Abstract
The mouse small intestine shows profound variability in gene expression along the crypt–villus axis1,2. Whether similar spatial heterogeneity exists in the adult human gut remains unclear. Here we use spatial transcriptomics, spatial proteomics and single-molecule fluorescence in situ hybridization to reconstruct a comprehensive spatial expression atlas of the adult human proximal small intestine. We describe zonated expression and cell type representation for epithelial, mesenchymal and immune cell types. We find that migrating enterocytes switch from lipid droplet assembly and iron uptake at the villus bottom to chylomicron biosynthesis and iron release at the tip. Villus tip cells are pro-immunogenic, recruiting γδ T cells and macrophages to the tip, in contrast to their immunosuppressive roles in mouse. We also show that the human small intestine contains abundant serrated and branched villi that are enriched at the tops of circular folds. Our study presents a detailed resource for understanding the biology of the adult human small intestine.
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Data availability
Crypt–villus gene expression zonation profiles can be browsed using our webapp (https://itzkovitzwebapps.weizmann.ac.il/webapps/home/session.html?app=Human_villus_zonation1_1; some browsers may need to refresh the page or disable extentions). Spatial transcriptomics data generated in this study can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.10715015)79, containing preprocessed 10x loupe browser files, raw count tables, tissue images and spot barcodes. Human villus zonation segmental tables are available at Zenodo (https://doi.org/10.5281/zenodo.11490477)80. LCM RNA-seq and proteomics raw data can be downloaded from Zenodo (https://doi.org/10.5281/zenodo.10715015)81. CODEX data together with haematoxylin and eosin stainings generated in this study are provided as QuPath project, including segmentation masks and cell annotations on Zenodo (https://doi.org/10.5281/zenodo.10724499)82. Previously published RNA-seq datasets that were used in the study are available at Zenodo (https://doi.org/10.5281/zenodo.3403670)1, in the Gut cell survey13 and under GEO accession codes GSE154714 (ref. 4), GSE185224 (ref. 14) and GSE201859 (ref. 36). Source data are provided in the Supplementary Information.
Code availability
The code used to process the initial spatial transcriptomics input is available at GitHub (https://github.com/yotamharnik/ST_Human_SI.git). This includes raw input data and scripts for generating processed data. Code for CODEX analysis is available at GitHub (https://github.com/tiroshlab/Spatial_small_intestine).
References
Moor, A. E., Harnik, Y., Ben-Moshe, S., Massasa, E. E., Rozenberg, M., Eilam, R., Bahar Halpern, K. & Itzkovitz, S. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).
Beumer, J. & Clevers, H. Cell fate specification and differentiation in the adult mammalian intestine. Nat. Rev. Mol. Cell Biol. 22, 39–53 (2021).
Bonis, V., Rossell, C. & Gehart, H. The intestinal epithelium—fluid fate and rigid structure from crypt bottom to villus tip. Front. Cell Dev. Biol. 9, 661931 (2021).
Manco, R. et al. Clump sequencing exposes the spatial expression programs of intestinal secretory cells. Nat. Commun. 12, 3074 (2021).
Bahar Halpern, K. et al. Lgr5+ telocytes are a signaling source at the intestinal villus tip. Nat. Commun. 11, 1936 (2020).
Shoshkes-Carmel, M. et al. Subepithelial telocytes are an important source of Wnts that supports intestinal crypts. Nature 557, 242–246 (2018).
McCarthy, N. et al. Distinct mesenchymal cell populations generate the essential intestinal BMP signaling gradient. Cell Stem Cell 26, 391–402 (2020).
Valenta, T. et al. Wnt ligands secreted by subepithelial mesenchymal cells are essential for the survival of intestinal stem cells and gut homeostasis. Cell Rep. 15, 911–918 (2016).
Sullivan, Z. A. et al. γδ T cells regulate the intestinal response to nutrient sensing. Science 371, eaba8310 (2021).
Bujko, A. et al. Transcriptional and functional profiling defines human small intestinal macrophage subsets. J. Exp. Med. 215, 441–458 (2017).
Brandtzaeg, P. et al. The B-cell system of human mucosae and exocrine glands. Immunol. Rev. 171, 45–87 (1999).
