd condition colored by pseudotime as inferred by Monocle 3. Trajectory from fibroblast-like cells to iPSCs depicted on UMAPs for each condition. FIG. 5B shows RNA velocity stream plots of iPSC reprogramming cells in (left) LacZ kd or (right) SHROOM3 kd condition. Arrows denote inferred general trajectory of cells from scVelo. FIG. 5C-5E shows module scores of fibroblast (FIG. 5C), pluripotency (FIG. 5D), and PCP (FIG. 5E) over pseudotime as calculated by Monocle 3 for LacZ and SHROOM3 kd conditions. FIG. 5F shows PAGA graphs depicting inferred transitions between cell types based on RNA velocities for (left) LacZ and (right) SHROOM3 kd conditions. FIG. 5G shows UMAP plots of iPSC reprogramming cells in (left) LacZ kd or (right) SHROOM3 kd condition colored by transitioning or stable status as inferred by MuTrans. FIG. 5H shows a bar plot depicting the proportion (%) of transitioning cells per cell type in LacZ and SHROOM3 kd conditions. FIG. 51 shows mean-CV plots of each cell type for LacZ and SHR00M3 kd conditions (top). Change in CV and mean expression between SHR00M3 (linear regression depicted by red line) and LacZ kd (linear regression depicted by black line) conditions with pluripotency genes colored in pink (bottom). FIG. 5J shows a schematic of new cell type transitions in SHR00M3 kd condition during reprogramming compared to LacZ kd condition.

[0023] FIG. 6A-6M shows knockdown of CSRP1 increases cancer sternness marker expression in human lung cancer cells. FIG. 6A shows a Kaplan-Meier survival plot of pan-cancer patients with or w ithout mutation(s) in adhesome genes from GDC Data Portal. Survival rates of patients with mutation(s) in adhesome genes were significantly lo er than patients without mutations in adhesome genes (log-rank test, ****P<0.0001). FIG. 6B shows a violin plot comparing expression (z-score) of (top) mesenchymal markers, such as VIM, and (bottom) epithelial markers, such as CDH1, in cohorts with / without a mutation in any of the 103 dynamic adhesome genes (two-sided Student's t-test; *P < 0.05; ***P < 0.001; dotted gray lines represent mean). FIG. 6C shows gene expression (TPM) from A549, a human lung cancer cell, of the top 15 knockdown genes from adhesome RNAi screen, of w hich 5 are highly expressed (TPM>50). The Venn diagram depicts all 5026 genes that are highly expressed in A549 cells (TPM>50), of which 5 also belong to the top 15 knockdown genes from the adhesome RNAi screen. FIG. 6D showsexpression of lung cancer sternness markers (ALDH1A3, P0U5F1. CD44, and EPCAM) in A549 cells with LacZ, SHROOM3, or CSRP1 knockdowns as measured by RT-qPCR (bars represent mean ± standard error; two-sided Student's t-test, *P<0.05, **P<0.01, ***P<0.001, ****p<0.0001: n=8). FIG. 6E shows a schematic of scRNA-seq time point collection and sequencing during acquisition of sternness of A549 cells. FIG. 6F shows a row-normalized expression heatmap of differentially expressed genes from each cluster of cells. FIG. 6G shows UMAP plot colored by cluster assignment and labeled cell subpopulation from LacZ (left) or CSRP1 (right) knockdown conditions. FIG. 6H shows a dotplot depicting expression of cancer sternness genes (ALDH1A3, POU5F1, CD44, EPCAM, ABCG2, and NOTCH3) in cells from clusters 1 and 2 from LacZ and CSRP1 conditions. Color of circles represents average expression and size represents percent of cells in clusters that express the respective gene. FIG. 61 shows violin plots comparing the expression of PCP, LUAD EMT, MET, and CSC signatures, respectively, between CSRP1 and LacZ kds (two-sided Student’s t-test; ****P<0.000I; dotted gray lines represent mean). FIG. 6J shows UMAP plots of CSC cells in (left) LacZ kd or (right) CSRP1 kd condition colored by pseudotime as inferred by Monocle 3. Trajectory from cluster 1 to cluster 2 depicted on UMAPs for each condition. FIG. 6K shows RNA velocity plot depicting inferred transitions between cell ty pes for (left) LacZ and (right) CSRP1 kd conditions. FIG. 6L shows UMAP plots of CSC cells in (left) LacZ kd or (right) CSRP1 kd condition colored by transitioning or stable status as inferred by MuTrans. FIG. 6M shows a bar plot depicting the proportion (%) of transitioning cells per cluster in LacZ and CSRP1 kd conditions.

[0024] FIG. 7A-7E shows CSRP1 knockdown in human lung cancer cells increases metastasis in a mouse xenograft model. FIG. 7A shows schematic of knockdown, addition of reprogramming media, injection, and collection timeline for in vivo mouse model of metastasis. FIG. 7B shows representative gross (top) and fluorescent (bottom) images of lungs for scramble (left) and CSRP1 (right) knockdown conditions. FIG. 7C shows flow cytometry CD298 (human marker) gating results for representative lung (top) and blood (bottom) samples for scramble (left) and CSRP1 (right) knockdown conditions. FIG. 7D shows a bar plot depicting the percentage of CD298+ cells in the blood (left) and lungs (right) in each condition (bars represent mean ± standard deviation; unpaired Wilcoxon test, *P<0.05, **P<0.01, ***P<0.00I, ****P<0.0001). Each point represents a single animal subject for a total of n = 16 in the scramble kd condition and n = 11 in the CSRP1 kd condition. FIG. 7E shows a bar plot image quantification for mCherry+ area percentage in lungs (left) and mCherry+ foci count (right) in each condition (bars represent mean ± standard deviation; unpaired Wilcoxon test, *P<0.05, **P<0.01, ***P<0.001,****p<0.0001). Each point represents a single subject for a total of n = 17 in the scramble kd condition and n = 10 in the CSRP1 kd condition.

[0025] FIG. 8 shows the qPCR iPSC mutation frequency analysis.

[0026] FIG. 9A-9F shows adhesome disruption, and specifically SHROOM3 knockdown, improves reprogramming efficiency. FIG. 9A shows a schematic of cell-cell and cell-extracellular matrix interaction proteins and signaling cascades, representing various adhesome components. FIG. 9B shows a timeline of the shRNA knockdown screen of dynamic adhesome genes during reprogramming. hiF-Ts were transduced with plasmids containing either control or adhesome knockdowns, given media with doxycycline to induce reprogramming for 16 days, and stained and imaged on day 16 for TRA-1-60, a stem cell surface marker. FIG. 9C shows a number of iPSC colonies relative to controls for each dynamic adhesome gene in the shRNA screen. Values show n are the logarithm base 2 of the average of up to 3 shRNA targets per gene divided by the average of the non-targeting control conditions which are represented by y=0. Representative images of TRA-1-60+ iPSC colonies (black) for selected knockdow ns. FIG.9D shows a number of colonies for each individual SHROOM3 shRNA compared to each non-target shRNA control for hiF-T reprogramming with ROCK and LSD1 inhibitors. The mean of the non-target controls and the mean of the negative controls are shown as dashed black lines. FIG. 9E show s an exon map of protein-coding SHROOM3 transcripts and shRNA target regions (top) with their corresponding fold change in Nanog+ colony counts (bottom). In the diagram, exons are represented as yellow boxes, introns as black lines, and shRNAs as shaded boxes according to the exon they target. Fold change calculations are relative to the mean colony counts of single isoform knockdowns (bars represent mean ± standard deviation; two-sided Student’s t-test, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001). Each point represents a single well for a total of n = 8 targeting exon 1 and n = 4 targeting each of exons 6, 11, and 12 (numbered on the x-axis and in the diagram). FIG. 9F shows the fold change Nanog+ colony count data relative to the mean colony counts of the lacZ knockdown condition. Dual isoform-targeting shRNAs from (e) are grouped and distinguished by point fill color (bars represent mean ± standard deviation; two-sided Student’s t-test, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001). Each point represents a single well for a total of n = 4 in the lacZ knockdown condition and n = 12 in the dual isoform-targeting SHROOM3 knockdown condition.

[0027] FIG. 10A-10F shows SHROOM3 knockdown confers a genome-protecting effect. FIG.10A shows variant count during reprogramming for control vs SHROOM3 knockdown as ioinferred using scAllele (left) and SComatic (right; Poisson regression, ****P<0.0001). FIG. 10B shows copy numbers at common karyotypic abnormality loci in PSCs for control DNA, control knockdown, and SHR00M3 knockdown! (two-sided Student’s t-test, *P<0.05, **P<0.01, ***p<0.001, ****P<0.0001). FIG. IOC shows row-scaled heatmap depicting antioxidant protein gene expression in pseudo bulked hiF-T single-cell RNA sequencing data during lacZ (left) or SHR00M3 (right) knockdown. Select genes are grouped by protein family. FIG. 10D-19E show s gene expression for key components of the base excision repair (FIG. 10D) and mismatch repair pathways (FIG. 10E) during lacZ or SHR00M3 knockdown shown along reprogramming trajectories (given in order of 1 to 4). FIG. 10F shows schematic of proposed time-resolved genomic characterization during episomal reprogramming with and without Shroom protein perturbation. Stars represent somatic mutations which are amplified by reprogramming (purple) and reprogramming-induced mutations (orange).

[0028] 11A-11D shows Shroom protein repression can be optimally delivered via episomal plasmid transfection. FIG. 11 A shows a schematic depicting an episomal reprogramming timeline. Upon optimal transfection via electroporation, all five plasmids will enter the cell. Different levels of reprogramming success are represented and contain fewer than five plasmids. FIG. 11B shows brightfield (left) and fluorescent (right) images of cells from select wells (of 16) with different electroporation parameters for GFP plasmid transfection. Well 2 (bottom) was considered the most successful during the optimization study. FIG. 11C shows pCXB-EBNAl plasmid engineered to include U6 promoter and top performing SHROOM3 shRNA. FIG. 11D shows a domain map of main protein-coding SHROOM3 transcripts (top) and artificial constructs for repression studies (bottom).DETAILED DESCRIPTION OF THE INVENTION

[0029] Disclosed are the various compounds, solvents, solutions, carriers, and / or components to be used to prepare the compositions to be used within the methods disclosed herein. Also disclosed are the various steps, elements, amounts, routes of administration, symptoms, and / or treatments that are used or observed when performing the disclosed methods, as well as the methods themselves. These and other materials, steps, and / or elements are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these materials are disclosed, while specific reference of each various individual and collective combination and permutation of these compounds may not be explicitly disclosed, each is specifically contemplated and described herein.

[0030] Unless otherwise explained, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skills in the art to which a disclosed invention belongs. The singular terms "a," "an," and "the" include plural referents unless context clearly indicates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly indicates otherwise. The term "comprising" means that other elements can also be present in addition to the defined elements presented. The use of "comprising" indicates inclusion rather than limitation. Stated another way, the term "comprising" means "including principally, but not necessarily solely". Furthermore, variations of the word "comprising", such as "comprise" and "comprises", have correspondingly the same meanings. In one respect, the technology described herein related to the herein described compositions, methods, and respective component(s) thereof, as essential to the invention, yet open to the inclusion of unspecified elements, essential or not ("comprising").

[0031] Suitable methods and materials for the practice and / or testing of embodiments of the disclosure are described below. Such methods and materials are illustrative only and are not intended to be limiting. Other methods and materials similar or equivalent to those described herein can be used. For example, conventional methods well known in the art to which the disclosure pertains are described in various general and more specific references, including, for example, Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed., Cold Spring Harbor Laboratory Press, 1989; Sambrook et al., Molecular Cloning: A Laboratory Manual, 3d ed., Cold Spring Harbor Press, 2001; Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates, 1992 (and Supplements to 2000); Ausubel et al.. Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, 4th ed., Wiley & Sons, 1999; Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, 1990; and Harlow and Lane, Using Antibodies: A Laboratory' Manual, Cold Spring Harbor Laboratory Press, 1999, Gene Expression Technology (Methods in Enzymology. Vol. 185, edited by D. Goeddel, 1991. Academic Press, San Diego, Calif.), "Guide to Protein Purification” in Methods in Enzymology (M. P. Deutshcer, ed., (1990) Academic Press, Inc.); PCR Protocols: A Guide to Methods and Applications (Innis, et al. 1990. Academic Press, San Diego, Calif), Culture of Animal Cells: A Manual of Basic Technique, 2nd Ed. (R. I. Freshney. 1987. Liss. Inc. New York, N.Y.), Gene Transfer and Expression Protocols, pp.109-128, ed. E. J. Murray, The Humana Press Inc., Clifton, N.J.). and the Ambion 1998 Catalog (Ambion, Austin, Tex.), the disclosures of which are incorporated in their entirety7herein by reference.

[0032] All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety for all purposes. In case of conflict, the present specification, including explanations of terms, will control.

