High‐throughput data‐driven analysis of myofiber composition reveals muscle‐specific disease and age‐associated patterns

Raz, Vered; Raz, Yotam; Vijver, Davy van de; Bindellini, Davide; Putten, Maaike van; Akker, Erik B. van den

doi:10.1096/fj.201801714r

Cited by 2 publications

(4 citation statements)

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“…Eight-weeks-old male C57BL/6J wild-type mice were cultivated in the same cage and were sacrificed by cervical dislocation after CO 2 euthanasia. The TA muscles were collected from the same nest and mice that grew in the same cage, and immediately snap-frozen in liquid nitrogen in the presence of iso-pentane and were stored at À80 C, as described in [16,17]. Only the right TA muscles from two mice were used in this study.…”

Section: Animalsmentioning

confidence: 99%

“…The immunohistochemical procedure was performed with a staining protocol that is detailed in [16]. Sections were stretched in a PBS solution before the immunofluorescence procedure.…”

Section: Sectioning and Immunofluorescencementioning

confidence: 99%

“…Using this antibody mix, procedures to quantify myofiber type together with high-throughput imaging and myofiber quantification procedures resulted in large myofiber datasets, from which myofiber composition and the spatial arrangement could be studied using data-driven analyses [7,15,16]. Data-driven analysis can further improve the understanding of myofiber transitions in disease and physiological conditions [16][17][18]. Those studies on myofiber composition and changes in disease animal models are most often carried out in cross-sections taken from a median region of the muscle.…”

mentioning

confidence: 99%

“…Methods to identify myofiber types using an antibody mix containing fluorescently conjugated anti MyHC isoforms have been developed [13,14]. Using this antibody mix, procedures to quantify myofiber type together with high-throughput imaging and myofiber quantification procedures resulted in large myofiber datasets, from which myofiber composition and the spatial arrangement could be studied using data-driven analyses [7,15,16]. Data-driven analysis can further improve the understanding of myofiber transitions in disease and physiological conditions [16][17][18].…”

mentioning

confidence: 99%

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Discovering fiber type architecture over the entire muscle using data‐driven analysis

et al. 2021

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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

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Section: Animalsmentioning

confidence: 99%

“…The immunohistochemical procedure was performed with a staining protocol that is detailed in [16]. Sections were stretched in a PBS solution before the immunofluorescence procedure.…”

Section: Sectioning and Immunofluorescencementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Discovering fiber type architecture over the entire muscle using data‐driven analysis

et al. 2021

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A data‐driven methodology reveals novel myofiber clusters in older human muscles

et al. 2020

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Skeletal muscles control posture, mobility and strength, and influence whole‐body metabolism. Muscles are built of different types of myofibers, each having specific metabolic, molecular, and contractile properties. Fiber classification is, therefore, regarded the key for understanding muscle biology, (patho‐) physiology. The expression of three myosin heavy chain (MyHC) isoforms, MyHC‐1, MyHC‐2A, and MyHC‐2X, marks myofibers in humans. Typically, myofiber classification is performed by an eye‐based histological analysis. This classical approach is insufficient to capture complex fiber classes, expressing more than one MyHC‐isoform. We, therefore, developed a methodological procedure for high‐throughput characterization of myofibers on the basis of multiple isoforms. The mean fluorescence intensity of the three most abundant MyHC isoforms was measured per myofiber in muscle biopsies of 56 healthy elderly adults, and myofiber classes were identified using computational biology tools. Unsupervised clustering revealed the existence of six distinct myofiber clusters. A comparison with the visual assessment of myofibers using the same images showed that some of these myofiber clusters could not be detected or were frequently misclassified. The presence of these six clusters was reinforced by RNA expressions levels of sarcomeric genes. In addition, one of the clusters, expressing all three MyHC isoforms, correlated with histological measures of muscle health. To conclude, this methodological procedure enables deep characterization of the complex muscle heterogeneity. This study opens opportunities to further investigate myofiber composition in comparative studies.

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High‐throughput data‐driven analysis of myofiber composition reveals muscle‐specific disease and age‐associated patterns

Cited by 2 publications

References 34 publications

Discovering fiber type architecture over the entire muscle using data‐driven analysis

Discovering fiber type architecture over the entire muscle using data‐driven analysis

A data‐driven methodology reveals novel myofiber clusters in older human muscles

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