Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Comparative analysis of cell–cell communication at single-cell resolution

Nature Biotechnology volume 42, pages 470–483 (2024)Cite this article

Subjects

Abstract

Inference of cell–cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell–cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell–cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche–phenotype relationships in health and disease.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic overview of cell-resolved communication analysis with Scriabin.
Fig. 2: Benchmarking and robustness analysis of cell-resolved communication analysis.
Fig. 3: Scriabin accurately recovers expected CCC edges.
Fig. 4: Scriabin reveals communicative pathways obscured by agglomerative techniques.
Fig. 5: Cell–cell interaction programs of the developing fetal gut.
Fig. 6: Longitudinal circuits of CCC in acute SARS-CoV-2 infection.

Similar content being viewed by others

Data availability

Raw and processed scRNA-seq data generated in this manuscript are available on the Gene Expression Omnibus (GEO) as accession GSE228415 (ref. 101). Spatial transcriptomic datasets and datasets of PBMCs (pbmc5k and pbmc10k) were downloaded from 10x Genomics; for comparison of mouse and human PBMCs, datasets from 10x Genomics’ cell multiplexing oligo demonstration were used (https://www.10xgenomics.com/resources/datasets). Processed scRNA-seq data of SCC and matched normals29 were provided directly by the study authors. Processed count matrices from the Smart-Seq2 human HNSCC dataset were downloaded from GEO accession GSE103322 (ref. 8). Processed count matrices from the Smart-Seq2 human uterine decidua dataset were downloaded from European Bioinformatics Institute accession E-MTAB-6678 (ref. 13). Processed Seurat objects of the Fluidigm C1 pancreas islet dataset are available through the R package SeuratData21,33. Processed CRISPRa Perturb-seq data were downloaded from Zenodo record 5784651 (ref. 40). scRNA-seq data of human leprosy granulomas41 were downloaded from https://github.com/mafeiyang/leprosy_amg_network. Data from developing fetal intestine49 were acquired from the CELLxGENE portal: https://cellxgene.cziscience.com/collections/60358420-6055-411d-ba4f-e8ac80682a2e. Data of longitudinal responses to SARS-CoV-2 infection in HBECs58 were downloaded from GEO accession GSE166766. The GRCh38.p13 reference genome is available from the National Center for Biotechnology Information.

Code availability

Scriabin is available for download and use as an R package at https://github.com/BlishLab/scriabin (ref. 102).

References

  1. Almet, A. A., Cang, Z., Jin, S. & Nie, Q. The landscape of cell–cell communication through single-cell transcriptomics. Curr. Opin. Syst. Biol. 26, 12–23 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yosef, N. & Regev, A. Writ large: genomic dissection of the effect of cellular environment on immune response. Science 354, 64–68 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schraivogel, D. et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17, 629–635 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Pavličev, M. et al. Single-cell transcriptomics of the human placenta: inferring the cell communication network of the maternal–fetal interface. Genome Res. 27, 349–361 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zepp, J. A. et al. Distinct mesenchymal lineages and niches promote epithelial self-renewal and myofibrogenesis in the lung. Cell 170, 1134–1148 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cohen, M. et al. Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175, 1031–1044 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Raredon, M. S. B. et al. Single-cell connectomic analysis of adult mammalian lungs. Sci. Adv. 5, eaaw3851 (2019).

  15. Jin, S. et al. Inference and analysis of cell–cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang, Y. et al. iTALK: an R package to characterize and illustrate intercellular communication. Preprint at bioRxiv https://doi.org/10.1101/507871 (2019).

  17. Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A. & Forrest, A. R. R. Predicting cell-to-cell communication networks using NATMI. Nat. Commun. 11, 5011 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Türei, D., Korcsmáros, T. & Saez-Rodriguez, J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat. Methods 13, 966–967 (2016).

