Single-cell and multi-omic characterization of ex vivo expanded ASTRLs from stable kidney transplant recipients reveals a regulatory T cell phenotype

Single-cell and multi-omic characterization of ex vivo expanded ASTRLs from stable kidney transplant recipients reveals a regulatory T cell phenotype | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell and multi-omic characterization of ex vivo expanded ASTRLs from stable kidney transplant recipients reveals a regulatory T cell phenotype Sudipta Tripathi, Amélie M Julé, Zhu Zhuo, Brittany L Schreiber, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7464625/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Therapeutic application of ex vivo expanded regulatory T cells is a promising approach to prolong allograft survival. In this work we performed a detailed characterization of a preclinical heterogenous antigen specific T enriched regulatory cell line (ASTRL) expanded ex vivo from PBMC of stable kidney transplant recipients. We used three different approaches: scRNA-seq, flow cytometry and mass cytometry, to compare pre-expansion PBMC to post-expansion ASTRL. Results show the CD4 + T cell compartment in ASTRL clonally expanded in response to donor antigen stimulation and showed decreased TCR diversity. ASTRL CD4 + T cells demonstrated a Treg associated transcriptome with upregulated CD39 and TIGIT together with other classical Treg genes like IL2RA, IKZF4, TNFRSF9, CXCR6, DUSP10 and HLA-DRA. Comparison of differentially expressed genes (DEGs) in ASTRL with classical Treg gene signatures showed strong overlap of genes associated with both peripheral and uterine Tregs together with a Th2-like Treg transcriptomic profile. In conclusion the CD4 + T cell compartment of ASTRL acquire a regulatory T cell transcriptomic profile in response to donor antigen specific stimulation. This suggests a promising approach towards the development of a regulatory cell therapy in organ transplantation. Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Regulatory T cell (Treg) therapy has emerged as a promising strategy to promote and maintain immunological tolerance in organ transplantation and autoimmune diseases. However, the clinical application of Treg-based therapies faces several challenges. Notably, variability in ex vivo expanded Treg products, combined with the lack of standardized identifying markers and functional assays, hampers the consistency and reproducibility of these therapies. Another significant limitation lies in the cellular heterogeneity of Treg preparations. Due to technical constraints in current isolation methods, expanded Treg products often include other T cell populations, such as conventional T cells, memory T cells, and T follicular helper cells. This heterogeneity is widely regarded as a potential barrier to therapeutic efficacy and safety. Furthermore, attempts to eliminate non-Treg populations often lead to increased manufacturing complexity and costs, ultimately limiting accessibility. Despite these concerns, cellular heterogeneity may also offer therapeutic advantages. Recent studies suggest that functionally distinct immunoregulatory subsets can work cooperatively to achieve layered and context-specific immune suppression( 1 ),( 2 ). For example, in the tumor microenvironment, regulatory T cells and myeloid-derived suppressor cells exhibit crosstalk that enhances immunosuppressive efficacy beyond what is observed in homogeneous populations ( 3 ). Synergistic interactions are physiologically more relevant as cells rarely interact in isolation in vivo. This “division of labor” allows for more nuanced and robust control of immune responses. Tregs employ multiple mechanisms of suppression, which can function independently or synergistically depending on the immune context. Importantly, the failure of a single mechanism does not necessarily compromise overall immunoregulation( 4 ). Additionally, distinct regulatory memory cell subsets can provide both immediate and long-term tolerance( 5 ), suggesting that diversity within the regulatory compartment may be essential for sustained immune control. Harnessing these synergies among diverse regulatory populations may enable the development of integrated, flexible, and durable cell therapy products that better mimic physiological immune regulation in transplantation and autoimmunity( 6 ). Single-cell genomic technologies offer a powerful means to dissect cellular heterogeneity and gain deeper insights into complex cell populations. Recent single-cell RNA sequencing studies have revealed that Tregs are not a uniform population but consist of distinct subsets with specialized suppressive functions and varying degrees of stability( 7 – 10 ). These findings highlight the importance of understanding Treg heterogeneity in the context of developing effective cell-based therapies( 11 ). In our previous work, we demonstrated that donor alloantigen-specific regulatory T cell clones could prevent chronic rejection and prolong graft survival in rat models of kidney transplantation( 12 , 13 ). We subsequently identified these regulatory clones in human kidney transplant recipients and confirmed their functional role in promoting allograft tolerance( 14 ) ( 15 ). Building on these findings, we successfully expanded donor alloantigen-specific immunoregulatory T cells ex vivo from both rejecting and stable kidney transplant recipients. This led to the development of antigen-specific T cell-enriched regulatory lines (ASTRLs) derived from kidney transplant recipients with stable function and intended for future development as individualized, autologous cell therapy products. These ASTRLs exhibit immunoregulatory phenotypes and function through the adenosinergic pathway( 16 ). Preliminary phenotypic analyses have shown enrichment of T cells expressing canonical regulatory markers( 16 ). However, as ASTRLs are derived from peripheral blood mononuclear cells (PBMCs) rather than a purified CD4⁺CD25⁺ Treg population, they exhibit cellular heterogeneity. Given the potential impact of this heterogeneity on therapeutic performance, we employed a multiomics approach—including single-cell RNA sequencing, CyTOF, and flow cytometry—to characterize ASTRLs in greater depth. Our objective is to determine whether cellular diversity within ASTRLs enhances their capacity for multifaceted immune regulation and could be leveraged to improve their efficacy as a potent cell therapy for transplantation tolerance. Results Upregulation of CD39 Expression in ASTRLs from Stable Kidney Transplant Recipients Peripheral blood samples were collected from five stable kidney transplant recipients (KTRs). Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation and cryopreserved prior to expansion. Antigen-specific T cell-enriched regulatory lines (ASTRLs) were subsequently generated by stimulating these PBMCs ex vivo with donor-specific antigens in the presence of low-dose IL-2 (Fig. 1 A). Following expansion, ASTRLs were harvested and stored frozen until further analysis. Preliminary phenotypic assessment using flow cytometry demonstrated that ASTRLs contained both CD3 + T cells and CD3 − non-T cell subsets. Within the CD3 + compartment, both CD4 + and CD8 + T cell subsets were notably enriched following expansion. Similarly, a substantial CD56 + population was identified within the CD3 − subset, which was also increased post-expansion (Fig. 1 B). Given the known role of the adenosinergic pathway in immunosuppression mediated by ASTRLs( 16 ), we specifically analyzed the expression of CD39, the rate-limiting enzyme of this pathway( 17 , 18 ),( 19 ). CD39 expression was consistently elevated in the expanded ASTRLs compared to their pre-expansion PBMC counterparts across all five patients. Functionally, this increased expression corresponded to significantly enhanced extracellular ATP (eATP) hydrolysis activity in the ASTRLs (Fig. 1 C). This relationship between higher CD39 expression and increased eATP hydrolysis was further confirmed in additional patient samples (Fig. 1 D). These results indicate that, despite patient-to-patient variability, ex vivo antigen-specific expansion reliably produces a T cell-enriched population with elevated, functionally active CD39 expression. Previous studies have demonstrated that inhibition of CD39 reduces the immunosuppressive capacity of ASTRLs. Additionally, the critical regulatory function of CD39 has been well-documented in tumor immunology( 18 – 20 ) ( 21 ) and is increasingly recognized as important in the context of organ transplantation( 22 – 24 ). ASTRLs Are Enriched in T Cells but Exhibit Cellular Heterogeneity Previous observations indicated that donor antigen-specific expansion of PBMCs ex vivo generates heterogeneous ASTRL populations, predominantly enriched in T cells. This cellular heterogeneity can complicate the clinical translation of regulatory cell therapies, making it essential to thoroughly characterize the cellular composition and variability within ASTRLs. To define the cellular diversity of ASTRLs comprehensively, we performed single-cell RNA sequencing along with T and B cell immune repertoire sequencing on paired PBMC and ASTRL samples from five kidney transplant recipients (KTRs). This integrative approach enabled simultaneous characterization of cellular phenotypes and antigen-specific immune receptor diversity. Global clustering analysis of single-cell gene expression data revealed seven distinct cellular clusters, identified as monocytes, dendritic cells (DCs), B cells, CD4 + T cells, CD8 + T cells, NK/NKT/MAIT/γδT cells, and other T cell subsets (Fig. 2 A). Quantitative comparison between pre-expansion PBMCs and post-expansion ASTRLs using Propeller ( 25 ) testing showed significant shifts in cell composition. Specifically, ASTRLs were markedly enriched in lymphoid populations—particularly CD4 + T cells ( p = 0.01735546, FDR = 0.02429764)—accompanied by a notable reduction in B cells ( p = 0.00118788, FDR = 0.00207878) and myeloid populations ( p = 1.09E-05, FDR = 7.65E-05) (Fig. 2 B). However, detailed individual analyses highlighted considerable variability among patients, especially regarding the proportions of different T cell subsets and NK cell populations within ASTRLs (Fig. 2 C, D). The identity of these cell clusters was validated by differential gene expression analyses of known canonical markers, visualized clearly in dot plots (Fig. 2 E) Further validation using CyTOF analysis on additional PBMC and ASTRL samples from eight KTRs supported these findings (Fig. 3 A). Visualization by t-distributed Stochastic Neighbor Embedding (tSNE) confirmed enrichment of the CD4 + T cell subset (red cluster) and smaller increases in CD8 + T cells (blue cluster) and CD56 + NK cells (purple cluster) in ASTRLs. In contrast, both B cells and monocytes were significantly depleted compared to pre-expansion PBMCs (Fig. 3 A). Additionally, CyTOF analyses showed significant upregulation of surface CD39 specifically in the CD3 + T cell compartment within ASTRLs. This contrasts with pre-expansion PBMCs, in which CD39 expression was primarily detected in the CD19 + B cell compartment—significantly diminished after ASTRL expansion (Fig. 3 B). Collectively, these data confirm that donor antigen-specific ex vivo expansion selectively enriches T cell populations within ASTRLs while introducing considerable heterogeneity, which varies between individual patients. Donor Alloantigen Drives Clonal Expansion Primarily in the CD4 T Cell Compartment of ASTRLs To investigate how donor-specific antigen stimulation affects the clonal composition of ASTRLs, we analyzed the T cell receptor (TCR) repertoires in paired PBMC and ASTRL samples from the five kidney transplant recipients (KTRs). UMAP visualization revealed prominent clonal expansions predominantly within the CD4 + T cell clusters across all five subjects. In contrast, fewer expansions were observed within CD8 + T cell clusters (Fig. 4 A). We also assessed B cell receptor (BCR) repertoires and found no significant clonal expansion in the small B cell clusters that remained in some ASTRL samples (Supplementary Fig. 1). Further analyses comparing TCR diversity between PBMC and ASTRL samples highlighted a consistent reduction in diversity post-expansion. The frequency distributions of TCR V gene usage (TRBV and TRAV) demonstrated selective enrichment of specific clonotypes in the ASTRLs, indicative of antigen-driven clonal expansion (Fig. 4 B). Quantitative comparisons further revealed substantial oligoclonality within ASTRLs, with a dramatic reduction in unique clonotypes—defined by exact nucleotide sequences and complete V(D)J gene usage—from approximately 100% unique clonotypes in PBMCs to around 20–40% in ASTRLs (Fig. 4 C). Additionally, analysis of TCR V gene diversity clearly demonstrated reduced V gene usage in ASTRLs compared to PBMCs for both beta and alpha chains, reflecting antigen-driven selective pressures (Figs. 4 D, 4 E). Statistical indices for diversity—including Shannon entropy, Simpson index, Chao, ACE, and Inverse Pielou index—uniformly confirmed this significant reduction in TCR diversity in ASTRLs compared to their corresponding PBMC samples (Fig. 4 F). Lower Shannon and Inverse Simpson indices indicated reduced overall TCR diversity. Decreased scores for Chao and ACE indices suggested a lower richness of unique clonotypes, whereas a higher Inverse Pielou index indicated dominance of a