TREG molecular fingerprints

Abstract

Provided herein are Treg fingerprints that can be used to differentiate between Treg and Teff cells regardless of their expansion state, and to assess the expansion of Treg after the manufacturing process.

Description

Attorney Docket No.: 237752002040TREG MOLECULAR FINGERPRINTS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and benefit of U. S. Provisional Application No.63 / 780,120, filed March 28, 2025, and U. S. Provisional Application No. 63 / 878,717, filed September 9, 2025, the contents of each of which are incorporated herein by reference in their entireties.FIELD

[0002] The present disclosure relates to Treg fingerprints that can be used to identify Treg cells and differentiate between Treg cells and Teff cells regardless of their expansion state, and to Treg fingerprints that can be used to assess the expansion of Treg cells or Teff cells.BACKGROUND

[0003] The use of regulatory T cell (Treg) therapies to restore immune homeostasis and self-tolerance is a promising new modality for the treatment of autoimmune disorders. Early clinical trials in conditions such as type 1 diabetes (T1D) (1), multiple sclerosis (MS) (2), and inflammatory bowel disease (IBD) (3, 4) have demonstrated that these therapies are safe and well-tolerated, and have shown promise in controlling autoimmune inflammation. Most Treg cell therapies developed thus far are autologous and require drug products to be manufactured for each individual patient. To ensure the highest quality, the identity, strength (potency), quality, and purity of each drug product must be assessed, posing challenges for autologous cell therapies where these parameters require consistency across individual lots for each patient treated. Current release assays are focused on assessment of the phenotype and function of the final drug product and ensuring that these metrics align with endogenous Treg, however these methods alone may not reliably identify and validate the functional integrity of expanded Treg. Accurately assessing the function of Treg cell therapies also poses additional challenges in that Treg can suppress immune responses through several mechanisms (immunosuppressive cytokines, metabolic disruption, inhibitory coreceptors) that may not all be operational in all tissues or disease settings. Current potency assays cannot measure multiple mechanisms simultaneously, requiring individual assays to be developed and validated for use to capture the totality of the suppressive potential of Treg cell therapies. In addition to these methods, a molecular profiling approach might identify specific gene or protein expression profiles that capture multiple aspects of an optimal Treg cell therapy drug1MF-367695757.6Attorney Docket No.: 237752002040product, allowing for an additional measure that can help to ensure these treatments meet therapeutic standards.

[0004] RNA sequencing (RNAseq) allows for the measurement of the average gene expression across a population of cells (bulk RNAseq) or at the individual cell level (single cell [sc]RNAseq). These technologies, along with computational methods such as machine learning algorithms and pathway-based tools like Gene Set Enrichment Analysis (GSEA), (7) single sample GSEA (ssGSEA) (8), and gene set variation analysis (GSVA) (9), have enabled the identification of gene expression signatures (or “fingerprints”) that can help to define immune cell subsets. These analyses have become an essential tool in drug development and in the treatment of diseases, and have been used in applications ranging from drug discovery to driving new insights into disease mechanisms and enabling precision medicine approaches to tailor treatments specifically to individual patients. In cellular therapies, gene expression analysis has been employed to identify gene signatures linked to successful and poor treatment outcomes, provide insights into treatment resistance mechanisms, and to examine interactions between CAR T cells and tumor microenvironments, demonstrating practical utility of this approach and further highlighting the need to develop fingerprints for products currently in development.

[0005] Although well-established Treg markers such as FOXP3 and CD25 (IL2RA) have been identified, they are insufficient to reliably identify Treg as single markers due to overlapping, albeit transient, up-regulation in activated Teff. Demethylation of the Treg-specific demethylated region (TSDR), which is linked to stable FOXP3 expression (16), can also be used to identify Treg, however this method may not capture the functional status of these cells (17). Advances in transcriptomics now offer a high-resolution view of gene expression, and several Treg identity gene fingerprints have been published in the literature (18-20). These studies identified genes critical to Treg identity, including FOXP3, IL2RA, CTLA4, and TNFRSF18 (GITR), and other genes such as ENTPD1 (CD39), TGFB1, and LRRC32 (GARP), which have been identified as markers of Treg suppressive function. Of these 3 studies, however, only 1 (Pesenacker, et al.) considered the activation status of Treg in the analysis, generating an activation-independent Treg identity fingerprint. In the context of Treg cell therapies that typically undergo multiple rounds of activation and expansion prior to infusion into patients, and because of the overlap in gene expression between Treg and activated Teff cells, an activation-independent Treg identity fingerprint would be required to remove genes upregulated upon activation from the gene signature that defines cell identity.2MF-367695757.6Attorney Docket No.: 237752002040

[0006] Like most other immune cells, Tregs demonstrate some level of plasticity in their phenotype under certain conditions. It has also been shown that Treg can be divided into T helper (Th)-like subsets that exhibit unique combinations of transcription factors expressed by effector Th subsets, and can phenotypically resemble these cells (including cytokine secretion) while retaining suppressive function; however, in many autoimmune diseases, these Th-like Treg may lose suppressive function and may ultimately contribute to autoimmune inflammation. Because of this overlap in gene expression with Teff cells and the potential of Treg to acquire Teff-like function, Treg molecular fingerprints that have been derived from Treg gene expression alone may not have the resolution to identify cells that have gained effector function. To address this, additional studies are needed to identify genes that are uniquely expressed in expanded Treg and Teff and to incorporate this information into a molecular fingerprint that provides a more holistic view of the final drug product.SUMMARY

[0007] Treg fingerprints were developed as described herein that can be used to assess a final drug product. The Treg fingerprints described herein include a Treg identity fingerprint that can be used to identify or classify Treg cells and differentiate between Treg and Teff cells regardless of their expansion state, and a Treg expansion fingerprint that can be used to assess the expansion of Treg after the manufacturing process. The identity fingerprint as described herein comprises predefined gene sets comprising Treg markers and Teff markers. The expansion fingerprint as described herein comprises predefined gene sets comprising post-expansion markers and pre-expansion markers. Both fingerprints were validated using published and internal data and were applied to both nonclinical and clinical datasets to demonstrate practical applications of the fingerprints. The present disclosure supports the development and use of Treg fingerprints based upon expression level of the markers described herein as part of the drug development process, not only for use in quality control (QC) of the final drug product, but also to inform the design of clinical trials.

[0008] In one aspect, provided herein is a method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level 3MF-367695757.6Attorney Docket No.: 237752002040of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs.

[0009] In another aspect, provided herein is a method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs.

[0010] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in a cell or a population of cells in the Treg cell therapy, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

[0011] In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers.

[0012] In some embodiments, the method comprises detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

[0013] In some embodiments, the method further comprises detecting an expression level of one or more Teff markers, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.4MF-367695757.6Attorney Docket No.: 237752002040

[0014] In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more of the Teff markers.

[0015] In some embodiments, the method comprises detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0016] In another aspect, provided herein is a method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprising obtaining gene expression data for one or more Treg markers and one or more Teff markers associated with the cell or the population of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2, determining, based on the gene expression data, a Treg score for the cell or the population of cells, and classifying the cell as a Treg or classifying the population of cells as comprising Tregs if the Treg score for the cell exceeds a threshold.

[0017] In some embodiments, the one or more Treg markers are selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the one or more Teff markers are selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0018] In some embodiments, the Treg score is determined using a Treg marker subscore and a Teff marker sub-score.

[0019] In some embodiments, the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.

[0020] In some embodiments, the threshold is zero.5MF-367695757.6Attorney Docket No.: 237752002040

[0021] In some embodiments, the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is a positive value greater than zero.

[0022] In some embodiments, the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0023] In some embodiments, the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is a negative value less than zero.

[0024] In some embodiments, the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0025] In some embodiments, the Treg score is determined using a Treg marker subscore and a Teff marker sub-score, and wherein the sub-scores are determined using a gene set enrichment analysis.

[0026] In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0027] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

[0028] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

[0029] In another aspect, provided herein is a method of determining cell identity, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1,6MF-367695757.6Attorney Docket No.: 237752002040CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells.

[0030] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a Treg cell therapy product comprising a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

[0031] In some embodiments, the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.

[0032] In some embodiments, the cell identity is determined to be Treg or Treg-like if the Treg score meets a threshold.

[0033] In some embodiments, the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score does not meet a threshold.

[0034] In some embodiments, the threshold is zero.7MF-367695757.6Attorney Docket No.: 237752002040

[0035] In some embodiments, the cell identity is determined to be Treg or Treg-like if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0036] In some embodiments, the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0037] In some embodiments, the Treg marker sub-score and the Teff marker sub-score are each determined using a gene set enrichment analysis.

[0038] In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0039] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

[0040] In some embodiments, the predefined gene set comprising the one or more Treg markers comprises one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

[0041] In some embodiments, a higher Treg marker sub-score increases the likelihood that the cell identity is determined to be Treg or Treg-like.

[0042] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

[0043] In some embodiments, the predefined gene set comprising the one or more Teff markers comprises one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0044] In some embodiments, a higher Teff marker sub-score increases the likelihood that the cell identity is determined to be Teff, Teff-like, or non-Treg.

[0045] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers for generating the Treg marker sub-score and a predefined gene set comprising the one or more Teff markers for generating the Teff marker 8MF-367695757.6Attorney Docket No.: 237752002040sub-score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.

[0046] In some embodiments, the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

[0047] In some embodiments, the Treg marker sub-score and the Teff marker sub-score are given equal weight in determining the Treg score.

[0048] In some embodiments, a higher Treg score indicates a higher quality and / or a higher purity for the plurality of cells.

[0049] In some embodiments, a lower Treg score indicates a lower quality and / or a lower purity for the plurality of cells.

[0050] In some embodiments, a lower Treg score indicates contamination of the plurality of cells.

[0051] In another aspect, provided herein is a method of identifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs.

[0052] In some aspects, provided herein is a method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more post-expansion markers in a cell or a population of cells in the Treg cell therapy, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more post-expansion markers.

[0053] In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the post-expansion markers.9MF-367695757.6Attorney Docket No.: 237752002040

[0054] In some embodiments, the method comprises detecting the expression level of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0055] In some embodiments, the method further comprises detecting an expression level of one or more pre-expansion markers, wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0056] In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the pre-expansion markers.

[0057] In some embodiments, the method comprises detecting the expression level of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0058] In some aspects, provided herein is a method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers associated with the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells as comprising expanded Tregs if the expansion score for the cell exceeds a threshold.

[0059] In some embodiments, the one or more post-expansion markers are selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the one or more pre-expansion markers are selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0060] In some embodiments, the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score.10MF-367695757.6Attorney Docket No.: 237752002040

[0061] In some embodiments, the expansion score is based on a comparison between the pre-expansion marker sub-score and the post-expansion marker sub-score.

[0062] In some embodiments, the threshold is zero.

[0063] In some embodiments, the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is a positive value greater than zero.

[0064] In some embodiments, the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0065] In some embodiments, the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is a negative value less than zero.

[0066] In some embodiments, the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0067] In some embodiments, the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score, and the sub-scores are determined using a gene set enrichment analysis.

[0068] In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0069] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.

[0070] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.11MF-367695757.6Attorney Docket No.: 237752002040

[0071] In another aspect, provided herein is a method of determining cell expansion status, the method comprising: (a) obtaining gene expression data for one or more postexpansion markers and one or more pre-expansion markers in a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the preexpansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker subscore and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells.

[0072] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a Treg cell therapy product comprising a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker sub-score and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

[0073] In some embodiments, the expansion score is based on a comparison between the pre-expansion marker sub-score and the post-expansion marker sub-score.

[0074] In some embodiments, the cell expansion status is determined to be expanded if the expansion score meets a threshold.12MF-367695757.6Attorney Docket No.: 237752002040

[0075] In some embodiments, the cell expansion status is determined to be unexpanded or endogenous if the expansion score does not meet a threshold.

[0076] In some embodiments, the threshold is zero.

[0077] In some embodiments, the cell expansion status is determined to be expanded if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0078] In some embodiments, the cell expansion status is determined to be unexpanded or endogenous if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0079] In some embodiments, the post-expansion marker sub-score and the pre-expansion marker sub-score are each determined using a gene set enrichment analysis.

[0080] In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0081] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.

[0082] In some embodiments, the predefined gene set comprising the one or more postexpansion markers comprises one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0083] In some embodiments, a higher post-expansion marker sub-score increases the likelihood that the cell expansion status is determined to be expanded.

[0084] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.

[0085] In some embodiments, the predefined gene set comprising the one or more preexpansion markers comprises one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.13MF-367695757.6Attorney Docket No.: 237752002040

[0086] In some embodiments, a higher pre-expansion marker sub-score increases the likelihood that the cell expansion status is determined to be unexpanded or endogenous.

[0087] In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers for generating the post-expansion marker sub-score and a predefined gene set comprising the one or more pre-expansion markers for generating the pre-expansion marker sub-score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.

[0088] In some embodiments, the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

[0089] In some embodiments, the post-expansion marker sub-score and the pre-expansion marker sub-score are given equal weight in determining the expansion score.

[0090] In some embodiments, a higher expansion score indicates a higher quality and / or a higher purity for the plurality of cells.

[0091] In some embodiments, a lower expansion score indicates a lower quality and / or a lower purity for the plurality of cells.

[0092] In some embodiments, a lower expansion score indicates contamination of the plurality of cells.

[0093] In some embodiments, the expression level is mRNA expression level.

[0094] In some embodiments, the gene expression data comprises mRNA expression levels for one or more genes.

[0095] In some embodiments, the gene expression data is obtained using next generation sequencing, whole genome sequencing, whole exome sequencing, targeted sequencing, direct sequencing, Sanger sequencing, or microarray.

[0096] In some embodiments, the gene expression data comprises data for genes other than the one or more Treg markers, the one or more Teff markers, the one or more postexpansion markers, or the one or more pre-expansion markers.14MF-367695757.6Attorney Docket No.: 237752002040

[0097] In some embodiments, the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0098] In some embodiments, the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0099] In some embodiments, the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0100] In some embodiments, the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0101] In some embodiments, the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0102] In some embodiments, the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0103] In some embodiments, the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0104] In some embodiments, the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0105] In some embodiments, the expression level of the one or more post-expansion markers and / or the one or more pre-expansion markers is detected in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.15MF-367695757.6Attorney Docket No.: 237752002040

[0106] In some embodiments, the expression level of the one or more post-expansion markers and / or the one or more pre-expansion markers is detected in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0107] In some embodiments, the expression level of the one or more post-expansion markers and / or the one or more pre-expansion markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0108] In some embodiments, the expression level of the one or more post-expansion markers and / or the one or more pre-expansion markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0109] In some embodiments, the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0110] In some embodiments, the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0111] In some embodiments, the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cells.

[0112] In some embodiments, the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor.

[0113] In some embodiments, the comparison comprises subtracting the Teff marker subscore from the Treg marker sub-score.

[0114] In some embodiments, the comparison comprises subtracting the pre-expansion marker sub-score from the post-expansion marker sub-score.16MF-367695757.6Attorney Docket No.: 237752002040BRIEF DESCRIPTION OF THE DRAWINGS

[0115] FIGS. 1A-1C: Overview of the Sonoma Biotherapeutics (SBT) Treg product molecular fingerprints. FIG. 1A: Molecular fingerprints are defined by 2 components: signatures underlying different Treg phenotypes, and metrics for scoring each signature. Signatures that have been developed and discussed here are illustrated by blue bars, with potential signatures for future development illustrated by white bars. Hypothetical score values for each signature are represented by a horizontal line. The signatures represent, e.g., predefined gene sets comprising markers that can be used to assess cell phenotype as described herein. Together, these signatures and their respective scores could be used to differentiate cell therapy drug products, as demonstrated by hypothetical products A and B, shown here. FIG. IB: For each phenotype, a positive (Score Pos) (i.e., a sub-score associated with “favorable” features such as a Treg marker sub-score or a post-expansion sub-score as described herein) and negative (Score_Neg) (i.e., a sub-score associated with “unfavorable” features such as a Teff marker sub-score or a pre-expansion sub-score as described herein) score is derived using the ssGSEA approach based on predefined phenotype-specific gene sets, with each score carrying equal weight. The final score is computed by subtracting Score Neg from Score Pos. FIG. 1C: Signatures were primarily derived using DO (endogenous / unexpanded) Treg (CD4+CD25+CD1271ow) and Teff (CD4+CD251owCD127+) cell sorted from PBMC, and D14 Treg and Teff that have undergone activation and expansion for 14 days. Representative flow cytometry plots demonstrate expression of FOXP3 and Helios in D14 Treg and Teff.

[0116] FIGS. 2A-2D: SBT Treg identity signature (i.e., predefined set of markers) distinguishes Treg samples from Teff samples in both resting and expanded states. FIG. 2A:UpSet plot illustrating shared gene expression across 4 populations: Treg and Teff at DO and D14. The matrix below represents intersections, with filled circles indicating which populations contribute to each intersection. The bar chart above the matrix shows the number of shared genes for each intersection, and the bar chart to the right displays the total number of expressed genes in each population, highlight the extent of overlap in gene expression across cell subsets and time points. The final Treg and Teff signatures were selected from the genes overlapping between DO and D14, to make up a predefined gene set comprising Treg markers and a predefined gene set comprising Teff markers. FIG. 2B: Protein-protein interaction networks from StringDB for Treg signature genes (n=32, top) and Teff signature genes (n=l 1, bottom). Lines connecting genes represent functional and physical protein17MF-367695757.6Attorney Docket No.: 237752002040associations, with the line thickness indicating the strength of data support (minimum interaction confidence of 0.5). FIG. 2C: Comparison of SBT Treg identity scores (Treg scores) between D14 Treg samples (n=6) that were freshly thawed and the same samples that were rested for 24 hours in IL-2 (Paired t-test, P=0.44). FIG. 2D: Comparison of scores derived from the SBT Treg identity fingerprint (left) with published fingerprints from Ferraro et al. (18) (middle) and Pesenacker et al. (19) (right) applied to DO and D14 Teff and Treg generated by SBT. Each point represents an individual sample.

[0117] FIGS. 3A-3D: The SBT Treg expansion signature distinguishes samples before versus after expansion. The Treg expansion signature comprises a predefined gene set with post-expansion markers and a predefined gene set with pre-expansion markers. FIG. 3A: UpSet plot illustrating shared gene expression across DO (top) and D14 (bottom) Treg. The matrix below represents intersections, with filled circles indicating which experiment contributes to each intersection. The bar chart above the matrix shows the number of shared genes for each intersection, and the bar chart to the right displays the total number of expressed genes in each experiment, illustrating the extent of overlap in gene expression across experimental replicates and time points. The final Treg expansion signature was selected from the genes overlapping between the 2 experiments. FIG. 3B: Exemplary protein-protein interaction networks from StringDB for proteins encoded by select genes from the DO (top) and D14 (bottom) signatures. Lines connecting genes represent functional and physical protein associations, with the line thickness indicating the strength of data support (minimum interaction confidence of 0.5). FIG. 3C: Comparison of SBT expansion scores between D14 Treg samples (n=6) that were freshly thawed and the same samples that were rested for 24 hours in IL-2 (Paired t-test, P=0.44). FIG. 3D: SBT expansion scores applied to DO and D14 Teff and Treg generated by SBT. Each point represents an individual sample.

[0118] FIGS. 4A-4C: Application of the SBT Treg identity signature to identify destabilized Treg. The Treg identity signature comprises a predefined gene set with Treg markers and a predefined gene set with Teff markers. FIG. 4A: To generate destabilized Treg, Treg expressing a high tonic signaling CAR (D14 tsTreg) were stimulated using anti-CD3 / anti-CD28 beads for 3 days and rested for 4 days before repeating for a total of 4 rounds of stimulation prior to transcriptional analysis. D14 Teff cells were also stimulated in the same manner as a control (4stim Teff). Representative flow plots demonstrate expression of FOXP3 and Helios in D14 tsTreg and destabilized Treg. FIG. 4B: SBT Treg identity18MF-367695757.6Attorney Docket No.: 237752002040signature applied to Treg (DO, D14 tsTreg, and destabilized) and to Teff (D14 and 4stim) from 3 donors. Identity scores are shown in the bar chart (top) with normalized expression of the genes comprising the negative (i.e., Teff marker sub-score) and positive (i.e., Treg marker sub-score) Treg identity signature scores (bottom). FIG. 4C: Comparison of scores derived from the SBT Treg identity fingerprint (left) with published fingerprints from Ferraro et al. (18) and Pesenacker et al. (19) when applied to D14 Treg, destabilized Treg, or D14 Teff generated by SBT. Each point represents an individual sample.

[0119] FIGS. 5A-5B: Application of SBT Treg signatures (Treg markers and Teff markers; post-expansion markers and pre-expansion markers) to differentiate Treg products.FIG. 5A: SBT Treg identity score (Treg score) applied to activated Treg, ectopic FOXP3 CD4+ cells (eTreg) and activated Teff from data generated by Honaker, et al. (9). Identity scores (Treg scores) are shown in the bar chart (top) with normalized expression of the genes comprising the Teff marker and Treg marker sub-scores (bottom). FIG. 5B: Comparison of SBT Treg identity (left) and expansion (right) scores between D14 Treg derived by SBT and eTreg product. P-values calculated using Welch’s two-sample, two-sided t-test.

[0120] FIGS. 6A-6B: Application of SBT Treg gene signatures (Treg markers and Teff markers; post-expansion markers and pre-expansion markers) to phase 2 T1D clinical data. FIG. 6A: SBT Treg identity (left) and expansion (right) scores of DO (baseline / pre-expansion) and D14 (infusion product / post-expansion) Treg from a phase 2 clinical trial in T1D (28). Each color indicates individual participants with dashed lines connecting data from the same participant. FIG. 6B: Correlation between clinical outcome (patient’s percent change in C-peptide AUC at 1 year) and SBT Treg identity scores of D14 Treg infusion product. Blue line represents a linear regression model with the 95% confidence interval shown in shaded gray (Pearson’s r=0.37, Wald test p-value p=0.157).

[0121] FIG. 7: FOXP3 RNA expression before (Day 0) and after expansion (Day 14) in Treg and Teff. The FOXP3 RNA expression (log2TPM, right panel) is measured by bulk RNAseq from all datasets (Table 1).

[0122] FIGS. 8A-8C: Destabilized Tregs exhibit reduced suppression capacity that correlates with SBT Treg identity score (Treg score). Suppression function of D14 tsTregs (Day 14 Tregs expressing high-tonic signaling CAR) (closed circle), destabilized Tregs (open circle), and 4 stim Teffs (open triangle) on CD4+ T cells (FIG. 8A) and CD8+ T cells (FIG.8B) in total CD3+ T cells. n=5. FIG. 8C depicts the correlation between SBT Treg identity19MF-367695757.6Attorney Docket No.: 237752002040score (Treg score) and suppression capacity, quantified by maximum percentage suppression (top) and area under the curve (AUC) across Treg: Tresp ratios from 1: 1 to 1: 128 (bottom). Data is from two donors with matched suppression and identity scores. The line represents a linear regression model with the 95% confidence interval shaded. Pearson correlation coefficients (r) and p-values are shown.

[0123] FIG. 9: Level of expression of SBT Treg and Teff gene signatures in phase 2 T1D clinical samples from Bender et al. 2024 and a SBT dataset. Boxplot of log2TPM values for all genes, highlighting the median expression of Treg (red) and Teff (blue) signature genes across three sample groups: baseline Treg samples from T1D patient (left), expanded Treg products from T1D patients (middle) and an internal SBT control dataset (right).

[0124] FIGS. 10A-10C: The reduced SBT Treg expansion fingerprint has similar performance with the full expansion fingerprint. The expansion fingerprints as described herein comprise predefined gene sets comprising post-expansion markers and pre-expansion markers. FIGS. 10A-10B: Protein-protein interaction networks from StringDB for the reduced DO (FIG. 10 A) and D14 (FIG. 10B) fingerprints. Lines connecting genes represent functional and physical protein associations, with the line thickness indicating the strength of data support (minimum interaction confidence of 0.5). FIG. 10C: The reduced SBT expansion scores applied to DO and D14 Teff and Treg generated by SBT. Each point represents an individual sample.