    Article  PubMed  Google Scholar 

  19. Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  21. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Cortal, A., Martignetti, L., Six, E. & Rausell, A. Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nat. Biotechnol. 39, 1095–1102 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  25. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang, J. et al. Evaluation of ultra-low input RNA sequencing for the study of human T cell transcriptome. Sci Rep. 9, 8445 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  27. Wu, L. et al. Blockade of TIGIT/CD155 signaling reverses T-cell exhaustion and enhances antitumor capability in head and neck squamous cell carcinoma. Cancer Immunol. Res. 7, 1700–1713 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Kiner, E. et al. Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes. Nat. Immunol. 22, 216–228 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Joller, N. & Kuchroo, V. K. Tim-3, Lag-3, and TIGIT. Curr. Top. Microbiol. Immunol. 410, 127–156 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  32. Linderman, G. C. et al. Zero-preserving imputation of single-cell RNA-seq data. Nat. Commun. 13, 192 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lawlor, N. et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 27, 208–222 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44–73 (2016).

    Article  PubMed  Google Scholar 

  36. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Cabello-Aguilar, S. et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55–e55 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Raredon, M. S. B. et al. Computation and visualization of cell–cell signaling topologies in single-cell systems data using Connectome. Sci. Rep. 12, 1–12 (2022).

    Article  Google Scholar 

  39. Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat. Commun. 13, 1–13 (2022).

    Article  Google Scholar 

  40. Schmidt, R. et al. CRISPR activation and interference screens decode stimulation responses in primary human T cells. Science 375, eabj4008 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ma, F. et al. The cellular architecture of the antimicrobial response network in human leprosy granulomas. Nat. Immunol. 22, 839–850 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gordon, S. Alternative activation of macrophages. Nat. Rev. Immunol. 3, 23–35 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Ridley, D. S. & Jopling, W. H. Classification of leprosy according to immunity. A five-group system. Int. J. Lepr. Other Mycobact. Dis. 34, 255–273 (1966).

    CAS  PubMed  Google Scholar 

  44. Flynn, J. L. et al. An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection. J. Exp. Med. 178, 2249–2254 (1993).

    Article  CAS  PubMed  Google Scholar 

  45. Herbst, S., Schaible, U. E. & Schneider, B. E. Interferon gamma activated macrophages kill mycobacteria by nitric oxide induced apoptosis. PLoS ONE 6, e19105 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ní Cheallaigh, C. et al. A common variant in the adaptor mal regulates interferon gamma signaling. Immunity 44, 368–379 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Verhagen, C. E. et al. Reversal reaction in borderline leprosy is associated with a polarized shift to type 1-like Mycobacterium leprae T cell reactivity in lesional skin: a follow-up study. J. Immunol. 159, 4474–4483 (1997).

    Article  CAS  PubMed  Google Scholar 

  48. Teles, R. M. B. et al. Identification of a systemic interferon-γ inducible antimicrobial gene signature in leprosy patients undergoing reversal reaction. PLoS Negl. Trop. Dis. 13, e0007764 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Biton, M. et al. T helper cell cytokines modulate intestinal stem cell renewal and differentiation. Cell 175, 1307–1320 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Goto, N. et al. Lymphatics and fibroblasts support intestinal stem cells in homeostasis and injury. Cell Stem Cell 29, 1246–1261.e6 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Niec, R. E. et al. Lymphatics act as a signaling hub to regulate intestinal stem cell activity. Cell Stem Cell 29, 1067–1082.e18 (2022).

  53. Darling, T. K. & Lamb, T. J. Emerging roles for Eph receptors and ephrin ligands in immunity. Front. Immunol. 10, 1473 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kim, M. J. et al. PAF-Myc-controlled cell stemness is required for intestinal regeneration and tumorigenesis. Dev. Cell 44, 582–596 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang, N. et al. ID1 is a functional marker for intestinal stem and progenitor cells required for normal response to injury. Stem Cell Rep. 3, 716–724 (2014).

