Quantification of Cell Death Using an Impedance-Based Microfluidic

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Quantification of Cell Death Using an Impedance-Based Microfluidic Device Amin Mansoorifar, Anil Koklu, and Ali Beskok Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b05890 • Publication Date (Web): 22 Feb 2019 Downloaded from http://pubs.acs.org on February 25, 2019

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Analytical Chemistry

Quantification of Cell Death Using an ImpedanceBased Microfluidic Device Amin Mansoorifar1, Anil Koklu1, and Ali Beskok1,*

1Department

of Mechanical Engineering, Southern Methodist University, Dallas, TX 75205, USA

*Corresponding

author: [email protected]

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Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Abstract Dielectric spectroscopy (DS) is a nondestructive method to characterize dielectric properties by measuring impedance data over a frequency spectrum. This method has been widely used for various applications such as counting, sizing, and monitoring biological cells and particles. Recently, utilization of this method has been suggested in various stages of the drug discovery process due to low sample consumption and fast analysis time. In this study, we used a previously developed microfluidic system to confine single PC-3 cells in micro-wells using dielectrophoretic forces and perform the impedance measurements. PC-3 cells are treated with 100 µM Enzalutamide drug, and their impedance response is recorded till the cells are totally dead as predicted with viability tests. Four different approaches are used to analyze the impedance spectrum. Equivalent circuit modeling is used to extract the cell electrical properties as a function of time. Principal component analysis (PCA) is used to quantify cellular response to drug as a function of time. Single frequency measurements are conducted to observe how the cells response over time. Finally, opacity ratio (OR) is defined as an additional quantification method. This device is capable of quantitatively measuring drug effects on biological cells and detect the cell death. The results show that the proposed microfluidic system has the potential to be used in early stages of drug discovery process.


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The process from discovery of a new drug to commercialization usually takes 10-15 years and about $800-$1000 million [1]. As a result, there is a great need for a tool to monitor and test different drug candidates in the early stages of diseases [2]. Over the past decade, with the tremendous advances in microfabrication techniques, microfluidic devices have been extensively used at various stages of the drug discovery process [3-6]. They offer several advantages including but not limited to low reagent consumption and reduced experimental time and analysis [7]. Moreover, they provide the potential for parallelization and reduced final costs [8, 9]. Microfluidic chips are small platforms with microchannels, valves, reservoirs, tubing systems etc. handling minute amounts of fluids [10, 11]. In addition, integrated tools such as electrodes can provide many analysis steps within the microfluidics platform [12]. Pre-clinical drug testing using a high throughput microfluidics assay can reduce the number of potential drug candidates. Cell-based screening is usually performed in 96 or 384-well microtiter plates [13]. Microtiter plate created by Dr. Gyula Takatsy, is a plate with a certain number of wells used as test tubes [14]. This setup has several limitations such as inefficient removal of reagents and difficulty in washing out the wells [15]. Moreover, because of large size of individual wells and consequently large amounts of expensive reagents and cells, the total analysis cost is high. Microfluidics technology could be applied here to fabricate miniaturized cell-based assays to solve the aforementioned limitations [13]. Lee and colleagues developed a miniaturized cell-culture array for toxicity screening of drug candidates and their cytochrome P450-generated metabolites, and they showed that 2,000-fold miniaturization does not change the cytotoxicity response compared with conventional 96-well microtiter plate [16]. In another study, Wada and colleagues used a microfluidic cell culture system in combination with sensor cells to quantitatively detect cytotoxic reagents [17]. Moreover, personalized treatment can be performed using microfluidic chips [18, 19]. For example, Xu et al. developed an effective drug sensitivity platform, where they successfully assayed the sensitivities of different single and combined-drug chemotherapy schemes for eight patients [20]. To monitor cellular response to external stimuli such as drug uptake, it is often desirable to localize individual cells in an array format to retain their position and prevent their migration. Cell trapping, and immobilization have been done using several techniques such as optical [21], acoustic [22], mechanical [23], magnetophoretic [24], and dielectrophoretic [25]. Glass et al. used a non-invasive triple-spot optical trap to directly position the cells of interest and eliminated the need of 3 ACS Paragon Plus Environment


