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

Muscle fibres are the dominant, multinucleated cell type in skeletal muscle. In humans, they can be classified as slow (type 1) and fast (type 2) fibres, traditionally based on distinct properties of their contractile machinery. Slow and fast fibres are characterized by shared and specific cellular complexities that are pivotal for their adaptive capacity to exercise or role in metabolic disease progression. In this era of omics research, there is a critical need to accurately infer muscle fibre type proportions from bulk tissue omics datasets because single-fibre approaches are not feasible in large-scale or retrospective studies. Here we present FibeRtypeR, an easy-to-use web application to accurately estimate fibre type proportions from bulk transcriptomics and proteomics datasets (https://muscleapps.ugent.be). FibeRtypeR exploits transcriptomics profiles of 1000 fibre-typed individual human skeletal muscle fibres and sex-specific fibre type proteomes as reference datasets and is validated against paired immunohistochemical fibre type determinations in 160 muscle biopsies. We show that FibeRtypeR can be applied to public datasets, illustrating the application potential across a wide range of biological contexts such as ageing, disease and exercise training. This new freely accessible computational tool will prove valuable to the skeletal muscle research community. KEY POINTS: Bulk muscle omics datasets lack fibre type specific information. Our new tool, FibeRtypeR, leverages in-house collected single-fibre profiles allowing for accurate fibre type inference. FibeRtypeR is methodologically robust across omics technologies and workflows. We host FibeRtypeR as an intuitive open-access Shiny app, applicable to new and publicly available transcriptomics and proteomics datasets.