Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in ovarian cancer
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Date
2023-11-27
Publication Type
Journal Article
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Abstract
Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 were not previously reported. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-β/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.
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published
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Journal / series
Volume
14 (1)
Pages / Article No.
7780
Publisher
Nature
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Organisational unit
02892 - NEXUS Personalized Health / NEXUS Personalized Health
09595 - Snijder, Berend (ehemalig) / Snijder, Berend (former)
02072 - Proteomics Plattform D-HEST
03790 - Beerenwinkel, Niko / Beerenwinkel, Niko
09568 - Rätsch, Gunnar / Rätsch, Gunnar
09735 - Bodenmiller, Bernd / Bodenmiller, Bernd
Notes
Funding
190413 - Single-cell transcript isoform sequencing for precision oncology diagnostics and treatment (SNF)
766030 - Computational ONcology TRaining Alliance (EC)
766030 - Computational ONcology TRaining Alliance (EC)
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