[0033] Although methods and materials similar or equivalent to those described herein can be used to practice or test the disclosed technology, suitable methods and materials are described below. The materials, methods, and examples are illustrative only and not intended to be limiting.

[0034] A '■subject' ’ is an individual and includes, but is not limited to. a mammal (e.g., a human, horse, pig, rabbit, dog, sheep, goat, non-human primate, cow, cat, guinea pig, or rodent), a fish, a bird, a reptile or an amphibian. The term does not denote a particular age or sex. Thus, adult and newborn subjects, as well as fetuses, whether male or female, are intended to be included. A ’■patient" is a subject afflicted with a disease or disorder. The term “patient” includes human and veterinary subjects.

[0035] As used herein, a “biological sample’' or “clinical sample” may be used interchangeably and may refer to any biological material taken from a subject. Non-limiting examples of a biological material to be taken for a biological sample may include but are not limited to, saliva, blood, or urine, and nasopharyngeal specimens.

[0036] As used herein, the term “stem cell” refers to, in multicellular organisms, undifferentiated or partially differentiated cells that can differentiate into various types of cells and divide indefinitely to produce more of the same stem cell. They are the earliest type of cell in a cell lineage. They are found in both embryonic and adult organisms, but they have slightly different properties in each. They are usually distinguished from progenitor cells, which cannot divide indefinitely, and precursor or blast cells, which are usually committed to differentiating into one cell type.

[0037] As used herein, the term “RNA-based inhibitory' agent” refers to a nucleic acid molecule that reduces, suppresses, or prevents expression or activity of a target gene or gene product at the transcriptional or post-transcriptional level. The RNA-based inhibitory agent may comprise RNA, DNA, or a combination thereof, and may include natural, synthetic, or chemically modified nucleotides. In certain embodiments, the RNA-based inhibitory' agent functions through mechanisms such as RNA interference (RNAi). antisense inhibition, or RNA-guided degradation or modification of nucleic acids. Such agents include, but are not limited to, a short hairpin RNA(shRNA), a small interfering RNA (siRNA) and antisense oligonucleotides (ASOs).

[0038] Referring now to FIGs. 1A-9F, the present invention relates to cell engineering and cell therapies, providing methods and compositions for reprogramming adult / somatic cells, e.g., derived from human biopsy samples, into stem cells or other therapeutically relevant cell types. In some embodiments, the compositions and methods described herein reduce mutagenesis during the reprogramming of somatic cells into induced pluripotent stem cells (iPSCs).

[0039] The present invention features compositions for generating induced pluripotent stem cells (iPSCs) from a biological sample. In some embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and a factor configured to modulate an adhesome component. In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and a factor configured to modulate an adhesome component. In further embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and a factor configured to modulate an adhesome component.

[0040] In some embodiments, the reprogramming factors (e.g., the Yamanaka Factors) comprise transcription factors. In some embodiments, the reprogramming factors (e.g., the Yamanaka Factors) comprise one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc. For instance, the compositions described herein may include the aforementioned reprogramming factors. Alternatively, the composition described herein may only comprise one of the aforementioned reprogramming factors. In other embodiments, the reprogramming factors comprise one or more small molecule compounds configured to reprogram a cell through chemical means. Accordingly, the present disclosure encompasses both reprogramming achieved via ectopic expression of transcription factors and reprogramming mediated by small molecule compounds.

[0041] In some embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and a factor configured to modulate gene expression of an adhesome component, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO). In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and a factor configured to modulate gene expression of an adhesome component, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO). In some embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and a factor configured to modulate gene expression of an adhesomecomponent, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO).

[0042] In some embodiments, the composition comprises one reprogramming factor (e.g., one Yamanaka Factor) and a factor configured to modulate gene expression of an adhesome component, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO). In other embodiments, the composition comprises two reprogramming factors (e.g., two Yamanaka Factors) and a factor configured to modulate gene expression of an adhesome component, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO). In some embodiments, the composition comprises three reprogramming factors (e.g., three Yamanaka Factors) and a factor configured to modulate gene expression of an adhesome component, e.g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO). In other embodiments, the composition comprises four reprogramming factors (e.g., four Yamanaka Factors) and a factor configured to modulate gene expression of an adhesome component, e g., an RNA-based inhibitory agent (e.g., shRNA, siRNA, or ASO).

[0043] In some embodiments, the RNA-based inhibitor)' agent knocks down the expression of the adhesome component, e.g., SHROOM3. In some embodiments, the RNA-based inhibitory agent modulates the gene expression of SHROOM3. In some embodiments, the RNA-based inhibitor}' agent knocks down the expression of SHROOM3. In some embodiments, the RNA-based inhibitory' agent modulates the gene expression of DLC1. In some embodiments, the RNA-based inhibitory agent knocks down the expression of DLC1. In some embodiments, the RNA-based inhibitory agent modulates the gene expression of LM07. In some embodiments, the RNA-based inhibitory agent knocks down the expression of LM07.

[0044] In some embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In some embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3.

[0045] In some embodiments, the composition comprises one reprogramming factor (e.g., one Yamanaka Factor) and an shRNA configured to modulate gene expression of an adhesomecomponent, e.g., SHROOM3. In other embodiments, the composition comprises two reprogramming factors (e.g., two Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In some embodiments, the composition comprises three reprogramming factors (e.g., three Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In other embodiments, the composition comprises four reprogramming factors (e.g., four Yamanaka Factors) and an shRNA configured to modulate gene expression of an adhesome component, e.g., SHR00M3.

[0046] In some embodiments, the short hairpin RNA (shRNA) may have a sequence according to SEQ ID NO: 1 (GGCTATGATAAATGGTCTAAT). In some embodiments, the shRNA comprises a sequence at least 71% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 76% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 80% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 85% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 90% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 95% identical to SEQ ID NO: 1. In some embodiments, the shRNA comprises a sequence at least 100% identical to SEQ ID NO: 1. In some embodiments, the aforementioned shRNA (e.g., SEQ ID NO: 1) is designed to target exon 6 of SHROOM3.

[0047] In other embodiments, the short hairpin RNA (shRNA) may have a sequence according to SEQ ID NO: 2 (CCTTGGTGAAGATGCCAGTAA). In some embodiments, the shRNA comprises a sequence at least 71% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 76% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 80% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 85% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 90% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 95% identical to SEQ ID NO: 2. In some embodiments, the shRNA comprises a sequence at least 100% identical to SEQ ID NO: 2. In some embodiments, the aforementioned shRNA (e g., SEQ ID NO: 2) is designed to target exon 11 of SHROOM3.

[0048] In alternative embodiments, the short hairpin RNA (shRNA) may have a sequence according to SEQ ID NO: 3 (GTTTGAACTATCTGGGTTATT). In some embodiments, the shRNA comprises a sequence at least 71% identical to SEQ ID NO: 3. In some embodiments, the shRNA comprises a sequence at least 76% identical to SEQ ID NO: 3. In some embodiments, theshRNA comprises a sequence at least 80% identical to SEQ ID NO: 3. In some embodiments, the shRNA comprises a sequence at least 85% identical to SEQ ID NO: 3. In some embodiments, the shRNA comprises a sequence at least 90% identical to SEQ ID NO: 3. In some embodiments, the shRNA comprises a sequence at least 95% identical to SEQ ID NO: 3. In some embodiments, the shRNA comprises a sequence at least 100% identical to SEQ ID NO: 3. In some embodiments, the aforementioned shRNA (e.g., SEQ ID NO: 3) is designed to target exon 12 of SHROOM3.

[0049] In some embodiments, shRNA sequences are provided in DNA format (i.e., thymidine (T) substituted for uridine (U)) for convenience. It will be understood that the corresponding RNA molecules comprise uridine (U) in place of thymidine (T). The shRNA molecules may be expressed from DNA constructs to produce functional RNA hairpins that mediate RNA interference. Unless otherwise indicated, shRNA, siRNA, and ASO sequences disclosed herein encompass both DNA and RNA forms, their expression constructs, and functional equivalents thereof.

[0050] In some embodiments, the shRNA targets exon 1, 6, 11, 12, or a combination thereof.

[0051] In some embodiments, the shRNA knocks down the expression of the adhesome component, e.g., SHROOM3. In some embodiments, the shRNA modulates the gene expression of SHROOM3. In some embodiments, the shRNA knocks down the expression of SHROOM3. In some embodiments, the shRNA modulates the gene expression of DLC1. In some embodiments, the shRNA knocks down the expression of DLC1. In some embodiments, the shRNA modulates the gene expression of LMO7. In some embodiments, the shRNA knocks down the expression of LMO7.

[0052] In some embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and an siRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and an siRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3. In some embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and an siRNA configured to modulate gene expression of an adhesome component, e.g., SHROOM3.

[0053] In some embodiments, the composition comprises one or more reprogramming factors(e.g.. one or more Yamanaka Factors) and an ASO configured to modulate gene expression of an adhesome component, e.g., SHR00M3. In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and an ASO configured to modulate gene expression of an adhesome component, e g., SHR00M3. In some embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and an ASO configured to modulate gene expression of an adhesome component, e.g., SHR00M3.

[0054] In some embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule. In other embodiments, the composition comprises two or more reprogramming factors (e.g., two or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule. In some embodiments, the composition comprises three or more reprogramming factors (e.g.. three or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule.

[0055] In some embodiments, the composition comprises one reprogramming factor (e.g., one Yamanaka Factor) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule. In other embodiments, the composition comprises two reprogramming factors (e.g., two Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule. In some embodiments, the composition comprises three reprogramming factors (e.g., three Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a small molecule. In other embodiments, the composition comprises four reprogramming factors (e.g., four Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g.. a small molecule.

[0056] In some embodiments, the small molecule disrupts the function of the adhesome component, e.g., SHROOM3. In some embodiments, the small molecule modulates the function of SHROOM3. In some embodiments, the shRNA disrupts the function of SHROOM3. In some embodiments, the small molecule modulates the gene expression of DLC1. In some embodiments, the small molecule knocks down the expression of DLC1. In some embodiments, the small molecule modulates the gene expression of LMO7. In some embodiments, the small molecule knocks down the expression of LM07.

[0057] Non-limiting examples of small molecules configured to modulate the function of an adhesome component, e.g., SHROOM3, include but are not limited to, Y-27632 or CHEMBL6067279.

[0058] In certain embodiments, modulation of the adhesome component does not necessarily occur at the level of gene expression, but rather at the level of protein activity, for example, through dominant negative mechanisms. In such embodiments, modulation may be achieved by overexpression of dominant negative or competitive binding peptides comprising one or more wild-type and / or mutated protein domains of the SHROOM3 protein. Thus, in some embodiments, the factor configured to modulate the function of an adhesome component is a dominant negative mutant that modulates (interferes with) the function of the endogenous wild-ty pe protein, e.g., a SHROOM3 protein.

[0059] In alternative embodiments, the composition comprises one or more reprogramming factors (e.g., one or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a dominant negative mutant of the adhesome component or a functional portion thereof. In other embodiments, the composition comprises two or more reprogramming factors (e.g.. two or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e g., a dominant negative mutant of the adhesome component or a functional portion thereof. In some embodiments, the composition comprises three or more reprogramming factors (e.g., three or more Yamanaka Factors) and a factor configured to modulate the function of an adhesome component, e.g., a dominant negative mutant of the adhesome component or a functional portion thereof.

[0060] The present invention may also feature methods of generating iPSCs from a biological sample. In some embodiments, the method comprises obtaining or having obtained a biological sample and contacting the biological composition with a composition as described herein. In some embodiments, the reprogramming factors and the factor configured to modulate the adhesome component are simultaneously added to the biological sample. In other embodiments, the reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample in a stepwise manner.

[0061] The present invention may further comprise a method of generating induced pluripotent stem cells (iPSCs) from a biological sample. In some embodiments, the method comprises a) obtaining or having obtained a biological sample, b) contacting the biological sample with an anRNA-based inhibitory agent (e.g.. shRNA, siRNA, or ASO), wherein the RNA-based inhibitory agent is configured to module gene expression of an adhesome component, c) reprogramming the biological sample to generate iPSCs and d) isolating and expanding the iPSCs.

[0062] In other embodiments, the method comprises a) obtaining or having obtained a biological sample, b) contacting the biological sample with a small molecule, wherein the molecule is configured to module the function of an adhesome component to generate iPSCs, c) reprogramming cells in the biological sample, and d) isolating and expanding the iPSCs.

[0063] In some embodiments, reprogramming is considered complete when the cells exhibit gene expression patterns characteristic of fully reprogrammed pluripotent stem cells, including expression of TRA-1-60, Nanog, Oct4, and other pluripotency-associated markers. In certain embodiments, completion of reprogramming is further confirmed based on cell or iPSC colony morphology consistent with pluripotent stem cell phenotypes.

