Specific Nanoporous Geometries on Anodized Alumina Surfaces

Article Cite This: ACS Biomater. Sci. Eng. XXXX, XXX, XXX−XXX

Specific Nanoporous Geometries on Anodized Alumina Surfaces Influence Astrocyte Adhesion and Glial Fibrillary Acidic Protein Immunoreactivity Levels D. Ganguly,†,§ C. D. L. Johnson,‡,§ M. K. Gottipati,‡,§,∥ D. Rende,⊥ D.-A. Borca-Tasciuc,†,# and R. J. Gilbert*,‡,§,# †

Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States ‡ Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States § Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States ∥ Department of Neuroscience and the Center for Brain and Spinal Cord Repair, The Ohio State University, Columbus, Ohio 43210, United States ⊥ Center for Materials, Devices and Integrated Systems, Rensselaer Polytechnic Institute, Troy, New York 12180, United States # Rensselaer Nanotechnology Center, Rensselaer Polytechnic Institute, Troy, New York 12180, United States S Supporting Information *

ABSTRACT: Electrodes implanted in the brain or spinal cord trigger the activation of resident astrocytes. In their reactive state, astrocytes surrounding the electrode form a glial scar, compromising the ability of the electrode to interface with the surrounding neural tissue. One approach to reduce the inhibiting scar tissue is to incorporate nanoarchitecture on the surface of the implanted materials to modify the astrocytic response. The incorporated nanoarchitecture changes both the surface characteristics and the material properties of the implant interface. We investigated the response of rat cortical astrocytes to nanoporous anodic aluminum oxide (AAO) surfaces. Astrocytes were seeded onto nonporous aluminum control surfaces and AAO surfaces with average nanopore diameters of 20 and 90 nm. The surfaces were characterized by assessing their nanomorphology, hydrophobicity, surface chemistry, mechanical properties, and surface roughness. For cell response characterization, calcein-based viability and adhesion studies were performed. Plasmid-assisted vinculin live cell imaging was done to characterize focal adhesion number and distribution. Immunocytochemistry was used to assess glial fibrillary acidic protein (GFAP) expression. We found that astrocyte adhesion was significantly higher on small pore surfaces compared to large pore surfaces. Astrocytes produced more focal adhesions (FA) and distributed these FA peripherally when cultured on small pore samples compared to the other groups. Astrocyte GFAP expression was lower when astrocytes were cultured on surfaces with small nanopores compared to the control and large pore surfaces. These results indicate that unique surface nanoporosities influence astrocyte adhesion and subsequent cellular response. The reduction in GFAP immunoreactivity exhibited by the smaller pore surfaces can improve the long-term performance of the implanted neurodevices, thus making them credible candidates as a coating material for neural implants. KEYWORDS: nanopores, alumina, neural interface, astrocyte, glial scar, cell adhesion

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dystonia.2 However, there is a major barrier to successful integration and functioning of NE in vivo, and it results from the activation of multiple inflammatory and wound-healing cascades in and around the implantation area. These cascades are initiated by the damage to the blood vasculature and extracellular matrix (ECM) during NE implantation, which in turn triggers a chronic response1,3 This chronic response involves the formation of a dense fibrotic tissue called glial scar around the implanted NE. The glial scar is composed of

INTRODUCTION Neural interfaces are bioengineered devices that can be implanted directly in the nervous system and are used to aid in the diagnosis, repair, or supplementation of compromised neural functions in cases of disease as well as injury/insult to the central nervous system (CNS).1 Implantable neural interfaces in the form of neural electrodes (NE), neural prostheses, and scaffolds have revolutionized our approach toward neural engineering.1,2 NE in particular have been extensively used for both monitoring and stimulation of neural activity in the brain. Deep brain stimulation using chronically implanted electrodes is a recognized therapeutic option for patients suffering from Parkinson’s disease, tremors, and © XXXX American Chemical Society

Received: October 9, 2017 Accepted: November 17, 2017

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DOI: 10.1021/acsbiomaterials.7b00760 ACS Biomater. Sci. Eng. XXXX, XXX, XXX−XXX

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ACS Biomaterials Science & Engineering

(SEM) was used to examine the surface morphology, atomic force microscopy (AFM) was used to characterize the surface topography, and nanoindentation experiments were performed to assess the mechanical properties. Contact angle goniometry was used to determine the surface hydrophilicity/hydrophobicity of the AAO surfaces. For the cell work, primary rat cortical astrocytes were cultured onto the AAO substrates to assess how AAO surfaces influenced astrocyte properties. The astrocyte adhesion and viability response to AAO surfaces were studied using calcein−propidium iodide live−dead staining. To better understand astrocyte adhesion to our unique AAO surfaces, the production and distribution of focal adhesions were examined by plasmid-assisted live cell imaging of vinculin. Finally, glial fibrillary acidic protein (GFAP) was quantified using immunocytochemistry to determine if our unique surfaces stimulated a differential GFAP response.

multiple cells types, but one of the most important components, in number and functionality, is the astrocytes. Astrocytes outnumber neurons heavily in the human brain and perform many diverse functions in the CNS.4 Astrocytes interface with the circulatory system to form the blood brain barrier and regulate blood flow.4 Astrocytes maintain the fluid, ion, and pH homeostasis in addition to regulating neurotransmitter concentrations in the CNS. 4,5 Additionally, astrocytes deliver growth factors to neurons during CNS development and provide mechanical support for neuronal circuits.6,7 The function of astrocytes frequently investigated and of prime importance to this study is the astrocyte response to CNS trauma. Following an insult to the CNS, similar to the mechanical injury observed post NE implantation, astrocytes around the site of the injury undergo a conversion to a reactive phenotype in a process known as astrogliosis. During astrogliosis, astrocytes proliferate abnormally, grow in size, and become highly metabolically active.8,9 Subsequently, reactive astrocytes interact with monocytes and locally recruited fibroblasts and endothelial cells and form a glial scar around the implanted NE. This scar formation electrically insulates the NE, thus disrupting or disabling the device’s capability of monitoring or stimulating the targeted neural circuits.10,11 A considerable research effort is underway for developing therapeutic materials and interfaces for CNS injuries and diseases that could integrate seamlessly with CNS tissue without eliciting a glial scarring response.12,13 Glial scarring response involves multiple cell types, and we have concentrated on astrocytes, the chief components in the encapsulation or the glial scar4 formed around the implant, for this study. Literature is replete with disparate approaches to reduce astrocyte reactivity and glial scar caused by the implantation of biomaterials or devices into the CNS. These approaches include scaffolds with modifiable and unique surface features, implant surfaces coated with anti-inflammatory pharmaceuticals, methods for altering protein adhesion on interfaces, and surfaces that incorporate bioactive molecules.1,12,14,15 Of these, one emerging approach is to modify cell response by placing micro- and nanoscale features on the NE surface which mimic the nanoenvironment found in the ECM.16−18 Nanotopography has been shown to have considerable influence on cells’ morphology, alignment, adhesion, and proliferation.18,19 These topographies also affect cellular gene expression, the production of cytokines, growth factors, cell signaling molecules, and cytoskeleton linked molecules.20,21 In this study, we explored how unique nanoporous anodic aluminum oxide (AAO) surfaces with different pore diameters would provide a topographical cue to elicit changes in adhesion and subsequent cellular response of astrocytes seeded on these surfaces. Alumina, an insulating ceramic, has been used in complementary metal oxide semiconductor microelectrodes integrated in circuits within neural electrodes.22 It has been used as scaffold material for other biomedical implants and is considered biocompatible.23−25 Nanoporous AAO surfaces are resistant to erosion under physiological conditions and are even capable of acting as drug delivery vehicles.26 Here, a simple two-step electrochemical etching technique was used to fabricate AAO surfaces from aluminum films. Additionally, we present methods for thorough characterization of our AAO surfaces and the assessment of astrocyte response. Several surface characterization techniques were used to confirm the consistency of porous features between the different batches of fabrication. Scanning electron microscopy

