Semiautomatic morphometric analysis of skeletal muscle obtained by needle biopsy in older adults

Analysis of skeletal muscle mass and composition is essential for studying the biology of age-related sarcopenia, loss of muscle mass, and function. Muscle immunohistochemistry (IHC) allows for simultaneous visualization of morphological characteristics and determination of fiber type composition. The information gleaned from myosin heavy chain (MHC) isoform, and morphological measurements offer a more complete assessment of muscle health and properties than classical techniques such as SDS-PAGE and ATPase immunostaining; however, IHC quantification is a time-consuming and tedious method. We developed a semiautomatic method to account for issues frequently encountered in aging tissue. We analyzed needle-biopsied vastus lateralis (VL) of the quadriceps from a cohort of 14 volunteers aged 74.9 ± 2.2 years. We found a high correlation between manual quantification and semiautomatic analyses for the total number of fibers detected (r² = 0.989) and total fiber cross-sectional area (r² = 0.836). The analysis of the VL fiber subtype composition and the cross-sectional area also did not show statistically significant differences. The semiautomatic approach was completed in 10-15% of the time required for manual quantification. The results from these analyses highlight some of the specific issues which commonly occur in aged muscle. Our methods which address these issues underscore the importance of developing efficient, accurate, and reliable methods for quantitatively analyzing the skeletal muscle and the standardization of collection protocols to maximize the likelihood of preserving tissue quality in older adults. Utilizing IHC as a means of exploring the progression of disease, aging, and injury in the skeletal muscle allows for the practical study of muscle tissue down to the fiber level. By adding editing modules to our semiautomatic approach, we accurately quantified the aging muscle and addressed common technical issues.