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IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data
Arianna Barbetta1, Sarah Bangerth1, Jason T C Lee1
1University of Southern California.
Research Square
|July 23, 2024
Summary
IMmuneCite is a new computational tool that accurately identifies 32 immune cell phenotypes in spatial proteomics data. This framework enhances immune microenvironment analysis across species and applications.
Area of Science:
- Immunology
- Computational Biology
- Proteomics
Background:
- Spatial proteomics offers single-cell resolution for tissue analysis.
- Accurate cell segmentation and phenotyping remain challenging.
- Complex immune landscapes require sophisticated computational tools.
Purpose of the Study:
- Introduce IMmuneCite, a computational framework for spatial proteomics.
- Improve the creation of single-cell datasets for immune cell analysis.
- Enable high-fidelity investigation of the immune microenvironment.
Main Methods:
- Developed IMmuneCite for comprehensive image pre-processing.
- Applied the framework to human and murine liver tissue spatial proteomics data.
- Focused on defining discrete immune cell phenotypes and reducing nonbiological clusters.
Main Results:
- Identified 32 discrete immune cell phenotypes in human liver samples.
- Reduced nonbiological cell clusters caused by marker co-localization.
- Demonstrated versatility across species and antibody panels.
- Enabled deep characterization of immune cell functional states.
Conclusions:
- IMmuneCite is a user-friendly, integrated computational platform.
- Facilitates cross-species investigation of the immune microenvironment.
- Ensures creation of focused, spatially resolved single-cell proteomic datasets for high-fidelity analysis.
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