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MATERIALS AND METHODS

Fabrication of AAO Surfaces. AAO surfaces were fabricated by a two-step anodization process similar to previously described procedures.27 Aluminum foils of high purity (99.99%, Alfa Aesar, Haverhill, MA) and 0.5 mm thickness were subjected to extensive mechanical polishing to reduce macro-level surface roughness. The foils were polished for 5 min using an ammonia-based abrasive metal cleaning solution (Brasso Metal Polish, Reckitt Benckiser, Parsippany, NJ) followed by another 5 min polishing with diamond paste (MetaDi 3 μm, Buehler, Lake Bluff, IL). At the end of each step, the samples were cleaned by spraying them vigorously with acetone (95%, SigmaAldrich, St Louis, MO) and deionized (DI) water. The internal stresses generated during the polishing process were relieved by annealing the samples at 350 °C for 1 h.27 For the control group samples, the nonporous aluminum surfaces obtained at this stage were cut into 10 × 10 mm pieces, while the nanoporous group samples underwent further electrochemical polishing (ECP). ECP was performed in a solution of 2:1:1 ratio of phosphoric acid (85%, Sigma-Aldrich), sulfuric acid (85%, Sigma-Aldrich), and DI water, respectively, for 6 min at 60 °C with 20 V applied between the sample at the anode and the aluminum cathode (Figure 1). The thin oxide layer formed during ECP was removed by suspending the samples for 1 h in an aqueous solution of 3.5% phosphoric acid and 45 g/L chromium dioxide (99%, Alfa Aesar). Anodization was performed next at room temperature (RT) in two steps using the same set up shown in Figure 1 with the electropolished foil as the anode and a thicker piece of aluminum as cathode. A solution of 0.3 M oxalic acid (99%

Figure 1. Schematic of anodization setup. B


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ACS Biomaterials Science & Engineering dihydrate, Fisher Scientific, Pittsburgh, PA) was used as the electrolyte with anodizing voltages of 20 V for samples with small pores (20 nm) and 60 V for large pores (∼90 nm). The pores formed during the first anodization step were nonuniform, although they self-organized as they grew deep into the substrate. Hence, this first porous alumina layer was etched away using the same solution described earlier to remove the oxide formed during ECP, and a second anodization was performed with the same anodization settings as before. In the second step, more regular surfaces were formed as pore growth initiated from the dents left after dissolution of the first anodization layer.27 The anodized foils were then cut into 10 × 10 mm pieces to be used as the cell seeding substrates. Surface Porosity Characterization. The nonporous controls and the AAO surfaces were imaged using a Versa 3D dual beam scanning electron microscope (FEI, Hillsboro, OR) equipped with Everhart Thornley detector to ensure surface uniformity and to determine the nanopore diameter and interpore distance. The micrographs were collected under 10 kV, 93 pA beam current (spot size 5) and 4.5 mm working distance. Images were taken at 2.07 μm horizontal field width (100 kX) to capture the morphology, and higher magnification images were taken at 600 nm horizontal field width (350 kX) to quantify pore diameter and pore distance. Cross sectional images were taken to analyze pore depth. One surface sample per batch from three independently fabricated batches (batch size of three) was selected for nanopore diameter and interpore distance quantification. All image analyses were performed using NIH ImageJ software (NIH, Bethesda, MD). Surface Wettability Measurement. Surface wettability or hydrophilicity was compared across the samples using water contact angle as the metric. A sessile water drop test was performed using a Rame-Hart 500 goniometer (Rame-Hart Instrument Co., Succasunna, NJ). Prior to goniometry, the substrates were cleaned by spraying them with DI water and ethanol for 2 min each and then dried with compressed air. A 1 μL water drop was brought in contact with the surface, and subsequently, the volume of the drop was increased and decreased to advance and retract the liquid front, respectively. This procedure was repeated three times on three independently fabricated samples to obtain the data. All tests were carried out in an air environment at RT and a relative humidity of ∼70%. The images obtained were analyzed to determine the water contact angles using ImageJ software and the associated low-bond axisymmetric drop shape analysis plugin (LBADSA Plugin, Lausanne, Switzerland). Surface Roughness Measurement. The surface roughness of the samples were measured by using AFM (Bruker Multimode IIIa, Billerica, MA) in tapping mode, which created topographic images of the surfaces with a lateral resolution of 1 Å and a vertical resolution of 0.1 Å.28 The samples were cleaned by spraying for 2 min with DI water and ethanol and then dried using compressed gas. Each sample was then attached to the AFM specimen metal disc (Ted Pella, Redding, CA) with superglue (Loctite, Westlake, OH) to prevent drift during testing. The samples were scanned with a tip of 10 nm radius (OTESPA, Bruker, f = 339 kHz, k = 42 N/m). Images (2 × 2 μm) were collected at 512 samples/line resolution and a scan rate of 0.4 Hz. Because of the localized nature of the measurements, the scans and roughness calculations were completed at three different locations for each group of three independently fabricated samples. The AFM images were analyzed using the accompanying Nanoscope software. First order plane fit was applied to all images before any calculation (as suggested by the manufacturer). The root-mean-square of height deviations taken from the mean data plane (RMS Rq), and arithmetic average of the absolute values of the surface height deviations (Ra) were used as measures to quantify the surface roughness. Modulus and Hardness Characterization. Nanoindentation is commonly used to measure the mechanical properties such as hardness and elastic modulus of samples.29 This technique uses a depth gauging indentation test where the indenter tip is forced into the surface of the sample and the indenter force (P) and the indenter displacement (h) into the bulk of the material are simultaneously recorded during the testing period. This data is plotted as a load−

