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Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates

Nature Cell Biology volume 23, pages 23–31 (2021)Cite this article

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An Author Correction to this article was published on 16 April 2021

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Abstract

A detailed understanding of intestinal stem cell (ISC) self-renewal and differentiation is required to treat chronic intestinal diseases. However, the different models of ISC lineage hierarchy1,2,3,4,5,6 and segregation7,8,9,10,11,12 are subject to debate. Here, we have discovered non-canonical Wnt/planar cell polarity (PCP)-activated ISCs that are primed towards the enteroendocrine or Paneth cell lineage. Strikingly, integration of time-resolved lineage labelling with single-cell gene expression analysis revealed that both lineages are directly recruited from ISCs via unipotent transition states, challenging the existence of formerly predicted bi- or multipotent secretory progenitors7,8,9,10,11,12. Transitory cells that mature into Paneth cells are quiescent and express both stem cell and secretory lineage genes, indicating that these cells are the previously described Lgr5+ label-retaining cells7. Finally, Wnt/PCP-activated Lgr5+ ISCs are molecularly indistinguishable from Wnt/β-catenin-activated Lgr5+ ISCs, suggesting that lineage priming and cell-cycle exit is triggered at the post-transcriptional level by polarity cues and a switch from canonical to non-canonical Wnt/PCP signalling. Taken together, we redefine the mechanisms underlying ISC lineage hierarchy and identify the Wnt/PCP pathway as a new niche signal preceding lateral inhibition in ISC lineage priming and segregation.

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Fig. 1: FVR labels the secretory enteroendocrine and Paneth cell lineages.
Fig. 2: Wnt/β-catenin and non-canonical Wnt/PCP-activated Lgr5+ ISCs are indistinguishable at the transcriptional level.
Fig. 3: Subtype enrichment of crypt cells reveals distinct progenitor states for all intestinal lineages.
Fig. 4: Temporally resolved lineage labelling and pseudotemporal ordering of intestinal crypt cells shows that EECs and PCs directly allocate from ISCs via unipotent transition states.
Fig. 5: FVR marks Lgr5+ label-retaining cells that give rise to PCs.

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Data availability

Microarray data have been deposited in NCBI/GEO under accession code GSE94092. scRNAseq data have been deposited in NCBI/GEO under accession code GSE152325. All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Custom R scripts of the single-cell qRT-PCR bioinformatics analysis are available in a jupyter notebook upon request. Single-cell RNAseq analysis is available under https://github.com/theislab/gut_lineage/.

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References

  1. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature https://doi.org/10.1038/nature06196 (2007).

  2. Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell https://doi.org/10.1016/j.stem.2016.05.010 (2016).

  3. Li, N., Nakauka-Ddamba, A., Tobias, J., Jensen, S. T. & Lengner, C. J. Mouse label-retaining cells are molecularly and functionally distinct from reserve intestinal stem cells. Gastroenterology https://doi.org/10.1053/j.gastro.2016.04.049 (2016).

  4. Li, N. et al. Single-cell analysis of proxy reporter allele-marked epithelial cells establishes intestinal stem cell hierarchy. Stem Cell Rep. https://doi.org/10.1016/j.stemcr.2014.09.011 (2014).

  5. Kim, T. H. et al. Single-cell transcript profiles reveal multilineage priming in early progenitors derived from Lgr5+ intestinal stem cells. Cell Rep. https://doi.org/10.1016/j.celrep.2016.07.056 (2016).

  6. Potten, C. S. Stem cells in gastrointestinal epithelium: numbers, characteristics and death. Philos. Trans. R. Soc. B Biol. Sci. https://doi.org/10.1098/rstb.1998.0246 (1998).

  7. Buczacki, S. J. A. et al. Intestinal label-retaining cells are secretory precursors expressing Lgr5. Nature https://doi.org/10.1038/nature11965 (2013).

  8. Grün, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature https://doi.org/10.1038/nature14966 (2015).

  9. Heuberger, J. et al. Shp2/MAPK signaling controls goblet/Paneth cell fate decisions in the intestine. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1309342111 (2014).

  10. Shroyer, N. F., Wallis, D., Venken, K. J. T., Bellen, H. J. & Zoghbi, H. Y. Gfi1 functions downstream of Math1 to control intestinal secretory cell subtype allocation and differentiation. Genes Dev. https://doi.org/10.1101/gad.1353905 (2005).

