The hair-growth
cycle is an example of a cyclic process that is well characterized
morphologically but understood incompletely at the molecular level.
As an initial step in discovering regulators in hair-follicle
morphogenesis and cycling, we used DNA microarrays to profile mRNA
expression in mouse back skin from eight representative time points.
We developed a statistical algorithm to identify the set of genes
expressed within skin that are associated specifically with the
hair-growth cycle. The methodology takes advantage of higher
replicate variance during asynchronous hair cycles in comparison
with synchronous cycles. More than one-third of genes with
detectable skin expression showed hair-cycle-related changes in
expression, suggesting that many more genes may be associated with
the hair-growth cycle than have been identified in the literature.
By using a probabilistic clustering algorithm for replicated
measurements, these genes were grouped into 30 time-course profile
clusters, which fall into four major classes. Distinct genetic
pathways were characteristic for the different time-course profile
clusters, providing insights into the regulation of hair-follicle
cycling and suggesting that this approach is useful for identifying
hair follicle regulators. In addition to revealing known
hair-related genes, we identified genes that were not previously
known to be hair cycle-associated and confirmed their temporal and
spatial expression patterns during the hair-growth cycle by
quantitative real-time PCR and in situ hybridization. The same
computational approach should be generally useful for identifying
genes associated with cyclic processes from complex tissues.
Publications
Lin KK, Chudova D, Hatfield GW, Smyth P, Andersen B.
Identification of hair cycle-associated genes from time-course gene
expression profile data by using replicate variance. Proc Natl
Acad Sci U S A. 2004; 101(45): 15955-60.
Zhu, Z., Lin, K., Kasamatsu, T. Artifactual synchrony
via capacitance coupling in multi-electrode recording from cat striate
cortex. Journal of Neuroscience Methods. 2002; 115: 45-53.
