Transcriptional engineering of tr1 cells

Abstract

Methods and cell compositions are provided in which expression of one or more of the transcription factors are modulated to direct T cell phenotype and function, including differentiation of Tr1 cells. The resulting Tr1 cells are useful in the treatment of inflammatory and other conditions.

Description

TRANSCRIPTIONAL ENGINEERING OF TR1 CELLSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] Pursuant to 35 U.S.C. § 1 19 (e), this application claims priority to the filing date of United States Provisional Patent Application Serial No. 63 / 537,736 filed September 1 1 , 2023, the disclosure of which application is herein incorporated by reference.BACKGROUND

[0002] Regulatory T cells (Tregs) play a critical role in human health by inducing and maintaining tolerance to endogenous and exogenous antigens. Type 1 regulatory T (Tr1 ) cells are Tregs that arise from effector CD4+T cells (Teff) in response to repeated antigen stimulation in a tolerogenic environment, e.g., in the presence of immunoregulatory cytokines interleukin (IL)-10 and IL-27. In humans, Tr1 cells have been identified in antigen-driven chronic inflammatory conditions, such as allogeneic hematopoietic stem cell transplantation (allo-HSCT), allergy (to bee venom, pollen, dust mites), autoimmunity (celiac disease), infections (leishmania, malaria, influenza), as well as in cancer. However, transcription factors (TFs) that control human Tr1 cell differentiation, phenotype and functions are largely unknown. Tr1 cells do not express or depend on TF FOXP3, which determines the differentiation and function of thymic (t)Tregs and bestows the Treg-like phenotype and functions to FOXP3+inducible (i)Tregs. Tr1 lineage-determining TFs have been described in the murine, IL-27- inducible T r1 differentiation model, which has not yet been recapitulated in human cells. Thus, the identification of TFs that govern human Tr1 cells requires the use of human Tr1 cell differentiation model. Revealing the key TFs of human antigen-specific Tr1 cells is critical to understand Tr1 biology, identify Tr1 cells in vivo and to provide a mechanistic basis for rational design of antigen-specific Tr1 cell therapies.

[0003] T-allo10 is a human alloantigen-inducible Tr1 differentiation model designed to recreate the in vivo Trl induction observed after allo-HSCT, the condition where Tr1 cells were first identified. During T-allo10 culture, allogeneic tolerogenic dendritic cells stimulate the alloreactive fraction of peripheral CD4+Teff to clonally expand and differentiate into Tr1 cells. The resulting Tr1 cells are oligoclonal and alloantigen-specific; namely, they selectively suppress alloreactive Teff, but not Teff activated by irrelevant antigens. This alloantigen- specific suppressive activity makes T-allo10 Tr1 cells an attractive therapy to induce tolerance after allo-HSCT, as they could prevent graft-vs-host disease (GvHD) mediated by alloreactive Teff, without impairing Teff responses against tumor cells or pathogens. HSCT donor-derived T-allo10 Tr1 products are already being tested in two phase l / lb clinical trials, with promising early clinical results.

[0004] The low frequency of antigen-specific Tr1 cells in Tr1 cell products currently made for clinical application, called T-allo10 cells, also poses a challenge in their clinical application. An optimized strategy to generate purer antigen-specific Tr1 cell products is of clinical interest, as are means for in vivo therapeutic targeting of Tr1 cells.SUMMARY

[0005] Compositions and methods are provided for increasing the efficiency of Tr1 cell differentiation and generation by engineering the cell’s expression of transcription factors. It is shown that transcription factors IRF4 and BATF are necessary for type 1 regulatory T cell (Tr1 ) differentiation, and that transcription factor MAF potentiates Tr1 cell differentiation.

[0006] In some embodiments, an in vitro method is provided for generating polyclonal and / or antigen-specific Tr1 cells. Antigen-specific Tr1 cells may be alloantigen-specific cells. The cells are engineered to over-express one or more of the transcription factors IRF4, MAF and BATF, for example IRF4 and MAF; IRF4 and BATF; MAF and BATF; or IRF4, MAT and BATF. Introduction of one or more of the transcription factors VDR, FOXB1 , and TOX2 is also of interest.

[0007] The efficiency of generation of Tr1 cells from a starting cell population, e.g. naive or CD4+ cells is improved relative to the efficiency in the absence of the transcription factor engineering by at least 2.5%, at least 5%, at 7.5%, at least 10%, at least 12.5%, at least 15%, at least 17.5%, at least 20%, at least 25%, at least 30%, at least 50%, at least 75%, at least 100%, or more.

[0008] An in vitro method for generating Tr1 cells comprises: a) providing naive or polarized CD4+T cells; b) activating the CD4+T cells; c) modifying the CD4+T cells to overexpress one or more of the transcription factors IRF4, MAF and BATF; and d) expanding the transduced cells. In some embodiments the modifying step (c) comprising transducing the cells with a vector encoding one or more of the transcription factors IRF4, MAF and BATF operably linked to a promoter active in the cells. In some embodiments the modifying step (c) comprising altering the genome of the cells with a recombinase to introcude a modification to the one or more of the transcription factors IRF4, MAF and BATF genes or promoters.

[0009] Tr1 cells may be purified from the expanded cell population. At the end of the expansion cycle, the cells may be assessed for a Tr1 phenotype, which phenotype may include, without limitation, expression of LAG3, CD49b, CTLA-4 ± granzyme B expression, stability of Tr1 phenotype through a secondary stimulation, and suppressive and cytotoxic functions. The resulting Tr1 cells can be characterized as LAG3+CD49b+. In some embodiments the differentiated cell population is selected for expression of LAG3 and CD49b, e.g. by flow cytometry, affinity purification methods, etc. to provide for a purified Tr1 cellpopulation. A Tr1 genetic signature may be used to confirm the identity of the cells, and to assess their presence following transfer to an individual.

[0010] Where the T r1 cells are polyclonal, the initial activation step (b) may be performed with one or more of anti-CD3 antibody, anti-CD28 mAb, and IL-2; or IL-2, IL-7 and IL-15 alone or in various combinations. The transduced cells are then expanded in step (d) using a polyclonal T cell stimulus, e.g. targeting CD3 and CD28 with antibodies or artificial antigen-presenting cells, or with feeder cell mixture (e.g., irradiated peripheral blood mononuclear cells, PBMC, cell lines such as JY cells, L cells, K562 cells, others, with or without anti-CD3 antibody). Alternatively, to generate antigen-specific cells, the transduced cells are expanded in step (d) using using allogeneic or autologous, or antigen-loaded tolerogenic myeloid cells such as IL- 10-tolerized dendritic cells (DC-10) instead of using anti-CD3 / CD28 stimulus.

[0011] To generate alloantigen-specific Tr1 cells, the CD4+T cells may be activated in step (b) by culturing with allogeneic DC-10. In other embodiments the CD4+T cells are activated in step (b) by culturing with autologous DC-10 cells pre-loaded with an antigen of interest. The activated cells may be selected from the population after this step. The transduced cells are then expanded in step (d) using a polyclonal T cell stimulus, e.g. by targeting CD3 and CD28 or with feeder cells.

[0012] In other embodiments, a method is provided for decreasing Tr1 cells in a population, the method comprising inhibiting expression of one or more of the factors IRF4, MAF, and BATF in the cells. Expression can be inhibited by introducing siRNA, CRISPR knock-out of the transcription factor gene or promoter, and the like.

[0013] In some embodiments, an engineered cell is provided, in which the engineered cell has been engineered to regulate expression of one or more of the transcription factors IRF4, MAF and BATF, e.g. by transient down-regulation, introduction of a coding sequence to upregulate expression, genetically modified to knock out the coding sequence or promoter, and the like. In some embodiments the cell is a human cell. In some embodiments, the cell is a human T cell. In some embodiments the human T cell is a Tr1 cell such as a Tallol O cells, e.g. a LAG3+CD49b+cell. Other T cells of interest include naive CD8+T cells, cytotoxic CD8+T cells, naive CD4+T cells, helper T cells, e.g. TH1 , TH2, TH9, TH11 , TH22, TFH; regulatory T cells, e.g. TR1 , natural Tneg, inducible TReg; memory T cells, e.g. central memory T cells, effector memory T cells, NKT cells, exp T cells, y§ T cells and engineered variants of such T cells including CAR T cells; etc. The engineered cell can be provided in a unit dose for therapy, and can be allogeneic, autologous, etc. with respect to an intended recipient.

[0014] In some embodiments, a vector comprising a polynucleotide coding sequence is provided, where a coding sequence for one or more of the transcription factors IRF4, MAF and BATF is operably linked to a promoter active in the desired T cell. An active promoter may be constitutively active or may be regulated. In some embodiments, the vector comprises apolynucleotide coding sequence that downregulates one or more of the transcription factors IRF4, MAF and BATF, e.g. encoding guide RNA, a knockout construct, RNAi construct, antisense construct, etc. Various vectors are known in the art and can be used for this purpose, e.g. replication competent, replication deficient or conditionally replicating viral vectors, plasmid vectors, minicircle vectors. In some embodiments, the vector maybe integrated into the target cell genome or can be episomally maintained.

[0015] In some embodiments a therapeutic method is provided, the method comprising introducing into a subject in need thereof a therapeutically effective quantity of an engineered cell population, in which the engineered cell has been treated to regulate expression of one or more of the transcription factors IRF4, MAF and BATF. The cell population may be engineered ex vivo, and may be autologous or allogeneic with respect to the subject. In some embodiments the engineered cell is a Tr1 cell. In some embodiments the engineered cell is a Tallo cell.

[0016] In some embodiments, a method is provided to determining the presence of Tr1 cells in a cell population. Populations of interest may include, for example, expanded T-allo10 cell populations. In some embodiments the population of cells comprises peripheral blood CD4+T cells, for example taken from an individual treated with T-allo10 cells; peripheral blood taken from a cancer patient, e.g. multiple myeloma, colorectal cancer, breast cancer, etc.; a solid tumor sample from a cancer patient, etc. A subject may have been treated with CART cell therapy, etc. Tr1 cells are identified by determining the presence of a gene expression profile as provided herein. In some embodiments a gene expression profile comprises expression information from one or more transcription factors. In some embodiments a gene expression profile is combined with an epigenomic profile, e.g. for transcription factor binding motifs. In some embodiments, the determination of the presence of Tr1 cells is used in the determination of efficacy of a therapeutic intervention, including administration of a cell composition comprising Tr1 cells; administration of an immune checkpoint inhibitor; and the like.BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The invention is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures.

[0018] FIGS. 1 A-1J. Single-cell RNA+TCR-seq analysis identifies antigen-specific Tr1 cells as a distinct population within T-allo10 cells. A. U AP visualization of CD4+T cells subpopulations within T-allo10 cells (n = 2). Overlaid contour plot shows clonally expanded cells. The lowest clonal frequency displayed is 5 cells. Dim = dimension. B. Dot plot illustratingthe representative differentially expressed genes (DEG) per cluster. Dot color intensity corresponds to the average expression level per cluster. Dot size represents the percentage of cells expressing the gene within the cluster. C. Heatmap of average gene expression of 49 differentially expressed genes identified between Tr1 and FOXP3+Treg cell clusters. Adjusted p (padj) value < 0.001 and Iog2 fold change (log2FC) > 1 . D. Volcano plot depicting the results of the differential gene expression analysis between Tr1 and all other clusters. DEG that passed the filtering (padj < 0.001 , log2FC > 1 ) are in black; representative genes are labeled. E. Top: chord diagram showing clonotypes shared between clusters. Each arc represents a UMAP cluster from panel A, and ribbons connecting arcs represent shared clonotypes. The width of the ribbons corresponds to the number of shared clonotypes. Bottom: summary table provides the count of shared clonotypes between clusters. F. UMAP projection displaying the re-clustering of Tr1 cell cluster from A. G. Chord diagram showing the total number of unique and shared clones among Tr1 sub-clusters. H. Volcano plot illustrating the differential gene expression analysis among subclusters of Tr1 cells. DEG (padj < 0.001 and log2FC > 1 ) are in black. I. Developmental trajectory of Tr1 cells predicted by RNA velocity. J. Heatmap representing the average gene expression of 9 differentially expressed transcription factors between Tr1 cells and the remaining clusters.

[0019] FIGS. 2A-2F. ATAC-seq reveals the chromatin accessibility landscape and transcriptional regulators of antigen-specific Tr1 cells. A. Experimental outline. Tr1 = CD49b+LAG3+CD45RA CD4+type 1 regulatory T cells from T-allo10 cell product; DN = double-negative (non-Tr1 ) CD49b LAG3 CD45RA CD4+cells from T-allo10 product; Teff = CD49b l_AG3 CD45RA CD4+ effector T cells from control T-allo product; DC-10 = allogeneic tolerogenic dendritic cells; matDC = allogeneic mature dendritic cells; MPLA = monophosphoryl lipid A, a TLR4 agonist given at day 5 of matDC induction. B. Quality control analysis (QC) of bulk ATAC libraries showing nucleosome periodicity (left) and transcription start site enrichment (right). Data are averaged from n = 5 per condition. C. Changes in chromatin accessibility in Tr1 cells (n = 5) relative to all other non-Tr1 cells (CD4, Teff, DN, total n = 15). Each dot represents one ATAC-seq peak. D. Gene Set Enrichment Analysis (GSEA) of ATAC-seq peaks of Tr1 cells versus all non-Tr1 cells within MsigDB C7: Immune Signatures database; representative enrichment plots are shown. NES = normalized enrichment score; FDR = FDR q value. E. Upstream regulator Ingenuity Pathway (IPA) analysis of the top 2,336 accessible peaks in Tr1 cells, ranked by the number of times found differentially accessible between Tr1 cells and all other T cell subsets (> 0.7) and filtered on transcriptional regulators that were present in the dataset, showing the predicted top three most significant upstream regulators (hub centers) in a network with downstream genes. Color = log2FC. F. Gene accessibility track visualization of Tr1 -specific transcription factors IRF4 and MAF. Promoter regions are highlighted in red, while enhancer regions are highlighted inyellow. Significantly accessible regions in Tr1 cells compared to the non-Tr1 cells are marked with asterisks (*), with corresponding p-value indicated numerically.

[0020] FIGS. 3A-3G. Antigen-specific T r1 cells have a distinct signature of active transcription factors. A. tSNE visualization of ATAC-seq profiles using 452 transcription factor (TF) motif deviations identified by chromVAR package. Samples are colored by cell type. B. Volcano plot showing transcription factor enrichment (chromVAR) between Tr1 and all non-Tr1 cells. P- values were calculated using a two-tailed t-test. C. Volcano plot showing the differential binding activity, calculated by TOBIAS package, between Tr1 and all non-Tr1 cells; each dot represents one motif. Representative Tr1 -specific TFs are highlighted in red, and non-Tr1 TFs in blue. D. Bias corrected ATAC-seq footprints between Tr1 and DN (non-Tr1 ) cells from T- allol 0 products. Selected motifs that have differential binding activity between T r1 and all non- Tr1 cells (panel C) are shown. E. Representative histograms of TF expression within Tr1 and non-Tr1 (DN) populations of T-allo10 cells, n = 2 - 6 per TF. F. Enhancers scores predicted by GREAT, ranked by chromatin accessibility. Representative ranked-enhancers among the top 50 are shown on the right. G. Paired correlation between differential expression and differential accessibility, computed using the Domains of Regulatory Chromatin (DORC) calling method from the FigR package. Highlighted top right quadrant shows the Tr1 -specific DORC and top associated genes (normal font, black dots) and TFs (bold font, red dots).

[0021] FIGS. 4A-4Q.Transcription factors IRF4, BATF and MAF play non-redundant roles in Tr1 cell differentiation, phenotype, and function. A. Strategy for the transcription factor knockout (KO) in parental CD4+T cells, combined with their differentiation into antigen-specific Tr1 cells. DC = dendritic cells. B. Knock-out score (percent) in CD4+T cells 72h post-editing, as calculated by ICE (Inference of CRISPR Edits, Synthego) using Sanger sequencing data. Each dot represents a healthy donor sample, red full lines the median, and red dotted lines interquartile range. IRF4 KO, n = 25; BATF KO, n = 6; MAF KO, n = 8. C. Left: cumulative flow cytometry data of Tr1 cell frequency at the end of 10-day T-allo10 culture, started from nontargeted (wild-type, WT) or IRF4 KO parental CD4+T cells that were co-cultured in 10:1 ratio with allogeneic DC-10; n = 25. Right: representative flow cytometry plots showing Tr1 cell staining in WT and IRF4 KO memory CD4+T cells within T-allo10 products. Numbers on plot indicate frequency of gated cells. D. Representative flow cytometry histograms depicting the expression of LAG3 and CTLA-4 in memory CD4+T cells of WT and IRF4 KO T-alo10 products. E. Frequency of LAG3+, CD49b cells (left), and CTLA-4+cells (right) in CD4+memory T cells (Tmem) in WT or IRF4 KO T-allo10 cells; n = 25, flow cytometry F. Frequency of CTLA-4+cells in CD49+LAG3 (white circles), CD49b LAG3+(black circles), and CD49b+LAG3+Tr1 cells (grey circles), within Tmem of T-allo10 cells; n = 25, flow cytometry. G. Left: Suppression of responder (“R”) T cell proliferation by WT or IRF4 KO T-allo10 cells. R = ex vivo isolated CD4+ T cells autologous to T-allo10 cell donors. Suppression assay wasperformed as described. Right: representative histograms of dividing responder (“R”) CD4+ T cells labeled with CellTrace Violet (CTV) proliferation dye, cultured for 5 days alone (blue), or stimulated with allogeneic mature dendritic cells (DC) ± WT or KO T-allo10 cells. Flow cytometry, n = 6. H. Frequency of granzyme B (GZMB)+cells in Tr1 cells from WT or BATF KO T-allo10 cells; n = 6, flow cytometry. I. Frequency of Tr1 cells in WT or BATF KO T-allo10 cells (as in C); n = 6, flow cytometry. J. Frequency of LAG3+, CD49b cells (left), CTLA-4+cells (middle), and granzyme B (GZMB)+cells in CD4+memory T cells (Tmem) from WT or BATF KO T-allo10 cells; n = 6, flow cytometry. K. Frequency of Tr1 cells in WT or MAF KO T-allo10 cells (as in C); n = 6, flow cytometry. L. Suppression of responder (“R”) T cell proliferation by WT or MAF KO T-allo10 cells (as in G); n = 4, flow cytometry. M. Frequency of IL-10* CD49b+LAG3+Tr1 cells or non-Tr1 (CD49b LAG3 DN or double-negative) cells in Tmem of WT or MAF KO T-allo10 products. Left: cumulative data, n = 5; right: representative dot plots, flow cytometry. N. Strategy for the transcription factor (TF) overexpression in parental naive CD4+ T cells using CRISPRa (CRISPR-activation), combined with their differentiation into Tr1 cells. dCas9-VPR = “dead”, nuclease-inert Cas9 fused to transcriptional activation domains VP64, p65 and Rta; RT-qPCR = real-time quantitative PCR. O. CRISPRa efficiency, measured by upregulation of MAF gene 24h after CRISPRa (MAFa) relative to non-targeted controls (NT) using RT-qPCR; n = 8. P. Frequency of Tr1 cells after MAF CRISPRa in comparison to NT in Tmem cells of T-allo10 products. Left: cumulative data; right: representative dot plots. * p < 0.05, *** p < 0.001 , **** p < 0.0001 ; Wilcoxon test.

[0022] FIGS. 5A-5K. Identification of peripheral blood Tr1 cells in vivo using Tr1 -specific transcription factor signature. A. Heatmap of transcription factor (TF) motifs identified as specific to T-allo10-derived Tr1 cells by CIBERSORTx algorithm, as expressed in total CD4+T cells isolated from peripheral blood mononuclear cells (PBMC). HD and BC = healthy donors; PT = patients 1 -3; D35, 90 and 300 indicate days post-allo-HSCT and T-allo10 therapy. B. Frequency of Tr1 cells estimated by digital cytometry (CIBERSORTx; y-axis) and measured by flow cytometry (x-axis) in total CD4+T cells; Spearman rank correlation. Line indicates linear regression trendline. C. Frequency of Tr1 cells in total CD4+T cells of healthy donors (n = 3) and a patient at month 9 post-allo-HSCT and T-allo10 infusion, which were subsequently analyzed by sc-multiome (single-cell ATAC+RNA-seq, 10X Genomics). D. Donor IDs after integration of samples with Harmony; ArchR package. E. sc-multiome clusters of CD4+T cell samples from panel C; ArchR package. F. Distribution of donors across clusters. G. Gene expression of selected genes characteristic for Tr1 cells, cytotoxic T cells, and FOXP3+Tregs. H. Gene accessibility of genes depicted in panel G. I. From top to bottom: gene accessibility of cytokines IL10 and IFNG and motif accessibility of TFs MAF and TBX21 that induce IL10 and IFNG, respectively. J. Motif accessibility of TFs that act as key regulators of T-helper cell subsets. K. Gene Set Enrichment Analysis (GSEA) of the accessible TFsignature specific to in v / 'fra-induced antigen-specific Tr1 cells in CD4+T cells of healthy donors and a patient analyzed by sc-multiome. NES = normalized enrichment score; q-value = FDR-corrected p-value. Color scale = q-value-adjusted NES.

