Sierra

Sierra detects differential transcript usage from polyA-captured single-cell RNA sequencing (scRNA-seq) data to enable analysis of alternative mRNA isoform expression across cell populations.

Key Features:

• Detection of Differential Transcript Usage: Identifies variations in transcript usage across cell populations by analyzing coordinates of read pileups (peaks) within scRNA-seq data.
• UMI Counting: Incorporates Unique Molecular Identifier (UMI) counting to quantify transcripts for more accurate expression estimates.
• Comparative Analysis with Bulk RNA-seq: Facilitates comparative studies between single-cell and matched bulk RNA sequencing datasets to assess concordance of differential transcripts.
• Validation in Cardiac Cell Types: Validated using cardiac scRNA-seq cell types compared to matched bulk RNA-seq populations, showing overlap in detected differential transcripts.
• Application in Human and Murine Datasets: Applied to human peripheral blood mononuclear cells (PBMCs) and the Tabula Muris dataset to detect differential transcript usage across species and tissues.
• Detection of 3' UTR Shortening: Identifies 3' untranslated region (UTR) shortening events, exemplified in cardiac fibroblasts.

Scientific Applications:

• Alternative splicing and isoform diversity: Enables investigation of alternative splicing events and isoform-level expression at single-cell resolution.
• Cell-type-specific transcriptomics: Supports characterization of cell-type-specific transcriptomic profiles that contribute to cellular heterogeneity.
• Post-transcriptional regulation: Facilitates exploration of regulatory mechanisms such as 3'UTR shortening that can impact gene expression regulation.

Methodology:

Tailored for polyA-captured scRNA-seq data, Sierra identifies read pileups (peaks) by genomic coordinates, performs UMI counting for transcript quantification, and conducts differential transcript usage analysis between defined cell populations.