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Oct 2019

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Measuring Cell Growth and Junction Development in Epithelial Cells Using Electric Cell-Substrate Impedance Sensing (ECIS)
使用电子细胞基质阻抗传感(ECIS)测量上皮细胞生长和连接发育   

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Abstract

Electric Cell-substrate Impedance Sensing (ECIS) is an automated method that can be used to quantify processes such as cell attachment, growth, migration and barrier functions (i.e., the properties of tight junctions). The method provides simultaneous information on cell number and tight junction function by detecting electric parameters of cells grown on electrodes. Samples are probed with small alternating current (AC) over a range of frequencies, and changes in capacitance and impedance are measured over time. Capacitance reflects the degree of electrode coverage by cells, that correlates with cell number, and can be used to assess cell proliferation or migration. Impedance values inform about barrier function. Obtaining real-time simultaneous information on these parameters is unique to this system and is of great value for addressing fundamental questions such as the role of tight junction proteins in cell growth and migration. This protocol describes the use of ECIS to follow cell growth and tight junction-dependent barrier generation in tubular epithelial cells. We used this method to explore how depleting claudin-2, a tight junction protein affects tubular cell growth and barrier function. During the process, cells are transfected with control or claudin-2-specific siRNA, and 24h later plated on electrodes. ECIS automatically collects information on cell growth and barrier as the monolayer develops. The data are initially analyzed using the ECIS software and exported into a graph software for further processing.

Keywords: Impedance sensing (阻抗传感), Epithelial cells (上皮细胞), Cell growth (细胞生长), Transepithelial resistance (上皮细胞跨膜阻抗), Tight junction development (紧密连接发育)

Background

Epithelial cells generate monolayers characterized by well-developed intercellular junctions. Input from the junctions determines cell behavior, including proliferation and migration. As junctions develop, cell proliferation and migration slows and then halts due to contact inhibition (McClatchey and Yap, 2012). Although the past decades rapidly augmented our understanding of the molecular mechanisms of proliferation and migration, many aspects of these processes, and the exact nature of contact inhibition remain incompletely defined. As cells establish a confluent layer, intercellular junctions, including tight junctions develop and mature, ensuring well-controlled paracellular permeability (barrier functions) (Zihni et al., 2016). Paracellular seal and permeability pathways are established by the claudin family of tight junction proteins (Tsukita et al., 2019). Interestingly, accumulating evidence suggests that in addition to their role as permeability regulators, many claudins also modulate proliferation and migration (Hagen, 2017; Venugopal et al., 2019). These roles, however, remain incompletely understood. Methods that follow barrier function and cell growth or migration simultaneously over time are crucial for obtaining new insights. Electric Cell-substrate Impedance Sensing (ECIS), that was developed by Applied BioPhysics offers this possibility. ECIS is an impedance-based, automated method that allows the quantification of various aspects of cell behavior through detection of electric properties (Wegener et al., 2000). The cells are grown in chambers containing small gold electrodes (referred to as arrays), and the system continuously probes the electrodes using small, biologically inert alternating current. A range of AC frequencies from 62.5-64,000 Hz is used to measure impedance and capacitance across the electrodes over time. The basis of the measurement is that the current can flow through the cells or under and between them, and the magnitude of resistance of these compartments depends on the frequency of the AC current applied. Thus, specific frequencies can be used to probe different parameters, and this can be utilized to follow various aspects of cell behavior in the same sample (Stolwijk et al., 2015; Wegener et al., 2000). During measurment the system measures capacitance and impedance at the full frequency range, and the user can select the required parameters during analysis. Capacitance detected using high AC frequency reflects coverage of the electrode, with little influence from other parameters. Capacitance data collected in a typical experiment are shown in Figure 1A (top graph). This demonstrates that following seeding cells generate an insulating layer on the electrodes that is reflected by a drop in capacitance. Capacitance reaches its minimum and stabilizes when a fully confluent layer is generated. The initial rate of drop reflects cell attachment, that is completed by 3h. After this time, the rate of drop reflects cell growth. Figure 1A bottom graph shows the parallel changes in impedance (Z) values measured at low AC frequencies. In epithelial cells with well-developed tight junctions, impedance measured at 500 Hz or lower frequencies reflects predominantly the properties of the paracellular space. Impedance increases as cells reach confluence and tight junctions develop. Many epithelial cells show an increase in the Z value, followed by a decrease as junctions mature (Amoozadeh et al., 2017). Interestingly, simultaneous measurement of capacitance and impedance reveals that tight junction development and maturation does not stop as confluence is reached (Amoozadeh et al., 2018). More detailed analysis to separate different components of impedance can be performed using mathematical modelling, built into the software, which can be advantageous in cells with weaker paracellular resistance (e.g., endothelial cells) (Wegener et al., 2000; Stolwijk et al., 2015; García et al., 2019). Although the theory of mathematical modelling in ECIS is complex, the measurement itself is relatively straight forward, and simple analysis can provide valuable information. ECIS is also equipped with an elevated field module for wounding of the layer, and capacitance values can be used for an automated cell migration assay (not discussed in this protocol) (Szaszi et al., 2012).