introducing external bodies such as beads to the system [26]. In another study, Kim and colleagues used short ultrasound pulses to trap the single cells from the sample and to measure their physical properties [27]. Among the mentioned methods, dielectrophoresis as a label free technique has attracted numerous researchers. Ho et al. designed a microfluidic chip based on DEP that orients liver cells in a radial pattern facilitating construction of liver tissue in vitro [28]. Recently, Mansoorifar et al. showed positioning of single cells inside individual micro-wells using dielectrophoresis [29]. There have been several ways to measure properties of biological cells after trapping/immobilization including dielectric spectroscopy (DS), surface plasmon resonance (SPR), chemiluminescence, and fluorescence [30]. DS is a non-invasive method which is used to measure dielectric properties of biological cells [25, 31]. This method has been applied to distinguish between normal (SV-HUC-1) and cancerous (TCCSUP) human urothelial cell lines [32]. It has been shown that the two cell lines are significantly different in terms of amplitude and phase angle at a specific frequency. Recently, we showed that DS can be utilized for real-time measurement of prostate cancer cells’ response to pH and conductivity changes [29]. Previously we developed an impedance based microfluidic device which can capture biological cells such as yeast and cancer cells, performing impedance measurements, and finally releasing them [25, 29]. In this study, we focused on capturing and impedance measurements of PC-3, a highly metastatic prostate cancer cell line, and its response to Enzalutamide anti-cancer drug using different quantification methods. This paper is organized as follows. First, a previously developed microfluidic DS technique was used to determine the dielectric properties of PC-3 cells and their DEP response [33]. Following that, PC-3 cells were introduced to the microfluidic device and captured inside the micro-wells. 100 µM Enzalutamide drug was introduced to the cell medium and impedance spectra was recorded for 8 hours till all the cells are dead as predicted with viability tests. Using equivalent circuit model, cell data was extracted. Moreover, impedance magnitude and phase angle at single frequencies were measured. Principal component analysis was used to quantify the changes in PC3 cells. Finally, opacity ratio was calculated as another method to quantify cells changes. The results obtained in this paper suggest that this impedance-based microfluidic device can be used to quantitatively monitor cell state and has potential applications in early stages of drug discovery process.


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Materials and Methods Device Fabrication The microfluidics chips are fabricated using standard photolithography on glass substrates. Microfabrication process is extensively explained in our previous study [29]. In summary, after cleaning glass substrates, transparent gold electrodes are fabricated using lift-off process. Microwells are transferred to the electrodes using SU-8 photolithography. Finally, inlet, outlet and electrical ports are attached, and the slides are aligned. The only difference here is that micro-wells geometry is changed from cubic to cylindrical. As a result, the sharp corners that may induce extra charge concentrations are eliminated. Figure 1a shows side view of the device. Figure 1b is the experimental setup with syringe pump for providing cell suspension and function generator/impedance analyzer for cell capture/impedance measurements. Moreover, the schematic of experimental setup is presented in figure S1. Figures 1c and 1d illustrate the Scanning Electron Microscopy (SEM) images of micro-wells array and a single micro-well. There exist 576 microwells with 30 µm diameter and 15 µm well separation distance.

Figure 1 a) Side view of the microfluidic device, b) experimental setup, c) SEM image of micro-wells array, and d) SEM image of an individual micro-well.



Cell Preparation PC-3 cells were obtained from the American Type Culture Collection (ATCC) which were extracted from a 62 years old Caucasian male. Cell line is cultured in RPMI 1640 growth medium (Sigma Aldrich) supplemented with 5% fetal bovine serum (FBS), penicillin (100 IU/ml), and streptomycin (100 µg/ml). Cells are grown in an incubator (Thermo Scientific) at 37oC with 5% CO2 atmosphere. Figure S2 shows snapshots of PC-3 cell culture during three-day period. The harvested cells were spherical and 22.0 ± 4 µm. After about 90% of the petri dish area is covered with cells in 72 hours, the growth medium is extracted, and cells are washed with 1X PBS and TrypLE is added to detach the cells from the surface. TrypLE is a direct replacement for trypsin with a high specificity and low damage to the cells. After incubating the cells with TrypLE for 5 minutes, the complete growth medium is added, and the suspension is transferred to centrifuge tubes. The cell suspension is centrifuged at 1000 rpm for 5 minutes and the supernatant is extracted, and fresh medium is added. The final solution is transferred to a petri dish to be cultured inside the incubator. For DEP experiments and DS measurements, cells are suspended in low conductivity buffer (LCB) solution containing 229 mM sucrose, 16 mM glucose, and 1 µM CaCl2 in DI water. The solution pH is adjusted to 7.3 by adding sodium phosphate mono/dibasic (NaH2PO4/Na2HPO4) solution. For viability tests, the Trypan Blue extraction test is performed using a 1:1 dilution with 0.4% Trypan Blue Solution.

Dielectrophoresis DEP is the motion of polarizable particles suspended in ionic solution and subjected to spatially non-uniform electric field. Figures 2a and 2b show a schematic of how DEP works. When the particle is more polarizable than the medium (Figure 2a), the force at higher electric field site is higher dragging the particle to the high electric field region which is known as positive dielectrophoresis (pDEP). However, when the medium is more polarizable than the particle (Figure 2b), the higher electric force at high electric field region repels the particle to the lower electric field region which is known as negative dielectrophoresis (nDEP). The dielectrophoretic force exerted on a homogenous dielectric sphere with no conductive losses and no net charge surrounded by a conducting medium is given by: 6 ACS Paragon Plus Environment

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𝑭𝑫𝑬𝑷 = 2𝜋𝑟𝑝3𝜀0𝜀𝑟 𝑅𝑒(𝐶𝑀)∇|𝑬|2

