Heterogeneity and clonal relationships of adaptive immune cells in ulcerative colitis revealed by single-cell analyses – Science

Profiles of rogue gut lymphocytes

Dysregulated human gut B and T lymphocytes contribute to the immunopathogenesis of ulcerative colitis (UC), a type of inflammatory bowel disease (IBD) characterized by mucosal damage in the colon. Boland et al. probed the immunologic perturbations associated with active UC by sequencing mRNA and clonal antigen receptors from individual CD45+ cells isolated from rectal biopsies or blood of patients and healthy controls. The resulting single-cell sequencing resource revealed heterogeneity among tissue-resident memory T cells (TRM) in UC, including expansion of an inflammatory CD8+ TRM subset expressing the Eomesodermin transcription factor. The identification of this TRM population and other disease-associated T and B cell subsets provides a platform for future functional studies addressing how these subsets conspire to trigger chronic mucosal injury in UC.

Inflammatory bowel disease (IBD) encompasses a spectrum of gastrointestinal disorders driven by dysregulated immune responses against gut microbiota. We integrated single-cell RNA and antigen receptor sequencing to elucidate key components, cellular states, and clonal relationships of the peripheral and gastrointestinal mucosal immune systems in health and ulcerative colitis (UC). UC was associated with an increase in IgG1+ plasma cells in colonic tissue, increased colonic regulatory T cells characterized by elevated expression of the transcription factor ZEB2, and an enrichment of a T cell subset in the peripheral blood. Moreover, we observed heterogeneity in CD8+ tissue-resident memory T (TRM) cells in colonic tissue, with four transcriptionally distinct states of differentiation observed across health and disease. In the setting of UC, there was a marked shift of clonally related CD8+ TRM cells toward an inflammatory state, mediated, in part, by increased expression of the T-box transcription factor Eomesodermin. Together, these results provide a detailed atlas of transcriptional changes occurring in adaptive immune cells in the context of UC and suggest a role for CD8+ TRM cells in IBD.

Inflammatory bowel disease (IBD) encompasses a spectrum of complex intestinal disorders characterized by dysregulated innate and adaptive immune responses to gut microbiota in genetically susceptible hosts (1). IBD is typically categorized as Crohns disease or ulcerative colitis (UC) on the basis of anatomic, clinical, and histopathologic criteria (2). A number of studies aimed at uncovering the cellular and molecular basis of IBD have been undertaken, thereby implicating a number of diverse immune cell types in its pathogenesis, such as macrophages (3, 4), innate lymphoid cells (57), and subsets of CD4+ T cells (810). The vast majority of gene expression analyses in IBD have used whole intestinal tissue or bulk populations of cells fluorescence-activated cell sorting (FACS)purified from peripheral blood or intestinal tissue on the basis of phenotypic cell surface markers. However, intestinal tissue is heterogeneous, composed of diverse epithelial, stromal, and immune cells, and it has been increasingly appreciated that substantial heterogeneity can exist even within the same immune cell type (11, 12). Thus, gene expression measurements of whole-tissue samples likely detect the most highly expressed mRNA transcripts in the most abundant cells, thereby masking many potentially important cell typespecific transcriptional signatures.

Emerging data from a number of laboratories across many fields have demonstrated the necessity and power of an unbiased, marker-agnostic approach in investigating known cell types and discovering previously unidentified cell subsets and states (1116). In particular, single-cell RNA sequencing (scRNA-seq) has been used to investigate the heterogeneity of intestinal cells in the context of IBD (1720). In addition to transcriptomic analyses, the ability to delineate T cell (scTCR-seq) and B cell (scBCR-seq) receptor sequences at the single-cell level has enabled characterization of TCR and BCR repertoire diversity and identification of clonal relationships (21, 22). Together, these studies have generated exciting insights that could only have been revealed by analyses performed at the single-cell level.

Here, we integrated scRNA-seq, scTCR-seq, and scBCR-seq approaches to elucidate key components, cellular states, and clonal relationships of the gastrointestinal mucosal and peripheral immune systems in health and UC. Substantial heterogeneity among T and B cell subsets was observed, including within plasma B cells, gamma delta () T cells, regulatory T (Treg) cells, and CD8+ tissue-resident memory T (TRM) cells. It is well established that TRM cells mediate protective responses to microbial infection (23), but a potentially pathogenic role for these cells in autoimmune and inflammatory diseases has been increasingly appreciated (24, 25). We observed clonally related CD8+ TRM cells in four putative differentiation states across health and disease. In UC, CD8+ TRM cells exhibited a marked shift toward an inflammatory differentiation state associated with increased expression of the T-box transcription factor Eomesodermin (Eomes). By virtue of binding to gene targets encoding molecules with inflammatory and effector properties, such as cytokines, cytolytic granules, and killer cell lectin receptors, Eomes may be a critical molecular regulator of a pathogenic CD8+ TRM cell differentiation state in UC.

UC affects the rectum in the vast majority of cases, and inflammation extends proximally in a contiguous manner. This uniformity enabled us to sample this same region in every participant and minimize potential nonbiologic sources of variability, such as regional differences along the gastrointestinal tract and variable exposures to luminal contents. To probe for transcriptional signatures that might be specific to health or disease, we obtained rectal mucosal biopsy and peripheral blood samples from nine healthy individuals and seven patients with active UC (Fig. 1A and table S2). Cells from mucosal biopsies and peripheral blood were processed into single-cell suspensions, FACS-purified on the basis of CD45, a pan-immune cell marker (fig. S1A), and subjected to scRNA-seq, scTCR-seq, and scBCR-seq using the 10x Genomics Chromium platform.

(A) Overview of the experimental design and analysis. QC, quality control. (B) t-SNE plots of cells from all participants across all anatomic sites (upper left; C labels indicate healthy controls and U labels indicate patients with UC), colored by anatomic location from which cells were derived (upper right) and major immune cell groups (lower left) or by health status (lower right; patients with UC versus healthy individuals). (C) Proportion of each major immune cell group in healthy individuals and patients with UC, across both anatomic sites for each participant, as a percentage of all cells.