[0125] FIG. 11 provides a non-limiting example of a process for determining a status for a plurality of cells using gene expression data.DETAILED DESCRIPTION

[0126] Regulatory T cells are a key player in the maintenance of organismal homeostasis to prevent the destruction of otherwise healthy tissues. Tregs are a subset of T cells that inhibit the cytotoxic or pro-inflammatory activity of effector CD4+ or effector CD8+ T cells. Tregs differentiate from the parent T lymphocyte lineage upon the upregulation of key Treg genes, in particular IL-2Ra and FOXP3 (see, e.g., Chen, ML et al. (2005), Proc Natl Accad Set USA,- 102(2):419-424 and Liu, VC etal. (2007), J Immunol,' 178(5):2883-2892, each of which is hereby incorporated by reference in its entirety). Upon T cell receptor (TCR) activation, these Tregs are responsible for directly suppressing effector T cell activity via cytokine production, e.g., TGF-P and IL-10 (see, e.g., Chen, J et al. (2019), Trends Mol Med; 25(11): 1010-1023, hereby incorporated by reference in its entirety), and the engagement of20MF-367695757.6Attorney Docket No.: 237752002040immune checkpoint receptors, e.g., TIGIT- or CTLA-4-engagement (see, e.g., Knochelmann, HM et al. (2018), Cell Mol Immunol, 15(5):458-469, hereby incorporated by reference in its entirety).

[0127] Treg cell therapies have the potential to radically change treatment paradigms for patients with autoimmune diseases. Rather than treating the rampant proinflammatory response with drugs that cause broad immunosuppression in patients, or with drugs that target only one specific aspect of the immune response, bolstering the body’s natural immunosuppressive response by infusion with expanded Treg is an attractive strategy to restore immune homeostasis. As with all autologous cellular therapies, those derived from Treg require careful characterization to ensure that the final product consists of a highly pure population of cells. Unlike Teff cell therapies, however, Treg present some additional challenges in this respect. For example, one of the primary markers for Treg identification, the transcription factor FOXP3, is only expressed intracellularly, making it impossible to select for using viable cells, and can also be expressed in activated Teff cells. Analysis of the levels of demethylation of the Treg-specific demethylated region (TSDR) located in the FOXP3 gene can be used to distinguish between Treg (highly demethylated) and Teff (low or no demethylation). However, multiple studies together with our own internal data (data not shown) indicate there might be a gender difference or bias in the TSDR methylation level and its impact on Treg phenotypes (43-45). More in-depth methods of characterizing Treg are needed to ensure the purity and function of these promising therapies.

[0128] The rise of high throughput “omics” technologies, including transcriptomics, epigenomics, and proteomics, has revolutionized cellular characterization, enabling unprecedented depth in defining cell states and functions, also known as “fingerprints.” Many published molecular fingerprints have been developed using the gene expression of resting cells, and while it has been demonstrated that these fingerprints can be highly specific in their ability to identify cells of interest, they may not be applicable in cases such as cellular therapies, where cells are stimulated, expanded, and often times, transduced prior to infusion into patients. To address these gaps, we developed transcriptional fingerprints that can be used to analyze transduced Treg cell therapies using an algorithm that uses bi-directional signatures which define “favorable,” or positive, gene expression signatures and “unfavorable,” or negative, signatures; calculating a final score (by subtracting a negative sub-score generated from the negative signature from a positive sub-score generated from the positive signature) provides a more nuanced measure of the quality of Treg cell therapies.21MF-367695757.6Attorney Docket No.: 237752002040

[0129] Thus, in some aspects, provided herein is a method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the one or more Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs.

[0130] In some aspects, provided herein is a method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprising detecting an expression level of one or more Treg markers in the Treg cell or the population of Treg cells, the one or more Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs.

[0131] In some aspects, provided herein is a method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the one or more Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

[0132] In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3, or more, 4 or more, 5, or more 7 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers. In some embodiments, the method comprises detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.22MF-367695757.6Attorney Docket No.: 237752002040

[0133] In some embodiments, the method further comprises detecting an expression level of one or more Teff markers, wherein the one or more Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more Teff markers. In some embodiments, the method comprises detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0134] In some aspects, provided herein is a method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprising obtaining gene expression data of one or more Treg markers associated with the cell or the population of cells, determining, based on the gene expression data, a Treg score for the cell or the population of cells, and classifying the cell as a Treg or classifying the population of cells as comprising Tregs, if the Treg score for the cell exceeds a threshold, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the method further comprises obtaining gene expression data for one or more Teff markers associated with the cell or the population of cells, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0135] In some embodiments, the Treg score is determined using single-sample gene set enrichment analysis (ssGSEA). In some embodiments, the Treg score is determined at least partially based on predefined gene signatures for a Treg phenotype and an associated weight for each signature. In some embodiments, the predefined gene signatures comprise predefined gene sets comprising certain markers, e.g., a predefined gene set comprising Treg markers, a predefined gene set comprising Teff markers, a predefined gene set comprising post-expansion markers, a predefined gene set comprising pre-expansion markers. In some embodiments, the Treg phenotype comprises Treg identity and Treg expansion. In some embodiments, the Treg phenotype comprises Treg identity and / or Treg expansion. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from unexpanded Tregs and from Tregs23MF-367695757.6Attorney Docket No.: 237752002040that have been expanded in cell culture. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff cells. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff unexpanded Teff and Teff that have been expanded in culture.

[0136] In some aspects, provided herein is a method of identifying a cell as an expanded Treg or a composition as comprising expanded Tregs in a cell or a population of cells comprising, detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the one or more post-expansion markers comprise one or more of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1, wherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs. In some aspects, provided herein is a method of identifying a cell as an expanded Treg or a composition as comprising expanded Tregs in a cell or a population of cells comprising, detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the one or more post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, wherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs.

[0137] In some embodiments, the method comprises detecting 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more post-expansion markers. In some embodiments, the method comprises detecting expression of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1. In some embodiments, the method comprises detecting expression of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0138] In some embodiments, the method further comprises detecting expression of one or more pre-expansion markers, wherein the one or more pre-expansion markers comprise one or more of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3. In some 24MF-367695757.6Attorney Docket No.: 237752002040embodiments, the one or more pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0139] In some embodiments, the method comprises detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more pre-expansion markers. In some embodiments, the method comprises detecting the expression level of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3. In some embodiments, the method comprises detecting the expression level of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0140] In some aspects, provided herein is a method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data of one or more post-expansion markers associated with the cell or the population of cells, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising expanded Tregs if the expansion score for the cell exceeds a threshold, wherein the one or more post-expansion markers comprise one or more of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1. In some aspects, provided herein is a method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data of one or more post-expansion markers associated with the cell or the population of cells, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising expanded Tregs if the expansion score for the cell exceeds a threshold, wherein the one or more post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0141] In some embodiments, the expanded Treg score is determined using single-sample gene set enrichment analysis (ssGSEA). In some embodiments, the Treg score is determined at least partially based on predefined gene signatures for a Treg phenotype and an associated weight for each signature. In some embodiments, the Treg phenotype comprises Treg identity and Treg expansion. In some embodiments, the Treg phenotype comprises Treg identity 25MF-367695757.6Attorney Docket No.: 237752002040and / or Treg expansion. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from unexpanded Tregs and from Tregs that have been expanded in cell culture. In some embodiments, the expression level is mRNA expression level. In some embodiments, the gene expression data is mRNA expression level. In some embodiments, mRNA expression level is determined using RNAseq or microarray.Treg Fingerprints

[0142] Provided herein are two Treg fingerprints: an identity fingerprint to differentiate between Treg and Teff regardless of their expansion state, and a fingerprint to differentiate cells (either Treg or Teff) that have undergone expansion. The present disclosure is based on the inventors’ development of these two fingerprints, which together provide a robust molecular framework for characterizing Treg cell therapy products. Unlike conventional approaches that rely on single markers such as FOXP3 or epigenetic assays, the fingerprints of the present disclosure incorporate a bidirectional scoring system comprising both “favorable” and “unfavorable” gene signatures. This bidirectional component enables simultaneous assessment of Treg-like characteristics (“favorable”) and Teff-like characteristics (“unfavorable”) (for the identity fingerprint), or simultaneous assessment of post-expansion markers (“favorable”) and pre-expansion markers (“unfavorable”) (for the expansion fingerprint). The bidirectional architecture can be used to generate a composite identity score (Treg score) or expansion score that more accurately reflects cell identity and state as compared to other approaches. Surprisingly, the inventors observed that the present fingerprinting approach achieves highly sensitive and specific discrimination between Treg and Teff cells independent of activation or expansion status, and further reveals functional attributes such as stability and product quality in an improved manner over existing approaches. The ability of the present fingerprints to distinguish, e.g., destabilized Treg cells, and to outperform other conventional markers of Treg identity or expansion state, illustrates the unexpected advantage of a bidirectional fingerprint design.

[0143] Described herein are Treg scores, which may also be described and referred to herein as identity scores, Treg identity scores, or SBT Treg identity scores.26MF-367695757.6Attorney Docket No.: 237752002040

[0144] Also described herein are expansion scores, which may also be described and referred to herein as expanded Treg scores or SBT expansion scores.

[0145] Also described herein are predefined gene sets comprising one or more Treg markers and / or predefined gene sets comprising one or more Teff markers, which may be described and referred to herein as identity fingerprints, Treg identity fingerprints, Treg fingerprints, SBT identity fingerprints, SBT Treg identity fingerprints, SBT Treg fingerprints, identity signatures, Treg identity signatures, Treg signatures, SBT Treg identity signatures, or SBT Treg signatures.

[0146] Also described herein are predefined gene sets comprising one or more postexpansion markers and / or predefined gene sets comprising one or more Teff markers, which may be described and referred to herein as expansion fingerprints, Treg expansion fingerprints, SBT expansion fingerprints, SBT Treg expansion fingerprints, expansion signatures, Treg expansion signatures, or SBT Treg expansion signatures.

[0147] As described herein, a Treg cell is defined as being CD4+CD25hlCD127lo, and a Teff cell is defined as being CD4+CD25loCD127+.

[0148] In one aspect, provided herein is a method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs. In some embodiments, the method further comprises detecting an expression level of one or more Teff markers in the cell or the population of cells, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0149] In another aspect, provided herein is a method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprising detecting an expression level of one or more Treg markers in the Treg cell or the population of Treg cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A,27MF-367695757.6Attorney Docket No.: 237752002040SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs. In some embodiments, the method further comprises detecting an expression level of one or more Teff markers in the cell or the population of cells, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, the method further comprises generating a Treg score based upon the expression level of the one or more Treg markers and the expression level of the one or more Teff markers. In some embodiments, the cell is identified as a destabilized Treg or the population of cell is identified as comprising destabilized Tregs if the Treg score does not exceed a threshold. In some embodiments, the threshold is zero.

[0150] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers. In some embodiments, the method further comprises detecting an expression level of one or more Teff markers in the cell or the population of cells, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0151] In another aspect, provided herein is a method of identifying a cell as an expanded Treg or a composition as comprising expanded Tregs, the method comprising detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs. In some embodiments, the method further comprises detecting an expression level of one or more pre-expansion markers, wherein the pre-expansion markers comprise28MF-367695757.6Attorney Docket No.: 237752002040one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0152] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more post-expansion markers. In some embodiments, the method further comprises detecting an expression level of one or more pre-expansion markers, wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.Markers for Differentiating Between Treg and Teff

[0153] In some aspects, the methods described herein comprise detecting an expression level of one or more Treg markers in a cell or a population of cells. In some embodiments, based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs. In some embodiments, based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs. In some embodiments, the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

[0154] In some embodiments, the one or more Treg markers comprise ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and / or IL2RA. Thus, in some embodiments, the cell is identified as a Treg or the population of cells is identified as comprising Tregs if an increased expression level of one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and / or IL2RA is detected, compared to expression in a Teff cell. In some embodiments, the cell is identified as a Treg or the population of cells is identified as comprising Tregs if differential expression of one or more of ACTA2, TNFRSF1B, OAS1,29MF-367695757.6Attorney Docket No.: 237752002040SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA is detected, compared to expression in a Teff cell. In some embodiments, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs based upon the expression level of one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the expression is mRNA expression.

[0155] In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers. For example, in some embodiments, the method comprises detecting 2 to 32 Treg markers, such as 3 to 32 Treg markers, 4 to 32 Treg markers, 5 to 32 Treg markers, 7 to 32 Treg markers, 10 to 32 Treg markers, 15 to 32 Treg markers, 20 to 32 Treg markers, 25 to 32 Treg markers, or 30 to 32 Treg markers selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the Treg markers are detected prior to expansion (i.e., DO). In some embodiments, the Treg markers are detected following 14 days of expansion (i.e., D14). In some embodiments, the present methods include detecting expression level of Treg markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include obtaining gene expression data for Treg markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include detecting expression level of Treg markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor. In some embodiments, the present methods include obtaining gene expression data for Treg markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen30MF-367695757.6Attorney Docket No.: 237752002040receptor. In some embodiments, the method comprises detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the expression is mRNA expression.

[0156] In some embodiments, the methods described herein further comprise detecting an expression level of one or more Teff markers. In some embodiments, the one or more Teff markers comprise STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and / or ID2. Thus, in some embodiments, the cell is identified as a Teff or the population of cells is identified as comprising Teffs if an increased expression level of one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2 is detected, compared to expression in a Treg cell. Thus, in some embodiments, the cell is identified as a Teff or the population of cells is identified as comprising Teffs if differential expression of one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2 is detected, compared to expression in a Treg cell. In some embodiments, the expression is mRNA expression.

[0157] In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more of the Teff markers. For example, in some embodiments, the method comprises detecting 2 to 10 Teff markers, such as 3 to 10 Teff markers, 4 to 10 Teff markers, 5 to 10 Teff markers, 7 to 10 Teff markers, 8 to 10 Teff markers, or 9 to 10 Teff markers selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, the Teff markers are detected prior to expansion (i.e., DO). In some embodiments, the Teff markers are detected following 14 days of expansion (i.e., D14). In some embodiments, the present methods include detecting expression level of Teff markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include obtaining gene expression data for Teff markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include detecting expression level of Teff markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor. In some embodiments, the31MF-367695757.6Attorney Docket No.: 237752002040present methods include obtaining gene expression data for Teff markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor. In some embodiments, the method comprises detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, the expression is mRNA expression.

[0158] In some embodiments, the methods described herein comprise detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, IL2RA, STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, the expression is mRNA expression.Markers of T cell Expansion

[0159] T cell expansion refers to the growth, proliferation, and / or activation of a population of T cells. In some embodiments, T cells described herein have undergone ex vivo expansion. In some aspects, the methods provided herein can be used to detect T cells that have been expanded, as distinct from T cells that have not been expanded. In some embodiments, the T cells have undergone expansion in the presence of anti-CD3 / anti-CD28 beads. In some embodiments, an expanded Treg or Teff cell has undergone expansion for 14 days (e.g., D14). In some embodiments, the present methods include detecting expression level of certain markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include obtaining gene expression data for certain markers in cell populations after the cells have undergone expansion for about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days. In some embodiments, the present methods include detecting expression level of certain markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor. In some embodiments, the present methods include obtaining gene expression data for certain markers in cell populations about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor. In some aspects, provided herein are methods of identifying a cell as an expanded Treg or a composition as comprising expanded Tregs in a cell or a population of cells. In 32MF-367695757.6Attorney Docket No.: 237752002040some aspects, provided herein are methods of identifying a cell as an expanded Teff or a composition as comprising expanded Teffs in a cell or a population of cells. In some embodiments, the method comprises detecting an expression level of one or more postexpansion markers in the cell or the population of cells. In some embodiments, based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or expanded Teff or the population of cells is identified as comprising expanded Tregs or expanded Teff. In some embodiments, the expression is mRNA expression.

[0160] In some embodiments, the methods described herein comprise detecting the expression level of a post-expansion marker for Tregs. In some embodiments, the one or more post-expansion markers comprise PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2, and / or CDK1. In some embodiments, the one or more post-expansion markers comprise FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and / or MZB1. In some embodiments, the one or more postexpansion markers are detected in a Treg cell or a population of cells comprising Treg cells. In some embodiments, the one or more post-expansion markers are detected in a Teff cell or a population of cells comprising Teff cells. In some embodiments, the one or more postexpansion markers are detected a population of cells comprising both Treg cells and Teff cells. In some embodiments, a cell or a population of cells is identified as comprising expanded Tregs based upon differential expression of one or more of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2, and CDK1. In some embodiments, a cell or a population of cells is identified as comprising expanded Tregs based upon differential expression of one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1. In some embodiments, the expression is mRNA expression.

[0161] In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more post-expansion markers selected from the group consisting of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1. In some embodiments, the methods described herein comprise detecting the expression level of33MF-367695757.6Attorney Docket No.: 2377520020402 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more post-expansion markers selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1. In some embodiments, the expression is mRNA expression.

[0162] In some embodiments, the method comprises detecting the expression level of one or mor pre-expansion markers. In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more pre-expansion markers selected from the group consisting of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3. In some embodiments, the methods described herein comprise detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more pre-expansion markers selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS. In some embodiments, a cell or a population of cells is determined to be pre-expansion based upon differential expression of one or more of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3. In some embodiments, a cell or a population of cells is determined to be pre-expansion based upon differential expression of one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS. In some embodiments, the expression is mRNA expression.Methods of Detection

[0163] In some aspects, the methods provided herein comprise detecting expression level of one or more genes. In some embodiments, the expression level is mRNA expression level. In some aspects, the methods provided herein comprise obtaining gene expression data. In some embodiments, the gene expression data is mRNA expression level. In some embodiments, the mRNA expression level is determined using RNAseq. In some embodiments, the mRNA expression level is determined using microarray. In some embodiments, the methods provided herein comprise obtaining gene expression data that comprises mRNA expression levels for one or more genes. In some embodiments, the gene expression data is obtained using next generation sequencing, whole genome sequencing, whole exome sequencing, targeted sequencing, direct sequencing, Sanger sequencing, or microarray. In some embodiments, the gene expression data comprises data for genes other 34MF-367695757.6Attorney Docket No.: 237752002040than the one or more Treg markers, the one or more Teff markers, the one or more postexpansion markers, or the one or more pre-expansion markers as described herein.

[0164] In some embodiments, the mRNA expression level is measured pre-expansion of the Treg cells (e.g., DO cells) and post-expansion of the Treg cells (e.g., D14 cells). In some embodiments, the mRNA expression level has a Log2 fold change of greater than 1.

[0165] In some embodiments, the expression level of one or more genes, e.g., the mRNA expression level, is measured pre-expansion of the Treg cells (e.g., DO cells) and postexpansion of the Treg cells (e.g., D14 cells). In some embodiments, the mRNA expression level is measured pre-expansion of the Teff cells (e.g., DO cells) and post-expansion of the Teff cells (e.g., D14 cells). In some embodiments, the mRNA expression level has a Log2 fold change of greater than 1. In some embodiments, the gene expression data, e.g., the mRNA expression level data, is represented as log2 TPM data.

[0166] In some embodiments, the expression level of one or more genes, e.g., the mRNA expression level, is used to determine a fingerprint score (e.g., a Treg score, an expansion score). In some embodiments, the fingerprint score is calculated by calculating a sub-score for favorable signatures and a sub-score for unfavorable signatures separately, followed by subtracting the unfavorable sub-score from the favorable sub-score. In some embodiments, the fingerprint score is determined by an algorithm or computational model. In some embodiments, the model uses gene set variation analysis (GSVA). In some embodiments, the model uses single-sample gene set enrichment analysis (ssGSEA). In some embodiments, the model is singscore. In some embodiments, the model is a machine learning classifier model.

[0167] In some embodiments, a model can be used to determine a Treg score. In some embodiments, the Treg score is determined at least partially based on predefined gene signatures for a Treg phenotype and an associated weight for each signature. In some embodiments, the Treg phenotype comprises Treg identity. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from unexpanded Tregs and from Tregs that have been expanded in cell culture. In some embodiments, the method comprises determining a positive Treg identity signature score (i.e., a Treg marker sub-score as described herein) and a negative Treg identity signature score (i.e., a Teff marker sub-score as described herein). In35MF-367695757.6Attorney Docket No.: 237752002040some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff cells. In some embodiments, the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff unexpanded Teff and Teff that have been expanded in culture.

[0168] In some embodiments, a model can be used to determine one or more sub-scores that are used to determine a Treg score or an expansion score as described herein. In some embodiments, a sub-score is determined at least partially based on predefined gene signatures for a phenotype and an associated weight for each signature. In some embodiments, the predefined gene signatures comprise a predefined list of genes or markers which may be associated with, e.g., Treg identity, Teff identity, a pre-expansion state, or a post-expansion state. In some embodiments, the predefined gene signatures and the associated weight for each signature are determined using gene expression data. In some embodiments, the predefined gene signatures and the associated weight for each signature are determined using gene expression data from unexpanded or endogenous Tregs, from Tregs that have been expanded in cell culture, from unexpanded or endogenous Teffs, and / or from Teffs that have been expanded in cell culture. In some embodiments, the weight of each signature is equal, e.g., a signature associated with a Treg or Treg-like cell is given equal weight to a signature associated with a Teff or Teff-like cell.

[0169] Thus, in some aspects, described herein is a method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprising obtaining gene expression data for one or more Treg markers associated with the cell or the population of cells, determining, based on the gene expression data, a Treg score for the cell or the population of cells, and classifying the cell as a Treg or classifying the population of cells as comprising Tregs, if the Treg score for the cell exceeds a threshold, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the method further comprises obtaining gene expression data for one or more Teff markers associated with the cell or the population of cells, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.36MF-367695757.6Attorney Docket No.: 237752002040

[0170] In some embodiments, the Treg score is determined using a Treg marker subscore and a Teff marker sub-score. In some embodiments, the Treg score is determined by subtracting the Teff marker sub-score from the Treg marker sub-score. In some embodiments, the threshold is zero. In some embodiments, the cell is classified as a Treg or the population of cells is classified as comprising Tregs if the Treg score is a positive value greater than zero. In some embodiments, the cell is classified as a Treg or the population of cells is classified as comprising Tregs if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9. In some embodiments, the cell is classified as a Teff or a non-Treg or the population of cells is classified as comprising Teffs or non-Tregs if the Treg score is a negative value less than zero. In some embodiments, the cell is classified as a Teff or a non-Treg or the population of cells is classified as comprising Teffs or non-Tregs if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0171] In some embodiments, the Treg score is determined using a Treg marker subscore and a Teff marker sub-score, and the sub-scores are determined using a gene set enrichment analysis. In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA). In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and wherein the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers. In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and wherein the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

[0172] In some aspects, described herein is a method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data for one or more post-expansion markers associated with the cell or the population of cells, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising expanded Tregs if the expansion score for the cell exceeds a threshold, wherein the post-expansion markers comprise one or more of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1. In some aspects, described herein is a method of classifying a cell as an expanded Treg or a37MF-367695757.6Attorney Docket No.: 237752002040population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data for one or more post-expansion markers associated with the cell or the population of cells, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising expanded Tregs if the expansion score for the cell exceeds a threshold, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0173] In some embodiments, the method further comprises obtaining gene expression data for one or more pre-expansion markers, wherein the pre-expansion markers comprise one or more of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3. In some embodiments, the method further comprises obtaining gene expression data for one or more pre-expansion markers associated with the cell or the population of cells, wherein the preexpansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0174] In some embodiments, the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score. In some embodiments, the expansion score is determined by subtracting the pre-expansion marker sub-score from the post-expansion marker sub-score. In some embodiments, the threshold is zero. In some embodiments, the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is a positive value greater than zero. In some embodiments, the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9. In some embodiments, the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is a negative value less than zero. In some embodiments, the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9. In some embodiments, the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score, and wherein the sub-scores are determined using a38MF-367695757.6Attorney Docket No.: 237752002040gene set enrichment analysis. In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA). In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more postexpansion markers, and wherein the gene set enrichment analysis generates the postexpansion marker sub-score based on the one or more post-expansion markers. In some embodiments, the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and wherein the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers. In some aspects, described herein is a method of classifying a cell as a pre-expanded Treg or a population of cells as comprising pre-expanded Tregs, the method comprising obtaining gene expression data of one or more post-expansion markers associated with the cell or the population of cells, determining, based on the gene expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising pre-expanded Tregs if the expansion score for the cell does not exceed a threshold. In some embodiments, the threshold is zero. In some embodiments, the one or more post-expansion markers comprise one or more of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2, and CDK1, or the one or more post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1. In some embodiments the one or more pre-expansion markers comprise one or more of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3, or the one or more pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.Treg Scores

[0175] In some embodiments, a cell is identified or classified as a Treg or as a Treg-like cell, or a population of cells is identified or classified as a population comprising Tregs or Treg-like cells, based on a Treg score. In some embodiments, the Treg score is calculated based on a comparison between two sub-scores. In some embodiments, the two sub-scores include a Treg marker sub-score associated with a cell being Treg-like, and a Teff marker sub-score associated with a cell being Teff-like. In some embodiments, the comparison between the two sub-scores is a subtraction, e.g., the Teff marker sub-score is subtracted from 39MF-367695757.6Attorney Docket No.: 237752002040the Treg marker sub-score. Thus, in some embodiments, the Treg score is calculated by subtracting the Teff marker sub-score from the Treg marker sub-score. In some embodiments, if the Treg score is greater than 0, the cell is identified or classified as a Treg or as a Treg-like cell, or a population of cells is identified or classified as a population comprising Tregs or Treg-like cells. In some embodiments, if the Treg score is less than 0, the cell is identified or classified as a Teff or as a Teff-like cell, or a population of cells is identified or classified as a population comprising Teffs or Teff-like, or the cell is identified or classified as a non-Treg, or a population of cells is identified or classified as a population comprising non-Treg cells. In some embodiments, if the Treg score is less than 0, the cell is identified or classified as a destabilized Treg cell.

