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New Single-Cell 'Total' RNA-seq Method Promises More Comprehensive Transcriptome View


This story has been updated to correct information about methods developed by Hao Wu. 

NEW YORK – A new method for single-cell RNA sequencing may provide a triple threat: high sensitivity, full-length coverage of RNA, and high throughput.

The researchers behind the method — dubbed "vast transcriptome analysis of single cells by dA-tailing" (VASA-seq) — claim it offers the ability to capture more RNAs than existing high-throughput methods, namely 10x Genomics' Chromium platform, resulting in more genes per cell as well as isoforms and long noncoding RNAs analyzed. It works with both plate- and droplet-based approaches to cell isolation, costing approximately $.98 and $.11 per cell, respectively.

In a proof-of-concept study published this week in Nature Biotechnology, researchers at the University of Cambridge, the Hubrecht Institute in the Netherlands, and elsewhere used VASA-seq to sequence the transcriptome of more than 30,000 cells from the developing mouse embryo. They reported 9,825 genes per cell with VASA-drop, at a sequencing depth of 75,000 trimmed reads per cell, and 9,425 for VASA-plate.

"We discovered cell type markers, many based on noncoding RNA, and performed in vivo cell cycle analysis via detection of non-polyadenylated histone genes," the authors wrote. "Moreover, our VASA-seq data provide a comprehensive analysis of alternative splicing during mammalian development, which highlighted substantial [genome] rearrangements during blood development and heart morphogenesis."

"Research laboratories are increasingly relying on single-cell RNA-seq methods to deliver key insights on the underlying heterogeneity of the biological system they want to investigate," Florian Hollfelder, a Cambridge biochemist and a senior author on the paper, said in an email. "Being able to detect more molecules and markers per cell will not only increase the precision of cell type annotation but will enable more comprehensive identification of unique gene expression patterns that are intrinsic to that cell type."

Moreover, these data could be used to "provide new mechanistic insights into the gene regulatory function of the previously poorly characterized non-polyadenylated noncoding RNAs in developmental processes and diseases," said Hao Wu, a single-cell gene expression researcher at the University of Pennsylvania Perelman School of Medicine.

The plate-based version of the assay is already available from a commercial services partner, Netherlands-based Single Cell Discoveries, Hollfelder said, adding that he and his team have filed for IP and are considering different avenues of commercialization. He suggested that commercially available instruments able to perform multi-step reactions at high throughput, such as Mission Bio's Tapestri single-cell platform, currently used mostly for single-cell DNA sequencing and proteomics, "may be the most immediate solutions for commercial adaptation of the VASA-drop protocol."

The method goes further than most existing high-throughput methods for single-cell transcriptomics, which use barcodes that hybridize to polyadenylated transcripts and only sequence a small portion of those transcripts.

VASA-seq captures both polyadenylated transcripts — namely, mRNAs — and non-polyadenylated ones, such as long noncoding, short noncoding, and certain protein-coding RNAs. And with full-length sequences, it can also detect alternative splicing. This is "technically difficult to achieve in one assay," Wu said.

After cell lysis, RNAs are fragmented, followed by end repair and poly(A) tailing steps. These molecules are then reverse transcribed and undergo second-strand synthesis. The resulting DNA molecules are then transcribed in vitro and depleted of ribosomal RNA, followed by sequencing adapter ligation and another reverse transcription step before indexing PCR creates the final sequencing library.

In the droplet version of VASA-seq, these steps must happen by picoinjection subsequent to single-cell encapsulation, so it would be unlikely to be compatible with the 10x Chromium instrument.

The researchers used a workflow "similar to inDrop," a single-cell encapsulation method developed by researchers at Harvard University and commercialized by 1CellBio, Hollfelder said, "but other custom systems such as Drop-seq or HyDrop would also be suitable."

This droplet instrumentation costs approximately $60,000; the plate-based version can be automated with liquid handlers, which could cost up to $100,000, he said.

Sequencing costs are about $.50 per cell, Hollfelder said, "but this may shift depending on user requirements." For example, users may want to perform shallow sequencing and investigate specific libraries with higher coverage, he suggested, adding that "increased RNA capture offers higher degrees of flexibility around experimental design in that way."

The doublet rate is less than 5 percent, he estimated, depending on cell loading concentration.

For standard gene expression profiling, VASA-seq data is compatible with existing analysis tools, such as 10x's Cell Ranger software, Kallisto, zUMIs, Alevin, and STARsolo. "However, the analysis of small noncoding RNAs may be complicated by high multi-mapping rates and will require further development for VASA-seq applications," Hollfelder said.

In addition to single-cell transcriptomics and RNA isoform sequencing, the method could be used in studies of gene expression dynamics, where time is considered, and which can "to some degree predict shifts in cell states," Hollfelder said. One such approach, RNA velocity, does so by comparing reads mapped to introns — and thus derived from unspliced RNA — with other reads mapped to exons. But existing methods capture only the most 5' or 3' mRNA fragments, he said, "and therefore capture lower amounts of intronic reads, which may impact the quality of RNA velocity [measurements]."

Wu, who has developed a method for determining the age of transcripts in single-cell RNA-seq experiments called single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), agreed. "Higher capture efficiency of spliced and unspliced RNA fragments can provide more accurate input for RNA dynamics analysis including RNA velocity," he said.

"We show that VASA-seq performs better than current methods," Hollfelder said. "Not only are certain genes uniquely detected, and hence their dynamics are uniquely captured, but also, most genes have a more balanced ratio of intronic and exonic reads, which, in the case of RNA velocity, enhances dynamical modeling."

Aside from Single Cell Discoveries, Hollfelder said both the plate- and droplet-based protocols are being implemented in other labs, but he did not provide more details.