Predicting and comparing transcription start sites in single cell populations

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables analysis of transcription start sites (TSSs) at single-cell resolution.
• Existing computational methods for 5' RNA sequencing data often neglect single-end data and lack single-cell specific evaluations.
• Understanding transcription initiation and alternative TSS usage is crucial for cell type and condition-specific analyses.

Purpose of the Study:

• To develop and validate scTSS, a computational pipeline for analyzing 5' scRNA-seq data.
• To enable joint analysis of multiple single-cell samples, including TSS prediction, quantification, and differential usage analysis.
• To accommodate both paired-end and single-end 5' scRNA-seq data.

Main Methods:

• Development of the scTSS computational pipeline.
• TSS cluster prediction and quantification.
• Application of a Binomial generalized linear mixed model for differential TSS usage detection.
• Analysis of single-cell data from two distinct disease contexts.

Main Results:

• scTSS successfully analyzes both paired-end and single-end 5' scRNA-seq data.
• The pipeline accurately detects differential TSS usage between cell types and conditions.
• scTSS identifies cell subpopulations with unique TSS-level expression profiles.
• Demonstrated utility in disease-specific transcriptional initiation analysis.

Conclusions:

• scTSS provides a robust and versatile tool for single-cell TSS analysis.
• The pipeline enhances the understanding of transcriptional regulation complexities at a single-cell level.
• scTSS facilitates the discovery of novel cell subpopulations and disease-associated regulatory mechanisms.