Beumer, J. et al. BMP gradient along the intestinal villus axis controls zonated enterocyte and goblet cell states. Cell Rep. 38, 110438 (2022).
Elmentaite, R. et al. Cells of the human intestinal tract mapped across space and time. Nature 597, 250–255 (2021).
Burclaff, J. et al. A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics. Cell. Mol. Gastroenterol. Hepatol. https://doi.org/10.1016/j.jcmgh.2022.02.007 (2022).
Holloway, E. M. et al. Mapping development of the human intestinal niche at single-cell resolution. Cell Stem Cell 28, 568–580 (2021).
Egozi, A. et al. Single-cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis. PLoS Biol. 21, e3002124 (2023).
Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826 (2021).
Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature 619, 572–584 (2023).
Zilbauer, M. et al. A Roadmap for the Human Gut Cell Atlas. Nat. Rev. Gastroenterol. Hepatol. https://doi.org/10.1038/s41575-023-00784-1 (2023).
Forrest, A. R. R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
Bausch-Fluck, D. et al. The in silico human surfaceome. Proc. Natl Acad. Sci. USA 115, E10988–E10997 (2018).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Tuganbaev, T. et al. Diet diurnally regulates small intestinal microbiome-epithelial-immune homeostasis and enteritis. Cell 182, 1441–1459 (2020).
Harnik, Y. et al. Spatial discordances between mRNAs and proteins in the intestinal epithelium. Nat. Metab. 3, 1680–1693 (2021).
Kelly, J., Weir, D. G. & Feighery, C. Differential expression of HLA-D gene products in the normal and coeliac small bowel. Tissue Antigens 31, 151–160 (1988).
Scott, H., Solheim, B. G., Brandtzaeg, P. & Thorsby, E. HLA-DR-like antigens in the epithelium of the human small intestine. Scand. J. Immunol. 12, 77–82 (1980).
Mansbach, C. M. & Siddiqi, S. A. The biogenesis of chylomicrons. Annu. Rev. Physiol. 72, 315 (2010).
Mahmood Hussain, M. A proposed model for the assembly of chylomicrons. Atherosclerosis 148, 1–15 (2000).
Chung, J. et al. LDAF1 and seipin form a lipid droplet assembly complex. Dev. Cell 51, 551–563 (2019).
Hung, Y.-H., Carreiro, A. L. & Buhman, K. K. Dgat1 and Dgat2 regulate enterocyte triacylglycerol distribution and alter proteins associated with cytoplasmic lipid droplets in response to dietary fat. Biochim. Biophys. Acta 1862, 600–614 (2017).
Barker, H. G., Malm, J. R. & Reemtsma, K. Comparative fat and fatty acid intestinal absorption test utilizing radioiodine labeling; results in normal subjects. Proc. Soc. Exp. Biol. Med. 92, 471–474 (1956).
Lawen, A. & Lane, D. J. R. Mammalian iron homeostasis in health and disease: uptake, storage, transport, and molecular mechanisms of action. Antioxid. Redox Signal. 18, 2473–2507 (2013).
Moor, A. E. et al. Global mRNA polarization regulates translation efficiency in the intestinal epithelium. Science 357, 1299–1303 (2017).
Zwick, R. K. et al. Epithelial zonation along the mouse and human small intestine defines five discrete metabolic domains. Nat. Cell Biol. https://doi.org/10.1038/s41556-023-01337-z (2024).
Meran, L., Baulies, A. & Li, V. S. W. Intestinal stem cell niche: the extracellular matrix and cellular components. Stem Cells Int. 2017, e7970385 (2017).
Palikuqi, B. et al. Lymphangiocrine signals are required for proper intestinal repair after cytotoxic injury. Cell Stem Cell 29, 1262–1272 (2022).
Niec, R. E. et al. Lymphatics act as a signaling hub to regulate intestinal stem cell activity. Cell Stem Cell 29, 1067–1082 (2022).
Bernier-Latmani, J. et al. ADAMTS18+ villus tip telocytes maintain a polarized VEGFA signaling domain and fenestrations in nutrient-absorbing intestinal blood vessels. Nat. Commun. 13, 3983 (2022).