(1)

where 𝜀0, 𝜀𝑟, and 𝑟𝑝 represent vacuum permittivity, medium relative permittivity, and particle radius, respectively. |𝑬| is electric field strength and 𝐶𝑀 is Clausius-Mossotti factor which is a frequency dependent parameter defined as: 𝐶𝑀(𝜔) =

𝜀𝑝∗ ― 𝜀𝑚∗

(2)

𝜀𝑝∗ + 2𝜀𝑚∗

where 𝜀𝑝∗ and 𝜀𝑚∗ represent particle and medium complex permittivity. Complex permittivity is defined as: 𝜀∗ = 𝜀 ― 𝑗

𝜎 𝜔

(3)

In this equation, 𝜀, 𝜎, and 𝜔 refer to electrical permittivity, conductivity, and angular frequency, respectively.

Figure 2 Schematics for a) pDEP and b) nDEP, c) the equivalent circuit model used for extracting electrical properties.



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Dielectric Spectroscopy DS is a non-invasive method to determine electrical properties of the material under test. In this method, a small AC voltage is applied, and impedance is calculated based on the current response. Finally, using mathematical models lead to determination of electrical properties of materials. One of the most common methods to analyze impedance spectrum is equivalent circuit modeling. For the current microfluidic system, equivalent circuit model shown in figure 2c is used [25]. Based on this model, impedance at the electrode/electrolyte interface is considered as constant phase element (CPE) model which is a practical representation of the electrode polarization (EP) effects. This model is given as: 𝑍𝐶𝑃𝐸 =

1

(4) 𝛼

𝐾(𝑗𝜔)

where 𝐾 and 𝛼 are the CPE coefficient and exponent, respectively. The total impedance of the system is given as the following if 𝑛𝑒 and 𝑛𝑓 are the number of empty and full micro-wells, respectively. 𝑍𝑡𝑜𝑡 =

1 𝑛𝑓 𝑍𝑓

+

𝑛𝑒 𝑍𝑒

(5)

+ 𝑗𝜔𝐶𝑓

where 𝑍𝑒 and 𝑍𝑓 represent the impedance of empty and filled micro-wells, and 𝐶𝑓 shows all the parasitic effects. Figure S3 shows microscopy images of empty and filled micro-wells. For the empty micro-wells, the CPE element is in series with channel resistance (𝑅𝑐ℎ) and parallel combination of empty wells resistance (𝑅𝑤,𝑒) and SU8 capacitance (𝐶𝑆𝑈8). For the filled microwells, the cell will fill the wells and a higher resistance in the solution in micro-well exists (𝑅𝑤,𝑓). The cell itself is modeled as cytoplasmic resistance (𝑅𝑐𝑦𝑡) in series with membrane capacitance ( 𝐶𝑚𝑒𝑚). As a result, the empty and filled micro-wells impedances are given as: 𝑍𝑒 = 𝑍𝐶𝑃𝐸 + 𝑅𝑐ℎ +

1 1 + 𝑗𝜔𝐶𝑆𝑈8 𝑅𝑤,𝑒

, 𝑍𝑓 = 𝑍𝐶𝑃𝐸 + 𝑅𝑐ℎ +

1 1 + 𝑗𝜔𝐶𝑆𝑈8 + 𝑅𝑤,𝑓


(6) 1

(

𝑅𝑐𝑦𝑡 +

1 𝑗𝜔𝐶𝑚𝑒𝑚

)

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Principal Component Analysis Principal component analysis (PCA) is a statistical method for reduction of a large set of variables into a smaller set with preserving as much of the variance in the data. This method is probably the most popular dimension-reduction method in all scientific fields. For a given set of data vectors 𝑋 = [𝑥1 𝑥2… 𝑥𝑛], 𝑥𝑖 ∈ 𝑅𝑑 , the p principal axis are those orthonormal axes (𝑢𝑖) onto which the 𝑇

variance retained under projection is maximal. In another words, 𝑣𝑎𝑟(𝑢𝑖 𝑋) is maximal for 𝑇

𝑇

principal components. However, 𝑢𝑖 𝑋 = 𝑢𝑖 𝑆𝑢𝑖, where 𝑆 is 𝑑 × 𝑑 covariance matrix of 𝑋. As a result, the optimization problem to find principal components (PCs) is: 𝑇

𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝑢𝑖 𝑆𝑢𝑖

(7)

𝑇

𝑠𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑢𝑖 𝑢𝑖 = 1 Using Langrangian method for the abovementioned optimization problem, 𝜆𝑖𝑢𝑖 = 𝑆𝑢𝑖

(8)

However, since impedance values at lower frequencies are multiple orders larger than high frequency values, it is better to use correlation matrix instead of covariance matrix. The correlation matrix is related to the corresponding covariance matrix using the below equation: 𝑅 = 𝐷 ―1𝑆𝐷

(9)

In this equation, D is a vector of square roots of the diagonal of S and is written as: 𝐷 = 𝑑𝑖𝑎𝑔(𝑆)

(10)

As a result, the eigenvectors of sample covariance or correlation matrix denote the principal components. In this paper, IBM SPSS Statistics 24 software was used to analyze correlation matrix of the impedance spectra data to perform PCA.

