Review pubs.acs.org/CR

Cell-Based Biosensors and Their Application in Biomedicine Qingjun Liu,†,‡ Chunsheng Wu,† Hua Cai,† Ning Hu,† Jun Zhou,† and Ping Wang*,†,‡ †

Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of the Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China ‡ State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Shanghai 200050, China 9.4. Stem-Cell-Based Biosensors 9.5. Bioinspired Olfactory- and Taste-Cell-Based Biosensors 9.6. Cell-Based Biosensors for Cellomics Author Information Corresponding Author Notes Biographies Acknowledgments References

AC AD AD AE AE AE AE AF AG

CONTENTS 1. Introduction 2. Hybridization of Cells to Chips 2.1. Cell Immobilization and Surface Modification 2.2. Microfabricated Cell Culture Chips 3. Microelectrode Array Sensors 3.1. Theories of MEAs 3.2. Design and Fabrication of MEAs 3.3. Pharmacological Applications 4. Electrical Cell−Substrate Impedance Sensors 4.1. Theories and Structure of ECIS 4.2. Monitoring of Cell Morphology and Migration 4.3. Barrier Function Assessment and Drug Discovery 5. Field Effect Transistor Sensors 5.1. Principles of FETs 5.2. Cell Microenvironment Monitoring 5.3. Electrophysiological Detection 6. Light Addressable Potentiometric Sensors 6.1. Principles of the LAPS 6.2. Microphysiometers Based on the LAPS 6.3. Cell−Semiconductor Hybrid for Electrophysiological Detection 7. Patch Clamp Chips 7.1. Theories and Fabrication of Patch Clamp Chips 7.2. Ion Channel Research 7.3. High-Throughput Drug Screening 8. Affinity Cell-Based Biosensors 8.1. Quartz Crystal Microbalance 8.2. Surface Plasmon Resonance 9. Future Trends of Cell-Based Biosensors 9.1. Cell-Based Biosensors Using Nanotechnology 9.2. Cell-Based Biosensors with Microfluidic Technology 9.3. Immune-Cell-Based Biosensors © XXXX American Chemical Society

A B

1. INTRODUCTION A biosensor is an analytical device that can be used for detecting analytes and combines a biological component with a physicochemical detecting transducer. In recent years, biosensors have rapidly expanded and evolved in many new fields such as molecularly sensitive receptors, biomimetic sensors, and nanotechnologies.1−4 One of the most enduring biosensors is the cell-based biosensor, which can detect biochemical effects directly via living cells and convert these effects into digital electrical signals by sensors or transducers.5−8 Hence, it serves as the bridge between biology and electronics. Cell-based biosensors combine living cells and sensors or transducers for cellular physiological parameter detection, pharmaceutical effect analysis, environmental toxicity test, etc.9−11 In contrast to molecule-based approaches, cell-based biosensors have a broad spectrum of detection capabilities. Moreover, in addition to analyte sensing and detecting, cellbased biosensors can provide the advantages of rapid and sensitive analysis for in situ monitoring with cells.12−14 Cells naturally encapsulate molecular sensor arrays. Enzymes, receptors, and ion channels, all with a stable status, could respond to their corresponding analytes via a native cellular mechanism. Compared with molecular biosensors, cell-based biosensors are expected to respond optimally to bioactive analytes. Therefore, cell-based biosensors provide a useful tool to study the physiological effects of analytes. However, cellbased biosensors still suffer from some intrinsic shortcomings. The common problems faced by the optimization of cell-based biosensors include how to achieve satisfactory stability, how to improve the selectivity of a special sensor design, and how to prolong the cells’ lifetime. Fortunately, cellular mimicking and sensing are expected to be exploited in the near future, with the development of biotechniques such as nanotechnology, microfluidics, and high-content screening.

B D E E G G I I K M N N O P R R T U V V X X Y Y Z AA AA AB AC

Received: August 9, 2011

A

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

patch clamp chips, the quartz crystal microbalance (QCM), and surface plasmon resonance (SPR) will be introduced and discussed in detail with conclusions and future prospects. Some emerging technologies involving combining cell-based biosensors with up-to-date technologies in science and engineering are discussed in detail, including the use of nanotechnology, microelectronics, and molecular biology to fabricate the integrated, intelligent, and bioinspired biosensors used for cellomics studies.

Because of the obvious advantages of cell-based biosensors, e.g., long-term recording in a noninvasive way, fast response time, and label-free experimentation, they have been widely utilized in many fields such as cellular physiological analysis, pharmaceutical evaluation, environmental monitoring, and medical diagnosis.12,15−17 In these applications, cell lines and primary cultured cells are mainly selected as the cell sources. Cell lines divide actively in vitro, which offers the convenience of preparation and culture, if the desired cell type is available. Primary cultured cells are often extracted from animals directly, with the advantages of having numerous available cell types and the similarity function of in vivo cells. Cell lines can serve as renewable biosensor elements in biomedical assays, such as toxin detection and drug screening.12 Primary cultured cells with different cell types are usually used in bionic research, mimicking the sensing processes of organisms, such as artificial olfaction and gustation.18−21 To be specific, with the crucial advantages of in situ physiological monitoring, the biosensors can be successfully used in different biological applications along with the special characteristics of the cells.22,23 Electric excitability plays a significant physiological role in neurons and cardiomyocytes, so the characteristics of excitable cells have been commonly studied in cell-based biosensors and are used to acquire functional information on the direct effects of moduators/blockers to ion channels, agonists/antagonists to ligands and receptors, and the release of neurotransmitters.10,13,24−26 On the other hand, microphysiometers are employed to monitor the acidic metabolites of cell populations (both adherent and nonadherent), which have good performance in receptor analysis and drug analysis.27 Besides, electrical cell−substrate impedance sensing provides a useful method to study adhesion, proliferation, morphology, and motility of adherent cells with modeling of the cell as a resistor and a capacitor.28,29 Basically, the cell type used in each sensing case is determined by the different purposes in the biomedical applications. The conventional cell-based biosensors usually use cell population recording to study the behavior of cell populations on the basis of sensor platforms. The recorded data are often the collective responses of many cells detected by the sensor electrodes, which can be applied for cell−cell interaction study, such as cell invasion and barrier function assessment.9,22,23 However, these cell population recordings cannot realize the study of single cells and tiny bioactive units (e.g., special ion channels or receptors). On the contrary, single-cell sensing systems can record the cellular responses to stimuli, without mixing information about cell−cell interaction.30 Therefore, the single-cell sensing of cell populations is just a kind of in situ physiological monitoring of individual cells to reflect the behaviors of integrated cellular systems. In this review we will systematically discuss cell-based biosensor theories, technologies, and developments. We will combine the descriptions of microelectronics and information technology with those of chemical and biological fundamentals to introduce the principles and novel applications of cell-based biosensors. We will also provide a topical description of the research progress of cell-based biosensors over the past two decades. In addition, many innovative applications of cell-based biosensors, in areas such as biomedicine, will be detailed. The principles, developments, and typical applications of microelectrode array sensors (MEAs), electrical cell−substrate impedance sensors (ECIS), field effect transistor sensors (FETs), light-addressable potentiometric sensors (LAPS),

2. HYBRIDIZATION OF CELLS TO CHIPS Intermolecular interactions between biomolecules are highly specific. To enhance the performance of biosensor devices for the research of biological molecular interactions, it is crucial to achieve highly efficient coupling between the biological molecules and transducers. In these systems, living tissues or cells are directly immobilized onto the sensors or transducers, which can specifically respond to chemical substances and potentially changes in or interactions with the intra- or extracellular microenvironment.9,31−33 Living cells or tissue is one of the most significant components of biosensors to produce biological signals, such as changes in ion concentration, electrical current, or voltage fluctuation. The other components of the biosensor are mainly physical or chemical transducers, including sensing devices and their peripheral equipment. Fast advancements of silicon- and glass-based microfabrication technologies have made silicon- and glass-based sensors/devices the most common materials for the fabrication of biosensors, such as MEA, FET, LAPS, and patch clamp chips. In this section we will focus on the surface modification of silicon and glass for improving biocompatibility to facilitate the incorporation and delivery of cells and biomolecules. The modification of other types of sensors with different kinds of surfaces, such as metal surfaces, with poly(dimethylsiloxane) (PDMS) and nanomaterials will be introduced in the corresponding related sections. Typical biosensor substrate materials are glass and silicon. Silicon dioxide is a stable, nontoxic, and inert biomaterial. In modern biosensors, many types of substrates, such as silicon nitride,34,35 silicon carbide,36 photoresist SU-8,37,38 polyimide,39,40 and other organic materials, are used. Metals and metal oxides are also widely used.41−43 Cell immobilization on biosensor chips is one of the most important protocols and impacts the performance of the entire cell-based biosensor system. At present, a major research focus of surface chemistry has been concentrated on the control of chemical properties on the surface of sensors to satisfy the need for effective cell immobilization. For example, many efforts have been concentrated on the surface preparation to satisfy specific requirements in well-defined cell−sensor interfaces, which provide many opportunities for future development to obtain a good interface for precise cell localization, cell network construction, and even cell micromanipulation on biosensor chips. 2.1. Cell Immobilization and Surface Modification

Immobilization of cells on microchips is essential for biosensor design and downstream applications. Cells immobilized on biosensors are quite different from common cell culture. For biosensors, fine coupling with a silicon electric bilayer, metal surface, PDMS surface, or nanomaterial surface is desired. Several chemical procedures have been employed to modify the B

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Figure 1. Different chemical modifications of a sensor surface for improving the cell immobilization efficiency. (a) Schematic diagram of a cell coupled with a sensor surface by different surface modifications, including peptide, ECM, and SAM. (b) Schematic of the chemical process of cells immobilized on a gold surface via SAM.

surface of sensors for high-efficiency cell immobilization (Figure 1). The commonly applied methods are extracellular immobilization methods, which mainly include two categories: uniform chemical coating and integrated chemical coating with surface topology. Uniform chemical coating usually uses peptides or other extracellular matrix (ECM) components as summarized in Table 1. Surface modification by uniform chemical coating can greatly improve the biocompatibility of the sensor surface, which makes cells grow and couple well onto the sensors. However, uniform chemical coating could also reduce the surface stability of environmental factors such as temperature, mechanical shear, and solutions. In addition, the uniform coating could also lead to current leakage to the culture medium or other cells, resulting in a reduction of the signal-to-noise ratio. To achieve cell-based biosensors with higher performance, it is crucial to control the chemical and physical properties of the coating materials, including the thickness, distribution, and stability of the coating layer. On the other hand, integrated chemical coating usually forms micropatterns on the sensor surface by microcontact printing, inkjet printing, or a self-assembled monolayer (SAM), which could often provide covalent linkages between cells and the sensor surface. The micropatterns could also create precise cell immobilization on the sensor surface, which can greatly facilitate the measurement of cellular responses. Moreover, covalent modifications with SAMs are experimentally simple and quick, which improve the surface robustness to thermal, mechanical, and solvolytic instabilities. However, not all kinds of cells or sensors are suitable for integrated chemical coating. For cell-based biosensors, it is important to choose the appropriate method for cell immobilization by considering the cellular processes and surface properties of the sensors. Cellular processes such as adhesion, growth, migration, secretion, and gene expression are related to the dissolved molecules in the ECM and/or the adjacent cells. These cellular processes can be triggered, influenced, or controlled by the

specific biomolecules distributed throughout the neighboring surfaces in three dimensions. In vitro, cell-membrane-bound molecules mediate the cell adhesion. ECM complex molecules play a similar role when cells adhere onto a biosensor substrate. One common method for facilitating the cellular immobilization onto a biosensor surface is to create a uniform adherent layer. To accommodate different factors such as cell type, substrate geometry, and chemical characteristics, different ECM components and effective functional groups are immobilized. Furthermore, ECM immobilization is combined with micropattern geometry. The target region for cell sensing is modified with an attractive ECM, whereas other background regions are untreated. ECM modification methods are classified in Table 1. Because different types of microchips are applied in biosensors, the methods of cell immobilization also vary. Coating a biocompatible material is a convenient way to form the functional group on the biosensor surface.44,45 ECM components, such as polylysine,46,47 laminin,48,49 fibronectin,50−52 and collagen,53−55 can provide better adherence and spreading of cells and tissues on the biosensors. Generally, most of these patterning techniques are focused on guiding cells onto substrates including uniform materials. Nowadays, innovative technologies, integrating both the topology and biochemical coating, have developed to assist in precisely placing the cells. Methods such as microcontact printing,56 inkjet printing,57 and use of an SAM,58−61 can guide cells to effectively attach to the target region. Recent studies on cell growth factors and an ECM revealed that some segments of the peptides, such as RGD, IKVAV, and YIGSR,48,61,62 could govern the properties of the whole factors or matrix. These segments could bind with the integrin on the cell membrane to promote a series of biochemical reactions both on and inside the membrane. It may help the cell adhere and spread onto the sensor surface. However, additional coating might cause the current to leak into the culture medium, other cells, or even other electrodes C

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Review

via material on the plating layer. As a result, many extraneous noises are transferred to the electrode. The study has found that neurons can adhere and spread on silicon surface with the appropriate roughness.63 For enhancement of surface roughness, silicon chips are dipped into hydrofluoric acid, which efficiently enhances the surface roughness from the original 3 to 25 nm.64 However, to maintain efficient signal detection, either complicated 3D structures or more complex structural changes are allowed. Electric properties make it impossible to etch or implant ions into the chip surface. Some other methods are also used for cell immobilization, such as hydroxyl ion implantation,49 aminosilanization,65−67 amino acid immobilization,68 etc., which can significantly affect the surface properties of the silicon chips. However, the surface modifications should be limited to maintain their electrical properties. 2.2. Microfabricated Cell Culture Chips

ECM micropattern

inkjet printing57 self-assembled monolayer58−61

The fast development of microelectromechanical systems (MEMSs) has noticeably transformed biological, chemical, and medical research. Technologies such as DNA chips,69,70 lab-on-a-chip microfluidic devices,71 and micro total analysis system (μTAS) chips,72 are all based on biological theories and MEMS fabrication technologies. Lithography is the basis of modern microfabrication technology. In general, ultraviolet (UV) light is shone through a mask with a desired pattern. The photoresist is spattered onto a flat substrate to form a thin film and is dried before exposure. Biosensor substrates are generally made of glass and silicon. Microfabrication techniques make it possible for researchers to design the biochemistry and topology of the substrate in the vicinity of each cell with micrometer-level controls. With the development of microfabrication technology, BioMEMSs have been greatly encouraged to assist with cell immobilization on biosensors. Some BioMEMS strategies are applied in immobilizing cells on sensors. First, biosensor design is combined with topology methods, which guides cell orientation by artificial microstructures.38 The BioMEMS helps to record the distribution and changes in the current of the neuronal networks, typically via the FET array.73 This provides a practical method for longterm monitoring of cells via a microelectronic circuit. Various microstructures are fabricated on silicon for trapping cells and are tested separately.73−75 Some researchers used microholes with a diameter of 150 μm to facilitate cell adherence.76 Figure 2 shows neurons that were immobilized in a microfence.77 Others managed to design micropyramids,34,78 microchannels,79,80 and microwells74,81 on the chips to guide cell adhesion.74,81 Neuron was positioned that the network was oriented as the ideal model and formed synapses artificially. However, it is too difficult to control the processes precisely enough to fabricate complicated microstructures on the chips. Soft lithography is a modern and economic technology to generate a transparent rubber pattern utilizing PDMS, due to the reusability of the master mold. Soft lithography is a promising and attractive technology for biologists for cell immobilization. For scaffolds made of PDMS, a stencil and agar could be used to hold the cells and support them for more complex 3D structures. PDMS is satisfactorily biocompatible, stable, nontoxic, and suitable for cells as a novel organic material. It is also suitable for cell culture on the desirable region of biosensors.61 Microstamping, which was originally named microcontact

integrated coating with topology

ECM immobilization

SIKVAV, CDPGYIGSR, PDSGR, YFQRYLI, RNIAEIIKDA46 RDIAEIIKDI, CDPGYIGSR49 polylysine,46,47 laminin,48,49 fibronectin,50−52 collagen53−55,57 microcontact printing41

description

Additional coating might cause current leakage to the culture medium or other cells, which reduces the signal-to-noise ratio. The careful control of the chemical and physical properties of the coating materials is crucial to the cell-based biosensors. In addition to the covalent modifications with SAMs being experimentally simple and quick, they could improve the surface robustness to thermal, mechanical, and solvolytic instabilities.

modification material

uniform coating

YIGSR, RGD, IKVAV48

main factor

peptide section immobilization

ECM coating classification

Table 1. ECM Immobilization Methods for Cell Culture on Chips

Chemical Reviews

D

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Figure 3. Application of microfluidic cell arrays based on PDMS: (a) overview of the microfluidic chip design with a chamber that contains a low-density cell positioning array, (b, c) design for hydrodynamic positioning of single cells and on-chip cell culture. Reprinted with permission from ref 101. Copyright 2010 Expert Reviews Ltd.

elements. It is a crucial part of the entire biological component hybrid system and has great influence on cell viability, biomolecular activity, and the final system output signals. An extensive amount of surface chemistry research work has been performed in developing a sensor surface with higher biocompatibility to achieve reliable and reproducible cell− sensor hybrid systems. However, a great deal of uncertainty regarding the means and mechanisms still exists, and they have not been determined. Many studies on surface modification of sensors have been developed for various desired applications. Nowadays, biological components have been widely used in increasingly diversified and complex situations, as well as in more and more applications involving invasive sensors, tissue engineering, gene transfection systems, drug delivery, and biomedical nanotechnologies. Materials used for biosensors are specifically selected or developed on the basis of their possession of desired properties without toxicity, immunogenicity, thrombogenicity, carcinogenicity, and irritation. Silicon, glass, metals, polymers, and photoresists are mainly used to fabricate the sensors and devices. However, the surface biocompatibility of these sensors or devices is not good enough for high-efficiency incorporation of cells or biological molecules, so surface modification is usually required for improving the biocompatibility by changing the surface hydrophilicity, roughness, and chemical properties. One common method is to modify the surface with a thin layer of functional proteins such as enzymes, antigens, and antibodies that subsequently enhance the performance of the entire biological component hybrid system.

Figure 2. Neurons immobilized in a microfence: (a) polyimide microfence on a neuron-based biosensor, (b) single neuron trapped inside poles, with neurites spreading outside and connecting with others. Reprinted with permission from ref 77. Copyright 2001 National Academy of Sciences.

printing, transfers the material to a substrate from a PDMS stamp. Utilizing this method to easily organize a neuronal network in vitro, some researchers have utilized chemicals or reagents to guide neurite spreading along the tracks.48,77 Chemical molecules such as polystyrene,82,83 polylysine,46,47 laminin,48,49 collagen,53−55 some peptide sections,84 and other complex compounds could promote cell adherence to biosensors. Many groups have reported some microfluidic systems that utilize PDMS; for example, a multiwell PDMS chip combined with frequency-modulated ultrasound can realize parallelized manipulation of cells.85 It has shown unique advantages in performing analytical functions, such as cell transportation,86−89 cell sorting,90−94 cell electroporation,95,96 and cell manipulation.97−100 As shown in Figure 3, a functional cell-based array was successfully built using this method.101 The parallelized perfusion microarrays provided higher throughput analysis and clinical screening, even for a single tumor cell. They exhibit great potential for high-throughput drug screening. At the same time, microfluidic cell arrays are flexible enough for parallelized and fully addressable functional cell-based assays, on magnetic and ultrasonic platforms, which are widely used for treatments such as cell lysis, electroporation, and electrofusion, along with attracting cells to target locations. Therefore, microfluidic arrays based on PDMS provide new opportunities to develop cellbased biosensors. Biocompatibility is an important consideration for biosensors in which cells or other biological molecules are used as sensitive

3. MICROELECTRODE ARRAY SENSORS 3.1. Theories of MEAs

Advancements in microfabrication techniques have paved the way to develop MEAs for stimulating and recording the electrical activity of excitable cells and tissue cultures with high resolution.9 Figure 4a displays a schematic overview of the action potential MEA measurement setup. Multiple metallic film sites with a diameter of several micrometers are fabricated on a glass substrate, and cells are cultured in the chamber, adhering to the substrate and microelectrodes. The intracellular E

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

resulting in a net current of zero. The electric field, which is generated by these electron transfer reactions, causes the formation of the inner Helmholtz plane (IHP) and the outer Helmholtz plane (OHP) in the electrolyte. The net result of all these reactions is the creation of the electrical double layer (EDL), as illustrated in Figure 4b. The space charge region has a graded profile with the strongest field at the interface, diminishing to zero in the bulk electrolyte. The equivalent circuit of the layer can be expressed as the Randles model102,103 as specially marked in Figure 4c. According to the theories developed by Helmholtz,104 the interfacial capacitance of the EDL (CI) is determined by the permittivity of free space (εo), the relative permittivity of the electrolyte(εr), the area of the interface (A), and the distance of the OHP from the metal electrode (dOHP):105 CI = A

εoεr dOHP

(1)

The rough estimate of εr and dOHP is often useful as a design guide, although numerous factors can affect both εr and dOHP. From 1910 to 1913, Gouy and Chapman modified the simple Helmholtz model (a rigid sheet of solvated ions) by considering mobile solvated ions at the electrode surface.106,107 These mobile ions were influenced by thermal forces in addition to the applied electrical forces, resulting in an ion cloud near the interface. In 1924, Stern rectified this inconsistency by combining the Gouy−Chapman model with that of Helmholtz. He combined a layer of bound ions in the OHP with a diffuse ion cloud beyond it.108 The charge transfer resistance (Rct), which represents the resistive path parallel to the capacitor in the electrical model of this interface, is determined by the exchange current (per unit area) between the metal and electrolyte (JO, A/cm2), the involved ionic valence in the charge transfer reaction (z), and the thermal voltage(Vt):

R ct =

Vt JOz

(2)

The spreading resistance (Rspread) affects the current from the localized electrode to a distant counter electrode, which can be calculated by integrating the series resistance of solution shells moving outward from the electrode, and the solution resistance (R, Ω) is determined by

R=

Figure 4. (a) Schematic of the measurement setup of an MEA. (b) Schematic representation of the electric double layer. (c) Equivalent circuit of the signaling pathway in the MEA system.

ρL A

(3)

where ρ is the resistivity of the electrolyte (W·cm), L is the length (cm), and A is the cross-sectional area (cm2) of the solution through which the current passes. For a circular electrode of radius r (cm), Rspread (Ω) is given by

action potential originating at the cell itself is generated by ions passing through gated protein channels in the cell membrane. The movement of ions in the extracellular solution creates potentials that can be detected at different sites. When adhesion occurs between cells and microelectrodes, some areas of the electrolytes form a cell−electrolyte interface. The electrochemical properties of the interface are the basis for the MEA sensing mechanism. On the basis of electrochemical theory, when the metal interface makes contact with the ionic conducting solution, an equilibrium condition is eventually established whereby the currents due to the electron transfer to and from the metal are equal. This equilibrium exchange current density flows across the interface in both directions,

R spread =

ρ ρ π = 4r 4 A

(4) 2 109

where A is the area of the circular electrode (cm ). The equivalent circuit model of a cell−electrode junction is shown in Figure 4c, which illustrates relationships between intracellular action potentials and extracellular field potentials. It shows that the point-contact model developed from the Hodgkin−Huxley model, with the capacitance and resistance of the cellular membrane in parallel, is used to describe the cellular electrophysiology and the electrical source of the circuit.9,102 F

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

The current flowing through the cellular membrane is the sum of the capacitive current and ionic flow and is given by Itotal = K

d2Vmem dt

2

= Cmem

dVmem + Iionic dt

deposited on the electrodes.110 The development of MEA designs is primarily related to the aspects of layout, the structures of the electrodes, and functional expansion. Changing the spatial distribution or layout of the microelectrodes has been an effortless but effective way to meet the demands of specific biological and operational problems. For example, the “HexaMEA” of MultiChannel Systems, equipped with a hexagonal layout and partial high-density distribution of a tissue-conformal MEA, was specially designed to study the extracellular field potentials of neuronal tissues, such as the retina and hippocampal slices.111 By bonding a layer of PDMS polymer with defined microfluidic channels, cultured neuronal networks can grow according to the scale of the channels. The signal transduction between neuron network groups or within individual cells can be mapped.112−114 These methods could be used to obtain more valuable electrophysiological information, with no serious negative effects on the performance of individual electrodes. One drawback of this approach, however, is that when the planar MEA is used for extracellular recording from organotypic tissues such as the retina and brain slices, there is a distal gap between the neurons and the two-dimensional electrodes. The coupling between the tissue and the electrode is also poor due to the damaged cell layer on the chip surface. Thus, twodimensional electrodes may not be optimal for effectively recording the electrical activities of organotypic slices. Threedimensional electrodes, however, could penetrate the dead cell layer because they increase the contact between the slice and electrodes and therefore increase the signal-to-noise ratio. Electrodes shaped with pyramid tips are mainly designed to record extracellular field potentials of slicing tissues with an improved signal-to-noise ratio.115,116 Another way to improve the quality of the signal is to perforate a microhole (i.e., 3 μm) in the center of the electrode and fix the cells on the electrode sites by applying a small amount of pressure from the backside of the chip.117 Maher et al. also designed a “neurochip” by depositing a thin Si3N4 layer with a hole on the silicon well to form a space for the growth of individual neurons.7 To further develop automation and higher efficiency, some groups focused on the functional expansion of the MEAs. Several techniques (e.g., microcontact printing, microfluidic channels, and the microelectronic fabrication process) are combined with MEAs for cell manipulation or trapping,118 addressable recording,119 high integration with functions of stimulation, recording, and signal processing by complementary metal oxide semiconductor (CMOS) technology,42 and highthroughput drug screening.120 In our laboratory, we integrated MEAs with interdigitated electrodes on the same chip to jointly analyze the electrophysiological activity and physical state of cultured cells in a time-switching way.121 With nearly 40 years of development, the MEA has been commercialized, and its products are mainly made by the companies MultiChannel Systems, Ayanda Biosystems (United States), and Panasonic (Japan). The MEA has had a greater and greater significant role in the study of cellular networks and is still undergoing development to become a mature and practical tool for high-throughput pharmacological screening.

