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Primer-Based RNA Sequencing Analyzes RNA Transcripts in as Few as 50 Cells

By BiotechDaily International staff writers
Posted on 05 Nov 2013
Bioengineers have devised a new way to analyze RNA transcripts from as few as samples of 50 to 100 cells. The new strategy could be used to develop cost-effective and rapid techniques for detecting cancer at early stages, as well as better tools for drug discovery, forensics, and developmental biology.

The bioinformatic protocols, which were published in April 2013 in the journal Nature Scientific Reports, are now being applied to a wide range of biologic and medical research questions from brain cancer, to liver function and stem cell biology. The approach, developed by the University of California (UC) San Diego (USA) bioengineers, is called designed primer-based RNA sequencing (DP-seq). It is a new tool for generating comprehensive images of RNA—the “transcriptome”—gathered from as little as 50 pg of RNA. Transcriptome analysis provides clues into what biologic mechanisms are happening at a specific time. RNA transcripts serve as a substitute for which genes are being expressed and at what levels.

“In the months since we published the DP-seq protocol, there has been tremendous interest from the scientific community,” said. Shankar Subramaniam a bioengineering professor at the UC San Diego Jacobs School of Engineering and the corresponding author of the study. “When you are not restricted to samples of thousands of cells, there are so many more system-wide gene expression questions you can ask, and answer,” said Prof. Subramaniam. The investigators are looking for answers for questions such as what transcription factors will determine cell fates, such as cancer versus normal, and what pathways are likely to be activated in a tissue upon treatment with a drug?

In spite of the small amounts of RNA inputs required, the DP-seq protocol conserves the comparative profusion of RNA transcripts, even for low and moderately expressed transcripts. Furthermore, DP-seq can be used to target amplification of specific, medium, or lower abundance RNA transcripts by reducing amplification of highly abundant RNA transcripts. With targeted amplification, researchers can gain clues into the low and moderate frequency RNA transcripts that can get lost in the amplification process in other protocols.

“One of the exciting things about our protocol is that it has the potential to perform targeted amplification of genes of interest and/or specific regions of the transcriptome which carry disease-causing mutations or SNPs [single nucleotide polymorphisms]. Selective amplification of these transcripts will allow massive multiplexing of the samples, opening the door to cost-effective diagnostics,” said Vipul Bhargava, a graduate student in the Subramaniam laboratory and the first author on the article in Nature Scientific Reports.

To demonstrate this selective amplification, the researchers designed primers that suppressed amplification of highly expressing ribosomal transcripts in embryonic stem cells of mice. This, along with high sensitivity and the large dynamic range offered by DP-seq, uncovered RNA transcripts that had previously only been detected in later stages of embryonic development known as germ layer segregation.

“The majority of the novel transcripts that we identified in our study were low expressed. The high sensitivity in quantification of those transcripts and the large dynamic range offered by our protocol—over five orders of magnitude in RNA concentration—allowed us to detect the expression of these transcripts,” said Prof. Bhargava.

The DP-seq protocol is one of several approaches developed over the last two years capable of generating transcriptomes from approximately 50 pg of RNA. The new protocols from UC San Diego, however, offer clear advantages, the researchers reported. For example, by using 44 heptamer primers for cDNA amplification, the DP-seq approach generates transcriptomes faster and more economically than approaches that rely on full-length cDNA amplification of extremely small RNA samples.

The researchers hope to develop their technology for routine diagnosis of pathologies as well as for discovery of mechanisms and targets for therapeutic interventions. A patent has been filed for this technology, which is available for licensing.

DP-seq is a next-generation sequencing-based approach to whole transcriptome analysis. At its base is one of the central dogmas of biology: DNA is transcribed to form RNA, which is then translated to generate proteins, which may be modified before the proteins carry out their prescribed tasks.

Analyzing which RNA transcripts are present, and at what levels--transcriptome analysis—provides an outlook of system-wide gene expression patterns and a state of the system. “If you want to address a particular disease, the days of just looking into one gene, one protein or one signaling pathway are over. You need to look at all levels of complexity, all the way from genomic DNA to RNA to proteins, as well as how different modifications happen at the RNA and protein levels,” said Prof. Bhargava.

Applications such as DP-seq, which provide quantitative data on gene expression levels system-wide, are part of a move toward “systems health” in which researchers build systems-level models that describe biologic events and clarify what causes disease. Once the genes are identified that are changing their expression patterns, these genes can be studied as to how they interact with each other and build networks. Through these network models, one can begin to understand how specific changes within one network can affect the overall system. Understanding how perturbations in a system cause disease can lead to developing new therapies.

The early research raised issues that led the researchers better determine from a systems-level perspective such as how data from the continually growing list of “omics” tools can be integrated to construct models of biochemical pathways and mechanisms that will help understand healthy physiology as well as disease.

UC San Diego researchers are now using these types of models, and other new developments made possible by systems biology, to treat diseases, discover new drugs, and determine how organisms work at the molecular level.

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University of California, San Diego



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