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:

Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition

Nature Biotechnology (2024)Cite this article

Subjects

Abstract

Tissue-level and organism-level biological processes often involve the coordinated action of multiple distinct cell types. The recent application of single-cell assays to many individuals should enable the study of how donor-level variation in one cell type is linked to that in other cell types. Here we introduce a computational approach called single-cell interpretable tensor decomposition (scITD) to identify common axes of interindividual variation by considering joint expression variation across multiple cell types. scITD combines expression matrices from each cell type into a higher-order matrix and factorizes the result using the Tucker tensor decomposition. Applying scITD to single-cell RNA-sequencing data on 115 persons with lupus and 83 persons with coronavirus disease 2019, we identify patterns of coordinated cellular activity linked to disease severity and specific phenotypes, such as lupus nephritis. scITD results also implicate specific signaling pathways likely mediating coordination between cell types. Overall, scITD offers a tool for understanding the covariation of cell states across individuals, which can yield insights into the complex processes that define and stratify disease.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: General overview of scITD and demonstration of functionality.
Fig. 2: SLE scRNA-seq dataset overview and main scITD analysis.
Fig. 3: Comparing scITD to other methods using simulated data and SLE dataset.
Fig. 4: Example LR interaction and genetic evidence for its causal role in influencing a multicellular pattern.
Fig. 5: Multicellular pattern that stratifies persons with COVID-19 by disease severity.

Similar content being viewed by others

Data availability

The IFNβ stimulation dataset7 can be obtained from the Gene Expression Omnibus (GEO) under accession number GSE96583. The count matrices for the Perez et al. SLE scRNA-seq dataset9 can be obtained from the GEO under accession number GSE174188. The Nehar-Belaid et al. pSLE scRNA dataset11 can be obtained from the GEO under accession number GSE135779. The Stephenson et al. COVID-19 dataset is publicly available at https://www.covid19cellatlas.org/index.patient.html, titled ‘COVID-19 PBMC Ncl-Cambridge-UCL’. The van der Wijst et al. COVID-19 dataset can be found at https://cellxgene.cziscience.com/collections/7d7cabfd-1d1f-40af-96b7-26a0825a306d. The CellChat LR pair database can be found at https://github.com/LewisLabUCSD/Ligand-Receptor-Pairs/blob/master/Human/Human-2020-Jin-LR-pairs.csv. NicheNet regulatory potential scores were obtained from https://zenodo.org/record/3260758/files/ligand_target_matrix.rds. NicheNet ligand treatment datasets were obtained from https://zenodo.org/record/3260758/files/expression_settings.rds.

Code availability

Our computational method, scITD55, can be found on GitHub (https://github.com/kharchenkolab/scITD) or the Comprehensive R Archive Network (CRAN) (https://cloud.r-project.org/web/packages/scITD/index.html). A walkthrough tutorial for using scITD is also available at http://pklab.med.harvard.edu/jonathan/. The code used to produce all figures in this paper can be found at https://github.com/j-mitchel/scITD-Analysis/tree/main/figure_generation.

References

  1. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ramirez Flores, R. O., Lanzer, J. D., Dimitrov, D., Velten, B. & Saez-Rodriguez, J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife 12, e93161 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tucker, L. R. Some mathematical notes on three-mode factor analysis. Psychometrika 31, 279–311 (1966).

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  7. Targ, S. Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96583 (2017).

  8. Perez, R. K. et al. Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus. Science 376, eabf1970 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Perez, R. K. et al. Multiplexed scRNA-seq reveals the cellular and genetic correlates of systemic lupus erythematosus. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174188 (2021).

  10. Nehar-Belaid, D. et al. Mapping systemic lupus erythematosus heterogeneity at the single-cell level. Nat. Immunol. 21, 1094–1106 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nehar-Belaid, D., Flynn, W. F., Banchereau, J., Pascual, V. & Robson, P. A single cell approach to map cellular subsets involved in systemic lupus erythematosus (SLE) heterogeneity. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135779 (2020).

  12. Hooks, J. J. et al. Immune interferon in the circulation of patients with autoimmune disease. New Engl. J. Med. 301, 5–8 (1979).

    Article  CAS  PubMed  Google Scholar 

  13. Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kirou, K. A. et al. Activation of the interferon-α pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum. 52, 1491–1503 (2005).

