High-Throughput Muscle Fiber Typing From RNA Sequencing Data

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 = 0.65 [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.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. For the first time, it is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

The menopausal transition does not influence skeletal muscle capillary growth in response to cycle training in women

The influence of the menopausal transition, with a consequent loss of oestrogen, on capillary growth in response to exercise training remains unknown. In the present study, we evaluated the effect of a period of intense endurance training on skeletal muscle angiogenesis in late pre-menopausal and recently post-menopausal women with an age difference of <4 years. Skeletal muscle biopsies were obtained from the thigh muscle prior to and after 12 weeks of intense aerobic cycle training, and analyzed for capillarization, fiber type distribution and content of vascular endothelial growth factor (VEGF). At baseline, there was no difference in capillary per fiber ratio (C:F; 1.41 ± 0.22 vs 1.40 ± 0.30), capillary density (CD; 305±61 vs 336±52 mm2), muscle fiber area (MFA) or percentage distribution of muscle fiber type I (47.3±10.1 vs 49.3±15.1 %) and type II (52.7±10.1 vs 50.7±15.1%) between the pre- and post-menopausal women. The training period resulted in a similar increase in C:F in pre- and post-menopausal women (by 9.2 vs 12.1 %, respectively) and CD (by 6.9 vs 8.9 %, respectively), whereas MFA and fiber type distribution remained unaltered. Skeletal muscle VEGF protein content was similar between groups at baseline and increased to a similar extent with training (by 21.1 vs 27.2 %, respectively) in the pre- and post-menopausal women. In conclusion, the loss of oestrogen per se at menopause does not influence the capillary growth response to intense aerobic exercise training.

MyoSight—semi-automated image analysis of skeletal muscle cross sections

Background Manual analysis of cross-sectional area, fiber-type distribution, and total and centralized nuclei in skeletal muscle cross sections is tedious and time consuming, necessitating an accurate, automated method of analysis. While several excellent programs are available, our analyses of skeletal muscle disease models suggest the need for additional features and flexibility to adequately describe disease pathology. We introduce a new semi-automated analysis program, MyoSight, which is designed to facilitate image analysis of skeletal muscle cross sections and provide additional flexibility in the analyses. Results We describe staining and imaging methods that generate high-quality images of immunofluorescent-labelled cross sections from mouse skeletal muscle. Using these methods, we can analyze up to 5 different fluorophores in a single image, allowing simultaneous analyses of perinuclei, central nuclei, fiber size, and fiber-type distribution. MyoSight displays high reproducibility among users, and the data generated are in close agreement with data obtained from manual analyses of cross-sectional area (CSA), fiber number, fiber-type distribution, and number and localization of myonuclei. Furthermore, MyoSight clearly delineates changes in these parameters in muscle sections from a mouse model of Duchenne muscular dystrophy (mdx). Conclusions MyoSight is a new program based on an algorithm that can be optimized by the user to obtain highly accurate fiber size, fiber-type identification, and perinuclei and central nuclei per fiber measurements. MyoSight combines features available separately in other programs, is user friendly, and provides visual outputs that allow the user to confirm the accuracy of the analyses and correct any inaccuracies. We present MyoSight as a new program to facilitate the analyses of fiber type and CSA changes arising from injury, disease, exercise, and therapeutic interventions.

Heterogeneous distribution of mRNAs within flight muscle fibers, and implications for function

Muscle heterogeneity has been explored in terms of fiber-type distribution, structural organisation, and differences at their junctions with neurons and tendons. We amplify on such observation to additionally suggest that muscle syncytia have nonuniform protein requirements along their length, deployed for developmental and functional uses. An exploration of regionalized proteins or their mRNA across muscle syncytia has not been done. We investigated mRNA localization in regions of Drosophila melanogaster dorsal longitudinal muscle (DLM) syncytia over their entire transcriptome. Dissection of muscle regions, their RNA-seq and stringent Differential Gene Expression analysis indeed reveals statistically significant regionalization of nearly a hundred mRNA over the length of DLMs. Functions of over half of these genes require experimental verification. A preponderance of mRNA coding for catabolic and proteolytic enzymes is conspicuous among transcripts enriched in the posterior of DLMs. Our findings provide a foundation for exploring molecular processes that contribute to syncytial maturation and muscle homeostasis in a spatially non-homogenous manner.