DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data

DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data

Abstract

Deciphering the functional interactions of cells in tissues remains a major challenge. Here we describe DIALOGUE, a method to systematically uncover multicellular programs (MCPs)—combinations of coordinated cellular programs in different cell types that form higher-order functional units at the tissue level—from either spatial data or single-cell data obtained without spatial information. Tested on spatial datasets from the mouse hypothalamus, cerebellum, visual cortex and neocortex, DIALOGUE identified MCPs associated with animal behavior and recovered spatial properties when tested on unseen data while outperforming other methods and metrics. In spatial data from human lung cancer, DIALOGUE identified MCPs marking immune activation and tissue remodeling. Applied to single-cell RNA sequencing data across individuals or regions, DIALOGUE uncovered MCPs marking Alzheimer’s disease, ulcerative colitis and resistance to cancer immunotherapy. These programs were predictive of disease outcome and predisposition in independent cohorts and included risk genes from genome-wide association studies. DIALOGUE enables the analysis of multicellular regulation in health and disease.

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Data availability

The datasets analyzed in this study include seq-FISH data37 and HMRF37 annotations obtained from https://bitbucket.org/qzhu/smfish-hmrf/src/master/hmrf-usage/; scRNA-seq from the mouse neocortex obtained via the Gene Expression Omnibus (GEO), accession number GSE115746 (ref. 38); Slide-seq data11 obtained via the Single Cell Portal: https://singlecell.broadinstitute.org/single_cell/study/SCP354/slide-seq-study#study-summary; MERFISH data10 obtained from the DRYAD repository: https://datadryad.org/stash/dataset/doi: 10.5061/dryad.8t8s248; and SMI data from human NSCLC samples39, including cell type annotations, from https://www.nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/. scRNA-seq data of colon biopsies16 were obtained from the Single Cell Portal: https://singlecell.broadinstitute.org/single_cell/study/SCP259/intra-and-inter-cellular-rewiring-of-the-human-colon-during-ulcerative-colitis#study-download; snRNA-seq data from human prefrontal cortex17 were obtained via the AMP-AD Knowledge Portal: https://adknowledgeportal.synapse.org/ (Synapse IDs: syn18686381, syn18686382, syn18686372 and syn3505720; available through controlled access and subject to the use conditions set by human privacy regulations); and melanoma and brain organoid single-cell data were obtained via the GEO under accession numbers GSE120575 (ref. 55), GSE115978 (ref. 18) and GSE86153 (ref. 43).

Code availability

DIALOGUE is implemented as an R package and can be installed using the devtools::install(‘DIALOGUE’) command. Further documentation and tutorials are provided in the package help pages (for example, ?DIALOGUE). We also provide DIALOGUE via GitHub (https://github.com/livnatje/DIALOGUE) and the Klarman Cell Observatory repository10, along with additional guidelines and specifications.

References

  1. Hong, S. & Stevens, B. Microglia: phagocytosing to clear, sculpt, and eliminate. Dev. Cell 38, 126–128 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  2. Ribeiro, M. et al. Meningeal γδ T cell-derived IL-17 controls synaptic plasticity and short-term memory. Sci. Immunol. 4, eaay5199 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  3. Schwartz, M. Can immunotherapy treat neurodegeneration? Science 357, 254 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  4. Baruch, K. et al. PD-1 immune checkpoint blockade reduces pathology and improves memory in mouse models of Alzheimer’s disease. Nat. Med. 22, 135–137 (2016).

    Article 
    CAS 

    Google Scholar
     

  5. Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  6. Corrigan-Curay, J. et al. T-cell immunotherapy: looking forward. Mol. Ther. 22, 1564–1574 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  7. Hauser, S. L. et al. B-cell depletion with rituximab in relapsing-remitting multiple sclerosis. N. Engl. J. Med. 358, 676–688 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  8. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  9. McDavid, A. et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  10. Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  11. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  12. Burgess, D. J. Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  13. Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-seq. eLife 8, e43803 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  14. Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  15. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  16. Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  17. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  18. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  19. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  20. van der Maaten, L. & Hinton, G. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

  21. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  22. Vieth, B., Parekh, S., Ziegenhain, C., Enard, W. & Hellmann, I. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat. Commun. 10, 4667 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  23. Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  24. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  25. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  26. Yang, Z. & Michailidis, G. A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data. Bioinformatics 32, 1–8 (2016).

    PubMed 
    Article 

    Google Scholar
     

  27. Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  28. Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  29. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  30. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  31. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  32. Kumar, M. P. et al. Analysis of single-cell RNA-seq identifies cell–cell communication associated with tumor characteristics. Cell Rep. 25, 1458–1468 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  33. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  34. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  35. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  36. Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184, 5482–5496 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  37. Zhu, Q., Shah, S., Dries, R., Cai, L. & Yuan, G.-C. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat. Biotechnol. https://doi.org/10.1038/nbt.4260 (2018)

  38. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  39. He, S. et al. High-plex multiomic analysis in FFPE tissue at single-cellular and subcellular resolution by spatial molecular imaging. Preprint at https://www.biorxiv.org/content/10.1101/2021.11.03.467020v1 (2021).

