Single-cell analyses of Crohn’s disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions

Crohn’s disease (CD) is a chronic transmural inflammation of intestinal segments caused by dysregulated interaction between microbiome and gut immune system. Here, we profile, via multiple single-cell technologies, T cells purified from the intestinal epithelium and lamina propria (LP) from terminal ileum resections of adult severe CD cases. We find that intraepithelial lymphocytes (IEL) contain several unique T cell subsets, including NKp30+γδT cells expressing RORγt and producing IL-26 upon NKp30 engagement. Further analyses comparing tissues from non-inflamed and inflamed regions of patients with CD versus healthy controls show increased activated TH17 but decreased CD8+T, γδT, TFH and Treg cells in inflamed tissues. Similar analyses of LP find increased CD8+, as well as reduced CD4+T cells with an elevated TH17 over Treg/TFH ratio. Our analyses of CD tissues thus suggest a potential link, pending additional validations, between transmural inflammation, reduced IEL γδT cells and altered spatial distribution of IEL and LP T cell subsets.

I nflammatory bowel disease (IBD) encompasses intermittent chronic inflammatory disorders of the gastrointestinal tract that significantly impair the quality of life in affected individuals and can result in comorbidities and complications requiring repeated surgery 1,2 . IBD includes two major subtypes, Crohn's disease (CD) and ulcerative colitis (UC). CD inflammation spans across all layers of the gut, while tissue damage in UC is confined to the mucosa. Both genetic and environmental factors contribute to IBD by generating abnormal interactions between the commensal microbiome and the mucosal immune system that result in uncontrolled intestinal inflammation. The conventional therapy with anti-inflammatory and immunomodulatory drugs has been recently integrated with biologicals that can effectively target cytokines, such as TNF-α, IL-12 and IL-23, or inflammatory cell recruitment with α4β7 blockers 3 . However, IBD still poses significant therapeutic challenges.
High-dimensional single-cell profiling approaches, such as single-cell RNA sequencing (scRNA-seq) and mass cytometry, have been recently performed on intestinal specimens from patients with CD or UC and controls. These studies provided unbiased analyses of cell lineages and their functional states in IBD, deconvoluted pathways underlying IBD pathogenesis and supplied biomarkers predicting the course of disease and the response to therapy [4][5][6][7][8][9][10] . While these studies have analyzed whole mucosal biopsies or the lamina propria (LP) selectively, few studies have analyzed intraepithelial lymphocytes (IEL) purified from CD specimens. IEL comprise a quite diverse and complex repertoire of TCRαβ + and TCRγδ + T cells 11,12 , which are strategically located at the interphase between the luminal environment and the intestinal barrier, contributing to intestinal homeostasis and mucosal protection 13,14 .
Here, we examine T cells from 90 intestinal specimens of CD and controls by either scRNA-seq, multi-parameter flow cytometry or CyTOF, with CD specimens derived from surgical resections of the terminal ileum of adult patients with severe CD. Comparing IEL T cell profiles with those of T cells purified from the LP, our data not only provide an unbiased view of T cell lineages diversity and functional states in the intestinal mucosa under both healthy and CD conditions, but also identify an altered spatial distribution of T cell subsets between the IEL and the LP compartments that potentially correlates with transmural inflammation, although this remains to be validated with larger patient cohorts.
Results scRNA-seq analysis identifies multiple IEL T cell subsets. IEL were prepared from surgical resections of the terminal ileum of CD patients, which included both macroscopically inflamed tissue (II) and adjacent non-inflamed tissue (NI) ( Supplementary  Fig. 1a-c). Most CD cases required surgical treatment because of severity and resistance to medical therapy (patient information: Supplementary Data 1-3). Ileal resections from patients undergoing surgery for colonic polyposis or cancer were used as controls. In an initial survey, we defined the baseline heterogeneity of IEL T cells by scRNA-seq of about 15,000 cells sorted from two CD patients and two controls (sorting strategy Supplementary  Fig. 1d). Unsupervised clustering by UMAP of gene expression data from both CD and control samples identified ten cell clusters (Fig. 1a, b). Differential expression of marker genes was used to annotate the different cell types and states. Three T cell clusters expressed CD8A (0, 1, and 6) and five expressed CD4 (2, 3, 4, 5, and 8) (Fig. 1c). Among the CD8 + clusters, cluster 6 expressed genes indicating a canonical effector phenotype, including KLRG1 (Fig. 1d), GZMB, GZMK, PRF1, IFNG, and FCRL6 (Supplementary Fig. 2a, b). Cluster 6 also expressed high level of KLF2 ( Supplementary Fig. 2c), a transcription factor that promotes lymphocytes circulation 15 . In contrast, cluster 1 and cluster 0 shared expression of ITGAE, the receptor for E-cadherin, which is indicative of tissue residency 16 , and CD160, a receptor for HVEM expressed on epithelial cells 17 (Fig. 1d). Expression of ENTPD1, which encodes the activation marker CD39, distinguished cluster 1 from cluster 0 (Fig. 1d). The expression of γδTCR, NK cell receptors, cytotoxic mediators, and chemokines ( Supplementary Fig. 2a, b) in a subset of cells within these two clusters suggested that additional subpopulations may be present (see below).
