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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
Published on: April 8, 2016
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Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms.
Erik A Burlingame1,2, Jennifer Eng2, Guillaume Thibault2
1Computational Biology Program, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
Cell Reports Methods
|September 6, 2021
Summary
New open-source tools enable scalable analysis of large multiplex tissue imaging datasets for cell phenotyping and spatial analysis in breast cancer research. This facilitates reproducible discovery of subtype-specific features.
Area of Science:
- Computational Biology
- Biotechnology
- Oncology
Background:
- Megascale single-cell multiplex tissue imaging (MTI) datasets are emerging, requiring advanced computational tools.
- Existing methods for cell phenotyping and spatial analysis lack reproducibility and scalability.
Purpose of the Study:
- To develop open-source, GPU-accelerated tools for MTI data analysis.
- To enable robust cell phenotyping and microenvironment characterization.
- To facilitate cross-validation of MTI platforms and reveal breast cancer subtype-specific features.
Main Methods:
- Developed open-source, GPU-accelerated software for intensity normalization, phenotyping, and microenvironment characterization.
- Deployed the toolkit on human breast cancer (BC) tissue microarrays stained by cyclic immunofluorescence.
- Performed cross-validation of cell phenotypes across two different MTI platforms.
- Conducted integrative phenotypic and spatial analysis.
Main Results:
- The developed toolkit provides reproducible and scalable analysis of MTI data.
- Cross-validation confirmed the reliability of MTI-derived breast cancer cell phenotypes.
- Integrative analysis identified BC subtype-specific phenotypic and spatial features.
Conclusions:
- Open-source, GPU-accelerated tools are essential for analyzing megascale MTI datasets.
- The developed toolkit enhances reproducibility and scalability in cell phenotyping and spatial analysis.
- This approach enables novel discoveries in breast cancer biology by integrating phenotypic and spatial information.
