Patterned networks of mouse hippocampal neurons on peptide-coated gold surfaces

Patterned networks of hippocampal neurons were generated on peptide-coated gold substrates prepared by microscope projection photolithography and microcontact printing. A 19 amino acid peptide fragment of laminin A (PA22-2) that includes the IKVAV cell adhesion domain was used to direct patterns of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Biomaterials 2005-03, Vol.26 (8), p.883-889
Hauptverfasser: Heller, Daniel A., Garga, Veronika, Kelleher, Keith J., Lee, Tai-Chou, Mahbubani, Sunil, Sigworth, Laura A., Lee, T.Randall, Rea, Michael A.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Patterned networks of hippocampal neurons were generated on peptide-coated gold substrates prepared by microscope projection photolithography and microcontact printing. A 19 amino acid peptide fragment of laminin A (PA22-2) that includes the IKVAV cell adhesion domain was used to direct patterns of cell adhesion in primary culture. Microscale grid patterns of peptide were deposited on gold-coated glass cover slips by soft lithography using “stamps” fashioned from polydimethylsiloxane. Strong coordination bonding between gold atoms on the surface and the sulfur atoms of the N-terminal cysteine residues supported stable adhesion of the peptide, which was confirmed by immunofluorescence using anti-IKVAV antiserum. Dispersed hippocampal cells isolated from neonatal mouse pups were grown on peptide-patterned gold substrates for 7 days. Neurons preferentially adhered to peptide-coated regions of the gold surface and restricted their processes to the peptide patterns. Whole cell recordings of neurons grown in patterned arrays revealed an average membrane potential of −50 mV, as well as the presence of voltage-gated ion conductances. Peptide-modified gold surfaces serve as convenient and effective substrates for growing ordered neural networks that are compatible with existing multi-electrode array recording technology.
ISSN:0142-9612
1878-5905
DOI:10.1016/j.biomaterials.2004.03.029