    Article  CAS  PubMed  Google Scholar 

  15. Nikpour, M., Dempsey, A. A., Urowitz, M. B., Gladman, D. D. & Barnes, D. A. Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosus. Ann. Rheum. Dis. 67, 1069–1075 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Weckerle, C. E. et al. Network analysis of associations between serum interferon α activity, autoantibodies, and clinical features in systemic lupus erythematosus. Arthritis Rheum. 63, 1044–1053 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Baechler, E. C. et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl Acad. Sci. USA 100, 2610–2615 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Crow, M. K., Kirou, K. A. & Wohlgemuth, J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 36, 481–490 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Feng, X. et al. Association of increased interferon-inducible gene expression with disease activity and lupus nephritis in patients with systemic lupus erythematosus. Arthritis Rheum. 54, 2951–2962 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Iwata, Y. et al. p38 mitogen-activated protein kinase contributes to autoimmune renal injury in MRL-Faslpr mice. J. Am. Soc. Nephrol. 14, 57–67 (2003).

    Article  CAS  PubMed  Google Scholar 

  21. Jin, N. et al. The selective p38 mitogen-activated protein kinase inhibitor, SB203580, improves renal disease in MRL/lpr mouse model of systemic lupus. Int. Immunopharmacol. 11, 1319–1326 (2011).

    Article  CAS  PubMed  Google Scholar 

  22. Azodi, C. B., Zappia, L., Oshlack, A. & McCarthy, D. J. splatPop: simulating population scale single-cell RNA sequencing data. Genome Biol. 22, 341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Klumpe, H. et al. The context-dependent, combinatorial logic of BMP signaling. Cell Syst. 13, 388–407 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 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 

  25. Dodeller, F. & Schulze-Koops, H. The p38 mitogen-activated protein kinase signaling cascade in CD4 T cells. Arthritis Res. Ther. 8, 205 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wikenheiser, D. J. & Stumhofer, J. S. ICOS co-stimulation: friend or foe?. Front. Immunol. 7, 304 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Katan, M. B. Apolipoprotein E isoforms, serum cholesterol and cancer. Lancet 1, 507–508 (1986).

    Article  CAS  PubMed  Google Scholar 

  28. Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. Burgess, S., Small, D. S. & Thompson, S. G. A review of instrumental variable estimators for Mendelian randomization. Stat. Methods Med. Res. 26, 2333–2355 (2017).

    Article  PubMed  Google Scholar 

  30. Fox, J., Kleiber, C., Zeileis, A. & Kuschnig, N. ivreg: instrumental-variables regression by ‘2SLS’, ‘2SM’, or ‘2SMM’, with diagnostics. The Comprehensive R Archive Network https://zeileis.github.io/ivreg/ (2023).

  31. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Quach, H. et al. Genetic adaptation and neandertal admixture shaped the immune system of human populations. Cell 167, 643–656 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53, 1300–1310 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chun, S. et al. Limited statistical evidence for shared genetic effects of eQTLs and autoimmune disease-associated loci in three major immune cell types. Nat. Genet. 49, 600–605 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Stephenson, E. et al. Single-cell multi-omics analysis of the immune response in COVID-19. Nat. Med. 27, 904–916 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Cruikshank, W. W., Berman, J. S., Theodore, A. C., Bernardo, J. & Center, D. M. Lymphokine activation of T4+ T lymphocytes and monocytes. J. Immunol. 138, 3817–3823 (1987).

    Article  CAS  PubMed  Google Scholar 

  37. Winkler, M. S. et al. Human leucocyte antigen (HLA-DR) gene expression is reduced in sepsis and correlates with impaired TNFα response: a diagnostic tool for immunosuppression? PLoS ONE 12, e0182427 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Olwal, C. O. et al. Parallels in sepsis and COVID-19 conditions: implications for managing severe COVID-19. Front. Immunol. 12, 91 (2021).

    Article  Google Scholar 

  39. Giamarellos-Bourboulis, E. J. et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe 27, 992–1000 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Spinetti, T. et al. Reduced monocytic human leukocyte antigen-DR expression indicates immunosuppression in critically ill COVID-19 patients. Anesth. Analg. 131, 993–999 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. van der Wijst, M. G. P. et al. Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19. Sci. Transl. Med. 13, eabh2624 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Barkas, N., Petukhov, V., Kharchenko, P. V. & Biederstedt, E. pagoda2: single cell analysis and differential expression. The Comprehensive R Archive Network https://github.com/kharchenkolab/pagoda2 (2021).

