Spatial Multiomics Analysis Identifies SOCS1 as a Key Immune Checkpoint in T cell Tolerance Induction of HSCT

doi:10.21203/rs.3.rs-150508/v1

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Spatial Multiomics Analysis Identifies SOCS1 as a Key Immune Checkpoint in T cell Tolerance Induction of HSCT

https://doi.org/10.21203/rs.3.rs-150508/v1

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Achieving T cell tolerance ensures superior clinical outcomes in hematopoietic stem cell transplantation (HSCT). However, the in vivo T cell tolerance profiles in physiological state need to be further delineated. Here, we characterized the gene expression profile in tolerant T cells which was induced in healthy donors by granulocyte colony-stimulating factor, a stem cell mobilizer extensively used in HSCT. We identified suppressor of cytokine signaling 1 (SOCS1) as an essential immune checkpoint for T cell tolerance in the mouse models and primary T cells in the HSCT context. Further spatial multiomics analysis characterized the distinct three-dimensional genome architecture and the gene regulatory network in tolerant T cells. We found STAT3 competes with CTCF and mediates the formation of a new chromatin loop between the SOCS1 promoter and upstream super enhancers during the induction of T cell tolerance. This study identifies SOCS1 as a key immune checkpoint and potential immune target for improving outcome of patients with HSCT.

Immunology

Computational Biology

Bioinformatics

Hematopoietic Stem Cell Transplantation

Tolerance Profiles in Physiological State

Granulocyte Colony-stimulating Factor

Cytokine Signaling

Spatial Multiomics Analysis

T cell tolerance, which ensures that T cells can effectively eliminate foreign antigens while maintain hyporesponsive to self-antigens, promises superior clinical outcomes in transplantation and autoimmune diseases. Although hematopoietic stem cell transplantation (HSCT) became a curative therapy for a wide range of hematological malignancies, donor T cells failing to reach the tolerant state in host microenvironment leads to severe graft versus host disease (GVHD) and threatens the survival of patients after HSCT. Several strategies have been developed to induce tolerance and minimizing GVHD occurrence post-HSCT, such as blockade of costimulatory signals, transforming growth factor beta and regulatory T cells (Treg) ^{1, 2, 3}.

Granulocyte colony-stimulating factor (G-CSF), initially identified as a growth factor for neutrophils, has been widely used as a mobilizer for stem/progenitor cells in HSCT settings. In the past two decades, increasing evidence has supported the critical role of recombinant human G-CSF in the induction of T cell tolerance, which is characterized by decreased proliferation and interleukin-2 production, in healthy HSCT donors ^{4, 5, 6}. Experimental evidence suggests that G-CSF is a strong immune regulator of T cells and directly modulates T-cell immune responses via its receptor on T cells ^{5, 7}. These data indicate that human tolerant T cells induced by G-CSF provide a platform to elucidate the essential genomic and epigenomic factors in maintaining T cell tolerance in HSCT context.

Previous studies have profiled gene expression patterns or key transcription factors (TFs) in the induction and maintaining of T cell tolerance under pathological conditions^{8, 9, 10}. However, investigating gene expression alone provides limited information ignoring crucial role of regulatory networks. Moreover, few have investigated the potential role of chromatin reorganization in the induction of T cell tolerant state, especially under physiologic conditions. Recent advances in chromatin structure analytic technologies, including assays for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and genome-wide chromosome conformation capture (Hi-C), enable delineating the profile of trans-acting factors and cis-regulatory elements in chromatin reorganization^{11, 12, 13}. These approaches help us investigate the chromatin state and 3D chromatin interactions of T cells from steady state to tolerant state under physiologic conditions.

In this study, we systematically investigated the dynamics of transcriptomes and 3D chromatin interaction coordinate in a physiologic T cell tolerance model established by treating healthy donors with G-CSF. We characterized the expression profile in the tolerant T cells and identified the essential role of SOCS1 in the induction and maintaining of T cell tolerance in HSCT. Furthermore, spatial multiomics analysis revealed the chromatin reorganization of T cell from steady state to tolerant state and we demonstrated transcription factor STAT3, acting as epigenetic coordinator in promoting SOCS1 expression level during the induction of T cell tolerance. Our study provides insights into transcriptomes and epigenetic modifications in T cell tolerance induction in HSCT.

Tolerant state T cells exhibit distinct gene expression profiles and TF distributions.