[0023] FIGS. 6A-6J. Identification of solid tumor-resident Tr1 cells in vivo using Tr1 -specific transcription factor signature. A. Computational analysis workflow applied to publicly available single-cell ATAC-seq data from clear-cell renal cell carcinoma (ccRCC, n = 8). The 10x Genomics output was processed using ArchR to analyze CD4+T cells after filtering out myeloid cells, NK cells, and B cells from total CD45+cells from ccRCC samples. B. UMAP representation of 8 ccRCC samples after integration with Harmony. Color indicates distinct sample origins. C. Cluster identification of the integrated dataset, showing 12 clusters. D. Fractional distribution of donors across identified clusters. E. Fractional distribution of cancer stages (la or lb) within each cluster. F. Fractional distribution of cell types within each cluster; tumor = cells isolated from tumor tissue; normal adjacent = cells isolated from the adjacent normal tissue; PBMC = cells isolated from peripheral blood mononuclear cells. G. UMAP representation highlighting different cell origins listed in panel F. H. Enrichment of the Tr1 - specific TF motif signature using Gene Set Enrichment Analysis (GSEA). NES = normalized enrichment score, q-value = FDR-corrected p-value. Color scale = q-value-adjusted NES. I. UMAP embedding of accessible motifs for indicated transcription factors (TFs) based on chromVAR deviation scores, calculated by ArchR. J. UMAP embedding of accessible genes based on gene score accessibility per cell, as calculated by ArchR.

[0024] FIGS. 7A-7D. A. Schematic representation of 10x Genomics single-cell (sc) Immune Profiling (sc- RNA+TCR-seq) of T-allo 10 products. Aliquot of T-allo10 cells was analyzed by sc-lmmune Profiling on day 9 of T-allo10 culture, while the rest was cultured by day 10 and assessed for phenotype and anergy. B. Representative gating strategy of T-allo10 products. C. Percentage of Tr1 cells in two T-allo products analyzed by flow cytometry, defined as CD49b+LAG3+memory (CD45RA ) CD4+CD3+live singlet T cells; flow cytometry. D. Alloantigen-specific anergy of two T-allo10 products analyzed by flow cytometry, measured as lack of proliferation of carboxyfluorescein diacetate, succinimidyl ester (CFSE)-labeled T- allo10 cells after 3-day re-stimulation in vitro with allogeneic mature dendritic cells (matDC), in comparison to proliferation of control effector cells (T-allo) generated from the same parental CD4+T cells but in co-culture with matDC. Anergy was calculated as described. Proliferation was measured by decrease in CFSE fluorescence. Polyclonal stimulation of T-allo10 and T- allo cells with plate-bound anti-CD3 monoclonal antibody (mAb; clone OKT3, 10 pg / mL) and soluble anti-CD28 mAb (1 pg / mL) was used as a positive control for proliferation.

[0025] FIGS. 8A-8E. A. Clustering label annotation as predicted by: a) CIPR annotation tool (graph) and b) expression level of canonical markers of T cell subsets. B. Representative UMAP visualization of gene expression from each cluster’s defining markers. C. UMAPrepresentation of gene expression from canonical Tr1 marker genes. D. Distribution of cluster fractions for each donor, showing the average of 2 donors. E. Clone size distribution of clusters and clonal diversity calculated by inverse Simpson index.

[0026] FIGS. 9A-9D. A. Venn diagram illustrating the overlap of upregulated genes in the T- allol O Tr1 cell population between bulk RNA-seq (from Chen P and Cepika AM et al) and single-cell RNA-seq dataset. For genes unique to bulk- and sc-datasets, top 20 upregulated genes are shown. B. UMAP visualization of EOMES gene expression in sc-lmmune profiling of two T-allo10 products. C. Left: representative flow cytometry plots depicting the analysis of intracellular CTLA-4 and granzyme B (GZMB) expression in CD49b+LAG3+Tr1 and CD49b LAG3 non-Tr1 (DN) memory CD4+T cells from T-allo10 products. Right: cumulative data of intracellular CTLA-4 and GZMB expression, n = 6. *p < 0.05, Wilcoxon test. D. Left: representative flow cytometry plot showing the frequency of Tr1 cells expressing intracellular granzyme B (GZMB) and surface CD107a, which indicates degranulating cells, after a 6h- stimulation with anti-CD3 / anti-CD28 Dynabeads. Right: cumulative data of total CD107a expression on Tr1 or non-Tr1 (DN) cells after 6h-culture of T-allo10 products (n = 5) in cell culture media alone or in the presence of anti-CD3 / CD28 Dynabeads or myeloid tumor cell line U937 cells in 10:1 ratio. Friedman ANOVA with Dunn’s post hoc test was used to compare Tr1 cells or DN cells across conditions, while Wilcoxon test was used to compare Tr1 vs DN cells in each condition. Statistically significant adjusted p-values are indicated on the graph.

[0027] FIGS. 10A-10C. A. Sequence quality and unique alignment sequence distribution of the ATAC-seq dataset. B. ATAC-seq data quality control filters of four CD4+T cell populations from five donors. Shown are the numbers of unique ATAC-seq nuclear fragments and transcription start site (TSS) enrichment in each sample. Dashed lines represent the filters for high-quality data (1 x 106unique nuclear fragments and TSS score > 5). C. Visualization of ranked genes based on the number of significant gene-peak correlations (per gene), used to define domains of regulatory chromatin (DORC).

[0028] FIGS. 11 A-1 1 E. A. CRISPR / Cas9 single guide RNA targeting strategy for IRF4, BATF, and MAF, indicating guide RNA targets and primers used for PCR. CDS = coding sequence. B. Expression of granzyme B (GZMB) in memory CD4+T cells (Tmem, left) and Tr1 cells (right) in wild-type (WT) and IRF4 knock-out (KO) T-allo10 cells, flow cytometry, p = ns, n = 25, Wilcoxon test. C. IRF4 KO scores, measured by Synthego ICE (Inference of CRISPR Edits) analysis of Sanger sequencing data, of parental CD4+T cells after 72h rest post-eding (baseline), total T-allo10 cells after 10 day differentiation in vitro (bulk), FACS-sorted live singlet CD45RA+CD4+CD3+T cells from T-allo10 cells (naive), FACS-sorted live singlet CD49b LAG3 CD45RA CD4+CD3+T cells from T-allo10 cells (DN), and FACS-sorted live singlet CD49b+LAG3+CD45RA CD4+CD3+T cells from T-allo10 cells (Tr1 ). D. Expression of CTLA-4 in LAG3+CD49b+Tr1 cells (Tr1 , left) and in LAG3+CD49b memory CD4+T cells(LAG3+, right) in wild-type (WT) and BATF knock-out (KO) T-allo10 cells, flow cytometry, n = 6, Wilcoxon test. E. Representative flow cytometry plots depicting frequency of CTLA-4+cells and Tr1 cells in memory (mem) CD4+T cells (left and middle panels, respectively), and CTLA- 4 expression within Tr1 cells (right panel), in WT and MAF KO T-allo10 cells.

[0029] FIGS. 12A-12F. A. Schematic representation indicating T-allo10 adoptive cell therapy in recipients of allogeneic hematopoietic stem cell transplantation (allo-HSCT) and time points when CD4+T cells were isolated by FACS from cryopreserved patient peripheral blood mononuclear cells (PBMC) and analyzed by ATAC-seq. B. Top: Representative flow cytometry gating strategy to identify CD49b+LAG3+Tr1 cells in CD4+T cells within ex vivo PBMC. Bottom: frequency of Tr1 and CD49b LAG3 non-Tr1 (DN) cell populations in CD4+T cells of healthy donors used for CIBERSORTx analysis. C. Percentage of Tr1 cells within total CD4+T cells, assessed by flow cytometry in both patient (PT, n = 9) and healthy donor (HD, n = 3) samples that were also analyzed by ATAC-seq. Wilcoxon test. D. UMAP visualization of nuclear RNA-derived gene expression of T cell marker genes CD3 (encoded by CD3E, CD3D, CD3G) and CD4 in purified live CD4+CD3+T cells that were analyzed by sc-multiome and ArchR. E. Volcano plot illustrating differential gene expression between clusters C3 and C1 of the sc-multiome dataset. Genes highlighted in purple represent those characteristic of cluster C1 , while those in green represent cluster C3. Dotted lines indicate filtering criteria with a false discovery rate (FDR)- corrected p-value < 0.001 and a Iog2 fold change > 1 . F. Expression of intracellular CTLA-4 (n = 6) in ex vivo Tr1 and non-Tr1 (DN) cells within total CD4+T cells from unstimulated PBMC. Left: cumulative data, right: representative flow cytometry gating; Wilcoxon test.

[0030] FIGS. 13A-13C. Tr1 TF signature validation in B cells (A) and monocytes (B). Analysis encompasses initial clustering of CD45+ cells analyzed by sc-ATAC-seq (from the publicly available clear cell renal cell carcinoma (ccRCC) dataset) and identification of B cells (MS4A1 +) and monocytes (CD14+), followed by subsequent clustering of B cells and monocytes and examination of Tr1 signature enrichment using Gene Set Enrichment Analysis (GSEA). MS4A1 gene encodes CD20.

[0031] FIG. 14. Example of bicistronic lentiviral vector maps that can be used to generate Tr1 cells by transcription factor (TF) engineering. The transgene is driven by an EF1 oc promoter and followed by an internal ribosome entry site (IRES) to facilitate bicistronic expression of the transgene and a truncated version of CD19 (tCD19) for identifying and selecting transduced cells.

[0032] FIG. 15. Simplified diagram of T r1 cell transcription factor engineering for cell therapy, on an example a bicistronic lentiviral vector encoding IRF4 TF and truncated (t)CD19 marker gene.

[0033] FIGS 16A-B show a comparison of Tr1 cells versus other cells in a single-cell multiome analysis. (A) gene expression analysis; (B) epigenomic analysis.DETAILED DESCRIPTION

[0034] Before the present methods and compositions are described, it is to be understood that this invention is not limited to particular method or composition described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0035] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. It is understood that the present disclosure supercedes any disclosure of an incorporated publication to the extent there is a contradiction.

[0037] It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells and reference to "the peptide" includes reference to one or more peptides and equivalents thereof, e.g. polypeptides, known to those skilled in the art, and so forth.

[0038] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates which may need to be independently confirmed.

[0039] The terms "polypeptide," "peptide" and "protein" are used interchangeably herein to refer to a polymer of amino acid residues. The terms also apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non- naturally occurring amino acid polymer.

[0040] The term "sequence identity," as used herein in reference to polypeptide or DNA sequences, refers to the subunit sequence identity between two molecules. When a subunit position in both of the molecules is occupied by the same monomeric subunit (e.g., the same amino acid residue or nucleotide), then the molecules are identical at that position. The similarity between two amino acid or two nucleotide sequences is a direct function of the number of identical positions. In general, the sequences are aligned so that the highest order match is obtained. If necessary, identity can be calculated using published techniques and widely available computer programs, such as the GCS program package (Devereux et al., Nucleic Acids Res. 12:387, 1984), BLASTP, BLASTN, FASTA (Atschul et al., J. Molecular Biol. 215:403, 1990).

[0041] By "protein variant" or "variant protein" or "variant polypeptide" herein is meant a protein that differs from a wild-type protein by virtue of at least one amino acid modification. The parent polypeptide may be a naturally occurring or wild-type (WT) polypeptide, or may be a modified version of a WT polypeptide. Variant polypeptide may refer to the polypeptide itself, a composition comprising the polypeptide, or the amino sequence that encodes it. Preferably, the variant polypeptide has at least one amino acid modification compared to the parent polypeptide, e.g. from about one to about ten amino acid modifications, and preferably from about one to about five amino acid modifications compared to the parent.

[0042] By "parent polypeptide", "parent protein", "precursor polypeptide", or "precursor protein" as used herein is meant an unmodified polypeptide that is subsequently modified to generate a variant. A parent polypeptide may be a wild-type (or native) polypeptide, or a variant or engineered version of a wild-type polypeptide. Parent polypeptide may refer to the polypeptide itself, compositions that comprise the parent polypeptide, or the amino acid sequence that encodes it.

[0043] The term "amino acid" refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g., hydroxyproline, gamma-carboxyglutamate, and O-phosphoserine. “Amino acid analogs” refers to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., an a- carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g.,homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. “Amino acid mimetics” refers to chemical compounds that have a structure that is different from the general chemical structure of an amino acid, but that functions in a manner similar to a naturally occurring amino acid.

[0044] Amino acid modifications may include amino acid substitutions, deletions and insertions, particularly amino acid substitutions. Variant proteins may also include conservative modifications and substitutions at other positions of the cytokine and / or receptor (e.g., positions other than those involved in the affinity engineering). Such conservative substitutions include those described by Dayhoff in The Atlas of Protein Sequence and Structure 5 (1978), and by Argos in EMBO J., 8:779-785 (1989). For example, amino acids belonging to one of the following groups represent conservative changes: Group I: Ala, Pro, Gly, Gin, Asn, Ser, Thr; Group II : Cys, Ser, Tyr, Thr; Group III: Vai, He, Leu, Met, Ala, Phe; Group IV: Lys, Arg, His; Group V: Phe, Tyr, Trp, His; and Group VI: Asp, Glu. Further, amino acid substitutions with a designated amino acid may be replaced with a conservative change.

[0045] The term “isolated” refers to a molecule that is substantially free of its natural environment. For instance, an isolated protein is substantially free of cellular material or other proteins from the cell or tissue source from which it is derived. The term refers to preparations where the isolated protein is sufficiently pure to be administered as a therapeutic composition, or at least 70% to 80% (w / w) pure, more preferably, at least 80%-90% (w / w) pure, even more preferably, 90-95% pure; and, most preferably, at least 95%, 96%, 97%, 98%, 99%, or 100% (w / w) pure. A “separated” compound refers to a compound that is removed from at least 90% of at least one component of a sample from which the compound was obtained. Any compound described herein can be provided as an isolated or separated compound.

[0046] The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a mammal being assessed for treatment and / or being treated. In some embodiments, the mammal is a human. The terms “subject,” “individual,” and “patient” encompass, without limitation, individuals having a disease. Subjects may be human, but also include other mammals, particularly those mammals useful as laboratory models for human disease, e.g., mice, rats, etc.

[0047] The term “sample” with reference to a patient encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof. The term also encompasses samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations, such as T cells. The definition also includes samples that have been enriched for particular types of molecules, e.g., nucleicacids, polypeptides, etc. The term “biological sample” encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like. A “biological sample” includes a sample obtained from a patient, e.g. peripheral blood cells, a cell lysate or other cell extract comprising polynucleotides and / or polypeptides, etc.

[0048] The term “diagnosis” is used herein to refer to the identification of a molecular or pathological state, disease or condition in a subject, individual, or patient.

[0049] The term “prognosis” is used herein to refer to the prediction of the likelihood of death or disease progression, including recurrence, spread, and drug resistance, in a subject, individual, or patient. The term “prediction” is used herein to refer to the act of foretelling or estimating, based on observation, experience, or scientific reasoning, the likelihood of a subject, individual, or patient experiencing a particular event or clinical outcome. In one example, a physician may attempt to predict the likelihood that a patient will survive.

[0050] As used herein, the terms “treatment,” “treating,” and the like, refer to administering an agent, or carrying out a procedure, for the purposes of obtaining an effect on or in a subject, individual, or patient. The effect may be prophylactic in terms of completely or partially preventing a disease or symptom thereof and / or may be therapeutic in terms of effecting a partial or complete cure for a disease and / or symptoms of the disease. “Treatment,” as used herein, may include treatment of graft rejection, or graft versus host disease in a mammal, particularly in a human, and includes: (a) inhibiting the disease, i.e., arresting its development; and (b) relieving the disease or its symptoms.

[0051] Treating may refer to any indicia of success in the treatment or amelioration or prevention of a disease, including any objective or subjective parameter such as abatement; remission; diminishing of symptoms or making the disease condition more tolerable to the patient; slowing in the rate of degeneration or decline; or making the final point of degeneration less debilitating. The treatment or amelioration of symptoms can be based on objective or subjective parameters; including the results of an examination by a physician. Accordingly, the term "treating" includes the administration of engineered cells to prevent or delay, to alleviate, or to arrest or inhibit development of the symptoms or conditions associated with disease or other diseases. The term "therapeutic effect" refers to the reduction, elimination, or prevention of the disease, symptoms of the disease, or side effects of the disease in the subject.

[0052] As used herein, a "therapeutically effective amount" refers to that amount of the therapeutic agent, e.g. engineered Tr1 cells, sufficient to treat or manage a disease or disorder. A therapeutically effective amount may refer to the amount of the cells sufficient to delay or minimize the onset of disease, e.g., to delay or minimize development of grant versushost disease, rejection of organ transplants, and the like. A therapeutically effective amount may also refer to the amount of therapeutic cells that provides a therapeutic benefit in the treatment or management of a disease. Further, a therapeutically effective amount with respect to a therapeutic cell of the invention means the amount of therapeutic cell alone, or in combination with other therapies, that provides a therapeutic benefit in the treatment or management of a disease.

[0053] As used herein, the term “dosing regimen” refers to a set of unit doses (typically more than one) that are administered individually to a subject, typically separated by periods of time. In some embodiments, a given therapeutic agent, e.g. Tr1 cells, has a recommended dosing regimen, which may involve one or more doses. In some embodiments, a dosing regimen comprises a plurality of doses each of which are separated from one another by a time period of the same length; in some embodiments, a dosing regimen comprises a plurality of doses and at least two different time periods separating individual doses. In some embodiments, all doses within a dosing regimen are of the same unit dose amount. In some embodiments, different doses within a dosing regimen are of different amounts. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount different from the first dose amount. In some embodiments, a dosing regimen comprises a first dose in a first dose amount, followed by one or more additional doses in a second dose amount same as the first dose amount. In some embodiments, a dosing regimen is correlated with a desired or beneficial outcome when administered across a relevant population (i.e., is a therapeutic dosing regimen).

[0054] "In combination with", "combination therapy" and "combination products" refer, in certain embodiments, to the concurrent administration to a patient of the engineered cells described herein in combination with additional therapies, e.g. HSCT transplantation, surgery, radiation, chemotherapy, and the like. When administered in combination, each component can be administered at the same time or sequentially in any order at different points in time. Thus, each component can be administered separately but sufficiently closely in time so as to provide the desired therapeutic effect.

[0055] "Concomitant administration" means administration of one or more components, such as engineered Tr1 cells, known therapeutic agents, etc. at such time that the combination will have a therapeutic effect. Such concomitant administration may involve concurrent (i.e. at the same time), prior, or subsequent administration of components. A person of ordinary skill in the art would have no difficulty determining the appropriate timing, sequence and dosages of administration.

[0056] The use of the term "in combination" does not restrict the order in which agents or cells are administered to a subject with a disorder. A first prophylactic or therapeutic agent can beadministered prior to (e.g., 5 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks 6 weeks, 8 weeks, or 12 weeks before), concomitantly with, or subsequent to (e.g., 5 minutes, 15 minutes, 30 minutes, 45 minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 8 weeks, or 12 weeks after) the administration of a second prophylactic or therapeutic agent to a subject with a disorder.Transcription Factors

[0057] Interferon regulatory factor 4 (IRF4) also known as MUM1 is a key regulatory transcription factor in the development of human immune cells. The refseq for the human protein is NP 001 182215, NP_002451 . IRF4 expression is promoted by a variety of bioactive stimuli, including antigen receptor engagement, lipopolysaccharide (LPS), IL-4, and CD40. IRF4 can function either as an activating or an inhibitory transcripdtion factor depending on its transcription cofactors. IRF4 frequently cooperates with the cofactors B-cell lymphoma 6 protein (BCL6) and nuclear factor of activated T-cells (NFATs). IRF4 expression is limited to cells of the immune system, in particular T cells, B cells, macrophages and dendritic cells.

[0058] MAF (musculoaponeurotic fibrosarcoma) is a basic leucine zipper (bZIP) transcription factor belonging to the AP-1 superfamily. The Maf transcription factor family is composed of 7 members divided into two subclasses: the large Maf proteins composed of MAFA / L-MAF, MAFB, MAF / c-Maf, and NLR (neural retina leucine zipper), and the small Maf proteins, MAFK, MAFG, and MAFF, which lack the amino-terminal transactivation domain. C-Maf is of particular interest for the present disclosure. The basic regions of dimeric Maf factors allows them to recognize a palindromic sequence referred to as the Maf Recognition Element (MARE). This sequence is composed of a 7-bp TPA-Responsive Element (TRE) or a 8-bp cyclic AMP- Responsive Element (CRE) core region and a TGC flanking sequence bound by the Extended Homology Region (EHR), exclusively found in Maf proteins. Maf proteins can form homo- and heterodimers with other compatible bZIP proteins, such as Jun and Fos. Maf proteins can also interact with other non-bZIP proteins including specific transcription factors, such as Sox family members. The refseq for the human protein is NP_001026974, NP_005351 .

[0059] Three isoforms exist for human c-Maf: a short form (373 amino acids), a medium form (383 amino acids), and a long form (30 amino acids more than the short form) of 38.5, 39.6, and 42 kDa respectivelyThe distinct c-Maf products have not shown functional differences.

[0060] In T cells, TCR stimulation induces the transcription of the Maf gene. However, distinct additional stimuli are required to sustain the expression of Maf, such as co-stimulatory signals or the presence of cytokines, including IL-4, IL-6, TGF-[3, and IL-27.