Results and Discussion A conventional parallel plate configuration is used to extract Re(CM) of PC-3 cells suspended in LCB. The method to extract this parameter has been extensively explained in our previous study 9 ACS Paragon Plus Environment


[33]. Figure S4 shows the extracted Re(CM) for PC-3 cells suspended in LCB. The frequency at which Re(CM) changes sign is referred as the cross over frequency (fc), which is about 40 kHz for the PC-3 cells. According to the DS measurements, 5 MHz was chosen to trap the PC-3 cells inside the micro-wells, since this frequency yields the highest positive value for Re(CM) Viability Test Trypan Blue extraction test was used to calculate the viability of PC-3 cells exposed to 100 µM Enzalutamide drug. PC-3 cell is androgen independent and Enzalutamide at low concentrations cannot cause fast damages to the cell [34, 35]. That is the reason we used a high drug concentration, so the changes can be observed in several hours. Figure S5 represents the percentile viability of PC-3 cells suspended in LCB and 100 µM Enzalutamide drug mixture. Based on this figure, cells exposed to 100 µM drug will die after 8 hours. As a result, the on-chip experiments were performed for about 8 hours to make sure that all cells will die till the end of the experiment. DS Measurements For drug tests, PC-3 cells were mixed with LCB containing 100 µM Enzalutamide. The mixture is pumped through the microfluidic channel at 2 µl/min volumetric flowrate. Afterwards, the electrodes are energized at 4 Vpp with 5 MHz frequency to capture the cells. The cell capture process is monitored under the microscope and it is observed that all the micro-wells are filled with PC-3 cells within 30 s. More information on cell loading process is given in our previous study [29]. Finally, the microfluidic device is connected to a high precision impedance analyzer and the impedance spectra of trapped cells population are recorded continuously for 8 hours. Each impedance measurement is repeated three times and the averaged data is reported in this article. Figure S6 shows the normalized standard deviation of the healthy cells’ impedance value and phase angle in the frequency range. It is evident that normalized standard deviation is less than 1% in the whole frequency spectrum. Based on the equivalent circuit illustrated in figure 3c, impedance spectrum of empty and filled wells can be calculated separately. Figure S7 shows the impedance value and phase angle of single empty and filled micro-well for freshly captured cells. Based on this figure, filled micro-well experiences higher impedance values due to the presence of the cell. However, phase angle changes are very similar. Using the equivalent circuit model explained, EP effect is extracted. Figures 3a-b show the impedance and phase angle spectra before and after the EP effects extraction 2 hours after drug injection. It is evident that EP overshadows 10 ACS Paragon Plus Environment

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impedance spectrum at lower frequencies, and it is necessary to extract this effect before further analyses. Figures 3c-d show the impedance and phase angle spectra of freshly captured cells, and the same cell sample after 2 and 8 hours, after subtraction of the EP effects. Based on the impedance spectra, the freshly captured cells show higher impedance magnitudes at lower frequencies while they experience a lower value at higher frequencies. Undamaged cells have healthy integrated membranes, while damaged cells lose their membrane integrity resulting in outflow of ions to the exterior medium, which in turn increases overall impedance value at higher frequencies. Based on figure 3d, the phase angle changes at higher frequencies is low. However, there is a recognizable change in the phase angle at low and medium frequencies. This can be explained since healthy cells have an integrated membrane which acts as an insulator and make the system more capacitive compared to the damaged cells. The other important point here is the presence of two distinct dispersion regions for fresh sample. These two dispersion regions are related to the cell membrane polarization (~ 100kHz) and SU8-medium polarization (~ 10 MHz), respectively. However, when the cells are exposed to Enzalutamide for a longer time, the first relaxation region starts to vanish due to the membrane damage.



Figure 3 a) Impedance spectra and b) phase angle of PC-3 cells treated with 100 µM Enzalutamide for two hours before and after EP extraction. c) Impedance spectra and d) phase angle spectra of PC-3 cells treated with 100 µM Enzalutamide at 0, 2, and 8 hours.