(5)

Therefore, the voltage at node A is proportional to the second derivative of the action potential. In Figure 4c, ZEDL (the impedance of the Randles model), Relectrode (the resistance of the electrodes), and Rin (the input resistance of the preamplifier) are connected in series and compose one of the current passages. In addition, the minute volume of the electrolyte also induces a side track for ionic flow to the bulk electrolyte. It is expressed as the sealing resistance (Rseal) parallel to the series branch. The total transmembrane current flows through both the series branch and the Rseal branch, so the recorded extracellular field potential (VEP) can be calculated as VEP = Itotal

R seal R in Z EDL + R electrode + R in + R seal

(6)

This equation can provide clues required for explaining the relationship between biological signals and signals recorded using microelectrodes. As discussed above, the amplitude of the extracellular action potential detected by the MEA is much smaller than the transmembrane action potential. The signal shape and time course are also quite different. However, the extracellular potential is sufficient enough to provide the desired information about the activities of the cells. For example, the permeability change of ion channels in the cell membrane induced by drugs or chemicals would significantly affect the membrane potential and also alter the shape/amplitude of the recorded signals. By analyzing some characteristic parameters of the recorded signals, the exact effects of drugs or chemicals can be determined. 3.2. Design and Fabrication of MEAs

The layout and structure of MEAs continue to be developed to meet the demands of specific and operational biological problems. The essential design principle has obeyed the “sandwich” structure since Thomas et al. pioneered it in 1972.9 The metallic film is deposited between the insulative substrate and the passivation layer. Only the electrode sites and pad are exposed for recording and output of the signal, respectively. A typical commercial MEA chip from MultiChannel Systems (Germany) is shown in Figure 5. Generally, the electrodes diameter is 10−100 μm. To improve the performance, a TiN layer with regular “column” morphology is

3.3. Pharmacological Applications

Over the past 10 years, cell cultures have progressively been used more frequently as pharmacological models to study functional characterizations of drugs, pathogens, and toxicants. In 1995, Gross’s group proposed the concept of using neuronal

Figure 5. An 8 × 8 channel MEA chip: (a) chip layout of the electrodes, (b) TiN-coated electrode, (c) surface morphology of the electrode. G

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

networks cultured on MEAs as biosensors.122 Without the homeostatic control of the central nervous system, the in vitro neuronal networks are still a highly stable system and are sensitive to minute chemical agent changes in the culture environment. Similarly, some other electrogenic cells and tissues, such as cardiomyocytes, brain slices and retinal networks, are also gradually becoming acceptable pharmacological models for MEAs. Drug discovery using MEAs with electrogenic cells or tissues is done by monitoring the extracellular potential changes due to the influence of drugs on cellular activities. In the following section, we will review these applications on cardiomyocytes and neuronal networks. Cardiotoxicity is a severe side effect of many clinical drugs that impairs the normal function of the heart in some patients. Recently, there have been at least a dozen top-selling drugs withdrawn from the market due to their cardiotoxicity, and this caused huge economic losses for the related companies. Of course, the most important issue is the safety of the drugs, and proper pharmacology is necessary to ensure that each drug is not detrimental to human health. MEAs can record the extracellular potentials of cardiomyocytes. Multisite recording would reveal the spatiotemporal behaviors of the culture, including the generation of extracellular field potential and the propagation pathway. The electrophysiological mapping of cardiomyocytes provides an important method for understanding the mechanisms underlying some serious heart diseases. These attributes of electrophysiological mapping could be sensitively influenced by the stimulus, including mechanical stress, the electrical pulse, and chemical agents, including drugs. Ion channels, primarily sodium, potassium, and calcium ion channels, are important targets for drugs that affect cardiomyocytes. They are important cellular components in the physiological function and are implicated in a variety of cardiac system disorders. A fast inward Na+ current can drive depolarization. The amplitude of extracellular field potential recorded by MEAs is greatly decreased and the conduction delay increased after treatment with Na+ channel blockers.123,124 Ca2+ is closely linked with cardiac contractility, and drugs such as quinidine and nifedipine could cause changes in the extracellular field potentials and beating behaviors.125,126 Some groups have used MEAs to study drug-induced cardiotoxicity, including that of potassium channel activators, pesticides, angiotensin II, etc.42,127 Many drugs affect several types of ion channels and cellular organs. Thus, the MEA provides a convenient and versatile system for in vitro study of cardiomyocytes. Prolongation of the QT interval (expanded QT) of an electrocardiogram (ECG) is known to be associated with arrhythmia.128,129 Studies have found that many drugs can cause the prolongation of the QT interval, which indicates the necessity of assessing the drug effects on QT interval prolongation. QT interval prolongation directly corresponds to the prolonged ventricular action potential, which is also correlated with the duration of field potentials measured by the MEA. To achieve high-throughput pharmacological screening of QT prolongation, a six-well multichannel electrode chip was used, where each of the wells contained 10 microelectrodes.130 To validate the system, the QT-prolonging effect of some compounds with known effects, such as antiarrhythmic agents such as quinidine and E-4031 and noncardiac agents such as cisapride, sparfloxacin, etc. were tested. Figure 6 shows an example of the field potential for human embryonic stem-cell-

Figure 6. (a) Six-well multichannel electrode chip for the QT-Screen and the micrograph of electrodes in one well with hESC-CMs attached and cultured on them. (b) Field potential for hESC-CMs in the presence of increasing amounts of E-4031. Reprinted with permission from ref 130. Copyright 2010 Elsevier.

derived cardiomyocytes (hESC-CMs) measured by a QTScreen system in the presence of increasing amounts of E-4031, indicating the prolongation of field potential duration in response to E-4031. Compared with conventional cell-based assays, such as human ether-a-go-go-related gene (hERG) assays, MEAs can detect cardiomyocyte action potential regulation with full mechanisms. In conclusion, this novel biosensor has been successfully applied primarily to biomedical studies such as drug screening and environment detection in a long-term and noninvasive way. The MEA is also an important tool to map the electrophysiology of neuronal networks in a parallel way for long-term recording of basic neuronal activity. The electrophysiology of neuronal networks underlies memory storage, learning ability, and signal processing in the human brain. On the basis of synaptic connections, most of the neuronal networks developed spontaneous bursting activities and reached synchronicity when the cultures matured.122,124−129,131−134 Figure 7 shows the neuronal networks from stem cells grown on an MEA.135 Since H

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Electrical stimuli are also widely used to study the mechanisms of various neuronal networks. For instance, the effects of various forms of electric pulses applied to the retina can be reflected in the ganglion cells, which could help advance studies on the rehabilitation of retinal diseases.142,143 Synaptic plasticity is the primary mechanism for learning and memory. Long-term potentiation (LTP) and long-term depression (LTD) are two types of persistent changes in synaptic efficacy which can also be induced by electrical stimulation on the MEA.144,145 The MEA provides a means for locally measuring the electrical activities of cell cultures and their stimulations. The designed system is capable of electrically stimulating cell cultures with a defined stimulus in specific sites or channels. Under different stimulations, neuronal networks respond differently. Also, the functional connectivity induced by electrical stimulations can be learned from the multirecording. On the basis of fundamental research methods, the neuron− MEA platform has been widely used in pharmacological applications. The neuronal activity deviation from physiological levels can be mapped when the networks are exposed to chemical compounds such as drugs and environmental toxins. On the basis of inherent electrophysiological mechanisms of neuronal networks, their influences are classified as transmission effects, specific synaptic effects, direct metabolic effects, and generic membrane effects.146 In the past decade, many substances with excitation or inhibition effects have been studied, including neurotransmitters (γ-aminobutyric acid (GABA), dopamine, serotonin),147−149 a glycine receptor antagonist (strychnine),31,146 a 5-hydroxytryptamine (5-HT) receptor antagonist (fluoxetine),150 N-methyl-D-aspartate (NMDA) receptor antagonists (2-amino-5-phosphonopentanoic acid (APV), 2,3-dihydroxy-6-nitro-7-sulfamoylbenzo[f ]quinoxaline-2,3-dione (NBQX)),151 GABA receptor antagonists (gabazine, bicuculline, trimethylolpropane phosphate (TMPP)),152 cannabinoid receptor-mediated apoptosis (anandamide and methanandamide),153 ethanol,154 a Na−K−ATPase inhibitor (ouabain),122 the Gp120 protein of the AIDS virus,146 and even a heavy metal (Zn).155,156 The profiles of the neuroactive substances are usually expressed by the firing rate, the waveform shape, and other attributes such as the synchronicity, burst structure, regularity of oscillation, and connectivity.157,158 The neuronal networks were mainly dissociated from the central nervous system and the peripheral nervous system. Until now, the dissociated and cultured neuronal networks, tissues, and slices all have been widely used as pharmacological models. Table 2 shows some typical pharmacological analyses of various types of neuronal networks based on an MEA. The studies demonstrated that neuronal networks from different brain regions show different pharmacological responses. Through the MEA platform, one can generate a database of well-characterized profiles of different drugs and toxins. Moreover, by comparisons of the induced changes in spike patterns, unknown neuronally active substances could be discovered.

Figure 7. Basic activities of neuronal networks on an MEA: (a) neuroepithelial cells isolated from the embryonic cortex expand and differentiate into neurons and astrocytes on the MEA, (b) cells differentiate into neurons and astrocytes, identified by double immunostaining (bar = 30 μm), (c) typical spike trains on an electrode of the network after several treatments, i.e., bicuculline, 6cyano-7-nitroquinoxaline-2,3-dione (CNQX), and 2-amino-5-phosphonopentanoic acid (APV). Reprinted with permission from ref 135. Copyright 2009 Elsevier.

the stem-cell-derived neuronal networks on the MEA share similar properties with dissociated neuronal networks from mammalian tissues, they can serve as a promising resource for a variety of applications. The firing patterns detected by the MEA could also help to explain the neuronal topology. The bursting derives from regulation of excitation and inhibition balance, which is related to the sodium currents and calcium current.136,137 The patterns of spike bursting depend on the stage of development of the neuronal networks.138 The relationship between the development of neuronal connectivity and intrinsic bioelectric network activity determines the specialty of different neuronal networks.139,140 Different methods have aided the development of neuronal signal extraction and analysis.141 The spatial−temporal attributes of the spikes are vital to the neuronal information encoded.

4. ELECTRICAL CELL−SUBSTRATE IMPEDANCE SENSORS 4.1. Theories and Structure of ECIS

ECIS is an electrochemical technique which is used to study cell adhesion, proliferation, growth, and migration in real time.51,169−171 Figure 8 presents a schematic of the ECIS system proposed by Giaever and Keese.172 Microelectrodes are I

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Table 2. Pharmacology on Various Neuronal Networks by an MEA cellular component frontal cortical neurons cortex networks cortex networks cortex networks auditory cortex network auditory cortex networks spinal cord networks spinal cord networks hippocampal neuronal networks retinal ganglion slice olfactory epithelium olfactory bulb slice olfactory placode neurons

stimulus and concentration chloroquine (0−30 μM)

description

glutamate (0−100 μM)

A concentration-dependent depression of the spike rate and inhibition of the whole-cell calcium current were found.159 A low concentration (50 nM) of DomA increased the basal spontaneous electrical activity.160 Activity-dependent enhancement of neuronal network activity was selectively blocked.161 An increased duration and number of spikes and unique oscillatory behavior with each burst were aroused by treatment with BoNT.162 Excitation, initial inhibition, and activity cessation were caused at 1−20, 15, and 20−25 μM, respectively.150 Regularity and coordination of bursting were increased in the auditory cortex network. The response of auditory cortex networks to quinine was in the excitatory phase followed by inhibition.163 A concentration-dependent increase in the spike rate was caused at elevated levels.164

strychnine (0 nM to 20 μM)

Bursting was increased at 5−20 nM and coordinated above 5 μM.146

gabazine (10 μM), bicuculline (10 μM)

Gabazine increased the firing. Bicuculline exhibited heterogeneity of action on the firing rate.149

AP-4 (100 μM)

Activated with AP-4, the cells were hyperpolarized both in the presence of light and in darkness.165

acetic acid (25 μM), butanedione (25 μM) glutamic acid (Glu; 10 μM to 5 mM) GABA (10 μM), bicuculline (10 μM)

Different firing patterns are generated after treatment of these two different odorants.166

domoic adic (DomA; 0−2 μM) Borna disease virus (BDV) botulinum toxin (BoNT; 10 ng/mL) fluoxetine (1−25 μM) quinine (1−40 μM)

Glu increased the amplitude and the firing rate of the signals.167 GABA almost immediately inhibited the firing activities. Bicuculline had a facilitatory effect or lacked an effect.168

Figure 8. Schematic of the ECIS system. The phase-sensitive impedance measurement unit contains a lock-in amplifier with a phase-sensitive detector and computer interface for collecting data. Certain cell responses, such as changes in the cellular metabolism, morphology, and cytoskeleton, cause impedance changes. Reprinted with permission from ref 172. Copyright 2009 Elsevier.

fabricated on the sensor surface where the cells attach. An ac current is applied to the electrodes while the voltage is recorded by a phase-sensitive impedance measurement instrument. The ion current is blocked when the cells attach and spread on the electrodes. Thus, as the area of the electrodes covered by cells increases, the detected impedance increases concurrently. When the impedance is measured at a low frequency, the current tends to flow through the gaps between the cells and electrodes or cell−cell tight junctions. This reflects the tightness of the cell−electrode attachment as well as cell−cell connections. At higher frequencies, the current flows through the cell membrane and cytoplasm, and the information in the cell membrane is reflected. ECIS is a noninvasive method to monitor cellular physiology; therefore, it is suitable to carry out long-term measurements in cell bioassays. A schematic of the cell positioned over an electrode and its equivalent circuit model are displayed in Figure 9 to give an insight into the detecting principle of ECIS. Before cells are attached onto the electrodes, the measured impedance includes the double layer capacitance (CD) (metioned in section 3.1)

Figure 9. (a) Schematic of a cell on an ECIS sensor. (b) Equivalent model of a cell-free ECIS sensor. (c) Equivalent model of a cellcovered ECIS sensor. (d) Impedance for cell-free and cell-covered ECIS sensors under different frequencies. (e) Corresponding sensitivity under different frequencies. Reprinted with permission from ref 173. Copyright 2008 Elsevier.

and the cell culture medium spreading resistance (Rsol). In cellbased bioassays, cells are cultured on the electrodes, and the equivalent circuit cell-covered ECIS sensor is displayed as shown in Figure 9c. Cells on electrodes can be presented as a capacitance (Ccell), which represents the dielectric property of the cell membrane. The adjacency resistance between cells can be defined as Rcell. Rgap and Cgap are the electrical properties of the small cell−substrate gap. On the basis of the two equivalent circuits shown in Figure 9b,c, the electrochemical impedance spectrum of ECIS can be obtained and is exhibited in Figure 9d. Wang defined f low and J

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

f high by simplifying the circuit to classical high-pass circuits in different frequency ranges:173 1 1 flow = 5 2π (R s + R cell + R gap)C I (7) fhigh = 5

2πR s(C I

−1

1 + Ccell −1 + Cgap−1)−1

each well with a higher sensitivity and lower differences than in high-throughput cell-based bioassay. On the basis of experimental results and theoretical analysis, some rules are generated for optimization of IDE design. The parameters of IDEs include the electrode width, gap, length, and total area. These parameters influence the sensitivity and the number of effective cells in different ways. Since more cells are needed in response to the same percentage of change in the large area compared to the small electrode area, the sensitivity increases with a reduction of the electrode width and length, namely, minimizing the total area of the electrodes. However, the number of effective cells decreases as the electrode width and length are reduced, which will weaken the average effect of IDEs, thus reducing the repeatability of cell experiments. Therefore, there is a tradeoff in the effective cell number and the sensitivity in sensor design. Additionally, the impedance of the electrodes will increase as the electrode width is reduced, which will cause electrical field differences along the electrode. The cell attachment at the different positions of the electrodes will contribute undesirable differences in the total impedance. Therefore, the width and length of the electrodes should be suitable. Moreover, the edge effect of the plane electrode will lead to particularly large changes in the impedance when a cell attaches to the electrode edge. Thus, the IDE distance should be larger than the cell size. Generally, IDEs with a short length, a moderate width, and high-density branches have a uniform electrical field and a high sensitivity. ECIS was pioneered by Giaever and Keese three decades ago and is an effective and useful label-free technology for in vitro cell-based assay with real-time, noninvasive, and dynamic detection. Current applications of ECIS in cell biology assays and biomedicine are listed in Table 3.

(8)

When the frequency increases from zero, CDl decreases but mainly contributes to the total decreasing impedance until f low, where the impedance of this capacitor becomes lower than Rsol; thus, there is almost no difference between cell-free and cellcovered sensors. Then an uplift of cell-covered sensors versus cell-free sensors is obviously seen in the frequency range from f low to f high because of the attachment of cells contributing additional increased impedance to the total impedance of the ECIS sensors. When the frequency becomes higher than f high, the cell membrane becomes invisible; thus, again the difference is hardly seen beyond f high. The most appropriate frequency for cell impedance sensing is the frequency with highest sensitivity, which is defined to be f middle, the root of eq 7: d(|Zcell‐covered(f )| − |Zcell‐free(f )|) d(sensitivity(f )) = df df

(9)

Typically, two types of electrodes are primarily used for impedance sensing: monopolar structure electrodes174,175 and interdigitated electrodes (IDEs).176,177 The typical structures of monopolar electrodes and IDEs are shown in Figure 10. The

4.2. Monitoring of Cell Morphology and Migration

ECIS is constructed on the basis of cell adhesion, proliferation, and migration on the IDEs. A series of morphological changes occur until full adhesion.220,221 Thus, enhancement of cell− substrate interactions may improve the cell culture quality and cell-based assays in vitro. Traditional cell-based assays are indirect, laborious, timeconsuming, and invasive (e.g., WST-1, XTT/MTT, BrdU). Impedance sensing provides a way to probe the kinetic aspects of this complex process. The ECIS is used to study the cell− ECM interactions in two ways: one is cell−substrate adhesion with different ECMs, such as laminins, fibronectin, and collagens.222,223 The other is reagent interferences of cell− ECM interaction, the cytoskeletal architecture, and signaling pathways, such as G-protein-coupled receptor (GPCR) activation and protein tyrosine kinase activation.224,225 In the above studies, the researchers focused on how to improve cell adherence. Other researchers have evaluated the quality of biocompatible materials by recording the state of cell attachment. ECIS works as cell-based biosensors in these studies. Chang’s group estimated the seeding cell density by coating electrodes with RGD-C peptide. A positive correlation between changes in admittance and cell attachment from 102 to 107 cells/cm2 was found.177 Using this method, one can characterize and screen biocompatible materials with suitable cell affinity.226 Chang et al. quantitatively evaluated the bioavailability of an alkanethiolate self-assembled monolayer of varied chain lengths adsorbed on Au-coated microelectrodes by analyzing the impedance change.227 Thein et al. recorded

Figure 10. Typical geometries of ECIS electrodes. (a, b) Monopolar electrodes: small electrodes are used as working electrodes, and large electrodes are used as the counter electrodes. Although the area of two electrodes is large, the total impedance is dominated by the impedance of the working electrodes. (c, d) IDEs: two electrodes are interdigitated, and the impedance changes can be differentially recorded for both electrodes.

effective sensing area is limited due to the monopolar electrode structure, so only a few cells contribute to impedance measurement, which results in large differences in highthroughput cell-based bioassay. Furthermore, a large counter electrode hampers the miniaturization of ECIS sensors. In contrast, the effective sensing area of IDEs is much larger in K

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews fibroblast cells (kidney175,187), endothelial cells (human peritoneal mesothelial cells,188 renal microvascular endothelial cells189), carcinoma cells (Hela,175 ovarian,188 hepatoma,155,190 lung,191 breast,192−194 prostate194)

epithelial cells (bronchial,195 laryngeal,196 choroid plexus,197 corneal198), endothelial cells (pulmonary microvessel,199 brain microvascular,200 brain capillary,201−203 umbilical vein,204 lung microvascular,205,206 corneal198,207) fibroblast cells (lung,174,208−210 orbital210), epithelial cells (ovarian211), adenocarcinoma cells (colon,176,212 cervix213−215), carcinoma cells (hepatoma,215,216 epithelial,217 hepatoma,215 lymphoma,213 lung,185 ovarian218), sarcoma cells (fibrosarcoma,219 osteosarcoma218)

Migration causes local area impedance increases or decreases showing time and spatial resolution.

Biochemicals induce barrier dysfunction by perturbing the extracellular matrix and cytoskeleton. Cytotoxicity and drugs influence cell adhesion and the cell morphology, which induces a change of cell−substrate interaction or intercellular impedance in an acute or chronic way.

cell types

fibroblast cells (lung,51,174,178 kidney,179 liver,180,181 muscle182−184), carcinoma cells (hepatoma,185 pancreatic (BxPC3)186)

description

Biochemicals increase or decrease adhesion and proliferation by interference with cell− ECM interaction, cell-signaling pathways, or the cytoskeletal architecture.

application

cell adhesion and proliferation monitoring cell migration and invasion monitoring barrier function assessment cytotoxicity assays and drug discovery

Table 3. ECIS for Cell-Based Assays and Biomedical Applications

Review

the impedance changes of the cell−electrode heterostructure using a single-cell-based integrated electrode array, modified by either KRGD short peptide or fibronectin.228 The biofunctionalization of the electrode could achieve strong or tight cell adhesion and therefore improve the transduced signals. Utilizing cell-repulsive or cell-affinitive characteristics of surface-modifying materials should provide a better impedance recording sensitivity.229−231 After cell adhesion, ECIS can be used for the study of cell proliferation, cell cycles,232 cell quality and cell differentiation,233,234 and tissue culture.235−237 ECIS is a noninvasive, real-time, label-free method compared to conventional methods (e.g., flow cytometry and fluorescence), and ECIS is essential in the field of biomedical applications such as cytotoxicity, stem cell research, and high-throughput drug assessment. Cell migration and invasion are significant in cellular physiological processes, such as cancer metastasis, wound healing, immune response, and homeostasis. Wound healing and cancer metastasis were used as examples of ECIS applications for monitoring cellular migration and invasion. Classic stages of wound repair include inflammation, new tissue formation, and remodeling. Cellular migration of different cell types occurs at the second stage of wound healing to bring the edges of the wound together. Wound healing by ECIS was first reported by Noiri et al.189 They applied a dc current to the cell monolayer to build wound models on the electrodes. After wound formation, cells on the edge of the wound migrated collectively onto the electrode. The measured impedance increased slowly during this time, until it was restored to the value observed before wounding. Then the measured impedance remained steady, which indicated that the wound had completely healed. However, the dc signal was unstable, which may cause some types of electrochemical reaction on the electrode. Keese et al. improved the wound generation using milliampere ac currents at high frequency.187 Wang et al. introduced wound healing using selfassembled monolayers.175 The small electrode restricts the wound without electrode damage, and cell-based assays have a high consistency (Figure 11).187 Migration of some cancer cells occurs from the initial tumor to new locations to form a new tumor. The invasion of tumor cells and angiogenesis are foundations for tumor development and have been investigated by numerous assays.238 The typical cell-based assay by ECIS for cancer metastasis was first presented by Keese et al. and is based on previous microscopic observation in which metastatic cells attached to and invaded the cell layer.239 The metastatic potential of tumor cells was tested with impedance drop. There are many factors related to the tumor invasion process, such as proteases, cytoskeletal proteins, and adhesion molecules.240 Efforts were made to study these factors by ECIS.188,190,193 In those studies, the experimental schemes were similar to those of Keese.239 Migration was enhanced by certain molecules or proteases, and pharmacologic inhibitors of the signaling pathways effectively inhibited invasion and metastasis.194,241,242 Here, ECIS serves as a valuable approach for biomarkers or therapeutic target exploration compared to other chemotaxis assays (e.g., Boyden chamber assays), with real-time monitoring, high sensitivity, and similarity with in vivo events. Ordinary ECIS measurements provide time course information, but lack spatial distribution. To gain more intuitive insight into the dynamic process of cell migration, the electrical impedance tomography (EIT) technique has been combined L

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Cell−cell tight junctions seal adjacent epithelial or endothelial cells grouped together to form a cohesive layer to maintain tissue integrity. Many biochemical molecules, such as histamine,204 tumor necrosis factor-α,207 bacteria,196 and toxin impact epithelial and endothelial barriers by perturbing tight junctions and the cytoskeleton. This leads to loss of barrier integrity, which facilitates the passage of damaging agents and sensitizing allergens into their underlying tissue. Barrier permeability is not only modulated by tight junction proteins but also affected by changes in cell−substrate adhesion patterns,197 as well as integrin signal transductions,198,205,206 which may result in dramatic changes in the endothelial cell barrier dysfunction during lung disease, brain stroke, and heart infarction. Metastatic cell line invasion can be another cause of barrier dysfunction. Typical barrier function assessments in metastatic cells have been established by ECIS (Figure 12). The resisitance of the human umbilical vein endothelial cell (HUVEC) dramatically decreased within 1 h after the highly metastatic sublines were added.239 Figure 11. Wound-healing sensing with ECIS. (a) Photomicrographs of Madin−Darby canine kidney (MDCK) cells before and after wounding: ECIS electrode with cells before wounding (i), immediately after wound formation (ii), and 15 h after wounding (iii). (b) Typical electrical wound-healing data. BS-C-1 cells were cultured in three individual wells before the measurement was begun. Approximately 10 min into the run, two wells were applied with an elevated field pulse, one for 10 s and the other for 30 s. The resistive portion of the impedance at 4 kHz is displayed for the two treatment groups and one control group. Reprinted with permission from ref 187. Copyright 2004 National Academy of Sciences.

with ECIS. EIT images the conductivity distribution inside the samples by measuring between multiple electrodes.243−245 Hou et al. imaged the 2D conductivity distribution of the carbon nanotube thin film by patterning 32 electrodes in a rectangular configuration.246 Chai et al. designed 4 × 4 metal oxide semiconductor microelectrodes to image the culture environment of cells.247,248 Linderholm et al. used an IDE array to monitor the migration and stratification of cells in scratching and wound-healing processes.236,249,250 Sun et al. fabricated a circular distribution ECIS chip on which cells can be cultured at any location for imaging.251 They provided a noninvasive technology for patterning the electrical properties of the cell population, even for a single cell.