[0061] BATF. Basic leucine zipper transcription factor, ATF-like (BATF), a member of the AP- 1 family, forms heterodimers with Jun and blocks AP-1 transactivation. Unlike other AP-1 family members, BATF expression is restricted to lymphoid organs, and is induced in T cells and natural killer T cells upon stimulation. The refseq for the human protein is NP_006390.

[0062] The vitamin D receptor (VDR also known as the calcitriol receptor) is a member of the nuclear receptor family of transcription factors. Calcitriol (the active form of vitamin D, 1 ,25- (OH)2vitamin D3) binds to VDR, which then forms a heterodimer with the retinoid-X receptor. The VDR heterodimer then enters the nucleus and binds to Vitamin D responsive elements (VDRE) in genomic DNA. VDR binding results in expression or transrepression of many specific gene products. VDR is also involved in microRNA-directed post transcriptional mechanisms. Many types of immune cells express VDR. Reference sequences for the human protein sequence include Genbank: NP_001361591 .1 ; NP_001361590.1 ; NP_001351014.1 ; and XP 024304946.1 .

[0063] FOXB1 (forkhead box B1 ) is a transcription factor. The reference sequence for the human protein may be accessed at NP_036314.

[0064] TOX2, also known as C20orf100, is located on q13.12 of human chromosome 20 and has four transcripts. The TOX2 transcript encoding the longest isoform contains nine exons with an mRNA length of 2491 bp that is translated into a protein consisting of 506 amino acids. TOX2, similar to TOX, affects the development of NK cells by regulating the expression of T- bet. Reference human sequences may be accessed at NC_000020.1 1 ; NC_060944.1 ; XP_006723947.1 ; XP_016883598.1 ; XP_047296516.1 ; XP_047296517.1 ; andXP_047296518.1 .Cell Compositions

[0065] In some embodiments a cell composition is provided. The cell can be provided in a unit dose for therapy, and can be allogeneic, autologous, etc. with respect to an intended recipient. Methods may include a step of obtaining desired cells, e.g., CD4+T cells, etc., which may be isolated from a biological sample, or may be derived in vitro from a source of progenitor cells. The cells are engineered as described herein, which may be performed in any suitable culture medium. For example, cells may be collected from a patient, modified ex vivo, and reintroduced into the subject. The cells collected from the subject may be collected from any convenient and appropriate source, including e.g., peripheral blood (e.g., the subject’s peripheral blood), a biopsy (e.g., a biopsy from the subject), and the like.

[0066] Engineered cells can be provided in pharmaceutical compositions suitable for therapeutic use, e.g. for human treatment. Therapeutic formulations comprising such cells can be frozen, or prepared for administration with physiologically acceptable carriers, excipients or stabilizers (Remington's Pharmaceutical Sciences 16th edition, Osol, A. Ed. (1980)), in theform of aqueous solutions. The cells will be formulated, dosed, and administered in a fashion consistent with good medical practice. Factors for consideration in this context include the particular disorder being treated, the particular mammal being treated, the clinical condition of the individual patient, the cause of the disorder, the site of delivery of the agent, the method of administration, the scheduling of administration, and other factors known to medical practitioners.

[0067] The cells can be administered by any suitable means, usually parenteral. Parenteral infusions include intramuscular, intravenous (bolus or slow drip), intraarterial, intraperitoneal, intrathecal or subcutaneous administration.Tr1 cells

[0068] Type 1 regulatory T (Tr1 ) cells are an inducible subset of regulatory T cells that play a pivotal role in promoting and maintaining tolerance. The main mechanisms by which Tr1 cells control immune responses are: the secretion of high levels of IL-10, expression of CTLA-4, and expression of PD-1 and PD-L1 , as well as the killing of myeloid myeloid cells through the release of cytotoxic intracellular granules that contain perforin and granzyme B. Tr1 cells secrete high levels of IL-10 and minimal amounts of IL-4 and IL-17, which distinguish them from Th2 and Th17 cells. Furthermore, Tr1 cells secrete low levels of IL-2, and depending on the local cytokine milieu can produce variable levels of IFN-y. Similar to other T cell subsets, Tr1 cells can transiently express FOXP3 upon activation; however, in Tr1 cells FOXP3 expression is not constitutive and never reaches the high levels characteristic of CD25+ Foxp3+ regulatory T cells. Tr1 cells from human peripheral blood can be identified by coexpression of surface proteins CD49b and LAG3.

[0069] In some embodiments a method to generate allo-specific Tr1 cells comprises culturing in vitro with IL-10 a population of naive T cells or total CD4+T cells, engineered to increase expression of one or more of the transcription factors IRF4, MAF and BATF, for example IRF4 and MAF; IRF4 and BATF; MAF and BATF; or IRF4, MAT and BATF. In other embodiments, one or more of the transcription factors TOX2, VDR and FOXB1 are introduced to increase expression, optionally in combination with one or more of IRF4, MAF and BATF. A population of naive T cells can be obtained from a patient in which allo-specific tolerance is desired, e.g. a donor of an allogeneic graft. A population of naive T cells can be selected for the phenotype of CD45RA+CD4+, and may be obtained from cord blood, peripheral blood, etc.

[0070] The method to generate allo-specific Tr1 cells can utilize a Tallol 0 differentiation protocol, for example as disclosed in clinical trial ID NCT03198234. Such a method differentiates Tallol 0 cells by culture of CD4+ T cells in the presence of recombinant IL-10, and / or allogenic-tolerogenic dendritic cells, referred to as DC-10 (see, for example, Amodio and Gregori (2012) Transplant Res. 1 : 14). DC-10 are differentiated from peripheral bloodmonocytes cultured in the presence of GM-CSF, IL-4, and IL-10. The resulting DC-10 cells are CD1 1 c+CD11 b+CD14+CD16+CD1 a , and display a mature myeloid phenotype, being CD83+, CD86+and HLA-DR+. The cells secrete high amounts of IL-10; secrete IL-6, low levels of TNFa and no IL-12. DC-10 are phenotypically different and more potent than immature DCs or IL-10-modulated mature DC in promoting allo-specific Tr1 cells. The DC-10 cells may be be cultured from cells obtained from an individual to which allo-tolerance is desired, e.g. a transplant recipient.

[0071] The resulting Tr1 cells present after this differentiation protocol are anergic, secrete significant levels of IL-10, and suppress T cell responses in vitro in an IL-10, CTLA-4, and PD- 1 / PD-L1 -dependent manner. Adoptive transfer of ex vivo induced alloantigen-specific Trl cells has proven to be feasible and safe, and can be applied in allogeneic hematopoietic stem cell transplantation among other uses.

[0072] An alternative strategy for the induction of high numbers of human Tr1 cells is lentiviral- mediated gene transfer of human IL-10. Stable ectopic expression of IL-10 can efficiently generate homogeneous populations of Tr1 -like cells. These cells display potent suppressive functions both in vitro and in vivo in xenogeneic graft versus host disease model, while preserving the graft versus leukemia effects.

[0073] An engineered cell can be modified by introduction of a construct to over-express one or more of the transcription factors IRF4, MAF and BATF by introduction of IRF4, MAF or BATF coding sequences operably linked to a promoter active in T cells. The sequences can be introduced on a vector, e.g. a viral vector, etc., or can be introduced through genome editing. For example, a coding sequence may be introduced into a target cell using CRISPR technology. CRISPR / Cas9 system can be directly applied to human cells by transfection with a plasmid that encodes Cas9 and sgRNA. The viral delivery of CRISPR components has been extensively demonstrated using lentiviral and retroviral vectors. Gene editing with CRISPR encoded by non-integrating virus, such as adenovirus and adenovirus-associated virus (AAV), has also been reported. Recent discoveries of smaller Cas proteins have enabled and enhanced the combination of this technology with vectors that have gained increasing success for their safety profile and efficiency, such as AAV vectors.

[0074] The nucleic acid encoding a reprogramming factor is inserted into a vector for expression and / or integration. Many such vectors are available. The vector components generally include, but are not limited to, one or more of the following: an origin of replication, one or more marker genes, an enhancer element, a promoter, and a transcription termination sequence. Vectors include viral vectors, plasmid vectors, integrating vectors, and the like.

[0075] Expression vectors may contain a selection gene, also termed a selectable marker. This gene encodes a protein necessary for the survival or growth of transformed host cellsgrown in a selective culture medium or a truncated gene encoding a surface marker that allows for antibody based detection. Host cells not transformed with the vector containing the selection gene will not survive in the culture medium. Typical selection genes encode proteins that (a) confer resistance to antibiotics or other toxins, e.g., ampicillin, neomycin, methotrexate, or tetracycline, (b) complement auxotrophic deficiencies, or (c) supply critical nutrients not available from complex media, or (d) enable surface antibody based detection for isolation via fluoresences activating cell sorting (FACS) or magnetic separation e.g. truncated forms of NGFR, EGFR, CD19.

[0076] Nucleic acids are "operably linked" when placed into a functional relationship with another nucleic acid sequence. For example, DNA for a signal sequence is operably linked to DNA for a polypeptide if it is expressed as a preprotein that signals the secretion of the polypeptide; a promoter or enhancer is operably linked to a coding sequence if it affects the transcription of the sequence; and a ribosome binding site is operably linked to a coding sequence if it is positioned so as to facilitate translation. Generally, "operably linked" means that the DNA sequences being linked are contiguous, and, in the case of a secretory leader, contiguous and in reading phase. However, enhancers do not have to be contiguous.

[0077] Expression vectors will contain a promoter that is recognized by the host organism and is operably linked to the coding sequence. Promoters are untranslated sequences located upstream (5') to the start codon of a structural gene (generally within about 100 to 1000 bp) that control the transcription and translation of particular nucleic acid sequence to which they are operably linked. Such promoters typically fall into two classes, inducible and constitutive. Inducible promoters are promoters that initiate increased levels of transcription from DNA under their control in response to some change in culture conditions, e.g., the presence or absence of a nutrient or a change in temperature. A large number of promoters recognized by a variety of potential host cells are well known.

[0078] Transcription from vectors in mammalian host cells may be controlled, for example, by promoters obtained from the genomes of viruses such as polyoma virus, fowlpox virus, adenovirus (such as Adenovirus 2), bovine papilloma virus, avian sarcoma virus, cytomegalovirus, a retrovirus LTR (such as murine stem cell virus), hepatitis-B virus and Simian Virus 40 (SV40), from heterologous mammalian promoters, e.g., the actin promoter, PGK (phosphoglycerate kinase), or an immunoglobulin promoter, or from heat-shock promoters, provided such promoters are compatible with the host cell systems. The early and late promoters of the SV40 virus are conveniently obtained as an SV40 restriction fragment that also contains the SV40 viral origin of replication.

[0079] Transcription by higher eukaryotes may be increased by inserting an enhancer sequence into the vector. Enhancers are cis-acting elements of DNA, usually about from 10 to 300 bp in length, which act on a promoter to increase its transcription. Enhancers arerelatively orientation and position independent, having been found 5' and 3' to the transcription unit, within an intron, as well as within the coding sequence itself. Many enhancer sequences are now known from mammalian genes (globin, elastase, albumin, oc-fetoprotein, and insulin). Typically, however, one will use an enhancer from a eukaryotic virus. Examples include the SV40 enhancer on the late side of the replication origin, the cytomegalovirus early promoter enhancer, the polyoma enhancer on the late side of the replication origin, and adenovirus enhancers. The enhancer may be spliced into the expression vector at a position 5' or 3' to the coding sequence, but is preferably located at a site 5' from the promoter.

[0080] Expression vectors for use in eukaryotic host cells will also contain sequences necessary for the termination of transcription and for stabilizing the mRNA. Such sequences are commonly available from the 5' and, occasionally 3', untranslated regions of eukaryotic or viral DNAs or cDNAs. Construction of suitable vectors containing one or more of the abovelisted components employs standard techniques.

[0081] Suitable host cells for cloning a construct are the prokaryotic, yeast, or other eukaryotic cells described above. Examples of useful mammalian host cell lines are mouse L cells (L- M[K-], ATCC#CRL-2648), monkey kidney CV1 line transformed by SV40 (COS-7, ATCC CRL 1651 ); human embryonic kidney line (293 or 293 cells subcloned for growth in suspension culture; baby hamster kidney cells (BHK, ATCC CCL 10); Chinese hamster ovary cells / -DHFR (CHO); mouse Sertoli cells (TM4); monkey kidney cells (CV1 ATCC CCL 70); African green monkey kidney cells (VERO-76, ATCC CRL-1 587); human cervical carcinoma cells (HELA, ATCC CCL 2); canine kidney cells (MDCK, ATCC CCL 34); buffalo rat liver cells (BRL 3A, ATCC CRL 1442); human lung cells (W138, ATCC CCL 75); human liver cells (Hep G2, HB 8065); mouse mammary tumor (MMT 060562, ATCC CCL51 ); TRI cells; MRC 5 cells; FS4 cells; and a human hepatoma line (Hep G2).

[0082] Host cells, including CD4+ T cells, naive T cells, stem cells, etc. can be transfected with the above-described expression vectors for construct expression. Cells may be cultured in conventional nutrient media modified as appropriate for inducing promoters, selecting transformants, or amplifying the genes encoding the desired sequences. Mammalian host cells may be cultured in a variety of media. Commercially available media such as Ham's F10 (Sigma), Minimal Essential Medium ((MEM), Sigma), RPMI 1640 (Sigma), and Dulbecco's Modified Eagle's Medium ((DMEM), Sigma) are suitable for culturing the host cells. Any of these media may be supplemented as necessary with hormones and / or other growth factors (such as insulin, transferrin, or epidermal growth factor), salts (such as sodium chloride, calcium, magnesium, and phosphate), buffers (such as HEPES), nucleosides (such as adenosine and thymidine), antibiotics, trace elements, and glucose or an equivalent energy source. Any other necessary supplements may also be included at appropriate concentrations that would be known to those skilled in the art. The culture conditions, such as temperature,pH and the like, are those previously used with the host cell selected for expression, and will be apparent to one of skill in the art.

[0083] In other embodiments, an engineered cell is provided, in which the cell has been modified by introduction of a expression vector or a vector for knocking out or reducing expression of one or more of the transcription factors IRF4, MAF and BATF. In other embodiments the engineered cell has been treated to transiently down-regulate expression of one or more of the transcription factors IRF4, MAF and BATF, e.g. by contacting the cell population with anti-sense oligonucleotides, with siRNA, etc.

[0084] An anti-l RF4, MAF or BATF agent may be an shRNA or an antisense oligonucleotide (ODN). By RNAi agent is meant an agent that modulates expression by a RNA interference mechanism. The RNAi agents employed in one embodiment are small ribonucleic acid molecules (also referred to herein as interfering ribonucleic acids), i.e., oligoribonucleotides, that are present in duplex structures, e.g., two distinct oligoribonucleotides hybridized to each other or a single ribooligonucleotide that assumes a small hairpin formation to produce a duplex structure. By oligoribonucleotide is meant a ribonucleic acid that does not exceed about 100 nt in length, and typically does not exceed about 75 nt length, where the length in certain embodiments is less than about 70 nt. Where the RNA agent is a duplex structure of two distinct ribonucleic acids hybridized to each other, e.g., an siRNA, the length of the duplex structure typically ranges from about 15 to 30 bp, usually from about 15 to 29 bp, where lengths between about 20 and 29 bps, e.g., 21 bp, 22 bp, are of particular interest in certain embodiments. Where the RNA agent is a duplex structure of a single ribonucleic acid that is present in a hairpin formation, i.e., a shRNA, the length of the hybridized portion of the hairpin is typically the same as that provided above for the siRNA type of agent or longer by 4-8 nucleotides. The weight of the RNAi agents of this embodiment typically ranges from about 5,000 daltons to about 35,000 daltons, and in many embodiments is at least about 10,000 daltons and less than about 27,500 daltons, often less than about 25,000 daltons.

[0085] dsRNA can be prepared according to any of a number of methods that are known in the art, including in vitro and in vivo methods, as well as by synthetic chemistry approaches. Examples of such methods include, but are not limited to, the methods described by Sadher et al. (Biochem. Int. 14:1015, 1987); by Bhattacharyya (Nature 343:484, 1990); and by Livache, et al. (U.S. Pat. No. 5,795,715), each of which is incorporated herein by reference in its entirety. Single-stranded RNA can also be produced using a combination of enzymatic and organic synthesis or by total organic synthesis. The use of synthetic chemical methods enable one to introduce desired modified nucleotides or nucleotide analogs into the dsRNA. dsRNA can also be prepared in vivo according to a number of established methods (see, e.g., Sambrook, et al. (1989) Molecular Cloning: A Laboratory Manual, 2nd ed.; Transcription andTranslation (B. D. Hames, and S. J. Higgins, Eds., 1984); DNA Cloning, volumes I and II (D. N. Glover, Ed., 1985); and Oligonucleotide Synthesis (M. J. Gait, Ed., 1984, each of which is incorporated herein by reference in its entirety).

[0086] In certain embodiments, instead of the RNAi agent being an interfering ribonucleic acid, e.g., an siRNA or shRNA as described above, the RNAi agent may encode an interfering ribonucleic acid, e.g., an shRNA, as described above. In other words, the RNAi agent may be a transcriptional template of the interfering ribonucleic acid. In these embodiments, the transcriptional template is typically a DNA that encodes the interfering ribonucleic acid. The DNA may be present in a vector, where a variety of different vectors are known in the art, e.g. , a plasmid vector, a viral vector, etc.

[0087] Alternatively, an antisense sequence is complementary to the targeted RNA, and inhibits its expression. One or a combination of antisense molecules may be administered, where a combination may comprise multiple different sequences. Antisense molecules may be produced by expression of all or a part of the target RNA sequence in an appropriate vector, where the transcriptional initiation is oriented such that an antisense strand is produced as an RNA molecule. Alternatively, the antisense molecule is a synthetic oligonucleotide. Antisense oligonucleotides will generally be at least about 7, usually at least about 12, more usually at least about 20 nucleotides in length, and not more than about 25, usually not more than about 23-22 nucleotides in length, where the length is governed by efficiency of inhibition, specificity, including absence of cross-reactivity, and the like.

[0088] Anti-sense molecules of interest include antagomir RNAs, e.g. as described by Krutzfeldt et al. (2005) Nature 438:685-689, herein specifically incorporated by reference. Small interfering double-stranded RNAs (siRNAs) engineered with certain “drug-like” properties such as chemical modifications for stability and cholesterol conjugation for delivery have been shown to achieve therapeutic silencing of an endogenous gene in vivo. To develop a pharmacological approach for silencing miRNAs in vivo, chemically modified, cholesterol- conjugated single-stranded RNA analogues complementary to miRNAs were developed, termed “antagomirs”. Antagomir RNAs may be synthesized using standard solid phase oligonucleotide synthesis protocols. The RNAs are conjugated to cholesterol, and may further have a phosphorothioate backbone at one or more positions.

[0089] In some embodiments an anti- IRF4, MAF or BATF agent utilizes a class 2 CRISPR / Cas effector protein (or a nucleic encoding the protein), e.g., as targeted endonuclease to alter the genomic sequence at the IRF4, MAF or BATF locus in a manner that decreases expression of IRF4, MAF or BATF, e.g. by deletion of a portion of the coding sequence or ptomoter. In class 2 CRISPR systems, the functions of the effector complex (e.g., the cleavage of target DNA) are carried out by a single protein (which can be referred to as aCRISPR / Cas effector protein) - where the natural protein is an endonuclease (e.g., see Zetsche et al, Cell. 2015 Oct 22;163(3)759-71 ; Makarova et al, Nat Rev Microbiol. 2015 Nov;13(11 )722-36; Shmakov et al., Mol Cell. 2015 Nov 5;60(3):385-97; and Shmakov et al., Nat Rev Microbiol. 2017 Mar;15(3):169-182: “Diversity and evolution of class 2 CRISPR-Cas systems”). As such, the term “class 2 CRISPR / Cas protein” or “CRISPR / Cas effector protein” is used herein to encompass the effector protein from class 2 CRISPR systems - for example, type II CRISPR / Cas proteins (e.g., Cas9), type V CRISPR / Cas proteins (e.g., Cpf1 / Cas12a, C2c1 / Cas12b, C2C3 / Cas12c), and type VI CRISPR / Cas proteins (e.g., C2c2 / Cas13a, C2C7 / Cas13c, C2c6 / Cas13b). Class 2 CRISPR / Cas effector proteins include type II, type V, and type VI CRISPR / Cas proteins, but the term is also meant to encompass any class 2 CRISPR / Cas protein suitable for binding to a corresponding guide RNA and forming a ribonucleoprotein (RNP) complex.

[0090] A nucleic acid that binds to a class 2 CRISPR / Cas effector protein (e.g., a Cas9 protein; a type V or type VI CRISPR / Cas protein; a Cpf 1 protein; etc.) and targets the complex to a specific location within a target nucleic acid is referred to herein as a “guide RNA” or “CRISPR / Cas guide nucleic acid” or “CRISPR / Cas guide RNA.” A guide RNA provides target specificity to the complex (the RNP complex) by including a targeting segment, which includes a guide sequence, which is a nucleotide sequence that is complementary to a sequence of a target nucleic acid.

