Real-time Targeted Enrichment in Single-cell Long-read Sequencing

The vast majority of multi-exonic genes are alternatively spliced, generating diverse and cell-type-specific isoforms exhibiting functional differences. To better capture this heterogeneity using single-cell long-read sequencing data, we previously developed an exome-probe-based approach to enrich for exonic reads of target genes. While effective, this procedure is time-consuming and expensive. Real-time targeting offers a more cost-efficient solution for selectively sequencing reads of interest. Here, we performed real-time enrichment of exonic sequences of single-cell long reads by targeting spliced transcripts from 3377 genes implicated in brain functions and related diseases. Our approach increased the total number of spliced on-target reads to up to 1.82 times the control level. Notably, targeting lowly expressed subsets yielded spliced on-target reads 1.39 to 1.89 times the control. While these gains do not rival those achieved using chemical probe-based enrichment, they are sufficient to significantly enhance the power of downstream statistical analyses, such as testing for cell-type-specific isoform abundance. Specifically, compared to naïve single-cell long-read sequencing, our approach yielded 2.42 times as many genes with significant differences in isoform usage between neurons and glia. Real-time targeting confirms cell-type-specific splicing in two early Mapt exons and newly reveals such events in > 100 genes, including Bak1 and Atp8a1. Overall, our findings highlight real-time targeting as a versatile method for enhancing resolution in detecting differential isoform usage across cell types in single-cell long-read data, offering the potential to obtain a fuller view of cellular isoform diversity.

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Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2026-07-01
DOI
https://doi.org/10.1093/gpbjnl/qzag051
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Real-time Targeted Enrichment in Single-cell Long-read Sequencing

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Genomics Proteomics & Bioinformatics
Single-cell and spatial transcriptomics
article

Real-time Targeted Enrichment in Single-cell Long-read Sequencing

Wen Hu, Julien Jarroux, Anoushka Joglekar, Andrey D. Prjibelski, X. Li, Alexandru I. Tomescu, Careen Foord, Hagen Tilgner, Justine Hsu, Natan Belchikov
article en

Abstract

The vast majority of multi-exonic genes are alternatively spliced, generating diverse and cell-type-specific isoforms exhibiting functional differences. To better capture this heterogeneity using single-cell long-read sequencing data, we previously developed an exome-probe-based approach to enrich for exonic reads of target genes. While effective, this procedure is time-consuming and expensive. Real-time targeting offers a more cost-efficient solution for selectively sequencing reads of interest. Here, we performed real-time enrichment of exonic sequences of single-cell long reads by targeting spliced transcripts from 3377 genes implicated in brain functions and related diseases. Our approach increased the total number of spliced on-target reads to up to 1.82 times the control level. Notably, targeting lowly expressed subsets yielded spliced on-target reads 1.39 to 1.89 times the control. While these gains do not rival those achieved using chemical probe-based enrichment, they are sufficient to significantly enhance the power of downstream statistical analyses, such as testing for cell-type-specific isoform abundance. Specifically, compared to naïve single-cell long-read sequencing, our approach yielded 2.42 times as many genes with significant differences in isoform usage between neurons and glia. Real-time targeting confirms cell-type-specific splicing in two early Mapt exons and newly reveals such events in > 100 genes, including Bak1 and Atp8a1. Overall, our findings highlight real-time targeting as a versatile method for enhancing resolution in detecting differential isoform usage across cell types in single-cell long-read data, offering the potential to obtain a fuller view of cellular isoform diversity.

Genomics Proteomics & Bioinformatics
University of Helsinki (FI), Cornell University (US), MIND Research Institute (US)
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
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