[0176] In some embodiments, the sub-scores are determined using gene expression data obtained for a cell or a population of cells. In some embodiments, the gene expression data comprises data obtained using a next-generation sequencing approach. In some embodiments, the gene expression data comprises normalized sequence read data obtained from a nextgeneration sequencing approach, for example, transcripts per million (TPM), or log2(TPM). In some embodiments, the gene expression data is obtained and processed as described herein, e.g., as described in Example 1. In some embodiments, the gene expression data comprises data from one or more samples or cell populations, for example, from endogenous DO Treg cells, endogenous DO Teff cells, expanded D14 Treg cells, and / or expanded D14 Teff cells.

[0177] In some embodiments, the sub-scores (e.g., the Treg marker sub-score and the Teff marker sub-score) are each determined using the set of markers described herein. For example, the Treg marker sub-score is determined using the Treg markers and the Teff marker sub-score is determined using the Teff markers. In some embodiments, the Treg markers used for determining the Treg marker sub -score or the Teff markers used for determining the Teff marker sub-score are described herein as a fingerprint, signature, or a predefined gene set.

[0178] In some embodiments, the predefined gene set used for determining the Treg marker sub-score comprises one or more Treg markers selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, the predefined gene set used for determining the 40MF-367695757.6Attorney Docket No.: 237752002040Teff marker sub-score comprises one or more Teff markers selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0179] In some embodiments, the Treg marker sub-score is determined using a gene set enrichment analysis of the gene expression data obtained as described herein, wherein the gene set comprises one or more Treg markers, and wherein the gene set enrichment analysis is used to identify sample-specific gene set enrichment. For example, the gene set enrichment analysis can be applied to normalized gene expression data (e.g., TPM or log2 -transformed TPM data) to determine the extent of enrichment for the Treg marker gene set in a given sample, which may be a sample comprising an unknown cell population or a sample with a predicted but unvalidated cell population. In some embodiments, the Treg marker sub-score is a numerical value generated by the gene set enrichment analysis, wherein the numerical value describes the extent of enrichment of the Treg marker gene set in one sample as compared to one or more other samples. In some embodiments, a higher value for the Treg marker sub-score indicates a greater extent of enrichment of the Treg marker gene set in a sample as compared to one or more other samples. In some embodiments, a lower value for the Treg marker sub-score indicates a lesser extent of enrichment of the Treg marker gene set in a sample as compared to one or more other samples.

[0180] In some embodiments, the Teff marker sub-score is determined using a gene set enrichment analysis of the gene expression data obtained as described herein, wherein the gene set comprises one or more Teff markers, and wherein the gene set enrichment analysis is used to identify sample-specific gene set enrichment. For example, the gene set enrichment analysis can be applied to normalized gene expression data (e.g., TPM or log2 -transformed TPM data) to determine the extent of enrichment for the Teff marker gene set in one sample as compared to one or more other samples. In some embodiments, the Teff marker sub-score is a numerical value generated by the gene set enrichment analysis, wherein the numerical value describes the extent of enrichment of the Teff marker gene set in one sample as compared to one or more other samples. In some embodiments, a higher value for the Teff marker sub-score indicates a greater extent of enrichment of the Teff marker gene set in a sample as compared to one or more other samples. In some embodiments, a lower value for the Teff marker sub-score indicates a lesser extent of enrichment of the Teff marker gene set in a sample as compared to one or more other samples.41MF-367695757.6Attorney Docket No.: 237752002040

[0181] In some embodiments, the gene set enrichment analysis comprises an unsupervised method for estimating variation within a predefined gene set that generates an expression statistic, i.e., a numerical value, that summarizes relative gene expression (e.g., gene expression in a sample as compared to one or more other samples). In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA), wherein genes within each sample are rank-ordered based on expression and an expression statistic or enrichment score is calculated using a running-sum statistic that increases when genes in the gene set are encountered and decreases when genes not in the gene set are encountered. In some embodiments, the enrichment score reflects the relative positioning of the gene set within the ranked expression profile of the sample. In some embodiments, the resulting enrichment score represents the degree to which genes in the gene set are coordinately upregulated or downregulated within the sample relative to the overall gene expression distribution. Methods for gene set variation or enrichment analysis, e.g., ssGSEA, are described further in Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformaiics, 14, 7 (30). Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7 is specifically incorporated herein in its entirety.

[0182] Thus, in some embodiments, the methods described herein can be used to generate a Treg marker sub-score and a Teff marker sub-score. In some embodiments, each sub-score has an equal weight in a bidirectional calculation for the Treg score for a sample. In some embodiments, the Treg score is determined by subtracting the Teff marker sub-score from the Treg marker sub-score. In some embodiments, the Treg score being greater than 0 identifies a sample as comprising Tregs or Treg-like cells. In some embodiments, the Treg score being less than 0 identifies a sample as comprising Teffs or Teff-like cells, or as not comprising Treg cells.Expansion Scores

[0183] In some embodiments, a cell is identified or classified as an expanded Treg or Teff, or a population of cells is identified or classified as a population comprising expanded Tregs or Teffs, based on an expansion score. In some embodiments, the score is calculated based on a comparison between two sub-scores. In some embodiments, the two sub-scores include a post-expansion marker sub-score associated with a cell being a post-expansion (D14) cell, and a pre-expansion marker sub-score associated with a cell being a pre- 42MF-367695757.6Attorney Docket No.: 237752002040expansion (DO) cell. In some embodiments, the comparison between the two sub-scores is a subtraction, e.g., the pre-expansion marker sub-score is subtracted from the post-expansion marker sub-score. Thus, in some embodiments, the expansion score is calculated by subtracting the pre-expansion marker sub-score from the post-expansion marker sub-score. In some embodiments, if the expansion score is greater than 0, the cell is identified or classified as a post-expansion or D14 cell, which may be an expanded Treg cell or an expanded Teff, or a population of cells is identified or classified as a population comprising post-expansion cells. In some embodiments, if the expansion score is less than 0, the cell is identified or classified as a pre-expansion or DO cell, which may be a pre-expansion Treg cell or a preexpansion Teff cell, or a population of cells is identified or classified as a population comprising pre-expansion cells.

[0184] In some embodiments, the sub-scores are determined using gene expression data obtained for a cell or a population of cells. In some embodiments, the gene expression data comprises data obtained using a next-generation sequencing approach. In some embodiments, the gene expression data comprises normalized sequence read data obtained from a nextgeneration sequencing approach, for example, transcripts per million (TPM), or log2(TPM). In some embodiments, the gene expression data is obtained and processed as described herein, e.g., as described in Example 1. In some embodiments, the gene expression data comprises data from one or more samples or cell populations, for example, from endogenous DO Treg cells, endogenous DO Teff cells, expanded D14 Treg cells, and / or expanded D14 Teff cells.

[0185] In some embodiments, the sub-scores (e.g., the post-expansion marker sub-score and the pre-expansion marker sub-score) are each determined using the set of markers described herein. For example, the post-expansion marker sub-score is determined using the post-expansion markers and the pre-expansion marker sub-score is determined using the preexpansion markers. In some embodiments, the post-expansion markers used for determining the post-expansion marker sub-score or the pre-expansion markers used for determining the pre-expansion marker sub-score are described herein as a fingerprint, signature, or a predefined gene set.

[0186] In some embodiments, the predefined gene set used for determining the postexpansion marker sub-score comprises one or more post-expansion markers selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1. In some embodiments, the predefined gene set used for 43MF-367695757.6Attorney Docket No.: 237752002040determining the pre-expansion marker sub-score comprises one or more pre-expansion markers selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0187] In some embodiments, the post-expansion marker sub-score is determined using a gene set enrichment analysis of the gene expression data obtained as described herein, wherein the gene set comprises one or more post-expansion markers, and wherein the gene set enrichment analysis is used to identify sample-specific gene set enrichment. For example, the gene set enrichment analysis can be applied to normalized gene expression data (e.g., TPM or log-transformed TPM data, for example, log2 -transformed TPM data) to determine the extent of enrichment for the post-expansion marker gene set in a given sample, which may be a sample comprising an unknown cell population or a sample with a predicted but unvalidated cell population. In some embodiments, the post-expansion marker sub-score is a numerical value generated by the gene set enrichment analysis, wherein the numerical value describes the extent of enrichment of the post-expansion marker gene set in one sample as compared to one or more other samples. In some embodiments, a higher value for the postexpansion marker sub-score indicates a greater extent of enrichment of the post-expansion marker gene set in a sample as compared to one or more other samples. In some embodiments, a lower value for the post-expansion marker sub-score indicates a lesser extent of enrichment of the post-expansion marker gene set in a sample as compared to one or more other samples.

[0188] In some embodiments, the pre-expansion marker sub-score is determined using a gene set enrichment analysis of the gene expression data obtained as described herein, wherein the gene set comprises one or more pre-expansion markers, and wherein the gene set enrichment analysis is used to identify sample-specific gene set enrichment. For example, the gene set enrichment analysis can be applied to normalized gene expression data (e.g., TPM or log2 -transformed TPM data) to determine the extent of enrichment for the pre-expansion marker gene set in one sample as compared to one or more other samples. In some embodiments, the pre-expansion marker sub-score is a numerical value generated by the gene set enrichment analysis, wherein the numerical value describes the extent of enrichment of the pre-expansion marker gene set in one sample as compared to one or more other samples. In some embodiments, a higher value for the pre-expansion marker sub-score indicates a greater extent of enrichment of the pre-expansion marker gene set in a sample as compared to one or more other samples. In some embodiments, a lower value for the pre-expansion44MF-367695757.6Attorney Docket No.: 237752002040marker sub-score indicates a lesser extent of enrichment of the pre-expansion marker gene set in a sample as compared to one or more other samples.

[0189] In some embodiments, the gene set enrichment analysis comprises an unsupervised method for estimating variation within a predefined gene set that generates an expression statistic, i.e., a numerical value, that summarizes relative gene expression (e.g., gene expression in a sample as compared to one or more other samples). In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA), wherein genes within each sample are rank-ordered based on expression and an expression statistic or enrichment score is calculated using a running-sum statistic that increases when genes in the gene set are encountered and decreases when genes not in the gene set are encountered. In some embodiments, the enrichment score reflects the relative positioning of the gene set within the ranked expression profile of the sample. In some embodiments, the resulting enrichment score represents the degree to which genes in the gene set are coordinately upregulated or downregulated within the sample relative to the overall gene expression distribution. Methods for gene set variation or enrichment analysis, e.g., ssGSEA, are described further in Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bi()informaiics, 14, 7 (30). Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7 is specifically incorporated herein in its entirety.

[0190] Thus, in some embodiments, the methods described herein can be used to generate a post-expansion marker sub-score and a pre-expansion marker sub-score. In some embodiments, each sub-score has an equal weight in a bidirectional calculation for the expansion score for a sample. In some embodiments, the expansion score is determined by subtracting the pre-expansion marker sub-score from the post-expansion marker sub-score. In some embodiments, the expansion score being greater than 0 identifies a sample as comprising expanded cells, i.e., post-expansion cells. In some embodiments, the expansion score being less than 0 identifies a sample as comprising unexpanded cells, i.e., preexpansion cells.Determining Identity and Expansion States Using Scores

[0191] In some aspects, the methods provided herein comprise generating scores and using the scores to determine a status for a plurality of cells. In some embodiments, the45MF-367695757.6Attorney Docket No.: 237752002040generated score is a Treg score as described herein. In some embodiments, the generated score is an expansion score as described herein. FIG. 11 provides a non-limiting exemplary schematic showing general process 1100 for determining a status for a plurality of cells using gene expression data and a score. Process 1100 is performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 1100 is performed using a client-server system, and the blocks of process 1100 are divided up in any manner between the server and one or more client devices. In process 1100, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 1100. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0192] The method for determining a status for a plurality of cells can include: receiving gene expression data for a plurality of cells (1102); determining a first sub-score for a first predefined gene set (1104); determining a second sub-score for a second predefined gene set (1106); generating a score based on the first sub-score and the second sub-score (1108); and determining a status for the plurality of cells based on the score.

[0193] At 1102 in FIG. 11, gene expression data is received for a plurality of cells. The plurality of cells can be provided from a sample, for example, a sample comprising a population of cells with unknown or unvalidated identity, or an unknown or unvalidated expansion state. In some embodiments, the sample comprises Treg cells. In some embodiments, the sample comprises Teff cells. In some embodiments, the sample comprises endogenous cells that are pre-expansion. In some embodiments, the sample comprises postexpansion cells. In some embodiments, the sample is a single sample comprising a drug product, for example, a Treg cell therapy drug product. In some embodiments, the gene expression data is obtained using a sequencing approach that generates a plurality of sequencing reads. In some embodiments, the gene expression data is obtained from a nextgeneration sequencing approach, or a massively parallel sequencing approach. In some embodiments, the gene expression data is obtained from a microarray assay. In some embodiments, the gene expression data comprises data for a first predefined gene set, for a second predefined gene set, and for genes that are not included in either of the predefined gene sets.

[0194] At 1104 in FIG. 11, a first sub-score is determined for a first predefined gene set. The gene expression data received at 1102 can comprise expression data for genes within the 46MF-367695757.6Attorney Docket No.: 237752002040first predefined gene set and expression data for genes that are not part of the first predefined gene set. In some embodiments, the first sub-score is determined using at least a portion of the expression data received at 1102. In some embodiments, the first sub-score is determined using all of the expression data received at 1102. In some embodiments, the first predefined gene set comprises a predefined list of markers.

[0195] In some embodiments, the first predefined gene set comprises a predefined list of Treg markers and the first sub-score is a Treg marker sub-score. In some embodiments, the Treg markers comprise one or more genes selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA. In some embodiments, a higher Treg marker sub-score increases the likelihood that the cell identity is determined to be Treg or Treg-like.

[0196] In some embodiments, the first predefined gene set comprises a predefined list of post-expansion markers and the first sub-score is a post-expansion marker sub-score. In some embodiments, the post-expansion markers comprise one or more genes selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, MZB1. In some embodiments, a higher post-expansion marker subscore increases the likelihood that the cell expansion status is determined to be expanded.

[0197] In some embodiments, the first predefined gene set comprises one or more markers relating to a stability phenotype, a suppression potency phenotype, a Thl phenotype, a Th2 phenotype, and / or a Th 17 phenotype. In some embodiments, the first predefined gene set was selected using gene expression data and differential gene expression analysis of one or more reference samples. In some embodiments, the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

[0198] The first sub-score determined for the first predefined gene set can be determined using gene set enrichment analysis. In some embodiments, the gene set enrichment analysis is performed for only the plurality of cells for which gene expression data is obtained at 1102, wherein the plurality of cells is from a single sample. In some embodiments, a single-sample gene set enrichment analysis is used. In some embodiments, the gene set enrichment analysis47MF-367695757.6Attorney Docket No.: 237752002040comprises an unsupervised method, e.g., an algorithm that finds patterns or structures in data without being given any labeled examples or predefined outcomes. In some embodiments, the gene set enrichment analysis estimates variation within the first predefined gene set and generates an expression statistic, i.e., a numerical value, that summarizes relative gene expression (e.g., gene expression for genes that are in the first predefined gene set as compared to gene expression for genes that are not part of the first predefined gene set). In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA), wherein genes within each sample are rank-ordered based on expression, for example, expression as measured using log-normalized TPM data, i.e., Iog2 TPM data, and an expression statistic or enrichment score is calculated using a running-sum statistic that increases when genes in the first predefined gene set are encountered and decreases when genes not in the first predefined gene set are encountered. In some embodiments, the enrichment score reflects the relative positioning of the first predefined gene set within the ranked expression profile of the sample. In some embodiments, the resulting enrichment score represents the degree to which genes in the first predefined gene set are coordinately upregulated or downregulated within the sample relative to the overall gene expression distribution within the sample for all genes for which gene expression data was obtained. In some embodiments, the enrichment score is computed for a single biological sample without requiring a matched control sample. In some embodiments, the method relies on within-sample gene expression rank distribution and not between-sample comparison. In some embodiments, the method can be applied independently to each sample in a plurality of samples. Methods for gene set variation or enrichment analysis, e.g., ssGSEA, are described further in Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7 (30). Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7 is specifically incorporated herein in its entirety. In some embodiments, the methods herein involve ssGSEA implemented using the GSVA package. See, e.g., Example 1.

[0199] In some embodiments, the first sub-score determined for the first predefined gene set is determined using a machine learning model, e.g., a classifier model. In some embodiments, a trained machine learning model is used to generate the first sub-score using gene expression data. In some embodiments, the trained machine learning model comprises a classifier selected from logistic regression, random forest, or other supervised learning48MF-367695757.6Attorney Docket No.: 237752002040models. In some embodiments, the first sub-score determined for the first predefined gene set is determined using a single-set gene set enrichment analysis approach, a singscore approach, a machine learning approach, a gene set variation analysis approach, a PLAGE approach, or a z- score approach.

[0200] At 1106 in FIG. 11, a second sub-score is determined for a second predefined gene set. The gene expression data received at 1102 can comprise expression data for genes within the second predefined gene set and expression data for genes that are not part of the second predefined gene set. In some embodiments, the second sub-score is determined using at least a portion of the expression data received at 1102. In some embodiments, the second sub-score is determined using all of the expression data received at 1102. In some embodiments, the second predefined gene set comprises a predefined list of markers.

[0201] In some embodiments, the second predefined gene set comprises a predefined list of Teff markers and the second sub-score is a Teff marker sub-score. In some embodiments, the Teff markers comprise one or more genes selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2. In some embodiments, a higher Teff marker sub-score increases the likelihood that the cell identity is determined to be Teff, Teff-like, or non-Treg.

[0202] In some embodiments, the second predefined gene set comprises a predefined list of pre-expansion markers and the second sub-score is a pre-expansion marker sub-score. In some embodiments, the pre-expansion markers comprise one or more genes selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, FOS. In some embodiments, a higher pre-expansion marker sub-score increases the likelihood that the cell expansion status is determined to be unexpanded or endogenous.

[0203] In some embodiments, the second predefined gene set comprises one or more markers relating to a stability phenotype, a suppression potency phenotype, a Thl phenotype, a Th2 phenotype, and / or a Th 17 phenotype. In some embodiments, the second predefined gene set was selected using gene expression data and differential gene expression analysis of one or more reference samples. In some embodiments, the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.49MF-367695757.6Attorney Docket No.: 237752002040

[0204] The second sub-score determined for the second predefined gene set can be determined using gene set enrichment analysis. In some embodiments, the gene set enrichment analysis is performed for only the plurality of cells for which gene expression data is obtained at 1102, wherein the plurality of cells is from a single sample. In some embodiments, a single-sample gene set enrichment analysis is used. In some embodiments, the gene set enrichment analysis comprises an unsupervised method, e.g., an algorithm that finds patterns or structures in data without being given any labeled examples or predefined outcomes. In some embodiments, the gene set enrichment analysis estimates variation within the second predefined gene set and generates an expression statistic, i.e., a numerical value, that summarizes relative gene expression (e.g., gene expression for genes that are in the second predefined gene set as compared to gene expression for genes that are not part of the second predefined gene set). In some embodiments, the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA), wherein genes within each sample are rank-ordered based on expression, for example, expression as measured using log-normalized TPM data, i.e., Iog2 TPM data, and an expression statistic or enrichment score is calculated using a running-sum statistic that increases when genes in the second predefined gene set are encountered and decreases when genes not in the second predefined gene set are encountered. In some embodiments, the enrichment score reflects the relative positioning of the second predefined gene set within the ranked expression profile of the sample. In some embodiments, the resulting enrichment score represents the degree to which genes in the second predefined gene set are coordinately upregulated or downregulated within the sample relative to the overall gene expression distribution within the sample for all genes for which gene expression data was obtained. In some embodiments, the enrichment score is computed for a single biological sample without requiring a matched control sample. In some embodiments, the method relies on within-sample gene expression rank distribution and not between-sample comparison. In some embodiments, the method can be applied independently to each sample in a plurality of samples. Methods for gene set variation or enrichment analysis, e.g., ssGSEA, are described further in Hanzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics, 14, 7 (30). In some embodiments, the methods herein involve ssGSEA implemented using the GSVA package. See, e.g., Example 1.

[0205] In some embodiments, the second sub-score determined for the second predefined gene set is determined using a machine learning model, e.g., a classifier model. In some50MF-367695757.6Attorney Docket No.: 237752002040embodiments, a trained machine learning model is used to generate the second sub-score using gene expression data. In some embodiments, the trained machine learning model comprises a classifier selected from logistic regression, random forest, or other supervised learning models. In some embodiments, the second sub-score determined for the second predefined gene set is determined using a single-set gene set enrichment analysis approach, a singscore approach, a machine learning approach, a gene set variation analysis approach, a PLAGE approach, or a z-score approach.

[0206] At 1108 in FIG. 11, a score is generated based on the first sub-score and the second sub-score. In some embodiments, the score is based on a comparison between the first sub-score and the second sub-score. In some embodiments, the score is based on a subtraction of the second sub-score from the first sub-score. In some embodiments, the first sub-score comprises a Treg marker sub-score, the second sub-score comprises a Teff marker sub-score, and the score that is generated is a Treg score that can be used to determine the identity of the plurality of cells. In some embodiments, the first sub-score comprises a post-expansion marker sub-score, the second sub-score comprises a pre-expansion marker sub-score, and the score that is generated is an expansion score that can be used to determine the expansion state of the plurality of cells.

[0207] At 1110 in FIG. 11, a status for the plurality of cells is determined using the score. In some embodiments, the score is a Treg score, and the status that is determined for the plurality of cells comprises the identity of the plurality of cells. In some embodiments, the cell identity is determined to be Treg or Treg-like if the Treg score meets a threshold. In some embodiments, the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score does not meet a threshold. In some embodiments, the cell identity is determined to be a destabilized Treg if the Treg score does not meet a threshold. In some embodiments, the threshold is zero. In some embodiments, the Treg score is described herein as meeting a threshold because the Treg score exceeds the threshold, i.e., is greater than the threshold. In some embodiments, the Treg score is described herein as not meeting a threshold because the Treg score does not exceed the threshold, i.e., is less than the threshold. In some embodiments, the cell identity is determined to be Treg or Treg-like if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9. In some embodiments, the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9. In some embodiments, the Treg score is51MF-367695757.6Attorney Docket No.: 237752002040greater than 0 and the plurality of cells is identified as comprising Tregs. In some embodiments, the Treg score is less than 0 and the plurality of cells is identified as comprising Teffs, non-Tregs, or destabilized Tregs.

[0208] In some embodiments, the score is an expansion score, and the status that is determined for the plurality of cells comprises the expansion state of the plurality of cells. In some embodiments, the cell expansion status is determined to be expanded if the expansion score meets a threshold. In some embodiments, the cell expansion status is determined to be unexpanded or endogenous if the expansion score does not meet a threshold. In some embodiments, the threshold is zero. In some embodiments, the expansion score is described herein as meeting a threshold because the expansion score exceeds the threshold, i.e., is greater than the threshold. In some embodiments, the expansion score is described herein as not meeting a threshold because the expansion score does not exceed the threshold, i.e., is less than the threshold. In some embodiments, the cell expansion status is determined to be expanded if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9. In some embodiments, the cell expansion status is determined to be unexpanded or endogenous if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9. In some embodiments, the expansion score is greater than 0 and the plurality of cells is identified as a post-expansion plurality of cells. In some embodiments, the expansion score is less than 0 and the plurality of cells is identified as a pre-expansion plurality of cells.