[0064] In some embodiments, the adhesome component is SHROOM3. In some embodiments, reprogramming the cells of the biological sample comprises introducing or having introduced one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc.

[0065] In some embodiments, the biological samples comprise human dermal fibroblasts (skin cells) or cells found in the blood (peripheral blood mononuclear cells or umbilical cord blood mononuclear cells).

[0066] The present invention features a method for reducing mutagenesis in induced pluripotent stem cells (iPSCs). The method may comprise obtaining or having obtained a biological sample and contacting the biological sample with a composition as described herein (e.g.. a composition comprising one or more reprogramming factors and a factor configured to modulate an adhesome component). In some embodiments, contact of the composition with the somatic cell reduces mutagenesis during reprogramming.

[0067] The present invention may also feature methods of treating cancer in a subject in need thereof. In some embodiments, the method comprises administering to the subject a composition configured to overexpress an adhesome component. In some embodiments, the cancer is lung cancer. In some embodiments, the adhesome component CSRP1. In some embodiments, the composition comprises a small molecule.

[0068] EXAMPLE 1

[0069] The following is a non-limiting example of the present invention. It is to be understood that said example is not intended to limit the present invention in any way. Equivalents or substitutes are within the scope of the present invention.

[0070] Here, how adhesion-associated molecules impact sternness acquisition during iPSC reprogramming was examined. Using an inducible reprogramming system, a genetic knockdown (shRNA) screen of the integrin and cadherin “adhesome” was performed, which served as a literature-derived complement of genetically encoded cell-cell and cell-matrix receptors (and their associated proteins). Notably, the adhesome has increasingly been implicated in developmental disorders and cancer pathogenesis, including tumor stromal development and metastasis. From the screen, a robust adhesome expression acts, in general, as a barrier to reprogramming and SHROOM3 was identified the top shRNA knockdown (kd) targets. The beneficial effects of SHROOM3 shRNAs were confirmed within a primary cell reprogramming system using normal human dermal fibroblasts. Counter to its known role in cadherin-junction signaling, SHROOM3 knockdown enabled improved reprogramming through disruption of cell-matrix communication via non-canonical WNT / planar cell polarity' (PCP) and fibronectin signaling.

[0071] Transcriptome profiling of -36,000 single cells across the reprogramming timeline revealed that heightened SHROOM3 expression was localized to cell populations emerging late during reprogramming and, when downregulated, reprogramming occurred primarily through a neural crest-like cell intermediate, as an alternative to trophoblast-like intermediates. Notably, an increased transcriptional noise was observed within pluripotency gene networks upon loss of SHROOM3, which was associated with more favorable transitions towards iPSCs. Analysis of direct cell-cell and cell-matrix communication networks via CellChat revealed a dramatic decrease in fibronectin signaling upon SHROOM3 knockdown (kd) and ectopic exposure to fibronectin through ECM patterning suppressed colony formation and enabled matrix-guided spatially-resolved reprogramming, which was dependent on SHROOM3 expression.

[0072] Given emerging evidence suggesting that the effects of biomechanical cues on somatic cell reprogramming also extend to cancer stem cell (CSC) generation, a second adhesome gene, CSRP1 (which was highly expressed in lung cancer cells), was also examined in the context of cancer cell sternness acquisition. The knockdown of CSRP1 facilitated the reactivation of sternness programs in A549 lung cancer cells. Transcriptome profiling of -17,000 lung cancercells during CSRP1 kd showed that, similar to iPSC reprogramming, adhesome disruption mediated sternness acquisition through dynamic rewiring cell fate trajectories and, based on multiscale stochastic dynamics, increased the number of cells undergoing transition.

[0073] To further explore the proposed connection between the acquisition of cancer cell sternness, fate plasticity, and metastatic aggressiveness, a xenograft mouse model of metastasis was implemented, establishing primary tumor sites through subcutaneous injections of A549 lung cancer cells subjected to either CSRP1 knockdown or control (scramble shRNA) knockdown. A significant increase in the number of A549 cells present within circulation and at metastatic tumor sites was observed within the lungs following CSRP1 kd compared to control. These findings demonstrate how adhesome-mediated cell fate rewiring may influence oncogenesis and cancer pathology7.

[0074] Reduced adhesome expression globally enhances cellular reprogramming: To investigate the role of the adhesome on iPSC reprogramming, temporal adhesome expression dynamics was examined throughout the reprogramming process. Differential expression analysis was performed using an existing RNA-seq dataset generated from a human fibroblasts-like cell line (hiF-T) undergoing reprogramming through activation of doxycycline (dox.) inducible polycistronic OCT4 / KLF4 / c-MYC / SOX2 expression cassette. Using this dataset, 103 adhesome genes (out of 347) were dynamically regulated across 24 days of reprogramming. The expression profile of these genes was i) at or above a fragment per kilobase million (FPKM) of 5 at any point during the reprogramming process and ii) dynamically regulated (showing a fold change in expression of 6 or greater between any two timepoints) (FIG. 1A). Adhesome expression changed in a largely coordinated fashion, and hierarchical clustering revealed four major patterns of expression dynamics (FIG. 1A), where over 60% of genes w ere rapidly downregulated w ithin 2 days of the onset of reprogramming while a smaller subset of genes showed a more gradual decrease in expression. The remainder of genes were either transiently or permanently upregulated, largely during the later phases of reprogramming. The integrin adhesome was significantly overrepresented (compared to its representation across all adhesome genes) within the dynamic gene set while the cadherin adhesome was underrepresented (FIG. IB).

[0075] To test how the expression of these dynamically regulated genes impacts the reprogramming process, an RNA interference (RNAi) knockdow n screen w as performed in hiF-T cells, where short hairpin RNAs (shRNAs) obtained through the RNAi Consortium (TRC) (Broad Institute) were used to target all 103 dynamic adhesome genes, with each gene beingindependently targeted by 3 unique shRNAs. Given that cytoskeletal tension disruption via Rho-associated protein kinase inhibitor (ROCKi) Y-27632 is known to enhance reprogramming efficiency, two screens were performed in parallel - one with ROCKi (as well as a lysine-specific histone demethylase 1 inhibitor (LSDli), which is known to accelerate the reprogramming process) and a second without any inhibitors (FIG. 1C). Cells were transduced using lentiviral delivery of adhesome shRNAs as well as shRNAs targeting non-human genes as non-targeting controls (e.g., RFP, LacZ, Luciferase, and GFP). Following a one-day recovery period, reprogramming was initiated through the addition of dox. Reprogramming cells cultured w ith LSDli (either by itself or with ROCKi) typically produce iPSCs after 13-15 days compared to over 20 days in the absence of inhibitors. Therefore, reprogramming was carried out for 16 days with inhibitors and 24 days without inhibitors. iPSC colonies were identified based on pluripotency marker TRA-1-60 expression and quantified using a custom ImageJ script (FIG. ID). Reprogramming efficiency was calculated as the average log fold change of all shRNAs targeting a given adhesome gene relative to the average colony count of non-targeting controls. In the ‘'no inhibitor’’ media condition, the average colony count for non-targeting clones w as 5, 11.3, 23.8, and 27 for shRNAs targeting RFP, Luciferase, GFP and LacZ, respectively. All non-targeting controls were normally distributed around a mean performance of -15.3 ± a standard deviation of 11.7. Strikingly, over 90% of all adhesome knockdown conditions showed improved reprogramming efficiency over the average for non-targeting controls (FIG. ID).When the relative fold changes in reprogramming efficiency was compared between both reprogramming conditions (with and without inhibitors), there w as a strong positive correlation between the two RNAi screens (FIG. IE). These results suggest that, although the magnitude in fold change was lessened with the addition of ROCKi and LSDli. the relative impact of each gene disruption is largely conserved and independent of well-established cytoskeletal perturbations experienced through pharmacological inhibition of ROCK.

[0076] To further investigate the coordinated role of the adhesome in reprogramming, a protein-protein interaction network of dynamic adhesome genes grouped by functional category was integrated with the fold change of colony counts from our RNAi screen (FIG. IF).Permutation testing of colony count fold change betw een individual functional categories and all other functional categories revealed that knockdown of genes under the phosphatidylinositol phosphatase (Ptdins phosphatase) functional category showed significant enhancement in reprogramming (p=0.040), where gene products of INPPD5 and INPPL1 are known to act as mediators of AKT signaling through receptor ty rosine kinase activity7.

[0077] Notably, SHROOM3 shRNAs collectively had the most potent impact on reprogramming, increasing reprogramming efficiency by 27-fold in the absence of inhibitors (FIG. IE).SHROOM3 is a member of the cadherin adhesome that is activated downstream of FGF signaling to drive apical constriction during rosette formation in zebrafish and plays a critical role in non-canonical WNT / planar cell polarity (PCP) signaling, which is required for the organized alignment of polarized epithelial cells in response to cell-cell communication and biophysical cues. SHR00M3 expression was transiently upregulated during reprogramming, peaking around day 14 of reprogramming (without inhibitors) (FIG. 1A). While all three SHR00M3 shRNAs outperformed the non-targeting controls, the best performing shRNA (clone 1) targeted exon 6 which is shared by both known protein-coding isoforms of SHR00M3. In contrast, clones 2 and 3 targeted exon 1 which is present only within a single isoform. Notably, in both reprogramming conditions, shRNA targeting exon 6 outperformed shRNAs targeting exon 1 by ~ 4- to 18-fold. Given the difference in performance between shRNA clones according to the exon being targeted, the clone space was expanded beyond the original set to include two additional clones (4 and 5) from TRC whose knockdowns were mediated by targeting exons 11 and 12, respectively. Similar to clone 1, these shRNA clones also targeted exons included in both known protein-coding isoforms of SHR00M3.

[0078] The performance of all five clones was then compared within a primary cell reprogramming system using lentiviral transduction of reprogramming factors in normal human dermal fibroblast (NHDF). In the context of the primary cell reprogramming system, a similar trend was observed where clone 1 outperformed clones 2 and 3 by an average of approximately 7.2-fold. Both clones 4 and 5 also outperformed clones 2 and 3 by an average of approximately 2.9- to 3.4-fold. The performance of LacZ shRNA was also compared to ’‘dual isoform-targeting” SHROOM3 shRNAs and observed improvement in reprogramming by an average of approximately 2.0-fold (approximately 3.1-, 1.3-, and 1.5-fold increase for clones 1, 4, and 5, respectively). The ability of shRNA clone 1 to disrupt SHROOM3 expression was also confirmed at the protein level and its beneficial effects on primary reprogramming across multiple viral dosages relative to a scramble shRNA control.

[0079] Single-cell transcriptomics reveals conserved and novel human reprogramming subpopulations and PCP-EMT-Pluripotency axis regulated by SHROOM3 expression: Due to the ability of dual isoform-targeting SHROOM3 shRNAs to substantially improve reprogramming efficiency, and their potential to reveal alternative targets of FGF signaling thatimpact the reprogramming process (beyond ERK. MAPK, and AKT). how SHROOM3 kd facilitates iPSC generation through its impact on cell fate and developmental trajectories during reprogramming was determined. During development, SHR00M3 plays a central role in the apical constriction of polarized epithelial cells, which is necessary' for tissue morphogenesis, neural tube closure, and the progression of EMT in neural crest cells. Given this, the role of SHR00M3 in EMT - a process that requires downregulation during iPSC generation, may partially explain its inhibitory role in reprogramming. However, cell types known to express SHR00M3 during development have not been identified in previous reprogramming experiments. Recently, temporal single-cell RNA sequencing (scRNA-seq) of mouse reprogramming identified various distinct and dynamic cell subpopulations, including neural-, trophoblast-, and iPSC-like cell types, that emerged over time and were derived from cells undergoing a mesenchymal -to-epithelial transition (MET, the reverse process of EMT). Utilizing the hiF-T experimental system, the study aimed to identify and characterize subpopulations that emerge throughout the timeline of human cell reprogramming, which may be critical to understanding the role of SHR00M3 in mediating EMT and related “outside-in” signaling (i.e., cell-cell and cell-ECM signaling) during the reprogramming process.