Figure 12. Barrier function assessment by ECIS: (a) schematic overview of the metastatic subline causing damage to the barrier integrity of the HUVEC layer, (b) HUVEC layer in the presence of the highly metastatic AT3 subline and weakly metastatic G subline. The integrity of the endothelial cell layer is damaged by the subline activities, resulting in a resisitance decrease. Reprinted with permission from ref 239. Copyright 2002 Informa plc.

4.3. Barrier Function Assessment and Drug Discovery

The blood−brain barrier is a membranous structure between the brain capillaries and blood. It can prevent certain substances (mostly harmful) from traveling from the blood into the brain, maintaining the basic stability of the internal environment of the brain. Studies of the blood−brain barrier using ECIS focus on initial invasion of the barrier caused by an extracellular foreign substance via certain transcellular mechanisms,200 cell− cell contact change, cell−substrate attachment, cytoskeletal rearrangements which cause blood−brain barrier alterations,202 and extracellular matrixes which cause barrier function disturbance.203 Moreover, combined with other in vitro assays (e.g., immunohistochemistry or molecular biology), ECIS can be used to study time-dependent pathogenic mechanisms, which are induced by barrier systemic infections. In the above studies, researchers studied barrier functions with cells that adhere to a flat substrate, which is different from the in vivo conditions. The development of microfluidic

Barrier function assessment is an important ECIS experiment. Epithelial tissue contains epithelial cells, which cover all the surfaces of animals. A cell layer forms the endothelium in the blood vessels, which is an active interface between tissue and blood. Both epithelial and endothelial cell layers are permeable barriers with a high selectivity between two compartments. Tiruppathi et al. quantified endothelial cell morphological changes by measuring the impedance of a monolayer of bovine pulmonary microvessel endothelial cells on microelectrodes.199 They employed ECIS to monitor the dynamics of endothelial cells in real time, which may be used to study the signal transduction events by the increasing endothelial permeability. Wegener et al. developed special impedance measuring chambers that could measure the electrical resistances of transepithelial and transendothelial cells at different locations using microelectrodes.252 M

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

technology253 and tissue engineering254 brings opportunities to address this problem. Sun’s group reported that the disruption of the epithelial barrier function by Triton X-100 and ethylene glycol tetraacetic acid (EGTA) was successfully measured by a bioimpedance chip with an air−liquid interface.195 Primary airway epithelial cells grown and fully differentiated on such a chip can perform as a better representation of the human airway barrier than the submerged culture system. Cells generate a series of changes in the presence of cytotoxins. First, the cells lose their adhesion and become round, then membrane protrusions or blebbing occurs, and subsequently apoptotic states form and are ultimately engulfed via phagocytosis. These apoptotic responses usually change cellular adhesion and the cellular morphology. They will ultimately induce a cell−substrate impedance decline, which depends on the cell type, compound concentration, and exposure duration. Keese et al. employed an ECIS system for toxicological testing using epithelial cells and fibroblasts.255 Some reference chemicals recommended for evaluating cytotoxicity (e.g., trichlorfon, antipyrine, and dimethylformamide), as well as some familiar toxins, such as cycloheximide, sodium arsenite, enzalkonium chloride, mercury chloride, Triton X-100, and cadmium chloride, have been tested to determine the effects which were presented with time− response curves and dose−response curves by a real-time impedance-based cell electronic sensing system with different types of cells.209,256,257 In the case of drug discovery, cell-based assays using ECIS for investigation of pharmacodynamics can provide a more physiological approach when compared to biochemical assays and are therefore highly suitable for cell-based high-throughput drug screening. ACEA Biosciences (United States), in partnership with Roche (Switzerland), has developed a series of products for in vitro cell monitoring during drug challenge that utilizes cell−substrate impedance. The study trend for cellular processes is cellular parameters in parallel measurement in the screening application.258−262 Bionas (Germany) and Molecular Devices (United States) have developed products based on multiparametric microsensor chips. Measuring impedance from a large cell population attached to one electrode results in complicated and doubtful data. Because the average signals may result from many cellular activities, such as cell attachment, proliferation, growth, and motility, it is difficult to precisely know the membrane property changes of individual cells with the current ECIS systems. In the single-cell sensing system, cellular response to drugs alone can be accurately recorded without having cell−cell interaction.214,263 Recently, vertically aligned carbon-nanotubecovered electrodes have been demonstrated to be a more rapid, sensitive, and specific device for the detection of cancer cells.264 Thus, the response of a single cell may reveal more information on pharmacological or toxic doses.

semiconductor field effect transistor (OSFET).266 The OSFET consists of a metal oxide semiconductor (MOS) transistor configuration, where the gate metal has been omitted and can be used directly in the extracellular fluid as an active probe. Changing the detection object from tissues to cells is another landmark work done by Fromherz et al. in 1991.13 The FET sensor has become a kind of cell-based biosensor. More parameters are available for detection, including not only ionic effluxes around a neuron, but also oxygen consumption and other metabolic parameters. The past decade has witnessed significant improvement in semiconductor technology, which has led to both low cost and robust performances of FET-based devices. Currently, cell-based FET sensors have been applied in the field of medicine and biotechnology.6,267−269 In this portion of the review we focus on cell microenvironment monitoring and cell electrophysiological detection. The FET uses an electric field to control the shape and the conductivity of the channel in a semiconductor structure.270 First, we briefly introduce the principle of field effect in the fundamental structure named the insulated-gate field effect transistor (IGFET). Due to a metallic plate deposited on the surface of the insulator, the insulated-gate transistor is frequently designated as an MOSFET. The FET controls the electron flow from the source to the drain by changing the size and shape of the “conductive channel”, which is generated and influenced by the voltage applied across the gate and source. Take an n-channel enhancement-mode IGFET which has a sort of N−P−N sandwich with two junctions among the source, channel, and drain as an example (Figure 13a).

5. FIELD EFFECT TRANSISTOR SENSORS

Normally, current cannot flow through the channel between the source and the drain due to no channel existing with the two opposite PN junctions. When a positive gate-to-source voltage (UGS) is applied, the voltage causes the holes to be repelled from the interface, and a depletion region is formed which contains immobile negatively charged acceptor ions and also attracts free electrons within the body toward the gate.271 Further increasing the gate voltage will cause the electrons to move to the interface, which is called an inversion layer. The threshold voltage, commonly abbreviated as UGS(th), is defined

Figure 13. (a) Basic structure of an n-channel enhancement-mode MOSFET with a positive gate voltage. (b) I−V characteristics and output plot of an n-channel enhancement-mode MOSFET.

5.1. Principles of FETs

Among various types of biochemical sensors, the integration of biological elements together with an FET arouses fascinating attention. An FET without gate metallization was originally used for measuring ion concentration and was dubbed an ionsensitive FET (ISFET) in 1970.265 Then the extracellular potential from the muscle of a locust leg was recorded in 1976 by Bergveld et al., who defined this modified FET as an oxide N

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

to detecting electrophysiological signals, the ISFET can measure more cell physiology information. The ISFET mainly focuses on the extracellular acidification rate because protons act as an important regulator in the microenvironment of living cells and changes among various biochemical reactions. Usually, cellular pH is measured by microelectrodes or magnetic resonance spectroscopy. However, microelectrodes can cause damage when the probes enter the cells, and magnetic resonance spectroscopy is unsuitable for continuous, longterm monitoring of the extracellular microenvironment. At the same time, other important ions in cellular metabolism, such as potassium, sodium, and calcium, can also be monitored by ionsensitive membrane-modified ISFET sensor arrays. These membranes are usually treated with polymer membranes or carbon tetrafluoride plasmas.276−278 Living cells are always performing complex biochemical and biophysical processes to maintain their physiological functions. In this case, only monitoring the extracellular ionic concentration is not enough. To collect more information from living cells, cell monitoring chips were designed to integrate with other sensors, such as temperature sensors, conductivity sensors, and oxygen sensors. This technology enables the integration of sensors located on the same silicon wafer at the bottom of a cell culturing chamber. With a microfluidic system for exchange of cell culture media, cells can endure long-term studies, such as responses to drug stimulation and recovery from drug exposure. Baumann et al. developed a cell monitoring system which allowed for parallel and noninvasive measurement of multiple parameters.279 Another multisensor platform for cellular culture monitoring has been developed which includes potassium- and proton-sensitive ISFETs, amperometric microelectrodes for dissolved O2, and pseudoreference microelectrodes for cell metabolism.280 One commercialized system, the Bionas 2500 (Germany) analyzing system, can record multiparameter cellular physiological information on disposable chips. These parameters include cell impedance, acidification rate, and oxygen consumption.281 In recent years, FETs configured from nanowires (NWs) and carbon nanotubes (NTs) have been attractive components in the FET family. Silicon nanowires (SiNWs) have extremely high surface-to-volume ratios and 1-D characteristics with a sensitive carrier at their surface. While working as the gate in the FET, the essential one-dimensional characteristics of the nanowires offer a unique merit over macroplanar FETs and overcome the sensitivity limitations of the latter.282,283 Through the surface modification of a SiOx-covered SiNW-based FET or an oxide-free SiNW-based FET, these sensor devices can be used for ultrasensitive, highly selective, real-time, and label-free detection of various biological and chemical species, including proteins, nucleic acids, small molecules, and viruses.284,285 For example, the SiNW sensor based on a poly(vinyl chloride) (PVC) membrane which contains valinomycin (VAL) as the sensitive part could monitor the K+ release of live chromaffin cells.286 From Figure 14a, we can see a fabricated structure of SiNW-FETs, which were fabricated from 4 in. n-type silicon-on-insulator (SOI) wafers with a 50 nm thick device layer and 400 nm thick buried oxide layer. The chip was 14 mm × 14 mm in size, containing eight SiNWFETs. A metal film (Ni/Au, 15 nm/65 nm) was coated onto the backside of the chip as a back gate electrode. A valinomycin PVC membrane was coated onto the device surface and airdried. Then 106 chromaffin cells were cultured inside the chamber (Figure 14b). The cells were washed and cultivated in

as the gate voltage by which an inversion layer forms at the interface between the substrate and insulating layer. For either an enhancement- or a depletion-mode FET, the changing UGS will change the resistance of the channel, and the drain current is proportional to the drain-to-source voltage (UDS). The performance of the FET is similar to that of a variable resistor operating in a linear mode of the ohmic mode.272,273 As UDS continues to increase, a significant asymmetrical change is created in the channel where a gradient of voltage potential is formed from the source to the drain. The channel becomes “pinched off” at the drain end. If UDS increases further, the “pinch-off” point moves away from the drain to the source in the channel. Then the FET enters the saturation mode, in which the drain current (ID) is constant (Figure 13b). Basically the relationship between drain current (ID) and gate-to-source voltage (UGS) is expressed as follows:274 ⎛ u ⎞2 GS ⎜ iD = IDO⎜ − 1⎟⎟ ⎝ UGS(th) ⎠

(10)

Generally, the ion-selective field-effect transistor (ISFET) has the same basic structural key element as BioFETs, even if ionselective properties of the ISFET are not necessary for recording electrical signals. The charge (or potential) effect is used to recognize phenomena in FETs, so ISFET is taken as an example for demonstrating the sensing principle. The drain current (ID) of the ISFET working in the linear region can be expressed as ID = μCeff

V 2⎤ W⎡ ⎢(VGS − Vth)VDS − DS ⎥ L⎣ 2 ⎦

(11)

where ID is the drain current, Ceff is the capacitance of the gate insulator per unit area, VGS is the gate-to-source voltage, VDS is the drain-to-source voltage, Vth is the threshold voltage, μ is the electron mobility rate, W is the channel width, and L is the channel length. The threshold voltage (Vth) of eq 10 is described in the following equation: Vth = VFB + 2ϕB −

QB Ceff

(12)

ϕB represents the separation of the intrinsic and the actual Fermi level, which is determined by doping, and QB/Ceff in eq 12 is the potential drop over the oxide caused by the applied gate voltage, resulting in charge QB in the substrate. The flat voltage (VFB) is the potential where no excess charge is present in the substrate without the electric field. The ISFET-related expression for VFB becomes VFB = Vref − ψ0 − χ sol −

Φs q

(13)

where Vref is the contribution of the reference electrode, Φs is the semiconductor work function, and χsol is the surface dipole potential of the solvent. ψ0 is the surface potential at the oxide solution interface, which determines the pH-dependent nature of an ISFET, and chemical domain information is converted into the electrical domain information by this parameter.275 5.2. Cell Microenvironment Monitoring

Cell metabolism parameters include changes of the extracellular pH value, ion concentrations, oxygen consumption, carbon dioxide production, and other metabolic products. In addition O

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Fromherz et al. mounted a leech neuron on an open gate oxide of an FET in electrolytes and recorded the action potential from the cell.25,288 They built a simple model describing the junction between the cell membrane and the gate oxide. After that, recordings from individual invertebrate neurons, vertebrate neurons, and cardiomyocyte monolayers with stimulation from an impaled micropipet electrode were reported (Figure 15a).6 The transient extracellular voltage waveforms between the cell and gate oxide detected by calibrated transistors vary in shape, as shown in Figure 15b. The upper graphs show the intracellular voltage (VM(t)) measured by pipet electrode, and the lower graphs show the corresponding extracellular voltage (VJ(t)) recorded by the transistor. By microscopy, type A and type B couplings were observed with the cell body on a transistor, while type C couplings were observed with an axon stump on a transistor. There are three types of VJ(t) recordings with two positions of the cells on the FET. The different signal shapes originate from different cell−chip couplings. This implies an inhomogeneous distribution of ionic conductance in the cell membrane. For a better understanding of this issue, ionic and capacitive currents through the attached cell membrane are taken into consideration. The capacitive current is driven by currents through the upper free membrane, while the ionic currents are driven by currents that flow through the attached membrane with a sharp inward current of the Na+ channel and a delayed outward current of the K+ channel. Different attachments of cells to the transistor result in different ratios of contribution by these two types of currents.289 The coupling of neurons and FET sensors was first described by the two-dimensional cable theory.13,290 Later, voltage-gated ion channels in the contact regions were considered to build the point contact model.291 The equivalent circuit is shown in Figure 15c. The extracellular voltage VJ is determined by Kirchhoff’s law according to eq 14. The current through the silicon dioxide and along the junction is equal to the current passing through the attached membrane, where CJM is the capacitance of the membrane in the junction, giJM is the specific conductance of ion species i, and gf is the average conductance per unit area of the adhesion site along the cleft.

Figure 14. (a) SiNW device arrays based on VAL−PVC with PDMS coupled to detect the alkali metals. (b) Cells washed and cultivated in NMG buffer release K+ under the nicotine stimulation. (c) Sensor response to K+ released by the chromaffin cells that were cultivated in various doses of nicotine stimulation. Reprinted with permission from ref 286. Copyright 2012 Elsevier.

C JG

dVJ dt

d(VM − VJ)

+ gJVJ = C JM +

dt



i g JM (VM

i

i ) − VJ − V J0

(14)

Proper approximations and simplifications are made on the basis of simulated results. The capacitive current through the gate oxide and potential at the contact point can be neglected. The current density (iM) can be replaced by the conductance multiplied by the membrane voltage. The scaling factor (Xi) represents the differences between the conductance of the average cellular membrane and the attached cellular membrane.292 Therefore, eq 14 can be written as

an N-methyl-D-glucamine (NMG) buffer as a chemical stimulation. Nicotine was then dissolved in NMG buffer and pumped into the measuring chamber. Figure 14c illustrates sensor response to K+ released by the chromaffin cells that were cultivated in the presence of various nicotine dosages. 5.3. Electrophysiological Detection

FET sensors were the first to record electrical signals of muscle tissue and neuronal slices in the middle of the 1970s into the early 1980s.287 This led to a new method of analysis in the field of bioelectronics. Thermally grown silicon dioxide suppresses the transfer of electrons and the concomitant electrochemical processes, which guarantees silicon to be a suitable electronically conductive substrate. Preliminary experiments from these pioneers have demonstrated the possibility of communication between microionics and microelectronics.

VJ =

1 ⎛ dVM ⎜c M + gJ ⎜⎝ dt



∑ X iiMi ⎟⎟ i



(15)

Some studies suggest that the ion concentrations that change in the cleft should not be neglected. This slow course change raises the surface potential of the gate oxide, which causes differences between the n- and p-channel FET signals.293,294 P

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Figure 15. Transistor recording of the excitatory response of neurons. (a) The cell body of a leech neuron impaled with a micropipet posits the FET sensor. (b) A-, B-, and C-type couplings. The intracellular voltage (VM(t)) is shown in the upper row, and the extracellular voltage (VJ(t)) on the gate oxide is shown in the lower row. (c) Equivalent circuit of the neuron−silicon junction. Reprinted with permission from ref 6. Copyright 2003 Elsevier.

Besides the effort on robust sensor designs,294 electronic neuronal circuitry has also been developed for a bidirectional electrical communication between silicon chips and neuronal cells. After development of capacitive stimulation by an insulated spot of silicon,295 an integrated chip with an insulated-gate FET and an insulated spot of silicon is used to stimulate the neuron and record the extracellular potentials synchronously.296 The two-way interface of the neuroelectronic hybrids is shown in Figure 16.77 Figure 16a shows the ideal cell−chip communication example by depositing a single neuron on both the stimulation spot and a transistor. Voltage pulses applied to the stimulation spot elicit action potentials in the neuron. At the same time, voltage changes in the neuron membrane modulate the source-to-drain current through the gate oxide of the FET.

The cell−chip communication inspires two types of neuroelectronic hybrid design. The first design is the “neuron− silicon−silicon−neuron” pathway (Figure 16b), where the activity of one neuron stimulates another neuron through electronic processing on the chip.297 Two neurons are attached to the chip with no direct contact. The spontaneous activity of a neuron can be recorded by the transistor. The device elicits a digital signal to create a voltage pulse burst which is added to the capacitor to stimulate the second neuron. The other neuroelectronic hybrid design is the “silicon−neuron−neuron− silicon” pathway (Figure 16c), where a capacitive stimulates neuron transmits action potential to another neuron through a chemical synapse, and the postsynaptic response of another neuron can be detected with a transistor.298 Two neurons contact each other via chemical synapses. The presynaptic neuron is stimulated by a capacitor, and then the signal Q

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

the neurons growing on the arrays can be found sitting on a two-contact site. The experiments were performed by the modified chip under complex pattern stimulation, which induces spatially distributed neuronal memory. Random outgrowth of neurons wastes the stimulating and recording electrodes if the somas do not sit on the desired position. Topographically controlled neuronal networks can be used to overcome the above problem. Chemical patterning approaches are applied on chips using microcontact printing or inkjet printing reviewed by Didier Falconnet et al.302 However, the strong contracting forces of growing neurites induce an unstable chemical guidance. The geometrically defined wells and grooves for neuronal growth strengthen this force. Using an organic polymer, wells and connecting grooves are fabricated on neuro-electronic hybrids with 16 two-way interfaces.303 Snail neurons placed in the wells outgrow their electrical synapses guided by the grooves. In this way, it is easy to construct specially defined architectures on the flat sensor surface, so that the supervised neuronal networks can be developed on the FET chip. Organotypic brain slices are particularly promising; they are a few cell layers thick which can conserve their natural neuronal connections.304 However, understanding brain tissue activities requires high spatiotemporal resolution on recording tools over a large area of tissue. The semiconductor technology allows for high-density stimulation and recording site design of FET chips. These chips are fabricated to detect the excitable neurons in a functional area for spatiotemporal correlation analysis. A sheet conductor model quantitatively illustrated the recording across a layered brain slice and the stimulation by circular electrodes.305 This model provided basic theories for neurophysical experiments using brain slices. Recently, high-density FET arrays with 16 384 contacts yielded time-resolved images of electrical field potentials in brain slices with a spatial resolution of 7.8 μm and a time resolution of 0.5 ms on an area of 1 mm2.306 When CA3 regions of the hippocampal slice are stimulated by capacitors, excitatory synapses in the CA1 regions are activated via connected axons. The transistor recording provides time-resolved images of presynaptic and postsynaptic activities with continuous space. Compared to dissociated cells, there are still some new problems existing for interfacing individual neuronal cells in a tissue culture. First, it is not certain whether individual stimulation capacitors and transistors can contact individual neurons with enough distance. Second, as many neuronal cell bodies are embedded in glial cells and a network of dendrites and axons, it is hard to obtain precise information about a particular cell and a particular stimulation or recording site correlation. Furthermore, the loss of the signal amplitude from the center of a slice to the bottom of the slice needs to be carefully investigated. For example, the vertical profile of evoked field potential has been investigated by micropipet electrodes, and the loss of signal amplitude is about 60% from the center to the bottom.307 Therefore, the reconstruction of neuronal activities requires current-source density analysis.

Figure 16. Neuro-electronic hybrids. (a) Capacitive stimulation and transistor recording of individual neurons by the FET. (b) Transistor recording, signal processing by microelectronics on the chip, and capacitive stimulation of another neuron. (c) Capacitive stimulation, signal transmission through the neuronal network, and transistor recording at another neuron. Reprinted with permission from ref 77. Copyright 2001 National Academy of Sciences.

activates the chemical synapse. The postsynaptic excitation is recorded by a transistor. The two types of neuron hybrids inspired the idea of neuronal prosthesis to establish selfmemorable chips. However, these systems still have many drawbacks that need to be addressed, such as crosstalk between the capacitor and transistor and the strength of the synaptic coupling on the chip. For the purpose of adding more recording sites to FET sensors for random neuronal network detection, the FET array was fabricated at the beginning of the 1990s. However, increasing the density of the FET array was limited by the integration scale of available silicon technology. Efforts have now been made to improve the efficient utility of the current recording site. When dissociated cells are displaced on the recording site, the cells’ outgrowth on the sensor will be well controlled to form defined neuronal networks. Synapses can alter the efficacy of synaptic transmissions between neurons. These changes of synaptic strength can occur rapidly through several mechanisms (e.g., long-term potentiation and long-term depression).299 Investigation of the function of networks in neuronal connectivity as a whole requires highlevel resolution. The density of FET sensor arrays ranges from 16 to 64 sites, up to the scale of 128 × 128 sites.11,300,301 A typical culture of cardiomyocytes shows spontaneous excitation and the electrical excitation spreading process on the FET. Pharmacological bioassay experiments can be implemented in this platform on cardiomyocytes. Neuronal cells in a closely placed capacitor and recording transistor arrays are used so that

6. LIGHT ADDRESSABLE POTENTIOMETRIC SENSORS 6.1. Principles of the LAPS

The LAPS was first proposed by Hafeman in 1988 as a semiconductor device for biochemical systems.26,308 In the following 20 years, LAPS was widely studied in biological analysis and commonly used as an ISFET.309 Different LAPS R

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

systems are designed to obtain better sensitivity, stability, and compatibility for bioassays. The cytosensor microphysiometer for extracellular acidification and the threshold unit for immunoassays have been commercialized by Molecular Devices Corp. (Sunnyvale, CA).308 The potentiometric alternating biosensor system has also been commercialized by Technobiochip (Marciana, Livorno, Italy).310 These systems have been used widely in various areas, including cellular physiology, toxicology, and pharmacology. An LAPS is typically structured as a conventional electrolyte/ insulator/semiconductor (EIS) sensor (Figure 17a),309 and the LAPS surface is chemically deposited with silicon oxynitride as an insulating layer, which can also be silicon oxide and silicon nitride. The insulating layer separates the silicon chip from the solution. The sensor surface forms silamine and silanol groups under hydration. The solution pH can affect the surface potential by changing the proportion of silamine and silanol groups. In the high-pH condition, the LAPS surface has a strong negative charge. In the low-pH condition, the LAPS surface has a weak charge. An electric field is generated in the LAPS when a dc voltage is applied on the sensor chip. A photocurrent is produced by a pulsing infrared light from a light-emitting diode (LED) at the backside of the chip. The photocurrent amplitude depends on the sum of the surface potential and applied potential, and the former depends on the solution pH of the insulating layer311 (Figure 17b). In the conventional detection mode, a dc voltage is used to keep the LAPS photocurrent constant, and the voltage changes correspond to the pH changes (60 mV/pH unit). Therefore, biological events could induce corresponding fluctuations in the photocurrent output by modifying the electrochemical parameters of the interface. In the biological application, the LAPS commonly monitors the cellular metabolism, especially energy metabolism. The heterotrophic cells obtain various nutrients, produce the energy, and release the wastes for growth. The carbon source (e.g., sugars, amino acids, and fatty acid) mainly produces metabolic energy. The schematic overview of the cellular metabolic process is presented in Figure 17c.312 In the natural aerobic condition, cells convert glucose into CO2. In the anaerobic condition, cells convert glucose into lactic acid. The acidic products (e.g., H+, CO2, and lactic acid) generated by cellular energy metabolism induce a pH drop in the extracellular microenvironment, which can be detected by a microphysiometer.309 On the basis of this principle, a large amout of experiments have been carried out to monitor the extracellular acidification rate (ECAR), which is the most significant parameter indicating the state of cellular metabolism. Many biochemical factors, such as receptor−ligand reactions, will affect the ECAR of the cells, and the effects of these factors can be measured via a microphysiometer. Additionally, a microphysiometer can analyze and evaluate pharmaceutical effects on ECAR, such as antitumor drugs for chemotherapy.313,314 In constructing cell-based biosensors for extracellular microenvironment monitoring, the LAPS is preferred over the ISFET because of its compatibility with MEMS fabrication, less critical and easier encapsulation, and incorporation into microvolume measuring chambers for bioassays. Also, when cells are cultured on a silicon surface, extracellular action potential coupled to the sensor surface can also generate corresponding spikes in the photocurrent output.30 Electrophysiological study with the

Figure 17. Illustration of an LAPS as a microphysiometer: (a, b) principle and structure of the LAPS, (c) proton release in cellular metabolism. Panel a reprinted with permission from ref 309. Copyright 2000 Elsevier. Panel b reprinted with permission from ref 311. Copyright 2001 Elsevier. Panel c reprinted with permission from ref 312. Copyright 2002 Elsevier.