    Article  CAS  Google Scholar 

  56. Kazer, S. W. et al. Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection. Nat. Med. 26, 511–518 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Strunz, M. et al. Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis. Nat. Commun. 11, 3559 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ravindra, N. G. et al. Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes. PLoS Biol. 19, e3001143 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gressner, O. A., Peredniene, I. & Gressner, A. M. Connective tissue growth factor reacts as an IL-6/STAT3-regulated hepatic negative acute phase protein. World J. Gastroenterol. 17, 151–163 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Sack, G. H. Jr. Serum amyloid A—a review. Mol. Med. 24, 46 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Xu, J. et al. SARS-CoV-2 induces transcriptional signatures in human lung epithelial cells that promote lung fibrosis. Respir. Res. 21, 182 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Doitsh, G. et al. Cell death by pyroptosis drives CD4 T-cell depletion in HIV-1 infection. Nature 505, 509–514 (2013).

    Article  ADS  Google Scholar 

  63. Vignuzzi, M. & López, C. B. Defective viral genomes are key drivers of the virus–host interaction. Nat. Microbiol. 4, 1075–1087 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. López, C. B. Defective viral genomes: critical danger signals of viral infections. J. Virol. 88, 8720–8723 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Raredon, M. S. B. et al. Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinform. 39, btac775 (2023).

  66. Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  67. Svensson, V. Droplet scRNA-seq is not zero-inflated. Nat. Biotechnol. 38, 147–150 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Cao, Y., Kitanovski, S., Küppers, R. & Hoffmann, D. UMI or not UMI, that is the question for scRNA-seq zero-inflation. Nat. Biotechnol. 39, 158–159 (2021).

    Article  CAS  PubMed  Google Scholar 

  69. Ghaddar, B. & De, S. Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq. Nucleic Acids Res. 50, e82 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).

    Article  CAS  PubMed  Google Scholar 

  71. Pasqual, G. et al. Monitoring T cell–dendritic cell interactions in vivo by intercellular enzymatic labelling. Nature 553, 496–500 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  72. Guidolin, D., Marcoli, M., Tortorella, C., Maura, G. & Agnati, L. F. Receptor–receptor interactions as a widespread phenomenon: novel targets for drug development? Front. Endocrinol. 10, 53 (2019).

    Article  Google Scholar 

  73. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).

  74. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018).

  75. Waltman, L. & van Eck, N. J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).

    Article  ADS  Google Scholar 

  76. Yip, A. M. & Horvath, S. Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 8, 22 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Dong, J. & Horvath, S. Understanding network concepts in modules. BMC Syst. Biol. 1, 24 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Dimitrov, D. et al. Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data. Nat. Commun. 13, 3224 (2022).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  79. Wilk, A. J. et al. Charge-altering releasable transporters enable phenotypic manipulation of natural killer cells for cancer immunotherapy. Blood Adv. 4, 4244–4255 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hughes, T. K. et al. Second-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies. Immun. 53, 878–894.e7 (2020).

  82. Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Wilk, A. J. et al. Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19. J. Exp. Med. 218, e20210582 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  85. Petukhov, V. et al. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19, 78 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).

  88. Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018).

    Article  CAS  PubMed  Google Scholar 

  89. Freytag, S., Tian, L., Lönnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res. 7, 1297 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Chibueze, C. E., Yoshimitsu, M. & Arima, N. CD160 expression defines a uniquely exhausted subset of T lymphocytes in HTLV-1 infection. Biochem. Biophys. Res. Commun. 453, 379–384 (2014).

    Article  CAS  PubMed  Google Scholar 

  92. Agresta, L., Hoebe, K. H. N. & Janssen, E. M. The emerging role of CD244 signaling in immune cells of the tumor microenvironment. Front. Immunol. 9, 2809 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  93. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Polo, J. M., Ci, W., Licht, J. D. & Melnick, A. Reversible disruption of BCL6 repression complexes by CD40 signaling in normal and malignant B cells. Blood 112, 644–651 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. McKinlay, C. J. et al. Charge-altering releasable transporters (CARTs) for the delivery and release of mRNA in living animals. Proc. Natl Acad. Sci. USA 114, E448–E456 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. McKinlay, C. J., Benner, N. L., Haabeth, O. A., Waymouth, R. M. & Wender, P. A. Enhanced mRNA delivery into lymphocytes enabled by lipid-varied libraries of charge-altering releasable transporters. Proc. Natl Acad. Sci. USA 115, E5859–E5866 (2018).