[0091] In some embodiments an engineered cell is a T cell, including without limitation naive CD8+T cells, cytotoxic CD8+T cells, naive CD4+T cells, helper T cells, e.g. TH1 , TH2, TH9, TH11 , TH22, TFH; regulatory T cells, e.g. TR1 , natural TReg, inducible TReg; memory T cells, e.g. central memory T cells, effector memory T cells, NK T cells, y5 T cells and variants of such T cells including CAR T cells, etc. Tr1 cells are of particular interest. In some embodiments the cell is genetically modified in an ex vivo procedure, prior to transfer into a subject, to introduce a sequence to upregulate or down-regulate expression. The engineered cell can be provided in a unit dose for therapy, and can be allogeneic, autologous, or xenogeneic with respect to an intended recipient, usually autologous.

[0092] T cells useful for engineering with the genetic agents disclosed above include naive T cells, total CD4+T cells, etc. T cells for treatment as described above can be collected from a subject or a donor, and may be separated from a mixture of cells by techniques that enrich for desired cells or may be engineered and cultured without separation. An appropriate solution may be used for dispersion or suspension of the cells. Such solution will generally be a sterile balanced salt solution, e.g. normal saline, PBS, Hank’s balanced salt solution, etc., conveniently supplemented with fetal calf serum or other naturally occurring factors, in conjunction with an acceptable buffer at low concentration, generally from 5-25 mM.Convenient buffers include HEPES, phosphate buffers, lactate buffers, etc. Techniques for affinity separation may include magnetic separation, using antibody-coated magnetic beads, affinity chromatography, cytotoxic agents linked to a monoclonal antibody or used in conjunction with a monoclonal antibody, e.g., complement and cytotoxins, and "panning" with antibody attached to a solid matrix, e.g., a plate, or other convenient technique. Techniques providing accurate separation include fluorescence activated cell sorters, which can have varying degrees of sophistication, such as multiple color channels, low angle and obtuse light scattering detecting channels, impedance channels, efc. The cells may be selected against dead cells by employing dyes associated with dead cells (e.g., propidium iodide). Any technique may be employed which is not unduly detrimental to the viability of the selected cells. The affinity reagents may be specific receptors or ligands for the cell surface molecules indicated above. In addition to antibody reagents, peptide-MHC antigen and T cell receptor pairs may be used; peptide ligands and receptor; effector and receptor molecules, and the like.

[0093] The separated cells may be collected in any appropriate medium that maintains the viability of the cells, usually having a cushion of serum at the bottom of the collection tube. Various media are commercially available and may be used according to the nature of the cells, including dMEM, HBSS, dPBS, RPMI, Iscove’s medium, etc., frequently supplemented with fetal calf serum (FCS). The collected and optionally enriched cell population may be used immediately for genetic modification, or may be frozen at liquid nitrogen temperatures and stored, being thawed and capable of being reused. The cells will usually be stored in 10% DMSO, 50% FCS, 40% RPMI 1640 medium.

[0094] The cell population is then engineered as disclosed herein. The engineered cells may be further enriched for the desired phenotype. The cells may be frozen following genetic modification. The cells may be formulated in a suitable buffer for administration to an individual in need thereof. The engineered cells may be infused to a subject in any physiologically acceptable medium by any convenient route of administration, normally intravascularly, although they may also be introduced by other routes, where the cells may find an appropriate site for growth. Usually, at least 1 x106cells / kg will be administered, at least 1 x107cells / kg, at least 1 x108cells / kg, at least 1x109cells / kg, at least 1 x101° cells / kg, or more, which may be limited by the number of T cells that are obtained during collection and culture.Therapeutic Cell Formulations

[0095] Methods and compositions are provided for enhancing TR1 cellular responses, by engineering cells from a recipient or donor to modulate one or more of the transcription factors IRF4, MAF and BATF. As discussed above, the subject methods include a step of obtainingthe targeted cells, e.g. T cells, which may be isolated from a biological sample, or may be derived in vitro from a source of progenitor cells. The cells are transduced or transfected or treated with introduction of a vector, e.g. a lentivirus vector, encoding one or more of the transcription factors; by genomic engineering sequences to upregulate expression; or alternatively to introduce anti-sense oligonucleotides, RNAi, etc.. This step can be performed in any suitable culture medium. In some embodiments, a population of cells is obtained from a subject and genetically modified ex vivo to introduce a nucleic acid (e.g. a vector) comprising a nucleic acid sequence encoding one or more of the transcription factors IRF4, MAF and BATF operably linked to one or more expression control sequences functional in the isolated cell; or inactivating expression of one or more of the transcription factors IRF4, MAF and BATF, and the genetically modified cell is reintroduced into the subject from which it was obtained.

[0096] In some embodiments a population of T cells is engineered or treated in vitro to increase expression of one or more of the transcription factors IRF4, MAF and BATF, and then differentiated to a Tr1 phenotype, e.g. a Tallol 0 protocol. The cells may be reintroduced into the subject from which it is obtained.

[0097] In some embodiments a therapeutic method is provided, the method comprising introducing into a recipient in need thereof of an in vitro engineered cell population, wherein the cell population has been modified as described herein, and is usually autologous or allogeneic with respect to the recipient.

[0098] Re-programmed T cells, e.g. Tr1 (Tallol 0) cells, can be provided in pharmaceutical compositions suitable for therapeutic use, e.g. for human treatment. Therapeutic formulations comprising such cells can be frozen, or prepared for administration with physiologically acceptable carriers, excipients or stabilizers (Remington's Pharmaceutical Sciences 16th edition, Osol, A. Ed. (1980)), in the form of aqueous solutions. The cells will be formulated, dosed, and administered in a fashion consistent with good medical practice. Factors for consideration in this context include the particular disorder being treated, the particular mammal being treated, the clinical condition of the individual patient, the cause of the disorder, the site of delivery of the agent, the method of administration, the scheduling of administration, and other factors known to medical practitioners.

[0099] Generally at least about 104reprogrammed cells / kg are administered, at least about 105reprogrammed cells / kg; at least about 10sreprogrammed cells / kg, at least about 107reprogrammed cells / kg, at least about 108reprogrammed cells / kg, or more. For example, typical ranges for the administration of cells for use in the practice of the present invention range from about 1 x105to 5x108viable cells per kg of subject body weight per course of therapy. Consequently, adjusted for body weight, typical ranges for the administration of viable cells in human subjects ranges from approximately 1 x106to approximately 1 x1013viable cells,alternatively from approximately 5x106to approximately 5x1012viable cells, alternatively from approximately 1 x107to approximately 1x1012viable cells, alternatively from approximately 5x107to approximately 1 x1012viable cells, alternatively from approximately 1 x108to approximately 1 x1012viable cells, alternatively from approximately 5x108to approximately 1 x1012viable cells, alternatively from approximately 1 x109to approximately 1 x1012viable cells per course of therapy. In one embodiment, the dose of the cells is in the range of 2.5-5x109viable cells per course of therapy.

[0100] A course of therapy may be a single dose or in multiple doses over a period of time. In some embodiments, the cells are administered in a single dose. In some embodiments, the cells are administered in two or more split doses administered over a period of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 21 , 28, 30, 60, 90, 120 or 180 days. The quantity of engineered cells administered in such split dosing protocols may be the same in each administration or may be provided at different levels. Multi-day dosing protocols over time periods may be provided by the skilled artisan (e.g. physician) monitoring the administration of the cells taking into account the response of the subject to the treatment including adverse effects of the treatment and their modulation as discussed above.

[0101] In one embodiment, the present invention provides a method of treating a subject suffering from a disease, disorder or condition amendable to treatment with regulatory T cell therapy by the administration of a suitably reprogrammed T cell population. In one embodiment, the present invention provides for a method of treatment, the method comprising the steps of (a) obtaining a biological sample comprising T cells, e.g. naive CD4+ T cells from the individual; (b) enriching the biological sample for the presence of T cells; (c) increasing expression of one or more of the transcription factors I RF4, MAF and BATF in the T cells; (d) differentiating the T cells to a Tr1 phenotype ex vivo; (e) administering a pharmaceutically effective amount of the reprogrammed T cells to the mammal.

[0102] The preferred formulation depends on the intended mode of administration and therapeutic application. The compositions can also include, depending on the formulation desired, pharmaceutically-acceptable, non-toxic carriers or diluents, which are defined as vehicles commonly used to formulate pharmaceutical compositions for animal or human administration. The diluent is selected so as not to affect the biological activity of the combination. Examples of such diluents are distilled water, physiological phosphate-buffered saline, Ringer's solutions, dextrose solution, and Hank's solution. In addition, the pharmaceutical composition or formulation may also include other carriers, adjuvants, or nontoxic, nontherapeutic, nonimmunogenic stabilizers and the like.

[0103] In still some other embodiments, pharmaceutical compositions can also include large, slowly metabolized macromolecules such as proteins, polysaccharides such as chitosan, polylactic acids, polyglycolic acids and copolymers (such as latex functionalized Sepharose™,agarose, cellulose, and the like), polymeric amino acids, amino acid copolymers, and lipid aggregates (such as oil droplets or liposomes).

[0104] Acceptable carriers, excipients, or stabilizers are non-toxic to recipients at the dosages and concentrations employed, and include buffers such as phosphate, citrate, and other organic acids; antioxidants including ascorbic acid and methionine; preservatives (such as octadecyidimethylbenzyl ammonium chloride; hexamethonium chloride; benzalkonium chloride, benzethonium chloride; phenol, butyl or benzyl alcohol; alkyl parabens such as methyl or propyl paraben; catechol; resorcinol; cyclohexanol; 3-pentanol; and m-cresol); low molecular weight (less than about 10 residues) polypeptides; proteins, such as serum albumin, gelatin, or immunoglobulins; hydrophilic polymers such as polyvinylpyrrolidone; amino acids such as glycine, glutamine, asparagine, histidine, arginine, or lysine; monosaccharides, disaccharides, and other carbohydrates including glucose, mannose, or dextrins; chelating agents such as EDTA; sugars such as sucrose, mannitol, trehalose or sorbitol; salt-forming counter-ions such as sodium; metal complexes (e.g., Zn-protein complexes); and / or non-ionic surfactants such as TWEEN™, PLURONICS™ or polyethylene glycol (PEG).Therapeutic Methods

[0105] In some embodiments the subject compositions, methods and kits are used to enhance a T cell mediated immune response, including regulatory T cell responses. In some embodiments the immune response is directed towards a condition where it is desirable to regulate target cells, e.g. immune cells, including without limitation immune cells involved in autoimmune disease, immune cells involved in transplantation, undesirable inflammatory responses, enhancing erythropoiesis, enhancing thrombopoiesis, etc. Immune conditions may include, without limitation, autoimmune diseases, graft v host disease, hematopoietic bone marrow transplantation, adoptive cell therapy, tumor infiltrating cell (TIL) therapy, inflammation, graft rejection, and the like.

[0106] A significant number of patients with hematologic malignancies need a hematopoietic stem cell transplant (HSCT) to be cured. Only about 50% of these patients have a fully matched donor, the remaining patients will require an HSCT from a mismatched related or unrelated donor. Almost 60% of these mismatched donor HSCTs will result in graft-versus- host disease (GvHD), which can cause significant morbidity and increased non-relapse mortality. GvHD is caused by the donor effector T cells present in the HSC graft that recognize and react against the mismatched patient's tissues. In some embodiments a Tallol 0 cell composition modified as disclosed herein, is administered to prevent GvHD and induce graft tolerance in patients receiving mismatched unmanipulated donor HSCT. The cell therapy may comprise a cell preparation from the same donor of the HSCT (T-allo10) containing Tregulatory type 1 (Tr1 ) cells able to suppress allogenic (host-specific) responses, thus decreasing the incidence of GvHD.

[0107] In some embodiments the condition is an autoimmune disease. Conditions for which the reprogrammed cells described herein find use in treatment include a number of inflammatory conditions where inhibition of activated B or T cells is of interest. Inflammation contributes to pathogenesis of inflammatory diseases, or diseases associated with inflammation, such as autoimmune diseases including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and autoimmune hepatitis; degenerative diseases with an inflammatory component such as osteoarthritis (OA), Alzheimer's disease (AD), and macular degeneration; chronic infections such as HIV; metabolic diseases with an inflammatory component such as type II diabetes, metabolic syndrome, and atherosclerosis.

[0108] Many diseases have an underlying inflammatory component that contributes to disease initiation and / or progression. Thus, the spectrum of inflammatory diseases and diseases associated with inflammation is broad and includes autoimmune diseases such rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and autoimmune hepatitis; degenerative diseases such as osteoarthritis (OA), Alzheimer's disease (AD), and macular degeneration; chronic infections such as human immunodeficiency virus (HIV), chronic hepatitis C virus (HCV) infection, chronic hepatitis B virus (HBV), chronic cytomegalovirus (CMV) infection, tuberculosis (TB) infection, as well as other chronic viral and bacterial infections; metabolic diseases including type II diabetes, metabolic syndrome, nonalcoholic steatohepatitis (NASH), and alcoholic steatohepatitis; cardiovascular diseases such as atherosclerosis; cancers which can arise from and induce inflammation; as well as other diseases with an inflammatory component.

[0109] In some embodiments treatment of autoimmune disease is combined with additional therapeutic agents, including, for example steroids and disease-modifying anti-rheumatic drugs (DMARDs). Conventional DMARDs include, for example, apremilast, azathioprine, cyclosporine, cyclophosphamide, hydroxychloroquine, leflunomide, methotrexate, mycophenolate, and sulfasalazine. Biological therapies include abatacept, belimumab, ixekizumab, rituximab, sarilumab, secukinumab, tocilizumab, and ustekinumab. Anti-TNF drugs include adalimumab, certolizumab pegol, etanercept, golimumab, and infliximab. JAK inhibitors include baricitinib and tofacitinib.

[0110] In other embodiments, e.g. where expression of the transcription factors is down- regulated, the condition is cancer. As used herein, the terms "cancer" (or "cancerous"), "hyperproliferative," and "neoplastic" to refer to cells having the capacity for autonomous or unregulated growth (e.g., an abnormal state or condition characterized by rapidly proliferating cell growth). Hyperproliferative and neoplastic disease states may be categorized aspathologic (e.g., characterizing or constituting a disease state), or they may be categorized as non- pathologic (e.g., as a deviation from normal but not associated with a disease state). The terms are meant to include all types of cancerous growths or oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness. "Pathologic hyperproliferative" cells occur in disease states characterized by malignant tumor growth. Examples of non-pathologic hyperproliferative cells include proliferation of cells associated with wound repair. The terms "cancer" or "neoplasm" are used to refer to malignancies of the various organ systems, including those affecting the lung, breast, thyroid, lymph glands and lymphoid tissue, gastrointestinal organs, and the genitourinary tract, as well as to adenocarcinomas which are generally considered to include malignancies such as most colon cancers, renal-cell carcinoma, prostate cancer and / or testicular tumors, non-small cell carcinoma of the lung, cancer of the small intestine and cancer of the esophagus.

[0111] The term "carcinoma" is art recognized and refers to hematologic malignancies, malignancies of epithelial or endocrine tissues including respiratory system carcinomas, gastrointestinal system carcinomas, genitourinary system carcinomas, testicular carcinomas, breast carcinomas, prostatic carcinomas, endocrine system carcinomas, and melanomas. An "adenocarcinoma" refers to a carcinoma derived from glandular tissue or in which the tumor cells form recognizable glandular structures.

[0112] Examples of tumor cells include but are not limited to AML, ALL, CML, adrenal cortical cancer, anal cancer, aplastic anemia, bile duct cancer, bladder cancer, bone cancer, bone metastasis, brain cancers, central nervous system (CNS) cancers, peripheral nervous system (PNS) cancers, breast cancer, cervical cancer, childhood Non-Hodgkin's lymphoma, colon and rectum cancer, endometrial cancer, esophagus cancer, Ewing's family of tumors (e.g. Ewing's sarcoma), eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumors, gestational trophoblastic disease, Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, liver cancer, lung cancer, lung carcinoid tumors, Non-Hodgkin's lymphoma, male breast cancer, malignant mesothelioma, multiple myeloma, myelodysplastic syndrome, myeloproliferative disorders, nasal cavity and paranasal cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumor, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcomas, melanoma skin cancer, non-melanoma skin cancers, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer (e.g. uterine sarcoma), transitional cell carcinoma, vaginal cancer, vulvar cancer, mesothelioma, squamous cell or epidermoid carcinoma, bronchial adenoma, choriocarinoma, head and neck cancers, teratocarcinoma, or Waldenstrom's macroglobulinemia.

[0113] The compositions and method of the present invention may be combined with additional therapeutic agents. For example, when the disease, disorder or condition to be treated is a neoplastic disease (e.g. cancer) the methods may be combined with conventional chemotherapeutic agents or other biological anti-cancer drugs such as checkpoint inhibitors (e.g. PD1 or PDL1 inhibitors) or therapeutic monoclonal antibodies (e.g., Avastin®, Herceptin®).

[0114] Examples of chemical agents identified in the art as useful in the treatment of neoplastic disease, include without limitation, abitrexate, adriamycin, adrucil, amsacrine, asparaginase, anthracyclines, azacitidine, azathioprine, bicnu, blenoxane, busulfan, bleomycin, camptosar, camptothecins, carboplatin, carmustine, cerubidine, chlorambucil, cisplatin, cladribine, cosmegen, cytarabine, cytosar, cyclophosphamide, cytoxan, dactinomycin, docetaxel, doxorubicin, daunorubicin, ellence, elspar, epirubicin, etoposide, fludarabine, fluorouracil, fludara, gemcitabine, gemzar, hycamtin, hydroxyurea, hydrea, idamycin, idarubicin, ifosfamide, ifex, irinotecan, lanvis, leukeran, leustatin, matulane, mechlorethamine, mercaptopurine, methotrexate, mitomycin, mitoxantrone, mithramycin, mutamycin, myleran, mylosar, navelbine, nipent, novantrone, oncovin, oxaliplatin, paclitaxel, paraplatin, pentostatin, platinol, plicamycin, procarbazine, purinethol, ralitrexed, taxotere, taxol, teniposide, thioguanine, tomudex, topotecan, valrubicin, velban, vepesid, vinblastine, vindesine, vincristine, vinorelbine, VP-16, and vumon.[001 15] Targeted therapeutics that can be administered in combination may include, without limitation, tyrosine-kinase inhibitors, such as Imatinib mesylate (Gleevec, also known as STI- 571 ), Gefitinib (Iressa, also known as ZD1839), Erlotinib (marketed as Tarceva), Sorafenib (Nexavar), Sunitinib (Sutent), Dasatinib (Sprycel), Lapatinib (Tykerb), Nilotinib (Tasigna), and Bortezomib (Velcade), Jakafi (ruxolitinib); Janus kinase inhibitors, such as tofacitinib; ALK inhibitors, such as crizotinib; Bcl-2 inhibitors, such as obatoclax, venclexta, and gossypol; FLT3 inhibitors, such as midostaurin (Rydapt), IDH inhibitors, such as AG-221 , PARP inhibitors, such as Iniparib and Olaparib; PI3K inhibitors, such as perifosine; VEGF Receptor 2 inhibitors, such as Apatinib; AN-152 (AEZS-108) doxorubicin linked to [D-Lys(6)]-LHRH; Braf inhibitors, such as vemurafenib, dabrafenib, and LGX818; MEK inhibitors, such as trametinib; CDK inhibitors, such as PD-0332991 and LEE01 1 ; Hsp90 inhibitors, such as salinomycin; and / or small molecule drug conjugates, such as Vintafolide; serine / threonine kinase inhibitors, such as Temsirolimus (Torisel), Everolimus (Afinitor), Vemurafenib (Zelboraf), Trametinib (Mekinist), and Dabrafenib (Tafinlar).

[0116] Examples of biological agents identified in the art as useful in the treatment of neoplastic disease, include without limitation, cytokines or cytokine antagonists such as IL-12, INFa, or anti-epidermal growth factor receptor, radiotherapy, irinotecan; tetrahydrofolate antimetabolites such as pemetrexed; antibodies against tumor antigens, a complex of amonoclonal antibody and toxin, a T cell adjuvant, bone marrow transplant, or antigen presenting cells (e.g., dendritic cell therapy), anti-tumor vaccines, replication competent viruses, signal transduction inhibitors (e.g., Gleevec® or Herceptin®) or an immunomodulator to achieve additive or synergistic suppression of tumor growth, cyclooxygenase-2 (COX-2) inhibitors, steroids, TNF antagonists (e.g., Remicade® and Enbrel®), interferon-pia (Avonex®), and interferon-|31 b (Betaseron®) as well as combinations of one or more of the foregoing as practiced in known chemotherapeutic treatment regimens readily appreciated by the skilled clinician in the art.

[0117] T umor specific monoclonal antibodies that can be administered in combination with an engineered cell may include, without limitation, Rituximab (marketed as MabThera® or Rituxan®), Alemtuzumab, Panitumumab, Ipilimumab (Yervoy®), etc.

