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A single-cell survey of the small intestinal epithelium

Nature volume 551, pages 333–339 (2017)Cite this article

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Abstract

Intestinal epithelial cells absorb nutrients, respond to microbes, function as a barrier and help to coordinate immune responses. Here we report profiling of 53,193 individual epithelial cells from the small intestine and organoids of mice, which enabled the identification and characterization of previously unknown subtypes of intestinal epithelial cell and their gene signatures. We found unexpected diversity in hormone-secreting enteroendocrine cells and constructed the taxonomy of newly identified subtypes, and distinguished between two subtypes of tuft cell, one of which expresses the epithelial cytokine Tslp and the pan-immune marker CD45, which was not previously associated with non-haematopoietic cells. We also characterized the ways in which cell-intrinsic states and the proportions of different cell types respond to bacterial and helminth infections: Salmonella infection caused an increase in the abundance of Paneth cells and enterocytes, and broad activation of an antimicrobial program; Heligmosomoides polygyrus caused an increase in the abundance of goblet and tuft cells. Our survey highlights previously unidentified markers and programs, associates sensory molecules with cell types, and uncovers principles of gut homeostasis and response to pathogens.

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Figure 1: Single-cell expression survey of intestinal epithelial cells.
Figure 2: Regional variation in cell type and differentiation.
Figure 3: Enteroendocrine cell taxonomy.
Figure 4: CD45+ tuft-2 cells express Tslp.
Figure 5: Microfold cell signatures.
Figure 6: Epithelial response to pathogen infections.

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Acknowledgements

We thank L. Gaffney for help with figure preparation, the Broad Flow Cytometry Facility (P. Rogers, S. Saldi and C. Otis), C. Hafemeister and R. Satija for use of the ‘How Many Cells’ tool, S. Riesenfeld and A. Dixit for statistical advice, and T. Tickle for help with the Single Cell Portal. This study was supported by the Klarman Cell Observatory at the Broad Institute, NIH RC2DK114784 (A.R. and R.J.X.), HHMI (A.R.), Food Allergy Science Initiative (FASI) at the Broad Institute (A.R. and R.J.X.), and a Broadnext10 award (A.R. and R.J.X.). M.B. is supported by a postdoctoral fellowship from the Human Frontiers Science Program (HFSP). R.J.X. is supported by NIH DK43351, DK097485 and the Helmsley Charitable Trust.

Author information

Author notes

  1. Adam L. Haber, Moshe Biton and Noga Rogel: These authors contributed equally to this work.

  2. Moshe Biton, Ramnik J. Xavier and Aviv Regev: These authors jointly supervised this work.

Authors and Affiliations

  1. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, 02142, Massachusetts, USA

    Adam L. Haber, Moshe Biton, Noga Rogel, Rebecca H. Herbst, Karthik Shekhar, Christopher Smillie, Grace Burgin, Toni M. Delorey, Itay Tirosh, Danielle Dionne, Raktima Raychowdhury, Wendy S. Garrett, Orit Rozenblatt-Rosen, Omer Yilmaz, Ramnik J. Xavier & Aviv Regev

  2. Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA

    Moshe Biton & Ramnik J. Xavier

  3. Department of Systems Biology, Harvard Medical School, Boston, 02114, Massachusetts, USA

    Rebecca H. Herbst & Yarden Katz

  4. Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, 01609, Massachusetts, USA

    Toni M. Delorey

  5. Departments of Immunology and Infectious Diseases and Genetics and Complex Diseases, Harvard T. H. Chan School of Public Health, Boston, 02115, Massachusetts, USA

    Michael R. Howitt & Wendy S. Garrett

  6. Department of Biology, The David H. Koch Institute for Integrative Cancer Research at Massachusetts Institute of Technology, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA

    Semir Beyaz & Omer Yilmaz

  7. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Semir Beyaz

  8. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Howard Hughes Medical Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Semir Beyaz

  9. Mucosal Immunology and Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Charlestown, 02129, Massachusetts, USA

    Mei Zhang & Hai Ning Shi

  10. Departments of Pathology, Gastroenterology, and Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, Massachusetts, USA

    Omer Yilmaz

  11. Gastrointestinal Unit and Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Boston, 02114, Massachusetts, USA

    Ramnik J. Xavier

  12. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, 02140, Massachusetts, USA