  99. Molfetta, R., Quatrini, L., Santoni, A. & Paolini, R. Regulation of NKG2D-dependent NK cell functions: the yin and the yang of receptor endocytosis. Int. J. Mol. Sci. 18, 1677 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Carlsten, M. et al. Primary human tumor cells expressing CD155 impair tumor targeting by down-regulating DNAM-1 on NK cells. J. Immunol. 183, 4921–4930 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. Wilk, A. J., Shalek, A. K., Holmes, S. & Blish, C. A. Comparative analysis of cell–cell communication at single-cell resolution. Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE228415 (2023).

  102. Wilk, A. J., Shalek, A. K., Holmes, S. & Blish, C. A. Comparative analysis of cell–cell communication at single-cell resolution. GitHub. https://github.com/BlishLab/scriabin (2023).

Download references

Acknowledgements

We thank W. J. Greenleaf and S. W. Kazer for helpful conversations in the conceptualization of Scriabin’s workflow. We thank P. A. Wender and N. L.-B. Weidenbacher for synthesis of CARTs. We thank C. Tzouanas and J. Ordovas-Montanes for insights on intestinal cell–cell communication pathways. We thank A. Ji for providing processed scRNA-seq data of SCC and matched normals. We also thank all current and former members of the Blish laboratory for helpful discussions of this work. A.J.W. is supported by the Stanford Medical Scientist Training Program (T32 GM007365-44) and the Stanford Bio-X Interdisciplinary Graduate Fellowship. Work with CARTs was supported by NIH/NIAID R01 AI161803 to C.A.B. This work was also supported by NIH/NIDA DP1 DA04508902 to C.A.B.; R01HD103571 to C.A.B.; NIH/NCI 1U54CA217377, U01 28020510 and 1U2CCA23319501 to A.K.S.; NIH/NIDA 1DP1DA053731 to A.K.S.; the Bill & Melinda Gates Foundation INV-027498 and OPP1202327 to A.K.S.; the MIT Stem Cell Initiative through Foundation MIT to A.K.S.; and a 2019 Sentinel Pilot Project from the Bill & Melinda Gates Foundation to C.A.B. and A.K.S.

Author information

Author notes

  1. These authors jointly supervised this work: Susan Holmes, Catherine A. Blish.

Authors and Affiliations

  1. Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA

    Aaron J. Wilk & Catherine A. Blish

  2. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Aaron J. Wilk & Catherine A. Blish

  3. Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA

    Aaron J. Wilk & Catherine A. Blish

  4. Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  5. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  6. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alex K. Shalek

  7. Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA

    Alex K. Shalek

  8. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Alex K. Shalek

  9. Department of Statistics, Stanford University, Stanford, CA, USA

    Susan Holmes

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

    Catherine A. Blish

Authors
  1. Aaron J. Wilk
    View author publications

    Search author on:PubMed Google Scholar

  2. Alex K. Shalek
    View author publications

    Search author on:PubMed Google Scholar

  3. Susan Holmes
    View author publications

    Search author on:PubMed Google Scholar

  4. Catherine A. Blish
    View author publications

    Search author on:PubMed Google Scholar

Contributions

A.J.W., A.K.S., S.H. and C.A.B. conceived of the work. A.J.W. built Scriabin and performed computational analyses, cell culture work, flow cytometric analysis and scRNA-seq profiling. A.J.W. wrote the manuscript, with input from all authors. S.H. and C.A.B. jointly supervised the work.

Corresponding author

Correspondence to Aaron J. Wilk.

