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Abstract Mitochondrial abnormalities underscore a variety of neurologic injuries and diseases and are well-studied in adult populations. Clinical studies identify critical roles of mitochondria in a wide range of developmental brain injuries, but models that capture mitochondrial abnormalities in systems representative of the neonatal brain environment are lacking. Here, we develop an organotypic whole-hemisphere (OWH) brain slice model of mitochondrial dysfunction in the neonatal brain. We extended the utility of complex I inhibitor rotenone (ROT), canonically used in models of adult neurodegenerative diseases, to inflict mitochondrial damage in OWH slices from term-equivalent rats. We quantified whole-slice health over 6 days of exposure for a range of doses represented in ROT literature. We identified 50 nM ROT as a suitable exposure level for OWH slices to inflict injury without compromising viability. At the selected exposure level, we confirmed exposure- and time-dependent mitochondrial responses showing differences in mitochondrial fluorescence and nuclear localization using MitoTracker imaging in live OWH slices and dysregulated mitochondrial markers via RT-qPCR screening. We leveraged the regional structures present in OWH slices to quantify cell density and cell death in the cortex and the midbrain regions, observing higher susceptibilities to damage in the midbrain as a function of exposure and culture time. We supplemented these findings with analysis of microglia and mature neurons showing time-, region-, and exposure-dependent differences in microglial responses. We demonstrated changes in tissue microstructure as a function of region, culture time, and exposure level using live-video epifluorescence microscopy of extracellularly diffusing nanoparticle probes in live OWH slices. Our results highlight severity-, time-, and region-dependent responses and establish a complimentary model system of mitochondrial abnormalities for high-throughput or live-tissue experimental needs. 

Multiple-particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT data sets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such as biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT data sets from the living brain ECS—age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5 h OGD treatment, and 1.5 h OGD treatment with 59% accuracy in the cortex and 66% in the striatum, and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion.