1Department of Electrical and Computer Engineering and 2Department of Biomedical Engineering, University of Iowa College of Engineering; 3Department of Internal Medicine, University of Iowa College of Medicine and Veterans Administration Hospital, Iowa City, Iowa; and 4Department of Aeronautical, Mechanical and Biomedical Engineering, University of Tennessee, Knoxville, Tennessee
Submitted 7 March 2005 ; accepted in final form 2 May 2005
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ABSTRACT |
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cell-cell adhesion; cell-matrix adhesion; cell membrane capacitance; mathematical computation
Identifying the specific cell adhesion sites and cytoskeletal membrane properties that regulate membrane integrity and function under physiological and pathological conditions represents a complex task, because an intervening cytoskeletal network mechanically couples cell-cell and cell-matrix adhesion sites. For example, if the cytoskeleton is viewed as an integrative structure, external stimuli can disrupt cell-cell adhesion through two basic mechanisms. Activation of signal transduction events could decrease adhesion at cell-matrix sites and cause cell rounding, which in turn could result in a secondary or reactive loss in cell-cell adhesion. Alternatively, activation of signal transduction pathways may directly target cell-cell adhesion sites and cause a direct loss in cell-cell adhesion with a reactive loss in cell-matrix adhesion. Because there are distinct adhesion proteins at cell-cell and cell-matrix sites that could be affected differentially by signal transduction pathways, it is important to identify the spatiotemporal characteristics by which molecular signals differentially affect cytoskeletal membrane properties. Thus numerical models and simulations are a critical part of evaluating the complexities of these signal transduction pathways.
Giaever and Keese (6) were the first to introduce a closed-form, mathematical model that characterizes cell-cell and cell-matrix adhesion in cultured fibroblasts by measuring transcellular impedance of a cultured monolayer grown on a microelectrode exposed to an alternating current. Measurements of cell-cell and cell-matrix adhesion were resolved in confluent monolayers by mathematically modeling the impedance across a cell-covered electrode as an electrical circuit consisting of a capacitor and resistor in series. The published closed-form solution was based on treating cells as a disk shape and organized in a fashion in which individual cells make contact with neighboring cells but with gaps between cells (Fig. 1). Of particular note was that the boundary geometry and conditions of this model were not previously disclosed in that report (6) or in a later one regarding cultured epithelial cells published by the same authors (14). The model characterizes transcellular impedance into three unknown solution parameters in confluent, cultured cells: Rb (the impedance due to cell-cell adhesion), (impedance due to cell-matrix adhesion), and Cm (membrane capacitance due to transcellular electrical conduction). In principle, the unknown parametric solutions of cell surface membrane properties are resolvable, provided that there are at least an equal number of experimental measurements for the number of unknown solution parameters. One of the key advantages of measuring barrier function using an alternating current rather than the conventional use of a direct current is that experimental impedance is measured at multiple frequencies, which permits simulating and solving for these unknown membrane properties.
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MATERIALS AND METHODS |
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Measurement of transendothelial impedance on a microelectrode biosensor.
Endothelial barrier function was measured using a previously reported electrical substrate impedance sensing (ESIS) technique (68, 18). In this system, cells were cultured on a small, gold electrode (5 x 104 cm2) using culture medium as the electrolyte and barrier function was measured dynamically by determining the electrical impedance of a cell-covered electrode. A variable voltage-alternating signal was supplied through a 1-M resistor between frequencies of 25 and 60,000 Hz. Voltage and phase data were measured using a model SRS830 lock-in amplifier (Stanford Research Systems) and then stored and processed using a personal computer. The same computer also controlled the output of the amplifier and mechanical relay switches to different electrodes using custom software written by Applied Biophysics. Cultured HUVECs were inoculated on electrodes at a confluent density of 105 cells/cm2. Proprietary algorithms (Applied Biophysics) mathematically converted the in-phase and out-of-phase voltage into the resistance and capacitance, respectively, on the basis of the assumption that both the naked electrode and the cell monolayer were being treated as resistor and capacitor in series.
Software architecture of the numerical modeling. A LabView version 6.0 graphics software application development environment for data acquisition, analysis, signal processing, and instrument control was obtained from National Instruments (Austin, TX). The Microsoft Visual Studio integrated development environment (IDE) was obtained from Microsoft (Redmond, WA). LabView algorithms called Electrical Impedance Modeling Analysis and Simulation (EMAS) were developed to model cell membrane parameters from the measured transendothelial impedance as discussed in the next subsection.