T cell tolerance could be induced by in vivo application of G-CSF in both mice and humans ^{4, 5, 6}. We assessed the transcriptome, chromatin accessibility and 3D genome landscape of steady state CD4⁺ and CD8⁺ T cells (CD4⁺ T_ss and CD8⁺ T_ss, respectively) together with tolerant state CD4⁺ and CD8⁺ T cells (CD4⁺ T_tol and CD8⁺ T_tol, respectively) in human bone marrow (BM) (Fig. 1A and Fig. S1). Steady state and tolerant state T cells were distinct from each other both in CD4⁺ and CD8⁺ T cells, as shown in dendrograms generated using transcriptome data (Fig. 1B). Then we explored and validated genes that were differentially expressed during the induction of tolerance in human CD4⁺ and CD8⁺ T cells (CD4⁺ T_tol and CD8⁺ T_tol) by G-CSF. We found that CD8⁺ T_tol cells differentially expressed multiple genes, such as SOCS1, PRDM1 and SEMA7A (Fig. 1C-D), compared to CD8⁺ T_ss cells. We observed changes in a core set of genes, including suppressor of cytokine signaling 1 (SOCS1), PR/SET domain 1 (PRDM1), in a similar manner in CD4⁺ T_tol and CD8⁺ T_tol cells (Fig. 1C-D, and S2A-B). However, both CD4⁺ T_tol and CD8⁺ T_tol cells also exhibited changes in specific sets of genes. Notably, genes with CD8⁺ T cell-based changes in expression included SEMA7A, whereas those with CD4⁺ T cell-based changes included nuclear factor-κB inhibitor alpha (NFKBIA) (Fig. 1C-D, and S2A-B). Thus, although there is a clear transcriptomic change shared by both CD4 and CD8 lineages, there were also unique differentially expressed genes and TFs in CD4⁺ and CD8⁺ T cells during tolerance induction in vivo. CD8 T_tol cells showed a significant downregulation of genes related to cell activation (Fig. 1E), and CD4 T_tol cells showed downregulation of cytokine signaling genes (Fig. S2C). These results indicated T cells from G-CSF administrated healthy donors exhibited tolerance phenotype. Furthermore, we validated the direct upregulation of SOCS1 expression level by G-CSF in highly purified CD3⁺ T cells from 7 independent heathy donor BM samples in vitro. The results showed that G-CSF stimulation led to a peak in SOCS1 mRNA production after 4 h of culture, followed by recovery after 72 h of culture (Fig. 1F, Fig. S3A-B). After cultured 72 h of CD3⁺ T cells with G-CSF stimulation in vitro, the G-CSFR expression level was significantly increased (Fig. S3C-D). Consistent with previous studies⁷, IL-2 was decreased in the G-CSF treatment group which indicated that G-CSF suppressed differentiation of T cells to the Th1 type (Fig. 1G, Fig. S3E). These results demonstrated that G-CSF directly upregulated SOCS1 expression levels via GCSFR.

G-CSF loses its protective role in a GVHD mouse model in the absence of Socs1.

To investigate the role of SOCS1 regulation on T cell function during G-CSF induction of tolerance in vivo, we established a T cell-specific Socs1 conditional knockout (cKO; LckCre-Socs1^fl/fl) mouse model. Consistent with previous studies¹⁴, most cKO mice survived longer than 6 months. Occasionally, cKO mice developed dermatitis at 4 weeks (3 in 50 mice, Fig. S4A). There was slight splenomegaly in the cKO mice compared with Socs1^fl/fl (WT) mice (Fig. S4B-C). Flow cytometry analysis showed that CD3⁺ T cell were decreased in cKO mice compared with WT mice (Fig. S4D). The transposition of the CD4/CD8 ratio in cKO mice represents a decrease in CD4⁺ T cells and an increase in CD8⁺ T cells in cKO mice compared with WT mice (Fig. S4E). The ratio of naïve CD4⁺ T cells was increased in the cKO mice compared with the WT mice (Fig. 2A-B). IFN-γ secretion in CD4⁺ and CD8⁺ T cells in cKO mice was increased compared with that in WT mice (Fig. 2C-D). These data suggested that loss Socs1 might induce severe GVHD in the HSCT mouse model.

To validate this hypothesis, we examined the effect of losing Socs1 in T cells on the protective role of G-CSF in a well-established murine GVHD model (C57BL/6 to BALB/c). Donor WT or cKO mice received 5 daily subcutaneous injections of either PBS or 5 µg human G-CSF, and spleens were harvested on day 6. BALB/c recipient mice received 8 Gy total body irradiation (TBI), and 3×10⁶ T cells from the spleen were transplanted intravenously from the respective donors the following day. We transplanted 5×10⁶ T cell-depleted bone marrow cells (TCD-BM) as protective cells from the WT PBS group donor mice to all groups of recipient mice. As shown in Fig. 2E, G-CSF prolonged the survival of WT mice compared with the PBS group (green vs. blue); however, G-CSF accelerated the death of GVHD mice in the cKO group (yellow vs. red). A validation experiment in which 5×10⁶ TCD-BM and 2×10⁶ T cells were transplanted from the spleen also indicated that G-CSF exacerbated GVHD and shortened the life span of cKO mice compared with WT mice (Fig. 2F). Flow cytometry analysis also showed that G-CSF inhibited CD62L expression levels in both WT and cKO mice (Fig. S4F-G), which is consistent with our previous studies in clinical samples.

