On-chip label-free determination of cell survival rate

Biosensors and Bioelectronics 148 (2020) 111820

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On-chip label-free determination of cell survival rate Qiang Zi a, b, Weiping Ding a, b, *, Chunli Sun a, b, Shibo Li a, b, Dayong Gao c, Liqun He d, Jing Liu e, Lei Xu b, Bensheng Qiu a, b, ** a

Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China c Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA d Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China e Key Laboratory of Biofabrication of Anhui Higher Education Institutes, Department of Biomedical Engineering and Environmental Science, Hefei University, Hefei, Anhui 230601, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Cell survival rate Microfluidic chip Hypertonic stimulus Impedance analysis Cell volume

Cell survival rate (CSR) is a very important parameter in biological and medical fields. Today, the routine method to determine this parameter is time-consuming; it also makes the labeled cells no longer useable for subsequent experiments. Here, we developed an on-chip label-free method for determining the CSR. For the method, a hypertonic stimulus was designed to create volume differences between living and dead cells, and then, the differences were characterized with measurements of impedance as the cells flowed through two electrodes. Based on the method, a microfluidic hypertonic stimulus-based impedance flow cytometry chip (HSIFC) was designed, and the localized function of the HSIFC was verified. Finally, the performance of the HSIFC was confirmed by measuring the different CSRs for the different types of cells. The results show that the HSIFC can accurately determine the CSR, and the accuracy is comparable to that of flow cytometry. This work paves the way for the label-free evaluation of CSR after various cell manipulations and treatments on the chip and pro­ motes the versatility of lab-on-a-chip devices.

1. Introduction Cell survival rate (CSR) or the percentage of living cells in a sample of living and dead cells is an important parameter in biological and medical fields. It is often used to evaluate the status of cells cultured on chips or plates (Halldorsson et al., 2015); to estimate the fragility of microfluidic chips after cell treatments (Lee et al., 2017; Song et al., 2009); to confirm the effect of cell manipulations, such as cell sorting (Kamande et al., 2013), cell separation (Hejazian et al., 2015; Hu et al., 2018), and cell trapping (Zhou et al., 2016b); and to assess the safety and effectiveness of drugs (Ramasamy et al., 2014). Compared with the viability or ac­ tivity of cells, the CSR (percentage) is difficult to determine because it is a challenge to detect the indicators directly related to cell life and death based solely on a morphological analysis of the cells via light refraction (Park et al., 2015) or specific molecules released from the cells (Ven­ nstrom et al., 2008). In the laboratory, the routine method to determine the CSR (per­ centage) is cell staining, in which the cells are stained with two dyes,

calcein-AM and propidium iodide (PI) (Kim et al., 2017) or Hoechst 33342 and PI (Puppulin et al., 2018; Sun et al., 2019), then washed using PBS, and finally counted manually or using ImageJ (Rueden et al., 2017). This method is tedious, laborious, and time-consuming. It also makes the cells (especially living cells) unsuitable for reuse in subse­ quent experiments, as the cells are stained. Flow cytometry (FCM), as an alternative, is often used for the rapid determination of the CSR (Jiang et al., 2016). However, the cells still need to be stained, causing the loss of living cells for further experimentation, which is particularly prob­ lematic when precious, rare cells, such as iPSCs or embryonic stem cells, are being studied (Ouyang et al., 2016; Sirenko et al., 2014). The impedance method is based on the dielectric properties of cells and is an effective way to assess the status of cells (Xu et al., 2016), and thus, the impedance flow cytometer (IFC) has been developed for various applications (Rho et al., 2018), such as platelet activation (Evander et al., 2013) and cell differentiation (Zhou et al., 2016b). Compared with FCM, IFC is label-free, compact, and easy to integrate. Furthermore, by coupling with microfluidics, IFC offers the merits of

* Corresponding author. Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui 230027, China. ** Corresponding author. Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China. E-mail addresses: [email protected] (W. Ding), [email protected] (B. Qiu). https://doi.org/10.1016/j.bios.2019.111820 Received 23 June 2019; Received in revised form 1 October 2019; Accepted 23 October 2019 Available online 28 October 2019 0956-5663/© 2019 Elsevier B.V. All rights reserved.