  11. van Es, J. H. et al. Dll1 marks early secretory progenitors in gut crypts that can revert to stem cells upon tissue damage. Nat. Cell Biol. https://doi.org/10.1038/ncb2581 (2012).

  12. Schonhoff, S. E., Giel-Moloney, M. & Leiter, A. B. Neurogenin 3-expressing progenitor cells in the gastrointestinal tract differentiate into both endocrine and non-endocrine cell types. Dev. Biol. https://doi.org/10.1016/j.ydbio.2004.03.013 (2004).

  13. Gehart, H. & Clevers, H. Tales from the crypt: new insights into intestinal stem cells. Nat. Rev. Gastroenterol. Hepatol. https://doi.org/10.1038/s41575-018-0081-y (2019).

  14. Fre, S. et al. Notch signals control the fate of immature progenitor cells in the intestine. Nature 435, 964–968 (2005).

    Article  CAS  Google Scholar 

  15. Yang, Q., Bermingham, N. A., Finegold, M. J. & Zoghbi, H. Y. Requirement of Math1 for secretory cell lineage commitment in the mouse intestine. Science https://doi.org/10.1126/science.1065718 (2001).

  16. Ritsma, L. et al. Intestinal crypt homeostasis revealed at single-stem-cell level by in vivo live imaging. Nature https://doi.org/10.1038/nature12972 (2014).

  17. Takeda, N. et al. Interconversion between intestinal stem cell populations in distinct niches. Science https://doi.org/10.1126/science.1213214 (2011).

  18. Sangiorgi, E. & Capecchi, M. R. Bmi1 is expressed in vivo in intestinal stem cells. Nat. Genet. https://doi.org/10.1038/ng.165 (2008).

  19. Montgomery, R. K. et al. Mouse telomerase reverse transcriptase (mTert) expression marks slowly cycling intestinal stem cells. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1013004108 (2011).

  20. Powell, A. E. et al. The pan-ErbB negative regulator lrig1 is an intestinal stem cell marker that functions as a tumor suppressor. Cell https://doi.org/10.1016/j.cell.2012.02.042 (2012).

  21. Kozar, S. et al. Continuous clonal labeling reveals small numbers of functional stem cells in intestinal crypts and adenomas. Cell Stem Cell https://doi.org/10.1016/j.stem.2013.08.001 (2013).

  22. Cortijo, C., Gouzi, M., Tissir, F. & Grapin-Botton, A. Planar cell polarity controls pancreatic β-cell differentiation and glucose homeostasis. Cell Rep. https://doi.org/10.1016/j.celrep.2012.10.016 (2012).

  23. Bader, E. et al. Identification of proliferative and mature β-cells in the islets of Langerhans. Nature 535, 430–434 (2016).

    Article  CAS  Google Scholar 

  24. Roscioni, S. S., Migliorini, A., Gegg, M. & Lickert, H. Impact of islet architecture on β-cell heterogeneity, plasticity and function. Nat. Rev. Endocrinol. https://doi.org/10.1038/nrendo.2016.147 (2016).

  25. Grumolato, L. et al. Canonical and noncanonical Wnts use a common mechanism to activate completely unrelated coreceptors. Genes Dev. https://doi.org/10.1101/gad.1957710 (2010).

  26. Niehrs, C. The complex world of WNT receptor signalling. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/nrm3470 (2012).

  27. Gegg, M. et al. Flattop regulates basal body docking and positioning in mono- and multiciliated cells. eLife 3, e03842 (2014).

    Article  Google Scholar 

  28. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature https://doi.org/10.1038/nature24489 (2017).

  29. Muñoz, J. et al. The Lgr5 intestinal stem cell signature: robust expression of proposed quiescent ‘+4’ cell markers. EMBO J. https://doi.org/10.1038/emboj.2012.166 (2012).

  30. Lange, A. et al. FltpT2AiCre: a new knock-in mouse line for conditional gene targeting in distinct mono- and multiciliated tissues. Differentiation 83, S105–S113 (2012).

    Article  CAS  Google Scholar 

  31. Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, N. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis https://doi.org/10.1002/dvg.20335 (2007).