Flow cytometry of IEL corroborates T cell diversity. To validate scRNA-seq data we analyzed IEL of CD and control patients by flow cytometry (gating strategy: Supplementary Fig. 3a, b; patient information Supplementary Data 4). Cells were clustered using viSNE analysis in Cytobank. To facilitate comparison of flow cytometry with scRNA-seq data, t-SNE clusters were labeled with the same numbers of the corresponding scRNA-seq cluster followed by an asterisk. We adopted CD103 as a marker of tissueresident CD8 + T cells (Fig. 2a, b). Most CD103non-resident cells expressed KLRG1 + (cluster 6*) (Fig. 2b), consistent with the KLRG1 + T effector cells identified by scRNA-seq (cluster 6) (see Fig. 1d). CD103 + cells were split into CD39 + and CD39clusters (1* and 0*), which corresponded to scRNA-seq clusters 1 and 0. Flow cytometry also identified a unique cluster of CD103 -KLRG1cells (U) that lacked all tested markers and thus could not match any subset identified by scRNA-seq (Fig. 2a).

IEL show an aberrant representation of T cell subsets in CD.
To examine the impact of CD on IEL T cells, we quantified percentages of CD8 + , CD4 + , and γδ T cells in a large cohort of patients (Controls: n = 33-34; CD NI: n = 19-20; CD II: n = 19) by flow cytometry (Fig. 3a-c, Supplementary Data 5 and Source Data 1). CD8 + T cells were significantly decreased in CD patients at the inflamed site (Fig. 3a), a finding that was further supported by immunohistochemistry data (Supplementary Fig. 3c-f). CD8 + T cell attrition was paralleled by a significant increase in CD4 + T cells (Fig. 3b). In addition, γδ T cells were significantly reduced in the inflamed tissue of CD patients, as compared to controls (Fig. 3c). We further examined whether CD impacted specific T cell subsets in a fraction of the larger cohort of patients described above (Controls: n = 15; CD NI n = 9; CD II n = 9). Quantification of resident CD103 + CD8 + T cells and non-resident CD103 -KLRG1 + CD8 + T cells did not reveal major differences between CD and controls ( Fig. 3d-f), indicating global rather than subsetspecific reduction of CD8 + T cells in CD. In contrast, quantification of different CD4 + T cell clusters showed a significant increase of CD39 + CD4 + T cells, CD39 + CCR6 + T H 17 cells (Fig. 3g, h) and CD39 + CD4 + T H 17 within the non-resident CD103population (Fig. 3i) in CD patients, suggesting that these cells may have recently settled into the tissue. In addition, BTLA + and TIGIT + CD4 + T cells were decreased in CD patients (Fig. 3j,  k), indicating reduced representation of T FH . Finally, we observed a Fig. 1 scRNA-seq of IEL T cells identifies discrete subsets of CD4 + and CD8 + T cells. a Unsupervised UMAP analysis of IEL T cell clusters. T cells were pooled from two controls and two CD patients (8822 cells control, 6909 cells CD). b Heat map displaying the top ten differentially expressed genes in each cell cluster. c Identification of CD4 and CD8A expressing cells. d UMAP of representative selected genes associated with the identified clusters. NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-22164-6 ARTICLE significant decrease in the relative proportion of CD25 hi CD4 + T cells in CD, which may correspond to Tregs (Fig. 3l). Altogether, these results suggested that the IEL compartment of CD patients exhibits an aberrant T cell landscape. Notably, some CD-associated alterations were evident in the non-inflamed tissue. The noninflamed tissue may exhibit early cellular changes reflecting ongoing disease without any macroscopic sign of inflammation and may predict risk of recurring inflammation.