    Aviv Regev

Authors
  1. Adam L. Haber
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  2. Moshe Biton
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Contributions

A.L.H., M.B. and N.R. contributed equally to this study; M.B., R.J.X. and A.R. co-conceived the study; M.B., N.R., A.L.H., R.J.X. and A.R. designed experiments and interpreted the results; N.R. and M.B. carried out all experiments; G.B., T.M.D., M.R.H., S.B., D.D., M.Z. and R.R. assisted with experiments; A.L.H. designed and performed computational analysis with assistance from R.H.H., K.S., C.S., Y.K., I.T. and A.R.; M.R.H. and W.S.G. assisted with tuft and follicle-associated epithelium experiments; M.Z. and H.N.S. assisted with pathogen infections; S.B. and O.Y. assisted with epithelial cell sorting; D.D. and O.R.-R. assisted with scRNA-seq; and A.L.H., M.B., N.R., R.J.X. and A.R. wrote the manuscript, with input from all authors.

Corresponding authors

Correspondence to Moshe Biton, Ramnik J. Xavier or Aviv Regev.

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Competing interests

A.R. is a member of the scientific advisory board of ThermoFisher, Syros Pharmaceuticals and Driver Group. R.J.X. is a consultant at Novartis, Janssen and Celgene. A.H., M.B., N.R., R.H., K.S., C.S., O.R., R.X. and A.R. are co-inventors on a provisional patent application filed by the Broad Institute relating to this manuscript.

Additional information

Reviewer Information Nature thanks L. Vermeulen and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Identifying intestinal epithelial cell types in scRNA-seq data by unsupervised clustering.

Related to Fig. 1. a, b, Quality metrics for scRNA-seq data. Shown are distributions of the number of reads per cell (left), the number of genes detected with non-zero transcript counts per cell (centre) and the fraction of reads mapping to the mm10 mouse transcriptome per cell (right) in the droplet-based 3′ scRNA-seq data (a) and the plate-based full-length scRNA-Seq data (b). c–f, Agreement across batches. c, Contribution of batches to each cluster. Each pie chart shows the batch composition (colour-coded legend) of each detected cluster (labelling and number of cells are marked above each pie chart) in the droplet-based 3′ scRNA-seq dataset (n = 6 mice). All ten replicates contribute to all clusters, and no major batch effect is observed. d, Cell type proportions across batches. Shown is the proportion of detected cells in each major cell type in the droplet-based 3′ scRNA-seq dataset in each of ten batches (points; n = 6 mice). Grey bar, mean; error bars, s.e.m. e, Agreement in expression profiles across mice. Box and whisker plot shows the Pearson correlation coefficients in average expression profiles (average log2(TPM + 1)) for cells in each cluster, across all pairs of mice. Black bar, median value; box edges, 25th and 75th percentiles; whiskers, a further 1.5 times the interquartile range. Clusters with additional subtypes (such as tuft and EEC cells) show more variation, as expected. f, Scatter plots comparing the average log2(TPM + 1) gene expression values between two scRNA-seq experiments from the droplet-based 3′ scRNA-seq dataset (top), between two scRNA-seq experiments from the plate-based full-length scRNA-seq dataset (centre), or between the average of a plate-based full-length scRNA-seq and a population control (bottom). Pearson correlation is marked top left. g, Additional quality control metrics and cluster annotation on the basis of the expression of known cell type markers. t-SNE visualization of 7,216 single cells is shown, where individual points correspond to single cells. Cells are coloured by, from top left to bottom right, their assignment to clusters using a k-nearest-neighbour graph-based algorithm (Methods; legend shows the cluster type) (identical to Fig. 1b), mean expression (log2(TPM + 1)) of several known marker genes for a particular cell type or state (indicated above each plot), the mouse from which they originate (see legend), the number of reads per cell, the number of genes detected per cell, or the number of transcripts as measured by UMIs per cell.

Extended Data Figure 2 Identification and characterization of intestinal epithelial cell types in plate-based full-length scRNA-seq data by unsupervised clustering.