LAPS can overcome the geometric restrictions of MEAs and FETs. LAPS modeling was studied in some of the existing research. An equivalent circuit model of the interface of the cell− semiconductor was established to describe the LAPS detection principle of the extracellular potential signals. LAPS modeling mainly focused on the EIS system. Circuit modeling theories were described by M. Sartore and M. Grattarola.315,316 Figure S

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Figure 18. (a) Qualitative description of the charge distribution within the EIS system. (b) EIS equivalent circuit. (c) Simulation LAPS characteristic curve.

induced by the extracellular potential. Therefore, the LAPS has a wide application in the study of metabolism and electrophysiological detection.

18a qualitatively displays the charge distribution within the EIS system. Charge is mainly distributed in four regions: the semiconductor charge (Qs), the interfacial charge (Q0), the electrolyte space−charge region charge (Qd), and the IHP counterion charge (Qβ). Qβ is negligible. Since the system is electrically neutral, the sum of the charge is zero: Qs + Q0 + Qd = 0

6.2. Microphysiometers Based on the LAPS

The LAPS is sensitive to pH variation in the electrolyte solution. By encapsulating the LAPS and cells in a microvolume chamber, the microphysiometer is used to indicate the ECAR (Figure 19).318 In the microphysiometer, cells are cultured in

(16) 317

According to the site-binding theory, the interfacial charge on a silicon oxide (SiO2) and silicon nitride (Si3N4) insulator is ⎛ ⎞ [H+]s 2 − K+K − ⎟Nsil Q 0 = e⎜ + 2 + ⎝ [H ]s + K+[H ]s + K+K − ⎠ ⎛ ⎞ [H+]s + e⎜ + ⎟Nnit ⎝ [H ]s + KN + ⎠

(17)

Here, Nsil and Nnit are the densities of silanol (SiOH) and silamine (SiNH2), respectively. K+, K−, and KN+ are the dissociation constants of the surface chemical reactions. The electrolyte space−charge region charge (Qd) can be estimated by the Gouy−Chapman−Stern theory: ⎡ e(V − Vd) ⎤ Q d = (8εeε0KTC0)1/2 sinh⎢ ⎥ ⎣ 2KT ⎦

(18)

The semiconductor charge (Qs) can be estimated from Piosson’s equation: Q s = ± 2kTεε0 [p0 (e−eVs/ kT + eVs/kT − 1) + n0(eeVs/ kT − eVs/KT − 1)]1/2 (19)

An LAPS equivalent circuit is shown in Figure 18b. V is the applied bias voltage, Cd is the Gouy−Chapman layer capacitance, Ch is the Helmoltz layer capacitance, Ci is the insulator capacitance, Cs is the depletion region capacitance, and Rs models the recombination process. From Figure 18b, two equations can be obtained: Vd − V0 = Q d /C h

(20)

V0 − Vs = Q 0/C i

(21)

Figure 19. Microphysiometer for extracellular acidification based on the LAPS: (a) schematic of the cytosensor microphysiometer, (b) ECAR measured by the flow-on/flow-off cycle of the microphysiometer. Reprinted with permission from ref 318. Copyright 1999 John Wiley & Sons, Inc.

In the equations, Ci and Ch are determined from the experiment; thus, Qd, Q0, Qs, Vd, Vs, and V0 can be calculated from these six equations. The relationship between the bias voltage and the photocurrent can be described, and the simulation result is shown in Figure 18c. On the basis of the theories and simulation results, it is obvious that the LAPS is a surface-potential-sensitive biosensor used to measure the pH change, which is induced by extracellular acidification, or surface potential change, which is

the microvolume chamber, which is formed by a multiporous polycarbonate membrane and an O-ring spacer. The microvolume chamber is tightly fixed on the LAPS chip, and H+ can diffuse adequately in the small volume, so the ECAR can be directly measured by the LAPS. A modulated infrared LED (light-emitting diode) illuminates the backside of the LAPS, and thus, the LAPS will generate a photocurrent with the same frequency. All of the experimental data are transmitted into a T

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Figure 20. Extracellular potential monitored with the LAPS: (a) schematic of the cell-based biosensor and LAPS detection system, (b) cell−sensor interface with the EIS structure, (c) simplified model of the cell−LAPS biosensor. Reprinted with permission from ref 348. Copyright 2006 Elsevier.

peptide receptors which activate the signal transduction mechanisms have been investigated.337 Signal pathways of gp130 family cytokines (interleukin-6 (IL-6), oncostatin M) and IL-1 were probed by the ECAR of HepG2 cells.338 Extracellular acidification measurement is a kind of in vitro bioassay, and H+ is used as an indicator of overall cellular metabolism. Initial studies of irritancy testing using human keratinocytes grown on coverslips tested half-log serial dilutions of eight irritants previously characterized as having in vivo ocular irritancy ranging from mild to severe.339 Ocular and skin irritancy testing materials represent the range of effects commonly encountered in cleaning products.340,341 Other cell sources, including hepatic cells342 and cancer cell lines,338,343 were treated with various toxins and drugs to evaluate their effects. The microphysiometer is a useful platform to monitor metabolic disturbance by measuring the recovery of the extracellular microenvironment. Further improvements of the microphysiometer system were proposed for monitoring several parameters in the extracellular microenvironment related to ATP usage and metabolic processes. A multianalyte microphysiometer has been proposed to simultaneously measure the acidification rate, glucose consumption, oxygen uptake, and lactate production in the extracellular environment by modifying the cytosensor microphysiometer system to study the cellular metabolism more comprehensively.319,321 Another attempt to use a multiparameter microphysiometer was made using poly(vinyl chloride)-based ion-sensitive membranes on the LAPS surface for extracellular monitoring of ions such as K+, Na+, Ca2+, and Cl−.344,345 These multianalyte and multiparameter detections can provide more biological information about cellular responses to achieve better evaluation of drug effects. Especially, if modified with a porous structure, the LAPS could display highly enhanced performance attributed to morphological changes in the sensing units.346

computer by the detection and analysis instrument. The culture medium is injected into the flow chamber by the peristaltic pump, the degasser, and finally the injection valve. The ECAR can be continually measured by refreshing the culture medium cycle. The basic function of the microphysiometer is to study the metabolic activities responsive to agents. The metabolic activity responses of different cells to many different agents have been successfully monitored.319−321 For example, for fibroblast cells, 2-deoxy-D-glucose reduces the acidification rate from −90 to −40 μV/s, which is directly induced by the decrease of lactic acid. Also, fluoride acts as a metabolic inhibitor which binds to the enolase and affects pyruvate formation, which subsequently decreases the ECAR significantly. Changes in the ECAR typically ranged from 110% to 200% of the basal ECAR in the receptor activations.27 Pharmacology assays with microphysiometers can measure the functional consequence of extracellular acidification by drugs binding to molecules. Additionally, microphysiometers can measure the ECAR induced by many receptors (e.g., cAMP and mitogenesis). Cells maintain homeostasis by releasing H+, so the overall cellular metabolism can be reflected by the ECAR. Applications have been proposed in studies including pharmacology-related signaling mechanisms,322 functional characterizations of ligand−receptor binding,323,324 and identification of specific and functional orphan receptors.325,326 Many activations of protein tyrosine kinases have been investigated using the effects of pharmacological factors and receptors, such as epidermal growth factor (EGF) receptor tyrosine kinase,322,323 epidermal growth factor,327 insulin-like growth factor-1 (IGF-1),328 ligand-gated ion channels,329 leptin receptor,330 T-cell receptor,331 G-protein-coupled receptors of angiotensin II receptor, type 1 (AT1 receptor),332,333 and bradykinin receptor B2.334 When the signaling pathways are coupled, the receptoractivation-induced cellular responses can be studied with the ECAR. For example, dimethyl amiloride (DMA) was used to study Na+/H+ exchanger activities in cells growing at different cell densities.335 Exposing cells at low density (104 cells/mL) to dust induced an initial release of acid not involving the exchanger, followed by a sustained DMA-sensitive Na+/H+ exchanger activation. In cells near high density (106 cells/mL), the Na+/H+ exchanger was not activated during exposure, resulting in a modest increase in the ECAR. Exposing cells at high density resulted in a biphasic ECAR pattern, and an initial increase in proton release was followed by an inhibition of the ECAR below baseline. Polar amino acids are important in the proton flux activity of Na+/H+ exchangers.336 A large number of G-protein- and non-G-protein-coupled hormone and neuro-

6.3. Cell−Semiconductor Hybrid for Electrophysiological Detection

In the above section, the microphysiometer is introduced to monitor metabolism in a cell population. However, it is far from the ability of cell−semiconductor hybrid systems to detect ion channels or potentials of cells. The LAPS surface is totally flat without any special structures, so the cells can easily culture and freely attach to the LAPS. Recently, the LAPS was used to study excitable cells by monitoring extracellular potentials.46,347 In principle, the modulated infrared laser is focused by the microscope to illuminate the position of the target cell. The inflow or outflow of cellular ionic currents (e.g., Na+, K+, and Ca2+) will induce fluctuation of the LAPS response signals. U

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

The LAPS consists of an EIS structure (Figure 20a,b).348 The insulator (SiO2 layer) is suitable for the attachment of cells without the addition of special structures. Therefore, the ionic current induced by the extracellular potential will couple with the LAPS surface, and the corresponding photocurrent change can be detected by the LAPS. This simple model of the cell− sensor interface is shown in Figure 20c. The LAPS has the light-addressable function, and the laser can scan at arbitrary positions on the LAPS surface. On the basis of the working principle, the extracellular potential of the cell can be detected when the focused laser illuminates the sensitive region of a cell.348 Thus, a membrane potential change can cause corresponding turbulence in the LAPS output. In recent research, cell−LAPS hybrid biosensors have been proposed as superior instruments for extracellular recording of potential signals.30,46,349,350 Most recordings were performed with excitable cells, such as ardiomyocytes and neurons. As discussed in this review, the FET array and MEA are at present the two main types of cell−semiconductor hybrid biosensor systems for extracellular detection. However, the MEA and FET are typically restricted by their surface structure because only a few electrodes are fabricated. Thus, a small effective detection region can be used to monitor the cellular state. Moreover, microelectronic fabrication restricts the distance of the electrodes on the MEA and FET,351 which ranges from 50 to 200 μm.347 Consequently, the spatial resolution of the sensor is restricted significantly, and it will hamper the development of the biosensors. However, the LAPS is different from the MEA and FET due to its special working mode. The surface potential of the LAPS can be monitored and detected by a scanning focused laser beam. The illuminated site will generate a photocurrent signal which reflects the surface potential. Taking this characteristic into consideration, the spatial resolution will be much improved. Therefore, an arbitrary site on the LAPS can sense the cellular state, so all of the cells cultured on the LAPS can be studied. The majority of related studies are focused on self-exciting cells such as neurons and cardiomycytes.352 Stem cells and tissues are also used as new sources for LAPS biosensors.311,353,354 Liu et al. have cultured the mouse embryoid bodies on the LAPS and induced them to differentiate into cardiomyocytes and neurons in vitro (Figure 21a).311 Typical signals of cardiomyocytes recorded are shown in Figure 21b. The spikes can be sorted and analyzed by several parameters shown in Figure 21c. Differences in these parameters can reflect cellular responses to drugs in a way similar to that of MEA- and FET-based biosensors. Electrophysiological responses are monitored using amiodarone, noradrenaline, sparfloxacin, and levofloxacin as cardiotoxic drugs. An obvious disadvantage of the LAPS is its single-channel output, which makes it difficult to record extracellular potentials of multisite cells simultaneously. The LAPS with submicrometer electrodes have been reported with a light-addressable function.119 This novel design concept will even benefit MEA systems to solve the problem of lower spatial resolution. Moreover, the functions and performance of the LAPS can be enhanced by modifying specific bioactive units and other materials, such as biological enzymes and ion-selective membranes, to detect many physiological parameters of cells simultaneously. Alongside parallel detection, another promising field is using the LAPS as a virtual electrode to stimulate excitable cells. The extracellular potential is determined by the membrane potential

Figure 21. Embryonic stem cells differentiated into cardiomyocytes and extracellular potentials detected by the LAPS: (a) mouse embryoid bodies differentiated into cardiomyocytes on the LAPS, (b) typical signals recorded, (c) automatic spike sorting. Reprinted with permission from ref 311. Copyright 2011 Elsevier.

and ionic current.355 One group has developed the LAPS biosensor technology by recording the extracellular stimulations of discrete neurons.356 The complicated spatiotemporal patterns of cell networks can be investigated by scanning the desired sites on the LAPS. Therefore, information at any site will be monitored continually. Compared to the site-restricted MEA, the LAPS can serve as a useful biosensor to carry out the spatiotemporal analysis. Additionally, visualized research on cells and tissues may be improved by the technology. The bioelectronics and bioelectrochemical imaging will facilitate the study of cell networks with the development of spatiotemporal pattern studies. The cell−silicon junction plays a significant role in the combination of neuronal dynamics and electronics.6 The integration of excitable cells and sensors will be used to exploit a new approach for both eliciting and recording neuronal networks on the same chip simultaneously. The neuronal networks can be formed on the sensors, and the signaling pathway of the neuronal networks can also be modeled and computed in further investigations.

7. PATCH CLAMP CHIPS 7.1. Theories and Fabrication of Patch Clamp Chips

The patch clamp has become a powerful laboratory tool to study single and multiple ion channels in cells. A fire-polished V

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

or the macroscopic current from the whole cellular membrane, both of which play crucial roles in cellular signal transduction, impulse conduction, and cellular microenvironment balance. On the other hand, ion channels are important drug targets because many drugs are agonists or antagonists of ion channels. However, this technique suffers from some intrinsic shortcomings, including being of low throughput and requiring highprecision micromanipulation, vibration damping, and ample experience and skills on behalf of the manipulator.359 The conventional microelectrode was initially improved by Flyion, who presented a flip-tip technology and developed a novel automatic patch clamp instrument, known as the Flyscreen 8500 system (Flyion, Germany).360 The cell− electrode interface was inverted, which made it possible to place the cells inside the microelectrode. Using this approach, cells can reach the pipet tip and form a seal from the inside. This single microelectrode-based system, however, cannot perform high-throughput measurements. Another improvement of a patch clamp chip began in the late 1990s. The glass microelectrode was replaced by a planar structure in the patch clamp chip as indicated in Figure 22b. In the patch clamp chip, cells were guided onto the aperture by negative pressure or a static electricity field. The high-resistance seal with the cell membrane was obtained by the application of another negative pressure. The patch clamp chip possesses many advantages over the conventional patch clamp. First, it is more convenient, and a rapid operation can be carried out on the patch clamp chip. This can also be easily combined with optical measurements and micromanipulation, such as atomic force microscopy (AFM) and fluorescence microscopy. Second, a patch clamp chip configured with microelectrodes could record multiple cells simultaneously, making it suitable for high-throughput measurements. Finally, due to its planar structure, its capacitance (about 1 pF) should be less than that of a glass pipet (several picofarads), and the resistance is reduced, resulting in a lower distribution of resistor−capacitor (RC) noise and a higher resolution. With the development of microfabrication technology, various patch clamp chips have been fabricated using different materials. The main challenge for chip fabrication is achieving a smooth aperture on the chip with a diameter of 1 μm or smaller. At present, patch clamp chips have been fabricated with silicon,361,362 glass,363−365 PDMS,366,367 polyimide,368 and amorphous Teflon.369 Each material has its own advantages and disadvantages as the basis for patch clamp chips, which are summarized in Table 4. The initial attempt for the fabrication of a patch clamp chip utilized silicon due to the availability of standard silicon fabrication techniques and its easy integration with microfluidic technology.361,362 Most silicon-based patch clamp chips are used for the research of ion channels constructed in liposomes or artificial lipid bilayers.370,371 On the other hand, Fertig et al. developed glass-based patch clamp chips using ion-track etching techniques to fabricate a smooth aperture diameter within 1 μm, which significantly reduces the capacitance of the device to below 1 pF and also greatly enhances the performance of the chips.363 It has proven to be useful for recording whole-cell and single-channel currents.364,365 PDMS is a common material for microfluidic devices. The most significant advantage of PDMS is easy fabrication and low cost, and micromolding can be employed to fabricate micrometer level apertures. In addition, a high-resistance seal with the cell membrane can be easily obtained due to the surface

glass pipet is used with an open tip diameter of approximately 1 μm to isolate a small membrane patch for recording. The micropipet tip produces a smooth surface that can facilitate formation of a gigaohm resistance seal with the cell membrane, which is the most important aspect of the patch clamp technique. The currents of one or a few ion channels can be measured by the cell membrane patch on the basis of the highresistance seal with the cell membrane and electronic isolation. It has been demonstrated that the membrane conductance of a neuron is due to the membrane permeability for Na+ and K+.357,358 The individual ionic conductances of sodium (gNa) and potassium (gK) (mΩ−1 cm−2) can be described by the equations gNa = INa /(Em − E Na)

(22)

gK = IK /(Em − E K )

(23) 2

where INa and IK represent the ionic currents (μA/cm ), ENa and EK represent the equilibrium potentials (mV), and Em represents the membrane potential (mV) expressed as the inside potential minus the outside potential. A leak conductance (gAn) that is probably mainly due to the anion conductance of chloride ions can be described by the equation gAn = IAn /(Em − EAn)

(24)

where IAn represents the anion current and EAn represents the anion equilibrium potential. The total membrane current (Im) is the sum of the ionic currents and the current flowing into the membrane capacity (Cm): Im = Cm

dEm + INa + IK + IAn dt

(25)

where t is time (ms). Figure 22a shows the schematics of the conventional patch clamp recording configuration. The patch clamp allows the study of single-channel behaviors on a small membrane patch

Figure 22. Configuration of the patch clamps: (a) conventional patch clamp, (b) planar patch clamp chip. W

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Table 4. Comparison of Patch Clamp Chips Based on Different Materials material silicon361,362

aperture diameter

seal resistance

glass363−365

300 nm to 12 μm ∼1 μm

200 kΩ to 47 MΩ 4 MΩ

PDMS366,367

2−20 μm

4−10 MΩ

polyimide368

2−4 μm

1.3−26 MΩ

amorphous Teflon369

2−800 μm

49−660 GΩ

advantages

shortcomings

simple fabrication and easy integration with microfluidic technology excellent dielectric qualities, good mechanical and naturally hydrophilic properties, transparent excellent insulation, low dielectric loss, transparent, cheap good biocompatibility, excellent insulation, low dielectric loss, transparent stable, low noise level, high effective bandwidth

high density of free charge, intensive photoelectric effect, hard to form a high gigaohm seal resistance lack of a standard microfabrication technique

hydrophobic properties after oxygen plasma modification. It can also be easily integrated with optical measurements due to its transparency. A simple and practical PDMS-based patch clamp chip consisting of a micrometer-sized hole was successfully developed by air-molding.367 An array based on PDMS was developed to achieve multisite recording simultaneously, in both cell-attached366 and whole-cell configurations.372 It has been used in the research of cell network cultures and can form high-resistance seals between the cells and microapertures to overcome the spatial problems of the conventional patch clamp. This makes it possible and promising to study neuron genesis, synaptic transmission, and cell differentiation. However, the PDMS-based patch clamp chip currently has the drawbacks associated with the fabrication of an aperture smaller than 2 μm and mass production. In contrast, a polyimide-based patch clamp chip with an aperture 2 μm in diameter was developed by the utilization of a focused ion beam technique.368 It is only suitable for loose patch recording due to the difficulties encountered in forming gigaohm seal resistance. Last but not least, amorphous Teflon-based patch clamp chips were developed for recording ion channels located in planar lipid bilayers with low noise.369

difficulties in smaller aperture fabrication and mass production difficulties in forming high gigaohm seal resistance difficulties in smaller aperture fabrication

Figure 23. Patch clamp instruments for ion channel research: (a) picture of an NPC1 Port-a-Patch, a semiautomated system for wholecell voltage-clamp recordings, (b) Patchliner platform for highthroughput and automated robotic measurement.

then mounted onto the instrument. This system can form a high-resistance seal larger than 1 GΩ under software control, which can ensure its low noise recording and allow its application in single-channel recordings. This system was used to study the BK channels expressed on CHO cells under various holding potentials, such as 60, 40, and 0 mV in a cellattached mode.376 The single-channel activities can be identified by the method of holding the membrane patch on a constant voltage due to open and close probabilities. This system is also suitable for the research of single-channel activities formed by the peptailbol alamethicin constructed in bilayers from giant unilamellar vesicles.377 In 2006, Nanion Technologies launched a fully automated patch clamp workstation, the NPC-16 Patchliner, which can perform gigaohm seal recordings with high throughput. Figure 23 shows a picture of the Patchliner platform for highthroughput and automated robotic measurement. Borosilicate glass chips are the recording substrates. This system can perform multimode recordings such as whole-cell, cell-attached, and perforated-patch recordings. In addition, internal and external solutions can be exchanged directly, which allows both internal and external modulation of the ion channels.

7.2. Ion Channel Research

Ion channels play crucial roles in the modulation and regulation of important physiological functions in organs and cells and are macromolecular protein complexes located in the cell membrane.373 With the continuous progress in medical molecular genetics, significant achievements have been made with regard to ion channel structure−function relationships. This has resulted in active ion channel target search for drug discovery around the world. With the development and availability of many commercial products, patch clamp chips have proven to be an attractive and promising tool for ion channel research. Many companies, including Nanion Technologies GMBH (Germany), Molecular Devices Corp. (United States), Sophion Bioscience A/S (Denmark), and Axon Instruments Inc. (United States), have already developed their own commercial planar patch clamp systems, which feature high-throughput capabilities, high seal resistance, automation, multimode clamps and recordings, and drug perfusion. The NPC1 Port-a-Patch, a miniature product launched by Nanion Technologies, was used to study the rNav1.2a expressed on HEK cells by whole-cell voltage-clamp recordings.374,375 It has been used in fast-gated channels by the blocking of sodium channels using tetrodotoxin (TTX). Figure 23a shows a picture of an NPC1 Port-a-Patch (Germany). The system is semiautomated because the cell suspension is added onto the chip manually, which is glued onto a small cap. It is

7.3. High-Throughput Drug Screening

Patch clamp chips offer a significant advantage that makes them suitable for high-throughput screening of drugs which target ion channels.378,379 Many ion channels are important drug targets, comprising up to 5% of the total known drug targets. Targeting these ion channels can make available numerous therapeutic agents, and these drugs play a crucial role in various disorder treatments, such as arrhythmias, hypertension, pain, seizures, diabetes, and stroke. With the continually growing understanding of the human genome and proteome, more and more drug targets will be discovered and utilized. It is estimated that X

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

8. AFFINITY CELL-BASED BIOSENSORS

ion channels would make up to 15% of around 5000 potential drug-effect targets. However, measurement of ion channel properties is complicated by both their complexity and variety, which is made clear by genomics and proteomics.380 The expression of diverse ion channels consequently leads to an inconsistency in the data recorded. The parallel, automated, and high-throughput patch clamp chip system can greatly improve the work efficiency during drug screening. An open-access patch clamp chip is integrated with raised and cell-trapping microfluidic channels, which can improve the whole-cell measurements and drug profiling compared to those of the traditional glass micropipet openings.381 It is an easy platform which is convenient for fluidic exchange due to open access to the main fluidic chamber. The surface of this open-access patch clamp site is fabricated by macroscale hole punching, which is smooth and facilitates the formation of a high-resistance seal. This platform can perform extensive experiments in whole-cell patch clamp mode on CHO cells with the expression of specific ion channels, which includes various characteristics of ion channel I−V, drug dose-dependent responses, and dynamic drug activities. This platform can perform high-throughput screening of ion channel electrophysiology due to its significant advantages over the traditional patch clamp, including easier handling and a shorter data turnaround time. To perform simultaneous multicell recording, Molecular Devices launched a novel product, the Ionworks Quattro system, by adapting the population patch clamp technology, which is suitable for fast analysis of the structure and function of massive mutant ion channels.382 In this system, multiple cells can be voltage clamped at the same time by multiple microapertures in multiwells, where approximately 7000− 10000 cells are added. This system was used to record the noninactivated channels hKv1.5 expressed in CHO cells in a whole-cell recording mode.380 Reliable and reproducible data can be obtained. Another system used in high-throughput ion channel screening is the 16-channel planar patch clamp system, known as PatchXpress (Axon Instruments, Molecular Devices., United States). When the effects of small molecules on hERG channels are investigated by screening ion channels, this system can obtain 2000 reliable data points per day with a 4-fold improvement in throughput.383 Qpatch 16 (Sophion Bioscience, Denmark) is also a 16-channel automated planar patch clamp screening system. Its throughput can reach 250−1200 data points in the case of whole-cell recordings on hERG channels expressed in CHO cells. When it was used to screen the hERG and KCNQ4 channels (voltage-gated potassium channels) expressed in CHO and HEK cells, respectively, the success rate of the gigaohm seal was 40−90%, and about 67% of the cells could sustain whole-cell recordings for more than 20 min.384 Similarly, compared with conventional patch clamps, the IonWorks HT planar patch clamp system (Molecular Devices., United States) can also achieve reliable and comparable data when used to screen hERG channels expressed in CHO cells using quinidine.378 It is evident that patch clamp chip technology has become an effective approach for the basic scientific research of ion channels, as well as drug discovery. In the future, some improvements, such as higher throughput, higher success rate, and primary cell recordings, are necessary for patch clamp chips to satisfy the present applications in basic research and drug discovery, as well as to explore new fields and applications.

