Cell-type-specific analysis of alternative polyadenylation using single-cell transcriptomics data.

Area of Science:

• Molecular Biology
• Genetics
• Bioinformatics

Background:

• Alternative polyadenylation (APA) is a key post-transcriptional gene regulatory mechanism affecting 3' UTR length.
• The majority of mammalian protein-coding genes utilize multiple polyadenylation (pA) sites, influencing gene expression.
• Current understanding of APA dynamics, especially in diverse cell types, remains limited.

Purpose of the Study:

• To develop and validate a method for analyzing APA patterns using 3'-end-biased single-cell RNA sequencing (scRNA-seq) data.
• To investigate cell-type-specific APA regulation across various tissues and biological conditions.
• To demonstrate the utility of public scRNA-seq datasets for large-scale APA exploration.

Main Methods:

• Implementation of a novel computational method to infer APA patterns from scRNA-seq data, leveraging 3'-end sequencing.
• Analysis of multiple publicly available scRNA-seq datasets from diverse tissue origins.
• Comparative analysis of APA profiles across different cell types and experimental conditions.

Main Results:

• Identification of widespread, cell-type-specific APA modulation, leading to global 3' UTR shortening and lengthening.
• Observation of increased cleavage at intronic polyadenylation sites in various cell types.
• Proof-of-concept for utilizing existing scRNA-seq data as a rich resource for studying APA.

Conclusions:

• scRNA-seq data provides a powerful and scalable resource for dissecting APA regulation at single-cell resolution.
• APA is a dynamic regulatory process significantly modulated by cell type and biological context.
• The developed method enables deeper insights into the role of APA in cellular function and disease.