Related to Fig. 1. a, Quality control metrics and cluster annotation on the basis of the expression of known cell type markers. t-SNE visualization of 1,522 single cells (n = 8 mice) is shown, where individual points correspond to single cells. Cells are coloured by, from top left to bottom right, their assignment to clusters, mean expression (log2(TPM + 1)) of several known marker genes for a particular cell type or state (indicated above each plot; same as in Extended Data Fig. 1g), the mouse from which they originate (see legend) and its genotype, the FACS gate used to sort them (see legend), the number of reads per cell, or the number of genes detected per cell. b, Cell-type-specific signatures. Heatmap shows the relative expression level (row-wise Z scores) of genes (rows) in cell-type-specific signatures (same genes as in Fig. 1c, with the exception of enterocyte markers), across the individual post-mitotic intestinal epithelial cells (columns) in the full-length scRNA-seq data. Colour code marks the cell type and their associated signatures. c, Mptx2 is a novel Paneth cell marker. t-SNE of the cells from the droplet-based 3′ scRNA-seq (left; as in Fig. 1b) and plate-based full-length scRNA-seq (right; as in a) datasets is shown, coloured by expression (log2(TPM + 1)) of the mucosal pentraxin Mptx2. d, Cell-type-enriched GPCRs. Heatmap shows the relative expression (row-wise Z scores) of genes encoding GPCRs (rows) that are significantly (FDR < 0.001; Mann–Whitney U test; Methods) upregulated or downregulated in the cells (columns) of a given cell type (top; colour coded as in a) compared to all other cells, in the plate-based full-length scRNA-seq data. e, Cell-type-specific leucine-rich repeat proteins (LRRs). Heatmap depicts the mean relative expression (column-wise Z score of mean log2(TPM + 1) values) of genes (columns) encoding leucine-rich repeat proteins that are significantly (FDR < 0.001; Mann–Whitney U test) upregulated or downregulated in a given cell type (rows) compared to all other cells, in the plate-based full-length scRNA-seq data. f, Cell-type transcription factors and GPCRs. Average relative expression (Z score of mean log2(TPM + 1); colour scale) of the top ten transcription factors (left) and GPCRs (right) (columns) enriched in each cell type (rows).

Extended Data Figure 3 Regional variation in Paneth cell subtypes and stem cell markers.

a, Paneth cell subsets. t-SNE of 10,396 single cells (points) was obtained using a large cell-enriched protocol (Methods), coloured by cluster annotation (n = 2 mice). b, Paneth cell subset markers. Shown is the expression (row-wise Z score; colour scale) of genes specific (FDR < 0.05; Mann–Whitney U test; log2(fold change) > 0.5) to each of the two Paneth cell subsets (average of 724.5 cells per subtype, down-sampled to 500 for visualization) shown in a. c, Two Paneth subsets reflect regional diversity. Shown is the expression of the same genes (rows) as in b in Paneth cells from each of three small intestinal regions (average of 176.3 cells per region; columns; Fig. 2a). 11 of 11 Paneth-1 markers are enriched in the ileal Paneth cells, whereas 7 of 10 Paneth-2 markers are enriched in duodenal or jejunal Paneth cells (FDR < 0.05; Mann–Whitney U test). d, Validation of regional enterocyte markers. Shown is smFISH of Lct (red) and Fabp6 (white) in the duodenum (proximal; left) and ileum (distal; right). Dotted line, boundary between crypt and villi; green and yellow arrows, proximal and distal enterocytes, respectively; scale bars, 50 μm. e, Regional variation of intestinal stem cells. Expression (row-wise Z score) of genes specific to stem cells from each intestinal region (FDR < 0.05; Mann–Whitney U test; log2(fold change) > 0.5). On average, 1,226.3 cells were obtained from each of the three regions, down-sampled to 500 for visualization (columns).

