CyNET—a network analysis framework for high dimensional, system level analyses of the functional immunome

Abstract The immune system is a complex “network of networks,” where interactions between various immune cell subsets determine immune competence and influence disease onset or control. These interactions dictate whether the body remains in a healthy state or develops pathological conditions. Traditional statistical methods largely ignore these interactions and rely only on statistical changes in cell frequencies. To address this gap, we developed CyNET (Cytometry Network), a network science-based analysis platform that quantifies immune system properties at both the systems and subset levels. We used CyNET to analyze immune development across different age groups, examining peripheral blood cells from healthy newborns, adults (20–55 yr), and elderly individuals (≥70 yr) using CyNET and single-cell RNA sequencing. The analysis revealed that changes in the centrality of immune subsets, rather than just their frequency alone, provide deeper insights into biological functions. For instance, although CD28− CD8 T cells increase in frequency with age, their reduced centrality and diminished intracellular interaction potential explain cellular senescence and exhaustion. Additionally, CyNET identified key systems properties—such as -network edge density, degree centralization, and assortativity score—that reflect immune system development and help characterize the immune network’s functional architecture across different ages.