Modeling Spatiotemporal Immune Dynamics in Inflammatory Diseases Using Integrative Multi-Modal Analytics and High-Dimensional Longitudinal Datasets

Comprehending the dynamic response of the immune system in inflammatory diseases is a major task due to the dynamic complexity, the cellular heterogeneity, and responses depend on the spatial context. The classic analytical methods are insufficient to handle the complex dynamics of the immune system, and especially of those diseases that advance, relapse, or remit temporally and spatially. To fill this void, the development of multi-modal analytics combined with high-dimensional, longitudinal data establishes a transformative framework for deciphering spatiotemporal immune signatures and regulatory networks. The aim of this study was to develop a global model system that uses integrative multi-omics data, imaging, clinical information, and spatial transcriptomics to model the immune dynamics in chronic inflammation including, e.g., rheumatoid arthritis (RA), IBD and SLE. By leveraging a number of sophisticated statistical learning algorithms, such as manifold alignment, tensor decomposition, and dynamic Bayesian networks, the model captures the sequential progression of disease progression, rewiring of immune cells, and microenvironmental cues over time. A key innovation is the method’s ability to mix data streams with different temporal and spatial resolutions — for example, bringing together single-cell RNA sequencing with time-stamped measurements of serum proteomics, or histopathological imaging. This enables the discovery early predictable markers, and context-dependent therapeutic targets in a mechanistically-informed manner. At its conclusion, this modeling approach will facilitate development of the next generation of precision immunology interventions for real-time monitoring of disease and personalized tide-turning intervention in the treatment of inflammatory diseases.