Flow Sensing of Single Cell by Graphene Transistor in a Microfluidic

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Flow Sensing of Single Cell by Graphene Transistor in a Microfluidic Channel Priscilla Kailian Ang,†,‡ Ang Li,§,|| Manu Jaiswal,† Yu Wang,† Han Wei Hou,||,^ John T. L. Thong,# Chwee Teck Lim,*,§,||,^,z and Kian Ping Loh†,* †

Graphene Research Centre, Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore 117543 NUS Graduate School for Integrative Sciences and Engineering, Singapore 117597 § Singapore-MIT Alliance for Research and Technology (SMART), 28 Medical Drive, 05-06M, Singapore 117456 Division of Bioengineering and Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576 ^ BioSystems and Micromechanics (BioSyM) IRG, Singapore-MIT Alliance for Research & Technology (SMART), 3 Science Drive 2, S16-07-08, Singapore 117543 z Mechanobiology Institute, National University of Singapore, 5A Engineering Drive 1, Singapore 117411 # Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117576

)

‡

bS Supporting Information ABSTRACT: The electronic properties of graphene are strongly influenced by electrostatic forces arising from long-range charge scatterers and by changes in the local dielectric environment. This makes graphene extremely sensitive to the surface charge density of cells interfacing with it. Here, we developed a graphene transistor array integrated with microfluidic flow cytometry for the “flowcatch-release” sensing of malaria-infected red blood cells at the single-cell level. Malaria-infected red blood cells induce highly sensitive capacitively coupled changes in the conductivity of graphene. Together with the characteristic conductance dwell times, specific microscopic information about the disease state can be obtained. KEYWORDS: Graphene, field-effect transistor, microfluidics, single cell detection, infectious disease, cell surface charge density

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robing an individual diseased cell can reveal specific microscopic information of the state of disease, as opposed to averaged information collected from an ensemble of healthy and infected cells. Detection at the single-cell level requires the development of specialized techniques for cell manipulation, identification, isolation, and sorting.1 The many advantages of microfluidic flow cytometry include dynamic cell identification and sorting, repetitive usage without surface contamination and small sample volume.2 Flow sensing of single cell has been demonstrated by optomechanical systems3 and optofluidic circuits.4 However, these techniques require high power laser, expensive microlens setup for focusing incident light to the detection volume and fluorescence labeling of cells. Identifying a new platform that allows label-free electrical detection of single cells will be highly useful. A diseased cell expresses specific surface antigens mediating pathological cell
cell interaction. Since cell
cell recognition largely involves charge-based interaction, the expression of disease-induced cell surface antigens should produce detectable differences in surface charge density. The electrical detection of changes in surface charge density requires a sensor platform that can interface effectively with the cell and respond sensitively to r 2011 American Chemical Society

changes in the interfacial capacitance. In addition, when the sensor is incorporated in a microfluidic channel, it must maintain current stability without interfering with the hydrodynamics in the microenvironment. Although nanowire transistor has shown good performance in sensing single virus,5 the areal dimension of nanowire is incompatible with the size of a single cell. In addition, the placement of an array of nanowire transistors along the microfluidic channel can induce complex variations in the flow dynamics. In this regard, the high electrical conductivity and twodimensional nature of graphene render it an attractive material to be used as a signal transduction platform in the microfluidic channel. The electrical properties of graphene are strongly influenced by long-range charge scatterers and the local dielectric environment within a few nanometers from the two-dimensional sheet.6 Signal transduction based on detecting electrical charges of the target analyte, for example, DNA,7 has been demonstrated by static immobilization on graphene. To achieve the flow sensing of Received: July 28, 2011 Revised: October 29, 2011 Published: November 11, 2011 5240

