Heterogeneity of CD8αα intraepithelial lymphocytes is transcriptionally conserved between TCRαβ and TCRγδ cell lineages

Intestinal intraepithelial lymphocytes (IELs) are a versatile population of immune cells with both effector and regulatory roles in gut immunity. Although this functional diversity is thought to arise from distinct IEL subpopulations, the heterogeneity of TCRαβ⁺ and TCRγδ⁺ IELs have not been well characterized. Using scRNAseq, we identified CD8αα⁺ T cell subsets with memory-like (Tcf7⁺) and effector-like (Prdm1⁺) profiles in both TCRαβ⁺ and TCRγδ⁺ IELs. Using CD160 and CD122 as markers of memory-like and effector-like cells, respectively, we found that while effector-like cells dominated the small intestine, memory-like IELs were more prevalent in the large intestine, suggesting a functional specialization of immune responses along the gut. Further transcriptional analysis revealed shared profiles between TCRαβ⁺ and TCRγδ⁺ small intestinal IEL subsets, suggesting conserved functional roles across these populations. Finally, our analysis indicated that TCRαβ⁺ memory-like IELs arise from Tcf7⁺ double-negative (DN) precursors, and that effector-like IELs subsequently differentiate from the memory-like population. In contrast, TCRγδ⁺ IELs appear to originate from two distinct precursor populations, one expressing Tcf7 and the other Zeb2, indicating the presence of parallel developmental pathways within this lineage. Overall, our findings reveal that both TCRαβ⁺ and TCRγδ⁺ cells contain memory-like and effector-like subsets, which may contribute to the functional heterogeneity of IELs.

Introduction

The intestinal epithelium is populated by intraepithelial lymphocytes (IELs), a diverse group of T cells that play a critical role in maintaining gut health through several mechanisms. These include effector functions via the secretion of inflammatory cytokines or antimicrobial peptides, cytotoxic activity through perforin and granzymes, regulatory functions by tolerating food antigens or commensal microbes, and even promoting epithelial barrier repair after inflammation (1–5). It is hypothesized that various IEL subpopulations are responsible for these different roles. These subpopulations have been categorized as “induced IELs,” and “natural IELs”, corresponding to conventional tissue-resident T cells and unconventional T cells, respectively. Induced IELs develop from circulating conventional T cells and consist of the following subsets: TCRαβ⁺ CD4⁺, TCRαβ⁺ CD8αβ⁺, and TCRαβ⁺ CD4⁺ CD8αα⁺ (TCRαβ⁺ double-positive or TCRαβ⁺ DP). Natural IELs derive directly from thymic precursors and include TCRαβ⁺ CD4^- CD8αβ^- (TCRαβ⁺ double-negative or TCRαβ⁺ DN), TCRαβ⁺ CD4^- CD8αβ^- CD8αα⁺ (TCRαβ⁺ CD8αα⁺), TCRγδ⁺ CD4^- CD8α^- Cd8β^- (TCRγδ⁺ double-negative or TCRγδ⁺ DN), and TCRγδ⁺ CD4^- CD8αβ^- CD8αα⁺ (TCRγδ⁺ CD8αα⁺) cells (2, 4, 6).

Induced IELs are conventional TCRαβ⁺ cells that migrate to the intestine after encountering antigens and can perform effector functions resembling tissue resident memory cells. Induced TCRαβ⁺ CD8αβ⁺ cells account for 10-15% of all IELs (7, 8). These cells are implicated in controlling viral infections and pathogenic bacteria by expressing high levels of effector markers including granzymes A and B, perforin, Fas-ligand and 2B4, while producing limited amounts of inflammatory cytokines such as IL-1, IL-6, IL-12 and IL-17 (9–11). Induced TCRαβ⁺ CD4⁺ IELs can exhibit both immune-suppressive and effector functions (5, 12, 13). Regulatory T cells (Tregs) and CD4 T cells produce IL-10 or TGF-β to suppress excessive inflammation in response to microbes (3, 14–16). Furthermore, upon entering the intestine, some TCRαβ⁺ CD4⁺ IELs begin to express CD8αα in a microbiota-dependent manner and acquire cytotoxic features such as production of granzymes and IFN-γ (17–19).

The function of natural IELs is incompletely understood, despite representing the majority of IELs. TCRαβ⁺ DN thymic emigrants begin expressing CD8αα upon entering the intestine, and acquire cytotoxic features such as granzymes, Fas-ligand, and NK cell receptors (20, 21). However, these cells do not initiate proinflammatory responses against infections. Rather, they are believed to play a regulatory role in suppressing colitis and have a high activation threshold to self-antigens due to the absence of co-stimulation from CD8αβ (22–24). TCRγδ⁺ IELs display a more pronounced cytotoxic profile through their expression of granzymes, perforin, proinflammatory cytokines and anti-microbial peptides. These cells have been shown to be critical in reducing tumor volume in colorectal cancer and lowering pathogenic microbial loads in colitis models (3, 25, 26). On the other hand, TCRγδ⁺ IELs also produce anti-inflammatory cytokines and profibrotic factors, which play important roles in intestinal epithelial repair and barrier maintenance (1, 27–30). Given the polyfunctional nature of CD8αα IELs and their critical role in mucosal immunity and tissue homeostasis, better characterization of IEL subsets is essential. A deeper understanding of their functional diversity may yield important insights into their roles in health and disease and inform novel therapeutic strategies for intestinal infections and inflammatory disorders.

Although various modality assays such as flow cytometry, proteomics, metabolomics, and sequencing have been implemented to analyze IEL subpopulations, they have not sufficiently addressed the basis for the IEL functional heterogeneity (31–33). In particular, investigations using single-cell RNA sequencing (scRNAseq) have been unable to fully distinguish between TCRαβ⁺ and TCRγδ⁺ cells (20, 26, 34–39). Here, we performed scRNAseq on sorted TCRαβ⁺ and TCRγδ⁺ small intestinal IELs and identified CD8αα⁺ T cell subsets with distinct profiles. Both datasets revealed subsets with memory T cell-like characteristics expressing Tcf7 (TCF1), and subsets with effector T cell-like characteristics expressing Prdm1 (BLIMP1). Additionally, TCRγδ⁺ IELs contained a unique CD8αα⁺ cluster expressing Zeb2. Using surface markers CD160 and CD122 to distinguish memory-like and effector-like CD8αα⁺ IELs, respectively, we found that effector-like cells were more prevalent in the small intestine. This population of effector-like TCRαβ⁺ cells gradually decreased along the segments of the small intestine and was absent in the large intestine, where only a single population of CD122^intCD160^int cells was observed. In contrast, effector-like TCRγδ⁺ cells also decreased along the small intestine but were retained in the large intestine. These observations suggest that environmental factors including diet and microbiota may influence the distribution of memory-like and effector-like IELs, with similar effects observed for both TCRαβ⁺ and TCRγδ⁺ cells in the small intestine but varying impacts in the large intestine. Further analysis of our small intestinal scRNAseq dataset revealed significant transcriptional similarities between TCRαβ⁺ and TCRγδ⁺ cells. Finally, based on our data, we predicted the precursor-progeny relationships among different IEL subsets.