Modeling approach. Transendothelial impedance across a cell-covered electrode was measured at 23 different frequencies. Figure 1 represents a diagram of the primary current flow paths across a confluent monolayer. Each current flow path is affected by small spatial changes in the cellular shape, which dynamically modify transendothelial impedance at each of the measured frequencies.
Three separate cardinal current flow paths govern the total modeled impedance across a confluent monolayer of endothelial cells. The first current flow path lies between the ventral surface of the monolayer and the surface of the naked electrode and is described by the parameter , which is expressed in units of
·cm. The
term is defined by the expression
= rc
, which is dependent on the average separation distance (h) between the ventral membrane surface and the substratum, the solution resistivity, Rho (
), of the culture medium, and the cell radius (rc). The current flow path between the adjacent edges of the cells within the monolayer is labeled with the parameter (Rb) and is expressed in units of
·cm2. The final current flow path is capacitive in nature and relates to transcellular current flow through a ventral and dorsal plasma membrane. The transcellular current is dominated by the membrane capacitance parameter (Cm) along with a transmembrane resistance and a transcytoplasmic component, which is fixed within the model. The parameter Cm is reported in µF/cm2.
Fundamental equations.
The following model characterizes endothelial cells as having a disk shape and being arranged in a repeating pattern. If rc is defined as the cell radius, then the cell area is ·r
and the cell perimeter is 2·
·rc. The general model predicts the experimental impedance spectrum by applying the classical Ohm's and Kirchoff's laws for electrical currents. Eqs. 13 are Ohm's law formulations, and Eq. 4 applies Kirchoff's current law, which couples the first three equations:
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The symbols I0(rc) and I1(
rc) represent Bessel functions of the first kind, orders 0 and 1, respectively, with arguments of
rc. In the formulation for
rc, the parameter for
, which may represent vascular attachment, is shown with alternate relationships exposing h and
. It is important to note that the original report by Giaever and Keese (6) characterized the impedance due to transcellular membrane conductance as two capacitors in series, one capacitor for the basal membrane and the other for the apical membrane (Eq. 19), in which (j) is used to represent the imaginary number that results from taking
and (f) represents the current frequency:
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Description of Levenberg-Marquardt nonlinear simulation procedure. The Levenberg-Marquardt nonlinear simulation (LM-NLS) routine is capable of providing optimal estimates for the parameter solutions using the mathematical model in full complex form, magnitude form, a real valued form, or an imaginary form. The magnitude form creates and curve fits a single quantity by taking the square root of the sum of the squares of the real and imaginary parts, respectively. The complex form curve fits two independent vector quantities of impedance in a balanced fashion by treating the real and imaginary parts separately. The full, two-dimensional, complex parameter estimation process attempts to find optimum parameter values that minimize the error that exists between the simulated and experimental total cell-covered responses in both the real and imaginary components simultaneously. The magnitude formulation of the parameter estimation process uses a one-dimensional real value result that combines the real and imaginary components simultaneously while balancing the minimized error in both the real and imaginary components between the simulated and experimental responses. The real or imaginary optimization modes are one-dimensional as well, but optimize only against the real or imaginary component alone, respectively, when selected.
Error evaluation.
The real and imaginary experimental data of the cell-covered electrode (Zc) and the naked electrode (Zn) were measured at 23 frequencies between 25 and 60,000 Hz. The values of Zc used in the model were measured 24 h after cell attachment at time points at which the endothelium achieved a steady-state transendothelial resistance (TER). Values of Zn were measured after trypsinization of the cultured monolayers and replacement with fresh medium. Final calculated real and imaginary value solutions (Zs), using Eq. 21, were generated. The solutions generated were based on a set of (parms), specifically , Rb, and Cm, over the desired frequency range (f). The function to be fit with an optimum parameter set is, of course, Eq. 18, which describes the simulated or calculated impedance, Zs.