displacement curve (P−h), from which the characteristic material properties hardness and elastic modulus are obtained.30 The mechanical properties of the samples were measured by a Nanoindentor/NanoDMA (Hysitron TI900 TriboIndentor, Eden Prairie, MN) using a 100 nm radius Berkovich tip with three-sided pyramid geometry (TI-0039, Hysitron). The instrument was initially calibrated with a fused quartz standard (Hysitron, PN:5−0098, Er = 72.4 GPa and H = 9.69 GPa). The tests were performed with a displacement control of 250 nm where the load function was set as 10s loading, 2s holding, and 10s unloading. The maximum displacement was limited to 250 nm to prevent the contribution from the nonoxidized layer. The indents were made on a 5 × 5 grid with 5 μm separation to avoid interactions with deformed material around previous indents. The tests were performed on 3 separate locations amounting to 75 data points for each specimen under each sample group. The process was repeated three times using independently fabricated samples. The data was processed using the inbuilt TriboIndentor software package. Astrocyte Isolation and Cell Culture. The culture of primary astrocytes from the cerebral cortices of newborn Sprague−Dawley rats was conducted according to previously reported procedures.31 All animal procedures in this study adhered to the NIH Guidelines for the Care and Use of Laboratory Animals, and approval was obtained from the Institutional Animal Care and Use Committee at the Rensselaer Polytechnic Institute. In brief, following rapid decapitation, cerebral cortices were isolated from two-day-old rat pups under sterile conditions, minced in Opti-MEM (Life Technologies, Grand Island, NY) and transferred into a solution containing an equal volume of recombinant protease TrypLE and Opti-MEM (both Life Technologies). Cells were extracted using three 10 min incubations with TrypLE/Opti-MEM additionally supplemented with 1 mg/mL DNase I (Sigma-Aldrich). The latter two extractions were diluted in astrocyte media containing Dulbecco’s minimal essential medium (Life Technologies), 10% heat inactivated horse serum (Life Technologies), 100 U/mL penicillin and 100 μg/mL streptomycin (Life Technologies). Cells were pelleted using centrifugation (500 rcf for 5 min) and resuspended in astrocyte media. Dissociated cells were plated into poly-D-lysine (Sigma-Aldrich) coated T75 culture flasks at a density of 200 000 cells/flask and cultured for 2−4 weeks in a 37 °C 90% air/5% CO2 incubator with change in cellular media every 4 days until they reached 80% confluency. The purity of these astrocyte cultures (>95% pure) was checked periodically by immunocytochemical staining with an antibody for the astrocytic marker, glial fibrillary acidic protein (Dako, Glostrup, Denmark).32 Upon reaching 80% confluency, astrocytes from the culture flasks were plated on the AAO and control surfaces. These cells were detached from the bottom surface of the flasks using TrypLE for 5 min and neutralized with equal volume of astrocyte media. This mixture was centrifuged, and the pellet was resuspended in fresh astrocyte media. The cell suspension was plated onto the AAO surfaces placed in 24-well plates and returned to the incubator. Different plating densities were employed based on the type of experiment. Specifically, a plating density of 10 000 cells/well was used for calcein−propidium iodide live−dead cell staining. A plating density of 5000 cells/well was used for vinculin imaging and immunocytochemical analysis to obtain single cell images for sufficient quantification of signal intensity. Prior to cell seeding, the substrates were sterilized by overnight incubation in 70% ethanol. The surfaces were used for culture upon complete drying of ethanol in the tissue culture hood. Astrocyte Viability Assay. To assess astrocytic viability, the cells were loaded with calcein (1 μg/mL calcein-AM with 0.025% w/v pluronic acid, Life Technologies) and propidium iodide (2 μg/mL, Thermo Fisher), diluted in external solution (140 mM NaCl, 5 mM KCl, 2 mM CaCl2·2H2O, 2 mM MgCl2·2H2O, 10 mM HEPES, and 10 mM D-glucose), at the 1 and 4 day postplating time points. After 30 min, the samples were cleaned 3 times with external solution and then imaged live bathed in the external solution. Imaging was done using an inverted Olympus DSU IX-81 confocal microscope (Olympus Scientific Solutions America, Waltham, MA) driven by Metamorph software (Molecular Devices LLC, Sunnyvale, CA) using a 10× C


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Figure 2. Surface morphology and characterization of the AAO Surfaces. (A−C) Example SEM images of the nonporous control (A), small pore (B), and large pore (C) samples imaged at 100 kX magnification. Scale bars, 2 μm. Inset images in B and C show higher magnification SEM images captured at 350 kX; scale bars, 500 nm. (D and E) Summary graphs showing the average pore size (D) and interpore distance (E) for the small pore and large pore samples. “*” denotes a statistically significant difference between the groups. *p < 0.05. objective and a fluorescein isothiocyanate (FITC) filter set for calcein and Texas red filter set for propidium iodide. The images obtained were analyzed using the ImageJ software. Plasmid-Assisted Vinculin Live Cell Imaging. Focal adhesions are commonly studied for their role in the regulation of cell-adhesion strength, and vinculin is used as a marker for focal complexes. Hence, astrocytes were transfected one day after plating with a pEGFP vinculin plasmid (Addgene plasmid no. 50513, kindly provided by Dr. Kenneth Yamada) encoding vinculin tagged at its N-terminus with an enhanced green fluorescent protein (EGFP). Each sample with astrocytes received 0.5 μg of the plasmid DNA and 1.5 μL of TransIT-LT1 (Mirus Bio, LLC, Madison, WI) in Opti-MEM, premixed pursuant to the manufacturer’s instructions, and incubated at RT for 30 min to allow for the transfection agent to form complexes with the plasmid DNA. After 4 h, the samples were washed with Hank’s balanced salt solution (Thermo Fisher) and replaced with fresh warm cell culture media. After 72 h, the transfected cells were visualized and imaged using a 40× objective and a FITC filter set. The images obtained were analyzed using a specially designed algorithm in Python to identify and quantify focal adhesions and characterize their spatial distribution. The program uses Otsu thresholding33 and edge detection using sobel filter followed by connected component analysis. These connected components are tested for pixel count, and the image is thresholded in a procedure closely resembling Otsu’s for adaptive removal of spurious cells or image noise. The program calculates the geometric median (the point from which the sum of distances of a discrete set of sample points is minimized) for each cell and characterizes each cell by the length of its major axis (l). The program also calculates the distance of every identified focal adhesion on the cell from the median (d) and characterizes the FA as being “central FA” if d < l/2 or as being “peripheral FA” if d > l/2. The median point for each cell was computed, and the distance of each FA in the cell to