The advantages of ECIS are as follows. It is an automated, continuous, real-time, label-free detection method. Since the arrays are housed in a tissue culture incubator, the environment is controlled and any measurement artifacts stemming from changes in temperature and CO2 levels are eliminated. In comparison with other methods that follow cell growth or migration, the key benefit of ECIS is the ability to simultaneously detect transepithelial resistance (TER), i.e., barrier function and cell growth or migration (Kakiashvili et al., 2011; Amoozadeh et al., 2015 and 2017; Stolwijk et al., 2015; Láng et al., 2017) (Figure 1). This can be valuable for correlating effects of various treatments on these highly interdependent parameters, thereby providing new insights into fundamental cell biological questions.

This protocol describes the use of ECIS to follow tubular epithelial cell growth, and the development of barrier via formation of the tight junctions. We used this protocol to assess the development of cell confluence (Amoozadeh et al., 2018) and to follow effects of silencing of a tight junction protein, claudin-2 rate on proliferation rate and barrier function (Dan et al., 2019). Silencing claudin-2 alters both barrier function (resulting in elevated resistance) (Amoozadeh et al., 2018) and cell growth (reducing proliferation) (Dan et al., 2019). Simultaneous detection of these parameters reveals both effects. We found that claudin-2 silencing slows the development of confluency, but does not prevent it, and claudin-2 depleted cells generate a barrier which is tighter that the control due to absence of the channel forming claudin-2. Adaptation of the method for other cell types and proteins requires optimization of the seeding conditions, electrode coating and transfection parameters. We provide notes on these issues to direct the reader.


Figure 1. Capacitance and impedance measurements of LLC-PK1 cells with or without claudin-2 silencing. A. Exported graphs showing simultanously detected changes in capacitance at 32 kHz (reflecting cell growth) and impedance (z) at 500 kHz (reflectig barrier). LLC-PK1 cells were transfected using control or claudin-2-specific siRNA and electrical properties were measured using ECIS, as described in the protocol. B. Impedance (z) was determied at 72 h after seeding of cells in two different experiments. The data from the same experiment are indicated by black and red for experiment #1 and blue and green for experiment #2 (control and claudin-2 siRNA, respectively).