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convenience and high sensitivity for detecting and analyzing rare cells in small volume samples. To date, progress has been made in IFC using alternating-current interrogations to count specific cells (Evander et al., 2013; Hassan et al., 2016; Watkins et al., 2013) and to discriminate different cell types (Han et al., 2012). Since cell impedance signaling at various frequencies can provide information on parameters directly related to the physiological condi­ tions of single cells, such as cell volume (<1 MHz), membrane capaci­ tance (1–4 MHz) and cytoplasmic conductivity (4–10 MHz) (Sun et al., 2008), some efforts have been made to determine the survival rate of cells at high frequency (>1 MHz). For example, Gou et al., (2011) and Pierzchalski et al., (2010) differentiated living cells from apoptotic cells, while Xie et al., (2017) identified living cells. However, the impedance signaling of cells at high frequency might be affected by many cellular activities and environments, such as cell differentiation (Bagnaninchi and Drummond, 2011), cell division and growth (Huang et al., 1999; Sohn et al., 2000), ion gating (Han and Frazier, 2006) and temperature (Bao et al., 1992), causing inaccuracies in the measurements of the CSR (percentage). In this study, therefore, a method was developed to determine the CSR based on the impedance signaling of cells at low frequency. In this method, the volumes of living and dead cells exposed to a hypertonic stimulus are detected and analyzed. Based on the method, a microfluidic hypertonic stimulus-based impedance flow cytometry chip (HSIFC) was designed as the first step toward a new biosensor. First, the design of the HSIFC is introduced. Then, the localized function of the HSIFC is veri­ fied. Finally, the performance of the HSIFC is confirmed by comparing the percentages measured using the HSIFC with the expected values. This work provides a new method of on-chip label-free determination of the CSR for moving cells. The method can be easily integrated into labon-a-chip devices and used for the routine assay of the CSR. 2. Materials and methods Fig. 1. Schematic of the HSIFC: (a) design principle of the HSIFC; (b) schematic of the HSIFC components (b-1: the PDMS layer with microfluidic patterns; b-2: the glass layer with indium tin oxide (ITO) electrodes covered with a PDMS film; b-3: the combination of the PDMS layer and the glass layer; b-4: the 3-D assembly of HSIFC as drawn); (c) images of the fabricated HSIFC; and (d) overview of experiment setup.

2.1. Design of the HSIFC The core concept of the HSIFC is as follows (Fig. 1): the cell sus­ pension (the living and dead cell sample) is driven through a micro­ fluidic chip where a hypertonic solution (e.g., high concentration NaCl solution) is added to the cell suspension to induce a volume (distribu­ tion) difference between living and dead cells, and then, this difference is detected or differentiated according to the cell impedance distribu­ tion. In the HSIFC (Fig. 1a), there are two pairs of electrodes: one is for counting the cells before exposure to the hypertonic stimulus, and the other is for both the detection of the volume distribution of the cells and recounting of the cells after exposure to the hypertonic stimulus. When a cell passes through the constriction pore between two electrodes, the cell replaces part of the medium in the pore, causing an instantaneous pulse induced by the change in cell impedance. Under an excitation voltage at the proper frequency, the amplitude of the pulse reflects cell size (Sun et al., 2008). By using two pairs of electrodes, the chip is able to cross-verify the accuracy of the counting before and after exposure to the stimulus; more importantly, the consistency of the counting also reflects the safety of the hypertonic stimulus on the cells. To fully and rapidly mix the cell suspension with the hypertonic solution, a mixing zone with a chaotic micromixer (Stroock et al., 2002) is embedded in the HSIFC. To reduce the difficulties in fabricating the HSIFC, a mm-scale contactless electrode (Emaminejad et al., 2012, 2016) and a constricted pathway for cell flow in the detection positions are included in the design. In HSIFC operation, the cell suspension is introduced via the Cell IN (Fig. 1b), and then, the number of cells is preliminarily counted with the first pair of electrodes (EP 1) at the first detection position. After passing through EP 1, the cell suspension encounters the hypertonic stimulus solution from the Stimulus IN, and then, the two solutions are mixed in the mixing zone. The hypertonic stimulus induces a volume difference between the living and dead cells (the volume of the dead cells remains

unchanged as the cells lose osmoregulatory function or membrane integrity (Ebrahimi and Alam, 2016), whereas the living cells shrink). As the post-stimulus cell suspension passes the second pair of electrodes (EP 2), the volume distribution difference between living and dead cells is detected. Here, we assume that the cell volume obeys a Gaussian dis­ tribution. Then, the percentage of living cells in the living and dead cell sample (i.e., the cell survival rate) can be determined by solving the mixed Gaussian distribution based on the living and dead cell volume distributions. 2.2. Fabrication of the HSIFC The HSIFC we fabricated was composed of a polydimethylsiloxane (PDMS) layer with microfluidic patterns (Fig. 1b-1) and a glass layer with indium tin oxide (ITO) electrodes covered with a PDMS film (~5 μm) (Fig. 1b-2). Both layers were bonded together using oxygen plasma (Fig. 1b-3). The 3-D assembly drawing is shown in Fig. 1b-4 (the product map is shown in Fig. 1c). The PDMS layer with a cross-shaped pattern (Fig. 1b-1) was fabricated using the photolithography tech­ nique well developed previously (McDonald et al., 2000). The schematic fabrication process of the PDMS layer is shown in Fig. S1 (Supporting Information). The width and height of the microchannels are 300 μm and 20 μm, respectively. The length and width of the detection pore are both 20 μm (Fig. 1c). A chaotic mixer with V-shaped grooves (the depth of grooves is 20 μm), as described in the literature (Stroock et al., 2002), 2