limited number of clones. Collectively, these findings demonstrate that donor antigen-specific expansion predominantly promotes CD4 + T cell clonal expansions, resulting in a narrowed TCR repertoire within ASTRLs, although significant inter-individual variability in the exact clonotypes expanded was observed. Transcriptional Signature of the CD4 T Cell Compartment in ASTRLs To further characterize the immunoregulatory properties of ASTRLs, we performed a pseudobulk gene expression analysis specifically focused on the CD4 + T cell compartment in matched PBMC and ASTRL samples from the five kidney transplant recipients (KTRs). Principal Component Analysis (PCA) clearly separated the ASTRL samples from their corresponding PBMC controls, indicating that CD4 + T cells in ASTRLs acquire a distinct transcriptional profile following donor antigen-specific stimulation (Fig. 5 A). Hierarchical clustering confirmed consistent and distinct transcriptional patterns between PBMC and ASTRL groups (Fig. 5 B). Differential gene expression analysis between PBMC and ASTRL CD4 + T cell populations identified a total of 2665 significantly altered genes (absolute log2 fold-change > 1.0, adjusted p-value < 0.05). Of these, 592 genes were significantly upregulated, and 699 genes were significantly downregulated in ASTRLs compared to PBMCs (Fig. 5 C). The heatmap of the top 30 differentially expressed genes clearly distinguished ASTRL samples from their PBMC counterparts (Fig. 5 D). Gene Set Enrichment Analysis (GSEA) ( 26 ) against the MSigDb Hallmarks database ( 27 ) and previously defined Treg-specific gene sets ( 28 ) revealed significant enrichment in pathways associated with Treg function, TNF signaling, and mTORC1 signaling in the ASTRLs (Figs. 5 E, 5 F). Notably, a Th2-like lineage signature was evident, characterized by the upregulation of genes such as Gata3, IL-4, and IL-13, along with downregulation of RORC and CCR6 (Fig. 5 G). Further analysis of the significantly upregulated genes revealed known markers associated with Treg suppressive functions, including IL2RA (CD25), ENTPD1 (CD39), TIGIT, HAVCR2, LGALS1, LGALS8, and LAYN ( 29 ). Additionally, several transcription factors critical for Treg function—such as RUNX2, IKZF4, BATF, BATF3, ETV7, NFIL3, and VDR—were also upregulated in ASTRLs (Fig. 5 H). In summary, CD4 + T cells in donor antigen-expanded ASTRLs display a distinct immunoregulatory transcriptional signature, characterized by a Th2-like Treg phenotype and significant upregulation of genes associated with suppressive functions. CD4 T Cell Compartment of ASTRLs Exhibits Key Regulatory Features We further examined the CD4 + T cell compartment in ASTRLs to assess the expression of established canonical regulatory T cell (Treg) markers, including CD25 (IL2RA), Foxp3, Helios (IKZF2), GITR (TNFRSF18), CTLA4, TIGIT( 30 ), PD-1, LAG3( 31 ) , ( 32 ), CD226( 33 ), and genes related to the adenosinergic pathway( 34 ), particularly CD39 (ENTPD1) and CD38. We used single-cell RNA sequencing (scRNA-seq), flow cytometry, and mass cytometry (CyTOF) for this detailed analysis. Single-cell RNA-seq data demonstrated consistent increases in gene expression levels of CD25, GITR, TIGIT, and CD39 in all ASTRL samples compared to PBMCs. However, expression of markers such as Helios, CTLA4, PD-1, and CXCR3 varied considerably between individual samples (Figs. 6 A, 6 B). CXCR3, classically considered as a Th1 specific gene, is consistently expressed by ASTRLs and is an important chemokine receptor for Tregs ( 35 ) ( 36 ). Protein-level assessments via flow cytometry and CyTOF confirmed these observations, showing that expression of these regulatory markers was similarly elevated in the CD4 + T cell compartment of ASTRLs (Figs. 6 C, 6 D). Although individual variability was noted in the expression levels of specific markers, the overall phenotype consistently reflected upregulation of multiple canonical Treg markers. Mass Cytometry Reveals Distinct Phenotypic Signatures in ASTRLs To provide deeper insight into the phenotypic differences between PBMC and ASTRL populations, we performed comprehensive mass cytometry analyses. Unsupervised clustering using tSNE on samples from six PBMCs and ten ASTRLs showed clear separation between the two groups (Fig. 7 A), with ASTRLs demonstrating a marked enrichment of CD4 + T cells (Fig. 7 B). Smaller populations of CD8 + T cells and CD56 + NK cells were also present in ASTRLs, while B cells and monocytes were notably depleted (Figs. 7 C–E). While all ASTRL samples showed enrichment in CD4 + T cells, the proportions of CD8 + and NK cells varied widely among individuals (Fig. 8 ), confirming the inter-patient heterogeneity also observed in scRNA-seq data. Using FlowSOM( 37 ) clustering, we identified 20 distinct meta-clusters (MCs) across PBMCs and ASTRLs (Figs. 9 A, 9 B). Relative abundance of each MC was visualized via box plots (Fig. 9 C), and differential abundance analysis using edgeR( 38 ) identified 12 significantly different clusters (Fig. 9 D). Metaclusters significantly enriched in ASTRL samples predominantly included subsets of CD4 + T cells (MC03, MC04, MC08, MC11), along with specific CD8+ (MC13), CD56 + NK cells (MC15), and CD11b + subsets (MC19) (Fig. 9 E). Conversely, PBMC samples predominantly featured CD8 + (MC09, MC10), CD19 + (MC14), and a different CD11b + (MC17) subsets (Fig. 9 F). We further compared regulatory marker expression across matched clusters. The CD8 + T cell subset enriched in ASTRLs (MC13) exhibited higher levels of CD39, TIGIT, CTLA4 (CD152), CXCR3 (CD183), CD38, Helios, and Foxp3 compared to the CD8 + subset in PBMCs (MC09) (Fig. 10A). Similarly, the CD11b + subset in ASTRLs (MC19) showed elevated expression of these same markers along with CD155, CD85k, and CD304 (Fig. 10B). The CD56 + NK cell cluster (MC15) in ASTRLs also expressed CD39, TIGIT, CD183, CD38, and Helios. Among the CD4 + subsets, both MC08 and MC04 expressed high levels of CD39, TIGIT, CTLA4, CD183, CD38, Helios, CD25, Foxp3, and PD-1 (Fig. 10C). CXCR3 (CD183) is consistently expressed by ASTRL subsets at the protein level similar to the earlier observation at the gene level. Although not significantly more abundant in ASTRLs, meta-cluster MC01 (a CD3 + T cell population present in both PBMCs and ASTRLs) exhibited distinct phenotypic changes after expansion. ASTRL-derived MC01 cells had increased median expression of CD39, TIGIT, CD183, CC R7 (CD197), and CD25 compared to their PBMC counterparts (Fig. 10D).Figure 10: Protein expression levels of regulatory markers in PBMC and ASTRL metaclusters. A. Heatmap of regulatory markers showing comparison of CD8 MCs between PBMC and ASTRL samples. B. Heatmap of regulatory markers showing comparison of CD11b MCs between PBMC and ASTRL samples. C. Heatmap of regulatory marker expression in MCs 4, 8 and 15 distinctive to ASTRL samples. D. Heatmap of regulatory marker expression in MC1 showing similar abundance in PBMCs and ASTRLs. These results demonstrate that ASTRLs—regardless of lineage—consistently contain subsets that express a core set of regulatory markers: CD39, TIGIT, CD183, CD38, and Helios. To quantitatively assess marker expression differences, we applied CITRUS ( 39 ) analysis to CyTOF data from 16 total samples (6 PBMCs and 10 ASTRLs; antibody panel listed in Supplementary Table). Using Significance Analysis of Microarrays (SAM), we identified significantly higher expression of CD39, CTLA4 (CD152), CD25, CD183, OX40 (CD134), ICOS (CD278), CD226, and Helios in ASTRL samples compared to PBMCs (Fig. 11 A). Figure 11 : CITRUS analysis and median expression levels of regulatory markers in the T cell subset of PBMCs and ASTRLs. A. Median expression levels of regulatory markers between the CD3 + T cells of PBMC (n = 9) and ASTRL (n = 13) samples. B. Median expression levels of regulatory markers between the CD4 + T cells of PBMC and ASTRL samples. These differences were consistent across both CD4 + (Fig. 11 B) and CD8 + (Fig. 11 C) T cell subsets. Interestingly, although TIGIT was frequently co-expressed with CD39 (Fig. 11 D), its median expression was not significantly different between groups in CITRUS analysis. Despite variability between individual samples, the combined results from scRNA-seq, flow cytometry, and CyTOF consistently reveal that ASTRLs upregulate multiple canonical Treg-associated markers. These include CD25 (IL2RA), Foxp3, Helios (IKZF2), GITR (TNFRSF18), CTLA4, TIGIT, CD226, PD-1, LAG3, as well as adenosinergic pathway components CD39 (ENTPD1) and CD38. Notably, a core immunoregulatory signature—featuring CD25, CD39, TIGIT, CD183, and Helios—was consistently observed across several ASTRL subsets, highlighting the robust regulatory phenotype of these donor antigen-expanded cells. ASTRL CD4 T Cells Share Features of Both Peripheral and Uterine Tregs Given the heterogeneous nature of ASTRLs, the variability observed across patients, and the minimal difference in Foxp3 expression between PBMCs and ASTRLs within the CD4 + compartment (Fig. 6 C), these cells do not qualify as “classical Tregs.” However, ASTRLs exhibit significant regulatory capacity( 12 ) , ( 13 ) , ( 16 ) and upregulate several key immunosuppressive markers—such as CD39, TIGIT, and CXCR6—at both gene and protein levels. These markers are also associated with mouse and human Tregs. Considering the limited reliability of Foxp3 as an exclusive marker for human Tregs and the known heterogeneity within Treg subsets, we compared the differentially expressed genes (DEGs) in ASTRLs to established gene signatures of human peripheral Tregs and Th cells. Specifically, we compared ASTRL gene signatures with four different human Treg datasets to characterize the immunoregulatory profile of the CD4 + compartment in ASTRLs. Additionally, we compared ASTRL DEGs to those of human Tr1 cells( 40 , 41 ), noting that both ASTRLs and Tr1 cells are antigen-specific, peripheral in nature, and lack Foxp3 expression. In the dataset from Hollbacher et al. ( 42 ), peripheral CD4 + CD25 + and CD4 + CD25– cells from healthy controls were used to define Treg and Th gene signatures. When ASTRL DEGs were compared with this dataset, we observed an upregulation of Treg signature genes and downregulation of Th signature genes. Volcano plots show the overlap of the Hollbacher gene set with ASTRL DEGs (Fig. 12 A). Of the 225 Treg genes, 62 were upregulated and 11 were downregulated in ASTRLs. Out of 360 Th genes, ASTRLs downregulated 111 and upregulated 21 (Fig. 12 B), indicating that ASTRLs resemble peripheral Tregs more than conventional Th cells. Ferraro et al. ( 43 ) identified a Treg gene signature by comparing CD4 + CD25 + CD127– (Tregs) to CD4 + CD25 – CD127 + (Tconv) cells. Comparison of ASTRL DEGs (shown in grey) with Ferraro’s Treg genes (shown in purple) via volcano plots (Fig. 12 C) showed 39 genes upregulated and 54 downregulated in both datasets (Fig. 12 D). A combined analysis of the Hollbacher, Ferraro, and ASTRL datasets identified 17 commonly upregulated genes in ASTRLs. Interestingly, the Hollbacher and Ferraro Treg signatures do not fully overlap—only 48 genes are shared between the two, despite both being derived from peripheral Tregs of healthy donors (Fig. 12 E). Still, 17 DEGs were common across all three signatures. When compared with uterine Treg (uTreg) gene signatures from Wienke et al. ( 29 ), ASTRLs showed substantial overlap: 61 upregulated and 15 downregulated genes (Fig. 12 F). ASTRLs shared more DEGs with uTregs than with peripheral Tregs. Seven Treg-associated genes— IL2RA, IKZF4, ENTPD1, TNFRSF9, CXCR6, DUSP10, and HLA-DRA —were commonly upregulated in ASTRLs and in all three Treg datasets (Fig. 12 G). TIGIT was shared only with the Ferraro and Hollbacher signatures, not with the core uTreg signature. The uTreg profile is known for its similarity to tumor-infiltrating Tregs (TITRs), which are often associated with Th2-like features( 29 , 44 ). This is particularly relevant because prior studies showed that expanded PBMCs from stable kidney transplant recipients (KTRs) exhibited Th2-like features, while those from rejecting KTRs were more Th1-like( 12 , 13 ). Accordingly, we compared ASTRL DEGs with those characterizing Th2-like Tregs. Of 99 such genes, 24 were upregulated and 7 were downregulated in ASTRLs (Fig. 13 A). The Th2-like Treg phenotype of ASTRLs was further supported by cytokine profiles: upon CD3/CD28 bead stimulation, ASTRLs produced significantly higher levels of IL-5 and IL-13 compared to PBMCs. IL-4 production was similar in both. ASTRLs also had significantly higher baseline IL-13 , with IL-5 trending similarly (Fig. 13 B). PBMCs produced more IL-17A post-stimulation, while ASTRLs produced more IL-10 (Fig. 13 C). Additionally, IL-2 , IL-6 , and IL-12 levels rose significantly in stimulated PBMCs but not in ASTRLs. ASTRLs had a higher baseline IFN-γ level, which remained unchanged upon stimulation, while PBMCs showed a significant increase (Fig. 13 D). Beyond IL-10 and Foxp3, ASTRLs expressed several markers shared with Tr1 cells, including CCR5, BATF, EGR2, GZMB, CD39, TIGIT , and TIM3 . However, none of these markers are unique to Tr1 cells and can be found in other Foxp3 + Tregs. ASTRLs also expressed BHLHE40 , a transcription factor regulating IL-10 and IFN-γ ( 45 ), and associated with Tr1 cells. Like Tr1 cells, ASTRLs showed intermediate IFN-γ levels and expressed CD39 . Though BHLHE40 can induce CD49b and LAG3 ( 46 ) (canonical Tr1 markers), these were not differentially expressed in ASTRLs (log2 fold changes: 0.91 and 0.17, respectively). Another Tr1-specific gene, EGR2 , was upregulated in ASTRLs and is essential for IL-10 production in Tr1 cells( 47 ). Interestingly, CD49a , a marker of tissue-resident memory T cells, was significantly upregulated in ASTRLs. A comparison across the four datasets revealed a shared set of genes consistently upregulated in ASTRLs: IL2RA, ENTPD1, HLA-DRA, IKZF4, DUSP10, TNFRSF9, CXCR6 , and TIGIT . In addition, ASTRLs showed distinct enrichment of two gene subsets: 39 genes shared with peripheral Tregs and 38 with uTregs. A further 20 genes from the Th2-like Treg signature were also upregulated in ASTRLs (Fig. 13 E). These findings collectively indicate that ASTRLs possess a Th2-like regulatory gene signature and share functional features with human Tregs. Discussion In this study, we demonstrate that ASTRLs, when expanded ex vivo from stable kidney transplant recipients (KTRs), exhibit a Th2-like regulatory T cell (Treg) profile and upregulate a gene signature associated with Tregs. While Treg heterogeneity and gene signatures have been well characterized in healthy individuals( 48 ) , ( 43 ), human diseases( 49 ), and the tumor microenvironment( 50 ) , ( 51 ), this is, to our knowledge, the first detailed report describing the regulatory gene signature of an ex vivo expanded autologous cell therapy product derived from stable KTRs. This study identifies and validates the key functional cell subset within a heterogeneous cell therapy product, providing critical insights for clinical translation. The CD4 + T cell compartment is the primary active population within ASTRLs, showing clonal expansion in response to donor antigens. These ASTRL-CD4 + cells exhibit a Th2-like Treg gene signature that overlaps with both peripheral Tregs (pTregs) and uterine Tregs (uTregs). Despite low expression of Foxp3—similar to Tr1 cells—ASTRL-CD4 + cells maintain a regulatory phenotype and function. These findings support the idea that antigen-specific expansion of a heterogeneous T cell population can yield regulatory cells that do not necessarily meet the classical definition of Tregs. They also align with the growing recognition of substantial phenotypic and functional heterogeneity within Tregs( 48 , 52 ), which may shift depending on disease context. A notable example of this heterogeneity is the partial overlap of only 17 genes between ASTRLs and Foxp3 + Treg signatures. These 17 genes— ARHGAP11A, ARHGEF12, CSF2RB, CXCR6, DUSP10, DUSP4, ENTPD1, GK, HLA-DRA, IKZF4, IL2RA, NUSAP1, SLC1A4, SLC9A7, ST8SIA4, TIGIT , and TNFRSF9 —include several core Treg markers but exclude many genes unique to ASTRLs. This distinct signature likely arises from: the source of the initial cells (PBMCs from stable KTRs), the conditions used for ex vivo expansion, TCR-dependent alloantigen stimulation, and inter-patient variability. Treg heterogeneity is known to depend on TCR signaling( 10 ). TCR-induced markers such as GITR, TIGIT, IL-10 , and EBI3 are notably upregulated in ASTRLs, explaining their divergence from resting primary Treg signatures. Importantly, the ASTRL gene signature includes three non-overlapping gene sets: 39 pTreg genes, 38 uTreg genes, and 20 Th2-like Treg genes. The Th2-like gene set and associated cytokine production confirm the Th2 bias of ASTRLs, consistent with previous findings( 12 , 13 ). This bias is further supported by the downregulation of 111 Th genes, typically upregulated in conventional T cells. Comparisons of circulating, lymphoid, and tissue-resident Tregs have revealed distinct gene signatures related to tissue adaptation. Our results introduce a novel concept: that ex vivo expansion of cells previously exposed to donor alloantigens (as in PBMCs from stable KTRs) can activate a regulatory gene program. ASTRLs exhibit a unique regulatory gene combination not previously described. The 39 genes shared with pTregs are mainly involved in TCR signaling, MHC class II antigen presentation, and pathways linked to allograft rejection or tolerance. Genes such as TNFRSF18 (GITR), HAVCR2 (TIM3), BATF , and NFIL3 —found in the uTreg signature and upregulated in ASTRLs—are recognized for their roles in tissue-adapted Treg function. Despite patient-to-patient variability, ASTRLs consistently display a regulatory phenotype, as shown by multiomic analysis. Our earlier work demonstrated that their suppressive function is mediated by the adenosinergic pathway, a finding further supported by gene expression data in this study. Additional pathways contributing to ASTRL-mediated immunoregulation include TNF-α , mTORC1 , and TIGIT signaling. Among these, the TNFRSF signaling axis and BATF are crucial for Treg survival and maintenance and are enriched in ASTRLs. The co-expression of pTreg and uTreg signature genes within ASTRLs is unique. While a pTreg-like signature is expected due to the peripheral origin of the starting cells, the presence of uTreg-specific genes (e.g., BATF and the TNFRSF axis) likely reflects the influence of alloantigen-driven expansion. Interestingly, both uTregs and ASTRLs also contain NK cell subsets, though the role of NK cells in ASTRL expansion remains under investigation. Our findings suggest that cellular heterogeneity is not necessarily a drawback, especially in the context of antigen-specific cell therapies. In fact, our data indicate potential interactions between the innate and adaptive immune systems in response to alloantigen. This is evident from the gene expression profiles of ASTRLs, which show various costimulatory receptor-ligand pairs across different cell types—interactions likely crucial for maintaining a regulatory microenvironment. Tregs are known to mediate suppression through direct cell-cell interactions. In cancer immunobiology, such regulatory microenvironments help tumors evade immune detection. Adapting this concept to generate alloantigen-specific, heterogeneous regulatory T cells to prevent chronic allograft rejection is a rational and promising approach. Additionally, the data suggest dynamic crosstalk between innate and adaptive immune cells within ASTRLs. TIGIT and its ligand CD155 —both expressed on NK and T cells—imply a role for trained immunity. TIGIT-CD155 interactions inhibit the TCR-AKT-mTORC1 signaling pathway( 53 , 54 ), and preliminary data suggest ASTRL NK cells may exhibit features of trained immunity. The possible role of innate immune memory in contributing to ASTRL’s regulatory function warrants further exploration using multiomic techniques. Single-cell technologies are increasingly being used to design, understand, and refine novel cell therapies( 55 ) , ( 56 , 57 ). Our application of multiomic analyses has provided a comprehensive understanding of the regulatory landscape of ASTRLs. TCR repertoire and clonal expansion analyses further demonstrate the antigen specificity of these cells. Altogether, the evidence supports ASTRLs as a potent and effective regulatory cell therapy. This study offers a uniquely detailed preclinical characterization of a cell therapy product. A robust immune system depends on both heterogeneity and redundancy—not only for effective responses but also to maintain immune homeostasis. Tumors exploit this redundancy to evade immunity, and we can reverse-engineer that principle to support allograft tolerance. Our single-cell data confirm that despite individual variability, ASTRLs are phenotypically and functionally consistent and, regardless of their heterogeneity, represent a promising approach towards development of a regulatory cell therapy. Methods Study Subjects Peripheral blood samples were collected from five stable kidney transplant recipients (KTRs), each with at least one HLA-DR mismatch with their donor. Additional KTR samples ( ≥ 10) were used for complementary analyses such as flow cytometry and CyTOF. All participants provided written informed consent prior to participation in the study, and the study was approved by the institutional review board of Brigham and Women’s Hospital (Protocol# 2013P001293). All the methods were performed in accordance with the institutional guidelines and regulations. ASTRL Expansion PBMCs were isolated from patient blood samples using Lymphoprep density gradient centrifugation (Stemcell Technologies) and cryopreserved in liquid nitrogen until use. For expansion, thawed PBMCs were stimulated with donor-derived allopeptides (ProImmune, Littlemore, UK) in the presence of low-dose IL-2 (Proleukin). This donor antigen-specific stimulation protocol, described previously( 12 ), was used to generate Antigen-Specific T cell-enriched Regulatory Lines (ASTRLs). Flow Cytometry and Mass Cytometry (CyTOF) For phenotypic characterization, ASTRLs and corresponding PBMCs were stained with fluorophore-conjugated antibodies targeting markers such as CD3, CD4, CD8, CD25, CD127, CD39, CD73, CTLA4, GITR, ICOS, CD45RA, CD226, LAP, GARP, CD56, CD16, CD19, CD11b, CD38, CD27, and CD24 (BioLegend). Flow cytometry data were acquired on a Cytek Aurora spectral cytometer and analyzed using FlowJo software. For CyTOF analysis, antibodies from Standard BioTools were used according to the manufacturer’s protocols. Samples were run on a CyTOF-XT instrument at the Dana-Farber Cancer Institute Mass Cytometry Core. Data were analyzed using Cytobank (Beckman Coulter) and OMIQ (Dotmatics) with tSNE, FlowSOM, and CITRUS clustering algorithms. ATP Hydrolysis Assay To evaluate the functional activity of CD39, extracellular ATP hydrolysis was measured using a malachite green colorimetric assay kit (AnaSpec). Inorganic phosphate production, a byproduct of ATP hydrolysis, was quantified according to the manufacturer's protocol( 58 ). Proliferation Assays and Cytokine Measurements Previously frozen PBMC and ASTRL samples were stimulated with CD3/CD28 beads for 72h at 37ºC. Supernatants were collected for cytokine measurement using multiplex assay kits from Thermofisher following the manufacturer’s protocol. Single-Cell RNA Sequencing Sample Preparation and Library Construction Ten samples (five PBMCs and five matched ASTRLs) were prepared from stable kidney transplant recipients. Cryopreserved cells were thawed in a 37°C water bath and resuspended in DMEM containing 10% FBS. Following centrifugation and washing with PBS + 0.4% BSA, cell viability was assessed using Trypan Blue staining. Samples with ≥ 65% viability were included for further processing. Approximately 10,000 cells per sample were loaded into the 10x Genomics Chromium Controller using the Single Cell 5' v2 Reagent Kit for encapsulation into Gel Beads-in-Emulsion (GEMs). These GEMs were processed to generate barcoded, full-length cDNA libraries for gene expression, TCR, and BCR profiling. Library quality was assessed using an Agilent Bioanalyzer, and libraries were sequenced on Illumina NextSeq or NovaSeq platforms. Data Processing and Analysis Preprocessing and Quality Control Raw sequencing data were demultiplexed using Cell Ranger `mkfastq`, and reads were aligned to the human reference genome (hg38) using Cell Ranger `count`. Clustering and Cell Type Identification Dimensionality reduction was performed using PCA, followed by Uniform Manifold Approximation and Projection (UMAP) ( 59 ). Variable genes from TCR loci (TRAV, TRBV, etc.) were excluded to avoid clustering based on clonotype rather than transcriptional phenotype. Samples were batch-corrected using Harmony( 60 ). to minimize inter-sample variation. Clustering was done using Seurat’s Louvain algorithm( 61 )., and clusters were annotated with canonical immune markers. Additionally, reference mapping was performed using Azimuth( 62 ), which aligned cells to a curated PBMC reference dataset. B cell identities were corrected using matched BCR data. Pseudobulk Differential Expression Analysis For differential gene expression analysis, CD4 + T cells were extracted from each sample, and their transcript counts were aggregated (pseudobulk). Using DESeq2, we compared gene expression profiles between ASTRLs and PBMCs. Genes with adjusted p-values 1 were considered significant. Log2 fold changes were stabilized using the ashr shrinkage estimator( 63 ). Gene Set Enrichment and Pathway Analysis Significantly differentially expressed genes were analyzed for functional enrichment using clusterProfiler( 64 ), querying the MSigDB Hallmark database( 27 ), Reactome pathways( 65 ), and custom gene sets ( 28 ). Enrichment was assessed via GSEA ( 26 )and over-representation analysis (ORA). Heatmaps were generated using pheatmap and ComplexHeatmap R packages( 66 ). TCR and BCR Repertoire Analysis TCR/BCR clonotypes were identified using scRepertoire( 67 ), based on the most frequent alpha and beta chains per cell. Clonotype identity was defined by unique combinations of CDR3 nucleotide sequences and V(D)J gene usage. Additional integration into Seurat metadata allowed UMAP-based visualization of clonal expansion using immunarch. Cells with only partial receptor chain information (e.g., only alpha or beta) were retained where appropriate. Sex as a Biological Variable Sex was not considered as a biological variable. Statistical Analysis Data were visualized and analyzed using GraphPad Prism. Appropriate statistical tests—including one-way ANOVA, two-way ANOVA, and unpaired t-tests—were used to assess significance, depending on the dataset. Venn diagrams were created using the toll available at the following URL. http://bioinformatics.psb.ugent.be/webtools/Venn/ Study Approval All participants provided written informed consent prior to participation in the study, and the study was approved by the local institutional ethics committee. Data Availability The supporting data is available upon request from the corresponding author. Declarations Conflict-of-interest statement: The authors have declared that no conflict of interest exists. Funding: We thank the Saxena Kidney and Pancreas Transplantation Research Fund in aiding the research efforts. Author Contribution Author contributionsST: conception and designing research studies, conducting experiments, acquiring and analyzing data, interpretation of results, writing and editing the manuscriptBLS, PLM: conducting experiments, acquiring and analyzing data,AMJ, ZZ, SHS: analyzing data, writing and editing the manuscriptAMW: designing research studiesAC: conception and designing research studies, interpretation of results, editing the manuscript and overall supervision of research Acknowledgement We thank the Saxena Kidney and Pancreas Transplantation Research Fund in aiding the research efforts. 