Two thousand, one hundred sixteen genes with a mean expression of at least 1 UPM (unique molecular identifier per million reads) were detected, with a total of 10,160 genes detected across the dataset (fig. S1, B and C, and table S3). Data from all participants across both anatomic sites were merged, and unsupervised t-distributed stochastic neighborhood embedding (t-SNE) analysis was performed to visualize the clustering of single cells from all participants (Fig. 1B). We used expression of canonical genes (see Materials and Methods) to annotate the clusters into four broad groups of immune cell types: T lymphocytes, B lymphocytes, natural killer (NK) cells, and myeloid cells. No substantial differences in the proportions of each major immune cell group were observed between healthy individuals and patients with UC (Fig. 1C), nor was the level of overall BCR or TCR repertoire diversity notably different between healthy individuals and patients with UC (fig. S1, D and E).

Additional t-SNE and uniform manifold approximation and projection (UMAP) clustering analyses were performed on B lymphocytes, yielding 17 subclusters, all of which included cells from at least two participants (Fig. 2, A and B; fig. S2, A to E; and table S3). B cell clusters were broadly categorized into nave, memory, or plasma cell clusters on the basis of differential expression of canonical genes. For example, nave and memory cells expressed high levels of genes such as CD19 and could be further annotated as nave (clusters B3, B8, and B9) or memory (clusters B2, B4, and B5) on the basis of high or low expression of IGHD, respectively. Eleven plasma cell clusters were identified on the basis of high expression of PRDM1 and XBP1. Many clusters exhibited strong anatomic associations; only three clusters, the B3 and B9 nave clusters and the B5 memory cluster, were found predominantly in the peripheral blood, whereas the rest of the B cell clusters were found preferentially in the intestinal tissue (Fig. 2B and fig. S2, F and G). We next compared the absolute and relative numbers of each cluster in the healthy and disease states. The B1 plasma cell cluster was almost exclusively derived from patients with UC, and cells from this cluster were observed to have undergone increased clonal expansion in patients with UC, whereas the B6 plasma cell cluster was highly enriched in healthy individuals, and cells from this cluster were observed to have undergone clonal expansion preferentially in healthy individuals (Fig. 2, C to E, and fig. S2G).

(A) Phenotypic annotations of B lymphocyte clusters. (B) t-SNE plots of B lymphocyte clusters, colored by cluster identity (upper left), anatomic location from which cells were derived (upper right), health status (lower left), and immunoglobulin heavy chain expression, determined using scBCR-seq data (lower right). (C) Quantitation of selected B lymphocyte clusters enriched or depleted in health versus disease, expressed as absolute numbers. (D) Comparison of clonotypic expansion exhibited by cells from indicated plasma cell clusters, quantitated separately in healthy individuals versus patients with UC. (E) Absolute number (left) and percentage of cells (right) from each cluster derived from healthy individuals (blue) or patients with UC (red). Two-sided Wilcoxon rank sum test (C). *P < 0.05 and **P < 0.01.

To begin to explore the molecular basis of the heterogeneity among the plasma cell clusters, we performed differential expression analyses among the 11 plasma cell clusters. The UC-enriched B1 cluster exhibited 165 differentially expressed genes compared with all other plasma cell clusters (table S4). Moreover, we observed that cells from the UC-enriched B1 cluster were predominantly immunoglobulin G1 positive (IgG1+) (Fig. 3A); additional analyses indicated that the number and proportion of IgG1+ plasma cells were markedly increased in patients with UC, whereas the number and proportion of IgA2+ plasma cells were increased in healthy individuals (Fig. 3B). Last, we looked for evidence of BCR clonotypes shared among the B cell clusters that we annotated. In the healthy setting, clonotypes were shared across multiple plasma cell clusters, but not the B1 plasma cell cluster (Fig. 3C, left, and fig. S3A). By contrast, in the setting of UC, clonotypes were shared between cells from the B1 plasma cell cluster and most other plasma cell clusters. Together, these results suggest the possibility that plasma cells transit between distinct states, the relative proportions of which differ in the setting of health versus disease.

(A) Quantitation of IgA1+, IgA2+, and IgG1+ cells within each plasma cell cluster, determined using scBCR-seq data. (B) Absolute number and percentage of cells from patients with UC or healthy individuals are shown for each immunoglobulin isotype (IgA1+, IgA2+, IgD+, IgG1+, IgG2+, and IgM+ only; very few IgG3+, IgG4+, or IgE+ cells were detected). (C) t-SNE plots of plasma cell clusters, colored by cluster identity, with red lines indicating BCR clonotypes shared among clusters and line weight representing number of shared clonotypes, for healthy individuals (left) versus patients with UC (right).

Analogous to our B lymphocyte analyses, additional t-SNE and UMAP analyses were performed on T lymphocytes, yielding 17 subclusters, all of which included cells from at least two participants (Fig. 4, A and B; fig. S4, A to F; and table S3). These T cell clusters were annotated into subsets on the basis of differential expression of canonical genes. Cells from three clusters (T8, T13, and T16) exhibited high expression of genes encoding components of the TCR, although it should be noted that the T8 cluster also contained TCR alpha beta () T cells. Seven clusters (T3, T4, T6, T9, T11, T12, and T17) with high expression of SELL (CD62L), CCR7, LEF1, and TCF7 were annotated as nave/memory. Four CD8+ T cell clusters (T1, T2, T10, and T14) expressed transcripts suggestive of a TRM cell phenotype (23), with high expression of CD69, ITGAE (CD103), CD101, CCR6, and ITGA1 (CD49a) along with low expression of KLF2 and S1PR1. Cells from the T7 cluster expressed transcripts indicative of Treg cells (26), including high FOXP3 and IL2RA (CD25) expression along with low IL7R expression. Cells from the T5 cluster expressed transcripts suggestive of T follicular helper (TFH) cells (27), including high CXCR5, PDCD1 (PD1), ICOS, and BCL6 expression. Cells from the T15 cluster expressed transcripts indicative of mucosal-associated invariant T (MAIT) cells (28), including ZBTB16 and TRAV1-2. We next compared the numbers of cells from each of the T lymphocyte clusters derived from healthy individuals and patients with UC (Fig. 4C). In the peripheral blood, cluster T8 cells were enriched in patients with UC, raising the possibility of previously unrecognized T cell states or subsets that may be more abundant in the setting of disease. In the intestine, the T7 (Treg), T5 (TFH), and T10 (CD8+ TRM) cell clusters were enriched in patients with UC.

