Single-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
• Incomplete 3 -untranslated region (3 -UTR) annotations in reference genomes limit scRNA-seq data accuracy.
• Poor annotations lead to undercounting genes, hindering cell identity and expression pattern analysis.

Purpose of the Study:

• To evaluate single-cell isoform sequencing (sciso-seq) as a method to improve 3 -UTR annotations for scRNA-seq.
• To assess the impact of enhanced annotations on gene discovery and cell type identification.

Main Methods:

• sciso-seq was performed in tandem with scRNA-seq on threespine stickleback embryos.
• Gene models were generated using sciso-seq data and compared to Ensembl annotations alone.
• sciso-seq data was also combined with a bulk Iso-Seq dataset for annotation merging.

Main Results:

• Gene models from embryonic sciso-seq captured 26.1% more scRNA-seq reads than Ensembl annotations.
• Combining sciso-seq with bulk Iso-Seq yielded a marginal improvement (+0.8%) over sciso-seq alone.
• sciso-seq identified thousands of novel splicing variants, significantly improving gene models.

Conclusions:

• sciso-seq is an efficient and cost-effective approach to rapidly improve genome annotations for scRNA-seq.
• Enhanced gene models facilitate accurate cell type identification and increase gene detection sensitivity.
• This technique addresses a critical limitation in single-cell genomics, enabling more robust biological inferences.