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Simulation procedure. The LabView-based graphical user interface provides user control over the simulation and parameter estimation routines embedded in the C++ dynamic link library. The user interface also provides numerical and graphical feedback of simulation and analytic results. The user supplies both cell-covered and naked electrode data for graphical inspection, along with an overlay of the simulated cell-covered electrode. The user observes the results as the parameters are adjusted until a reasonably close match between the simulated and actual cell-covered responses occurs. This constitutes an initial guess for the parametric estimation process and can be used to update the simulation for greater accuracy.
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RESULTS |
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Curve fitting between the calculated and the experimental real data appears similar using visual inspection between the different optimization approaches. However, the derived values of , Rb, and Cm for the different visually based optimization approaches compared with the LM-NLS procedure are distinctly different (Table 1). The values of
, Rb, and Cm were similar between those derived by a LM-NLS procedure and a systematic visual inspection approach that considered the heuristic features of the real data, which would be considered the visual approach of an expert user. The percentage accuracy, defined as the percentage of the solution parameters derived by a heuristic approach of the values derived by the LM-NLS procedure, was small (<5%). However, the values derived using a typical nonheuristic approach, which would be considered the visual approach of a layperson or an inexperienced user, resulted in a much greater difference, which ranged from 8% for Cm, to 32% for Rb, and to 47% for
compared with the LM-NLS procedure solutions. Taken together, these results demonstrate that deriving parametric solutions solely on the basis of visual inspection lacks accuracy and supports the requirement for numerical nonlinear optimization approaches to derive parametric solutions regarding cell membrane properties.
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Frequency-dependent error and solution parameter stability when modeling the experimental imaginary, magnitude, and complex data.
If the numerical model is sufficient to derive model solution parameters, then the LM-NLS model should produce the same solution parameters, regardless of whether it is modeling the real data, imaginary data, magnitude data, or the complex data of the impedance. Before we can assess whether the LM-NLS can achieve stable solutions across these different data, it is critical to evaluate how modeling stability is affected by the frequency spectrum for each type of data. Figure 5 shows the correlation between numerical solution parameters and reduced 2 for imaginary data. Modeling only the imaginary data demonstrates similarities and unique behaviors compared with modeling only the real data. Like modeling the real data, model stability and error were worse at low frequencies. However, the impedance spectrum that achieved the most stable model solutions at the least
2 error was narrower than that observed when modeling the real data. In addition, the frequency spectrum for identifying the most stable solution parameter at a low plateau
2 error for the imaginary data (1015 kHz) did not overlap with the impedance spectrum for real data (210 kHz). Again, like the real data, modeling the imaginary data at >15 kHz resulted in a lower reduced
2 error as well as a less stable solution for
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DISCUSSION |
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The data show that the estimations of model solution parameters of cell membrane properties are dependent on the frequency spectrum and the type of impedance data subjected to the LM-NLS procedure. Modeling stability was assessed by examining how the LM-NLS estimated cell-membrane parameters and 2 error for the real, imaginary, complex, and magnitude transendothelial impedance data as a function of frequency bandwidth. For each type of experimental data, model solution parameters were dependent on unique frequency spectra. The frequency spectra that achieved the most optimal solutions at low plateau level
2 error were nonoverlapping when the LM-NLS method was used to estimate solution parameters from the real and imaginary experimental data. Optimization of solution parameters on the basis of the magnitude and complex modes took on the partial character of the real and imaginary formulations. The frequency bandwidths to identify stable solution parameters on the basis of magnitude and the complex data did not represent the sum of the bandwidths of the real and imaginary data alone. Because the optimal frequency bandwidths for the real and imaginary data were nonoverlapping, the real and the imaginary data have different impacts on the magnitude and the complex data. The magnitude data represent a one-dimensional quantity formed from the real and imaginary data components. If the bandwidths for identifying stable solution parameters for the real and imaginary data components did not overlap, then it would be anticipated that the bandwidth for identifying stable solutions on the basis of magnitude data would not increase, which was the case. In contrast, the complex mode formulation represents a two-dimensional quantity of the real and imaginary input components, which requires satisfying the model for both data types simultaneously. Because the frequency bandwidths for the real and imaginary data did not overlap, it was expected that optimization algorithms to derive stable solutions at minimal error would occur over a narrow-frequency bandwidth, which was the case.
