An imaging-based systems approach to understand the neuroimmunological manifestations of Dengue infection in humans

Dengue is a mosquito-borne illness for which approximately half of the world's population is at risk, with close to 100 million symptomatic cases per year. Classified as a Neglected Tropical Disease (NTD) that disproportionately affects the world's most vulnerable populations, basic and translational research regarding the cellular consequences of Dengue virus (DENV) infection is vastly under-represented. Even fewer studies examine the neuro-immunological manifestations of severe dengue which embodies the most fatal and dangerous form of disease. However, as the central nervous system (CNS) is inaccessible in humans with the rare exceptions of post-mortem brain tissue, the lack of appropriate human models and platforms to test large-scale functional perturbations have hindered efforts to conduct human studies in the context of DENV infection. Here, we propose to develop an autologous human model that represents the DENV neuro-immune interface by employing a two-step reprogramming method that differentiates donor blood to derive donor-matched induced neuronal cells (iNs) via induced pluripotent stem cells (iPSCs).We will then establish a DENV infection paradigm of both neural and immune lineage cells, and systematically screen for potential neuro- and immune-modulators of DENV pathogenicity and cell-cell interactions. High-content image-based profiling and convolutional neural networks (CNNs) will be implemented to untangle the contributions of different cell types and learn heterogeneous cell-type-specific or donor-specific phenotypes. Finally, for promising candidate modulators that either affect cellular DENV pathogenicity, those that alter the baseline neuro-immune interactions, or those in which the drug response differs among donors - we will conduct matched transcriptomic profiling to uncover functional gene networks that regulate features of DENV-mediated perturbation of neuro-immune homeostasis.