Issue 6, 2018

Quantifying orientational regeneration of injured neurons by natural product concentration gradients in a 3D microfluidic device

Abstract

Regeneration of injured neurons in complicated three-dimensional (3D) microenvironments is a key approach for treating neurodegenerative diseases. Microfluidics provides a versatile tool to recapitulate cellular microenvironments in vitro, but it still remains a big challenge to construct a microfluidic platform incorporating extracellular matrix (ECM) structures and highly controlled 3D gradients of soluble factors to study the regeneration of injured neurons. In this work, we developed a microfluidic device which can provide multiple adjustable gradients in a 3D ECM to investigate the regeneration of injured central nervous system (CNS) neurons in response to natural small molecules. With interconnecting but independently controlled central channels, asymmetrically designed side channels and a series of microgrooves connecting the central channels, spatially and temporally controlled 3D biochemical gradients can be generated inside collagen hydrogel in the central channels. This allows quantitative analysis of guided axon growth and the orientational regeneration of injured dopaminergic neurons by 3D chemical gradients of three natural molecules. This study demonstrates a promising microfluidic platform for the generation of highly controlled 3D biochemical gradients in an ECM to quantitatively study neuronal responses, thereby potentially facilitating drug screening and optimization of treatment protocols for neurodegenerative diseases.

Graphical abstract: Quantifying orientational regeneration of injured neurons by natural product concentration gradients in a 3D microfluidic device

Supplementary files

Article information

Article type
Paper
Submitted
22 Oct 2017
Accepted
21 Feb 2018
First published
21 Feb 2018

Lab Chip, 2018,18, 971-978

Quantifying orientational regeneration of injured neurons by natural product concentration gradients in a 3D microfluidic device

Y. Tang, Q. Qiu, F. Zhang, M. Xie and W. Huang, Lab Chip, 2018, 18, 971 DOI: 10.1039/C7LC01143A

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