[0209] In some embodiments, a Treg score as described herein can be used to assess identity of a Treg cell therapy product by distinguishing Treg cells from Teff cells independent of activation or expansion state. In some embodiments, a Treg score as described herein can be used as a quality control metric for release of autologous Treg cell therapy products. In some embodiments, a Treg score as described herein can be used to evaluate potency of a Treg cell therapy product. For example, an increased Treg score may be correlated with increased suppressive function of a Treg cell therapy product. In some embodiments, a Treg score as described herein can provide a composite measure of multiple immunosuppressive mechanisms of Treg cells that are not captured by single functional assays. In some embodiments, a Treg score as described herein can be used to assess consistency across patient-specific batches of autologous cell therapy products. In some embodiments, a Treg score as described herein can be used to detect phenotypic instability or52MF-367695757.6Attorney Docket No.: 237752002040loss of Treg identity in engineered or expanded cells. In some embodiments, a Treg score as described herein can be used to evaluate effects of genetic engineering, including CAR expression or FOXP3 overexpression, on Treg cell identity and function. In some embodiments, a Treg score as described herein can be used to compare starting material from different donors or patient populations to assess baseline cell quality. In some embodiments, a Treg score as described herein can be used to determine whether a manufacturing process restores or improves dysfunctional Treg cells derived from patients with autoimmune disease. In some embodiments, a Treg score as described herein can be used to monitor effects of culture conditions, activation protocols, or processing variations on Treg cell products. In some embodiments, a Treg score as described herein can be used to define acceptance criteria or thresholds for product release, including identification of suboptimal products. In some embodiments, a Treg score as described herein can be used to assess expansion state and proliferative capacity of Treg cell therapy products. In some embodiments, a Treg score and / or expansion score as described herein can be used to guide optimization of manufacturing processes for Treg cell therapies. In some embodiments, a Treg score as described herein can be used as to predict clinical response to Treg cell therapy. In some embodiments, a Treg score as described herein can be used to correlate pre-infusion product characteristics with patient outcomes in clinical trials. In some embodiments, a Treg score as described herein can be used to guide dose selection for Treg cell therapies based on predicted potency or quality. In some embodiments, a Treg score as described herein can be measured in patient samples to monitor treatment response or immune status. In some embodiments, a Treg score as described herein can be used to identify resistance mechanisms or functional deficiencies in Treg cell therapies. In some embodiments, a Treg score and / or expansion score as described herein can be implemented as part of a multi-omics evaluation framework for a Treg cell therapy product in combination with transcriptomic, epigenomic, proteomic, or functional assay data. In some embodiments, a Treg score and / or expansion score as described herein can be adapted for use in other immune cell therapies or cell types beyond Treg cells. In some embodiments, a Treg score and / or expansion score as described herein can be used to produce a quantitative score indicative of product quality. In some embodiments, a Treg score and / or expansion score as described herein can be used to support regulatory submissions by demonstrating identity, potency, and consistency of cell therapy products.53MF-367695757.6Attorney Docket No.: 237752002040

[0210] Thus, in one aspect, provided herein is a method of determining cell identity, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells.

[0211] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a Treg cell therapy product comprising a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

[0212] In another aspect, provided herein is a method of determining cell expansion status, the method comprising: (a) obtaining gene expression data for one or more postexpansion markers and one or more pre-expansion markers in a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM,54MF-367695757.6Attorney Docket No.: 237752002040FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the preexpansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker subscore and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells.

[0213] In another aspect, provided herein is a method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a Treg cell therapy product comprising a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker sub-score and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.Applications of Treg Fingerprints

[0214] Certain aspects of the present disclosure relate to assessing the quality of a Treg cell therapy product. In some embodiments, a Treg score and / or an expansion score, as described herein, can be used to assess the quality of a Treg cell product. In some embodiments, a higher Treg score indicates a higher quality and / or a higher purity for a plurality of cells in a Treg cell therapy product. In some embodiments, a lower Treg score indicates a lower quality and / or a lower purity for a plurality of cells in a Treg cell therapy product. In some embodiments, a lower Treg score indicates contamination of a plurality of cells in a Treg cell therapy product. In some embodiments, a higher expansion score indicates a higher quality and / or a higher purity for a plurality of cells in a Treg cell therapy product. In 55MF-367695757.6Attorney Docket No.: 237752002040some embodiments, a lower expansion score indicates a lower quality and / or a lower purity for a plurality of cells in a Treg cell therapy product. In some embodiments, a lower expansion score indicates contamination of a plurality of cells in a Treg cell therapy product.

[0215] In some embodiments, the Treg fingerprints described herein may be applied to one or more polyclonal Treg populations. In some embodiments, the Treg fingerprints described herein may be applied to one or more engineered Treg populations. In some embodiments, the Treg population is generated from and / or for healthy individuals and / or from individuals with a disease. In some embodiments, the Treg identity fingerprints described herein are applied to a Treg population for the treatment of an autoimmune disease. In some embodiments, the Treg identity fingerprints described herein are applied to a Treg population for the treatment of an inflammatory disease. For example, in some embodiments, the Treg identity fingerprints described herein are applied to an expanded polyclonal Treg population for the treatment of type-1 diabetes (T1D).

[0216] In some embodiments, the Treg fingerprints described herein are used to assess the quality of a Treg therapy. In some embodiments, the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers. For example, in some embodiments, a Treg cell therapy is determined to be of higher quality when expression of one or more Treg markers in increased compared to a population of Teff cells. In another example, in some embodiments, expression level of the Treg markers provided herein can be determined at various time points during a manufacturing process to assess the expansion of Treg cells. In some embodiments, expression level of the Treg or Teff markers provided herein can be used to assess purity of a T cell composition. For example, the Treg and Teff markers provided herein can be used to confirm whether a Treg therapeutic composition comprises sufficient purity of Treg cells, or whether such composition comprises undesired Teff cells. Similarly, the Treg and Teff markers provided herein can be used to confirm whether a Teff therapeutic composition comprises sufficient purity of Teff cells, or whether such composition comprises undesired Treg cells.

[0217] When these fingerprints were applied to a large set of data generated from both DO (pre-expansion) and D14 (post-expansion) Treg and Teff cells generated internally, both performed exceptionally well assigning positive scores to cells with favorable characteristics and negative scores to those with “unfavorable” ones. In the case of the Treg identity fingerprint, Treg at both time points had scores above 0, and Teff had scores below 0.Importantly, the Sonoma Biotherapeutics (SBT) Treg identity fingerprint was better at 56MF-367695757.6Attorney Docket No.: 237752002040differentiating D14 Teff from DO and D14 Treg than analysis of FOXP3 expression alone. For the expansion fingerprint, both Treg and Teff had scores below 0 at DO, and above 0 at DI 4, despite the fingerprint being developed solely using gene expression data from Treg. Importantly, the SBT identity fingerprint performed better at differentiating Treg and Teff than 2 published fingerprints. One fingerprint (Ferraro et al.) was developed using only resting DO Treg and when applied to SBT Treg and Teff, gave D14 Teff scores greater than 0, making them indistinguishable from D14 Treg. The second fingerprint (Pesenacker et al.) performed better, likely because this fingerprint was developed using DO Treg and Treg stimulated for 40 hours, resulting in an activation-independent Treg gene signature similar to the SBT Treg identity fingerprint. These data suggest that “one-size-fits-all” molecular fingerprints may not be appropriate in all cases.

[0218] Aside from being used to confirm the identity and expansion states of SBT Treg, we demonstrated that our Treg fingerprints could be applied to other data sets to glean additional insights that may not be captured in traditional analyses. We demonstrated one potential use by comparing Treg that expressed a CAR which demonstrates high levels of tonic signaling (leading to destabilization of FOXP3 expression and a decrease in immunosuppressive function) to DO and D14 Treg and Teff. The Treg identity scores of destabilized Treg were lower than those of DO and D14 Treg, and closer to scores of Teff, indicating that these chronically stimulated cells lose some aspects of the Treg phenotype. In autoimmune diseases such as RA, multiple studies have demonstrated that patient-derived Treg show some level of phenotypic abnormalities and dysfunction / destabilization. In the case of autologous Treg cell therapies that need to be manufactured using patients’ cells, little is known about the potential impacts of these abnormalities on the final drug product. The Treg fingerprints are sensitive enough to enable a more nuanced view of Treg identity; comparison of the identity scores of final drug product generated from healthy donors or patients with autoimmune diseases might lead to insights on whether the manufacturing process “restores” dysfunctional Treg or findings that could inform changes in manufacturing to overcome these deficiencies

[0219] In another case, we compared the Treg identity scores of ectopic Treg, or CD4+T cells that had forced expression of FOXP3 compared to DO Treg and Teff and showed that ectopic Treg had scores that mainly fell in between Teff and Treg. Over-expression of FOXP3 is one strategy to mitigate the potential for Treg cell therapies to lose expression of FOXP3 when exposed to inflammatory conditions, leading to Treg being converted to a more57MF-367695757.6Attorney Docket No.: 237752002040proinflammatory phenotype and exacerbating autoimmune disease. Despite it being demonstrated that ectopic Treg can be immunosuppressive in vitro and to some extent in in vivo models, their function has not yet been validated in humans. Lower SBT Treg identity scores in eTreg suggest that while overexpression of FOXP3 can imbue non-Treg cells with some aspects of a Treg phenotype, they still maintain a level of Teff gene expression.

[0220] The identity and expansion state of Treg cell therapies are important to fully characterize as part of the drug development process, though these fingerprints may not fully capture perhaps the most important aspect of a successful cell therapy: potency. Treg can exert immunosuppressive function in multiple ways, including secretion of suppressive cytokines (IL-10, TGF-P, IL-35), metabolic disruption of effector T cells (high IL-2 consumption, adenosine production, tryptophan depletion), and direct cell to cell contact (CTLA-4, LAG3), in a sense acting as a cellular “polypharmacy” to dampen inflammation. Current potency assays for Treg cell therapies primarily assess their ability to suppress T cells, however these assays can vary in format and endpoint. Additionally, Treg can affect other cell types, including antigen presenting cells, which is not captured in T cell suppression assays. As such, direct measurement of the potency of each of these functions is difficult to analyze for each patient-derived drug product. To address this, Treg fingerprints could be developed to identify gene expression that is correlated with optimal function of individual immunosuppressive mechanisms, which could then be used alongside Treg identity and expansion fingerprints to provide a more holistic view of the drug product prior to infusion.

[0221] Ultimately, the ideal results of using these fingerprints to characterize Treg cell therapy drug products would be to demonstrate some level of correlation with clinical results, enabling the prediction of whether or not a patient may respond to therapy and leading to insights that could lead to next-generation manufacturing and engineering strategies to improve upon outcomes. We demonstrated that Treg fingerprints could be applied to polyclonal Treg generated for a clinical trial in T1D, confirming that there were no significant differences between the identity score of baseline Treg and infusion product and that infusion product had higher expansion scores, indicating that SBT Treg fingerprints can be applied to other Treg cell therapies and can identify cells with high accuracy. When we compared SBT Treg identity scores with clinical response as measured by the change in C-peptide 4-hour AUC at 1 year, there was no significant correlation between the 2 metrics though there was a positive trend between higher Treg identity scores and better clinical58MF-367695757.6Attorney Docket No.: 237752002040outcome (r=0.37). Several factors may have hindered this analysis, including a small sample size (n=15), the fact that there were no infusion products with “poor” Treg identity scores below 0, and that the sequencing depth of these samples was generally lower than internally derived samples, suggesting that the data quality may not have been optimal for this purpose. Most importantly, however, there was no significant difference in the C-peptide 4-hour AUC at 1 year inTID patients that received Treg when compared to the placebo group, and the absence of a clinically meaningful treatment effect limits the interpretability of this association. Because the identity and expansion scores of the polyclonal Treg generated for this study were high, these data suggest that the quality of the Treg drug product is unlikely to be a contributing factor in the lack of clinical benefit; rather, it might suggest that the use of polyclonal Treg to treat autoimmune diseases is not optimal, and that engineering antigen specificity into could amplify therapeutic benefit. Despite these caveats, the results of this proof-of-concept analysis are encouraging, and additional analyses should be conducted using data from Treg cell therapy clinical trials to strengthen the rationale for Treg fingerprints in this context.

[0222] These fingerprints have been successfully applied across multiple applications, demonstrating their ability to distinguish between different cell states and conditions.However, as with any analytical approach, there are certain limitations to consider. For example, the Treg fingerprints assess immune cell subsets using subset-specific gene expression signatures at the bulk level. While Treg isolation protocols yield highly purified populations of cells prior to expansion, 100% purity is not guaranteed, and the fingerprints developed using bulk gene expression analysis is not optimized to detect rare contaminating cell subsets. However, the design of the fingerprint combining “favorable” cell subsets and “unfavorable” cell subsets using a weighted scheme allows further optimization based on the objective such as detecting minimally allowable “unfavorable” cell subsets. In addition, this approach could be complemented by single-cell sequencing, providing greater phenotyping and resolution for rare subsets.

[0223] In summary, we have devised Treg identity and expansion fingerprint algorithms that consider both the gene expression of both favorable and unfavorable cell characteristics in a Treg cell therapy drug product, resulting in a robust measure of cellular identity and expansion state. This ssGSEA-based scoring system provides a straightforward and reproducible framework that could be adapted by researchers for additional applications and further refined to evaluate other cell therapy products or immune cell types. The Treg59MF-367695757.6Attorney Docket No.: 237752002040fingerprint scores we outlined in these studies should not be viewed as the only tool to assess the quality and function of cellular therapies. Instead, they should be one component of a larger assessment of these therapies that includes multi-omics approaches and functional assays that can provide a more holistic understanding of these complex drug products. By addressing the challenges associated with cellular therapy manufacturing, our fingerprints provide a critical tool for assessing quality, consistency, and therapeutic potential. As the field of cell therapy continues to advance, these algorithms may provide a foundation for improving product characterization, ensuring product safety, and ultimately enhancing clinical outcomes.

[0224] As used herein, “Treg fingerprint” refers a set of biomarkers, such as mRNA expression level, that is indicative of Treg status.EXEMPLARY EMBODIMENTS

[0225] Exemplary embodiments of the methods described herein include:

[0226] Embodiment 1 A. A method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the one or more Treg markers comprising ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs.

[0227] Embodiment 2A. A method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprising detecting an expression level of one or more Treg markers in the Treg cell or the population of Treg cells, the one or more Treg markers comprising ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs.60MF-367695757.6Attorney Docket No.: 237752002040

[0228] Embodiment 3 A. A method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the one or more Treg markers comprising ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

[0229] Embodiment 4A. The method of any one of embodiments 1 A-3A, comprising detecting the expression level of 2 or more, 3, or more, 4 or more, 5, or more 7 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers.

[0230] Embodiment 5A. The method of any one of embodiments 1 A-4A, comprising detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

[0231] Embodiment 6A. The method of any one of embodiments 1 A-5A, comprising detecting an expression level of one or more Teff markers, wherein the one or more Teff markers comprise STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0232] Embodiment 7A. The method of embodiment 6A, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more Teff markers.

[0233] Embodiment 8A. The method of embodiment 6A, comprising detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0234] Embodiment 9A. A method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprising obtaining gene expression data of one or more Treg markers associated with the cell or the population of cells, determining, based on the gene expression data, a Treg score for the cell or the population of cells, and classifying the cell as a Treg or classifying the population of cells as comprising Tregs, if the Treg score for the cell exceeds a threshold, wherein the one or more Treg 61MF-367695757.6Attorney Docket No.: 237752002040markers comprise ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

[0235] Embodiment 10A. The method of embodiment 9A, wherein the Treg score is determined using single-sample gene set enrichment analysis (ssGSEA).

[0236] Embodiment 11 A. The method of embodiment 9A or 10A, wherein the Treg score is determined at least partially based on predefined gene signatures for a Treg phenotype and an associated weight for each signature.

[0237] Embodiment 12A. The method of embodiment 11 A, wherein the Treg phenotype comprises Treg identity, Treg expansion, Thl7 phenotype, Th2 phenotype, Thl phenotype, and / or suppression potency.

[0238] Embodiment 13A. The method of any one of embodiments 9A-12A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data.

[0239] Embodiment 14A. The method of any one of embodiments 9A-13A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from unexpanded Tregs and from Tregs that have been expanded in cell culture.

[0240] Embodiment 15 A. The method of any one of embodiments 9A-14A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff cells.

[0241] Embodiment 16A. The method of any one of embodiments 9A-15A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from Teff unexpanded Teff and Teff that have been expanded in culture.

[0242] Embodiment 17A. A method of identifying a cell as an expanded Treg or a composition as comprising expanded Tregs in a cell or a population of cells comprising, detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the one or more post-expansion markers comprise PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2,62MF-367695757.6Attorney Docket No.: 237752002040CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1, wherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs.

[0243] Embodiment 18 A. The method of embodiment 17A, comprising detecting 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more post-expansion markers.

[0244] Embodiment 19A. The method of any one of embodiments 17A-18A, comprising detecting expression of PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1.

[0245] Embodiment 20A. The method of any one of embodiments 17A-19A, further comprising detecting expression of one or more pre-expansion markers, wherein the one or more pre-expansion markers comprise KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3.

[0246] Embodiment 21 A. The method of embodiment 20A, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 7 or more, or 10 or more preexpansion markers.

[0247] Embodiment 22A. The method of embodiment 20A, comprising detecting the expression level of KLF4, NR4A2, TCEA3, MATN1, DUSP1, GOLGA8M, APBA2, ED AR, RASD2, DTX1, GPR25, SH3RF3, CCR9, FOS, JUNB NFKBIA, and TGFB3

[0248] Embodiment 23 A. A method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data of one or more expanded Treg markers associated with the cell or the population of cells, determining, based on the gene expression data, an expanded Treg score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells an comprising expanded Tregs if the Treg score for the cell exceeds a threshold, wherein the one or more expanded Treg markers comprise PCKS6, CLDN5, SPP1, ATRNL1, LRP4, PCDH8, APOL4, PDGFRB, PTGIS, NOVA2, RCAN2, CYP1B1, PTPRG, C6orf223, CCRG, LAG3, HLA-DRA, MDM2 and CDK1.

[0249] Embodiment 24A. The method of embodiment 23 A, wherein the expanded Treg score is determined using single-sample gene set enrichment analysis (ssGSEA).63MF-367695757.6Attorney Docket No.: 237752002040

[0250] Embodiment 25A. The method of embodiment 23 A or 24A, wherein the Treg score is determined at least partially based on predefined gene signatures for a Treg phenotype and an associated weight for each signature.

[0251] Embodiment 26A. The method of embodiment 25A, wherein the Treg phenotype comprises Treg identity, Treg expansion, Thl7 phenotype, Th2 phenotype, Thl phenotype, and / or suppression potency.

[0252] Embodiment 27A. The method of any one of embodiments 23 A-26A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data.

[0253] Embodiment 28A. The method of any one of embodiments 23 A-27A, wherein the predefined gene signatures for the Treg phenotype and the associated weight for each signature is determined using gene expression data from unexpanded Tregs and from Tregs that have been expanded in cell culture.

[0254] Embodiment 29A. The method of any one of embodiments 1 A-8A or 17A-22A, wherein the expression level is mRNA expression level.

[0255] Embodiment 30A. The method of any one of embodiments 9A-16A or 23A-28A, wherein the gene expression data is mRNA expression level.

[0256] Embodiment 31 A. The method of embodiment 29 A or 30 A, wherein mRNA expression level is determined using RNAseq or microarray.

[0257] Further exemplary embodiments of the methods described herein include:

[0258] Embodiment 1. A method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprising detecting an expression level of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs.

[0259] Embodiment 2. A method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprising detecting an expression level64MF-367695757.6Attorney Docket No.: 237752002040of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs.

[0260] Embodiment 3. A method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in a cell or a population of cells in the Treg cell therapy, the Treg markers comprising one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

[0261] Embodiment 4. The method of any one of embodiments 1-3, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers.

[0262] Embodiment 5. The method of any one of embodiments 1-4, comprising detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

[0263] Embodiment 6. The method of any one of embodiments 1-5, further comprising detecting an expression level of one or more Teff markers, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0264] Embodiment 7. The method of embodiment 6, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more of the Teff markers.65MF-367695757.6Attorney Docket No.: 237752002040

[0265] Embodiment 8. The method of embodiment 6 or 7, comprising detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0266] Embodiment 9. A method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprising obtaining gene expression data for one or more Treg markers and one or more Teff markers associated with the cell or the population of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2, determining, based on the gene expression data, a Treg score for the cell or the population of cells, and classifying the cell as a Treg or classifying the population of cells as comprising Tregs if the Treg score for the cell exceeds a threshold.

[0267] Embodiment 10. The method of embodiment 9, wherein the one or more Treg markers are selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the one or more Teff markers are selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0268] Embodiment 11. The method of embodiment 9 or 10, wherein the Treg score is determined using a Treg marker sub-score and a Teff marker sub-score.

[0269] Embodiment 12. The method of embodiment 11, wherein the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.

[0270] Embodiment 13. The method of any one of embodiments 9-12, wherein the threshold is zero.

[0271] Embodiment 14. The method of any one of embodiments 9-13, wherein the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is a positive value greater than zero.66MF-367695757.6Attorney Docket No.: 237752002040

[0272] Embodiment 15. The method of any one of embodiments 9-14, wherein the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0273] Embodiment 16. The method of any one of embodiments 9-15, wherein the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is a negative value less than zero.

[0274] Embodiment 17. The method of any one of embodiments 9-16, wherein the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0275] Embodiment 18. The method of any one of embodiments 9-17, wherein the Treg score is determined using a Treg marker sub-score and a Teff marker sub-score, and wherein the sub-scores are determined using a gene set enrichment analysis.

[0276] Embodiment 19. The method of embodiment 18, wherein the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0277] Embodiment 20. The method of embodiment 18 or 19, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and wherein the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

[0278] Embodiment 21. The method of any one of embodiments 18-20, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and wherein the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

[0279] Embodiment 22. A method of determining cell identity, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3,67MF-367695757.6Attorney Docket No.: 237752002040and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells.

[0280] Embodiment 23. A method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a Treg cell therapy product comprising a plurality of cells, wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2; (b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers; (c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers; (d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and (e) determining, based on the Treg score, a cell identity for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

[0281] Embodiment 24. The method of embodiment 22 or 23, wherein the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.

[0282] Embodiment 25. The method of any one of embodiments 22-24, wherein the cell identity is determined to be Treg or Treg-like if the Treg score meets a threshold.

[0283] Embodiment 26. The method of any one of embodiments 22-25, wherein the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score does not meet a threshold.

[0284] Embodiment 27. The method of embodiment 25 or 26, wherein the threshold is zero.68MF-367695757.6Attorney Docket No.: 237752002040

[0285] Embodiment 28. The method of any one of embodiments 22-27, wherein the cell identity is determined to be Treg or Treg-like if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0286] Embodiment 29. The method of any one of embodiments 22-28, wherein the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0287] Embodiment 30. The method of any one of embodiments 22-29, wherein the Treg marker sub-score and the Teff marker sub-score are each determined using a gene set enrichment analysis.

[0288] Embodiment 31. The method of embodiment 30, wherein the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0289] Embodiment 32. The method of embodiment 30 or 31, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and wherein the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

[0290] Embodiment 33. The method of embodiment 32, wherein the predefined gene set comprising the one or more Treg markers comprises one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2R. A.

[0291] Embodiment 34. The method of any one of embodiments 30-33, wherein a higher Treg marker sub-score increases the likelihood that the cell identity is determined to be Treg or Treg-like.

[0292] Embodiment 35. The method of any one of embodiments 30-34, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and wherein the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.69MF-367695757.6Attorney Docket No.: 237752002040

[0293] Embodiment 36. The method of embodiment 35, wherein the predefined gene set comprising the one or more Teff markers comprises one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

[0294] Embodiment 37. The method of any one of embodiments 30-36, wherein a higher Teff marker sub-score increases the likelihood that the cell identity is determined to be Teff, Teff-like, or non-Treg.

[0295] Embodiment 38. The method of any one of embodiments 30-37, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers for generating the Treg marker sub-score and a predefined gene set comprising the one or more Teff markers for generating the Teff marker sub -score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.

[0296] Embodiment 39. The method of embodiment 38, wherein the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

[0297] Embodiment 40. The method of any one of embodiments 22-39, wherein the Treg marker sub-score and the Teff marker sub-score are given equal weight in determining the Treg score.

[0298] Embodiment 41. The method of any one of embodiments 22-40, wherein a higher Treg score indicates a higher quality and / or a higher purity for the plurality of cells.

[0299] Embodiment 42. The method of any one of embodiments 22-41, wherein a lower Treg score indicates a lower quality and / or a lower purity for the plurality of cells.