[0118] In some embodiments the compositions and methods of the present invention may be combined with immune checkpoint therapy. Examples of immune checkpoint therapies include inhibitors of the binding of PD1 to PDL1 and / or PDL2. PD1 to PDL1 and / or PDL2 inhibitors are well known in the art. Examples of commercially available monoclonal antibodies that interfere with the binding of PD1 to PsDL1 and / or PDL2 include nivolumab (Opdivo®, BMS- 936558, MDX1 106, commercially available from BristolMyers Squibb, Princeton NJ), pembrolizumab (Keytruda®MK-3475, lambrolizumab, commercially available from Merck and Company, Kenilworth NJ), and atezolizumab (Tecentriq®, Genentech / Roche, South San Francisco CA). Additional examples of PD1 inhibitory antibodies include but are not limited to durvalumab (MEDI4736, Medimmune / AstraZeneca), pidilizumab (CT-01 1 , CureTech), PDR001 (Novartis), BMS-936559 (MDX1 105, Bristol Myers Squibb), and avelumab (MSB0010718C, Merck Serono / Pfizer) and SHR-1210 (Incyte). Additional antibody PD1 pathway inhibitors are described in United States Patent No. 8,217,149 (Genentech, Inc) issued July 10, 2012; United States Patent No. 8,168,757 (Merck Sharp and Dohme Corp.) issued May 1 , 2012, United States Patent No. 8,008,449 (Medarex) issued August 30, 201 1 , United States Patent No. 7,943,743 (Medarex, Inc) issued May 17, 201 1 . Additionally, small molecule PD1 to PDL1 and / or PDL2 inhibitors are known in the art. See, e.g. Sasikumar, et al as WO2016142833A1 and Sasikumar, et al. WO2016142886A2, BMS-1 166 and BMS-1001 (Skalniak, et al (2017) Oncotarget 8(42): 72167-72181 ).Tr1 Cell Detection

[0119] In some embodiments, a method is provided to determining the presence of Tr1 cells in a cell population. Populations of interest may include, for example, expanded T-allo10 cell populations. In some embodiments the population of cells comprises peripheral blood CD4+T cells, for example taken from an individual treated with T-allo10 cells; peripheral blood taken from a cancer patient, e.g. multiple myeloma, colorectal cancer, breast cancer, etc.; a solidtumor sample from a cancer patient, etc. A subject may have been treated with CART cell therapy, etc. Tr1 cells are identified by determining the presence of a gene expression profile as provided herein. In some embodiments a gene expression profile comprises expression information from one or more transcription factors. In some embodiments a gene expression profile is combined with an epigenomic profile, e.g. for transcription factor binding motifs. In some embodiments, the determination of the presence of Tr1 cells is used in the determination of efficacy of a therapeutic intervention, including administration of a cell composition comprising Tr1 cells; administration of an immune checkpoint inhibitor; and the like.

[0120] Analysis of T-allo10 cells with single-cell (sc) multiome comprising AT AC- and RNA- seq analysis enabled discrimination between Tr1 cells and other activated CD4+T cells that share some similar genes, with subsequent identification of a unique Tr1 gene expression signature. This Tr1 gene expression signature can be used with popular, publicly available platforms, e.g. scType (see lanevski et al. (2022)Nat Commun , 1246) to determine the presence of Tr1 cell clusters in CD4+ T cell populations across multiple disease indications, in both T-allo10 treated patients and other diseases.

[0011] A comparison of gene expression between Tr1 cells vs all other cells in the new sc- multiome data reveals a Tr1 -distinct transcriptional profile. These Tr1 cells analyzed on singlecell level also have a distinct epigenetic profile (see FIGS. 16A and 16B). The signature takes genes that overlap between Tr1 signature identified by sc-multiome and Tr1 signature identified by high-depth bulk RNA-seq in an independent dataset; and may further include “penalty” genes not expressed by Tr1 cells, but present in related CD4+ cells.

[0122] Upregulated genes comprise one, two, three or more of the following: CCR5; LGMN; RGS9; RDH10; IL10; G0S2; PTMS; ATP10D; P4HA2; TOX2; PHEX; KIAA1217; RDH10-AS1 ; MYO1 E; GZMA; MAF; PARD6G; ZC3H12C; GFOD1 ; CTLA4; SOCS6; COL5A3; FOXB1 ; ZDHHC14; CCR2; CCL4; LINC00299; LINC01146; SMOX; CXCR6; HAVCR2; IL21 ; CSF2; VDR; LINC01480; PDCD1 ; LAG3; ITGA2; CST7; SETBP1 ; GZMB; CD38; LYN; EVA1 B; CCL3; HOPX; IFNG; TNFRSF4; TRIB1 ; SLC27A2; NCALD; MIR4435-2HG; MZB1 ; DUSP6; DTHD1 ; TNFRSF18; TNFRSF1 B, and may include each of the genes. Down-regulated genes comprise one, two, three or more of the following: RNF144A; SCML1 ; AK5; TAF4B; RIMKLB; DENND5A; MAN1 C1 ; APBA2; MPP7; PCSK5; LEF1 ; TCF7; NR3C2; and DHRS3, and may include each of the genes.

[0123] Genes that may be included as a penalty to better discriminate from other CD4+ T cell subsets include one, two, three or more of FOXP3; RORC; BCL6; GATA3; IL23R; CCR6; CXCR5; and IKZF2, and may include each of the genes. Genes of interest that are transcription factors include TOX2, MAF, FOXB1 , VDR, LEF1 , and TCF7.

[0124] Methods of interest include single cell epigenomics and RNA sequence analysis to generate a dataset for the cell population of interest.

[0125] For example, ATAC-seq analysis (Assay for Transposase-Accessible Chromatin using sequencing) may be used to determine chromatin accessibility in a T cell sample. Cells are treated with a Tn5 transposase enzyme that inserts sequencing adapters into regions of open or accessible chromatin. The open regions are thus fragmented and tagged for sequencing. After the transposition, the tagged DNA fragments are amplified and prepared for high- throughput sequencing. Once the reads are mapped, regions of high read density (peaks) are identified, which represent accessible chromatin. The sequences under the peaks can be analyzed for transcription factor (TF) binding motifs. ATAC-seq data can be integrated with other omics data (e.g., RNA-seq or ChlP-seq) to provide a more comprehensive view of gene regulation.

[0126] RNA-seq analysis (RNA sequencing) may be used to analyze the transcriptome. Total RNA is extracted from the sample, which may come from tissues, cells, or individual cells. Typically single cell analysis is performed. The RNA is fragmented, reverse-transcribed, and sequencing adapters are added to the cDNA. Sequencing platforms include, for example, Illumina, PacBio, Oxford Nanopore, etc. Read quality is assessed, and the abundance of transcripts is quantified. Gene expression is typically measured in counts or Fragments Per Kilobase of transcript per Million mapped reads (FPKM), Reads Per Kilobase of transcript per Million mapped reads (RPKM), or Transcripts Per Million (TPM). Raw counts can be normalized to account for differences in sequencing depth and RNA composition between samples. Common normalization methods include DESeq2, edgeR, or limma-voom. Differentially expressed genes (DEGs) are identified based on their log-fold change and p- values (often adjusted for multiple testing using False Discovery Rate, FDR). RNA-seq data can be integrated with other omics data such as ChlP-seq, ATAC-seq, or proteomics data. The resulting dataset of RNA expression and / or epigenomics analysis can be input into a suitable computer based analysis to identify and characterize cells. For example, ScType can be used to identify and categorize Tr1 cells based on expression or epigenomics data. ScType (Single-cell type) classification involves identifying which specific cell types are present in a dataset by analyzing the expression levels of marker genes unique to each cell type. The gene expression of individual cells is measured, and gene expression patterns are used to group similar cells into clusters. By analyzing the expression of marker genes, ScType analysis assigns cells to specific types. ScType gene expression analysis utilizes computational methods to cluster cells, visualize data, and identify marker genes. A typical workflow involves data collection of single cell expression; preprocessing quality control, normalization, and filtering of the data. Cells are clustered based on their gene expression profiles into distinct ScTypes. The cluster corresponding to Tr1 cells can be identified and quantitated.

[0127] The invention now being fully described, it will be apparent to one of ordinary skill in the art that various changes and modifications can be made without departing from the spirit or scope of the invention.EXPERIMENTAL

[0128] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.EXAMPLE 1Regulation of antigen-specific type 1 regulatory T cell identity

[0129] In vitro-induced human antigen-specific Tr1 cells are distinct from FOXP3+Tregs and other CD4+T cell populations on a single-cell level. Previously, we used bulk RNA-sequencing (-seq) to reveal that in vitro-induced human alloantigen-specific Tr1 cells have a gene expression signature enriched for co-inhibitory genes, including LAG3, CTLA4, PDCD1 (encoding PD-1 ), HAVCR2 (encoding Tim-3), and ENTPD1 (encoding CD39), among others. Here, we further define the Tr1 gene expression signature at the single cell level, and compare it to FOXP3+Tregs and conventional CD4+T cells present in the T-allo10 product. Towards this goal, we generated two T-allo10 cell products enriched with alloantigen-specific Tr1 cells, using a 10-day co-culture of total peripheral CD4+T cells with allogeneic tolerogenic dendritic cells as described (FIG. 7A). An aliquot of T-allo10 cells at day 9 of T-allo10 culture was purified for live CD4+CD3+T cells prior to single-cell capture and 5’ single-cell (sc) RNA- and TCR-seq. The expected Tr1 cell phenotype and function was confirmed in the T-allo10 cell products at the end (day 10) of the T-allo10 culture, by measuring co-expression of surface proteins CD49b and LAG3 and anergy (FIG. 7B-D).

[0130] T-allo10 cells separated into 7 clusters (Fig.lA), which we annotated using data-driven and knowledge-based methods (Fig.l B, FIG. 8A-C). Except cluster C6, which was predominantly found in one donor, all other clusters were proportionally distributed ( FIG. 8D). The proportion of Tr1 cells identified by sc-RNA-seq was similar to the proportion of Tr1 cells identified by flow cytometry ( FIG. 8D, Fig. S1C). As expected from antigen-induced cells, the Tr1 cluster contained the majority of clonally expanded cells (Fig.lA, FIG. 8E); the othercluster with highest number of clones was the small Th1 cluster C6 (FIG. 8E). Overall, in T- allol O products, we detected clusters of FOXP3+Tregs, naive, resting, and polarized T cells that were distinguished from the Tr1 cell cluster (Fig.1 A). Even using stringent filtering (FDR- adjusted p-value < 0.001 and absolute Iog2 fold-change > 1 ), FOXP3+Tregs and Tr1 cells had 49 differentially expressed genes (Fig.l C). FOXP3+Tregs expressed high level of FOXP3, IL2RA (encoding CD25), TIGIT, CD27, and TF BATF, while Tr1 cells expressed high levels of LAG3, interferon (IFN)-regulated IFIT gene family (IFITM1 , IFITM2, IFITM3), FURIN (a protease which can activate latent transforming growth factor-p), genes conferring the cytotoxic phenotype (KLRB1 , GZMA), and TF BHLHE40, which regulates IFN-y and IL-10 production in Tr1 cells (Fig.l C).

[0131] A comparison of Tr1 cell transcriptome to all other clusters revealed a distinct gene expression signature comprised from 90 significantly upregulated genes in Tr1 cells, including LAG3, CTLA4, FURIN, TGFB1 , and cytotoxicity-associated genes KLRB1 , GZMA, GZMB, GNLY and CCL5 (Fig.lD). One quarter of these genes was also found in our published signature of alloantigen-specific Tr1 cells, derived from bulk RNA-seq analysis of Tr1 cells isolated from T-allo10 cell products based on co-expression of CD49b+and LAG3 ( FIG. 9A). We observed low expression of EOMES (FIG. 9B), which was previously described as a marker of polyclonal Tr1 cells and tissue Tr1 cells , and a positive regulator of proteins associated with cytotoxic function (perforin, granzyme B, IFN-y) in cytotoxic T cells. Notably, despite the low expression of EOMES, Tr1 cells express granzyme B protein (encoded by GZMB) and are functionally cytotoxic (FIG. 9C-D).

[0132] The TCR repertoire of in v / tro-induced antigen-specific Tr1 cells was oligoclonal and distinct from the repertoire of FOXP3+Tregs and other CD4+T cell populations in T-allo10 cell products. Tr1 cells shared only 8 clones with resting, Th17-like, and IFN-exhibiting (IFN) T cells (Fig.l E). Overall, clonal sharing between T cell populations in the T-allo10 cell product was minimal (Fig.l E), likely due to the antigen stimulation in Tr1 -polarizing conditions and clonally diverse parental CD4+T cells. Notably, FOXP3+Tregs did not share any clones with Tr1 cells (Fig.l E) nor did they clonally expand in response to alloantigen stimulus (Fig.1 A). These data suggest that the frequency of alloreactive FOXP3+Tregs in peripheral blood CD4+T cells is low, or that the starting Treg population in culture was too small to detect rare alloreactive FOXP3+Tregs38.

[0133] Next, we computationally selected Tr1 cells and re-clustered them, revealing two subclusters: Tr1 -A and Tr1 -B (Fig.l F). Tr1 sub-clusters shared 50 clones, indicating a common origin (Fig.1 G). While there were only 14 DEG between the two clusters, Tr1 cells in cluster Tr1 -B exhibited higher levels of activation-inducible genes such as LAG3, CTLA4, and dualspecificity phosphatases (DUSP4 and DUSP5), but lower IL7R (encoding the IL-7 receptor a- chain) (Fig.1 H,). RNA velocity analysis suggested that the less activated Tr1 phenotype incluster Tr1 -A precedes more activated Tr1 cell phenotype in cluster Tr1 -B (Fig.11). Only one TF, JUNB, was differentially expressed between two clusters (2.24-fold higher in Tr1-B; adjusted p-value = 6.24 x 1046). Since JUNB is downstream of T cell receptor (TCR) signaling, its expression in the Tr1 -B cluster is in accordance with the more activated state of Tr1 -B cells.

[0134] Finally, to identify the TFs that were significantly upregulated in Tr1 cells but not in other T cell populations, we examined the differentially expressed TFs between Tr1 cells and all other clusters. These analyses revealed 7 specifically upregulated TFs in Tr1 cells: CEBPD, JUNB, BHLHE40, MAF, PRDM1 , NFIL3, and CSRNP1 (Fig.U). Among these TFs, the role of CEBPD and CSRNP1 in T cells is poorly understood. We previously identified BHLHE40 as specific for ex vivo isolated Tr1 cells, showing it regulates IL-10 and IFN-y. NFIL3 was shown to repress FOXP3 and upregulate IL-10 and HAVCR2 (encoding Tim-3). Similarly, MAF and PRDM1 are known regulators of IL-10 , and have also been implicated as regulators of murine Tr1 cell differentiation. Altogether, our single-cell RNA- and TCR-seq analysis uncovered that in v / fro-induced antigen-specific Tr1 cells have a distinct transcriptional signature and a TCR repertoire, and express a unique TF profile.

[0135] Human antigen-specific Tr1 cells have a unique signature of accessible chromatin peaks. To further investigate the transcriptional regulation of Tr1 cell identity and obtain a comprehensive landscape of TF activity in antigen-specific Tr1 cells, we applied Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq. First, we generated five T-allo10 products and control T-allo products (which contain CD4+Teff cells) from the same parental CD4+T cells (Fig.2A). We isolated CD49b+LAG3+Tr1 cell and CD49b LAG3 non-Tr1 cell (DN, double-negative) populations from memory CD4+T cells of T-allo10 products, and CD49b LAG3 Teff cells from control T-allo products by FACS, thawed cryopreserved parental CD4+T cells, and analyzed these populations by ATAC- and RNA- seq. After quality control (Fig.2B; FIG. 10A-B), we compared the chromatin accessibility of Tr1 cells vs. all control populations combined, revealing 881 significantly accessible peaks in Tr1 (Fig.2C). Next, we annotated the peaks based on their nearest gene, and analyzed them using Gene Set Enrichment Analysis (GSEA), using Molecular Signature Database (MsigDB) gene set C7: Immune Signatures. Data show that the genes that are significantly accessible in Tr1 cells were enriched in the gene signatures of regulatory T cells and memory T cells comprised in the MsigDB (Fig.2D). These signatures were also identified in Tr1 cells in our published, bulk RNA-seq dataset, showing reproducibility across samples and platforms. Finally, we used Ingenuity Pathway Analysis (IPA) Upstream Regulator analysis to predict key transcriptional regulators of Tr1 -specific accessible genes, identifying IRF4, MAF, and BHLHE40 TFs as top transcriptional regulators of Tr1 cells (Fig.2E). These TFs were also significantly upregulated in Tr1 cells in either bulk or single-cell datasets or both (FIG. 9A). Chromatin peaks that were significantly accessible in Tr1 cells compared to controlpopulations overlapped with known IRF4 and MAF promoter and enhancer regions (Fig.2F). In summary, these data show that the differentiation of antigen-specific human Tr1 cells leads to significant chromatin remodeling, with a distinct pattern of accessible chromatin peaks observed in Tr1 cells. Network enrichment analysis of Tr1 -specific accessible peaks identified IRF4, MAF, and BHLHE40 TFs as top upstream regulators.

[0136] Human antigen-specific Tr1 cells have a distinct TF usage profile. In addition to revealing global chromatin accessibility, ATAC-seq can also identify TFs that are actively used by the cell even if their gene expression is low. TF usage can be identified by i) mapping the known TF binding sites (“motifs”) to the areas of accessible chromatin, ii) mapping the TF footprints to identify TFs bound to the chromatin at the time of sampling, iii) uncovering the TFs that colocalize with predicted enhancer regions, and iv) integrating AT AC- and RNA-seq data to identify the TFs with high peak accessibility-gene expression associations. Active TFs in Tr1 cells that are identified by both peak accessibility and TF usage analyses are likely to be biologically relevant. We analyzed TF motif accessibility in Tr1 cells and control cell populations using ChromVar, and showed that Tr1 cells cluster apart from other cell subsets (Fig.3A). The TF motifs that were significantly more accessible in Tr1 cells compared to control populations contained AP-1 (Fos, Jun and others), MAF, IRF (Interferon Regulatory Factor), and T-box TF family members, among others (Fig.3B). We also performed footprinting analysis using TOBIAS, which was concordant with the motif analysis, revealing many of the same TF family members as significantly more active in Tr1 cells (Fig.3C). TFs with prominent footprints included IRF4, which we previously identified as upregulated in Tr1 cells and which others have found to be important in polyclonal murine and human Tr1 cell differentiation; the BATF:JUN heterodimer, which may facilitate IRF4 expression; RUNX2, which was found in cytotoxic CD8+T cells and NK cells but not previously described in Tr1 cells; and BHLHE40, which regulates IL-10 and IFN-y production in ex vivo human Tr1 cells from peripheral blood (Fig.3D). TFs IRF4, MAF, PRDM1 , TBX21 and RUNX2, for which the antibodies for flow cytometry were available, were also upregulated in Tr1 cells at the protein level (Fig.3E).

[0137] Next, we asked which TFs co-localize with regions of the Tr1 genome exhibiting super- enhancer-like features. Super-enhancers are large clusters of enhancers with high TF binding activity, associated with expression of TFs and genes crucial to cell identity. GREAT algorithm extrapolates biological functions of non-coding genomic regions by querying the annotations of nearby genes, and can infer enhancers from ATAC-seq data. Highest ranked enhancers, which might represent super-enhancers, were found nearby TFs MAF, BHLHE40, and ZEB2, and archetypal Tri genes such as CTLA4, NAMPT and CCL3 (Fig.3F). Interestingly, ZEB2 was recently shown to induce GZMA (encoding granzyme A), which is highly expressed in in vitro- induced antigen-specific Tr1 cells (Fig.l E, FIG. 8C).

[0138] As a final step, we integrated ATAC-seq data with matched RNA-seq data (Fig.2A) using FigR, which identified domains of regulatory chromatin (DORC). DORC are chromatin regions with high-density associations between cis-regulatory elements and genes, which are enriched for lineage-determining genes and overlap with known super-enhancers. The Tr1 cell-specific DORC contained archetypal Tr1 genes such as CTLA4, PDCD1 , and FURIN (Fig.3G, FIG. 10C), and the following TFs: MAF, IRF4, BATF, TBX21 (encoding T-bet), TOX2, RBPJ, FOXB1 and ZNF212 (Fig.3G, FIG. 10C). Altogether, our comprehensive bioinformatic analysis of TF usage show that antigen-specific human T r1 cells have a unique profile of active TFs, and identified MAF and IRF4 as one of the top candidate TFs that regulate the key biological features of Tri cells.

[0139] IRF4, BATF and MAF regulate human antigen-specific Tr1 cells. Next, we investigated the biological relevance of top candidate TFs using functional genomics. We started with IRF4 and MAF, which were identified as top upstream regulators of Tr1 cells in network enrichment analysis (Fig.2F), in TF usage analysis (Fig.3B-C), in Tr1 DORC (Fig. 3G), and they were highly expressed in Tr1 cells (Fig.1 J). BATF expression was higher in FOXP3+Tregs than in Tr1 cells (Fig.lC), but we also validated its role because BATF is often co-regulated with IRF4, and was identified in Tr1 cells in the TF usage and DORC analyses (Fig.3B-C and Fig.3G, respectively). Our first approach was to knock-out (KO) the target TFs in parental CD4+T cells using CRISPR / Cas9 before Tr1 cell differentiation in T-allo10 culture (Fig.4A). The efficiency of CRISPR-based gene editing efficiency in primary T cells is usually increased by TCR prestimulation, but this strategy prevented antigen-inducible Tr1 cell differentiation in T-allo10 culture (our unpublished data). Instead, we knock-out the TFs from unstimulated, ex vivo isolated CD4+T cells with multiplex single guide (sg) RNAs, designed to disrupt the universal second exon of IRF4 and first exons of BATF and MAF ( FIG. 11 A). Multiplex sgRNA strategy achieved good KO efficiencies in unstimulated CD4+T cells (> 75% median; Fig.4B).

