Exploring the cellular landscape of circular RNAs using full-length single-cell RNA sequencing

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Circular RNAs (circRNAs) exhibit tissue-specific expression patterns.
• The cellular architecture and cell-type-specific expression of circRNAs remain largely uncharacterized.

Purpose of the Study:

• To explore the cellular landscape of circRNAs using single-cell RNA sequencing (scRNA-seq).
• To identify cell-type-specific circRNA expression patterns in human and mouse tissues.
• To provide a high-resolution resource for circRNA research.

Main Methods:

• Analysis of 171 full-length single-cell RNA-seq datasets from human and mouse tissues.
• Large-scale integrative analysis to identify and quantify circRNAs.
• Validation using 11 bulk RNA-seq resources.

Main Results:

• Identification of 139,643 human and 214,747 mouse circRNAs in scRNA-seq libraries.
• Validation confirmed 216,602 high-confidence circRNAs uniquely detected in the single-cell cohort.
• Revealed cell-type-specific circRNA expression in brain, embryonic, and tumor tissues.
• Identified uniquely expressed circRNAs for cell type identification and validated their use in immune cell deconvolution.

Conclusions:

• This study provides the first comprehensive characterization of circRNA expression at the single-cell level.
• The findings expand the understanding of circRNA heterogeneity across cell types.
• The generated dataset serves as a valuable resource for future circRNA research at single-cell resolution.