Rapid determination of myosin heavy chain expression in rat, mouse, and human skeletal muscle using multicolor immunofluorescence analysis

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Abstract

Skeletal muscle is a heterogeneous tissue comprised of fibers with different morphological, functional, and metabolic properties. Different muscles contain varying proportions of fiber types; therefore, accurate identification is important. A number of histochemical methods are used to determine muscle fiber type; however, these techniques have several disadvantages. Immunofluorescence analysis is a sensitive method that allows for simultaneous evaluation of multiple MHC isoforms on a large number of fibers on a single cross-section, and offers a more precise means of identifying fiber types. In this investigation we characterized pure and hybrid fiber type distribution in 10 rat and 10 mouse skeletal muscles, as well as human vastus lateralis (VL) using multicolor immunofluorescence analysis. In addition, we determined fiber type-specific cross-sectional area (CSA), succinate dehydrogenase (SDH) activity, and α-glycerophosphate dehydrogenase (GPD) activity. Using this procedure we were able to easily identify pure and hybrid fiber populations in rat, mouse, and human muscle. Hybrid fibers were identified in all species and made up a significant portion of the total population in some rat and mouse muscles. For example, rat mixed gastrocnemius (MG) contained 12.2% hybrid fibers whereas mouse white tibialis anterior (WTA) contained 12.1% hybrid fibers. Collectively, we outline a simple and time-efficient method for determining MHC expression in skeletal muscle of multiple species. In addition, we provide a useful resource of the pure and hybrid fiber type distribution, fiber CSA, and relative fiber type-specific SDH and GPD activity in a number of rat and mouse muscles. © 2012 Bloemberg, Quadrilatero.

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Bloemberg, D., & Quadrilatero, J. (2012). Rapid determination of myosin heavy chain expression in rat, mouse, and human skeletal muscle using multicolor immunofluorescence analysis. PLoS ONE, 7(4). https://doi.org/10.1371/journal.pone.0035273

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