The Roles of an Aluminum Underlayer in the Biocompatibility and Mechanical Integrity of Vertically Aligned Carbon Nanotubes for Interfacing with Retinal Neurons
Abstract:Retinal implant devices are becoming an increasingly realizable way to improve the vision of patients blinded by photoreceptor degeneration. As an electrode material that can improve restored visual acuity, carbon nanotubes (CNTs) excel due to their nanoscale topography, flexibility, surface chemistry, and double-layer capacitance. If vertically aligned carbon nanotubes (VACNTs) are biocompatible with retinal neurons and mechanically robust, they can further improve visual acuity—most notably in subretinal imp… Show more
“…VACNT flexibility also likely plays a role since neurons are known to readily adhere to and grow processes on softer substrates [ 94 , 95 ]. Although it has been suggested that CNT functionalization is necessary for biocompatibility, our observations support previous results demonstrating that appropriate degrees of texture and flexibility of pristine VACNTs are sufficient for neural network survival [ 83 ], which in turn favors recording and stimulation [ 96 ]. The increased process density at our electrode edges is also consistent with previous work showing that neurons respond to topographical cues [ 38 , 97 , 98 ] for a range of feature sizes [ 99 – 101 ] and in particular that they align with the direction of minimum surface curvature, so favoring paths that minimize process bending [ 102 ].…”
Section: Discussionsupporting
confidence: 88%
“…In addition to providing textured regions with intricate lateral shapes, the high aspect ratios of the VACNTs could aid penetration into neural tissue such as the retina [ 82 ]. Building on a broad range of CNT compatibility studies (including in vitro [ 83 ], rat in vivo [ 84 ], and human ECG tests [ 85 ]), we focus on in vitro experiments due to the controlled environment in which fluorescence and electron microscopy can be used to examine cell behavior and cell-electrode interactions as they evolve over periods of up to 17 days in vitro (DIV). Previous studies demonstrating that electrically-biased CNT electrodes stimulate neurons effectively [ 86 – 88 ] and even boost their signal transmission [ 77 , 78 , 89 , 90 ] indicate that our fundamental studies of cell arrangement have large potential for translation to future electrical measurements and applications.…”
Controlled assembly of retinal cells on artificial surfaces is important for fundamental cell research and medical applications. We investigate fractal electrodes with branches of vertically-aligned carbon nanotubes and silicon dioxide gaps between the branches that form repeating patterns spanning from micro- to milli-meters, along with single-scaled Euclidean electrodes. Fluorescence and electron microscopy show neurons adhere in large numbers to branches while glial cells cover the gaps. This ensures neurons will be close to the electrodes’ stimulating electric fields in applications. Furthermore, glia won’t hinder neuron-branch interactions but will be sufficiently close for neurons to benefit from the glia’s life-supporting functions. This cell ‘herding’ is adjusted using the fractal electrode’s dimension and number of repeating levels. We explain how this tuning facilitates substantial glial coverage in the gaps which fuels neural networks with small-world structural characteristics. The large branch-gap interface then allows these networks to connect to the neuron-rich branches.
“…VACNT flexibility also likely plays a role since neurons are known to readily adhere to and grow processes on softer substrates [ 94 , 95 ]. Although it has been suggested that CNT functionalization is necessary for biocompatibility, our observations support previous results demonstrating that appropriate degrees of texture and flexibility of pristine VACNTs are sufficient for neural network survival [ 83 ], which in turn favors recording and stimulation [ 96 ]. The increased process density at our electrode edges is also consistent with previous work showing that neurons respond to topographical cues [ 38 , 97 , 98 ] for a range of feature sizes [ 99 – 101 ] and in particular that they align with the direction of minimum surface curvature, so favoring paths that minimize process bending [ 102 ].…”
Section: Discussionsupporting
confidence: 88%
“…In addition to providing textured regions with intricate lateral shapes, the high aspect ratios of the VACNTs could aid penetration into neural tissue such as the retina [ 82 ]. Building on a broad range of CNT compatibility studies (including in vitro [ 83 ], rat in vivo [ 84 ], and human ECG tests [ 85 ]), we focus on in vitro experiments due to the controlled environment in which fluorescence and electron microscopy can be used to examine cell behavior and cell-electrode interactions as they evolve over periods of up to 17 days in vitro (DIV). Previous studies demonstrating that electrically-biased CNT electrodes stimulate neurons effectively [ 86 – 88 ] and even boost their signal transmission [ 77 , 78 , 89 , 90 ] indicate that our fundamental studies of cell arrangement have large potential for translation to future electrical measurements and applications.…”
Controlled assembly of retinal cells on artificial surfaces is important for fundamental cell research and medical applications. We investigate fractal electrodes with branches of vertically-aligned carbon nanotubes and silicon dioxide gaps between the branches that form repeating patterns spanning from micro- to milli-meters, along with single-scaled Euclidean electrodes. Fluorescence and electron microscopy show neurons adhere in large numbers to branches while glial cells cover the gaps. This ensures neurons will be close to the electrodes’ stimulating electric fields in applications. Furthermore, glia won’t hinder neuron-branch interactions but will be sufficiently close for neurons to benefit from the glia’s life-supporting functions. This cell ‘herding’ is adjusted using the fractal electrode’s dimension and number of repeating levels. We explain how this tuning facilitates substantial glial coverage in the gaps which fuels neural networks with small-world structural characteristics. The large branch-gap interface then allows these networks to connect to the neuron-rich branches.
“…The substrates were modified by immobilizing laminin following protocols explained elsewhere to increase the surface biocompatibility prior to the cell cultures [3]. The substrates were then covered with a suspension of dissociated mouse retinal cells, obtained following ethical approval and protocols previously described elsewhere [4]. Neuronal and glial cells were identified using immunofluorescence.…”
“…Previous studies have shown that neurons prefer to adhere to CNT structures [ 10 , 13 , 14 ] rather than to the smooth surfaces of traditional sensors [ 10 ]. Thin film CNT FETs have been employed for a range of electronic sensing applications, from hormone detection to the detection of molecular dynamics [ 15 , 16 , 17 , 18 , 19 ].…”
We propose a carbon-nanotube-based neural sensor designed to exploit the electrical sensitivity of an inhomogeneous fractal network of conducting channels. This network forms the active layer of a multi-electrode field effect transistor that in future applications will be gated by the electrical potential associated with neuronal signals. Using a combination of simulated and fabricated networks, we show that thin films of randomly-arranged carbon nanotubes (CNTs) self-assemble into a network featuring statistical fractal characteristics. The extent to which the network’s non-linear responses will generate a superior detection of the neuron’s signal is expected to depend on both the CNT electrical properties and the geometric properties of the assembled network. We therefore perform exploratory experiments that use metallic gates to mimic the potentials generated by neurons. We demonstrate that the fractal scaling properties of the network, along with their intrinsic asymmetry, generate electrical signatures that depend on the potential’s location. We discuss how these properties can be exploited for future neural sensors.
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