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:

Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability

Nature Genetics volume 55, pages 2200–2210 (2023)Cite this article

Subjects

Abstract

In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body’s own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent (‘dynamic’) chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant (‘cs-invariant’) peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Overview of approach: identification of dynamic regulatory elements from single-nucleus multimodal data marks genomic loci enriched for disease heritability.
Fig. 2: Dynamic peaks implicate cell-type-specific genes and regulatory processes.
Fig. 3: Dynamic peaks are more strongly enriched for disease heritability than cs-invariant peaks.
Fig. 4: Dynamic peaks help distinguish disease-critical T cell states for immune-mediated disease.
Fig. 5: Dynamic peaks help distinguish disease-critical myeloid cell states for immune-mediated disease.
Fig. 6: Autoimmune disease heritability in PBMC cell states.

Similar content being viewed by others

Data availability

This manuscript uses multimodal single-nucleus data from 12 donors that have been deposited at the NIH Database of Genotypes and Phenotypes (dbGaP accession number: phs003417.v1) and the Gene Expression Omnibus (GEO accession number: GSE243917). All studies used for corroboration from the GWAS Catalog (https://www.ebi.ac.uk/gwas/studies) can be found in Supplementary Data 15 and 16, and they have the following accession numbers: GCST90132222, GCST006048, GCST005569, GCST90013534, GCST002318 and GCST90018959. This work uses summary statistics from the UK Biobank study (http://www.ukbiobank.ac.uk). The summary statistics for UK Biobank used in this paper are available at https://data.broadinstitute.org/alkesgroup/UKBB. The 1000 Genomics Project Phase 3 data are available at ftp://ftp.1000genomes.ebi.ac.uk/vol1.ftp/release/2013050. We also used a publicly available PBMC 10x single-cell multimodal ATAC and gene expression dataset (https://cf.10xgenomics.com/samples/cell-arc/2.0.0/pbmc_granulocyte_sorted_10k/pbmc_granulocyte_sorted_10k_web_summary.html).

Code availability

This work uses the S-LDSC software (https://github.com/bulik/ldsc) to process GWAS summary statistics, the Harmony package for single-cell batch correction, and the scanpy package for single-cell data analysis. Code related to processing of the multimodal single-nucleus data, post-hoc heritability analyses, and generation of the tables and figures in this manuscript is available on GitHub (https://github.com/immunogenomics/sc-h2; https://doi.org/10.5281/zenodo.8329597)71. Python version 3.7.12 and R version 4.1.1 were used for all analyses in this study.

References

  1. Szekanecz, Z. et al. Autoinflammation and autoimmunity across rheumatic and musculoskeletal diseases. Nat. Rev. Rheumatol. 17, 585–595 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Rosenblum, M. D., Remedios, K. A. & Abbas, A. K. Mechanisms of human autoimmunity. J. Clin. Invest. 125, 2228–2233 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Wang, L., Wang, F.-S. & Gershwin, M. E. Human autoimmune diseases: a comprehensive update. J. Intern. Med. 278, 369–395 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Rao, D. A. et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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  PubMed  PubMed Central  Google Scholar 

  6. Argyriou, A. et al. Single cell sequencing identifies clonally expanded synovial CD4+ Tph cells expressing GPR56 in rheumatoid arthritis. Nat. Commun. 13, 4046 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jonsson, A. H. et al. Granzyme K+ CD8 T cells form a core population in inflamed human tissue. Sci. Transl. Med. 14, eabo0686 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang, F. et al. IFN-γ and TNF-α drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation. Genome Med. 13, 64 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Uzzan, M. et al. Ulcerative colitis is characterized by a plasmablast-skewed humoral response associated with disease activity. Nat. Med. 28, 766–779 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Christophersen, A. et al. Distinct phenotype of CD4+ T cells driving celiac disease identified in multiple autoimmune conditions. Nat. Med. 25, 734–737 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Akama-Garren, E. H. et al. Follicular T cells are clonally and transcriptionally distinct in B cell-driven mouse autoimmune disease. Nat. Commun. 12, 6687 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jaeger, N. et al. Single-cell analyses of Crohn’s disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions. Nat. Commun. 12, 1921 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Arazi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nakamizo, S. et al. Single-cell analysis of human skin identifies CD14+ Type 3 dendritic cells co-producing IL1B and IL23A in psoriasis. J. Exp. Med. 218, e20202345 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu, H. et al. Subsets of mononuclear phagocytes are enriched in the inflamed colons of patients with IBD. BMC Immunol. 20, 42 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Mitsialis, V. et al. Single-cell analyses of colon and blood reveal distinct immune cell signatures of ulcerative colitis and Crohn’s disease. Gastroenterology 159, 591–608 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Korsunsky, I. et al. Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases. Med. 3, 481–518.e14 (2022).

