Laura Lane has worked as a health and science journalist since 1997. She received her master's degree in biology from Stanford University. Since then, she has written for the Dallas Morning News, the Contra Costa Times, Shape magazine, WebMD, Yoga Journal, Diagnostic Imaging, the International Medical News Group, The Scientist, Bio IT World and Biocompare.
Tucked into the twists and turns of chromatin lie the DNA sequences that provide what is often referred to as our genetic blueprint. Only a fraction of these sequences are ever transcribed into RNA. By analyzing which sequences are transcribed, researchers can gain insight on which genes are expressed under normal conditions and which are associated with disease development. Looking at patterns of expression across the genome or certain disease states provides a “molecular signature”. When evaluated in combination with various phenotypes, this molecular signature allows researchers and physicians to work together to make more specific diagnoses, formulate better strategies for treatment and ultimately, hopefully, to mitigate the risk of developing disease in the first place.
In short, our understanding genomics continues to rapidly evolve. While traditional, well-established techniques are still prevalent and powerful, advantages gained through the use of new technologies are giving researchers the wherewithal to tackle problems and go the distance from the gene to the clinic.
Going digital
For years, real-time PCR (also referred to as quantitative PCR, or qPCR) has stood as the go-to technique for assessing gene expression. When it comes to genes that are rarely expressed, however, real-time PCR often reaches its limit. These genes require additional cycles of amplification, increasing the variability and thus decreasing the accuracy of quantification.
Digital PCR is helping researchers to overcome this challenge; the technology partitions the amplification into tens of thousands of separate, but simultaneous, reactions. In effect, each reaction theoretically starts with close to one copy of the target gene, negating the need for controls and computing results from standard curves. You simply count the number of resulting copies, essentially, with a resolution as low as just one copy.
Setting up these reactions by hand would be nearly impossible. That’s why companies have designed thermal cyclers specifically with digital PCR in mind. One of the biggest differences between the various models is the volume of the reaction mix. The smaller the sample volume, the fewer the copies of target strands starting out in the reaction.
Still, PCR depends on prior knowledge of target genes. You must design your primer based on those sequences so that it amplifies the target gene and nothing else. If you don’t successfully evade all the pitfalls of primer design, you may very well turn out false positives. Bad results could mean months of wasted time -- and money -- and other undesirable repercussions.
Next time, next generation
That’s why many researchers are turning to next generation sequencing (NGS) to study gene expression. Instead of looking for pre-identified targets, the sequencing approach defines what’s in the sample. NGS brings to light previously known as well as unknown strands of RNA that result from gene expression.
NGS takes its cue from tried-and-true shotgun sequencing. With the new approach, shorter stretches are sequenced. These short reads are normally associated with ambiguities. However, the technique compensates by processing hundreds of millions of short fragments. The result is a huge mound of data that, once analyzed, reveals the exact sequences of expression.
NGS instruments are similar to traditional sequencers but work on a much larger scale that reflects the technique’s common name of “high throughput massively parallel sequencing”. The different models of sequencers vary in throughput and size, as well as in the sophisticated engineering at their core. They range from huge units befitting core labs to small benchtop units.
Time for RNA-Seq
When it comes to studying RNA, the NGS approach is also called RNA-Seq. Or, because study of gene expression profiles transcription, the method owns the technical term “whole transcriptome shotgun sequencing”. But straightforward it is not. Sequencing the transcriptome involves a lot more complication than sequencing genomic DNA. Transcriptome analyses must “identify, characterize and catalogue all the transcripts expressed within a specific cell/tissue—at a particular stage…correct splicing patterns and the structure of genes, and…the differential expression of transcripts in both physio- and pathological conditions.” [1]
Potentially, RNA-Seq could deliver results more accurately than qPCR. Sequencing occurs with the pairing of base after base, independent of the primers of PCR. With such assumptions, RNA-Seq can reveal point mutations, single nucleotide polymorphisms, deletions, insertions, differential splicing and other significant phenomena.
Ongoing efforts, regardless of technique, will bring scientists closer to painting a complete picture of gene expression and cataloging all the RNA variants. As the technologies advance every year, research will get faster, easier and cheaper. Such progress will undoubtedly bring gene expression assays into clinics. Improved access will help physicians to make timely diagnoses and begin treatment immediately.
Reference
[1] eCosta, et al., “Uncovering the Complexity of Transcriptomes with RNA-Seq”, J Biomed Biotechnol. 2010