DNA microarrays, also known as DNA “chips,” have become an increasingly popular way to study the expression and function of thousands of genes simultaneously. Without proper controls and verification, however, the use of this new technology can result in nothing more than experimental “noise.”
That was one of the “take-home messages” from an international symposium on differential gene expression hosted by Vanderbilt University Medical Center earlier this month. More than 150 scientists attended the meeting, held Oct. 12-15 at the Nashville Marriott Hotel at Vanderbilt.
Although microarrays can generate an unprecedented amount of data on patterns of gene expression, care must be taken to ensure proper experimental controls and reproducibility so the data obtained is meaningful, said two of the speakers, David Lockhart, Ph.D., president and chief scientific officer of Ambit Biosciences, and John Quackenbush, Ph.D., investigator at The Institute for Genomic Research (TIGR) in Rockville, Md.
It is important to control the quality and integrity of the RNA used in the microarrays, and to make sure that the RNA samples being compared are equally labeled, they said. Differences in gene expression indicated by high-density oligonucleotide arrays also should be verified by an independent method, such as Northern blot analysis, Lockhart said.
Failure to take these factors into consideration can negatively affect research findings, added Stephen Cooper, Ph.D., professor of Microbiology and Immunology at the University of Michigan.
Using several microarray studies of the eukaryotic cell cycle published in high-impact journals, Cooper pointed out the potential hidden problems in analyzing large data sets, and concluded that most of the new cell cycle regulated genes discovered by microarrays were in fact statistical “noise” of the method.
Lin Zhang, Ph.D., and Jian Yu, Ph.D., of Johns Hopkins University presented the latest update of the SAGE (serial analysis of gene expression) technology that Zhang helped develop. Using the newly developed “SAGE Genie” Web browser, millions of SAGE tags from more than 100 different cell types can be analyzed at the National Center for Biotechnology Information Web site, they said.
Zhang stressed the importance of having a good SAGE library and using longer SAGE tags for a more accurate analysis of gene expression in global scale.
Other technologies discussed at the meeting included the imaging of proteins in tissues with mass spectrometry, a technology pioneered by Richard Caprioli, Ph.D., Stanley Cohen Professor of Biochemistry and director of the Mass Spectrometry Research Center at Vanderbilt; and siRNA expression in mammalian cells, a powerful method for efficient knock down of gene expression developed by Reuven Agami, Ph.D., research group leader at the Netherlands Cancer Institute in Amsterdam.
The keynote speaker, Mark M. Davis, Ph.D., Howard Hughes Medical Institute investigator and professor of Microbiology and Immunology at Stanford University, recounted his journey in hunting for the elusive T-cell receptor nearly two decades ago.
Using a subtractive hybridization technique, a primitive method by today’s standard, Davis’ group discovered one of the T-cell receptor subunits by comparing mRNA expression between T and B cells, both of which come from the same cell lineage.
Not only did he correctly envision that the T-cell receptor had to be expressed in a T-cell specific manner, but also he determined that the molecule should look like immunoglobulins (or antibody molecules), which mediate the B-cell arm of immunity. In addition, the gene encoding it should show chromosomal rearrangement in different T cells recognizing different antigens just like antibody encoding genes.
Davis’ success was due to more than good luck, but rather to a careful experimental design and attention to detailed execution of the experimental procedures with all the controls necessary, notes Peng Liang, Ph.D., associate professor of Cancer Biology at the Vanderbilt-Ingram Cancer Center, who organized and chaired the symposium.
“His pioneering, hypothesis-driven research is in sharp contrast to a growing number of hypothesis-generating studies in the current field of gene expression analysis,” Liang said.
(Editor’s note: This story was based on material provided by Dr. Liang.)