To understand an organism’s molecular machinery, a scientist often explores the transcription of genes. RNA sequencing (RNA-seq) reveals the information in the transcriptome. Making this method work as easily, quickly, and accurately as possible, though, will be an ongoing process.
In 2009, Stanford Medicine professor of genetics Michael Snyder and his colleagues wrote: “RNA-Seq is the first sequencing-based method that allows the entire transcriptome to be surveyed in a very high-throughput and quantitative manner.” More than a decade later, scientists want more throughput and ways to apply RNA-seq to more samples. Getting the best results, though, starts with the sample.
To sequence only the transcriptome—messenger RNA (mRNA)—scientists need to remove other forms of ribonucleic acids. “Effective removal of abundant RNAs upstream of library preparation is a major challenge for RNA-seq,” said Eileen Dimalanta, associate director of applications and product development at New England Biolabs. “This step is necessary to ensure that sequencing reads are not wasted, and the experiment can focus on the RNA of biological interest.” The RNA to remove might include ribosomal RNA (rRNA) and even “additional abundant RNAs, such as globin mRNAs,” Dimalanta pointed out.
In addition to sequencing only a specific type of RNA, scientists want to remove as many time-consuming steps as possible. “Hands-on time required for library preparation can be a challenge,” said Jason Liu, field applications scientist at Roche Diagnostics. Scientists can also spend time looking for ways to analyze the results. As Liu said, “It is difficult to find easy-to-use tools for RNA-seq data analysis.”
The sample challenges
In addition to getting the right RNA, sequencing needs enough of it, which is not always easy to get. “A major challenge in RNA-seq workflows is getting enough sample of sufficient quality for your selected application,” said Kevin Meldrum—vice president and general manager of the genomics division at Agilent Technologies. “For example, many ‘standard’ RNA-seq approaches—such as whole-transcriptome and mRNA-seq—require large amounts of high-quality RNA.”
The source of the sample also impacts its value. In samples from formalin-fixed, paraffin-embedded (FFPE) tissue, for example, the “RNA is heavily degraded by the fixation process,” Meldrum explained. Agilent’s new SureSelect XT HS2 RNA library prep kit uses target-enrichment technology, and it’s “optimized for low RNA inputs, challenging sample types, and is compatible with as little as 10 nanograms of total RNA from FFPE samples,” Meldrum said. This kit is in beta evaluation by customers. For example, “Caris Life Sciences is currently using the SureSelect XT HS2 RNA library prep kit in research to power and develop a target enrichment–sequencing assay for solid tumors,” Meldrum noted. “They’ve reported to us that the SureSelect XT HS2 RNA library prep kit has given them excellent capture of gene expression, gene fusions, and RNA splice variants from a single workflow.”
The source of an RNA sample can also require special reagents. For instance, “the NEBNext RNA Depletion line of products has recently expanded to address bacterial and blood samples,” said Dimalanta. The bacterial version of this product depletes “rRNA from gram-positive and gram-negative organisms, from monocultures or samples with mixed bacterial species” and the blood version depletes “both rRNA and globin mRNAs—adult, fetal, and embryonic,” Dimalanta explained. The company maintains a list of bacteria that are compatible with these products.
Controlling the quality
In sequencing any nucleic acids, the outcome depends on quality control throughout the workflow. To assess samples, scientists can use the Agilent 4150 TapeStation system for NGS QC. This platform “gives researchers the ability to objectively assess quality and quantity of up to 16 samples per run with constant costs per sample,” Meldrum said. “The instrument is also highly flexible and capable of switching between RNA and DNA assays.”
The more that scientists use RNA-seq, the more that cost comes into consideration. “Sequencing runs can be expensive, particularly if large sample numbers and/or high sequencing depth is needed, such as in splice-variant and gene-fusion analyses,” Meldrum said. “Poor quality samples can also cause cost overruns due to low-quality or inconsistent data, and these can necessitate library re-prep and resequencing.” This creates even more reasons to set up the needed QC along the steps of an RNA-seq workflow.
Speeding up steps
Scientists tend to pick kits to make lab life easier and for standardization. An RNA-seq kit can also speed up some steps. As an example, Roche Diagnostics’ “KAPA RNA HyperPrep kits offer streamlined, automation-friendly RNA library prep, as well as mRNA-capture and rRNA-depletion modules,” Liu said. “These kits deliver robust performance of low-input FFPE samples and generate sequencing-ready libraries within an 8-hour workday, including enrichment.”
A mixed sample can also slow down a process, unless scientists use a kit made for that situation. Here, Liu said, “We’ve recently started offering a design tool for determining the best custom probes for depletion of rRNA from single- and multi-species microbial mixes.”
Once researchers collect the RNA-seq data, the need for easier and faster options continues. “To help researchers address data-analysis challenges, we have partnered with Genialis, which has developed a qualified data analysis and visualization solution for our KAPA RNA HyperPrep Kit,” Liu said. “Taken together, these two solutions form a streamlined, end-to-end RNA library preparation and analysis workflow.” As examples of using this technology, Liu mentioned studying “the biological role of androgen receptor gene amplification and TP53 mutation in prostate tumorigenesis to map actionable pathways and mutations in brain tumors.”
In 2009, Snyder and his colleagues appreciated the value of RNA-seq, but only a handful of articles mentioned the technique in those early days. By 2013, according to PubMed, nearly 1,000 articles mentioned RNA-seq, and by 2019 that number surpassed 4,500. Such growth over a bit more than 10 years shows the importance of this method. To really see what genes are doing, scientists need to know what’s being transcribed and what’s not. There, RNA-seq reveals the molecular machines that are turned on, and helps scientists understand what that might mean.