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Fig. 2. Effects of hypertonic stimulus on the survival rate of HUVECs and the volume of cells: (a) the survival rates of the HUVECs exposed to 3 stimuli over time; (b) the images of the living cells and dead cells before and after exposure to the hypertonic stimulus (the red and blue fluo­ rescence indicates the dead and living cells, respectively); (c) the volume change of the living cells and dead cells exposed to hypertonic stim­ ulus over time; and (d) the histograms of the diameter measurements of the living cells and dead cells before and after exposure to the hy­ pertonic stimulus. (For interpretation of the ref­ erences to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3. Performance of the chaotic mixer: (a) the fluorescence images and the corresponding relative fluorescence intensity distributions before and after mixing; (b) the y-direction average relative fluorescence intensity distributions before and after mixing; and (c) the comparison of the average fluorescein concentrations be­ tween the measured and expected values.

3

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Fig. 4. Example of signal processing: (a) raw data; (b) close-up view of the filtering signal; (c) 2 typical peaks recognized by the wavelet-based algorithm; and (d) processed signal (the blue square dots denote peaks). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

was fabricated on the top side of the mixing zone, and its length is approximately 13 mm (Fig. 1b-1). The ITO electrodes (1.5 mm � 3.8 mm) were fabricated using the laser etching technique (Shenzhen South China Xiangcheng Technology Co., Ltd., China). The detailed parameters of the PDMS layer and the ITO electrodes are shown in Fig. S2.

Pump Systems Inc., Farmingdale, USA). For the experiments, the sinusoidal voltages at a fixed frequency (450 kHz) were applied to both pairs of detection electrodes (Fig. 1d). The electric impedance signal was delivered into lock-in amplifiers with an operational transimpedance amplifier (TIA) and then transferred into voltage pulses that were directly related to cell size. The output signal was recorded at a 250 kHz sampling rate with the DAQ card and analyzed with LabVIEW. The cell suspension was introduced into the HSIFC at a fixed flow rate of 20 μL/min using a syringe (Sigma-Aldrich, Shanghai, China), where it moved through PEEK tubing (ID 0.5 mm and OD 1.59 mm; Vici, Schenkon, Switzerland) and was collected with a centrifuge tube (Corning, NY, USA) through PEEK tubing. A high concentration of NaCl solution (4300 mOsm/kg H2O) was used as a hypertonic stimulus and introduced through the Stimulus IN at a fixed flow rate of 10 μL/min.

2.3. Experiment setup The experimental setup (Fig. 1d) consisted of a wave generator (DG4202; RIGOL Technology Co., Ltd., China), an electric impedance sensing circuit that we designed (Fig. S3), a 16-bit data acquisition (DAQ) card (NI-9205; National Instruments, USA), a desktop with a data processing program developed with LabVIEW 2013 (National In­ struments, USA), the HSIFC, and two syringe pumps (NE-1000; New Era 4

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2.4. Cell culture and suspension preparation

fluorescence intensity was analyzed using MATLAB R2015a (The Math Works, Inc., Natick, MA, USA).

Human umbilical vein endothelial cells (HUVECs), human colon cancer (HT-29) cells and immature bone-marrow-derived macrophage (iBMDM) cells were obtained from Culture Collection of the Chinese Academy of Sciences (Shanghai, China) and cultured with DMEM (Dulbecco’s modified Eagle’s medium; Gibco Life Technologies, USA) at 37 � C and 5% CO2 in an incubator (Thermo Scientific Forma 3131, Waltham, MA, USA). Here, the sample collected after cell manipulations and treatments was simulated using a mixture of pure living cells and pure dead cells. To prepare the sample with pure living cells, living cells cultured on dishes were treated with 0.25% trypsin containing 0.53 mM EDTA, concentrated by a centrifuge (HC-2066; USTC Zonkia Scientific In­ struments Co., Ltd., Anhui, China) at 200�g and resuspended in DMEM. To prepare the sample with pure dead cells, living cells were treated with 4% paraformaldehyde solution for 20 min and resuspended in DMEM (to obtain paraformaldehyde-treated cells) or were heated in a 65 � C water bath (HH-420; Tianjin Experimental Instrument Factory, Jintan, China) for 15 min (to obtain heat-treated cells). To prepare the sample with living and dead cells, the pure living and dead cell samples were mixed at desired volume ratios. The density of the cells for all samples was ~105 cells per mL.

