Towards systems immunology of critical illness at scale: from single cell ‘omics to digital twins

• Published in: Trends in immunology 2023-05, Vol.44 (5), p.345-355
• Main Author: Vodovotz, Yoram
• Format: Article
• Language: English
• Subjects: COVID-19; Critical Illness; Humans; Immune System; Inflammation
• ISSN: 1471-4906; 1471-4981
• DOI: 10.1016/j.it.2023.03.004

Systems immunology holds great basic and translational potential via integration of data-driven and mechanistic modeling.Critical illness due to bacterial or viral infection, or caused by severe traumatic injury, has yielded systems immunology insights.A concerted effort aimed at generating single-cell and bulk ‘omics data from multiple tissues and biofluids, combined with data-driven and mechanistic modeling at scale, could result in rational immune reprogramming for critical illness as well as related immune disorders. Data-driven and mechanistic computational modeling pose related but distinct visions of how to undertake systems immunology. There is a cognitive gap between these disparate modeling approaches and this gap must be bridged to drive the next generation of translational advances, especially in the context of critical illness. Ultimately, these computational strategies could yield novel approaches for rational immune reprogramming. Single-cell ‘omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of ‘digital twins’ and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.