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Figure 1. Graphene-based detection of single Plasmodium falciparum-infected erythrocyte (PE). (a) (Left) Schematic illustration of an array of graphene transistors on quartz. The electrodes are protected by a SU-8 photoresist that conveniently acts as the side wall for the microfluidic channel through which cells flow. (Right) Specific binding between ligands located on positively charged membrane knobs of parasitized erythrocyte and CD36 receptors on graphene channel produces a distinct conductance change. Conductance returns to baseline value when parasitized erythrocyte exits the graphene channel. (b) DIC image of independent graphene transistors with SU-8/PDMS microfluidic channel. Inset shows the etched graphene strip between source and drain electrodes. Scale bar is 30 μm. (c) Three-dimensional AFM images of (left) parasitized erythrocyte14 (scale bar is 1 μm) and (right) 3D height plot of the surface of parasitized erythrocyte revealing protruding knobs which overlaid with adhesion maps of knob ligands (PfEMP1) using CD36-functionalized AFM tip (yellow regions).

single cell, the challenges lie not only in the electrical detection, but also in the successful execution of a “flow-catch-release” sequence so that the sensing surface can be regenerated. In this case, the graphene sensor must trap the cell momentarily and sense it before it is released. To achieve that, functionalization of the graphene with biorecognition proteins that bind specifically to the target cell with the appropriate strength is needed. In this work, we demonstrate the flow-catch-release sensing of single malaria-infected red blood cell on the graphene sensor platform. Malaria is one of the world’s most deadly infectious diseases in which the parasite strain, Plasmodium (P.) falciparum, is the most virulent. The hallmarks of an infection by P. falciparum include several irreversible structural modifications of the parasitized red blood cells (or erythrocytes (PEs)) namely, (1) gradual loss of deformability,8,9 (2) the export of parasite proteins into the cytosol and membrane of the host erythrocytes,10 and (3) development of positively charged knob-like protrusions on the surfaces of the cells.11,12 The gradual manifestation of these structural modifications distinguish the two stages of infection known as the trophozoite and schizont stages. The pathophysiological process involves the cytoadherence of the PEs to endothelial cells lining the blood vessels and capillaries, mediated by specific interaction between a family of parasitic ligands on the knobs of the PEs (Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1)) and the CD36 receptor proteins on the endothelial cells.13 Therefore, in the flow-catch-release scheme, graphene is functionalized with the endothelial CD36 receptors

for the selective capture of the malaria-infected cells when the diseased blood (consisting of a mixture of healthy and infected red blood cells) flows through the microfluidic channel. In addition, the optical transparency of graphene allows the simultaneous optical monitoring of binding events via differential interference contrast (DIC) microscopy during the electrical sensing. The flow sensing of PEs is illustrated schematically in Figure 1a. An array of graphene field-effect transistors (FETs) is constructed on a quartz substrate (Figure 1b) using chemical vapor deposited (CVD) graphene film and patterned using photolithography. Chemically resistant SU-8 polymer (∼4.5 μm thick) insulates the electrodes and also acts as the side wall of the microfluidic channel. The inlet and outlet regions of the microfluidic channel are 20 μm wide, bearing an 8 μm wide channel constriction to allow the flow and electrical detection of a single cell. Any incidental debris will be trapped in the 20 μm wide channel. A slab of polydimethylsiloxane (PDMS) with two punched holes for the inlet and outlet is physically clamped on top of the SU-8 side walls. The Ag/AgCl-reference gate electrode remains in contact with the fluid at the outlet for electrochemical top-gating of the graphene device. The principle of sensing is based on changes in drain
source conductivity of the graphene channel upon the binding of the infected cells to the receptor-functionalized graphene. The positively charged membrane knobs, as shown in the 3D atomic force microscope (AFM) image in Figure 1c (right), exert a Coulomb potential on graphene, thereby doping graphene with electrons. 5241

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Figure 2. Characterization and PE detection of CD36-functionalized graphene transistor. (a) CD36 coupled with FITC-conjugated CD36 antibody on graphene (inset) shows significant fluorescence quenching as compared to that on quartz. Scale bar is 100 μm. (b) AFM image and height profile of CD36 coupled with FITC-conjugated CD36 antibody on graphene shows a combined protein height of ∼3 nm. (c) Device performance for graphene and CD36-functionalized graphene gated in culture medium. Inset shows the corresponding capacitance
voltage measurements. Device channel length and width are 10 and 20 μm, respectively. (d) Ids
Vg curves shows distinct shifts in charge neutrality point when PE adhered onto CD36-functionalized graphene channel. The increase in the width of the minimum conductivity plateau upon PE adhesion is also evident.