2.7. On-chip bead experiments The on-chip bead experiments were performed to demonstrate the process of particle detection, verify the accuracy of the counting of particles, and analyze the relationship between particle size/diameter and amplitude. In the experiments for demonstrating the process of particle detection, the PBS solution containing 5 μm, 10 μm and 15 μm beads (the number of beads was approximately 1.7 � 105 per mL, 1.3 � 105 per mL and 0.3 � 105 per mL, respectively) was introduced at the given flow rate (20 μL/min) through the Cell IN while the flow through the Stimulus IN was static. A sinusoidal voltage at a fixed fre­ quency (450 KHz) with a given amplitude (5 V) was applied to EP 2. The signals from EP 2 were sampled at 250 KHz using a 16-bit DAQ card, recorded and processed in the desktop. Then, the relationship between particle size/diameter and amplitude was analyzed. After the data were recorded, PBS was introduced to wash the chip for subsequent use. In experiments to verify the accuracy of the counting of particles, the PBS solution with only 5 μm beads (~1.5 � 105 per mL) was introduced at a given flow rate (20 μL/min) through the Cell IN, while the flow through the Stimulus IN was static. The applied excitation voltage, the sampling rate and the signal processing were the same as those in the previous experiments. The acquisition time was approximately 2 min.

2.5. Off-chip cell hypertonic stimulus In this study, 3 types of experiments were performed to show the effect of hypertonic stimulus on the survival rate of the cells, the mor­ phologies of living and dead cells before and after hypertonic stimulus, and the change in the size/diameter of the living and dead cells exposed to the hypertonic stimulus over time. In the first experiments, the living HUVEC suspension (~106 cells per mL) was mixed with the hypertonic NaCl solution (NaCl concentrations: 1400, 2300 and 4300 mOsm/kg H2O) at a volume ratio of 1:1; after 30 min, the mixed cell suspension was stained with PI and Hoechst dyes (Shanghai Yeasen Biotechnology Co., Ltd., China) and incubated at 37 � C for 10 min; then, the fluorescence images were recorded using an inverted fluorescence microscope (IX53; Olympus, Japan) with CCD (DP-73; Olympus, Japan), and the numbers of living and dead cells were counted manually. In the second experiments, the pure living HUVEC suspension (~106 cells per mL) was added to 2 wells (100 μL per well) of a 96-well plate (ThermoFisher Scientific Inc., USA) using a micropipette (Ther­ moFisher Scientific Inc., USA) and the pure dead HUVEC suspension was added to another 2 wells; then, the hypertonic NaCl solution (100 μL; NaCl concentration: 4300 mOsm/kg H2O) was added to 2 wells (one contained the living cells and the other contained the dead cells), while PBS (100 μL; 300 mOsm/kg H2O) was added to the remaining wells; next, the suspensions were stained with PI and Hoechst dyes; finally, the bright field and fluorescence images were recorded using an inverted fluorescence microscope. In the third experiments, the pure living and dead HUVEC suspen­ sions were added to 2 wells (100 μL per well) of a 96-well plate. When the hypertonic NaCl solution (100 μL; NaCl concentration: 4300 mOsm/ kg H2O) was added to the wells, bright-field images were recorded at a speed of 1 picture per minute using an inverted fluorescence micro­ scope. The size/diameter of the cells was analyzed using ImageJ.

2.8. On-chip cell experiments In the on-chip cell experiments for demonstrating the size distribu­ tion of the cells, the living iBMDM cells, HT-29 cells and HUVECs were separately introduced through the Cell IN while the flow through the Stimulus IN was static; 2 sinusoidal voltages at a fixed frequency (450 KHz) with a given amplitude (5 V) were applied to EP 1 and EP 2. The signals through EP 1 and EP 2 were recorded and processed. The acquisition time was approximately 2 min. In the on-chip cell experiments for demonstrating the performance of the HSIFC, the pure living cells, the pure dead cells and the mixed living and dead cells were separately introduced at a fixed flow rate (20 μL/ min) through the Cell IN, while the stimulus solution (NaCl concentra­ tion: 4300 mOsm/kg H2O) was introduced through the Stimulus IN at a given flow rate (10 μL/min). The excitation at EP 1 was 5 V @ 450 KHz, while at EP 2, it was 1 V @ 450 KHz because of the difference in the conductivity between the solutions at EP 1 and EP 2. The signals through EP 1 and EP 2 were separately recorded and processed. Here, iBMDM cells, HT-29 cells and HUVECs were used. The pure dead cells were obtained by treating the living cells with 4% paraformaldehyde or heating the living cells at 65 � C for 10 min. The ratio of living cells to dead cells was 1:2, 1:1 or 2:1 as prepared. The density of cells in the suspension through the Cell IN was approximately 105 per mL. 2.9. FCM experiments The PBS solution with only 5 μm beads (the densities of the beads were the same as those described in Section 2.7) was analyzed using a flow cytometer (CytoFLEX; Beckman Coulter, Inc., USA). For cells, 2 μL of calcein-AM and 10 μL of PI were added to 1 mL of each cell suspension (the cell suspension was the same as that described in Section 2.8), and the mixed sample was incubated in the dark at 4 � C for 15 min. Before experiments, negative (unstained mixed samples) and positive (calceinAM stained pure living cells and PI stained pure dead cells) controls were separately analyzed to determine an appropriate gate. In experiments, the forward scatter (FSC) and side scatter signals were recorded. With the same gate, fluorescein scatter plots of the mixed sample were analyzed.

