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Cell-type-specific signaling networks in heterocellular organoids

Abstract

Despite the widespread adoption of organoids as biomimetic tissue models, methods to comprehensively analyze cell-type-specific post-translational modification (PTM) signaling networks in organoids are absent. Here, we report multivariate single-cell analysis of such networks in organoids and organoid cocultures. Simultaneous analysis by mass cytometry of 28 PTMs in >1 million single cells derived from small intestinal organoids reveals cell-type- and cell-state-specific signaling networks in stem, Paneth, enteroendocrine, tuft and goblet cells, as well as enterocytes. Integrating single-cell PTM analysis with thiol-reactive organoid barcoding in situ (TOBis) enables high-throughput comparison of signaling networks between organoid cultures. Cell-type-specific PTM analysis of colorectal cancer organoid cocultures reveals that shApc, KrasG12D and Trp53R172H cell-autonomously mimic signaling states normally induced by stromal fibroblasts and macrophages. These results demonstrate how standard mass cytometry workflows can be modified to perform high-throughput multivariate cell-type-specific signaling analysis of healthy and cancerous organoids.

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Fig. 1: Cell-type and cell-state identification of organoid-derived single cells by MC.
Fig. 2: Cell-type- and cell-state-specific signaling analysis of intestinal organoids.
Fig. 3: TOBis for single-cell organoid multiplexing.
Fig. 4: Cell-type-specific signaling during intestinal organoid development.
Fig. 5: Single-cell signaling analysis of CRC TME organoids.
Fig. 6: Oncogenic mutations mimic stromal signaling networks.

Data availability

All raw data, processed data and working illustrations are available as a Community Cytobank project (https://community.cytobank.org/cytobank/experiments#project-id=1271). Source data for Figs. 2, 4 and 5 and Supplementary Figs. 1–3, 5, 7 and 8 are available with the paper.

References

  1. 1.

    Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Pastula, A. et al. Three-dimensional gastrointestinal organoid culture in combination with nerves or fibroblasts: a method to characterize the gastrointestinal stem cell niche. Stem Cells Int. 2016, 3710836 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598.e12 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Tuveson, D. & Clevers, H. Cancer modeling meets human organoid technology. Science 364, 952–955 (2019).

    Article  CAS  Google Scholar 

  5. 5.

    Pawson, T. & Scott, J. D. Protein phosphorylation in signaling—50 years and counting. Trends Biochem. Sci. 30, 286–290 (2005).

    Article  CAS  Google Scholar 

  6. 6.

    Miller-Jensen, K., Janes, K. A., Brugge, J. S. & Lauffenburger, D. A. Common effector processing mediates cell-specific responses to stimuli. Nature 448, 604–608 (2007).

    Article  CAS  Google Scholar 

  7. 7.

    Tape, C. J. et al. Oncogenic KRAS regulates tumor cell signaling via stromal reciprocation. Cell 165, 910–920 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Simmons, A. J. et al. Cytometry-based single-cell analysis of intact epithelial signaling reveals MAPK activation divergent from TNF-ɑ-induced apoptosis in vivo. Mol. Syst. Biol. 11, 835 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Tape, C. J. Systems biology analysis of heterocellular signaling. Trends Biotechnol. 34, 627–637 (2016).

    Article  CAS  Google Scholar 

  10. 10.

    Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Behbehani, G. K., Bendall, S. C., Clutter, M. R., Fantl, W. J. & Nolan, G. P. Single-cell mass cytometry adapted to measurements of the cell cycle. Cytom. A 81, 552–566 (2012).

    Article  Google Scholar 

  15. 15.

    Fienberg, H. G., Simonds, E. F., Fantl, W. J., Nolan, G. P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytom. A 81, 467–475 (2012).

    Article  CAS  Google Scholar 

  16. 16.

    Yin, X. et al. Niche-independent high-purity cultures of Lgr5+ intestinal stem cells and their progeny. Nat. Methods 11, 106–112 (2014).

    Article  CAS  Google Scholar 

  17. 17.

