Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables cell type and lineage identification in complex tissues.
• Current scRNA-seq methods struggle to directly associate cell clusters with specific phenotypes or clinical outcomes.

Purpose of the Study:

• To develop a computational method, Scissor, for identifying cell subpopulations linked to specific phenotypes using scRNA-seq data.
• To integrate bulk expression data with scRNA-seq data to enhance phenotype-associated subpopulation discovery.

Main Methods:

• Scissor quantifies similarity between individual cells and bulk samples.
• A regression model is optimized using the correlation matrix and sample phenotype to pinpoint relevant subpopulations.
• The method integrates phenotype-associated bulk expression data and single-cell data.

Main Results:

• Scissor identified cell subsets in lung cancer linked to poor survival and TP53 mutations.
• In melanoma, Scissor detected a T cell subpopulation associated with immunotherapy response.
• The method proved effective for facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets.

Conclusions:

• Scissor successfully identifies biologically and clinically relevant cell subpopulations from single-cell assays.
• The method leverages phenotype and bulk-omics data to bridge the gap between cell clusters and specific traits.
• Scissor offers a powerful tool for dissecting cellular heterogeneity in various disease contexts.