2.6. Perfusion of the fluorescence solution The experiments were performed to verify the effectiveness of the chaotic mixer. In the experiments, 0.08 mM fluorescein solution was introduced from the Stimulus IN at a given flow rate (10 μL/min), while deionized water was introduced from the Cell IN at a given flow rate (20 μL/min); after 15 min, the fluorescence images at positions P1 and P2 were recorded using the inverted fluorescence microscope. The 5

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2.10. Signal processing Here, we used a self-developed graphical user interface (GUI) pro­ gram coded in LabVIEW 2013 to automatically process the impedance signals and adjust the detection parameters. A wavelet-based algorithm was used for denoising and detecting peaks. The time-frequency prop­ erties of wavelet transforms make wavelet-based algorithms well suited for the detection of transient events. In this study, the signal processing included a preprocessing step and an amplitude extraction step (Fig. S3). The baseline wander signal was removed through a discrete wavelet transform (DWT) using the Daubechies 05 wavelet. Then, the detrended signal was further processed to increase the signal-to-noise ratio (SNR) by suppressing the wide-band noise coefficients before signal recon­ struction. After wide-band noise filtering, the reconstructed signal was decomposed into multiple levels (the decomposition level depends on the pulse width) using the Daubechies 05 wavelet. By comprehensively analyzing the zero-crossing points of the wavelet coefficients at multiple levels, the adopted algorithm can accurately confirm the peak position. Once the peak was detected, the amplitude (representing the bead/cell size) was extracted. Benefiting from the inherent multilevel nature of the wavelet analysis, the wavelet-based peak detection method is more robust and accurate compared with the peak-finding approach (Hassan et al., 2014), which suffers from sensitivity to noise. The amplitude data were recorded in a text file and presented as histograms.

difference between living and dead cells. Thus, we first needed to confirm that the hypertonic NaCl solution (or the hypertonic stimulus operation) does not kill the living cells in a defined period. The results show that the survival rates of HUVECs exposed to 3 stimuli was approximately 100% after 35 min (Fig. 2a). This finding means that the strategy we adopted is safe for living cells and negligibly affects the percentage of living cells in the sample to be tested. Second, we needed to confirm that the volume of the living and volume of the dead cells are distinguishable after the hypertonic stimulus is applied. The results show that the volume of the living and of the dead HUVECs was the same before the introduction of the stimulus (Fig. 2b), whereas after the stimulus was introduced (i.e., the hypertonic NaCl solution), the volume of the living cells was small, but the volume of the dead cells was un­ changed (the red and blue fluorescence indicates the dead and living cells, respectively). In addition, the volume of the living cells in the hypertonic solution initially decreased quickly and then remained almost unchanged (Fig. 2c). To further confirm that the volume of the living cells is distin­ guishable from the volume of the dead cells after being challenged by the stimulus, the diameter measurements of the living and dead cells were statistically analyzed (Fig. 2d). The results show that the average size of the living cells decreased from ~15 μm before exposure to the stimulus to ~10 μm after exposure to the stimulus, whereas the average size of the dead cells was unchanged at ~15 μm.