Since graphene exhibits ambipolar behavior, the p-type or n-type behavior can be tuned effectively by the gate voltage. A positively gated graphene will exhibit an increase in its channel conductance upon the binding of the infected cell because of electrostatic doping. When the infected cell leaves the sensing area, the conductance returns to baseline value and remains relatively stable until the next binding event. To sense cells, it is necessary to adsorb a thin layer of biorecognition proteins on the sensing platform. The aromatic scaffold of graphene15 allows it to bind strongly to biorecognition proteins through noncovalent interactions.16 The uniform distribution of CD36 on graphene and quartz can be visualized by labeling with fluorescein isothiocyanate (FITC)-conjugated CD36 antibody. The epifluorescence image of CD36 coated on quartz (Figure 2a) reveals uniform protein distribution. However, the visualization of FITC-conjugated CD36 antibody complex on graphene is hampered by fluorescence quenching (inset in Figure 2a), due possibly to resonance energy transfer between the FITC molecule and graphene π-electronic framework.17 Judging from the weakly fluorescent boundary between CD36coated graphene surface and the surrounding quartz, the AFM height profile of CD36-FITC-conjugated-CD36-antibody complex on graphene was measured to be ∼3 nm (Figure 2b). Therefore, we can deduce that the height of the CD36-coated graphene is less than 3 nm and charge-based detection of receptor
ligand binding between ligands on PE membrane knobs and CD36

receptors should occur within the Debye length of ∼3 nm for electrolyte ionic strength of ∼10 mM. The stability and viability of CD36 on graphene and quartz are determined from the adhesion force of CD36-functionalized AFM tip, which yielded 407 and 309 pN for graphene and quartz, respectively. The stronger adhesion force of CD36 on graphene compared to quartz suggests that the adsorbed biorecognition proteins are sufficiently stable for flow sensing (Figure S1 and S2 in Supporting Information). The binding/unbinding events of single PE at the graphene active area can be monitored simultaneously by DIC visualization and channel conductance. No specific labeling of fluorescent molecules to PEs is needed to differentiate PE-containing trophozoite or schizont parasite because the transparency of graphene allows the membrane deformation and parasite body to be clearly distinguishable under DIC visualization. Therefore, the implementation of simultaneous label-free optical and electrical detection of PE is viable on graphene without the need for fluorescence labeling. The PE-induced changes to graphene transport properties are evaluated using a combination of capacitance
voltage (C
V) and drain
source current
gate voltage (Ids
Vg) measurements. The characteristic ambipolar nature of graphene transistor gated in the culture medium is shown in Figure 2c. The charge neutrality point (CNP) is at ∼0.1 V, which can possibly be due to the p-type doping by SU-8 resist. The gate potential (Vg) is converted to 5242

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Figure 3. Parasite differentiation. (a) Conductance-time plots for (early to mid) trophozoite-PE and schizont-PE measured at Vg = 0.1 V and corresponding DIC images on the right. Device channel length and width is 8 and 15 μm, respectively. (b) Box plots of percentage conductance changes for trophozoite-PE and schizont-PE. The top and bottom of the box denote 75th and 25th percentiles of the population, respectively, while the top and bottom whiskers denote 90th and 10th percentiles, respectively. Maximum and minimum values are denoted by open squares. Gaussian distribution of the raw data points is shown. (c) Conductance-time plot for the occupation of 2 schizont-PE shows distinct conductance rise which corresponds to single schizont-PE.

charge carrier density, n, by integrating the capacitance
voltage curves (inset in Figure 2c) nðVg
Vg 3 min Þ ¼

1Z e

Vg Vg:min

CðV ÞdV

ð1Þ

where e is the electronic charge, C(V) is the voltage-dependent total capacitance at the interface of graphene and Vg,min is the potential at the CNP of graphene. The corresponding resistancecarrier density profiles can be fitted to the equation18 RðnÞ ¼ Rc þ

L qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 WeμFE n20 þ ðn
nÞ ̅

ð2Þ

where n is the modulated carrier concentration, n0 is the carrier density at the conductivity minimum or residual carrier density, n is the shift in minimum conductivity, and L and W are the length and width of the graphene device, respectively. The charge carrier mobility for a representative etched graphene strip is estimated from the fitting eq 2 to be 721 cm2/(V s) while that of CD36functionalized graphene strip is 681 cm2/(V s). With the integration of microfluidics, both static and flow sensing of single PE can be performed. Static sensing of trophozoite-PE and schizont-PE is achieved when the flow pressure is adjusted to 0.3 Pa8 to facilitate the stationary adhesion or arrest of rolling PEs. This allows the collection of Ids
Vg curves with a gate voltage sweep rate of 0.1 V/s for trophozoite-PE and

schizont-PE. Several key findings are shown in Figure 2d. First, we observed that CD36-functionalized graphene shows specific binding to PE and nonspecific interaction with healthy red blood cell is absent. Moreover, PE must be in contact with graphene to yield a detectable electrical response since electrostatic forces decay rapidly with separation from graphene. Second, the voltage shift in CNP (ΔVg,min) with respect to CD36-functionalized graphene (and the corresponding density change in the position of conductivity minimum) is given by
65 ( 10 mV (1.0  1011 cm
2) and
90 ( 15 mV (6.0  1011 cm
2) for trophozoite-PE and schizont-PE, respectively. Schizont-PE induces a larger negative shift in CNP due to higher charged knob density.12 Third, the width of the voltage plateau at CNP (n0), increases from 1.6  1012 to 2.2  1012 cm
2 upon adhesion of PE. This is an indicator of enhanced charge inhomogeniety in the graphene, which is more pronounced when the ratio of cell-to-graphene channel area is larger (Figure S3, Supporting Information). The flow-catch-release sensing of single PE is demonstrated when the flow pressure is adjusted to 5 Pa. Figure 3a shows the discrete time-dependent changes in conductance corresponding to the adherence of a single trophozoite-PE or schizont-PE as it rolls across the CD36-functionalized graphene. All transistors are gated at CNP to avoid sample-to-sample variation in the initial state of device. The different level of conductance rise measured at CNP (Vg = 0.1 V) shows the ability to differentiate between the trophozoite and schizont parasite development stage. The 5243

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Figure 4. Effect of charge impurity density on graphene device performance. (a) Conductance vs carrier density, n, near CNP for protein-functionalized graphene in solution. The blue curve is based on the experimental data for protein-functionalized graphene. Curves with increasing conductance represent graphene under the influence of charged cells with varying percentage of randomly distributed cell area in proximity with graphene: 0% (blue), 5% (red), 10% (black), 15% (yellow), and 20% (green). Input parameters: top-layer dielectric constant, ε = 10, cell
graphene separation z = 2 nm and density of Coulomb scatterers, Nic = 1  1012 cm
2 are chosen to simulate the local changes in graphene conductivity (b) Simulated changes in the minimum conductance of graphene upon adhesion of cells of different knob density. Baseline is given by protein-functionalized graphene doped at CNP to match experimental conditions.