We finally re-analyzed the CD4 + T cell cluster pair 2-4, because it contained a small group of CD8A + cells among a large population of T H 17 cells (see Fig. 1c). Reclustering of the 2-4 cluster pair generated four distinct subsets (Fig. 4e). Cluster 2-4 0 corresponded to activated cytokine-secreting IL23R + T H 17, whereas cluster 2-4 1 expressed CXCR4, GPR183, and CXCR3, which indicate quiescent T H 17 33 (Fig. 4f and Supplementary  Fig. 4c). Cluster 2-4 2 belonged to the γδ T cell lineage, as indicated by TRCG2 and CD8A expression. These cells expressed RORC, IL17A, and IL26, explaining the initial co-clustering with T H 17 cells and suggesting that they may represent a discrete subpopulation of γδ T cells with a type 17 polarization (Fig. 4f). This γδ T cell subset was also marked by NCR3 (Fig. 4f). To validate this γδ T cell subset, we stained IEL with a mAb for    TCRVδ2, followed by an antibody that recognizes all γδ TCRs, and a mAb for NKp30 (patient information in Supplementary Data 6). Among Vδ2γδ + T cells, about 25-50% of the cells expressed NKp30 in different individuals (n = 9), suggesting that NKp30 + γδ T cells represent approximately half of the TCRVδ2 -Vδ1 + γδ T cells (Fig. 4g, h and Supplementary Fig. 4d). All of these cells expressed CD39 (Fig. 4h). In contrast, NKp30 was not expressed by terminal ileum CD8 + TCRγδ -T cells ( Fig. 4i and Supplementary Fig. 4d), contrary to what shown for colonic CD8 + T cells of UC patients 10 .
To validate the functional relevance of NKp30 on this unique γδ T cell subset, we sorted all γδ T cells from control patients and tested the expression of RORγt on NKp30 + vs. NKp30cells. As shown in Fig. 4j, NKp30 + γδ T cells expressed higher level of RORγt than NKp30 -. We further expanded NKp30 + γδ T cells in vitro to perform functional assays. Expanded γδ T cells Fig. 4 Reclustering of heterogenous IEL CD8 + and CD4 + T cells populations. a Unsupervised UMAP reclustering of clusters 0 and 1 from Fig. 1a. b UMAP of representative selected genes associated with the identified clusters. c Unsupervised UMAP reclustering of clusters 5 and 8 from Fig. 1a. d UMAP of representative selected genes associated with the identified clusters. e Unsupervised UMAP reclustering of clusters 2 and 4 from Fig. 1a. f UMAP of representative selected genes associated with the identified clusters. g Overlay expression of NKp30 in Vδ2 -(pink) vs. Vδ2 + (black) γδ T cells. h, i Representative flow plots showing the expression of NKp30 in TCRγδ + Vδ2 -CD39 + (h) and CD8 + TCRγδ -CD39 + (i). T cells in terminal ileum of control patients (one donor representative of nine is shown). j Overlay expression of RORγt in NKp30 -(black) vs. NKp30 + γδ T cells (pink). k Overlay expression of NKp30 in peripheral blood TCR αβ + T cells (black) vs. NKp30 + sorted γδ T cells (pink). l IL-26 production by NKp30 + γδ T cells upon antibody mediated crosslinking of NKp30. MFI mean fluorescence intensity, PB peripheral blood, IC isotype control. maintained high levels of NKp30 expression (Fig. 4k) and produced IL-26 upon engagement of NKp30 with a cognate antibody (Fig. 4l). Because of the reported antibacterial properties of IL-26 34 , our results suggest that this discrete γδ T cell subset may have a protective role in intestinal homeostasis.