[0140] After 10 days of in vitro differentiation, T-allo10 cell products generated from KO and mock-treated wild-type (WT) parental CD4+T cells were compared for Tr1 cell frequency, phenotype, and function. Compared to controls, IRF4 KO significantly decreased the yield of Tr1 cells, identified by the co-expression of CD49b and LAG3, within the T-allo10 cell products (Fig.4C). After IRF4 KO, the expression of LAG3 and CTLA-4 was also significantly decreased in memory CD4+T cells (Fig.4D-E), while CTLA-4 expression was significantly decreased in LAG3-single-positive memory CD4+T cells and Tr1 cells (Fig.4F). IRF4 KO did not affect the expression of granzyme B ( FIG. 11 B). Interestingly, the remaining WT CD4+T cells outgrew the IRF4-KO CD4+T cells in the Tr1 compartment, but not in non-Tr1 memory or naive CD4+T cells in IRF4-KO T-allo10 products, ( FIG. 11C), suggesting that IRF4 provides a competitive growth advantage to in vitro differentiating Tr1 cells. Finally, we observed a significantimpairment of the suppressive function of IRF4 KO T-allo10 cells (Fig.4G), indicating that IRF4 regulates suppressive capacity of Tr1 cells.

[0141] On the other hand, BATF KO significantly impaired the expression of granzyme B in Tr1 cells (Fig.4H), but it did not affect Tr1 cell differentiation (Fig.4l) and CTLA4 expression in Tr1 cells (Fig.4l, FIG. 11 D). Interestingly, BATF KO impaired the ability of memory CD4+cells to express LAG3, CTLA-4 and granzyme B (Fig.4l and FIG. 11 D). We also tested the effect of MAF KO on Tr1 cell differentiation, phenotype, and function. MAF KO did not modulate Tr1 cell differentiation (Fig.4K), the expression of LAG3 or CTLA-4 (FIG. 11 E), or suppressive function of T-allo10 cells (Fig.4L). However, in accordance with its described role in regulating IL-10, MAF KO significantly impaired the production of IL-10 in Tr1 cells (Fig.4M).

[0142] Because IL-10 drives the differentiation of Tr1 cells, we asked whether MAF overexpression (OE) can increase Tr1 cell yields in vitro. \Ne designed sgRNA guides to the MAF promoter region and upregulated MAF in naive CD4+T cells using CRISPR-activation (CRISPRa), which induces transient (3-5 days) OE of the target gene (Fig.4N). We achieved strong upregulation of MAF 24h after CRISPRa (Fig.40). MAF OE induced a trend towards an increase in soluble IL-10 at the end of 10-day T-allo10 culture compared to non-targeted controls (Fig.4P) and significantly increased Tr1 cell yield at the end of the T-allo10 culture (Fig.4Q).

[0143] In summary, we show that IRF4, BATF, and MAF, identified by the bioinformatic analyses as top transcriptional regulators of in wfro-induced antigen-specific Tr1 cells, play key roles in regulating various aspects of Tr1 cell biology: their differentiation, phenotype, and functions.

[0144] Tr1 -specific TF signature is detected in vivo in peripheral blood CD4+T cells. Next, we sought to understand if the Tr1 TF signature, which we identified in in vitro induced antigenspecific Tr1 cells, can be used to detect Tr1 cells in vivo. To this goal we applied CIBERSORTx, a digital cytometry method that estimates abundances of target cell types in a heterogenous bulk population using high-throughput data. We analyzed the Tr1 cell frequencies by CIBERSORTx in ATAC-seq data of bulk CD4+T cells isolated from peripheral blood of three patients with hematological malignancies, at three time points post-HSCT and treatment with donor-derived T-allo10 cells ( FIG. 12A). TF motif signatures of healthy donor CD4+T cells, which contain significantly fewer CD49+LAG3+Tr1 cells than T-allo10 recipients (FIG. 12B-C), were included to test the sensitivity of the method. We used the interactive CIBERSORTx user interface and previously identified TF motif signatures of purified parental CD4+T cells and Tri and non-Tr1 cells from the T-allo10 cell products (Fig.3A-B) to construct a CIBERSORTx signature matrix. Next, we investigated the relative accessibility of TF motifs, identified by the CIBERSORT signature matrix as specific for Tr1 cells, within the bulk CD4+T cells. Data show that all but one patient sample clustered apart from healthy donor samples(Fig.5A). Based on this TF matrix and TF motif accessibility values, CIBERSORTx determined the Tr1 cell frequencies in bulk CD4+T cell samples which we validated by comparing them to Tr1 cell frequencies obtained by flow cytometry. These data revealed that the frequencies of Tr1 cells in the three patients at all time points were significantly higher than the frequencies observed in the healthy donors ( FIG. 12-C), and that the frequency of Tr1 cells identified by digital cytometry significantly correlated with the frequency of Tr1 cells identified by flow cytometry (Fig.5B). These data confirm our previous TCR clonal tracking data that Tr1 cells persist long-term in vivo after adoptive transfer, and demonstrate that the Tr1 TF signature identified in in-vitro induced antigen-specific Tr1 cells can be leveraged to detect Tr1 cells in vivo.

[0145] To confirm that the TF motif signature of Tr1 cells can also be used to identify Tr1 cells on a single cell level, we analyzed the purified peripheral blood CD4+T cells from three healthy donors and one patient at 9 months post-T-allo10 infusion and allo-HSCT with sc-multiome (sc-ATAC + RNA-seq; 10X Genomics). All donors had detectable CD49b+LAG3+Tr1 cells by flow cytometry (Fig.5C), with higher frequency of Tr1 cells in healthy donor BC0202 and patient 1 at month 9 (M9) than the other two healthy donors (Fig.5C). Samples were integrated, then clustered based on accessibility data (Fig.5D) because nuclear RNA was sparse in ex vivo isolated, unstimulated T cells (FIG. 12D). Clustering revealed 15 CD4+T cell clusters (Fig.5E), some of which were evenly distributed across donors, while others predominantly contained cells from one donor (Fig.5F). Clusters C1 and C3 were identified as putative Tr1 clusters based on the high gene expression of LAG3, ITGA2 (encoding CD49b) and CD226 (Fig.5G), which were previously shown to be specific to peripheral blood Tr1 cells65. These two clusters predominantly originated from healthy donor BC0202 (C1 , Fig.5F) and the patient M9 (C3, Fig.5F). Clusters C1 and C3 expressed granzyme genes, but GZMB (encoding granzyme B) was higher in cluster C1 , GZMA (encoding granzyme A) was higher in cluster C3, while GZMK was present in both (Fig.5G). Because GZMA expression is higher than GZMB in Tr1 cells (Fig.l E), we compared the gene expression between clusters C1 and C3. This comparison revealed that cluster C1 has significantly higher expression of GZMB, granulysin (GNLY), TNF (encoding TNF-a), LTA (lymphotoxin-a), several I FN-inducible genes from the IFIT and 2’-5’-oligoadenylate synthase (OAS) families, and TRBV28 gene (the variable region 28 of the TCR |3-chain), among others (FIG. 12E, Fig.5G). On the other hand, cluster C3 contained more PDCD1 (PD-l )-expressing cells (Fig.5G), and higher level of archetypal Tr1 cell genes ENTPD1 (CD39), IRF4, and DUSP4 (FIG. 12D, Fig.5G). Thus, cluster C3 contains Tr1 cells, while cluster C1 likely contains a combination of Tri and Th1 or TEMRA (T effector memory cells re-expressing CD45RA) cells, given that ITGA2 (CD49b) is expressed by some cells in that cluster (Fig.5G). Interestingly, CTLA4 expression in Tr1 cell cluster C3 was low (Fig.5G). This may be due to sparsity of nuclear RNA data or a lessactivated state of these Tr1 cells in vivo. Indeed, we observed low CTLA4 expression in CD49b+LAG3+T r1 cells isolated from healthy donor peripheral blood by both bulk RNA-seq35 and on protein level, albeit still significantly higher than in non-Tr1 memory CD4+T cells ( FIG. 12F). As in T-allo10 products (Fig.1 ), FOXP3+Treg cluster, C8, was clearly identified by high expression of FOXP3, Helios (encoded by IKZF2), and CTLA4 (Fig.5G).

[0146] Next, we analyzed accessibility of the same genes as in Fig.5G. Accessibility of LAG3 and CD226 or granzymes was not exclusive to Tr1 cell cluster C3 (Fig.5H). In contrast to its RNA expression pattern, ITGA2 gene was inaccessible (Fig.5H), (Fig.5G), suggesting a difference in regulation between chromatin accessibility and nuclear RNA transcription. The accessibility of CTLA4 in Tr1 cluster C3 was lower, but PDCD1 accessibility was higher than in FOXP3+Tregs (Fig.5H). In addition, cluster C3 had accessible IL10 and IFNG genes, as well as their regulators MAF and TBX21 , respectively (Fig.5l). Furthermore, we analyzed TF motif accessibility in this dataset, showing that Tr1 cell cluster C3 had accessible motifs of TBX21 , MAF, BATF, IRF4 and PRDM1 , among others (Fig.5J). None of these TF motifs were exclusively accessible in Tr1 cell clusters (Fig.5J). Notably however, Tr1 clusters did not have accessible GAT A3, RORC, and BCL6 motifs, which are archetypal for Th2, Th17, and Tfh cells, respectively (Fig.5J).

[0147] To confirm our annotation of cluster 03 as the putative Tr1 cell cluster, we first derived a common signature of actively used TFs in in v / tro-induced antigen-specific Tr1 cells (Methods). Next, we used this Tr1 -specific TF signature and GSEA to determine its enrichment in the sc-multiome dataset. Indeed, C3 was the only cluster that was significantly enriched for the Tr1 -specific TF signature (Fig.5K). This TF signature was not enriched in independent datasets of B cells and monocytes, confirming its Tr1 cell specificity (FIG. 13A-B). Altogether, we demonstrate that we can identify Tr1 cells in the peripheral blood of patients and healthy donors using a Tr1 -specific TF signature.

[0148] Tr1 -specific TF signature identifies Tr1 cells in solid tumors. Next, we investigated if the Tr1 -specific TF motif signature, which was identified in in v / tro-induced antigen-specific Tr1 cells, can be broadly applied to identify Tr1 cells in tissues. To this aim, we re-analyzed published sc-ATAC-seq dataset from peripheral blood, tumor tissue and adiacent normal tissue of 8 patients with clear-cell renal cell carcinoma samples (ccRCC), 4 patients in stage 1 A and 4 patients in stage 1 B. We focused on CD4+T cells while filtering out CD8+T cells, B cells, NK cells, and myeloid cells (Fig.6A; Methods). By integrating the peripheral blood and tissue samples of 8 ccRCC patients (Fig.6B) we identified 12 CD4+T cell clusters (Fig.6C). Most clusters were found in all patient samples (Fig.6D) and proportionally distributed across patients with stage 1 A or 1 B ccRCC (Fig.6E). However, cells predominantly clustered together according to the tissue type (Fig.6F-G). GSEA analysis identified cluster C9, composed from naive CD4+T cells from peripheral blood, as significantly depleted from the Tr1 TF signature(Fig.6H). Cluster C6, composed of tumor CD4+T cells, was significantly enriched for Tr1 TF signature, with normalized enrichment score (NES) = 2.1371 (Fig.6H). Accordingly, Tr1 cluster C6 had highly accessible motifs for TBX21 , MAF, BATF and IRF4, but closed motifs characteristic for FOXP3+Tregs, Th2, Th17 cells, and Tfh cells (FOXP3, GAT A3, RORC, and BCL6, respectively) (Fig.6l).

[0149] Like in the peripheral blood CD4+T cells (Fig.5H-l), CTLA4 accessibility was lower in the tumor Tr1 cluster C6 than in blood and tumor FOXP3+Tregs (Fig.6J), and IL10 accessibility higher, but not specific to cluster C6. Perforin (PRF1 ) accessibility was notable in Th1 cluster C3 and IFN-activated cluster C4 (Fig.6J). We also observed that cluster C5 had a NES > 1 , but enrichment was not statistically significant (Fig.6H). To ensure that we correctly define C6 as a Tr1 cell cluster, we examined the marker genes of clusters C5 and C6. The accessibility of IL17RE was highest in cluster C5 (Fig.6J), likely indicating Th17-like cells. In contrast, cluster C6 had significantly more accessible genes encoding granzyme K (GZMK, Fig.6G), the TF CEBPD (detected in Tr1 cells in single cell analysis, Fig.U), chemokine receptor CCR5, which is highly expressed in human gut Tr1 cells, and genes found in Tr1 cell DORC or in the GREAT enhancer analyses (CXCR6, CCL3, and TF ZEB2, Fig.3G). Interestingly, accessibility of CXCL13 was elevated in cluster C6. CXCL13 can be expressed by Tfh and Tph cells, but cluster C6 did not have high accessibility of the Tfh marker CXCR5 or the Tph TF SOX4 ( FIG. 13C). These data support the identification of cluster C6 as a Tr1 cell cluster (Fig.6J). Altogether, we show that a TF signature, which is unique to antigenspecific human Tr1 cells induced in vitro, can be used to identify putative Tr1 cells within tumor-resident CD4+T cells.

[0150] Systematic investigation of the epigenetic landscape of human Tr1 cells and their transcriptional regulators has not been performed, despite increasing interest in Tr1 biology and clinical applications in various antigen-driven inflammatory conditions and diseases, and ongoing clinical trials of Tr1 -enriched T-allo10 cell therapy in allo-HSCT. Here, we show that human in v / tro-induced antigen-specific Tr1 cells have a unique transcriptome, oligoclonal TCR repertoire, and a distinct signature of open chromatin, TF motifs, and TF footprints in comparison to the parental CD4+T cells, as well as other CD4+cell populations. Integrating the epigenetic data with the Tr1 cell transcriptome enabled us to identify IRF4, BATF, and MAF as top transcriptional regulators of Tr1 cells. Using functional genomics, we confirm that IRF4, BATF and MAF play critical and non-redundant roles in Tr1 cell differentiation, phenotype, and functions. We demonstrate that the TF signature discovered in antigen- induced Tr1 cells in vitro can also track Tr1 cells in vivo, in CD4+T cells isolated from peripheral blood of T-allo10 treated patients. Furthermore, this TF signature can identify Tr1 cells in CD4+T cells present in solid tumor tissue.

[0151] Current lack of wide consensus on the identity of Tr1 cell-regulating TFs may result from the use of different experimental models. In the mouse, where Tr1 differentiation is IL- 27-dependent, systems biology- and mechanistic studies have been performed to identify their key transcriptional regulators. Polyclonal Tr1 cell induction model pointed to Irf1 , Batf, Maf, and Ahr as murine Tr1 lineage-defining TFs, antigen-specific induction model pointed to Eomes, Tbx21 , and Prdml , while peptide-nanoparticle induction model pointed to Bcl6, Irf4 and Prdml . In humans, Tr1 cell induction is IL-10 dependent, and mechanistic studies to validate the functional role of lineage-defining Tr1 TFs are limited. In one study, lentiviral vector mediated overexpression of EOMES in CD4+T cells induced polyclonal Tr1-like cells, suggesting its role in Tr1 differentiation. Another study used shRNA and small molecule inhibitors to show that IRF4 cooperates with AHR to regulate IL-10 production and suppressive function of activin-A-induced polyclonal Tr1 cells. We investigated the lineage-defining TFs in human antigen-specific Tr1 cells, which are induced in vitro in response to alloantigens presented by allogeneic tolerogenic dendritic cells. Our integrative analysis of the epigenome and transcriptome of human antigen-specific Tr1 cells showed that Tr1 cells utilized IRF4, BATF, MAF, TBX21 , EOMES and PRDM1 , but not BCL6 or AHR as suggested in murine nanoparticle and human activin-A induction models, respectively, likely because of the intrinsic differences in these models.

[0152] Our functional genomics studies showed that IRF4, BATF and MAF are key regulators of antigen specific Tr1 cells. While IRF4 and BATF often co-operate, we show that IRF4 is necessary for Tr1 cell differentiation and CTLA-4 expression, and BATF regulates granzyme B expression in Tr1 cells. We found that BATF is also highly expressed in FOXP3+Tregs, which did not express granzyme B in the condition we tested. Hence, BATF may play a different role in FOXP3+Tregs than in Tr1 cells. This finding is in line with other reports suggesting that lineage-defining TFs of one CD4+T-helper (Th) subset can have various functions in other Th subsets. In addition, IRF4 and BATF have been implicated in T cell activation, prevention of exhaustion, and Th17 differentiation. It is possible that the activation of IRF4 and BATF in developing Tr1 cells does not lead to Th17 differentiation due to concomitant activation of EOMES and TBX21 , which repress Th17 phenotype. IRF4 also regulates the differentiation of T-follicular helper (Tfh cells) and production of their hallmark cytokine IL-21 . In our hands, antigen-specific Tr1 cells express IL-21 and genes associated with Tfh cells such as ICOS, but do not have accessible the TF BCL6, master regulator of Tfh cells. This may result from high levels of PRDM1 (encoding Blimp-1 ) in Tr1 cells, which can repress BCL6.

[0153] We identified MAF as another key transcriptional regulator of antigen-specific human Tr1 cells. MAF is a universal regulator of IL-10 production across different CD4+T cell subsets. In mice, Maf cooperates with Ahr and Prdml (encoding Blimp-1 ) to achieve optimaltranscription of IL-10. In addition, murine Maf and Prdml cooperate to upregulate the expression of a co-inhibitory receptor module that includes LAG3, an archetypal Tr1 cell gene. Interestingly, while we show that MAF regulates IL-10 production in human Tr1 cells, MAF KO or OE did not change the expression of LAG3. This may be because PRDM1 , which is highly expressed in Tr1 cells, compensates for the lack of MAF to upregulate LAG3. Furthermore, it is conceivable that MAF can play a role in promoting human Tr1 cell differentiation that is distinct from its effect on IL-10 production. Indeed, we show that even transient OE of MAF significantly increased Tr1 cell yields after their antigen-induced differentiation.

[0154] Our study also provides a novel way to identify Tr1 cells in vivo. Co-expression of CD49b and LAG3 has been shown to identify Tr1 cells in human peripheral blood. Studies of tissue Tr1 cells proposed that the co-expression of PD-1 and CCR5 identified human Tr1 cells in the gut, and EOMES expression identified Tr1 cells that infiltrate human solid tumors. These discrepancies in the phenotype of blood and tissue Tr1 cells may stem from their adaptation to the local microenvironment, without changes in the core Tr1 cell identity and function. Indeed, ex vivo isolated Tr1 cells from blood, gut, and tumor tissue all suppressed Teff cell proliferation and produced IL-10 and IFN-y, suggesting that their key transcriptional regulators are likely similar. Accordingly, we show that T r1 cell-specific TF signature, which we identified in in v / tro-induced antigen-specific Tr1 cells, can be used to detect Tr1 cells in vivo.

[0155] TF motif-based digital cytometry showed that we can quantify Tr1 cells using their epigenetic signature in a heterogenous bulk population of total peripheral blood CD4+T cells, isolated from patients treated with Tr1 -enriched T-allo10 cell therapy up to 300 days posttreatment. Previously, we used TCR clonotype-based tracking to demonstrate that Tr1 cells from infused T-allo10 cell products persist long term in patients, but these data did not inform us on their phenotypic stability. Herein, our data indicates that the molecular signature of Tr1 cells is stable. We also showed enrichment of the Tr1 TF signature in peripheral blood of healthy donors, indicating that the Tr1 TF signature we identified in in v / tro-induced antigenspecific Tr1 cells is a general signature of Tr1 cells, independent from antigens and in vitro vs in vivo conditions. This is further supported by the identification of a putative Tr1 cell cluster among resident tumor CD4+T cells of patients with kidney cancer. As ATAC-seq profiling is becoming more accessible, the Tr1 -specific TF signature can be applied to study antigenspecific Tr1 cells in tumor biology and clarify their role in solid tumors vs. hematological malignancies.

[0156] In summary, we describe the epigenetic landscape of antigen-specific Tri cells, and identify their unique signature of actively used TFs. This signature contains key transcriptional regulators of Tr1 cells - TFs IRF4, BATF, and MAF - and can be used to identify Tr1 cells in epigenomics datasets in vivo. Thus, the data provided herein illuminates two key challenges in Tr1 cell biology: their transcriptional regulation and their identification. These data can beused to develop Tr1 cell-based therapies using TF engineering, inhibit Tr1 cell differentiation in vivo using targeted TF degradation approaches, and serve as a basis for mechanistic studies of Tr1 cell biology. In addition, the discovery of a Tr1 -specific epigenetic signature will advance our understanding on the role of Tr1 cells in autoimmune disease, inflammatory conditions, and cancer.Materials and methods

[0157] Primary cells. Human peripheral blood mononuclear cells (PBMC) were sourced from buffy coats of deidentified donors (Stanford Blood Center, Palo Alto, CA, USA) and isolated using Ficoll-Paque Plus (GE Healthcare) density gradient. CD4+T cells were isolated from PBMC using Human CD4+ T Cell Isolation Kits (Miltenyi Biotec, Inc., Bergisch Gladbach, Germany). Naive CD4+ T cells were isolated using Human Naive CD4+ T Cell Isolation Kit II (Miltenyi Biotec). CD14+monocytes were isolated using Human CD14 Microbeads (Miltenyi Biotec). Cell purity was verified by flow cytometry. CD4+T cells were cultured in a T cell medium of X-VIVO-15 (Lonza, Basel, Switzerland) with 5% Human AB Serum (MilliporeSigma, Burlington, MA, USA). Cells isolated from patients and allo-HSCT donors enrolled in the T-allo10 clinical trial were collected in accordance with the Administrative Panels on Human Subjects in Medical Research, Stanford University T-allo10 eProtocol #38734, after an informed consent. Clinical study design and patient inclusion and exclusion criteria can be found at ClinicalTrials.gov. Demographics, treatment and clinical data of the patients were reported previously.