motion of the trophozoite-PE and schizont-PE rolling across the graphene channel is captured in Movie S1 and Movie S2 in the Supporting Information, respectively. While the adherence of trophozoite-PE produces a conductance change of 5.1 ( 0.3%, schizont-PE induces a change of 8.4 ( 1.3% (Figure 3b). Even at a high flow pressure of 5 Pa, the baseline conductance values remain relatively constant and any conductance change corresponding to a sudden increase in flow pressure is small (∼2%) compared to that induced by PE adherence (Figure S4, Supporting Information). This shows good device reproducibility with minimum fouling of the graphene sensor platform. The ambipolar nature of graphene allows detection at negative gate voltage as well. A reverse trend in conductance drop is obtained in this case when time-dependent conductance was measured at Vg =
0.2 V. The adherence of schizont-PE typically induces a conductance drop of ∼10%, as shown in Figure S5 and Movie S3 (Supporting Information). The sensitive detection of single cell binding event is further shown in Figure 3c and Movie S4 (Supporting Information) where the adherence of two schizontPEs on graphene induces two sharp conductance rises when measured at Vg = 0.1 V. When a single schizont-PE rolls across graphene, the change in conductance is ∼8%. When a second schizont-PE squeezed past a restricted area occupied by the first schizont-PE, the resultant conductance change is ∼14%. This slight decrease from the expected conductance change of ∼16% is due to the slightly reduced surface interaction when two PEs squeeze into the channel. Any model that accounts for cell
surface interaction should consider the cell as a highly charged and irregular entity. In this case, several undulations and knobs characterize the PE cell surface (Figure 1c). Only knob sites that adhere to the thin protein layer can exert significant Coulomb potential on charge carriers in the graphene sheet.18 The scattering arising from a majority of the charged cell surface is exponentially screened by a factor exp(
qz) where q is the momentum transfer upon

scattering and z is the cell
graphene separation (see Supporting Information for a different case that treats cell as a charged planar surface).19 For pristine graphene doped at CNP, the energy landscape is usually described by the formation of electron
hole puddles with typical energy, EF = pνF(πn0)1/2, where n0 is the disorder-induced residual carrier density.20 However, in regions where the heavily charged cell surface is in close proximity to the graphene sheet, the size of these local electron-doped charged puddles is expected to be much larger in energy space. Under the assumption that the typical spatial extent of such charged puddles is comparable or larger than the mean free path, the cell
graphene system can be modeled as a two-dimensional random two-resistor network comprising of graphene regions with and without cell-induced Coulomb disorder.21 For graphene areas which are in close proximity to the cell, an additional Coulomb disorder term is introduced. The shift in Fermi level and change in mobility in these puddles is calculated based on the selfconsistent theory for charge impurity scattering in graphene.19 A charge impurity density of Nic = 1  1012 cm
2 and a cell
graphene separation of z = 2 nm is assumed. The total conductivity of graphene is then estimated for various fractions of such randomly distributed puddles based on the theory for twodimensional networks.21 Figure 4a shows the conductivity changes near CNP arising from this inhomogeneous charge density configuration, considered for the case of low density (5 - 20%) of knobs and undulations. Key features of detection involving conductivity changes near CNP (simulated in Figure 4b) can be explained using this model, while remote charge scatterers may contribute to the shift in the position of conductivity minimum (see Supporting Information for a discussion on density shifts arising from charged scatterers). The different output signals associated with the adherence of trophozoite-PE and schizontPE can be understood within the density inhomogeneity model, since these cells are characterized by different charged knobs densities on their surface.12 5244

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Figure 5. Adhesive dynamics and parasite differentiation. (a) PE containing early trophozoite rolls along the surface with tank-threadinglike motion. (b) PE housing late trophozoite exhibits tumbling or flipping dynamics. (c) PE bearing schizont peels off the surface under hydrodynamic flow, followed by crawling dynamics possibly due to strain of parasite body on the membrane. (d) Box plots of cell velocities for trophozoite-PE and schizont-PE velocity obtained from the time taken for PE to cross graphene channel of 15 μm. The top and bottom of the box denote 75th and 25th percentiles of the population, respectively, while the top and bottom whiskers denote 90th and 10th percentiles, respectively. Maximum and minimum values are denoted by open squares. Gaussian distribution of raw data points is shown.

