Genomics: Back From the Dead

Wed, 11/04/2009 - 11:11am
James Netterwald, PhD, MT (ASCP)

Researchers that use the pre-genomic tools, such as the microarray, to study modern biological problems in stem cells think so.

The microarray was first commercialized in the mid-1990s, during what has become known as the pre-genomic era. Once the first “nearly complete” version of the human genome was published circa 2001, biology was suddenly launched into the post-genomic era, prompting and fueling many “genomics is dead” rumors. For most of the pre-genomic era—and some of the post-genomic era—microarrays utilized a hybridization-based method, but that has changed. SABiosciences, Frederick, Md., has forgone the use of traditional hybridization-based microarrays in favor of real-time PCR Arrays. The reason for the switch: PCR arrays have greater sensitivity and a wider linear dynamic range than traditional arrays.

Heatmap of expression differences for genes after rapamycin treatment (12 and 24 hr). Directions and amounts of change are indicated by the color and intensity of the block. D and R denote control and rapamycin-treated cells, respectively. (Source: Fei Wang, PhD)

“The use of real-time PCR means that we can achieve exquisite sensitivity and wide linear dynamic ranges to analyze genes expressed at a wide variety of levels,” says George Quellhorst, PhD, R&D Manager at SABiosciences. Their platform, called RT2 Profiler PCR arrays, is a SYBR Green-based real-time PCR technology. The arrays are in the form of 96- or 384-well plates, with each well containing a gene-specific primer pair optimized for real-time reverse transcriptase PCR. Each array enables the user to query the relative expression of 84 different pathway-focused or cell type-specific genes, including stem cells.

“Our stem cell-focused, real-time PCR Arrays analyze the expression of genes defining the ‘stemness’ and regulating early steps in the differentiation of embryonic, hematopoietic, mesenchymal, and neurogenic stem cells,” says Shankar Sellappan, PhD, associate product manager at SABiosciences. “PCR Arrays are also available that characterize both upstream regulatory and downstream effector genes in the Hedgehog, Notch, TGF Beta/BMP, and WNT signaling pathways.” In summary, “our stem cell PCR Arrays offer an easy-to-use, accurate system for analyzing stem cell development, differentiation-related pathway genes, and epigenetic changes,” says Jeffrey Hung, PhD, director of marketing at SABiosciences.

Keeping it global
Stem cell research is generally regarded as a relatively recent addition to modern biology. Given that fact, two questions remain. First, is the pre-genomic hybridization-based array still a viable option for stem cell research? And second, is an array solely designed for analysis of stem-cell-specific genes a better tool for stem cell research than using a more global approach? The answers to these questions vary, based on the purpose of the study and, of course, the researcher’s personal preferences.

“The general microarrays are even better because you have all of the micro-RNA (miRNA) genes. So if you look at stem cells or different types of stem cells, you can have a better idea of the miRNA in which you might be interested. It is always better to have a global view,” says Carlo M. Croce, MD, chairman, Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University (OSU), Columbus, Ohio.

Beyond Expression 
It is no secret that researchers can use microarrays to study more than just gene expression. “We’ve used microarrays for mapping the molecular composition of hundreds of cell types, and discovered that pluripotent stem cells are in a class by themselves,” says Jeanne F. Loring, PhD, professor of developmental neurobiology and director of the Center for Regenerative Medicine at The Scripps Research Institute, La Jolla, Calif. In fact, by using various microarray platforms to measure mRNA expression, miRNA expression, DNA methylation, and copy number variation, Loring’s lab has discovered that pluripotent stem cells share common characteristics with each other, characteristics not found in all other cell types. By integrating the data from these various array platforms, she has been able to answer questions such as “does a duplication of a gene result in higher mRNA expression?” OR “Does high expression of a particular class of miRNAs affect the production of a particular protein?

Such integration is logical given Loring’s preference for the systems biology approach. “We view the cells in a global context, letting the data tell us what pathways are important,” says Loring. “DNA methylation, SNPs, copy number variations, miRNAs, mRNAs, chromatin structure, protein modification, are all pieces of the stem cell puzzle … and we’re working on the whole puzzle, not just one little corner.”