[0080] Two scRNA-seq time course experiments were performed on hiF-Ts undergoing reprogramming. Given the SHROOM3 expression profile within bulk RNA-seq (FIG. 1A), The analyses focused on later time points of reprogramming. A total of 52,991 cells were captured for scRNA-seq across days 6, 9, 12, and 15 of reprogramming - 28,604 and 24,387 cells for SHROOM3 (clone 1) and LacZ control knockdown conditions, respectively (FIG. 2A). Quality control metrics were chosen to focus on cell populations with a high degree of unique genes detected per cell. These populations represented about 83% of the cells captured for SHROOM3 kd condition and 54% for the LacZ kd condition. These cells captured the most dynamic cell populations during the reprogramming process. In both time course experiments, LSDli was added to accelerate the reprogramming timeline. However, ROCKi was not included to avoid confounding effects on mechanosignaling due to its known impact on cytoskeletal disruption. Cells from each of the reprogramming time courses were visualized in a two-dimensional Uniform Manifold Approximation and Projection (UMAP) space and annotated cells according to sampling time (FIG. 2B). Cells generally clustered within their respective reprogramming timepoint and showed a temporally contiguous progression from day 6 to 15 (e.g., from right to left in the UMAP space, respectively) in both SHROOM3 and LacZ kd conditions (FIG. 2B).Cell types were identified using Seurat and annotated based on expression of unique genesignatures within clusters (FIG. 2C-2D). Cells with expression profiles similar to those identified during mouse reprogramming (e.g., neural-, trophoblast-, fibroblast-, epithelial- and pluripotent-like cells) were found in addition to novel cell types yet to be reported, including distinct clusters of smooth muscle- and neural crest-like populations. Altered proportions of each cell type between SHR00M3 and LacZ kd conditions were also observed (FIG. 2E). High expression of SHR00M3 is primarily restricted to cell types that emerged later in our time course (e.g., day 12;), which include neural crest (NCC), proliferating trophoblast (Prolif. Troph.), epithelial (Epi.), and smooth muscle (SMC) cell types (FIG. 2F). SHR00M3 was also expressed to a lesser extent in pluripotent (iPSC) and neural (Neur.) cells.

[0081] SHR00M3 is activated downstream of the PCP pathw ay and is expressed in neural crest and cardiac muscle cells during development. Thus, the overlap between cells highly expressing SHR00M3 and PCP-related genes was examined and both SHR00M3 and PCP signaling (see Methods) are highly active in NCC and SMC subsets. Furthermore, PCP signaling has been demonstrated to drive EMT signaling during NCC differentiation / migration and cancer cell migration. Therefore, the effect of SHROOM3 knockdown on PCP pathway and EMT signaling was evaluated, with a focus on high-expressing cell type subsets. SHR00M3 knockdown was found to reduce SHR00M3 expression across all cell types (though not significantly in neural cells) and notably decreased PCP signaling and EMT signatures (see Methods) in NCCs and SMCs (FIG. 2F-2H). SHR00M3 kd also significantly increased the pluripotency signature of SHR00M3 kd cells in general and, most notably, in SMCs and in iPSCs to a modest degree (FIG. 21) To further investigate whether these effects were the result of off-target effects, BLAST was used to identify genomic locations that partially matched the SHR00M3 shRNA target sequence This analysis revealed only one gene, STIM2, with a significant e-value (e=0.67), while all other partial matches had e-values much greater than 1, indicating non-significant similarity. Expression analysis of STIM2 revealed no statistically significant difference between conditions overall.

[0082] SHROOM3 kd reduces heterogeneity and magnitude of cell-cell communication during reprogramming: Given the importance of incoming and outgoing cell-environment communication in the activation and propagation of PCP and EMT signaling, how SHROOM3 kd impacts cell-cell communications was next examined. Here, CellChat, an extensive ligand-receptor signaling database and open source software (CellChat) that enables identification and quantitative analysis of key intercellular communication networks withinsingle-cell transcriptome datasets, was utilized to identify dynamic changes in cell-cell communication between reprogramming subpopulations upon SHROOM3 kd. The analysis was limited to cell-cell contact and cell-ECM-directed ligand-receptor interactions within the CellChat database. Intercellular communication in the SHROOM3 and LacZ kd datasets were inferred separately and then analyzed together in a shared-two-dimensional manifold based on functional similarity. CellChat pattern recognition analysis was first employed to identify which cell types shared similar incoming and outgoing communication signaling profiles. In both conditions, several late-stage cell populations (e.g., iPSCs, NCCs, epithelial, and proliferating trophoblasts) consistently had more similar outgoing and incoming communication patterns (FIG. 3A). Interestingly, the number of incoming and outgoing signaling patterns were reduced under SHR00M3 kd (3 and 4 unique patterns were reduced to 2 for incoming and outgoing patterns, respectively), suggesting intercellular signaling was consolidated. SMCs showed a unique incoming communication pattern within LacZ kd conditions that was lost upon SHR00M3 kd. A similar change in iPSCs for outgoing communication patterns was also noted. Comparing the relative number of interactions between specific ’sender’ and ‘receiver’ cell populations according to each reprogramming condition, we found that, upon SHR00M3 kd, signaling interactions were largely attenuated for NCCs, neuroepithelial, fibroblasts, and trophoblasts as sender-cells, while iPSCs increased communication frequency globally (FIG.3B). Conversely, major histocompatibility complex class I negative (cIMHC Neg.) cells (an early reprogramming population), showed broad increases in communication as receiver-cells. To explore the single-cell signaling findings within a broader reprogramming context, a publicly available annotated scRNA-seq dataset from a chemically induced human reprogramming system that captured cells across multiple time points along successful versus failed iPSC fate trajectories was reanalyzed. Secondary analysis of these data corroborates a more consolidated cell-cell signaling network amongst cells identified as progressing towards successful iPSC reprogramming relative to cells identified to be on a failed reprogramming trajectory'. Notably, cells identified along successful reprogramming trajectories exhibited a marked downregulation of SHR00M3 expression relative to cells along failed trajectories at similar stages of the reprogramming timeline. Taken together, these findings suggest that SHR00M3 kd may impact reprogramming by reducing the magnitude and complexify of active cell-to-cell signaling networks.

[0083] SHROOM3 kd remodels cell communication networks through fibronectin signaling: Next, the analysis focused on identifying specific cell-cell communication pathwaysmost affected by SHROOM3 disruption. After projecting cell-cell communication networks from both SHROOM3 and LacZ kd into a two-dimensional manifold, Euclidean distance was calculated between all pairs of signaling pathways. The generated distances between the SHR00M3 and LacZ kd datasets revealed that EphA, Notch, and fibronectin signaling were the most differentially expressed signaling pathways between conditions (FIG. 3C). The importance of EphA, Notch, and fibronectin signaling in iPSC generation were also echoed in our observations between cells on failed versus successful (or ‘progressing’) reprogramming trajectories in the previously referenced chemically induced reprogramming system. The expression signature of fibronectin signaling localized primarily to NCC and SMC subpopulations (where SHR00M3 is also most dominantly expressed), while the Notch and EphA pathway signatures (see Methods) were found to be expressed primarily between NCC and iPSC subpopulations. Furthermore, module scores for fibronectin (FN1) signaling showed a much more dynamic pattern of regulation between these cell types compared to EPHA or NOTCH signaling (FIG. 3D). SMCs had the highest module score in FN1 signaling while iPSCs had the lowest, suggesting cells are required to downregulate FN1 signaling during pluripotency acquisition. SHR00M3 kd conditions showed consistent and highly significant downregulation in FN1 signaling within NCC and SMC populations. In iPSCs, SHR00M3 kd led to modest increases in module scores across all three signaling pathways.

[0084] Further analysis of inferred intercellular communication networks (limited to Notch, EphA, and fibronectin signaling pathways) revealed an overall increase in EphA signaling and a decrease in Notch and fibronectin signaling within the SHR00M3 kd condition (FIG. 3E), particularly across interactions involving iPSCs, NCCs, epithelial cells, SMCs and proliferating trophoblast cells. To understand temporal differences in signaling pathways, normalized functional similarity of EphA, Notch, and fibronectin signaling between the SHR00M3 and LacZ kd conditions across each timepoint was assessed. This analysis showed that, in general, all pathways become more distinct (less similar) between conditions over time (coinciding with upregulation of SHR00M3 during reprogramming), with SHR00M3 and LacZ kd conditions peaking in dissimilarity at day 12 of reprogramming (FIG. 3F). Notably, fibronectin showed the most dynamic changes in functional similarity over the course of the reprogramming timeline. Interestingly, SMCs were an important hub for Notch, EphA, and FN1 cell communications within both conditions, however, this was less apparent in fibronectin and Notch pathways under SHROOM3 kd (FIG. 3G). iPSCs primarily mediated EphA signaling in both conditions. NCCs in the SHROOM3 kd condition were less active in mediating fibronectin and Notch signaling butwere more active in mediating EphA signaling.

[0085] All cell communication pathways were jointly projected on the same two-dimensional manifold and clustered based on the functional similarity of inferred networks for both conditions (FIG. 3H). The majority of the differentially active signaling pathways (FIG. 3C) localized to the same clusters independent of SHROOM3 or LacZ kd condition. However, FN1 and CDH signaling were the only pathways to the group in different clusters between the conditions, suggesting that the two signaling pathways have different functional roles in each condition. We also noted an inversion of FN1 and FGF (where SHROOM3 is a known downstream effector) signaling between reprogramming conditions, where FN1 signaling moved from cluster 1 to 3 upon SHROOM3 kd and FGF signaling moved from cluster 3 to 1. Given the dynamics observed in fibronectin signaling — reduced in iPSCs but highly active in more differentiated cell types such as SMCs and fibroblasts — it was proposed that SHR00M3, despite its association with the cadherin adhesome. may exert an inhibitory role in the reprogramming process through cell-ECM interactions.

[0086] ECM composition directs focal adhesion morphology and reprogramming efficiency, enables spatially-resolved reprogramming: The extracellular matrix (ECM) is known to influence key cell fate decisions during differentiation and cancer progression (including metastasis). Manipulation of substrate properties in manners that generally decrease cell adhesion strength and maturity is known to also increase iPSC reprogramming efficiency - e.g., softer substrates, micro-grooved topographies, and 3D compared to 2D matrix environments. Thus, it is plausible that the enhanced reprogramming observed within these contexts can partially be attributed to a modulation in adhesion-mediated signaling, which our CellChat comparative analysis supports.

[0087] The effects of cell adhesions on reprogramming were functionally investigated. First, ECM surface coating composition was varied for hiF-T cells undergoing reprogramming. ECM proteins were absorbed to polystyrene tissue culture surfaces, including fibronectin, type I collagen, gelatin (hydrolyzed collagen), and two commercially available tumor-derived ECMs, Matrigel® and Geltrex™. The latter two are composed of a chemically complex mixture of matrix proteins and thus likely represent a more biologically relevant ECM presentation. After reprogramming on the respective ECM coatings, TRA-l-60+colonies were counted to assess reprogramming efficiency. Fibronectin, Matrigel®, and Geltrex™ had the most significant impact on reprogramming, inhibiting efficiencies by 44%, 33%, and 21%, respectively (FIG.4A). Collagen also significantly reduced reprogramming efficiency, but to a lesser extent. To further highlight that this ECM-mediated suppression of reprogramming was due to direct cell-ECM interaction at the cell-material interface, a “UCI” PDMS stamp was employed to spatially block Geltrex™ adsorption within a single polystyrene dish while the remaining (unblocked) surface was coated with ECM. After 16 days of reprogramming, colony formation occurred almost exclusively in regions of the dish protected from Geltrex™ coating (FIG. 4B), demonstrating the potential for spatial control of reprogramming. Notably, all ECMs except gelatin repressed reprogramming efficiency (some to a greater extent than others), suggesting that integrin binding, regardless of specific integrins, may negatively impact iPSC reprogramming.

[0088] As cells adhere to stiffer substrates, focal adhesions increase in both area and elongation, while the inverse is true on softer surfaces. Furthermore, soft substrates promote more efficient reprogramming. Therefore, changes in focal adhesion shape were characterized in hiF-Ts seeded onto glass wells coated with the different ECMs. After 2 days of culture in growth media (GM) without dox or inhibitors, immunostaining was performed on the cells to visualize focal adhesion proteins (vinculin) and the actin cytoskeleton (phalloidin) (FIG. 4C). The cells on Matrigel®, Geltrex™, and fibronectin had visibly more elongated focal adhesions compared to the cells seeded on control surfaces (without ECM), gelatin, and collagen conditions.

[0089] To quantify differences in focal adhesion morphology, a custom ImageJ script was developed to identify individual focal adhesions and quantify the morphological properties of the cellular focal adhesions in each ECM condition. This analysis revealed that Matrigel®, Geltrex™, and fibronectin indeed had significantly lower circularity and significantly higher areas compared to the control surface (FIG. 4D-4E). Additionally, gelatin had a significantly reduced circularity relative to the control but to a much milder degree compared to Matrigel®, Geltrex™, and fibronectin. These findings suggest that general integrin binding and focal adhesion maturity, in the absence of varying substrate stiffness or topography, are associated with lower reprogramming efficiency.

[0090] Cell-adhesive forces are dynamically regulated during late reprogramming:Proteomic studies performed during iPSC reprogramming reported that various proteins associated with focal adhesions, integrin complexes, ECM interactions, and the actin cytoskeletal exhibit transient upregulation during both early and late phases of reprogramming (the first and last 3 days, respectively). Given that SHROOM3 coordinates myosin contraction at thecytoskeletal apex during apical constriction through activation of mechano-regulatory proteins such as RhoA and ROCK1 / 2, it is possible that SHR00M3 disruption may also influence traction forces between the cell and ECM. Traction force dynamics during human somatic reprogramming are currently unknown as are the effects of SHR00M3 kd in this context. To understand the dynamics of cell-matrix adhesive forces during reprogramming, traction force microscopy was performed in hiF-T cells during normal reprogramming and in the context of SHR00M3 disruption.