(A) Phenotypic annotations of T lymphocyte clusters. (B) t-SNE plots of T lymphocyte clusters, colored by cluster identity (upper left), anatomic location from which cells were derived (upper right), and health status (lower left). (C) Quantitation of T lymphocyte clusters that were enriched or depleted in health versus UC, expressed as absolute numbers. (D) Mean expression of selected genes that were differentially expressed between T7 cluster CD4+ Treg cells derived from healthy individuals (blue) versus patients with UC (red); see also table S5. UMI, unique molecular identifier. (E) In vitro suppression assay using induced Treg cells transduced with nontargeting (NT) shRNA (n = 3) or Zeb2 shRNA (n = 3) or (F) induced Treg cells from tamoxifen-treated ERWtZeb2fl/+ (n = 3) and ERCreZeb2fl/fl (n = 3) mice. Putative Treg cells were sorted on the basis of high CD4 and CD25 expression, and these cells expressed high levels of Foxp3; see also fig. S5. Error bars indicate SEM. Two-sided Wilcoxon rank sum test (C); unpaired Students t test for each Tconv:Treg ratio (E and F). Tconv, conventional T cell (CD4+ CD25). *P < 0.05, **P < 0.01, and ***P < 0.001.

Because Treg cells are known to prevent inflammation and autoimmunity (26), it might have been predicted that Treg cells would be numerically reduced in patients with UC. However, we observed an enrichment of the T7 Treg cell cluster in patients with UC (Fig. 4C), consistent with previously published reports (8, 9), suggesting that inflammation was not due to insufficient numbers of Treg cells. Rather, we hypothesized that this finding might reflect an expansion of Treg cells to control ongoing inflammation. We further reasoned that Treg cells from patients with UC might express transcripts leading to defective Treg cell function that could contribute to disease development despite increased numbers of these cells. Comparison of the gene expression profiles of Treg cells from healthy individuals and patients with UC revealed 288 differentially expressed genes (Fig. 4D and table S5), some of which have previously been reported to play a role in Treg cell function, suggesting that the current approach was capable of identifying functionally important genes. For example, expression of SATB1, repression of which is necessary for Treg cell suppressive function (29, 30), was increased in Treg cells from patients with UC; conversely, KLF2, MYC, and ITGB1 expression was reduced in Treg cells from patients with UC, consistent with previously published work demonstrating a role of these factors in Treg cell function (3133). However, most of the genes differentially expressed between Treg cells from healthy individuals versus patients with UC had not been previously linked to Treg cell function. As a first step toward demonstrating that genes identified by these analyses might indeed represent previously unrecognized regulators of Treg cell function, we selected one such gene, ZEB2, which has been previously shown to promote effector function in murine CD8+ T cells (34, 35), for further studies. The observation that ZEB2 was more highly expressed in Treg cells derived from patients with UC raised the possibility that down-regulation of ZEB2 may be required for optimal Treg cell function. To test this possibility, we transduced in vitroinduced murine Treg cells with a short hairpin RNA (shRNA) construct targeting Zeb2. Treg cells transduced with a Zeb2 shRNA construct exhibited enhanced suppressive function compared with control Treg cells transduced with a nontargeting shRNA construct (Fig. 4E). To confirm these results, we treated ERWtZeb2fl/+ and ERCreZeb2fl/fl mice with tamoxifen to induce Zeb2 deletion, FACS-purified nave CD4+CD25 T cells, and cultured these cells in Treg cellinducing cytokine conditions. Control and Zeb2-deficient Treg cells were then FACS-purified and tested in an in vitro suppression assay; we observed that Zeb2-deficient Treg cells exhibited enhanced suppressive function compared with control Treg cells (Fig. 4F and fig. S5).

The aforementioned analyses (Fig. 4C) raised the possibility that distinct T cell states or subsets might be differentially enriched in the setting of health versus UC. To begin to understand the differences among these putative T cell states, we performed differential gene expression analyses of the three T cell clusters (T8, T13, and T16) that we defined. These analyses revealed a number of genes that were differentially expressed among the three T cell clusters (Fig. 5, A and B, and table S6). For example, T8 cluster cells expressed high levels of CCR7; T13 cells expressed high levels of KLRB1 (CD161) and S100B; and T16 cells expressed high levels of KLRC1 (NKG2A), GNLY, and XCL1. To confirm whether these T cell states could be discerned in human peripheral blood, we constructed a panel of 35 protein markers based on differentially expressed genes among T8, T13, and T16 cluster cells derived from our scRNA-seq analyses. We then performed mass cytometry using peripheral blood from an independent cohort of healthy individuals and patients with UC (fig. S6 and table S7). In addition to unbiased analysis of the dataset using PhenoGraph (fig. S6), targeted analysis of T cells was performed. T cells were electronically gated on the basis of CD45+CD3+TCR+ positivity and then subjected to UMAP analysis, revealing three clusters of T cells (Fig. 5C).

(A) Heatmap of mean expression of selected genes differentially expressed between T cell clusters (T8, T13, and T16). (B) Violin plots of selected genes differentially expressed by T cell clusters (T8, T13, and T16). (C) Overview of the design for mass cytometry (CyTOF) analysis of peripheral blood from healthy individuals (n = 5) and patients with UC (n = 5), with UMAP analysis of T cells from all patients. DE, differential gene expression. (D) Gating strategy to identify CCR7highCD161lowNKG2Alow (T8), NKG2Ahigh (T13), and CCR7lowCD161high NKG2Alow (T16) clusters, with the proportion of each cell cluster represented among all T cells for each participant, calculated separately for healthy individuals versus patients with UC. Error bars indicate SEM. Unpaired Students t test (D). *P < 0.05.