Extended Data Figure 4 Differentiation from stem cells to mature enterocytes.

a-d. Diffusion-map embedding of 5,282 cells (points) progressing through stages of enterocyte differentiation (Methods). a, b, Cells are coloured by their cluster assignment (Fig. 1b). Diffusion components 1 and 3 (DC-1 and DC-3) are associated with the transition from stem cells to progenitors (a), whereas DC-2 distinguishes between proximal and distal enterocyte fate commitment (b). c, d, Cells are coloured by the expression (log2(TPM + 1)) of known and newly identified transcription factors associated with stages of differentiation (c), or with proximal or distal enterocyte differentiation (d). e, Transcription factors that are differentially expressed between proximal and distal cell fate. Heatmap shows the mean expression level of 44 transcription factors differentially expressed between the proximal and distal (rows) enterocyte clusters of Fig. 1b (FDR < 0.05; Mann–Whitney U test). f, Newly identified regional stem cell markers (Extended Data Fig. 3e) identify distinct populations in diffusion-map space. Shown are close-ups of the stem-cell region in diffusion space (b, inset square), coloured by expression level (log2(TPM + 1)) of pan-stem cell marker Lgr5 (left), proximal stem cell marker Gkn3 (centre) or distal stem cell marker Bex1 (right). Dashed line helps to visualize separation of stem cells on the basis of region-specific markers.

Extended Data Figure 5 Heterogeneity within EEC cells.

Related to Fig. 3. a, EEC subset discovery and regional location. Shown is the t-SNE of the 533 EEC cells identified from the droplet-based datasets for whole small intestine (SI) and regional samples (colour legend; n = 8 mice; Methods). b, Agreement in hormone detection rates between droplet-based 3′ and full-length scRNA-seq. Scatter plot shows the detection rate (fraction of cells with non-zero expression of a given transcript) for a set of known EEC hormones, transcription factors and marker genes (see legend) in EEC cells from the full-length dataset, and from the droplet-based 3′ dataset. Linear fit (dashed line) and 95% confidence interval (shaded) are also shown. c, Expression of key genes across subset clusters. t-SNE plot shows cells coloured by their assignment to the 12 clusters (top left; identical to Fig. 3a) or by the expression (log2(TPM + 1)) of markers of immature EEC cells (Neurog3), genes encoding gut hormones (Sct, Sst, Cck, Gcg, Ghrl, GIP, Nts, PYY) or markers of enterochromaffin cells (Tac1, Reg4). d, Co-expression of gastrointestinal hormones by individual cells. Left, heatmap shows the expression of canonical gut hormone genes (rows) in each of 533 individual EEC cells (columns), coloured on the basis of their assignment to the clusters in Fig. 3a (top). Right, heatmap shows for each cluster (columns) the percentage of cells (inset text) in which the transcript for each hormone (rows) is detected.

Extended Data Figure 6 Classification and specificity of EEC subsets.

Related to Fig. 3. a, b, Relationships between EEC subsets. a, Dendrogram shows the relationship between EEC clusters as defined by hierarchical clustering of mean expression profiles of all of the cells in a subset (Methods). Estimates for the significance of each split are derived from 100,000 bootstrap iterations using the R package pvclust (■P < 0.1, *P < 0.05, **P < 0.01, P < 0.001; χ2 test). b, Heatmap shows cell–cell similarities (Pearson’s r) between the 11 significant principal component scores (P < 0.05; Methods) across the 533 EEC cells (rows, columns). Rows and columns are ordered using cluster labels obtained using unsupervised clustering (Methods). c. Subset specificity of gut hormones and related genes. Scatter plot shows each the specificity of each gene to its marked cell subset (defined as the proportion of cells not in a given subset that do not express a given gene) and its sensitivity in that subset (defined as the fraction of cells of a given type that do express the gene (Methods). Subsets are colour coded as in the legend. Genes are assigned to the subset where they are most highly expressed on average. Genes were chosen on the basis of their known annotation as gut hormones (Cck, Gal, Gcg, Ghrl, GIP, Iapp, Nucb2, Nts, Pyy, Sct, Sst), enterochromaffin markers (Tph1, Tac1) and canonical EEC markers (Chga, Chgb). d, GPCRs enriched in different EEC subtypes. Heatmap shows the expression levels (row-wise Z score) averaged across the cells in each of the EEC subtypes (columns) of 11 GPCR-encoding genes (rows) that are differentially expressed (FDR < 0.25; Mann–Whitney U test) in one of the EEC subtypes. The free fatty acid receptors (Ffar) 1 and 4 show specific expression patterns: Ffar1 is highest in SIN cells and is also expressed by the Cck-expressing subsets previously termed I cells (SIL-P, SILA and SIK-P), whereas Ffar4 is highest in the GIP-expressing subsets (SIK and SIK-P). These receptors are known to induce the expression of GIP and Gcg to maintain energy homeostasis64. Ffar2 was expressed by some progenitors and by enterochromaffin cells, but absent from GIP-expressing cells, whereas the oleoylethanolamide receptor Gpr119, which is important for food intake and glucose homeostasis21, is most highly expressed in SILA cells.

