High-throughput muscle fiber typing from RNA sequencing data.

Oskolkov N, Santel M, Parikh HM, Ekström O, Camp GJ, Miyamoto-Mikami E, Ström K, Mir BA, Kryvokhyzha D, Lehtovirta M, Kobayashi H, Kakigi R, Naito H, Eriksson K, Nystedt B, Fuku N, Treutlein B, Pääbo S, Hansson O

Skelet Muscle 12 (1) 16 [2022-07-02; online 2022-07-02]

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. 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). The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13-0.67], [95% CI], and rmyosin = 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. 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.

Bioinformatics Long-term Support WABI [Collaborative]

Bioinformatics Support, Infrastructure and Training [Collaborative]

NGI Short read [Service]

National Genomics Infrastructure [Service]

PubMed 35780170

DOI 10.1186/s13395-022-00299-4

Crossref 10.1186/s13395-022-00299-4

pii: 10.1186/s13395-022-00299-4
pmc: PMC9250227

Publications 7.1.3