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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for studying cellular transcriptome diversity.
• Existing scRNA-seq methods often struggle with sensitivity and accuracy in isoform quantification.
• Characterizing transcript isoforms is essential for understanding post-transcriptional gene regulation.

Purpose of the Study:

• To introduce SCALPEL, a Nextflow-based tool for quantifying and characterizing transcript isoforms from 3' scRNA-seq data.
• To evaluate SCALPEL's performance against existing methods using synthetic and real-world datasets.
• To demonstrate SCALPEL's utility in discovering novel cell populations and regulatory insights.

Main Methods:

• Development of SCALPEL, a Nextflow pipeline for isoform analysis.
• Utilizing synthetic scRNA-seq data to benchmark SCALPEL's sensitivity and specificity.
• Applying SCALPEL to real scRNA-seq datasets for biological discovery.
• Integrating SCALPEL with paired long- and short-read scRNA-seq data.

Main Results:

• SCALPEL exhibits superior sensitivity and specificity compared to other tools on synthetic data.
• SCALPEL predictions show high concordance with existing methods and experimental validation in real datasets.
• SCALPEL identified novel cell populations missed by standard gene expression analysis.
• SCALPEL confirmed 3' UTR length changes during differentiation and uncovered cell-type specific miRNA regulatory signatures.
• SCALPEL improved isoform quantification when using paired long- and short-read scRNA-seq data.

Conclusions:

• SCALPEL is a sensitive and accurate tool for isoform quantification from 3' scRNA-seq data.
• SCALPEL expands the capabilities of scRNA-seq for exploring post-transcriptional gene regulation.
• The tool facilitates deeper understanding of gene regulatory mechanisms at the single-cell level across diverse biological contexts.