8.1. Quartz Crystal Microbalance

Resonant devices are sensitive to mass and viscosity changes, which makes them a promising and powerful tool for the study of biological molecular interactions, especially for affinity cellbased biosensors. The typical resonant device is the QCM, which can detect tiny mass changes by monitoring the oscillation frequency shifts of a crystal resulting from pressure changes on the crystal surface caused by mass loading. An increase of mass loading on the sensitive surface causes a decrease in oscillation frequency. Consequently, the masssensing sensor can respond to the mass changes of coated materials on the sensitive area. The oscillation frequency decrease is proportional to the mass change.385 The QCM was first discovered by the Curie brothers.386 Sauerbrey then revealed that the relationship between the mass changes and the oscillation frequency changes in the quartz crystal is linear.385 In the 1980s, Nomura and Okuhara designed oscillator circuits that could utilize this sensor not only in a vacuum or air detection environment, but also in a liquid environment.387 It helped to make the QCM an attractive and promising analytical tool for biosensors. In the case of cellbased biosensors, living cells behave as elastic masses on the QCM surface, which implies the QCM can provide information about mass changes, reactions, and conditions at the liquid− solid interface. The resonant resistance (R) of the quartz crystal combined with the oscillation frequency ( f) is used to characterize the viscoelastic properties of inelastic masses deposited on the crystal surface, whose values are affected by the medium density and viscosity. The f decrease is related to both behaviors of mass binding and viscoelastic energy dissipation, while R increases. When solution with different densities (ρi) and viscosities (ηi) and a crystal surface form a tight contact without relative sliding, the oscillation frequency shift can be calculated by the Kanazawa equation:388 Δf = −f 3/4 (πρi μi )−1/2 ηρ i i

(26)

where Δf is the oscillation frequency shift, f is the frequency, and μq is the quartz shear modulus. This equation predicts the shift in resonance frequency upon immersion of the dry crystal into solutions, which has been applied to study the cell layer on the crystal. The resonant resistance change is calculated by the Muramatsu equation:389 ΔR = (2πfρi ηi)1/2

A k2

(27)

where ΔR is the resonant resistance change, f is the frequency, A is the electrode area, and k is the electromechanical coupling factor. When the QCM operates in a solution, total frequency changes may result from a bound mass, as well as contributions from the solution. The viscoelastic and energy-dissipating properties of the surface with masses attached, such as living cells, can be evaluated relative to the density−viscosity changes of a liquid solution. According to these equations, the QCM technique has a sensitive quantitative capability suitable for studying responses of adherent cells to chemical, biological, or physical changes in the environment. In addition, the QCM has a wide detection range from a monolayer of small molecules to greater masses, even complex arrays of cells. Some properties of cultured cells (e.g., cell attachment, proliferation, and cell− substrate interaction) are monitored with the QCM under different conditions (Figure 24). The QCM can also measure Y

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

approach to characterize the electrical and viscoelastic properties of epithelial and endothelial cell monolayers as controlled barriers in vivo.397 A QCM with dissipation (QCM-D) (Q-Sense AB, Sweden) is a novel and promising technique for the real-time monitoring of cell attachment and spreading in vitro. QCM-D can perform in situ monitoring in real time with much higher accuracy. It has been successfully used in the characterization of fibroblast attachment and spreading on biocompatible surfaces precoated with protein.398 The QCM-D technique can also be applied in the characterization of dense-core vesicle release and retrieval, which is a complex and dynamic process. The mass change and rigidity change resulting from the process of exocytosis and subsequent retrieval of dense-core vesicles can be simultaneously monitored. In the case of two cell lines, NG 108-15 and PC 12, it was demonstrated that differences in cellular release and retrieval on different morphologies, sizes, and numbers of dense-core vesicles can be effectively monitored by culturing on piezoelectric quartz crystals.399 The QCM has become a very valuable tool in cell-based assays and can provide detailed physiological and functional information on cells in a real-time and label-free way, such as cell pharmaceutical information, cell attachment, and cell proliferation. In addition, cell exocytosis and vesicle retrieval can also be monitored by the QCM in a noninvasive way in situ. All these advantages have made the QCM a very attractive and promising cell-based analytical technique which could be widely applied in the field of biomedicine in the near future.

exocytosis, vesicle retrieval of cell populations, and cell− microparticle interactions in real time.

Figure 24. Schematic diagram showing the mechanisms of cell-based assays using QCM devices.

The specific binding of ligands and receptors will lead to cell responses via the intracellular signal transduction pathway. The cell responses can be detected by monitoring the shifts in QCM oscillation frequency and energy dissipation on the basis of the mass changes and viscoelastic properties of the QCM surface. QCM biosensors integrated with living cells are used to study drug discovery and analysis and can detect the kinetics of whole-cell biological responses. Living endothelial cells adhered to the QCM surface were used for detecting the nocodazole actions, which is a microtubule-binding drug that can alter the cytoskeletal properties of living cells.390 By simultaneous measurements of the shift values in the QCM steady-state frequency and motional resistance, the cell-based QCM biosensor can effectively monitor the process of cell microtubule disruption induced by nocodazole. The cell-based QCM biosensor has also been used in the prediction of the drug resistance of tumor cells. A cell-based QCM biosensor was developed for the real-time screening of cell lines and their sensitivity to anticancer drugs and can predict tumor responses to drugs before therapy and before drug resistance or hypersensitivity develops.391 For example, the results demonstrated that QCM analysis accurately predicted which cell line is more resistant to taxanes and provided very useful information for the clinical treatment of related tumors. The underlying mechanisms of QCM signaling for a particular cell type were explored by using ac impedance analysis in a frequency range.392 This approach revealed that many subcellular compartments contribute to the resonators’ fundamental frequency, including the extracellular matrix, liquid compartment, and medium, as well as the actin cytoskeleton. Recently, adhesion and growth of rat epithelial cells and lung melanoma cells on a QCM was monitored in real time by the continuous measurement of changes in the oscillation frequency and resistance values of the piezoelectric resonators.393 The behavior of human patellar tendon fibroblasts was also monitored by a QCM coated with indium−tin oxide (ITO).394 On the other hand, a QCM with a polyporphyrinfilm-modified gold electrode surface was employed to monitor the growth of MCF-7 cells in real time, as well as to investigate the chemical cytotoxicities.395 The responses of human oral epithelial cells to microspheres were monitored by a cell-based QCM biosensor in real time.396 In addition, a QCM combined with impedance analysis can be used as a novel and promising

8.2. Surface Plasmon Resonance

Traditional chemical analytical methods, such as electrochemical measurements, nuclear magnetic resonance (NMR), and optical spectroscopy, can resolve the small signal changes above nonspecific binding and background noise in the samples. In recent years, studies have trended to using alternative approaches to sense changes of physicochemical properties at the solution−transducer interface, such as the dielectric constant and refractive index. As an optical technique, SPR can measure changes in the refractive index just on the sensor surface using the evanescent wave phenomenon. Now, biosensors based on SPR have been widely used as exceedingly powerful and quantitative probes to study biomolecular interactions. SPR-based biosensor systems (i.e., BIAcore, The Netherlands; AutoLAB, Sweden) are developed and commercialized as a significant improvement in the field of molecular sensor research. As a real-time and label-free sensing system, SPR has made it possible to analyze the binding amount of ligand to a receptor with a high precision rate regarding association and dissociation of the molecules.400 SPR-based biosensors have been widely used in molecular interaction research including a variety of analyte molecular weights and biofilm binding events. Recently, cell-based biosensors using SPR have also been used for detecting and analyzing molecules which induce reactions from cells and also the interactions between living cells. SPR is an optical detection method. A polarized light passes through and illuminates a thin metal layer and is then reflected from the metal layer and verified by the arrangement of the Kretschmann configuration.401 On the metal layer surface, incident photons are absorbed by free electrons and converted into surface plasmon waves under certain conditions, such as a particular wavelength, polarization, and incidence angle. In the correct conditions, a dip in reflected light is monitored by the Z

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

insufficient to explain the entire change in the resonance angle in response to the cellular activation.403,405 Thus, the SPR system can be more and more widely used for detection in cellbased biosensors, with the mechanisms illuminated step by step. Recently, Bittova et al. used SPR to study biomolecules binding to the lipid bilayer plasma membrane containing phosphatidylserine and diacylglycerol spreading on a sensor chip.406 They found that SPR can reflect the number of proteins and other molecules which are associated with and dissociated from the lipid membrane. The changes in the resonance angles also reflected the conformational changes of individual proteins in the evanescence field.407 Therefore, they posited that conformational changes of proteins may also contribute to the resonance angle changes in or near the lipid membrane near the sensor surface. At the same time, when stimulated with extracellular ligands to membrane receptors, the cells initiated different intracellular events, such as translocation of the proteins into the cytosol or plasma membrane and changes in the membrane potentials, temperature, and pH. All of these local microenvironmental changes may be factors affecting angle changes. Some researchers also have tried to fix living cells, especially the nonadherent cells, onto the gold surface with their functions preserved for SPR analysis.405 The studies suggested that an SPR sensor can successfully detect bioreactions of basophil cells and B cells. These immune cells are spheroid cells with diameters of 8−10 μm, just like adherent cells. Evanescence waves may penetrate hundreds of nanometers above the gold layer of the chip. The thickness of the membrane is approximately 10 nm. Thus, the SPR-detected events should be in or near the cellular membrane near the sensor surface. By bioengineering mutations, receptors with knock-up and/or knock-down functions can clarify the relationship between intracellular signaling and SPR signals in further studies.403 Thus, changes in the resonance angle in the SPR system can reflect cell events beyond changes in the limited size of cell-adhering areas detected by other sensors. Different kinds of cell-based biosensors, such as impedance biosensors, have been designed to detect cell attachment and migration on sensor surfaces. However, these techniques for cell adherence and morphology sensing do not reflect intracellular activities of signal transductions well. On the basis of the sensitive dependence of SPR on surface charge density, a very exciting study of a plasmonic-based electrochemical impedance microscope was presented recently, which allowed for simultaneous SPR and optical imaging.263 It can optically resolve local impedance in a submicrometer grade without using microelectrodes. The SPR can not only study individual cells, but also resolve subcellular structures with high temporal and spatial resolution. For example, it can be used to monitor the dynamic cellular processes, such as apoptosis and electroporation, with millisecond temporal resolution. Thus, with sensing mechanisms illuminated step by step, SPR cellbased biosensors provide a useful and promising approach to detect intracellular events of living cells in a real-time, label-free way.

SPR system. Perturbations on the metal layer, such as interactions of immobilized probe molecules with captured target molecules, cause refraction condition changes in the surface plasmon waves, which are in turn seen as changes in the local index of refraction, which can be measured. The SPR measurement is often expressed in millidegree light incident angles. The angle shift is proportional to the increase in perturbations of the solution−metal interface and the mass increase at the gold surface. Usually, if the molecule is larger than 1000 Da, it is big enough to change the refractive index with well-binding sites. Molecular adsorption, such as that of polymers, proteins, or DNA, can be detected by SPR reflectivity measurements with the angle changes, and the gold surface is well suited for cell culture, with good electrochemical behavior and excellent biocompatibilities. In cell-based biosensors, cells are often directly cultured on the sensor surface and used as sensitive elements for target molecules. Various groups have demonstrated that spheroid cells with diameters of about 10 μm, as well as those with adherent cells, can be cultured on the gold film in SPR.402,403 Hide et al. first reported SPR sensing signals generated after immunoglobulin E (IgE)-sensitized cells were stimulated by antigens.402 Figure 25 shows the principle of the SPR-based

Figure 25. Principle of the SPR-based biosensor for living cell reactions. The cell cultured on the gold layer is coated on a glass plate. Different receptors (FceRI, IgE, and A3) are illustrated on the cell surface. The reaction medium containing antigens flows in the direction indicated by the curve arrow. The laser beam illuminates the cell and is reflected to the monitor, which detects the shift in the resonance angle. The reflected laser beam is affected by the amount of substances in the evanescent field from the surface within several hundred nanometers. Reprinted with permission from ref 402. Copyright 2002 Elsevier.

biosensor for this kind of cell with different receptors reacting to antigens. Then Lee et al. also designed an olfactory-cellbased biosensor with SPR for characterizing molecular interactions of odorant molecules and olfactory receptors in the cell membrane.404 However, the thickness of the evanescent field on the SPR surface is around several hundred nanometers, while each of the cells is in the range of several micrometers. This suggests that cells cultured on the SPR surface are often out of the range of the detectable evanescent field. Therefore, those studies considered that the SPR signals stemmed from the intracellular signal transduction rather than simple binding kinetics between ligands and cellular receptors. Some recent experimental results suggested that changes in the cell adhesion area of the SPR surface and cell membrane structure are also

9. FUTURE TRENDS OF CELL-BASED BIOSENSORS 9.1. Cell-Based Biosensors Using Nanotechnology

In the past decade, nanotechnologies have greatly improved the status of science and technology. Nanotechnology includes the AA

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

measurement even over and within subcellular components of the cardiac muscle and nerve cells for the investigation of intercellular bioelectricity with regard to health and disease.417 They represent very successful applications of nanotechnology for cell-based biosensor research.

study, creation, manipulation, and use of materials, devices, and systems which focus on dimensions of less than 100 nm. The common nanomaterials include nanowires, nanotubes, nanoparticles, nanoporous membranes, etc. Now that the performance of sensors, especially their sensitivities, can be improved greatly by nanomaterials, nanotechnology has played an important role in the biosensors. Many unique physical and chemical features are displayed by nanomaterials. For example, the nanosurface effect, minisize effect, quantum size effect, and even macroquantum tunnel effect are all very important characteristics of nanomaterials. Nanobiosensors have a rapid and simple analysis function with their submicrometer dimensions. Even portable equipment with nanobiosensors for multiple component analysis is also becoming available. Some researchers have reviewed the status of nanotechnology-based biosensors.4,408 However, the design and fabrication of the nanotechnology-based biosensors also hold promise for a variety of biomedical uses. The combination of biology, nanotechnology, and photonics helps to manipulate and detect molecules and atoms by novel nanosensors and nanoprobes for evolutional diagnostics and also for therapeutic medical devices at the cellular level. Nanosensors have great potential in monitoring biological processes at the single-cell level and can even sense individual biochemical species of a living cell in specific locations, in both in vivo and in vitro research. In biomedical implants, materials of nanostructure could mimic the nanometer topography in native tissues, which can greatly benefit the tissue integration by improving the biocompatible responses. At the same time, micropatterned cell cultures could provide ideal in vitro models to study fundamental interactions between cells and cells with substrates.409 Nanofabrication techniques can control the arrangement of molecules well with site-specific surface biochemical functionalization. For example, researchers have combined site-selective biochemistry with molecular-scale nanolithography to create biomimetic arrays of individual integrin binding sites.410 The spatial arrangement of cellular adhesion can be controlled by this nanolithography, and the nanopatterned surfaces with different surface morphologies can greatly affect the cell behavior. In addition, with spatial confinement and an adhesion site, the attachment, selection, growth, morphology, and differentiation of cells could be controlled geometrically with an adjustable elasticity on the surface.411,412 Understanding various aspects of nanotopography is extremely important for better design of cell-sensing devices. Integrated design of the nanointerface could greatly aid the probing of living cells. Extremely small nanosensors could be fabricated and used to detect intracellular or intercellular physiological parameters in cell microenvironments. Patolsky et al. have used silicon nanowire FET arrays to record neuronal signals.413 They analyzed individual dendrites and axons of cultured neuron hybrids with the FET arrays. The biosensors with nanoscale junctions are used for detection, stimulation, and inhibition of neuronal signals. Recently, other groups have also demonstrated that nanoscale electrodes of nanotubes and nanopillars can record action potentials of cardiomyocytes and neurons with unprecedented spatial resolution.414−416 The amplitude of the recorded potentials was about 70−100 mV with greater axial resistances, and the signal-to-noise ratio was more than 1000. These nanoscale approaches have great promise for electrical

9.2. Cell-Based Biosensors with Microfluidic Technology

To be highly integrated and miniaturized is one of the developmental trends of cellular analysis systems. The lab-on-achip, which is often indicated by μTAS as well, is a type of microfluidic device which integrates one or more laboratory experimental processes just on a single microchip. It assembles the microfluidics to mechanically control the activity of biological samples with control devices such as pumps, valves, and sensors. Advanced cell culture microsystems and models could be provided by microfluidic designs. Many very important biochemical and biophysical characteristics of cell cultures in the microenvironment will be illuminated by microfluidics. Usually, with the size of only several millimeters to even several square centimeters, microfluidics also can be used for cell manipulation in small fluid channels, even down to subpicoliter volumes. If integrated with analytical units, such as sensing elements, the microfluidic design could perform highthroughput detection for cell biology applications. Compared with the traditional cellular analysis system, the volume of the sample is smaller, which causes lower consumption and lower contamination. It could also reduce the response time and improve the efficiency for analysis. It could precisely control the cellular activities and easily complete the high-throughput analysis for massive parallelization due to its compactness. With the mass production of microfabrication, the cost of the chips is also very low. However, the technology for microfluidics is still under development. Even though it has precise geometry, it may not reach the precision of the traditional analysis system. In smallscale assays, this condition could be more easily affected by the complex physical and chemical factors. The cell-based microfluidics are usually designed to analyze the cellular activity and structure from the cellular level to the molecular level. This covers all of the steps from cell culture and growth to surface treatment, selection, cellular lyses, separation, and componential analysis.418,419 The microfluidic channels are fabricated using substrates including silicon, glass, and polymers such as PDMS. The motion of cells is driven by the continuous liquid flow through the microfluidic channel. The flow is actuated by the external or integrated pumps or other electrokinetic mechanisms. By integrating the processing of cellular manipulation, chemical analysis, and lysis on a chip, microfluidic devices can analyze single cells. Various types of intracellular components could be separated and analyzed in the microchannels quantitatively.420 At the same time, more wide ranges of cellular applications have been reported in microfluidics, including cell immobilization, cell sorting, cell culture, cell electroporation, patch clamp recording, and cell network signaling.419 Microfluidic devices hold promise for both cell-based biosensors and basic cell biology research. The characteristics of the microfluidic cell culture are critical for understanding cellular features scaled down from macroscale to microscale.421 Many reports have described novel discoveries in cell biology of cell microenvironments provided by microfluidics.422 Complete microenvironments have been established sufficiently for cell AB

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

cultures by microfluidic systems. The microsystem can provide a useful in vitro model to predict specific responses of cell populations. Microfluidic devices used as cell culture platforms could greatly enhance the research of cell biology and could illuminate the mechanisms of many diseases. However, finding available detection or analysis devices is still one of the most important issues for microfluidic systems. The use of microsensors as the detection unit for microfluidic devices will greatly improve the development of microfluidic chips for biological and clinical medicine.

provide pathogen information to the pathogenic pathway. As the pathway is a hybrid immune system that disseminates to different relative cells, it will benefit from the illumination of the important time and space issues of the integrated immune signals.426,427 Cell function analysis and cellular communications have been developed for the intricacies of antigen-specific immune responses beyond traditional evaluations of gene regulation, cell surface molecule expression, and signal molecule secretion. On the basis of microtechnology, novel biosensors have been designed for the analysis of immune cell function as well as for monitoring secreted inflammatory markers.427 Analysis of antigen-specific immune cell responses and screening of cellular communication via secreted signals provide sensing platforms for the immunoinflammatory responses. The technologies hold promise in applications for antibody screening, inflammation detection, and clinical infectious disease diagnosis.

9.3. Immune-Cell-Based Biosensors

As one of the most complex biological systems, the immune system can identify and kill pathogens to protect against disease. The immune system has great sensitivity to a wide variety of pathogenic agents, such as viruses, bacteria, parasitic worms, and even cancer cells. Immune cells play a very important role in immune mechanisms. Besides synthesizing and secreting immune molecules which act as regulators, messengers, and helpers in the immune process of defending against pathogens, immune cells can recognize and respond to antigens with very high sensitivity and specificity. These features make immune cells ideal candidates to serve as sensitive elements in cell-based biosensors for antigen or pathogen detection. A number of immune cells have been investigated to explore the feasibility of being used as sensitive elements in cell-based biosensors such as mast cells, B cells, and dendritic cells.423−425 Mast cells are an important type of immune cell that can recognize many specific antigens. The sensitization of mast cells to specific antigens can begin with IgE antibodies binding to Fc receptors on the cell membrane surface. The binding interactions between antigen and antibody on the cell surface can lead to a significant cell response, which is often characterized by cell morphology changes, metabolism rate increases, and biochemical mediator release. Dendritic cells are also immune cells strategically located at the environmental interface, serving as an immunological sentinel and tissueresident antigen-presenting cell. As antigen-presenting cells, the important function of the dendritic cells is to process antigens and present them to other immune cell surfaces.424 At the same time, the dendritic-cell-based biosensor system is a novel platform for time- and cost-efficient discovery of dendritic cell stimulatory drugs. Despite the growing interest in academia and industry in the clinical use of immunostimulants as therapeutics for cancer and infectious diseases, and also as vaccine adjuvants, no systematic effort has been reported in the various relevant literature on the discovery of newer immunostimulants.425 The detected concentration was 100- to 300-fold lower than the ordinary detection limits by phenotypic or functional assays, with the advantages of responding to the varieties of biologic and pharmacologic agents. Cells of the immune system are all sensitive to stimuli. They can transfer immune information rapidly via different kinds of contacts with other cells after triggering by the stimulus. The cells can even remember their recent immune experiences and transmit the signals to successive cells with direct contacts or secreted cyokines. Thus, the cell contacts or cytokine-releasing events of the immune system are just like the information transducer of the biosensor system. Biosensors used for immune cells can recognize bioinformation and measure them the same way as cells do by contacting their signaling pathways in the immune system. The sensing approach can

9.4. Stem-Cell-Based Biosensors

In cell-based biosensor applications, tumor cell lines and primary cultured cells are mainly selected as the cell sources. Cell lines divide actively in vitro, while primary cells are often extracted directly from animals. Cell lines derived from tumor cells are used to facilitate preparation and culture. However, cells may suffer the loss of the desired function of in vivo cells, while primary cells have numerous available cell types and functions similar to those of in vivo cells. Nevertheless, the processes of cell separation and harvesting are often inefficient and even have ethical issues. Therefore, the reliance on colony cell lines or primarily animal-derived cells has become a continuous barrier to increase the applications of cell-based biosensors.22 Stem cells are biological cells found in many multicellular organisms and have the ability to proliferate by mitosis and the potential to differentiate into at least one specialized cell type. Therefore, stem cells can be artificially grown and transformed into cell cultures with characteristics consistent with their primary counterpart cells of different tissues. Compared with tumor-derived cell lines and primary cultured cells, stemderived cells can fill a gap between the behavior of cultured cells and cells in vivo. In recent years, both highly plastic adult stem cells and autologous embryonic stem cell lines have been used as promising candidates for medical therapies.408 The application of mammalian stem cells in biomedicine provides a useful tool for evaluating targets and novel therapeutics in cell-based assays. In our previous studies, neurons and cardiomyocytes from embryonic stem cells were differentiated from mouse, both manifesting their electrophysiological properties while being recorded by an LAPS cell-based biosensor system.352,353 With physiological reactions to different drugs or toxins, the cells served as renewable biosensor elements and could be used in biomedical assays, such as toxin detection and drug screenings. Meanwhile, myocardiocytes and neurons are difficult to regenerate after cell injury. Stem cells are ideal cell types for in vitro research. Sensor techniques are very good sensing and controlling units of complex tissues for medical applications.428 Development of hybrid systems of stem cells and semiconductor devices will greatly benefit tissue-implanting therapies. At the same time, with noninvasive and long-term recording merits, biosensors also have great potential to be used for stem cell differentiation studies. For example, the observation that AC

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

noses and tongues. On the basis of the bioinspired artificial olfaction design, they imagined the sensor would be used for detection of drugs and explosives similarly to dogs’ noses. However, there are many difficulties for approaches using olfactory-cell-based biosensors. For example, neuron cultures are more rigorous in their choice of substrates. Therefore, the measurement of the electrical responses of neuronal cells or even neuronal networks presents a major challenge. Another approach is to knock in or knock out special receptors on cell lines to get measurements of the receptor−ligand interactions incorporated with cell-based biosensors. Cell-based biosensors can collect functional information on bioactive analytes using living cells as sensing elements. Compared to sensitive materials of traditional electronic noses, the bioactive cell units are extracted from primary sensing organs and cultured in vitro. By doing this, the cells have higher sensitivity, better selectivity, and faster response. Additionally, the extracellular activities related to cell functions can be measured directly by microelectronic sensors. In our studies, we have explored the cultured olfactory cells as sensing elements to establish microchips of bioelectronic noses348 and managed to create a bioelectronic nose by combining an electrode array with an intact olfactory epithelium,166,354 which was extracted from the primary olfactory system with structural and functional integrity. Olfactory epithelium and sensor hybrid systems, taking advantage of both elements, can detect realtime extracellular signals under odor stimulations for prolonged periods. The performance of biosensors will be gradually improved by better preservation of the biological tissue function and the development of new sensor technology. As the performance of biosensors improve, we believe that sensor technology will be applied in the field of food safety, environmental monitoring, and health care.

many rely on conventional cell morphology and immunohistochemical markers to assess maturation of the stem cells may be insufficient. Assessed by an MEA, the cells’ electrophysiological activities were only observed 2 weeks later to standardize assessment of morphological differentiation and immunohistochemical markers of myocardiocytes. A cell’s full maturation comes long after the phenotypic and immunohistochemical criteria.429 This suggests that stem-cell-based biosensors may provide a useful platform for drug assessment and screening in biomedical applications. However, new approaches should be considered to widen the field of application in biosensors. First, for correctly evaluating the cell characteristics, a comparison between cell lines and primary cultures is required. Second, metabolic systems of biosensors as an adjunct system to the validated embryonic stem protocol should be developed. Embryonic stem cell in vitro differentiation can be affected greatly in its present form. Multi-cell-type-specific end points of differentiation have to be established in real time to avoid the negative results and to improve the precision of embryonic stem cell differentiation. 9.5. Bioinspired Olfactory- and Taste-Cell-Based Biosensors