Santaolalla, R., Fukata, M. & Abreu, M. T. Innate immunity in the small intestine. Curr. Opin. Gastroenterol. 27, 125–131 (2011).
Moghaddami, M., Cummins, A. & Mayrhofer, G. Lymphocyte-filled villi: comparison with other lymphoid aggregations in the mucosa of the human small intestine. Gastroenterology 115, 1414–1425 (1998).
Crosnier, C., Stamataki, D. & Lewis, J. Organizing cell renewal in the intestine: stem cells, signals and combinatorial control. Nat. Rev. Genet. 7, 349–359 (2006).
Brügger, M. D. & Basler, K. The diverse nature of intestinal fibroblasts in development, homeostasis, and disease. Trends Cell Biol. 33, 834–849 (2023).
Chiquet-Ehrismann, R. Tenascins. Int. J. Biochem. Cell Biol. 36, 986–990 (2004).
Treuting, P. M., Arends, M. J. & Dintzis, S. M. in Comparative Anatomy and Histology (Second Edition) (eds. Treuting, P. M. et al.) Ch. 11, 191–211 (Academic, 2018). https://doi.org/10.1016/B978-0-12-802900-8.00011-7.
Subiran Adrados, C., Yu, Q., Bolaños Castro, L. A., Rodriguez Cabrera, L. A. & Yun, M. H. Salamander-Eci: an optical clearing protocol for the three-dimensional exploration of regeneration. Dev. Dyn. 250, 902–915 (2021).
Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
Ben-Moshe, S. & Itzkovitz, S. Spatial heterogeneity in the mammalian liver. Nat. Rev. Gastroenterol. Hepatol. https://doi.org/10.1038/s41575-019-0134-x (2019).
Trautmann, A. Extracellular ATP in the immune system: more than just a ‘danger signal’. Sci. Signal. 2, pe6 (2009).
Mabley, J. G. et al. Inosine reduces inflammation and improves survival in a murine model of colitis. Am. J. Physiol. Gastrointest. Liver Physiol. 284, G138–G144 (2003).
Liu, T. et al. ADAMDEC1 promotes skin inflammation in rosacea via modulating the polarization of M1 macrophages. Biochem. Biophys. Res. Commun. 521, 64–71 (2020).
O’Shea, N. R. et al. Critical role of the disintegrin metalloprotease ADAM-like decysin-1 [ADAMDEC1] for intestinal immunity and inflammation. J. Crohns Colitis 10, 1417–1427 (2016).
Matsumoto, T. et al. Serrated adenoma in familial adenomatous polyposis: relation to germline APC gene mutation. Gut 50, 402–404 (2002).
Snover, D. C. Update on the serrated pathway to colorectal carcinoma. Hum. Pathol. 42, 1–10 (2011).
Rubio, C. A. Serrated adenoma of the duodenum. J. Clin. Pathol. 57, 1219–1221 (2004).
Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protoc. 8, 1743–1758 (2013).
Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465 (2009).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Bagnoli, J. W. et al. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9, 2937 (2018).
Kohen, R. et al. UTAP: User-friendly Transcriptome Analysis Pipeline. BMC Bioinform. 20, 154 (2019).
Elinger, D., Gabashvili, A. & Levin, Y. Suspension trapping (S-Trap) is compatible with typical protein extraction buffers and detergents for bottom-up proteomics. J. Proteome Res. 18, 1441–1445 (2019).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Gu, Z. Complex heatmap visualization. iMeta 1, e43 (2022).
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinform. Oxf. Engl. 30, 2811–2812 (2014).
Ni, Z. et al. SpotClean adjusts for spot swapping in spatial transcriptomics data. Nat. Commun. 13, 2971 (2022).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Cunningham, F. et al. Ensembl 2022. Nucleic Acids Res. 50, D988–D995 (2022).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Caliński, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat. Methods 19, 1634–1641 (2022).
Hickey, J. W., Tan, Y., Nolan, G. P. & Goltsev, Y. Strategies for accurate cell type identification in CODEX multiplexed imaging data. Front. Immunol. 12, 727626 (2021).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).
Shannon, C. E. The mathematical theory of communication. 1963. MD Comput. 14, 306–317 (1997).
