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Updated: May 9, 2025
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Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
Published on: April 30, 2019
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Highly Adaptable Analysis Tools for Mapping Spatial Features of Cellular Aggregates in Tissues
Andrew Sawyer1,2,3, Nick Weingaertner1,3, Ellis Patrick4
1School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
Current Protocols
|May 5, 2025
Summary
New metrics quantify cellular structure in entire tissue lesions, offering simple readouts for spatial analysis of tumors and granulomas. This approach enhances prognostic biomarker discovery and lesion comparison in multiplex imaging studies.
Area of Science:
- Pathology
- Computational Biology
- Biomedical Imaging
Background:
- Multiplex imaging advances reveal spatial cell organization as key prognostic biomarkers.
- Current spatial analysis tools often focus on small regions, missing patterns in larger cellular aggregates like tumors.
- Analyzing entire lesions is crucial for understanding disease progression and cellular distribution.
Purpose of the Study:
- To develop novel quantitative metrics for analyzing the cellular structure of entire tissue lesions.
- To provide simple, interpretable readouts for spatial analysis applicable to any lesion size or shape.
- To enable cross-lesion comparisons and enhance prognostic biomarker discovery.
Main Methods:
- Development of two novel metrics: Total Cell Preference Index and Immune Cell Preference Index.
- Quantitative description of cellular structure within entire tissue lesions.
- Application of open-source QuPath software for image analysis.
Main Results:
- The Total Cell Preference Index quantifies lesion density changes (central vs. peripheral), indicating necrosis extent.
- The Immune Cell Preference Index maps immune cell type distribution (central vs. peripheral) across the entire lesion.
- Both indexes provide single-number readouts for simplified interpretation and visualization.
Conclusions:
- The novel metrics enable comprehensive spatial analysis of entire tissue lesions, overcoming limitations of traditional methods.
- This approach simplifies cross-lesion comparisons and is compatible with various multiplex imaging systems.
- The user-friendly protocol, utilizing open-source software, can be rapidly implemented by researchers.
