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Research Techniques Made Simple: Mass Cytometry Analysis Tools for Decrypting the Complexity of Biological Systems
Tiago R Matos1, Hongye Liu2, Jerome Ritz2
1Division of Hematologic Malignancies, Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Harvard Medical School, Boston, Massachusetts, USA; Academic Medical Center, Department of Dermatology, University of Amsterdam, Amsterdam, The Netherlands.
The Journal of Investigative Dermatology
|April 17, 2017
Summary
Mass cytometry enables deep single-cell analysis, but manual methods are limited. New computational tools like SPADE and t-SNE reveal complex cellular networks for breakthroughs in immunology and dermatology.
Area of Science:
- Single-cell analysis
- Computational biology
- Immunology
- Dermatology
Background:
- Mass cytometry allows simultaneous analysis of over 40 cellular markers at the single-cell level.
- Manual analysis of mass cytometry data is subjective, labor-intensive, and struggles with high dimensionality.
- The complexity of cellular networks necessitates advanced computational approaches for data interpretation.
Purpose of the Study:
- To review frequently used and accessible mass cytometry data analysis tools.
- To highlight the potential of mass cytometry combined with computational methods for scientific discovery.
- To underscore the importance of these tools in areas like immunology and investigative dermatology.
Main Methods:
- Review of dimensionality reduction and data analysis tools for mass cytometry.
- Mention of Principal Component Analysis (PCA), SPADE, viSNE, ACCENSE, and CITRUS.
- Focus on computational approaches to overcome manual analysis limitations.
Main Results:
- Emerging recognition of cellular network diversity and complexity.
- Discovery of unexpected cell subpopulations, hierarchies, and developmental pathways.
- Advancements in visualizing and presenting high-dimensional mass cytometry data.
Conclusions:
- Mass cytometry, coupled with advanced analytic tools, drives significant discoveries.
- Potential applications include identifying predictive signaling phenotypes for diagnosis and prognosis.
- These methods facilitate characterization of tumor heterogeneity and disease-resistant cells, unveiling novel therapeutic strategies.