CyNET- a network analysis framework for high dimensionality, system level analyses of the functional Immunome

• Published in: bioRxiv
• Main Authors: Kumar, Pavanish, Yeo, Joo Guan, Poh, Su Li, Hazirah, Sharifah Nur, Sutamam, Nursyuhadah Binte, Ang, WX Gladys, Chellamuthu, Vasuki Ranjani, Wasser, Martin, Thai, Yeo Kee, Leong, Jing Yao, Ramakrishna, Lakshmi, Arkachaisri, Thaschawee, Albani, Salvatore
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Laboratory 04.08.2024
• Edition: 1.2
• Subjects: Immunology
• ISSN: 2692-8205
• DOI: 10.1101/2024.07.15.603495

The immune system is a complex “Network of Networks”, in which various immune cell subsets interact and influence each other’s functions, ultimately determining immune competence and the control or onset of disease. The coordinated interactions between these cell subsets determine whether the outcome is a normal physiological state or a pathological condition. Established statistical procedures largely ignore the interactions between subsets and rely on statistically significant changes in the frequency of cell subsets. We developed CyNET (Cytometry Network)— an analysis platform based on a network science approach to understand the immune system holistically. CyNET enables us to quantify the systems level and subsets level properties. We show that changes in the centrality of the nodes (immune subsets) reflect better biological functions than changes in frequency. We used CyNET to analyze the immune development along the chronological age gradient. Peripheral blood cells from healthy newborns (cord blood), adults (20 to 55 years), and elderly (70 years and above) human subjects were further used for validation using single-cell transcriptomics. We found that network edge density, degree centralization, and assortativity score reflect the maturation and development of the immune system along the age axis, thus enabling the characterisation of the functional architecture of the Immunome and the identification of key functional hubs within the immune networks.