Harnik, Y. et al. Spatial transcriptomics data for ‘A spatial expression atlas of the adult human proximal small intestine’. Zenodo https://doi.org/10.5281/zenodo.10715015 (2024).
Harnik, Y. et al. Human villus zonation segmental tables for ‘A spatial expression atlas of the adult human proximal small intestine’. Zenodo https://doi.org/10.5281/zenodo.11490477 (2024).
Harnik, Y. et al. LCM RNA-seq and proteomics raw data for ‘A spatial expression atlas of the adult human proximal small intestine’. Zenodo https://doi.org/10.5281/zenodo.10715015 (2024).
Harnik, Y. et al. CODEX data for ‘A spatial expression atlas of the adult human proximal small intestine’. Zenodo https://doi.org/10.5281/zenodo.10724499 (2024).
Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Wang, Y. et al. Bile acid-dependent transcription factors and chromatin accessibility determine regional heterogeneity of intestinal antimicrobial peptides. Nat. Commun. 14, 5093 (2023).
Hortsch, M. The Michigan Histology website as an example of a free anatomical resource serving learners and educators worldwide. Anat. Sci. Educ. 16, 363–371 (2023).
Acknowledgements
We thank D. Hershkovitz and I. Barshack for discussions; and the staff at Abcam for donating the carrier-free antibodies for CODEX experiments. S.I. is supported by the Helen and Martin Kimmel Award for Innovative Investigation, the Yad Abraham Research Center for Cancer Diagnostics and Therapy, the Moross Integrated Cancer Center, the Minerva Stiftung grant, a Weizmann-Sheba grant, the Israel Science Foundation grants no. 908/21 and 3663/21, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant no. 768956 and a grant from the Ministry of Innovation, Science & Technology,Israel. Microscopy imaging was made possible thanks to the de Picciotto-Lesser Cell Observatory in memory of Wolf and Ruth Lesser. Y.K.K. is supported by the JSMF Postdoctoral Fellowship in Understanding Dynamic and Multi-scale Systems. R.H. is funded by the Walter Benjamin Programme from the German Research Foundation. Illustrations in Figs. 2g and 5c and Extended Data Figs. 1a, 9a and 10a were created using BioRender.
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Authors and Affiliations
Contributions
S.I., O.Y. and Y.H., conceived the study. I.N., R.P. and N.P. operated on patients and provided samples. Y.H., O.Y., K.B.H. and T.B. collected and processed samples. Y.H. and R.N. performed ST and smFISH experiments. ST experiments were performed under the guidance of M.K. and H.K.-S. R.N. performed LCM experiments and analysis, smFISH and HCR-FISH experiments and quantification. R.N., Y.L. and A.S. performed the proteomics experiments and analysis. T.K.H. provided ground truth anatomical classification of H&E images. R.H. and I.T. performed CODEX experiments and data analysis. O.G. and Y.A. contributed to all microscopy imaging and pixel classification development. Y.H., Y.K.K. and A.E. performed ST data processing and scRNA-seq integration. Y.H. and S.I. performed the ST data analysis and wrote the manuscript. C.M. and D.S.S. contributed to the histology slide evaluation. D.S.S. also provided biopsies from healthy patients. All of the authors discussed the results and commented on the manuscript.
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Abcam provided carrier-free antibodies for CODEX experiments (to R.H.). The other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Spatial Expression atlas outline and evaluation.
a, Study design. Spatial transcriptomics (10X Visium) n = 8, Spatial proteomics (CODEX) n = 3, smFISH n = 8. The diagram was created using BioRender. b, H&E images of tissues from ST experiments. Capture area within the fiducial frame is 6.5 ×6.5 mm. P7 and P8 maintain the circular-fold anatomy marked with green asterisk for circular fold top and green diamond for circular fold bottom. Spot annotation for circular fold top and bottom are available at Supplementary Table 8. c-e, Quality metrics of Visium data, n = 8 patients examined over 2 independent experiments. g,h, Violin plots showing distributions of the numbers of UMIs and genes in each of the patients, n = 2281, 3108, 3110, 3741, 2577, 1581, 1558 spots in P1-P8 respectively. White circles represent medians; whiskers represent 25–75 percentiles. f, Representative (P3) H&E staining of Visium slide fiducial frame (blow-up below). g, Visium spots coloured by Leiden clusters inferred from the analysis of 15,145 spots from eight patients (blow-up below). SM – Submucosa; EEC.K\M – Enteroendocrine K \ M cells; ILF – Isolated lymphoid follicle; LP – Lamina propria; RBC – Red blood cells; V-Villus. h, Visium spots coloured by zone along the crypt-villus axis (blow-up below). i, Mucosal spots (Crypt to V6) coloured by zone along the stromal-epithelial axis (blow-up below). Scale bar top 1 mm; bottom 100 µm.