  32. Burtscher, I., Barkey, W. & Lickert, H. Foxa2-venus fusion reporter mouse line allows live-cell analysis of endoderm-derived organ formation. Genesis https://doi.org/10.1002/dvg.22404 (2013).

  33. Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. https://doi.org/10.1186/s13059-019-1663-x (2019).

  34. Kim, T. H. et al. Broadly permissive intestinal chromatin underlies lateral inhibition and cell plasticity. Nature https://doi.org/10.1038/nature12903 (2014).

  35. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science https://doi.org/10.1126/science.aaw3381 (2020).

  36. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. https://doi.org/10.1038/s41576-020-0223-2 (2020).

  37. Bastidas-Ponce, A. et al. Comprehensive single cell mRNA profiling reveals a detailed roadmap for pancreatic endocrinogenesis. Development https://doi.org/10.1242/dev.173849 (2019).

  38. Curtin, J. A. et al. Mutation of Celsr1 disrupts planar polarity of inner ear hair cells and causes severe neural tube defects in the mouse. Curr. Biol. https://doi.org/10.1016/S0960-9822(03)00374-9 (2003).

  39. Andersson-Rolf, A., Fink, J., Mustata, R. C. & Koo, B. K. A video protocol of retroviral infection in primary intestinal organoid culture. J. Vis. Exp. https://doi.org/10.3791/51765 (2014).

  40. Yin, X. et al. Niche-independent high-purity cultures of Lgr5+ intestinal stem cells and their progeny. Nat. Methods https://doi.org/10.1038/nmeth.2737 (2014).

  41. Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. https://doi.org/10.1038/nbt.3154 (2015).

  42. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. https://doi.org/10.1016/j.cels.2018.11.005 (2019).

  43. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. https://doi.org/10.1038/ncomms14049 (2017).

  44. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. https://doi.org/10.1186/s13059-017-1382-0 (2018).

  45. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. https://doi.org/10.21105/joss.00861 (2018).

  46. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. https://doi.org/10.1038/nbt.3192 (2015).

  47. Blondel, V. D., Guillaume, J. L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).

  48. Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv007 (2015).

  49. Drost, H. G. & Paszkowski, J. Biomartr: genomic data retrieval with R. Bioinformatics https://doi.org/10.1093/bioinformatics/btw821 (2017).

  50. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  Google Scholar 

  51. Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. https://doi.org/10.1038/75556 (2000).

  52. Carbon, S. et al. Expansion of the gene ontology knowledgebase and resources: the Gene Ontology Consortium. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw1108 (2017).

  53. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv1070 (2016).

  54. Reimand, J. et al. g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. https://doi.org/10.1093/nar/gkw199 (2016).

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Acknowledgements

We thank K. Diemer, J. Schultheiss, I. Kunze, A. Ludwig and A. Bettenbrock for excellent technical assistance, A. Raducanu and P. Mahaddalkar for assistance with the FACS and H. Farin for teaching regarding intestinal single cell culture. We are grateful to J. Murdoch for providing Celsr1crsh animals and H. Edlund for providing the Ngn3 antibody. We thank R. Böttcher, M. Tschöp and S. Woods for critical reading of the manuscript. This work was supported by an Emmy-Noether Fellowship and the European Union with ERC starting grant Ciliary Disease (project no. 242807) and HumEn 7th Framework Programme FP7 Health–2013–Innovation 1 (contract no. 602889). For financial support we also thank the Helmholtz Society, Helmholtz Portfolio Theme ‘Metabolic Dysfunction and Common Disease’ (H.L. and J.B.), the Helmholtz Alliance ‘Aging and Metabolic Programming, AMPro’ (H.L. and J.B.) and the Helmholtz Alliance ICEMED—Imaging and Curing Environmental Metabolic Diseases (H.L.). This project was funded by ExNet-0041-Phase2–3 (‘SyNergy-HMGU’) through the Initiative and Network Fund of the Helmholtz Association (H.L. and F.J.T.), by Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (grant no. ZT-I-PF-5-01; F.J.T.), and the German Research Foundation and the German Center for Diabetes Research (DZD e.V.; H.L.).