Materials and Reagents

  1. Clear tissue culture-treated 6-well microplates (Corning Costar®, Millipore-Sigma, catalog number: CLS3513 )
  2. Microcentrifuge tubes (1.5 ml) (Fisherbrand, catalog number: 2043-05408129 )
  3. CountessTM Cell Counting Chamber Slides (Thermo Fisher, Invitrogen, catalog number: C10228 )
  4. Cell line: LLC-PK1 kidney tubule epithelial cell line (European Collection of Authenticated Cell Cultures, catalog number: 86121112 )
  5. Cell culture media and reagents:
    Dulbecco's Modified Eagle Medium (D-MEM), low glucose, with L-glutamine and 110 mg/L sodium pyruvate (Gibco, Thermo Fisher, catalog number: 11885084 )
    Fetal Bovine Serum (Gibco, Thermo Fisher, catalog number: 12483-020 )
    Penicillin-Streptomycin, 100x, sterile-filtered, cell culture tested (Sigma-Aldrich, catalog number: P-4333. 25300062 )
    Trypsin-EDTA (0.05% Trypsin with EDTA 4Na) (Thermo Fisher, Gibco, catalog number 25300062 )
    Opti-MEMTM I Reduced Serum Medium for transfection (Thermo Fisher, Gibco, catalog number: 31985088 )
  6. Trypan Blue Stain (0.4%) (Gibco, catalog number: 15250061 ) for use with the CountessTM Automated Cell Counter (Invitrogen, Thermo Fisher, catalog number T10282)
  7. LipofectamineTM RNAiMAX Transfection Reagent (Invitrogen, Thermo Fisher, catalog number 13778150)
  8. Two custom-designed siRNAs targeting porcine claudin-2 (Thermo Fisher/Dharmacon) (sequence #1, CCAGAACTCTCGCGCCAAA, and #2 CCCTGATAGCTGGGATCAT)
  9. SilencerTM Negative Control No. 1 siRNA (non-related (NR)) siRNA (Applied Biosystems/Ambion, Thermo Fisher, catalog number: AM4611 )
  10. ECIS electrode array, type: 8W10E or 8W10E+ PET (Applied Biophysics, catalog number: 8W10E or 8W10E+ )
  11. L-cysteine (Millipore-Sigma-Aldrich catalog number: C7352 )
  12. 10 mM L-cysteine solution for electrode stabilization (see Recipes)

Equipment

  1. ECIS® Z-Theta instrument with 16-well array module housed in incubator (Applied Biophysics)
  2. CountessTM Automated Cell Counter (Thermo Fisher, Invitrogen, catalog number C10228 )
  3. Tissue culture equipment (sterile hood, incubator, centrifuge)

Software

  1. ECIS Zθ software
  2. Graph software (e.g., Excel or GraphPad)

Procedure

  1. Electrode stabilization with cysteine pretreatment
    1. Pretreat electrodes with 200 μl of filtered 10 mM L-cysteine for 15 min at room temperature.
    2. Aspirate the cysteine solution and wash with 200 μl of autoclaved dH2O twice. Do not wash electrodes with PBS.
    3. Air dry the electrodes before seeding cells. Alternatively, the treated and dried electrodes can be stored at room temperature till its expiry.

  2. Splitting epithelial cells for transfection with siRNA
    1. Grow LLC-PK1 cells in low glucose DMEM supplemented with 10% FBS and 1% Pen-Strep in an incubator at 37 °C with 5% CO2.
    2. Split cells when they are grown to about 90% confluence.
    3. Seed cells for transfection in 6-well plates using standard cell culture procedures.
    4. Use 2 ml antibiotic-free, serum-containing DMEM/well to seed cells. Aim at obtaining 30-40% confluence the next day.

  3. Transfecting cells
    The following day (16-24 h later) transfect cells with 100 nM of the negative control (non-related, NR) or claudin-2-specific siRNA using the Lipofectamine RNAiMAX transfection reagent as follows:
    1. Mix reagents:
      Tube A: For each condition (well) to be transfected, prepare siRNA: dilute 2.4 μl of 100 nM stock siRNA duplex in 200 μl Opti-MEM medium without serum in a sterile microcentrifuge tube. Mix gently. For more wells, scale up amounts as needed.
      Tube B: For each condition (well) prepare LipofectamineTM: add 3 μl LipofectamineTM RNAiMAX to 200 μl Opti-MEM medium. Mix gently. For more wells, scale up amounts as needed.
    2. Incubate the tubes for 5 min at room temperature.
    3. Mix the content of tube A and B to generate the siRNA-Lipofectamine complex. Incubate for 15 min.
    4. At the end of the incubation time, add 400 μl of the siRNA-Lipofectamine mix to the corresponding well. Gently shake to mix and return to the incubator.
    5. Grow cells for 24 h.