Results

TCRαβ⁺ and TCRγδ⁺ IELs consist of diverse groups of cells

While many studies have explored the heterogeneity of IELs using single-cell sequencing techniques, it has been a challenge to clearly distinguish between TCRαβ⁺ and TCRγδ⁺ cells at the transcriptional levels, in part due to the shared usage of Vα and Vδ gene locus segments. To better characterize the populations of TCRαβ⁺ and TCRγδ⁺ IELs, we performed scRNAseq on FACS-sorted TCRαβ⁺ and TCRγδ⁺ IELs collected from the small intestine of naïve healthy 6-8-week-old C57BL/6 mice from the Jackson Laboratory. Uniform manifold approximation and projection (UMAP) analysis was performed separately for the TCRαβ⁺ and TCRγδ⁺ cells, revealing 16 and 17 clusters, respectively (Figures 1a, b, Supplementary Figures 1a, b). Based on the expression level of Cd4, Cd8a, and Cd8b1 coreceptor genes, we noticed that minor IEL populations defined in flow cytometry experiments (TCRαβ⁺ DN, TCRαβ⁺ DP, and TCRγδ⁺ DN; Supplementary Figure 2a) were not represented as distinct clusters (Figures 1c, d), possibly due to the relatively small population sizes or stringent quality control filtering. Among the conventional TCRαβ⁺ cells, CD4⁺ cells comprised clusters 9, 14 and part of cluster 3, while CD8αβ⁺ cells consisted of clusters 2, 8, 12 and part of cluster 3. Surprisingly, our analysis of natural TCRαβ⁺ CD8αα⁺ cells revealed two distinct cluster groups: clusters 1, 4, 6, 15, and clusters 5, 7, 10 (Figures 1a, c). Similarly, the TCRγδ⁺ CD8αα⁺ clusters formed diverging groups on the UMAP plot (Figures 1b, d).

Figure 1

Figure 1. TCRαβ⁺ and TCRγδ⁺ IELs consist of diverse groups of cells. (a, b). UMAP plots of sorted CD45⁺ TCRαβ⁺ TCRγδ^- IELs (a) or CD45⁺ TCRαβ^- TCRγδ⁺ IELs (b) after quality control filtering. (c, d). Representative UMAP plots for the expression of Cd3e and coreceptor genes Cd4/Cd8a/Cd8b1 for TCRαβ⁺ IELs (c) and TCRγδ⁺ IELs (d). (e, f). Representative UMAP plots for the expression of marker genes descriptive of cluster group phenotypes for TCRαβ⁺ (e) and TCRγδ⁺ (f) datasets. (g, h). Annotated UMAP plots of 4,845 TCRαβ⁺ IELs (g) and 10,418 TCRγδ⁺ IELs (h).

To define the phenotype of each cluster group, we summarized the expression level of marker genes (Supplementary Figures 2b, c). The conventional TCRαβ⁺ CD4⁺ clusters consist of T-follicular helper-like cells expressing Cxcr5 and Il21 (cluster 9), Foxp3⁺ Tregs (cluster 14), and naïve cells expressing Sell, Ccr7, and Dapl1 (cluster 3) (40–43). The CD8αβ⁺ clusters 2 and 8 were enriched for effector-like T cell markers, including Prdm1 (which encodes the transcription factor BLIMP1) and effector molecules Gzmb and Gzmk (38) (Figure 1e). In contrast, cluster 12 was enriched for memory-like T cell markers, such as Tcf7 (encoding the transcription factor TCF1) and Id3. CD8αβ⁺ cells were also detected in the naïve cell cluster (cluster 3). Interestingly, within the TCRαβ⁺ CD8αα⁺ clusters, we also observed two distinct groups: clusters 1, 4, 6 and 15 were enriched for effector-like markers, including Prdm1, Tyrobp, Gzma and Gzmb, whereas clusters 5, 7 and 10 were enriched for memory-like markers Tcf7 and Id3 (37, 38) (Figure 1e). A similar separation between effector-like and memory-like profiles was found among the TCRγδ⁺ cells (clusters 1, 3, 4, 6, 7, 8, 11 and clusters 2, 5, 10, 13, respectively; Figure 1f).

Collectively, we annotated cluster groups according to the expression of enriched transcription factors: the effector-like cluster groups were labeled as “Prdm1⁺” clusters, and the memory-like cluster groups were labeled as “Tcf7⁺” clusters (Figures 1g, h). Additionally, a unique effector-like population within the TCRγδ⁺ IELs characterized by Zeb2, Gzmk, and Ccr5, was labeled as “Zeb2⁺” cells (former clusters 9 and 15). Finally, cluster groups enriched in proliferation and cell cycle genes (Neil3, Bub3, Esco2, Aspm, Mxd3 for TCRαβ⁺ dataset and Dtl, Ung, Chek1, Cdc6 and Mcm10 for TCRγδ⁺ dataset) were annotated as “Mki67⁺” clusters, corresponding to former TCRαβ⁺ cluster 11 and TCRγδ⁺ clusters 12, 14, 17. Clusters with poorly defined identities or nonspecific cluster localization (former TCRαβ⁺ clusters 13 and 16, and TCRγδ⁺ cluster 16) were excluded from further analysis. In our final annotation, we retained 4,845 TCRαβ⁺ IELs and 10,418 TCRγδ⁺ IELs.

CD8αα⁺ IEL subsets have distinct transcriptional features and preference in colonization pattern of the intestine

TCRαβ⁺ and TCRγδ⁺ CD8αα⁺ IELs are considered “non-conventional” T cells, due to their unique developmental pathways, TCR-independent activity, and other innate-like features. These cells play versatile roles in gut immunity, including the secretion of antimicrobial peptides during infections, exhibiting cytotoxic potential, and supporting repair after inflammation, while also displaying higher activation thresholds for self-reactivity (1, 3, 44, 45). Our observation of the distinct CD8αα⁺ subsets may provide insight into their functional diversity.

To better understand the differences between the CD8αα⁺ sub-clusters, we filtered out cells that expressed Cd4 or Cd8b1 coreceptor genes and re-clustered the populations (Supplementary Figures 3a, b). When confirming the expression of Cd8a, we noticed the re-clustered cells included those with no Cd8a expression in each of the annotated populations (Supplementary Figures 3c, d), The coreceptor negative cells contributed to 4.7% and 11.9% of the re-clustered TCRαβ⁺ and TCRγδ⁺ cells, respectively, and were localized to the edges of the UMAP plots (Supplementary Figures 3e, f). We speculated that these cells represent the population of DN IELs identified by flow cytometry experiments (Supplementary Figure 2a), which are known as precursors to CD8αα⁺ IELs (13). Accordingly, we performed the subsequent analyses separately for the Cd8a⁺ (CD8αα⁺) IELs and coreceptor negative DN IELs, with the cluster labels identified in Figure 1.