By choosing the appropriate bandwidths for the analysis and by minimizing the 2 result in each case, the LM-NLS procedure achieved very consistent results. Upon analyzing the model, we found that our data show that the extrapolation error also needs to be minimized. This notion is supported by the notion that the cell membrane parameters were consistent between the real, imaginary, complex, and magnitude data sets when a strategy was used to identify the frequency spectrum that minimized error in terms of the measured Zerror or
2. Graphed plots with the measured Zerror were used to extract the actual extrapolation error. In contrast, when a fixed frequency spectrum that was suited to the real data was applied to the imaginary, magnitude, and complex data sets, there was added variability in the estimates in the cell membrane parameters, which indicates that such an approach leads to greater modeling error and parametric estimate instability. In particular, the greatest variability in the solution estimates occurred in cell-matrix adhesion when modeling the different types of impedance data. Furthermore, the model solution parameter instability was observed most frequently for the cell-matrix adhesion parameter in a frequency-dependent fashion. Extremely low values for
2 can be achieved if not enough data are used for fitting. For example, if only three data points were used to derive solution parameters, a
2 of 0 would likely result. However, there is a tradeoff in choosing a too small data subset to minimize
2, which would unduly affect the model predictions for the larger original data set.
Under ideal conditions, the instrumental noise would also be known at each frequency and the model would provide a true representation of the experimental system. In these cases, the successful optimization of the model parameters would produce a reduced 2 on the order of unity. Frequency data points with large deviations would be weighted less than those with smaller deviations. In cases in which the instrumental noise is not known, one begins by assuming that noise remains constant. If the underlying noise distribution is frequency dependent, large numerical instabilities can arise during the optimization process and determining a stable range of sampling frequencies would be necessary. Frequency-dependent systematic errors can introduce an additional complication.
Although introducing noise measurements into the 2 analysis can improve the stability, it can introduce additional numerical artifacts. In cases in which the noise fluctuations are insignificant, singularities could arise during the computation. This can occur, for example, when filtering successfully reduces the electrical fluctuations to the level of the analog-to-digital discretization level. If the noise is non-Gaussian, the estimation would not be a maximum likelihood. Filtering, 60-Hz noise, and other artifacts, for example, could introduce non-Gaussian noise into the data.
By their nature, nonlinear optimization algorithms can produce optimized parameters that are dependent on the starting parameters. By preceding the nonlinear optimization with a visual fit, a more appropriate starting parameter can be chosen.
Identifying the optimal frequency bandwidth was first accomplished by identifying the upper- and lower-frequency bands at which Zerror was typically low (<10%) by first applying the LM-NLS procedure to the entire experimental data set between 25 and 60,000 Hz. Next, the optimization algorithms were repeated with the restricted data subset at the targeted frequency bandwidth. Our data demonstrate that using criteria that derived cell-membrane parameters on the basis of minimizing the 2 error in the LM-NLS optimization resulted in the most stable and reproducible cell membrane parameters.
The present data also demonstrate the principle that deriving solution parameters on the basis of visual inspection criteria alone is prone to potential error. Choosing a visual fit between the calculated model and the experimental data without regard to the heuristically guided approach potentially leads to significant error. Yet, even with a heuristically guided approach, some remaining error cannot be eliminated. The current data demonstrate the importance of a numerically guided approach that automates and finds model solution parameters on the basis of recognized numerical optimization approaches.
In our study, we also evaluated the impact of the membrane-resistive component of the cell monolayer impedance. Previous reports by Giaever and Keese (6) did not document the impact of the membrane resistance on modeling error and parametric solution estimates. As defined in Eq. 20, transcellular membrane impedance is inversely related to the value of Rm. We observed that the impact of Rm on model solution parameters of , Rb, and Cm as well as
2 becomes inconsequential as values of Rm exceed 200
/cm2. The limited role of Rm is consistent with the empirical evidence showing why it is possible to measure the very low ion conductance using patch-clamping techniques (2). To measure the typical picoampere levels of ion channel conductance on the basis of Ohm's law, Rm must be large so that it can be treated as a constant.
The model originally proposed by Giaever and Keese (6) has a systematic Zerror between the experimental and the calculated data on the basis of the model. While the model captures the experimental data for the real data at frequencies >2,000 Hz (<5% error), the model does not fit the data at low frequencies. Furthermore, the model captures an even narrower subset of the data for the imaginary, magnitude, and complex data. To compensate for this systematic error and achieve modeling stability, we developed computational algorithms that can select a subset of the original database at variable-frequency bandwidths. In this fashion, we are able to exclude data in the frequency range <2 kHz, where the error is most prevalent.
