NEW YORK (GenomeWeb News) - Some life-sciences tool vendors have pledged that digital gene-expression applications performed on their second-generation sequencing platforms will eventually dominate the gene-expression market at the expense of arrays, but several experts familiar with both technologies say cost, data analysis, and throughput issues are continuing to encourage researchers to choose arrays over DGE.
Both Illumina and Applied Biosystems, now called Life Technologies after merging with Invitrogen last month, have launched gene-expression applications on their sequencing instruments over the past two years. But while these firms look to bite off a chunk of the expression array market, some users familiar with both array-based and sequencing-based expression profiling say the two applications are different and predict that arrays will continue to be a valuable platform for researchers, at least for the next few years.
Gary Hardiman, director of the Biomedical Genomics Microarray Facility at the University of California in San Diego, told GenomeWeb Daily News sister publication BioArray News recently that researchers may split in regards to what platform they use based on their different research projects.
“I think that researchers just interested in which genes are altered with regards to expression in response to a biological phenomenon will stick with arrays for the time being — this analog information, the lists of genes that are altered, and the pathway and ontology analyses you can do with these lists, will continue to provide valuable insights into biological questions,” Hardiman said.
Hardiman — whose facility offers both Illumina and Agilent expression arrays — said that he has performed DGE on the Genome Analyzer and was impressed with the results. He believes that those who are interested in looking at splice variants and alternative transcripts will be the “initial heavy users of DGE.” According to Hardiman, researchers studying organisms with limited or no sequence data are also likely to move projects over to DGE.
At the same time, Hardiman said that experiment design will not be the only indicator of which scientists use arrays and which ones use sequencers for expression profiling. He said that cost will continue to be a “major factor in the minds of many researchers.”
“I really don’t think there is going to be erosion in array demand due to the sequencers,” he said.
In the long term, though, Hardiman said that added multiplexing capacity on sequencers, such as enabling many samples per flow cell, with each bar coded with distinct tag sequences, will drive costs down, spurring adoption of DGE. “It is only a matter of time until this technology replaces arrays entirely,” Hardiman said.
Shawn Levy, director of the Vanderbilt University Microarray Shared Resource in Nashville, Tenn., told BioArray News that DGE is currently “compatible and complementary” to arrays.
“As it stands today and for the next year, I wouldn’t consider it to be competitive to arrays for the majority of experimental designs,” he said. Levy agreed that DGE could become competitive with arrays over the next few years, but argued that array vendors are also likely to develop more-efficient and less-expensive products too.
Vanderbilt currently offers expression on the Affymetrix, Agilent, and Roche NimbleGen platforms. Levy said that while there has been notable buzz around DGE, users of the VMSR still tend to pick arrays over DGE due to cost constraints, lack of data analysis tools for DGE, and throughput considerations.
“We have seen demand, but a different kind of demand for what we have seen with arrays,” said Levy. “It absolutely is a key technology, but I would say that, by and large, the experimental designs that benefit most from arrays are a little bit different than the experimental designs that you would use on the sequencers, at least in the short term.”
Levy predicted that users will probably seek to take the best from both platforms by, for instance, surveying a larger number of patient samples with arrays, identifying representative samples or outliers, and then applying DGE to that smaller number of samples.
Neil Winegarden, head of operations at the University Health Network Microarray Centre in Toronto, said that “people have definitely bought into the idea” that DGE is “the future,” but cautioned that data analysis challenges were likely to encourage users to stick with arrays.
“People tend to like the [DGE] data, but the biggest issue is that the data analysis support just is not there,” Winegarden said. “Even if you talk to ABI and Illumina, they are going to tell you that right up front, that it is a big challenge. The technology is just developing so much faster than the data analysis is right now,” he said.
Winegarden also said that cost will motivate users to pick arrays over DGE, but he suggested that the two technologies “may very well be synergistic with one another, rather than competitive.”
While users weigh the merits of using arrays or sequencers in their studies, companies that sell microarrays for expression profiling are in different positions, while most appear to agree that data analysis challenges and the cost of sequencing applications will persuade some users to choose arrays for their projects in the near term.
Joel McComb, senior vice president and general manager of life sciences at Illumina, last month told analysts at the firm’s analyst day event in San Diego that the “ability to do sample preparation and then the imaging of the analysis and the time required is going to continue the balance between how much work goes on in sequencing and how much work goes on in the array business.”
McComb argued that arrays “continue to be easier to prep, as far as the sample goes, and image analysis is faster and more convenient on the array technology.” According to McComb, Illumina sees the “two technologies working together,” and agrees that “bioinformatics will play a key role in how that business transitions from one area to the other area.”
While Illumina sees the two applications it sells as complementary, Affymetrix is in a different position in the market, having focused on acquiring lower-throughput technologies, rather than a second-generation sequencing platform that could support digital gene expression. Moreover, Affy is still the market leader in regards to array-based gene expression, which means it could take the most substantial hit to its business should DGE gain wider adoption at the expense of arrays.
Jeremy Preston, associate director of product marketing at Affy, told BioArray News that the company views “next-generation sequencing as a complementary technology” that “may generate content which we can put on microarrays.”
Preston said that researchers who currently use DGE are “early technology adopters” and it will be “some time before DGE makes its way into the mainstream market.” According to Preston, customers consider three issues when choosing between DGE and microarray-based research, namely cost, throughput, and data analysis.
A more comprehensive version of this article appears in the most recent issue of BioArray News.