We first compared the transcriptional profiles of the CD8αα⁺ sub-clusters (Supplementary Figure 4, Supplementary Table 1). In both the TCRαβ⁺ and TCRγδ⁺ datasets, Prdm1⁺ clusters were significantly enriched for effector T cell-like transcription factors including Prdm1, Runx1, Runx2, Plek and Irf8. In contrast, Tcf7⁺ clusters showed marked enrichment for memory-like transcription factors such as Tcf7, Id3, Aff3, Bach2, Nfkb1 and Satb1 (Figures 2a–d). Additionally, several transcription factors displayed lineage-specific expression pattern. For example, Foxo3, Jazf1, Zbtb16 and Lef1 were differentially expressed among clusters within the TCRαβ⁺ lineage, whereas Zmat4 and Zfnx3 and Elk4 were specific for TCRγδ⁺ cells. Interestingly, within the TCRγδ⁺ dataset, the Zeb2⁺ cluster exhibited a transcriptional profile intermediate between Prdm1⁺ and Tcf7⁺ clusters, while also expressing unique TFs such as Zeb2, Bcl11b and Rora (Figure 2d). Effector-like and memory-like transcriptional profiles of the Prdm1⁺ and Tcf7⁺ clusters, respectively, were corroborated by unbiased gene ontology (GO) analysis. GO terms associated with activation, effector function and cell motility were enriched in Prdm1⁺ clusters in comparison to Tcf7⁺ clusters in both TCRαβ⁺ and TCRγδ⁺ T cell populations. In contrast, GO terms related to ribosome biogenesis, which have been associated with memory formation (46, 47), were predominantly overrepresented in Tcf7⁺ clusters relative to Prdm1⁺ clusters (Supplementary Figures 4a, b). The Zeb2⁺ population appeared more similar to TCRγδ⁺ Prdm1⁺ cluster and was enriched in effector-related GO terms (Supplementary Figures 4c, d). The CD8αα⁺ Prdm1⁺ and Tcf7⁺ clusters differentially expressed genes encoding surface proteins, with Il2rb (CD122) being enriched in the Prdm1⁺ clusters, and Cd160 (CD160) predominantly expressed in Tcf7⁺ clusters (Figures 2e, f). Both CD122 and CD160 have been previously studied for their roles in IEL maintenance, cytotoxic activity, or protective ability, but their heterogeneous expression within total IEL populations has not been fully clarified (44, 48–51). To validate whether CD160 and CD122 can be used to distinguish memory-like and effector-like subsets of CD8αα⁺ IELs, we performed flow cytometry on small intestinal IELs stained for these surface markers. The analysis revealed two distinct populations, with approximately 20% of CD8αα⁺ IELs being CD122^intCD160⁺ and 75% being CD122^hiCD160^- (Figure 2g). These results suggest that the effector-like and memory-like phenotypes are distinctly distributed among total IELs, with surface markers CD160 and CD122 providing a means to distinguish between these functional subsets.

Figure 2

Figure 2. CD8αα⁺ IEL subsets have distinct transcriptional features and colonization pattern of the intestine. (a) UMAP plot of re-clustered TCRαβ⁺ CD8αα⁺ cells. (b) Differentially expressed transcription factors between the (non-Mki67⁺) CD8αα⁺ cluster groups in TCRαβ⁺ CD8αα⁺ cells. (c) UMAP plot of re-clustered TCRγδ⁺ CD8αα⁺ cells. (d) Differentially expressed transcription factors between the (non-Mki67⁺) CD8αα⁺ cluster groups in TCRγδ⁺ CD8αα⁺ cells. (e, f). Expression of differentially expressed surface protein-coding genes represented as UMAP plots and ridge plots for TCRαβ⁺ CD8αα⁺ cells (e) and TCRγδ⁺ CD8αα⁺ cells (f). (g) Representative flow cytometry plots of CD122 and CD160 expression among TCRαβ⁺ CD8αα⁺ cells and TCRγδ⁺ CD8αα⁺ cells from the small intestine. (h, i). Representative flow cytometry plots and quantification of CD122 and CD160 expression along five sections of the intestine for TCRαβ⁺ CD8αα⁺ cells (h) and TCRγδ⁺ CD8αα⁺ cells (i). Data were analyzed by Paired T-test (h, i).

The maintenance and activity of IELs can be modulated by both diet and gut microbiota, which vary along the intestinal tract (17, 18, 52–57). These variations may shape the distribution of IEL subsets to ensure appropriate immune responses in different regions of the intestine. Therefore, we next examined the composition of CD8αα⁺ IELs along five different sections of the intestine. In both TCRαβ⁺ and TCRγδ⁺ CD8αα⁺ IELs, the proportion of CD160⁺ cells gradually increased from 10% to 30% along the small intestine (duodenum, jejunum, ileum), while the proportion of CD122^hi cells decreased reciprocally from 90% to 60% (Figures 2h, i). However, the distribution of these populations shifted drastically in the large intestine. Among TCRαβ⁺ IELs, CD122⁺ cells were nearly absent in cecum and colon, leaving one major population of CD122^intCD160^int cells (Figure 2h). In contrast, despite collecting very few of TCRγδ⁺ IELs in the large intestine, an equal proportion of CD122^hiCD160^- and CD122^intCD160^int populations were observed (Figure 2i).

To confirm whether the CD160⁺ population in all five sections of the intestine correspond to the Tcf7⁺ cluster IELs, we used Tcf7-GFP mice, where Tcf7-expressing cells are concurrently labeled by GFP (58). Indeed, the CD8αα⁺ CD160⁺ IELs expressed higher levels of GFP compared to CD8αα⁺ CD122⁺ cells in most tissue sections and contained a greater frequency of GFP+ cells, for both TCRαβ⁺ and TCRγδ⁺ subsets (Supplementary Figures 5a, c). These observations suggest that some yet unknown factors influence the abundance of CD8αα⁺ IELs along the intestine, promoting a high and similar prevalence of effector-like IELs in TCRαβ⁺ and TCRγδ⁺ cells in the small intestine but favoring the colonization of memory-like TCRαβ⁺ CD8αα⁺ IELs in the large intestine.

