Semi-automated technique for bovine skeletal muscle fiber cross-sectional area and myosin heavy chain determination

Lay Summary Properties of muscle tissue are affected by cellular-level changes in the isoform of myosin, a protein involved in muscle contraction. The heavy chain subunit of myosin (MyHC) is affected by breed type, changes as animals mature, and interacts with muscle fiber size when growth-promoting technologies are used in meat animals. While MyHC type and muscle fiber size are important for growth potential and meat quality of livestock, measurement of these variables is time consuming. The objective of this study was to validate a semi-automated workflow for identification of MyHC type and measurement of muscle fibers compared to a previously published manual technique. The semi-automated workflow evaluated approximately six times more myofibers in one-sixth of the time compared to the manual workflow. While the semi-automated technique identified the muscle profile with greater relative abundance of glycolytic muscle fibers and 14% smaller fibers, results from both techniques were strongly correlated and found similar biological results. An additional benefit of the semi-automated workflow was the use of objective thresholds to classify MyHC types as opposed to subjective human judgement of the manual workflow. This study demonstrated that the semi-automated workflow efficiently and objectively imaged, classified, and measured muscle fibers. Myosin heavy chain (MyHC) type and muscle fiber size are informative but time-consuming variables of interest for livestock growth, muscle biology, and meat science. The objective of this study was to validate a semi-automated protocol for determining MyHC type and size of muscle fibers. Muscle fibers obtained from the longissimus and semitendinosus of fed beef carcasses were embedded and frozen within 45 min of harvest. Immunohistochemistry was used to distinguish MyHC type I, IIA, and IIX proteins, dystrophin, and nuclei in transverse sections of frozen muscle samples. Stained muscle cross sections were imaged and analyzed using two workflows: 1) Nikon workflow which used Nikon Eclipse inverted microscope and NIS Elements software and 2) Cytation5 workflow consisting of Agilent BioTek Cytation5 imaging reader and Gen5 software. With the Cytation5 workflow, approximately six times more muscle fibers were evaluated compared to the Nikon workflow within both the longissimus (P < 0.01; 768 vs. 129 fibers evaluated) and semitendinosus (P < 0.01; 593 vs. 96 fibers evaluated). Combined imaging and analysis took approximately 1 h per sample with the Nikon workflow and 10 min with the Cytation5 workflow. When muscle fibers were evaluated by the objective thresholds of the Cytation5 workflow, a greater proportion of fibers were classified as glycolytic MyHC types, regardless of muscle (P < 0.01). Overall mean myofiber cross-sectional area was 14% smaller (P < 0.01; 3,248 vs. 3,780) when determined by Cytation5 workflow than when determined by Nikon workflow. Regardless, Pearson correlation of mean muscle fiber cross-sectional areas determined by Nikon and Cytation5 workflows was 0.73 (P < 0.01). In both workflows cross-sectional area of MyHC type I fibers was the smallest and area of MyHC type IIX fibers was the largest. These results validated the Cytation5 workflow as an efficient and biologically relevant tool to expedite data capture of muscle fiber characteristics while using objective thresholds for muscle fiber classification. A semi-automated technique for evaluating muscle fiber size and myosin heavy chain type can image and analyze six times more myofibers in one-sixth of the time compared to the manual workflow with similar biological results.