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1","display":"","copyAsset":false,"role":"figure","size":54118,"visible":true,"origin":"","legend":"\u003cp\u003eEx vivo expanded antigen specific T enriched regulatory cell lines (ASTRLs) from Kidney Transplant Recipients upregulate surface expression of functional CD39. A. Schematic presentation of ex vivo expansion of PBMC to generate ASTRL. B. Surface expression of CD39 by CD3\u003csup\u003e+\u003c/sup\u003e and CD3\u003csup\u003e- \u003c/sup\u003ecell subsets of PBMC and ASTRL. Representative dot plots showing CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e and CD56\u003csup\u003e+\u003c/sup\u003e cell subsets in both PBMC and ASTRL. C. Increase in median CD39 expression and eATP hydrolysis of 5 ASTRL samples in comparison to the respective PBMCs. D. Median CD39 expression levels and eATP hydrolysis by PBMCs and ASTRLs of additional KTRs.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/fddba4e0824ea72830cf4efd.jpg"},{"id":92205315,"identity":"c753b647-61f8-44df-b444-8477ffcae347","added_by":"auto","created_at":"2025-09-25 18:20:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34483,"visible":true,"origin":"","legend":"\u003cp\u003eSingle cell gene expression profile of different cell clusters in PBMC and ASTRL of stable KTRs. A. UMAP of global clustering of PBMC and ASTRL samples. B. UMAP of PBMC and ASTRL samples separately showing comparisons between cell compositions. C. and D. Comparisons of cell compositions and variability between individual PBMC and ASTRL samples. E. Canonical gene expression markers used for identification of cell clusters.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/f979b831b94b673e8621e0e8.jpg"},{"id":92205839,"identity":"08a20b21-96bb-48f9-8fd9-96e421076d95","added_by":"auto","created_at":"2025-09-25 18:28:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33328,"visible":true,"origin":"","legend":"\u003cp\u003ePBMC and ASTRL phenotyping by mass cytometry. A. tSNE plots show enrichment of CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment in ASTRLs (n=8) in comparison to PBMC (n=8). B. Representative tSNE plots showing CD39 expression in CD19\u003csup\u003e+\u003c/sup\u003e cells in PBMC and CD4\u003csup\u003e+ \u003c/sup\u003ecells in ASTRL.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/13d4a72926f1bc905128502f.jpg"},{"id":92204915,"identity":"0d2b9e2c-69d3-4e89-a5f3-a2b0cd92c493","added_by":"auto","created_at":"2025-09-25 18:12:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31395,"visible":true,"origin":"","legend":"\u003cp\u003eClonal Expansion and TCR Diversity of ASTRL. A. UMAP of 5 ASTRL sample showing clonal proliferation in the CD4\u003csup\u003e+\u003c/sup\u003e compartment in comparison to the respective PBMC samples. B. Frequency of top 10 clonotypes in five PBMC samples and the corresponding ASTRLs C. Percentage of unique clonotypes in individual PBMC and ASTRL samples. D. Clonal diversity in TCR Vb usage in PBMC and ASTRL samples. E. Clonal diversity in TCRa chain variable region usage in PBMCs and ASTRLs. F. Statistical Indices comparing TCR diversity metrics between PBMCs and ASTRLs.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/c84ba17c62ea6045a2ffe874.jpg"},{"id":92204918,"identity":"6d893873-f7fd-4330-85f7-3bf8a0e102d7","added_by":"auto","created_at":"2025-09-25 18:12:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38358,"visible":true,"origin":"","legend":"\u003cp\u003ePseudobulk analysis of differentially expressed genes in the CD4 compartment of ASTRL. A. PCA analysis shows distinct separation between PBMC and ASTRL samples. B. Hierarchical clustering distinguishes between PBMC and ASTRL samples. C. Volcano plot showing differentially expressed genes (DEGs) between PBMCs (n=5) and ASTRLs (n=5). D. Heat map of top 50 DEGs in ASTRLs vs PBMCs. E. GESE showing enrichment pathways of ASTRL DEGs. F. DEGs of the TNF signaling pathway in ASTRLs. G. Th2 lineage DEGs in ASTRLs. H. Differentially expressed transcription factor genes in ASTRLs.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/11f3b0cf25956a8ad5ed9356.jpg"},{"id":92205323,"identity":"761b6458-5373-4a67-a64f-11a64b850575","added_by":"auto","created_at":"2025-09-25 18:20:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50321,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of canonical Treg markers in ASTRL. A. and B. Differentially expressed canonical Treg genes in the CD4 compartment of ASTRLs by scRNAseq. C. Surface expression of canonical Treg markers on CD4\u003csup\u003e+\u003c/sup\u003e cells in ASTRLs by flow cytometry. D. Surface expression of canonical Treg markers on CD4\u003csup\u003e+\u003c/sup\u003e cells in ASTRLs by mass cytometry (CyTOF)\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/4226fe2b0e9a1cb98065f993.jpg"},{"id":92204930,"identity":"46e286bf-d31e-43b3-9c1b-b7dec72263c1","added_by":"auto","created_at":"2025-09-25 18:12:08","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":38569,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypic characterization of PBMC and ASTRL samples using Mass Cytometry. A. tSNE plot of overlapping PBMC (n=9) and ASTRL (n=13) samples. B. tSNE plot showing different cell subsets in global clustering of PBMC and ASTRL samples. C. and D. tSNE plot of different cell subsets of PBMC and ASTRL samples separately. E. Abundances of different cell subsets in PBMCs and ASTRLs\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/e10dcef812a15e0b3f07ed3e.jpg"},{"id":92204926,"identity":"12cff63d-b5bf-4877-b899-6c5045b13309","added_by":"auto","created_at":"2025-09-25 18:12:08","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41076,"visible":true,"origin":"","legend":"\u003cp\u003eAbundances of different cell subsets in individual PBMC samples and the corresponding ASTRLs (n=6)\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/4b9e8dc3c7939416c09aa832.jpg"},{"id":92205317,"identity":"635b70a5-7885-4d5f-8afb-13d891584221","added_by":"auto","created_at":"2025-09-25 18:20:07","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":32146,"visible":true,"origin":"","legend":"\u003cp\u003eUnsupervised clustering analysis of PBMC and ASTRL samples using FlowSOM. A. and B. FlowSOM metaclusters (MCs) in PBMC and ASTRL samples. C. and D. Significantly different MCs between PBMCs and ASTRLs E. Significantly abundant MCs in PBMCs. F. Significantly abundant MCs in ASTRLs.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/34075ee28a384ea69b40f73e.jpg"},{"id":92205318,"identity":"3acfffc4-f81f-4e7f-ae33-847df794abf7","added_by":"auto","created_at":"2025-09-25 18:20:07","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":38049,"visible":true,"origin":"","legend":"\u003cp\u003eProtein expression levels of regulatory markers in PBMC and ASTRL metaclusters. A. Heatmap of regulatory markers showing comparison of CD8 MCs between PBMC and ASTRL samples. B. Heatmap of regulatory markers showing comparison of CD11b MCs between PBMC and ASTRL samples. C. Heatmap of regulatory marker expression in MCs 4, 8 and 15 distinctive to ASTRL samples. D. Heatmap of regulatory marker expression in MC1 showing similar abundance in PBMCs and ASTRLs.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/cb565465267cf2e3c714e593.jpg"},{"id":92204919,"identity":"0cd26861-a565-4542-ae84-8f8c810a0cc6","added_by":"auto","created_at":"2025-09-25 18:12:07","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":84053,"visible":true,"origin":"","legend":"\u003cp\u003eCITRUS analysis and median expression levels of regulatory markers in the T cell subset of PBMCs and ASTRLs. A. Median expression levels of regulatory markers between the CD3\u003csup\u003e+ \u003c/sup\u003eT cells of PBMC (n=9) and ASTRL (n=13) samples. B. Median expression levels of regulatory markers between the CD4\u003csup\u003e+ \u003c/sup\u003eT cells of PBMC and ASTRL samples.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/372c364da77420d20b3a77f0.jpg"},{"id":92205324,"identity":"e178c32d-b8d3-40a4-9de0-8ab073babdb9","added_by":"auto","created_at":"2025-09-25 18:20:08","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":68792,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed Th and Treg signature genes by CD4 compartment of ASTRL. A. Upregulation of Treg signature genes and downregulation of Th signature genes by ASTRL. B. Venn diagram of shared genes between ASTRL and peripheral Treg and Th signature genes. C. Signature peripheral Treg genes upregulated and downregulated in ASTRL CD4 compartment. D. Venn diagram of ASTRL DEGs shared with peripheral Treg signature genes. E. and F. Venn diagrams of upregulated and downregulated genes shared by ASTRL with classical peripheral Tregs. G. and H. Comparison of differentially expressed genes in ASTRLs with the uterine Treg signature.\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/204d04cd22ba3af885fdcdcb.jpg"},{"id":92204935,"identity":"7f24c735-e246-4160-928e-082f31928bd5","added_by":"auto","created_at":"2025-09-25 18:12:08","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":32278,"visible":true,"origin":"","legend":"\u003cp\u003eTh2 -like Treg gene signature of ASTRL DEGs. A. Venn diagram of genes shared by ASTRL and Th2-like Tregs. B. Th2 type Cytokine production by stimulated ASTRLs. C. Comparison of proinflammatory cytokine production by PBMCs and ASTRLs. E. Transcriptomic signature of ASTRL DEG.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/f12897f971b03af8d81b7a17.jpg"},{"id":100614500,"identity":"e323d896-8ad3-45b8-b2ae-3f2729a0d30e","added_by":"auto","created_at":"2026-01-19 17:20:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1923643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/6ca8f114-0db1-44bf-a2a6-18d4b5590814.pdf"},{"id":92205840,"identity":"9a6cec47-a27f-4872-a269-563e96ca2560","added_by":"auto","created_at":"2025-09-25 18:28:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1709546,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/a6ed9b4d3e2c69c8be1ebccc.docx"},{"id":92204916,"identity":"01ffb124-7f17-4532-99a5-ba6a770a0cc5","added_by":"auto","created_at":"2025-09-25 18:12:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2146378,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7464625/v1/7d3679d7cde3fe678c5041ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell and multi-omic characterization of ex vivo expanded ASTRLs from stable kidney transplant recipients reveals a regulatory T cell phenotype","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRegulatory T cell (Treg) therapy has emerged as a promising strategy to promote and maintain immunological tolerance in organ transplantation and autoimmune diseases. However, the clinical application of Treg-based therapies faces several challenges. Notably, variability in ex vivo expanded Treg products, combined with the lack of standardized identifying markers and functional assays, hampers the consistency and reproducibility of these therapies.\u003c/p\u003e\u003cp\u003eAnother significant limitation lies in the cellular heterogeneity of Treg preparations. Due to technical constraints in current isolation methods, expanded Treg products often include other T cell populations, such as conventional T cells, memory T cells, and T follicular helper cells. This heterogeneity is widely regarded as a potential barrier to therapeutic efficacy and safety. Furthermore, attempts to eliminate non-Treg populations often lead to increased manufacturing complexity and costs, ultimately limiting accessibility.\u003c/p\u003e\u003cp\u003eDespite these concerns, cellular heterogeneity may also offer therapeutic advantages. Recent studies suggest that functionally distinct immunoregulatory subsets can work cooperatively to achieve layered and context-specific immune suppression(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e),(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For example, in the tumor microenvironment, regulatory T cells and myeloid-derived suppressor cells exhibit crosstalk that enhances immunosuppressive efficacy beyond what is observed in homogeneous populations (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Synergistic interactions are physiologically more relevant as cells rarely interact in isolation in vivo. This \u0026ldquo;division of labor\u0026rdquo; allows for more nuanced and robust control of immune responses.