Loring’s lab uses the Illumina BeadStation platform to perform analysis of miRNA expression, mRNA expression, DNA methylation, and SNP genotyping for several reasons. First, this platform allows her to perform all of these analyses on the same instrument. With performing the all of these analyses on the same samples, there is an internal quality control system that allows her to integrate these data, thus achieving the systems approach. Second, among the platform’s strengths, she can analyze “hundreds or thousands of cell lines, and find common themes quickly and relatively cheaply.”

Croce’s use of microarray technology to measure miRNA expression began earlier in this decade at Thomas Jefferson University in Philadelphia, where he and his colleagues developed the first microarray assay for a miRNA expression analysis, publishing the results of this study in 2004 (Calin et al. PNAS. 2004 Aug 10; 101(32):11755-60). In 2008, Croce et al. published a paper in Nature Protocols describing the minutiae of this array platform (Liu et al. 2008. Nature Protocols 3:563-578). The platform is essentially a glass slide containing 4,104 oligonucleotide probes capable of simultaneously interrogating the expression of over 1500 mature miRNAs and their corresponding precursors from 474 human and 373 mouse miRNA genes. This large number of probes also includes predicted miRNAs because these molecules eventually turn out to be true miRNAs. Each new version of the array (now on its fifth iteration) contains newly-discovered miRNAs, in addition to those found on previous versions.

“I suspect the most widely expressed miRNA have been discovered. But we are still to discover miRNAs that are more cell-type-specific or tissue-specific, for example, in cell types or tissues that has not been previously investigated, such as stem cells.”

Croce uses this platform to perform whole-genome-wide miRNA expression analysis. Samples of total RNA are derived from tumors and blood from cancer patients in OSU’s Cancer Center. “We have the largest database of cancer specimens because we have tested, to date, over 10,000 human cancer specimens,” says Croce. Total RNA is reverse-transcribed and hybridized to the array; target-probe hybridization is the principle of this array platform. Croce has used this platform to establish miRNA signatures and prognostic miRNA for colon cancer and breast cancer. The ultimate goal of Croce’s lab is to use these molecular signatures to develop diagnostic and prognostic procedures for specific types of cancer, as well as to develop miRNA-based therapeutics for those cancers.

Hot off the presses
Another example of the use of traditional, genome-wide arrays for stem cell gene expression analysis resulted in a 2009 PNAS paper (Zhou et al. PNAS. 2009 May 12; 106(19):7840-5), published by the laboratory of Fei Wang, PhD, assistant professor of cell and developmental biology at University of Illinois, Urbana-Champaign. Wang and his colleagues used genome-wide microarrays from Affymetrix to identify key genes involved in the regulation of pluripotency in human embryonic stem cells (hESC).

“The reason we are able to identify a number of genes actually important for pluripotency instead of thousands of genes is simply because we have a very-defined purpose,” says Wang.

In this published exploratory study followed by functional dissection, Wang and his colleagues first treated hESC with rapamycin, a rapid MTOR kinase inhibitor, capable of shutting down activity of this protein within a matter of minutes. The main observation was that MTOR inhibition induces differentiation of human embryonic stem cells (mesoderm and endoderm lineages). After making this phenotypic observation, they were interested in dissecting the molecular mechanism involved, which is where the microarray was implemented into the research schema. For the molecular dissection, MTOR inhibition in hESC was followed by genome-wide gene expression analysis. “The major reason to use a genome-wide array instead of subset arrays is that we didn’t know which genes are downstream of MTOR. Using a genome-wide array, we were actually able to identify genes that we had no idea about,” says Wang. “It’s really hard to use a candidate approach [in this case].”

Through this experiment, the lab was able to identify 100 genes that are directly impacted by MTOR inhibition. Wang lab members were able to narrow down the number of potential MTOR effector genes to approximately 20, based on the level of change in gene expression. And, in the end, six to seven important mediators of MTOR function, as it pertains to maintenance of pluripotency in hESC, were identified.

Despite the fact that biology has entered the post-genomic era and that stem cell researchers address post-genomic biological problems with pre-genomic tools, “the microarray is still important, at least as part of your arsenal, says Wang. “But you cannot totally rely on a single approach. Instead, this approach has to be combined with other methods such as cell biology [methods].”

However, although other methods exist, there is no doubt the microarray is still viable even when solving post-genomic biological problems in new systems such as stem cells. Maybe now, all of the “genomics is dead” claims will finally be put to rest.

This article was published in Bioscience Technology magazine: Vol. 32, No. 10, October, 2009, pp. 1, 10-11.


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