[0091] After validating that hiF-T cells generate ROCK-dependent traction forces, a time course experiment was designed where hiF-Ts were singularized and collected (following MEF depletion) across days 0, 1, 3, 6, 9, 12, and 15 of reprogramming for analysis by traction force microscopy (TFM; FIG. 4F). Cell -generated traction forces were highly dynamic and occurred in two distinct waves (early and late; FIG. 4G-4H).

[0092] These waves of traction force during reprogramming also coincide with notable changes in adhesome gene expression, where the majority of adhesome genes are rapidly downregulated during early reprogramming, and other genes, including SHROOM3, are transiently upregulated towards the later phases. TFM was performed on SHROOM3 kd reprogramming cells and a significant difference in traction forces generated from uninfected cells was found compared to cells infected with SHROOM3 shRNA at day 12 of reprogramming (during SHROOM3’s peak expression). SHROOM3 kd conditions generated significantly lower traction forces compared to no-virus controls and slightly, but not significantly, lower traction forces compared to LacZ kd .

[0093] SHROOM3 kd rescues repression of iPSC reprogramming by fibronectin: Functional fibronectin signaling can act as a barrier to reprogramming. In addition, the loss of SHROOM3 is associated with decreases in PCP signaling within NCC and SMC subpopulations throughout our reprogramming timeline (FIG. 2G). During development, PCP is necessary during embryogenesis and neural tube closure, and is particularly critical for the regulation of ECM proteins including fibronectin organization, assembly, and stability. Given the potential for PCP signaling to regulate fibronectin signaling, the communication strength of PCP signaling was characterized between all cell types across the four different reprogramming timepoints (FIG.41). Interestingly, communication strength inference via CellChat suggested that PCP signaling undergoes a sharp increase beginning on day 12 of reprogramming, coinciding with peak SHROOM3 expression and the second wave of cell-generated traction forces (FIG. 4H-4I).Notably, day 12 of reprogramming showed incoming and outgoing PCP signaling was highest inNCCs, SMCs, and iPSCs (FIG. 41).

[0094] Reprogramming of hiF-Ts seeded on fibronectin coated wells was performed with either SHROOM3 or LacZ kd. After 12 days of reprogramming, fibronectin significantly reduced reprogramming efficiency of LacZ kd cells, and the suppressive effects were abrogated in cells experiencing SHROOM3 kd (FIG. 4J). These data suggest that SHROOM3 expression is critical for repression of reprogramming through ectopic fibronectin signaling.

[0095] SHROOM3 kd remodels cell fate trajectories towards iPSCs: The CellChat analysis suggested that cells experiencing SHROOM3 disruption undergo dynamic remodeling in adhesion-associated cell communication networks. Given the known effects of paracrine-associated cell communications on reprogramming, SHROOM3 kd impact on cell fate trajectories towards pluripotency was characterized in the reprogramming system with a focus in cell-ECM and physical cell-cell interactions. Using Monocle3, separate pseudotime trajectories were constructed for cells undergoing reprogramming exposed to either LacZ or SHROOM3 kd (FIG. 5A) LacZ pseudotime analysis revealed that cells primarily traverse a path originating from fibroblasts towards iPSCs by way of an intermediate trophoblast subpopulation. Conversely, cells experiencing SHROOM3 kd exhibited a primary trajectory towards iPSCs from fibroblasts that traverse through epithelial and NCC subpopulations. RNA velocity analysis was also performed with consideration of the ratios of immature (unspliced) and mature (spliced) RNA transcripts, which could provide an orthogonal perspective on the impact of SHROOM3 kd on cell fate transitions during reprogramming. Utilizing scVelo, separate RNA velocity analyses were performed on each of our scRNA-seq experimental conditions (FIG. 5B). From the single cell velocities, cell transition vectors were generally observed to flow from fibroblasts to epithelial or trophoblast cells as well as between NCCs and trophoblast cells to iPSCs, which was consistent with our Monocle3 pseudotime trajectories (FIG. 5A). Furthermore, as pseudotime trajectory progressed, expression of fibroblast genes decreased for both conditions (FIG. 5C), while pluripotency and PCP gene expressions (see Methods) increased (FIG. 5D-5E). Of note, the pluripotency gene signature peaked transiently during pseudotime in the LacZ kd condition, however under SHR00M3 kd, the pluripotency gene signature monotonically peaked at the end of pseudotime progression (FIG. 5D). The PCP gene signature was generally weaker in magnitude and less dynamic under SHROOM3 kd compared to LacZ (FIG. 5E).

[0096] Following RNA velocity analysis, partition-based graph abstraction (PAGA) was applied to the velocities generated from scVelo (FIG. 5F). The PAGA trajectories corroborated theexistence of transitions from epithelial cells to NCCs as well as transitions from trophoblast cells to iPSCs; however, while the latter transition was apparent in the LacZ kd condition, the inferred connectedness between trophoblast cells and iPSCs was significantly weaker in the context of SHR00M3 kd. Interestingly, the PAGA analysis also suggested that SHR00M3 kd uniquely supported transitions between iPSCs and NCCs, and the emergence of a new transition to iPSCs from neuroepithelial cells. In the LacZ kd condition, NCCs exhibited transitions towards SMCs, however this transition reversed directionality under SHR00M3 kd condition, suggesting SMCs are potentially less stable under SHR00M3 disruption.

[0097] This analysis also showed that upon SHR00M3 kd, NCCs formed incoming transitions with all neighboring cell types (FIG. 5F). Thus, NCCs might serve as a more stable intermediate attractor state for cells during late reprogramming. Next, a multiscale dynamical systems framework was applied to our single-cell transcriptome data where state transitions between reprogramming subtypes could be more explicitly defined. Here, MuTrans was utilized to distinguish between “stable” versus “transitioning” cell types (see Methods). From the single-cell data, MuTrans generated a mixture distribution fitted dynamical manifold of the reprogramming landscape and applied transition path theory' to provide quantitative discrimination between single cells that reside in stable attractor basins versus those that are in transition. Transition entropy scores for individual cells, where “transitioning cells” have greater entropy scores, in the LacZ and SHR00M3 kd conditions were calculated separately. Entropy scores were bimodally distributed in both conditions. Transitioning cells were defined as having a transition entropy score greater than 50% of the max entropy score observed. In both conditions, transitioning cells were concentrated in the cell populations that bridge fibroblast and iPSC subsets (FIG. 5G).However, upon SHR00M3 kd, a new stable population of cells did emerge within the NCC subset, while the SMC subset showed a notable loss in stability (FIG. 5G-5H). Additionally, the loss of SHR00M3 may attenuate spontaneous cell fate transitions (differentiation) from NCCs into SMCs, thus increasing the fate stability of NCCs, which could serve as a more robust cell reservoir for transitions towards pluripotency (e.g., into iPSCs).

[0098] To further explore possible mechanisms of fate stability, the relationship between changes in gene expression mean and coefficient of variation (CV) (between LacZ and SHR00M3 kd conditions) was also investigated for all genes within a given cell population (FIG. 51), as well as for pluripotency related genes specifically (see Methods). For pluripotency genes (versus all genes), neuroepithelial, NCC, and SMC populations experienced greater changes in noise (asmeasured by CV) relative to changes in mean expression under SHR00M3 disruption compared to the LacZ kd control (FIG. 51). Interestingly, proliferating trophoblast cells were the only subset that showed a weaker change in noise relative to mean expression in pluripotency genes. Together, these data suggest that SHR00M3 helps negotiate developmental traj ectories towards pluripotency through NCC and / or neuroepithelial subpopulations as an alternative to trophoblast subsets, and potentially through SHROOM3-dependent transcriptional noise modulation (FIG.5J).

[0099] Adhesome gene mutations are associated with poor patient outcomes and reduced EMT expression: The tumor microenvironment presents many diverse cell-cell and cell-ECM interactions that are thought to contribute to the activity of stem-cell-like programs. Cancer cell dedifferentiation utilizes numerous signaling pathways that also regulate normal stem cell biological processes, such as proliferation and the repression of differentiation. Adhesome gene expression is able to regulate sternness acquisition of non-cancerous cells, but it is not well understood if and how adhesome gene expression affects properties of sternness in cancer cells, although biophysical cues have recently been shown to trigger sternness acquisition within a breast cancer cell line. As increases in cancer cell sternness may lead to worse patient outcomes, we first used the Genomic Data Commons (GDC) Cohort Comparison tool to understand the effect of adhesome gene mutations on cancer patient outcomes. Pan-cancer patient cases were characterized as either containing a mutation in any of our 103 dynamic adhesome genes or not containing any dynamic adhesome gene mutations. From the Cohort Comparison tool, the cases harboring adhesome gene mutations were associated with significantly lower patient survival rates (FIG. 6A).

[0100] Targeting EMT has recently been suggested as a potential therapy for treating lung cancer and lung tumor-derived cancer stem cells (CSCs). Thus, the role of adhesome mutation was investigated on EMT / MET expression, specifically in lung cancer patient samples. Lung adenocarcinoma (LU AD) patient samples were divided into cohorts of those with or without a mutation in the 103 dynamic adhesome genes. The expression of EMT genes (e.g., VIM and related mesenchymal markers) was lower in the cohort harboring adhesome gene mutations, while the opposite trend was observed in the expression of MET genes (e.g., CDH1 and related epithelial markers) (FIG. 6B). These data suggest that adhesome gene mutations are associated with worse patient outcomes and modulate the expression of genes involved in EMT (decreased) and MET (increased) progression within patient tumors.

[0101] CSRP1 knockdown modulates PCP-EMT signaling and induces expression of genes associated with cancer sternness and metastasis: Given such a strong association between EMT and LU AD patient survival rates, the potential role of adhesome expression in the acquisition of cancer sternness was explored, leveraging lung cancer as a model system. First, the top 15 RNAi gene knockdowns that enhanced reprogramming were evaluated to determine which were also highly expressed in the human lung adenocarcinoma cell line A549. Using expression values from the Human Cell Line Atlas, 5 genes w ere identified that were both highly expressed in A549 cells (TPM >50) and fell within the top 15 of gene knockdowns showing the greatest improvement in reprogramming efficiency (FIG. 6C). SHROOM3 was lowly expressed in A549 cells, while CSRP1 showed the highest expression of all top 15 adhesome genes (FIG. 6C).Furthermore, as with SHROOM3, CSRP1 is expressed in NCCs and is required for neural tube closure and PCP signaling.

[0102] A549s were transduced with lentivirus containing shRNA constructs targeted to CSRP1, LacZ, or SHROOM3 and subsequently cultured in the hiF-T reprogramming media (without inhibitors or dox) for 8 days. Knockdown at the protein level was confirmed for CSRP1 and SHROOM3. To assess the potential for cancer sternness activity', gene expression was measured using qRT-PCR targeting cancer-sternness related genes (AI.DH1A3. POU5F1, CD44, and EPCAM). All genes were upregulated and POU5F1 (P=0.0391), CD44 (P=0.0215), and EPC AM (P=0.0003) were upregulated significantly upon CSRP1 kd compared to the LacZ and SHROOM3 kd conditions (FIG.6D).

[0103] Next, this cancer cell reprogramming protocol was applied to further investigate CSRPl-mediated fate transitions that might contribute to sternness aquation again using single-cell transcriptome analysis. After CSRP1 or LacZ kd followed by 8 days of feeding with our hiF-T reprogramming media, replicate samples from each condition were singularized and prepared for scRNA-seq (FIG. 6E). Different subpopulations arise in response to CSRP1 knockdown compared to LacZ (FIG. 6F-6G). To identify a cancer stem cell (CSC)-like population, the expression of aldehyde dehydrogenase (ALDH) family genes as well as lung cancer sternness genes (forming a CSC gene signature, see Methods) was investigated. Clustering revealed 4 distinct cell subpopulations, and one cluster in particular (cluster 2) contained cells with a high expression of both signatures. Furthermore, cells from cluster 2 had increased expression of CSC marker genes (i.e., ALDH1A3, POU5F1, (4)44. EPCAM, ABCG2, and NOTCH 3) compared to cluster 1 (FIG. 6H). Notably, cluster 2 contains proportionately morecells in the CSRP1 kd condition relative to LacZ kd, suggesting an increase in the number of cells occupying cancer stem cell-like states (FIG. 6F).

[0104] The role of EMT in sternness acquisition of tumor cells is complex. The expression level was analyzed of EMT. CSC. as well as PCP gene signature across the LacZ and CSRP1 kd conditions. CSRP1 and PCP gene signature expressions were both significantly lower in CSRP1 kd relative to LacZ (FIG. 61). Furthermore, genes from our LUAD EMT signature (see Methods) were significantly decreased while those from the CSC signature were significantly increased upon CSRP1 kd (FIG.61).

