Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits
- Peter Orchard1,9,
- Nandini Manickam1,9,
- Christa Ventresca1,2,9,
- Swarooparani Vadlamudi3,
- Arushi Varshney1,
- Vivek Rai1,
- Jeremy Kaplan1,
- Claudia Lalancette4,
- Karen L. Mohlke3,
- Katherine Gallagher5,6,
- Charles F. Burant7 and
- Stephen C.J. Parker1,2,8
- 1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 2Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 3Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA;
- 4Epigenomics Core, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 5Department of Surgery, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 6Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 7Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
- 8Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
Abstract
Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell–specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We additionally perform multi-omics profiling (gene expression and chromatin accessibility) on human and rat muscle samples. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 33,862 nuclei and identify seven cell types ranging in abundance from 59.6% to 1.0% of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site–distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome-wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3.5% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, transcription factor motifs, and target genes for type 2 diabetes signals. These chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for nominating causal GWAS SNPs and cell types.
Footnotes
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↵9 Co-first authors
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.268482.120.
- Received July 8, 2020.
- Accepted September 16, 2021.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
