Deconvoluting fibre type proportions from human skeletal muscle transcriptomics and proteomics data using FibeRtypeR

Area of Science:

• Skeletal muscle physiology and omics research.
• Computational biology and bioinformatics applications in human health.

Background:

• Skeletal muscle fibers (type 1 and type 2) have distinct roles in adaptation and disease.
• Accurate muscle fiber type proportion estimation is vital but challenging with bulk omics data due to limitations of single-fiber analysis in large studies.
• Existing methods struggle to provide precise fiber type information from bulk tissue omics datasets.

Purpose of the Study:

• To introduce FibeRtypeR, an accessible web application for estimating muscle fiber type proportions from bulk transcriptomics and proteomics data.
• To provide a robust computational tool for researchers studying skeletal muscle in various contexts like aging, disease, and exercise training.
• To enable accurate fiber type inference where single-fiber analyses are not feasible.

Main Methods:

• Development of FibeRtypeR utilizing transcriptomics profiles from 1000 individually fiber-typed human skeletal muscle fibers and sex-specific proteomes as references.
• Validation of FibeRtypeR against immunohistochemical fiber type determinations in 160 muscle biopsies.
• Application of FibeRtypeR to public omics datasets across diverse biological contexts.

Main Results:

• FibeRtypeR accurately estimates muscle fiber type proportions from bulk omics data.
• The tool demonstrates robustness across different omics technologies and analytical workflows.
• Successful application to public datasets highlights its utility in aging, disease, and exercise research.

Conclusions:

• FibeRtypeR is a valuable, freely accessible computational tool for the skeletal muscle research community.
• The application overcomes limitations of bulk omics data by enabling precise muscle fiber type inference.
• This tool facilitates deeper understanding of skeletal muscle adaptations and pathologies.