[0158] Flow cytometry. Purified primary cells, DC-10 and mDC cells, T-allo10 cells, and cells used for T cell degranulation and suppression assays were stained to assess purity, phenotype, and / or proliferation via flow cytometry. Cells were first stained for viability for 15 minutes at room temperature using 100 uL of a 1 :1000 (Live / Dead InfraRed) or 1 :500 (Live / Dead Aqua and Live / Dead Violet) fixable viability dye in PBS. Cells were then washed and incubated for 5 minutes at room temperature (RT) with 5 uL of Fc receptor blocking reagent (Miltenyi) in 50 uL of staining buffer composed of PBS with 0.02% sodium azide and 2% fetal bovine serum, then incubated for 15 minutes at RT using an extracellular antibody cocktail with staining buffer added to 100 uL. Cells were then washed and acquired on a flow cytometer, or fixed and stained intracellularly. For cytoplasmic antigens (e.g., cytokines, intracellular proteins like granzyme B), cells were fixed with BD Cytofix or Cytofix / Cytoperm Buffer (BD Biosciences, San Jose, CA, USA) at +4°C (min. 20 min up to overnight), washed with saponin-based BD Permeabilization Buffer, then stained with intracellular antibody cocktail diluted in 50 uL BD Permeabilization Buffer for 30 min at +4°C. After intracellular staining, cells were washed with Permeabilization Buffer, and resuspended in Staining Buffer before acquisition. For intranuclear antigens (e.g., transcription factors), cells were fixed,permeabilized and stained using Pharmingen Transcription Factor Buffer (BD Biosciences) or eBioscience FOXP3 / Transcription Factor Staining Buffer Set (Invitrogen) following manufacturer instructions. Samples were acquired on a BD FACSymphony A5 or a Beckman- Coulter CytoFLEX. Flow cytometry data was analyzed using FlowJo v10.8 (BD).

[0159] In vitro Tr1 cell differentiation. The T-allo10 protocol from Chen P and Cepika AM et al. was adapted to accommodate gene expression modulation with CRISPR. Briefly, tolerogenic dendritic cells (DC-10) and mature DC (matDC) were differentiated from CD14+monocytes using IL-4, GM-CSF, and IL-10 or MPLA, respectively. For knockout experiments, CD4+T cells were rested for 72 hours after electroporation in T cell media supplemented with 50 U / mL rhlL-2, then collected and plated 10:1 with allogeneic DC-10s in 24-well plates at a concentration of 7 x 105to 1 x 106T cells / mL in T cell media with 10 U / mL rhlL-10 (Gibco, Thermo Fisher Scientific). For CRISPRa experiments, naive CD4+T cells were rested for 4 hours after electroporation in T cell media, washed, and plated 10:1 with allogeneic DC-10s in 24-well, 48-well, or 96-well flat-bottom plates at a concentration of 7 x 105- 1 x 106T cells / mL in T cell media with 10 U / mL rhlL-10 (Gibco). For both workflows, half of the medium volume was replaced at day 5 of coculture with T cell media supplemented with rhlL-10 for a final concentration of 10 U / mL. Medium was replaced with base T cell media as necessary up until day 10 of co-culture, at which point cells were collected.

[0160] T cell anergy. T cell anergy assay was performed as described. Briefly, T-allo10 cells or control T-allo Teff cells were labeled with CellTrace carboxyfluorescein succinimidyl ester (CFSE; Thermo Fisher Scientific) for 5 to 8 minutes at room temperature, and cultured alone or stimulated with allogenic or third party matDCs at 10:1 ratio, 20:1 ratio of Human T-Activator CD3 / CD28 Dynabeads (Thermo Fisher Scientific) for 3 days. After culture, the cells were collected, stained for viability and surface markers, and the percentage of CFSE , dividing cells within the CD3+CD4+population was measured by flow cytometry and anergy was calculated as ((% CFSE T-allo - % CFSE T-allo10) I % CFSE T-allo) x 100.

[0161] Degranulation assay. To assess degranulation, 100,000 to 200,000 T-allo10 cells were cultured in a 96-well plate for 6h in total of 200 uL of the base T cell medium (see Primary Cells section) with 3 ug / mL brefeldin A (Sigma Aldrich), 2 uM monensin (BD) and 5 uL of BV421 mouse anti-human CD107a antibody (clone H4A3, BD Biosciences), with or without anti-CD3 / anti-CD28 Dynabeads or U937 myeloid leukemia cell line in 10:1 celkbead ratio. After the culture, supernatant was removed by centrifugation, cells were stained for surface and intracytoplasmic antigens using the protocol above, and acquired.

[0162] T cell suppression. Autologous, responder CD4+T cells (2.5 x 104per well), labeled with 5 pM CellTrace Violet (Thermo Fisher Scientific) for 5 to 8 min at room temperature, were co-cultured with or without equal numbers of CFSE-labeled T-allo10 cells and stimulated with 2.5 x 103allogeneic matDCs for 5 days as described. Both responder and T-allo10 cells werealso plated alone and with anti-CD3 / anti-CD28 Dynabeads (Gibco) as proliferation controls. At day 5 of this assay, the percentage of divided CellTrace Violet (CTV)-negative responder cells was assessed by flow cytometry. Suppression was calculated as follows: % suppression = ((% divided responder cells with only matDCs added) - (% divided responder cells with T- allol O and matDCs added)) / (% divided responder cells with only matDCs added).

[0163] Fluorescence-activated cell sorting (FACS). Purification of T-allo10 cell populations, ex vivo isolated CD4+T cells, and ex vivo Tr1 cells was performed as described. Briefly, to isolate Tr1 cells and other subsets from T-allo10 cultures, cells were collected at day 10 of culture and stained as described (see Flow Cytometry section) using PBS with 2% FBS at all steps. Samples were then filtered through a 40-mm cell filter (BD). Cells were sorted using a BD FACSAria II with a 100-um nozzle by gating through singlets, live, lymphocyte, CD3+CD4+, CD45RA (memory) and finally LAG3+CD49b+for Tr1 cells; LAG3 CD49b for DN cells; and LAG3+CD49b for Teff cells. To isolate total CD4+T cells, cells were gated through singlets, live, lymphocyte, and CD3+CD4+gate. To isolate naive CD4+T cells, cells were gated through singlets, live, lymphocyte, CD3+CD4+and CD45RA+gate.

[0164] Sample processing and library generation for sc-lmmune Profiling (RNA- and TCR- seq). Live singlet CD3+CD4+T cells were purified by FACS, counted, and their cell concentration adjusted in PBS with 0.04% BSA to allow capture up to 10,000 cells per well of the Chromium Next GEM chip, which was then loaded onto a Chromium Single Cell Instrument (10x Genomics, Pleasanton, CA). RNA-seq and V(D)J libraries were prepared using the Chromium Single Cell 5' Library, Gel Beads, & Multiplex Kit according to manufacturer’s instructions. Paired-end sequencing was conducted on an Illumina NovaSeq 6000. The Cell Ranger Single-Cell Software Suite (1 OX Genomics) was employed for sample demultiplexing, alignment, filtering, and UMI counting for RNA-seq libraries, using the human GRCh38 genome assembly and RefSeq gene model. For V(D)J libraries, the Cell Ranger Single-Cell Software (10X Genomics) performed sample demultiplexing, de novo assembly, alignment, annotation against germline segment V(D)J reference sequences and clonotype grouping.

[0165] scRNA-seq data were processed using Seurat (v.3.0) with specific criteria: nFeature_RNA between 500 and 5000, percent. mito < 0.25, and nCount_RNA < 50,000. Additionally, cells with multiple TCR alpha or beta chains were filtered out. The filtered data were integrated, batch-effect-corrected, clustered, and analyzed using the standard dataset integration and analysis workflow in Seurat. After merging samples into a single Seurat object, gene expression counts underwent normalization and scaling. Graph-based clustering was performed with the top 20 principal components at a resolution of 0.5. Marker genes for each cluster were identified and clusters expressing canonical marker genes from different cell types were manually annotated. Differential gene expression analysis between T cell populations was conducted using Seurat FindMarkersQ function.

[0166] TCR-seq data were analyzed using scRepertoire (v.1.0.2). A TCR clonotype was defined as the combination of genes in the TCR and the nucleotide sequence of the CDR3 region (gene + nucleotide) for paired TCR alpha and beta chains. We generated an overlay of the position of clonally expanded cells using clonalOverlay(). To look at the network interaction of clonotypes shared between clusters along the single-cell dimensional reduction we used the chord diagrams from the circlize R package, using functions getCirclize() and chordDiagram(). Additionally, the count of cells by cluster assigned into specific frequency ranges was obtained with the occupiedscRepertoire() function and clonal diversity was calculated using clonalDiversity().

[0167] Analysis of expression dynamics of the Tr1 cell cluster. \Ne used SCANPY, a python package for large-scale differential gene expression analysis, to load our 10x output as anndata object. Structuring the data within an anndata matrix allowed for downstream compatibility with other python-based implementation methods. Preprocessing, filtering, clustering, and initial visualizations were performed using SCANPY followed by RNA velocity analysis using scVelo. Velocyto, a command line interface, was used to prepare the data to be analyzed in scVelo by generating a spliced / unspliced expression matrix that is necessary for estimating RNA velocities. Vector fields were plotted using scVelo's velocity_embedding_grid function to visualize expression dynamics across all genes. To compare velocity and expression values of key Tr1 genes, the velocity function was used to plot the ratio of spliced and unspliced transcript as well as UMAP plots with colored gradient values of velocity and expression.

[0168] Bulk RNA- and ATAC-seq sample preparation and sequencing. T-allo10 and control T-allo cells were generated from total CD4+ T cell I dendritic cell donor pairs generated from five healthy donors as previously described (Fig.2A). Tr1 and non-Tr1 cells were isolated from T-allo10 cell products at day 10 of culture by FACS, as live singlet CD4+CD3+CD45RA cells either co-expressing CD49b and LAG3 (Tr1 ) or CD49b LAG3 (non-Tr1 , double-negative or DN cells). Effector T cells (Teff) were isolated from T-allo10 products as live singlet CD4+CD3+CD45RA CD49b LAG3 cells. Along with an aliquot of previously cryopreserved CD4+T cells, these populations were either lysed with RNAqueous Lysis Buffer, RNA isolated and libraries prepared as described, or their native DNA was tagmented and libraries prepared following the OMNI-ATAC protocol, with two modifications; transposition reaction was done with 350 rpm mixing and using the transposase from the Illumina Tagment DNA TDE1 Enzyme and Buffer Kit (Illumina, San Diego, CA, USA). Libraries were quantified using KAPA Library Quantification kit (Roche, Indianapolis, IN, USA), then sequenced using Hi-seq 4000 sequencer (Illumina).

[0169] For bulk ATAC-seq of CD4+T cells, CD4+T cells were isolated using FACS as live singlet CD3+CD4+cells from previously cryopreserved PBMC from the first cohort of patientswith hematological malignancies treated with T-allo10 cell infusion a day before their unmanipulated allo-HSCT (ClinicalTrials.gov ID: NCT03198234) at day 35, 90 and 300 posttreatment (n = 3), and four healthy donors. Patient characteristics have been described previously. Frequency of Tr1 cells in ex vivo CD4+T cells was measured using flow cytometry as described for all patients and 3 / 4 healthy donors. Live purified CD4+T cells were processed using OMNI-ATAC protocol and libraries sequenced using NextSeq 500 sequencer (Illumina).

[0170] Bulk ATAC-seq Data Analysis. Adapters were trimmed using cutadapt, and reads were mapped to the hg38 genome using bowtie2 with a maximum fragment length of 2000bp. Filtering criteria included non-mitochondrial reads, mapq > 20, and properly paired reads. Duplicates were removed using Picard tools. Peak calling was performed using macs2 with specific parameters (-shift -75 -extsize 150 -nomodel -call-summits -nolambda -p 0.01 -B -SPMR) on Tn5 insertion sites.

[0171] ChrAccR was utilized for differential accessibility analysis between sorted T cell populations. Regions and fragments on chromosomes chrM, chrX, chrY and regions with a coverage of less than 1 in more than 50 % of samples were removed. A count matrix was generated as insertion counts across samples at consensus peakset regions using the ChrAccR regionAggregation() function. DESeq2 was employed for calculating differentially accessible peaks and visualized using MA plots from the ggmaplot package. Transcription factor motif enrichment was calculated using ChromVAR, deviation scores were obtained with the ChrAccR getChromVarDev() function. Footprint plots were generated using aggregations of insertion counts in windows surrounding all occurrences of a motif genome-wide. ChrAccR normalizes these counts using kmer frequencies and uses all insertion sites to calculate the background distribution. Differential footprint analysis was performed with TOBIAS using the function BINDetect.

[0172] We utilized merged peak files to visualize peak accessibility in the WashU Epigenome Browser. Differential accessibility between Tr1 and the other CD4 T cells subsets was calculated as described previously, focusing on peaks overlapping with IRF4 and MAF promoter and enhancers regions predicted by TRANSFAC 2.0.

[0173] Bulk RNA-seq data processing and analysis. Bulk RNA-seq was processed as described. Briefly, sequencing reads were mapped and aligned to the reference human genome (GENCODE v32) and quantified using Salmon (v1.10.2), then aggregated to gene level, and imported into R using tximport. Samples were filtered to remove reads with low or no expression across multiple samples. Normalization, visualization, and differential gene expression analysis was performed using DESeq2 package. The integration of RNA and ATAC data was accomplished through a paired analysis of corresponding donors. This involved identifying and examining the correlation between highly expressed genes in the RNAdataset and accessible genes in the ATAC dataset using an adaptation of functions of the FigR package (v 0.1 .0).

[0174] Functional Analysis of cis-Regulatory Regions. The GREAT gene ontology tool (v.4.0.4, was employed to assign gene ontology terms to cis-regulatory regions with high accessibility in the Tr1 cluster (Tr1 peaks were defined as those with FDR less than 0.01 and log2FoldChange greater than 1.5 compared with other CD4 T cell clusters). We used the following parameters: Species assembly: hg38, Association rule: Basal+extension: 5000 bp upstream, 1000 bp downstream, 1000000 bp max extension, curated regulatory domains included. We also used FigR package (v 0.1 .0) to discern cis-regulatory interactions and delineate domains of regulatory chromatin, referred to as DORCs. For visualization we use the function dorcJPIot.

[0175] CRISPR / Cas9 knockouts. Single-guide (sg) RNAs were purchased from Synthego (Menlo Park, CA, USA). The sgRNAs were modified to incorporate 2'-O-methyl-3'- phosphorothioate bonds at the three terminal nucleotides of the 5' and 3' ends. For each gene, 1 to 3 sgRNAs were selected using CRISPOR and used in combination. HiFi Cas9 was purchased from Integrated DNA Technologies (Coralville, IA, USA).

[0176] When using frozen CD4+T cells, cells were thawed and rested in a 37°C / 5% CO2incubator in the T cell medium with 50 U / mL rhlL-2 (Peprotech, Thermo Fisher Scientific, Waltham, MA, USA), for 4-24 hours prior to electroporation, while freshly isolated CD4+T cells were electroporated immediately following isolation. 37.2 pmol of Cas9 and 105 pmol of sgRNA were incubated for 30’ at room temperature to form RNPs. T cells were resuspended in P3 buffer and mixed with complexed RNPs and 10 pmol of Cas9 Electroporation Enhancer (Integrated DNA Technologies). Cells were electroporated using the Lonza 4D Nucleofector X unit, program EO-1 15, then plated at 1 x 106to 4 x 10scells / mL in T cell media with 50 U / mL rhlL-2 and rested for 72h before lysis for Sanger sequencing analysis or T-allo 10 culture initiation.

[0177] Indel frequency analysis. 60-72h post-targeting, CD4+T cells were collected and gDNA was isolated with Quick Extract (Lucigen) following manufacturer instructions. The following PCR primers were used to amplify cut sites with Q5 Hot Start High-Fidelity 2X Master Mix (New England Biolabs, Ipswich, MA, USA): IRF4 5’ - AGGTGCCTTCTTCCGGGG - 3’ & 5’ - TTGCGTGGAAACGAGAACGC - 3’; BATF 5’ - GAAGTTTCCGCCCATGTGAC - 3’ & 5’ - CGGCCCACTTGAAAACTCCT - 3’; MAF 5’ - CGCCGCGCAAGCTAGAA - 3’ & 5’ - GGGTAGCCGGTCATCCAGT - 3’. PCR products were Sanger sequenced using one of the primers shown above. Resulting Sanger chromatograms were then used as input for indel analysis (Synthego Performance Analysis, ICE Analysis. 2019. v3.0. Synthego; February 2023)).

[0178] CRISPRa. Chemically modified sgRNAs for CRISPRa were selected using CRISPick. Two guides for each gene were chosen based on GC content and proximity to TSS, then used in combination. sgRNAs, chemical modifications identical to those used for CRISPR / Cas9 knockouts, were purchased from Synthego. dCas9-VPR mRNA was obtained from Horizon Discovery (Waterbeach, UK). Cells were resuspended in P3 buffer (Lonza) and mixed with 105 pmol of sgRNA and 0.6 ug of dCas9-VPR mRNA, followed by electroporation on a Lonza 4D Nucleofector X unit with program EO-115, using a Lonza electroporation strip with no more than 1.5 x 106cells per well. Cells were plated at 1 x 106to 4 x 106cells / mL in T cell media and rested before lysis for qPCR and T-allo10 culture initiation.

[0179] RNA and qPCR. 24-48 hours after electroporation of CRISPRa components, cells were collected and lysed using RNeasy Micro Kit RLT buffer (Qiagen, Hilden, Germany), then passed through Chromatrap gDNA removal columns (Porvair Sciences, King’s Lynn, UK). From here, Qiagen RNeasy Micro Kit instructions were followed for RNA extraction, minus DNase treatment steps. RNA yield was quantified using a Qubit 4 fluorometer (Invitrogen, Thermo Fisher Scientific) and reverse transcription carried out using SuperScript VILO IV mastermix (Invitrogen). cDNA input for qPCR was normalized between samples based on pre- cDNA synthesis RNA concentrations. Taqman Fast Universal PCR master mix and Taqman gene expression assays (Applied Biosystems, Thermo Fisher Scientific) were used for qPCR. The assay IDs of the Taqman assays used are as follows: HS00943570_m1 for RUBCN, Hs_00193519_m1 for MAF. qPCR was performed on a QuantStudio 7 Pro (Applied Biosystems) and analyzed using Design and Analysis v2.6 software (Thermo Fisher Scientific). qPCR was run with 3 technical replicates per sample. Target transcript abundance was normalized to endogenous control (RUBCN) abundance within samples before assessing CRISPRa efficiency by comparing non-targeted and targeted conditions.

[0180] Estimation of Tr1 cell abundances with CIBERSORTx. The CIBERSORTx algorithm was employed to generate a signature matrix of accessible motifs for isolated T r1 cells (Create Signature Matrix module). This involved analyzing motif deviations from bulk ATAC-seq data obtained from isolated CD4+T cells, Tr1 cells, and non-Tr1 (DN) cells within T-allo10 products. Subsequently, the generated signature matrix was applied to predict the composition of Tr1 cells within total CD4+T cells from both healthy donors and post-allo-HSCT patients, as well as following T-allo10 infusion, using bulk AT AC data (Input Cell Fractions module).

[0181] Sample processing and library generation for sc-multiome (AT AC- and RNA-seq). CD4+T cells were purified by FACS as for the bulk ATAC-seq. Nuclei were isolated from 40,000 cells according to the Demonstrated Protocol: Nuclei Isolation for Single Cell Multiome AT AC + Gene Expression Sequencing (10x Genomics CG000365). In summary, cells were washed twice by centrifugation with PBS with 0.04% bovine serum albumin (BSA; Miltenyi Biotec), then lysed with 45 pl chilled Lysis Buffer. After 3.5 min incubation on ice, the lysatewas washed with 50 pl chilled Wash Buffer, supernatants removed, and pellets washed again without disruption by adding 45 pl of chilled Diluted Nuclei Buffer to each tube. Nuclei were then resuspended in 7 pl of Diluted Nuclei Buffer, and an 2 pl aliquot used for manual counting with hemocytometer and 0.4% Trypan Blue solution. After counting, nuclei concentration was adjusted to achieve maximum targeted nuclei recovery and promptly processed for single-cell library generation using the manufacturer’s protocol. Libraries were prepared according to the Chromium Next GEM Single Cell Multiome AT AC + Gene Expression User Guide (CG000338 Rev F). Paired-end 150-bp sequencing was done either on NexSeq 500 using the v2.5high- output kit (sample from healthy donor 10242) or using Illumina NovaSeq 6000, targeting a depth of 250 million read pairs per sample. Over 5,000 nuclei were captured per donor, and samples passed the quality control post sequencing.

