High-throughput muscle fiber typing from RNA sequencing data.

IF 5.3 2区医学 Q2 CELL BIOLOGY

Skeletal Muscle Pub Date : 2022-07-02 DOI:10.1186/s13395-022-00299-4

Nikolay Oskolkov, Malgorzata Santel, Hemang M Parikh, Ola Ekström, Gray J Camp, Eri Miyamoto-Mikami, Kristoffer Ström, Bilal Ahmad Mir, Dmytro Kryvokhyzha, Mikko Lehtovirta, Hiroyuki Kobayashi, Ryo Kakigi, Hisashi Naito, Karl-Fredrik Eriksson, Björn Nystedt, Noriyuki Fuku, Barbara Treutlein, Svante Pääbo, Ola Hansson

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引用次数: 5

Abstract

Background: 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 a larger number of individuals to be tested.

Methods: By using single-nuclei RNA 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 ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22).

Results: The correlation between the sequencing-based method and the other two were r_ATPas = 0.44 [0.13-0.67], [95% CI], and r_myosin = 0.83 [0.61-0.93], with p = 5.70 × 10^-3 and 2.00 × 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 ~ 10,000 paired-end reads.

Conclusions: This new method ( https://github.com/OlaHanssonLab/PredictFiberType ) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

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基于RNA测序数据的高通量肌纤维分型。

背景:骨骼肌纤维类型分布与人体健康、肌肉功能和运动表现有关。这些知识是通过劳动密集型和昂贵的方法收集的，这限制了这些研究。在这里，我们提出了一种基于肌肉组织RNA测序数据(totRNAseq)的方法来估计冷冻人体样本中骨骼肌纤维类型的分布，从而允许更多的个体进行测试。方法:以单核RNA测序(snRNAseq)数据为参考，对聚类基因标记的基因表达进行平均，得到聚类表达特征，并将其应用于totRNAseq数据，通过线性矩阵分解推断肌纤维核类型。然后将这一估计与两个独立队列(n = 39和22)中62个肌肉样本的atp酶染色测量的纤维类型分布或肌球蛋白重链蛋白异构体分布进行比较。结果:基于测序的方法与其他两种方法的相关性为rATPas = 0.44 [0.13-0.67]， [95% CI]， rmyosin = 0.83 [0.61-0.93]， p分别为5.70 × 10-3和2.00 × 10-6。即使对于非常低的totRNAseq测序深度，即平均到~ 10,000对末端读取，纤维类型组成的反卷积推断也是准确的。结论:这种新方法(https://github.com/OlaHanssonLab/PredictFiberType)因此可以使用totRNAseq以一种成本和劳动效率高的方式测量大量样品的纤维类型分布。现在，研究纤维类型分布与健康结果之间的关系是可行的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Skeletal Muscle CELL BIOLOGY-

CiteScore

9.10

自引率

0.00%

发文量

审稿时长

12 weeks

期刊介绍： The only open access journal in its field, Skeletal Muscle publishes novel, cutting-edge research and technological advancements that investigate the molecular mechanisms underlying the biology of skeletal muscle. Reflecting the breadth of research in this area, the journal welcomes manuscripts about the development, metabolism, the regulation of mass and function, aging, degeneration, dystrophy and regeneration of skeletal muscle, with an emphasis on understanding adult skeletal muscle, its maintenance, and its interactions with non-muscle cell types and regulatory modulators. Main areas of interest include: -differentiation of skeletal muscle- atrophy and hypertrophy of skeletal muscle- aging of skeletal muscle- regeneration and degeneration of skeletal muscle- biology of satellite and satellite-like cells- dystrophic degeneration of skeletal muscle- energy and glucose homeostasis in skeletal muscle- non-dystrophic genetic diseases of skeletal muscle, such as Spinal Muscular Atrophy and myopathies- maintenance of neuromuscular junctions- roles of ryanodine receptors and calcium signaling in skeletal muscle- roles of nuclear receptors in skeletal muscle- roles of GPCRs and GPCR signaling in skeletal muscle- other relevant aspects of skeletal muscle biology. In addition, articles on translational clinical studies that address molecular and cellular mechanisms of skeletal muscle will be published. Case reports are also encouraged for submission. Skeletal Muscle reflects the breadth of research on skeletal muscle and bridges gaps between diverse areas of science for example cardiac cell biology and neurobiology, which share common features with respect to cell differentiation, excitatory membranes, cell-cell communication, and maintenance. Suitable articles are model and mechanism-driven, and apply statistical principles where appropriate; purely descriptive studies are of lesser interest.