Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering...

Area of Science:

• Immunology
• Computational Biology
• Bioinformatics

Background:

• Mass cytometry generates complex data requiring advanced visualization tools.
• Existing clustering methods (Cytosplore, FlowSOM) identify cell populations but need efficient downstream analysis.
• Rapid interpretation of immune cell responses in the tumor microenvironment is crucial for therapeutic development.

Purpose of the Study:

• Introduce Cytofast, an R package for rapid visualization of mass cytometry data.
• Demonstrate Cytofast's utility in analyzing immune cell modulation, specifically natural killer (NK) cells, in response to immunotherapy.
• Quantitatively compare immune cell cluster abundance and phenotypic characteristics between experimental groups.

Main Methods:

• Utilized Cytofast R package for downstream analysis of mass cytometry data.
• Applied clustering methods (FlowSOM, Cytosplore) for cell population identification.
• Analyzed data from a tumor challenge model with PD-L1 blockade immunotherapy to assess NK cell responses.

Main Results:

• Cytofast rapidly generated visual representations of immune cell clusters and their group-related correlations.
• The R package effectively visualized differences in cell cluster abundance and phenotypic markers.
• PD-L1 treatment induced observable patterns, including increased activated NK cell subsets with higher CD54 or CD11c expression.

Conclusions:

• Cytofast provides a valuable tool for rapid and quantitative analysis of mass cytometry data.
• The package enhances the visualization of immune cell responses, particularly NK cells, in the tumor microenvironment.
• Cytofast aids in understanding treatment effects, such as PD-L1 blockade, on immune cell dynamics.