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Authors and Affiliations

  1. Institute of Diabetes and Regeneration Research, Helmholtz Diabetes Center, Helmholtz Center Munich, Neuherberg, Germany

    Anika Böttcher, Michael Sterr, Alexandra Aliluev, Lena Oppenländer, Ingo Burtscher & Heiko Lickert

  2. Institute of Stem Cell Research, Helmholtz Center Munich, Neuherberg, Germany

    Anika Böttcher, Michael Sterr, Alexandra Aliluev, Lena Oppenländer, Ingo Burtscher & Heiko Lickert

  3. German Center for Diabetes Research (DZD), Neuherberg, Germany

    Anika Böttcher, Michael Sterr, Alexandra Aliluev, Lena Oppenländer, Ingo Burtscher, Martin Irmler, Johannes Beckers & Heiko Lickert

  4. Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany

    Maren Büttner, Sophie Tritschler, Steffen Sass & Fabian J. Theis

  5. School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

    Sophie Tritschler

  6. Institute of Experimental Genetics, Helmholtz Center Munich, Neuherberg, Germany

    Martin Irmler & Johannes Beckers

  7. Technical University of Munich, Chair of Experimental Genetics, Freising, Germany

    Johannes Beckers

  8. Department Biology II, Anthropology and Human Genomics, Ludwig-Maximilians-University of Munich, Martinsried, Germany

    Christoph Ziegenhain & Wolfgang Enard

  9. Comprehensive Pneumology Center, Helmholtz Center Munich, Munich, Germany

    Andrea C. Schamberger, Fien M. Verhamme & Oliver Eickelberg

  10. Technical University of Munich, Munich, Germany

    Fabian J. Theis & Heiko Lickert

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Contributions

A.B. designed experiments, performed experiments, analysed data and wrote the manuscript. M.B. analysed single-cell qRT-PCR and RNAseq data and helped write the manuscript. S.T. analysed single-cell qRT-PCR data and helped write the manuscript. M.S. analysed microarray data. A.A. performed western blot analysis of sorted cell populations. L.O. performed and analysed immunostainings. I.B. generated the Ngn3-VF mouse line. S.S. analysed microarray data. M.I. and J.B. performed the microarrays and data analysis. C.Z. and W.E. contributed to single-cell qRT-PCR experiments and discussions. A.C.S., F.M.V. and O.E. provided single-cell qRT-PCR resources and contributed to discussions. F.J.T. supervised M.B., S.T. and S.S. and analysed single-cell RNAseq and qRT-PCR data and helped write the manuscript. H.L. supported study design, data analysis and writing, and acquired financial support.

Corresponding authors

Correspondence to Fabian J. Theis or Heiko Lickert.

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Competing interests

F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. and Dermagnostix. S.T. reports receiving consulting fees from Cellarity, Inc. The other authors declare no competing interests.

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Peer review information Nature Cell Biology thanks Boudewijn Burgering and the other, anonymous, reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 FVR labels all Paneth and enteroendocrine subtypes.

a, LSM images of immune-stained flow-sorted FVRhi/low/neg SI crypt cells isolated from FltpZV/+ mice showing that three distinct crypt cell populations are distinguishable by FVR activity. FVR (Venus, green), DAPI (blue, nuclei). Scale bar, 75 µm. b, Fluorescent intensity of GFP (Venus) in FVRlow and FVRhi cells isolated from FltpZV/+ cells by flow cytometry. n = 12 mice. Data are presented as mean values +/– s.e.m. c, Heatmap depicting the P-values determined by one-sided Fisher’s exact test of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways which were significantly enriched in at least one of the populations. d, FACS plot depicting the experimental strategy to obtain Lgr5hi cells (intestinal stem cells, ISCs) from Lgr5-EGFP-ires-CreERT2 (Lgr5-ki) mice. e,f, Confirmation of the microarray results. Comparison of the crypt cell populations distinguishable by FVR activity with Lgr5hi ISCs by qRT-PCR, for the expression of intestinal lineage markers (Lyz1, Paneth cells; Chga, enteroendocrine cells; Muc2, goblet cells) (e) and proliferation markers (Ccnd1, Ki67) and cell-cycle exit marker (Cdkn1a) (f). n = 3 mice. Data are presented as mean values +/– s.e.m. Two-tailed Student’s t-test. g–i, Heatmaps depicting the enrichment of the enteroendocrine cell (EEC) gene signature in the FVRlow population (g), and the enrichment of genes expressed in Paneth cells (PC) formed via Lgr5hi ISCs (h) or via a Goblet/Paneth cell precursor (i) in FVRhi cells. Gene signatures are derived from Grün et al.2 Expression is scaled row-wise and the colours range from dark blue (low expression) to orange (high expression) and represent normalized expression (row z-score).