The UMAP analyses suggested that the combination of 35 protein markers together could distinguish these putative T cell states. We next asked whether these putative T cell states could be discerned using only a few protein markers selected from our differential gene expression analyses. T cells were electronically gated on the basis of CD45+CD3+TCR+ positivity, and expression of CCR7, CD161, and NKG2A was analyzed. Putative T13 cluster cells could be distinguished from T8 and T16 cluster cells on the basis of high NKG2A expression (Fig. 5D). Putative T8 and T16 cluster cells, both of which expressed low levels of NKG2A, could be distinguished on the basis of CCR7 and CD161 protein expression, with T8 cluster cells tending to express higher levels of CCR7 and T16 cluster cells expressing higher levels of CD161. Last, we observed that, as predicted by our scRNA-seq analyses (Fig. 4C), putative T8 cluster cells represented a higher proportion of T cells in the peripheral blood of patients with UC compared with healthy individuals (Fig. 5D).

As described above, we annotated four putative CD8+ TRM-like cell clusters (T1, T2, T10, and T14) and hypothesized that these clusters might represent states between which TRM cells transit differentially during health and disease. To investigate this possibility, we looked for evidence of TCR clonotypes shared among the T cell clusters we previously defined. In healthy individuals, clonotypes were shared across cells from the CD8+ TRM T1, T2, and T14 clusters (Fig. 6A and fig. S3B); by contrast, in patients with UC, clonotypes were shared among the CD8+ TRM T1, T2, and T10 cell clusters (Fig. 6B and fig. S3B). In line with these observations, there was increased clonal expansion of cells from the T1 and T14 clusters preferentially in healthy individuals; conversely, there was increased clonal expansion of cells from the T10 cluster in patients with UC (Fig. 6C). Moreover, we observed that in the setting of UC, there was a marked increase in clonotypes shared between the T10 and T8 clusters; because T10 cluster cells were derived predominantly from intestinal tissue whereas T8 cluster cells were almost exclusively derived from peripheral blood (fig. S4D), these results indicated trafficking of clonally related cells between the two compartments. We detected a significantly greater proportion of clonally related T10 and T8 cluster cells in patients with UC compared with healthy individuals (Fig. 6D). Moreover, for certain clonotypes, the number of cells in the tissue (T10) was greater than that in the blood (T8), whereas for other clonotypes, the number of cells in the blood was greater than that for tissue (Fig. 6E). Differential expression analyses revealed changes in genes encoding trafficking molecules (23), including decreased expression of CD69 and CRTAM and increased expression of SELL, KLF2, and S1PR1 in blood T8 cells compared with clonally related tissue T10 cells (Fig. 6, F and G). Together, these results indicate that in the setting of UC, clonally related CD8+ T cells may modulate their gene expression to enable trafficking between blood and intestinal tissue.

t-SNE plots of T cell clusters of healthy individuals (A) and patients with UC (B), colored by cluster identity. For summary plots (left plots), red lines indicate TCR clonotypes shared among clusters and line weight represents number of shared clonotypes; three selected clonotypes from three representative healthy individuals (A; right three plots) or patients with UC (B; right three plots) are shown as examples. (C) Comparison of clonotypic expansion exhibited by cells from CD8+ TRM cell clusters (T1, T2, T10, and T14) from healthy individuals and patients with UC. (D) Quantitation of clonally related T8 and T10 cells in healthy controls and patients with UC, represented as proportion of clonally related T8 and T10 cells among all T8 and T10 cells in each individual. (E) Anatomic origin of clonally related blood T8 and rectal T10 cells. Each column represents a single clonotype and is colored on the basis of anatomic location (rectum, blue; peripheral blood, red); numbers of clonally related cells derived from each location are indicated on the y axis. (F and G) Violin plots (F) and heatmap (G) of selected differentially expressed genes in clonally related and clonally unrelated T8 and T10 cells (peripheral blood, red; rectum, blue). Two-sided Wilcoxon rank sum test (D). **P < 0.01.

These findings also suggested a spectrum of states between which clonally related CD8+ TRM cells transit differentially in health versus UC. To begin to understand the differences between these putative CD8+ TRM cell states, we performed differential gene expression and pathway analyses of the four TRM cell clusters (T1, T2, T10, and T14) (Fig. 7, A and B, and table S8). These analyses revealed 481 genes differentially expressed between T10 cluster cells and cells from the other three CD8+ TRM clusters. These included increased expression of transcripts encoding inflammatory molecules and cytolytic granules, such as perforin and granzymes A, B, H, K, and M; metabolic regulators such as FABP5, which has been shown to support TRM cell differentiation (36); and a number of key transcription factors, including ZEB2 (34, 35) and Eomes (37), both of which have previously been implicated in circulating effector and memory CD8+ T cell differentiation (38). Because Eomes has been previously shown to promote its own expression (39), we hypothesized that Eomes might be a critical regulator of the T10 CD8+ TRM transcriptional program.

(A) Violin plots of selected genes differentially expressed by the four CD8+ TRM cell clusters (T1, T2, T10, and T14). (B) Pathway analysis of genes differentially expressed by the T10 CD8+ TRM cluster compared with all other CD8+ TRM clusters. (C) Representative hematoxylin and eosinstained images of unaffected and affected colonic tissue from a patient with UC used for RNA in situ hybridization (ISH) analyses shown in (D) and (E). Representative ISH images of affected colonic tissue (D) and quantitation of CD8+CD69+EOMES+ cells from unaffected and affected regions of colonic tissue from patients with UC (n = 5) (E). Paired Students t test (E). **P < 0.01.