Biometric engineers have already made many devices taking advantage of the phenomena learned from nature. The studies of organisms often suggest new ways to design materials and dynamic structures to create imaging and communication techniques. In carrying out such work, the inspiration often comes from organism functions that can be mimicked as key features or properties. Current research in mechatronics and robotics is oriented toward the development of high-performance sensing components able to mimic biological structures. By the communion of biology and micro/nanotechnology, bioinspired systems intend to provide opportunities for integrating living bioactive units and proper biosensors to overcome current technological limitations of actuating and sensing performances. The biological olfactory and gustatory systems can discriminate and recognize a large number of chemicals. Sensation processes are all initiated by the target chemical molecules binding to their corresponding receptors or ion channels. Subsequently, through cellular signaling pathways, chemical signals are translated to electrical signals. The electrical signals are propagated along the neuron axons to the upper organs, where the signals are processed and encoded, and the output signals are then transmitted to the brain. At last the brain can decode the signal and discriminate the corresponding olfaction and taste. Olfactory and gustatory systems play important roles in environment detection, so olfactory and gustatory sensor research has been performed for the purpose of potential commercialization.430 Artificial olfaction and gustation, electronic noses and electronic tongues, are sensor technologies that mimic olfactory and gustatory systems to detect smell and taste with sensitive materials. The performance of these sensors mainly depends on the catalysis or absorbability of the sensitive materials for the target chemical(s). Although great improvements have been made in recent years, electronic noses and tongues still have many limitations, especially in sensitivity and specificity, including biological binding of the odorants and tastants to the receptor cells in the biological systems. Göpel and his colleagues first utilized bioactive units as sensitive elements to develop novel artificial olfaction systems.19 They suggested using olfactory neurons as biomolecular functional units for highly sensitive electronic

9.6. Cell-Based Biosensors for Cellomics

Cellomics is an inventor of high-content screening, which was coined from “the study of cells” or “the knowledge of cellular phenotype and function”. It is a high-throughput analysis approach of the cell phenotype and function in mammalian cells. The technique is mainly based on the development of integrated imaging and sensing with automated cell cultures. With high-throughput analysis of functional interactions of genes and proteins, it aims to provide systematic screening based on genomics and proteomics as high-content screening. Cellomics research focuses on the design of analysis platforms of cells in culture, especially for providing a method to facilitate the detection of cellular responses of drugs or potential therapeutic molecules. Therefore, cellomics could monitor changes of the structures and functions of cells in response to extracellular stimuli, such as drug compounds and toxicities, in multiparametric assays. It is an online approach to analyze a living cell function in a rigorously controlled physiological environment. In the future, biosensors will achieve much quicker development by using biological organs, cells, and molecules and such active materials as sensing elements combined with microfabrication technology to realize the true potential of cellomics sensor chips. Such biosensors have superhigh sensitivity and selectivity to the surrounding environments and measured objects, like humans and animals. Cell-based biosensors using living cells can detect the measured information qualitatively and quantitatively to determine the existence of some substances with their concentration. What is AD

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Biographies

more, cell-based biosensors can detect functional information on an external physical or chemical stimulant. Also, cell-based biosensors used as true biomimetic sensors will also find practical applications in the medical fields of repairing and substituting human sense organs, etc. Integrated cell-based biosensors can provide either high-throughput information or different functional parameters of living cells by the integration of electronic sensors with living cells. A sensor array integrates the same or similar sensors. Multisensors involve different sensor elements with different functions. A multifunction chip can monitor the different parameters in different environments. Multifunctional cellbased biosensors are also an important improvement. Different from the multisensors, they are constructed with identical or similar sensor elements. For example, as one of the typical multifunctional sensors, our group has developed a multifunctional cell-based biosensor with an LAPS array431 and dual functional electrode arrays121 which can detect the metabolism, electrophysiology, and physical state of living cells on several different detection parts under different detection conditions. This integration can greatly simplify the fabrication and reduce the size of biosensors compared to multisensors, which makes miniaturization possible and is a great approach for studying emergent behaviors of integrated cellular systems which emerge due to interactions among different components. A higher level of integration is one of the important features of integrated cell-based biosensors for cellomics. Most integrated cell-based biosensors involve different types of integrated sensors at the same time. High-level integration can provide more comprehensive information to give precise determination of cell function. At the same time, if hybrid sensors are used with microfluidic devices and cocultured cells, integrated cell-based biosensors can be designed in the future to mimic the behavior of a human liver, lung, and heart to test some new suites of in vitro and cell-culture-based methods for drug and toxin screening, minimizing expensive and timeconsuming animal studies. Therefore, more precise and rigorous preselection of identified compounds can be achieved with these integrated cell-based biosensors. Cell-based biosensors will provide high-content screening and high-content analysis to drug discovery and biological research, which comprise automated sensing instrumentation, sensing analysis software, reagents, laboratory automation, and services for life science research with the fastest possible “timeto-decision” in the process of drug discovery, from targeting and validation, through screening. Therefore, cell-based biosensors will hold high promise for systems-level understanding of cell biology. With an enormous amount of functional cellular information, the resulting newly gained knowledge will play an important role in personalized medicine in the future.

Qingjun Liu received his Ph.D. degree in biomedical engineering from Zhejiang University, Hangzhou, China, in 2006. He is currently a professor in the Biosensor National Special Laboratory, Zhejiang University. He received the Nomination Award for an Excellent Ph.D. Dissertation of China in 2008. He wrote the book Cell-Based Biosensors: Principles and Applications published by Artech House Publishers, Norwood, MA, in October 2009 and the book Biomedical Sensors and Measurement published by Springer-Verlag GmbH, Berlin, Heidelberg, in July 2011. He is also a visiting scholar in the Department of Health Technology and Informatics of the Hong Kong Polytechnic University, Hong Kong, and the Micro and Nanotechnology Laboratory (MNTL) at the University of Illinois at Urbana-Champaign (UIUC). His research is focused on the development and application of cell- and molecule-based biosensors and systems.

Chunsheng Wu received his Ph.D. degree in biomedical engineering from Zhejiang University, Hangzhou, China, in 2009. He is currently an associate researcher in the Biosensor National Special Laboratory at Zhejiang University. He was a joint Ph.D. student in the Micro Systems Laboratories at the University of California, Los Angeles (UCLA) from 2008 to 2009. He was a postdoctoral fellow in Instrument Science and Technology at Zhejiang University from 2010 to 2012. He was a visiting scholar in the Institute of Nano- and Biotechnologies at the Aachen University of Applied Sciences, Germany, from 2012 to 2013. His research is focused on the development and application of cell- and molecule-based biosensors and systems.

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest. AE

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

Jun Zhou received his B.S. and Ph.D. degrees in biomedical engineering from Zhejiang University, Hangzhou, China, in 2007 and 2012, respectively. In 2009, he joined the International Society for Olfaction and Chemical Sensing (ISOCS) as a student member. His current research interests lie in cell-based MEA sensors, cell-based FET sensors, and microsensor arrays for in vivo signal detection application. He received the First Class Award for Excellent Graduate Students of Zhejiang University in 2011.

Hua Cai received his B.S. and Ph.D. degrees in biomedical engineering from Zhejiang University, Hangzhou, China, in 2004 and 2010, respectively. Then he was a postdoctoral researcher in the Department of Biomedical Engineering at Zhejiang University from 2010 to 2012. He is currently an assistant researcher in the Biosensor National Special Laboratory at Zhejiang University. His research interests are focused on developing cell-based biosensors and biomedical sensors. Currently, a particular focus of his research is the development of cellbased biosensor related devices or instruments and their applications. He received the Best Student Paper Award at the Third Asia-Pacific Conference of Transducers and Micro-Nano Technology, Singapore, in 2006.

Ping Wang was born in May 1962. He received his B.S., M.S., and Ph.D. degrees in electrical engineering from the Harbin Institute of Technology, Harbin, China, in 1984, 1987, and 1992, respectively. From 1992 to 1994, he was a postdoctoral fellow in the Biosensor National Special Laboratory, Department of Biomedical Engineering, Zhejiang University. At present, he is a professor of Biomedical Engineering, Director of the Biosensor National Special Laboratory, and Director of the Key Laboratory of Biomedical Engineering of the Ministry of Education of China, Zhejiang University. He is a member of the International Society for Olfaction and Chemical Sensing and a member of the International Steering Committee of the Asia Chemical Sensors Society. He was also a visiting scholar in the Edison Sensors Laboratory at Case Western Reserve University, Cleveland, OH, and the Biosensor and Bioinstrumentation Laboratory at the University of Arkansas, Fayetteville, in 2002 and 2005, respectively.

Ning Hu received his B.S. and Ph.D. degrees in biomedical engineering from Zhejiang University, Hangzhou, China, in 2009 and 2014, respectively. His research includes electric cell−substrate impedance

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 30970765, 81027003, 81371643, and 31228008), the National Basic Research Program of China (Grant No. 2009CB320303), the National High Technology Research and Development Program of China (Grant No. 2007AA09210106), the International Cooperation Project of the NSFC (Grant No. 61320106002), Marine Public Welfare Project of China (No.

sensing, light addressable potentiometric sensors, and development of cell-based biosensor related devices or instruments and their applications. He received the Ph.D. National Scholarship of China in 2013, the highest award of Zhejiang University (Chu Kochen Scholarship) in 2013, and the GE Foundation TECH Award in 2012. AF

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(37) Nicolau, D. V.; Taguchi, T.; Taniguchi, H.; Tanigawa, H.; Yoshikawa, S. Biosens. Bioelectron. 1999, 14, 317. (38) Wang, L.; Wu, Z. Z.; Xu, B. Q.; Zhao, Y. P.; Kisaalita, W. S. Sens. Actuators, B 2009, 140, 349. (39) Chen, Y. Y.; Lai, H. Y.; Lin, S. H.; Cho, C. W.; Chao, W. H.; Liao, C. H.; Tsang, S.; Chen, Y. F.; Lin, S. Y. J. Neurosci. Methods 2009, 182, 6. (40) Schlosshauer, B.; Brinker, T.; Muller, H. W.; Meyer, J. U. Brain Res. 2001, 903, 237. (41) Franks, W.; Tosatti, S.; Heer, F.; Seif, P.; Textor, M.; Hierlemann, A. Biosens. Bioelectron. 2007, 22, 1426. (42) Heer, F.; Franks, W.; Blau, A.; Taschini, S.; Ziegler, C.; Hierlemann, A.; Baltes, H. Biosens. Bioelectron. 2004, 20, 358. (43) Moore, E.; Rawley, O.; Wood, T.; Galvin, P. Sens. Actuators, B 2009, 139, 187. (44) Clark, P.; Britland, S.; Connolly, P. J. Cell Sci. 1993, 105 (Part 1), 203. (45) Antonik, M. D.; D’Costa, N. P.; Hoh, J. H. IEEE Eng. Med. Biol. Mag. 1997, 16, 66. (46) Ismail, A. B.; Yoshinobu, T.; Iwasaki, H.; Sugihara, H.; Yukimasa, T.; Hirata, I.; Iwata, H. Biosens. Bioelectron. 2003, 18, 1509. (47) Jun, S. B.; Hynd, M. R.; Dowell-Mesfin, N.; Smith, K. L.; Turner, J. N.; Shain, W.; Kim, S. J. J. Neurosci. Methods 2007, 160, 317. (48) Matsuzawa, M.; Weight, F. F.; Potember, R. S.; Liesi, P. Int. J. Dev. Neurosci. 1996, 14, 283. (49) Matsuzawa, M.; Liesi, P.; Knoll, W. J. Neurosci. Methods 1996, 69, 189. (50) Fatisson, J.; Azari, F.; Tufenkji, N. Biosens. Bioelectron. 2011, 26, 3207. (51) Wegener, J.; Keese, C. R.; Giaever, I. Exp. Cell Res. 2000, 259, 158. (52) Xiao, C.; Luong, J. H. T. Biotechnol. Prog. 2003, 19, 1000. (53) Kapyla, J.; Ivaska, J.; Riikonen, R.; Nykvist, P.; Pentikainen, O.; Johnson, M.; Heino, J. J. Biol. Chem. 2000, 275, 3348. (54) Medina, M. B. Proc. SPIE 1999, 3544, 97. (55) Munakata, H.; Takagaki, K.; Majima, M.; Endo, M. Glycobiology 1999, 9, 1023. (56) StJohn, P. M.; Kam, L.; Turner, S. W.; Craighead, H. G.; Issacson, M.; Turner, J. N.; Shain, W. J. Neurosci. Methods 1997, 75, 171. (57) Sanjana, N. E.; Fuller, S. B. J. Neurosci. Methods 2004, 136, 151. (58) Faucheux, N.; Schweiss, R.; Lutzow, K.; Werner, C.; Groth, T. Biomaterials 2004, 25, 2721. (59) Palyvoda, O.; Chen, C. C.; Auner, G. W. Biosens. Bioelectron. 2007, 22, 2346. (60) Veiseh, M.; Wickes, B. T.; Castner, D. G.; Zhang, M. Biomaterials 2004, 25, 3315. (61) Saneinejad, S.; Shoichet, M. S. J. Biomed. Mater. Res. 1998, 42, 13. (62) Huber, M.; Heiduschka, P.; Kienle, S.; Pavlidis, C.; Mack, J.; Walk, T.; Jung, G.; Thanos, S. J. Biomed. Mater. Res. 1998, 41, 278. (63) Fan, Y. W.; Cui, F. Z.; Chen, L. N.; Zhai, Y.; Xu, Q. Y. Surf. Coat. Technol. 2000, 131, 355. (64) Fan, Y. W.; Cui, F. Z.; Hou, S. P.; Xu, Q. Y.; Chen, L. N.; Lee, I. S. J. Neurosci. Methods 2002, 120, 17. (65) Cho, Y. K.; Kim, T. H.; Lee, J. G. J. Micromech. Microeng. 2010, 20, 065010. (66) Erickson, D.; Li, D. Q.; Krull, U. J. Anal. Biochem. 2003, 317, 186. (67) Yang, N.; Su, X. D.; Tjong, V.; Knoll, W. Biosens. Bioelectron. 2007, 22, 2700. (68) Hajjoul, H.; Kocanova, S.; Lassadi, I.; Bystricky, K.; Bancaud, A. Lab Chip 2009, 9, 3054. (69) Reyes, D. R.; Iossifidis, D.; Auroux, P. A.; Manz, A. Anal. Chem. 2002, 74, 2623. (70) Vilkner, T.; Janasek, D.; Manz, A. Anal. Chem. 2004, 76, 3373. (71) Chang, J. C.; Brewer, G. J.; Wheeler, B. C. Biomed. Microdevices 2000, 2, 245.

201305010), and the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR13H180002). We also give many thanks to Wei Zhang, Lidan Xiao, Hui Yu, Chengxiong Wu, Jie Zhou, Da Ha, Diming Zhang, Liujing Zhuang, Manas Gartia, and Dan Coursolle for their help in compiling and correcting the manuscript.

REFERENCES (1) Yano, K.; Karube, I. TrAC, Trends Anal. Chem. 1999, 18, 199. (2) Kriz, D.; Ramström, O.; Mosbach, K. Anal. Chem. 1997, 69, 345. (3) Kriz, D.; Ramstroem, O.; Svensson, A.; Mosbach, K. Anal. Chem. 1995, 67, 2142. (4) Jianrong, C.; Yuqing, M.; Nongyue, H.; Xiaohua, W.; Sijiao, L. Biotechnol. Adv. 2004, 22, 505. (5) Gopal, K. V. Neurotoxicol. Teratol. 2003, 25, 69. (6) Fromherz, P. Physica E (Amsterdam, Neth.) 2003, 16, 24. (7) Maher, M.; Pine, J.; Wright, J.; Tai, Y.-C. J. Neurosci. Methods 1999, 87, 45. (8) Neher, E. Nat. Biotechnol. 2001, 19, 114. (9) Thomas, C. A., Jr.; Springer, P. A.; Loeb, G. E.; Berwald-Netter, Y.; Okun, L. M. Exp. Cell Res. 1972, 74, 61. (10) Keefer, E. W.; Gramowski, A.; Stenger, D. A.; Pancrazio, J. J.; Gross, G. W. Biosens. Bioelectron. 2001, 16, 513. (11) Ecken, H.; Ingebrandt, S.; Krause, M.; Richter, D.; Hara, M.; Offenhausser, A. Electrochim. Acta 2003, 48, 3355. (12) Bousse, L. Sens. Actuators, B 1996, 34, 270. (13) Fromherz, P.; Offenhausser, A.; Vetter, T.; Weis, J. Science 1991, 252, 1290. (14) Rudolph, A. S.; Reasor, J. Biosens. Bioelectron. 2001, 16, 429. (15) Paddle, B. M. Biosens. Bioelectron. 1996, 11, 1079. (16) Rogers, K. R. Biosens. Bioelectron. 1995, 10, 533. (17) Wilkins, E.; Atanasov, P. Med. Eng. Phys. 1996, 18, 273. (18) Chang, J. C.; Brewer, G. J.; Wheeler, B. C. Biosens. Bioelectron. 2001, 16, 527. (19) Göpel, W. Sens. Actuators, B 2000, 65, 70. (20) Miyamoto, T.; Miyazaki, T.; Okada, Y.; Sato, T. J. Neurosci. Methods 1996, 64, 245. (21) Men, H.; Zou, S.; Li, Y.; Wang, Y.; Ye, X.; Wang, P. Sens. Actuators, B 2005, 110, 350. (22) Pancrazio, J.; Whelan, J.; Borkholder, D.; Ma, W.; Stenger, D. Am. J. Prev. Med. 1999, 27, 697. (23) Ziegler, C. Fresenius’ J. Anal. Chem. 2000, 366, 552. (24) Denyer, M. C.; Riehle, M.; Britland, S.; Offenhauser, A. Med. Biol. Eng. Comput. 1998, 36, 638. (25) Vassanelli, S.; Fromherz, P. Appl. Phys. A: Mater. Sci. Process. 1998, 66, 459. (26) Hafeman, D. G.; Parce, J. W.; McConnell, H. M. Science 1988, 240, 1182. (27) McConnell, H. M.; Owicki, J. C.; Parce, J. W.; Miller, D. L.; Baxter, G. T.; Wada, H. G.; Pitchford, S. Science 1992, 257, 1906. (28) Ito, Y. Sens. Actuators, B 1998, 52, 107. (29) Lehmann, M.; Baumann, W.; Brischwein, M.; Ehret, R.; Kraus, M.; Schwinde, A.; Bitzenhofer, M.; Freund, I.; Wolf, B. Biosens. Bioelectron. 2000, 15, 117. (30) Xu, G.; Ye, X.; Qin, L.; Xu, Y.; Li, Y.; Li, R.; Wang, P. Biosens. Bioelectron. 2005, 20, 1757. (31) Krause, G.; Lehmann, S.; Lehmann, M.; Freund, I.; Schreiber, E.; Baumann, W. Biosens. Bioelectron. 2006, 21, 1272. (32) van Bergen, A.; Papanikolaou, T.; Schuker, A.; M?ller, A.; Schlosshauer, B. Brain Res. Protoc. 2003, 11, 123. (33) Jungblut, M.; Knoll, W.; Thielemann, C.; Pottek, M. Biomed. Microdevices 2009, 11, 1269. (34) Choi, C. K.; English, A. E.; Jun, S. K.; Kihm, K. D.; Rack, P. D. Biosens. Bioelectron. 2007, 22, 2585. (35) Gao, H.; Luginbuhl, R.; Sigrist, H. Sens. Actuators, B 1997, 38, 38. (36) Iliescu, C.; Chen, B.; Poenar, D. P.; Lee, Y. Y. Sens. Actuators, B 2008, 129, 404. AG

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(72) Jenkner, M.; Muller, B.; Fromherz, P. Biol. Cybern. 2001, 84, 239. (73) Fromherz, P. ChemPhysChem 2002, 3, 276. (74) Wu, Z. Z.; Zhao, Y.; Kisaalita, W. S. Colloids Surf., B 2006, 52, 14. (75) Peterson, S. L.; McDonald, A.; Gourley, P. L.; Sasaki, D. Y. J. Biomed. Mater. Res. 2005, 72A, 10. (76) Chen, T. S.; Chen, C. P.; Tsai, Y. C.; Shih, W. P.; Lin, W. C. Proc.IEEE Micro Electro Mech. Syst., 19th 2006, 957, 506. (77) Zeck, G.; Fromherz, P. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 10457. (78) Choi, A.; Kim, J. Y.; Lee, J. E.; Jung, H. I. Curr. Appl. Phys. 2009, 9, E294. (79) Griscom, L.; Degenaar, P.; LePioufle, B.; Tamiya, E.; Fujita, H. Sens. Actuators, B 2002, 83, 15. (80) Walker, G. M.; Ozers, M. S.; Beebe, D. J. Biomed. Microdevices 2002, 4, 161. (81) Liu, L.; Luo, C. X.; Ni, X. F.; Wang, L.; Yamauchi, K.; Nomura, S. M.; Nakatsuji, N.; Chen, Y. Biomed. Microdevices 2010, 12, 505. (82) Folch, A.; Jo, B. H.; Hurtado, O.; Beebe, D. J.; Toner, M. J. Biomed. Mater. Res. 2000, 52, 346. (83) Toepke, M. W.; Beebe, D. J. Lab Chip 2006, 6, 1484. (84) Takayama, S.; McDonald, J. C.; Ostuni, E.; Liang, M. N.; Kenis, P. J.; Ismagilov, R. F.; Whitesides, G. M. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 5545. (85) Vanherberghen, B.; Manneberg, O.; Christakou, A.; Frisk, T.; Ohlin, M.; Hertz, H. M.; Onfelt, B.; Wiklund, M. Lab Chip 2010, 10, 2727. (86) Hunt, T. P.; Issadore, D.; Westervelt, R. M. Lab Chip 2008, 8, 81. (87) Huang, W. H.; Cheng, W.; Zhang, Z.; Pang, D. W.; Wang, Z. L.; Cheng, J. K.; Cui, D. F. Anal. Chem. 2004, 76, 483. (88) Hsiao, A. P.; Barbee, K. D.; Huang, X. H. Proc. SPIE 2010, 7759, 190. (89) Sasuga, Y.; Iwasawa, T.; Terada, K.; Oe, Y.; Sorimachi, H.; Ohara, O.; Harada, Y. Anal. Chem. 2008, 80, 9141. (90) Studer, V.; Jameson, R.; Pellereau, E.; Pepin, A.; Chen, Y. Microelectron. Eng. 2004, 73−74, 852. (91) Wang, M. M.; Tu, E.; Raymond, D. E.; Yang, J. M.; Zhang, H. C.; Hagen, N.; Dees, B.; Mercer, E. M.; Forster, A. H.; Kariv, I.; Marchand, P. J.; Butler, W. F. Nat. Biotechnol. 2005, 23, 83. (92) Weibel, D. B.; Siegel, A. C.; Lee, A.; George, A. H.; Whitesides, G. M. Lab Chip 2007, 7, 1832. (93) Valero, A.; Braschler, T.; Demierre, N.; Renaud, P. Biomicrofluidics 2010, 4, 022807. (94) Cheng, W.; Klauke, N.; Smith, G.; Cooper, J. M. Electrophoresis 2010, 31, 1405. (95) Mottet, G.; Villemejane, J.; Mir, L. M.; Pioufle, B. L. J. Micromech. Microeng. 2010, 20, 047001. (96) Wang, J.; Stine, M. J.; Lu, C. Anal. Chem. 2007, 79, 9584. (97) Zeringue, H. C.; Wheeler, M. B.; Beebe, D. J. Lab Chip 2005, 5, 108. (98) Taylor, A. M.; Dieterich, D. C.; Ito, H. T.; Kim, S. A.; Schuman, E. M. Neuron 2010, 66, 57. (99) Heller, M. J.; Dehlinger, D.; Esener, S.; Sullivan, B. Proceedings of the 2nd Frontiers in Biomedical Devices Conference, Irvine, CA, June 7−8, 2007; American Society of Mechanical Engineers: New York, 2007; p 53. (100) Tresset, G.; Takeuchi, S. Biomed. Microdevices 2004, 6, 213. (101) Wlodkowic, D.; Cooper, J. M. Expert Rev. Mol. Diagn. 2010, 10, 521. (102) Joye, N.; Schmid, A.; Leblebici, Y. Neurocomputing 2009, 73, 250. (103) Berggren, C.; Bjarnason, B.; Johansson, G. Instrum. Sci. Technol. 1999, 27, 131. (104) Helmholtz, H. Ann. Phys. Chem. 1879, 7, 377. (105) Kovacs, G. T. A. Enabling Technologies for Cultured Neural Networks; Academic Press: New York, 1994. (106) Gouy, M. J. Phys. Theor. Appl. 1910, 9, 457.