[0300] Embodiment 43. The method of any one of embodiments 22-42, wherein a lower Treg score indicates contamination of the plurality of cells.

[0301] Embodiment 44. A method of identifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising detecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein based upon the expression level of the one or more post-expansion markers, the cell70MF-367695757.6Attorney Docket No.: 237752002040is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs.

[0302] Embodiment 45. A method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more post-expansion markers in a cell or a population of cells in the Treg cell therapy, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more post-expansion markers.

[0303] Embodiment 46. The method of embodiment 44 or 45, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the post-expansion markers.

[0304] Embodiment 47. The method of any one of embodiments 44-46, comprising detecting the expression level of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0305] Embodiment 48. The method of any one of embodiments 44-47, further comprising detecting an expression level of one or more pre-expansion markers, wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0306] Embodiment 49. The method of embodiment 48, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the pre-expansion markers.

[0307] Embodiment 50. The method of embodiment 48 or 49, comprising detecting the expression level of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0308] Embodiment 51. A method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprising obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers associated with the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS, determining, based on the gene 71MF-367695757.6Attorney Docket No.: 237752002040expression data, an expansion score for the cell or the population of cells, and classifying the cell as an expanded Treg or classifying the population of cells as comprising expanded Tregs if the expansion score for the cell exceeds a threshold.

[0309] Embodiment 52. The method of embodiment 51, wherein the one or more postexpansion markers are selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the one or more pre-expansion markers are selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0310] Embodiment 53. The method of embodiment 51 or 52, wherein the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score.

[0311] Embodiment 54. The method of embodiment 53, wherein the expansion score is based on a comparison between the pre-expansion marker sub-score and the post-expansion marker sub-score.

[0312] Embodiment 55. The method of any one of embodiments 51-54, wherein the threshold is zero.

[0313] Embodiment 56. The method of any one of embodiments 51-55, wherein the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is a positive value greater than zero.

[0314] Embodiment 57. The method of any one of embodiments 51-56, wherein the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0315] Embodiment 58. The method of any one of embodiments 51-57, wherein the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is a negative value less than zero.

[0316] Embodiment 59. The method of any one of embodiments 51-58, wherein the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as72MF-367695757.6Attorney Docket No.: 237752002040comprising unexpanded or endogenous Tregs if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0317] Embodiment 60. The method of any one of embodiments 51-59, wherein the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score, and wherein the sub-scores are determined using a gene set enrichment analysis.

[0318] Embodiment 61. The method of embodiment 60, wherein the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0319] Embodiment 62. The method of embodiment 60 or 61, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and wherein the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.

[0320] Embodiment 63. The method of any one of embodiments 60-62, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and wherein the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.

[0321] Embodiment 64. A method of determining cell expansion status, the method comprising: (a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker subscore and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells.

[0322] Embodiment 65. A method of assessing the quality of a Treg cell therapy product, the method comprising: (a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a Treg cell therapy product comprising a 73MF-367695757.6Attorney Docket No.: 237752002040plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS; (b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers; (c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers; (d) generating an expansion score for the plurality of cells, using the post-expansion marker sub-score and the pre-expansion marker sub-score; and (e) determining, based on the expansion score, a cell expansion status for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

[0323] Embodiment 66. The method of embodiment 64 or 65, wherein the expansion score is based on a comparison between the pre-expansion marker sub-score and the postexpansion marker sub-score.

[0324] Embodiment 67. The method of any one of embodiments 64-66, wherein the cell expansion status is determined to be expanded if the expansion score meets a threshold.

[0325] Embodiment 68. The method of any one of embodiments 64-67, wherein the cell expansion status is determined to be unexpanded or endogenous if the expansion score does not meet a threshold.

[0326] Embodiment 69. The method of embodiment 67 or 68, wherein the threshold is zero.

[0327] Embodiment 70. The method of any one of embodiments 64-69, wherein the cell expansion status is determined to be expanded if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

[0328] Embodiment 71. The method of any one of embodiments 64-70, wherein the cell expansion status is determined to be unexpanded or endogenous if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

[0329] Embodiment 72. The method of any one of embodiments 64-71, wherein the postexpansion marker sub-score and the pre-expansion marker sub-score are each determined using a gene set enrichment analysis.74MF-367695757.6Attorney Docket No.: 237752002040

[0330] Embodiment 73. The method of embodiment 72, wherein the gene set enrichment analysis comprises single-sample gene set enrichment analysis (ssGSEA).

[0331] Embodiment 74. The method of embodiment 72 or 73, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and wherein the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.

[0332] Embodiment 75. The method of embodiment 74, wherein the predefined gene set comprising the one or more post-expansion markers comprises one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

[0333] Embodiment 76. The method of any one of embodiments 72-75, wherein a higher post-expansion marker sub-score increases the likelihood that the cell expansion status is determined to be expanded.

[0334] Embodiment 77. The method of any one of embodiments 72-76, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and wherein the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.

[0335] Embodiment 78. The method of embodiment 77, wherein the predefined gene set comprising the one or more pre-expansion markers comprises one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

[0336] Embodiment 79. The method of any one of embodiments 72-78, wherein a higher pre-expansion marker sub-score increases the likelihood that the cell expansion status is determined to be unexpanded or endogenous.

[0337] Embodiment 80. The method of any one of embodiments 72-79, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more postexpansion markers for generating the post-expansion marker sub-score and a predefined gene set comprising the one or more pre-expansion markers for generating the pre-expansion marker sub-score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.75MF-367695757.6Attorney Docket No.: 237752002040

[0338] Embodiment 81. The method of embodiment 80, wherein the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

[0339] Embodiment 82. The method of any one of embodiments 64-81, wherein the postexpansion marker sub-score and the pre-expansion marker sub-score are given equal weight in determining the expansion score.

[0340] Embodiment 83. The method of any one of embodiments 64-82, wherein a higher expansion score indicates a higher quality and / or a higher purity for the plurality of cells.

[0341] Embodiment 84. The method of any one of embodiments 64-83, wherein a lower expansion score indicates a lower quality and / or a lower purity for the plurality of cells.

[0342] Embodiment 85. The method of any one of embodiments 64-84, wherein a lower expansion score indicates contamination of the plurality of cells.

[0343] Embodiment 86. The method of any one of embodiments 1-8 or 44-50, wherein the expression level is mRNA expression level.

[0344] Embodiment 87. The method of any one of embodiments 9-43 or 51-85, wherein the gene expression data comprises mRNA expression levels for one or more genes.

[0345] Embodiment 88. The method of embodiment 87, wherein the gene expression data is obtained using next generation sequencing, whole genome sequencing, whole exome sequencing, targeted sequencing, direct sequencing, Sanger sequencing, or microarray.

[0346] Embodiment 89. The method of embodiment 87 or 88, wherein the gene expression data comprises data for genes other than the one or more Treg markers, the one or more Teff markers, the one or more post-expansion markers, or the one or more preexpansion markers.

[0347] Embodiment 90. The method of any one of embodiments 1-8, wherein the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0348] Embodiment 91. The method of any one of embodiments 1-8, wherein the expression level of the one or more Treg markers and / or the one or more Teff markers is76MF-367695757.6Attorney Docket No.: 237752002040detected in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0349] Embodiment 92. The method of any one of embodiments 1-8, wherein the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0350] Embodiment 93. The method of any one of embodiments 1-8, wherein the expression level of the one or more Treg markers and / or the one or more Teff markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0351] Embodiment 94. The method of any one of embodiments 9-43, wherein the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0352] Embodiment 95. The method of any one of embodiments 9-43, wherein the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0353] Embodiment 96. The method of any one of embodiments 9-43, wherein the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0354] Embodiment 97. The method of any one of embodiments 9-43, wherein the gene expression data is obtained for the one or more Treg markers and / or the one or more Teff markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0355] Embodiment 98. The method of any one of embodiments 44-50, wherein the expression level of the one or more post-expansion markers and / or the one or more preexpansion markers is detected in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.77MF-367695757.6Attorney Docket No.: 237752002040

[0356] Embodiment 99. The method of any one of embodiments 44-50, wherein the expression level of the one or more post-expansion markers and / or the one or more preexpansion markers is detected in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0357] Embodiment 100. The method of any one of embodiments 44-50, wherein the expression level of the one or more post-expansion markers and / or the one or more preexpansion markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cell or the population of cells.

[0358] Embodiment 101. The method of any one of embodiments 44-50, wherein the expression level of the one or more post-expansion markers and / or the one or more preexpansion markers is detected in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cell or the population of cells with a chimeric antigen receptor.

[0359] Embodiment 102. The method of any one of embodiments 51-85, wherein the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at DO and / or before culturing the cell or the population of cells.

[0360] Embodiment 103. The method of any one of embodiments 51-85, wherein the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells before transduction of the cell or the population of cells with a chimeric antigen receptor.

[0361] Embodiment 104. The method of any one of embodiments 51-85, wherein the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after culturing the cells.

[0362] Embodiment 105. The method of any one of embodiments 51-85, wherein the gene expression data is obtained for the one or more post-expansion markers and / or the one or more pre-expansion markers in the cell or the population of cells at about 10 days, about 11 days, about 12 days, about 13 days, or about 14 days after transduction of the cells with a chimeric antigen receptor.78MF-367695757.6Attorney Docket No.: 237752002040

[0363] Embodiment 106. The method of embodiment 12 or 24, wherein the comparison comprises subtracting the Teff marker sub-score from the Treg marker sub-score.

[0364] Embodiment 107. The method of embodiment 54 or 66, wherein the comparison comprises subtracting the pre-expansion marker sub-score from the post-expansion marker sub-score.EXAMPLESExample 1 — Transcriptional fingerprinting of regulatory T cells: ensuring quality in cell therapy applications

[0365] The success of regulatory T cell (Treg) therapies depend on the source of Treg and the quality of Treg manufacturing product that maintains Treg identity. Commonly used methods to identify Treg, including assessment of FOXP3 expression and demethylation of the Treg-specific demethylated region (TSDR), may not be sufficient on their own to ensure that Treg cell therapy drug products have an optimal identity and phenotype prior to infusion into patients. To address this critical need, we developed a robust framework to molecularly characterize Treg products using next-generation sequencing. By systematically profiling Treg and effector T cells (Teff) pre- and post-expansion, we defined the molecular fingerprints for expanded Treg products. A non-parametric algorithm was employed to score Treg manufacturing products for their cell identity and expansion fingerprints. The identity fingerprint reflects Treg cell identity by effectively distinguishing Treg from Teff irrespective of their activation status, with increased sensitivity and specificity, while the expansion fingerprint discriminates expanded vs endogenous Treg or Teff. We also showed that the identity fingerprint predicts Treg stability in in vitro settings and can be used to illustrate differences in drug products generated using distinct strategies. We further applied fingerprinting to bulk RNA sequencing (RNA-seq) data from endogenous and expanded Treg in a Phase 2 clinical trial for type 1 diabetes (T1D), demonstrating its ability to capture Treg identity and expansion in an independent study. Together, our Treg fingerprinting method provides a powerful tool to molecularly characterize Treg products, potentially enabling correlative analysis with the safety and efficacy outcomes of Treg-based cell therapies.

[0366] Described herein are experiments for designing and implementing Treg fingerprints. Using both internal and published data, the inventors developed two Treg fingerprints that can be used to assess a final drug product: a Treg identity fingerprint that can differentiate between Treg and Teff cells regardless of their expansion state, and a Treg79MF-367695757.6Attorney Docket No.: 237752002040fingerprint to characterize expanded Treg after the manufacturing process. Both were validated using published and internal data and were applied to both nonclinical and clinical datasets to demonstrate practical applications of the fingerprints. Together, these data support the development and use of Treg fingerprints as part of the drug development process, not only for use in QC of the final drug product (e.g., an autologous Treg cell therapy engineered with a chimeric antigen receptor (CAR) to respond to activation signals in inflamed tissues), but also to inform the design of clinical trials.Materials & MethodsGeneration of Treg and Teff cells

[0367] Treg (CD4+CD25hiCD127lo) and Teff (CD4+CD25loCD127+) were isolated from peripheral blood mononuclear cells (PBMC) by fluorescence-activated cell sorting (FACS). Purified cells were then either frozen (DO) or activated and expanded for 14 days with anti-CD3 / anti-CD28 Dynabeads (ThermoFisher Scientific) (D14). Treg were transduced with CAR constructs on day 3.Treg Suppression Assays

[0368] CD3+responder T cells (Tresp) were isolated from cryopreserved PBMC (StemCell Technologies; Cat# 19051) and labeled with CellTrace™ CFSE (ThermoFisher; Cat# C34554) before plating 5×104cells / well in a 96-well plate. D14 Treg, destabilized Treg, or 4 stim Teff cells were plated at indicated cell:Tresp ratios after overnight rest in cRPMI + 300 lU / mL IL-2. Cocultures and Tresp only were cultured in cRPMI media + anti-CD3 / anti-CD28 Dynabeads for 3 days. CD4+and CD8+Tresp cell proliferation was measured by CFSE-dilution measured by flow cytometry. The % suppression was calculated by the following equation:(% CFSE dilution of Tresp only-% CFSE dilution of Tresp from coculture) 100 / ° % CFSE dilution of Tresp onlyRNA-Seq Experiments

[0369] RNA sequencing (RNA-Seq) experiments were conducted to investigate transcriptomic changes under various conditions. Cell pellets were sent to an external vendor (SeqMatic), where they performed library prep via Illumina mRNA kit and sequencing by NovaSeq X Plus.80MF-367695757.6Attorney Docket No.: 237752002040Treg transcriptional profiling post-thaw and after 24-hour culture

[0370] Cryopreserved D 14 Treg generated from 6 donors were thawed and harvested for transcriptional analysis either immediately after thaw, or after being cultured for 24 hours in complete RPMI 1640 (cRPMI) medium with 10% FBS and 300 lU / mL IL-2.Transcriptional profiling on Tregs and Teffs before / after destabilization

[0371] Cryopreserved D 14 Treg expressing a high tonic stimulating CAR (high-affinity scFv specific for myelin oligodendrocyte glycoprotein [MOG], fused to a CD28 costimulatory domain and CD3(^ signaling domain) and D14 Teff were thawed and cultured at 37°C for 24 hours in complete RPMI 1640 (cRPMI) medium + 10% FBS with 300 lU / mL IL-2. Cells were then plated at 5xl05cells / mL in cRPMI with 300 lU / mL IL-2 and anti-CD3 / anti-CD28 Dynabeads at a 2:1 cell to bead ratio. Beads were removed on Day 3, then cells were cultured for 4 days. The stimulation process was repeated 3 times, for a total of 4 rounds of stimulation over 28 days. Cells were collected immediately after thaw and after 4 rounds of stimulation for transcriptional profiling. Unstimulated DO and D14 Treg from the same donors were also analyzed.RNA-Seq data processingInternal Datasets

[0372] FASTQ files for each dataset was processed using the nf-core / rnaseq pipeline (version 3.14.0) (26). The reference genome used was GRCh38 (Homo_sapiens. GRCh38.dna_sm. primary assembly), and gene annotation was the Ensembl release 110. The pipeline performed the following steps: 1) Quality control of raw reads (FastQC), 2) Adapter trimming and quality filtering (Trim Galore!), and 3) Transcript quantification (Salmon). The output of gene’s transcript-per-million (TPM) matrix was used for downstream fingerprinting score calculation. The counts files were used for downstream differential gene expression analysis.External Datasets

[0373] Three external datasets were included in the analysis: 1) Honaker et al. 2020 (data downloaded from doi:10.5061 / dryad.02v6wwq08) (9), 2) GSE253540 (27), and 3) GSE243270 (28). The FASTQ files for these datasets were obtained using the nf-81MF-367695757.6Attorney Docket No.: 237752002040core / fetchngs pipeline. Subsequently, they were processed following the same pipeline and parameters as the internal datasets to ensure consistency in data processing (Table 1).Table 1: Datasets used for Treg fingerprint discovery, validation, and application.D14 D14 DO DO D14 D14 Treg Teff Dataset name Source Use Teff Treg Teff Treg (Destab) (Stim) SBT dataset 1 Internal Discovery 0 0 3 9 0 0 (Identity)SBT dataset 2 Internal Discovery 8 8 18 36 0 0 (Identity &Expansion)SBT dataset 3 Internal Discovery 0 22 0 12 0 0 (Expansion)SBT dataset 4 Internal Validation 0 0 0 8 0 0 SBT dataset 5 Internal Validation 0 0 0 56 0 0 SBT dataset 6 Internal Validation 0 0 0 24 72 0 SBT dataset 7 Internal Validation 16 18 9 35 0 0 SBT dataset 8 Internal Validation 0 3 3 6 0 3 &ApplicationMensink et al. External Validation 0 0 10 10 0 0 2024 (27)SBT dataset 9 Internal Validation 0 0 4 4 16 0 SBT dataset Internal Validation 0 0 0 3 0 0 10SBT dataset 11 Internal Validation 0 3 0 13 11 0 SBT dataset Internal Validation 0 0 12 36 0 0 12Totals 24 54 59 252 99 3 Honaker et al. External Application 0 0 4 8 0 0 2020 (9)Bender et al. External Application 0 14 0 15 0 02024 (28)Differential Gene Expression Analysis

[0374] Differential gene expression analysis was performed using DESeq2 (29). Genes were considered differentially expressed if they met the following criteria: Log2 fold change > 1 and Benjamini-Hochberg adjusted p-value < 0.05.Gene Signature Curation82MF-367695757.6Attorney Docket No.: 237752002040Treg Identity Signatures

[0375] The Ferraro Treg identity signatures were obtained from Dataset S2 of Ferraro et al. 2014 (18) comprising 194 Treg-up and 192 Treg-down genes. The Pesenacker et al. signature was extracted from Figure IF of their study (19).

[0376] To derive the SBT expansion-independent Treg identity signatures, we employed a two-step process: 1) Differential expression analysis between Treg and Teff was conducted at DO and D14 separately. 2) Intersection of the gene lists was derived as: Treg identity signature genes — genes upregulated in both DO and D14 Treg (compared to Teff) (i.e., Treg markers) and Teff identity genes — genes upregulated in both DO and D14 Teff (compared to Treg) (i.e., Teff markers). Further, due to the availability of only one dataset containing both DO Teff and Treg, we used the Treg / Teff signatures from Ferraro et al. (18) to perform the intersection. Two internal discovery datasets were included in the derivation of the Treg identity signature: SBT dataset 1 and SBT dataset 2 (Table 1). The Treg identity signatures as described herein comprise predefined gene sets comprising Treg markers and Teff markers.Treg Expansion Signatures

