% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@ARTICLE{Albers:819304,
author = {Albers, Jonas and Offenhäusser, Andreas},
title = {{S}ignal {P}ropagation between {N}euronal {P}opulations
{C}ontrolled by {M}icropatterning},
journal = {Frontiers in Bioengineering and Biotechnology},
volume = {4},
issn = {2296-4185},
address = {Lausanne},
publisher = {Frontiers Media},
reportid = {FZJ-2016-05005},
pages = {46},
year = {2016},
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.},
cin = {ICS-8},
ddc = {570},
cid = {I:(DE-Juel1)ICS-8-20110106},
pnm = {552 - Engineering Cell Function (POF3-552)},
pid = {G:(DE-HGF)POF3-552},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:27379230},
UT = {WOS:000390385500001},
doi = {10.3389/fbioe.2016.00046},
url = {https://juser.fz-juelich.de/record/819304},
}