Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing.

Area of Science:

• Single-cell genomics
• Transcriptomics
• Computational biology

Background:

• Understanding cellular heterogeneity requires high-resolution analysis of gene expression.
• Existing single-cell methods have limitations in comprehensively analyzing transcript isoforms and mutations.

Purpose of the Study:

• To develop an integrated single-cell protocol for simultaneous short-read and long-read sequencing.
• To create a computational pipeline (FLAMES) for advanced transcriptomic analysis, including isoform discovery and mutation detection.

Main Methods:

• A modified Chromium 10x droplet-based protocol for subsampling cells.
• Integration of short-read and nanopore long-read sequencing.
• Development and application of the FLAMES computational pipeline.

Main Results:

• Identification of thousands of previously unannotated isoforms.
• Discovery of conserved functional modules enriched for alternative splicing across cell types and species.
• Successful integration with scATAC-seq data and improved correlation with protein expression.

Conclusions:

• The developed method enables comprehensive single-cell transcriptomic analysis, including novel isoform discovery.
• This approach links transcriptomic heterogeneity to functional modules and drug resistance mutations.
• It provides a powerful tool for dissecting cellular complexity and gene regulation.