    Rapsomaniki, M. A. et al. CellCycleTRACER accounts for cell cycle and volume in mass cytometry data. Nat. Commun. 9, 632 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Kim, T. H. et al. Broadly permissive intestinal chromatin underlies lateral inhibition and cell plasticity. Nature 506, 511–515 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Orlova, D. Y. et al. Earth Mover’s Distance (EMD): a true metric for comparing biomarker expression levels in cell populations. PLoS ONE 11, e0151859 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Nusse, R. & Clevers, H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell 169, 985–999 (2017).

    Article  CAS  Google Scholar 

  23. 23.

    Gehart, H. & Clevers, H. Tales from the crypt: new insights into intestinal stem cells. Nat. Rev. Gastroenterol. Hepatol. 16, 19–34 (2019).

    Article  Google Scholar 

  24. 24.

    Massague, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 13, 616–630 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Zunder, E. R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Willis, L. M. et al. Tellurium-based mass cytometry barcode for live and fixed cells. Cytom. A 93, 685–694 (2018).

    Article  CAS  Google Scholar 

  28. 28.

    McCarthy, R. L., Mak, D. H., Burks, J. K. & Barton, M. C. Rapid monoisotopic cisplatin based barcoding for multiplexed mass cytometry. Sci. Rep. 7, 3779 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Isella, C. et al. Stromal contribution to the colorectal cancer transcriptome. Nat. Genet. 47, 312–319 (2015).

    Article  CAS  Google Scholar 

  31. 31.

    Calon, A. et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320–329 (2015).

    Article  CAS  Google Scholar 

  32. 32.

    Lan, J. et al. M2 macrophage-derived exosomes promote cell migration and invasion in colon cancer. Cancer Res. 79, 145–158 (2018).

    Google Scholar 

  33. 33.

    Tape, C. J. The heterocellular emergence of colorectal cancer. Trends Cancer 3, 79–88 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Dow, L. E. et al. Apc restoration promotes cellular differentiation and reestablishes crypt homeostasis in colorectal cancer. Cell 161, 1539–1552 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    O’Rourke, K. P. et al. Transplantation of engineered organoids enables rapid generation of metastatic mouse models of colorectal cancer. Nat. Biotechnol. 35, 577–582 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Spitzer, M. H. et al. IMMUNOLOGY. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Cruz-Acuna, R. et al. Synthetic hydrogels for human intestinal organoid generation and colonic wound repair. Nat. Cell Biol. 19, 1326–1335 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988.e16 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Machado, L. et al. In situ fixation redefines quiescence and early activation of skeletal muscle stem cells. Cell Rep. 21, 1982–1993 (2017).

    Article  CAS  Google Scholar 

  42. 42.

    Han, G., Spitzer, M. H., Bendall, S. C., Fantl, W. J. & Nolan, G. P. Metal-isotope-tagged monoclonal antibodies for high-dimensional mass cytometry. Nat. Protoc. 13, 2121–2148 (2018).

    Article  CAS  Google Scholar 

  43. 43.

    Han, G. et al. Atomic mass tag of bismuth-209 for increasing the immunoassay multiplexing capacity of mass cytometry. Cytom. A 91, 1150–1163 (2017).

    Article  CAS  Google Scholar 

  44. 44.

    Chevrier, S. et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 6, 612–620 e615 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytom. A 83, 483–494 (2013).

    Article  CAS  Google Scholar 

  46. 46.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  Google Scholar 

  47. 47.

    Khalil, H., Nie, W., Edwards, R. A. & Yoo, J. Isolation of primary myofibroblasts from mouse and human colon tissue. J. Vis. Exp. https://doi.org/10.3791/50611 (2013).

  48. 48.

    McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  49. 49.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to L. Dow (Cornell University) for sharing CRC organoids, X. Lu (University of Oxford) for sharing murine intestines and O. Ornatsky (Fluidigm) for providing monoisotopic cisplatin (195Pt and 196Pt). We thank the UCL CI Flow-Core for MC support and L. McInnes for UMAP advice. Graphical organoid renderings were designed by J. Claus (Phospho Biomedical Animation). This work was supported by Cancer Research UK (grant no. C60693/A23783, C.J.T.), UCLH Biomedical Research Centre (grant no. BRC422, C.J.T.), The Royal Society (grant no. RSG\R1\180234, C.J.T.) and Rosetrees Trust (grant no. A1989, C.J.T.).