    Article  CAS  PubMed  Google Scholar 

  18. Seldin, M. F. The genetics of human autoimmune disease: a perspective on progress in the field and future directions. J. Autoimmun. 64, 1–12 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Stahl, E. A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Redondo, M. J., Jeffrey, J., Fain, P. R., Eisenbarth, G. S. & Orban, T. Concordance for islet autoimmunity among monozygotic twins. N. Engl. J. Med. 359, 2849–2850 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Guerra, S. G., Vyse, T. J. & Cunninghame Graham, D. S. The genetics of lupus: a functional perspective. Arthritis Res. Ther. 14, 211 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gordon, H., Trier Moller, F., Andersen, V. & Harbord, M. Heritability in inflammatory bowel disease: from the first twin study to genome-wide association studies. Inflamm. Bowel Dis. 21, 1428–1434 (2015).

    PubMed  Google Scholar 

  23. Selmi, C. et al. Primary biliary cirrhosis in monozygotic and dizygotic twins: genetics, epigenetics, and environment. Gastroenterology 127, 485–492 (2004).

    Article  PubMed  Google Scholar 

  24. Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Raychaudhuri, S. et al. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang, Y.-F. et al. Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups. Nat. Commun. 12, 772 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Patsopoulos, N. A. et al. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).

    Article  CAS  Google Scholar 

  28. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Amariuta, T. et al. Impact: genomic annotation of cell-state-specific regulatory elements inferred from the epigenome of bound transcription factors. Am. J. Hum. Genet. 104, 879–895 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hu, X. et al. Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Farh, K. K.-H. et al. Genetic and epigenetic fine-mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Trynka, G. et al. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat. Genet. 45, 124–130 (2012).

    Article  PubMed  Google Scholar 

  36. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jagadeesh, K. A. et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics. Nat. Genet. 54, 1479–1492 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jia, P., Hu, R., Yan, F., Dai, Y. & Zhao, Z. SCGWAS: landscape of trait–cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies. Genome Biol. 23, 220 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Weinand, K. et al. The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis. Preprint at bioRxiv https://doi.org/10.1101/2023.04.07.536026 (2023).

  43. Yenyuwadee, S., Sanchez-Trincado Lopez, J. L., Shah, R., Rosato, P. C. & Boussiotis, V. A. The evolving role of tissue-resident memory T cells in infections and cancer. Sci. Adv. 8, eabo5871 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Clark, R. A. Resident memory T cells in human health and disease. Sci. Transl. Med. 7, 269rv1 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Zhang, F. et al. Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes. Nature https://doi.org/10.1038/s41586-023-06708-y (2023).

  46. 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 

  47. Jostins, L. et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ge, X. et al. Functional genomics atlas of synovial fibroblasts defining rheumatoid arthritis heritability. Genome Biol. 22, 247 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kang, J. B. et al. Efficient and precise single-cell reference atlas mapping with Symphony. Nat. Commun. 12, 5890 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gellatly, K. J. et al. scRNA-seq of human vitiligo reveals complex networks of subclinical immune activation and a role for CCR5 in Treg function. Sci. Transl. Med. 13, eabd8995 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Soskic, B. et al. Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation. Nat. Genet. 54, 817–826 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ha, E., Bae, S.-C. & Kim, K. Large-scale meta-analysis across East Asian and European populations updated genetic architecture and variant-driven biology of rheumatoid arthritis, identifying 11 novel susceptibility loci. Ann. Rheum. Dis. 80, 558–565 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Eyre, S. et al. High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ishigaki, K. et al. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat. Genet. 54, 1640–1651 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 53, 1415–1424 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Kim, K. et al. High-density genotyping of immune loci in Koreans and Europeans identifies eight new rheumatoid arthritis risk loci. Ann. Rheum. Dis. 74, e13 (2015).

