ArticleNeuroscience letters2019
Morphometric and computational assessments to evaluate neuron survival and maturation within compartmentalized microfluidic devices: The influence of design variation on diffusion-driven nutrient transport.
Article in Neuroscience letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Burgeoning use of segregated microfluidic platforms that parse somas and neurites into discrete compartments is fueling unique examinations of neuronal structure and physiology in a manner impossible to achieve with non-compartmentalized systems. However, even though this line of axon-soma polarizing microfluidic devices stems from the same general design of a Campenot chamber set-up, slight deviations in device geometry appear to induce vastly different nutrient transport profiles that influence neuron survival and maturation. Here we examine the uptake of nerve growth factor (NGF) by a pheochromocytoma PC12 cell line cultured using two Campenot-like device designs, a "Standard" layout, representative of a commercial device, and a custom "Notch" layout, predicted to encourage more efficient nutrient transfer that gives rise to sustained neuron viability and extensive neurite elaboration. Exploiting in vitro culture schemes coupled with computational analyses, we identify the influence of device design geometry on the interplay between neuronal survival and maturation, gauged from morphometric assessments and the spatiotemporal distribution of NGF. Computer simulations of NGF transport within the devices revealed that the microfluidic neuron culture system is highly sensitive to change, where nutrient transport is intricately linked to device geometry and cell plating density, and premature depletion of nutrients is observed if specific design criteria are not met. This study underscores the importance of validating specific device geometries for a particular neuro-based assessment, while showcasing computational modeling as a powerful tool to achieve this goal.
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