Interlaboratory and interplatform comparison of microarray gene expression analysis of HepG2 cells exposed to benzo(a)pyrene
van Delft, J.H.M.
TNO Kwaliteit van Leven
Microarray technology is being used increasingly to study gene expression of biological systems on a large scale. Both interlaboratory and interplatform differences are known to contribute to variability in microarray data. In this study we have investigated data from different platforms and laboratories on the transcriptomic profile of HepG2 cells exposed to benzo(a)pyrene (BaP). RNA samples generated in two different laboratories were analyzed using both Agilent oligonucleotide microarrays and Cancer Research UK (CR-UK) cDNA microarrays. Comparability of the expression profiles was assessed at various levels including correlation and overlap between the data, clustering of the data and affected biological processes. Overlap and correlation occurred, but it was not possible to deduce whether choice of platform or interlaboratory differences contributed more to the data variation. Principal component analysis (PCA) and hierarchical clustering of the expression profiles indicated that the data were most clearly defined by duration of exposure to BaP, suggesting that laboratory and platform variability does not mask the biological effects. Real-time quantitative PCR was used to validate the two array platforms and indicated that false negatives, rather than false positives, are obtained with both systems. All together these results suggest that data from similar biological experiments analyzed on different microarray platforms can be combined to give a more complete transcriptomic profile. Each platform gives a slight variation in the BaP-gene expression response and, although it cannot be stated which is more correct, combining the two data sets is more informative than considering them individually. © 2009 Mary Ann Liebert, Inc. 2009.
To reference this document use:
Toxicology and Applied Pharmacology
cell strain HepG2
false negative result
gene expression profiling
principal component analysis
real time polymerase chain reaction
OMICS A Journal of Integrative Biology, 13 (2), 115-125