Ethics declarations

Competing interests

A.K.S. reports compensation for consulting and/or scientific advisory board (SAB) membership from Merck, Honeycomb Biotechnologies, Cellarity, Repertoire Immune Medicines, Ochre Bio, Third Rock Ventures, Hovione, Relation Therapeutics, FL82, Empress Therapeutics, IntrECate Biotherapeutics, Senda Biosciences and Dahlia Biosciences. C.A.B. reports compensation for consulting and/or SAB membership from Catamaran Bio, DeepCell, Immunebridge and Revelation Biosciences. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Yvan Saeys and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Additional analyses of exhausted intratumoral SCC T cells.

a) UMAP projection of all T cells from the dataset published by Ji, et al.29, colored by author-annotated T cell subtype. b) Dot plot depicting average and percent expression of the exhaustion signature score by author-annotated T cell subtypes. c) ROC curves depicting the ability of each cluster from the single-cell T cell object (left) or Scriabin generated T cell-CD1C+ DC CCIM (right) to be classified as exhausted or non-exhausted. Each line corresponds to a single cluster. The diagonal black line corresponds to an AUC = 0.5, where there is no predictive power of classification. AUC = 0, the cluster can be perfectly classified as non-exhausted; AUC = 1, the cluster can be perfectly classified as exhausted.

Extended Data Fig. 2 Comparison of Scriabin to agglomerative CCC analysis techniques and validation with spatial transcriptomic data.

a) Runtime of Scriabin and five published CCC methods on the 10X PBMC 5k dataset. For each dataset size, the dataset was randomly subsampled to six different indicated sizes and the same subsampled dataset was used for all methods. b) Runtime of Scriabin and Connectome comparative workflows. The 10X PBMC 5k and 10k datasets were merged into a single dataset which was subsampled as in (a), and the comparative workflows performed between cells from the 5k vs. 10k dataset. Six different dataset sizes were compared. c) Jaccard index heatmaps depicting the degree of overlap in the top 1,000 ligand-receptor CCC edges from each method-resource pair for four datasets: 10X PBMC 5k, Fluidigm C1 pancreas islets21,33, Smart-seq2 uterine decidua13, and Smart-seq2 HNSCC8. d, e) The procedure described in Fig. 3a was repeated for 11 datasets, and the median distance quantile of a percentile of the most highly interacting cell-cell pairs was calculated using real cell distances relative to randomly permuted cell distances. d) Each facet shows the median distance quantile of the top 0.1%, 0.5%, 2.5%, 5%, 10%, and 20% most highly interacting cell-cell pairs. e) Each facet shows the median distance quantile of cell-cell pairs within the range of interaction quantile shown. In each facet, an exact two-sided p-value from the Wilcoxon rank-sum test is shown.

Extended Data Fig. 3 Flow cytometry and transcriptional analysis of B and NK cell transfection and co-culture.

a) Flow cytometry gating scheme used to identify B and NK cells. b) Scatter plots of flow cytometry data showing expression of CD40 and CD40L by B cells and NK cells. Left: B cells and NK cells transfected with GFP-encoding mRNA at start of co-culture. Middle: B cells transfected with CD40-encoding mRNA and NK cells transfected with CD40L-encoding mRNA at start of co-culture. Right: B cells transfected with CD40-encoding mRNA and NK cells transfected with CD40L-encoding mRNA at end of co-culture. For (a-b), percentages of the parent gate are shown for each gate. c) UMAP projections of full dataset colored by cell condition of origin (left) or annotated cell type (right). d) Dot plot depicting average and percent expression of exogenous mRNAs in the four co-cultures.