    Article  PubMed  Google Scholar 

  57. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Yang, S.-K. et al. Genome-wide association study of Crohn’s disease in Koreans revealed three new susceptibility loci and common attributes of genetic susceptibility across ethnic populations. Gut 63, 80–87 (2013).

    Article  PubMed  Google Scholar 

  59. PBMCs from a healthy donor - granulocytes removed through cell sorting (10k). Single Cell Multiome ATAC + Gene Expression Dataset by Cell Ranger ARC 2.0.0. 10x Genomics (2021).

  60. Donlin, L. T. et al. Methods for high-dimensional analysis of cells dissociated from cryopreserved synovial tissue. Arthritis Res. Ther. 20, 139 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Amemiya, H. M., Kundaje, A. & Boyle, A. P. The encode blacklist: identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Granja, J. M. et al. ARCHR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Nathan, A., Baglaenko, Y., Fonseka, C. Y., Beynor, J. I. & Raychaudhuri, S. Multimodal single-cell approaches shed light on T cell heterogeneity. Curr. Opin. Immunol. 61, 17–25 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. meta.R. GitHub https://github.com/cran/rmeta/blob/master/R/meta.R (2018).

  66. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Human PBMC. Azimuth https://app.azimuth.hubmapconsortium.org/app/human-pbmc (2020).

  69. GWAS Catalog (European Bioinformatics Institute, 2023); https://www.ebi.ac.uk/gwas/

  70. Yang, J. et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gupta, A. immunogenomics/sc-h2: v3.0. Zenodo https://doi.org/10.5281/zenodo.8329597 (2023).

Download references

Acknowledgements

We thank K. Dey, K. Jagadeesh, J. Kang, S. Kim, B. Strober, P.-R. Loh, S. Sunyaev and L. Rumker for helpful conversations and L. Gaffney for help with figures. This work is supported in part by funding from the National Institutes of Health. S.R. is supported by the following grants: R01AR063759, U01HG012009, UC2AR081023, P01AI148102, U19AI095219 and R01AR063709. A.N. and K. Weinand are supported by T32AR007530. A.L.P. is supported by the following grants: U01 HG012009 and R01 MH101244. K. Wei is supported by NIH NIAMS K08AR077037, Rheumatology Research Foundation Innovative Research Award, Burroughs Wellcome Fund Career Award in Medical Sciences and Doris Duke Clinical Scientist Development Award. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Martin Jinye Zhang

    Present address: Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

  2. A full list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Anika Gupta, Kathryn Weinand, Aparna Nathan, Saori Sakaue & Soumya Raychaudhuri

  2. Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Anika Gupta, Kathryn Weinand, Aparna Nathan, Saori Sakaue, Kevin Wei & Soumya Raychaudhuri

  3. Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Anika Gupta, Kathryn Weinand, Aparna Nathan, Saori Sakaue & Soumya Raychaudhuri

  4. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Anika Gupta, Kathryn Weinand, Aparna Nathan, Saori Sakaue, Martin Jinye Zhang, Alkes L. Price, Tiffany Amariuta & Soumya Raychaudhuri

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

    Anika Gupta, Kathryn Weinand, Aparna Nathan & Soumya Raychaudhuri

  6. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Martin Jinye Zhang, Alkes L. Price & Tiffany Amariuta

  7. Weill Cornell Medicine, New York, NY, USA

    Laura Donlin

  8. Hospital for Special Surgery, New York, NY, USA

    Laura Donlin

  9. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Alkes L. Price

  10. Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA

    Tiffany Amariuta

  11. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Tiffany Amariuta

Authors
  1. Anika Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathryn Weinand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aparna Nathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saori Sakaue
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Jinye Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laura Donlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kevin Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alkes L. Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tiffany Amariuta
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Soumya Raychaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Accelerating Medicines Partnership RA/SLE Program and Network