Source data

Extended Data Fig. 2 Fltp+ cells possess limited organoid forming capacity in vitro.

a, Schematic depicting the FltpT2AiCre/+; Gt(ROSA)26mTmG/+ lineage-tracing model. Fltp lineage− cells (mTomato, red) convert into Fltp lineage+ cells (mGFP, green) upon Fltp-promoter driven Cre expression via an intermediate (mTmG, yellow) cell state. b, MA-plot comparing the expression of Notch pathway genes (KEGG mmu04330) in mTmG and Lgr5hi cells. The y-axis depicts the fold change in log2 and the x-axis depicts the mean log2 expression value. n = 6 mice for Lgr5hi ISC. n = 4 mice for mTmG. c, MA-plot comparing the expression of Lgr5+ label-retaining cell signature genes7 in mTmG and Lgr5hi cells. Differentially expressed genes (FDR < 0.01) are shown in red. The y-axis depicts the fold change in log2 and the x-axis depicts the mean log2 expression value. n = 6 mice for Lgr5hi cells. n = 4 mice for mTmG. d, e, Representative LSM images showing Lgr5hi and mTmG cells stained for Ki67 (white) and DAPI (blue, nucleus) (d) and quantification of Ki67 positive cells (e). n = 5 mice for Lgr5hi cells. n = 5 mice for mTmG cells. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. Scale bars, 75 µm. f, g, Representative LSM images showing live organoids derived from single Lgr5hi ISCs (f) and mTmG cells after 6 days culture (g). Images represent data of 3 mice for Lgr5hi cells and 5 mice for mTmG cells. Scale bars, 75 µm. h, i, Representative bright-field images of 6,000 FACS-purified mTmG and Lgr5hi cells cultured for 12 days (h) and quantification of organoid number (i). n = 3 mice for Lgr5hi cells. n = 5 mice for mTmG cells. Data are presented as mean values ± s.d.; two-tailed Student’s t-test. Scale bars, 5 mm.

Source data

Extended Data Fig. 3 Fltp+ cells possess limited multi-lineage potential in vivo, are resistant to chemical injury but do not contribute to regeneration after intestinal injury.

a, Experimental setup to assess stress response of Fltp+ cells (mTmG) compared to Lgr5hi ISCs. b, c, LSM images of duodenal sections from Lgr5-ki (b) and FltpT2AiCre/+; Gt(ROSA)26mTmG/+ mice (c). Sections were stained for DAPI (blue, nucleus), GFP (Lgr5+ cells, green) and Lyz1 (white, Paneth cells) (b) or mT (RFP, red), mG (GFP, green), and Lyz1 (white, Paneth cells) (c). Images represent data of 6 untreated Lgr5-ki mice; 4 treated Lgr5-ki mice; 6 untreated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice; 7 treated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice. Scale bars, 75 µm. d-g, Representative FACS plots of Lgr5-GFP cell populations (d) and mT/mTmG/mG cell populations (e) from untreated and 5-FU treated mice and quantification of frequencies referred to total crypt cells (f, g). n = 6 untreated Lgr5-ki mice; n = 4 treated Lgr5-ki mice; n = 6 untreated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice; n = 7 treated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. h, i, LSM images (h) and quantification (i) of EdU positive (white) Lgr5hi and mTmG cells. DAPI (blue) stains the nucleus. n = 3 untreated Lgr5-ki mice; n = 5 treated Lgr5-ki mice; n = 3 untreated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice; n = 7 treated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. Scale bars, 75 µm. j, k, l, LSM images showing immune-stained duodenal sections from untreated and 5-FU treated FltpT2AiCre/+;Gt(ROSA)26mTmG/+ mice. DAPI (blue, nucleus), mGFP (green, Fltp lineage+ cells), mTomato (red, Fltp− cells), Lyz1 (Paneth cells, white) and ChgA (enteroendocrine cells, red) (j). Scheme showing mGFP expression pattern in crypt-villi units that are considered as stem-cell tracings (mGFP expression in all intestinal lineages in crypt-villi units) (k) and quantification of stem-cell tracings from sections (l). n = 5 untreated mice; n = 3 treated mice. Scale bars, 75 µm. Data are presented as mean values ± s.d.