[0105] Similar to observations from the iPSC reprogramming experiments, CellChat analysis showed cell-to-cell communications through fibronectin, Notch, and EphA were all significantly dissimilar between CSRP1 and LacZ knockdown, although only EphA remained in the top three most differential regulated communication pathways. Interestingly, clusters 1, 2 and 4 interact more strongly with each other in the CSRP1 kd condition compared to the LacZ kd control. Furthermore, under CSRP1 kd cells in cluster 2 increased cell communications with all other clusters present (clusters 1 and 4). The cell type proportions were highly dynamic upon CSRP1 disruption. CSRP1 kd showed near total loss of cluster 3 subsets in conjunction with the emergence of cluster 4 (which uniquely expresses the metastatic marker SI 00 A), which could be indicative of changes in cell fate trajectories and / or transitions between A549 subsets.

[0106] CSRP1 kd dynamically regulates cell fate trajectories in A549 cancer cells: To understand how cell type populations arise throughout sternness acquisition, a pseudotime trajectory' and RNA velocity analyses was performed on the scRNA-seq data for A549 cells undergoing CSRP1 kd versus LacZ kd. In both trajectory inference analyses, CSRP1 kd more directly connected cluster 1 to cluster 2, potentially bypassing an intermediary state (cluster 3) and enabling more efficient sternness acquisition (FIG. 6J-6K). As pseudotime trajectory progresses in A549 cells, gene expression of LUAD EMT signature decreased and the CSC signature increased in the CSRP1 kd condition. Interestingly, MuTrans analysis revealed that a larger proportion of cells from cluster 2 are transitioning in the CSRP1 kd condition compared to the LacZ kd condition, suggesting a new transit! onary state within the C SC-like cluster (FIG.6L 6M)

[0107] CSRP1 kd augments cancer metastasis in xenograft mouse models: To investigate the effect of CSRP1 kd on metastatic potential. A549 cell lines were first transducedusing lentivirus containing an mCherry fluorescent reporter and constitutively active shRNA constructs targeting either the CSRP1 gene (CSRP1 kd) or a control scramble sequence. Disruption of CSRP1 at the protein level in the CSRP1 kd A549 cell line was confirmed by Western blot. Both transduced A549 cell lines were cultured in reprogramming media for 8 days before being subcutaneously injected into immunodeficient NSG™ mice. After 6 weeks, organs were dissected, imaged, dissociated into single cells, and compositionally analyzed using fluorescence-based flow cytometry (FC) to quantify the percentage of metastatic cells (FIG. 7A).Cells isolated from lung and whole blood tissues were sorted using a gating scheme that filtered for cells which were singlets, live, and CD298+(a human-specific marker). Intact lung tissues were analyzed using gross imaging and epifluorescence microscopy to capture mCherry signal (FIG. 7B-7C).

[0108] Animals injected with A549 cells undergoing CSRP1 kd exhibited a significant increase in the percentage of CD298+cells found in the lungs and blood circulation based on FC analysis (FIG. 7D). The observed increase in the abundance of tumor cells found in dissociated lungs was corroborated through non-destructive tissue analysis (from on our gross and fluorescence images), determined by calculating the percentage of mCherry4lung area and by counting the number of mCherry4foci using a custom ImageJ script (FIG. 7E). In addition to lung and blood analyses, primary tumor volume and weight for each condition was measured. At 6 weeks, tumor volume and weight were not statistically different from one another, suggesting that comparable primary tumor sites were established for both conditions. Taken together, the data provide evidence that, in addition to the induction of a CSC-like expression program, CSRP1 kd in A549 cells supports increases in several critical aspects of metastasis in vivo, including increases in the frequency of both circulating tumor cells and in the establishment of distal metastatic tumor sites w ithin the lungs

[0109] hiF-T Somatic Cell Reprogramming: Reprogramming was carried out using an immortalized BJ fibroblast derived cell line known as hiF-Ts. These cells contain a doxycycline inducible OKSM polycistronic gene cassette as well as telomerase expressing vector. These cells were expanded in GM consisting of DMEM F12 with 10% embryonic stem cell grade fetal bovine serum (ES-FBS) with 0.5 pg / mL (-918 nM) puromycin to ensure clonal purity. Prior to reprogramming, puromycin was removed from the GM. On day -1 of reprogramming, the hiF-Ts were seeded at 75,000 cells per cm2in GM and transduced with shRNA expressing lentiviral vectors as needed. On day 0, irradiated mouse embry onic fibroblasts (MEFs) were added at150,000 cells per cm2in GM. On day 1, GM was supplemented with 2 pg / mL (~3.9 pM) dox to initiate reprogramming. On day 2, the 10% ES-FBS in the GM with dox is replaced with 20% knockout serum replacement. Reprogramming was completed on day 24. If reprogramming with LSDli (or LSDli and ROCKi), 1 pM of LSDli (and 1 pM of ROCKi) were added to the GM on day 0. On day 2. the concentration of LSDli in the GM was reduced to 0.1 pM. Reprogramming with inhibitors was completed on days 15-16.

[0110] shRNA Dynamic Adhesome Screening: Adhesome-gene-targeting lentiviral shRNA and DNA constructs were obtained from the Broad Institute (Cambridge, MA) through The RNAi Consortium (TRC). Inducible hiF-T cells w ere transduced using lentivirus containing constitutively active shRNA constructs in a single-well format and subsequently reprogrammed through the addition of dox. Each dynamic adhesome gene was targeted using 3 shRNAs while each non-targeting control (GFP, RFP, lacZ, and Luciferase) was targeted using 4 shRNAs. Viral titer concentrations were standardized prior to experimentation. Reprogramming was conducted with both ROCKi and LSDli or neither. Results were filtered to only include conditions where the viral titer was within 3 standard deviations of the mean. Fold changes (or log fold changes) were calculated for each condition (ROCKi / LSDli or no inhibitors) using the ratio of the mean colony count of a single gene over the mean colony count of all non-targeting controls. The best performing shRNA from each trio was selected for further experimentation as needed.

[0111] Primary Cell Reprogramming: Primary cell reprogramming experiments were conducted using primary Normal Human Dermal Fibroblasts (NHDF) cells. The cells were expanded in Fibroblast Growth Medium (FGM-2, Lonza Bioscience) and seeded at a density7of 50,000 cells per cm2on day -1. On day 0, the cells were transduced with shRNA (SHROOM3: TRCN0000414634 and non-target control: MISSION® SHC016) expressing lentiviral vectors. On day 1, cells were transduced with lentiviral vectors containing OCT4 / KLF4 / c-MYC / SOX2 and fed a commercial reprogramming media (ReproTeSR, STEMCELL Technologies). Reprogramming media was changed daily until reprogramming was completed on days 24-25.

[0112] Cellular Assays: Reprogramming efficiency was assessed by staining and identifying TRA-l-60+colonies. First, cells were fixed with 1% paraformaldehyde for 20 minutes, washed, and subsequently blocked and permeabilized for 30 minutes with a 3% bovine serum albumin and 0.02% Triton-X detergent. Next the samples were incubated with a biotinylated TRA-1-60 antibody at 4°C for 16 hours, washed and then incubated with a horse radish peroxidase conjugated streptavidin for 3 hours at room temperature. Finally, DABPeroxidase substrate kit (Vector Laboratories) was used to darken the TRA-l-60+colonies. The wells were then digitally scanned and positive colonies were counted using ImageJ (vl.52p; see Code Availability). Western blot confirming SHR00M3 kd used anti-human SHR00M3 antibody produced in rabbit (Atlas Antibodies, UNSPSC 12352203) and anti-human GAPDH antibody produced in mouse (BioLegend, 919501).

[0113] For primary cell reprogramming assays, iPSC colonies were identified using NANOG (abeam, abl73368) as a positive marker. Cells were fixed, washed, and blocked similarly to the hiF-Ts. Next, primary antibody incubation was conducted overnight followed by washing, incubation with secondary antibody, DAPI staining, and a final washing. The wells were imaged using a Zeiss Merlin high-resolution microscope and positive colonies were counted using ImageJ (vl.54j).

[0114] Isoform Visualization and Targeting: Transcript information, including chromosome location and annotations, was gathered from the Ensembl genome browser. Subsequent visualization of primary SHROOM3 protein-coding isoform exon information and annotation with shRNAs was performed using Gviz (v 1.52.0) and biomaRt (v2.64.0) in R (V4.3.2).

[0115] scRNA-seq of Reprogramming hiF-Ts: Reprogramming hiF-Ts was conducted as previously described in replicate with SHROOM3 or control LacZ shRNA knockdown. On days 6, 9. 12, and 15 of reprogramming, samples were singularized and MEF depleted. Singularized and MEF depleted samples were processed at the UCI Genomics Research and Technology' Hub (GRT Hub) for RNA extraction, quality control, library' preparation, and sequencing. Libraries were prepared using the Chromium Single Cell 3' v3.1 according to the manufacturer’s protocol (lOx Genomics), targeting 10,000 cells per sample. Sequencing was performed on S4 lanes in a NovaSeq 6000 (300 cycles, NovoGene

[0116] scRNA-seq Quality Control and Dataset Integration: The generated FastQ sequencing read files from scRNA-seq were analyzed with the CellRanger software pipeline (v5.0.0) (lOx Genomics) to align reads to the hg38 reference transcriptome (refdata-cellranger-GRCh38-3.0.0), filter out low' quality' reads, and identify unique cells. After preprocessing the read data with CellRanger, Seurat (v4.1.1) w as used to filter out apoptotic and doublet cells using previously established cutolf values of between 200 and 2500 unique feature counts and a mitochondrial ratio of less than 15%. After filtering, 36,767 (or 17,050) suitablesingle cells were identified in the iPSC reprogramming (or A549) dataset.

[0117] For iPSC reprogramming dataset, integration analysis was performed using the Harmony package (vO.l.O). To identify cell clusters, principal component analysis (PCA) was first performed on the list of highly variable genes. A set of high variable genes was then identified by binning the average expression of all genes into evenly sized groups and computing the median dispersion (variance divided by the mean) in each bin. 2000 highly variable genes were selected using FindVariableFeatures function. 40 significant principal components (PCs) were used in RunUMAP and FindNeighbors. Clustering was performed with the Louvain modularity-based community detection algorithm and a resolution of 0.44. We used Seurat’s implementation of the Wilcoxon Rank Sum test to perform differential gene expression analysis between the clusters, which returned p-value and fold-change differences.

[0118] For the human chemically induced reprogramming dataset, quality control was performed with no integration steps in R (v.4.3.2). Additionally, clusters, trajectories, and dimensional reduction coordinates were transferred from the original dataset. Module scores were added to the Seurat (v.5.1.0) object using AddModuleScore and the features from the CellChat (v2.1.0) database for each signaling pathway. CellChat analyses were performed by merging CellChat objects from each day and using the netAnalysis_signalingRole_heatmap function to visualize signaling roles across the reprogramming timeline. SHROOM3 expression was added using the Seurat pseudobulking function AggregateExpression and scaling expression across identities from 0 to 1 to match CellChat’s relative expression parameter.

[0119] For A549 datasets, integration analysis was performed using the scMC package (vl.0.0). 2000 highly variable genes were selected using identifylntegrationFeatures function. 40 significant PCs were used in RunUMAP and FindNeighbors from Seurat package. Clustering was performed with the Leiden algonthm and a resolution of 0.2.

[0120] scRNA-seq Cell Type Annotation: Positive differentially expressed genes unique to each of the clusters were identified using Seurat's FindAllMarkers function with a log fold change threshold of 0.25, adjusted P-value (Bonferroni correction) <0.01, and expression in more than 25% of cells in either cluster being compared. Subpopulations were annotated based on comparing the differentially expressed genes per cluster to canonical marker gene signatures and the Human Cell Atlas database.

[0121] scRNA-seq Off-target Analysis: Potential off-target genes were collected by using the N1H BLAST (Basic Local Alignment Search Tool) and PSI-BLAST (Position-Specific Iterative BLAST) to find sequence similarities to the SHR00M3 shRNA (TRCN0000414634) target sequence. Potential genes were sorted by BLAST e-value and kept if mean expression > 0.1 in either the SHR00M3 or LacZ kd condition then examined in the context of the scRNA-seq experiment cell types and conditions.

[0122] scRNA-seq Trajectory Inference and Pseudotime Analyses: Several approaches to trajectory inference were utilized: Monocle 3, RNA velocity, and MuTrans. First, the Monocle 3 package (vl.2.9) was used to perform pseudotime and trajectory analyses. UMAP embeddings and clusters from Seurat for each condition of each dataset were converted into cell_data_set objects using SeuratWrappers (v0.3.0) and used as input to construct trajectory7and pseudotime graphs via the leam_graph function with default settings.