  • Kathryn Weinand
  • , Aparna Nathan
  • , Saori Sakaue
  • , Laura Donlin
  • , Kevin Wei
  •  & Soumya Raychaudhuri

Contributions

A.G. and S.R. conceptualized the study. A.G. conducted all analyses on genetic and post-QC multimodal data. S.R. and T.A. supervised the study. K. Weinand conducted multimodal data QC and data preprocessing. T.A., A.N., A.L.P., S.S., M.J.Z. and AMP-RA/SLE provided input on statistical analyses and study design. L.D. and K. Wei recruited patients for multimodal samples, and K. Wei supervised sample processing and single-cell multimodal data generation. A.G., T.A. and S.R. wrote the initial manuscript. All authors contributed to editing the final manuscript.

Corresponding authors

Correspondence to Tiffany Amariuta or Soumya Raychaudhuri.

Ethics declarations

Competing interests

S.R. is a founder of Mestag, Inc, and a scientific advisor for Rheos, Jannsen and Pfizer and serves as a consultant for Sanofi and Abbvie. K. Wei has sponsored research agreements from Gilead Sciences and 10x Genomics and is a consultant for Mestag, Inc. The other authors declare no competing interest.

Peer review

Peer review information

Nature Genetics thanks John Ray, Monique van der Wijst and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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 Number of cells per sample and per cell state.

(a) Number of cells per donor and per cell subtype (discrete cell state) for each of the five cell types studied. Donor BRI-1281 has Osteoarthritis, while the other 11 donors have Rheumatoid Arthritis. (b) Number of accessible peaks (open in at least 50 cells) and expressed genes (expressed in at least 50 cells) in each of the five cell types studied.

Extended Data Fig. 2 Discrete and continuous cell state attributes for each of the five cell types studied.

Post-processing. (a-e) (left), snRNA-seq-based UMAPs of B, T, myeloid, fibroblast, and endothelial discrete cell states, respectively. (a-e) (right), percent RNA variance explained by each of the top 10 RNA-PCs (B, T, myeloid, fibroblast, and endothelial cells, respectively). (f) Top ten T cell RNA-PCs, with cells shaded by their PC loading. T cells shown as a case study of RNA-defined continuous cell states.

Extended Data Fig. 3 Poisson GLM regression QC checks and number of dynamic versus cell-state-invariant peaks, for the five cell types studied.

(a) Quantile-quantile plot of expected versus observed p-values in the actual, observed data (colored by cell type) and null data (grey; generated by permuting the cell-PC assignments; inflation≤1.1 for all five cell types’ null p-values), relative to the expected p-value distributions (uniform distribution). This Q-Q plot is shown for each of the five cell types studied. The y = x line is shown as a dashed black line. (b) Number of accessible but cell-state-invariant accessible (black) versus dynamic (grey) peaks in each cell type. The ratio of grey to the total value of black bars indicates the fraction of accessible peaks in each cell type that are also dynamic in that cell type. (c) Distribution of mean peak counts for cell-state-invariant and dynamic peaks (in T cells, as a representative example). We used a two-sided Wilcoxon rank sum test to determine that the two distributions are distinct (in T cells, µdynamic = 0.108 versus µcs-invariant = 0.096, p = 1e-20), suggesting that power differences are present but play a minimal role.

Extended Data Fig. 4 Cell types represented by each dynamic peak set.

In this snRNA-based UMAP of all five cell types analyzed, each dot represents a cell. Shading reflects the mean accessibility of the peaks in each cluster (0-11). For each peak set, the cell types with relatively higher mean peak accessibility are circled with their respective color. Supplementary Data 7 has the top Gene Ontology biological processes enriched by these peak sets (peaks were assigned to the gene whose promoter region (TSS + /− 1 kb) they overlapped with, and the gene set corresponding to each peak set was run through Gene Ontology gene set enrichment).

Extended Data Fig. 5 Per-SNP effect sizes across annotations for the five cell types.