Extended Data Figure 7 Characterization of tuft cell heterogeneity and identification of Tslp and the haematopoietic lineage marker Ptprc (CD45) in a subset of tuft cells.

Related to Fig. 4. a, Tuft-1 and tuft-2 cells. Shown is t-SNE visualization of 102 tuft cells (points; n = 8 mice) from the plate-based full-length scRNA-seq dataset (Extended Data Fig. 2a), labelled by their subclustering into tuft-1 (orange) and tuft-2 (brown) subtypes. b, Gene signatures for tuft-1 and tuft-2 cells. Heatmap shows the relative expression (row-wise Z scores) of the tuft-1 and tuft-2 marker genes (rows; orange and brown, respectively) across single cells from the plate-based dataset (columns) assigned to tuft-1 and tuft-2 cell clusters (orange and brown, respectively). The top 25 genes are shown for each subtype (all FDR < 0.01 and log2(fold change) > 0.1 in both plate- and droplet-based datasets). c, Tuft-2 signature genes are enriched in immune functions. Shown are the significantly enriched (Methods; FDR < 0.1; −log10(Q value)) gene ontology terms in the gene signature for the tuft-2 subset. d, Expression of neuron- and inflammation-related genes in tuft-1 and tuft-2 subsets, respectively. Plot shows for each gene (y axis) its differential expression (x axis) between Tuft-1 and Tuft-2 cells. Bar indicates Bayesian bootstrap65 estimates of log2(fold change); hinges and whiskers indicate 25% and 95% confidence intervals, respectively. e, Il33 is not detected in tuft cells. Distribution of expression of Il33 in cell subsets in full-length scRNA-seq. (*FDR < 0.1; Mann–Whitney U test). f, g, Tuft-2 cells are enriched for Tslp. f, Combined smFISH and immunofluorescence of Tslp (green) with DCLK1 (red). Scale bars, 10 μm. g, Relative quantification of mRNA expression by qPCR of Alpi, Tslp and Dclk1 (tuft cell markers) from tuft-1, tuft-2 or randomly selected EpCAM+ single cells identified from 96-well plate-based full-length scRNA-seq (16 cells per group) (*P < 0.05, **P < 0.005; t test). h, Validation of CD45 expression in tuft-2 cells. Immunofluorescence assay showing co-expression of the tuft cell marker DCLK1 and of CD45 (left) and CD45 (right, with increased brightness); yellow boxes show three representative tuft cells. Scale bars, 200 μm. i, Isolation of tuft-2 cells based on CD45 expression using FACS. Shown is t-SNE of 332 EpCAM+/CD45+ FACS-sorted single cells (points; n = 3 pooled mice), coloured by unsupervised clustering (top left), the expression of the Tuft cell marker Dclk1 (top right), or the signature scores for tuft-1 and tuft-2 cells (bottom left and right, respectively).

Extended Data Figure 8 Microfold cells from RANKL-treated intestinal organoids and in vivo.

Related to Fig. 5. a–d, Microfold cells in RANKL-treated organoids. a–c, t-SNE of 5,434 single cells (points) from control (left) or RANKL-treated (middle and right) intestinal organoids, or colouring each cell (b, c) by the expression (log2(TPM + 1)) of the canonical microfold cell markers TNF-α-induced protein 2 (Tnfaip2, M-sec; b) and glycoprotein 2 (Gp2; c) (n = 4 pooled wells per treatment condition). d, Expression of microfold cell marker genes35,37,66 in each of the organoid cell clusters. Violin plots show the distribution of expression levels (log2(TPM + 1)) for each of ten previously reported microfold cell marker genes37 (columns), in the cells (points) in each of 13 clusters, including mature microfold cells (red), identified by k-nearest-neighbour clustering of the 5,434 scRNA-seq profiles from organoids. e, f, Microfold cell gene signature in vitro. Heatmaps show for each mature or stem cell cluster of organoid-derived intestinal epithelial cells (columns) the mean expression of genes (rows) for known (grey bars) or newly identified (black bars) microfold cell markers (e) or transcription factors (f), identified as being specific (FDR < 0.05; Mann–Whitney U test) to microfold cells in vitro and in vivo (Methods). g, Congruence of in vitro- and in vivo-derived microfold cell gene signatures. Violin plot shows the distribution of the mean expression of the in vitro-derived signature genes across the in vivo microfold cells (red) and across all other cells derived from the follicle-associated epithelia (grey).