Source data

Extended Data Fig. 4 Identification of progenitors for each intestinal lineage by scRNAseq.

a, Scheme of a SI crypt depicting all described intestinal epithelial cell types. ISC, intestinal stem cell. EC, enterocyte. GC, goblet cell. PC, Paneth cell. EEC, enteroendocrine cell. TC, tuft cell. LRC, label-retaining cell. b, Overview of current models of secretory fate specification in the gut. In addition to multipotent secretory progenitors that specify during multiple rounds of division11,15, different lineage-restricted bi-potent secretory progenitors7,9,10,12 and direct differentiation from ISCs2 have been reported. +4 reserve stem cells might correspond to Lgr5+ label-retaining cells (LRCs). Paneth cell (PC), enteroendocrine cell (EEC), goblet cells (GC), tuft cells (TC). c, UMAPs depicting the gene score of major cell types for each cell. d, UMAP depicting all samples from control wild-type (no enrichment) and reporter mice (rare lineage enrichment) used in this study. e, Correlation map showing highly variable genes between progenitor cell types. f, UMAP depicting the cell-cycle score for each cell. g, Bar plot showing the cell-cycle score for progenitor and mature cell types.

Extended Data Fig. 5 Intestinal progenitors are characterized by distinct gene expression patterns.

a, Heatmap depicting the mean expression of marker genes in progenitor and mature cell populations. Distinct marker genes are already expressed in progenitor cells. ISC, intestinal stem cell. EC pro, enterocyte progenitor. EC, enterocyte. GC pro, goblet cell progenitor. Early GC, early goblet cell. GC, mature goblet cell. PC pro, Paneth cell progenitor. PC, Paneth cell. EEC pro, enteroendocrine cell progenitor. EEC, enteroendocrine cell. TC pro, tuft cell progenitor. TC, tuft cell. b, Heatmap showing differentially expressed transcription factors between progenitors. c, Heatmap depicting signalling pathways involved in stem-cell maintenance, differentiation and cell positioning. Genes in black show significant expression differences. d, Abstracted graph of the PAGA model plotted on UMAP, cell-type cluster are reduced to colored dots. Graph abstraction reveals connections between cell types and are depicted as lines. Line thickness describes confidence level of the connection between two cell types. Connections below the threshold confidence of 0.05 are not displayed. All progenitor populations connect to ISCs.

Extended Data Fig. 6 Pseudotemporal ordering of cells identifies unipotent transition states for EEC and PC lineage formation characterized by downregulation of stem-cell and co-expression of stem cell and secretory lineage genes, respectively.

a, Violin plots showing the distribution of selected marker genes from single-cell qRT-PCR analysis in ISCs, and PC and EEC lineage. Raw single-cell qRT-PCR data are available in the Source Data for Extended Data Fig. 6. b, Heatmap of gene expression along the lineage-trajectories inferred by PAGA (separated by white vertical bars). Within each trajectory cells are ordered by diffusion pseudotime (dpt). Colour bars at the bottom indicate cell-type clusters and FACS states. c, Expression of cell-cycle (top) and lineage (bottom) marker genes along the Paneth cell (PC) lineage trajectory. Cells are ordered by dpt. Expression is shown as the running average over 30 cells. d, Cell density of cell-type clusters along the PC trajectory. Cells are ordered by dpt. e, Expression of cell-cycle (top) and lineage (bottom) marker genes along the enteroendocrine (EEC) lineage trajectory. Cells are ordered by dpt. Expression is shown as the running average over 30 cells. f, Cell density of cell-type clusters along the EEC trajectory. Cells are ordered by dpt.

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Extended Data Fig. 7 Non-canonical Wnt/PCP signalling is activated during differentiation of ISCs towards PCs and EECs.

