Quantification of transcript isoforms at the single-cell level using SCALPEL

Franz Ake, Marcel Schilling, Sandra M. Fernández-Moya, Akshay Jaya Ganesh, Ana Gutiérrez-Franco, Lei Li and Mireya Plass ()
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Franz Ake: Bellvitge Institute for Biomedical Research (IDIBELL)
Marcel Schilling: Bellvitge Institute for Biomedical Research (IDIBELL)
Sandra M. Fernández-Moya: Bellvitge Institute for Biomedical Research (IDIBELL)
Akshay Jaya Ganesh: Bellvitge Institute for Biomedical Research (IDIBELL)
Ana Gutiérrez-Franco: Bellvitge Institute for Biomedical Research (IDIBELL)
Lei Li: Shenzhen Bay Laboratory
Mireya Plass: Bellvitge Institute for Biomedical Research (IDIBELL)

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Single-cell RNA sequencing (scRNA-seq) facilitates the study of transcriptome diversity in individual cells. Yet, many existing methods lack sensitivity and accuracy. Here we introduce SCALPEL, a Nextflow-based tool to quantify and characterize transcript isoforms from standard 3’ scRNA-seq data. Using synthetic data, SCALPEL demonstrates higher sensitivity and specificity compared to other tools. In real datasets, SCALPEL predictions have a high agreement with other tools and can be experimentally validated. The use of SCALPEL on real datasets reveals novel cell populations undetectable using single-cell gene expression data, confirms known 3’ UTR length changes during cell differentiation, and identifies cell-type specific miRNA signatures regulating isoform expression. Additionally, we show that SCALPEL improves isoform quantification using paired long- and short-read scRNA-seq data. Overall, SCALPEL expands the current scRNA-seq toolkit to explore post-transcriptional gene regulation across species, tissues, and technologies, advancing our understanding of gene regulatory mechanisms at the single-cell level.

Date: 2025
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DOI: 10.1038/s41467-025-61118-0

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