bioRxivCancer BiologyPreprint
Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids
T. Karakulak, N. Zajac, H. A. Bolck, A. Bratus-Neuenschwander, Q. Zhang, W. Qi, D. Basu, T. C. Oltra, H. Rehrauer, C. von Mering, H. Moch, A. Kahraman
Abstract
Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternative splice isoforms. This complex phenomenon can be revealed by an in-depth understanding of cellular heterogeneity at the single-cell level. Recent advances in single-cell long- read sequencing technologies enable comprehensive transcriptome sequencing at the single-cell level. In this study, we have generated single-cell long-read sequencing of Patient-Derived Organoid (PDO) cells of clear-cell Renal Cell Carcinoma (ccRCC), an aggressive and lethal form of cancer that arises in kidney tubules. We have used the Multiplexed Arrays Sequencing (MAS-ISO-Seq) protocol of PacBio to sequence full-length transcripts exceptionally deep across 2,599 single cells to obtain the most comprehensive view of the alternative landscape of ccRCC to date. On average, we uncovered 86,182 transcripts across PDOs, of which 31,531 (36.6%) were previously uncharacterized. In contrast to known transcripts, many of these novel isoforms appear to exhibit cell-specific expression. Nonetheless, >50% of these novel transcripts were predicted to possess a complete protein-coding open reading frame. This finding suggests a biological role for these transcripts within kidney cells. Moreover, an analysis of the most dominant transcript switching events between ccRCC and non-ccRCC cells revealed that many switching events were cell and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our research elucidates the intricate transcriptomic architecture of ccRCC, potentially exposing the mechanisms underlying its aggressive phenotype and resistance to conventional cancer therapies.
- organism: human
- assay: scRNA-seq
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
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