Obstacles to detecting isoforms using full-length scRNA-seq data.

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Early single-cell RNA sequencing (scRNA-seq) studies suggested limited isoform expression per gene within single cells.
• These initial findings may be confounded by unaddressed technical limitations such as dropout events and isoform quantification errors.

Purpose of the Study:

• To evaluate the feasibility of studying alternative splicing using scRNA-seq data.
• To identify key limitations and necessary improvements for accurate splicing analysis at the single-cell level.

Main Methods:

• A simulation-based approach was employed to explicitly model dropout events and isoform quantification errors.
• Simulations were used to assess the impact of these technical factors on alternative splicing analysis.

Main Results:

• High dropout rates in scRNA-seq represent a significant obstacle for alternative splicing studies.
• Isoform quantification errors, while present, were found to be a lesser impediment compared to dropouts in well-established model organisms.
• The choice of isoform expression models significantly influences simulation outcomes.

Conclusions:

• Accurate single-cell alternative splicing analysis requires a deeper understanding of isoform choice mechanisms.
• Enhancing the capture efficiency of scRNA-seq is crucial for improving splicing analysis.
• Current scRNA-seq methodologies are not recommended for resolving isoforms in individual cells without further advancements.