Equivalent Circuit Analysis The equivalent circuit model presented in Figure 2c was used to evaluate the membrane capacitance and cytoplasmic resistance of PC-3 cells treated with 100 µM Enzalutamide for 0, 2, 4, 6, and 8 hours. Figure 4 shows the extracted cell parameters for an 8-hour period. The error bars indicate the changes in Rcyt and Cmem due to variations in initial conditions used for fitting the impedance data with the equivalent circuit model. Based on Figure 4a, Cmem started from a value of 1.57×10-11 F and decreased slightly to 1.40×10-11 F as the cells exposed to the drug for 8 hours. This 12.1% decrease in membrane capacitance is basically related to rounding up of the cells to a spherical shape due to cell cytoskeletal tension after releasing in LCB [29]. Figure 4b shows that Rcyt has a value of 7.64×105 Ω for healthy cells and increases as the cells are degraded. The dead cells (after 8 hours) have an Rcyt of 1.43×106 Ω which is 87.2% larger than the live cells. This 12 ACS Paragon Plus Environment

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makes Rcyt a good indicator in discriminating between live and dead cells. The increase in Rcyt is due to efflux of cytoplasmic ions to extracellular medium which happens because of membrane degradation and ionic diffusion.

Figure 4 Extracted a) membrane capacitance and b) cytoplasmic resistance of PC-3 cells over an 8-hour period.

Single Frequency Measurement Figure 5 shows the impedance value and phase angle changes at 10 kHz, 100 kHz, and 1 MHz frequencies. Since at 10 kHz EP is high, there exists random variations in the impedance value over time. However, the trend is decreasing showing that cell impedance is descending because of the membrane damage (as evidenced by the cell viability experiments). Moreover, phase angle graph shows that the system becomes more resistive over time. At higher frequencies (100 kHz and 1 MHz), impedance value increases with time, which is due to outflow of ions from cell cytoplasm. Additionally, phase angle graphs predict more resistive system over time. However, for 1 MHz phase angle changes slightly which agrees with the results presented in Figure 3. 13 ACS Paragon Plus Environment


Figure 5 Changes over time in a) Impedance value and b) phase angle of PC-3 cells treated with 100µM Enzalutamide.

Principal Component Analysis Principal component analysis was performed on impedance value and phase angle spectra of PC3 cells exposed to 100 µM Enzalutamide drug. Figure 6a shows the scree plot of first 20 eigenvalues. Based on Kaiser Criterion, components with eigenvalues under 1.0 can be dropped because a factor with an eigenvalue of 1 account for as much variance as a single variable. However, the order of first two components is two orders larger than the third one. As a result, the first two components are enough to represent all the data. PCA results show that the first two principal components explain 99.6% of the variation in the data (PC1: 74.5%, PC2: 25.1%). Figure 6b illustrates the coefficients of the first two principal components. The coefficients indicate the relative weight of each variable in the component. This graph clearly shows what frequencies 14 ACS Paragon Plus Environment

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contribute more to principal components. PC1 is more related to high frequencies, while PC2 has more contribution from lower frequencies. These coefficient plots have also been calculated for impedance spectra after eliminating EP effect and stray capacitances and are shown in figure S8.

Figure 6 a) Scree plot of first 20 components, and b) first two principal component coefficients.

These figures suggest that eliminating EP and stray capacitance does not have a considerable effect on PC coefficients. As a result, PCA can be performed on raw data without losing accuracy. The value that the spectra have in the PC coordinate system are called scores. Figure 7a shows the score plot of the first two PCs. Each data point in this graph represent the impedance spectrum at a time. From this graph, the data points are finally accumulated on a region showing the biological cells are dead. Figure 7b show the time variation of PC1 and PC2 score values.



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Figure 7 a) Score plot of first two principal components and b) PC score variations with respect to time.

Based on this figure, PC1 score value reaches a constant value after 8 hours showing that the cells are completely dead. However, PC2 score value is still changing with time. It is mostly because PC2 is more related to lower frequencies where EP effect decreases the measurement accuracy. Another reason could be the smaller contribution of PC2 with respect to PC1 in representing the data. In order to clarify the changes in PC2 score values, PCA was performed by changing the starting frequency to 1, 10, 25, and 50 kHz. Table 1 shows the components eigenvalues and % of variance explained using various starting frequencies. Based on this table, the 1st PC eigenvalue is not very sensitive to the starting frequency while the 2nd PC eigenvalue decreases dramatically with increasing starting frequency. As a result, the % of variance explained by 2nd PC is sensitive to the starting frequency and it is practically unimportant for starting frequencies more than 25 kHz as its value and contribution to variance decreases beyond this frequency. Table 1 Eigenvalues and % of variance explained using different starting frequencies. Starting frequency