Extended Data Fig. 4 Analysis of CCC between CD40LG-transfected NK cells and CD40-transfected B cells with Connectome and NicheNet.

a) Circos plot summarizing Connectome’s38 results of significantly differentially-expressed ligand-receptor pair edges between the CD40LG-CD40 transfected condition (shades of red) and GFP-GFP transfected condition (shades of blue). CCC is analyzed between ligands expressed by sender NK cells (bottom) and receptors expressed by receiver B cells (top). b) Dot plot depicting percentage and average expression of differentially-expressed receptors by B cells (top) and ligands by NK cells (bottom) returned by Connectome’s DifferentialConnectome workflow. c) NicheNet20 was applied to predict ligand activities in B cells between the CD40LG-CD40 transfected condition and the GFP-GFP transfected condition. The bar plot depicts pearson coefficient outputs of NicheNet for this analysis. d) Dot plot depicting percentage and average expression of potentially-active ligands shown in (c) by NK cells.

Extended Data Fig. 5 Additional analyses of the scRNA-seq dataset of leprosy granulomas.

a) Bar graph depicting cell proportions per granuloma in the dataset of Ma, et al.41. Author-provided cell type annotations are used for analysis. b) Subclustering resolutions of T cells (left) and myeloid cells (right) required for comparative CCC analysis by agglomerative methods. Pink bars indicate the percentage of subclusters containing at least one cell from an LL granuloma and one cell from an RR granuloma. Blue bars indicate the percentage of subclusters containing at least one cell from all nine analyzed granulomas. c) NicheNet20 was applied to predict ligand activities in myeloid cells between RR granulomas relative to LL granulomas. The bar plot depicts pearson coefficient outputs of NicheNet for this analysis. d) UMAP projections of T cells (top) and myeloid cells (bottom) colored by author-generated subcluster cell type annotation (left), granuloma type (middle), or if the cell falls into a cluster 2 perturbed bin (right; see Fig. 4f). e) We applied a binomial test to determine if cells from a cluster 2 perturbed bin were significantly enriched or depleted in any T cell or myeloid cell subcluster. The bar plot depicts the -log(p-value) of the exact binomial test. When p < 0.05, the bars are colored to indicate if perturbed cells are either enriched (red) or depleted (blue) from the cluster. The dotted line indicates the point at which p = 0.05. Calculated p-values are two-sided.

Extended Data Fig. 6 Co-expressed interaction programs in intestinal development.

a) Scatter plot depicting expression of LEC marker LYVE1 and RSPO3. Shown are Pearson’s r, and an exact two-sided P value. b) UMAP projections of ligand (shades of purple) or receptor (shades of green) expression in 3 gut endothelial cell-specific modules. c) UMAP projection of gut endothelial cells colored by expression of ligands in the interaction programs depicted in (b). d) Dot plot depicting the expression fold-change and Bonferroni-corrected Wilcoxon rank-sum test 2-sided p-values of interaction program expression in each anatomical location. e) Intramodular connectivity scores for each ligand-receptor pair in each anatomical location for the module indicated by the arrow in (d). The black arrow in (e) indicates the genes whose average and percent expression are plotted to the right. Shown is an exact two-sided Bonferroni-corrected p-value from the Wilcoxon rank-sum test as described in panel (d). f-g) Connectome38 was used to analyze CCC in the human intestinal development dataset49 using author-annotated cell types for aggregation. Results are plotted for communication between gut endothelial cells (senders) and intestinal epithelial cells (receivers; f) or between fibroblasts (senders) and intestinal epithelial cells (receivers; g).

Extended Data Fig. 7 scRNA-seq dataset of SARS-CoV-2 infected HBECs.

UMAP projections of 64,008 cells from the dataset published by Ravindra, et al.58 colored by time point (a), annotated cell type (b), or the percentage of UMIs per cell of SARS-CoV-2 origin (c).