We further investigated the G-CSF-administered donor-derived T cell phenotype in recipient mice in GVHD models. Compared with the WT mice, the naïve populations of both CD4⁺ and CD8⁺ T cells were increased in the cKO group (Fig. 2G-H). The proliferation ability of CD4⁺ T cells from cKO mice was significantly increased compared with that of CD4⁺ T cells from WT mice (Fig. 2I). Moreover, we transplanted 1×10⁶ MLL-AF9-induced AML cells into nonirradiated WT or cKO mice to investigate T cell function in the context of leukemia. The results showed that the loss of Socs1 in T cells prolonged survival in leukemic mice and delayed leukemia progression (Fig. 2J). This result indicated that losing Socs1 might activate T cell function in the tumor environment. Taken together, these results demonstrated that Socs1 is the key mediator in G-CSF-induced T cell tolerance.

High expression levels of SOCS1 impairs T cell proliferation and decreases aGVHD occurrence after HSCT.

To further investigate the role of SOCS1 in maintaining T cell tolerance, we used lentivirus to overexpress SOCS1 in steady-state T cells and found that the SOCS1 expression level was increased approximately 30-fold in the SOCS1 overexpression group (SOCS1 OE) compared with the control group (CT) (Fig. 3A). High expression of SOCS1 inhibited T cell proliferation, and more T cells were blocked in the G0 stage in the SOCS1 OE group compared to the CT or noninfection control group (Fig. 3B). The proliferative ability of CD4⁺ T cells was decreased in the SOCS1 OE group compared to the CT or noninfection group, while CD8⁺ T cells showed no change (Fig. 3C-D, Fig. S5A). This result is consistent with the in vivo study in which the proliferation ability of CD4⁺ T cells from Socs1 cKO mice was increased compared with that of CD4⁺ T cells from WT mice (Fig. 2I). Moreover, high SOCS1 expression in T cells also promoted TIGIT expression (Fig. 3E, Fig. S5B-C). There were no significant differences in the secretion of cytokines, such as IFN-γ, IL-2, IL-17, IL-4, and IL-10, by CD4⁺ T and CD8⁺ T cells between the SOCS1 OE group and CT group (Fig. 3F, Fig. S6A-C).

We further validated the relationship between the expression level of SOCS1 in T cells and aGVHD occurrence in patients after allo-HSCT. The results showed that there was a lower expression level of SOCS1 in the patients with aGVHD than in the patients without aGVHD at the same timepoint after allo-HSCT (Fig. 3G). This result indicated that a low expression level of SOCS1 might induce aGVHD after allo-HSCT.

G-CSF Regulates Target Gene Expression by Chromatin structure alteration.

Previous results identified SOCS1 as a key immune checkpoint for T cell tolerance, thus the regulatory network of SOCS1 needs to be further investigated. To explore the regulatory mechanism of SOCS1 during T cell tolerance induction, we performed transcription factor enrichment analysis in chromatin regions with differences in accessibility between CD8⁺ T_tol and CD8⁺ T_ss cells using ATAC-seq data ¹⁵. The regions with high chromatin accessibility were located in promoter and distal intergenic and distinct in steady state and tolerant state T cells (Fig. S7A-B). We observed higher chromatin accessibility at the promoter and upstream elements of cell-type specific genes than at other regions in CD4⁺ and CD8⁺ T_ss (Fig. S7C-E), consistent with the characteristics of these two cell lineages ¹⁶. These results highlight the feasibility and reliability of ATAC-seq in investigating the genome landscape and chromatin accessibility of human T cells. We found that TFs such as STAT3 were specifically activated in CD8⁺ T_tol compared to CD8⁺ T_ss cells (Fig. 4A). T cell activation related TFs such as JunB, AP-1 were suppressed in CD8⁺ T_tol compared to CD8⁺ T_ss cells (Fig. 4B). Differential expression of genes and differential activation of TFs including TXNIP and RUNX1 between CD4⁺ T_tol and CD4⁺ T_ss were observed (Fig. S7E-F). According to the relationship between TFs and target genes ¹⁷, we plotted the network of upregulated genes and enhanced regulatory transcription factors in CD8 cells (Fig. 4C), which shows that SOCS1 is highly expressed and that the TF most strongly regulating SOCS1 is STAT3. We also identified a regulatory network of highly expressed genes and enhanced TFs in CD4⁺ cells (Fig. S7G).

Next, we analyzed the spatial regulatory relationship between transcription factors and target genes which is 3D genome interaction maps of CD4⁺ and CD8⁺ T cells from steady state to tolerant state. We obtained high-resolution maps (5 kb) of the 3D genome structure, including A/B compartments, TAD structure and loop structure, of all the samples (Fig. 4D). The HiCRep SCC scores ¹⁸ of the Hi-C matrices showed that cells of different lineages have different 3D genome structures (Fig. 4E). The loop length of CD4⁺ T_ss was significantly longer than that of CD8⁺ T_ss (median length: CD4 T_ss, 210 kb; CD8 T_ss, 190 kb; Fig. 4F-G). This finding indicates that the chromatin structure of CD4⁺ T cells is more variable than that of CD8⁺ T cells at the level of loops during the induction of tolerance.

