For 20 years, DNA microarrays have been the go-to method for revealing patterns of gene expression. But as the price of next-generation sequencing continues to fall, RNA sequencing (RNA-seq) has become an increasingly popular method for assessing transcriptomes.
A DNA microarray consists of a predetermined assortment of nucleic acid probes attached to a surface. To assess gene expression, researchers derive complementary DNA (cDNA) from cellular RNA, label the cDNA with a fluorescent marker, wash labeled cDNA over the array, and use lasers to assess how much cDNA has stuck to each probe. RNA-seq relies on converting RNA into a cDNA library and then directly sequencing the cDNA.
While learning how to deal with the raw data RNA-seq produces can be tricky, RNA-seq has capabilities that microarrays lack. The technique can reveal previously uncharacterized transcripts, gene fusions, and genetic polymorphisms, while microarrays only pull out transcripts that researchers explicitly fish for. With sufficient sequencing depth, RNA-seq can also detect high-abundance and low-abundance transcripts more effectively than microarrays can.
Scientists are voting with their feet. The revenue that Affymetrix, a leading microarray producer, received from the gene expression portion of its business decreased from $104.5 million in 2012 to $73.4 million by 2014, according to the company’s 2014 annual report. In 2009, nearly all NIH funding to grants in their first year concerning gene expression went to projects using microarrays, according to an analysis by Dave Delano, senior product manager for gene expression and regulation at Illumina. By 2013, microarrays’ share had fallen to approximately one-third of new funding.
But due to the ease of using microarrays to analyze large numbers of samples rapidly, the technology continues to dominate RNA-seq in terms of sheer numbers of samples analyzed. Weida Tong, director of bioinformatics and biostatistics at the US Food and Drug Administration (FDA) National Center for Toxicological Research in Jefferson, Arkansas, notes that, in 2014, data from more than 54,000 samples analyzed via arrays were deposited into the Gene Expression Omnibus (GEO) database, compared to data from just around 9,000 samples analyzed using RNA-seq (Genome Biol, 15:523, 2014).
Eventually, the research community will fully switch to RNA-seq, Tong says. Until then, microarray and RNA-seq data need to be more compatible and the data analysis and storage for RNA-seq must become easier. “This is just giving birth,” Tong says. “It’s painful, but once the process is finished, the community can enjoy this technology.”
Here, The Scientist discusses the transition from microarrays to RNA-seq, when researchers should make the switch, and strategies for making the process as painless as possible.
A WHOLE NEW WORLD
For applications such as exploratory work or research using nonmodel organisms, RNA-seq is a clear winner because it reveals transcriptomes without bias, uncovering novel splice junctions, small RNAs, and even novel genes that microarrays simply miss. (See “Transcriptomics for the Animal Kingdom,” The Scientist, July 2013.)
“Unlike microarray probes, RNA sequencing does not require a priori? sequence knowledge of the sample for analysis,” Kevin Poon, global product manager of gene regulation at Agilent Technologies in Santa Clara, California, writes in an e-mail to The Scientist. “In this way, it is an ideal platform for discovery research; obtaining the absolute sequence of transcripts enables the discovery of mutations and fusion transcripts.” Agilent produces both microarrays and RNA-seq tools.
Mariano Alvarez, a graduate student in the lab of Christina Richards at the University of South Florida (USF) in Tampa, studies how the 2010 Gulf oil spill has affected gene expression in the hexaploid salt marsh grass Spartina alterniflora. Alvarez and his collaborators started out using microarrays to assess gene expression in oil-exposed versus nonexposed plants. But for a new project surveying gene expression in invasive populations of Japanese knotweed (Fallopia japonica), the researchers are including RNA-seq data, in hopes of better understanding how expression of gene variants and isoforms differ in different habits.
RNA-seq has also been valuable for exploring the uncharted regions of even well-studied species’ transcriptomes. For instance, in December the University of Toronto’s Benjamin Blencowe and his colleagues used a novel RNA-seq computational method to demonstrate altered transcription patterns of tiny snippets of DNA called microexons in different brain tissues and in people with autism versus controls (Cell, 159:1511-23, 2014).
Researchers who switch to RNA-seq are often “seeing dimensions of the biology they just weren’t picking up in microarrays,” says Anup Parikh, a senior product manager at Thermo Fisher Scientific, which sells RNA-seq tools through its Ion Torrent brand….