LGALS3 encodes galectin 3 that has antibacterial and antifungal immunity 35 , corroborating antimicrobial properties of T H 17 36 . GPR65 is a proton sensing G-proteincoupled receptor that has been found to be a risk factor for IBD 37 . Cluster 3 corresponded to naïve T cells expressing CCR7 (Fig. 5d), LEF1, TCF7, SELL, and KLF2 ( Supplementary Fig. 5a, c). Cluster 5 and 6 were both T FH based on TOX2 and CXCR5 expression ( Fig. 5d and Supplementary Fig. 5c); cluster 6 was further distinguishable from cluster 5 based on the selective expression of CXCL13 (Fig. 5d), PDCD1, BTLA, and CD200 ( Supplementary  Fig. 5c). As opposed to IEL T FH , LP T FH were not readily distinguishable into P2RY8 + and CYSLTR1 + subsets. Cluster 4 corresponded to Treg expressing FOXP3, IL2RA (Fig. 5d), ENTPD1, BATF, IL10, LAIR2, TNFRSF4 (for OX40), and TNFRSF9 (for CD137) (Supplementary Fig. 5a-c). The Treg subset was enriched in GPX1 (Fig. 5d) and GLRX ( Supplementary Fig. 5b), which are induced by FOXP3 and encode molecules protecting from oxidative stress 38,39 . Cluster 5, 6, and 4 shared expression of CTLA4, TIGIT, ICOS ( Supplementary Fig. 5c) and the transcription factors TOX, TOX2, and MAF ( Fig. 5d and Supplementary Fig. 5a), corroborating that T FH and Treg lineages are closely related 31 . Finally, cluster 7 identified a unique cluster of CD4 + T cells exhibiting a heat-shock stress-activated pathway, as indicated by expression of HSP family members (HSPA1A, HSPA1B, and DNAJB1), JUN, TNF, and IL2 (Fig. 5b, d and Supplementary Fig. 5a, b). The activation of this pathway was also evident at the bottom of the CD8A + cluster 2 (Fig. 5c, d). Altogether, our data indicated that IEL and LP harbor overlapping T cell subsets, although T H 17 in LP are more quiescent than those in the IEL compartment ( Supplementary Fig. 6a). Furthermore, LP contained a unique subset of T cells that express heat-shock stress-pathway genes and secreted cytokines such as TNF and IL2.
Quantification of different LP clusters in CD and control patients revealed an opposite trend than what observed in IEL, with a significant increase in CD8 + T cells paralleled by a decrease in CD4 + T cells (Fig. 6c, d; fresh samples triangles). These data were confirmed by flow cytometry in a different cohort of frozen samples from control (n = 9) and CD (n = 11) patients and combined to the CyTOF data (Fig. 6c, d; frozen samples circles; patient information: Supplementary Data 7 and Source Data 2). Among CD8 + T cells, CD103 + resident cells were increased in CD compared to controls (Fig. 6e), as evidenced by expansion of clusters 2, 4, 12, and 16 ( Fig. 6f-i). Although total CD4 + T cells were reduced in CD, T H 17 of clusters 5 and 14 were significantly increased at the non-inflamed site of CD lesions (Fig. 6j). On the contrary, Treg (7) and T FH (9) were decreased (Fig. 6k, l). The increase in T H 17 cells (CD4 + CCR6 + CD161 + ) and decrease of T FH (TIGIT + ) was further confirmed by running CITRUS analysis (Supplementary Fig. 7a-c). Taken together, our data suggested that inflammatory T H 17 and CD8 + T cells are increased in the LP of CD, paralleled by an attrition of CD4 + T cells with regulatory properties, consistent with deepened gut wall inflammation.

Discussion
Through a high-resolution analysis of human intestinal IEL T cells in controls and severe adult CD, our study defined a vast heterogeneity of T cell lineages in the IEL compartment, including various subsets of CD8 + , γδ + , and CD4 + T cells. Compared to controls, CD was associated with major abnormalities in the composition of IEL T cells, which included: (a) an increase in inflammatory CD39 + T H 17; (b) a decrease in Treg, which might exacerbate inflammation; (c) a decrease in T FH , which may explain the impaired mucosal IgA production previously reported in CD 42 ; and (d) a global reduction of CD8 + T cells and γδ T cells. These changes in the IEL compartment were coupled with increased CD8 + T cells and T H 17, as well as reduced T FH and Treg in the LP, likely reflecting the deepening of inflammation and overt transmural damage 1,2 . Overall, despite some limitations of our study due to cohort heterogeneity of CD patients, our results offer insight into T cell correlates of transmural inflammation and relapsing/recurrent disease.