DN IEL subsets have distinct transcriptional features and no preference in colonization along the intestine

Given that DN IELs are considered precursors to CD8αα⁺ IELs, we sought to determine whether the distinct Prdm1⁺ and Tcf7⁺ transcriptional profiles observed in mature CD8αα⁺ IELs are already established at the DN stage. Therefore, using our dataset of coreceptor-negative DN IELs, we next examined whether these transcriptional differences could also be detected prior to CD8αα expression. Gene expression analysis across both TCRαβ⁺ and TCRγδ⁺ lineages revealed that DN subsets share many transcription factors with their corresponding CD8αα⁺ counterparts. Specifically, both Tcf7⁺ DN and CD8αα⁺ IELs expressed Tcf7 and Zfp706, while Prdm1⁺ subsets shared expression of Prdm1 and Runx1 (Figures 2a–d, 3a–d). Furthermore, we found a strong overlap in transcription factor expression between DN and CD8αα⁺ subsets within the TCRγδ⁺ lineage (Figures 2a–d, 3a–d). For example, Zmat4 was highly enriched in the Prdm1⁺ cluster; Zeb2, Runx2, Stat1, and Bcl11b were enriched in the Zeb2⁺ cluster; and Tcf7, Id3, Aff3, Satb1, Lef1, and Nfkb1 were enriched in the Tcf7⁺ cluster (Figures 2d, 3d). In addition, we identified several genes, which were specific to Tcf7⁺ DN subsets, including TFs associated with the cell cycle such as Hnrnpk, and Ybx1 (59–61) (Figures 3a–d), along with increased expression of ribosomal genes, a pattern also observed among the CD8αα⁺ subclusters (Supplementary Table 1). These findings suggest that DN cells represent distinct populations that nonetheless relate to CD8αα⁺ cells, supporting the possibility of developmental continuum between these subsets.

Figure 3

Figure 3. DN IEL subsets have distinct transcriptional features and static colonization pattern of the intestine. (a) UMAP plot of re-clustered TCRαβ⁺ DN cells. (b) Differentially expressed transcription factors between the (non-Mki67⁺) DN cluster groups in TCRαβ⁺ DN cells. (c) UMAP plot of re-clustered TCRγδ⁺ DN cells. (d) Differentially expressed transcription factors between the (non-Mki67⁺) DN cluster groups in TCRγδ⁺ DN cells. (e, f). Expression of differentially expressed surface protein-coding genes represented as UMAP plots and ridge plots for TCRαβ⁺ DN cells (e) and TCRγδ⁺ DN cells (f). (g) Representative flow cytometry plots of CD122 and CD160 expression among TCRαβ⁺ DN cells and TCRγδ⁺ DN cells from the small intestine. (h, i). Representative flow cytometry plots and quantification of CD122 and CD160 expression along five sections of the intestine for TCRαβ⁺ DN cells (h) and TCRγδ⁺ DN cells (i). Data were analyzed by one-way ANOVA (h, i).

We also examined the expression level of surface protein coding genes which were differentially expressed between CD8αα⁺ subclusters. Similarly to the CD8αα⁺ IELs, the Tcf7⁺ DN clusters had greater mRNA expression of Cd160, while the Prdm1⁺ DN clusters showed higher expression of Il2rb (Figures 3e, f). These markers also distinguished DN IELs at the protein level by surface expression. However, in contrast to the CD8αα⁺ IELs, which contained a greater proportion of CD122^hi cells (Figure 2g), approximately 70% of DN IELs were CD160⁺ and 20% were CD122^hi (Figure 3g). Based on Tcf7-GFP reporter expression, we observed a clear trend (although not always statistically significant) indicating that CD160⁺ DN cells expressed higher levels of Tcf7 and contained a higher frequency of GFP+ cells (Supplementary Figures 5b, d). These findings align with the predicted distribution from the sequencing data (Supplementary Figures 3e, f). Of note, unlike natural IELs, induced IELs were more uniform and did not clearly segregate into memory-like and effector-like phenotype based on expression of CD122 and CD160 (Supplementary Figure 5e). An exception was observed for TCRαβ⁺ CD8αα⁺ CD4⁺ (DP) cells, where the majority of cells displayed an effector-like CD160^-CD122^int phenotype.

Interestingly, unlike CD8αα⁺ IELs, the proportion of CD122⁺ and CD160⁺ cells along the intestine remained stable among both TCRαβ⁺ and TCRγδ⁺ DN IELs (Figures 3h, i). This suggests that CD8αα⁺ IEL subsets are uniquely sensitive to yet unidentified microenvironmental cues that may drive their “gradient-like” distribution along the small intestine. Notably, this unique sensitivity appears to be specific to the small intestine, as both the CD8αα⁺ and DN IELs in the large intestine (cecum and colon) are predominantly comprised of CD122^lowCD160^int cells (Figures 2h, i, 3h, i).

The transcriptional profiles of TCRαβ⁺ cells are similar to TCRγδ⁺ cells

The characteristics of CD8αα⁺ IELs have been suggested to vary between TCRαβ⁺ and TCRγδ⁺ cells, based on functional studies (62). However, the clustering profiles of the small intestinal CD8αα⁺ IELs appeared strikingly similar between the TCRαβ⁺ and TCRγδ⁺ datasets (Figures 2, 3), suggesting the presence of subsets with shared phenotypes across both cell lineages. To further investigate this, we examined the similarity of IEL subsets between the two datasets.

First, we identified the gene signatures representing the CD8αα⁺ Prdm1⁺ (effector-like) and CD8αα⁺ Tcf7⁺ (memory-like) clusters in the total TCRαβ⁺ dataset. We calculated the differentially expressed genes comparing each subcluster to the rest of the TCRαβ⁺ cells and sorted the genes by their statistical significance (Figure 4a). The top 10 representative genes of the TCRαβ⁺ CD8αα⁺ Prdm1⁺ cluster included genes associated with CD8 T cell activation such as Fcer1g, Cd244a (encoding 2B4), and Clnk (a SLP-76 member), or genes associated with cell-cell contacts such as Fgl2, Frmd5, and Osbpl3 (63–68). The top 10 genes representing the TCRαβ⁺ CD8αα⁺ Tcf7⁺ cluster included genes associated with memory or stem-like T cells such as Id3, Batf3, Kit, and AW112010 (42), and genes associated with intestinal resident T cells such as Cd160 and Xcl1. Next, we projected the signature of each TCRαβ⁺ CD8αα⁺ subcluster onto the TCRγδ⁺ dataset to evaluate whether similarly annotated clusters showed similar enrichment patterns. Indeed, the TCRαβ⁺ CD8αα⁺ Prdm1⁺ signature was significantly enriched in the TCRγδ⁺ CD8αα⁺ Prdm1⁺ clusters (Figure 4b), indicating strong similarities between effector-like clusters of the TCRαβ⁺ and TCRγδ⁺ datasets. In parallel, the TCRαβ⁺ CD8αα⁺ Tcf7⁺ signature was highly enriched in the TCRγδ⁺ CD8αα⁺ Tcf7⁺ clusters, highlighting a similar correspondence between memory-like populations. Together, these findings underscore striking parallels between IEL subsets across the TCRαβ⁺ and TCRγδ⁺ lineages.

Figure 4

Figure 4. The transcriptional profiles of TCRαβ⁺ cells are similar to TCRγδ⁺ cells. (a) Heatmap of top 10 marker genes for the annotated TCRαβ⁺ IEL subclusters sorted by statistical significance. (b) Expression of CD8αα⁺ cluster marker genes in (a) among total TCRγδ⁺ cells. Signature scores were compared by Mann-Whitney U test. (c) UMAP plot generated after integration of TCRαβ⁺ and TCRγδ⁺ datasets with original cluster identities overlaid. (d) Integrated clusters (left) and quantification of cluster composition by individual cluster groups represented as a bar plot (right).