(a) Genome annotation strategy for a toy peak set, to create input into S-LDSC. Each peak was 1.2 kb in size. (b) Open peaks’ τ* within each cell type, across the 12 autoimmune conditions studied. (c) τ* for dynamic versus cell-state-invariant (‘cs-invariant’) peaks within each cell type (error bars indicate 95% CI around the mean estimate). Dotted line indicates y = x. Sample size for the GWAS used for each trait can be found in Supplementary Table 2. Standard errors are computed from a genomic block jackknife. P-values for τ* are evaluated using a one-sided z-test. Trait acronyms: adult-onset asthma: AO Asthma, child-onset asthma: CO Asthma, Crohn’s disease: Crohn’s, hypothyroidism: HT, inflammatory bowel disease: IBD, primary biliary cirrhosis: PBC, respiratory ear-nose-throat disease: Resp:ENT, rheumatoid arthritis: RA, systemic lupus erythematosus: SLE, ulcerative colitis: UC.

Extended Data Fig. 6 Standardized effect size (τ*) for each discrete cell state in B, fibroblast, and endothelial cells.

(a) (top) B cell UMAP with cell state centroids. (bottom) τ* values for each B cell state present with at least 10 cells. All values are shown, and those significant at p < 0.05/14 (Bonferroni-adjusted p < 0.05) are marked with an asterisk. (b-c) Same as (a) but for fibroblasts and endothelial cells, respectively. Sample size for the GWAS used for each trait can be found in Supplementary Table 2. Standard errors are computed from a genomic block jackknife. P-values for τ* are evaluated using a one-sided z-test.

Extended Data Fig. 7 RA GWAS SNPs that disrupt TFs whose motifs are enriched in dynamic peaks that define T cell states that capture the most RA heritability. The position weight matrices were obtained from HOMER.

Here, we show position weight matrices, which indicate the sequence binding preferences of transcription factors (TFs). Arrows indicate the GWAS SNP position within each TF motif.

Extended Data Fig. 8 RA-focused benchmarking results.

(a) RA heritability enrichment for the five cell types in the synovial tissue, across four RNA-only approaches. (b) Same as (a), but showing RA τ*. (c) Same as (a), but showing RA τ* for the 23 T cell states present. (d) Same as (a), but showing RA τ* for the 14 myeloid cell states present. (e) Same as (a), but showing RA τ* for the T and myeloid cell states that were highly correlated with NMF factors in sc-linker. (f) Association between each cell type and RA using scGWAS. Approaches compared in a)-f): our approach (as presented in this paper), sc-linker (Jagadeesh*, Dey* et al., Nat Genet, 2022), our approach (using RNA only), and SEG (Finucane et al., Nat Genet, 2018). Error bars indicate 95% confidence intervals. (g) RA heritability enrichment and τ* for dynamic and cs-invariant peaks in the five synovial tissue cell types, using ATAC PCs and ATAC-defined cell states. (h) snATAC UMAP (left) and τ* (right) for ATAC-defined T (top) and myeloid (bottom) cell states in the synovial tissue. In g) and h), all values shown have p < 0.05/14 (Bonferroni-adjusted p < 0.05). (i) τ* values comparing (left) our approach in synovial tissue, (middle) our approach in healthy PBMCs, and (right) pseudobulk ATAC-based annotations in healthy PBMCs, for the three immune cell types present in PBMCs. Error bars indicate 95% confidence intervals. (j) τ* values resulting from running our approach in healthy PBMCs, for (top) T and (bottom) myeloid cell states present in the PBMC data, as defined by 10X. Error bars indicate 95% confidence intervals. For all panels with bar plots, error bars indicate the mean +/- a 95% confidence interval. Sample size for the GWAS used for each trait can be found in Supplementary Table 2. Standard errors are computed from a genomic block jackknife. P-values for τ* are evaluated using a one-sided z-test.

Extended Data Fig. 9 Heritability enrichment and τ* values at the cell type level in PBMCs.