The switch of A/B compartments from steady-state T cells to tolerant cells is shown for chromosome 10 in Fig. S8A. Approximately 7.4% and 7.3% of the genome regions in CD4⁺ and CD8⁺ T cells, respectively, switched from compartment A to B and were associated with downregulated gene expression after tolerance induction. Meanwhile, 6.6% and 4.5% of the genome regions in CD4⁺ and CD8⁺ T cells, respectively, switched from compartment B to A and were associated with upregulated gene expression (Fig. S8B-C). The TAD boundaries were more accessible and were enriched with CTCF and H3K27ac signals ¹³ (Fig. S8D). We found that most of the TAD boundaries (> 90%) are conserved and that the length of the TADs decreased slightly in the tolerant CD4⁺ and CD8⁺ T cells compared to the steady-state cells (Fig. S8E-F).

We demonstrated that more than 53% of the genes upregulated in CD8 T_tol cells compared with CD8⁺ T_ss cells, including SOCS1, PRDM1, and KLF9, are located in the loop anchor regions (Fig. 4H, P < 1e-16). These observations showed that most of the differentially expressed genes may contact distal cis-regulatory elements via chromatin loops during the in vivo induction of human T cell tolerance. We found 7,481 chromatin loops in CD8⁺ T_ss and 6,186 chromatin loops in CD8⁺ T_tol cells, with 4,786 chromatin loops appearing in both (Fig. 4I). Compared with those in CD8⁺ T_ss cells, STAT3 and PRDM1 motifs were enriched in the gained loop anchors and were highly expressed in CD8⁺ T_tol cells, which is consistent with the ATAC-seq results (Fig. 4A, 4J). This suggests that STAT3 may be a structural protein that mediates the gain of chromatin loops. The lost loop anchor-enriched TFs included ZNF416 and TCF4 in CD8⁺ T_tol cells (Fig. 4J, Fig. S8G).

Association of STAT3 with SOCS1 Expression in Tolerant T Cells

We next explored the regions of spatial interaction with the promoter of SOCS1 and determined which transcription factors bind to these regions. On chromosome 16, the SOCS1 gene is located within one TAD in CD8⁺ T_ss cells (Fig. 5). Then, we investigated the chromatin spatial structure, histone modification and TF binding sites around the SOCS1 gene. The genome-browser view of CTCF and STAT3 binding sites suggests that the CTCF protein mediates the interaction between the SOCS1 locus and the downstream chromatin region, and STAT3 proteins mediate the interaction between SOCS1 and upstream super enhancers. From Hi-C data, the interaction between the SOCS1 locus and downstream heterochromatin is weakened, and the interaction between SOCS1 and upstream super enhancers is enhanced in CD8⁺ T_tol cells compared to CD8⁺ T_ss cells (Fig. 5). These results suggest that a new association of STAT3 with SOCS1 expression emerged during the in vivo induction of human T cell tolerance. In support of this hypothesis, genome-wide statistics showed that genes with long-range interactions with heterochromatin tended to be expressed at low levels, while genes with long-range interactions with enhancers tended to be highly expressed (Fig. S8H).

STAT3 mediates the spatial interaction between enhancers and promoters in the whole genome.

To investigate whether STAT3 competes with CTCF in regulating target genes, we performed ChIP-seq and CUT&Tag experiments to detect the colocalization of STAT3 and CTCF. For example, many of the binding sites of STAT3 and CTCF are colocalized in and around SOCS1 (Fig. 6A) and TXNIP (Fig. 6B), which are upregulated after G-CSF mobilization. Furthermore, STAT3 and CTCF colocalized in the whole genome analysis of human CD8 T cells and GM12878 cell lines (Fig. 6C-E and Fig. S9A-D). There was a significant overlap between the CTCF peaks and the STAT3 peaks in CD8 T cells, as shown by Venn diagram (P < 1e-10, Fig. 6F). The peaks of STAT3 binding are enriched in promoter and enhancer regions (Fig. 6G-H). Then, we classified the peaks of STAT3 binding into promoter regions or enhancer regions (Fig. 6H and Fig. S9E-G), and there is a significant spatial interaction between the promoter regions and enhancer regions (Fig. 6I-J). These results strongly suggest that the STAT3 complex is involved in enhancer and promoter interactions. Consistent with our observation, previous studies showed STAT3 could regulate chromatin topology and mediate transcription during T cell differentiation ^{19, 20, 21}. STAT4 binding in the genome contributes to the specification of the nuclear architecture around Ifng during Th1 differentiation²². Furthermore, we observed both CTCF and STAT3 foci in the nuclei of Jurkat cells by immunofluorescence staining (Fig. S9H). Collectively, these results suggest that a STAT3-mediated enhancer-promoter interaction induces SOCS1 expression during the in vivo induction of T cell tolerance (Fig. S9I).