[0377] Treg expansion signatures were derived by comparing D14 to DO Tregs from two internal datasets: SBT dataset 2 and SBT dataset 3 (Table 1). The final gene signatures (Table 2) consisted of overlapping genes from these two datasets, as illustrated in FIG. 3A. The Treg expansion signatures as described herein comprise predefined gene sets comprising post-expansion markers and pre-expansion markers.Table 2: Genes included in Treg identity and Treg expansion fingerprints.SBT Identity Treg SBT Identity Teff SBT Expansion DO SBT Expansion D14 ACTA2 ABCB1 AAED1 AACS BCL2L11 CD40LG AAK1 AARS CCDC141 CFH ABCD2 ABAT CCNG2 GAB3 ABCG1 ABCB6 CDK14 GNLY ABHD11 ABHD4 CTLA4 ID2 ABL2 ABHD6F5 MCOLN2 ABLIM2 ABI3FCRL3 MCTP2 AC007551.3 ABRACL FOXP3 NKG7 AC058791.1 AC003102.3 GBP5 STXBP1 ACACB AC020571.3 GCNT4 TARP (CD3G) ACTG1P4 AC093391.2 GPA33 ACTR5 ACAA2ID3 ACVR2B AC ATIIKZF2 ADAMTS17 ACAT2IL2RA ADARB1 ACLYINPP5F ADCY10P1 ACO1METTL7A ADCY3 ACOT1383MF-367695757.6Attorney Docket No.: 237752002040OASl ADM5 ACOX1 PYHIN1 ADRA2B ACP2 RBMS3 ADSSL1 ACSS2 RTKN2 AFAP1 ACTA2 SELP AFF3 ACTB SGPP2 AGPAT2 ACTN1 SLC12A6 AHNAK ACY1 SMPD3 AHR ADAMI 0 ST8SIA6 AK5 ADAMTS1 TIGIT AKAP6 ADAMTS4 TNFRSF1B AKIRIN2 ADAMTS6 TRIB1 AKT3 ADH5 TSHR ALMS1 ADIPOR2 VAV3 ALMS 1 -IT 1 ADPRH ZCCHC14 ALOX5AP ADRBK2ALPK1 AFMID AMIGO 1 AGMAT AMY2B AGPAT5 AMZ1 AHRR ANKK1 AIFM1 ANKMY1 AIMP2 ANKRD31 AK4 ANKRD36BP2 AKR1C3 ANKRD37 ALAS1 ANKRD42 ALDH1B1 ANKS3 ALDH4A1 ANKS6 ALDH6A1 ANXA1 ALG1 AOC2 AMOTL1 AOC3 ANG APBA2 ANLN APBB1 ANO 10 APOE ANPEP AREG ANXA4 ARHGAP32 AP1S1 ARHGAP33 AP3M1 ARHGEF10 AP3M2 ARIH2OS APB Al ARL4A APEX2 ARMCX4 APOBEC3C ARRDC2 APOBEC3F ARRDC3 APOBEC3G ARRDC4 APOBEC3H ARVCF APOL1 ASAP1 APOL2 ASIC3 APOL4 ASPH APOLD1 ATAD3B APP ATF3 AQP3 ATF7IP AQP7 ATHL1 ARF3 ATP1A3 ARG2 ATP2B1 ARHGEF39 ATP6V1E2 ARID3A ATXN7 ARL11 ATXN7L1 ARL2 ATXN7L2 ARL6IP1 AUTS2 ARL6IP5 AVIL ARMC7 AVPI1 ARNTL2 AXIN2 ARPC5 B3GNT2 ARSB BACH1 ASB2BACH2 ASB984MF-367695757.6Attorney Docket No.: 237752002040BAG3 ASF IB BAI1 ASIC1 BAI2 ASPHD2 BAMBI ASPM BCAS1 ATOX1 BCAS2 ATP 13 A3 BCL2A1 ATP5F1 BCL9L ATP6V0A1 BCLAF1 ATP6V0D1 BEX2 ATP6V0E1 BMP8B ATP6V1A BRICD5 ATP6V1B2 BTG2 ATP6V1D BZRAP1 ATP7A C10orfl05 ATRNL1 ClOoiflll AURKA C10orf32-ASMT AURKB C12orf42 B3GALNT1 C12orf57 B3GALT2 C12orf61 B3GALT4 C12orf68 B3GNT8 C14orfl82 B3GNT9 C15orf52 B4GALT5 C16orf80 BAG2 C17orfl07 BAK1 C19orf71 BATF C19orf73 BAX Clorfl62 BBS12 Clorf228 BCAT2 C1QTNF3 BCL2L1 C1QTNF6 BDH2 C20orfll2 BHLHE40 C21orf2 BIRC5 C22orf43 BIVM C3orf33 BLM C3orf35 BLVRB C4orf46 BMF C6orf226 BMP2K C6orf48 BPGM C6orf99 BPNT1 C7orf60 BRCA1 C8orf44-SGK3 BRE C9orf72 BRI3 CAB YR BRK1 CACHD1 BRSK1 CACNA1F BSPRY CACNA1H BUB1 CALY BUB IB CAMK2N1 BYSL CAMK4 C10orfl28 CAND2 C10orf88 CAPN7 Cllorf49 CAPRIN2 C12orf49 CAPS C12orf73 CASZ1 C14orfl CATSPER2 C14orfll9 CBX3P2 C16orf62 CBX4 C17orf58 CBX7 C17orf72 CCDC101 C17orf77 CCDC136 C17orf80 CCDC141 C17orf96 CCDC17 C19orfl0CCDC176 C19orf3885MF-367695757.6Attorney Docket No.: 237752002040CCDC57 C1GALT1C1 CCDC59 Clorfll2 CCDC64 Cl orl233 CCDC88A Clorl85 CCDC96 C3AR1 CCNL1 C3orfl8 CCNL2 C4orf48 CCNT1 C5AR2 CCR9 C5orf51 CCRN4L C6orl211 CCSAP C6orl223 CCT6P1 C9orf69 CD69 CA6 CD83 CABLES 1 CD84 CALD1 CDAN1 CALR CDC14A CAMK1 CDC14B CAMKK2 CDC42BPG CANX CDC42EP4 CAPNS1 CDH23 CAPZA1 CDH24 CARD6 CDHR3 CASC5 CDK13 CASP3 CDK16 CASP9 CDK20 CASR CDK5R1 CBLN3 CDKN1B CBR1 CDKN2AIP CBR3 CDKN2D CBS CDRT1 CBX2 CEACAM4 CCDC102A CEBPA CCDC106 CELSR2 CCDC125 CENPV CCDC127 CEP 120 CCDC47 CEP290 CCDC71 CEP72 CCL4 CEP85L CCL5 CEP95 CCNA2 CHCHD6 CCNB1 CHD1 CCNB2 CHD2 CCND2 CHMP1B CCNF CHN1 CCR1 CHRM4 CCR4 CHEF 18 CCR5 CIB1 CCRL2 CIB2 CD109 CIDEB CD226 CIDECP CD300LD CILP CD33 CIRBP CD3EAP CITED4 CD4 CLASRP CD53 CLCF1 CD59 CLEC18A CD68 CLEC2B CD70 CLK1 CD74 CLK4 CD79B CLOCK CD80 CMPK2 CD81 CNOT6L CD86CNTD2 CD99L286MF-367695757.6Attorney Docket No.: 237752002040CNTNAP1 CDC123 COL5A1 CDC20 COL5A3 CDC25A COL6A1 CDC25C COL6A3 CDC45 CORO2A CDCA2 CPNE2 CDCA3 CRABP2 CDCA5 CREBRF CDCA7L CRIP2 CDCA8 CROCC CDIP1 CSRNP1 CDK1 CUBN CDK2AP1 CXCL16 CDK4 CXCR5 CDK5 CYB5D1 CDK6 CYP2D6 CDKL2 CYP2D7P CDKN2A CYTH3 CDKN2B DANCR CEACAM1 DBNDD1 CECR2 DBNDD2 CENPA DCHS1 CENPBD1 DCLRE1B CENPE DDIT3 CENPF DDIT4 CENPI DDX3Y CEP55 DDX5 CES3 DDX51 CES4A DENND4A CHAC2 DENND6A CHCHD1 DGKD CHEK1 DHRS3 CHP1 DIP2C CHPF DISCI CHRNA6 DKFZP586I1420 CHST14 DLEU2 CISDI DLGAP3 CISH DLL1 CKAP2L DMPK CKAP5 DMTN CKLF DMWD CLCN3 DNAH8 CLDN5 DNAI2 CLDND1 DNAJB1 CLIC3 DNAJB9 CLIC4 DNAJC25 CLIP3 DNAJC27 CLTA DNLZ CLTC DOCK6 CMBL DOCK7 CMTM3 DPH7 CMTM6 DPY19L2P2 CMTR2 DTX1 CNOT6 DUSP1 COA3 DUSP10 COA7 DUSP2 COL15A1 DUSP3 COLGALT2 DUSP4 COMMD3 DUSP5 COMMD4 DUSP8 COMT DXO COPB2 DYRK1A COPG1EBF4 COQ587MF-367695757.6Attorney Docket No.: 237752002040EDAR CORO1B EDN1 COROIC EEA1 CPLX1 EEF1A2 CPOX EEPD1 CPPED1 EGR1 CPSF2 EGR4 CPT1A EHBP1L1 CPT2 EID3 CREB3L3 EIF1 CREG2 EIF1AD CRIM1 EIF4A2 CRYBG3 EIF5 CSF1 ELF1 CSF2RB ELF2 CST3 ELF3 CSTB ELMSAN1 CSTF1 ENGASE CTIF EPB41L5 CTNS EPCI CTSA EPC2 CTSC EPHA1 CTSD EPHB4 CTSH EPHB6 CUX2 EPHX2 CXCR1 EPN2 CXCR3 EPPK1 CXCR6 ERO 1 LB CYBRD1 ERP27 CYFIP1 ERRFI1 CYP1A1 ETNK2 CYP1B1 EVA1C CYP51A1 EVC CYSTM1 EVC2 DACT1 EXPH5 DARS FAM117B DARS2 FAM129C DBI FAM134B DCAF12 FAM159A DCBLD1 FAM160A1 DCLRE1A FAM160B2 DCPS FAM169A DDB1 FAM179A DDB2 FAM193B DENND5B FAM19A2 DEPDC1 FAM211A DEPDC1B FAM229A DERL3 FAM46C DGAT2 FAM76B DHCR24 FAM95C DHCR7 FANK1 DHDDS FBF1 DHFRL1 FBP1 DEIRS 1 FBXO33 DHRS2 FBXW4 DHRS7B FBXW4P1 DIAPH3 FBXW7 DIXDC1 FCGBP DLGAP5 FCGRT DNAJB5 FCRL1 DNAJC14 FCRL2 DNAJC28 FEM1C DNAJC5 FGF11 DNAJC6FGF9 DNALI188MF-367695757.6Attorney Docket No.: 237752002040FLJ10038 DNASE2 FLJ13197 DNMBP FL JI 3224 DOK1 FLJ27354 DOLK FLJ31104 DPP3 FLJ31945 DPP4 FLJ38717 DPYSL2 FLJ45513 DQX1 FNBP4 DRAM1 FOS DRP2 FOXJ1 DSCC1 FOXO1 DST FOXO4 DLL FOXP1 DUSP14 FRMD3 DUSP7 FSCN3 DYNC1I2 FUT5 DYNLL1 FXYD2 E2F1 FYB E2F3 GADD45B E2F7 GALNT12 E2F8 GAREML EBB GAS8 EBP GCC2 ECE2 GCNT4 ECT2 GCOM1 EDA2R GDF9 EFCAB4A GDPD3 EFNB1 GEM EGFL6 GFRA2 EGLN3 GGT7 EHHADH GKAP1 EIF1AY GLCCI1 ELOVL6 GNA11 EMC2 GNG7 EMILIN2 GOLGA8M EMP1 GOLGA8N ENO1 GPLD1 ENPP4 GPR174 EPAS1 GPR18 EPHB1 GPR183 EPRS GPR25 ERAP1 GPR35 ERMP1 GPRASP1 ESCO2 GRASP ESPL1 GRK5-IT1 ESYT1 GRM2 ETFDH GRPEL1 ETHE1 GSAP ETS2 GSTM3 EVA IB GTSCR1 EVI5 GUSBP11 EXO1 GUSBP2 EXOC6B GYLTL1B EXOSC4 GZF1 EYA2 GZMK F2R H1FX FADD H3F3B FADS1 HABP4 FADS2 HAR1A FAH HAS3 FAM110A HAUS3 FAM114A1 HEG1 FAM115CHELB FAM118B 89MF-367695757.6Attorney Docket No.: 237752002040HELZ2 FAM122A HERPUD2 FAM126A HEXIM1 FAM129B HID1 FAM150B HIP1R FAM155B EtPKl FAM171A1 HIST1H1D FAM173B HIST1H1E FAM212B HIST1H2AE FAM214B HIST1H2AH FAM63A HIST1H2AM FAM64A HIST1H2BC FAM69A HIST1H2BG FAM72A HIST1H3A FAM72B HIST1H4J FAM72C HIST2H2AA4 FAM72D HIST2H2AC FAM86C1 HIST2H2BE FAM98B HIST2H2BF FANCI HIST3H2A FBN1 HIST4H4 FBXL8 HIVEP1 FBXO10 HIVEP2 FBXO22 HNRNPAO FBXO43 HNRNPDL FCER1G HNRNPH1 FCER2 HOOK1 FDFT1 HOTAIRM1 FDPS HSF2 FDXR HTR7P1 FEN1 HUS1B FES ICA1 FGGY ID1 FH ID2 FKBP1A IER2 FLNB IER5 FLVCR2 IER5L FNIP2 IFFO2 FOCAD IFIT2 FOXM1 IFITM1 FOXRED2 IFNG FPGT IFNLR1 FREM3 IFRD1 FURIN IFT172 FUT8 IKZF5 FZD6 IL12A G3BP1 IL17RC G6PD IL17RE GAB2 IL6ST GAB ARAP IL7R GALE INADI. GALK2 ING1 GALNT2 ING2 GAPDH INO80D GART IPCEF1 GAS2L1 IQCC GBA IRAK3 GBE1 IRF2BP2 GCAT IRF8 GCHFR IRGM GCNT1 IRS2 GEMIN6 ISM1 GEMIN7 ISYNA1 GGHITGA5 GIMAP490MF-367695757.6Attorney Docket No.: 237752002040ITPKB GINS1 ITPRIP GJA3 JADE1 GJB2 JAM3 GK JAZF1 GLB1 JMJD1C GLO1 JMJD7 GLRX2 JMY GLTPD1 JUN GLUL JUNE GM2A JUND GNB4 KALRN GNG10 KANSL1 GNGT2 KANSL2 GNPDA1 KAT6A GNPTAB KAT6B GNS KCNA3 GOLIM4 KCNA6 GOLPH3L KCNC4 GP5 KCNH3 GPR114 KCNJ14 GPR146 KDM2A GPR15 KDM6B GPR171 KIAA0754 GPR56 KIAA1683 GPR63 KLF10 GPRC5A KLF11 GPSM2 KLF12 GPT2 KLF4 GPX1 KLF6 GRIK5 KLF9 GRN KLHDC1 GRPEL2 KLHL11 GSDMA KLHL15 GSTZ1 KLHL28 GTF2IRD1 KLKB1 GTF3C6 KLRB1 GTSE1 KMT2E GYG1 KRT18 GZMB KRT72 HAVCR2 KRT73 HBEGF KTI12 HCP5 L3MBTL3 HEATR6 LAMP3 HES1 LCN10 HEXA LCOR HEXB LEF1 HEXIM2 LEPREL2 HEE LGALS4 HIATL1 LGR4 HIBCH LIPT2 HJURP LMCD1 HKDC1 LMNA HLA-DRA LMO7 HMBS LONRF3 HMG20A LPAL2 HMGCL LPHN1 HMGCR LRMP HMGCS1 LRP6 HMMR LRRC37B HMOX1 LRRC70 HN1 LSMEM1 HOMER2 LTBP3 HOMEZLUC7L HPGDS 91MF-367695757.6Attorney Docket No.: 237752002040LY6G5B HPS5 LY9 HRH2 LYPD3 HSD3B7 LYSMD2 HSF4 LZTS3 HSPA1A MAFF HTATSF1 MAGEE 1 HYAL2 MALAT1 HYLS1 MAML2 ICAM2 MAP3K1 ICAM4 MAP3K14 ICMT MAP3K8 IDH1 MAP4K5 IFI30 MAPK8IP1 IGF1 MAPRE3 IGF2BP3 MARCKSL1 IGF2R MATN1 IGFBP3 MBNL2 IGFBP4 MBTD1 IGFLR1 MC1R IGSF9B MCF2 IKBIP MCF2L IKZF4 MCF2L2 IL12RB2 MDK IL1R1 MDM4 IL1R2 MDS2 IL1RN MED17 IL2RA MED21 IL32 MED30 IL3RA MEF2BNB-MEF2B IL9R MEF2D ILDR1 MEGF6 ILK MEST INIP METAP ID INPPI METTL12 INSIGI MEX3B INTS5 MICALL2 IQGAP3 MID2 IQSEC2 MLF1 ITGAM MOAP1 ITGAX MPP7 ITSN1 MRC1 JDP2 MRPL55 JUP MS4A1 KANK3 MSL2 KAT2B MSTO2P KBTBD7 MTSS1L KCNK1 MTURN KCNK6 MUSTN1 KCNQ3 MVB12B KCTD10 MX2 KCTD21 MYADM KCTD3 MYH3 KCTD5 MYLIP KDELC1 MYLK4 KDSR MYO15B KEAP1 MYZAP KIAA0040 NAA16 KIAA0895L NAB1 KIAA1211 NAIP KIAA1524 NAP1L5 KIAA1671 NAV2 KIF11 NBPF19 KIF14NCAPH2 KIF1592MF-367695757.6Attorney Docket No.: 237752002040NCK2 KIF18A NDNL2 KIF18B NDRG2 KIF20A NEC API KIF23 NEK10 KIF24 NELL2 KIF26A NFIA KIF2C NFIX KIF4A NFKBIA KIFAP3 NFKBIE KIFC1 NFKBIZ KLF8 NIPAL4 KLHL2 NKD1 KNSTRN NKPD1 LACTB2 NKTR LAG3 NLGN2 LAMP2 NLRP6 LAPTM4B NOTCH1 LARS2 NOXA1 LAT2 NPAS2 LATS2 NPIPA1 LCN12 NPIPA5 LDLR NPIPA8 LEO1 NPIPB11 LEPR NPIPB5 LEPROT NPIPB6 LGALS1 NPTXR LGALS3 NR1D1 LGALS3BP NR1D2 LGALSL NR3C1 LGMN NR3C2 LIF NR4A1 LIG4 NR4A2 LILRA6 NR4A3 LIMS2 NRARP LMAN2 NRF1 LMO4 NSMF LOXL3 NUAK2 LPAR3 NUDT15 LPAR5 NXF1 LPAR6 NXT1 LPCAT2 NYAP1 LPCAT3 OBSCN LPXN OCM LRP4 ODC1 LRP5 ODF2L LRRC32 OMG LRRC34 OSBPL1A LRRC61 OSER1 LEA OTUD1 LTB OTUD3 LY96 OVGP1 LYPLAL1 OXNAD1 LYZ PAIP2B LZTS1 PAPD5 MAF PAPD7 MAFG PAQR7 MAGI1 PARD6B MAGT1 PARK2 MAN1A1 PARP6 MANF PARP8 MAOA PBX3 MAP1A PCBP2-OT1 MAP7D3PCBP3 MAPKAPK393MF-367695757.6Attorney Docket No.: 237752002040PCDHl MAST1 PCED1A MAT2A PCF11 MB21D2 PCNX MBOAT7 PCNXL2 MBTPS2 PCP2 MCM10 PCSK5 MCM6 PDE7A MCOLN2 PDE8A MCOLN3 PDXDC2P MDFIC PEG10 MDM2 PELI1 MED11 PERI MED18 PER2 MED20 PHC1 MED7 PHC3 MELK PHF1 MESDC1 PHF12 MESDC2 PHF3 METTL13 PHKA2 MF API PHKG1 MFSD1 PHLDA1 MFSD5 PHLDB1 MGAT1 PIBF1 MGAT3 PIGA MGME1 PIK3R1 MGST2 PILRB MIB1 PIM3 MICAL2 PITPNC1 MINA PIWIL2 MINPP1 PIWIL4 MIR4697HG PKD1 MKI67 PKD1P1 MLC1 PLA2G4B MLEC PLAC8 MMAB PLCL1 MMACHC PLEK MMP25 PLEKHA1 MMRN1 PLEKHF2 MOB3A PLEKHG1 MORN4 PLEKHG5 MOSPD1 PLEKHH2 MOSPD3 PLEKHM1P MPDU1 PLK2 MPST PLXDC1 MPV17 PLXNA1 MPZL1 PLXNB1 MR1 PM20D2 MRC2 PMAIP1 MRFAP1L1 PNPLA2 MRPL12 PNRC1 MRPL13 POC1B-GALNT4 MRPL15 POFUT2 MRPL17 POLR2J4 MRPL19 POMT2 MRPL27 POU2F1 MRPL35 POU3F1 MRPL37 POU6F1 MRPL51 PPM1N MRPS15 PPP1R15A MRPS6 PPP1R15B MSANTD4 PPP1R2 MSMO1 PPP2R2B MSRB1PPP3CC MTCH294MF-367695757.6Attorney Docket No.: 237752002040PRDM11 MTHFD1 PRDM15 MTHFD2 PRDM2 MTMR11 PRELID2 MUC1 PRKCE MUT PRKCZ MVD PRO2852 MVK PROC Al MYBL2 PRR7 MYCBP PRRT1 MYH10 PRRT2 MYL6B PSD MYLK PTBP2 MYO18B PTCHI MYO IE PTGER4 MYO5C PTK2 MYO6 PTMA MYOF PTP4A1 MYRF PTPLA MZB1 PTPRS NACC1 PUM1 NACC2 PVRIG2P NANP PVRL1 NANS PYGM NAPEPLDR3HDM2 NAPG RAB11FIP3 NAT1 RAB33B NAT8L RABL2B NCALD RALGPS1 NCAPG RAP1GAP2 NCAPH RARRES3 NCEH1 RASA2 NCF2 RASD1 NCR3 RASD2 NCS1 RASGRF2 NCSTN RASGRP2 NDFIP2 RBBP6 NDN RBM39 NDUFA12 RBM7 NDUFAF1 RCAN3AS NDUFAF3 RCN3 NDUFB3 REL NDUFB5 RELT NDUFC2 REM2 NDUFS2 RFX3 NDUFV2 RGCC NEAT1 RGL4 NEC AB 3 RGMB NEIL3 RGPD1 NEK2 RGPD5 NFE2L3 RGPD8 NIPA1 RGS1 NKD2 RGS12 NKG7 RGS16 NLN RGS2 NMNAT1 RHOB NOL3 RHOXF1 NOP 10 RIC3 NOS3 RICTOR NOVA2 RILPL1 NPC1 RIMKLB NPC2 RENT NPDC1 RIPK4 NPTNRND1 NPTX195MF-367695757.6Attorney Docket No.: 237752002040RNF130 NQO1 RNF138 NR6A1 RNF139 NRN1 RNF144A NRP1 RNF169 NRROS RNF175 NRSN2 RNF212 NSDHL RNF216 NUCB1 RNF222 NUDT5 RNF38 NUDT6 RNF43 NUF2 ROBO3 NUP37 RORA NUP43 RP11-143J12.2 NUSAP1 RP11-265P11.2 OIP5 RP11-35612.4 OLAH RP11-395P17.3 OLFM2 RP11-430B1.2 OPN3 RP11-696N14.1 ORAI2 RP11-996F15.2 ORAI3 RP3-395M20.9 ORMDL2 RP5-1073O3.7 OSBPL6 RP5-115904.2 OSGIN2 RPGR OTUB2 RRN3P1 P2RX4 RRN3P2 P4HB RRP7B PA2G4P4 RSBN1 PACSIN1 RSPH4A PAICS RSRC2 PAK1 RYK PALLD SAMD12 PAM SAMD8 PANK1 SAP25 PANX2 SARDH PARP1 SBDS PBK SBDSP1 PBLD SCARNA17 PCAT19 SCARNA9 PCCA SCMH1 PCDH8 SCML1 PCED1B SCML4 PCK2 SCNN1D PCSK6 SCXB PCTP SDK2 PDCD1LG2 SEC14L1P1 PDE6G SECISBP2 PDGFA SELM PDGFRB SEMA4C PDHB SENP3-EIF4A1 PDIA4 SEPN1 PDIA5 SEPT7P2 PDIA6 SERPINB6 PDP2 SERTAD2 PDXK SF1 PEAK1 SFI1 PEPD SGK1 PET112 SGMS1 PEX11B SH3GL1P1 PFKFB2 SH3RF3 PFKM SIAH2 PGAM1 SIGIRR PGD SIK1 PHF23SIN3B PHF696MF-367695757.6Attorney Docket No.: 237752002040SIRT1 PHGDH SIX5 PHLDA3 SLC14A1 PHPT1 SLC16A6 PHTF2 SLC22A23 PIF1 SLC22A5 PIGF SLC25A25 PIGM SLC25A27 PIGN SLC25A28 PIGV SLC25A34 PIK3R3 SLC25A36 PIM1 SLC25A45 PIM2 SLC2A4RG PIP5K1B SLC30A1 PIR SLC40A1 PKD1L3 SLC45A1 PKD2 SLC5A2 PKMYT1 SLC5A6 PLA2G15 SLC7A5P1 PLAU SLC7A5P2 PLAUR SMA4 PLCG2 SMAD7 PLEKHB2 SMAGP PLEKHF1 SMG1P1 PLEKHN1 SMIM5 PLEKHO2 SMURF2 PLK1 SMYD5 PLOD1 SNAI1 PLXNB2 SNED1 PMCH SNHG1 PNPO SNHG12 POC1A SNHG15 PODXL SNHG3 POLH SNHG6 POLQ SNHG8 POLR3K SNORD3A POP1 SNRK POU2AF1 SNRNP70 POU4F1 SORBS3 PPAP2A SOWAHC PPIL1 SPATA2 PPM1G SPATA6L PPP1R26 SPEF2 PPT1 SPEG PRDX1 SPON1 PRDX3 SPRY1 PRDX4 SPTY2D1 PREPL SRRM1 PRF1 SRRM2 PRICKLE4 SRRT PRKAR1A SRSF2 PRKAR1B SRSF3 PRKCDBP SRSF5 PROB1 SRSF7 PROS1 SSPO PRR11 ST3GAL3 PRRG4 ST8SIA1 PRSS23 STAG3L5P PSAT1 STAG3L5P-PVRIG2P-PILRB PSEN2 SPAM PSMA4 STK17A PSMB5 STMN3 PSMD1 STRC PSPHSUCO PTGDR297MF-367695757.6Attorney Docket No.: 237752002040SUSD2 PTGER2 SUSD4 PTGIR SUV420H2 PTGIS SVIL PTPN14 SYCP2 PTPN9 SYDE2 PTPRG SYS1 PTPRK SYS1-DBNDD2 PTPRO TAF3 PTRH2 TAF4B PVR TAGAP PVRIG TBC1D27 PYCARD TBCC PYCR1 TBX19 RAB10 TCAP RAB11FIP1 TCEA3 RAB19 TCF7 RAB23 TCF7L2 RAB31 TCTE1 RAB33A TEN1-CDK3 RAB38 TENC1 RACGAP1 TFAP2E RAD51 t RAD54L TGFBI RALB TGIF1 RAMP1 THAP9 RAP2A THRA RAP2B THSD1 RARS THUMPD1 RASGRP4 TIAM1 RBBP8 TIAM2 RBKS TIFA RBM12B TIGD1 RBM47 TLR5 RBM4B TMEM191A RBX1 TMEM30B RCAN2 TMEM71 RCBTB2 TMEM86B RDH10 TMEM88 REEP5 TMEM8B RENBP TMIE REXO2 TMOD2 RGS9 TNFAIP3 RHOC TNFRSF13C RHOU TNFRSF19 RMDN1 TNFRSF25 RMND1 TNK1 RNASE4 TNK2 RNF14 TNRC6B RNF185 TNRC6C RNF213 TNXB RNF34 TOBI RP5-1057J7.6 TOE1 RP5-1103G7.4 TOPI MT RPE TOPORS RPN1 TOX2 RPP25 TP53I11 RPP25L TP53INP2 RRAGD TPCN1 RRAS TPH1 RRAS2 TPM2 RRM2 TPPP RRM2B TRA2B RTN4RL1TRABD2A RTP498MF-367695757.6Attorney Docket No.: 237752002040TRAF4 RWDD2B TRAF6 RYR1 TRERF1 RYR2 TRIM39 S100A11 TRIM7 SI OOP TRMT61B S100Z TSC22D1 S1PR4 TSC22D2 SAMHD1 TSC22D3 SAPCD2 TSNARE1 SASH1 TSPYL1 SCAMP4 TSPYL2 SCARE 1 TSPYL4 SCD TTC39C SCGB3A1 TTLL11 SCN2A TUBA1A SCPEP1 TUBB2A SDF2 TUBB2B SDHC TUBE1 SDHD TVP23C SEC22B TWF1 SEC23B TXLNG SEC23IP UBAP1L SEC24D UCP3 SEC31B ULK2 SEL1L UPB1 SELPLG UPK3B SEMA4A USP3 SEPHS2 USP53 SERAC 1 USP54 SERPINE2 USPL1 SERPINI1 VAMP2 SFN VASH1 SFXN1 VAV3 SGOL1 VCPKMT SGOL2 VILL SH2D1A WDPCP SH3BP5L WDR27 SH3D21 WDR52 SHCBP1 WDR74 SIGLEC17P WEE1 SIT1 WHAMM SKA1 WHAMMP1 SKA3 WHAMMP2 SLAMF1 WHAMMP3 SLAMF7 WNT10A SLC15A4 WNT7A SLC16A13 YPEL5 SLC16A3 YRDC SLC16A4 ZBTB10 SLC16A9 ZBTB11 SLC17A5 ZBTB20 SLC1A4 ZBTB21 SLC1A5 ZBTB25 SLC20A2 ZC3H12A SLC23A1 ZC3H12D SLC25A12 ZC3H6 SLC25A15 ZCCHC2 SLC25A20 ZCCHC7 SLC25A23 ZCCHC8 SLC26A4 ZCWPW1 SLC27A2 ZDHHC11B SLC29A1 ZEB1 SLC30A6ZFAND2A SLC35A599MF-367695757.6Attorney Docket No.: 237752002040ZFAND4 SLC35B2 ZFP28 SLC35C1 ZFP36 SLC35F3 ZFP36L2 SLC35F6 ZFP69 SLC37A2 ZFYVE9 SLC38A7 ZGLP1 SLC39A1 ZMAT1 SLC39A14 ZNF135 SLC39A8 ZNF136 SLC41A2 ZNF14 SLC47A1 ZNF204P SLC4A11 ZNF208 SLC50A1 ZNF223 SLC5A3 ZNF23 SLC7A11 ZNF239 SLC9B2 ZNF26 SLCO4A1 ZNF273 SLFN12 ZNF276 SMC2 ZNF281 SMCO4 ZNF300 SMG8 ZNF331 SMIM15 ZNF333 SMIM3 ZNF354A SMPDL3B ZNF365 SMTN ZNF367 SNAI3 ZNF394 SNAPC3 ZNF415 SNAPIN ZNF439 SNCA ZNF44 SND1 ZNF460 SNRNP25 ZNF467 SNTB1 ZNF483 SNX20 ZNF492 SNX29 ZNF506 SNX32 ZNF541 SNX5 ZNF548 SNX8 ZNF554 SOCS1 ZNF558 SOCS2 ZNF571 SOCS6 ZNF574 SORT1 ZNF599 SOS1 ZNF628 SPATS2L ZNF629 SPC24 ZNF630 SPC25 ZNF638-IT1 SPHK2 ZNF639 SPINT1 ZNF662 SPIRE 1 ZNF681 SPN ZNF703 SPOCK1 ZNF705E SPP1 ZNF800 SPPL2A ZNF831 SPTLC2 ZNF844 SPTSSA ZNF853 SQLE ZNF888 SQRDL ZNRF3 SRD5A1 ZSCAN18 SREBF2 ZSWIM5 SRP54 ZXDA SRP9 ZXDB SSR3 ZXDC SSTR3STAC3STAP2100MF-367695757.6Attorney Docket No.: 237752002040STARD4 STARD8 STIP1 STMN1 STRADB STRIP2 STT3A STT3B STX3 STX7 STXBP1 SUCLA2 SULT1B1 TALDO1 TARS TBC1D14 TBC1D22B TBCA TBXA2R TCAIM TCEAL8 TCN2 TEX 14 TGFBR1 THAP8 TICRR TIMM8B TIMP1 TJP2 TK1 TLR1 TLR6 TLX2 TM7SF2 TM7SF3 TM9SF1 TM9SF2 TMC4 TMCO1 TMED10 TMED9 TMEM101 TMEM102 TMEM140 TMEM168 TMEM169 TMEM178B TMEM184B TMEM19 TMEM200A TMEM217 TMEM223 TMEM256 TMEM30A TMEM38A TMEM53 TMEM60 TMEM68 TMEM97 TNFAIP8L2 TNFRSF11A TNFRSF1B TNFRSF8TNFSF11101MF-367695757.6Attorney Docket No.: 237752002040TNFSF14 TNFSF8 TNFSF9 TNIP3 TNS1 TOLLIP TOMM40L TOP2A TOR4A TP53I3 TP53RK TPCN2 TPM4 TPP1 TPX2 TRAPPCI TRAPPC6B TRAT1 TREML2 TRIB3 TRIM46 TRIM47 TRIO TRIP 10 TRIP 13 TRIP6 TRMT1L TROAP TRUB1 TSPAN15 TSPAN31 TSPO TST TSTA3 TTC9C TTK TTYH3 TUBB TUBB4A TUBB6 TUBG1 TWIST 1 TWSG1 TXLNA TXLNB TXN TXN2 TXNDC15 TXNDC17 TXNDC5 TXNDC9 TXNRD1 TYMS TYRO3 UBE2C UBE2F UBE2T UBL4A UBTD2 UCK2 UCP2 UGDH UGP2UHRF1102MF-367695757.6Attorney Docket No.: 237752002040UNQ6494 UQCC1 USHBP1 USP28 USP49 UTP11L UTP14C UTP3 VANGL1 VCL VDAC1 VDAC3 VDR VKORC1 VMP1 VPS25 VPS35 VPS41 VWA5A VWCE WARS WARS2 WDFY1 WDFY4 WDHD1 WDR12 WDR25 WDR31 WDR41 WDR92 WDYHV1 WIPI1 WRB WSB2 XPNPEP2 YARS YBX3 YIPF4 YIPF6 YWHAE YWHAG ZAK ZBED2 ZBTB32 ZBTB6 ZC2HC1C ZDHHC12 ZDHHC14 ZDHHC16 ZDHHC23 ZFHX2 ZFYVE21 ZG16B ZMAT3 ZMPSTE24 ZNF174 ZNF20 ZNF213 ZNF226 ZNF227 ZNF234 ZNF28 ZNF282ZNF594103MF-367695757.6Attorney Docket No.: 237752002040ZNF613 ZNF672 ZNF691 ZNF780B ZSCAN22 ZSCAN9ZWINTStringDB Protein-Protein Interaction map

