Ribonucleic acid—RNA—gives scientists a way to analyze gene expression. Getting the best results, though, depends on analyzing the right RNA in an effective way. Increasingly, scientists select RNA sequencing (RNA-seq), but there’s more than one method for that. New tools and techniques allow scientists to use this approach in more scientific situations, from environmental science to medicine.

RNA-seq is not the only way to study DNA expression. That can also be accomplished with microarrays or quantitative polymerase chain reactions (qPCR), also called real-time PCR (RT-PCR). In comparing these methods, Brian Lilhanand, single cell multiomics leader for BD, pointed out several advantages of RNA-seq: uncovering novel transcripts, because the technique does not require species- or transcript-specific probes; larger dynamic range; better ability to detect rare transcripts; and easier multiplexing for the number of transcripts being analyzed.

Other experts mentioned similar benefits of RNA-seq. For example, Samuel J. Rulli, Jr.—associate director global product management, RNAseq profiling, NGS assay technologies at QIAGEN—said, “RNA-seq’s biggest advantages are that it’s an unbiased approach and the amount of sample that you need is very small.” He added, “You can start with a fragmented sample—such as a formalin-fixed, paraffin-embedded sample or certain environmental samples—and still get a nice signal.”

One expert discussed very specific differences between approaches to analyzing RNA. “RNA-seq covers the entire transcriptome, while RT-PCR is usually limited to one or a few genes at a time and microarray is limited by the number of genes on the microarray,” said Kateri Moore, professor of cell, developmental, and regenerative biology at the Icahn School of Medicine at Mount Sinai. “So, the basic advantage is the broader coverage of the whole transcriptome of the cells.” She added, “If the population is very heterogeneous then you would not be able to identify individual cell types within your sample or even heterogeneity that is within let’s say a FACS [fluorescence-activated cell sorting] purified cell population.” Moore pointed out that scientists can resolve those issues with single-cell RNA-seq.

Various kits and techniques make it easier for scientists to apply RNA-seq methods to a range of questions in basic and applied science.

Counting on kits

With most any molecular method, things get easier on scientists when vendors produce a collection of kits, and that’s true in RNA-seq. Scientists can use kits to clean up an RNA sample, amplify areas of interest, and more.

Obtaining the intended RNA takes some effort. “In most samples, 85 to 95% of the RNA is ribosomal, and you don’t want to sequence that,” said Rulli. Scientists usually want to sequence the RNA related to transcription. So, QIAGEN developed its QIAseq FastSelect ribosomal RNA removal kits. “These don’t affect the expression values of mRNA or change it,” Rulli said. “The process to remove ribosomal RNA can be accomplished in 14 minutes, so RNA-seq library construction can be reasonably accomplished in less than a day.”

When research calls for analyzing particular RNAs from single cells, scientists can use “BD’s Whole Transcriptome Analysis Amplification Kit and targeted gene panels to generate either an unbiased readout of the differentially expressed genes or focus on a specific set of genes of interest in their samples,” Lilhanand noted.

Multiplexing molecules

To increase throughput, scientists turn to various forms of multiplexing. One option comes from QIAGEN’s ultraplexing UPX line. “Through our sample bar-coding strategy after the cDNA reaction, you can combine hundreds or thousands of samples in one sequencing reaction,” Rulli explained. “With this, you can address anything from single-cell samples down to low-input samples with 1 nanogram of RNA or less.”

A growing trend arises in studies that analyze more than one area of ’omics, such as genomics and transcriptomics. “In life science and moving to the clinic, having a DNA and RNA signature is more powerful,” said Rulli. “You can use multimodality panels for Illumina instruments, for example.” He added, “Just because a DNA mutation is there doesn’t mean that it’s expressed.” That’s the reason for analyzing both types of nucleic acids.

Scientists can also combine RNA-seq with other molecules. As an example, Lilhanand said, “BD’s multiomic reagents utilize the RNA-seq technology for analyzing both the RNA and protein information at a single-cell level.”

Sequencing salmon

At the University of Edinburgh, Diego Robledo—a career track fellow at The Roslin Institute—and his colleagues used RNA-seq to study the health of gills in salmon. As Robledo explained, his team built this research from previous information on quantitative trait loci (QTL). “In a previous study, we had found several QTLs associated with resistance to amoebic gill disease in Atlantic salmon—animals with the correct genotypes in this genomic position tended to show healthier gills,” he said. “We wanted to investigate the functional basis of those QTL, and RNA-seq is the best tool to assess the functional differences at genome-wide level between two groups of animals.”

To make such comparisons, Robledo needed the right fish to study. “In this specific case, where we wanted to compare animals with opposite phenotypes for a polygenic trait, the hardest part was selecting the animals,” he said. “In this work, we used both phenotypes—gill damage, amoebic load—and genetic-estimated breeding values to have the best chance of getting truly resistant and truly susceptible animals.”

From applying RNA-seq studies to the different groups, Robledo and his colleagues discovered “several potential candidates for the previously discovered amoebic gill disease resistance QTLs—some of them showing allele differential expression,” he noted. “This information can potentially be exploited in salmon breeding programs to enhance the resistance of salmon stocks to this disease.”

So, it’s not just the future of human health that RNA-seq might improve, but also agricultural programs and more. With the ability to gather large amounts of information on more than one type of molecule, scientists can learn about the pathways from genes to proteins and beyond.