Furthermore, we detected STAT3 protein levels in SOCS1 overexpressed Jurkat T cell line. The results showed that a high SOCS1 expression level inhibited the phosphorylation of STAT3 (Fig. S10A-B). Moreover, the Western blotting results in the spleen cells from mice with T cell-specific Socs1 knock out showed that the phosphorylation of STAT3 was upregulated when Socs1 expression was inhibited (Fig. S10C-D). These results indicated that SOCS1 regulated the activation of STAT3 through a negative feedback loop.

Here, we have taken advantage of a well-defined physiologic T cell tolerant state which is induced by G-CSF involving multiomics analysis revealed the chromatin reorganization during T cell tolerance induction. Unsupervised clustering of accessible chromatin regions, specifically distal elements, groups individual cell types with high cluster purity, suggesting that these distal regulatory elements precisely define T cell immunological characteristics during the induction of tolerance. In addition, changes in the 3D genome compartment status might influence the accessibility of genomic regions to transcription factors or other regulatory proteins, which could be particularly important for certain subsets of genes. Furthermore, we identified SOCS1 as a key immune checkpoint in the induction and maintaining of T cell tolerance in HSCT. Integration of multiomics data enabled us to identify a novel regulatory model for SOCS1 expression during T cell tolerance induction (Fig. 7). The methodologies developed here might have important implications for addressing immunological profiles, such as those of dendritic cells and B cells, in other contexts of tolerance induction ⁹ as well as in cellular therapy.

SOCS1 belongs to classic negative feedback inhibitor family and plays an indispensable role in attenuating IFN-γ signaling^{23, 24}. Consistent with previous studies, we also found Socs1 cKO mice showed aberrant CD4/CD8 ratio, which is indicated Socs1 involved in T-cell development in the thymus^{25, 26}. Interestingly, although we observed enlarged spleen in Socs1 cKO mice as previous studies reported^{14, 27}, there is no statistical significance and cKO mice occasionally developed dermatitis. The Socs1 cKO mice didn’t develop severe inflammatory symptom in steady state. However, in the GVHD transplantation mouse models, G-CSF treated Socs1 cKO donor mice group developed more severe GVHD and shortened lifespan compared with PBS treated Socs1 cKO donor mice group (Fig. 2E-F). These results indicated the essential role of Socs1 in G-CSF induced T cell tolerance. Moreover, clinical data showed that there is low expression level of SOCS1 in T cells from patients with GVHD compared those without GVHD at the same period after HSCT (Fig. 3G). It might indicate SOCS1 involved in the maintaining of T cell tolerance in HSCT. Recently published work revealed SOCS1 haploinsufficiency causes autoimmune diseases and related to cytokine hypersensitivity of immune cells ²⁸. Taken together, these findings strongly indicated SOCS1 as a potential immune target for the clinical therapy of autoimmune diseases and transplantation.

In addition of SOCS1, we also found classical T cell tolerance regulators involved in G-CSF induced tolerant T cells. In CD8⁺ T_tol cells, the expression of AP-1 was significantly downregulated, accompanied by the upregulation of NFATC2. In CD4⁺ T_tol cells, the expression of Jun was also significantly downregulated, accompanied by the upregulation of NFKBIA (Fig. 1C-D and Fig. S2A-B). Previous studies showed that T cell anergy could be induced if the interaction of NFAT with its transcriptional partner AP-1 (Fos/Jun) was prevented ¹⁰. We also found an immune tolerance mediator, B lymphocyte-induced maturation protein-1 (Blimp-1) which is a zinc finger-containing transcriptional repressor encoded by PRDM1 ^{29, 30}, was upregulated in CD4⁺ T_tol and CD8⁺ T_tol cells compared with CD4⁺ T_ss and CD8⁺ T_ss cells, respectively (Fig. 1D and Fig. S2B). Overall, our study confirmed the G-CSF induced T cell tolerance partially via classical tolerance regulator TFs.

From the spatial multiomic data analysis, we observed a STAT3-mediated enhancer/promoter interaction for SOCS1 gene expression and proposed a novel model in which STAT3 could replace CTCF and form new chromatin loops, leading to the expression of SOCS1 (Fig. 7). Similarly, another TF NF-κB has been reported could compete with CTCF, forming a new loop and enhancing PD-L1 expression ³¹. Previous studies also showed STAT3 could regulate chromatin topology and mediate transcription during T cell differentiation ^{19, 20, 21}. These data suggest that STAT3 activating downstream target genes during T cell activation and tolerance might via reorganizing the chromatin structure and involved in distal regulatory elements.

In summary, based on physiologic T cell tolerance model and multiomics analyses, we established a platform for discovering novel genes and TFs that induce immune tolerance. Our data resource will serve as a valuable tool for the community to further elucidate the gene regulatory networks controlling the induction of human T cell tolerance. In addition, the essential role of SOCS1 in the induction and maintaining of T cell tolerance suggests that this gene could be a potential target for clinical therapy.

