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Developmental refinement of synaptic transmission on micropatterned single layer graphene
Sandeep Keshavan1, Shovan Naskar2, Alberto Diaspro3
1Department of Nanophysics, Istituto Italiano di Tecnologia, Genova, Italy.
Acta Biomaterialia
|November 11, 2017
Summary
Graphene (a single atomic layer of carbon) supports healthy neuron growth and function for neural prosthetics. Both patterned and non-patterned graphene interfaces maintain synaptic efficacy, enabling large-area neural sensing and stimulation devices.
Area of Science:
- Neuroscience
- Materials Science
- Bioengineering
Background:
- Interfacing neurons with graphene is crucial for developing advanced neural prosthetics and biosensors.
- Reliable large-area patterning methods for graphene are essential for fabricating graphene-based circuitry.
- Single Layer Graphene (SLG) offers unique properties for bioelectronic applications.
Purpose of the Study:
- To investigate the in vitro neuronal development on patterned Single Layer Graphene (SLG) surfaces.
- To compare neuronal and glial cell proliferation and viability on glass, SLG, and patterned SLG.
- To assess the efficacy of synaptic transmission on different graphene substrates.
Main Methods:
- Laser micromachining to create microscale patterns of SLG stripes.
- Surface characterization of SLG using techniques like Immunohistochemical (IHC) staining.
- Recording miniature post synaptic currents (mPSCs) to evaluate synaptic transmission.
Main Results:
- Neurons and glial cells show viability and proliferation on SLG and patterned SLG surfaces.
- Glial cell proliferation is enhanced on SLG compared to glass.
- Synaptic transmission efficacy is preserved on both homogeneous and patterned SLG interfaces.
Conclusions:
- Single Layer Graphene (SLG) supports healthy neuronal development and synaptic function in vitro.
- Patterned SLG interfaces maintain neuronal viability and synaptic efficacy.
- Graphene-based interfaces, both patterned and homogeneous, are promising for large-area neural sensing, stimulation, and brain-interface applications.