Extended Data Figure 9 Intestinal epithelial cell response to pathogenic stress.

Related to Fig. 6. a, Generalized and pathogen-specific response genes. Volcano plots show for each gene (points) the differential expression and its associated significance (−log10(Q value); likelihood-ratio test) in response to either Salmonella (top) or H. polygyrus (bottom). Genes strongly upregulated in Salmonella (FDR < 10−6) or H. polygyrus (FDR < 5 × 10−3) are highlighted in purple or red, respectively. All highlighted genes are significantly differentially expressed (FDR < 0.05) in both 3′ and full-length scRNA-seq datasets. Left, all genes differentially expressed in the noted pathogen infection versus uninfected controls; middle, the subset differentially expressed in both pathogens versus control; right, the subset differentially expressed in only the noted pathogen, but not the other (Methods). b, Global induction of enterocyte-specific genes across cells during Salmonella infection. Shown is t-SNE of 5,010 single intestinal epithelial cells from control wild-type mice (left) and mice infected with Salmonella (right). Cells are coloured by the expression of the indicated genes, all specific to enterocytes in control mice (Supplementary Tables 2–4) and strongly upregulated by infection (FDR < 10−10 in both 3′ and full-length scRNA-seq datasets). c, Intestinal epithelial cell programs in Salmonella infection. Enriched (–log10(Q value)) gene ontology terms in genes induced in Salmonella-treated intestinal epithelial cells versus control. d, Cell-intrinsic changes after Salmonella infection. Relative expression (row-wise Z scores; colour scale) of 104 genes (top), of which 58 (bottom) are specific to Salmonella infection, significantly upregulated (FDR < 0.05; Mann–Whitney U test; log2(fold change) > 0.1) in enterocytes (columns) from Salmonella infection. Ten representative genes are labelled. e, Upregulation of pro-inflammatory apolipoproteins serum amyloid A 1 and 2 (Saa1 and Saa2) in distal enterocytes under Salmonella infection. Violin plot shows log2(TPM + 1) expression level of Saa1 (top) and Saa2 (bottom) across all post-mitotic cell types from control and Salmonella-treated mice (n = 4 mice; sample identity shown in the legend) (*FDR < 0.01, **FDR < 0.0001; Mann–Whitney U test). f, Upregulation of antimicrobial peptides by Paneth cells after Salmonella infection. Violin plots show log2(TPM + 1) expression levels of genes encoding antimicrobial peptides and the mucosal pentraxin Mptx2 in the cells (points) from control and Salmonella-infected mice (n = 4 mice; sample identity shown in the legend) (*FDR < 0.1, **FDR < 0.01, ***FDR < 0.0001; Mann–Whitney U test). g, Paneth cell numbers detected (using graph clustering; Methods) after Salmonella infection. Frequencies of Paneth cells in each mouse (points) under each condition (see legend) (**FDR < 0.01; Wald test). Error bars, s.e.m.

Extended Data Figure 10 Goblet and tuft cell responses to H. polygyrus show a unique defence mechanism.