Eigenvalue, 1st PC

Eigenvalue, 2nd PC

% of variance explained, 1st

PC

% of variance explained, 2nd PC

1 kHz

297.831

100.890

74.272

25.160

10 kHz

265.535

47.472

84.565

15.118

25 kHz

261.333

17.233

93.668

6.177

50 kHz

252.796

7.838

96.857

3.003


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Additionally, PCA was done on the data set with different starting frequencies and the PC1 and PC2 score results are shown in figure S9. This figure clearly shows that both PC1 and PC2 score values are consistent with changing starting frequency up to 50 kHz. As a result of this and the low contribution of PC2, it is concluded that PC1 score value itself is enough to represent the cellular condition. Moreover, PCA was performed on phase angle spectrum. Figure S10 show the score plot of PC1 and PC2 and their variation with respect to time. Comparing figures 7 and S10, it is concluded that both impedance and phase angle spectra follow approximately the same trend and values for the first two principal components. As a result, performing PCA on either impedance or phase angle spectrum will be enough to quantify cell condition. Opacity Ratio Measurement The ratio of the impedance magnitude at a high frequency to a low frequency is known as the opacity ratio (OR). This parameter is often utilized to normalize the impedance data for cell size heterogeneity in different cell cultures. In this study, we used 1 MHz and 10 kHz as the high and low frequency signal and the opacity itself is normalized with the initial opacity ratio value. The initial opacity ratios for 100 µM was 0.812. Figure 8 shows the changes in normalized opacity ratio (OR) for PC-3 cells exposed to 100 µM Enzalutamide. This figure illustrates that normalized OR increases with time. Basically, opacity increases with either decreasing of the membrane capacitance or increasing of the cytoplasmic resistance. As a result, opacity changes are in good agreement with cell parameter calculations. After 8 hours, normalized OR reaches a plateau of about 1.16. This plateau means that the cells totally lost their function and cell death has been completed.

Figure 8 Normalized OR of PC-3 cells treated with 100 µM Enzalutamide. Initial OR was 0.812.



Conclusion In this study, we fabricated and tested an impedance based microfluidic device to position biological cells inside micro-wells and performed impedance measurements. PC-3 prostate cancer cell line was used to demonstrate the performance of the device for drug testing purposes. We used conventional dielectric spectroscopy devices to measure the frequencies at which PC-3 cells could be captured inside 30 µm diameter micro-wells. Clausius-Mossotti factor results show that at 5 MHz, PC-3 cells experience the highest pDEP force. After capturing the cells inside the microwells, 100 µM Enzalutamide (Xtandi) was injected into the microfluidic device and impedance measurements were performed in the 10 kHz-40 MHz frequency range. Viability tests on PC-3 cells treated with 100 µM Enzalutamide suggest that after about 8 hours all the cells will die. As a result, impedance measurements were performed in an 8-hour period. Impedance results show that cells undergoing death process exhibit different spectrum than the healthy ones. Live cells impedance spectra have two dispersion regions, while for dead cells, the first dispersion disappears. Equivalent circuit model was used to extract cellular electric properties. Cmem started from a value of 1.57×10-11 F and decreased slightly to 1.40×10-11 F while Rcyt started from a value of 7.64×105 Ω for healthy cells and increases to 1.43×106 Ω for dead cells. Single frequency measurements show that at higher frequencies (>100 kHz) cell dynamics could be captured while lower frequencies are overshadowed with EP effects. PCA was performed on impedance spectra and principal component score values at each time step was used to quantify cell state at each time step. PCA results show that eliminating EP effect or stray capacitance does not affect PCA outcome. As a result, PCA can be performed on raw data. Moreover, it was shown that PC score values do not change considerably by changing the starting frequency value up to 50 kHz. It means that data acquisition would be faster. Additionally, it was shown that PC1 score starts from -3.43 and converged to 0.95 for the dead cells. Finally, opacity ratio was introduced to better capture cell dynamics undergoing death process. The results show that OR increases by about 16% for the dead cells compared to fresh cells. It is concluded that this device could be used for quantification of drug effectiveness on various cell lines as well as for heterogenous cell mixtures. In our previous studies [36, 37], we have shown that EP effect dominating the impedance spectrum for smaller electrode sizes and higher conductivities could be reduced using fractal gold


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nanostructured electrodes [36, 37]. We will use this concept to increase the sensitivity of the microfluidic device for high conductivity measurements.

Acknowledgement We would like to thank Prof. Ganesh Raj and Dr. Shihong Ma for providing PC-3 cell line and Enzalutamide drug as well as discussions for the course of this work. Research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institute of Health (NIH) under the award number R21AR063334. The content is solely the responsibility of the authors and does not necessarily represents the views of the NIH.

Conflict of interest statement All authors declare complete absence of financial/commercial conflicts of interest.

Supporting Information Supporting Information Available: Schematic of the experimental setup, snapshots of PC-3 cell culture, microscopy images of empty and filled micro-wells, Re(CM) of PC-3 cells, Percentile viability of PC-3 cells exposed to Enzalutamide drug, normalized standard deviation of impedance spectrum, impedance spectrum of empty and filled micro-wells, PC coefficients of raw and compensated impedance data, time variations of PC scores for different starting frequencies, score plots and its time variation for phase angle spectra. This material is available free of charge via the Internet at http://pubs.acs.org

References 1. 2.

Wu, M.H., Huang, S.B., and Lee, G.B., Microfluidic cell culture systems for drug research. Lab Chip, 2010. 10(8): p. 939-56. Dambach, D.M., Andrews, B.A., and Moulin, F., New technologies and screening strategies for hepatotoxicity: use of in vitro models. Toxicol. Pathol., 2005. 33(1): p. 17-26. 19 ACS Paragon Plus Environment


3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23.