Extended Data Fig. 2 Analysis of spatial transcriptomics and villus zonation assembly.
a-d, UMAP of 15,145 spots (n = 8) coloured by metadata as indicated in the figure (Methods). e, Heat map showing relative gene expression per cluster. Columns represent 100 down-sampled spots from each cluster indicated by colour and title above columns. Row represents genes. f-h, Stacked bar plots showing the relative abundances of spots from each cluster by patient (f), crypt-villus axis zone (g) and stromal-epithelial axis zone (h). EEC.K\M – Enteroendocrine K \ M cells; ILF – Isolated lymphoid follicle; LP – Lamina propria; RBC – Red blood cells; V-Villus. SM – Submucosa. i, H&E image (up) and a colour-coded pixel classification results for the same image (down) with blow-ups (n = 8) (Methods). Scale bars 100 µm. j, Representative (n = 12) field of views showing H&E (left), certified pathologist annotations serving as ground truth (middle) and the pixel classifier prediction (right). Prediction shown as maximal probability given by the classifier, while probabilities were used to compute Visium spot composition (Methods). Scale bars 5 µm. k, Visium spots for white dashed area in i. spots are shown as pie charts coloured by the composition of pixels for each spot. Colour-code as in i. l, Visium spots on k, colour-coded by their inferred stromal-epithelial axis relative coordinate. m, Zonation profiles for genes peaking at different zones along the crypt-villus axis. Top panels show Visium spots coloured by log10(UMI fraction); bottom panels show zonation profile for P3 (Methods). Lines are normalized to mean, patches show SEM. n, Genes that are zonated along the epithelial stromal axis. Left panels show Visium spots coloured by log10(UMI fraction); right panels show expression levels stratified to a zone for P3 (n = 3105 spots, Methods). Error-bars show SEM. Apical – Apical epithelium; Basal – Basal epithelium; LP – Lamina propria; MMuc – Muscularis mucosa; SMuc – Submucosa. C – Crypt.
Extended Data Fig. 3 Single cell phenotyping using CODEX and scRNAseq.
a, Table showing proteins that were profiled using CODEX together with morphological features. Unique markers are coloured same as the cell type in b. b, Heatmap showing the relative protein abundances for each cell type. For each cell type, the three rows show the protein abundances in patients P3,P4 and P8 from top to bottom. Columns represent proteins and morphological features used for phenotyping. c, Voronoi diagram showing cell type/state abundances across all samples. Thick grey lines separate epithelial from mesenchymal and immune cells. d, CODEX image (left) for P4 as also shown in Fig. 4a. Proteins are coloured as indicated at the bottom. e Cell type pheno-map corresponding to d. Each dot represents a cell centroid. Colours represent cell type as indicated below. f, Representative CODEX image (n = 3 biological repeats, 2 technical repeats each) of a serrated (left) next to a straight villus (right). Proteins are coloured as indicated at the bottom. Scale bars are 100 µm. g, Cell type pheno-map corresponding to f. Epi – Epithelium. Endo – Endothelium. cDC – Conventional dendritic cells. Fibro – Fibroblast. BM – basal membrane (Muscularis mucosa). h-j, UMAP of scRNAseq published datastets13,14 coloured by data origin (h), lineage (i) or cell type (j). Cell type and lineage names were changed for pooling purpose, while preserving the published original annotation essence.
Extended Data Fig. 4 In-situ validation using smFISH for zonation profiles computed from spatial transcriptomics.