its respective median was used to classify the radial region to which it belongs. The total numbers of central FA and peripheral FA were counted for the transfected cells on all the surfaces. This analysis of focal adhesion distribution was conducted to best communicate the observed difference in focal adhesion distribution on specific alumina surfaces. Astrocyte GFAP Immunoreactivity Assessment. Indirect immunocytochemistry (ICC) was done to assess the immunoreactivity of glial fibrillary acidic protein (GFAP-ir), 1 and 4 days postplating. Prior to labeling, the astrocytes were incubated for 1 h with a fluorescent dipeptide derivative, β-Ala-(L)-Lys-Nε-7-amino-4-methylcoumarin-3-acetic acid (β-Ala-Lys-AMCA) (Biotrend Chemicals, Zurich, Switzerland), by diluting it in the culture media. β-Ala-LysAMCA is specifically taken up by the astrocytes into their cytoplasm, and its fluorescence was used to delineate the cell outline.34 After βAla-Lys-AMCA incubation, the samples were cleaned with phosphate buffered saline (PBS) and fixed in 4% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA) for 30 min at RT. Astrocytes were blocked and permeabilized with 5% bovine serum albumin (Thermo Fisher) and 0.4% Triton-X (Sigma-Aldrich) in PBS for 30 min to prevent nonspecific binding followed by an incubation with a primary antibody against GFAP (1:700, Z0334, Dako) for 6 h at RT. The cells were then washed thrice with PBS and incubated for 1 h with Alexa Fluor 488 secondary antibody (1:1000, Life Technologies) at RT. Astrocytes labeled for GFAP were imaged with a 40× objective and a standard FITC filter set, while the fluorescence of β-Ala-Lys-AMCA was visualized using a standard 4′,6-diamidino-2-phenylindole (DAPI) filter set. The β-Ala-Lys-AMCA images obtained were manually traced along the perimeter to obtain the total area of the cell. In parallel, no primary antibody controls were run for a subset of the AAO surfaces with astrocytes to estimate the nonspecific secondary antibody binding and ultimately calculate a threshold value for the GFAP labeled D


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Figure 3. Large pore samples show greater hydrophobicity compared to small pore and control samples (A−C). Representative water contact angle measurement images for the nonporous control (A), small pore (B), and large pore (C) samples. (D) Summary graph showing the average contact angle measurements of the different AAO samples. “*” and “∧” denote a statistically significant difference between the groups marked by the same symbol. *p < 0.05, ∧p < 0.05. astrocytes. The threshold was calculated as the background subtracted mean intensity of the no primary control cells + six standard deviations. Background fluorescence was obtained from regions of the AAO surfaces that did not contain any astrocytes. Using Metamorph software, we performed relevant background subtraction and subsequently calculated the GFAP integrated intensity. To determine the integrated intensity, the images were thresholded to create a mask for each cell, which represents the GFAP positive in the cells. The area of the mask and the mean gray value of the pixels in the mask were calculated and multiplied to get the integrated intensity of GFAP for each cell. The integrated intensity is the product of the area of the cell and the mean gray value of the pixels within the cell area. The GFAP occupancy is the percentage of total cell area marked by β-Ala-LysAMCA that is positive for GFAP. Our analysis was limited to astrocytes that were alone, with no touching cells. With this criterion, at least three cells were analyzed from each sample. Therefore, n = 9 astrocytes from three independently fabricated alumina surfaces for each condition and time point were analyzed. Statistical Methods. All material characterization experiments were conducted on at least three independently fabricated samples and at least three different locations per sample (N = 3). All the biological experiments were conducted on at least three independently fabricated samples and three independent cell cultures (N = 3). All data was analyzed using one way ANOVA followed by post hoc Tukey tests to determine statistical differences between groups (p < 0.05). Data presented in all graphs and text show the mean and standard deviation (mean ± standard deviation).

glial cells and neurons have been directly correlated to the presence, extent, and dimensions of nanotopographical cues on the nanoporous materials.35 Hence, the morphology of the control and AAO surfaces were characterized by SEM (Figures 2A−C). The SEM images were analyzed to get the distribution of pore diameters (Figure 2D) and interpore distances (Figure 2E). The control surfaces contained microscale ridges and some nanofeatures but, as expected, did not contain nanopores. The nanopores in the small and large pore samples were vertically oriented and showed regular organization. The average pore diameter of the smaller pore surfaces was 21.1 ± 2.3 nm, while the pore diameter for larger pore surfaces was 90.3 ± 3.5 nm. Average interpore distances were 17.8 ± 1.4 nm for the smaller pore surfaces and 86.3 ± 2.2 nm for the larger pore surfaces. Because our material fabrication process involved subjecting the surfaces to chemical processing for different durations and using different anodizing voltages to generate distinct pore sizes, X-ray photoelectron spectroscopy (XPS) was used to determine if the different surfaces possessed varying surface compositions. The results confirmed the absence of impurities and lack of significant differences in the surface chemical composition between the small and large nanoporous samples (Supporting Information and Figure S1). When exposed to air, the aluminum control surfaces develop a native protective coating of aluminum oxide, which is similar to the porous surfaces. XPS indicated that the unprocessed control samples had a high amount of carbon adsorbed to the surfaces compared to the two nanoporous surfaces. We suspect that the

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RESULTS Topography Visualization and Porous Geometry Assessment by SEM. Cellular adhesion and proliferation of E


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Figure 4. Small pore surfaces have the lowest surface roughness. (A−C) Representative 3D AFM images of the nonporous control (A), small pore (B), and large pore (C) samples with X = 0.5 μm/division and Z = 150 nm/division. The images represent an area of 2 × 2 μm on the surface. (D) Summary graph showing the average RMS roughness (Rq) and mean roughness (Ra) of the different AAO samples. “*” and “∧” denote a statistically significant difference between the groups marked with the same symbol. *p < 0.05, ∧p < 0.05.

