et al.

Life Science Technologies
Genomics
860
Featured Participants
Affymetrix
www.affymetrix.com
Agilent
www.agilent.com
Ambry Genetics
www.ambrygen.com
Expression Analysis
www.expressionanalysis.
com
Illumina
www.illumina.com
Life Technologies
www.lifetechnologies.com
Oxford Gene Technology
www.ogt.co.uk
Pathwork Diagnostics
www.pathworkdx.com
PerkinElmer
www.perkinelmer.com
Washington University
School of Medicine in St.
Louis
medschool.wustl.edu
tools, the concept of what makes a microarray has begun to evolve.
Traditionally, microarrays consisted only of nucleotides attached to
a solid surface, but variations on this theme also exist. For example,
Life Technologies (Carlsbad, California) developed its TaqMan
OpenArray Real-Time PCR plates, which include 3,072 wells.
ìThe arrays can be formatted from our inventory of eight million
TaqMan assays,î says Jami Elliott, market development manager
at Life Technologies. The real-time PCR assays, which use TaqMan
probes (so named because these assays rely on the Taq polymerase),
can be used to measure gene expression, identify biomarkers,
and more.
If eight million choices arenít enough, Joshua Trotta, director, business
development genetic analysis at Life Technologies, says, ìWe can
custom design one.î
Ongoing Data Dilemmas
Rather than making microarrays, Expression Analysis, a Quintiles
company in Durham, North Carolina, uses arrays to conduct a wide
range of studies for customers, such as gene-expression profiling. In
doing so, Expression Analysis uses microarrays from several vendors,
including Affymetrix, Illumina, and Fluidigm in South San Francisco,
California. According to Pat Hurban, vice president of R&D at
Expression Analysis, ìMicroarrays are very mature as a technology,
but there are still a number of challenges in working with the data,
especially when you want to drill down into the biology.î He adds,
ìItís one thing to provide a statistical treatment of data, but another to
understand the pathways involved and translate that into biology.î This
company uses a collection of proprietary tools in an effort to bridge that
knowledge gap.
In fact, Hurban advises researchers to reassess the best technology to
use as a project advances. ìYou must be mindful of the technical limitations
of microarrays,î he says. For example, he points out that microarrays
provide excellent discovery tools. ìItís not uncommon to identify
specific genes of interest with microarrays,î Hurban says. ìWhen
it comes to translational research, the question becomes: Is it advisable
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to continue on a microarray platform as you get closer to the clinic or
transition to a more suitable and robust technology, such as [quantitative]
PCR or sequencing.î
In a recent project, Expression Analysis worked with a client who had
what Hurban describes as ìa preliminary gene-signature panel that was
very useful as a diagnostic in a certain indication area.î Researchers at
Expression Analysis worked with patient samples from the sponsor to
put that signature on microarrays. ìWe showed the validity of this panel,î
Hurban says. ìUltimately, the sponsor wanted to turn this signature
into a diagnostic and became concerned with the microarray results
because the precision was a bit of a challenge.î Consequently, the client
eventually turned to a PCR-based platform for the final diagnostic. As
a result, Hurban says, ìYou might use a microarray to some point, and
then go to another technology.î
Tomorrowís Tools
The ongoing advances in sequencing technology have made more than a
few experts predict the demise of microarrays. For example, Elizabeth
Chao, director of translational medicine at Ambry Genetics in Aliso
Viejo, California, says, ìThe expression arrays that Iíve been using for
14 years are incredible tools, but RNA sequencing is starting to replace
microarrays in research and translation.î She adds, ìSequencing
is not replacing microarrays in the clinical setting yet, but it probably
will soon.î
The data generated by sequencing can be both beneficial and challenging.
Sequencing provides a gigantic amount of data in a short period of
time, but it can be difficult to interpret so much data. Chao is confident
that interpreting sequencing data will improve rapidly. She says, ìBioinformatics
has really come up, and new methods are making it possible
to look at sequences across the entire genome.î
To evolve with changes in technology, some companies provide services
that teach researchers to use the growing amounts of data. For
example, Todd Smith, senior leader, research and application at PerkinElmer,
says, ìWe can help people as they go from microarrays to
DNA sequencing.î This can include analytical techniques for handling
the higher volume of data. These technologies, though, will likely
complement each other, according to Smith and his colleagues. ìThere
are applications where microarrays work best, and others where sequencing
works best,î says Williams. ìThere are areas where sequencing
wonít work well, but microarrays can.î As an example, Williams says
they are about to start a study that involves 160 samples that must be
processed in a matter of weeks. ìThereís no way we could go through
that with sequencing and get it turned around in time to have meaningful
data,î he says. Moreover, Smith says microarrays are superior
to sequencing when it comes to searching for structural variations in
a genome.
Though some experts may have differing opinions, the general consensus
predicts that microarrays will continue to benefit basic research
and provide clinical tools related to genomics. In the end, microarrays
will advance where they work the best.
Mike May is a publishing consultant for science and technology.
DOI: 10.1126/science.opms.p1300072
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