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  41. Witten, D. M., Tibshirani, R. & Hastie, T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10, 515–534 (2009).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  42. Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

  43. Quadrato, G. et al. Cell diversity and network dynamics in photosensitive human brain organoids. Nature 545, 48–53 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  44. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B (Methodol.) 57, 289–300 (1995).


    Google Scholar
     

  45. Andero, R. Nociceptin and the nociceptin receptor in learning and memory. Prog. Neuropsychopharmacol. Biol. Psychiatry 62, 45–50 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  46. Mellor, A. L., Lemos, H. & Huang, L. Indoleamine 2,3-dioxygenase and tolerance: where are we now? Front. Immunol. 8, 1360 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  47. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  48. Jeyakumar, T. et al. Inactivation of interferon regulatory factor 1 causes susceptibility to colitis-associated colorectal cancer. Sci. Rep. 9, 18897 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  49. Gerecke, C. et al. Hypermethylation of ITGA4, TFPI2 and VIMENTIN promoters is increased in inflamed colon tissue: putative risk markers for colitis-associated cancer. J. Cancer Res. Clin. Oncol. 141, 2097–2107 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  50. de Lange, K. M. et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  51. Arijs, I. et al. Mucosal gene signatures to predict response to infliximab in patients with ulcerative colitis. Gut 58, 1612–1619 (2009).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  52. De Jager, P. L. et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research. Sci. Data 5, 180142 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  53. Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimers Dis. 64, S161–S189 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  54. Hernandez, D. G. et al. Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain. Neurobiol. Dis. 47, 20–28 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  55. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  56. Tavazoie, M. F. et al. LXR/ApoE activation restricts innate immune suppression in cancer. Cell 172, 825–840 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  57. Ostendorf, B. N. et al. Common germline variants of the human APOE gene modulate melanoma progression and survival. Nat. Med. 26, 1048–1053 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  58. Jerby-Arnon, L. et al. Pan-cancer mapping of single T cell profiles reveals a TCF1:CXCR6-CXCL16 regulatory axis essential for effective anti-tumor immunity. Preprint at https://www.biorxiv.org/content/10.1101/2021.10.31.466532v1 (2021).

  59. Ruffin, N. & Guerreiro-Cacais, A. O. A pan-cancer signature for dysfunctional T cells. Nat. Rev. Immunol. 22, 74 (2022).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  60. Di Pilato, M. et al. CXCR6 positions cytotoxic T cells to receive critical survival signals in the tumor microenvironment. Cell 184, 4512–4530 (2021).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  61. Steinke, F. C. et al. TCF-1 and LEF-1 act upstream of Th-POK to promote the CD4+ T cell fate and interact with Runx3 to silence Cd4 in CD8+ T cells. Nat. Immunol. 15, 646–656 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  62. Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  63. Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  64. Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  65. Jin, X. et al. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  66. Wang, C., Lu, T., Emanuel, G., Babcock, H. P. & Zhuang, X. Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization. Proc. Natl Acad. Sci. USA 116, 10842 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  67. Dhainaut, M. et al. Spatial CRISPR genomics identifies regulators of the tumor microenvironment. Cell 185, 1223–1239.e20 (2022).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  68. Ji, Y., Lotfollahi, M., Wolf, F. A. & Theis, F. J. Machine learning for perturbational single-cell omics. Cell Syst. 12, 522–537 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  69. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article 

    Google Scholar
     

  70. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

  71. Jerby-Arnon, L. et al. Opposing immune and genetic mechanisms shape oncogenic programs in synovial sarcoma. Nat. Med. 27, 289–300 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  72. Ramilowski, J. A. et al. A draft network of ligand–receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

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Acknowledgements

We thank L. Gaffney and A. Hupalowska for help with figure preparation. L.J.A. is a Chan Zuckerberg Biohub Investigator and holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. L.J.A. was a Cancer Research Institute (CRI) Irvington Fellow supported by the CRI and a fellow of the Eric and Wendy Schmidt Postdoctoral Program. A.R. was a Howard Hughes Medical Institute (HHMI) Investigator. Work was supported by the Klarman Cell Observatory, National Institute of Diabetes and Digestive and Kidney Diseases RC2 DK114784, the Food Allergy Science Initiative, the Manton Foundation and the HHMI. The AD dataset was provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center. Data collection was supported through funding by National Instutute on Aging grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984, U01AG46152 and U01AG61356, the Illinois Department of Public Health and the Translational Genomics Research Institute.

Author information

Author notes

  1. Aviv Regev

    Present address: Genentech, South San Francisco, CA, USA

Affiliations

  1. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

    Livnat Jerby-Arnon

  2. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Livnat Jerby-Arnon

  3. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Livnat Jerby-Arnon & Aviv Regev

  4. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aviv Regev

Contributions

L.J. and A.R. conceived the study. L.J. devised the method and performed the analyses, with guidance and input from A.R. L.J. and A.R. wrote the manuscript.

Corresponding authors

Correspondence to
Livnat Jerby-Arnon or Aviv Regev.