Figure 5. Small pore samples exhibit the highest hardness and elastic modulus. (A and B) Summary graphs showing the average hardness (A) and elastic modulus (B) of the different AAO surfaces. “*” and “∧” denote a statistical difference between the groups marked with the same symbol. *p < 0.05, ∧p < 0.05.

carbon on the control surface comes from a longer exposure to the atmosphere. At the time of XPS, the two porous samples had recently been etched to create the surface pores and therefore had short exposure times to the atmosphere. The control came from the manufacturer and was not etched, which would have removed the accumulated carbon. The 3D nature of the nanopores was confirmed by obtaining cross-sectional SEM images (Supporting Information and Figure S2), and the minimum pore depth for both the nanopore groups was approximately 400 nm. Surface Wettability. The wettability of a surface is descriptive of its surface energy. Changes in wettability can influence cell adhesion, proliferation, and differentiation.36

Contact angle goniometry was used to better understand the effect of nanopores on surface wettability. Images were analyzed using low-bond axisymmetric drop shape analysis technique to determine the contact angles.37 The mean contact angles for the control (Figure 3A), small pore (Figure 3B), and large pore samples (Figure 3C) were 25.42, 21.51, and 34.90°, respectively. We found that the mean contact angle for the large pore samples was significantly higher than that of the control and the small pore samples (Figure 3D). The reduced hydrophilicity of the large pore sample is most likely due to the presence of bigger air columns in the larger pores which prevent the water from completely filling the pores, thus creating larger contact angles at the liquid−pore interface.38 F


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Figure 6. Small pore surfaces show enhanced cell adhesion and cell viability in culture. (A−F) Representative merge images of astrocytes loaded with the live cell fluorescent marker calcein (green) and stained with the dead cell marker propidium iodide (red) on the nonporous control (A, D), small pore (B, E), and large pore (C, F) surfaces, 1 day (A−C) and 4 days (D−F) postplating. Scale bars are 20 μm. (G and H) Summary graphs showing the average adherent astrocytes/mm2 (G) and the average percentage cell viability (H) on the different AAO surfaces. “*” and “∧” denote a statistically significant difference between the groups marked with the same symbol at 1 and 4 days postplating time points, respectively. *p < 0.05, ∧p < 0.05.

architecture presented by the surfaces to seeded astrocytes varies considerably across the substrate groups. Characterization of Mechanical Properties by Nanoindentation. Astrocyte behavior can change when cultured on surfaces with differential stiffness.42 Thus, we conducted nanoindentation studies to examine the stiffness of the AAO surfaces. Specifically, nanoindentation was used to measure the hardness and modulus of elasticity. Before starting the measurements, 100 single crystal aluminum standard was tested for comparison. The hardness and elastic modulus values were compared across the three sample groups (Figures 5A and B). The results indicate that the surfaces with small pores have the highest hardness (H) and elastic moduli (Er) values with H = 4.96 ± 0.15 GPa and Er = 97.31 ± 0.07 GPa. The corresponding values for controls were H = 0.6 ± 0.01 GPa and Er = 54.5 ± 12.22 GPa, and those for large pore samples were H = 1.3 ± 0.18 GPa and Er = 45.2 ± 4.02 GPa. The small pore samples had significantly higher values of hardness and elastic modulus compared to those of the other groups. Between the

Higher contact angles indicate relatively lower surface energy and greater degree of hydrophobicity, which may inhibit the adhesion and subsequent behavior of seeded cells. The values for contact angle obtained here were comparable to those reported elsewhere in literature.39 Surface Roughness Characterization by AFM. Surface roughness influences cellular interaction with biomaterials.40,41 Therefore, surface roughness of the three surfaces highlighted here was assessed using AFM (Figures 4A−C). The comparison of RMS roughness and mean roughness for the three sample groups (Figure 4D) showed that the nonporous aluminum samples had the highest surface roughness (RMS Rq = 17.04 ± 3.85 nm, Ra = 13.30 ± 2.96 nm) followed by the large pore samples (RMS Rq = 8.97 ± 1.37 nm, Ra = 6.96 ± 1.13 nm) and the small pore samples (RMS Rq = 4.76 ± 0.47 nm, Ra = 3.72 ± 0.43 nm). All of the individual differences between the groups for both the roughness measurements were statistically significant. This demonstrates that the nanoG


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Figure 7. Nanoporous surfaces show differential expression and distribution of astrocytic focal adhesions in culture as assessed using vinculin. (A−C) Representative images of pEGFP Vinculin transfected astrocytes on the nonporous control (A), small pore (B), and large pore (C) surfaces 72 h post-transfection. Scale bars, 20 μm. (D) Summary graph showing the average number of focal adhesions per astrocyte on the different AAO surfaces. “*” and “∧” denote a statistically significant difference between the groups marked with the same symbol. *p < 0.05, ∧p < 0.05. (E) Pie charts displaying the average number of central and peripheral focal adhesions on each surface type, expressed as a percentage of all the cells analyzed for the respective surface groups.

if these differences were due to a change in the astrocytes’ ability to form focal adhesions. Differences in focal adhesion number and distribution can change cellular adhesion and motility.45 Focal adhesions are also responsive to the mechanical stresses that result from the cell’s interactions with the ECM/substrate.46 To study this, we transfected astrocytes on the AAO surfaces with a plasmid encoding vinculin tagged to EGFP and the fluorescence of EGFP (Figures 7A−C) was used to quantify the number and distribution of the focal adhesions. We found that the number of astrocytic focal adhesions were similar between the control and small pore surfaces and significantly higher than that observed on the large pore surfaces (Figure 7D). We also assessed the distribution profile of the astrocytic focal adhesions by dividing the FA in each astrocyte into two groups (central FA and peripheral FA) based on their localization and quantifying their respective counts for each cell. We found that the astrocytes on the control surfaces showed an almost even distribution of central and peripheral FA (Figure 7E). However, the astrocytes on small pore surfaces had twice the number of peripheral FA compared to central FA. Conversely, the astrocytes on the large pore samples had a higher concentration of central FA and a lower percentage of peripheral FA. The higher number of focal adhesions and their peripheral distribution in the small pore surfaces is consistent with the enhanced astrocyte adherence, as shown in Figure 6. GFAP Immunoreactivity. To assess if the surfaces with and without nanoporosity induce a change in astrocytic GFAP