T cell isolation and culture

Human bone marrow mononuclear cells (BMMCs) were isolated from the BM of healthy donors before and after in vivo G-CSF application by Ficoll density centrifugation. CD3⁺ T cells were purified by positive selection (CD3 MACS MultiSort beads; Miltenyi Biotec, Bergische Gladbach, Germany). The isolated CD3⁺ T cells were cultured in IMDM (Gibco, Invitrogen, Carlsbad, CA) containing 10% BIT 9500 (Stemcell Technologies, Vancouver, CA) and stimulated with Dynabeads Human T-Activator CD3/CD28 (Gibco, Invitrogen, Carlsbad, CA). The study was approved by the Institutional Review Board of Peking University. Written informed consent was obtained from all healthy donors in accordance with the Declaration of Helsinki.

In vitrostimulation with G-CSF

CD3⁺ T cells isolated from healthy donors were incubated with G-CSF (100 ng/ml) for 4 h or 72 h at 37°C and 5% CO₂.

Mice

LckCre-Socs1^fl/fl (T cell-specific Socs1-cKO) and Socs1^fl/fl (littermate control; WT) mice (sex- and age-matched) were used. All mice were maintained in the specific pathogen-free animal facility of Peking University People’s Hospital. All experiments were performed according to the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals.

GVHD mouse models

Acute GVHD was induced as described previously ³². In brief, donor cKO or WT mice (C57BL/6 background) were subcutaneously injected with G-CSF (250 µg/kg daily) or the same volume of PBS for 5 days. These donor mice were sacrificed the day after the last dose was given. Splenic T cells were isolated from G-CSF- or PBS-treated donor mice by negative selection using a Pan T Cell Isolation Kit II (Miltenyi-Biotec, Germany), and the obtained cells had a purity of > 95%. BM cells from PBS-treated WT mice were T cell-depleted with anti-90.2 MicroBeads (Miltenyi-Biotec). Once splenic T cells and TCD-BM cells were purified, they were injected into the tail veins of prepared recipient mice. BALB/c hosts were subjected to total body irradiation from a [⁶⁰Co] source (8 Gy). They were randomly grouped.

Leukemia mouse models

The generation of the MLL-AF9 leukemia mouse model was described in detail previously ³³. Leukemia cells were thawed, and live cells were counted by staining with trypan blue. The ratio of GFP⁺ cells was detected with flow cytometry. A total of 1×10⁶ live GFP⁺ MLL-AF9 leukemia cells were transplanted into nonirradiated recipient mice, and mouse survival was monitored.

Lentivirus-mediated SOCS1 overexpression in T cells

The SOCS1-overexpressing lentivirus was purchased from Sangon Biotech (Shanghai, China). CD3⁺ T cells were prestimulated for 24 h with Dynabeads Human T-Activator CD3/CD28 in IMDM containing 10% BIT 9500, and rhIL-2 was added at a dose of 100 U/ml. After 24 h, the cells were transduced with thawed lentiviruses that were added directly to the plate. Then, 6 µg/ml polybrene (Sigma, USA) was added. The cells were incubated for another 24 h at 37°C and 5% CO_2, and fresh medium was changed. GFP⁺ cells were isolated after a 72-h infection and cultured in IMDM containing 10% BIT 9500 with rhIL-2 routinely used.

Flow cytometric analysis

Surface staining was performed with directly conjugated monoclonal antibodies for 20 min at room temperature for human samples. The cells were washed and resuspended in phosphate-buffered saline (PBS) before flow cytometric analysis. The monoclonal antibodies used were anti-human CD4-Percp-Cy5.5/APC-H7, CD8-APC-R700/V500, PD-1-PE-Cy7, Tim-3-APC, Tigit-BV605, 2B4-AF700, and CD160-PE (BD Bioscience San Diego, CA, USA). Intracellular staining was carried out by using a fixation/permeabilization kit (BD Bioscience) after resuspension according to the manufacturer's instructions. Ki-67-PE (BD Pharmingen) was added and incubated for 20 min at room temperature.

We stained the surfaces of mouse samples with direct-conjugated monoclonal antibodies for 30 min at 4°C. After incubation, the cells were washed and resuspended in phosphate-buffered saline before flow cytometry analysis. The monoclonal antibodies used were anti-mouse CD3-Percp, CD4-APC-H7, CD8-FITC, CD44-PE-Cy7, and CD62L-APC (BD Bioscience San Diego, CA, USA).

Cytokine detection by flow cytometry

T cells were stimulated with Dynabeads Human/Mouse T-Activator CD3/CD28 (Gibco, Invitrogen, Carlsbad, CA). After 72 h of culture, GolgiPlug (BD Pharmingen, San Diego, CA, USA) was added for 4 h. Cells were harvested for surface staining as described above. Intracellular staining was carried out by using a fixation/permeabilization kit (BD Bioscience) after resuspension according to the manufacturer's instructions. For human samples, IL-2-V450, IFN-γ-BV510, IL-17-PE, IL-4-APC, and IL-10-PE (BD Pharmingen) were added and incubated for 20 min at room temperature. For mouse samples, IFN-γ-PE (BD Pharmingen) was added and incubated for 30 min at 4°C.