  4. Seeding cells on the electrode arrays
    1. The next day, prepare the electrode arrays for seeding:
      Add 400 μl pre-warmed complete culture medium (growth medium with serum and antibiotics) to the wells, and place into the incubator for 15 min prior to seeding cells. This will coat the surface with serum and warm the array to reduce convection of the medium/cell suspension due to heat differences, that affects even settling of cells on the electrode.
    2. Trypsinize cells transfected with control siRNA and claudin-2-specific siRNA. When cells are floating, add warm medium, collect, and spin down cells using 300 x g. Resuspend in fresh medium. Keep warm while performing the cell counting.
    3. Count cells using the Countess automated cell counter:
      Using a sterile tip remove 10 μl of the cell suspension and mix with 10 μl of Trypan blue in a microcentrifuge tube. Add to a CountessTM Cell Counting Chamber Slide and count cells using the automated cell counter to obtain number of viable cells.
      Note: Manual counting can be used instead of the Countess.
    4. Adjust volume using warm medium to obtain a total of 0.5 x 105 cells/electrode in a final volume of 400 μl.
    5. Remove array from the incubator and aspirate the medium from the wells. Add 400 μl of cell suspension to each well.
    6. Insert the electrode into the ECIS array holder in the incubator and screw the holder tight.

  5. Software setup and acquiring data
    1. Turn on the computer and the ECIS Zθ machine.
    2. Open the ECIS software. Click the “Setup” button. The system will check the connection. If the connection is good, the wells show up as green. If the connection is bad (red wells), reinsert the electrode, then click “Check” to confirm connectivity of all electrodes by using the “Check” button.
    3. Select the type of electrode. Choose the “Multiple Frequency/Time (MFT)” option (the required frequency can be selected during analysis).
    4. Click on “Start”. Enter the file name and any comments you want to save. Collect data for 24-48 h.
    5. As cells grow, C at high frequency (32 or 64 kHz) decreases (see Note 6 for comments on the choice of frequency). Confluence is achieved when C is stable at the minimum. At this point, the measurement can be stopped.

Data analysis

  1. Prepare data for analysis
    In the ECIS software on the right top click the “Analyze” tab. Figure 2 shows a screenshot of the software with a typical measurement, with the corresponding commands labelled. You can select the parameters and curves you want to export. This limits the amount of data exported. Alternatively, you can export all data (see below) and process them in the graph software. However, selecting the data needed for your analysis prior exporting makes managing the dataset easier.
    1. Select display parameters: In “Well configuration,” select the wells you want to work with (Figure 2). On the top, select to display the parameter you would like to use and under the graph select to display the frequency. For cell growth, use capacitance (C) at a high frequency (32 or 64 kHz). For transepithelial resistance, select Z at a low frequency (we use 500 Hz).
    2. Normalize data: This can be useful for comparing measurements from different days, or if the variability of the starting values makes comparisons difficult. In the “Time series” option, click “Normalize”. Use the 0 h point for normalization. This will ensure that data collected on different days can be compared. Figures 3A and 3B show a measurement before and after normalization.
    3. Combine parallel measurements: You have the option to combine all parallel measurements and export them as a single dataset, to limit the size of the exported dataset. For this in “Well Configuration” select the wells that represent parallel measurements. Click on Grp (group). This will calculate the average of your measurements and display them as one curve. Calculate the average for each condition (Figure 3C).
    4. Make any other adjustment for exporting data: Adjust the graph to reflect the data you want to export: In “Well configuration” (bottom left), select the wells to export (either single measurements or the grouped value of the different conditions). Under the graph, adjust the X-axis to display the required time frame (Figure 2).


    Figure 2. Screenshot from the ECIS software displaying results from a typical measurement described in the protocol. The cells were grown in an 8W10E+ array, placed in Holder A. Conditions were as follows: 1-4 (left side): NR siRNA transfected; 5-8 (right side) Cldn-2 siRNA transfected. The capacitance displayed is set to 32 kHz. The various options in the software described in the protocol are marked with green arrows and blue text boxes indicate the various options.