Although these findings validate the overlap between corresponding clusters, it is possible that additional parallels exist between the two datasets that were not detected using a signature projection approach. For example, neither of the signatures showed enrichment in the TCRγδ⁺ CD8αα⁺ Zeb2⁺ cluster. To further evaluate the similarities between TCRαβ⁺ and TCRγδ⁺ IEL clusters, we applied a second approach, where we integrated populations from the two datasets and re-clustered them with low resolution (Figures 4c, d). In addition to the CD8αα⁺ and DN IELs analyzed in Figures 2, 3, we also included the TCRαβ⁺ CD8αβ⁺ clusters as they represent a major portion of TCRαβ⁺ IELs (Supplementary Figure 2a). As expected, the integrated UMAP analysis showed that the “Tcf7⁺”, “Prdm1⁺” and “Mki67⁺” clusters from TCRαβ⁺ and TCRγδ⁺ cells grouped together (Figure 4d). Similar to the previous observation (Figure 2d), the TCRγδ⁺CD8αα⁺ Zeb2⁺ cluster grouped together with a portion of the TCRαβ⁺ CD8αβ⁺ Prdm1⁺ cells, despite these clusters representing separate lineages of IELs. Altogether, our results demonstrate that many of the TCRαβ⁺ and TCRγδ⁺ cells have similar transcriptional profiles, suggesting a potential convergence of their functional roles in intestinal epithelial immunity.

Prediction of precursor-progeny relationship between memory-like and effector-like IELs

Next, we investigated precursor-progeny relationships among natural IEL subsets across TCRαβ⁺ and TCRγδ⁺ lineages. Given that conventional memory T cells can differentiate into effector T cells, we asked whether a similar relationship exists between memory-like IEL clusters and effector-like IEL clusters, and whether such transitions could be inferred from our scRNAseq datasets. Since thymic IEL-precursors are phenotypically DN cells, we also explored the possibility of a developmental trajectory from DN cells to CD8αα⁺ IELs. Upon re-clustering of TCRαβ⁺ CD8αα⁺ and DN cells, we observed that only the Tcf7⁺ cluster contained a substantial proportion of DN cells (Supplementary Figures 3c, e), suggesting a single precursor population for TCRαβ⁺CD8αα⁺ IELs. In contrast, the TCRγδ⁺ dataset contained two clusters, Tcf7⁺ and Zeb2⁺, that were both enriched for DN cells (Supplementary Figures 3d, f), indicating the potential existence of two distinct precursor populations for TCRγδ⁺CD8αα⁺ IELs. To test whether these hypotheses are supported by computational predictions, we performed RNA velocity analysis on combined datasets of TCRαβ⁺ CD8αα⁺ and DN cells, as well as TCRγδ⁺ CD8αα⁺ and DN cells (Figures 5a, b).

Figure 5

Figure 5. Memory-like and effector-like CD8αα⁺ IELs may represent separate IEL lineages. (a, b). UMAP plots with the overlay of RNA velocity trajectory predictions among re-clustered TCRαβ⁺ CD8αα⁺ and DN cells (a) and re-clustered TCRγδ⁺ CD8αα⁺ and DN cells (b). (c) Trajectory models between IEL clusters predicted by RNA velocity analysis.

The TCRαβ⁺ dataset contained distinct trajectories within both the Tcf7⁺ and Prdm1⁺ clusters. Notably, Tcf7⁺ DN cells were positioned at the beginning of the trajectory, clustering together at the earliest point, and therefore appeared as sole precursors to more differentiated Tcf7⁺ CD8αα⁺ cells (Supplementary Figure 3e, Figure 5a). Along this trajectory, cells within the Tcf7⁺ cluster progressively downregulated the expression of transcription factor Lef1 and the activating receptors Klrk1 and Klrc2, while upregulating the memory-associated transcription factor Batf3 (69, 70). In contrast, the trajectory within the Prdm1⁺ cluster started near the Ki67⁺ population and was characterized by increased expression of Zbtb16, which encodes the innate-like T cell transcription factor PLZF (71, 72) (Supplementary Figures 6a–c). These findings indicate the presence of phenotypic gradients within TCRαβ⁺ IEL compartment. Moreover, the directionality of the RNA velocity vectors suggested a developmental transition from the Tcf7⁺ cluster to Prdm1⁺ cluster, with the Mki67⁺ population acting as an intermediate proliferating stage. Thus, our results support the notion that DN Tcf7⁺ cells function as precursors to natural TCRαβ⁺ CD8αα⁺ IELs (Figure 5c).

The TCRγδ⁺ dataset also revealed lineage trajectories originating from DN cells (Supplementary Figure 3f, Figure 5b). Within the Tcf7⁺ cluster, two potential trajectory directions were observed. The first followed a linear progression from DN cells to CD8αα⁺ cells, marked by upregulation of memory-associated genes Xcl1 and Batf3, and downregulation of genes involved in early T cell development, including Rgs10 and Eya2 (73, 74) (Supplementary Figures 6d–f). The second trajectory, directed toward cluster 9 (Supplementary Figure 6d) and included contributions from both the DN and CD8αα⁺ cells, but lacked clearly distinguishable features. In contrast, the trajectory within the effector-like clusters appeared to originate from the DN cells in the Zeb2⁺ cluster and progress toward CD8αα⁺ cells in the Prdm1⁺ cluster, and was characterized by high expression of effector-associated genes such as Tyrobp, Fcgr3, and Ccrl2 (Supplementary Figures 6d–f). To better characterize precursor-progeny relationships among TCRγδ⁺ cells, we analyzed the distribution of Vγ chains within the DN and CD8αα⁺ cell populations (Supplementary Figures 7a–c). Vγ7⁺ cells, the predominant intestinal TCRγδ⁺ subset (75, 76), were primarily located in the Prdm1⁺ cluster and in a subset of Tcf7⁺ cluster within CD8αα⁺ cells. Interestingly, they were largely absent from the DN cells within the Tcf7⁺ cluster but were present in the DN cells within the Zeb2⁺ cluster. In contrast, Vγ1⁺ cells were enriched in the Tcf7⁺ cluster in both CD8αα⁺ and DN populations. Vγ4⁺ cells were relatively evenly distributed across clusters, while Vγ5⁺ and Vγ6⁺ cells were virtually absent. These findings suggest that Vγ7⁺ cells have largely completed the transition from the DN to the CD8αα⁺ state, consistent with their relatively early seeding of the gut (77). In contrast, the broader distribution of Vγ1⁺ and Vγ4⁺ cells across both DN and CD8αα⁺ clusters may reflect ongoing differentiation and migration from thymic precursors into the intestinal IEL compartment (78, 79). We also cannot rule out a possibility that similar to what was observed in the TCRαβ⁺ dataset, a transition from the Tcf7⁺ cluster to the Prdm1⁺ cluster may occur in TCRγδ⁺ lineage as well, via an intermediate Mki67⁺ proliferative population. Collectively, our data support the hypothesis that TCRγδ⁺ IELs arise from two independent precursor subsets (Figure 5c).