[0378] Protein-protein interaction maps were generated using R Package “rbioapi:: rba string network image”, with the following parameters: “required score = 500” and “network_flavor = actions”.SBT Fingerprint Score Calculation

[0379] The SBT Fingerprint Scores described herein (e.g., Treg score; expansion score) were calculated in two steps: 1) calculate the sub-scores for favorable signature (e.g., Treg marker sub-score or post-expansion marker sub-score) and the unfavorable signature (e.g., Teff marker sub-score or pre-expansi on marker sub-score) separately and 2) subtract the unfavorable sub-score from the favorable sub-score. The markers or genes that comprise the “favorable” signature that is used for calculating the “favorable” sub-scores (e.g., the Treg marker sub-scores or post-expansion marker sub-scores) may also be described herein as comprising a “positive” signature that is used for calculating a “positive” sub-score. The use of “positive” in this context does not represent the mathematical sign of the sub-score (i.e., whether the value of the sub-score is greater than 0 or not), it refers only to the “positive” nature of the markers or genes in their association with a certain cell phenotype. Similarly, the markers or genes that comprise the “unfavorable” signature that is used for calculating the “unfavorable” sub-scores (e.g., the Teff marker sub-scores or pre-expansion marker subscores) may also be described herein as comprising a “negative” signature that is used for calculating a “negative” sub-score. The use of “negative” in this context does not represent the mathematical sign of the sub-score (i.e., whether the value of the sub-score is less than 0 or not), it refers only to the “negative” nature of the markers or genes in their association with a certain cell phenotype. Calculation was performed using the R package GSVA with the following parameters: “method=ssgsea”, “diffscore=FALSE” for the input log2 -transformed TPM expression matrix (per dataset).ResultsOverview of SBT Treg molecular fingerprints104MF-367695757.6Attorney Docket No.: 237752002040

[0380] Several statistical methods were studied for the development of the SBT Treg molecular fingerprint algorithms, including gene set variation analysis (GSVA) (30), singlesample gene set enrichment analysis (ssGSEA) (8), and singscore (31, 32). Ultimately, ssGSEA was chosen as the computational method to provide a sample specific summary of gene expression of the SBT Treg cell therapy product due to its precision, sensitivity, and robustness in analyzing single samples (data not shown), as well as its established used in similar applications (33).

[0381] The SBT Treg molecular fingerprint algorithm is defined by two components: gene signatures underlying different Treg phenotypes and metrics for scoring each signature (FIG. 1A). Each fingerprint was developed using bidirectional gene signatures where each sample was scored on positive (“favorable”) and negative (“unfavorable”) gene signatures, or “sub-scores”. Positive and negative sub-scores were given equal weight, and the final score was calculated by subtracting the negative sub-score from the positive sub-score, enabling the ability to evaluate a cell product and determine whether the product exhibits the desired characteristics while avoiding unwanted characteristics (FIG. IB).

[0382] SBT Treg cell therapy drug product (D14 Treg) was generated from endogenous (DO) Treg (CD4+CD25+CD127low) isolated from peripheral blood mononuclear cells (PBMC) and activated, transduced with a CAR construct, and expanded for 14 days using anti-CD3 / anti-CD28 beads. In these studies, we also included counterpart endogenous (DO) Teff (CD4+CD25lowCD127+) and Teff that were activated and expanded similar to Treg without genetic engineering (D14 Teff). All SBT-derived Treg samples used in this study (except those following destabilization experiments, described later) were good quality (FIG. 7). A comparison of the protein expression of the Treg markers FOXP3 and Helios between representative D14 Treg and D14 Teff samples demonstrated that Treg maintain high expression of both markers (typically > 95% double positive) with significantly lower expression in D14 Teff (FIG. 1C).The Treg core identity fingerprint can accurately identify Treg cells irrespective of expansion state

[0383] The SBT Treg identity fingerprint was developed using data from 3 studies: 2 internal bulkRNA-seq datasets (SBT dataset 1 and 2, Table 1) containing DO and D 14 Treg (n=8 and n=45, respectively) and Teff (n=8 and n=21, respectively), and one public microarray dataset containing DO Teff and Treg gene signatures from healthy donors (n=78),105MF-367695757.6Attorney Docket No.: 237752002040or donors with type-1 (n=60) or type-2 diabetes (n=30) (18). To identify fingerprints that could differentiate between Treg and Teff irrespective of expansion state, only genes differentially expressed in both DO and D14 cells were considered. These analyses uncovered 32 genes in Treg and 11 genes in Teff that were differentially expressed at both time points and across different datasets (FIG. 2A, Table 2). The positive Treg identity gene signature (i.e., Treg markers) contains genes typical of the cell type, including FOXP3, IL2RA (CD25), IKZF2, and CTLA4. The negative Treg identity gene signature (containing genes expressed in Teff), on the other hand, contains genes typically associated with Teff cells such as CD40LG, and 2 genes (GNLY and NKG7) which are predominantly expressed in cytotoxic lymphocytes (FIG. 2B). The reproducibility of the SBT Treg identity score was assessed by comparing scores of 6 D 14 Treg samples immediately following thaw as well as following an overnight culture in the presence of IL-2, demonstrating there were no significant differences in Treg identity scores between the 2 groups (FIG. 2C).

[0384] We then compared the performance of the SBT Treg identity fingerprint to fingerprints based on other published signatures to determine whether there were any significant differences by applying each signature to a group of 12 internally derived and 1 externally derived validation datasets containing a total of 54 DO Treg, 252 D14 Treg, 24 DO Teff, and 59 D14 Teff samples (Table 1). The SBT Treg identity fingerprint accurately differentiated between Teff and Treg at both DO and D14, with Teff scores below 0, and Treg scores above, and little variation between experiments. The Treg fingerprint published by Ferraro, et al. (18) based on DO Treg signatures (vs DO Teff) accurately identified DO Teff and DO and D14 Treg, however D14 Teff scores were generally above 0 and not distinguished from Treg. A third fingerprint based on activation-independent Treg signatures published by Pesenacker, et al. (19) performed similarly to the SBT Treg identity fingerprint, differentiating cells at both time points (FIG. 2D).

[0385] To determine whether the SBT Treg identity fingerprint enabled better resolution between cell types and time points than identifying Treg by expression of FOXP3 only, we analyzed DO and D14 Treg and Teff gene expression of FOXP3. As expected, FOXP3 was highly expressed in DO and D14 Treg, with only minimal expression in DO Teff. D14 Teff, however, had moderate expression of FOXP3, which has been well described as being transiently upregulated in activated Teff cells (34, 35). Although FOXP3 expression in D14 Teff was lower than in Treg, it was still higher than in DO Teff; in this case, the SBT Treg identity fingerprint by incorporating information from other relevant genes provides better106MF-367695757.6Attorney Docket No.: 237752002040resolution between Treg and Teff, with Teff having similar scores below 0 irrespective of expansion. (FIG. 7).

[0386] The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the SBT Treg identity fingerprint on the validation datasets were all 100%. The identity fingerprints described by Pesenacker et al. (19) also performed well with all metrics >98%, while the fingerprint described by Ferraro et al. (18) showed lower accuracy (86%), specificity (34%) and PPV (85%) than the other 2 fingerprints (Table 3). Table 3: Comparison of diagnostic performance metrics across SBT and published fingerprints.Metric SBT Identity Fingerprint Ferraro et al. (18) Pesenacker et al. (19) Accuracy 100% 86% 99%Sensitivity 100% 100% 100%Specificity 100% 34% 98%PPV 100% 85% 99%NPV 100% 100% 100%Abbreviations: NPV = negative predictive value; PPV = positive predictive value; SBT = Sonoma BiotherapeuticsThe Treg expansion fingerprint identi fies T cells that have expanded, regardless of subset

[0387] The Treg identity fingerprint can accurately distinguish between Treg and Teff regardless of expansion, but it was not designed to assess whether the cells have properly expanded. To evaluate expansion, we developed a second fingerprint that is independent of cell identity, and specifically distinguishes between unexpanded and expanded cells.

[0388] Two internal datasets were used to generate the Treg expansion fingerprint containing a total of 30 DO and 48 D14 Treg samples. Genes that overlapped at each timepoint in each dataset were selected for the gene signature (FIG. 3A). This analysis identified 1103 genes (Table 2) differentially expressed in unexpanded DO Treg (negative gene signature), including genes involved in regulating proliferation (FOS, JUNB, NFKBIAf 1304 genes (Table 2) were uniquely expressed in post-expansion Treg (positive gene signature). An initial review identified several biologically relevant genes included in the differential expression set, for example, the coinhibitory marker I G3, CCR5, which allows for homing to sites where Teff are activated, HLA-DRA, linked to T cell activation, and CDK1 XV MDM2, both of which drive proliferation. FIG. 3B depicts a protein-protein interaction network from StringDB for these genes. Similar to the Treg identity fingerprint,107MF-367695757.6Attorney Docket No.: 237752002040this initially defined expansion fingerprint (FIG. 3B) was applied to 6 D14 Treg samples immediately after thaw and again after overnight culture in the presence of IL-2. Expansion scores were slightly lower in freshly thawed Treg than those that had been rested overnight, however the difference was not statistically significant (FIG. 3C, p-value = 0.18).

[0389] When applied to the group of 13 validation data sets, the SBT expansion fingerprint demonstrated 100% accuracy, sensitivity, specificity, PPV, andNPV. The SBT Treg expansion fingerprint accurately differentiated the expansion state of Treg with little inter-experimental variability, and despite being developed using only Treg gene signatures, the expansion fingerprint also accurately differentiated the expansion state of Teff (FIG. 3D).Practical application o f the Treg identity fingerprint to identi fy “destabilized” Treg

[0390] The applications of Treg fingerprinting can extend beyond confirming the identity and expansion of final drug product. For example, it has been observed that in some stressful conditions (36) such as repetitive stimulation (36) or the expression of a high tonic signaling CAR that leads to a cell receiving chronic activation signals in the absence of antigen (37-39), FOXP3 expression in Treg can become unstable, leading to a CD4+CD25lowFOXP3lowpopulation of destabilized Treg termed “exTregs” (40, 41). Although the precise mechanism of conversion of Treg to exTreg in vivo is somewhat controversial, they are hypothesized to have decreased regulatory function and may even adopt some effector characteristics (42).

[0391] To assess the ability of the Treg identity fingerprint to differentiate between Treg and destabilized Treg, we employed an in vitro system wherein Treg were transduced with a CAR construct that was demonstrated to have high levels of tonic signaling (data not shown). D14 Treg expressing the high tonic signaling CAR (DI 4 tsTreg) and were repeatedly stimulated every 7 days through the TCR / CD28 for 28 days (4 times total), resulting in cells defined as destabilized Treg. These cells were compared to D14 Teff and D14 Teff that were stimulated in the same manner as destabilized Treg (4 stim D14 Teff). Compared to unstimulated D14 tsTreg, destabilized Treg had lower expression of FOXP3 and Helios as measured by flow cytometry (FIG. 4A) and reduced suppression function on both CD4 and CD8 T cells (FIGS. 8A-8B).

[0392] The SBT Treg identity scores of these populations quantitively reflected the phenotype difference (FIG. 4B). DO Treg had the highest Treg identity scores, followed by D14 tsTreg which had reduced scores compared to DO Treg due to the high tonic CAR108MF-367695757.6Attorney Docket No.: 237752002040signaling during the expansion protocol. Destabilized Treg had the lowest identity scores of the Treg included in this study with scores less than 0, due both to higher expression of “negative” (unfavorable) Teff genes and lower expression of “positive” (positive) Treg genes, however these scores were still higher than D14 Teff and 4 stim D14 Teff (FIG. 4B). The gene expression profiles of the fingerprint genes match with the score evaluation displaying a transition from stable DO Treg to destabilized Treg to Teff.

[0393] The relationship between Treg identity score and suppressive potential was further examined in 2 of the 5 donor samples for which matching identity scores and suppression assay data were available. The identity score significantly correlated with the maximum percent suppression of both CD4+and CD8+T cells at a 1:1 Treg: Tresp ratio. However, when suppression was quantified using the area under the curve (AUC) (Akimova et al.Standardization, evaluation, and area-under-curve analysis of human and murine treg suppressive function. Methods Mol Biol. (2016) 1371:43-78. doi: 10.1007 / 978-1-4939-3139-2_4) across Treg: Tresp ratios from 1: 1 to 1: 128, the correlation did not reach the same statistical significance as with maximum percent suppression (FIGS. 8A-8C).

[0394] We further compared the performance of the SBT Treg identity fingerprint with those based on Ferraro et al. (18) and Pesenacker et al. (19) by including the gene expression data generated from this experiment. The three identity scores showed the same decreasing trend from D14 Treg to destabilized Treg and D14 Teff (FIG. 4C). However, only the SBT Treg identity fingerprint assigned scores generally below 0 to destabilized Treg while the scores assigned by the 2 published identity fingerprints were generally above 0 and closer to scores of D14 tsTreg (FIG. 4C).Application of SBT Treg signatures to compare different Treg products

[0395] Treg identity fingerprints might also be used to better understand differences between Treg drug products. Currently, there are multiple methods used to generate Treg cell therapies, including variations on the cell type used as the starting material. One alternative to isolating and expanding Treg from patients is to use CD4+T cells as the starting material and to overexpress FOXP3, which has been shown to upregulate Treg associated genes such as CTLAF TL2RA, and TNFRSF18, increase the production of suppressive cytokines IL-10 and TGF-P, and to enable the cells to exert some suppressive effects in vitro and in vivo (9). This method provides some advantage over the use of Treg which have smaller numbers in PBMC compared to CD4+T cells and require extensive expansion to generate enough cells for109MF-367695757.6Attorney Docket No.: 237752002040infusion, however it is unknown whether forced expression of FOXP3 alone is sufficient to drive gene expression similar to Treg.

[0396] We applied the SBT Treg identity fingerprint to a published dataset that compared activated bulk CD4+T cells edited to overexpress FOXP3 (ectopic Treg; eTreg) to Treg (CD4+CD25++CD127‘) and Teff cells (CD4+CD25 ) that were isolated from PBMC and activated and expanded for 12 days (9). As expected, Teff cells had Treg identity scores less than 0 and Treg had scores greater than 0. Of the 4 eTreg samples analyzed, 2 had scores that were above 0 but lower than the 4 Treg samples, however the other 2 samples had scores close to 0. Analysis of individual genes in these donors demonstrated both higher expression of some Teff genes and lower expression of some Treg genes (FIG. 5A). Compared to SBT Treg-derived cells, eTreg had significantly higher expansion scores (p=0.0002), however their Treg identity score illustrated greater variability and was significantly lower (p=0.03) (FIG. 5B)Application o f the SBT Treg fingerprints to Treg generated in a phase 2 clinical trial

[0397] SBT Treg fingerprints also have the potential to be a tool in the analysis of clinical data. Application of these fingerprints to Treg cell therapy products at multiple points in the treatment process (e.g. baseline, pre-infusion, or isolated from patients post-infusion) could lead to insights into the characteristics of the cell product that correlate with efficacy. To test this hypothesis, we applied the SBT Treg identity fingerprint to published results of a study examining the use of expanded polyclonal Treg for the treatment of T1D (28). Gene expression data was available from baseline (DO) and infusion product (D14) Treg from 14 participants in this phase 2 clinical trial. Applying the SBT Treg identity fingerprint to these samples showed that both baseline and infusion product samples had positive scores, suggesting that the starting material for the drug product had an appropriate Treg fingerprint and that the expansion process did not affect the identity of these cells. A comparison of DO and D14 Treg scores from individual donors showed no significant trends, with some drug products having higher D14 Treg identity scores, while others had lower scores (FIG. 6A).Though the primary intention of this analysis was to determine if the SBT Treg identity and expansion fingerprints could be successfully applied to DO Treg and corresponding D14 Treg cell therapy infusion product in this trial, we did further analysis to determine if there was any correlation between the Treg identity score of D14 Treg and a clinical readout from the trial (change in C-peptide percentage AUC after 1 year). While there was a trend in patients 110MF-367695757.6Attorney Docket No.: 237752002040who received D14 Treg that had higher Treg identity scores having a smaller decrease in C-peptide 4-hour AUC values 1 year after treatment (correlating with higher insulin production and thus better outcome), significance was not reached (r=0.37; p=0.157) (FIG. 6B);however, this study was limited by the number of samples available and skewed gene expression distribution because of overall lower sequencing depth compared to internally derived Treg (FIG. 9).References

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[0417] 20. Zemmour D, Zilionis R, Kiner E, Klein AM, Mathis D, Benoist C. Single-Cell Gene Expression Reveals a Landscape of Regulatory T Cell Phenotypes Shaped by the Ter. Nat Immunol (2018) 19(3):291-301. Epub 20180212. doi: 10.1038 / s41590-018-0051-0.

[0418] 21. McClymont SA, Putnam AL, Lee MR, Esensten JH, Liu W, Hulme MA, et al. Plasticity of Human Regulatory T Cells in Healthy Subjects and Patients with Type 1 Diabetes. J Immunol (2011) 186(7):3918-26. Epub 20110302. doi:10.4049 / jimmunol.1003099.

[0419] 22. Duhen T, Duhen R, Lanzavecchia A, Sallusto F, Campbell DJ. Functionally Distinct Subsets of Human Foxp3+ Treg Cells That Phenotypically Mirror Effector Th Cells. Blood (2012) 119(19):4430-40. Epub 20120321. doi: 10.1182 / blood-2011-11-392324.113MF-367695757.6Attorney Docket No.: 237752002040

[0420] 23. Komatsu N, Okamoto K, Sawa S, Nakashima T, Oh-hora M, Kodama T, et al. Pathogenic Conversion of Foxp3+ T Cells into Thl7 Cells in Autoimmune Arthritis. Nat Med (2014) 20(1):62-8. Epub 20131222. doi: 10.1038 / nm.3432.

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[0425] 28. Bender C, Wiedeman AE, Hu A, Ylescupidez A, Sietsema WK, Herold KC, et al. A Phase 2 Randomized Trial with Autologous Polyclonal Expanded Regulatory T Cells in Children with New-Onset Type 1 Diabetes. Sci Transl Med (2024) 16(746):eadn2404. Epub 20240508. doi: 10.1126 / scitranslmed.adn2404.

[0426] 29. Love MI, Huber W, Anders S. Moderated Estimation of Fold Change and Dispersion for Rna-Seq Data with Deseq2. Genome Biol (2014) 15( 12): 550. doi:10.1186 / s13059-014-0550-8.

[0427] 30. Hanzelmann S, Castelo R, Guinney J. Gsva: Gene Set Variation Analysis for Microarray and Rna-Seq Data. BMC Bioinformatics (2013) 14:7. Epub 20130116. doi:10.1186 / 1471-2105-14-7.

[0428] 31. Foroutan M, Bhuva DD, Lyu R, Horan K, Cursons J, Davis MJ. Single Sample Scoring of Molecular Phenotypes. BMC Bioinformatics (2018) 19(l):404. Epub 20181106. doi: 10.1186 / s12859-018-2435-4.114MF-367695757.6Attorney Docket No.: 237752002040

[0429] 32. Bhuva DD, Cursons J, Davis MJ. Stable Gene Expression for Normalisation and Single-Sample Scoring. Nucleic Acids Res (2020) 48(19):ell3. doi:10.1093 / nar / gkaa802.

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[0432] 35. Allan SE, Crome SQ, Crellin NK, Passerini L, Steiner TS, Bacchetta R, et al. Activation-Induced Foxp3 in Human T Effector Cells Does Not Suppress Proliferation or Cytokine Production. Int Immunol (2007) 19(4):345-54. Epub 20070227. doi:10.1093 / intimm / dxm014.

