Single-cell transcriptomic analysis of mIHC images via antigen mapping

Area of Science:

• Biotechnology
• Molecular Biology
• Immunology

Background:

• Highly multiplexed immunohistochemistry (mIHC) allows detailed analysis of cell populations in tissues.
• Challenges exist in annotating similar cell populations or those lacking specific markers in mIHC data.
• Recent advances enable concurrent antigen and transcriptomic measurements at the single-cell level.

Purpose of the Study:

• To develop a computational approach for integrating single-cell RNA sequencing (scRNA-seq) data with mIHC images.
• To improve the annotation of nuanced cell populations and spatial patterns in histological analyses.
• To enable a deeper understanding of cellular architecture and interactions within tissues.

Main Methods:

• Developed Spatially-resolved Transcriptomics via Epitope Anchoring (STvEA), a novel computational framework.
• STvEA integrates scRNA-seq data to guide the annotation of mIHC cytometry datasets.
• Applied STvEA to analyze published murine spleen cytometry and mIHC data.

Main Results:

• STvEA enables transcriptome-guided annotation of mIHC data with high resolution.
• The approach facilitates the systematic identification of subtle cell populations and their spatial distribution.
• Revealed the architecture of previously poorly characterized cell types in the murine spleen.

Conclusions:

• STvEA significantly enhances the analytical power of mIHC by incorporating transcriptomic information.
• This method provides deeper insights into tissue architecture, cellular heterogeneity, and cell-cell interactions.
• STvEA is a valuable tool for advancing histological and spatial biology research.