The remarkable heterogeneity of human T cells identified in the IEL compartment reflected the presence of different T cell lineages, as well as various stages of differentiation, activation, and tissue residency within each lineage. T H 17 cells included quiescent cells and cells capable of immediate effector functions, which were readily distinguished based on the mutually exclusive expression of CXCR4 and CD39, among other distinctive markers. IEL CD39 + T H 17 may be a two-edged sword. On one hand, they exhibit pathogenic features, such as expression of GZMB and CCL4, suggesting that they may be cytotoxic against epithelial cells and recruit inflammatory cell types that promote tissue destruction. On the other hand, production of IL-17 and IL-26 may enhance barrier function, providing protection. Given that CD39 has been shown to sustain T FH survival by degrading proapoptotic ATP released in the intestinal environment 43  T FH also included two major subsets: one subset expressed P2RY8, a G-protein-coupled receptor that inhibits cell migration upon binding S-geranylgeranyl-L-glutathione, which is present in bile salts 29 ; another subset expressed CYSLTR1, which binds to another glutathione-conjugated lipid mediator, LTC4 29 . T FH expressing high levels of CYSLTR1 were not previously identified in lymph nodes and may be specific to IEL 30 . These T FH subsets showed other distinctive Fig. 6 Quantification of CD8 and CD4 T cell clusters by CyTOF analysis in LP of control and CD patients. a Schematic t-SNE of CD4 + and CD8 + T cells from LP of all donors concatenated together (n = 18) controls (N), n = 8; CD, non-inflamed site (NI), n = 9; CD, inflamed site (II), n = 6. Total of 23 samples. b t-SNE of the indicated markers in CD4 + and CD8 + T cells. c, d Quantification of total CD8 + (c), total CD4 + (d) in LP of controls and CD patients by CyTOF (triangles = fresh samples) and FACS (circles = frozen samples). c, d Control (N), n = 17 (8 fresh, 9 frozen); CD, non-inflamed site (NI) n = 19 (9 fresh, 10 frozen); CD, inflamed site (II), n = 14 (6 fresh, 8 frozen). e-i Quantification of total CD8 + T RM (e) and CD8 + clusters 2 (f), 4 (g), 12 (h), and 16 (i) in LP of controls and CD patients by CyTOF. j-l Quantification of the CD4 + clusters 5 and 14 (j), 7 (k), and 9 (l) in LP of controls and CD patients by CyTOF. e-l Controls (N), n = 8; CD, non-inflamed site (NI), n = 9; CD, inflamed site (II), n = 6. Circles and triangles on the boxplots show data collected for each individual donor. Data were median and interquartile range. Significance was calculated using an ordinary, one-way ANOVA, multiple comparisons test with Prism v8 software. c **P = 0.0014; d **P = 0.028; e *P = 0.0139; f *P = 0.0178; h *P = 0.0178; i *P = 0.0219; j N vs. NI *P = 0.0156, NI vs. II *P = 0.0465; k **P = 0.0014; l **P = 0.0283. T RM tissue-resident memory T cell. Source data are provided as a Source Data file (Source Data 2). markers: P2RY8 + T FH expressed CXCR5 and TNFSF8 (for CD30L); CysLTR1 + T FH expressed BTLA and CD200. P2RY8 mediates the retention of T FH and B cells in the germinal center, while BTLA restrain T FH germinal center responses 29 . TNFSF8 gene polymorphisms have been associated with risk of CD 44 . Although IEL T FH did not show mRNA for the B cell stimulatory cytokine IL-21, P2RY8 + , and CysLTR1 + T FH subsets may control mucosal B cell responses through other mechanisms, such as BTLA-HVEM and TNFSF8-TNFRSF8 interactions.
IEL CD8 + T cells were also quite heterogeneous, including canonical CD103 -KLRG1 + cytotoxic CD8 + T cells and multiple subsets of tissue-resident CD8 + T cells, which shared the expression of CD103 and CD160 that may secure retention of T cells through binding to E-cadherin and HVEM on epithelial cells, respectively. Moreover, CD160 may shape the function of CD8 + T cells by inducing IFN-γ 45 . Tissue-resident CD8 + T cells included a subset of IL-7R + TCF7 + cells producing the DC chemoattractants XCL1 and XCL2 26 , which may recruit DCs from the LP. Other resident CD8 + T cells subsets were prone to effector functions: one subset expressed IFN-γ and the NK receptor KLRC1 specific for HLA-E; another expressed cytotoxicity mediators, lacked NK receptors but expressed the membrane protein EMP3, which has yet unknown function in immune responses. Finally, a group of resident CD8 + T cells expressed EIF5A, MDM4, and SET, which control p53 activity 28 , suggesting evasion from apoptosis and senescence. Many tissueresident CD8 + T cells expressed S100A family members, which may contribute to antimicrobial functions.