Related to Fig. 6. a, Genes induced significantly in response to infection in a non-cell-type-specific manner. Shown is t-SNE visualization of 9,842 single intestinal epithelial cells (points) from control wild-type mice (left), mice infected with H. polygyrus for 3 or 10 days (middle) and mice infected with Salmonella (right). Cells are coloured by the expression (log2(TPM + 1)) of the indicated genes. Genes were selected as significantly differentially expressed in response to infection in a non-cell-type-specific manner (FDR < 0.001 in both the 3′ scRNA-seq and full-length scRNA-seq datasets). b, c, Identification of the tuft-1 and tuft-2 subsets in the dataset of control, Salmonella- and H. polygyrus-infected cells. b, Violin plots of the distribution of the respective signature scores (left and middle) and the expression of Dclk1 (right; log2 (TPM+1)) in cells (points) in each of the tuft subsets. c, t-SNE mapping of the 409 tuft progenitor, tuft-1 and tuft-2 cells, coloured by the scores for each signature (left and middle) and by their assignment to subtype clusters via k-nearest-neighbour graph clustering (right). d, Induction of antiparasitic genes by goblet cells after helminth infection. Shown is the distribution of expression (log2(TPM+1)) of three antiparasitic immunity genes67 upregulated by goblet cells in response to H. polygyrus infection (FDR < 0.05; Mann–Whitney U test), in control and infected mice. e, Antiparasitic protein secretion by goblet cells during H. polygyrus infection. Immunofluorescence assay of formalin-fixed paraffin-embedded (FFPE) sections of RELMβ (top left; red) and E-cadherin (bottom left; green), and their merged view including DAPI nuclear stain (blue) (right), after 10 days of helminth infection. Arrow, sections of H. polygyrus; scale bars, 200 μm.

Supplementary information

Life Sciences Reporting Summary (PDF 75 kb)

Supplementary Table 1 - Summary of single-cell RNAseq experiments.

This table provides the number (after quality filtering, see Methods) of individual intestinal epithelial cells profiled in each of the in this study. (XLSX 31 kb)

Supplementary Table 2 - Cell-type specific signature genes – droplet-based dataset.

This table provides the lists of genes specific to each of the identified clusters of intestinal epithelial cells, identified using 3’ droplet-based scRNA-seq data (Figure 1b). (XLSX 2008 kb)

Supplementary Table 3 - Cell-type specific signature genes – plate-based dataset.

This table provides the lists of genes specific to each of the identified clusters of intestinal epithelial cells, identified using full-length scRNA-seq data (Extended Data Fig. 2a). (XLSX 1253 kb)

Supplementary Table 4 - Consensus cell-type specific signature genes – both datasets.

This table provides high-confidence lists of genes specific to each subtype of intestinal epithelial cells in both 3’ droplet-based and full-length scRNA-seq datasets. (XLSX 13 kb)

Supplementary Table 5 - Cell-type specific TFs and receptors.

This table provides lists of genes annotated as either transcription factors (TFs), G protein-coupled receptors (GPCRs), or leucine-rich repeat (LRR) proteins, enriched in each subtype of intestinal epithelial cells in full-length plate-based scRNA-seq data. (XLSX 13 kb)

Supplementary Table 6 - Enteroendocrine cell subset signature genes.

This table provides the lists of genes specific to each of the identified subsets of tuft cells, identified using both 3’ droplet-based and full-length scRNA-seq data. (XLSX 1482 kb)

Supplementary Table 7 - Consensus tuft cell subset signature genes.

This table provides the lists of genes specific to each of the identified subsets of tuft cells, identified using both 3’ droplet-based and full-length scRNA-seq data. (XLSX 611 kb)

Supplementary Table 8 - In vitro and in vivo M cell signature genes.

This table provides the lists of genes specific to intestinal microfold (M) cells, using 3’ droplet-based scRNA-seq data from in vitro cells derived from RANKL-treated organoids, and in vivo cells derived from the mouse follicle associated epithelia (FAE). (XLSX 319 kb)

Supplementary Table 9 - Intestinal epithelial response to pathogenic infection.

This table provides estimates of differential gene expression in response to infection with H. polygyrus and Salmonella enterica, for each epithelial cell type, using both full-length and 3’ droplet-based scRNA-seq data. (XLSX 6005 kb)

Supplementary Table 10 - Markers of proximal and distal Paneth cells.

This table provides estimates of differential gene expression between two subsets of Paneth cells identified by clustering and interpreted (post-hoc) as derived from proximal and distal small intestine (Extended Data Fig. 3). (XLSX 1541 kb)

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Haber, A., Biton, M., Rogel, N. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017). https://doi.org/10.1038/nature24489

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