    Figure 3. Data exported using the ECIS software with or without various processing. A-C. Exported graphs with various steps of analysis, as described in the protocol. A. All data are shown (graph from Figure 1). B. Data were normalized to the first point of the measurement. C. Data grouped according to the conditions. Black: NR siRNA; pink: Claudin-2 siRNA. D. Determination of half revovery time. Data were exported to Excel and graphed using a scatter graph with line option. Blue: NR siRNA; red: Claudin-2 siRNA. The green horizontal line indicates 0.5 C value (Y axis), and the blue and red dashed lines indicate the corresponding time (X axis value).

  2. Exporting graph image and data
    The graph can be exported as an image using the “File”, “Export graph” command. Once the image to be exported opens, chose the save icon on top, and set the file name and format you wish to use. This image however will be of low quality with small fonts. Another option is to export the data from the graph and process using a graph software. For this open “File”, select “Export data” and choose “Graph data”. This mode of exporting will only save the data displayed on the graph in a *.csv format. You can also export all the data with the corresponding option in the “Export data” command. This will result in exporting all data collected at all frequencies.
  3. Processing using a graphing software
    Although the graph image can be exported, and the resulting picture has all the labels, these cannot be altered, and the graph and labels are of low quality. Therefore, we usually export the data to a graphing program for further processing and for generating publication quality graphs (Figure 3D).
  4. Analyzing TER
    One mode of comparing barriers in two conditions is to compare Z at a given timepoint after junctions developed. In Figure 1B we graphed the Z values from two measurements (those shown in Figure 1A bottom graph and a similar series of experiment from a different day).
  5. Analyzing cell growth
    To compare data obtained in different experiments, we calculate for each measurement the time required for the cells to cover half the electrode (the time corresponding to 0.5 C value). For this analysis we use the data from the capacitance (C) values at 32 kHz, normalized to the first measurement point (see step 2) (Figure 3D). The time values for all conditions are collected, and the mean ± S.D. for each condition is calculated.
  6. Statistics
    The measurements must be performed at least in 3 different experiments. We use at least duplicates for every condition on every array. Direct comparison between arrays can pose a challenge, as the raw values show variability in different experiments. For example, TER data combined from two arrays obtained on different days shown on Figure 1B reveal that data from the same experiment cluster closer together, but the values differ from those measured on another array. While in both cases the claudin-2 siRNA transfected cells show elevated TER, unpaired t-test on the combined dataset shows that the change is not significant. Normalizing and expressing data as fold changes can solve this problem, as described for the C values. If that method is chosen, a non-parametric statistical test should be used.