2.11. CSR calculation

3.2. Performance of the chaotic mixer

In this study, the size distributions of both the living and dead cells were assumed to be Gaussian distributions, as shown in Equations (1) and (2), respectively. Thus, the data of the mixed living and dead cell samples can be presumed to follow a Gaussian mixed distribution (GMD), as shown in Equation (3). � � 1 ðx μL Þ2 fL ðxÞ ¼ pffiffiffiffiffi exp (1) 2σ1 2 2π σ 1

In the HSIFC, the speed of the response of the living cells to the extracellular stimulus mainly depends on the mixing speed of the cell suspension and the hypertonic solution because the osmoregulationinduced change in the volume of living cells is very quick (Dobbs et al., 1998; Fang et al., 2017). Thus, the chaotic mixer plays a key role in quickly measuring the percentage of living cells in the sample. To verify the performance of the mixer, the stimulus solution was replaced with a fluorescein solution to facilitate the visualization of the mixing process, and the fluorescence images were recorded before and after mixing at positions P1 and P2 (Fig. 3a). The distribution of fluorescein became uniform within 1 s, indicating that the two solutions were quickly mixed well (Fig. 3b). To further demonstrate the ability of the mixer, the fluorescein concentration measured experimentally was compared with the concentration calculated theoretically (Fig. 3c). The result shows that the measured value agrees well with the expected value. Thus, the mixer in the HSIFC worked very well.

� 1 fD ðxÞ ¼ pffiffiffiffiffi exp 2π σ 2

ðx

fM ðxÞ ¼ α fL ðxÞ þ ð1

αÞ fD ðxÞ

μD Þ2

�

2σ 2 2

(2) (3)

where μL and μD represent the mean size of the living cells and the dead cells after exposure to the stimulus, respectively; σ1 and ​ σ 2 are the standard deviations; α represents the percentage of the living cells in the mixed samples or the CSR. The parameters μL , μD , σ1 , and σ 2 can be predetermined from the pure living and dead cell samples. Then, the CSR is calculated by means of solving the GMD based on least squares with Python.

3.3. Signal analysis To demonstrate the signal analysis ability of the auxiliary sensing circuit and the wavelet-based algorithm for the HSIFC, a mixed sus­ pension of microbeads with diameters of 5 μm, 10 μm and 15 μm was introduced into the HSIFC. Fig. 4a shows a typical raw signal acquired by the DAQ card in 0.5 s. Each pulse represents one particle passing through the detection pore. The 10 μm and 15 μm microbeads are clearly distinguished according to the amplitude of pulses. However, the 5 μm microbeads (~50 mV) are undistinguishable from the noise of the sys­ tem (~40 mV). To solve this problem, wavelet-based broadband filtering was performed with the raw data (Fig. 4b). Before the detection of peaks, the filtered signals were reconstructed for further denoising, and the wavelet-based algorithm was used to identify the peak location as well as to calculate the start/end points of the pulses (Fig. 4c). After denoising and reconstruction, the noise amplitude was attenuated to below 2 mV. Then, the 5-μm microbeads were clearly observed (Fig. 4d). In the HSIFC, the insulating PDMS layer may block the coupling of the electrodes with the solution, causing low detection sensitivity (Emaminejad et al. 2012, 2016). To reduce the effect of the insulating layer, the thickness of the PDMS membrane that we fabricated was reduced to less than 5 μm by curing the PDMS premixture dissolved in

2.12. Statistical analysis The P values were calculated using two-tailed unpaired Student’s ttests: **P < 0.01; *P < 0.05 and N.S. denotes no significance. 3. Results 3.1. Behaviors of the cells exposed to the hypertonic stimulus In this study, the osmoregulatory strategy was designed to make the volume of living cells shrink (under hypertonic conditions, the intra­ cellular water will efflux to maintain the balance between the intracel­ lular and extracellular osmotic pressures, and thus, the volume of the cells will decrease) and then to make the volume of living cells distin­ guishable when compared with the unshrunk dead cells. Consequently, the percentage of living cells in the living and dead cell sample (or CSR) can be determined according to the osmoregulatory-induced volume 6

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toluene on the glass side (Shetty and Prasad, 2017); after this process, the membrane was much thinner than those described in previous re­ ports (Emaminejad et al., 2016; Guo et al., 2014). In addition, because of the wavelet-based algorithm, the SNR of the filtered signals was improved to 26 dB, which was comparable to those measured with coplanar microelectrodes (Hassan and Bashir, 2014; Hassan et al., 2014) and overlap microelectrodes (Caselli and Bisegna, 2016). It is note­ worthy that the differential electrode pair can also be adopted in the HSIFC for further denoising (Watkins et al., 2013).

