Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage, thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrates high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA, which allows analysis to be combined with existing short-read RNA-seq data sets. In our investigation of <i>BTK</i> and <i>SF3B1</i> genes in samples from patients with chronic lymphocytic leukemia (CLL), we detect <i>SF3B1</i> isoforms and mutations with high sensitivity. Integration with short-read single-cell RNA sequencing (scRNA-seq) data reveals significant gene expression differences in <i>SF3B1</i>-mutated CLL cells, although it does not impact the sensitivity of the anticancer drug venetoclax. scRaCH-seq's capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

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Publication Details

Journal
Genome Research
Published
2025-01-10
DOI
https://doi.org/10.1101/gr.279322.124
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article

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article

Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes

Andrew H. Wei, N. Davidson, Noorul Amin, Matthew E. Ritchie et al.
6 citations
Genome Research
Single-cell and spatial transcriptomics
article

Single-cell Rapid Capture Hybridization sequencing reliably detects isoform usage and coding mutations in targeted genes

Andrew H. Wei, N. Davidson, Noorul Amin, Matthew E. Ritchie, Andrew W. Roberts, David C.S. Huang, Chong Chyn Chua, Hongke Peng, Natasha S. Anstee, Jafar S. Jabbari, Luyi Tian, Yupei You, Changqing Wang, Rachel Thijssen
article en
6 citations

Abstract

Single-cell long-read sequencing has transformed our understanding of isoform usage and the mutation heterogeneity between cells. Despite unbiased in-depth analysis, the low sequencing throughput often results in insufficient read coverage, thereby limiting our ability to perform mutation calling for specific genes. Here, we developed a single-cell Rapid Capture Hybridization sequencing (scRaCH-seq) method that demonstrates high specificity and efficiency in capturing targeted transcripts using long-read sequencing, allowing an in-depth analysis of mutation status and transcript usage for genes of interest. The method includes creating a probe panel for transcript capture, using barcoded primers for pooling and efficient sequencing via Oxford Nanopore Technologies platforms. scRaCH-seq is applicable to stored and indexed single-cell cDNA, which allows analysis to be combined with existing short-read RNA-seq data sets. In our investigation of <i>BTK</i> and <i>SF3B1</i> genes in samples from patients with chronic lymphocytic leukemia (CLL), we detect <i>SF3B1</i> isoforms and mutations with high sensitivity. Integration with short-read single-cell RNA sequencing (scRNA-seq) data reveals significant gene expression differences in <i>SF3B1</i>-mutated CLL cells, although it does not impact the sensitivity of the anticancer drug venetoclax. scRaCH-seq's capability to study long-read transcripts of multiple genes makes it a powerful tool for single-cell genomics.

Genome ResearchVol. 35(4)
The Royal Melbourne Hospital (AU), The University of Melbourne (AU), Northern Health (AU), Walter and Eliza Hall Institute of Medical Research (AU), Dutch Cancer Society (NL), Peter MacCallum Cancer Centre (AU), Amsterdam Neuroscience (NL), Monash Health (AU), Amsterdam University Medical Centers (NL), University of Amsterdam (NL)
Silicon Valley Community Foundation, Australian Government, Amsterdam University Medical Centers, Medical Research Council, Australian Research Council, National Health and Medical Research Council
Good health and well-being
Single-cell and spatial transcriptomics
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