Teraflop 73 - Novembre - cesca

Genomic technologies have
been hailed as a boon for
the study of the nervous system,
as it is believed that they will help
us unravel the complexity of neural
function in health and disease. The potential
of genomics to fulfil this
promise is undeniable, but a dose of
healthy scepticism is warranted when
thinking about the brain because
some properties of this organ have already
hampered the progress of its
genomic analysis. If the study of the
nervous system is to profit from the
post-genomic era, it will be necessary
to find ways to circumvent the limitation
that such properties impose on us.
Brain heterogeneity
The ethos of the genomic analysis is to
simultaneously analyse differences in
the expression of a large number of
genes between samples of interest.
Several methods have been developed
to achieve this goal, but the neuroscience
community has embraced one
of them in particular as its genomic
tool of choice —cDNA microarrays.
A microarray is a solid surface to
which many DNA molecules are attached
at high density. Two differentially
labelled cDNA samples can then
be hybridized to it, enabling us to
compare the expression levels of transcripts
that are present in the samples
(see Figure). Owing to their highly
parallel nature, microarrays can quickly
interrogate complex mixtures that
contain thousands of cDNA molecules.
As the brain expresses at least half of
the genome, this property of microarrays
makes them quite appealing for
neurogenomic studies.
Unfortunately, the brain is a highly
heterogeneous organ, and this heterogeneity
limits our ability to take
The Present and Future
of Neurogenomics
Juan Carlos López
Editor, Nature Reviews Neuroscience
advantage of the use of microarrays.
In addition to containing different cell
types (neurons and several classes of
glia), the brain is divided in a staggering
number of nuclei, each of which has
a specific molecular signature —each
nucleus expresses a subset of genes
that is only partly shared with other nuclei.
To make matters worse, neurons
within a given nucleus are molecularly
different from each other, often in profound
ways.
As a result of this heterogeneity,
we must decide on the level at which
to focus our genomic analysis: whole
brains, specific nuclei, single neurons?
Although the level of analysis clearly
depends on the biological problem
that we are trying to solve, each of
them has conceptual and methodological
problems that need to be addressed
to push the field forward. For
example, comparing whole brains
might give us enough material for hybridization,
but any subtle differences
between the samples might get lost
owing to the variability across regions
and among neurons.
Similarly, comparing single neurons
or specific nuclei to disclose
those subtle differences is difficult. For
example, we might want to study a nucleus
that might be less than 1 mm in
diameter or might be interspersed
with other nuclei. These facts will
make it extremely hard to dissect,
resulting in the availability of insufficient
cDNA for our analysis. Although
progress has been made to solve
problems of this sort, a more worrying
concern is our ability to decide at
what point the differences between
two small neuronal populations or between
two neurons stop being meaningful
to become spurious. If we
consider that microarrays have
disclosed an important degree of variability
in the expression profiles of genetically
identical animals, this concern
is not trivial.
Suitable models of disease
An area of neuroscience in which genomic
studies are expected to bring
unprecedented success is the study of
neurological and psychiatric conditions.
Although many brain diseases
have a genetic component, the nature
of the relevant genes remains elusive,
largely because most of these diseases
are multigenic —many genes
make small contributions to the resulting
phenotype. But owing to their ability
to analyse many genes in parallel,
genomic technologies are thought to
be the solution for this problem.
We can approach the genomic
analysis of brain disorders in at least
two ways: to compare gene expression
patterns of normal and diseased
human brains, or to compare it between
normal animals and animals in
which the disorder is reproduced. In
addition to the problems I outlined
above, both of these approaches have
other shortcomings that limit their
usefulness.
By contrast to tumours and other
physical manifestations of pathology,
human nervous tissue is often collected
post mortem.As a result, the inherent
variability of the brain is further
confounded by the conditions in
which the autopsy was performed and
by the state of the patient during the
so-called agonal state. For example,
pyrexia and hypoxia have significant
effects on the viability of tissue and
the preparation of RNA.
More problematic is the fact that,
even if the tissue is collected efficiently,
it will contain a mixture of healthy,