Visium-inferred zonation profiles and smFISH validations for MBOAT1 (a,b), GSTA2 (c,d), SLC40A1 (e,f), SERPINA1 (g,h), IL32 (I,j) and DGAT2 (k,l). In panels a, c, e, g, i and k black lines show the median zonal expression across patients, patches are MAD (n = 8). Red lines show the in-situ quantification of at least 10 villi from 2–3 patients. Quantification was performed by dividing each villus to equal size segments along the villus. Dots were counted per segment normalized to each segment size (Methods). Panels b, h, j and l insets show the bottom (1), middle (2) and top (3) parts of the villus (red dashed frames on the villus image). MBOAT1, GSTA2, SERPINA1, DGAT2 in-situ was performed with the Stellaris smFISH protocol, SLC40A1 and IL32 were performed using the HCR-FISH protocol (Methods). All scale bars are 50 µm in complete villi and 5 µm in insets.
Extended Data Fig. 5 Spatial concordance between mRNA and translated proteins in the intestinal epithelium.
Visium-inferred mRNA zonation profiles (left) and protein staining (right) for CA9 (a), GPX2 (b), DMBT1 (c), AQP1 (d), NDRG1 (e), SLC2A2 (f), HLA-DRA (g), TMPRSS15 (h), CYP3A4 (i) and AQP10 (j). Black lines show the median zonal expression across patients, patches are MAD (n = 8). In panels a, e and g, protein staining was performed by immunofluorescence using the CODEX protocol (n = 3 biological repeats, 2 technical repeats each, Methods), Nuclear staining in blue (DAPI), Telocytes marked in green (PDGFRA) and zonated protein staining in white. In panels b-d, f and h-j, protein staining was performed by Immunostainings (n = 3 biological repeats), retrieved from the Human Protein Atlas83 (http://www.proteinatlas.org). Scale bars are 50 µm.
Extended Data Fig. 6 Zonated gene expression in secretory epithelial cells.
a, Zonation profiles for key EEC secreted hormones and peptides in human (cyan) and mouse4 (red). Profiles were interpolated to fit same number of zones. Lines show the mean normalized UMI fractions. Patches show SEM. b-e, Heat maps showing max-normalized zonation profiles. Columns represent zones. Rows represent cell type enriched genes (max expression above 1e-5; fold-change to any other cell type above 5 for EEC and Tuft; above 2 for Goblet cells and Best4 cells).
Extended Data Fig. 7 Zonated gene expression for gene groups and signaling pathways.
a, Heat maps showing max-normalized zonation profiles. rows represent zones. Columns represent highly expressed and zonated transcription factors20 (max expression above 2.5e-5; dynamic range above 2.8). Heatmap was split to two panels (top and bottom) for easier presentation. Genes mentioned in the main text are highlighted in red. b, Heat maps showing max-normalized zonation profiles. rows represent zones. Columns represent highly expressed and zonated surface markers21 (max expression above 6e-5; dynamic range above 3). Heatmap was split to two panels (top and bottom) for easier presentation. Genes mentioned in the main text are highlighted in red. SM - Submucosa; MM - Muscularis mucosa. c,d, Left, heat maps showing max-normalized zonation profiles. Columns represent zones. Rows represent key genes in the BMP (c) and WNT (d) signaling pathways. Right, bar plots showing the ratio between the mean expression in the epithelial cells and non-epithelial cells based on the scRNAseq data (Extended Data Fig. 3i) for all genes in the heat maps.
Extended Data Fig. 9 Laser capture microdissection of epithelial specific segments recapitulates Visium zonated expression patterns at the mRNA and protein levels.