The simultaneous DIC visualization and time-dependent conductance measurements also provide an avenue to elucidate the adhesive dynamics of PE, thereby allowing for parasite differentiation. The rolling of PEs along the microfluidic channel lined with parallel graphene active channels is facilitated by fluid stresses and random bombardment with other flowing red blood cells.22 As the PE enters the graphene channel, conductance increases and remains relatively constant for duration, t, before dropping to baseline value when the PE exits the graphene channel (Figure 3a). The duration, t, is known as the conductance dwell time, and from it the cell velocity can be calculated. The cell velocity is closely related to the dynamics of adhesion. As the cell progresses from the trophozoite to schizont stage of infection, its membrane stiffness increases. With a larger surface area of contact and lower membrane stiffness, it is noticed that early trophozoite-PE crawls along the surface in a tank-threadinglike motion (Figure 5a).8 As the intracell parasite develops to late trophozoite and schizont (Figure 5b and c), the dynamics of adhesion changes to fast flipping or tumbling from side to side, some rolling motions and occasional firm adhesion.9 The motions of the early trophozoite-PE, late trophozoite-PE and schizont-PE are captured in Movie S5 in Supporting Information. The mean cell velocity calculated from the conductance dwell time for trophozoite-PE (13 μm/s) is lower than schizont-PE (19 μm/s) with a statistical difference of p = 0.02865 at the 0.05 level using the two-sample Student’s t test. This increase in cell velocity as a function of membrane stiffness can be understood as such: a higher membrane stiffness results in a smaller contact area as the cell is less deformable, and thus PE rolls faster across the graphene active channel. The relationship between the calculated cell velocities and membrane stiffness correlates very well with numerical simulations.8 This shows that the computation of conductance dwell time and resultant cell velocity could also be used as additional parameters for statistical differentiation of the diseased cells. In summary, we have constructed a graphene sensor that is integrated with microfluidics and demonstrated successful sensing of malaria-infected red blood cells at the single-cell level. The graphene sensor is able to generate dynamic disease diagnostic

patterns in terms of conductance changes and characteristic dwell times. Coulomb impurity potential exerted by charged protrusions on cell surfaces induces local doping and produces characteristic conductance changes. The ability of graphene to interface readily with cell-recognition proteins, coupled with its optical transparency, sensitivity toward capacitively induced conductance changes and easy integration into microfluidic flow cytometry, exhibit great promise in clinical diagnostic applications.

’ ASSOCIATED CONTENT

bS

Supporting Information. Additional details on experimental methods, figures showing the device characteristics of larger graphene channel area, current stability of graphene transistor, time-dependent conductance plot for schizont-PE gated at Vg =
0.2 V, and influence of adherent cells on graphene electrical properties. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: (K.P.L.) [email protected]; (C.T.L.) ctlim@nus. edu.sg.

’ ACKNOWLEDGMENT K.P.L. acknowledges the support of NRF-CRP Grant Graphene Related Materials and Devices (R-143-000-360-281). ’ REFERENCES (1) Bhagat, A. A.; Bow, H.; Hou, H. W.; Tan, S. J.; Han, J.; Lim, C. T. Med. Biol. Eng. Comput. 2010, 48, 999–1014. (2) Hou, H. W.; Bhagat, A. A.; Chong, A. G.; Mao, P.; Tan, K. S.; Han, J.; Lim, C. T. Lab Chip 2010, 10, 2605–2613. (3) Wang, Z.; El-Ali, J.; Engelund, M.; Gotsaed, T.; Perch-Nielsen, I. R.; Mogensen, K. B.; Snakenborg, D.; Kutter, J. P.; Wolff, A. Lab Chip 2004, 4, 372–377. (4) Kim, M.; Hwang, D. J.; Jeon, H.; Hiromatsu, K.; Grigoropoulos, C. P. Lab Chip 2009, 9, 311–318. 5245

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