期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 104, 期 1, 页码 65-75出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/S0165-0270(00)00325-3
关键词
microcontact printing; guidance; pattern; cell adhesion; hippocampal neurons
The control of neuronal cell position and outgrowth is of fundamental interest in the development of applications ranging from cellular biosensors to tissue engineering. We have produced rectangular networks of functional rat hippocampal neurons on silicon oxide surfaces. Attachment and network formation of neurons was guided by a geometrical grid pattern of the adhesion peptide PA22-2 which matches in sequence a part of the A-chain of laminin. PA22-2 was applied by contact printing onto the functionalised silicon oxide surface and was immobilised by hetero-bifunctional cross-linking with sulfo-GMBS. Geometric pattern matching was achieved by microcontact printing using a polydimethylsiloxane (PDMS) stamp. In this way the produced grid pattern ranged from 3 to 20 mum in line width and from 50 to 100 mum in line distances. As shown by atomic force microscopy (AFM), line widths and line distances of the peptide pattern differ less than 0.5 mum from the used PDMS stamp. The height of the layer of immobilised PA22-2 was approximately 3.5 nm implying the layer to be monomolecular. Immobilised PA22-2 was capable of binding anti-PA22-2 antibodies indicating that the function of the peptide was not compromised by immobilisation. Rat hippocampal neurons, cultured at low density in serum-free medium, were applied to the growth matrix of PA22-2-coated substrates and, within 1-3 h of culture, formed a network-like pattern that mure or less matched the printed grid. Reliability and reproducibility of neuronal network formation depended on the geometry, line width and node diameter of the grid pattern. The immobilised neurons showed resting membrane potentials comparable with controls and, already after 1 day of culture, were capable of eliciting action potentials. The suitability of the immobilised neurons fur the study of man-made neural networks and for multi-site recordings from a functional neuronal network is discussed. (C) 2000 Elsevier Science B.V. All rights reserved.
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