Graphical and interactive spatial proteomics image analysis workflow

Area of Science:

• Proteomics
• Bioinformatics
• Computational Biology

Background:

• Spatial proteomics offers insights into protein expression and localization within cells and tissues.
• Existing spatial proteomic analysis workflows often lack integration, flexibility, and interactive visualization capabilities.
• Biomedical researchers require robust, reproducible, and customizable imaging analysis workflows.

Purpose of the Study:

• To present a modular, interactive, and containerized spatial proteomic image analysis workflow.
• To empower researchers with a tool for reproducible and customizable complex spatial proteomic data analysis.
• To facilitate visualization and interpretation of spatial proteomic data.

Main Methods:

• The workflow integrates cell segmentation, unsupervised clustering with optional batch correction, and cluster validation.
• A form-based graphical interface allows for single-click execution, customization, and interactive adjustment of analysis steps.
• Containerization ensures reproducibility and flexibility across different datasets and parameters.

Main Results:

• The workflow successfully performs multi-step spatial proteomic analyses, including cell type clustering and visualization.
• Demonstrated functionality on human normal tonsil and colorectal cancer tissues using high-plex immunohistochemistry.
• The interactive interface allows for dynamic adjustment of image processing and analysis parameters.

Conclusions:

• The presented workflow provides an end-to-end solution for spatial proteomic image analysis.
• Its modular and interactive design enhances reproducibility and customization for biomedical researchers.
• This tool facilitates deeper understanding of protein expression and localization in complex biological samples.