A new RNA sequencing approach allows scientists to hone in on rare, often obscured transcripts that reflect the finer details of cellular function.
Within every cell is a genome: a complete manual, coded in deoxyribonucleic acid (DNA), that reveals how that cell—or the creature it belongs to—is put together. However, more insights about how that creature adapts to life’s challenges can be found in the cell’s transcriptome: the ever-shifting landscape of ribonucleic acids (RNA) that the genome produces as protein-building messengers.
“High-throughput RNA sequencing is commonly used to profile transcriptomes for both research and diagnosis,” said Yue Wan, a Principal Investigator at A*STAR’s Genome Institute of Singapore (GIS). “RNA is more dynamic than genomic RNA, which creates more avenues for its use as a biomarker or drug target.”
Transcriptomes tell us which genes a cell is actively using in a given situation. By measuring that activity and detecting RNA transcript variants, RNA sequencing can narrate how cells and tissues operate in health and disease.
However, current RNA sequencing methods, such as nanopore sequencing, often struggle to fully capture the transcriptome.
“While a cell may express 5,000 to 15,000 genes at any time, roughly half of the resulting RNA molecules come from just 100 highly active genes, which usually aren’t research targets,” said Wan.
These repetitive molecules can obscure rare but important RNA types, as well as potential RNA transcripts new to science. To help scientists cut through the noise, Wan’s team worked with Oxford Nanopore Sequencing to explore how adaptive sampling methods can filter out common RNA sequences and amplify rarer ones during RNA studies.
In nanopore sequencing, RNA strands are threaded through tiny charged pores that identify each base (or ‘letter’ of RNA code) that passes through. This allows researchers to both identify and quantify sequences in an RNA sample.
“If you compare nanopore sequencing to a sorting and counting machine at a mixed fruit warehouse, adaptive sampling helps that machine selectively pick and pack apples from thousands of fruits,” said Wan.
Wan and colleagues mapped the nanopore sequencer’s ‘read until’ function to a pre-defined list of RNA sequences of interest, which would induce a voltage reversal in each nanopore if it began reading an unlisted transcript. The reversal causes nanopores to eject unwanted strands and move on to others, allowing the tool to efficiently enrich targeted RNAs and deplete unwanted RNAs when sequencing.
Tested on a pool of lab-created RNA transcripts, the team’s strategy managed to enrich quantities of desired transcripts by 20 to 30 percent. When tested on the transcriptome of Candida albicans, a common skin yeast, it increased reads of target transcripts by 11 to 13.5 percent, with longer and more abundant RNA strands being easier to enrich and deplete.
“We also detected 26 novel RNA transcripts in C. albicans through this adaptive sample sequencing method,” said Wan.
The team noted that their study is an early demonstration of adaptive sampling in RNA research, with more room to optimise calculation speeds and reading efficiency.
“We plan on other collaborations for more RNA structural studies using this approach, such as with viral RNA,” Wan added.
The A*STAR-affiliated researchers contributing to this research are from the Genome Institute of Singapore (GIS).
Source – Agency for Science, Technology and Research (A*STAR) Singapore