دورية أكاديمية
High-throughput muscle fiber typing from RNA sequencing data
| العنوان: | High-throughput muscle fiber typing from RNA sequencing data |
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| المؤلفون: | Oskolkov, Nikolay, Santel, Malgorzata, Parikh, Hemang M., Ekstrom, Ola, Camp, Gray J., Miyamoto-Mikami, Eri, Strom, Kristoffer, Mir, Bilal Ahmad, Kryvokhyzha, Dmytro, Lehtovirta, Mikko, Kobayashi, Hiroyuki, Kakigi, Ryo, Naito, Hisashi, Eriksson, Karl-Fredrik, Nystedt, Bjorn, Fuku, Noriyuki, Treutlein, Barbara, Paabo, Svante, Hansson, Ola |
| المساهمون: | Viikki Molecular Nutrition Group, Institute for Molecular Medicine Finland, University of Helsinki |
| بيانات النشر: | BMC |
| سنة النشر: | 2022 |
| المجموعة: | Helsingfors Universitet: HELDA – Helsingin yliopiston digitaalinen arkisto |
| مصطلحات موضوعية: | HUMAN SKELETAL-MUSCLE, CONTRACTILE PROPERTIES, ENZYME-ACTIVITIES, DETERMINANTS, EXPRESSION, PROPORTION, ENDURANCE, TYPOLOGY, ISOFORMS, SAMPLES, 1182 Biochemistry, cell and molecular biology |
| الوصف: | 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 x 10(-3) and 2.00 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 similar to 10,000 paired-end reads. Conclusions: This new method (https://github.com/OlaHanssonLab/PredictFiberTypeTest) 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. ; Peer reviewed |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| ردمك: | 978-0-00-820254-5 0-00-820254-0 |
| العلاقة: | 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, Pahlsson 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.; Oskolkov , N , Santel , M , Parikh , H M , Ekstrom , O , Camp , G J , Miyamoto-Mikami , E , Strom , K , Mir , B A , Kryvokhyzha , D , Lehtovirta , M , Kobayashi , H , Kakigi , R , Naito , H , Eriksson , K-F , Nystedt , B , Fuku , N , Treutlein , B , Paabo , S & Hansson , O 2022 , ' High-throughput muscle fiber typing from RNA sequencing data ' , Skeletal Muscle , vol. 12 , no. 1 , 16 . https://doi.org/10.1186/s13395-022-00299-4Test; ORCID: /0000-0001-9165-0516/work/150770614; 4b172d72-7dd6-4b49-8549-41b659503181; http://hdl.handle.net/10138/346479Test; 000820254000001 |
| الإتاحة: | http://hdl.handle.net/10138/346479Test |
| حقوق: | cc_by ; openAccess ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.D03A0A0 |
| قاعدة البيانات: | BASE |
| ردمك: | 9780008202545 0008202540 |
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