Single-cell long-read targeted sequencing reveals transcriptional variation in ovarian cancer

Ashley Byrne, Daniel Le, Kostianna Sereti, Hari Menon, Samir Vaidya, Neha Patel, Jessica Lund, Ana Xavier-Magalhães, Minyi Shi, Yuxin Liang, Timothy Sterne-Weiler, Zora Modrusan () and William Stephenson ()
Additional contact information
Ashley Byrne: Genentech
Daniel Le: Genentech
Kostianna Sereti: Genentech
Hari Menon: Genentech
Samir Vaidya: Genentech
Neha Patel: Genentech
Jessica Lund: Genentech
Ana Xavier-Magalhães: Genentech
Minyi Shi: Genentech
Yuxin Liang: Genentech
Timothy Sterne-Weiler: Genentech
Zora Modrusan: Genentech
William Stephenson: Genentech

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Single-cell RNA sequencing predominantly employs short-read sequencing to characterize cell types, states and dynamics; however, it is inadequate for comprehensive characterization of RNA isoforms. Long-read sequencing technologies enable single-cell RNA isoform detection but are hampered by lower throughput and unintended sequencing of artifacts. Here we develop Single-cell Targeted Isoform Long-Read Sequencing (scTaILoR-seq), a hybridization capture method which targets over a thousand genes of interest, improving the median number of on-target transcripts per cell by 29-fold. We use scTaILoR-seq to identify and quantify RNA isoforms from ovarian cancer cell lines and primary tumors, yielding 10,796 single-cell transcriptomes. Using long-read variant calling we reveal associations of expressed single nucleotide variants (SNVs) with alternative transcript structures. Phasing of SNVs across transcripts enables the measurement of allelic imbalance within distinct cell populations. Overall, scTaILoR-seq is a long-read targeted RNA sequencing method and analytical framework for exploring transcriptional variation at single-cell resolution.

Date: 2024
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DOI: 10.1038/s41467-024-51252-6

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