Improving the Reliability of

Microarrays for Toxicology Research:
A Collaborative Approach

Jerilyn A. Timlin1, Ronglin Wang2, Ann Miracle2, Edward V. Thomas1, Grant Heffelfinger1,

Greg Toth2

'Sandia National Laboratories*, Albuquerque, NM, 2USEPA**, Cincinnati, OH

EPA Microarray / Sandia Hyperspectral Scanning

EPA is developing a Pimephales promelas (fathead
minnow) microarray as part of Computational
Toxicology initiative to develop biomarkers for risk

assessments

Capitalizing on SNL's unique capabilities in
hyperspectral imaging (HSI) of microarrays to ensure the
highest confidence in EPA microarray data
HSI performs QC of arrays by identifying and
quantitating all emitting species
Previous studies revealed many arrays possess
contaminant emissions that can skew results, HSI is the
only analytical technique that can detect and correct for
these

Statistical Design and Analysis

Leveraging EPA strengths in high volume experimental data
and SNL strengths in statistical analysis of microarrays to
provide improved Pimephales promelas array data

Better facilitates data mining and biomarker identification

Statistically robust array designs

Multivariate data analysis

Improved visualization tools

Statistical Design Reduces Variability

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This integrated approach will permit extraction of highest quality data from a
minimal set of experiments, leading to more accurate, more sensitive determination

of specific molecular diagnostic indicators of bioavailable stressors in aquatic
ecosystems - a critical component in the EPA's exposure characterization as part of

its ecological risk assessment mission

""Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.
"""Although this work was reviewed by the U.S. EPA and approved for publication, it may not necessarily reflect official Agency policy.

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