Navigating your way through RNA-Seq
Part 1: Removal of highly abundant ribosomal RNA

Navigating your way through RNA-Seq:
Part 1: Removal of highly abundant ribosomal RNA

It’s a very interesting time to sequence RNA. Ten years into its introduction, RNA Sequencing (RNA-Seq) is proving to be an enormously empowering technique. It gives us the ability to get a glimpse of dynamic alterations in gene expression, to measure rare transcripts while simultaneously detecting alternative splice variants, non-coding RNA species and single-nucleotide polymorphisms. RNA-Seq also enables us to discover oncogenic gene fusions, gene isoforms and previously unidentified genes. It’s an open platform for the detection and measurement of any transcript without the need for genomic data availability, and provides the ability to construct a transcriptome “de novo” without a reference genome. In addition, RNA-Seq has a broader dynamic range and increased sensitivity compared to microarrays and can be performed on any species.¹ RNA-Seq has the ability to detect genomic alterations at a single molecule level, the power to identify a variety of alterations such as gene fusions and splice variations, and the potential to discover new and unknown variants of significance. With all these capabilities, RNA-Seq turns out to be unparalleled as a qualitative and quantitative analysis tool.¹

With all these advantages, RNA-Seq is well poised to dominate the clinical diagnostic space, but challenges still remain. This two-part series will outline some of the challenges with RNA-Seq and provide some guidelines on how to overcome them.

Sample preparation

As the idiom GIGO (garbage in, garbage out) goes, quality of sequencing results depends entirely on the quality of the sample that went into the workflow.

More than 90% of total RNA in the cell consists of “less interesting” ribosomal RNA (rRNA), which offers very little information about the transcriptome. Therefore, it is often desirable to remove rRNA before sequencing, so as to save sequencing efforts, time and resources. There are a variety of ways to remove the rRNA fraction prior to sequencing, each with its own pros and cons. Some of these methods include enrichment of mRNA, preferential digestion of abundant transcripts, amplification of non-ribosomal RNA or direct RNA depletion.² To increase sequencing economy, one of these methods can be utilized before the conversion of RNA to cDNA through reverse transcription and downstream library construction steps.

mRNA enrichment

The most common method for enriching mRNA is by selectively hybridizing 3'-polyadenylated RNA with oligo-dT capture beads. This approach allows for the recovery of mature RNA species from high-quality input material and provides the ability to detect only the expressed regions, which saves time, sequencing efforts and cost. However, this enrichment method has a few drawbacks:

• Because only the mature RNA species are retained and sequenced, non-coding RNAs expressed from inter-exonic and inter-genic regions are lost. Non-coding RNAs such as miRNA, ssRNA and circRNA may be of high significance when studying specific applications.³ In these applications, selective depletion of rRNA may help to preserve the non-coding RNA species of interest. 
• Variability in polyA tail length is shown to affect the efficiency of enrichment resulting in sequencing bias.⁴
• This method requires high-quality RNA input. Since only the polyadenylated RNA is enriched, for degraded samples such as FFPE, oligo dT selection will primarily isolate the 3' portion of the transcript, which leads to loss of valuable information from the 5’ end. Therefore, for low-quality and fragmented RNA samples, oligo dT capture method is not an ideal solution.
• The method is suitable only for eukaryotic mRNA due to the absence of polyA tail in prokaryotes.

Ribosomal RNA (rRNA) depletion

Unwanted transcripts such as rRNA, globin or other highly-abundant mRNA species can be selectively depleted. Currently two strategies for rRNA depletion exist: hybridization-based and enzyme-based. In hybridization-based workflows, rRNA depletion is carried out using sequence-specific oligos that hybridize to rRNA transcripts, and then removed from the total RNA sample using streptavidin beads. However, this method could result in off-target depletion requiring deep sequencing in order to detect relatively low-abundant transcripts.

Alternatively, unwanted transcripts can also be removed using RNase H, after hybridization with sequence-specific oligos. RNaseH is an enzyme that depletes RNA transcripts participating in RNA:DNA duplexes (i.e. bound to the DNA oligos). The depletion of highly abundant RNA species or rRNA improves the sensitivity and sequencing economy, enabling enhanced detection of rare transcripts and providing the ability to detect subtle changes in gene expression patterns.

The choice of RNA enrichment is critical, especially for fragmented samples such as FFPE or samples with very low quantity of RNA. Depletion using RNase H has been shown to provide superior sequencing metrics in terms of continuity of coverage, expression level, and detection of GC bias compared to other methods used for low-quality and low quantity RNA samples.⁵ Learn more about workflows optimized for depleting selective transcripts or library preparation solutions specifically geared for using FFPE samples. Using optimized workflows for specific applications could save you significant time, effort and improve sequencing economy.

With the hurdles associated with the “less interesting” RNA removed, you are a step closer to a successful RNA-Seq procedure. But a few more challenges have to be conquered in the subsequent steps. In part 2 of the series, we’ll describe challenges associated with pre-sequencing steps such as cDNA synthesis, library amplification and quantification, and post-sequencing steps such as data standardization and analysis.

References

1. Whitley et al. J of Investigative Dermatology. 2016; 136, e77-e82. doi:10.1016/j.jid.2016.06.003
2. O’Neil et al. Curr. Protocols in Mol Biol. 2013; DOI: 10.1002/0471142727.mb0419s103
3. Byron et al. Nature Genetics;2016; 17: 257-271.
4. Choi YH and Hagedorn CH. Proc. Natl. Acad. Sci. USA. 2003; 100:7033-7038.
5. Adiconis et al. Nature Methods;2013 ; 10(7):623-629.