Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data.

Area of Science:

• Immunology
• Bioinformatics
• Computational Biology

Background:

• Polychromatic flow cytometry (PFC) enables simultaneous detection of multiple cellular antigens, generating large datasets.
• Analyzing these complex datasets presents a significant challenge in flow cytometry research.

Purpose of the Study:

• To demonstrate the utility of cluster analysis (CA) and principal component analysis (PCA) for simplifying multicolor flow cytometry data visualization.
• To enable subject classification and identification of age-related T cell phenotypes using bioinformatic approaches.

Main Methods:

• Eight-color cytofluorimetric analysis was performed on T cell compartments from young, middle-aged, and centenarian donors.
• T cell subsets were defined by antigen expression profiles.
• Data were subjected to cluster analysis (CA) and principal component analysis (PCA).

Main Results:

• CA successfully clustered individuals based on their age-related cytofluorimetric profiles.
• PCA distinguished centenarians from young donors, with middle-aged donors showing intermediate profiles.
• Specific T cell phenotypes associated with aging were identified, such as shifts in CD127 and CD95 expression on memory T cells.

Conclusions:

• Bioinformatic tools (CA and PCA) are effective for analyzing large PFC datasets.
• These methods facilitate rapid identification of key cell populations that characterize distinct subject groups.
• The study highlights age-related changes in T cell subsets identifiable through advanced data analysis.