There are several potential explanations for the systematic error between the calculated and the experimental data, which require an understanding of the assumptions of the experimental measurement and the numerical model originally proposed by Giaever and Keese (6). First, there may be systematic error introduced by the instrumentation circuit, which affects how biological activity is measured. The original model proposed by Giaever and Keese assumes that both the naked electrode and the cell-covered electrode behave as resistor and capacitor in series. However, this cannot be validated directly, because the ESIS system provides only the resistance and capacitance measurements, which are mathematical conversions of the raw data (in-phase and out-of-phase voltage). The mathematical conversion algorithms are proprietary and protected by trade secret.
Second, the model assumes that there is no drift in the instrumentation system. Zn is not modeled but is simply mathematically divided into Zc, which holds true only if both Zn and the transendothelial impedance are both resistors and capacitors in series. If either the monolayer or the electrode does not behave as resistor and capacitor in series, then different numerical expressions for Zs and Zc are required. Furthermore, the model must assume that Zn behaves as a constant and exhibits no measurable drift over time. If there is significant electrical drift, then Zn needs to be modeled numerically. Thus, for this reason, we chose experimental data for Zn after removing cells from the electrode with trypsin to reduce Zerror.
Third, the model assumes that the cell geometry is disk shaped and contains gaps between cells. Because endothelial cells are not disk shaped, it remains to be validated whether the model solution parameters and the model stability are affected by selecting a different cell geometry.
Fourth, the LM-NLS optimization assumes a Gaussian distribution of noise across all measured frequencies. For these analyses, the value for the
2 was assumed to be unity, because
was unknown and was not experimentally measured. If there is variable distribution of noise as a function of frequency, then the optimization algorithms require a weighted function to compensate for frequency-dependent noise levels.
The results of these data document that modeling transendothelial impedance as a circuit that consists of a repeating pattern of disks and a resistor and capacitor in series is not sufficient to model the entire impedance frequency spectrum. The present data indicate that a more complicated numerical model is required to characterize the entire impedance spectrum between 25 and 60,000 Hz. More complicated models should provide a more complete fit between the simulated and experimental measurements at all measured frequencies and for both real and imaginary data.
It is important to emphasize that the experimental measurement derived using ESIS does not provide indices of cell-cell adhesion, cell-matrix adhesion, and membrane capacitance directly. Rather, these parameters must be derived by numerical modeling because the electrode area is greater than the diameter of a single cell. Changes in transendothelial resistance in response to physiological stimuli are frequently assumed such that changes are targeted at cell-cell adhesion sites. However, we recently reported that PKC activation initiates a disruption in barrier function that targeted primarily cell-matrix adhesion sites (17). In addition, we recently reported heterologous expression of low-molecular-weight, caldesmon-attenuated, adenovirus-mediated reduction in transcellular resistance in cultured fibroblasts predominately through effects on cell membrane capacitance (9). Only by numerical modeling the experimental transcellular impedance at multiple frequencies can one elucidate and localize the membrane sites at which inflammatory stimuli and pathogens mediate cellular injury.
The numerical model and algorithms presented in this report can be used by cell biologists and cell physiologists in several essential applications. First, the model can be applied to evaluate how exogenous physiological and pathological stimuli regulate endothelial and epithelial barrier function. Also, the numerical model can be used to quantify and evaluate how cell-cell and cell-matrix adhesion contribute to cell motility and wound repair under different experimental conditions in cultured cell systems (15, 17, 19). Second, the model can be applied to quantify and elucidate with precision cell-cell interactions between leukocyte-endothelium and pathogen-host interactions, for example (9). Third, the model is useful in elucidating signal transduction pathways that regulate cell membrane properties under different experimental conditions (15, 17, 19). Fourth, the model has utility in evaluating precisely how the genomics and proteomics of the cytoskeleton regulate cell membrane properties in intact living cells in which the behavior of the cytoskeleton may not be predicted adequately using in vitro bioinformatics tools (9). Fifth, the numerical model can be used to evaluate molecular mechanisms of drug toxicity. The numerical model could be multiplexed with electrical and optics-based assays to evaluate molecular mechanisms of drug therapeutics and toxicity. Ultimately, the accuracy of these analyses and simulations is dependent on reliable numerical models and computational algorithms that not only resolve model solution parameters but also assess modeling stability and error for the model solutions. Without assessments of modeling stability and error, experimenters cannot be confident in the model solution parameters and render appropriate interpretations of cell membrane properties.