[0123] RNA velocity analysis was performed using the scVelo package (vO.2.4). To prepare the data for scVelo, spliced / unspliced counts matrices were created with inputs of Homo_sapiens.GRCh38.102.gtf and Homo sapiens. GRCh38.dna_rm. primary _assembly.fa.gz into the velocyto package (vO.17.17) runlOx command. The spliced / unspliced count matrices and Seurat object datasets (i.e., single cell barcodes, UMAP embedding coordinates, and clusters) yvere imported into Python yvith the anndata library- (v0.8.0). Briefly, scVelo normalized, preprocessed, and computed velocities the imported datasets with the pp.filter_and_normalize, pp.moments, and tl. velocity / tl. velocity _graph functions yvith default settings. PAGA analysis on cell clusters per dataset was performed yvith the tl.paga function yvith default settings.

[0124] MuTrans was applied to identify transition cells and quantify the likelihood of all possible transition trajectories between cell states. To prepare the inputs for MuTrans, Seurat objects of each dataset were converted into anndata using provided functions in Seurat (v4.1.1). MuTrans yvas then used to cluster cells into different attractor groups and predict their stability using the entropy score with user-select attractor numbers and other default settings. Different numbers of attractors were tested, and the results showed consistency (the number of attractors should be equal to or less than the number of cell population groups). Then transition likelihood between select two groups was quantified using MuTrans’ pm.infer lineage function yvith default settings. The transition cells yvere determined using the entropy score and user-defined cutoff threshold.

[0125] Development of Gene Signatures: Gene signatures were curated from existing literature and databases. LUAD EMT (and LUAD MET) genes were identified from EMTome.org by searching for genes with significant association with VIM (and CDH1) with a Spearman coefficient greater than 0.65 (and 0.50).

[0126] Traction Force Microscopy: Polyacrylamide (PA) gel substrates were prepared with a modified procedure. Briefly, glass bottom dishes were functionalized with 0.1 M NaOH and (3-aminopropyl) triethoxysilane followed by glutaraldehyde treatment33. Top glass coverslips were functionalized with Poly-D-Lysine (0.1 mg / mL) and a 1:800 dilution of red fluorescent micro-spheres (0.5 pm carboxylate-modified, Thermo Fisher Scientific) in w ater. A solution of 10% acrylamide and 0.1% bis-acrylamide was prepared. Polymerization was initiated with the addition of 1:100 tetramethylethylenediamine (TEMED, Bio-Rad) and 1:10 of a 10% ammonium per-sulfate (APS, Bio-Rad) solution. 20 pL was promptly pipetted onto the functionalized glass bottom dish and the functionalized top glass coverslip was placed on top. The dish was then turned upside down to minimize gravity effects that could cause fluorescent microspheres to polymerize lower into the substrate. After polymerization, fibronectin (20 pg / rnL) was conjugated to the surface of gels with sulfo-SANPAH (Thermo Fisher Scientific) according to previous protocols.

[0127] Reprogramming and cell singularization were conducted as described for transcription profiling. The prepared PA gels were rinsed before cells were seeded at 3,000 cells / cm2Traction force microscopy imaging was performed as previously described14. To quantify traction forces ImageJ (vl.52p) was used to register the unaligned images. Next, particle image velocimetry and Fourier transform traction cytometry were performed. A custom code was written in Python and IJ1 macro language to batch process the single cell traction forces. A custom code was written in R to perform statistical analysis on those results. Images were acquired using an Olympus 1X83 microscope.

[0128] iPSC Reprogramming on ECMs: Polystyrene 24-well plates were prepared by washing wells with 70% ethanol in water for 5 minutes followed by air dry ing. Porcine gelatin (Sigma- Adri ch, G1890) was reconstituted in distilled water at 25 pg / mL. Fibronectin (Coming, 354008) was reconstituted in distilled water at 40 pg / mL. Type I collagen (Advanced Biomatrix, 5005) was diluted to 100 pg / mL in distilled water. Matrigel (Coming, 354277) was diluted to 100 pg / mL in DMEM / F-12 media. Geltrex (Thermo Fisher Scientific, A1413302) was diluted to 300 pg / mL in DMEM / F-12 media. Wells were then coated with ECMs (fibronectin, type I collagen,gelatin. Matrigel® and Geltrex™) and incubated at room temperature for 4 hours. Excess ECM was aspirated off and wells were washed with deionized water. Reprogramming of hiF-Ts was then performed.

[0129] Robust Linear Regression: Robust linear regression using MM-estimation was performed with the robustbase package in R (v3.4.1).

[0130] qRT-PCR: A549 cells were collected using the Direct-zol RNA Miniprep Kit (Zymo) for total RNA extraction. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analyses were carried out on reverse-transcribed cDNAs with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Thermo Fisher Scientific) and analyzed with CFX™ Manager software (v2.1). Expression levels are always normalized to GAPDH.

[0131] Focal Adhesion Analysis: Glass bottom dishes (MatTek) were coated with various ECMs prior to seeding of hiF-Ts. After 24 hours of adherence, cells were fixed with 1% paraformaldehyde for 20 minutes, washed, and subsequently blocked and permeabilized for 30 minutes with a 3% bovine serum albumin and 0.02% Triton-X detergent. Staining for vinculin, actin, and nuclei were performed using the Actin Cytoskeleton / Focal Adhesion Staining Kit (Millipore Sigma). Stained cells were imaged with Zeiss LSM700 confocal microscope. Focal adhesion shape characterization was performed with Analyze Particles macro and custom script in IJ 1 macro language.

[0132] Coefficient of Variation (CV): Gene expression was first separated by cell population. Next, for each cell population, the CV was calculated for each gene using the (5following formula: CV —9<me, where ogenerepresents the standard deviation of individual(JCV.Cgene expression and pgenerepresents the mean expression of individual gene expression.

[0133] Animals: A549 cells were transduced using lentivirus containing constitutively active shRNA constructs targeting the CSRP1 gene or a scramble sequence (control). Each plasmid consisted of a pLV backbone with puromycin resistance and the constitutive EFla promoter which drove expression of mCherry and either CSRP1 or control scramble shRNA as part of an miR30-based expression cassette purchased from VectorBuilder; vector IDs: VB220216-1502bfa and VB220217-1436ysz, respectively). Once transduced, cells were sorted using fluorescence activated cell sorting (FACS) to obtain a pure population of mCherry positive cells. Knockdown of CSRP1 was confirmed via Western blot using anti-Human / Mouse / RatCSRP1 antibody, R&D Systems (cat. no. AF5739SP). Cells were then fed with hiF-T reprogramming media for 8 days or standard cell growth medium (DMEM, high glucose, pyruvate medium from Thermo Fisher, cat. no. 11995073, 5% FBS, 1% Glutamax lOOx, 1% MEM Non-Essential Amino Acids lOOx, 1% Penicillin Streptomycin, and 0.5% 2-Mercapotoethanol). Following trypsinization, 106cells were resuspended in 50pl of IxPBS and 50pl of Matrigel (Fisher Scientific, cat. no. CB40230) and subcutaneously injected into 4-6-week-old NSG™ mice (The Jackson Laboratory). Institutional Animal Care and Use Committee of the University' of California, Irvine reviewed and approved all animal experiments.

[0134] Tissue Collections: After 6 weeks, mice were euthanized by asphyxiation with CO2followed by' cervical dislocation and tissue samples were collected as follows.

[0135] Peripheral Blood: Blood was collected using a 20G needle and syringe from the chest cavity after the right atrium and left ventricle were punctured. Mice were perfused with 15 mL of 10 mM EDTA in IxPBS, and blood was collected. Blood samples were centrifuged at 500g for 5 min at 4°C. Cells were resuspended in 5 mL of IxRBC lysis buffer (Miltenyi Biotec, cat. no. 130-094-183) and incubated at RT for 5 min. Cells were quenched in 5 mL IxPBS and centrifuged at 500g for 5 min at 4°C. Samples were then resuspended in growth media and processed for flow cytometry.

[0136] Lung: Lungs were harvested from mice and lung metastasis was visualized on a dissection microscope (Leica Biosystems, DMC 2900) where images of the gross tumor and mCherry fluorescence signal were captured. Lungs were then mechanically dissociated using a razor blade. The dissociated tissue was then placed in cell growth media and 0.1 mg / mL collagenase ty pe IV (Sigma- Aldrich, cat. no. C5138) and were digested at 37°C on a shaker for 45 min. Samples were centrifuged at 300g for 5 min at 4°C. Cells were resuspended in IxPBS and centrifuged a second time at 300g for 5 min at 4°C. Cells were resuspended in 2 pg mL1DNase I (Worthington Biochemical, cat. no. LS002139) for five min at RT. Samples were then quenched with 10 mL of IxPBS and filtered through a 70 pM cell strainer. Samples were centrifuged at 300g for 5 min at 4°C and resuspended in growth media and processed for flow cytometry.

[0137] Primary Tumors: Primary' tumors were harvested from mice and measured using 1 2a caliper. Volumes were calculated as: — Length * Width)

[0138] Flow Cytometry: Tissue samples were harvested from mice and mechanically dissociated to generate single cell suspensions as described above. Cells were incubated for 30 min at 4°C with human-specific antibody APC-CD298 (diluted 1:100; Thermofisher, cat no. PA5-119425). Flow cytometry was performed using BD Fortessa X20. Cell viability was determined by negative staining with SYTOX blue (diluted 1:1,000; Thermo Fisher Scientific, cat. no. S34857). The forward-scatter area by forward-scatter width (FSC-H x FSC-A) and side-scatter area by side-scatter width (SSC-H x SSC-A) was used to discriminate single cells from doublet and multiplet cells. Human metastatic cells were selected by gating on Sytox7CD298+cells.

[0139] Mouse Lung Image Analysis: ImageJ (vl.54j) was used to quantify mCherry+area percentage and foci count in mouse lungs (see Code Availability). Gross images of the lungs were converted to 8 bits and thresholded to a value of 0 to 25 to obtain an outline of the lung and saved as a region of interest to be used for downstream analysis. mCherry fluorescent images of the lungs were then converted to 8 bits and the Brightness / Contrast was thresholded to a value of 0 to 55. Background pixel value was obtained via the mode function and was then subtracted to leave only high intensify pixel values which are indicative of metastatic nodules in the lung. Images were then saved as tif fdes and normalized using Quantile Based Normalization Plugin for ImageJ. Once normalized, images were thresholded to a value of 38 to 255. The watershed function was run and the Region of Interest for respective lung images were overlaid on top. Lastly, the Analyze Particles function was run with the following parameters: size (130 to infinity) and circularity (0.3 to 1.00). Percent area and count were recorded for each lung image within their respective Region of Interest.

[0140] Statistics: The number of biological and technical replicates are indicated in the figure legends, Main text, and Methods section. The Student's t-test, Wilcoxon test, log-rank test, and ANOVA analyses were performed as indicated in the captions of the figures and supplemental figures, with GraphPad Prism software (v8.4.0) or R software (v3.4.1 and 4.3.2).

[0141] EXAMPLE 2

[0142] The following is a non-limiting example of the present invention. It is to be understood that said example is not intended to limit the present invention in any way. Equivalents or substitutes are within the scope of the present invention

[0143] Preliminary data suggests that SHROOM3 knockdown may work to reducemutagenesis during reprogramming by activating key DNA repair pathways and promoting the expression of protective proteins and programs. The activation of these programs and pathways may be the result of cell fate rewiring (rather than gene- or pathway-specific activation) which enables a robust, biologically tractable genomic stabi 1 i ty method.

[0144] To assess the accumulation of point mutations and short insertions and deletions over the course of reprogramming, the number of inferred genomic variants was compared between Day 6 fibroblasts (baseline) and Day 15 iPSCs using two complementary single-cell mutation calling pipelines: (1) scAllele, which detects single-nucleotide polymorphisms (SNPs) and base substitutions in expressed coding regions using de Bruijn graph reconstruction of sequenced short-read RNA, and (2) SComatic, which detects somatic variants through base-level count statistics and filtering. In both pipelines, raw sequencing files were processed independently for each condition. Control knockdown cells acquired 5,390 (scAllele) and 6,995 (SComatic) new variants during reprogramming, whereas SHROOM3 knockdown cells acquired only 898 and 1,251 new variants, respectively (FIG. 10A). This >5-fold reduction in variant burden suggests that SHROOM3 knockdown may significantly protect cells from reprogramming-induced mutagenesis .

[0145] To further investigate this potential protective effect, an orthogonal, qPCR-based method was employed to assess CNVs in the iPSCs generated in each condition at the eight most common sites for karyotypic abnormalities in PSCs. The results revealed copy number gains in the control knockdown condition, with 6 of 8 loci showing significant deviations from diploid levels (average copy number: 2.84). In contrast, the SHROOM3 knockdown condition exhibited minimal CNVs with only 4 of 8 loci altered and an average copy number of 1.93, closely matching the diploid control DNA (FIG. 10B). Importantly, the autosomal CNV distribution in SHROOM3 knockdown iPSCs was not statistically different from the control DNA, suggesting near-native chromosomal integrity at these sites.