We also considered an alternative hypothesis that the memory-like and effector-like IELs arise from separate precursors with their fates predetermined prior to maturation in the intestine. In mice, TCRαβ⁺ CD8αα⁺ IELs are known to develop from two thymic precursor populations that seed the intestine at different developmental stages and express oligoclonal TCR variable chains (21, 80–82). To test whether memory-like or effector-like IELs exhibit distinct TCR Vα-chain usage, we measured the expression of Vα2 and Vα3.2 on the surface of TCRαβ⁺ CD8αα⁺ IELs from five sections of the intestine. However, we did not observe consistent enrichment of either Vα-chain among memory-like or effector-like subsets (Supplementary Figure 8), arguing against the idea that these populations originated from separate precursors. Altogether, these findings support a model in which memory-like and effector-like CD8αα⁺ IELs represent cells at different stages of shared differentiation pathways, enabling a spectrum of immune responses across the intestinal mucosa.

Discussion

The current understanding for the heterogeneity of IELs has been suggested by a wide array of functional phenotypes attributed to surface proteins, transcription factor activity, or even developmental programs. In order to better define IEL subpopulations, single-cell sequencing experiments have been performed on total T cells; however, the distinction between TCRαβ⁺ and TCRγδ⁺ cells is difficult to make based on mRNA levels of TCR genes (20, 34, 39). To overcome this challenge, we sorted TCRαβ⁺ and TCRγδ⁺ small intestinal IELs and performed single-cell RNAseq. Clustering analyses for TCRαβ⁺ cells and TCRγδ⁺ cells revealed distinct populations of natural IELs based on marker genes. One population resembled memory-T cells, as indicated by the expression of Tcf7 and Id3, and the other resembled effector-T cells, characterized by the expression of Gzma, Gzmb, Tyrobp, and Prdm1. The distinction between these memory-like and effector-like cells was present in both the coreceptor negative DN IELs and the CD8αα⁺ IELs. Our findings complement previous studies, which note similar memory-like and effector-like clusters in non-TCR-sorted IEL datasets (26, 35–38). Although we have not examined the functional capabilities of effector-like and memory-like CD8αα⁺ IELs, others have highlighted the roles for these subsets. For example, effector-like TCRγδ⁺ cells in the colon demonstrated anti-tumor activity which was suppressed by Tcf7 expression (83). Additionally, the cytotoxic potential of effector-like IELs has been shown in DSS-induced colitis, where tissue damage was reduced in Ikzf3 KO mice with decreased amounts of effector-like IELs (26). Further studies will be necessary to elucidate the role of memory-like subsets, including their potential stem-like properties and anti-inflammatory functions in intestinal tissue-specific models. Interestingly, the similarly annotated clusters in our TCRαβ⁺ and TCRγδ⁺ datasets exhibited comparable transcriptional profiles. This suggests an overlap of cell phenotypes or functional contributions to intestinal immunity between TCRαβ⁺ and TCRγδ⁺ cells, despite the conventional view that IELs from these two lineages exhibit separate anti-inflammatory, regenerative, or cytotoxic roles (1, 3, 5). Our data suggests the functional heterogeneity of IELs can be further defined across the subsets of TCRαβ⁺ and TCRγδ⁺ cells.

Within our TCRγδ⁺ dataset, we also identified a novel cluster of effector-like CD8αα⁺ cells with high expression of Zeb2, Ccr5 and Gzmk. Zeb2 is linked to the terminal differentiation of effector CD8 T cells in infection models, whereas Ccr5 is associated with infection induced migration to the gut mucosa (84–86). Furthermore, the transcriptional profile of this cluster overlapped with that of the effector-like TCRαβ⁺ CD8αβ⁺ cluster, suggesting that it may have been overlooked in previous studies that did not separate the TCRαβ⁺ and TCRγδ⁺ cells. This TCRγδ⁺Zeb2⁺ subset may represent a transitional population of cells with a differentiation program resembling that of induced TCRαβ⁺ IELs. Further work is necessary to better define the phenotype and function of this IEL population.

We observed that the proportions of CD160⁺ and CD122⁺ cells among the CD8αα⁺ population are comparable between TCRαβ⁺ and TCRγδ⁺ IELs, suggesting that the distribution of these cell types in intestinal tissue may be influenced by environmental factors, such as dietary food and microbial antigens. While many studies have highlighted the importance of these factors in the development of major IEL subsets, few have inspected their impact on the more specific subsets of CD8αα⁺ cells (38, 83). Yakou et al. observed a partial influence of microbiota, where germ free mice had a reduced number of TCF1⁺ memory-like cells in the colon but had no reduction of TCF1⁺ cells in the small intestine (83). Wang et al. demonstrated a partial impact of altered diet, where mice fed a high-fat high-sucrose “Western” diet reduced the amount of effector-like CD8αα⁺ IELs but increased the abundance of memory-like CD8αα⁺ IELs in the small intestine, with no difference in cell viability between the two populations (38).

We also identified transcriptionally distinct DN IEL populations. In both TCRαβ⁺ and TCRγδ⁺ lineages, the majority of DN cells belonged to the Tcf7⁺ cluster. Additionally, the Zeb2⁺ cluster within TCRγδ⁺ IELs contained a unique subset of DN cells. Unlike CD8αα⁺ IELs, where the ratio between Tcf7⁺ and Prdm1⁺ populations changes along the intestinal tract, the dominance of Tcf7⁺ DN IELs remained consistent throughout the gut. This suggests that DN IELs are less responsive to local microenvironmental cues compared to their CD8αα⁺ counterparts. Additional studies exploring the relationship between the proportions of IEL subsets, microbial communities, and diet will be needed to clarify the heterogenous distribution of these cells across different parts of the intestine, and to determine the factors that render the CD8αα⁺ IELs sensitive to their dynamic tissue environments.

Through our analysis of the TCRαβ⁺ IEL subsets, we predicted a developmental relationship between memory-like and effector-like clusters. RNA velocity analysis of TCRαβ⁺ dataset displayed a clear trajectory originating from Tcf7⁺ DN cells progressing toward CD8αα⁺ cells, with directional transition from memory-like to effector-like clusters, consistent with findings reported by Wang et al. (38). In contrast, the presence of two transcriptionally distinct DN subsets within the TCRγδ⁺ IELs may suggest the existence of two independent precursor populations: one Tcf7⁺ subset giving rise to memory-like CD8αα⁺ cells, and a Zeb2⁺ subset giving rise to effector-like populations. This model is supported by a recent study showing that TCRγδ⁺ CD8αα⁺ IELs from the colon of Tcf7 knockout mice exhibited reduced Cd160 expression but increased expression of effector and cytotoxic genes including Il2rb, Gzma, Gzmb, and Tnfrsf9 (83). These findings suggest that effector-like cells can develop independently of memory-like cells likely originating from a distinct precursor lineage. An open question remains regarding the mechanisms underlying transitions between clusters. Based on our current data, we cannot definitively determine whether the enrichment of specific pathways drives these transitions or if these changes are a consequence of the state shifts. Additional functional studies will be necessary to address this issue.