Extended Data Fig. 8 Parameter tuning for ligand activity ranking and interaction program discovery workflows.

a) Heatmaps depicting Jaccard overlap index between DE testing results from CCIMs constructed with 217 different combinations of ligand activity ranking parameters. Three different datasets were used for testing: a pancreas islet dataset21,33, a uterine decidua dataset13, and a dataset of HNSCC8. b-d) 217 different parameter combinations were used to analyze CCC between NK cells transfected with CD40L-encoding mRNA and B cells transfected with CD40-encoding mRNA. Ligand activity-weighted CCIMs were calculated from each of these combinations and differential expression testing performed to identify which parameter combinations returned CD40L-CD40 as a differential edge with the highest specificity. b) Box plot depicting the difference between the log(fold-change) for CD40L-CD40 and the mean log(fold-change) for all other ligand-receptor pairs, with and without application of ligand activity ranking. n = 1 for analysis without ligand activity ranking; n = 216 for with ligand activity ranking. c) β coefficients and p-values from multiple regression analysis modeling the impact of each ligand ranking parameter on relative predictive power for the CD40L-CD40 edge. d) Scatter plots depicting relative predictive power for the CD40L-CD40 edge for all combinations of ligand ranking parameters. The mean for each parameter is shown within the plot. e) Example ligand activity distributions to aid in selection of the appropriate Pearson coefficient threshold. Generally, ligand activity coefficients form a right-skewed distribution, similar to the distributions shown here. The right tails of these distributions represent the putative biological activity and are the coefficients that should be used for CCIM weighting. We therefore encourage users to consider the number of ligands that are expected to display biological activity and the number of cells that are expected to have downstream signaling induced by those ligands. If there are very few ligands expected to be biologically active, and only a subset of cells responding to them, this threshold should be increased to include less of the right tail of the distribution. f) The interaction program discovery workflow was repeated on 35 random subsamples of the inDrop panc8 dataset21,34, using 19 different R2 thresholds to define the appropriate softPower parameter. Scatter plots depict association between R2 threshold and (clockwise from top left): recommended softPower, percentage of identified programs that failed significance testing, percentage of programs composed of only 1 ligand or receptor, and the average number of ligands and receptors composing a program. Shown are Pearson’s r, and an exact two-sided P value.

Extended Data Fig. 9 Highly perturbed samples require a higher degree of aggregation for dataset alignment.

A toy dataset of peripheral blood monocytes from a longitudinal dataset was analyzed. a) UMAP projection colored by time point. b-d) UMAP projections (left) colored by cluster identity, and bar plot depicting per timepoint cluster membership in the cluster principally occupied by sample Week 04 (right). Cluster resolutions: 1 (default, b), 0.3 (c), 0.05 (d).

Extended Data Fig. 10 Robustness analysis of Scriabin’s binning workflow.

a-d) Mouse and human PBMC scRNA-seq datasets from 10X Genomics were analyzed. a) UMAP projections of mouse and human PBMCs colored by the sample of origin (left) and by manually-annotated cell types (right). b) Heatmap depicting overlap between bin identity and cell type annotations. Each row sums to 100%, and the annotations at left show the number of cells within each bin and maximum degree of overlap of each bin with a given cell type identity (ie. the highest value in each row). c) UMAP projection highlighting cells in bin #191. d) Bar plot depicting differentially-expressed genes in bin #191 relative to other B cells shared between the human and mouse cells in bin #191. Differential expression tests were run individually for human and mouse cells. e-g) A toy dataset of ~14,000 peripheral blood mononuclear cells (PBMCs) with nine sub-datasets was analyzed. e) Density plot depicting the number of cells in each bin. The median bin size in this analysis is 25 cells. f) As in (b) An SNN graph was used to assess cell-cell connectivity for the binning workflow. Cell type annotations are transferred from a reference dataset and are thus orthogonal to the data used to generate the bins. g) Dot plot depicting the cell type annotations and scores for the anchor pairs used to generate the bins depicted in (f).

Supplementary information

Supplementary Information

Supplementary Text and Supplementary Tables 1 and 2

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wilk, A.J., Shalek, A.K., Holmes, S. et al. Comparative analysis of cell–cell communication at single-cell resolution. Nat Biotechnol 42, 470–483 (2024). https://doi.org/10.1038/s41587-023-01782-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41587-023-01782-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing