Graphical and Interactive Spatial Proteomics Image Analysis Workflow

Area of Science:

• Biomedical research
• Proteomics
• Computational biology

Background:

• Spatial proteomics offers detailed protein expression and localization data within cells and tissues.
• Existing imaging workflows lack flexibility, high-throughput capabilities, and interactive visualization for spatial proteomic analysis.

Purpose of the Study:

• To present a modular, interactive, and containerized spatial proteomics imaging workflow.
• To empower biomedical researchers with reproducible and customizable complex analysis tools.

Main Methods:

• The workflow includes cell segmentation, unsupervised clustering, cluster validation, and visualization.
• A form-based graphical interface allows single-click execution, interactive adjustments, and parameter modification.
• The workflow was tested on a high-plex immunohistochemistry-stained cancer tissue microarray (TMA).

Main Results:

• The workflow successfully processed a cancer imaging dataset with diverse cell types.
• Demonstrated applicability to various biopsy types and reproducible execution of complex analyses.
• Enabled interactive customization of image processing and analysis steps.

Conclusions:

• The developed spatial proteomics imaging workflow is flexible, high-throughput, and user-friendly.
• It facilitates reproducible and customizable analysis of spatial proteomic data for biomedical research.
• The workflow supports interactive visualization and broad applicability across different tissue types.