Biology Reference
In-Depth Information
2.2. Expression profiling of developmentally regulated genes
Gene profiling datasets are available for developing RGCs and CST neu-
rons, both of which undergo an age-dependent loss in regenerative ability
(
Bregman et al., 1989; Chen et al., 1995; Goldberg, Klassen, Hua, & Barres,
2002
). Both datasets derive from purified neurons, as opposed to whole
tissue, which is essential to track cell-specific changes.
Wang et al. (2007)
profiled immunopurified RGCs at 13 time points between E17 and P21,
identifying more than 2000 genes that change threefold or more between
two or more time points.
Arlotta et al. (2005)
used retrograde tracing and
flow cytometery to purify and profile CST neurons via microarray at four
time points between embryonic day 18 (E18) and postnatal day 14 (P14).
Although not created with axon regeneration in mind, this dataset
fortuitously spans the period when CST neurons lose regenerative
capacity
in vivo
(
Bregman et al., 1989
). Reanalysis of these data identified
237 genes that decreased and 834 genes that increased in expression
between E18 and P14 (
Blackmore, Moore, et al., 2010
). Interestingly, of
the genes regulated more than twofold between E18 and P14 in CST
neurons, approximately one-third also show correlated more than
twofold changes in RGCs (i.e., coordinately up- or downregulated with
age in both datasets; unpublished analysis). In contrast, only 1% of genes
show inversely correlated changes. In summary, microarray analysis has
identified thousands of genes that are developmentally regulated in RGC
or CST neurons and hundreds of genes that are similarly regulated in
both. Identifying which of these developmentally regulated genes
contribute functionally to axon growth has become an important goal for
regenerative research.
2.3.
screening of developmentally regulated genes
With the emergence of microarray technology, the identification of devel-
opmentally regulated genes far outpaced the ability to test gene function. To
close this gap, a set of technologies referred to as HTS and high-content
analysis (HCA) have begun to enter the field. HTS was first developed
by pharmaceutical companies to aid in drug discovery efforts and was
designed to test millions of compounds in noncellular assays (e.g., colorimet-
ric assays of enzyme activity; reviewed in
Jain &Heutink, 2010
). Living cells
were eventually incorporated into HTS assays using population-level read-
outs. Starting in the late 1990s, image-based analysis of individual cells was
introduced into screening experiments. This cell-based data acquisition,
In vitro