Kang, L., Chung, B. G., Langer, R., and Khademhosseini, A., Microfluidics for drug discovery and development: from target selection to product lifecycle management. Drug discovery today, 2008. 13(1-2): p. 1-13. hembekar, N., Chaipan, C., Utharala, R. and Merten, C.A., Droplet-based microfluidics in drug discovery, transcriptomics and high-throughput molecular genetics. Lab Chip, 2016. 16(8): p. 1314-1331. Skommer, J. and Wlodkowic, D., Successes and future outlook for microfluidics-based cardiovascular drug discovery. Expert Opin. Drug Discovery, 2015. 10(3): p. 231-244. Eribol, P., Uguz, A., and Ulgen, K., Screening applications in drug discovery based on microfluidic technology. Biomicrofluidics, 2016. 10(1): p. 011502. Pihl, J., Karlsson, M., and Chiu, D.T., Microfluidic technologies in drug discovery. Drug Discovery Today, 2005. 10(20): p. 1377-1383. Saadi, W., Wang, S.J., Lin, F. and Jeon, N.L., A parallel-gradient microfluidic chamber for quantitative analysis of breast cancer cell chemotaxis. Biomed. Microdevices, 2006. 8(2): p. 109118. Yang, Y., Le Gac, S., Terstappen, L.W. and Rho, H.S., Parallel probing of drug uptake of single cancer cells on a microfluidic device. Electrophoresis, 2018. 39(3): p. 548-556. Whitesides, G.M., The origins and the future of microfluidics. Nature, 2006. 442(7101): p. 368. Stone, H.A., Stroock, A.D., and Ajdari, A., Engineering flows in small devices: microfluidics toward a lab-on-a-chip. Annu. Rev. Fluid Mech., 2004. 36: p. 381-411. Erickson, D. and Li, D., Integrated microfluidic devices. Anal. Chim. Acta, 2004. 507(1): p. 11-26. Fernandes, T.G., Diogo, M.M., Clark, D.S., Dordick, J.S. and Cabral, J.M., High-throughput cellular microarray platforms: applications in drug discovery, toxicology and stem cell research. Trends Biotechnol., 2009. 27(6): p. 342-349. Sever, J.L., Application of a microtechnique to viral serological investigations. J. Immunol., 1962. 88(3): p. 320-329. Dunn, D.A. and Feygin, I., Challenges and solutions to ultra-high-throughput screening assay miniaturization: submicroliter fluid handling. Drug discovery today, 2000. 5(12): p. 84-91. Lee, M.Y., Park, C.B., Dordick, J.S. and Clark, D.S., Metabolizing enzyme toxicology assay chip (MetaChip) for high-throughput microscale toxicity analyses. Proc. Natl. Acad. Sci., 2005. 102(4): p. 983-987. Wada, K.I., Taniguchi, A., Kobayashi, J., Yamato, M. and Okano, T., Live cells-based cytotoxic sensorchip fabricated in a microfluidic system. Biotechnol. Bioeng., 2008. 99(6): p. 1513-1517. Ruppen, J., Wildhaber, F.D., Strub, C., Hall, S.R., Schmid, R.A., Geiser, T. and Guenat, O.T., Towards personalized medicine: chemosensitivity assays of patient lung cancer cell spheroids in a perfused microfluidic platform. Lab Chip, 2015. 15(14): p. 3076-3085. Jain, K., Applications of biochips: from diagnostics to personalized medicine. Curr. Opin. Drug Discovery Dev., 2004. 7(3): p. 285-289. Xu, Z., Gao, Y., Hao, Y., Li, E., Wang, Y., Zhang, J., Wang, W., Gao, Z. and Wang, Q., Application of a microfluidic chip-based 3D co-culture to test drug sensitivity for individualized treatment of lung cancer. Biomaterials, 2013. 34(16): p. 4109-4117. Ribeiro, A.R.d.S.R., Optical fiber tools for single cell trapping and manipulation, 2017, Universidade do Porto (Portugal). Hammarström, B., Evander, M., Barbeau, H., Bruzelius, M., Larsson, J., Laurell, T. and Nilsson, J., Non-contact acoustic cell trapping in disposable glass capillaries. Lab Chip, 2010. 10(17): p. 22512257. Jin, D., Deng, B., Li, J.X., Cai, W., Tu, L., Chen, J., Wu, Q. and Wang, W.H., A microfluidic device enabling high-efficiency single cell trapping. Biomicrofluidics, 2015. 9(1): p. 014101. 20 ACS Paragon Plus Environment

Page 20 of 30

Page 21 of 30 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


24. 25. 26. 27. 28. 29. 30. 31. 32.

33. 34. 35. 36. 37.

