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Jul 1, 2014 (Vol. 34, No. 13)

RNA-Seq Dissects the Transcriptome

  • With the rapid rise of next-generation sequencing (NGS), one of its technologies, RNA sequencing (RNA-Seq), has taken center stage for analyzing whole transcriptomes. Although RNA-Seq is still the new kid on the block, this technology, its proponents say, could revolutionize transcriptomics, revealing the architecture of gene expression in unprecedented detail.

    RNA-Seq applications are proliferating and include the elucidation of disease processes, targeted drug development, and personalized medicine. RNA-Seq also has veterinary and agricultural applications. RNA-Seq, however, will realize its promise only if certain impediments are overcome. These include consistency of methodology, cost, and optimization of data analysis.

    To orient researchers who are unfamiliar with the differences between  RNA-Seq platforms, Kelli Bramlett, R&D scientist, Life Technologies, poses two key questions:

    1. Are you interested in pure discovery, in a nonguided fashion, of every RNA species present in your test samples?

    2. Are you mainly focused on measuring expression levels of well-annotated coding RNA transcripts?

    “You might have a set of genes crucial to identifying a disease state, or profiling the stage of a specific type of cancer, or monitoring development in your experimental system,” elaborates Dr. Bramlett. “You then would want to employ a system that allows you to quickly and efficiently focus on just your genes of interest and screen through many different samples in a short amount of time.”

    Researchers who are clear about their experimental goals are better able to choose the technology that best suits their needs.

    “Some RNA-Seq allows for true discovery but will require greater sequencing depth to get all the information. Oftentimes, this type of deep sequencing requires significant additional time for analysis,” continues Dr. Bramlett. “If a focused panel targeting specific RNAs will better meet your needs, this can be accomplished on systems with much faster turnaround time and less sequencing depth. Also, the analysis of a less complex targeted library is much more straightforward.”

  • Enhancing Sensitivity

    RNA-Seq has advanced our ability to characterize transcriptomes at high resolution, and the laboratory and data analysis techniques used for this NGS application continue to mature, notes John Tan, Ph.D., senior scientist, Roche NimbleGen. “High sequencing costs combined with the omnipresence of pervasive, abundant transcripts decrease our power to study rare transcripts, decrease throughput, and limit the routine use of this technology.”

    For example, notes Dr. Tan, a small number of highly expressed housekeeping genes can be responsible for a large fraction of total sequence reads in an experiment, thus increasing the amount of sequencing required to characterize less abundant transcripts of interest.

    To improve the cost-effectiveness, throughput, and sensitivity of RNA-Seq, Dr. Tan and colleagues are developing methods to perform targeted RNA-Seq. “Targeted enrichment of transcripts of interest circumvents the need to perform separate rRNA depletion or polyA enrichment steps on input RNA,” explains Dr. Tan. “By targeting their sequencing, researchers can avoid wasting resources on housekeeping transcripts and focus instead on genes or genomic regions of interest.”

    Targeted RNA-Seq can allow deeper sequence coverage, increased sensitivity for low-abundance transcripts, less total sequencing per sample, and more samples processed per sequencing instrument run. “Significantly, we observe that the enrichment step also preserves quantitative information very well,” adds Dr. Tan. “These advances will facilitate a more routine use of RNA-Seq technology.”

  • Sample Integrity Issues

    Some samples present challenges for accurate RNA-Seq. “Formalin-fixed, paraffin-embedded (FFPE) patient tissue archives and the clinical data associated with them can be invaluable when dissecting the etiology and prognosis of disease. However, these may provide only limited amounts of sample that may also be degraded,” comments Gary Schroth, Ph.D., distinguished scientist, Illumina. “Standard techniques for RNA-Seq often don’t work.”

    Dr. Schroth says that most labs currently gauge RNA integrity via the RIN (RNA integrity number). “Labs often will look at the RIN as a measure of quality control prior to working with sample. We found that the RIN number from FFPE samples is not a sensitive measure of RNA quality or a good predictor for library preparation. A better predictor is RNA fragment size. We developed the DV200 metric, the percentage of RNA fragments greater than 200 nucleotides, a size needed for accurate construction of libraries.”

    Illumina offers its TruSeq® RNA Access Library Preparation Kit especially for FFPE samples. This kit, when used with the DV200 metric, provides cleaner and more accurate library preparation. This new approach allows researchers to start with five-to tenfold less material when making libraries from FFPE samples.

  • Strand Specificity

    Most NGS requires initial construction of libraries that may not provide the specificity desired even when prepared from mRNA. “Traditional RNA-Seq library preparation loses the strandedness of transcripts—information that is critical in understanding cellular transcription,” says Jungsoo Park, senior marketing and sales manager, Lexogen.

    According to Park, Lexogen tackled this problem by developing a method to generate libraries with greater than 99.9% strand specificity with a simplified process that takes 4.5 hours to complete. Lexogen’s SENSE mRNA-Seq library kit initially isolates mRNA via the poly A tail and utilizes random hybridization of the transcripts that  are bound to the magnetic beads without transcript fragmentation. “This is a revolutionary method, which keeps high strandedness of the transcripts,” asserts Park. “And it takes only half a day to finish.”

    One of the novel aspects of this approach is the use of starter/stopper heterodimers containing platform-specific linkers that hybridize to the mRNA. “The starters serve as primers for reverse transcription, which then terminates once the stopper from the next heterodimer is reached,” explains Park. “At this point, the newly synthesized cDNA and the stopper are ligated while still bound to the RNA template.” According to Park, there is no need for a time-consuming fragmentation step, and library size is determined simply by the protocol itself.

    For researchers only intending to see the expression levels, sequencing of the entire mRNA transcript will require subsequent bioinformatics processes such as RPKM, a measure of relative molar RNA concentration. To meet this challenge, Lexogen developed a kit, QuantSeq, which counts the 3′ end of each transcript. “Sequencing mRNA at or close to the 3′ end and counting their reads,” says Park, “provides an economical alternative to microarrays for gene expression and other studies.”

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