[0146] The analysis first examined whether SHROOM3 knockdown cells exhibit an altered oxidative stress response that could account for their maintained genome integrity. Reactive oxygen species (ROS) levels are known to transiently rise early during reprogramming, and oxidative DNA lesions — particularly 8-oxoguanine and 2-hydroxyadenine — are major contributors to point mutations, especially transversions. Notably, antioxidant treatment has been shown to mitigate these effects and reduce reprogramming-induced mutagenesis.

[0147] Preliminary data indicate that SHR00M3 knockdown reprogramming is associated with a marked upregulation of antioxidant defense genes, particularly those from the glutathione peroxidase (GPX) and glutathione S-transferase (GST) families (FIG. IOC). These proteins are central to ROS detoxification and redox homeostasis, and their elevation suggests a biological, rather than pharmacologic, strategy’ for mitigating oxidative DNA damage.

[0148] To investigate other potential genome-protecting mechanisms, the expression of genes involved in key DNA repair pathways were analyzed across each reprogramming trajectory. Initially the focus was on the base excision repair (BER) pathway, which repairs single-stranded DNA breaks often caused by oxidative stress. Notably, cells undergoing successful reprogramming in the SHROOM3 knockdown condition exhibited consistently higher expression of core BER genes compared to those in the control condition (FIG. 10D). Similarly, genes in the mismatch repair (MMR) pathway — critical for correcting replication errors and suppressing mutagenesis under conditions of replicative stress in reprogramming and PSCs — were more highly expressed in the SHROOM3 knockdown trajectory (FIG. 3E). These preliminary findings suggest that cells under SHROOM3 knockdown conditions may not only experience fewer DNA lesions, but could also possess elevated capacity to repair them, possibly- explaining the reduced mutational burden described previously. This elevated BER and MMR pathway activity may synergize with enhanced oxidative stress response to provide multi-tiered genome protection.

[0149] EMBODIMENTS

[0150] The following embodiments are intended to be illustrative only and not to be limiting in any w ay.

[0151] Embodiment 1: A composition for generating induced pluripotent stem cells (iPSCs) from a biological sample, the composition comprising: a) one or more reprogramming factors; and b) a factor configured to modulate an adhesome component.

[0152] Embodiment 2: The composition of embodiment 1, wherein the reprogramming factor is one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc. Embodiment 3: The composition of embodiment 1, wherein the reprogramming factor is one or more small molecule compounds configured to reprogram a cell.

[0153] Embodiment 4: The method of any one of embodiments 1-3, w herein the factoris configured to modulate gene expression of the adhesome component. Embodiment 5: The composition of embodiment 4, wherein the factor configured to modulate gene expression is an RNA-based inhibitor}' agent. Embodiment 6: The composition of embodiment 5, wherein the RNA-based inhibitory agent selected from the group consisting of a short hairpin RNA (shRNA), an antisense oligonucleotide (ASO). small interfering RNA (siRNA). Embodiment 7: The composition of embodiment 6, wherein the RNA-based inhibitory agent knocks down the expression of the adhesome component. Embodiment 8: The composition of embodiment 6 or embodiment 7, wherein the RNA-based inhibitory' agent modulated the gene expression of SHR00M3. Embodiment 9: The composition of embodiment 8, wherein the RNA-based inhibitory agent knocks down the expression of SHR00M3.

[0154] Embodiment 10: The method of any one of embodiments 1-3, wherein the factor is configured to modulate the function of the adhesome component. Embodiment 11: The composition of embodiment 10, wherein the factor configured to modulate the function of the adhesome component is a small molecule. Embodiment 12: The composition of embodiment 11, wherein the small molecule disrupts the function of the adhesome component. Embodiment 13:The composition of embodiment 12, wherein the small molecule disrupts the function of SHROOM3.

[0155] Embodiment 14: The method of any one of embodiments 1-3, wherein the factor is configured to modulate the function of the adhesome component. Embodiment 15: The composition of embodiment 14, wherein the factor configured to modulate the function of the adhesome component comprises a dominant negative mutant of the adhesome component or a functional portion thereof. Embodiment 16: The composition of embodiment 15, wherein the factor disrupts the function of the adhesome component.

[0156] Embodiment 17: The composition of embodiment 16, wherein the factor disrupts the function of SHROOM3. Embodiment 18: The composition of any' one of embodiments 1-17, wherein the composition reduces mutagenesis in the generated iPSCs. Embodiment 19:The composition of any one of embodiments 1-17, wherein administration of the composition during cellular reprogramming reduces the frequency of DNA mutations in generated iPSCs.

[0157] Embodiment 20: A method of generating iPSCs from a biological sample, the method comprising: a) obtaining or having obtained a biological sample and b) contacting the biological sample with the composition according to any one of embodiments 1-19.Embodiment 21: A method for reducing mutagenesis in induced pluripotent stem cells (iPSCs), the method comprises: a) obtaining or having obtained a biological sample and b) contacting the biological sample with the composition according to any one of embodiments 1-19; wherein contact of the composition with the somatic cell reduces mutagenesis during reprogramming.Embodiment 22: The method of embodiment 20 or embodiment 21, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample simultaneously. Embodiment 23: The method of embodiment 20 or embodiment 21, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample in a stepwise manner.

[0158] As used herein, the term ‘’about” refers to plus or minus 10% of the referenced number.

[0159] Although there has been shown and described the preferred embodiment of the present invention, it will be readily apparent to those skilled in the art that modifications may be made thereto which do not exceed the scope of the appended claims. Therefore, the scope of the invention is only to be limited by the following claims. In some embodiments, the figures presented in this patent application are drawn to scale, including the angles, ratios of dimensions, etc. In some embodiments, the figures are representative only and the claims are not limited by the dimensions of the figures. In some embodiments, descriptions of the inventions described herein using the phrase “comprising” includes embodiments that could be described as “consisting essentially of’ or “consisting of’, and as such the written description requirement for claiming one or more embodiments of the present invention using the phrase “consisting essentially of’ or “consisting of’ is met.

Claims

WHAT IS CLAIMED IS:

1. A composition for generating induced pluripotent stem cells (iPSCs) from a biological sample, the composition comprising:a) one or more reprogramming factors; andb) a factor configured to modulate an adhesome component.

2. The composition of claim 1, wherein the reprogramming factor is one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc.

3. The composition of claim 1, wherein the reprogramming factor is one or more small molecule compounds configured to reprogram a cell.

4. The method of any one of claims 1-3, wherein the factor is configured to modulate gene expression of the adhesome component.

5. The composition of claim 4, wherein the factor configured to modulate gene expression is an RNA-based inhibitory agent.

6. The composition of claim 5, wherein the RNA-based inhibitory agent selected from the group consisting of a short hairpin RNA (shRNA), an antisense oligonucleotide (ASO), small interfering RNA (siRNA).

7. The composition of claim 6, wherein the RNA-based inhibitory agent knocks down the expression of the adhesome component.

8. The composition of claim 6 or claim 7, wherein the RNA-based inhibitory agent modulated the gene expression of SHR00M3.

9. The composition of claim 8, wherein the RNA-based inhibitory agent knocks down the expression of SHROOM3.

10. The method of any one of claims 1-3, wherein the factor is configured to modulate the function of the adhesome component.

11. The composition of claim 10, wherein the factor configured to modulate the function of the adhesome component is a small molecule.

12. The composition of claim 11, wherein the small molecule disrupts the function of the adhesome component.

13. The composition of claim 12, wherein the small molecule disrupts the function of SHROOM3.

14. The method of any one of claims 1-3, wherein the factor is configured to modulate the function of the adhesome component.

15. The composition of claim 14, wherein the factor configured to modulate the function of the adhesome component comprises a dominant negative mutant of the adhesome component or a functional portion thereof.

16. The composition of claim 15, wherein the factor disrupts the function of the adhesome component.

17. The composition of claim 16, wherein the factor disrupts the function of SHR00M3.

18. The composition of any one of claims 1-17, wherein the composition reduces mutagenesis in the generated iPSCs.

19. The composition of any one of claims 1-17, wherein administration of the composition during cellular reprogramming reduces the frequency of DNA mutations in generated iPSCs.

20. A method of generating iPSCs from a biological sample, the method comprising:a) obtaining or having obtained a biological sample andb) contacting the biological sample with the composition according to any one of claims 1-19.

21. The method of claim 20, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample simultaneously.

22. The method of claim 20, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample in a stepwise manner.

23. A method of generating induced pluripotent stem cells (iPSCs) from a biological sample, the method comprising:a) obtaining or having obtained a biological sample;b) contacting the biological sample with an RNA-based inhibitory agent, wherein the RNA-based inhibitory agent is configured to modulate gene expression of an adhesome component;c) contacting the biological sample with one or more reprogramming factors; d) reprogramming the biological sample to generate iPSCs; ande) isolating and expanding the iPSCs.

24. The method of claim 23. wherein the RNA-based inhibitory agent selected from the group consisting of a short hairpin RNA (shRNA), an antisense oligonucleotide (ASO), small interfering RNA (siRNA).

25. A method of generating induced pluripotent stem cells (iPSCs) from a biological sample, the method comprising:a) obtaining or having obtained a biological sample;b) contacting the biological sample with a small molecule, wherein the molecule is configured to module the function of an adhesome component to generate iPSCs; c) contacting the biological sample with one or more reprogramming factors; d) reprogramming cells in the biological sample; ande) isolating and expanding the iPSCs.

26. The method of any one of claims 23-25, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample simultaneously.

27. The method of any one of claims 23-25, wherein the one or more reprogramming factors and the factor configured to modulate the adhesome component are added to the biological sample in a stepwise manner.

28. The method of any one of claims 23-27. wherein the reprogramming factor is one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc.

29. The method of any one of claims 23-27, wherein the reprogramming factor is one or more small molecule compounds configured to reprogram a cell.

30. The method of any one of claims 23-29, wherein the adhesome component is SHR00M3.

31. A method for reducing mutagenesis in induced pluripotent stem cells (iPSCs), the method comprises:a) obtaining or having obtained a biological sample andb) contacting the biological sample with the composition according to any one of claims 1-19;wherein contact of the composition with the somatic cell reduces mutagenesis during reprogramming.

32. A method for reducing mutagenesis in induced pluripotent stem cells (iPSCs), the method comprises:a) obtaining or having obtained a biological sample;b) contacting the biological sample composition comprising:i. one or more reprogramming factors; andii. a factor configured to modulate an adhesome component;wherein contact of the composition with the somatic cell reduces mutagenesis during reprogramming.

33. The method of claim 32, wherein the reprogramming factor is one or a combination of Oct4 (Pou5fl), Sox2, Klf4, and cMyc.

34. The method of claim 32, wherein the reprogramming factor is one or more small molecule compounds configured to reprogram a cell.

35. The method of any one of claims 32-34, wherein the factor is configured to modulate gene expression of the adhesome component.

36. The method of claim 35, wherein the factor configured to modulate gene expression is an RNA-based inhibitory agent.

37. The method of claim 36, wherein the RNA-based inhibitory agent selected from the group consisting of a short hairpin RNA (shRNA), an antisense oligonucleotide (ASO), small interfering RNA (siRNA).

38. The method of claim 36, wherein the RNA-based inhibitory agent knocks down the expression of the adhesome component.

39. The method of claim 36 or claim 37, wherein the RNA-based inhibitory agent modulated the gene expression of SHR00M3.

40. The method of claim 39, wherein the RNA-based inhibitory agent knocks down the expression of SHR00M3.

41. The method of any one of claims 32-34, wherein the factor is configured to modulate the function of the adhesome component.

42. The method of claim 41, wherein the factor configured to modulate the function of the adhesome component is a small molecule.

43. The method of claim 42, wherein the small molecule disrupts the function of the adhesome component.

44. The method of claim 43, wherein the small molecule disrupts the function of SHR00M3.

45. The method of any one of claims 32-34, wherein the factor is configured to modulate the function of the adhesome component.

46. The method of claim 45. wherein the factor configured to modulate the function of the adhesome component comprises a dominant negative mutant of the adhesome component or a functional portion thereof.

47. The method of claim 46, wherein the factor disrupts the function of the adhesome component.

48. The method of claim 47, wherein the factor disrupts the function of SHR00M3.

49. A method of treating cancer in a subject in need thereof, the method comprising administering to the subject a composition configured to overexpress an adhesome component.

50. The method of claim 49, wherein the cancer is lung cancer.

51. The method of claim 49 or claim 50, wherein the adhesome component CSRP1.

52. The method of any one of claims 49-51, wherein the composition comprises a small molecule.

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