of cells. 3.5. Performance of the HSIFC in determining CSR In the presence of hypertonic stimulus, the volume of living cells decreases, but the degree of drop is insufficient to cause the complete separation of the dead and living cell volume distributions even when a very high NaCl concentration is adopted because of the presence of osmotically inactive cells (Weng et al., 2017). In addition, the size uniformity of the cells is low, making it more difficult to distinguish the volume distributions of the dead and living cells. Therefore, to address this issue, an algorithm is used to mathematically distinguish the size distributions of the dead and living cells in the presence of the hyper­ tonic stimulus. For the algorithm, the distribution baselines for the pure living and dead cells are needed. Fig. 6a and b illustrates the amplitude distribution of the pure living and dead iBMDM cells. When the mixture of living and dead iBMDM cells is run through the HSIFC, the distribu­ tion data show two peaks (Fig. 6c). By using the algorithm, the per­ centage of the living or dead cells in the mixed sample can be determined. For a comparison (Fig. 6d–f), the cells were also analyzed using FCM, for which they were stained with calcein-AM and PI (the FSC of FCM cannot directly differentiate the volumes of unstained cells before or after a stimulus; Fig. S4). The percentage obtained using the method presented here is very close to the one measured using FCM (Fig. 6g); in fact, at various percentages (Fig. 6h), the results with the HSIFC agree well with the results from FCM. Thus, the HSIFC works very well. To further demonstrate the performance of the HSIFC (Fig. 6i–l), the percentage of living cells (or the CSR) was determined under various operating conditions (the types of cells, the causes of cell death and the percentage prepared all varied). The results show that the percentages measured from the HSIFC agree well with the expected/prepared values and are very close to the values from FCM (the HUVECs presented a

3.4. Counting beads/cells and characterizing their sizes To determine the percentage of living cells in the mixed cell sample (or CSR), the key step for the HSIFC is to count cells with differentiated volumes. Thus, one should first confirm that the HSIFC is able to correctly count the number of cells. To verify the ability of the HSIFC to count cells, 5 μm beads were driven through the chip. The results show that the number of microbeads counted using the HSIFC is close to the number obtained using FCM (Fig. 5a). One should also confirm that the HSIFC is able to differentiate cells of different sizes. To verify the ability of the HSIFC to differentiate cells, a mixture of 5 μm, 10 μm and 15 μm beads was adopted. The results show that 3 distributions were presented perfectly separated (Fig. 5b; the results also indicate that the average diameter of the microbeads is proportional to the average amplitude). When the cells were run through the HSIFC (Fig. 5c), the distribution of the cells was similar to the distribution of the microbeads (Fig. 5a). However, as the size uniformity of these cells is low (i.e., the standard deviation in cell size is large), unlike the case with microbeads, the overlap appears in the distribution at the measured amplitudes (Fig. 5c). To check whether the mixing process causes the loss of cells, the number of cells counted using EP 1 was compared with the number of cells counted using EP 2 (Fig. 5d). The number of cells before and after the mixing was determined to be very close, indicating that there was no loss

Fig. 5. Counting particles and characterizing sizes of particles: (a) 5 μm beads counted by the HSIFC and FCM; (b) the histogram of the amplitudes of the 5, 10 and 15 μm beads mixed together; (c) the amplitude distributions of the iBMDM cells, HT-29 cells and HUVECs; (d) the comparison of the counted HUVECs as determined by EP 1 and EP 2. 7

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Fig. 6. Comparison between the HSIFC and FCM under various operating conditions: (a–c) analysis of the iBMDM cells by the HSIFC; (d–f) analysis of the iBMDM cells by FCM; (g) the comparison between the HSIFC and FCM with the living and dead iBMDM cell ratio 1:2 as prepared; (h) the comparison of the performances between the HSIFC and FCM for iBMDM; and the comparison between the prepared values and the values measured by the HSIFC and FCM for (i) paraformaldehydetreated iBMDM cells, (j) heat-treated iBMDM cells, (k) paraformaldehyde-treated TH-29 cells and (l) paraformaldehyde-treated HUVECs.

larger deviation between the measured values and the expected values because some HUVECs approach or even exceed the size of the detection pore). Compared with FCM, the HSIFC has two distinctive advantages. First, the HSIFC is very suitable to be integrated onto a chip to realize insitu, rapid determination of the CSR for many lab-on-a-chip applications because it is compact and requires no complex optic systems. Second, the living cells from the HSIFC can be reused in subsequent experiments because they do not need to be stained or labeled.