Author information

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Authors

Contributions

X.Q. designed the study, performed organoid and MC experiments, analyzed the data and wrote the paper. J.S. developed TOBis, designed rare-earth metal antibody panels, performed MC analysis and analyzed data. P.V. isolated and characterized colonic fibroblasts, macrophages and cultured organoids, and analyzed data. P.K. performed UMAP, EMD, DREMI and PCA data analysis. M.N. developed TeMal barcodes. S.E.A. provided murine monocytes and intestines for fibroblast isolation. V.S.W.L. provided murine small intestines for organoid isolation. C.J.T. designed the study, analyzed the data and wrote the paper.

Corresponding author

Correspondence to Christopher J. Tape.

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Competing interests

M.N. has pending intellectual property on the use of tellurium reagents for mass cytometry applications which has been licensed to Fluidigm Corporation.

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Peer review information Nina Vogt and Nicole Rusk were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Integrated supplementary information

Supplementary Figure 1 Rare-Earth Metal-Conjugated Antibodies for Organoid Cell-Type Identification.

a) Summary of reagents used in organoid directed differentiation. b) Fuzzy logic diagram of organoid directed differentiation. c) Confocal immunofluorescence (IF) of small intestinal organoids stained with rare-earth metal-conjugated mass cytometry (MC) antibodies showing individual cell-type markers (red), F-Actin (white), and DAPI (blue) following directed differentiation, scale bars = 50 μm. Images are representative of three independent experiments. d) UMAP (Uniform Manifold Approximation and Projection) distributions of ~20,000 single organoid cells analyzed by MC following directed differentiation. e) Force-directed Scaffold maps of directedly differentiated small intestinal organoids. Cell-type landmark nodes are identified from untreated small intestinal organoids. f) Earth Mover’s Distance (EMD) quantification of cell-type identification markers from MC analysis of Lgr5-EGFP-ires-CreERT2 small intestinal crypt-cells. Source data

Supplementary Figure 2 Organoid Cell-Type and Cell-State Identification.

a) Strategy for organoid cell-type identification. b) Strategy for organoid cell-state classification. c) EMD quantification of cell-type identification markers across all small intestinal organoid cell-types from Figs. 1 and 2. d) Cell-state quantification of all small intestinal organoid cell-types from Figs. 1 and 2. Source data

Supplementary Figure 3 Cell-Type and Cell-State Specific Signaling Networks in Intestinal Organoids.

Cell-type and cell-state specific signaling networks of 27 PTMs from ~1 million single organoid cells analyzed by MC, with nodes colored by PTM-EMD scores quantifying PTM intensity (relative to all organoid cells), and edges colored by DREMI scores quantifying PTM-PTM connectivity. Source data

Supplementary Figure 4 Maxpar versus TOBis Organoid Barcoding in situ.

a) De-barcoded cell counts from small intestinal organoids barcoded using the Maxpar Cell-IDTM Barcoding Kit either after removal from Matrigel (ex situ) or while still in Matrigel (in situ) (n = 2 independent samples). b) Mean dual counts of 110Pd from Matrigel incubated in PBS, Maxpar Cell-IDTM barcode #20 (106Pd, 108Pd, and 110Pd) resuspended in PBS (PBS + BC), or Maxpar Cell-IDTM barcode #20 resuspended in Maxpar Cell Staining Buffer (CSB + BC) (two-tailed unpaired t-test, p < 0.0001, n = 3 independent samples). Error bars represent standard deviation (SD). c) Maximum-gain confocal IF images of NHS ester-/C2 maleimide-Alexa 647 fluorescent probes shown in Fig. 3a to highlight background Matrigel staining. Each IF image is representative of three independent experiments. d) Small intestinal organoids concurrently stained in situ with Maxpar Cell-IDTM (102Pd, 104Pd, 105Pd, 106Pd, 108Pd, and 110Pd) and TOBis (124Te, 126Te, 128Te, 130Te, 196Pt, and 198Pt) barcodes using analogous 20-plex 6-choose-3 matrices. Data is representative of three independent experiments.