control and large pore groups, there was a significant difference in the hardness but not the elastic modulus. The hardness and elastic modulus measurements are in agreement with previous studies that reported fabrication of AAO under similar processing conditions.43,44 Astrocyte Adhesion and Viability Assays. To examine if the porous features influence astrocyte adhesion and subsequent viability, astrocytes were plated onto the AAO surfaces, loaded with calcein, and labeled with propidium iodide 1 day (Figures 6A−C) and 4 days (Figures 6D−F) postplating. The cells positive for calcein were considered live, while the cells positive for propidium iodide were considered dead. The total number of live and dead cells were calculated for both the time points which were, in turn, used to calculate the percentage cell viability. We found that the astrocytes adhered strongly to the small pore surfaces at both 1 and 4 day time points compared to the corresponding control and large pore surfaces (Figure 6G). Astrocytes on the small and large pore surfaces showed similar cell viability at both 1 and 4 day time points compared to that of the corresponding control surfaces (Figure 6H). However, the cells on the large pore surfaces showed a significant decrease in cell viability compared to the small pore surfaces at both time points assessed. Together, these results imply that the small pore surfaces enhance the adherence and survival of astrocytes in culture, while the large pore surfaces have a potential negative effect on the viability of astrocytes. Focal Adhesion Distribution. Because adhesion differences were observed on the different AAO surfaces, we assessed H


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Figure 8. Small pore surfaces modulate the GFAP immunoreactivity of astrocytes in culture. (A−F) Representative images of astrocytes in culture plated onto the nonporous control (A, D), small pore (B, E), and large pore (C, F) surfaces and stained for GFAP 1 (A−C) and 4 days (D−F) postplating. Images a−f represent the corresponding β-Ala-Lys-AMCA images of the GFAP images A−F, which were used to identify the cell outlines. GFAP intensity is shown in fluorescence intensity units (iu). Scale bars, 10 μm. (G, H) Summary graphs showing the integrated intensity (G) and mean occupancy (H) of GFAP immunoreactivity on the different AAO surfaces. “*” denotes a statistically significant difference between the groups marked by the same symbol. *p < 0.05.

value than either the control or large pore samples at the 488 nm wavelength used to measure GFAP content. The higher relative reflectance would skew the small pore samples by making the GFAP content seem greater than it is. Because we found a significant decrease in GFAP-ir on the small pore surfaces, these reflectance findings strengthen our confidence in the GFAP-ir results.

levels, we labeled astrocytes on the AAO surfaces for GFAP using indirect immunocytochemistry 1 and 4 days postplating (Figures 8A−F). Because GFAP labeling does not disclose the entire cell, astrocytes were preloaded with β-Ala-Lys-AMCA to assist in the delineation of the cell membrane, which was in turn used to measure the total cell area (Figures 8a−f). We calculated the integrated intensity of GFAP-ir on all the AAO surfaces and found that the astrocytes on the small pore surfaces showed a significant decrease in the integrated GFAP intensity compared to that of the large pore surfaces at the 1 day postplating time point (Figure 8G). On the other hand, after 4 days in culture, there were no significant differences in astrocyte GFAP-ir between the porous surfaces or the control. There were no statistical differences in the occupancy of GFAPir between the sample groups. The lower levels of GFAP immunoreactivity for the small pore group compared to those of both control and large pore samples taken along with the higher astrocyte adhesion on the substrates is indicative of a correlation between initial adhesion characteristics and GFAP immunoreactivity. There is one known and uncontrollable potential source of error in the GFAP-ir data. Chapman and colleagues reported that different diameter pores in gold substrates altered the reflected fluorophore intensity, which introduced error in quantitative immunofluorescence data.47 To understand the error introduced by the different size pores, we measured the reflectance on each individual surface (Figure S4). The results show that the small pore surfaces have a higher reflectance

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DISCUSSION

In this study, we created AAO surfaces with two different pore sizes and studied their surface properties extensively along with their effect on cultured cortical astrocytes. We report here that (1) AAO surfaces as well as control samples exhibited no acute cytotoxicity and were permissive to the cultured astrocytes for the time points studied; (2) small pore surfaces showed significantly higher astrocyte adhesion and a higher number of astrocyte focal adhesions predominantly distributed along the periphery of the cells; and (3) GFAP immunoreactivity was less intense for astrocytes on small pore surfaces relative to that of the other groups. Depending on the presence of nanopores and their size, the different AAO surfaces present disparate topographies to the adherent astrocytes. These topographical disparities are accompanied by differences in material and surface properties as well. The differences observed in astrocytic behavior on the surfaces suggest that the astrocytes sense the subtle changes in the surface topography and material properties through cellular projections such as filopodia and react with a complex physiochemical response. I


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We hypothesize that the differences in astrocyte adhesion are most prominently influenced by the ambient nanotopography of the substrates and the size of the porous features. Cells have dimensions that are significantly larger than the ambient topography. However, actin-rich cell membrane projections (filopodia) with nanoscale probing tips provide the cells an ability to use contact guidance from features as small as 10 nm in height.52 The width of the extracellular portion of integrin receptors is typically ∼23 nm, and the length of the portion of the integrin α- and β-chains responsible for ligand binding is 5 nm.52 It is interesting to note that for small pore surfaces, the pore diameter is ∼20 nm and the roughness is ∼5 nm, which makes the small pores topographical inversions of the receptors responsible for topography sensing. This could potentially provide a basis for developing cell interfacing capacities at the single-integrin scale. Chapman et al. reported reduction in astrocyte adhesion and proliferation on nanoporous gold compared to that on control surfaces and concluded that nanotopography on the dealloyed nanoporous surface was the factor influencing differential astrocyte adhesion.35 Popat et al. reported that the adhesion of human fetal osteoblasts seeded on AAO surfaces with pore size around 50 nm was significantly increased in comparison to nonporous alumina, glass, and latex controls as a result of the tunable nanoarchitecture of the surface.53 It has been suggested that the nanoscale features come into contact with the cell membrane and inflict tensile and contraction mechanical forces on the cell. These provide mechanical cues that cause the reshuffling of the components of the membrane, which can determine cell fate with respect to adhesion and proliferation.54 This not only activates cell fate determining pathways but also causes the redistribution of FA that affects both adhesion and motility of the cells. Astrocytes on the nanoporous alumina surfaces had negligible amounts of proliferation between the 1 day and 4 day time points. This response has also been observed on nanoporous gold surfaces, which also showed reduced astrocyte adhesion and proliferation.47 Astrocytes have shown a sensitivity to nanoscale features on other materials. Astrocytes have shown reduced proliferation on carbon nanotubes as the nanotube diameter is reduced from the microscale to the nanoscale,55 and astrocytes exhibited reduced adhesion and proliferation that correlated with increasing the number of nanoscale bumps created by zinc oxide nanoparticles embedded in polyurethane.56 In this study, the reduced size of the nanopores improved adhesion but did not affect proliferation, which remained near zero. Nanotopography aside, it is likely that the subtle differences in material properties between the groups affect astrocytic behavior to some extent. On the basis of the contact angle goniometry experiments, smaller pore surfaces were found to be more hydrophilic than nonporous controls and large pore surfaces (Figure 3). Sorkin et al., in their study of astrocytic response to titania nanotube arrays with nanofeature sizes of 100−120 nm, reported that hydrophilic surfaces were found to promote cellular adhesion, possibly due to its effect on adsorption of ECM proteins required for adhesion.57 In a study by Hao et al., osteoblast-like MG-63 cells seeded on nanoporous titanium oxide films were more likely to adhere to surfaces consisting of smaller pores (∼30 nm) than surfaces with larger pores (∼90 nm).58 This difference in cellular adhesion was attributed to the small pore samples possessing a higher surface energy, similar to what we observe here.

