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Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics

Abstract

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space—termed neighborhoods—that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

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Fig. 1: Method schematic.
Fig. 2: Power and signal recovery assessed in simulation.
Fig. 3: CNA captures Notch activation gradient in RA dataset.
Fig. 4: CNA refines sepsis-associated blood cell populations.
Fig. 5: CNA characterizes biologically meaningful structure in TB dataset.
Fig. 6: CNA improves characterization of diverse sample attributes in a TB cohort.

Data availability

All data analyzed during this study are available in three previously published articles6,7,8. Source data are provided with this paper.

Code availability

An open-source repository containing code for running CNA is available at https://github.com/immunogenomics/cna; an open-source repository containing code underlying all figures and tables is available at https://github.com/immunogenomics/cna-display; and an open-source repository containing code underlying all simulations is available at https://github.com/immunogenomics/cna-sim. Source data are provided with this paper.

References

  1. 1.

    Kashima, Y. et al. Single-cell sequencing techniques from individual to multiomics analyses. Exp. Mol. Med. 52, 1419–1427 (2020).

    CAS  Article  Google Scholar 

  2. 2.

    Andrews, T. S. et al. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat. Protoc. 16, 1–9 (2021).

    CAS  Article  Google Scholar 

  3. 3.

    Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  Google Scholar 

  4. 4.

    Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    CAS  Article  Google Scholar 

  5. 5.

    Burkhardt, D. B. et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat. Biotechnol. 39, 619–629 (2021).

  6. 6.

    Nathan, A. et al. Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Nat. Immunol. 22, 781–793 (2021).

    CAS  Article  Google Scholar 

  7. 7.

    Reyes, M. et al. An immune-cell signature of bacterial sepsis. Nat. Med. 26, 333–340 (2020).

    CAS  Article  Google Scholar 

  8. 8.

    Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).

    CAS  Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  10. 10.

    Butler, A. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Haghverdi, L. et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Liu, J. et al. Jointly defining cell types from multiple single-cell datasets using LIGER. Nat. Protoc. 15, 3632–3662 (2020).

    CAS  Article  Google Scholar 

  13. 13.

    Fonseka, C. Y. et al. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci. Transl. Med. 10, eaaq0305 (2018).

  14. 14.

    Millard, N. et al. Maximizing statistical power to detect clinically associated cell states with scPOST. Preprint at https://www.biorxiv.org/content/10.1101/2020.11.23.390682v1 (2020).

  15. 15.

    Liu, Z. et al. Notch signaling in postnatal joint chondrocytes, but not subchondral osteoblasts, is required for articular cartilage and joint maintenance. Osteoarthritis Cartilage 24, 740–751 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Wang, X. & Astrof, S. Neural crest cell-autonomous roles of fibronectin in cardiovascular development. Development 143, 88–100 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).

    Article  Google Scholar 

  18. 18.

    Sanlioglu, S. et al. Lipopolysaccharide induces Rac1-dependent reactive oxygen species formation and coordinates tumor necrosis factor-α secretion through IKK regulation of NF-κB. J. Biol. Chem. 276, 30188–30198 (2001).

    CAS  Article  Google Scholar 

  19. 19.

    Pan, C. et al. Suppression of the RAC1/MLK3/p38 signaling pathway by β-elemene alleviates sepsis-associated encephalopathy in mice. Front. Neurosci. 13, 358 (2019).

  20. 20.

    von Knethen, A. & Brüne, B. Histone deacetylation inhibitors as therapy concept in sepsis. Int. J. Mol. Sci. 20, 346 (2019).

  21. 21.

    Wu, H.-P. et al. Serial increase of IL-12 response and human leukocyte antigen-DR expression in severe sepsis survivors. Crit. Care 15, R224 (2011).

    Article  Google Scholar 

  22. 22.

    Steinhauser, M. L. et al. Multiple roles for IL-12 in a model of acute septic peritonitis. J. Immunol. 162, 5437–5443 (1999).

    CAS  PubMed  Google Scholar 

  23. 23.

    Oliveira, N. M. et al. Sepsis induces telomere shortening: a potential mechanism responsible for delayed pathophysiological events in sepsis survivors? Mol. Med. 22, 886–891 (2016).

    CAS  Article  Google Scholar 

  24. 24.

    Gutierrez-Arcelus, M. et al. Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions. Nat. Commun. 10, 687 (2019).

    CAS  Article  Google Scholar 

  25. 25.

    Cano-Gamez, E. et al. Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines. Nat. Commun. 11, 1801 (2020).

    CAS  Article  Google Scholar 

  26. 26.

    Luecken, M. et al. Benchmarking atlas-level data integration in single-cell genomics. Preprint at https://www.biorxiv.org/content/10.1101/2020.05.22.111161v1 (2020).

  27. 27.

    Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  Article  Google Scholar 

  28. 28.

    Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626–638 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Silva, C. L. et al. Cytotoxic T cells and mycobacteria. FEMS Microbiol. Lett. 197, 11–18 (2001).

    CAS  Article  Google Scholar 

  30. 30.

    Li, M. et al. Age related human T cell subset evolution and senescence. Immun. Ageing 16, 24 (2019).

    Article  Google Scholar 

  31. 31.

    Shirai, T. et al. TH1-biased immunity induced by exposure to Antarctic winter. J. Allergy Clin. Immunol. 111, 1353–1360 (2003).

    CAS  Article  Google Scholar 

  32. 32.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at https://www.biorxiv.org/content/10.1101/060012v2 (2021).

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Acknowledgements

We thank A. Gupta, D. Kotliar, Y. Luo, N. Millard, M. Reyes, S. Sakaue, F. Zhang, the members of the CGTA discussion group and the Raychaudhuri lab for helpful discussions and feedback. This work is supported, in part, by funding from the National Institutes of Health (NIH) including UH2AR067677, U19 AI111224, U01 HG009379 and 1R01AR063759. S.A. was supported by the Swiss National Science Foundation postdoctoral mobility fellowships P2ELP3_172101 and P400PB_183823 and NIH grant T32HG010464. L.R. was supported, in part, by NIH 5T32HG2295-17. J.K. was supported by NIH grant T32GM007753. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.

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Contributions

Y.A.R., L.R. and S.R. designed and conceptualized the study. Y.A.R. and L.R. designed and implemented the algorithm and performed simulations. Y.A.R., L.R. and J.K. performed analysis of real data. A.N., S.A. and I.K. provided input on methodologic design and real data analysis. D.B.M., M.M., A.N. and I.K. provided dataset-specific expertise. Y.A.R., L.R. and S.R. wrote the manuscript with input from the remaining authors.

Corresponding author

Correspondence to Soumya Raychaudhuri.

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Competing interests

S.R. serves as a consultant for Gilead, Pfizer, Janssen and Rheos Medicines and is a founder of Mestag Therapeutics. I.K. serves as a consultant for Mestag Therapeutics. The other authors declare no competing interests.

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Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Tables 1–20 and Supplementary Figs. 1–13

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Source Data Fig. 2

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Source Data Fig. 3

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Source Data Fig. 5

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Source Data Fig. 6

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Reshef, Y.A., Rumker, L., Kang, J.B. et al. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01066-4

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