Supplementary Figure 5 Maxpar versus TOBis Cell-Recovery Comparison.

a) Example 10-plex barcoding workflow for organoid MC using Maxpar Cell-IDTM Barcoding Kit. b) Example 10-plex barcoding workflow for organoid MC using TOBis. c) Dissociation and d) centrifugation steps (s) required for multiplexed organoid experiments using Maxpar or TOBis barcoding. e) Single-cell recovery of serially-titrated small intestinal organoids barcoded with either Maxpar (ex situ) or TOBis (in situ) (two-tailed ratio-paired t-test, p < 0.001, n = 2 independent set of serially-titrated samples). f) Cell-type recovery (cell count) from equally seeded small intestinal organoids analyzed by MC and de-barcoded by Maxpar or TOBis. g) Cell-type recovery (population percentage) from equally sampled small intestinal organoid cells analyzed by MC and de-barcoded by Maxpar or TOBis (linear regression of correlation, R2 = 0.86, n = 2 independent samples). h) Mean debarcoded cell counts in the absence or presence of glutathione (GSH) in PBS washes following TOBis barcoding (two-tailed unpaired t-test, p < 0.0001, n = 3 independent samples). Error bars represent SD.. Source data

Supplementary Figure 6 Colorectal Cancer (CRC) Tumor Microenvironment (TME) Model Cells.

a) Confocal IF of CRC organoid genotypes (wild-type (WT), shApc (A), shApc and KrasG12D/+ (AK), and shApc, KrasG12D/+ and Trp53R172H/- (AKP)) stained with Pan-CK, LRIG1, CD44, and C-MYC (red), F-Actin (white), and DAPI (blue), scale bars = 50 μm. Images are representative of three independent IF experiments. Pan-CK and LRIG1 (in combination with CEACAM1) were subsequently used for epithelial and stem cell identification in MC. b) Confocal IF of colonic fibroblasts cultured in 2D (plastic) and 3D (Matrigel) demonstrates positive staining for colonic fibroblast markers PDPN, Vimentin, PDGFRɑ, FOXL1, and GLI1 (red), F-Actin (white), and DAPI (blue), scale bars = 50 μm. Images are representative of three independent IF experiments. PDPN and RFP were subsequently used for fibroblast identification in MC. c) Confocal IF of primary bone marrow derived macrophages cultured in 2D (plastic) and 3D (Matrigel) demonstrates positive staining for macrophages markers CD68, F4/80, CD45, CD11b, and CX3CR1 (red), F-Actin (white), and DAPI (blue), scale bars = 50 μm. Images are representative of three independent IF experiments. CD68 and F40/80 were subsequently used for macrophage identification in MC.

Supplementary Figure 7 CRC Organoid Signaling Networks in the CRC TME Model.

Signaling networks of 28 PTMs in colonic organoid cells from each genotype (wild-type (WT), shApc (A), shApc and KrasG12D/+ (AK), or shApc, KrasG12D/+, and Trp53R172H/- (AKP)) and microenvironment condition in the CRC TME model analyzed by MC. Signaling nodes are connected by DREMI edges and PTM activity is displayed as EMD relative to all cells across all conditions. Source data

Supplementary Figure 8 Macrophage and Fibroblast Signaling Profiles in the CRC TME Model.

a) and b) Signaling networks of 28 PTMs in macrophages and colonic fibroblasts from each genotype and microenvironment condition in the CRC TME model analyzed by MC. Signaling nodes are connected by DREMI edges and PTM activity is displayed as EMD relative to all cells across all conditions. c) and d) PCA of 28 PTM-EMDs for macrophages and colonic fibroblasts across all genotype/microenvironment combinations (10 conditions each). e) and f) PCA of 756 PTM-DREMIs for macrophages and colonic fibroblasts across all genotype/microenvironment combinations (10 conditions each). Source data

Supplementary Figure 9 CRC Organoid Microenvironment and Genotype Scaffold Maps.

a) Force-directed Scaffold maps were constructed from WT colonic organoids alone or co-cultured with macrophages and/or colonic fibroblasts. b) Force-directed Scaffold maps were constructed from WT, A, AK, and AKP colonic organoid monocultures.

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Supplementary Information

Supplementary Figs. 1–9 and Tables 1–4.

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Supplementary Table 5

Parameters for single-cell signaling data analysis markers selected and parameters used for UMAP, PCA and Scaffold analysis.

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Qin, X., Sufi, J., Vlckova, P. et al. Cell-type-specific signaling networks in heterocellular organoids. Nat Methods 17, 335–342 (2020). https://doi.org/10.1038/s41592-020-0737-8

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