Reporting in Nature Methods, the firm’s SOLiD System and the university’s method to construct short quantitative random RNA libraries were used.
Scientists from the University of Queensland, Australia and Applied Biosystems working in mice report the completion of the most comprehensive analysis of a mammalian transcriptome. RNA expression analysis data from this study was derived from differentiated cells and stem cells.
The team generated more than 10 billion sequence bases from all RNA transcripts by profiling the RNA transcripts generated from the genomes of mouse embryoid body cells and embryoid stem cells (ESC). Their study revealed thousands of previously unknown RNA transcripts. Researchers were also able to discern between RNAs transcribed from the coding, or sense strand, and noncoding RNAs that reside on the antisense strand of double-stranded DNA.
The investigators also identified an unexpectedly large number of variant transcripts derived from genomic loci of stem cells. They say that this helped them gain insights on the complexity of biological pathways involved in regulating the pluripotency of stem cells.
Applied Biosystems’ SOLiD™ System was used to perform a sequencing-based transcriptome profiling technique, using methodology developed at the University of Queensland to construct short quantitative random RNA libraries (SQRL).
Using the SQRL method, the scientists created random cDNA libraries that gave them 25-35 base-pair length sequence tags, each tag representing a particular RNA transcript generated from the mouse genome. The SOLiD System was able to detect minute quantities of RNA transcripts and generated up to 240 million sequence tags per run. This enabled the researchers to perform a digital RNA expression analysis application and obtain an exact count of the number of RNA sequence tags generated from the genome of the different cell lines.
“For the first time we are starting to accumulate data sets that allow us to look at that entire complexity of all of the RNA present in a mammalian cell,” remarks Sean Grimmond, Ph.D., an associate professor at the Institute of Molecular Bioscience, University of Queensland, and senior author of the study. “This finding demonstrates that a digital gene-expression methodology performed with the SOLiD System is far superior to array-profiling approaches in terms of having a higher sensitivity and being able to see more RNAs in a transcriptome.”
By counting the number of sequence tags, and finding tags that map to previously discovered genes in archived data bases, the researchers were able to calculate the number of variant RNA transcripts that originate from specific regions or loci of the genome. From these short sequence tags, they were able to characterize RNAs as splice variants, identify single base changes, and detect other kinds of variants.
Results are published in this month’s issue of Nature Methods.