Topographical control of astrocyte-like C6 glioma cells has previously been reported on surfaces containing nanodots of varying diameter (10, 50, 100, and 200 nm) that protrude from the surface. The results indicated that glioma cells showed the highest viability and the most complex networking on the smaller 50 and 10 nm nanodot surfaces. Similarly, our results indicate that primary astrocytes on the AAO surfaces exhibited increased adhesion, viability, and focal adhesions on the smaller (20 nm) porous surface compared to the larger (90 nm) pore surfaces. The primary astrocytes respond to alumina nanopores in a similar way as the glioma cells respond to protruding tantalum nitride nanodots. Interestingly, the nanodots produced by Lee and colleagues were etched onto the surface using porous alumina as the template, producing a pattern of nanodots with a similar layout and distribution as the alumina pores. The similarity in the astrocyte response suggests that the feature size may be more important than the specific feature shape. Furthermore, the alumina pores exert similar topographic control of astrocytes as the nanodot surfaces presented by Lee and colleagues with three fewer materials processing steps.48 Neurons, in contrast, appear to prefer larger pore dimensions. Cho et al. compared primary neurons cultured on pitted AAO surfaces with 60 nm concave pits to neurons cultured on AAO surfaces with 400−450 nm concave pits. The results showed that the larger (400−450 nm) pits accelerated neuron polarization and promoted axon formation compared to the smaller pits.49 Cho et al. also reported that the neuron response was similar on shallow concave pits to deep pores of the same diameter. These results further suggest that the feature size in the surface topography affects neurons and astroglia differently and is an important parameter to control. The cell viability studies showed that the AAO surfaces were biocompatible and not cytotoxic to the adherent astrocytes at both time points studied (Figure 6). These results suggest that the low astrocyte adhesion observed on large pore and control surfaces is not due to postadherence death of the cells. This preferential adhesion of astrocytes onto the small pore surfaces was also confirmed by analyzing the cell suspension supernatant 4 h postplating, thus precluding the reduction of astrocytic adhesion on large pore surfaces through noncytotoxic mechanisms (Supporting Information, Figure S3). Such noncytotoxic mechanisms have previously been implicated by Chapman et al. for their observed decrease in astrocyte adhesion on nanoporous gold substrates with nanofeatures of dimensions ∼170 nm at similar 4 h time point.50 While astrocyte culture on nanoporous AAO has not been reported before, to the best of our knowledge, the biocompatibility of AAO surfaces with comparable pore dimensions has been previously demonstrated for multiple cell types.51 Viability studies indicated that the difference in the number of adherent viable cells between the surfaces were caused by differences in adhesion, not cytotoxicity. We observed a strong adhesion of astrocytes on the small pore surfaces, which could be due to a combination of the various surface properties we assessed in this study. Cell adhesion and subsequent cell behavior present complex, multivariable problems, and it is implausible that any one material property is singularly responsible for the differences observed between the surface groups. It is most likely a combination of these material properties, as summarized in Supporting Information, Table S4, and the specific topographies involved for each surface group that influences astrocytic adhesion, as reported here. J


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adhesion response. While to the best of our knowledge, no study has quantitatively analyzed astrocytic GFAP-ir on nanoporous surfaces with differing nanofeature sizes, Gottipati et al. reported significantly lower levels of GFAP-ir accompanied by an increase in adhesion and proliferation for mouse cortical astrocytes plated on carbon nanotube films of ∼60 nm thickness.34 From our study and that of Gottipati and colleagues, it can be deduced that nanoporous features can cause adhesion-linked reduction in astrocytic GFAP-ir. Nanoporous alumina has been researched extensively for biomedical implants due to a variety of favorable properties such as high biocompatibility, low rates of wear, and small values for coefficient of friction.69 In addition to the effects of AAO on astrocyte GFAP-ir, AAO surfaces have been investigated for their effects on immune and inflammatory cell response in macrophages and monocytes which form the other major components of the immune response and chronic glial scarring cascade. Pujari and colleagues reported significantly higher levels of immune response activation in murine macrophages seeded on AAO surfaces of 200 nm pore size compared to those on the 20 nm pore. This is evidenced by significant increase in reactive oxygen species (ROS) which is also linked with promoting proinflammatory cytokines. This activated response also was observed in vivo for AAO membranes that were implanted in the dorsal subcutaneous cavity, as a dense fibrotic scar formed around the membranes with 200 nm pores.70 This indicates the possibility of having greater control over the composite foreign body response to trauma or implant insertion by using nanotopographical cues.