[0182] Demultiplexed scRNA- and scATAC-seq fastq files were processed using the Cell Ranger ARC pipeline (version 2.0.0) from 10x Genomics, generating barcoded count matrices for gene expression and ATAC data. The R package ArchR (v 1 .0.1 ) was used for downstream analysis. In ArchR, count matrices for each sample were imported and filtered for barcodes present in both scRNA-seq and scATAC-seq datasets. ArchR quality control included filtering for nuclei with 200-50,000 RNA transcripts, <1 % mitochondrial reads, <5% ribosomal reads, TSS enrichment >4, and >1 ,000 ATAC fragments. Subsequently, the filterDoublets() function in ArchR automatically removed doublets by identifying and eliminating the nearest neighbors of simulated doublets. For visualization and cluster identification we first identify variable peaks and perform dimensionality reduction using the ArchR addlterativeLS()l function. When the Latent Semantic Indexing (LSI) approach is not enough to correct strong batch effect differences, ArchR implements a commonly used batch effect correction tool called Harmony with the function addHarmony(). For visualization we used Uniform Manifold Approximation and Projection (UMAP) embeddings. To do cell type annotation, we use prior knowledge of cell type-specific marker genes and data-driven annotation tool CIPR. Gene expression was estimated from chromatin accessibility data using ArchR gene scores. Pseudo-bulk ATAC replicates were created for each cell type, and chromatin accessibility peaks were called using MACS2. Marker peaks were identified using Wilcoxon pairwise comparisons using ArchR getMarkerFeatures. For motif analysis, ArchR also calculates motif deviations using ChromVAR. Transcription Factor motif enrichment analysis on scATAC peaks was executed using the peakAnnoEnrichment() function in ArchR. Default parameters were employed, utilizing position frequency matrices from Cis-BP. Differential analysis between clusters was conducted using the getMarkerFeatures() function within the GeneScoreMatrix (for accessibility), GeneExpressionMatrix, and MotifMatrix.

[0183] Renal cell carcinoma scATAC data processing and analysis. The fragment files corresponding to each patient from the sc-ATAC-seq dataset published in Kourtis et al wereobtained from the NCBI Gene Expression Omnibus (GEO) repository under accession number GSE181062 and analyzed in the same manner as above. ArchR was used for the analysis of total CD45+cells from all patients. Potential doublets were identified and subsequently removed, followed by batch correction utilizing the Harmony. Following dimensionality reduction and clustering, the cell clusters corresponding to CD20 (MS4A1 )+B cells, CD14+monocytes, and NCAM1+ / FCGR3A+natural killer (NK) cells were excluded from downstream analysis, focusing exclusively on T cells. Specifically, T cell clusters were identified based on the expression of CD3D and CD3E markers. Subsequently, the CD8+T cell subset was selectively removed, retaining only the CD4+T cell population for further analysis.

[0184] Tr1 signature and enrichment. Gene Set Enrichment Analysis (GSEA) (version 4.2.3) was employed to compute functional enrichment scores for Tr1 cells in each patient’s dataset (including healthy donors and CD45+cells from clear-cell renal cell carcinoma (ccRCC) samples). This calculation method was established using the identified TF signatures specific to Tr1 cells. These signatures were determined by overlapping Tr1 -accessible peaks, footprints, and differential TF motif deviations identified by chromVAR. Additionally, we incorporated the TFs contained in the Tr1 cell-regulatory domains identified using FigR. The average motif deviations per cluster were exported and employed in GSEA enrichment analysis. Specifically, a list of 23 distinct Tr1 motifs served as the gene set for this analysis. Each cluster was treated as a unique phenotype, and its enrichment score was determined by comparing it to the remaining clusters with phenotype permutation (1000 permutations). When the resulting FDR q-value equaled zero, it was reported as <0.001 , following the convention established in GSEA that a p-value of zero (0.0) signifies an actual p-value smaller than 1 divided by the number of permutations performed. To validate the specificity of this approach and motif signature, we employed a similar methodology to assess ‘Tr1 enrichment' by reanalyzing B cells and monocytes sourced from the same clear-cell renal cell carcinoma dataset.

[0185] Statistical Analysis. Sample sizes were not predetermined using statistical methods but were comparable to those in previous publications. Except in cases of technical failure, no data were excluded from the analyses, and the experiments were not randomized. Investigators were not blinded during experiments or outcome assessment, and data collection and analysis were not performed blind to experimental conditions. Statistical analysis was carried out in GraphPad Prism 9 and 10 (GraphPad Software, Inc, Boston, MA, USA) or R v.4.0.3. Data analyzed using GraphPad Prism used non-parametric tests that do not assume equal variances between groups: Mann-Whitney or Wilcoxon test for groups of two (unpaired or paired samples, respectively), and Kruskal-Wallis or Friedman analysis of variance (ANOVA) with Dunn’s post hoc test for > 2 groups (independent or dependent samples, respectively), and Spearman rank r for correlation, with testing level (a) of 0.05.Tests were two-sided unless otherwise indicated. As applicable, center bars and whiskers represent the median with interquartile range. The number of biological replicates (i.e., donors), n, is indicated in figure captions for each experiment. The R packages used in the analysis of sequencing data have embedded statistical algorithms that include multiple testing correction for p values. False discovery rate (FDR) of 0.05 was applied. All measurements displayed were taken from distinct samples, i.e. biological replicates.Example 2Methods for generation of engineered Tr1 cells using master transcription factor (TF) lentiviral gene transfer

[0186] It was demonstrated that T-allo10 cells suppress the proliferation of other T cells via CTLA-4 expressed on alloatigen-specific Tr1 cells. The effect of IRF4 KO on the suppression capacity of T-allo10 cells was investigated. A suppression assay showed that WT T-allo10 cells potently suppress the proliferation of responder CD4+T cells, and an IRF4 KO significantly impaired their suppressive capacity (see FIGS. 4A-4P). Because MAF is known to regulate the production of cytokine interleukin (IL)-10, which is a hallmark Tr1 cytokine that contributes to their suppressive activity, we also compared the functional capacity of WT and MAF KO T-allo10 cells to produce IL-10. We observed that Tr1 cells were the dominant source of IL-10 in T-allo10 cells, and that MAF KO significantly impaired the ability of Tr1 cells to produce IL-10.

[0187] Because IL-10 is important for Tr1 cell differentiation, and Tr1 -endogenous IL-10 production and autocrine signaling could aid Tr1 cell differentiation, we decided to overexpress MAF in parental CD4+T cells using CRISPR-activation (CRISPRa). CRISPRa uses inactive, “dead” (d) Cas9 protein and sgRNA to bring synthetic transcription factors to the promoter of the gene of interest, inducing transiently their expression (3-5 days). We were able to obtain high overexpression efficiency with MAF CRISPRa (MAFa) relative to non-targeted CRISPRa (NTa), with over 10-fold induction. In T-allo10 culture, MAFa T-allo10 cells contained higher frequency of Tr1 cells compared to NTa, despite the comparable viability and naive / memory phenotype. These data indicate that permanent overexpression of TFs such as MAF and other key Tr1 TFs such as IRF4 and BATF using lentiviral vectors provides for development of TF- engineered Tr1 cells that can be easily purified using a marker gene and used for cell therapy (FIG. 15). In addition, these TFs can be targeted therapeutically to prevent Tr1 cell differentiation in clinical situations where their presence could be undesirable.

[0188] Cloning and Lentivirus Generation. To generate lentiviral plasmids overexpressing the desired TF (IRF4, BATF, and MAF), the TF cDNA with cloning sites for BamHI and EcoRI at the 5’ and 3’ end, respectively, from commercial vendors is used, see FIG. 14. This cDNA is cloned into the lentiviral backbone to generate a third generation, bicistronic lentiviral vectorplasmid that contains the marker gene (truncated CD19 or other) for selection and tracking of edited cells. Third generation lentivirus is produced by transfecting HEK 293T cells using calcium phosphate coprecipitation or other methods such as polyethanolamine or lipofectamine. Cells are transfected with transfer plasmid containing our TF of interest, packaging plasmids pMDLg / pRRE and pILVVOI , and envelope plasmid VSV-G. Plasmid pAdVAntage is co-transfected to increase viral titer. Virus is concentrated using ultracentrifugation or other methods and titer is assessed by ddPCR and transduction of HEK 293T cells with limiting dilutions of virus followed by marker (dCD19 or other) readout on FACS.

[0189] Alternatively to bicistronic vectors, bidirectional vectors can also be used to overexpress IRF4, MAF and BATF in CD4+T cells to induce Tr1 cells. Our group has generated and characterized two clinically optimized bidirectional vectors. First vector, containing the FOXP3 gene under the EF1 a promoter and truncated (A)NGFR marker gene under the mCMV promoter (Sato Y et al, Clin Transl Immunol 2020), is already being used in a clinical trial (ClinicalTrials.gov ID: NCT05241444). Second vector, containing the ILIO gene under the hPGK promoter and ANGFR marker gene under the mCMV promoter (Liu JMH et al, Cytotherapy 2021 ), has undergone extensive in vitro and in vivo characterization. Both vectors are highly efficient in transducing human CD4+T cells and have undergone modifications to comply with the US Federal Drug Administration (FDA) guidelines and address any potential safety concerns, including removal of the N-terminal hemagglutinin (HA) tag to avoid potential immunogenicity, removal of the LacZ and F1 origin cassettes in the vector backbone to reduce the plasmid size, and replacement of the ampicillin selection marker by kanamycin one to remove the potential risk B-lactam analog allergy. The same design strategy is used to generate IRF4, MAF, and BATF bidirectional lentiviral vectors, or optimize bicistronic vectors for clinical use.

[0190] Transduction of CD4+T Cells and Generation of Polyclonal and (allo)antigen-Specific CD4IRF4, CD4BATFand CD4MAFTr1 cells (briefly, CD4TFTr1 cells where TF is either IRF4, MAF or BATF). Peripheral blood mononuclear cells (PBMCs) are isolated from human peripheral blood using Ficoll-Hypaque density gradient. NaTve or polarized CD4+T cells (a.k.a. parental CD4+T cells) are purified from PBMCs using FACS or magnetic bead isolation based- methods. Samples with > 95% purity are used for downstream applications.

[0191] To generate polyclonal CD4TFTr1 cells, the CD4+T cells are activated with soluble anti-CD3 monoclonal antibody (mAb; 30 ng / mL), anti-CD28 mAb (1 pg / mL) and recombinant human (rh) IL-2 (50 U / mL), or with rh cytokines (IL-2, IL-7 and IL-15 alone or in various combinations) for > 48h before transduction with the lentiviral vector at multiplicity of infection (MOI) of 20 (range: 10 - 100 MOI). Successfully transduced cells are purified using the markergene expression via FACS or magnetic cell isolation between day 5-14 post-transduction, then expanded using a polyclonal T cell stimulus targeting CD3 and CD28 such as TransAct (Sato Y et al, Clin Transl Immunology 2020) or feeder cells (Liu JMH et al, Cytotherapy 2021 ) for additional 7-14 days. At the end of the expansion cycle, the cells are assessed for Tr1 phenotype as defined by LAG3, CD49b, CTLA-4 ± granzyme B expression, stability of Tr1 phenotype through a secondary stimulation, suppressive and cytotoxic functions as described (Chen P and Cepika AM et al, Sci Transl Med 2021 ; Liu JMH et al, Cytotherapy 2021 ), or a gene expression signature as disclosed herein.

[0192] To generate alloantigen-specific CD4TFTr1 cells, parental CD4+T cells are cultured at 10:1 ratio with allogeneic tolerogenic dendritic cells called DC-10 (production described in Gregori et al. 2010, Blood). After > 48h of culture, T cells expressing surface proteins that indicate antigen-specific stimulation (0X40 ± PD-L1 ± CD137 ± CD154 ± LAG3) are sorted using FACS or magnetic cell isolation to enrich for alloantigen-specific CD4+T cells. These cells are transduced with IRF4, BATF, or MAF lentivirus at a MOI of 20 (range: 10 - 100). After 5-14 days of culture, the successfully transduced T cells are purified using FACS or magnetic cell isolation based on the expression of the marker gene. Purified transduced T cells are expanded over the course of 7-14 days as described above. After expansion, cells are assessed for Trl phenotype as defined by LAG3, CD49b, CTLA-4 ± granzyme B expression, stability of Tr1 phenotype through a secondary stimulation, cytotoxic function, and alloantigen- specific suppressive capacity.

[0193] To generate antigen-specific CD4TFTr1 cells, purified CD4+T cells are cultured at 10:1 ratio with autologous DC-10 cells pre-loaded with the antigen of choice (method of choice for antigen-loading depends on the desired antigen; it can encompass antigen pulsing such as described in Pellerin L et al, J Allergy Clin Immunol 2018, doi: 10.1016 / j.jaci.2017.05.045, or lentiviral peptide loading such as described in Norton et al Mol Ther 2020), then processed anc characterized as outlined above, in the section for generation of alloantigen-specific CD4TFTr1 cells.

[0194] Alternatively, parental CD4+T cells are activated and transduced as described for generation of polyclonal CD4TFTr1 cells, but expanded using allogeneic DC-10 or antigen- loaded DC-10 instead of using anti-CD3 / CD28 stimulus such as TransAct (Sato Y et al, Clin Transl Immunology 2020) or feeder cells (Liu JMH et al, Cytotherapy 2021 )

[0195] Overall, the methods described generate stable and scalable 2ndgeneration engineered Tr1 cells, which suppress the proliferation and function of effector T cells.

[0196] Lentiviral vector maps are shown on FIG. 14. Simplified method of Tr1 cell engineering using lentiviral gene transfer of master TFs is in FIG. 15.Example 3Identification of a unique Tr1 cell gene expression signature

[0197] Analysis of T-allo10 cells with single-cell (sc) multiome comprising AT AC- and RNA- seq analysis enabled discrimination between Tr1 cells and other activated CD4+T cells that share some similar genes, with subsequent identification of a unique Tr1 gene expression signature. This Tr1 gene expression signature can be used with popular, publicly available platforms, e.g. scType (see lanevski et al. (2022) / Vat Commun 13, 1246) to determine the presence of Tr1 cell clusters in CD4+ T cell populations across multiple disease indications, in both T-allo10 treated patients and other diseases.

[0198] A comparison of gene expression between Tr1 cells vs all other cells in the new sc- multiome data reveals a Tr1 -distinct transcriptional profile. These T r1 cells analyzed on singlecell level also have a distinct epigenetic profile (see FIGS. 16A and 16B). The signature takes genes that overlap between Tr1 signature identified by sc-multiome and Tr1 signature identified by high-depth bulk RNA-seq in an independent dataset; and may further include “penalty” genes not expressed by Tr1 cells, but present in related CD4+ cells.

[0199] Upregulated genes comprise one or more of the following: CCR5; LGMN; RGS9; RDH10; IL10; G0S2; PTMS; ATP10D; P4HA2; TOX2; PHEX; KIAA1217; RDH10-AS1 ; MYO1 E; GZMA; MAF; PARD6G; ZC3H12C; GFOD1 ; CTLA4; SOCS6; COL5A3; FOXB1 ; ZDHHC14; CCR2; CCL4; LINC00299; LINC01146; SMOX; CXCR6; HAVCR2; IL21 ; CSF2; VDR; LINC01480; PDCD1 ; LAG3; ITGA2; CST7; SETBP1 ; GZMB; CD38; LYN; EVA1 B; CCL3; HOPX; IFNG; TNFRSF4; TRIB1 ; SLC27A2; NCALD; MIR4435-2HG; MZB1 ; DUSP6; DTHD1 ; TNFRSF18; TNFRSF1 B. Down-regulated genes comprise one or more of the following: RNF144A; SCML1 ; AK5; TAF4B; RIMKLB; DENND5A; MAN1 C1 ; APBA2; MPP7; PCSK5; LEF1 ; TCF7; NR3C2; and DHRS3.

[0200] Genes that may be included as a penalty to better discriminate from other CD4+ T cell subsets include, for example, FOXP3; RORC; BCL6; GATA3; IL23R; CCR6; CXCR5; and IKZF2. Genes of interest that are transcription factors include TOX2, MAF, FOXB1 , VDR, LEF1 , and TCF7.

[0201] In specific examples, the Tr1 gene list was used with the scType platform to identify Tr1 cells in single-cell RNA-seq datasets of CD4+ T cells. These samples included single-cell RNA-seq data of peripheral blood CD4+ T cells from a patient treated with T-allo10 cells.

[0202] It was demonstrated by this analysis that a Tr1 enrichment in peripheral blood CD4+ T cells from multiple myeloma patients of Tr1 -like cells disappear after CAR T therapy.

[0203] This analysis identified Tr1 cell enrichment in single-cell data of CRC patients with microsatellite instability.

[0204] This analysis identified Tr1 cell enrichment in single-cell data of patients with triple negative breast cancer.

[0205] As discussed in Example 2, a Tr1 -specific TF signature can identify Tr1 cells in solid tumors. In an analysis of an sc-ATAC-seq dataset from peripheral blood, tumor tissue and adiacent normal tissue of 8 patients with clear-cell renal cell carcinoma samples (ccRCC), 4 patients in stage 1 A and 4 patients in stage 1 B. By integrating the peripheral blood and tissue samples of 8 ccRCC patients, CD4+T cell clusters were identified. The Tr1 cluster had highly accessible motifs for TBX21 , MAF, BATF and IRF4, but closed motifs characteristic for FOXP3+Tregs, Th2, Th17 cells, and Tfh cells (FOXP3, GATA3, RORC, and BCL6, respectively). It was shown that a TF signature, can be used to identify Tr1 cells within tumorresident CD4+T cells.References1 . Roncarolo MG, Gregori S, Bacchetta R, et al. The Biology of T Regulatory Type 1 Cells and Their Therapeutic Application in Immune-Mediated Diseases. Immunity. 2018;49:1004- 19.2. Groux H, O'Garra A, Bigler M, et al. 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[0206] The preceding merely illustrates the principles of the invention. It will be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. The scope of the present invention, therefore, is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims.

Claims

THAT WHICH is CLAIMED IS:1 . An in vitro method of altering differentiation of Tr1 T cells, the method comprising: a) providing naive or polarized CD4+T cells; b) activating the CD4+T cells; c) modifying the CD4+ cells to increase expression of one or more of the transcription factors IRF4, MAF and BATF; and d) expanding the transduced cells.

2. The method of claim 1 , wherein the T cells are human T cells.

3. The method of any of claims 1-2, wherein IRF4 is upregulated.

4. The method of any of claims 1-3, wherein MAF is upregulated.

5. The method of any of claims 1-4, wherein BATF is upregulated.

6. The method of any of claims 1-5, wherein each of IRF4, MAF and BATF are upregulated.

7. The method of any of claims 1-6, wherein the cells are modified in step (c) by transducing with a vector encoding one or more of IRF4, MAF and BATF operably linked to a promoter active in the cells.

8. The method of any of claims 1-6, wherein the cells are modified in step (c) by genomic modification with a recombinase.

9. The method of any of claims 1-8, wherein cells modified to increase expression of one or more of the transcription factors IRF4, MAF and BATF are differentiated to Tr1 cells in vitro in a mixed lymphocyte reaction that contains: allogenic-tolerogenic dendritic cells, referred to as DC-10, CD4+ T cells, and IL-10, which induces allo-antigen specific anergic T cells.

10. The method of any of claims 1 -9, wherein the cells are modified to modulate expression of one or more of the transcription factors VDR, FOXB1 and TOX2.

11. The method of claim 9, wherein the allo-antigen specific anergic T cells are administered to an individual for down-regulation of an immune response.

12. The method of any of claims 1 -1 1 , wherein the T r1 cells are selected for expression of LAG3 and CD49b.

13. An engineered cell population produced by the method of any of claims 1 -12.

14. A therapeutic method, comprising introducing into a subject in need thereof a therapeutically effective quantity of an engineered cell population of claim 13.

15. A method of altering differentiation of Tr1 T cells, the method comprising: modifying CD4+ cells to downregulate expression of one or more of the transcription factors IRF4, MAF and BATF.

16. The method of claim 15, wherein the T cells are human T cells.

17. The method of any of claims 15-16, wherein IRF4 is downregulated.

18. The method of any of claims 15-16, wherein MAF is downregulated.

19. The method of any of claims 15-16, wherein BATF is downregulated.

20. The method of any of claims 15-19, wherein each of IRF4, MAF and BATF are downregulated.21 . The method of any of claims 15-20, wherein the cells are transiently modified.

22. The method of any of claims 15-20, wherein the cells are modified by genomic modification with a recombinase.

23. A method of identifying human Tr1 cells in a population, the method comprising analyzing single cell transcription profiles for the presence of a Tr1 profile, comprising one, two, three or more of the upregulated genes CCR5; LGMN; RGS9; RDH10; IL10; G0S2; PTMS; ATP10D; P4HA2; TOX2; PHEX; KIAA1217; RDH10-AS1 ; MYO1 E; GZMA; MAF; PARD6G; ZC3H12C; GFOD1 ; CTLA4; SOCS6; COL5A3; FOXB1 ; ZDHHC14; CCR2; CCL4; LINC00299; LINC01 146; SMOX; CXCR6; HAVCR2; IL21 ; CSF2; VDR; LINC01480; PDCD1 ; LAG3; ITGA2; CST7; SETBP1 ; GZMB; CD38; LYN; EVA1 B; CCL3; HOPX; IFNG; TNFRSF4; TRIB1 ; SLC27A2; NCALD; MIR4435-2HG; MZB1 ; DUSP6; DTHD1 ; TNFRSF18; TNFRSF1 B;and one, two, three or more of the down-regulated genes: RNF144A; SCML1 ; AK5; TAF4B;RIMKLB; DENND5A; MAN1 C1 ; APBA2; MPP7; PCSK5; LEF1 ; TCF7; NR3C2; and DHRS3.

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