a, LCM of villus epithelium top and bottom segments for P1 before and after tissue dissection. Collected tissue was used for RNAseq (n = 8) and Proteomics (n = 3). Scale bar, 50 μm. The diagram was created using BioRender. b, Spearman correlation (R = 0.59, two-sided p = 6.31e-42) of the villus height bias for epithelial specific genes between Visium and LCMseq. Representative genes from Extended Data Fig. 10 are highlighted in red. Values are mean of three biological repeats. Genes with expression below 5e-5 or that were identified in less than tree samples were excluded due to technical noise. c,d, LCMseq max normalized expression in bottom and tip segments for genes in chylomicrons biosynthesis pathway (c) and iron metabolism (d) pathways. e, Visium and LCMseq mRNA zonation profiles for genes highlighted in b. Visium black lines are median and patches are absolute deviation (MAD) between all patients (n = 8) zonation profiles for villus bottom (V1) to villus tip (V6). Profiles are normalized to their maximal expression across zones. LCMseq violin plots of (n = 3 patients with zone-matched data). Coloured dots are maximal normalized values for each patient. Grey whiskers show 25-75 percentiles, white circles show medians. f, Spearman-based clustering of LCM-proteomics. Clustering was performed for proteins which had sum-normalized protein abundance of 5e-4. g, Spearman correlation (R = 0.54, two-sided p = 2.2e-16) of the villus height bias of mRNA (based on spatial transcriptomics) and the corresponding proteins (based on LCM proteomics). Shown are genes with sum-normalized expression above 1e-6 (in either RNA or protein). Spearman R is calculated on significantly polarized genes between the LCM samples, coloured by blue (exact Wilcox rank-sum with BH correction, Q-val<0.3). Circled in red – examples of genes mentioned throughout the study.
Extended Data Fig. 10 Spatial reconstruction of human enterocyte along the crypt-villus axis.
a, Schematic of the experimental and computational approach. The diagram was created using BioRender. b, Selection of enterocyte specific genes from scRNAseq data (Extended Data Fig. 3h–j, methods). c, Retaining a set of enterocyte specific genes that are highly zonated as measured in the Visium spatial transcriptomics data (Fig. 1a). d, UMAP of 939 enterocyte from14 coloured by donor (up) or the inferred villus zone (down).C – crypt. V1-villus bottom. V6 – villus top.e, spatially reconstructed gene expression zonation profiles. right, heatmap showing profiles which are normalized to their maximum and sorted according to their center of mass. Shown are genes with a maximal zonation value above 5e-5. Left, UMAP of single enterocytes coloured by their log10(UMI fraction) for zonated genes highlighted in the heatmap to the right.
Extended Data Fig. 11 Zonation profiles are robust to patient clinical features.
a, Heatmap showing max normalized zonation profiles in individual patients (P1-P8) based on Visium ST and spatial reconstruction of single enterocyte from two patients in Burclaff et al.14 (methods) for genes in Extended Data Fig. 10 that have a maximal UMI fraction > 3e-4 in all samples. b, Comparison of zonation profiles patterns for patients with similar clinical feature. P value of two sided Kruskal Wallis test and the clinical feature are indicated for each violin plot; white circles represent medians; whiskers represent 25-75 percentiles. For each comparison n = 4 patients in each of the two groups (n = 12 within group pairs, n = 16 between group pairs). Correlation distance computed over 7175 genes with maximal UMI fraction above 1e-4. c, Table summarizing the results of differential gene expression analyses for patients grouped (Sex and Chemotherapy) or sorted (Age and BMI) by clinical feature. Shown are genes with a FDR < 0.25.
Extended Data Fig. 12 Spatial analysis of the stroma.
a, Violin plots showing the spatial distribution of immune (left) and mesenchymal (right) cell types across the stromal-epithelial axis. White circles represent medians; whiskers represent 25-75 percentiles. Cell-types were phenotyped using the combined signal from the 37-plex protein panel (Extended Data Fig. 3a–g, Methods). n(cells) = 89,142, n(ROIs) = 94, n(samples) = 3. b, Heatmap showing the maximal expression for genes associated with dominant cell-type. c, Crypt-villus zonation profiles of immunomodulatory genes found in mouse peak in the villus tip in mouse but not it human. Lines show zonation profiles from spatial reconstruction of enterocytes scRNAseq data (Fig. 3e–g, Methods). Patches show SEM. Values are UMI-summed normalized expression d, Heatmap showing max-normalized expression in human scRNAseq (Extended Data Fig. 3j) of immunomodulatory genes found in mouse. IDO1 is not expressed in the epithelium. cDC – Conventional dendritic cells. EC – endothelial cell.
