Jo Vandesompele at Ghent University discusses taking a deep dive into single-cell total RNA sequencing

Jo Vandesompele, PhD, Professor in Functional Cancer Genomics and Applied Bioinformatics at Ghent University, and his team are pioneering new methods in nucleic acid quantification to help understand cancer biology cell by cell. In the past five years, his research has shifted to focus on deciphering long noncoding RNA in the human genome with the goal of informing the development of better cancer diagnostics and therapeutics in the future.

In collaboration with Standard BioTools™, Vandesompele developed a single-cell total RNA sequencing method that allows for the study of protein-coding and noncoding RNA. It is the first time investigators have been able to study both full-length long noncoding RNA and mRNA at the same time in each single cell in a cost-efficient manner using the C1™ system.

“The transcriptome is much more complex than only polyadenylated RNA, or protein coding RNA,” Vandesompele said. “There are about 60,000 long noncoding RNA genes in the human genome, or about three times more than coding RNA, and this number continues to grow as we learn more about the transcriptome.”

Previous research has demonstrated that noncoding regions of the genome can influence the regulatory components of cells. This single-cell sequencing method can bring a better understanding to transcriptional regulation, specifically concerning how cells transition to disease states.

“Most of the other methods are just scratching the tip of the iceberg,” Vandesompele said. “With this application, we can see what’s below the surface. I think that's important in terms of single cell atlases—that you're not just scratching the surface, but going deep into the cellular identity of these different cell types.”

Programming the C1 platform for noncoding RNA

Vandesompele acquired the C1 system in 2015 because of its simplicity, ease of use and credibility for single-cell genomics studies.

“If you are looking to observe new cell types or cellular heterogeneity, for instance, you don’t need to sequence very deep,” Vandesompele explained. “However, in transcriptional regulation, you have a need for accurate quantification of low-abundant genes. And for these applications, we have found the library prep methods in combination with the C1 platform are best.”

Karen Verboom, a doctoral fellow who works with Vandesompele, optimized the single-cell application. The flexibility of the C1 platform, delivered through Script Builder™, allowed the team to modify the scripts controlling the integrated fluidic circuit (IFC), and enabled the creation of this new workflow.

“Our lab was not satisfied with off-the-shelf solutions,” Vandesompele said. “We knew we wanted to study more than just polyadenylated RNA. The C1 provided us with the flexibility to incorporate our own changes and workflows.”

While designing this method, the team faced a few challenges. Since nearly all single-cell RNA sequencing methods capture only polyadenylated RNAs, its first challenge was enabling quantification of RNA transcripts without polyadenylation tail. However, using random primers in a so-called total RNA sequencing workflow produces an abundance of unwanted ribosomal cDNA, which can dominate the sequenced reads and create bias. So the team introduced Takara Bio USA’s method that uses probes and enzymatic digestion to remove ribosomal cDNA during single-cell library preparation.

A second challenge the team confronted was ensuring that its methods preserved strand orientation. Tracking the RNA strand orientation allows the team to differentiate sense and antisense overlapping genes.

Celine Everaert, another doctoral fellow in the lab, noted, “The deeper we look into the human transcriptome, the more of these overlapping sense and antisense gene pairs we observe. If you want to understand transcriptional regulation, you need to be able to differentiate these, too. Preserving strand orientation allows you to quantify genes with much higher accuracy.”

Using C1 and Script Builder enabled the team to overcome such challenges, and now Script Hub™ houses several C1 Total RNA-Seq workflows.

Single-cell research on long noncoding RNA provides many potential benefits. First, cell specificity is much higher for noncoding RNA, so investigators have a higher chance of finding specific markers for cell states. C1 Total RNA-Seq also allows for an additional layer of information on how transcriptional regulation networks are composed. This can give researchers a better understanding of transcriptional regulation, which is often rewired in diseased cells. Lastly, full-length sequencing allows for uncovering structural information encoded in a transcriptome. Taken together, these total RNA solutions housed on Script Hub enable complete characterization of the transcriptome of single cells.

“A lot of structural information is encoded in a transcriptome, such as alternative splicing (where different exons are fused together), which often brings new functions,” Vandesompele said. “Also, other forms of structural information such as small insertions or deletions or fusing genes are extremely important in studying cancer cells.”

Circular RNAs and long reads: future potential for single-cell total RNA sequencing

Vandesompele and his team are excited about this new method, as are others in the research community. In fact, Vandesompele has started to collaborate with investigators studying other classes of noncoding RNA, such as circular RNAs, which originate from back-splicing of an otherwise linear RNA to form a circular structure.

“Scientists are realizing that the human transcriptome is full of these circular, back-spliced transcripts,” Vandesompele said. “And again, only by sequencing entire genes and not focusing on polyadenylated genes, you're able to exploit the structural information in a rich transcriptome.”

Vandesompele, Verboom and Everaert plan to continue using the flexibility of C1 to optimize their method to encompass a workflow for the investigation of circular RNAs and for full-length single-molecule long-read sequencing. Vandesompele also sees potential for this method in studying microRNAs, which are also crucial regulators of cell identity in health and disease.

“I think the better you understand the deregulated transcriptome, the better you will be able to identify biomarkers and therapeutic targets,” Vandesompele said. “As scientists are developing RNA-targeted therapies, it will be crucial to have a very accurate and detailed view of the transcriptome.”