NAR Genomics and BioinformaticsGenomicsPeer-reviewed journal article
A single-cell strategy for the identification of intronic variants related to mis-splicing in pancreatic cancer
Emre Taylan DumaniD, Maren SitteiD, Karly ConradsiD, Adi Danieli‐MackayiD, Fabian Ludewig, Philipp StröbeliD, Volker EllenriederiD, Elisabeth HeßmanniD, Argyris PapantonisiD, Gabriela SalinasiD
Abstract
Most clinical diagnostic and genomic research setups focus almost exclusively on coding regions and essential splice sites, thereby overlooking other non-coding variants. As a result, intronic variants that can promote mis-splicing events across a range of diseases, including cancer, are yet to be systematically investigated. Such investigations would require both genomic and transcriptomic data, but there currently exist very few datasets that satisfy these requirements. We address this by developing a single-nucleus full-length RNA-sequencing approach that allows for the detection of potentially pathogenic intronic variants. We exemplify the potency of our approach by applying pancreatic cancer tumor and tumor-derived specimens and linking intronic variants to splicing dysregulation. We specifically find that prominent intron retention and pseudo-exon activation events are shared by the tumors and affect genes encoding key transcriptional regulators. Our work paves the way for the assessment and exploitation of intronic mutations as powerful prognostic markers and potential therapeutic targets in cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/539836v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@18ddba7org.highwire.dtl.DTLVardef@4fc53aorg.highwire.dtl.DTLVardef@11d8d0dorg.highwire.dtl.DTLVardef@1178b86_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- approach: experimental assay
- Citations
- 5
- FWCI
- 0.68
- Field percentile
- top 37%
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