One remarkable result of this study is the identification of at least two subsets of γδ T cells within the IEL. One subset expressed T H 17 markers, such as RORC, IL-23R, IL-22, and IL-26. RORγt + γδ T cells have been extensively described in mouse 47,48 but not in human. Distinctive features of these γδ T cells included the expression of TCRVδ1, CD39, and NKp30, an activating cell surface receptor specific for B7H6 49 . Importantly, NKp30 + γδ T cells expressed higher levels of RORγt ex vivo as compared to NKp30γδ T cells and engagement of NKp30 resulted in IL-26 production, suggesting that these cells may have a protective function during homeostasis. Some traits of these γδ T cells, such as NKp30 expression and IL-26 production, were recently reported in a subset of CD8 T cells expanded in colon of UC patients 10 . Whether these colonic cells are bona fide CD8 T cells or γδ T cells expressing CD8, as we find in the small intestine, remains to be established. Another γδ T cell subset expressed the canonical γδ T cell transcription factor ID3 32 ; this subset expressed NK cell receptors and produced CSF1 and PDGFD, pointing to a potential crosstalk with macrophages and epithelial cells. While subsets of human γδ secreting CSF1 have been reported 46 , PDGFD secretion by γδ T cells has not been described before. In addition, a rare cell subset expressing γ-constant region of γδTCR and CD8A expressed FOXP3. Future studies will be required to precisely identify these cells and to test their regulatory function.
Notably, our cohort of severe adult CD showed a reduction of IEL CD8 + T cells and γδ T cells. Consistently, a population of CD39 + CD8 + T cells and γδ T cells has been recently reported to decrease in colonic mucosa biopsies of pediatric CD patients 9 . This population overlaps with the γδ and CD8 + T cell subsets expressing NK cell receptors and CD39 reported here. While we observed a global reduction rather than a selective loss of specific subsets of CD8 and γδ T cells, this discrepancy may depend on differences in patient ages (children vs. adults), sampling location (colon vs. ileum), type of specimen (biopsies vs. surgical resections) or degree of disease.
Finally, analysis of LP T cell transcriptomes identified unique functional features not immediately related to the classical T cell functional modules. A subset of LP CD4 + T cells expressed heatshock induced stress-pathway genes and cytokines, such as TNF and IL-2. A T cell subset expressing heat-shock proteins was also among five IEL populations recently reported in CD 50 . Intriguingly, a recent study showed that febrile temperature in mice induces T H 17 differentiation and augments pathogenicity through heat-shock response genes 51 . Together, these observations highlight the involvement of heat-shock response in the differentiation of T cells in the intestine. Expression of GPX1 in Tregs of the LP revealed the activation of anti-oxidative pathways in intestinal T cells, which may be particularly relevant to CD, as GPX1 gene polymorphisms have been associated with risk of CD 52,53 . These results will prompt future studies to determine the impact of these unique functions in CD pathogenesis.

Methods
Preparation of single-cell suspension from intestinal samples. Single-cell suspension was prepared as previously described 54 . Briefly, mucosal tissue from terminal ileum was separated from the muscular layer and serosa and cut into small pieces. Intraepithelial lymphocyte cells were extracted by rotating the tissue at room temperature for 40 min in Hank's balanced salt solution, 10% FCS, and 5 mM ethylenediaminetetraacetic acid (EDTA). Cells were filtered through 100-μm cell strainers and dithiothreitol (DTT) was added at a final concentration of 5 mM. After intraepithelial lymphocyte removal, LP cells were extracted by digesting tissue in complete RPMI medium containing 1 mg ml -1 Collagenase IV (Sigma, C-5138) at 37°C for 1 h under agitation. Cells were filtered and subjected to density gradient centrifugation using 40 and 70% Percoll solutions. Cells were collected, sorted, and processed for scRNA-seq or collected and stained for CyTOF. From a set of patients cells were processed and frozen for later CyTOF or flow cytometry analysis. Control patients for the present study were patients undergoing abdominal surgery for colon cancer or polyposis, which had non-involved terminal ileum removed, as part of the surgical procedure.
All human studies were conducted under the approval of the Institutional Review Boards of Washington University. All ileum samples were provided as surgical waste with no identifiers attached on written informed consent to the Digestive Disease Research Cores Center at Washington University. The demographic data provided in this study will not allow patient identification.