T-cell proliferation analysis (CSFE)

CD3⁺ spleen T cells were isolated with a pan T cell isolation kit (Miltenyi-Biotec, Germany). The purified T cells were stained with CFSE (BD Bioscience) to achieve a final concentration of 5 µM for 5 min at 37°C and washed twice with complete medium with 10% FBS. Labeled T cells (2×10⁵ cells/well) were stimulated with CD3/28 beads in flat-bottomed 96-well plates in complete RPMI. After 96 h, cells were harvested, stained with CD4-APC-H7 and CD8-APC-R700 (BD Bioscience), and then analyzed by flow cytometry.

RT-PCR

RNA was extracted using the RNeasy Mini Kit (Qiagen, 74106) according to the manufacturer’s protocol. For quantitative PCR, first strand synthesis was performed using a cDNA reverse transcription kit (TaKaRa, RR047A) according to the manufacturer’s protocol. Quantitative PCR assays were performed in 96-well MicroAmp Fast Optical 96-Well Reaction Plates (Applied Biosystems, 4344904) using SYBR Green (Roche, 04913914001). Signals were detected using a 7500 Real-Time PCR System (Applied Biosystems). Target gene cycle numbers were normalized to the housekeeping gene 18S to obtain the ΔCT values. The 2^−ΔΔCT method was used. The primer sequences were as follows: SOCS1 forward: 5'-CACGCACTTCCGCACATTC-3'; SOCS1 reverse: 5'-TAAGGGCGAAAAAGCAGTTCC-3'; human 18S forward: 5'-ACCGATTGGATGGTTTAGTGAG-3'; and human 18S reverse: 5'-CCTACGGAAACCTTGTTACGAC-3'.

RNA-seq experiments and analysis

Total mRNA with a polyA tail was extracted and reverse transcribed to cDNA for sequencing. Three biological repeats were performed for each sample, and 20 million reads were sequenced for each repeat. The sequenced reads were mapped to the human reference genome (hg19) by TopHat2 ³⁴, and gene expression was quantified by Cufflinks ³⁵. We used RStudio software for the downstream statistical analyses.

ATAC-seq experiments and analysis

The ATAC-seq experiment was performed following Buenrostro’s protocol ¹⁵. Two biological repeats were used for each sample, and 20 million reads were sequenced for each repeat. The sequenced reads were mapped to the human reference genome (hg19) by Bowtie2 ³⁴, and peak signals were quantified by MACS2 and deepTools. We used RStudio software for the downstream statistical analyses.

Hi-C experiments

The cells were resuspended in fresh PBS. Cell counts were performed. Then, a cell suspension with a final concentration of 1x10⁶ cells per 1 ml of PBS was prepared. A total of 1 x 10⁶ cells was isolated and crosslinked with 1% formaldehyde for 10 min at room temperature, and then, 2.5 M glycine solution was added to a final concentration of 0.2 M. Then, the cells were collected, flash-frozen in liquid nitrogen and stored at -80°C. The Hi-C experiment was performed following the in situ Hi-C protocol ¹³.

Hi-C data analysis

We performed read mapping and filtering of the Hi-C data following previous methods ³⁶. All Hi-C sequencing reads were mapped to the human reference genome (hg19) using Bowtie2 ³⁷. The two ends of the paired-end reads were mapped independently using the first 36 bases of each read. We filtered out redundant and nonuniquely mapped reads and kept the reads within 500 bp upstream of enzyme cutting sites (Mbol) for size selection. We used the iterative correction and eigenvector decomposition (ICE) method ³⁸ to normalize raw interaction matrices.

A/B compartment analysis

We used ICE-normalized interaction matrices at 500 kb resolution to detect chromatin compartment types with the R package HiTC ³⁹. Positive or negative values of the first principal component separated the chromatin regions into two spatially segregated compartments. The compartment with a higher gene density was assigned as the A compartment, and the other compartment was assigned as the B compartment ⁴⁰.

TAD analysis

We used ICE-normalized interaction matrices at 40 kb resolution to call TAD by a Perl script, matrix2insulation.pl (https://github.com/blajoie/crane-nature-2015). A higher resolution was possible because TADs are smaller than A/B compartments. Insulation scores (IS) were calculated for each chromosome bin, and the valleys of the IS identified the TAD boundaries. TADs smaller than 200 kb or located in telomeres/centromeres were filtered out as in previous methods ⁴¹. In comparisons of TADs between two cell lines, at least 70% overlap between two TADs was considered to indicate TAD conservation ⁴². We used BEDtools with the option of “intersectBed − f 0.70 – r” to identify conserved TADs ⁴³.

Gene ontology analysis

We used DAVID Bioinformatics Resources 6.7 for Gene Ontology analysis ⁴⁴. The set of all human genes was used as the background gene list.

Chromatin immunoprecipitation

Jurkat cells were fixed in 1% formaldehyde (Sigma-Aldrich, F8775) for 10 min at 37°C. Subsequently, glycine was added to 125 mM and incubated at 37°C for 5 min at 37°C. Next, the cells were pelleted and washed twice with cold PBS. The pellets were stored at − 80°C until use.

