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CyNET-a network analysis framework for high dimensional, system level analyses of the functional immunome
Pavanish Kumar1,2, Joo Guan Yeo1,2,3, Su Li Poh1
1Translational Immunology Institute, Singhealth Duke-NUS Academic Medical Centre, Singapore, Singapore.
Insights
Network science reveals immune system changes with age. Analyzing immune cell interactions, not just numbers, offers deeper insights into health and disease, particularly cellular senescence.
Area of Science:
- Immunology
- Network Science
- Computational Biology
Background:
- The immune system is a complex network where cell interactions dictate health and disease.
- Traditional methods focus on cell frequency, neglecting crucial interaction dynamics.
- Understanding immune network changes with age is vital for health and disease management.
Purpose of the Study:
- To develop and apply a network science-based platform, CyNET, for analyzing immune system properties.
- To investigate how immune cell network properties change across different age groups.
- To identify key network features that characterize immune system development and function.
Main Methods:
- Development of CyNET, a network science analysis platform for immune cells.
- Analysis of peripheral blood cells from newborns, adults, and elderly individuals using CyNET.
- Integration of CyNET analysis with single-cell RNA sequencing data.
Main Results:
- Immune subset centrality, not just frequency, provides deeper biological insights.
- Age-related changes in immune cell centrality correlate with cellular senescence and exhaustion (e.g., CD28- CD8 T cells).
- CyNET identified key systems properties (e.g., network density, centralization, assortativity) reflecting immune network architecture across ages.
Conclusions:
- Network science approaches like CyNET offer a more comprehensive understanding of immune system dynamics.
- Changes in immune cell network centrality are critical indicators of immune aging and function.
- CyNET provides a powerful tool for characterizing the functional architecture of the immune network across the lifespan.
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.
