Letter pubs.acs.org/ac
Graphene Oxide Interfaces in Serum Based Autoantibody Quantification Qiao Xu, Ho Cheng, Joshua Lehr, Amol V. Patil, and Jason J. Davis* Department of Chemistry, University of Oxford, Oxford, OX1 3QZ, United Kingdom S Supporting Information *
ABSTRACT: A reliable quantification of protein markers will undoubtedly underpin profound developments in disease surveillance, diagnostics, and improved therapy. Although there potentially exist numerous means of achieving this, electrochemical impedimetric techniques offer scale of sensitivity, cost, convenience, and a flexibility with which few alternatives can compete. Though there have been marked developments in electroanalytical protein detection, the demands associated with accessing the inherent assay sensitivity in complex biological media largely remains. We report herein the use of cysteamine-graphene oxide modified gold microelectrode arrays in underpinning the ultrasensitive and entirely label free non-faradaic quantification of Parkinson’srelevant autoantibodies in human serum.
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faradaic EIS insulin sensor capable of assaying neat patient serum samples. Graphene based biosensors have been attracting a great deal of interest during the past few years. Graphene is especially suited for impedimetric biosensing because of its metallic conductance, high associated rates of heterogeneous electron transfer, and large, potentially tailorable, accessible surface area.13 Graphene oxide (GO), in particular, possesses hydroxyl, epoxy, and carboxyl functional groups that can be selectively functionalized with appropriate reagents.14 To the best of our knowledge, there exists no prior report on the use of GO in the label free quantification of protein by impedance based methods. α-Synuclein is a profibrillar protein aggregated in Lewy bodies and, as such, much proposed as a potential marker of Parkinson’s disease (PD) onset and progression.15−17 The misfolding/aggregation of a native protein is accompanied by an autoimmune response as the host system attempts to clear its build up and reattains physiological balance.18,19 There have been attempts to align a quantification of α-synuclein autoantibodies with disease status; these have, to date, been encouraging enough to substantiate further investigation.8,15 Herein, we presented a novel and simple GO-cysteamine based ultrasensitive sensory interface with antifouling characteristics sufficiently good to support autoantibody quantification with high sensitivity in undiluted human serum samples.
aradaic and non-faradaic electrochemical sensing systems, due to their potential offering of a low cost and highly sensitive detection of clinically relevant biomarkers, have been subject to ever-increasing interest.1 While faradaic methods of analyte detection allow for enhancement of sensitivity through electrocatalysis2 and, thus, signal amplification,3 they require a redox active molecule to be present in solution or to be immobilized on the interface,4 complicating analysis. In seeking to assay, in a single label free and quantitative manner, nonelectroactive proteins, peptides, DNA fragments, aptamers, or cells (i.e., the vast majority of currently validated biomarkers), label free electrochemical impedance spectroscopy (EIS) is particularly powerful.5,6 Within this (steady state) method, the current response to the application of an AC voltage on a constant DC bias is tracked as a function of the frequency. From the acquired data, interfacial resistance or capacitative terms are quantifiable, some of which can respond, with very high sensitivity, to an electrode-confined capture event. If combined with miniaturized hardware, appropriate fluidic integration, high affinity/specificity receptors, and effective surface engineering, highly potent EIS based biosensors can be realized. As with all such label free methods, achieving a highly selective response to low levels of target within real biological samples, such as plasma, serum, cerebrospinal fluid, saliva, or urine, remains highly challenging, due to non-specific interactions. The principal interfacial forces responsible for (non-specific) surface−protein interactions are hydrophobic, electrostatic, and hydrogen bonding in nature. With this in mind, there exists a broad volume of “protein resistive” interfaces that have been developed. The most common approach has been to utilize hydrophilic and highly hydrated films, many of which have been based on PEGylated chemistries.7,8 More recently, we9 and others10−12 have investigated zwitterionic polymers as antifouling interfaces for biosensing applications. Significantly, we have reported9 a non© 2014 American Chemical Society
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EXPERIMENTAL SECTION Antihuman α-synuclein (α-Syn) was purchased from Santa Cruz Biotechnology, Inc. Recombinant human α-synuclein was Received: October 17, 2014 Accepted: December 16, 2014 Published: December 16, 2014 346
dx.doi.org/10.1021/ac503890e | Anal. Chem. 2015, 87, 346−350
Analytical Chemistry
Letter
Figure 1. Schematic representation of the sensory surface preparation. Immersion of cysteamine modified gold electrode in aqueous GO solution results in the electrostatic assembly of graphene oxide on the positively charged SAM terminus. Activation of the graphene oxide carboxylic acid groups allows for the covalent attachment of α-Syn antigens.
modified, characterized by giving unstable or nonlinear responses or associated with poor correlation (R2 < 0.95), was not used. Non-faradaic electrochemical impedance spectroscopy (EIS) measurements were carried out in PBS (10 mM, pH 7.4) in the frequency range from 0.05 to 10 000 Hz (greatest signal response in this range can be obtained at this frequency of 0.05 Hz, Figure 3A inset) with a waveform magnitude of 5 mV at a settled potential of 0.0 V. To quantify α-Syn Ab levels in any given solution, each chip (6 modified electrodes) was incubated in 60 μL of PBS or serum with specific α-Syn Ab concentrations for 30 min at room temperature, and EIS responses were recorded after washing with water. Measurements were repeated until they were tightly aligned (coefficient of variance 0.96. 349
dx.doi.org/10.1021/ac503890e | Anal. Chem. 2015, 87, 346−350
Analytical Chemistry
Letter
V. V.; Sherstnev, V. V.; Morozova-Roche, L. A. J. Neuroimmunol. 2011, 233, 221−227. (16) Yanamandra, K.; Gruden, M. A.; Casaite, V.; Meskys, R.; Forsgren, L.; Morozova-Roche, L. A. PLoS One 2011, 6, No. e18513. (17) Besong-Agbo, D.; Wolf, E.; Jessen, F.; Oechsner, M.; Hametner, E.; Poewe, W.; Reindl, M.; Oertel, W. H.; Noelker, C.; Bacher, M.; Dodel, R. Neurology 2013, 80, 169−175. (18) Yanamandra, K.; Alexeyev, O.; Zamotin, V.; Srivastava, V.; Shchukarev, A.; Brorsson, A. C.; Tartaglia, G. G.; Vogl, T.; Kayed, R.; Wingsle, G.; Olsson, J.; Dobson, C. M.; Bergh, A.; Elgh, F.; MorozovaRoche, L. A. PLoS One 2009, 4, No. e5562. (19) Papachroni, K. K.; Ninkina, N.; Papapanagiotou, A.; Hadjigeorgiou, G. M.; Xiromerisiou, G.; Papadimitriou, A.; Kalofoutis, A.; Buchman, V. L. J. Neurochem. 2007, 101, 749−756. (20) Stankovich, S.; Piner, R. D.; Nguyen, S. T.; Ruoff, R. S. Carbon 2006, 44, 3342−3347. (21) Bourlinos, A. B.; Gournis, D.; Petridis, D.; Szabo, T.; Szeri, A.; Dekany, I. Langmuir 2003, 19, 6050−6055. (22) Bhalla, V.; Carrara, S.; Sharma, P.; Nangia, Y.; Suri, C. R. Sens. Actuators, B 2012, 161, 761−768.
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