Applying in situ RNA hybridization using EOMES as a marker of T10 CD8+ TRM cells, we first asked whether these cells could be detected in intestinal tissue specimens from an independent cohort of UC patients with active disease. We performed pairwise comparisons between affected and unaffected tissue derived from the same patients with UC and observed that T10-like cells were increased in affected compared with unaffected tissue in this cohort (Fig. 7, C to E). Next, we investigated whether CD8+ T cells might play a role in mediating intestinal inflammation using an established interleukin-10 (IL-10)deficient piroxicam-induced enterocolitis mouse model (40). We observed a significant increase in colonic CD8 + T cells in IL-10deficient mice that were fed piroxicam-containing chow compared with control mice that were fed control chow (fig. S7A). To test whether depletion of CD8+ T cells might ameliorate disease, we treated IL-10deficient mice that were fed piroxicam-containing chow with either depleting anti-CD8 or isotype control antibodies and their weight monitored daily over 2 weeks. Treatment with anti-CD8 antibodies resulted in a substantial depletion of CD8+ T cells in the peripheral blood and colonic tissue (fig. S7B). Moreover, compared with treatment with isotype control antibodies, treatment with anti-CD8 antibodies resulted in a reduction of weight loss and colonic pathology induced by piroxicam (Fig. 8A and fig. S7C), suggesting a role for CD8+ T cells in the piroxicam-induced IL-10deficient mouse model, consistent with prior reports (40, 41), although it remains possible that CD8+ dendritic cells and/or CD8+ T cells may also play a contributing role in this model. To determine whether ectopic expression of Eomes in CD8+ T cells could influence disease severity in a model of intestinal inflammation, we adoptively transferred CD8+ T cells transduced with control or Eomes retroviral constructs into recombination-activating gene 1 (RAG1)deficient mice before challenging them with dextran sulfate sodium (DSS). We observed that mice receiving CD8+ T cells transduced with the Eomes construct lost significantly more weight and exhibited more colonic pathology than mice that received CD8+ T cells transduced with the control construct (Fig. 8B and fig. S7D), suggesting that ectopic Eomes expression is sufficient to confer CD8+ T cells with enhanced pathogenic properties.

(A) Percent weight change observed with CD8 depletion (n = 5) versus isotype control (n = 5) in an IL-10-deficient piroxicam-induced enterocolitis mouse model, expressed as percent of weight at the start of the experiment. Error bars indicate SEM. Data are representative of two independent experiments. (B) Percent weight change observed in RAG1-deficient mice receiving 5 105 FACS-sorted, green fluorescent proteinpositive controlretrovirus (RV) (n = 13) or Eomes-RV (n = 8) CD8+ T cells and treated with DSS, expressed as percent of weight at the start of the experiment. Error bars indicate SEM. Data are representative of two independent experiments. (C) ATAC-seq tracks with putative Eomes motifs (indicated with red lines) near accessible promoter regions for selected genes are shown. P14 CD8+ T cells were adoptively transferred into congenic recipients subsequently infected with LCMV Armstrong; cells were FACS-sorted at days 7 and 30 after infection (two technical replicates per time point) and subjected to ATAC-seq. Representative day 7 post-infection ATAC-seq tracks are shown. (D) P14 CD8+ T cells were transduced with control-RV (CD45.1) or Eomes-RV (CD45.1.2) constructs and adoptively transferred into congenic recipients (CD45.1.2) subsequently infected with LCMV Armstrong (n = 5). Expression of selected proteins by control-RV versus Eomes-RVexpressing CD8+ T cells was analyzed by FACS at 7 days after infection; staining of nave CD8+ T cells from an uninfected mouse are shown as a control. Data are representative of two independent experiments. PE, R-phycoerythrin; PE-Cy7, R-phycoerythrin-cyanine7 tandem fluorochrome; BV421, Brilliant Violet 421; BV605, Brilliant Violet 605. Unpaired Students t test (A and B) or paired Students t test (D). *P < 0.05, **P < 0.01, and ***P < 0.001.

We next investigated what gene targets Eomes might be acting upon to mediate these effects. Putative gene targets of Eomes have been previously identified in thymic innate memory CD8+ T cells (42) and in vitrodifferentiated CD8+ T cells (39) using chromatin immunoprecipitation sequencing approaches, but such approaches are not feasible in intestinal CD8+ TRM cells because of technical challenges with cell numbers. Moreover, the low numbers of intestinal CD8+ T cells that can be recovered in commonly used intestinal inflammation models precluded their use as a model system with which to identify Eomes gene targets in intestinal CD8+ TRM cells. Thus, to identify putative gene targets of Eomes specifically in intestinal CD8+ TRM cells, we applied the assay for transposase-accessible chromatin using sequencing (ATAC-seq) in the context of the lymphocytic choriomeningitis virus (LCMV) model system in which intestinal CD8+ TRM cells have been widely studied (43). P14 CD8+CD45.1+ T cells, which have transgenic expression of a TCR that recognizes an immunodominant epitope of LCMV, were adoptively transferred into congenic CD45.2+ wild-type recipient mice subsequently infected with LCMV 1 day later. Donor CD45.1+ P14 T cells were FACS-sorted from the small intestine epithelial lymphocyte compartment of recipient mice at 7 and 30 days after infection and processed for ATAC-seq. We searched for predicted Eomes binding motifs in accessible enhancer and promoter regions and looked for overlap of these genes with the T10 CD8+ TRM cluster transcriptional signature. These analyses confirmed known Eomes gene targets such as Ifng and Gzma (37) but identified other molecules, such as Klrg1, a killer lectin receptor, and Icos, a costimulatory molecule, as putative Eomes gene targets (Fig. 8C and table S9). To test whether ectopic expression of Eomes resulted in increased expression of putative gene targets that we identified by ATAC-seq analysis, we transduced congenically distinct CD8+ T cells with control (CD45.1) or Eomes (CD45.1.2) retroviral constructs before adoptive transfer into recipient mice (CD45.2) subsequently infected with LCMV and used flow cytometry to examine the protein expression of several putative targets in intestinal CD8+ T cells at 7 days after infection. Compared with intestinal CD8+ T cells expressing control constructs, intestinal CD8+ T cells expressing Eomes constructs expressed higher levels of interferon-(IFN), granzyme A, and KLRG1 protein and lower levels of inducible T cell costimulator (ICOS) protein (Fig. 8D). Together, these findings suggest the possibility that in UC, up-regulation of key factors such as Eomes in intestinal CD8+ TRM cells may promote their conversion into a pathogenic state exhibiting enhanced inflammatory and cytolytic properties.