\u003c/p\u003e\u003cp\u003eTregs employ multiple mechanisms of suppression, which can function independently or synergistically depending on the immune context. Importantly, the failure of a single mechanism does not necessarily compromise overall immunoregulation(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Additionally, distinct regulatory memory cell subsets can provide both immediate and long-term tolerance(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), suggesting that diversity within the regulatory compartment may be essential for sustained immune control. Harnessing these synergies among diverse regulatory populations may enable the development of integrated, flexible, and durable cell therapy products that better mimic physiological immune regulation in transplantation and autoimmunity(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSingle-cell genomic technologies offer a powerful means to dissect cellular heterogeneity and gain deeper insights into complex cell populations. Recent single-cell RNA sequencing studies have revealed that Tregs are not a uniform population but consist of distinct subsets with specialized suppressive functions and varying degrees of stability(\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). These findings highlight the importance of understanding Treg heterogeneity in the context of developing effective cell-based therapies(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our previous work, we demonstrated that donor alloantigen-specific regulatory T cell clones could prevent chronic rejection and prolong graft survival in rat models of kidney transplantation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We subsequently identified these regulatory clones in human kidney transplant recipients and confirmed their functional role in promoting allograft tolerance(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Building on these findings, we successfully expanded donor alloantigen-specific immunoregulatory T cells ex vivo from both rejecting and stable kidney transplant recipients. This led to the development of antigen-specific T cell-enriched regulatory lines (ASTRLs) derived from kidney transplant recipients with stable function and intended for future development as individualized, autologous cell therapy products.\u003c/p\u003e\u003cp\u003eThese ASTRLs exhibit immunoregulatory phenotypes and function through the adenosinergic pathway(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Preliminary phenotypic analyses have shown enrichment of T cells expressing canonical regulatory markers(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, as ASTRLs are derived from peripheral blood mononuclear cells (PBMCs) rather than a purified CD4⁺CD25⁺ Treg population, they exhibit cellular heterogeneity.\u003c/p\u003e\u003cp\u003eGiven the potential impact of this heterogeneity on therapeutic performance, we employed a multiomics approach\u0026mdash;including single-cell RNA sequencing, CyTOF, and flow cytometry\u0026mdash;to characterize ASTRLs in greater depth. Our objective is to determine whether cellular diversity within ASTRLs enhances their capacity for multifaceted immune regulation and could be leveraged to improve their efficacy as a potent cell therapy for transplantation tolerance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eUpregulation of CD39 Expression in ASTRLs from Stable Kidney Transplant Recipients\u003c/h2\u003e\u003cp\u003ePeripheral blood samples were collected from five stable kidney transplant recipients (KTRs). Peripheral blood mononuclear cells (PBMCs) were isolated using density gradient centrifugation and cryopreserved prior to expansion. Antigen-specific T cell-enriched regulatory lines (ASTRLs) were subsequently generated by stimulating these PBMCs ex vivo with donor-specific antigens in the presence of low-dose IL-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Following expansion, ASTRLs were harvested and stored frozen until further analysis.\u003c/p\u003e\u003cp\u003ePreliminary phenotypic assessment using flow cytometry demonstrated that ASTRLs contained both CD3\u003csup\u003e+\u003c/sup\u003e T cells and CD3\u003csup\u003e\u0026minus;\u003c/sup\u003e non-T cell subsets. Within the CD3\u003csup\u003e+\u003c/sup\u003ecompartment, both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cell subsets were notably enriched following expansion. Similarly, a substantial CD56\u003csup\u003e+\u003c/sup\u003e population was identified within the CD3\u003csup\u003e\u0026minus;\u003c/sup\u003e subset, which was also increased post-expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGiven the known role of the adenosinergic pathway in immunosuppression mediated by ASTRLs(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), we specifically analyzed the expression of CD39, the rate-limiting enzyme of this pathway(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e),(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). CD39 expression was consistently elevated in the expanded ASTRLs compared to their pre-expansion PBMC counterparts across all five patients. Functionally, this increased expression corresponded to significantly enhanced extracellular ATP (eATP) hydrolysis activity in the ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This relationship between higher CD39 expression and increased eATP hydrolysis was further confirmed in additional patient samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eThese results indicate that, despite patient-to-patient variability, ex vivo antigen-specific expansion reliably produces a T cell-enriched population with elevated, functionally active CD39 expression. Previous studies have demonstrated that inhibition of CD39 reduces the immunosuppressive capacity of ASTRLs. Additionally, the critical regulatory function of CD39 has been well-documented in tumor immunology(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and is increasingly recognized as important in the context of organ transplantation(\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eASTRLs Are Enriched in T Cells but Exhibit Cellular Heterogeneity\u003c/h3\u003e\n\u003cp\u003ePrevious observations indicated that donor antigen-specific expansion of PBMCs ex vivo generates heterogeneous ASTRL populations, predominantly enriched in T cells. This cellular heterogeneity can complicate the clinical translation of regulatory cell therapies, making it essential to thoroughly characterize the cellular composition and variability within ASTRLs.\u003c/p\u003e\u003cp\u003eTo define the cellular diversity of ASTRLs comprehensively, we performed single-cell RNA sequencing along with T and B cell immune repertoire sequencing on paired PBMC and ASTRL samples from five kidney transplant recipients (KTRs). This integrative approach enabled simultaneous characterization of cellular phenotypes and antigen-specific immune receptor diversity.\u003c/p\u003e\u003cp\u003eGlobal clustering analysis of single-cell gene expression data revealed seven distinct cellular clusters, identified as monocytes, dendritic cells (DCs), B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, NK/NKT/MAIT/γδT cells, and other T cell subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Quantitative comparison between pre-expansion PBMCs and post-expansion ASTRLs using Propeller (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) testing showed significant shifts in cell composition. Specifically, ASTRLs were markedly enriched in lymphoid populations\u0026mdash;particularly CD4\u003csup\u003e+\u003c/sup\u003e T cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01735546, FDR\u0026thinsp;=\u0026thinsp;0.02429764)\u0026mdash;accompanied by a notable reduction in B cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00118788, FDR\u0026thinsp;=\u0026thinsp;0.00207878) and myeloid populations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09E-05, FDR\u0026thinsp;=\u0026thinsp;7.65E-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). However, detailed individual analyses highlighted considerable variability among patients, especially regarding the proportions of different T cell subsets and NK cell populations within ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). The identity of these cell clusters was validated by differential gene expression analyses of known canonical markers, visualized clearly in dot plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther validation using CyTOF analysis on additional PBMC and ASTRL samples from eight KTRs supported these findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Visualization by t-distributed Stochastic Neighbor Embedding (tSNE) confirmed enrichment of the CD4\u003csup\u003e+\u003c/sup\u003e T cell subset (red cluster) and smaller increases in CD8\u003csup\u003e+\u003c/sup\u003e T cells (blue cluster) and CD56\u003csup\u003e+\u003c/sup\u003e NK cells (purple cluster) in ASTRLs. In contrast, both B cells and monocytes were significantly depleted compared to pre-expansion PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, CyTOF analyses showed significant upregulation of surface CD39 specifically in the CD3\u003csup\u003e+\u003c/sup\u003e T cell compartment within ASTRLs. This contrasts with pre-expansion PBMCs, in which CD39 expression was primarily detected in the CD19\u003csup\u003e+\u003c/sup\u003e B cell compartment\u0026mdash;significantly diminished after ASTRL expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eCollectively, these data confirm that donor antigen-specific ex vivo expansion selectively enriches T cell populations within ASTRLs while introducing considerable heterogeneity, which varies between individual patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDonor Alloantigen Drives Clonal Expansion Primarily in the CD4 T Cell Compartment of ASTRLs\u003c/h3\u003e\n\u003cp\u003eTo investigate how donor-specific antigen stimulation affects the clonal composition of ASTRLs, we analyzed the T cell receptor (TCR) repertoires in paired PBMC and ASTRL samples from the five kidney transplant recipients (KTRs). UMAP visualization revealed prominent clonal expansions predominantly within the CD4\u003csup\u003e+\u003c/sup\u003e T cell clusters across all five subjects. In contrast, fewer expansions were observed within CD8\u003csup\u003e+\u003c/sup\u003e T cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We also assessed B cell receptor (BCR) repertoires and found no significant clonal expansion in the small B cell clusters that remained in some ASTRL samples (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eFurther analyses comparing TCR diversity between PBMC and ASTRL samples highlighted a consistent reduction in diversity post-expansion. The frequency distributions of TCR V gene usage (TRBV and TRAV) demonstrated selective enrichment of specific clonotypes in the ASTRLs, indicative of antigen-driven clonal expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eQuantitative comparisons further revealed substantial oligoclonality within ASTRLs, with a dramatic reduction in unique clonotypes\u0026mdash;defined by exact nucleotide sequences and complete V(D)J gene usage\u0026mdash;from approximately 100% unique clonotypes in PBMCs to around 20\u0026ndash;40% in ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Additionally, analysis of TCR V gene diversity clearly demonstrated reduced V gene usage in ASTRLs compared to PBMCs for both beta and alpha chains, reflecting antigen-driven selective pressures (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eStatistical indices for diversity\u0026mdash;including Shannon entropy, Simpson index, Chao, ACE, and Inverse Pielou index\u0026mdash;uniformly confirmed this significant reduction in TCR diversity in ASTRLs compared to their corresponding PBMC samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Lower Shannon and Inverse Simpson indices indicated reduced overall TCR diversity. Decreased scores for Chao and ACE indices suggested a lower richness of unique clonotypes, whereas a higher Inverse Pielou index indicated dominance of a limited number of clones.\u003c/p\u003e\u003cp\u003eCollectively, these findings demonstrate that donor antigen-specific expansion predominantly promotes CD4\u003csup\u003e+\u003c/sup\u003e T cell clonal expansions, resulting in a narrowed TCR repertoire within ASTRLs, although significant inter-individual variability in the exact clonotypes expanded was observed.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTranscriptional Signature of the CD4 T Cell Compartment in ASTRLs\u003c/h3\u003e\n\u003cp\u003eTo further characterize the immunoregulatory properties of ASTRLs, we performed a pseudobulk gene expression analysis specifically focused on the CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment in matched PBMC and ASTRL samples from the five kidney transplant recipients (KTRs).