Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits [snRNA-seq]

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.平均而言，骨骼肌是占人体质量比例最高的组织，亦是复杂疾病与运动机能相关研究的关键靶组织。其由多种不同的细胞类型与肌纤维亚型组成。本研究对单细胞核ATAC测序（single-nucleus Assay for Transposase-Accessible Chromatin sequencing, snATAC-seq）进行优化，在冻存的人类与大鼠样本中绘制骨骼肌细胞特异性染色质可及性图谱；同时针对人类样本开展单细胞核RNA测序（single-nucleus RNA sequencing, snRNA-seq），以获取细胞特异性转录组数据。此外，本研究还针对人类与大鼠的肌肉样本开展多组学分析，涵盖基因表达与染色质可及性检测。本研究成功捕获到I型与II型肌纤维的特征信号，而现有单细胞RNA测序方法通常无法检测到这类信号。本研究对33862个细胞核开展多模态与跨物种整合分析，鉴定出7种细胞类型，其在全部细胞核中的占比区间为1.0%至59.6%。本研究提出一种基于回归的细胞类型推断方法：通过比较转录起始位点远端的ATAC测序峰与参考增强子图谱完成细胞类型注释，且该方法的结果与基于RNA标记基因的细胞类型分配结果具有高度一致性。本研究发现，在细胞特异性ATAC测序峰中，与英国生物库（UK Biobank）及糖尿病全基因组关联研究（Genome-Wide Association Study, GWAS）中的复杂表型相关的遗传变异富集存在异质性，其中肌肉间充质干细胞（约占细胞核总数的3.5%）的富集模式最为显著。最后，本研究将上述染色质可及性图谱与全基因组关联研究（GWAS）数据进行整合，为2型糖尿病的关联信号筛选出潜在的致病细胞类型、单核苷酸多态性（Single Nucleotide Polymorphism, SNPs）、转录因子基序与靶基因。上述针对人类与大鼠骨骼肌细胞类型的染色质可及性图谱资源，可为2型糖尿病关联信号的致病单核苷酸多态性及致病细胞类型的筛选提供宝贵的研究支撑。