ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Area of Science:

• Biomedical Imaging
• Computational Biology
• Cellular Biology

Background:

• Tissue functionality relies on resident cells and their microenvironment.
• Imaging Mass Cytometry (IMC) provides high-resolution spatial data on cell types and their interactions.
• Analyzing large, high-dimensional IMC datasets presents significant computational challenges.

Purpose of the Study:

• To develop an interactive visual analysis workflow for end-to-end Imaging Mass Cytometry data analysis.
• To create a tool, ImaCytE, that facilitates cell type discrimination and microenvironment analysis.
• To aid researchers in formulating and verifying hypotheses regarding tissue architecture and function.

Main Methods:

• Development of an interactive visual analysis workflow tailored for IMC data.
• Implementation of the workflow into a user-friendly tool named ImaCytE.
• Collaboration with domain experts to ensure practical utility and effectiveness.

Main Results:

• ImaCytE enables precise cell type identification based on protein expression profiles.
• The tool facilitates the analysis of cellular neighborhoods and spatial relationships within tissues.
• Demonstrated effectiveness of the workflow and ImaCytE through a collaborative case study.

Conclusions:

• The developed workflow and ImaCytE tool effectively address the challenges of IMC data analysis.
• ImaCytE empowers researchers to explore complex spatial biology data.
• This approach enhances the understanding of tissue organization and cellular interactions.