Ethics declarations

Competing interests

A.R. is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and, until 31 July 2020, was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov and Neogene Therapeutics. From 1 August 2020, A.R. is an employee of Genentech, a member of the Roche group, and has equity in Roche. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Rickard Sandberg, Qing Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 DIALOGUE identified MCPs in the mouse hypothalamus that are not recovered with other dimensionality reduction and clustering approaches.

(a)Pearson correlation coefficient between genes, PCs, NMF, and DIALOGUE MCPs from either the training or the test set (x axis) across different pairs of cell types (panels) in spatial niches in the mouse hypothalamus. (b) Pearson correlation coefficient (red/blue, color bar) between the Overall Expression of the relevant MCP component when considering only defined subsets of the pertaining cell types (rows, columns), as previously identified by clustering10. White: missing values (cell subtypes that cannot be compared). (c) MCPs are not merely driven by cell subtype composition in a niche. Fraction of cells from different clusters (as previously defined10, y axis) among cells of a given type (label on top) that over- or under-express the relevant component of each pair-wise MCP1 (top or bottom 25%, respectively, x axis) involving that cell type. (d)Similarity (y axis, Spearman’s r) between the gene loadings of MCPs identified in the microenvironment setting (x axis) and the gene loadings of matching MCPs identified in the macro-environment setting, when computed for different pairs of cell types using MERFISH data. *In both (a) and (d) middle line: median; box edges: 25th and 75th percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5; further outliers are marked individually.

Extended Data Fig. 2 DIALOGUE captures spatial patterns.

(a) Average Overall Expression in a niche (dot, 15 cells on average) of the first MCP (MCP1) in the first (x axis) and second (y axis) cell type in that MCP. In red is the locally weighted polynomial (LOWESS) regression line. (b) As in (a), but depicting the Overall Expression residuals after regressing out impact of cell clusters, as previously defined10. (a-b) Spearman correlation coefficient (R) and significance (P, one-sided). (c) Performance (AUROC, y axis) when predicting the expression of the corresponding DIALOGUE component in the neighboring cells located in the same macro-environment (dark blue, ~500 cells) or micro-environment (purple, and light blue, ~15 cells), when testing on unseen test set; the training data includes either spatial coordinates and single-cell profiles (light blue, ‘spatial data’) or only single cell profiles from ~500 cell aggregates, without spatial information (‘dissociated’, Methods).

Extended Data Fig. 3 DIALOGUE vs. HMFR.

(a) Overall Expression of HMRF37 domain-specific programs in neighboring pairs of glutamatergic (y axis) and GABAergic (x axis) neurons from different regions (colors). (b) Overall Expression of the relevant components of MCPs 1-5 in glutamatergic (y axis) and their adjacent GABAergic (x axis) neurons from different regions (colors). (a-b) Spearman correlation coefficient (R) and significance (P, one-sided).

Extended Data Fig. 4 MCPs mark spatial patterns and phenotypes.

(a) Spatial distribution of MCPs and HMFR programs. Overall Expression of MCPs identified by DIALOGUE and the HMRF37 domain programs in glutamatergic (circles) and GABAergic (dots) neurons in the mouse visual cortex. As shown, while many of the patterns follow either a more layered or salt and pepper pattern, MCP2 distinguished a more discrete region. While such boundaries sometimes reflect measurement artifacts, we did not find an association with number of genes/reads (typical quality measures) nor with simple alignment with Fields of View (FOV). (b,c) Shared and cell type specific components in DIALOGUE MCP1s in the mouse hypothalamus. (b) Fraction of genes (y axis) that are shared (yellow) or specific to one (A, dark blue) or another (B, light blue) of the cell types in each of the hypothalamus MCPs (x axis). (c) The two cell programs in each of the MCPs in (b) and their specific and shared (intersection) genes. P-values denote association with naïve animal behavior (mixed-effects models, two-sided test).

Extended Data Fig. 5 DIALOGUE identifies mis-localized cells and disease MCPs in single-cell data.

(a) ROC curves showing the true positive (y axis) and false positive (x axis) rate when predicting mis-localized cells of each major subset (panels) with different types of ‘contamination’ with cells that are either from the same layer (LP/EPI) within control (black, from replicate biopsy) or UC (blue; from adjacent biopsy with a different clinical status: inflamed or non-inflamed); or from a different layer but same clinical status, when considering either all samples (green) or only samples from control (yellow) or UC patients (red). (b) UC multicellular program genes. Average expression (Z score residuals after regressing out the associations with the LP/EPI location, red/blue color bar) of top genes (columns) from the UC multicellular program, sorted by their pertaining cell type (top color bar), across samples (rows), sorted by Overall Expression (right, Methods), and labeled by clinical status, location and patient ID (left color bars). (c) Melanoma MCP1. Average expression (Z score, red/blue color bar) of top genes (columns) from MCP1 identified in four different cell types (top color bar), across melanoma tumor samples (rows), sorted by Overall Expression of MCP1 (right, Methods), and labeled by treatment status and ICB response (left color bar).

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Jerby-Arnon, L., Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data.
Nat Biotechnol (2022). https://doi.org/10.1038/s41587-022-01288-0

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