In summary, we provide the first comprehensive assessment of modeling error and stability on the basis of a numerical model that characterizes transcellular impedance across a cell-covered electrode as disk shaped and as a resistor and capacitor in series as originally described by Giaever and Keese (6). We demonstrate that there are potential data-type and frequency-dependent modeling instabilities and systematic errors in the solution parameters. Understanding these experimental factors can allow investigators to produce reproducible and reliable numerical solutions of cell-cell and cell-matrix adhesion and membrane capacitance from measured transendothelial impedance. Use of a numerically stable parameter estimation process and inclusion of the appropriate range of frequencies in the parametric estimation process can allow one to obtain more accurate and reproducible parametric estimates of cell membrane properties. Because the diameter of the electrode exceeds the diameter of a single cell, the experimental measurement of ESIS cannot derive spatial measurements of cell membrane properties without reliable numerical models and computational algorithms. By quantifying the sources of error and parameter estimate instabilities, a method of impedance spectroscopy for determining in vitro cell monolayer properties can elucidate more precisely the cytoskeletal membrane properties that regulate endothelial barrier function.
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GRANTS |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
* E.-W. Bai and A. B. Moy contributed equally to this work as senior coauthors.
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REFERENCES |
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2. Baldursson O, Berger HA, and Welsh MJ. Contribution of R domain phosphoserines to the function of CFTR studied in Fischer rat thyroid epithelia. Am J Physiol Lung Cell Mol Physiol 279: L835L841, 2000.
3. Carbajal JM, Gratrix ML, Yu CH, and Schaeffer RC Jr. ROCK mediates thrombin's endothelial barrier dysfunction. Am J Physiol Cell Physiol 279: C195C204, 2000.
4. Carbajal JM and Schaeffer RC Jr. RhoA inactivation enhances endothelial barrier function. Am J Physiol Cell Physiol 277: C955C964, 1999.
5. Carson MR, Shasby SS, and Shasby DM. Histamine and inositol phosphate accumulation in endothelium: cAMP and a G protein. Am J Physiol Lung Cell Mol Physiol 257: L259L264, 1989.
6. Giaever I and Keese CR. Micromotion of mammalian cells measured electrically. Proc Natl Acad Sci USA 88: 78967900, 1991.
7. Giaever I and Keese CR. Monitoring fibroblast behavior in tissue culture with an applied electric field. Proc Natl Acad Sci USA 81: 37613764, 1984.
8. Giaever I and Keese CR. Use of electric fields to monitor the dynamical aspect of cell behavior in tissue culture. IEEE Trans Biomed Eng 33: 242247, 1986.[ISI][Medline]
9. Haxhinasto K, Kamath A, Blackwell K, Bodmer J, Van Heukelom J, English A, Bai EW, and Moy AB. Gene delivery of L-caldesmon protects cytoskeletal cell membrane integrity against adenovirus infection independently of myosin ATPase and actin assembly. Am J Physiol Cell Physiol 287: C1125C1138, 2004.
10. Iyer S, Ferreri DM, DeCocco NC, Minnear FL, and Vincent PA. VE-cadherin-p120 interaction is required for maintenance of endothelial barrier function. Am J Physiol Lung Cell Mol Physiol 286: L1143L1153, 2004.
11. Johnson A. PMA-induced pulmonary edema: mechanisms of the vasoactive response. J Appl Physiol 65: 23022312, 1988.
12. Johnson A, Phillips P, Hocking D, Tsan MF, and Ferro T. Protein kinase inhibitor prevents pulmonary edema in response to H2O2. Am J Physiol Heart Circ Physiol 256: H1012H1022, 1989.
13. Langeler EG and van Hinsbergh VW. Norepinephrine and iloprost improve barrier function of human endothelial cell monolayers: role of cAMP. Am J Physiol Cell Physiol 260: C1052C1059, 1991.