\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA) clearly separated the ASTRL samples from their corresponding PBMC controls, indicating that CD4\u003csup\u003e+\u003c/sup\u003e T cells in ASTRLs acquire a distinct transcriptional profile following donor antigen-specific stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Hierarchical clustering confirmed consistent and distinct transcriptional patterns between PBMC and ASTRL groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eDifferential gene expression analysis between PBMC and ASTRL CD4\u003csup\u003e+\u003c/sup\u003e T cell populations identified a total of 2665 significantly altered genes (absolute log2 fold-change\u0026thinsp;\u0026gt;\u0026thinsp;1.0, adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Of these, 592 genes were significantly upregulated, and 699 genes were significantly downregulated in ASTRLs compared to PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The heatmap of the top 30 differentially expressed genes clearly distinguished ASTRL samples from their PBMC counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) against the MSigDb Hallmarks database (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and previously defined Treg-specific gene sets (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) revealed significant enrichment in pathways associated with Treg function, TNF signaling, and mTORC1 signaling in the ASTRLs (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Notably, a Th2-like lineage signature was evident, characterized by the upregulation of genes such as Gata3, IL-4, and IL-13, along with downregulation of RORC and CCR6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eFurther analysis of the significantly upregulated genes revealed known markers associated with Treg suppressive functions, including IL2RA (CD25), ENTPD1 (CD39), TIGIT, HAVCR2, LGALS1, LGALS8, and LAYN (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, several transcription factors critical for Treg function\u0026mdash;such as RUNX2, IKZF4, BATF, BATF3, ETV7, NFIL3, and VDR\u0026mdash;were also upregulated in ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eIn summary, CD4\u003csup\u003e+\u003c/sup\u003e T cells in donor antigen-expanded ASTRLs display a distinct immunoregulatory transcriptional signature, characterized by a Th2-like Treg phenotype and significant upregulation of genes associated with suppressive functions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCD4 T Cell Compartment of ASTRLs Exhibits Key Regulatory Features\u003c/h3\u003e\n\u003cp\u003eWe further examined the CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment in ASTRLs to assess the expression of established canonical regulatory T cell (Treg) markers, including CD25 (IL2RA), Foxp3, Helios (IKZF2), GITR (TNFRSF18), CTLA4, TIGIT(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), PD-1, LAG3(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), CD226(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), and genes related to the adenosinergic pathway(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), particularly CD39 (ENTPD1) and CD38. We used single-cell RNA sequencing (scRNA-seq), flow cytometry, and mass cytometry (CyTOF) for this detailed analysis.\u003c/p\u003e\u003cp\u003eSingle-cell RNA-seq data demonstrated consistent increases in gene expression levels of CD25, GITR, TIGIT, and CD39 in all ASTRL samples compared to PBMCs. However, expression of markers such as Helios, CTLA4, PD-1, and CXCR3 varied considerably between individual samples (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). CXCR3, classically considered as a Th1 specific gene, is consistently expressed by ASTRLs and is an important chemokine receptor for Tregs (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProtein-level assessments via flow cytometry and CyTOF confirmed these observations, showing that expression of these regulatory markers was similarly elevated in the CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment of ASTRLs (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Although individual variability was noted in the expression levels of specific markers, the overall phenotype consistently reflected upregulation of multiple canonical Treg markers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMass Cytometry Reveals Distinct Phenotypic Signatures in ASTRLs\u003c/h2\u003e\u003cp\u003eTo provide deeper insight into the phenotypic differences between PBMC and ASTRL populations, we performed comprehensive mass cytometry analyses. Unsupervised clustering using tSNE on samples from six PBMCs and ten ASTRLs showed clear separation between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), with ASTRLs demonstrating a marked enrichment of CD4\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Smaller populations of CD8\u003csup\u003e+\u003c/sup\u003e T cells and CD56\u003csup\u003e+\u003c/sup\u003e NK cells were also present in ASTRLs, while B cells and monocytes were notably depleted (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u0026ndash;E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhile all ASTRL samples showed enrichment in CD4\u003csup\u003e+\u003c/sup\u003e T cells, the proportions of CD8\u003csup\u003e+\u003c/sup\u003e and NK cells varied widely among individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), confirming the inter-patient heterogeneity also observed in scRNA-seq data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing FlowSOM(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) clustering, we identified 20 distinct meta-clusters (MCs) across PBMCs and ASTRLs (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Relative abundance of each MC was visualized via box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), and differential abundance analysis using edgeR(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) identified 12 significantly different clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eMetaclusters significantly enriched in ASTRL samples predominantly included subsets of CD4\u003csup\u003e+\u003c/sup\u003e T cells (MC03, MC04, MC08, MC11), along with specific CD8+ (MC13), CD56\u0026thinsp;+\u0026thinsp;NK cells (MC15), and CD11b\u003csup\u003e+\u003c/sup\u003e subsets (MC19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). Conversely, PBMC samples predominantly featured CD8\u003csup\u003e+\u003c/sup\u003e (MC09, MC10), CD19\u003csup\u003e+\u003c/sup\u003e (MC14), and a different CD11b\u003csup\u003e+\u003c/sup\u003e (MC17) subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe further compared regulatory marker expression across matched clusters. The CD8\u0026thinsp;+\u0026thinsp;T cell subset enriched in ASTRLs (MC13) exhibited higher levels of CD39, TIGIT, CTLA4 (CD152), CXCR3 (CD183), CD38, Helios, and Foxp3 compared to the CD8\u0026thinsp;+\u0026thinsp;subset in PBMCs (MC09) (Fig.\u0026nbsp;10A). Similarly, the CD11b\u003csup\u003e+\u003c/sup\u003e subset in ASTRLs (MC19) showed elevated expression of these same markers along with CD155, CD85k, and CD304 (Fig.\u0026nbsp;10B). The CD56\u003csup\u003e+\u003c/sup\u003e NK cell cluster (MC15) in ASTRLs also expressed CD39, TIGIT, CD183, CD38, and Helios. Among the CD4\u003csup\u003e+\u003c/sup\u003e subsets, both MC08 and MC04 expressed high levels of CD39, TIGIT, CTLA4, CD183, CD38, Helios, CD25, Foxp3, and PD-1 (Fig.\u0026nbsp;10C). CXCR3 (CD183) is consistently expressed by ASTRL subsets at the protein level similar to the earlier observation at the gene level.\u003c/p\u003e\u003cp\u003eAlthough not significantly more abundant in ASTRLs, meta-cluster MC01 (a CD3\u003csup\u003e+\u003c/sup\u003e T cell population present in both PBMCs and ASTRLs) exhibited distinct phenotypic changes after expansion. ASTRL-derived MC01 cells had increased median expression of CD39, TIGIT, CD183, CC R7 (CD197), and CD25 compared to their PBMC counterparts (Fig.\u0026nbsp;10D).Figure 10: Protein expression levels of regulatory markers in PBMC and ASTRL metaclusters. A. Heatmap of regulatory markers showing comparison of CD8 MCs between PBMC and ASTRL samples. B. Heatmap of regulatory markers showing comparison of CD11b MCs between PBMC and ASTRL samples. C. Heatmap of regulatory marker expression in MCs 4, 8 and 15 distinctive to ASTRL samples. D. Heatmap of regulatory marker expression in MC1 showing similar abundance in PBMCs and ASTRLs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results demonstrate that ASTRLs\u0026mdash;regardless of lineage\u0026mdash;consistently contain subsets that express a core set of regulatory markers: CD39, TIGIT, CD183, CD38, and Helios.\u003c/p\u003e\u003cp\u003eTo quantitatively assess marker expression differences, we applied \u003cb\u003eCITRUS\u003c/b\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) analysis to CyTOF data from 16 total samples (6 PBMCs and 10 ASTRLs; antibody panel listed in Supplementary Table). Using Significance Analysis of Microarrays (SAM), we identified significantly higher expression of CD39, CTLA4 (CD152), CD25, CD183, OX40 (CD134), ICOS (CD278), CD226, and Helios in ASTRL samples compared to PBMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e: CITRUS analysis and median expression levels of regulatory markers in the T cell subset of PBMCs and ASTRLs. A. Median expression levels of regulatory markers between the CD3\u003csup\u003e+\u003c/sup\u003e T cells of PBMC (n\u0026thinsp;=\u0026thinsp;9) and ASTRL (n\u0026thinsp;=\u0026thinsp;13) samples. B. Median expression levels of regulatory markers between the CD4\u003csup\u003e+\u003c/sup\u003e T cells of PBMC and ASTRL samples.\u003c/p\u003e\u003cp\u003eThese differences were consistent across both CD4\u003csup\u003e+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eB) and CD8\u003csup\u003e+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eC) T cell subsets. Interestingly, although TIGIT was frequently co-expressed with CD39 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eD), its median expression was not significantly different between groups in CITRUS analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDespite variability between individual samples, the combined results from scRNA-seq, flow cytometry, and CyTOF consistently reveal that ASTRLs upregulate multiple canonical Treg-associated markers. These include CD25 (IL2RA), Foxp3, Helios (IKZF2), GITR (TNFRSF18), CTLA4, TIGIT, CD226, PD-1, LAG3, as well as adenosinergic pathway components CD39 (ENTPD1) and CD38. Notably, a core immunoregulatory signature\u0026mdash;featuring CD25, CD39, TIGIT, CD183, and Helios\u0026mdash;was consistently observed across several ASTRL subsets, highlighting the robust regulatory phenotype of these donor antigen-expanded cells.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eASTRL CD4 T Cells Share Features of Both Peripheral and Uterine Tregs\u003c/h3\u003e\n\u003cp\u003eGiven the heterogeneous nature of ASTRLs, the variability observed across patients, and the minimal difference in Foxp3 expression between PBMCs and ASTRLs within the CD4\u003csup\u003e+\u003c/sup\u003e compartment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), these cells do not qualify as \u0026ldquo;classical Tregs.\u0026rdquo; However, ASTRLs exhibit significant regulatory capacity(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and upregulate several key immunosuppressive markers\u0026mdash;such as CD39, TIGIT, and CXCR6\u0026mdash;at both gene and protein levels. These markers are also associated with mouse and human Tregs.\u003c/p\u003e\u003cp\u003eConsidering the limited reliability of Foxp3 as an exclusive marker for human Tregs and the known heterogeneity within Treg subsets, we compared the differentially expressed genes (DEGs) in ASTRLs to established gene signatures of human peripheral Tregs and Th cells. Specifically, we compared ASTRL gene signatures with four different human Treg datasets to characterize the immunoregulatory profile of the CD4\u003csup\u003e+\u003c/sup\u003e compartment in ASTRLs. Additionally, we compared ASTRL DEGs to those of human Tr1 cells(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), noting that both ASTRLs and Tr1 cells are antigen-specific, peripheral in nature, and lack Foxp3 expression.\u003c/p\u003e\u003cp\u003eIn the dataset from Hollbacher et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), peripheral CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003eCD25\u0026ndash; cells from healthy controls were used to define Treg and Th gene signatures. When ASTRL DEGs were compared with this dataset, we observed an upregulation of Treg signature genes and downregulation of Th signature genes. Volcano plots show the overlap of the Hollbacher gene set with ASTRL DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). Of the 225 Treg genes, 62 were upregulated and 11 were downregulated in ASTRLs. Out of 360 Th genes, ASTRLs downregulated 111 and upregulated 21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eB), indicating that ASTRLs resemble peripheral Tregs more than conventional Th cells.\u003c/p\u003e\u003cp\u003eFerraro et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) identified a Treg gene signature by comparing CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e+\u003c/sup\u003eCD127\u0026ndash; (Tregs) to CD4\u003csup\u003e+\u003c/sup\u003eCD25\u003csup\u003e\u0026ndash;\u003c/sup\u003eCD127\u003csup\u003e+\u003c/sup\u003e (Tconv) cells. Comparison of ASTRL DEGs (shown in grey) with Ferraro\u0026rsquo;s Treg genes (shown in purple) via volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eC) showed 39 genes upregulated and 54 downregulated in both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA combined analysis of the Hollbacher, Ferraro, and ASTRL datasets identified 17 commonly upregulated genes in ASTRLs. Interestingly, the Hollbacher and Ferraro Treg signatures do not fully overlap\u0026mdash;only 48 genes are shared between the two, despite both being derived from peripheral Tregs of healthy donors (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eE). Still, 17 DEGs were common across all three signatures.