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High-throughput muscle fiber typing from RNA sequencing data

Oskolkov, Nikolay LU ; Santel, Malgorzata ; Parikh, Hemang M. LU ; Ekström, Ola LU ; Camp, Gray J. ; Miyamoto-Mikami, Eri ; Ström, Kristoffer LU ; Mir, Bilal Ahmad LU orcid ; Kryvokhyzha, Dmytro LU and Lehtovirta, Mikko LU , et al. (2022) In Skeletal Muscle 12. p.1-9
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.... (More)

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 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. 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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publishing date
type
Contribution to journal
publication status
published
subject
in
Skeletal Muscle
volume
12
article number
16
pages
1 - 9
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85133403486
  • pmid:35780170
ISSN
2044-5040
DOI
10.1186/s13395-022-00299-4
language
English
LU publication?
yes
additional info
Funding Information: The authors would like to acknowledge support from the Science for Life Laboratory, the National Genomics Infrastructure, NGI, and the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement no. 2018-05973 for providing assistance in massive parallel sequencing and computational infrastructure. Funding Information: Open access funding provided by Lund University. This work was financially supported by the following: the Knut and Alice Wallenberg Foundation for equipment, Swedish Research Council project grant 2018–02635, Crafoord Foundation, Novo Nordisk Foundation, Påhlsson Foundation, Diabetes Wellness, the Swedish Diabetes foundation, the Hjelt Foundation, JSPS KAKENHI, Dnr 16KK0188, and by the Institute of Health and Sports Science & Medicine, Juntendo University. LUDC-IRC: Swedish Foundation for Strategic Research, Dnr IRC15-0067, EXODIAB: Swedish Research Council, Strategic Research Area, Dnr 2009–1039. NO and BN are financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab. The funding bodies had no influence or were involved in any other way in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Publisher Copyright: © 2022, The Author(s).
id
e7d7060e-5cf8-4bd3-b4ea-7b2a52d957e3
date added to LUP
2022-09-29 08:15:22
date last changed
2024-06-27 21:04:41
@article{e7d7060e-5cf8-4bd3-b4ea-7b2a52d957e3,
  abstract     = {{<p>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<sub>ATPas</sub> = 0.44 [0.13–0.67], [95% CI], and r<sub>myosin</sub> = 0.83 [0.61–0.93], with p = 5.70 × 10<sup>–3</sup> and 2.00 × 10<sup>–6</sup>, 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.</p>}},
  author       = {{Oskolkov, Nikolay and Santel, Malgorzata and Parikh, Hemang M. and Ekström, Ola and Camp, Gray J. and Miyamoto-Mikami, Eri and Ström, Kristoffer and Mir, Bilal Ahmad and Kryvokhyzha, Dmytro and Lehtovirta, Mikko and Kobayashi, Hiroyuki and Kakigi, Ryo and Naito, Hisashi and Eriksson, Karl Fredrik and Nystedt, Björn and Fuku, Noriyuki and Treutlein, Barbara and Pääbo, Svante and Hansson, Ola}},
  issn         = {{2044-5040}},
  language     = {{eng}},
  pages        = {{1--9}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Skeletal Muscle}},
  title        = {{High-throughput muscle fiber typing from RNA sequencing data}},
  url          = {{http://dx.doi.org/10.1186/s13395-022-00299-4}},
  doi          = {{10.1186/s13395-022-00299-4}},
  volume       = {{12}},
  year         = {{2022}},
}