can be determined within 2 min. Living cells have the basic ability to respond to extracellular osmotic stimulus in volume (Lang, 2007; Mcmanus et al., 1995). However, it has rarely been used as a basis for biosensing assays. Maeno et al., (2000) discovered that disordered volume regulation may be an early prereq­ uisite for cell death. The experiments we conducted (Fig. 2b and c) also show that dead cells lose their volume regulation function when exposed to the hypertonic stimulus. Based on the osmotic-based volume response mechanism, Park et al., (2015) measured the viability of the cells treated with drugs. In their study, the fixed cells were treated with hypertonic solution, and the change in the refractive index induced by the changing cell volume was detected using a complex waveguide-based sensor. Here, the moving cells were stimulated with a hypertonic solution, and the change in cell volume was directly detected using a simple impedance-based sensor. The integrated design on a chip makes the HSIFC very transferable for in situ sensing. In addition, the high throughput of the cells through the HSIFC also ensures the accuracy of the results. It should be noted that the HSIFC has a limited ability to distinguish reproductively dead cells or early apoptotic cells (Franken et al., 1999, 2011; Zhou et al., 2016a) from living cells. The reason is that the HSIFC distinguishes between dead and living cells by analyzing the osmoreg­ ulation ability of cells; however, the cells in the stage of reproductive death or early apoptosis still maintain basic physiological and biochemical functions for some time (Elmore, 2007; Hall and Giaccia, 2006), and they may have the osmoregulation ability at the time of detection. Hence, if there are reproductively dead cells or early apoptotic cells in the collected sample, the accuracy of the CSR

4. Discussion The number of pulses per unit time is affected by the density of the cells and the flow rate of the cell suspensions. Here, approximately 120 pulses were recorded in 1 s under the described conditions. The HSIFC has the ability to reach a higher throughput that is close to that of previously developed chips (Xie et al., 2017). In addition, the sensitivity of the HSIFC can be increased by using a microelectrode group (Han et al., 2012; Hassan et al., 2016), and the stability of the flow (and thus the accuracy of the amplitude) can be enhanced by embedding flow focusing technology (Gnyawali et al., 2017) before the detection pore. For the HSIFC, the accuracy of the measured percentage of living cells depends on the number of living and dead cells that pass through the detection pore. That is, the more cells run through the HSIFC, the higher the accuracy of the CSR. For a given operating condition (i.e., the given density of the cells and the given flow rate of the cell suspension), the run time should be sufficiently long such that the number of living and dead cells passing through the pore is adequate for the data analysis. Under the operating conditions we designed, the accurate percentage 8

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determined with the HSIFC may decrease. To solve the abovementioned applicability problem of the HSIFC, an intensive study is needed in the future.

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5. Conclusions In this work, a microfluidic chip HSIFC was designed to determine the CSR or the percentage of living cells in a sample of mixed living and dead cells by analyzing the impedance signaling differences induced by the differing volume of the cells after hypertonic stimulus. The results show that the HSIFC can accurately determine the CSR, and the accuracy is comparable to that achieved with flow cytometry. This work realizes the label-free, on-chip and in-line determination of the CSR based on the osmotic-volume response of living cells and thus promotes the multifunctionalization of lab-on-a-chip systems. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Qiang Zi: Investigation, Data curation, Formal analysis, Software, Methodology, Writing - original draft, Writing - review & editing. Weiping Ding: Conceptualization, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review & editing. Chunli Sun: Formal analysis, Writing - original draft. Shibo Li: Soft­ ware, Formal analysis, Validation. Dayong Gao: Supervision, Method­ ology. Liqun He: Methodology, Resources. Jing Liu: Resources, Formal analysis. Lei Xu: Methodology, Resources. Bensheng Qiu: Project administration, Supervision, Funding acquisition. Acknowledgments This work was partially supported by the National Natural Science Foundation of China (81627806 and 81571768). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We would also like to thank the USTC Center for Micro and Nanoscale Research and Fabrication and the Research Center for Life Sciences at USTC for its assistance. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.bios.2019.111820. References Bagnaninchi, P.O., Drummond, N., 2011. Proc. Natl. Acad. Sci. U.S.A. 108, 6462–6467. Bao, J.Z., Davis, C.C., Schmukler, R.E., 1992. Biophys. J. 61, 1427–1434. Caselli, F., Bisegna, P., 2016. IEEE Trans. Biomed. Eng. 63, 415–422. Dobbs, L.G., Gonzalez, R., Matthay, M.A., Carter, E.P., Allen, L., Verkman, A.S., 1998. Proc. Natl. Acad. Sci. U.S.A. 95, 2991–2996. Ebrahimi, A., Alam, M.A., 2016. Proc. Natl. Acad. Sci. U.S.A. 113, 7059–7064. Elmore, S., 2007. Toxicol. Pathol. 35, 495–516. Emaminejad, S., Javanmard, M., Dutton, R.W., Davis, R.W., 2012. Lab Chip 12, 4499–4507. Emaminejad, S., Paik, K.H., Tabard-Cossa, V., Javanmard, M., 2016. Sens. Actuators B Chem. 224, 275–281. Evander, M., Ricco, A.J., Morser, J., Kovacs, G.T.A., Leung, L.L.K., Giovangrandi, L., 2013. Lab Chip 13, 722–729. Fang, C.F., Ji, F.J., Shu, Z.Q., Gao, D.Y., 2017. Lab Chip 17, 951–960.

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