Immunohistochemistry. Formalin-fixed paraffin-embedded tissue blocks used for this study were retrieved from the tissue bank of the Department of Pathology (ASST, Spedali Civili di Brescia, Brescia, Italy). Four-micron thick tissue sections were used for immunohistochemical staining. Sections were incubated with antihuman CD4 (clone 4B12 1:50 Thermo Scientific) and antihuman CD8 antibody (clone C8-144B Agilent 1:50) and the reaction was revealed using Novolink Polymer (Leica Microsistem). For double staining, after completing the first immune reaction, the second was visualized using Mach 4 MR-AP (Biocare Medical), followed by Ferangi Blue. Finally, the slides were counterstained with Meyer's Haematoxylin.
Antibodies. Information on the antibodies used for flow cytometry and sorting is available in Supplementary Data 8.
Flow cytometry and sorting. Cells were sorted on BD FACS Aria II and flow cytometry analyses were performed on BD Symphony A3 instrument. Data were analyzed by FlowJo software v10.7.1 (TreeStar).
scRNA-seq and data analysis. T cells were sorted from processed IEL and LP as CD45 + , lymphocyte gate, singlets, and alive CD3 + CD19expression. Sorted cells were sequenced using 10X Genomics platform with chemistry version 2. Cell Ranger pipeline (https://support.10xgenomics.com/single-cell-gene-expression/ software/over-view/welcome) was used to process Chromium single-cell RNA-seq output to align reads and generate gene-cell expression matrices. Briefly, short sequencing reads were aligned to the GRCh38 reference genome and Ensembl 55 transcriptome by STAR 56 . The uniquely aligned reads were used to quantify gene expression levels for all Ensembl genes. We filtered out low-quality cells from the dataset if the number of genes detected was <500 or >3000, or the percentage of mitochondrion reads was >15%. Mitochondrion and ribosomal genes usually consumed a large fraction of reads in our dataset, and their relative abundance varied significantly from sample to sample. Such genes were not interesting in our research, and thus were excluded for downstream data analysis. Additionally, all genes that were not detected in at least 1% of all our single cells were discarded. Average UMI were 2960 and 3020 for IEL and LP T cells, respectively.
scRNA-seq downstream analysis. Downstream analyses were performed using Seurat R software package version 3.0 (http://satijalab.org/seurat/). After removing unwanted cells and genes from the dataset, raw UMIs in each cell were first scaled by library size and then log-transformed. To improve downstream dimensionality reduction and clustering, we first regressed out unwanted source of variation arising from the number of detected molecules. Then highly variable genes were identified and selected for PCA reduction of high-dimensional data. Cells in this reduced spaced were harmonized to adjust for batch effects coming from multiple donors including both normal and CD using the Harmony tool implemented in Seurat v3 57 . These low dimensional corrected Harmony embeddings were used for downstream analyses. Graph-based clustering was performed on the reduced data for clustering analysis with Seurat v3. The resolution in the FindClusters function in Seurat was set to 0.6 and the clustering results were shown in a UMAP plot. For different cell types, cells were grouped based on top markers. MAST in Seurat v3 was used to perform differential analysis 58 . For each cluster, DEGs were generated relative to all of the other cells.
Ex vivo intracellular staining of γδ T cells for RORγt. Total γδ T cells were sorted from IEL of control patients and cell surface stained for NKp30, CD39, fixed and permeabilized and stained intracellularly for RORγt with eBioscience FoxP3 staining kit.
In vitro T cell culture and NKp30 crosslinking. NKp30 + γδ T cells were sorted from IEL of control patients and expanded in vitro with PHA (HA16, Remel), irradiated feeder and IL-2. Expanded cells were stimulated with plate bound anti-NKp30 (clone 30.95.1) or isotype control (CRL-1729, ATCC). IL-26 was measured in supernatants 72 h later by ELISA (CUSABIO).
Statistical Analysis. Statistical analysis was performed using Graphpad Prism 8.4.3 (GraphPad Software, La Jolla, CA) or R version 3.6.2 (2019), as indicated in the figure legends. Data were presented as median and interquartile range. Unless otherwise noted, statistically significant differences between groups were determined by ordinary one-way ANOVA. In all figures, the following symbols were used to designate significance: *P ≦ 0.05, **P ≦ 0.01, ***P ≦ 0.001.
Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability
The scRNA-seq have been deposited in GEO under the GSE157477. Source data are provided with this paper.