histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data

Area of Science:

• Biomedical research
• Computational biology
• Pathology

Background:

• Single-cell, spatially resolved omics analysis is revolutionizing biomedical research and clinical practice.
• Understanding tissue architecture and cellular interactions is crucial for disease diagnosis and treatment.

Purpose of the Study:

• To introduce histoCAT, an open-source computational toolbox for histology topography cytometry analysis.
• To enable interactive, quantitative, and comprehensive exploration of cell phenotypes, interactions, microenvironments, and morphology in intact tissues.

Main Methods:

• Development of an open-source computational toolbox named histoCAT.
• Application of histoCAT to analyze highly multiplexed mass cytometry images of human breast cancer tissues.

Main Results:

• histoCAT facilitates interactive and quantitative exploration of single-cell, spatially resolved omics data.
• The toolbox enables detailed analysis of cell phenotypes, cell-cell interactions, microenvironments, and morphological structures.
• Demonstrated unique abilities of histoCAT in analyzing complex human breast cancer tissue data.

Conclusions:

• histoCAT provides a powerful platform for advancing single-cell, spatially resolved omics analysis.
• The toolbox supports comprehensive exploration of tissue complexity, paving the way for new discoveries in cancer research and beyond.