14. Lo C, Keese C, and Giaever I. Impedance analysis of MDCK cells measured by electric cell-substrate impedance sensing. Biophys J 69: 28002807, 1995.[Abstract]
15. Moy AB, Blackwell K, and Kamath A. Differential effects of histamine and thrombin on endothelial barrier function through actin-myosin tension. Am J Physiol Heart Circ Physiol 282: H21H29, 2002.
16. Moy AB, Bodmer JE, Blackwell K, Shasby S, and Shasby DM. cAMP protects endothelial barrier function independent of inhibiting MLC20-dependent tension development. Am J Physiol Lung Cell Mol Physiol 274: L1024L1029, 1998.
17. Moy AB, Blackwell K, Wang N, Haxhinasto K, Kasiske MK, Bodmer J, Reyes G, and English A. Phorbol ester-mediated pulmonary artery endothelial barrier dysfunction through regulation of actin cytoskeletal mechanics. Am J Physiol Lung Cell Mol Physiol 287: L153L167, 2004.
18. Moy AB, Van Engelenhoven J, Bodmer J, Kamath J, Keese C, Giaever I, Shasby S, and Shasby DM. Histamine and thrombin modulate endothelial focal adhesion through centripetal and centrifugal forces. J Clin Invest 97: 10201027, 1996.
19. Moy AB, Winter M, Kamath A, Blackwell K, Reyes G, Giaever I, Keese C, and Shasby DM. Histamine alters endothelial barrier function at cell-cell and cell-matrix sites. Am J Physiol Lung Cell Mol Physiol 278: L888L898, 2000.
20. Noria S, Cowan DB, Gotlieb AI, and Langille BL. Transient and steady-state effects of shear stress on endothelial cell adherens junctions. Circ Res 85: 504514, 1999.
21. Patterson CE, Barnard JW, Lafuze JE, Hull MT, Baldwin SJ, and Rhoades RA. The role of activation of neutrophils and microvascular pressure in acute pulmonary edema. Am Rev Respir Dis 140: 10521062, 1989.[ISI][Medline]
22. Patterson CE, Davis HW, Schaphorst KL, and Garcia JG. Mechanisms of cholera toxin prevention of thrombin- and PMA-induced endothelial cell barrier dysfunction. Microvasc Res 48: 212235, 1994.[CrossRef][ISI][Medline]
23. Phelps JE and DePaola N. Spatial variations in endothelial barrier function in disturbed flows in vitro. Am J Physiol Heart Circ Physiol 278: H469H476, 2000.
24. Shasby DM, Shasby SS, and Peach MJ. Granulocytes and phorbol myristate acetate increase permeability to albumin of cultured endothelial monolayers and isolated perfused lungs: role of oxygen radicals and granulocyte adherence. Am Rev Respir Dis 127: 7276, 1983.[ISI][Medline]
25. Shi S, Verin AD, Schaphorst KL, Gilbert-McClain LI, Patterson CE, Irwin RP, Natarajan V, and Garcia JG. Role of tyrosine phosphorylation in thrombin-induced endothelial cell contraction and barrier function. Endothelium 6: 153171, 1998.[Medline]
26. Tiruppathi C, Malik AB, Vecchio PD, Keese C, and Giaever I. Electrical method for detection of endothelial cell shape change in real time: assessment of endothelial barrier function. Proc Natl Acad Sci USA 89: 79197923, 1992.
27. Vouret-Craviari V, Boquet P, Pouysségur J, and Van Obberghen-Schilling E. Regulation of the actin cytoskeleton by thrombin in human endothelial cells: role of Rho proteins in endothelial barrier function. Mol Biol Cell 9: 26392653, 1998.
28. Wojciak-Stothard B, Potempa S, Eichholtz T, and Ridley AJ. Rho and Rac but not Cdc42 regulate endothelial cell permeability. J Cell Sci 114: 13431355, 2001.
29. Wojciak-Stothard B and Ridley AJ. Rho GTPases and the regulation of endothelial permeability. Vascul Pharmacol 39: 187199, 2002.[CrossRef][ISI][Medline]
30. Yuan Y, Huang Q, and Wu HM. Myosin light chain phosphorylation: modulation of basal and agonist-stimulated venular permeability. Am J Physiol Heart Circ Physiol 272: H1437H1443, 1997.
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