Extended Data Fig. 13 Examples of serrated villi.
a, CODEX image of a bifurcated villi observed in P3 (n = 3 biological repeats with 2 technical repeats each). NDRG1 marking the epithelium (green), PDGFRA marking telocytes (white) and KI67 marking proliferating cells (magenta). Scale bar 50 µm. b, Circular fold in 3D space. Fold orientation indicated in the image. Leaf-like villi are shown with red asterisks. Scale bar 800 µm. c, DAPI nuclear staining (n = 2 biological repeats) showing a bifurcated villus. Image taken from a duodenal biopsy of a 17.5-year-old female during diagnostic endoscopy that was found healthy. Scale bar 50 µm. d, 3D image from a front view (left) and a section (right) of a leaf-like serrated villus. Circular fold light sheet image was cropped to expose the leaf-like anatomy. Scale bar 200 µm. yellow dashed line marks the leaf-like villus. e, Several leaf-like villi on a circular fold tip. Front (left) and perpendicular (right) angles are shown. Yellow arrows point to leaf-like villi; magenta star marks a fixed point in 3D space for orientation. Scale bar 300 µm. f, Bifurcated villus in a zoomed-out view (left, white dashed rectangle), side view (middle) and a section (right). Yellow dashed line marks the bifurcated villus; red star marks a fixed point in 3D space for orientation. Scale bar from left to right: 500 µm, 200 µm and 200 µm. In d-f, n = 1 patient with 2 technical repeats. g-h, Serrated leaf-like villi in 2D sections as they appear in the published histology database Michigan Histology and Virtual Microscopy Learning Resources85. Slide UCSF 246 (g) and Slide UCSF 247 (h). Scale bars were not provided.
Supplementary information
Supplementary Table 1
Patient data. Clinical and experimental data of eight patients used in the study.
Supplementary Table 2
Crypt–villus and stromal–epithelial zonation tables. Gene expression levels for all zones along the Crypt–villus and stromal–epithelial axes. Shown is the UMI fraction of all patients individually and collectively. Tables also contain P and q values (Methods).
Supplementary Table 3
Codex antibody panel. Description of the antibodies used in the CODEX experiments.
Supplementary Table 4
Functional patterns of zonation analysis. GSEA of zonation profiles clusters. Hallmark, KEGG and Gene Ontology pathways/terms that are significantly enriched in the submucosa-crypt, villus bottom and villus top. Tables show pathways/term data, and hypergeometric test inputs and results.
Supplementary Table 5
LCM proteomics data of bottom and top human villus epithelium. Raw and filtered (iBAQ) data of the Ms proteomics on epithelial segments isolated from the bottom and top of the human villus of three patients. The table also shows a comparison of the proteomics data and spatial transcriptomics (Visium).
Supplementary Table 6
scRNA-seq-based villus zonation profiles of human and mouse. UMI-summed normalized gene expression in six zones from the villus bottom to the villus top for orthologous genes.
Supplementary Table 7
Ligands and matching receptors correlation. List of ligands and their matching receptors. For each ligand and receptor, the table shows their spatial correlation, entropy (representing cell type specific expression), cell-type with maximal expression, and P and q values.
Supplementary Table 8
DGE circular fold bottom and top. Shown is the mean UMI fraction of the circular fold bottom and top, P and q values for patients P7 and P8 where the circular fold anatomy was kept in the Visium slide. Also shown are Visium spots’ annotations for each patient.
Supplementary Table 9
CODEX antibody validations. Conventional immunofluorescence validations for antibodies that were custom-conjugated with DNA barcodes.
Supplementary Video 1
3D structure of a circular fold. Light-sheet microscopy of a circular fold from a human small intestine tube from the duodenum–jejunum junction (ligament of Treitz). Tissue was cleared (Methods) and autofluorescence was used to image the epithelial layer. Leaf-like, bifurcated and finger-like villi are highlighted in yellow, green and blue, respectively.
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Harnik, Y., Yakubovsky, O., Hoefflin, R. et al. A spatial expression atlas of the adult human proximal small intestine. Nature 632, 1101–1109 (2024). https://doi.org/10.1038/s41586-024-07793-3
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DOI: https://doi.org/10.1038/s41586-024-07793-3
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