Short running title: Data-driven analysis of myofiber compositionNonstandard abbreviations LGMD Limb girdle muscular dystrophy MFI mean fluorescence intensity MyHC Myosin heavy chain SGCA-null α-sarcoglycan-null SGCD-null δ-sarcoglycan-null Abstract Contractile properties of myofibers are dictated by the abundance of myosin heavy chain (MyHC)isoforms. MyHC composition designates muscle function and its alterations could unravel differential muscle involvement in muscular dystrophies and aging. Current analyses are limited to visual assessments in which myofibers expressing multiple MyHC isoforms are prone to misclassifications.As a result, complex patterns and subtle alterations are unidentified. We developed a high-throughputdata-driven myofiber analysis to quantitatively describe the variations in myofibers across the muscle.We investigated alterations in myofiber composition between genotypes, two muscles and two age groups. We show that this analysis facilitates the discovery of complex myofiber compositions and its dependency on age, muscle type and genetic conditions.

Skeletal muscle function is inferred from the spatial arrangement of muscle fiber architecture, which corresponds to myofiber molecular and metabolic features.Myofiber features are often determined using immunofluorescence on a local sampling, typically obtained from a median region. This median region is assumed to represent the entire muscle. However, it remains largely unknown to what extent this local sampling represents the entire muscle. We present a pipeline to study the architecture of muscle fiber features over the entire muscle, including sectioning, staining, imaging to image quantification and data-driven analysis with Myofiber type were identified by the expression of myosin heavy chain (MyHC) isoforms, representing contraction properties. We reconstructed muscle architecture from consecutive cross-sections stained for laminin and MyHC isoforms. Examining the entire muscle using consecutive cross-sections is extremely laborious, we provide consideration to reduce the dataset without loosing spatial information. Data-driven analysis with over 150,000 myofibers showed spatial variations in myofiber geometric features, myofiber type, and the distribution of neuromuscular junctions over the entire muscle. We present a workflow to study histological changes over the entire muscle using high-throughput imaging, image quantification, and data-driven analysis. Our results suggest that asymmetric spatial distribution of these features over the entire muscle could impact muscle function. Therefore, instead of a single sampling from a median region, representative regions covering the entire muscle should be investigated in future studies. K E Y W O R D S data-driven analysis, muscle architecture, myofiber type, quantitative image analysis 1 | INTRODUCTION Skeletal muscles facilitate the mobilization and stability of the skeleton, which is greatly determined by muscle fiber architecture. Muscle function changes in physiological, pathological conditions, and during development [1, 2]. Muscle fiber spatial arrangement is broadly described by their orientation relative to the axis of force generation, which can be classified into three main classes: 1) fibers arrangement

Contractile properties of myofibers are dictated by the abundance of myosin heavy chain (MyHC) isoforms. MyHC composition designates muscle function, and its alterations could unravel differential muscle involvement in muscular dystrophies and aging. Current analyses are limited to visual assessments in which myofibers expressing multiple MyHC isoforms are prone to misclassification. As a result, complex patterns and subtle alterations are unidentified. We developed a high‐throughput, data‐driven myofiber analysis to quantitatively describe the variations in myofibers across the muscle. We investigated alterations in myofiber composition between genotypes, 2 muscles, and 2 age groups. We show that this analysis facilitates the discovery of complex myofiber compositions and its dependency on age, muscle type, and genetic conditions.—Raz, V., Raz, Y., van de Vijver, D., Bindellini, D., van Putten, M., van den Akker, E. B. High‐throughput data‐driven analysis of myofiber composition reveals muscle‐specific disease and age‐associated patterns. FASEB J. 33, 4046–4053 (2019). http://www.fasebj.org