Nature BiotechnologyGeneticsPeer-reviewed journal article
High-throughput RNA isoform sequencing using programmable cDNA concatenation
Aziz M. Al’KhafajiiD, Jonathan T. SmithiD, Kiran V. GarimellaiD, Mehrtash BabadiiD, Victoria PopiciD, Moshe Sade-FeldmaniD, Michael GatzeniD, Siranush SarkizovaiD, Marc A. SchwartziD, Emily M. BlaumiD, Allyson Day, Maura CostelloiD, Tera Bowers, Stacey GabrieliD, Eric BanksiD, Anthony PhilippakisiD, Genevieve Marie BolandiD, Paul C. BlaineyiD, Nir HacoheniD
Abstract
Alternative splicing is a core biological process that enables profound and essential diversification of gene function. Short-read RNA sequencing approaches fail to resolve RNA isoforms and therefore primarily enable gene expression measurements - an isoform unaware representation of the transcriptome. Conversely, full-length RNA sequencing using long-read technologies are able to capture complete transcript isoforms, but their utility is deeply constrained due to throughput limitations. Here, we introduce MAS-ISO-seq, a technique for programmably concatenating cDNAs into single molecules optimal for long-read sequencing, boosting the throughput >15 fold to nearly 40 million cDNA reads per run on the Sequel IIe sequencer. We validated unambiguous isoform assignment with MAS-ISO-seq using a synthetic RNA isoform library and applied this approach to single-cell RNA sequencing of tumor-infiltrating T cells. Results demonstrated a >30 fold boosted discovery of differentially spliced genes and robust cell clustering, as well as canonical PTPRC splicing patterns across T cell subpopulations and the concerted expression of the associated hnRNPLL splicing factor. Methods such as MAS-ISO-seq will drive discovery of novel isoforms and the transition from gene expression to transcript isoform expression analyses.
- organism: human
- assay: long-read sequencing
- assay: scRNA-seq
- research focus: differential expression
- research focus: clustering
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 200
- FWCI
- 16.76
- Field percentile
- top 1%
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