Manual cell selection in single cell transcriptomics using scSELpy supports the analysis of immune cell subsets

Area of Science:

• Immunological research
• Computational biology
• Genomics

Background:

• Single cell RNA sequencing (scRNA-seq) is crucial for understanding complex diseases like inflammatory bowel diseases (IBD).
• Existing scRNA-seq analysis pipelines often lack user-friendly tools for manual cell population selection and subsequent analysis.

Purpose of the Study:

• To develop and validate a novel tool, scSELpy, for manual selection and downstream analysis of cell populations in scRNA-seq data.
• To address the unmet need for intuitive tools in single-cell transcriptomic analysis for immunological research.

Main Methods:

• Development of scSELpy, a tool integrated into Scanpy-based pipelines.
• Manual cell selection via polygon drawing on data visualizations.
• Downstream analysis and plotting of selected cell populations.
• Application to existing scRNA-seq datasets for validation.

Main Results:

• scSELpy facilitates manual positive and negative selection of T cell subsets relevant to IBD, surpassing standard clustering methods.
• The tool enables subphenotyping of T cell subsets and corroboration of previous findings.
• scSELpy demonstrates utility in T cell receptor sequencing data analysis.

Conclusions:

• scSELpy is a valuable, user-friendly addition to single-cell transcriptomic analysis toolkits.
• The tool supports immunological research by enabling precise cell population selection and analysis.
• scSELpy addresses a significant gap in current bioinformatics workflows for scRNA-seq data.