Automated Methods for the Analysis of Skeletal Muscle Fiber Size and Metabolic Type - International Review of Cell and Molecular Biology - page 284

function. The fat content of skeletal muscle increases with endurance exer-
cise training, a phenomenon known as the “athlete's paradox” ( Goodpaster
et al., 2001 ). However, the fat content of muscle is also elevated in obese
compared to lean individuals, which, in this context, is associated with
development of insulin resistance of the muscle and increased tendency
to develop Type II diabetes ( Guo, 2007 ).
Lipid droplets can be assayed in skeletal muscle tissue sections via a vari-
ety of reagents. For example, Sudan Black, which is visible under bright
field, was used by Padykula and Gauthier (1963) to visualize lipid droplets
in rat diaphragm muscle and by Cherian et al. (1966) to visualize lipid drop-
lets in denervated pigeon skeletal muscle. Oil Red O is also commonly used;
examples include studies in which skeletal muscle lipid droplets were eval-
uated in lean versus obese humans ( Goodpaster et al., 2001 ) and to evaluate
effects of exercise training on human muscle ( Pruchnic et al., 2004 ). Oil Red
O is also fluorescent, and this property has been taken advantage of in studies
in which lipid droplets in skeletal muscle were quantified in an automated
fashion ( Koopman et al., 2001 ). In recent studies, BODIPY (493/503) has
often used for visualization of lipid droplets in skeletal muscle. It is a fast and
simple method that produces bright fluorescent staining that can be quan-
tified using imaging programs. Quantification methods for intramyocellular
lipid droplets in human skeletal muscle were reviewed by Schrauwen-
Hinderling et al. (2006) . Additionally, in a direct comparison, BODIPY
and Oil Red O yielded equivalent results in serially stained skeletal muscle
sections, and BODIPY also proved useful in labeling lipid droplets in iso-
lated single muscle fibers ( Spangenburg et al., 2011 ).
5. DEVELOPMENT OF AUTOMATED ANALYSIS
OF SKELETAL MUSCLE
Common data parameters that are of interest to muscle researchers
include the number of fibers in a muscle section, the CSAs of the fibers
(which changes in response to a variety of biological processes and patholog-
ical stimuli), characterization of the metabolic fiber subtypes that are present,
which can be done by quantifying relevant biomarkers (e.g., particular myo-
sin subtypes) on a fiber-by-fiber basis. The beginning point for these analyses
is to recognize the boundaries of the individual muscle fibers within the sec-
tion, which enables calculation of the fiber CSA, as this is a critical step in
understanding the response of the muscle to a variety of physiological and
pathological processes and stimuli.