a, Heatmap showing gene expression from single-cell qRT-PCR data along the Paneth cell (PC) lineage trajectory from ISCs to mature PCs. Cell-type clusters are ordered by the inferred PAGA trajectory and cells by diffusion pseudotime dpt). Expression is shown as the running average over 30 cells scaled to the maximum observed level per gene. Bars at the bottom indicate cell-type clusters and dpt. b, Heatmap showing gene expression from single-cell qRT-PCR data along the enteroendocrine (EEC) lineage trajectory from ISCs to mature EECs. Cell-type clusters are ordered by the inferred PAGA trajectory and cells by dpt. Expression is shown as the running average over 30 cells scaled to the maximum observed level per gene. Bars at the bottom indicate cell-type clusters and dpt. c, Expression analysis of non-canonical Wnt/PCP genes in FVRhi/low/neg crypt cells from FltpZV/+ mice by qRT-PCR. n = 3 mice for Ror2, Dvl2, c-Jun. n = 6 mice for Fzd6, Prickle1, Celsr1. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. d, e, Western blot (d) and quantification (e) of active, phosphorylated (p) Jun N-terminal kinase (Jnk) in flow-sorted FVR+ and FVR− crypt cells isolated from adult FltpZV/+ reporter mice. Gapdh, mTor and pmTor are presented as controls. n = 5 mice. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test.

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Extended Data Fig. 8 Disturbed Wnt/PCP signalling causes alterations in gene expression pattern and numbers of Paneth cells.

a, Analysis of Celsr1 and Fltp expression in crypt cells from control and mutant mice by qRT-PCR. n = 4 (for Celsr1)/6(for Fltp) for control mice. n = 5 (for Celsr1)/3(for Fltp) for Celsr1crsh/+;Fltp+/+ mice. n = 5 (for Celsr1)/9(for Fltp) for Celsr1crsh/+;FltpZV/+ mice. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. b, scRNAseq analysis of control and Wnt/PCP Celsr1crsh/+;FltpZV/ZV mutant crypt cells. Heatmap showing Pearson correlation of the highly variable genes in all cell types. In all cases, Celsr1crsh/+;FltpZV/ZV and control samples of the respective cell-type correlate the most. n = 10 mice for control. n = 4 mice for Celsr1crsh/+; FltpZV/ZV . c, Bar plot depicting cell-cycle annotation for all cell types. n = 10 mice for control. n = 4 mice for Celsr1crsh/+;FltpZV/ZV. d, Heatmaps depicting differential expression of transcription factors ordered by log-fold change in PC-primed ISCs and in PC progenitors. Benjamin-Hochberg adjusted p-value <10−5. n = 10 mice for control. n = 4 mice for Celsr1crsh/+; FltpZV/ZV. e, Heatmap depicting expression of Wnt target genes. Expression of Axin2, Ascl2 and Lgr5 were significantly different in mutant PC-primed ISCs. Benjamin-Hochberg adjusted p-value <10−5. n = 10 mice for control. n = 4 mice for Celsr1crsh/+;FltpZV/ZV. f, Heatmap depicting differential gene expression in signalling pathways. Grey boxes in the dot plot highlight significantly different genes. Benjamin-Hochberg adjusted p-value <10−5. n = 10 mice for control. n = 4 mice for Celsr1crsh/+;FltpZV/ZV. g, h, i, LSM images of duodenal sections stained for ChgA (red, enteroendocrine cells), Lyz1 (white, Paneth cells), DAPI (blue, nuclei) (g) and quantification of Paneth cells (h) and ChgA+ EECs (i). n = 7 control mice. n = 6 Celsr1crsh/+; FltpZV/ZV mice. Data are presented as mean values ± s.e.m.; two-tailed Student’s t-test. Scale bars, 75 µm.

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Supplementary information

Reporting Summary

Supplementary Table 1

Supplementary Table 1. Differentially expressed genes in the FVR populations. Differential expression analyses were performed with the R environment for statistical computing (R Development Core Team, http://www.R-project.org/) using the limma package (version 3.30.7) and P-values were adjusted for multiple testing by Benjamini–Hochberg correction. A gene was considered as differentially expressed if the adjusted P-value (FDR) was below a threshold of 0.05 (for FVR). Supplementary Table 2. Genes used to determine the gene scores in single-cell RNAseq analysis. Supplementary Table 3. Genes used in single-cell qRT-PCR analysis. Supplementary Table 4. Primers for single-cell qRT-PCR.

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Raw data of single-cell qRT-PCR.

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Unprocessed western blot.

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Böttcher, A., Büttner, M., Tritschler, S. et al. Non-canonical Wnt/PCP signalling regulates intestinal stem cell lineage priming towards enteroendocrine and Paneth cell fates. Nat Cell Biol 23, 23–31 (2021). https://doi.org/10.1038/s41556-020-00617-2

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