Signal Propagation between Neuronal Populations Controlled by Micropatterning

Albers, Jonas; Offenhäusser, Andreas

doi:10.3389/fbioe.2016.00046

Journal Article

FZJ-2016-05005

Signal Propagation between Neuronal Populations Controlled by Micropatterning

Albers, J. ; Offenhäusser, A. (Corresponding author)FZJ*

2016
Frontiers Media Lausanne

Frontiers in Bioengineering and Biotechnology 4, 46 (2016) [10.3389/fbioe.2016.00046]

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Please use a persistent id in citations: http://hdl.handle.net/2128/12984 doi:10.3389/fbioe.2016.00046

Abstract: The central nervous system consists of an unfathomable number of functional networks enabling highly sophisticated information processing. Guided neuronal growth with a well-defined connectivity and accompanying polarity is essential for the formation of these networks. To investigate how two-dimensional protein patterns influence neuronal outgrowth with respect to connectivity and functional polarity between adjacent populations of neurons, a microstructured model system was established. Exclusive cell growth on patterned substrates was achieved by transferring a mixture of poly-l-lysine and laminin to a cell-repellent glass surface by microcontact printing. Triangular structures with different opening angle, height, and width were chosen as a pattern to achieve network formation with defined behavior at the junction of adjacent structures. These patterns were populated with dissociated primary cortical embryonic rat neurons and investigated with respect to their impact on neuronal outgrowth by immunofluorescence analysis, as well as their functional connectivity by calcium imaging. Here, we present a highly reproducible technique to devise neuronal networks in vitro with a predefined connectivity induced by the design of the gateway. Daisy-chained neuronal networks with predefined connectivity and functional polarity were produced using the presented micropatterning method. Controlling the direction of signal propagation among populations of neurons provides insights to network communication and offers the chance to investigate more about learning processes in networks by external manipulation of cells and signal cascades.

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