[0433] 36. Lam AJ, Uday P, Gillies JK, Levings MK. Helios Is a Marker, Not a Driver, of Human Treg Stability. Eur J Immunol (2022) 52(l):75-84. Epub 20211013. doi:10.1002 / eji.202149318.

[0434] 37. Lamarthee B, Marchal A, Charbonnier S, Blein T, Leon J, Martin E, et al. Transient Mtor Inhibition Rescues 4-1bb Car-Tregs from Tonic Signal-Induced Dysfunction. Nat Commun (2021) 12(1):6446. Epub 20211108. doi: 10.1038 / s41467-021-26844-1.

[0435] 38. Dawson NAJ, Rosado-Sanchez I, Novakovsky GE, Fung VCW, Huang Q, McIver E, et al. Functional Effects of Chimeric Antigen Receptor Co-Receptor Signaling Domains in Human Regulatory T Cells. Sci Transl Med (2020) 12(557). doi:10.1126 / scitranslmed.aaz3866.

[0436] 39. Lamarche C, Ward-Hartstonge K, Mi T, Lin DTS, Huang Q, Brown A, et al. Tonic-Signaling Chimeric Antigen Receptors Drive Human Regulatory T Cell Exhaustion. Proc Natl Acad Sci U S A (2023) 120(14):e2219086120. Epub 20230327. doi:10.1073 / pnas.2219086120.

[0437] 40. Freuchet A, Roy P, Armstrong SS, Oliaeimotlagh M, Kumar S, Orecchioni M, et al. Identification of Human ExTreg Cells as Cd16(+)Cd56(+) Cytotoxic Cd4(+) T Cells. Nat Immunol (2023) 24(10): 1748-61. Epub 20230810. doi: 10.1038 / s41590-023-01589-9.115MF-367695757.6Attorney Docket No.: 237752002040

[0438] 41. Tang Q, Ho P, Rosenthal W, Bluestone J. [Pre-Print] Deletion of a Distal Irf4 Element Prevents Inflammation-Induced Reprogramming of Human Regulatory T-Cell Fate(2024. Available from: https: / / doi.org / 10.21203 / rs.3.rs-5356932 / v1.

[0439] 42. Saxena V, Lakhan R, Iyyathurai J, Bromberg JS. Mechanisms of Extreg Induction. Eur J Immunol (2021) 51(8): 1956-67. Epub 20210527. doi:10.1002 / eji.202049123.

[0440] 43. Wieczorek G, Asemissen A, Model F, Turbachova I, Floess S, Liebenberg V, et al. Quantitative DNA Methylation Analysis of Foxp3 as a New Method for Counting Regulatory T Cells in Peripheral Blood and Solid Tissue. Cancer Res (2009) 69(2):599-608. doi: 10.1158 / 0008-5472.CAN-08-2361.

[0441] 44. Sinicrope FA, Rego RL, Ansell SM, Knutson KL, Foster NR, Sargent DJ. Intraepithelial Effector (Cd3+) / Regulatory (Foxp3+) T-Cell Ratio Predicts a Clinical Outcome of Human Colon Carcinoma. Gastroenterology (2009) 137(4): 1270-9. Epub 20090703. doi: 10.1053 / j.gastro.2009.06.053.

[0442] 45. Carrel L, Willard HF. X-Inactivation Profile Reveals Extensive Variability in X-Linked Gene Expression in Females. Nature (2005) 434(7031):400-4. doi:10.1038 / nature03479.

[0443] 46. Flores-Borja F, Jury EC, Mauri C, Ehrenstein MR. Defects in Ctla-4 Are Associated with Abnormal Regulatory T Cell Function in Rheumatoid Arthritis. Proc Natl Acad Sci U S A (2008) 105(49): 19396-401. Epub 20081126. doi: 10.1073 / pnas.0806855105.

[0444] 47. Dikiy S, Rudensky AY. Principles of Regulatory T Cell Function. Immunity (2023) 56(2):240-55. doi: 10.1016 / j.immuni.2023.01.004.

[0445] 48. Cederbom L, Hall H, Ivars F. Cd4+Cd25+ Regulatory T Cells down-Regulate Co-Stimulatory Molecules on Antigen-Presenting Cells. Eur J Immunol (2000) 30(6): 1538-43. doi: 10.1002 / 1521-4141(200006)30:6<1538::AID-IMMU1538>3.0.CO;2-X.

[0446] 49. Tran DQ, Glass DD, Uzel G, Darnell DA, Spalding C, Holland SM, et al. Analysis of Adhesion Molecules, Target Cells, and Role of 11-2 in Human Foxp3+ Regulatory T Cell Suppressor Function. J Immunol (2009) 182(5):2929-38. doi:10.4049 / jimmunol.0803827.Example 2 — Treg expansion fingerprint116MF-367695757.6Attorney Docket No.: 237752002040

[0447] Described herein are experiments and data relating to the Treg expansion fingerprint. Further analysis of the differentially expressed pre-expansion and post-expansion markers from the full expansion fingerprint as described in Example 1 yielded a reduced SBT Treg expansion fingerprint as described herein.

[0448] An elastic-net bootstrap regression framework (Flynn et al., Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights, Biology Methods and Protocols, Volume 8, Issue 1, 2023) was applied to identify a minimal, yet highly discriminative, reduced SBT Treg expansion fingerprint. The input feature matrix (X) consisted of log2 TPM expression values for 2,407 genes (full SBT expansion fingerprint) measured across 104 samples (SBT dataset 2 and 3, discovery data set), and the response vector (y) encoded the sample class labels as pre-expansion (n = 38) versus postexpansion (n = 66).

[0449] Using repeated bootstrap resampling (100 iterations), elastic-net logistic regression models were fit, and the selection frequency of each gene was recorded. A reduced expansion fingerprint was defined by retaining genes that were selected in at least 60 out of 100 bootstrap runs. This yielded 24 genes in total: 12 associated with the pre-expansion state and 12 associated with the post-expansion state.

[0450] The pre-expansion markers identified were: CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, FOS (FIG. 10A; showing the DO pre-expansion genes identified; n=12).

[0451] The post-expansion markers identified were: FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, MZB1 (FIG. 10B; showing the D14 post-expansion genes identified; n=12).Results

[0452] Protein-protein interaction (PPI) networks were generated for the reduced preexpansion (DO) (FIG. 10A) and post-expansion (D14) (FIG. 10B) fingerprints using StringDB with a minimum interaction confidence of 0.5 to assess functional connectivity. The feature selection procedure identified a compact 24-gene panel that robustly distinguished pre-expansion from post-expansion samples, with 12 genes enriched in each state. The StringDB -derived PPI networks (FIGS. 10A-10B) show that both the DO and D14 reduced fingerprints form coherent interaction modules rather than scattered, unrelated markers, supporting the biological plausibility of the reduced panel. In the pre-expansion 117MF-367695757.6Attorney Docket No.: 237752002040(DO) network, the selected genes include immediate-early / activation-associated transcription factors and signaling components such as FOS, JUN, EGR1, NR4A1, and NR4A2, which are connected through multiple functional and physical interactions (FIG. 10A). In the postexpansion (D14) network, genes such as UBE2C, RRM2, TYMS, TOP2A, and SCD cluster into pathways related to proliferation, DNA synthesis, and metabolic remodeling, consistent with the biology of expanded T cells (FIG. 10B).

[0453] Reduced SBT expansion scores were calculated for each sample using the selected genes; these scores were applied to both Teff and Treg populations at DO and D14 (FIG. 10C). When the reduced SBT expansion score was applied to the SBT-derived Teff and Treg samples at DO and D14, a clear separation was observed between pre-expansion and postexpansion states. Treg_D14 samples exhibited high expansion scores, while Teff DO and Treg DO samples showed markedly lower values, indicating minimal expansion signal at baseline. The distribution of scores closely mirrored what was observed using the full expansion fingerprint, with Treg_D14 consistently shifted toward the “expanded” end of the scale and Teff DO remaining near or below zero. Individual points shown in FIG. 10C correspond to single samples, illustrating that the reduced fingerprint maintains strong persample discrimination and is not driven by a small subset of outliers.

[0454] Diagnostic performance (accuracy, sensitivity, specificity, PPV, NPV) of the reduced fingerprint was compared against the original full expansion fingerprint across all samples (Table 4). Quantitatively, the reduced SBT Treg expansion fingerprint performed nearly identically to the original full fingerprint when evaluated across all samples in this study. For the full fingerprint applied to the datasets tested, accuracy, sensitivity, specificity, PPV, and NPV were all 100%. For the reduced fingerprint applied to the datasets tested, accuracy was 99%, sensitivity was 100%, specificity was 99%, PPV was 96%, and NPV remained 100%.Table 4: Comparison of Diagnostic Performance Metrics Across SBT full and reduced expansion fingerprints when including all samples.Metric Full fingerprint Reduced fingerprint Accuracy 100% 99%Sensitivity 100% 100%Specificity 100% 99%PPV 100% 96%NPV 100% 100%Abbreviations: NPV = negative predictive value; PPV = positive predictive value.118MF-367695757.6Attorney Docket No.: 237752002040

[0455] These metrics indicate that the reduced panel preserves perfect sensitivity and NPV, while sacrificing only a very small amount of specificity and PPV relative to the full model. Importantly, this performance is achieved with a substantially smaller and more interpretable gene set, which is advantageous for assay development and potential clinical translation.Conclusion

[0456] The elastic-net bootstrap feature selection applied to 2,407 input genes yielded a compact 24-gene SBT Treg expansion fingerprint (12 pre-expansion and 12 post-expansion genes) that captures the core biology of Treg activation and expansion captured by the full fingerprint. The reduced gene set formed coherent, biologically meaningful interaction networks and produced expansion scores that cleanly separate pre-expansion from postexpansion samples in both Teff and Treg populations.

[0457] Critically, the reduced fingerprint maintains virtually the same diagnostic performance as the full expansion fingerprint (99-100% accuracy, 100% sensitivity, 99% specificity for the datasets tested), while greatly simplifying the assay footprint. Thus, the reduced gene panel for the Treg expansion fingerprint is a practically implementable and high-performing expansion biomarker that can be deployed in more streamlined diagnostic or monitoring assays without loss of performance.119MF-367695757.6

Claims

Attorney Docket No.: 237752002040CLAIMS1. A method of identifying a cell as a Treg or a population of cells as comprising Tregs, the method comprisingdetecting an expression level of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more ofACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, andwherein based upon the expression level of the one or more Treg markers, the cell is identified as a Treg or the population of cells is identified as comprising Tregs.

2. A method of identifying a cell as a destabilized Treg or a population of cells as comprising destabilized Tregs comprisingdetecting an expression level of one or more Treg markers in the cell or the population of cells, the Treg markers comprising one or more ofACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, andwherein based upon the expression level of the one or more Treg markers, the cell is identified as a destabilized Treg or the population of cells is identified as comprising destabilized Tregs.

3. A method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more Treg markers in a cell or a population of cells in the Treg cell therapy, the Treg markers comprising one or more ofACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and120MF-367695757.6Attorney Docket No.: 237752002040wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more Treg markers.

4. The method of any one of claims 1-3, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more of the Treg markers.

5. The method of any one of claims 1-4, comprising detecting the expression level of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.

6. The method of any one of claims 1-5, further comprising detecting an expression level of one or more Teff markers, wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

7. The method of claim 6, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, or 10 or more of the Teff markers.

8. The method of claim 6 or 7, comprising detecting the expression level of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

9. A method of classifying a cell as a Treg, or of classifying a population of cells as comprising Tregs, the method comprisingobtaining gene expression data for one or more Treg markers and one or more Teff markers associated with the cell or the population of cells,wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and121MF-367695757.6Attorney Docket No.: 237752002040wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2, determining, based on the gene expression data, a Treg score for the cell or the population of cells, andclassifying the cell as a Treg or classifying the population of cells as comprising Tregs if the Treg score for the cell exceeds a threshold.

10. The method of claim 9, wherein the one or more Treg markers are selected from the group consisting of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and wherein the one or more Teff markers are selected from the group consisting of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

11. The method of claim 9 or 10, wherein the Treg score is determined using a Treg marker sub-score and a Teff marker sub-score.

12. The method of claim 11, wherein the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.

13. The method of any one of claims 9-12, wherein the threshold is zero.

14. The method of any one of claims 9-13, wherein the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is a positive value greater than zero.

15. The method of any one of claims 9-14, wherein the cell is classified as a Treg cell or a Treg-like cell, or the population of cells is classified as comprising Treg cells or Treg-like cells if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.122MF-367695757.6Attorney Docket No.: 23775200204016. The method of any one of claims 9-15, wherein the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is a negative value less than zero.

17. The method of any one of claims 9-16, wherein the cell is classified as a Teff cell, a Teff-like cell, or a non-Treg cell, or the population of cells is classified as comprising Teff cells, Teff-like cells, or non-Treg cells if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

18. The method of any one of claims 9-17, wherein the Treg score is determined using a Treg marker sub-score and a Teff marker sub-score, and wherein the sub-scores are determined using a gene set enrichment analysis.

19. The method of claim 18, wherein the gene set enrichment analysis comprises singlesample gene set enrichment analysis (ssGSEA).

20. The method of claim 18 or 19, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and wherein the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

21. The method of any one of claims 18-20, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and wherein the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

22. A method of determining cell identity, the method comprising:(a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a plurality of cells,wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, and123MF-367695757.6Attorney Docket No.: 237752002040wherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2;(b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers;(c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers;(d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and(e) determining, based on the Treg score, a cell identity for the plurality of the cells.

23. A method of assessing the quality of a Treg cell therapy product, the method comprising:(a) obtaining gene expression data for one or more Treg markers and one or more Teff markers in a Treg cell therapy product comprising a plurality of cells,wherein the Treg markers comprise one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA, andwherein the Teff markers comprise one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2;(b) determining a Treg marker sub-score for the plurality of cells, using the gene expression data for the one or more Treg markers;(c) determining a Teff marker sub-score for the plurality of cells, using the gene expression data for the one or more Teff markers;(d) generating a Treg score for the plurality of cells, using the Treg marker sub-score and the Teff marker sub-score; and(e) determining, based on the Treg score, a cell identity for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

24. The method of claim 22 or 23, wherein the Treg score is based on a comparison between the Teff marker sub-score and the Treg marker sub-score.124MF-367695757.6Attorney Docket No.: 23775200204025. The method of any one of claims 22-24, wherein the cell identity is determined to be Treg or Treg-like if the Treg score meets a threshold.

26. The method of any one of claims 22-25, wherein the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score does not meet a threshold.

27. The method of claim 25 or 26, wherein the threshold is zero.

28. The method of any one of claims 22-27, wherein the cell identity is determined to be Treg or Treg-like if the Treg score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

29. The method of any one of claims 22-28, wherein the cell identity is determined to be Teff, Teff-like, or non-Treg, if the Treg score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

30. The method of any one of claims 22-29, wherein the Treg marker sub-score and the Teff marker sub-score are each determined using a gene set enrichment analysis.

31. The method of claim 30, wherein the gene set enrichment analysis comprises singlesample gene set enrichment analysis (ssGSEA).

32. The method of claim 30 or 31, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers, and wherein the gene set enrichment analysis generates the Treg marker sub-score based on the one or more Treg markers.

33. The method of claim 32, wherein the predefined gene set comprising the one or more Treg markers comprises one or more of ACTA2, TNFRSF1B, OAS1, SLC12A6, PYHIN1, BCL2L11, IKZF2, GPA33, ZCCHC14, INPP5F, METTL7A, SMPD3, GCNT4, CDK14, VAV3, GBP5, ID3, ST8SIA6, FCRL3, SELP, CCDC141, FOXP3, CTLA4, F5, RTKN2, TIGIT, TSHR, SGPP2, TRIB1, CCNG2, RBMS3, and IL2RA.125MF-367695757.6Attorney Docket No.: 23775200204034. The method of any one of claims 30-33, wherein a higher Treg marker sub-score increases the likelihood that the cell identity is determined to be Treg or Treg-like.

35. The method of any one of claims 30-34, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Teff markers, and wherein the gene set enrichment analysis generates the Teff marker sub-score based on the one or more Teff markers.

36. The method of claim 35, wherein the predefined gene set comprising the one or more Teff markers comprises one or more of STXBP1, MCOLN2, CFH, TARP, MCTP2, ABCB1, CD40LG, GNLY, NKG7, GAB3, and ID2.

37. The method of any one of claims 30-36, wherein a higher Teff marker sub-score increases the likelihood that the cell identity is determined to be Teff, Teff-like, or non-Treg.

38. The method of any one of claims 30-37, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more Treg markers for generating the Treg marker sub-score and a predefined gene set comprising the one or more Teff markers for generating the Teff marker sub-score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.

39. The method of claim 38, wherein the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

40. The method of any one of claims 22-39, wherein the Treg marker sub-score and the Teff marker sub-score are given equal weight in determining the Treg score.

41. The method of any one of claims 22-40, wherein a higher Treg score indicates a higher quality and / or a higher purity for the plurality of cells.

42. The method of any one of claims 22-41, wherein a lower Treg score indicates a lower quality and / or a lower purity for the plurality of cells.126MF-367695757.6Attorney Docket No.: 23775200204043. The method of any one of claims 22-42, wherein a lower Treg score indicates contamination of the plurality of cells.

44. A method of identifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprisingdetecting an expression level of one or more post-expansion markers in the cell or the population of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, andwherein based upon the expression level of the one or more post-expansion markers, the cell is identified as an expanded Treg or the population of cells is identified as comprising expanded Tregs.

45. A method of assessing the quality of a Treg cell therapy comprising detecting an expression level of one or more post-expansion markers in a cell or a population of cells in the Treg cell therapy, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1,wherein the quality of the Treg cell therapy is determined based upon the expression level of the one or more post-expansion markers.

46. The method of claim 44 or 45, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the post-expansion markers.

47. The method of any one of claims 44-46, comprising detecting the expression level of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

48. The method of any one of claims 44-47, further comprising detecting an expression level of one or more pre-expansion markers, wherein the pre-expansion markers comprise one or more ofCSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.127MF-367695757.6Attorney Docket No.: 23775200204049. The method of claim 48, comprising detecting the expression level of 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, or 11 or more of the pre-expansion markers.

50. The method of claim 48 or 49, comprising detecting the expression level of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

51. A method of classifying a cell as an expanded Treg or a population of cells as comprising expanded Tregs, the method comprisingobtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers associated with the cell or the population of cells,wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, andwherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS,determining, based on the gene expression data, an expansion score for the cell or the population of cells, andclassifying the cell as an expanded Treg or classifying the population of cells as comprising expanded Tregs if the expansion score for the cell exceeds a threshold.

52. The method of claim 51, wherein the one or more post-expansion markers are selected from the group consisting of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, and wherein the one or more pre-expansion markers are selected from the group consisting of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

53. The method of claim 51 or 52, wherein the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score.

54. The method of claim 53, wherein the expansion score is based on a comparison between the pre-expansion marker sub-score and the post-expansion marker sub-score.128MF-367695757.6Attorney Docket No.: 23775200204055. The method of any one of claims 51-54, wherein the threshold is zero.

56. The method of any one of claims 51-55, wherein the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is a positive value greater than zero.

57. The method of any one of claims 51-56, wherein the cell is classified as an expanded Treg or the population of cells is classified as comprising expanded Tregs if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

58. The method of any one of claims 51-57, wherein the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is a negative value less than zero.

59. The method of any one of claims 51-58, wherein the cell is classified as an unexpanded or endogenous Treg or the population of cells is classified as comprising unexpanded or endogenous Tregs if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

60. The method of any one of claims 51-59, wherein the expansion score is determined using a post-expansion marker sub-score and a pre-expansion marker sub-score, and wherein the sub-scores are determined using a gene set enrichment analysis.

61. The method of claim 60, wherein the gene set enrichment analysis comprises singlesample gene set enrichment analysis (ssGSEA).

62. The method of claim 60 or 61, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and wherein the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.129MF-367695757.6Attorney Docket No.: 23775200204063. The method of any one of claims 60-62, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and wherein the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.

64. A method of determining cell expansion status, the method comprising:(a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a plurality of cells,wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, andwherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS;(b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers;(c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers;(d) generating an expansion score for the plurality of cells, using the post-expansion marker sub-score and the pre-expansion marker sub-score; and(e) determining, based on the expansion score, a cell expansion status for the plurality of the cells.

65. A method of assessing the quality of a Treg cell therapy product, the method comprising:(a) obtaining gene expression data for one or more post-expansion markers and one or more pre-expansion markers in a Treg cell therapy product comprising a plurality of cells, wherein the post-expansion markers comprise one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1, andwherein the pre-expansion markers comprise one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS;130MF-367695757.6Attorney Docket No.: 237752002040(b) determining a post-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more post-expansion markers;(c) determining a pre-expansion marker sub-score for the plurality of cells, using the gene expression data for the one or more pre-expansion markers;(d) generating an expansion score for the plurality of cells, using the post-expansion marker sub-score and the pre-expansion marker sub-score; and(e) determining, based on the expansion score, a cell expansion status for the plurality of the cells, thereby assessing the quality of the Treg cell therapy product.

66. The method of claim 64 or 65, wherein the expansion score is based on a comparison between the pre-expansion marker sub-score and the post-expansion marker sub-score.

67. The method of any one of claims 64-66, wherein the cell expansion status is determined to be expanded if the expansion score meets a threshold.

68. The method of any one of claims 64-67, wherein the cell expansion status is determined to be unexpanded or endogenous if the expansion score does not meet a threshold.

69. The method of claim 67 or 68, wherein the threshold is zero.

70. The method of any one of claims 64-69, wherein the cell expansion status is determined to be expanded if the expansion score is greater than about 0.1, about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, or about 0.9.

71. The method of any one of claims 64-70, wherein the cell expansion status is determined to be unexpanded or endogenous if the expansion score is less than about -0.1, about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, or about -0.9.

72. The method of any one of claims 64-71, wherein the post-expansion marker sub-score and the pre-expansion marker sub-score are each determined using a gene set enrichment analysis.131MF-367695757.6Attorney Docket No.: 23775200204073. The method of claim 72, wherein the gene set enrichment analysis comprises singlesample gene set enrichment analysis (ssGSEA).

74. The method of claim 72 or 73, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers, and wherein the gene set enrichment analysis generates the post-expansion marker sub-score based on the one or more post-expansion markers.

75. The method of claim 74, wherein the predefined gene set comprising the one or more post-expansion markers comprises one or more of FADS2, DHCR24, SCD, UBE2C, ITGAM, FADS1, EGLN3, RRM2, GPR15, TYMS, TOP2A, and MZB1.

76. The method of any one of claims 72-75, wherein a higher post-expansion marker subscore increases the likelihood that the cell expansion status is determined to be expanded.

77. The method of any one of claims 72-76, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more pre-expansion markers, and wherein the gene set enrichment analysis generates the pre-expansion marker sub-score based on the one or more pre-expansion markers.

78. The method of claim 77, wherein the predefined gene set comprising the one or more pre-expansion markers comprises one or more of CSRNP1, RGS2, TCEA3, EGR1, GPRASP1, IL7R, NR4A1, TSC22D3, DUSP1, JUN, NR4A2, and FOS.

79. The method of any one of claims 72-78, wherein a higher pre-expansion marker subscore increases the likelihood that the cell expansion status is determined to be unexpanded or endogenous.

80. The method of any one of claims 72-79, wherein the gene set enrichment analysis uses a predefined gene set comprising the one or more post-expansion markers for generating the post-expansion marker sub-score and a predefined gene set comprising the one or more pre-expansion markers for generating the pre-expansion marker sub-score, wherein the predefined gene sets are determined using gene expression data from one or more reference samples.132MF-367695757.6Attorney Docket No.: 23775200204081. The method of claim 80, wherein the one or more reference samples comprise a reference plurality of cells selected from the group consisting of unexpanded or endogenous Tregs, Tregs that have been expanded in cell culture, unexpanded or endogenous Teffs, and Teffs that have been expanded in cell culture.

82. The method of any one of claims 64-81, wherein the post-expansion marker sub-score and the pre-expansion marker sub-score are given equal weight in determining the expansion score.

83. The method of any one of claims 64-82, wherein a higher expansion score indicates a higher quality and / or a higher purity for the plurality of cells.

84. The method of any one of claims 64-83, wherein a lower expansion score indicates a lower quality and / or a lower purity for the plurality of cells.

85. The method of any one of claims 64-84, wherein a lower expansion score indicates contamination of the plurality of cells.133MF-367695757.6

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