The cell adhesion strength, as well as subsequent cell proliferation and motility, depend on the total area of focal adhesions and spatial distribution of the same.59,60 Uniform peripheral distribution of a large number of FA leads to much higher effective cell adhesion area, and this could be key to stronger cell adhesion. Astrocytes attach to the substrate (or the ECM) through FA. FA formation and dissolution is a dynamic process that depends on the availability of attachment/ spreading area, the chemical microenvironment, and integrin expression.61,62 Vinculin live cell imaging was performed to test whether the higher adhesion on small pore surfaces was caused due to changes in FA number and arrangement as a result of the biomimetic nanotopography effects. Astrocytes seeded on small pore samples have many large FA concentrated principally around the periphery of the cell, compared to those on large pore surfaces which have fewer and smaller FA limited to the central area of the cell. The marked difference in FA distribution is consistent with the observed attachment results for the astrocytes on these respective surfaces. Chapman et al., in their studies of astrocytes cultured on nanoporous gold, reported a significant increase in the number of focal adhesion sites for astrocytes seeded on surfaces with smaller pore dimensions (∼30 nm). 63 This suggests that the dimensions of the nanoporosities on alumina surfaces may cause a redistribution of the FA, thereby influencing the astrocytic adhesion on these surfaces. Focal adhesions are also responsive to mechanical stresses resulting from the cell’s interactions with the ECM/substrate. The small pore surfaces possessed higher elastic moduli than the surfaces with larger pores and nonporous samples, and higher stiffness has previously been implicated in the formation of stable and mature focal adhesions in fibroblasts and epithelial cells.64 However, we expect the differences are beyond the mechano-sensing limit of the astrocytes. CNS cells prefer softer substrates. Neural stem cell differentiation can be controlled by changing the stiffness from 0.15 to 2 kPa.65,66 Even the stiffest substrates in the body are well below the 45−100 GPa stiffness of the materials measured here. Osteoblasts, which respond to mechanical properties within bone, one of the stiffest naturally occurring materials in the body, respond to stiffness that ranges from 25 to 40 kPa.66,67 While the differences in stiffness may be beyond the mechano-sensing limit of the astrocytes, the small nanoporous surface treatment does significantly increase the elastic modulus. Correlation between initial cellular adhesion and subsequent phenotypic response has been reported previously, with higher adhesion reported to be accompanied by lower GFAP-ir in astrocytes.34 To determine if stronger adhesion induced lower expression of GFAP levels, immunostaining was used to assess GFAP expression at 1 and 4 day time points. At the 1 day time point, the astrocytes cultured on small pore surfaces had levels of GFAP significantly lower than those on both the control and large pore surfaces. After the 4 day time point, the astrocytes on small pore samples showed GFAP-ir amounts lower than those on large pores, though no significant differences were found with respect to astrocytes on control samples. The initially high astrocytic GFAP-ir by the astrocytes on control and large pore surfaces may be linked to a distressed state of the cell68 that may be brought upon due to poor cellular adhesion and/or its inhibitive physical interaction with nanoscale topographical features. On the basis of our findings, we believe that the reduction in GFAP-ir in small pores compared to those on its larger counterpart is due to the marked difference in initial cell

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CONCLUSION In conclusion, we report the effect of nanotopography and especially specific nanopore dimensions on astrocytic adhesion, FA distribution, and GFAP-ir. For small pores, the initial astrocyte adherence is high, leading to less intense GFAP-ir compared to astrocytes cultured on surfaces with larger pores where low cell adhesion might lead to long-term phenotypic conversion as evidenced by higher intensity of GFAP-ir at both investigated time points. Material properties such as surface roughness, contact angle, hardness, and stiffness were investigated for their contribution to the size dependence of cellular response to the nanoporosities. Our findings indicate that through a combination of these properties the complex, multiscale nanotopography provides physicochemical triggers for cell adhesion. These triggers lead to the activation of cellular pathways that regulate the focal adhesion formation and redistribution of focal adhesions along the basement cell membrane, thus affecting cell adhesion. One of the next steps will be to look at astrocyte response in terms of proliferation. In the future, we also aim to study the effect of nanoporous alumina on macrophages which form the other active component of the immune response to neural interfaces. The results presented here are aimed at furthering our knowledge and understanding of glial cell response to unique nanotopographical features. This paves the way for more exploration of nanotopographies which can produce significant, tunable differences in cellular adhesion as a function of material nanostructure. In future work, we will experiment with intermediate sized pores to further study how pore size and surface area modify the astrocyte response. The differential effects of different porosities on astrocyte behavior along with the ease of fabrication, low cost of production, corrosion K


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saline; ICC, indirect immunocytochemistry; FITC, fluorescein isothiocyanate; DAPI, 4′,6-diamidino-2-phenylindole; EGFP, enhanced green fluorescent protein; FA, focal adhesion

resistance, biocompatibility, and general safety make nanoporous alumina an interesting choice for neural interfaces.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsbiomaterials.7b00760. X-ray spectrophotometry analysis, materials and methods, and results (S1); cross-sectional images of surfaces taken with SEM (S2); calcein−propidium iodide live/ dead staining images for early cell adhesion study at 4 h (S3); table of material properties (S4); surface reflectance data (S5) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Tel.: 518-276-2032; E-mail: [email protected]. ORCID

R. J. Gilbert: 0000-0002-3501-6753 Author Contributions

R.J.G. and D.AB.T. were responsible for the overall project conception, design, and supervision. D.G., C.D.L.J., M.K.G., and R.J.G. designed the experiments. D.G. synthesized the AAO surfaces and characterized them with C.D.L.J. assisting in contact angle goniometry. D.R. assisted with scanning electron microscopy, atomic force microscopy, and nanoindentation experiments. C.D.L.J. performed astrocyte isolation and assisted in astrocyte culture and plating. D.G. performed all the experiments on astrocytes and analyzed the data; M.K.G. performed plasmid expansion for vinculin live imaging and helped with GFAP immunoreactivity experiments. D.G. wrote the paper and generated the graphs. C.D.L.J. and M.K.G. assisted in the preparation of the manuscript and Supporting Information and also the conceptualization of the graphical abstract. All authors discussed the results and commented on the manuscript. All authors have given approval to the final version of the manuscript. Funding

Funding for this work was provided by the NSF career award (Grant 1150125), NIH (Grant R01 NS092754), and the New York Spinal Cord Injury Research Trust (Contract no. C030239) awarded to R.J.G. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank Dr. Eklavya Singh for assisting with contact angle goniometry, Ryan Smith for helping with calcein staining image analysis, Rob Planty for help in XPS surface analysis, and Rikhiya Ghosh for aiding in vinculin live staining image analysis. The authors acknowledge Blair Cooper for construction of Figure 1.

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ABBREVIATIONS CNS, central nervous system; NE, neural electrode; ECM, extracellular matrix; AAO, anodized aluminum oxide; GFAP, glial fibrillary acidic protein; DI, deionized; ECP, electrochemical polishing; RT, room temperature; SEM, scanning electron microscopy; XPS, X-ray photoelectron spectroscopy; AFM, atomic force microscopy; PBS, phosphate buffered L


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