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P2‐114: A HIGH‐POWER VIEW OF PERIPHERAL MACROPHAGES IN RAT ALZHEIMER DISEASE MODEL IN VIVO
Author(s) -
Der Balint,
Town Terrence
Publication year - 2019
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2019.06.2521
Subject(s) - immune system , intravital microscopy , in vivo , macrophage , pathology , alzheimer's disease , innate immune system , biology , neuroscience , medicine , immunology , disease , in vitro , microbiology and biotechnology , biochemistry
Background: Brain development, formation of memories, and brain tissue repair all depend on the coordinated activity of single cells. Characterizing cells as adaptive systems, we developed computational-experimental methods to study how cells communicate and coordinate with each other during growth and repair, with the overall goal of controlling cell-cell interactions to improve human health. The work presented here illustrates applications of graph theory and live imaging to uncover how human neural networks emerge from the coordination of single stem cells derived from healthy controls and patients with neurodegenerative diseases. Methods: We characterized the dynamics of human embryonic and induced neural stem cells (iNSCs) during differentiation by a mathematical framework that integrates spatiallyderived graphs with functional Ca recordings and multielectrode array analysis. Distinct neural differentiation stages identified by the graph-based approach were independently confirmed through patch-clamp electrophysiology and immunochemistry. Differentiation events including the formation of neural rosettes, Ca waves, and extensive dendritic arborization were also observed, while proteomic screens identified key proteins involved in NSC differentiation and correlated to graph features. Results: Results demonstrated that healthy human neural stem cells show a characteristic peak in spatial network efficiency – coupled with the highest levels of coordinated, spontaneous electrical activity – as they start to evolve into neurons in 2D and 3D neurogenesis assays. We hypothesize these observations indicate a shift in the communication mode of cells from one driven by localized, adjacency-dependent signaling to electrical conduction. Conclusions: We summarize our findings, contrasting them with results we obtained from applying the Living Neural Networks assay to study the differentiation of NSCs of children with the neurodevelopmental disorder Smith-Lemli Opitz Syndrome and from iNSCs derived from patients with Alzheimer’s disease. Results provide insight into the design of developing neural networks, open the door for applications that modulate neurodifferentiation, and provide personalized human in vitro models to test new therapies that optimize human neural regeneration both biochemically and electrically.