Huang, C.-Y. and Wei, Z.-H., Concentric Magnetic Structures for Magnetophoretic Bead Collection, Cell Trapping and Analysis of Cell Morphological Changes Caused by Local Magnetic Forces. PloS one, 2015. 10(8): p. e0135299. Mansoorifar, A., Koklu, A., Sabuncu, A.C. and Beskok, A., Dielectrophoresis assisted loading and unloading of microwells for impedance spectroscopy. Electrophoresis, 2017. 38(11): p. 1466-1474. Glass, D.G., McAlinden, N., Millington, O.R. and Wright, A.J., A minimally invasive optical trapping system to understand cellular interactions at onset of an immune response. PloS one, 2017. 12(12): p. e0188581. Kim, M.G., Park, J., Lim, H.G., Yoon, S., Lee, C., Chang, J.H. and Shung, K.K., Label-free analysis of the characteristics of a single cell trapped by acoustic tweezers. Sci. Rep., 2017. 7(1): p. 14092. Ho, C.T., Lin, R.Z., Chang, W.Y., Chang, H.Y. and Liu, C.H., Rapid heterogeneous liver-cell on-chip patterning via the enhanced field-induced dielectrophoresis trap. Lab Chip, 2006. 6(6): p. 724-734. Mansoorifar, A., Koklu, A., Ma, S., Raj, G.V. and Beskok, A., Electrical Impedance Measurements of Biological Cells in Response to External Stimuli. Anal. Chem., 2018. 90(7): p. 4320-4327. Dittrich, P.S. and Manz, A., Lab-on-a-chip: microfluidics in drug discovery. Nat. Rev. Drug Discovery, 2006. 5(3): p. 210. Mansoorifar, A., Ghosh, A., Sabuncu, A.C. and Beskok, A., Accuracy of the Maxwell–Wagner and the Bruggeman–Hanai mixture models for single cell dielectric spectroscopy. IET Nanobiotechnol., 2017. 11(7): p. 874-882. Park, Y., Kim, H.W., Yun, J., Seo, S., Park, C.J., Lee, J.Z. and Lee, J.H., Microelectrical impedance spectroscopy for the differentiation between normal and cancerous human urothelial cell lines: real-time electrical impedance measurement at an optimal frequency. BioMed Res. Int., 2016. 2016. Sabuncu, A.C., Zhuang, J., Kolb, J.F. and Beskok, A., Microfluidic impedance spectroscopy as a tool for quantitative biology and biotechnology. Biomicrofluidics, 2012. 6(3): p. 034103. Ghashghaei, M., Paliouras, M., Heravi, M., Bekerat, H., Trifiro, M., Niazi, T.M. and Muanza, T., Enhanced radiosensitization of enzalutamide via schedule dependent administration to androgen-sensitive prostate cancer cells. Prostate, 2018. 78(1): p. 64-75. Barrado, M., Blanco-Luquin, I., Andrea-Navarrete, P., Visus, I., Guerrero-Setas, D., Escors, D., Kochan, G. and Arias, F., Radiopotentiation of Enzalutamide over Human Prostate Cancer Cells as Assessed by Real-Time Cell Monitoring. bioRxiv, 2018: p. 324889. Koklu, A., Sabuncu, A.C., and Beskok, A., Rough gold electrodes for decreasing impedance at the electrolyte/electrode interface. Electrochim. Acta, 2016. 205: p. 215-225. Koklu, A., Sabuncu, A.C., and Beskok, A., Enhancement of dielectrophoresis using fractal gold nanostructured electrodes. Electrophoresis, 2017. 38(11): p. 1458-1465.



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Figure 1 a) Side view of the microfluidic device, b) experimental setup, c) SEM image of micro-wells array, and d) SEM image of an individual micro-well. 200x156mm (300 x 300 DPI)

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Figure 2 Schematics for a) pDEP and b) nDEP, c) the equivalent circuit model used for extracting electrical properties. 163x180mm (300 x 300 DPI)


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Figure 3 a) Impedance spectra and b) phase angle of PC-3 cells treated with 100 µM Enzalutamide for two hours before and after EP extraction. c) Impedance spectra and d) phase angle spectra of PC-3 cells treated with 100 µM Enzalutamide at 0, 2, and 8 hours. 152x178mm (300 x 300 DPI)



Figure 4 Extracted a) membrane capacitance and b) cytoplasmic resistance of PC-3 cells over an 8-hour period. 104x224mm (600 x 600 DPI)


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Figure 5 Changes over time in a) Impedance value and b) phase angle of PC-3 cells treated with 100µM Enzalutamide. 100x229mm (600 x 600 DPI)



Figure 6 a) Scree plot of first 20 components, and b) first two principal component coefficients. 261x138mm (600 x 600 DPI)


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Figure 7 a) Score plot of first two principal components and b) PC score variations with respect to time. 260x141mm (600 x 600 DPI)



Figure 8 Normalized OR of PC-3 cells treated with 100 µM Enzalutamide. Initial OR was 0.812.


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Quantification of Cell Death Using an Impedance-Based Microfluidic

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