High-throughput muscle fiber typing from RNA sequencing data

Skeletal muscle fiber type distribution has implications for human health, muscle function and performance. This knowledge has been gathered using labor intensive and costly methodology that limited these studies. Here we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for larger number of individuals to be tested. By using single-nuclei snRNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPas staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). The correlation between the sequencing-based method and the other two were rATPas = 0.65 [0.46 – 0.84], [95% CI] and rmyosin = 0.80 [0.71 – 0.89], with p = 7.96 x 10-6 and 8.06 x 10-6 respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~5.000 paired end reads. This new method consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor efficient way. For the first time it is now feasible to study the association between fiber type distribution and health outcomes in large well-powered studies. Overall design: snRNAseq data from 1 human and 1 chimpanzee skeletal muscle sample

骨骼肌纤维类型分布与人类健康、肌肉功能及运动表现密切相关。过往相关研究多依赖劳动密集且成本高昂的实验方法，极大限制了研究规模的拓展。本研究提出一种基于肌肉组织总RNA测序（totRNAseq）数据的分析方法，可从冰冻人类肌肉样本中估算骨骼肌纤维类型分布，从而支持更大规模的样本检测。该方法以单细胞核RNA测序（snRNAseq）数据作为参考基准：首先通过对聚类基因标记的基因表达量取平均，生成聚类表达特征；随后将该特征应用于totRNAseq数据，并通过线性矩阵分解推断肌纤维细胞核类型。随后，研究团队将该估算结果与两个独立队列（分别包含39和22个样本，总计62份肌肉样本）中通过ATP酶染色或肌球蛋白重链蛋白异构体分布测得的纤维类型分布进行对比。基于测序的分析方法与另外两种检测方法的相关性分别为：rATPas=0.65 [95%置信区间：0.46–0.84]，rmyosin=0.80 [95%置信区间：0.71–0.89]，对应P值分别为7.96×10^-6和8.06×10^-6。即便在极低的totRNAseq测序深度下（低至平均约5000条双端读段），该纤维类型组成的反卷积推断结果依然准确可靠。因此，这种新方法可借助totRNAseq以更低的成本与人力投入，实现更大规模样本的纤维类型分布检测。这使得首次在大规模且统计效力充足的研究中探索纤维类型分布与健康结局之间的关联成为可能。实验整体设计：使用来自1份人类及1份黑猩猩骨骼肌样本的单细胞核RNA测序（snRNAseq）数据。