In summary, our study characterizes the transcriptional heterogeneity of TCRαβ⁺ and TCRγδ⁺ IELs, complementing previous finings and offering deeper insight into their complexity. We identified memory-like and effector-like subpopulations of IELs that exhibit strikingly similar transcriptional profiles between TCRαβ⁺ and TCRγδ⁺ lineages, despite their presumed participation in different immune responses. Moreover, our analyses suggest precursor-progeny relationships between DN and CD8αα⁺ cells, as well as between memory-like and effector-like CD8αα⁺ IELs, supporting a model in which these populations represent different stages of differentiation pathway. Together, our findings expand upon previously described IEL heterogeneity and underscore the need for further investigation into the functional relevance of transcriptionally similar TCRαβ⁺ and TCRγδ⁺ subsets.

Methods

Animals

C57BL/6J (stock no. 000664) mice were obtained from the Jackson Laboratory. All breedings were maintained at the University of Massachusetts, Amherst. This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All animals were handled according to approved institutional animal care and use committee (IACUC) protocols of the University of Massachusetts.

IEL isolation

The small intestine and colon were first removed from the rest of the gastrointestinal tract, onto collection media (RPMI supplemented with 25mM HEPES, 1% L-glutamine, 1% penicillin/streptomycin, 50µM β-mercaptoethanol, and 3% FBS). Peyer’s patches lining the small intestine were removed. Tissues were cleaned by flushing out feces with collection media and rinsing in PBS. Tissue fragments were agitated at 37°C for 20 minutes in collection media containing 5mM EDTA and 1mM DTT, then further shaken in serum-free collection media containing 2mM EDTA. The suspension was washed several times in collection media, and the IELs were collected as cells that passed through a 70µm filter. Finally, IELs were resuspended in collection media containing 10% FBS.

Flow cytometry analysis

Flow cytometry data were acquired on BD LSR Fortessa. The following monoclonal antibodies from BioLegend were used: CD45.2 (104), CD45 (30-F11), TCRβ (H57-597), TCRδ (GL3), CD8α (53-6.7), CD8β (YTS156.7.7), Biotin-CD122 (5H3) and Streptavidin- AF647. The monoclonal CD4 (GK1.5) antibody and Brilliant Stain Buffer were obtained from BD Biosciences. The monoclonal CD160 (CNX46-3) antibody was obtained from eBiosciences.

Live cells were treated with anti-CD16/32 Fc block (2.4G2, BD Pharmingen) prior to staining with antibodies against surface markers. Staining for surface proteins was performed at 4 °C for 40 min, and FACS buffer (PBS + 0.5% BSA + 0.01% sodium azide) was used for washes. Data from BD LSR Fortessa were analyzed in FlowJo™ v10.9.0 Software. IEL populations were analyzed as shown in Supplementary Figure 9.

Single-cell RNA sequencing

Cells were stained for CD45.2, TCRβ, TCRδ, and sorted using the BD FACSAria Fusion instrument (BD Biosciences). After sorting, cells were counted using a Cellometer K2 cell counter (Nexcelom Bioscience) and by manual counting via hemocytometer. Single cell gene expression profiling was performed using the Chromium Next GEM Single Cell 3′ v3.1 (Dual Index) kit. Each cell suspension was loaded onto a well of Chip G on the 10x Genomics Chromium Controller System following the manufacturer's user manual (10x Genomics). Barcoding and cDNA synthesis were performed according to the manufacturer's instructions. Qualitative analysis of cDNA was performed using the 2100 Agilent Bioanalyzer High Sensitivity assay. The cDNA libraries were constructed using the 10x Chromium Single cell 3’ Library Kit v3.1 (dual index) according to the manufacturer’s protocol. Quality assessment of final libraries was done on Qubit fluorometer using a DNA High Sensitivity assay (Thermo Scientific) and a 2100 Agilent Bioanalyzer High Sensitivity assay (Agilent Technologies). Libraries were sequenced on an Illumina NextSeq 500 using the NextSeq 500/550 Mid Output Kit v2.5 (150 Cycles) sequencing kit, with the following read length: 28 bp Read1 for the 10x cell barcode and UMI, 90 bp Read 2 for the insert, and 10 bp I7 and I5 for the sample index. Phix (Illumina) was spiked in at 1% as per kit manual recommendation (10x Genomics).

The 10x Could Analysis Cell Ranger Pipeline (Cellranger version 7.1.0) was used to align reads and generate feature-barcode matrices. Reads were aligned to Mus musculus reference genome (Mouse GRCm39). The aggr pipeline was used to combine data from multiple samples into an experiment-wide feature-barcode matrix and analysis. The 10x Genomics Loupe Browser was used for visualization, initial quality assessment, and filtering of single cell gene expression data. Single Cell Gene Expression was performed at the Genomics Resource Laboratory, University of Massachusetts Amherst, MA.

Analysis of scRNA-seq

Data analyses were performed using the Seurat package (version 4.3) in the R software version 4.2.1. The TCRαβ⁺ and TCRγδ⁺ datasets were analyzed individually but with identical procedures. The Seurat object was first generated by keeping all genes expressed by at least 3 cells. Cells were kept if they contained at least 100 unique features, at least 100 reads, and less than 10% mitochondrial genes. Data were normalized, scaled, then the top 2000 variable genes were used for the Principal Component Analysis (PCA) and generation of Uniform Manifold Approximation and Projection (UMAP) plots. Immune cells were filtered by expression of Ptprc > 0.5, then for T cells by keeping cells with at least 0.05% expression of Cd3e/Cd3g/Cd3d. Cluster identities were interpreted by calculating unique cluster marker genes and analyzing the distribution of known marker genes. Clusters with similar transcriptional profiles and localization on the UMAP plot were merged into groups. Poorly defined clusters were removed from further analysis.

Re-clustering of “CD8αα⁺” annotated cells from Figure 1 was performed by subsetting the “CD8αα⁺ Tcf7⁺”, “CD8αα⁺ Prdm1⁺”, and “Mki67⁺” clusters for the TCRαβ⁺ dataset, or the “CD8αα⁺ Tcf7⁺”, “CD8αα⁺ Prdm1⁺”, “CD8αα⁺ Zeb2⁺”, and “Mki67⁺” clusters for the TCRγδ⁺ dataset. Cells expressing Cd4 or Cd8b1 were removed, and the remaining cells were processed by the normalization and clustering steps as above. After examining the expression of Cd8a, cells were further separated into the CD8αα⁺ cells which expressed Cd8a, or DN cells which did not express Cd8a. Differentially expressed genes between clusters were calculated by the Wilcoxon ranked test. Pathway enrichment analyses were performed with Metascape, using the differentially expressed genes filtered by significance (adjusted p-value < 0.05) and Average Log₂ Fold Change > ± 0.5. The list of transcription factors in the mouse genome were obtained from the FANTOM5 database. The most differentially expressed transcription factors between clusters were selected to show as bubble plots (Figures 2b, d, 3b, d) by significance (adjusted p-value < 0.05) and proportion (difference in the percentage of cells expressing the gene > 20% for Figures 2b, d, Figure 3b, > 30% for Figure 3d). RNA velocity analysis was performed using scVelo and Velocyto.