Nuclei from 10 M cells per ChIP-seq were extracted, and chromatin was sonicated with a Bioruptor Sonication Device. Immunoprecipitation reactions were performed overnight with STAT3 (Cell Signaling Technology, 9139S, MA), H3K27ac or CTCF (ABclonal, A1133, China) antibodies. The next morning, antibodies and chromatin were captured using Protein G Dynabeads (Thermo Fisher). The material was washed, eluted and treated with RNase A for 30 min at 37°C and proteinase K for 3 h at 65°C.

Library preparation and sequencing

Library preparation from ChIP-seq DNA was performed using the Ultra II Library Prep Kit (NEB E7103L) and Multiplex Oligos for Illumina (NEB E7335L) and sequenced on an Illumina NextSeq 2500 instrument (150 base pairs single end).

ChIP-seq data processing, heat map generation, and edgeR analysis

H3K27ac, CTCF, and STAT3 ChIP-seq analyses were performed with an average range of 20–25 × 10⁶ reads per independent ChIP-seq experiment. ChIP-seq reads were mapped to the hg19 genome with Bowtie2 using default parameters. Aligned reads were filtered for a minimum MAPQ of 30, and duplicates were removed using SAMtools. Signal tracks were generated by first using BEDTools to produce bedGraph files scaled to 10 million reads per data set. Then, the UCSC Genome Browser utility bedGraphToBigWig was used with default parameters to generate bigwig files. Peaks were called using MACS2 with default parameters. Heat maps of the ChIP-seq signal profiles were generated with the HOMER (http://biowhat.ucsd.edu/homer/index.html) tool annotatePeaks with the following parameters: -ghist 50, -size 10000. ChIP-seq peaks exhibiting differential H3K27ac or STAT3 signals across the time course were identified using edgeR, similar to the process described above.

CUT&Tag experiments and analysis

The CUT&Tag experiments were performed as previously described ⁴⁵ (Vazyme TD901 kit) to generate DNA libraries derived from human CD8 T_ss cells. We used the SEACR peak caller (http://seacr.fredhutch.org), which was expressly designed for CUT&RUN and CUT&Tag data, to call peaks.

Code and data

All essential codes used for the analysis are available at GitHub (https://github.com/ChengLiLab/T_cell_tolerance). The raw sequencing data generated by this project were deposited at the Genome Sequence Archive (GSA, http://gsa.big.ac.cn) with accession number PRJCA002316.

Conflict of Interest

The authors declare no conflicts of interest.

Author contributions

X.-J.H., C.L., and Y.-J.C. designed the study; R.-F.L. and H.-D.G. collected data; R.-F.L., H.-D.G., Y.-J.C., C.L., and X.-J.H. analyzed the data and drafted the manuscript; and all authors contributed to data interpretation and manuscript preparation and approved the final version.

Acknowledgements

This work was partly supported by grants from the National Key Research and Development Program of China (2017YFA0104500), the Beijing Municipal Science and Technology Commission (Z181110009618032), the National Natural Science Foundation of China (Grant Nos. 81670168, 81621001, 81530046, and 81770189), the Beijing Municipal Natural Science Foundation (Grant No. 7204319), and the Key Program of the National Natural Science Foundation of China (Grant No. 81230013). The CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant No. 2019-I2M-5-034) and the Fundamental Research Funds for the Central Universities also provided funding. R.L. and C.L. were supported by the National Natural Science Foundation of China (31871266), the Chinese National Key Research and Development Program of China (2016YFA0100103), and NSFC Key Research Grant 71532001. Part of the data analysis was performed on the High-Performance Computing Platform of the Center for Life Sciences, Peking University. We thank Shanghai Jiayin Biotechnology, Ltd., for their help and suggestions for the ChIP-seq and CUT&Tag experiments. We thank all the faculty members who participated in these studies.

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Spatial Multiomics Analysis Identifies SOCS1 as a Key Immune Checkpoint in T cell Tolerance Induction of HSCT

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Abstract

Figures

Introduction

Results

Association of STAT3 with SOCS1 Expression in Tolerant T Cells

Discussion

Materials And Methods

T cell isolation and culture

Mice

GVHD mouse models

Leukemia mouse models

Lentivirus-mediated SOCS1 overexpression in T cells

Flow cytometric analysis

Cytokine detection by flow cytometry

T-cell proliferation analysis (CSFE)

RT-PCR

RNA-seq experiments and analysis

ATAC-seq experiments and analysis

Hi-C experiments

Hi-C data analysis

A/B compartment analysis

TAD analysis

Gene ontology analysis

Chromatin immunoprecipitation

Library preparation and sequencing

ChIP-seq data processing, heat map generation, and edgeR analysis

CUT&Tag experiments and analysis

Code and data

Declarations

Conflict of Interest

Author contributions

Acknowledgements

References

Additional Declarations

Supplementary Files

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