Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits [snATAC-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.骨骼肌平均占人体体重的最大比例，是复杂疾病发生与运动机能维持的关键组织。其由多种不同的细胞类型与肌纤维类型构成。本研究对单细胞核转座酶可及性测序（single-nucleus ATAC-seq, snATAC-seq）技术进行优化，以绘制冷冻保存的人类与大鼠样本中骨骼肌细胞特异性染色质可及性图谱；同时优化单细胞核RNA测序（single-nucleus RNA-seq, snRNA-seq）技术，以绘制人类样本的细胞特异性转录组图谱。此外，我们还对人类与大鼠肌肉样本开展了多组学分析（涵盖基因表达与染色质可及性两个层面）。我们成功捕获了现有单细胞RNA测序方法通常难以检测到的I型与II型肌纤维特征谱。我们对33862个细胞核开展了跨模态与跨物种整合分析，鉴定出7种细胞类型，其丰度占总细胞核数的比例范围为1.0%至59.6%。我们提出了一种基于回归的细胞类型推断方法：通过比对转录起始位点远端的ATAC-seq峰与参考增强子图谱，实现细胞类型注释，并证实该方法的结果与基于RNA标记基因的细胞类型注释结果具有一致性。我们发现，来自英国生物库（UK Biobank）与糖尿病全基因组关联研究中与复杂表型相关的遗传变异，在细胞特异性ATAC-seq峰中存在富集异质性，其中肌肉间充质干细胞（占细胞核总数约3.5%）的富集模式最为显著。最后，我们将上述染色质可及性图谱与全基因组关联研究（Genome-Wide Association Study, GWAS）数据进行整合，为2型糖尿病关联信号推定了潜在的因果细胞类型、单核苷酸多态性（Single Nucleotide Polymorphism, SNP）、转录因子基序以及靶基因。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，可为推定全基因组关联研究中的因果单核苷酸多态性与细胞类型提供宝贵的研究资源。