Integration of the TCRαβ⁺ and TCRγδ⁺ datasets was performed after preliminary analysis of individual datasets with the SelectIntegrationFeatures(), FindIntegrationAnchors(), and IntegrateData() Seurat functions. The integrated dataset was preprocessed following standard methods, and clusters were defined at low resolution (0.1) to determine which cell types colocalize on the UMAP plot.

Statistical analysis

Data statistical analysis was performed with Prism 9 (GraphPad software). P-values were determined using a two-tailed paired t-test, or one-way ANOVA.

Data availability statement

The sequencing data that support the findings of this study has been deposited in the National Center of Biotechnology Information Gene Expression Omnibus (GEO) and is accessible through the accession number GSE284856.

Ethics statement

The animal study was approved by IACUC of The University of Massachusetts, Amherst. The study was conducted in accordance with the local legislation and institutional requirements.

Author contributions

KH: Formal Analysis, Visualization, Writing – original draft, Data curation, Methodology, Software, Validation, Investigation, Writing – review & editing. XL: Formal Analysis, Data curation, Writing – review & editing, Investigation. AL: Writing – review & editing, Investigation, Formal Analysis, Data curation. RR: Resources, Formal Analysis, Data curation, Writing – review & editing, Software, Methodology. EP: Data curation, Investigation, Project administration, Conceptualization, Supervision, Writing – review & editing, Writing – original draft. LP: Conceptualization, Supervision, Resources, Formal Analysis, Visualization, Project administration, Writing – review & editing, Writing – original draft, Validation, Funding acquisition.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by NIH grants AI146188 (LP), AI133041 (LP) and Biotechnology Training Program (BTP) of National Research Service Award T32 GM13096 (KH and AL).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025.1637209/full#supplementary-material

Supplementary Figure 1 | (a) Preliminary analysis and quality control metrics for the TCRαβ⁺ scRNAseq dataset. (b) Preliminary analysis and quality control metrics for the TCRγδ⁺ scRNAseq dataset.

Supplementary Figure 2 | (a) Flow cytometry plots representing the distribution of small intestinal IEL subpopulations among CD45⁺ cells. (b, c). Heatmaps of cluster marker genes for the TCRαβ⁺ (a) and TCRγδ⁺ (b) datasets.

Supplementary Figure 3 | (a, b) UMAP plots of re-clustered cells from the “CD8αα⁺” clusters in Figure 1 for the TCRαβ⁺ (a) and TCRγδ⁺ (b) datasets. Prior to re-clustering, cells expressing Cd4 or Cd8b1 coreceptor genes were removed. (c, d). Expression of Cd8a in the re-clustered cells by visualizing the distribution on the UMAP plot and by violin plot for TCRαβ⁺ (c) and TCRγδ⁺ (d) datasets. (e, f). Distribution of cell subsets either expressing the Cd8a coreceptor gene (CD8α⁺) or negative for all coreceptor genes (DN), and pie chart representation of annotated populations within each subset for TCRαβ⁺ (e) and TCRγδ⁺ (f) datasets.

Supplementary Figure 4 | (a) Volcano plot representation of differential expression between the TCRαβ⁺ CD8αα⁺ Prdm1⁺ cluster and Tcf7⁺ cluster with labels for the top 10 differentially expressed genes and transcription factors shown in Figure 2 (left); top differentially enriched pathways (right). (b) Comparison between the TCRγδ⁺ CD8αα⁺ Prdm1⁺ cluster and Tcf7⁺ cluster. (c) Comparison between the TCRγδ⁺ CD8αα⁺ Prdm1⁺ cluster and Zeb2⁺ cluster. (d) Comparison between the TCRγδ⁺ CD8αα⁺ Zeb2⁺ cluster and Tcf7⁺ cluster.

Supplementary Figure 5 | (a) Quantification of GFP expression between TCRαβ⁺ (left) or TCRγδ⁺ (right) CD8αα⁺ CD122^intCD160⁺ cells and CD122^hiCD160^- cells from Tcf7-GFP mice, represented as median fluorescence intensity fold change compared to WT controls. (b) Quantification of GFP expression between TCRαβ⁺ (left) or TCRγδ⁺ (right) DN CD122^intCD160⁺ cells and CD122^hiCD160^- cells from Tcf7-GFP mice, represented as median fluorescence intensity fold change compared to WT controls. (c) Quantification of GFP⁺ cells between TCRαβ⁺ (left) or TCRγδ⁺ (right) CD8αα⁺ CD122^intCD160⁺ cells and CD122^hiCD160^- cells from Tcf7-GFP mice. (d) Quantification of GFP⁺ cells between TCRαβ⁺ (left) or TCRγδ⁺ (right) DN CD122^intCD160⁺ cells and CD122^hiCD160^- cells from Tcf7-GFP mice. (e) Representative flow cytometry plots for the distribution of CD122 and CD160 among TCRαβ⁺ (a) or TCRγδ⁺ (b) IEL subpopulations. Data were analyzed by paired T-test (a–d).

Supplementary Figure 6 | (a) UMAP plots of re-clustered CD8αα⁺ and DN cells as in Supplementary Figure 2 with cluster number identities instead of phenotype annotations, for the TCRαβ⁺ dataset. (b) Heatmaps of cluster marker genes for the re-clustered cells in the TCRαβ⁺ dataset. (c) Representative UMAP plots for genes differentially expressed among re-clustered cells in the TCRαβ⁺ dataset. (d) UMAP plots of re-clustered CD8αα⁺ and DN cells as in Supplementary Figure 2 with cluster number identities instead of phenotype annotations, for the TCRγδ⁺ dataset. (e) Heatmaps of cluster marker genes for the re-clustered cells in the TCRγδ⁺ dataset. (f) Representative UMAP plots for genes differentially expressed among re-clustered cells in the TCRγδ⁺ dataset.

Supplementary Figure 7 | (a) Representative UMAP plots for the expression level of Trgv genes in re-clustered TCRγδ⁺ CD8αα⁺ and DN IELs. (b) Expression level of Trgv genes among TCRγδ⁺ DN IELs. (c) Expression level of Trgv genes among TCRγδ⁺ CD8αα⁺ IELs.

Supplementary Figure 8 | Percentage of TCRαβ⁺ CD8αα⁺ CD122^intCD160⁺ or CD122^hiCD160^- IELs expressing the TCRVα2 or TCRVα3.2 chain. Data were analyzed by Paired T-test.

Supplementary Figure 9 | Representative flow cytometry gating scheme used for IEL experiments.

Supplementary Table 1 | Differentially expressed genes between the TCRαβ+ CD8αα+ and TCRγδ+ CD8αα+ clusters (sheet 1) and between the TCRαβ+DN and TCRγδ+DN clusters (sheet 2).

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