Recent studies have begun to apply single-cell transcriptomic approaches to investigate the mechanisms underlying the complex dysregulation of the immune system in IBD (18, 19). Our integrated single-cell transcriptomic and antigen receptor sequencing analyses have resulted in several insights into the immunobiology of UC. First, we annotated multiple clusters of plasma cells in intestinal tissue and observed that BCR clonotypes were shared among cells from many of these clusters, raising the possibility that plasma cells may transit among a spectrum of states. Plasma cells from patients with UC exhibited a marked shift toward a specific IgG1+ cluster (B1), in contrast to plasma cells from healthy individuals, which were predominantly IgA+, in accordance with early immunohistochemical findings first reported in the 1970s (44) and confirmed in subsequent studies (4547). It has been shown that colitogenic intestinal bacteria can be coated by high levels of IgA (48), suggesting that the ability of healthy individuals to produce IgA in the gut microenvironment may enable them to control specific inflammatory commensals that might otherwise initiate intestinal inflammation. Alternatively or in addition, IgG antibodies may themselves be pathogenic because it was previously proposed that an increase in anticommensal IgG antibodies in patients with UC may lead to inflammation through IgG-mediated FcR receptor activation and type 17 immunity (45).

Second, we envision that the current dataset can be used as a starting point to identify genes previously unknown to be dysregulated in IBD in an immune cell typespecific manner for further investigation. As an example, we observed an enrichment of Treg cells (contained within cluster T7) in patients with UC, raising the possibility that these cells might be functionally impaired despite being present in adequate numbers. Further analyses revealed a number of transcripts that were differentially expressed between Treg cells derived from healthy individuals compared with those from patients with UC, many of which were not previously known to have a role in Treg cells. We selected ZEB2 for further study, which, owing to its observed up-regulation in Treg cells from patients with UC, was hypothesized to impair Treg cell function; knockdown or deletion of Zeb2 resulted in enhanced murine Treg cell suppressive activity. This finding, together with the observations that the expression of several previously known regulators of murine Treg cells was also altered in Treg cells from patients with UC, suggests the potential value of the dataset in selecting putative regulators of healthy versus disease Treg cell states for further study.

A third insight that derives from these analyses is the finding of heterogeneity among CD8+ TRM cells in the human intestine. TRM cells are a subset of memory T lymphocytes that reside within tissues and provide essential protection at body surfaces (23), but to date, there has been only limited evidence for heterogeneity among murine (49, 50) and human TRM cells (51). TRM cells have been implicated in human autoimmune diseases such as vitiligo and psoriasis (52, 53), and recent studies have suggested a role for CD4+ TRM cells (24, 25) in IBD. We detected four transcriptionally distinct clusters of CD8+ TRM cells, one of which (T10) contained cells that had undergone significant clonal expansion predominantly in patients with UC. The finding that TCR clonotypes were shared among cells from the four CD8+ TRM cell clusters supports the hypothesis that these clusters represent states between which CD8+ TRM cells transit; in the setting of UC, we observed a marked shift of cells toward the putative T10 differentiation state. Moreover, the observation that increased numbers of cells in the peripheral blood that were clonally related to T10 CD8+ TRM cells in the intestine were increased in UC is intriguing in light of recent reports that murine and human TRM cells can exit the tissue and recirculate (54, 55). Together, our data suggest a role for CD8+ TRM cells in UC and raise the possibility that during IBD exacerbations, CD8+ TRM cells may exit the intestinal tissue and recirculate, providing a potential explanation for the tendency for IBD to affect multiple organ systems outside of the gastrointestinal tract.

Compared with cells in the other TRM states, cells in the T10 differentiation state expressed higher levels of genes encoding molecules that confer inflammatory and effector properties, such as cytokines, cytolytic granules, and killer lectin receptors. Our analyses nominate the T-box transcription factor Eomes as a regulator of the putative T10 CD8+ TRM cell transcriptional state. Eomes and T-bet are highly homologous members of the T-box family of transcription factors and are highly expressed by activated CD8+ T cells and resting and activated NK cells (37). Eomes and T-bet have cooperative functions (56) in inducing effector functions and enhanced expression of CD122, the receptor controlling IL-15 responsiveness, which underlies proliferative renewal after clearance of microbial pathogen (57, 58). Eomes also has functions that are distinct from those of T-bet, such as promoting self-renewal of long-lived memory cells (59), and has been shown to be up-regulated in CD8+ T cells during chronic infection (60). In the context of murine skin TRM cell differentiation in response to microbial infection, both Eomes and T-bet undergo initial up-regulation but are subsequently down-regulated to enable responsiveness to transforming growth factor signaling and continued TRM cell differentiation (61). Eomes is extinguished by 2 to 4 weeks after infection, at least in skin TRM cells, but low levels of T-bet are necessary for the maintenance of CD122 and survival of TRM cells (6163); it is therefore intriguing that cells in the putative T10 CD8+ TRM transcriptional state expressed high levels of Eomes. This T10 CD8+ TRM cluster, which exhibited high expression of transcription factors such as EOMES, appears to be transcriptionally distinct from a previously described CD8+IL17A+ T cell cluster (19), which expressed high levels of RORA and RORC, identified by scRNA-seq; moreover, it remains unknown how the T10 CD8+ TRM cluster described here relates to a CD3+CD4CD8IL-17A+ T cell cluster recently identified using mass cytometry (9). Future work will further investigate the degree of heterogeneity among intestinal CD8+ T cells with respect to function and plasticity in health and IBD.