  44. Li, J., Bien, J. & Wells, M. T. rTensor: an R package for multidimensional array (tensor) unfolding, multiplication, and decomposition. J. Stat. Softw. 87, 1–31 (2018).

    Article  CAS  Google Scholar 

  45. Sheehan, B. N. & Saad, Y. Higher order orthogonal iteration of tensors (HOOI) and its relation to PCA and GLRAM. In Proceedings of the 2007 SIAM International Conference on Data Mining (eds Apte, C., Liu, B., Parthasarathy, S. & Skillicorn, D) (Society for Industrial and Applied Mathematics, 2007).

  46. Kolda, T. G. & Bader, B. W. Tensor decompositions and applications. SIAM Rev. 51, 455–500 (2009).

    Article  Google Scholar 

  47. Unkel, S., Hannachi, A., Trendafilov, N. T. & Jolliffe, I. T. Independent component analysis for three-way data with an application from atmospheric. J. Agric. Biol. Environ. Stat. 16, 319–338 (2011).

    Article  Google Scholar 

  48. Zhou, G. & Cichocki, A. Fast and unique tucker decompositions via multiway blind source separation. Bull. Pol. Acad. Sci. Tech. Sci. 60, 389–405 (2012).

    Google Scholar 

  49. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  51. Badea, L. Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. Pac. Symp. Biocomput. 2008, 267–278 (2008).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Mitchel, J., Biederstedt, E. & Kharchenko, P. V. Single-cell analysis of inter-individual variability by interpretable tensor decomposition. GitHub https://github.com/kharchenkolab/scITD (2024).

Download references

Acknowledgements

We thank S. Sunyaev (Harvard Medical School) for advice with the eQTL colocalization analysis and A. Igolkina (Gregor Mendel Institute) for advice on the cell proportion analysis.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Jonathan Mitchel, Evan Biederstedt & Peter V. Kharchenko

  2. Program in Health Sciences and Technology, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA

    Jonathan Mitchel

  3. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    M. Grace Gordon, Raymund Bueno & Chun Jimmie Ye

  4. UCSF Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    M. Grace Gordon & Raymund Bueno

  5. School of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Richard K. Perez

  6. San Diego Institute of Science, Altos Labs, San Diego, CA, USA

    Peter V. Kharchenko

Authors
  1. Jonathan Mitchel
    View author publications

    Search author on:PubMed Google Scholar

  2. M. Grace Gordon
    View author publications

    Search author on:PubMed Google Scholar

  3. Richard K. Perez
    View author publications

    Search author on:PubMed Google Scholar

  4. Evan Biederstedt
    View author publications

    Search author on:PubMed Google Scholar

  5. Raymund Bueno
    View author publications

    Search author on:PubMed Google Scholar

  6. Chun Jimmie Ye
    View author publications

    Search author on:PubMed Google Scholar

  7. Peter V. Kharchenko
    View author publications

    Search author on:PubMed Google Scholar

Contributions

P.V.K. formulated the study and, with J.M., developed the overall approach. J.M. developed the detailed algorithms with advice from P.V.K. and assistance from E.B. J.M., C.J.Y. and P.V.K. worked on the interpretation of results, with help from M.G.G., R.K.P. and R.B. J.M. and P.V.K. drafted the paper, with contributions from C.J.Y. and input from the other authors.

Corresponding authors

Correspondence to Chun Jimmie Ye or Peter V. Kharchenko.

Ethics declarations

Competing interests

P.V.K. is an employee of Altos Labs. C.J.Y. is a scientific advisory board member for and holds equity in Related Sciences and ImmunAI, is a consultant for and holds equity in Maze Therapeutics and is a consultant for Trex Bio. C.J.Y. has received research support from Chan Zuckerberg Initiative, Chan Zuckerberg Biohub and Genentech. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks the anonymous reviewers 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.

Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Notes.

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

Mitchel, J., Gordon, M.G., Perez, R.K. et al. Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02411-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41587-024-02411-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