\u003c/p\u003e\u003cp\u003eWhen compared with uterine Treg (uTreg) gene signatures from Wienke et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), ASTRLs showed substantial overlap: 61 upregulated and 15 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eF). ASTRLs shared more DEGs with uTregs than with peripheral Tregs. Seven Treg-associated genes\u0026mdash;\u003cb\u003eIL2RA, IKZF4, ENTPD1, TNFRSF9, CXCR6, DUSP10, and HLA-DRA\u003c/b\u003e\u0026mdash;were commonly upregulated in ASTRLs and in all three Treg datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eG). \u003cb\u003eTIGIT\u003c/b\u003e was shared only with the Ferraro and Hollbacher signatures, not with the core uTreg signature.\u003c/p\u003e\u003cp\u003eThe uTreg profile is known for its similarity to tumor-infiltrating Tregs (TITRs), which are often associated with Th2-like features(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). This is particularly relevant because prior studies showed that expanded PBMCs from stable kidney transplant recipients (KTRs) exhibited Th2-like features, while those from rejecting KTRs were more Th1-like(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccordingly, we compared ASTRL DEGs with those characterizing Th2-like Tregs. Of 99 such genes, 24 were upregulated and 7 were downregulated in ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). The Th2-like Treg phenotype of ASTRLs was further supported by cytokine profiles: upon CD3/CD28 bead stimulation, ASTRLs produced significantly higher levels of \u003cb\u003eIL-5\u003c/b\u003eand \u003cb\u003eIL-13\u003c/b\u003e compared to PBMCs. \u003cb\u003eIL-4\u003c/b\u003e production was similar in both. ASTRLs also had significantly higher baseline \u003cb\u003eIL-13\u003c/b\u003e, with \u003cb\u003eIL-5\u003c/b\u003e trending similarly (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). PBMCs produced more \u003cb\u003eIL-17A\u003c/b\u003e post-stimulation, while ASTRLs produced more \u003cb\u003eIL-10\u003c/b\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). Additionally, \u003cb\u003eIL-2\u003c/b\u003e, \u003cb\u003eIL-6\u003c/b\u003e, and \u003cb\u003eIL-12\u003c/b\u003e levels rose significantly in stimulated PBMCs but not in ASTRLs. ASTRLs had a higher baseline \u003cb\u003eIFN-γ\u003c/b\u003e level, which remained unchanged upon stimulation, while PBMCs showed a significant increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBeyond IL-10 and Foxp3, ASTRLs expressed several markers shared with Tr1 cells, including \u003cb\u003eCCR5, BATF, EGR2, GZMB, CD39, TIGIT\u003c/b\u003e, and \u003cb\u003eTIM3\u003c/b\u003e. However, none of these markers are unique to Tr1 cells and can be found in other Foxp3\u0026thinsp;+\u0026thinsp;Tregs. ASTRLs also expressed \u003cb\u003eBHLHE40\u003c/b\u003e, a transcription factor regulating \u003cb\u003eIL-10\u003c/b\u003e and \u003cb\u003eIFN-γ\u003c/b\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), and associated with Tr1 cells. Like Tr1 cells, ASTRLs showed intermediate \u003cb\u003eIFN-γ\u003c/b\u003e levels and expressed \u003cb\u003eCD39\u003c/b\u003e. Though \u003cb\u003eBHLHE40\u003c/b\u003e can induce \u003cb\u003eCD49b\u003c/b\u003e and \u003cb\u003eLAG3\u003c/b\u003e(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) (canonical Tr1 markers), these were not differentially expressed in ASTRLs (log2 fold changes: 0.91 and 0.17, respectively). Another Tr1-specific gene, \u003cb\u003eEGR2\u003c/b\u003e, was upregulated in ASTRLs and is essential for IL-10 production in Tr1 cells(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, \u003cb\u003eCD49a\u003c/b\u003e, a marker of tissue-resident memory T cells, was significantly upregulated in ASTRLs.\u003c/p\u003e\u003cp\u003eA comparison across the four datasets revealed a shared set of genes consistently upregulated in ASTRLs: \u003cb\u003eIL2RA, ENTPD1, HLA-DRA, IKZF4, DUSP10, TNFRSF9, CXCR6\u003c/b\u003e, and \u003cb\u003eTIGIT\u003c/b\u003e. In addition, ASTRLs showed distinct enrichment of two gene subsets: 39 genes shared with peripheral Tregs and 38 with uTregs. A further 20 genes from the Th2-like Treg signature were also upregulated in ASTRLs (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eThese findings collectively indicate that ASTRLs possess a Th2-like regulatory gene signature and share functional features with human Tregs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrate that ASTRLs, when expanded ex vivo from stable kidney transplant recipients (KTRs), exhibit a Th2-like regulatory T cell (Treg) profile and upregulate a gene signature associated with Tregs. While Treg heterogeneity and gene signatures have been well characterized in healthy individuals(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), human diseases(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and the tumor microenvironment(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), this is, to our knowledge, the first detailed report describing the regulatory gene signature of an ex vivo expanded autologous cell therapy product derived from stable KTRs. This study identifies and validates the key functional cell subset within a heterogeneous cell therapy product, providing critical insights for clinical translation.\u003c/p\u003e\u003cp\u003eThe CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment is the primary active population within ASTRLs, showing clonal expansion in response to donor antigens. These ASTRL-CD4\u003csup\u003e+\u003c/sup\u003e cells exhibit a Th2-like Treg gene signature that overlaps with both peripheral Tregs (pTregs) and uterine Tregs (uTregs). Despite low expression of Foxp3\u0026mdash;similar to Tr1 cells\u0026mdash;ASTRL-CD4\u003csup\u003e+\u003c/sup\u003e cells maintain a regulatory phenotype and function. These findings support the idea that antigen-specific expansion of a heterogeneous T cell population can yield regulatory cells that do not necessarily meet the classical definition of Tregs. They also align with the growing recognition of substantial phenotypic and functional heterogeneity within Tregs(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), which may shift depending on disease context.\u003c/p\u003e\u003cp\u003eA notable example of this heterogeneity is the partial overlap of only 17 genes between ASTRLs and Foxp3\u003csup\u003e+\u003c/sup\u003e Treg signatures. These 17 genes\u0026mdash;\u003cb\u003eARHGAP11A, ARHGEF12, CSF2RB, CXCR6, DUSP10, DUSP4, ENTPD1, GK, HLA-DRA, IKZF4, IL2RA, NUSAP1, SLC1A4, SLC9A7, ST8SIA4, TIGIT\u003c/b\u003e, and \u003cb\u003eTNFRSF9\u003c/b\u003e\u0026mdash;include several core Treg markers but exclude many genes unique to ASTRLs. This distinct signature likely arises from:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ethe source of the initial cells (PBMCs from stable KTRs),\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ethe conditions used for ex vivo expansion,\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTCR-dependent alloantigen stimulation, and\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003einter-patient variability.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eTreg heterogeneity is known to depend on TCR signaling(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). TCR-induced markers such as \u003cb\u003eGITR, TIGIT, IL-10\u003c/b\u003e, and \u003cb\u003eEBI3\u003c/b\u003e are notably upregulated in ASTRLs, explaining their divergence from resting primary Treg signatures.\u003c/p\u003e\u003cp\u003eImportantly, the ASTRL gene signature includes three non-overlapping gene sets: 39 pTreg genes, 38 uTreg genes, and 20 Th2-like Treg genes. The Th2-like gene set and associated cytokine production confirm the Th2 bias of ASTRLs, consistent with previous findings(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This bias is further supported by the downregulation of 111 Th genes, typically upregulated in conventional T cells.\u003c/p\u003e\u003cp\u003eComparisons of circulating, lymphoid, and tissue-resident Tregs have revealed distinct gene signatures related to tissue adaptation. Our results introduce a novel concept: that ex vivo expansion of cells previously exposed to donor alloantigens (as in PBMCs from stable KTRs) can activate a regulatory gene program. ASTRLs exhibit a unique regulatory gene combination not previously described. The 39 genes shared with pTregs are mainly involved in TCR signaling, MHC class II antigen presentation, and pathways linked to allograft rejection or tolerance. Genes such as \u003cb\u003eTNFRSF18 (GITR), HAVCR2 (TIM3), BATF\u003c/b\u003e, and \u003cb\u003eNFIL3\u003c/b\u003e\u0026mdash;found in the uTreg signature and upregulated in ASTRLs\u0026mdash;are recognized for their roles in tissue-adapted Treg function.\u003c/p\u003e\u003cp\u003eDespite patient-to-patient variability, ASTRLs consistently display a regulatory phenotype, as shown by multiomic analysis. Our earlier work demonstrated that their suppressive function is mediated by the adenosinergic pathway, a finding further supported by gene expression data in this study. Additional pathways contributing to ASTRL-mediated immunoregulation include \u003cb\u003eTNF-α\u003c/b\u003e, \u003cb\u003emTORC1\u003c/b\u003e, and \u003cb\u003eTIGIT\u003c/b\u003e signaling. Among these, the \u003cb\u003eTNFRSF\u003c/b\u003e signaling axis and \u003cb\u003eBATF\u003c/b\u003e are crucial for Treg survival and maintenance and are enriched in ASTRLs.\u003c/p\u003e\u003cp\u003eThe co-expression of pTreg and uTreg signature genes within ASTRLs is unique. While a pTreg-like signature is expected due to the peripheral origin of the starting cells, the presence of uTreg-specific genes (e.g., \u003cb\u003eBATF\u003c/b\u003e and the \u003cb\u003eTNFRSF\u003c/b\u003e axis) likely reflects the influence of alloantigen-driven expansion. Interestingly, both uTregs and ASTRLs also contain NK cell subsets, though the role of NK cells in ASTRL expansion remains under investigation.\u003c/p\u003e\u003cp\u003eOur findings suggest that cellular heterogeneity is not necessarily a drawback, especially in the context of antigen-specific cell therapies. In fact, our data indicate potential interactions between the innate and adaptive immune systems in response to alloantigen. This is evident from the gene expression profiles of ASTRLs, which show various costimulatory receptor-ligand pairs across different cell types\u0026mdash;interactions likely crucial for maintaining a regulatory microenvironment. Tregs are known to mediate suppression through direct cell-cell interactions. In cancer immunobiology, such regulatory microenvironments help tumors evade immune detection. Adapting this concept to generate alloantigen-specific, heterogeneous regulatory T cells to prevent chronic allograft rejection is a rational and promising approach.\u003c/p\u003e\u003cp\u003eAdditionally, the data suggest dynamic crosstalk between innate and adaptive immune cells within ASTRLs. \u003cb\u003eTIGIT\u003c/b\u003e and its ligand \u003cb\u003eCD155\u003c/b\u003e\u0026mdash;both expressed on NK and T cells\u0026mdash;imply a role for trained immunity. TIGIT-CD155 interactions inhibit the \u003cb\u003eTCR-AKT-mTORC1\u003c/b\u003e signaling pathway(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), and preliminary data suggest ASTRL NK cells may exhibit features of trained immunity. The possible role of innate immune memory in contributing to ASTRL\u0026rsquo;s regulatory function warrants further exploration using multiomic techniques.\u003c/p\u003e\u003cp\u003eSingle-cell technologies are increasingly being used to design, understand, and refine novel cell therapies(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Our application of multiomic analyses has provided a comprehensive understanding of the regulatory landscape of ASTRLs. TCR repertoire and clonal expansion analyses further demonstrate the antigen specificity of these cells. Altogether, the evidence supports ASTRLs as a potent and effective regulatory cell therapy.\u003c/p\u003e\u003cp\u003eThis study offers a uniquely detailed preclinical characterization of a \u003cb\u003ecell therapy product.\u003c/b\u003e A robust immune system depends on both heterogeneity and redundancy\u0026mdash;not only for effective responses but also to maintain immune homeostasis. Tumors exploit this redundancy to evade immunity, and \u003cb\u003ewe can\u003c/b\u003e reverse-engineer that principle to support allograft tolerance. Our single-cell data confirm that despite individual variability, ASTRLs are phenotypically and functionally consistent and, regardless of their heterogeneity, represent a promising approach towards development of a regulatory cell therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eStudy Subjects\u003c/h2\u003e\u003cp\u003ePeripheral blood samples were collected from five stable kidney transplant recipients (KTRs), each with at least one HLA-DR mismatch with their donor. Additional KTR samples ( \u0026ge; 10) were used for complementary analyses such as flow cytometry and CyTOF. All participants provided written informed consent prior to participation in the study, and the study was approved by the institutional review board of Brigham and Women\u0026rsquo;s Hospital (Protocol# 2013P001293). All the methods were performed in accordance with the institutional guidelines and regulations.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eASTRL Expansion\u003c/h2\u003e\u003cp\u003ePBMCs were isolated from patient blood samples using Lymphoprep density gradient centrifugation (Stemcell Technologies) and cryopreserved in liquid nitrogen until use. For expansion, thawed PBMCs were stimulated with donor-derived allopeptides (ProImmune, Littlemore, UK) in the presence of low-dose IL-2 (Proleukin). This donor antigen-specific stimulation protocol, described previously(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), was used to generate Antigen-Specific T cell-enriched Regulatory Lines (ASTRLs).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFlow Cytometry and Mass Cytometry (CyTOF)\u003c/h2\u003e\u003cp\u003eFor phenotypic characterization, ASTRLs and corresponding PBMCs were stained with fluorophore-conjugated antibodies targeting markers such as CD3, CD4, CD8, CD25, CD127, CD39, CD73, CTLA4, GITR, ICOS, CD45RA, CD226, LAP, GARP, CD56, CD16, CD19, CD11b, CD38, CD27, and CD24 (BioLegend). Flow cytometry data were acquired on a Cytek Aurora spectral cytometer and analyzed using FlowJo software.\u003c/p\u003e\u003cp\u003eFor CyTOF analysis, antibodies from Standard BioTools were used according to the manufacturer\u0026rsquo;s protocols. Samples were run on a CyTOF-XT instrument at the Dana-Farber Cancer Institute Mass Cytometry Core. Data were analyzed using Cytobank (Beckman Coulter) and OMIQ (Dotmatics) with tSNE, FlowSOM, and CITRUS clustering algorithms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eATP Hydrolysis Assay\u003c/h2\u003e\u003cp\u003eTo evaluate the functional activity of CD39, extracellular ATP hydrolysis was measured using a malachite green colorimetric assay kit (AnaSpec). Inorganic phosphate production, a byproduct of ATP hydrolysis, was quantified according to the manufacturer's protocol(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eProliferation Assays and Cytokine Measurements\u003c/h2\u003e\u003cp\u003ePreviously frozen PBMC and ASTRL samples were stimulated with CD3/CD28 beads for 72h at 37\u0026ordm;C. Supernatants were collected for cytokine measurement using multiplex assay kits from Thermofisher following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSingle-Cell RNA Sequencing\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003eSample Preparation and Library Construction\u003c/h2\u003e\u003cp\u003eTen samples (five PBMCs and five matched ASTRLs) were prepared from stable kidney transplant recipients. Cryopreserved cells were thawed in a 37\u0026deg;C water bath and resuspended in DMEM containing 10% FBS. Following centrifugation and washing with PBS\u0026thinsp;+\u0026thinsp;0.4% BSA, cell viability was assessed using Trypan Blue staining. Samples with \u0026ge;\u0026thinsp;65% viability were included for further processing.\u003c/p\u003e\u003cp\u003eApproximately 10,000 cells per sample were loaded into the 10x Genomics Chromium Controller using the Single Cell 5' v2 Reagent Kit for encapsulation into Gel Beads-in-Emulsion (GEMs). These GEMs were processed to generate barcoded, full-length cDNA libraries for gene expression, TCR, and BCR profiling.\u003c/p\u003e\u003cp\u003eLibrary quality was assessed using an Agilent Bioanalyzer, and libraries were sequenced on Illumina NextSeq or NovaSeq platforms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eData Processing and Analysis\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003ePreprocessing and Quality Control\u003c/h2\u003e\u003cp\u003eRaw sequencing data were demultiplexed using Cell Ranger `mkfastq`, and reads were aligned to the human reference genome (hg38) using Cell Ranger `count`.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eClustering and Cell Type Identification\u003c/h2\u003e\u003cp\u003eDimensionality reduction was performed using PCA, followed by Uniform Manifold Approximation and Projection (UMAP) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Variable genes from TCR loci (TRAV, TRBV, etc.) were excluded to avoid clustering based on clonotype rather than transcriptional phenotype.\u003c/p\u003e\u003cp\u003eSamples were batch-corrected using Harmony(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). to minimize inter-sample variation. Clustering was done using Seurat\u0026rsquo;s Louvain algorithm(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e)., and clusters were annotated with canonical immune markers. Additionally, reference mapping was performed using Azimuth(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), which aligned cells to a curated PBMC reference dataset. B cell identities were corrected using matched BCR data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003ePseudobulk Differential Expression Analysis\u003c/h2\u003e\u003cp\u003eFor differential gene expression analysis, CD4\u003csup\u003e+\u003c/sup\u003e T cells were extracted from each sample, and their transcript counts were aggregated (pseudobulk). Using DESeq2, we compared gene expression profiles between ASTRLs and PBMCs. Genes with adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered significant. Log2 fold changes were stabilized using the ashr shrinkage estimator(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eGene Set Enrichment and Pathway Analysis\u003c/h2\u003e\u003cp\u003eSignificantly differentially expressed genes were analyzed for functional enrichment using clusterProfiler(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), querying the MSigDB Hallmark database(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), Reactome pathways(\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), and custom gene sets (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Enrichment was assessed via GSEA (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)and over-representation analysis (ORA). Heatmaps were generated using pheatmap and ComplexHeatmap R packages(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eTCR and BCR Repertoire Analysis\u003c/h2\u003e\u003cp\u003eTCR/BCR clonotypes were identified using scRepertoire(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e), based on the most frequent alpha and beta chains per cell. Clonotype identity was defined by unique combinations of CDR3 nucleotide sequences and V(D)J gene usage. Additional integration into Seurat metadata allowed UMAP-based visualization of clonal expansion using immunarch.\u003c/p\u003e\u003cp\u003eCells with only partial receptor chain information (e.g., only alpha or beta) were retained where appropriate.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eSex as a Biological Variable\u003c/h2\u003e\u003cp\u003eSex was not considered as a biological variable.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were visualized and analyzed using GraphPad Prism. Appropriate statistical tests\u0026mdash;including one-way ANOVA, two-way ANOVA, and unpaired t-tests\u0026mdash;were used to assess significance, depending on the dataset. Venn diagrams were created using the toll available at the following URL. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eStudy Approval\u003c/h2\u003e\u003cp\u003e All participants provided written informed consent prior to participation in the study, and the study was approved by the local institutional ethics committee.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe supporting data is available upon request from the corresponding author.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict-of-interest statement:\u003c/h2\u003e\u003cp\u003e The authors have declared that no conflict of interest exists.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eWe thank the Saxena Kidney and Pancreas Transplantation Research Fund in aiding the research efforts.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contributionsST: conception and designing research studies, conducting experiments, acquiring and analyzing data, interpretation of results, writing and editing the manuscriptBLS, PLM: conducting experiments, acquiring and analyzing data,AMJ, ZZ, SHS: analyzing data, writing and editing the manuscriptAMW: designing research studiesAC: conception and designing research studies, interpretation of results, editing the manuscript and overall supervision of research\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Saxena Kidney and Pancreas Transplantation Research Fund in aiding the research efforts. We thank the Center for Cancer Genomics Services and the Longwood Medical Area CyTOF core at the Dana-Farber Cancer Institute for their support with scRNA-seq and CyTOF data acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLutter, L. et al. Human regulatory T cells locally differentiate and are functionally heterogeneous within the inflamed arthritic joint. \u003cem\u003eClin. Transl Immunol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (10), e1420 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIkebuchi, R. et al. Functional Phenotypic Diversity of Regulatory T Cells Remaining in Inflamed Skin. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1098 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaist, M., Stege, H. \u0026amp; Grabbe, S. and Bros M. 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L., Kraus, G. \u0026amp; scRepertoire An R-based toolkit for single-cell immune receptor analysis. \u003cem\u003eF1000Res\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 47 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7464625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7464625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTherapeutic application of ex vivo expanded regulatory T cells is a promising approach to prolong allograft survival. In this work we performed a detailed characterization of a preclinical heterogenous antigen specific T enriched regulatory cell line (ASTRL) expanded ex vivo from PBMC of stable kidney transplant recipients. We used three different approaches: scRNA-seq, flow cytometry and mass cytometry, to compare pre-expansion PBMC to post-expansion ASTRL. Results show the CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment in ASTRL clonally expanded in response to donor antigen stimulation and showed decreased TCR diversity. ASTRL CD4\u003csup\u003e+\u003c/sup\u003e T cells demonstrated a Treg associated transcriptome with upregulated CD39 and TIGIT together with other classical Treg genes like IL2RA, IKZF4, TNFRSF9, CXCR6, DUSP10 and HLA-DRA. Comparison of differentially expressed genes (DEGs) in ASTRL with classical Treg gene signatures showed strong overlap of genes associated with both peripheral and uterine Tregs together with a Th2-like Treg transcriptomic profile. In conclusion the CD4\u003csup\u003e+\u003c/sup\u003e T cell compartment of ASTRL acquire a regulatory T cell transcriptomic profile in response to donor antigen specific stimulation. This suggests a promising approach towards the development of a regulatory cell therapy in organ transplantation.\u003c/p\u003e","manuscriptTitle":"Single-cell and multi-omic characterization of ex vivo expanded ASTRLs from stable kidney transplant recipients reveals a regulatory T cell phenotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 18:12:02","doi":"10.21203/rs.3.rs-7464625/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-08T05:27:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T19:54:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T19:54:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-03T20:45:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213154636193111273882814849962428566034","date":"2025-09-23T12:52:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326456587504935559132932799851854564639","date":"2025-09-23T08:11:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T08:02:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59614737218772401897897937206500839296","date":"2025-09-17T08:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T09:20:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T07:52:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T06:10:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T17:43:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-28T17:38:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef2f6432-ec8a-4562-95a8-57d442eaa485","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":55199443,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55199444,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2026-01-19T16:45:06+00:00","versionOfRecord":{"articleIdentity":"rs-7464625","link":"https://doi.org/10.1038/s41598-025-33656-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-14 16:28:28","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-09-25 18:12:02","video":"","vorDoi":"10.1038/s41598-025-33656-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33656-6","workflowStages":[]},"version":"v1","identity":"rs-7464625","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7464625","identity":"rs-7464625","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}