Our data suggest a model in which TRM cells exist in equilibrium across several differentiation states in the healthy condition. In the setting of UC, TRM cells may up-regulate Eomes, which binds to a number of downstream gene targets. On the basis of our ATAC-seq analyses in murine intestinal CD8+ T cells, putative gene targets may include inflammatory cytokines (Ifng), cytolytic granules (Gzma), chemokines (Ccl3, Ccl4, and Ccl5), molecules that promote survival (Bach2, Cd27, and Il2rb), killer cell lectin receptors (Klrb1, Klrc1, Klrd1, Klrg1, and Krk1), costimulatory molecules [Tnfrsf18 (GITR), Tnfrsf4 (OX40R), and Icos], and trafficking molecules such as Crtam (64). A caveat of the current study is the use of an infection system, owing to technical limitations with experimental colitis models, with which to identify putative Eomes gene targets in intestinal CD8+ TRM cells. Nonetheless, it appears that up-regulation of Eomes in CD8+ TRM cells, through its actions on a diverse set of gene targets, may promote the acquisition of an inflammatory and pathogenic TRM cell transcriptional program.

Overall, our work has resulted in an integrated single-cell transcriptomic and antigen receptor sequencing dataset that expands the single-cell data available in human IBD. The study identifies alterations in immune cell types and clonal relationships that occur in the context of disease, including plasma cells, Treg cells, T cells, and CD8+ TRM cells, and will enable other investigators to identify additional UC-associated changes in a cell type and tissue-specific manner for further study. This is likely to accelerate mechanistic and functional investigations into the role of specific genes in relevant immune cell types and states in UC.

The purpose of this study was to gain a broader understanding of the heterogeneity and clonal relationships of adaptive immune cells in the context of human UC. To this end, we generated and analyzed scRNA and antigen receptor sequencing data generated from human peripheral blood and gastrointestinal mucosal tissue samples.

The Human Research Protection Programs at the University of California, San Diego (UCSD) and the VA San Diego Healthcare System approved the study. Intestinal biopsies and peripheral blood were obtained from patients undergoing colonoscopy at the UCSD and the VA San Diego Healthcare System after obtaining informed consent. Healthy individuals were undergoing colonoscopy as part of routine clinical care for colorectal cancer screening/surveillance or noninflammatory gastrointestinal symptoms that included constipation or rectal bleeding. Inclusion criteria included age over 18 years old and absence of significant comorbidities or colorectal cancer. UC patients with active endoscopic disease were selected. Details of the study participants are provided in table S2.

Blood was collected in a BD Vacutainer CPT tube and centrifuged at 400g for 25 min. The buffy coat layer was removed, washed, and counted. Cells were resuspended in freezing buffer {10% (v/v) dimethyl sulfoxide (DMSO; Sigma-Aldrich), 40% (v/v) complete RPMI [RPMI (Corning) + 10% (v/v) fetal bovine serum (FBS; Life Technologies) + penicillin (100 U/ml)/streptomycin (100 g/ml) (Life Technologies)], and 50% (v/v) FBS}, placed into a freezing container (Mr. Frosty), and stored at 80C. Cells were recovered, washed, filtered, and used for mass cytometry [cytometry by time of flight (CyTOF, Fluidigm)] as described below or labeled with anti-human CD45 (2D1) (BioLegend) for sorting. CD45+ immune cells were sorted on a FACSAria II (BD Biosciences) using gating strategy shown in fig. S1A.

Intestinal biopsies were obtained with endoscopic biopsy forceps from the rectum and collected in a conical tube with Hanks balanced salt solution (HBSS; Corning). Intestinal biopsies were transferred into freezing buffer [10% (v/v) DMSO, 40% (v/v) complete RPMI, and 50% (v/v) FBS] and stored at 80C. Biopsies were recovered, incubated in HBSS on a shaker, then incubated twice in HBSS and 5 mM dithiothreitol (Thermo Fisher Scientific) with shaking, and then washed in HBSS. Intestinal biopsies were mechanically dissociated, then placed into 10 ml of digestion mixture [complete RPMI, collagenase type VIII (1.5 mg/ml) (Sigma-Aldrich), and deoxyribonuclease I (50 g/ml) (Roche)] on a rocker at 37C for 20 min, filtered, and stained with anti-human CD45. CD45+ immune cells were sorted on a FACSAria II using the gating strategy shown in fig. S1A.

Cells were washed and resuspended in phosphate-buffered saline and 0.04% (w/v) bovine serum albumin per the manufacturers guidelines. Single-cell libraries were prepared according to the protocol for 10x Genomics for Single Cell V(D)J and 5 Gene Expression. About 20,000 sorted CD45+ cells were loaded and partitioned into Gel Bead In-Emulsions. scRNA libraries were sequenced on a HiSeq 4000 (Illumina). The BCR and TCR libraries were amplified according to the manufacturers protocol and sequenced on a NovaSeq S4 (Illumina).

All mice were housed under specific pathogenfree conditions in an American Association of Laboratory Animal Careapproved facility at UCSD, and all procedures were approved by the UCSD Institutional Animal Care and Use Committee. C57BLJ/6 CD45.1, CD45.2, CD45.1.2, P14 TCR transgenic (CD45.1 or CD45.1.2, both maintained on a C57BL6/J background), RAG1-deficient, and IL-10deficient mice were bred at UCSD or purchased from the Jackson Laboratories. Mice with a loxP-flanked Zeb2 allele (35, 65) were bred with Rosa26Cre-ERT2 (ERCre) mice (66) and were maintained on a C57BL/J6 background. Rosa26Cre-ERT2mediated deletion of the floxed Zeb2 gene was induced by oral gavage of 1 mg of tamoxifen (Cayman Chemical Company) emulsified in 100 l of sunflower seed oil (Sigma-Aldrich) for five consecutive days and then rested for 5 days. Cells for Treg cell suppression assays were obtained from male mice that were 12 to 28 weeks old.

immunology.sciencemag.org/cgi/content/full/5/50/eabb4432/DC1

Materials and Methods

Fig. S1. Overview of experimental design, quality control metrics of single-cell sequencing data, and assessment of overall antigen receptor diversity in healthy individuals and patients with UC.