(107) Chapman, D. L. London, Edinburgh Dublin Philos. Mag. J. Sci. 1913, 25, 475. (108) Stern, O. Z. Elektrochem. 1924, 30, 508. (109) Newman, J. J. Electrochem. Soc. 1966, 113, 501. (110) Janders, M.; Egert, U.; Stelzle, M.; Nisch, W. Bridging Disciplines for Biomedicine, Proceedings of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, Oct 31 to Nov 3, 1996; Institute of Electrical and Electronics Engineers: Piscataway, NJ, 1996; p 245. (111) Gholmieh, G.; Soussou, W.; Han, M.; Ahuja, A.; Hsiao, M. C.; Song, D.; Tanguay, A. R., Jr.; Berger, T. W. J. Neurosci. Methods 2006, 152, 116. (112) Berdondini, L.; Chiappalone, M.; van der Wal, P. D.; Imfeld, K.; de Rooij, N. F.; Koudelka-Hep, M.; Tedesco, M.; Martinoia, S.; van Pelt, J.; Le Masson, G.; Garenne, A. Sens. Actuators, B 2006, 114, 530. (113) Dworak, B. J.; Wheeler, B. C. Lab Chip 2009, 9, 404. (114) Pearce, T. M.; Wilson, J. A.; Oakes, S. G.; Chiu, S. Y.; Williams, J. C. Lab Chip 2005, 5, 97. (115) Chu, H. Y.; Kuo, T. Y.; Chang, B.; Lu, S. W.; Chiao, C. C.; Fang, W. Sens. Actuators, A 2006, 130−131, 254. (116) Heuschkel, M. O.; Fejtl, M.; Raggenbass, M.; Bertrand, D.; Renaud, P. J. Neurosci. Methods 2002, 114, 135. (117) Greve, F.; Lichtenberg, J.; Kirstein, K. U.; Frey, U.; Hierlemann, A. Digest of Technical Papers, TRANSDUCERS ’05. The 13th International Conference on Solid-State Sensors, Actuators and Microsystems, Institute of Electrical and Electronics Engineers: Piscataway, NJ, 2005; p 1745. (118) Gray, D. S.; Tan, J. L.; Voldman, J.; Chen, C. S. Biosens. Bioelectron. 2004, 19, 1765. (119) Bucher, V.; Brunner, B.; Leibrock, C.; Schubert, M.; Nisch, W. Biosens. Bioelectron. 2001, 16, 205. (120) Meyer, T.; Leisgen, C.; Gonser, B.; Günther, E. Assay Drug Dev. Technol. 2004, 2, 507. (121) Xiao, L.; Hu, Z.; Zhang, W.; Wu, C.; Yu, H.; Wang, P. Biosens. Bioelectron. 2010, 26, 1493. (122) Gross, G. W.; Rhoades, B. K.; Azzazy, H. M. E.; Wu, M. C. Biosens. Bioelectron. 1995, 10, 553. (123) Borkholder, D. A. Ph.d. Theses, Stanford University, Stanford, CA, 1998. (124) Halbach, M. D.; Egert, U.; Hescheler, J.; Banach, K. Cell. Physiol. Biochem. 2003, 13, 271. (125) Reppel, M. Cell. Physiol. Biochem. 2007, 19, 12. (126) Gilchrist, K. H. Ph.D. Thesis, Stanford University, Stanford, CA, 2003. (127) Yeung, C.; Sommerhage, F.; Wrobel, G.; Offenhäusser, A.; Chan, M.; Ingebrandt, S. Anal. Bioanal. Chem. 2007, 387, 2673. (128) Elming, H.; Sonne, J.; Lublin, H. Acta Psychiatr. Scand. 2003, 107, 96. (129) Kannankeril, P. J.; Roden, D. M. Curr. Opin. Cardiol. 2007, 22, 39. (130) Braam, S. R.; Tertoolen, L.; Van De Stolpe, A.; Meyer, T.; Passier, R.; Mummery, C. L. Stem Cell Res. 2010, 4, 107. (131) Natarajan, A.; Molnar, P.; Sieverdes, K.; Jamshidi, A.; Hickman, J. J. Toxicol. Vitro 2006, 20, 375. (132) Tsai, C. T.; Chiang, F. T.; Chen, W. P.; Hwang, J. J.; Tseng, C. D.; Wu, C. K.; Yu, C. C.; Wang, Y. C.; Lai, L. P.; Lin, J. L. Cell Calcium 2011, 49, 1. (133) Caspi, O.; Itzhaki, I.; Kehat, I.; Gepstein, A.; Arbel, G.; Huber, I.; Satin, J.; Gepstein, L. Stem Cell Dev. 2009, 18, 161. (134) Kamioka, H.; Maeda, E.; Jimbo, Y.; Robinson, H. P. C.; Kawana, A. Neurosci. Lett. 1996, 206, 109. (135) O’Shaughnessy, T. J.; Liu, J. L.; Ma, W. Biosens. Bioelectron. 2009, 24, 2365. (136) Streit, J.; Tscherter, A.; Heuschkel, M. O.; Renaud, P. Eur. J. Neurosci. 2001, 14, 191. (137) Robinson, H. P.; Kawahara, M.; Jimbo, Y.; Torimitsu, K.; Kuroda, Y.; Kawana, A. J. Neurophysiol. 1993, 70, 1606. (138) Van Pelt, J.; Wolters, P. S.; Corner, M. A.; Rutten, W. L. C.; Ramakers, G. J. A. IEEE Trans. Biomed. Eng. 2004, 51, 2051. AH

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(139) Corner, M. A. Prog. Brain Res. 1994, 102, 3. (140) Marom, S.; Shahaf, G. Q. Rev. Biophys. 2002, 35, 63. (141) Brown, E. N.; Kass, R. E.; Mitra, P. P. Nat. Neurosci. 2004, 7, 456. (142) Sekirnjak, C.; Hottowy, P.; Sher, A.; Dabrowski, W.; Litke, A. M.; Chichilnisky, E. J. J. Neurophysiol. 2006, 95, 3311. (143) Kolomiets, B.; Dubus, E.; Simonutti, M.; Rosolen, S.; Sahel, J. A.; Picaud, S. Neurobiol. Dis. 2010, 38, 47. (144) Jimbo, Y.; Kasai, N.; Torimitsu, K.; Tateno, T.; Robinson, H. P. C. IEEE Trans. Biomed. Eng. 2003, 50, 241. (145) Hofmann, F.; Bading, H. J. Physiol. 2006, 99, 125. (146) Gross, G. W.; Harsch, A.; Rhoades, B. K.; Göpel, W. Biosens. Bioelectron. 1997, 12, 373. (147) Eytan, D.; Minerbi, A.; Ziv, N.; Marom, S. J. Neurophysiol. 2004, 92, 1817. (148) Otto, F.; Görtz, P.; Fleischer, W.; Siebler, M. J. Neurosci. Methods 2003, 128, 173. (149) Sokal, D. M.; Mason, R.; Parker, T. L. Neuropharmacology 2000, 39, 2408. (150) Xia, Y.; Gopal, K. V.; Gross, G. W. Brain Res. 2003, 973, 151. (151) Mistry, S. K.; Keefer, E. W.; Cunningham, B. A.; Edelman, G. M.; Crossin, K. L. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 1621. (152) Rijal, S. O.; Gross, G. W. J. Neurosci. Metods. 2008, 173, 183. (153) Morefield, S. I.; Keefer, E. W.; Chapman, K. D.; Gross, G. W. Biosens. Bioelectron. 2000, 15, 383. (154) Xia, Y.; Gross, G. W. Alcohol 2003, 30, 167. (155) Dasari, S.; Yuan, Y. Toxicol. Lett. 2010, 193, 138. (156) Parviz, M.; Gross, G. W. NeuroToxicology 2007, 28, 520. (157) Johnstone, A. F. M.; Gross, G. W.; Weiss, D. G.; Schroeder, O. H. U.; Gramowski, A.; Shafer, T. J. NeuroToxicology 2010, 31, 331. (158) Novellino, A.; Scelfo, B.; Palosaari, T.; Price, A.; Sobanski, T.; Shafer, T. J.; Johnstone, A. F. M.; Gross, G. W.; Gramowski, A.; Schroeder, O.; Jugelt, K.; Chiappalone, M.; Benfenati, F.; Martinoia, S.; Tedesco, M. T.; Defranchi, E.; D’Angelo, P.; Whelan, M. Front. Neuroeng. 2011, 4, 26. (159) O’Shaughnessy, T. J.; Zim, B.; Ma, W.; Shaffer, K. M.; Stenger, D. A.; Zamani, K.; Gross, G. W.; Pancrazio, J. J. Brain Res. 2003, 959, 280. (160) Hogberg, H. T.; Sobanski, T.; Novellino, A.; Whelan, M.; Weiss, D. G.; Bal-Price, A. K. NeuroToxicology 2011, 32, 158. (161) Volmer, R.; Prat, C. M. A.; Le Masson, G.; Garenne, A.; Gonzalez-Dunia, D. J. Virol. 2007, 81, 8833. (162) Scarlatos, A.; Cadotte, A. J.; DeMarse, T. B.; Welt, B. A. J. Food Sci. 2008, 73, E129. (163) Gopal, K. V.; Gross, G. W. Hear. Res. 2004, 192, 10. (164) Manos, P.; Pancrazio, J. J.; Coulombe, M. G.; Ma, W.; Stenger, D. A. Neurosci. Lett. 1999, 271, 179. (165) Guenther, E.; Herrmann, T.; Stett, A. In Advances in Network Electrophysiology; Taketani, M., Baudry, M., Eds.; Springer: New York, 2006; Vol. 13. (166) Liu, Q.; Ye, W.; Xiao, L.; Du, L.; Hu, N.; Wang, P. Biosens. Bioelectron. 2010, 25, 2212. (167) Chen, Q.; Xiao, L.; Liu, Q.; Ling, S.; Yin, Y.; Dong, Q.; Wang, P. Biosens. Bioelectron. 2011, 26, 3313. (168) Suyama, K.; Daikoku, S.; Funabashi, T.; Kimura, F. Endocr. J. 2004, 51, 171. (169) Giaever, I.; Keese, C. Proc. Natl. Acad. Sci. U.S.A. 1984, 81, 3761. (170) Giaever, I.; Keese, C. R. Nature 1993, 366, 591. (171) Giaever, I.; Keese, C. R. IEEE Trans. Biomed. Eng. 1986, BME33, 242. (172) Banerjee, P.; Bhunia, A. K. Trends Biotechnol. 2009, 27, 179. (173) Wang, L.; Wang, H.; Mitchelson, K.; Yu, Z.; Cheng, J. Biosens. Bioelectron. 2008, 24, 14. (174) Xiao, C.; Lachance, B.; Sunahara, G.; Luong, J. H. T. Anal. Chem. 2002, 74, 1333. (175) Wang, L.; Zhu, J.; Deng, C.; Xing, W. l.; Cheng, J. Lab Chip 2008, 8, 872.

(176) Ehret, R.; Baumann, W.; Brischwein, M.; Schwinde, A.; Stegbauer, K.; Wolf, B. Biosens. Bioelectron. 1997, 12, 29. (177) Chang, B. W.; Chen, C. H.; Ding, S. J.; Chen, D. C. H.; Chang, H. C. Sens. Actuators, B 2005, 105, 159. (178) Mayer, M.; Brunner, P.; Merwa, R.; Scharfetter, H. Physiol. Meas. 2005, 26, 185. (179) Lo, C. M.; Keese, C. R.; Giaever, I. Biophys. J. 1995, 69, 2800. (180) Cascales-Sanchez, P.; Fernandez-Cornejo, V.; Tomas-Gomez, A.; Terol-Calpena, F.; Sanchez-Del Campo, F. Transplant. Proc. 2007, 39, 2118. (181) Park, J. Y.; Lee, Y. S.; Chang, B. Y.; Kim, B. H.; Jeon, S.; Park, S. M. Anal. Chem. 2010, 82, 8342. (182) Yang, M.; Lim, C. C.; Liao, R.; Zhang, X. Biosens. Bioelectron. 2007, 22, 1688. (183) Balasubramanian, L.; Yip, K. P.; Hsu, T. H.; Lo, C. M. Am. J. Physiol.: Cell Physiol. 2008, 295, C954. (184) Haas, S.; Jahnke, H. G.; Glass, M.; Azendorf, R.; Schmidt, S.; Robitzki, A. A. Lab Chip 2010, 10, 2965. (185) Kirstein, S. L.; Atienza, J. M.; Xi, B.; Zhu, J.; Yu, N.; Wang, X.; Xu, X.; Abassi, Y. A. Assay Drug Dev. Technol. 2006, 4, 545. (186) Atienza, J. M.; Zhu, J.; Wang, X.; Xu, X.; Abassi, Y. J. Biomol. Screening 2005, 10, 795. (187) Keese, C. R.; Wegener, J.; Walker, S. R.; Giaever, I. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 1554. (188) Ren, J.; Xiao, Y. J.; Singh, L. S.; Zhao, X.; Zhao, Z.; Feng, L.; Rose, T. M.; Prestwich, G. D.; Xu, Y. Cancer Res. 2006, 66, 3006. (189) Noiri, E.; Hu, Y.; Bahou, W. F.; Keese, C. R.; Giaever, I.; Goligorsky, M. S. J. Biol. Chem. 1997, 272, 1747. (190) Saxena, N. K.; Sharma, D.; Ding, X.; Lin, S.; Marra, F.; Merlin, D.; Anania, F. A. Cancer Res. 2007, 67, 2497. (191) Chen, J.; Ye, L.; Zhang, L.; Jiang, W. G. J. Cell. Biochem. 2008, 105, 313. (192) Earley, S.; Plopper, G. E. Biochem. Biophys. Res. Commun. 2008, 366, 476. (193) Saxena, N. K.; Taliaferro-Smith, L.; Knight, B. B.; Merlin, D.; Anania, F. A.; O’Regan, R. M.; Sharma, D. Cancer Res. 2008, 68, 9712. (194) Sgambato, A.; De Paola, B.; Migaldi, M.; Di Salvatore, M.; Rettino, A.; Rossi, G.; Faraglia, B.; Boninsegna, A.; Maiorana, A.; Cittadini, A. J. Cell. Physiol. 2007, 213, 528. (195) Sun, T.; Swindle, E. J.; Collins, J. E.; Holloway, J. A.; Davies, D. E.; Morgan, H. Lab Chip 2010, 10, 1611. (196) Ko, K. S. C.; Lo, C. M.; Ferrier, J.; Hannam, P.; Tamura, M.; McBride, B. C.; Ellen, R. P. J. Microbiol. Methods 1998, 34, 125. (197) Wegener, J.; Hakvoort, A.; Galla, H. J. Brain Res. 2000, 853, 115. (198) Yin, F.; Watsky, M. A. Invest. Ophthalmol. Visual Sci. 2005, 46, 1927. (199) Tiruppathi, C.; Malik, A. B.; Del Vecchio, P. J.; Keese, C. R.; Giaever, I. Proc. Natl. Acad. Sci. U.S.A. 1992, 89, 7919. (200) Chang, Y. C.; Stins, M. F.; McCaffery, M. J.; Miller, G. F.; Pare, D. R.; Dam, T.; Paul-Satyaseela, M.; Kim, K. S.; Kwon-Chung, K. J. Infect. Immun. 2004, 72, 4985. (201) Treeratanapiboon, L.; Psathaki, K.; Wegener, J.; Looareesuwan, S.; Galla, H. J.; Udomsangpetch, R. Biochem. Biophys. Res. Commun. 2005, 335, 810. (202) Weidenfeller, C.; Schrot, S.; Zozulya, A.; Galla, H. J. Brain Res. 2005, 1053, 162. (203) Hartmann, C.; Zozulya, A.; Wegener, J.; Galla, H. J. Exp. Cell Res. 2007, 313, 1318. (204) Moy, A. B.; Van, E. J.; Bodmer, J.; Kamath, J.; Keese, C.; Giaever, I.; Shasby, S.; Shasby, D. M. J. Clin. Invest. 1996, 97, 1020. (205) Usatyuk, P. V.; Vepa, S.; Watkins, T.; He, D.; Parinandi, N. L.; Natarajan, V. Antioxid. Redox Signaling 2003, 5, 723. (206) Usatyuk, P. V.; Parinandi, N. L.; Natarajan, V. J. Biol. Chem. 2006, 281, 35554. (207) Shivanna, M.; Rajashekhar, G.; Srinivas, S. P. Invest. Ophthalmol. Visual Sci. 2010, 51, 1575. (208) Boyd, J. M.; Huang, L.; Xie, L.; Moe, B.; Gabos, S.; Li, X. F. Anal. Chim. Acta 2008, 615, 80. AI

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(209) Caide, X.; John, H. T. L. Biotechnol. Prog. 2003, 19, 1000. (210) Wang, H.; Keese, C. R.; Giaever, I.; Smith, T. J. J. Clin. Endocr. Metab. 1995, 80, 3553. (211) Litkouhi, B.; Kwong, J.; Lo, C. M.; Smedley, J. G., III; McClane, B. A.; Aponte, M.; Gao, Z.; Sarno, J. L.; Hinners, J.; Welch, W. R. Neoplasia 2007, 9, 304. (212) Ehret, R.; Baumann, W.; Brischwein, M.; Lehmann, M.; Henning, T.; Freund, I.; Drechsler, S.; Friedrich, U.; Hubert, M. L.; Motrescu, E.; Kob, A.; Palzer, H.; Grothe, H.; Wolf, B. Fresenius’ J. Anal. Chem. 2001, 369, 30. (213) Ciambrone, G. J.; Liu, V. F.; Lin, D. C.; McGuinness, R. P.; Leung, G. K.; Pitchford, S. J. Biomol. Screening 2004, 9, 467. (214) Verdonk, E.; Johnson, K.; McGuinness, R.; Leung, G.; Chen, Y. W.; Tang, H. R.; Michelotti, J. M.; Liu, V. F. Assay Drug Dev. Technol. 2006, 4, 609. (215) Yeon, J. H.; Park, J. K. Anal. Biochem. 2005, 341, 308. (216) Guo, M.; Chen, J.; Yun, X.; Chen, K.; Nie, L.; Yao, S. Biochim. Biophys. Acta, Gen. Subj. 2006, 1760, 432. (217) Glamann, J.; Hansen, A. J. Assay Drug Dev. Technol. 2006, 4, 555. (218) Solly, K.; Wang, X.; Xu, X.; Strulovici, B.; Zheng, W. Assay Drug Dev. Technol. 2004, 2, 363. (219) Zhu, J.; Wang, X.; Xu, X.; Abassi, Y. A. J. Immunol. Methods 2006, 309, 25. (220) LeBaron, R. G.; Athanasiou, K. A. Tissue Eng. 2000, 6, 85. (221) Cuvelier, D.; Thery, M.; Chu, Y. S.; Dufour, S.; Thiery, J. P.; Bornens, M.; Nassoy, P.; Mahadevan, L. Curr. Biol. 2007, 17, 694. (222) Bouafsoun, A.; Helali, S.; Othmane, A.; Kerkeni, A.; Prigent, A. F.; Jaffrezic-Renault, N.; Bessueille, F.; Leonard, D.; Ponsonnet, L. Macromol. Biosci. 2007, 7, 599. (223) Luong, J. H. T.; Xiao, C.; Lachance, B.; Leabu, S. M.; Li, X.; Uniyal, S.; Chan, B. M. C. Anal. Chim. Acta 2004, 501, 61. (224) Atienza, J. M.; Yu, N.; Wang, X.; Xu, X.; Abassi, Y. J. Biomol. Screening 2006, 11, 634. (225) Kataoka, N.; Iwaki, K.; Hashimoto, K.; Mochizuki, S.; Ogasawara, Y.; Sato, M.; Tsujioka, K.; Kajiya, F. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 15638. (226) Zou, Z.; Kai, J.; Rust, M. J.; Han, J.; Ahn, C. H. Sens. Actuators, B 2007, 136, 518. (227) Chang, C.-H.; Liao, Jiunn-Der; Chen, J.-J. J.; Ju, M.-S.; Lin, C.C. K. Langmuir 2004, 20, 11656. (228) Thein, M.; Asphahani, F.; Cheng, A.; Buckmaster, R.; Zhang, M.; Xu, J. Biosens. Bioelectron. 2010, 25, 1963. (229) Dittami, G. M.; Ayliffe, H. E.; King, C. S.; Rabbitt, R. D. J. Microelectromech. Syst. 2008, 17, 850. (230) Sun, T.; Morgan, H. Microfluid. Nanofluid. 2010, 8, 423. (231) Asphahani, F.; Wang, K.; Thein, M.; Veiseh, O.; Yung, S.; Xu, J.; Zhang, M. Phys. Biol. 2011, 8, 015006. (232) Wang, L.; Yin, H.; Xing, W.; Yu, Z.; Guo, M.; Cheng, J. Biosens. Bioelectron. 2010, 25, 990. (233) Bieberich, E.; Anthony, G. E. Biosens. Bioelectron. 2004, 19, 923. (234) C. Maercker, T. R.; Mathis, H.; Ridinger, H.; Bieback, K. Eng. Life Sci. 2008, 8, 33. (235) Hediger, S.; Fontannaz, J.; Sayah, A.; Hunziker, W.; Gijs, M. A. M. Sens. Actuators, B 2000, 63, 63. (236) Linderholm, P.; Vannod, J.; Barrandon, Y.; Renaud, P. Biosens. Bioelectron. 2007, 22, 789. (237) Frampton, J. P.; Hynd, M. R.; Williams, J. C.; Shuler, M. L.; Shain, W. J. Neural Eng. 2007, 4, 399. (238) Kucik, D. F.; Wu, C. Cell Migr. 2005, 294, 43. (239) Keese, C. R.; Bhawe, K.; Wegener, J.; Giaever, I. BioTechniques 2002, 33, 842. (240) Wang, G. K.; Zhang, W. Histol. Histopathol. 2005, 20, 593. (241) Taliaferro-Smith, L.; Nagalingam, A.; Zhong, D.; Zhou, W.; Saxena, N. K.; Sharma, D. Oncogene 2009, 28, 2621. (242) Patani, N.; Douglas-Jones, A.; Mansel, R.; Jiang, W.; Mokbel, K. Cancer Cell Int. 2010, 10, 29. (243) Berryman, J. G.; Borcea, L.; Papanicolaou, G. C.; Tsogka, C. J. Acoust. Soc. Am. 2002, 112, 1509.

(244) Holder, D., Ed. Electrical Impedance Tomography: Methods, History and Applications; Institute of Physics Publishing Inc.: Philadelphia, 2005. (245) Bayford, R. H. Annu. Rev. Biomed. Eng. 2006, 8, 63. (246) Hou, T. C.; Loh, K. J.; Lynch, J. P. Nanotechnology 2007, 18, 315501. (247) Chai, K. T. C.; Hammond, P. A.; Cumming, D. R. S. Sens. Actuators, B 2005, 111−112, 305. (248) Chai, K.; Davies, J.; Cumming, D. Sens. Actuators, B 2007, 127, 97. (249) Linderholm, P.; Braschler, T.; Vannod, J.; Barrandon, Y.; Brouard, M.; Renaud, P. Lab Chip 2006, 6, 1155. (250) Linderholm, P.; Marescot, L.; Meng Heng, L.; Renaud, P. IEEE Trans. Biomed. Eng. 2008, 55, 138. (251) Sun, T.; Tsuda, S.; Zauner, K. P.; Morgan, H. Biosens. Bioelectron. 2010, 25, 1109. (252) Wegener, J.; Sieber, M.; Galla, H. J. J. Biochem. Biophys. Methods 1996, 32, 151. (253) Amy, L. P.; David, J. B. Bioessays 2008, 30, 811. (254) Ruei, Z. L.; Hwan, Y. C. J. Biotechnol. 2008, 3, 1172. (255) Keese, C.; Karra, N.; Dillon, B.; Goldberg, A.; Giaever, I. Vitro Mol. Toxicol. 1998, 11, 183. (256) Xing, J. Z.; Zhu, L.; Gabos, S.; Xie, L. Toxicol. Vitro 2006, 20, 995. (257) Xing, J. Z.; Zhu, L.; Jackson, J. A.; Gabos, S.; Sun, X. J.; Wang, X. B.; Xu, X. Chem. Res. Toxicol. 2005, 18, 154. (258) Otto, A. M.; Brischwein, M.; Niendorf, A.; Henning, T.; Motrescu, E.; Wolf, B. Cancer Detect. Prev. 2003, 27, 291. (259) Brischwein, M.; Motrescu, E. R.; Cabala, E.; Otto, A. M.; Grothe, H.; Wolf, B. Lab Chip 2003, 3, 234. (260) Geisler, T.; Ressler, J.; Harz, H.; Wolf, B.; Uhl, R. IEEE Trans. Autom. Sci. Eng. 2006, 3, 169. (261) Ceriotti, L.; Kob, A.; Drechsler, S.; Ponti, J.; Thedinga, E.; Colpo, P.; Ehret, R.; Rossi, F. Anal. Biochem. 2007, 371, 92. (262) Becker, B.; Lob, V.; Janzen, N.; Grundl, D.; Ilchmann, F.; Wolf, B. 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, Riga, Latvia, June 16−20, 2008; 2008; Springer: Berlin, p 186. (263) Wang, W.; Foley, K.; Shan, X.; Wang, S.; Eaton, S.; Nagaraj, V. J.; Wiktor, P.; Patel, U.; Tao, N. Nat. Chem. 2011, 3, 249. (264) Abdolahad, M.; Taghinejad, M.; Taghinejad, H.; Janmaleki, M.; Mohajerzadeh, S. Lab Chip 2012, 12, 1183. (265) Bergveld, P. IEEE Trans. Biomed. Eng. 1970, 17, 70. (266) Bergveld, P.; Wiersma, J.; Meertens, H. IEEE Trans. Biomed. Eng. 1976, 23, 136. (267) Bergveld, P. Sens. Actuators, B 2003, 88, 1. (268) Lee, C. S.; Kim, S.; Kim, M. Sensors 2009, 9, 7111. (269) Schoning, M. J. Sens. Actuators, B 2005, 5, 126. (270) Sedra, A. S.; Smith, K. C. Microelectronic Circuits; Oxford University Press: New York, 1997. (271) Millman, J.; Grabel, A. Microelectronics; McGraw-Hill: New York, 1987. (272) Galup-Montoro, C.; Schneider, M. C. MOSFET Modeling for Circuit Analysis and Design; World Scientific Publishing Co. Inc.: Hackensack, NJ, 2007. (273) Malik, N. R. Electronic Circuits: Analysis, Simulation, and Design; Prentice-Hall: Upper Saddle River, NJ, 1995. (274) Horowitz, P.; Hill, W. The Art of Electronics; Cambridge University Press: Cambridge, U.K., 1989. (275) Olthuis, W.; Faber, E.; Krommenhoek, E.; van den Berg, A. Dresdner Sensor-Symposium, Dresden, Germany, Dec 10−12, 2007; Forschungsgesellschaft für Messtechnik, Sensorik und Medizintechnik e.V. Dresden: Franfurt, Germany, 2007; p 12. (276) Ho, K. I.; Lu, T. F.; Chang, C. P.; Lai, C. S.; Yang, C. M. Sensors 2009, 1−3, 1079. (277) Rai, P.; Jung, S.; Ji, T.; Varadan, V. K. Sensors 2009, 9, 1987. (278) Sharma, S.; Moniz, A. R. B.; Triantis, I.; Michelakis, K.; Trzebinski, J.; Azarbadegan, A.; Field, B.; Toumazou, C.; Eames, I.; Cass, A. Microfluid. Nanofluid. 2011, 10, 1119. AJ