(a) (left) Enrichment for dynamic and cs-invariant peaks in B, T, and myeloid cells in PBMCs (B N = 766 cells, T N = 5,764 cells, myeloid N = 3,641 cells). (right) Standardized effect size (τ*) values. All values shown have p < 0.05/14 (Bonferroni-adjusted p < 0.05). (b) (left) Enrichment for cell-type-specific peaks in B, T, and myeloid cells in PBMCs. (right) Standardized effect size (τ*) values. All values shown have p < 0.05/14 (Bonferroni-adjusted p < 0.05). (c) The first group indicates B, T, and myeloid-level τ* values using our approach on synovial tissue, the second group reflects our approach but on PBMCs, and the third group reflects a pseudobulk ATAC-based annotation for each cell type in PBMCs. Error bars reflect the mean +/− a 95% confidence interval. Sample size for the GWAS used for each trait can be found in Supplementary Table 2. Standard errors are computed from a genomic block jackknife. P-values for τ* are evaluated using a one-sided z-test.

Supplementary information

Supplementary Information

Description of Supplementary Tables 1 and 2 and Data 1–25, Note and Figs. 1–14.

Reporting Summary

Peer Review File

Supplementary Tables 1 and 2

Supplementary Table 1: Donor characteristics for the 12 RA synovial samples analyzed in this study. Supplementary Table 2: Description of the GWAS summary statistics data for the 19 traits analyzed for heritability estimates in this study.

Supplementary Data 1–25

Supplementary Data 1: Poisson GLM regression results across accessible peaks in B cells. Supplementary Data 2: Poisson GLM regression results across accessible peaks in T cells. Supplementary Data 3: Poisson GLM regression results across accessible peaks in myeloid cells. Supplementary Data 4: Poisson GLM regression results across accessible peaks in fibroblasts. Supplementary Data 5: Poisson GLM regression results across accessible peaks in endothelial cells. Supplementary Data 6: Enriched Gene Ontology processes for cell-state-invariant peaks. Supplementary Data 7: Enriched Gene Ontology processes for each of the 12 dynamic peak clusters, from cluster 0 through cluster 11. Supplementary Data 8: Heritability enrichment and τ* results for cell type annotations. Open peaks. Supplementary Data 9: Heritability enrichment and τ* results for cell type annotations. Dynamic and invariant peaks. Supplementary Data 10: Biological process (Gene Ontology) enrichment for each set of dynamic and cs-invariant peaks within each of the five cell types analyzed in this study. Supplementary Data 11: Results from our S-LDSC cell state-based analysis on cell states in B, endothelial and fibroblast cells. Supplementary Data 12: Heritability enrichment and τ* results for cell type annotations. Discrete T cell states. Supplementary Data 13: Meta-τ* values for each of the discrete T cell states. Supplementary Data 14: P values for T and M cells, using the top 25% of peaks for each cell state. P values from independent and conditional τ* calculations are included. Supplementary Data 15: RA GWAS SNP associations with cell states analyzed in this study, focused on those T cell states with the most heritability signal. SNPs in LD that are reported all have MAF >0.05. Supplementary Data 16: Cell states, TFs whose motifs are enriched in peaks representative of those cell states, and GWAS SNPs for RA and IBD/CD/UC that overlap these TF motifs. Supplementary Data 17: Heritability enrichment and τ* results for cell type annotations. Discrete myeloid cell states. Supplementary Data 18: Meta-τ* values for each of the discrete myeloid cell states. Supplementary Data 19: Results from our S-LDSC cell type analysis, comparing our approach to three other, RNA-only, approaches. Supplementary Data 20: Results from our S-LDSC T cell state analysis, comparing our approach to three other, RNA-only, approaches. Supplementary Data 21: Results from our S-LDSC myeloid cell state analysis, comparing our approach to three other, RNA-only, approaches. Supplementary Data 22: Results from our ATAC-based cell type analyses in synovial tissue. Supplementary Data 23: Results from our ATAC-based cell state analyses in synovial tissue. Supplementary Data 24: Results from our cell type analysis on PBMCs. Supplementary Data 25: Results from our S-LDSC T and myeloid cell state analysis on PBMC-based immune cell states.

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

Gupta, A., Weinand, K., Nathan, A. et al. Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability. Nat Genet 55, 2200–2210 (2023). https://doi.org/10.1038/s41588-023-01577-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-023-01577-7

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