Fig. S2. scRNA-seq reveals heterogeneous B lymphocyte clusters.

Fig. S3. Analyses of clonal relationships in health and UC.

Fig. S4. scRNA-seq reveals heterogeneous T lymphocyte clusters.

Fig. S5. Analysis of Zeb2 knockdown or Zeb2-deficient Treg cells.

Fig. S6. Mass cytometry analysis of peripheral blood.

Fig. S7. CD8+ T cell analysis and histopathology in mouse models of intestinal inflammation.

Table S1. Raw data file.

Table S2. Demographics for patient samples.

Table S3. Quality control metrics for single-cell sequencing data.

Table S4. Differential gene expression analyses of plasma cell clusters.

Table S5. Differential gene expression analyses between T cell clusters T8, T13, and T16.

Table S6. Differential gene expression analyses between T7 cluster cells from patients with UC versus healthy individuals.

Table S7. Metal-conjugated antibodies for mass cytometry.

Table S8. Differential gene expression analyses of CD8+ tissue-resident memory cell clusters.

Table S9. Putative Eomes gene targets in intestinal CD8+ T cells.

References (6775)

Acknowledgments: We thank L. Arambula Tsai for assistance with figures. We thank members of the Chang, Goldrath, and Yeo laboratories for technical advice, helpful discussion, and critical reading of the manuscript. scRNA-seq, TCR-seq, and BCR-seq using the 10x Genomics platform were performed at the UCSD IGM Genomics Center and supported by NIH grants P30KC063491, P30CA023100, and S10OD026929. Microscopy was performed at the UCSD Microscopy Core and supported by NIH grant NS047101. Mass cytometry was performed at the La Jolla Institute for Immunology Flow Cytometry Core and supported by NIH grant S10OD018499. Funding: This work was supported by the NIDDK-funded San Diego Digestive Diseases Research Center (P30DK120515) and the CCSG grant (P30CA23100) and funded by grants from the Kenneth Rainin Foundation (W.J.S., G.W.Y., and J.T.C.) and the NIH: TR001444 and DK123406 (B.S.B.); DK007202 (M.S.T.); AI082850 and AI00880 (Z.H. and A.B.); AI123202, AI129973, and BX003424 (J.T.C.); AI132122 (A.W.G., G.W.Y., and J.T.C.); and MH107367 (G.W.Y.). Author contributions: G.W.Y. and J.T.C. conceived the study. B.S.B., Z.H., M.S.T., J.G.O., K.D.O., W.J., H.G.D., E.S.K., A.E.L., T.T., J.J.M., B.Y., S.A.P., T.L.L., N.S.K., A.B., L.K.Q., J.N.K., A.M., P.S.D., and S.S. performed data acquisition, analysis, or interpretation. D.H., M.A.V., C.M., L.-F.L., J.D.B., W.J.S., A.W.G., G.W.Y., and J.T.C. supervised the study. B.S.B., Z.H., M.S.T., G.W.Y., and J.T.C. wrote and edited the manuscript. Competing interests: B.S.B. was a consultant for AbbVie, Prometheus Laboratories, and Pfizer in the past 3 years. P.S.D. is on the steering committee for Takeda, is a consultant for Takeda and Janssen, and has received honoraria for speaker events from Takeda, travel support from Takeda and Janssen, and grant support from Takeda and Pfizer. G.W.Y. is cofounder, member of the Board of Directors, on the Science Advisory Boards (SAB), equity holder, and paid consultant for Locanabio and Eclipse BioInnovations. A.W.G. is on the SAB of Pandion Therapeutics and ArsenalBio. W.J.S. reports research grants from Atlantic Healthcare Limited, Amgen, Genentech, Gilead Sciences, AbbVie, Janssen, Takeda, Eli Lilly, Celgene/Receptos, Pfizer, and Prometheus Laboratories (now Prometheus Biosciences); consulting fees from AbbVie, Allergan, Amgen, Arena Pharmaceuticals, Avexegen Therapeutics, BeiGene, Boehringer Ingelheim, Celgene, Celltrion, Conatus, Cosmo, Escalier Biosciences, Ferring, Forbion, Genentech, Gilead Sciences, Gossamer Bio, Incyte, Janssen, Kyowa Kirin Pharmaceutical Research, Landos Biopharma, Eli Lilly, Oppilan Pharma, Otsuka, Pfizer, Progenity, Prometheus Biosciences (merger of Precision IBD and Prometheus Laboratories), Reistone, Ritter Pharmaceuticals, Robarts Clinical Trials [owned by Health Academic Research Trust (HART)], Series Therapeutics, Shire, Sienna Biopharmaceuticals, Sigmoid Biotechnologies, Sterna Biologicals, Sublimity Therapeutics, Takeda, Theravance Biopharma, Tigenix, Tillotts Pharma, UCB Pharma, Ventyx Biosciences, Vimalan Biosciences, and Vivelix Pharmaceuticals; and stock or stock options from BeiGene, Escalier Biosciences, Gossamer Bio, Oppilan Pharma, Prometheus Biosciences (merger of Precision IBD and Prometheus Laboratories), Progenity, Ritter Pharmaceuticals, Shoreline Biosciences, Ventyx Biosciences, and Vimalan Biosciences. Spouse: Iveric Bio, consultant on stock options; Progenity, consultant on stock; Oppilan Pharma, consultant on stock options; Escalier Biosciences, prior employee (stock options); Prometheus Biosciences (merger of Precision IBD and Prometheus Laboratories), employee (stock options); Shoreline Biosciences, stock options; Ventyx Biosciences, stock options; and Vimalan Biosciences, stock options. J.T.C. reports research grant support from Takeda and Eli Lilly. Data and materials availability: The data have been deposited in the Gene Expression Omnibus under accession number GSE125527. All other data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials.

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