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(279) Baumann, W. H.; Lehmann, M.; Schwinde, A.; Ehret, R.; Brischwein, M.; Wolf, B. Sens. Actuators, B 1999, 55, 77. (280) Burdallo, I.; Jimenez-Jorquera, C. Procedia Chem. 2009, 1, 289. (281) Thedinga, E.; Axel, K.; Holst, H.; Keuer, A.; Drechsler, S.; Niendorf, R.; Baumann, W.; Freund, I.; Lehmann, M.; Ehret, R. Toxicol. Appl. Pharmacol. 2007, 220, 33. (282) Cui, Y.; Lieber, C. M. Science 2001, 291, 851. (283) Cui, Y.; Zhong, Z.; Wang, D.; Wang, W. U.; Lieber, C. M. Nano Lett. 2003, 3, 149. (284) Cui, Y.; Wei, Q.; Park, H.; Lieber, C. M. Science 2001, 293, 1289. (285) Patolsky, F.; Zheng, G.; Lieber, C. M. Nat. Protoc. 2006, 1, 1711. (286) Chang, K. S.; Sun, C. J.; Chiang, P. L.; Chou, A. C.; Lin, M. C.; Liang, C.; Hung, H. H.; Yeh, Y. H.; Chen, C. D.; Pan, C. Y. Biosens. Bioelectron. 2011, 31, 137. (287) Jobling, D. T.; Smith, J. G.; Wheal, H. V. Med. Biol. Eng. Comput. 1981, 19, 553. (288) Sprossler, C.; Denyer, M.; Britland, S.; Knoll, W.; Offenhausser, A. Phys. Rev. E 1999, 60, 2171. (289) Schätzthauer, R.; Fromherz, P. Eur. J. Neurosci. 1998, 10, 1956. (290) Weis, R.; Fromherz, P. Phys. Rev. E 1997, 55, 877. (291) Fromherz, P. Eur. Biophys. J. 1999, 28, 254. (292) Ingebrandt, S.; Yeung, C.-K.; Krause, M.; Offenhäusser, A. Eur. Biophys. J. 2005, 34, 144. (293) Wrobel, G.; Seifert, R.; Ingebrandt, S.; Enderlein, J.; Ecken, H.; Baumann, A.; Kaupp, U. B.; Offenhäusser, A. Biophys. J. 2005, 89, 3628. (294) Meyburg, S.; Wrobel, G.; Stockmann, R.; Moers, J.; Ingebrandt, S.; Offenhausser, A. Appl. Phys. Lett. 2006, 89, 013901. (295) Fromherz, P.; Stett, A. Phys. Rev. Lett. 1995, 75, 1670. (296) Stett, A.; Muller, B.; Fromherz, P. Phys. Rev. E 1997, 55, 1779. (297) Bonifazi, P.; Fromherz, P. J. Adv. Mater. 2002, 14, 1190. (298) Kaul, R. A.; Syed, N. I.; Fromherz, P. Phys. Rev. Lett. 2004, 92, 038102. (299) Malenka, R. C.; Bear, M. F. Neuron 2004, 44, 5. (300) Eversmann, B.; Jenkner, M.; Hofmann, F.; Paulus, C. IEEE J. Solid-State Circuits 2003, 38, 2306. (301) Offenhäusser, A.; Sprossler, C.; Matsuzawa, M.; Knoll, W. Biosens. Bioelectron. 1997, 12, 819. (302) Falconnet, D.; Csucs, G.; Michelle Grandin, H.; Textor, M. Biomaterials 2006, 27, 3044. (303) Merz, M.; Fromherz, P. Adv. Funct. Mater. 2005, 15, 739. (304) Besl, B.; Fromherz, P. Eur. J. Neurosci. 2002, 15, 999. (305) Fromherz, P. Eur. Biophys. J. 2002, 31, 228. (306) Hutzler, M.; Lambacher, A.; Eversmann, B.; Jenkner, M.; Thewes, R.; Fromherz, P. J. Neurophysiol. 2006, 96, 1638. (307) Stangl, C.; Fromherz, P. Eur. J. Neurosci. 2008, 27, 958. (308) Owicki, J. C.; Bousse, L. J.; Hafeman, D. G.; Kirk, G. L.; Olson, J. D.; Wada, H. G.; Parce, J. W. Annu. Rev. Biophys. Biomol. Struct. 1994, 23, 87. (309) Hafner, F. Biosens. Bioelectron. 2000, 15, 149. (310) Adami, M.; Sartore, M.; Nicolini, C. Biosens. Bioelectron. 1995, 10, 155. (311) Liu, Q.; Yu, H.; Tan, Z.; Cai, H.; Ye, W.; Zhang, M.; Wang, P. Sens. Actuators, B 2011, 155, 214. (312) Hu, N.; Wu, C.; Ha, D.; Wang, T.; Liu, Q.; Wang, P. Biosens. Bioelectron. 2012, 40, 167. (313) Wang, P.; Xu, G.; Qin, L.; Xu, Y.; Li, Y.; Li, R. Sens. Actuators, B 2005, 108, 576. (314) Miller, D. L.; Olson, J. C.; Parce, J. W.; Owicki, J. C. Biophys. J. 1993, 64, 813. (315) Grattarola, M.; Martinoia, S.; Massobrio, G.; Cambiaso, A.; Rosichini, R.; Tetti, M. Sens. Actuators, B 1991, 4, 261. (316) Sartore, M.; Adami, M.; Nicolini, C. Biosens. Bioelectron. 1992, 7, 57. (317) Siu, W. M.; Cobbold, R. S. IEEE Trans. Electron Devices 1979, 26, 1805. (318) Nice, E. C.; Catimel, B. Bioessays 1999, 21, 339.

(319) Eklund, S. E.; Snider, R. M.; Wikswo, J.; Baudenbacher, F.; Prokop, A.; Cliffel, D. E. J. Electroanal. Chem. 2006, 587, 333. (320) Eklund, S. E.; Taylor, D.; Kozlov, E.; Prokop, A.; Cliffel, D. E. Anal. Chem. 2003, 76, 519. (321) Eklund, S. E.; Thompson, R. G.; Snider, R. M.; Carney, C. K.; Wright, D. W.; Wikswo, J.; Cliffel, D. E. Sensors 2009, 9, 2117. (322) Garnovskaya, M. N.; Mukhin, Y. V.; Vlasova, T. M.; Raymond, J. R. J. Biol. Chem. 2003, 278, 16908. (323) Ellis, A. G.; Doherty, M. M.; Walker, F.; Weinstock, J.; Nerrie, M.; Vitali, A.; Murphy, R.; Johns, T. G.; Scott, A. M.; Levitzki, A.; McLachlan, G.; Webster, L. K.; Burgess, A. W.; Nice, E. C. Biochem. Pharmacol. 2006, 71, 1422. (324) Okada, Y.; Taniguchi, T.; Akagi, Y.; Muramatsu, I. Eur. J. Pharmacol. 2002, 455, 19. (325) Fujii, R.; Hosoya, M.; Fukusumi, S.; Kawamata, Y.; Habata, Y.; Hinuma, S.; Onda, H.; Nishimura, O.; Fujino, M. J. Biol. Chem. 2000, 275, 21068. (326) Kramarenko, I. I.; Bunni, M. A.; Morinelli, T. A.; Raymond, J. R.; Garnovskaya, M. N. Biochem. Pharmacol. 2009, 77, 269. (327) Coaxum, S. D.; Garnovskaya, M. N.; Gooz, M.; Baldys, A.; Raymond, J. R. Biochim. Biophys. Acta, Mol. Cell Res. 2009, 1793, 1174. (328) Arboleda, G.; Huang, T.-J.; Waters, C.; Verkhratsky, A.; Fernyhough, P.; Gibson, R. M. Eur. J. Neurosci. 2007, 25, 3030. (329) Fischer, H.; Seelig, A.; Beier, N.; Raddatz, P.; Seelig, J. J. Membr. Biol. 1999, 168, 39. (330) de Oliveira, V. X.; Fazio, M. A.; Santos, E. L.; Pesquero, J. B.; Miranda, A. J. Pept. Sci. 2008, 14, 617. (331) Douglas, E. S.; Hsiao, S. C.; Onoe, H.; Bertozzi, C. R.; Francis, M. B.; Mathies, R. A. Lab Chip 2009, 9, 2010. (332) Cunha, F. M.; Berti, D. A.; Ferreira, Z. S.; Klitzke, C. F.; Markus, R. P.; Ferro, E. S. J. Biol. Chem. 2008, 283, 24448. (333) Santos, E. L.; Reis, R. I.; Silva, R. G.; Shimuta, S. I.; Pecher, C.; Bascands, J. L.; Schanstra, J. P.; Oliveira, L.; Bader, M.; Paiva, A. C.; Costa-Neto, C. M.; Pesquero, J. B. Regul. Pept. 2007, 141, 159. (334) Santos, E. L.; de Picoli Souza, K.; Sabatini, R. A.; Martin, R. P.; Fernandes, L.; Nardi, D. T.; Malavolta, L.; Shimuta, S. I.; Nakaie, C. R.; Pesquero, J. B. Int. Immunopharmacol. 2008, 8, 293. (335) Burvall, K.; Palmberg, L.; Larsson, K. Life Sci. 2002, 71, 299. (336) Wiebe, C. A.; Rieder, C.; Young, P. G.; Dibrov, P.; Fliegel, L. Mol. Cell. Biochem. 2003, 254, 117. (337) Smart, D.; Wood, M. D. Biochem. Cell Biol. 2000, 78, 281. (338) Roth, C. M.; Kohen, R. L.; Walton, S. P.; Yarmush, M. L. Biophys. Chem. 2001, 89, 1. (339) Parce, J. W.; Owicki, J. C.; Kercso, K. M.; Sigal, G. B.; Wada, H. G.; Muir, V. C.; Bousse, L. J.; Ross, K. L.; Sikic, B. I.; McConnell, H. M. Science 1989, 246, 243. (340) Bruner, L. H.; Miller, K. R.; Owicki, J. C.; Parce, J. W.; Muir, V. C. Toxicol. Vitro 1991, 5, 277. (341) Landin, W. E.; Mun, G. C.; Nims, R. W.; Harbell, J. W. Toxicol. Vitro 2007, 21, 1165. (342) Gioli-Pereira, L.; Nascimento, É. A.; Santos, E. L.; Bracht, A.; Juliano, M. A.; Pesquero, J. B.; Borges, D. R.; Kouyoumdjian, M. Ital. J. Gastroenterol. Hepatol. 2005, 20, 463. (343) Waldenmaier, D. S.; Babarina, A.; Kischkel, F. C. Toxicol. Appl. Pharmacol. 2003, 192, 237. (344) Wu, Y.; Wang, P.; Ye, X.; Zhang, Q.; Li, R.; Yan, W.; Zheng, X. Biosens. Bioelectron. 2001, 16, 277. (345) Wu, Y.; Wang, P.; Ye, X.; Zhang, G.; He, H.; Yan, W.; Zheng, X.; Han, J.; Cui, D. Sens. Actuators, B 2001, 80, 215. (346) Siqueira, J. R., Jr.; Werner, C. F.; Bäcker, M.; Poghossian, A.; Zucolotto, V.; Oliveira, O. N., Jr.; Schöning, M. J. J. Phys. Chem. C 2009, 113, 14765. (347) Stein, B.; George, M.; Gaub, H.; Parak, W. Sens. Actuators, B 2004, 98, 299. (348) Liu, Q.; Cai, H.; Xu, Y.; Li, Y.; Li, R.; Wang, P. Biosens. Bioelectron. 2006, 22, 318. (349) Parak, W. J.; George, M.; Domke, J.; Radmacher, M.; Behrends, J. C.; Denyer, M. C.; Gaub, H. E. IEEE Trans. Biomed. Eng. 2000, 47, 1106. AK

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(350) Zhang, W.; Li, Y.; Liu, Q.; Xu, Y.; Cai, H.; Wang, P. Sens. Actuators, B 2008, 131, 24. (351) George, M.; Parak, W. J.; Gaub, H. E. Sens. Actuators, B 2000, 69, 266. (352) Liu, Q.; Cai, H.; Xu, Y.; Xiao, L.; Yang, M.; Wang, P. Biosens. Bioelectron. 2007, 22, 3224. (353) Liu, Q.; Cai, H.; Xiao, L.; Li, R.; Yang, M.; Wang, P. IEEE. Sens. J. 2007, 7, 1625. (354) Liu, Q.; Ye, W.; Yu, H.; Hu, N.; Du, L.; Wang, P.; Yang, M. Sens. Actuators, B 2010, 146, 527. (355) Fromherz, P. Neuroelectronic Interfacing: Semiconductor Chips with Ion Channels, Nerve Cells, and Brain; Wiley-VCH: Berlin, 2003. (356) Starovoytov, A.; Choi, J.; Seung, H. S. J. Neurophysiol. 2005, 93, 1090. (357) Hodgkin, A. L.; Huxley, A. F. J. Physiol. 1952, 116, 449. (358) Noble, D. J. Physiol. 1962, 160, 317. (359) Chen, P. H.; Zhang, W.; Zhou, J.; Wang, P.; Xiao, L. D.; Yang, M. Prog. Nat. Sci. 2009, 19, 153. (360) Lepple-Wienhues, A.; Ferlinz, K.; Seeger, A.; Schafer, A. Recept. Channels 2003, 9, 13. (361) Py, C.; Denhoff, M. W.; Martina, M.; Monette, R.; Comas, T.; Ahuja, T.; Martinez, D.; Wingar, S.; Caballero, J.; Laframboise, S. Biotechnol. Bioeng. 2010, 107, 593. (362) Suzuki, H.; Tabata, K.; Kato-Yamada, Y.; Noji, H.; Takeuchi, S. Lab Chip 2004, 4, 502. (363) Fertig, N.; Blick, R. H.; Behrends, J. C. Biophys. J. 2002, 82, 3056. (364) Fertig, N.; George, M.; Klau, M.; Meyer, C.; Tilke, A.; Sobotta, C.; Blick, R. H.; Behrends, J. C. Recept. Channels 2003, 9, 29. (365) Fertig, N.; Klau, M.; George, M.; Blick, R. H.; Behrends, J. C. Appl. Phys. Lett. 2002, 81, 4865. (366) Klemic, K. G.; Klemic, J. F.; Reed, M. A.; Sigworth, F. J. Biosens. Bioelectron. 2002, 17, 597. (367) Klemic, K. G.; Klemic, J. F.; Sigworth, F. J. Pfluegers ArchEur. J. Physiol. 2005, 449, 564. (368) Stett, A.; Bucher, V.; Burkhardt, C.; Weber, U.; Nisch, W. Med. Biol. Eng. Comput. 2003, 41, 233. (369) Mayer, M.; Kriebel, J. K.; Tosteson, M. T.; Whitesides, G. M. Biophys. J. 2003, 85, 2684. (370) Wilk, S. J.; Petrossian, L.; Goryll, M.; Thornton, T. J.; Goodnick, S. M.; Tang, J. M.; Eisenberg, R. S.; Saraniti, M.; Wong, D.; Schmidt, J. J.; Montemagno, C. D. e-J. Surf. Sci. Nanotechnol. 2005, 3, 184. (371) Suzuki, H.; Takeuchi, S. Anal. Bioanal. Chem. 2008, 391, 2695. (372) Mealing, G.; Bani-Yaghoub, M.; Tremblay, R.; Monette, R.; Mielke, J.; Voicu, R.; Py, C.; Barjovanu, R.; Faid, K. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Montreal, July 31 to Aug 4, 2005; Institute of Electrical and Electronics Engineers: Piscataway, NJ, 2005; p 3116. (373) Bennett, P. Trends Biotechnol. 2003, 21, 563. (374) Bruggemann, A.; George, M.; Klau, M.; Beckler, M.; Steindl, J.; Behrends, J. C.; Fertig, N. Assay Drug Dev. Technol. 2003, 1, 665. (375) Brueggemann, A.; George, M.; Klau, M.; Beckler, M.; Steindl, J.; Behrends, J. C.; Fertig, N. Curr. Drug Discovery Technol. 2004, 1, 91. (376) Bruggemann, A.; Stoelzle, S.; George, M.; Behrends, J. C.; Fertig, N. Small 2006, 2, 840. (377) Sondermann, M.; George, M.; Fertig, N.; Behrends, J. C. Biochim. Biophys. Acta, Biomembr. 2006, 1758, 545. (378) Kiss, L.; Bennett, P. B.; Uebele, V. N.; Koblan, K. S.; Kane, S. A.; Neagle, B.; Schroeder, K. Assay Drug Dev. Technol. 2003, 1, 127. (379) Dunlop, J.; Bowlby, M.; Peri, R.; Vasilyev, D.; Arias, R. Nat. Rev. Drug Discovery 2008, 7, 358. (380) Schroeder, K.; Neagle, B.; Trezise, D. J.; Worley, J. J. Biomol. Screening 2003, 8, 50. (381) Lau, A. Y.; Hung, P. J.; Wu, A. R.; Lee, L. P. Lab Chip 2006, 6, 1510. (382) Finkel, A.; Wittel, A.; Yang, N.; Handran, S.; Hughes, J.; Costantin, J. J. Biomol. Screening 2006, 11, 488.

(383) Tao, H. M.; Ana, D. S.; Guia, A.; Huang, M. X.; Ligutti, J.; Walker, G.; Sithiphong, K.; Chan, F.; Tao, G. L.; Zozulya, Z.; Saya, S.; Phimmachack, R.; Sie, C.; Yuan, J.; Wu, L.; Xu, J.; Ghetti, A. Assay Drug Dev. Technol. 2004, 2, 497. (384) Kutchinsky, J.; Friis, S.; Asmild, M.; Taboryski, R.; Pedersen, S.; Vestergaard, R. K.; Jacobsen, R. B.; Krzywkowski, K.; Schroder, R. L.; Ljungstrom, T.; Helix, N.; Sorensen, C. B.; Bech, M.; Willumsen, N. J. Assay Drug Dev. Technol. 2003, 1, 685. (385) Sauerbrey, G. Z. Phys. 1959, 155, 206. (386) Curie, J.; Curie, P. C. R. Acad. Sci. Paris 1880, 91, 294. (387) Nomura, T.; Okuhara, M. Anal. Chim. Acta 1982, 142, 281. (388) Keiji Kanazawa, K.; Gordon, J. G. Anal. Chim. Acta 1985, 175, 99. (389) Muramatsu, H.; Tamiya, E.; Karube, I. Anal. Chem. 1988, 60, 2142. (390) Marx, K. A.; Zhou, T.; Montrone, A.; McIntosh, D.; Braunhut, S. J. Anal. Biochem. 2007, 361, 77. (391) Braunhut, S. J.; McIntosh, D.; Vorotnikova, E.; Zhou, T.; Marx, K. A. Assay Drug Dev. Technol. 2005, 3, 77. (392) Wegener, J.; Seebach, J.; Janshoff, A.; Galla, H. J. Biophys. J. 2000, 78, 2821. (393) Fohlerová, Z.; Skládal, P.; Turanek, J. Biosens. Bioelectron. 2007, 22, 1896. (394) Li, F.; Wang, J. H. C.; Shun, T.; Wang, Q. M. 2004 IEEE Ultrasonics Symposium, Montreal, Aug 23−27, 2004; Institute of Electrical and Electronics Engineers: Piscataway, NJ, 2004; p 333. (395) Guo, M.; Chen, J.; Zhang, Y.; Chen, K.; Pan, C.; Yao, S. Biosens. Bioelectron. 2008, 23, 865. (396) Elsom, J.; Lethem, M. I.; Rees, G. D.; Hunter, A. C. Biosens. Bioelectron. 2008, 23, 1259. (397) Steinem, C.; Janshoff, A.; Wegener, J. Biosens. Bioelectron. 1997, 12, 787. (398) Lord, M. S.; Modin, C.; Foss, M.; Duch, M.; Simmons, A.; Pedersen, F. S.; Milthorpe, B. K.; Besenbacher, F. Biomaterials 2006, 27, 4529. (399) Cans, A. S.; H k, F.; Shupliakov, O.; Ewing, A. G.; Eriksson, P. S.; Brodin, L.; Orwar, O. Anal. Chem. 2001, 73, 5805. (400) Rich, R. L.; Myszka, D. G. Curr. Opin. Biotechnol. 2000, 11, 54. (401) Cooper, M. A. Nat. Rev. Drug Discovery 2002, 1, 515. (402) Hide, M.; Tsutsui, T.; Sato, H.; Nishimura, T.; Morimoto, K.; Yamamoto, S.; Yoshizato, K. Anal. Biochem. 2002, 302, 28. (403) Yanase, Y.; Suzuki, H.; Tsutsui, T.; Hiragun, T.; Kameyoshi, Y.; Hide, M. Biosens. Bioelectron. 2007, 22, 1081. (404) Lee, J. Y.; Ko, H. J.; Lee, S. H.; Park, T. H. Enzyme Microb. Technol. 2006, 39, 375. (405) Yanase, Y.; Suzuki, H.; Tsutsui, T.; Uechi, I.; Hiragun, T.; Mihara, S.; Hide, M. Biosens. Bioelectron. 2007, 23, 562. (406) Mozsolits, H.; Thomas, W. G.; Aguilar, M. I. J. Pept. Sci. 2003, 9, 77. (407) Gestwicki, J. E.; Hsieh, H. V.; Pitner, J. B. Anal. Chem. 2001, 73, 5732. (408) Vaddiraju, S.; Tomazos, I.; Burgess, D. J.; Jain, F. C.; Papadimitrakopoulos, F. Biosens. Bioelectron. 2010, 25, 1553. (409) Stevens, M. M.; George, J. H. Science 2005, 310, 1135. (410) Schvartzman, M.; Palma, M.; Sable, J.; Abramson, J.; Hu, X.; Sheetz, M. P.; Wind, S. J. Nano Lett. 2011, 11, 1306. (411) Hochbaum, A. I.; Aizenberg, J. Nano Lett. 2010, 10, 3717. (412) Bucaro, M. A.; Vasquez, Y.; Hatton, B. D.; Aizenberg, J. ACS Nano 2012, 6, 6222. (413) Patolsky, F.; Timko, B. P.; Yu, G.; Fang, Y.; Greytak, A. B.; Zheng, G.; Lieber, C. M. Science 2006, 313, 1100. (414) Duan, X.; Gao, R.; Xie, P.; Cohen-Karni, T.; Qing, Q.; Choe, H. S.; Tian, B.; Jiang, X.; Lieber, C. M. Nat. Nanotechnol. 2011, 7, 174. (415) Robinson, J. T.; Jorgolli, M.; Shalek, A. K.; Yoon, M.-H.; Gertner, R. S.; Park, H. Nat. Nanotechnol 2012, 7, 180. (416) Xie, C.; Lin, Z.; Hanson, L.; Cui, Y.; Cui, B. Nat. Nanotechnol. 2012, 7, 185. (417) Parpura, V. Nat. Nanotechnol. 2012, 7, 143. AL

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Chemical Reviews

Review

(418) Hung, P. J.; Lee, P. J.; Sabounchi, P.; Lin, R.; Lee, L. P. Biotechnol. Bioeng. 2005, 89, 1. (419) El-Ali, J.; Sorger, P. K.; Jensen, K. F. Nature 2006, 442, 403. (420) Zare, R. N.; Kim, S. Annu. Rev. Biomed. Eng. 2010, 12, 187. (421) Meyvantsson, I.; Beebe, D. J. Annu. Rev. Anal. Chem. 2008, 1, 423. (422) Young, E. W.; Beebe, D. J. Chem. Soc. Rev. 2010, 39, 1036. (423) Pizziconi, V. B.; Page, D. L. Biosens. Bioelectron. 1997, 12, 287. (424) Mizumoto, N.; Gao, J.; Matsushima, H.; Ogawa, Y.; Tanaka, H.; Takashima, A. Blood 2005, 106, 3082. (425) Kim, H.; Doh, J.; Irvine, D. J.; Cohen, R. E.; Hammond, P. T. Biomacromolecules 2004, 5, 822. (426) Krummel, M. F. Nat. Immunol. 2010, 11, 554. (427) Revzin, A.; Maverakis, E.; Chang, H.-C. Biomicrofluidics 2012, 6, 021301. (428) Kehat, I.; Khimovich, L.; Caspi, O.; Gepstein, A.; Shofti, R.; Arbel, G.; Huber, I.; Satin, J.; Itskovitz-Eldor, J.; Gepstein, L. Nat. Biotechnol. 2004, 22, 1282. (429) Banach, K.; Halbach, M. D.; Hu, P.; Hescheler, J.; Egert, U. Am. J. Physiol.: Heart. Circ. Physiol. 2003, 284, H2114. (430) Gilbert, A. N.; Firestein, S. Nat. Neurosci. 2002, 5, 1043. (431) Yu, H.; Cai, H.; Zhang, W.; Xiao, L.; Liu, Q.; Wang, P. Biosens. Bioelectron. 2009, 24, 1462.

AM

dx.doi.org/10.1021/cr2003129 | Chem. Rev. XXXX, XXX, XXX−XXX

Cell-based biosensors and their application in biomedicine.

Cell-based biosensors and their application in biomedicine. - PDF Download Free
21MB Sizes 1 Downloads 3 Views