Notes

  1. Array type: There are various types of electrode arrays available, that differ in the number of electrodes. The more electrodes an array has, the more cells are being measured and averaged, resulting in reduced noise, but also smaller sensitivity. It is important to choose the appropriate electrode based on the experiment type. For a detailed description of each of the ECIS cultureware, users are referred to the Applied BioPhysics website: https://www.biophysics.com/cultureware.php.
    For detecting cell growth and junction development, we used both 8W10E or 8W10E+ . The 10E+ has more electrodes to collect information. Importantly, for a given series of experiment, always use the same array.
  2. Electrode coating: Depending on the properties of the cell type used, collagen, fibronectin, laminin or other extracellular matrix proteins can be used to coat the electrode prior to seeding cells. Extracellular matrix proteins aid in cell adhesion and regulate a wide range of cell functions such as cell proliferation, differentiation, migration and survival. The type of protein used for coating might also affect proliferation and the development of the barrier, although this has to be systematically explored for specific cells. Therefore, it is important to standardize conditions of coating across experiments. We found that LLC-PK1 cells do not require special coating, as they grow well on the gold electrodes.
  3. Cell number and seeding: The inoculation technique is key for comparability and reproducibility between wells. Difference in proliferation can be masked by a high variability between wells. This usually results from inconsistency of seeding, and variation in cell number. For accurate measurements, cell numbers must be kept constant across the wells. Preparing an even cell suspension for seeding is key. Thoroughly trypsinize cells and mix suspension to eliminate clusters of cells. Accurate counting of the cell suspension to obtain comparable numbers when using cells from different conditions (e.g., transfection) is also important. Finally, prewarming of the array and the cell suspension to eliminate temperature difference between the well bottom and the suspension is also vital. For following cell growth, low cell number must be used to allow the cells to proliferate after adhering. If the cell number is too high, the cells will be confluent after adhering and no proliferation will occur. Cell adhesion and spreading is usually complete in the first 3 h, and after this time frame the cells establish the confluent layer via proliferation.
  4. Mycoplasma contamination of the cell lines should be routinely tested.
  5. Transfection parameters: Transfection conditions vary depending on the cell dilines used and the target proteins, therefore, optimization of the conditions may be required. Knockdown efficiency of the target protein(s) should be confirmed by western blotting or qPCR in parallel. Changes in confluence in the transfected cells should be assessed using the capacitance measurement. The cells on the arrays can be visualized using a microscopy. In addition, effects on individual tight junction proteins are also informative and can be assessed by immunofluorescence.
  6. Choice of frequency for capacitance and impedance measurement: When setting up the system, it is recommended to use the setting that collects data at all available frequencies. Due to differences in the resistance against the flow of current through the cells and between the cells, parameters measured at different AC frequencies will reflect different aspects of cell properties. During analysis, the user can choose the frequency for the parameters of interest. Capacitance determined at high frequency reflects the coverage of the electrode, with minimal influence by other parameters, such as barrier function. The current recommendation and default in the software for analysis of capacitance values is 64 kHz. Of note, an earlier version of the software had 32 kHz as the highest and default C value, and therefore we used this setting in our earlier studies. At this frequency, C also reflects electrode coverage. In contrast, resistance of the paracellular space (referred to as barrier function, generated by the tight junctions) is best reflected by low AC frequencies. Impedance (Z) values at 500 Hz and below reflect dominantly resistance of the paracellular space. The software is also able to perform a mathematical modelling that allows further analysis and better distinction between resistance of the paracellular space and the space underneath the cells (cell-substrate attachment). For details the author is referred to Stolwijk et al. (2015).

Recipes

  1. 10 mM L-cysteine solution for electrode stabilization
    1. Add 0.06 g of L-Cysteine to 50 ml dH2O
    2. Filter to sterilize
    3. Cysteine can easily oxidize when dissolved in water and therefore, this solution must be prepared fresh

Acknowledgments

Funding: Kidney Foundation of Canada; Canadian Institutes of Health Research (CIHR) grants PJT-149058 and MOP-142409.
    This protocol was derived from: Amoozadeh et al., 2015; Dan et al., 2019.

Competing interests

The authors declare that they have no financial or non-financial conflicts of interest.

References

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  2. Amoozadeh, Y., Dan, Q., Anwer, S., Huang, H. H., Barbieri, V., Waheed, F., Maishan, M. and Szaszi, K. (2017). Tumor necrosis Factor-α Increases claudin-1, 4, and 7 expression in tubular cells: role in permeability changes. J Cell Physiol 232(8): 2210-2220. 
  3. Amoozadeh, Y., Dan, Q., Xiao, J., Waheed, F. and Szaszi, K. (2015). Tumor necrosis factor-α induces a biphasic change in claudin-2 expression in tubular epithelial cells: role in barrier functions. Am J Physiol Cell Physiol 309(1): C38-50.
  4. Dan, Q., Shi, Y., Rabani, R., Venugopal, S., Xiao, J., Anwer, S., Ding, M., Speight, P., Pan, W., Alexander, R. T., Kapus, A. and Szászi, K. (2019). Claudin-2 suppresses GEF-H1, RHOA, and MRTF, thereby impacting proliferation and profibrotic phenotype of tubular cells. J Biol Chem 294(42): 15446-15465.
  5. García, E., Pérez, P., Olmo, A., Díaz, R., Huertas, G. and Yúfera, A. (2019). Data-analytics modeling of electrical impedance measurements for cell culture monitoring. Sensors (Basel) 19(21): e4639.
  6. Hagen, S. J. (2017). Non-canonical functions of claudin proteins: Beyond the regulation of cell-cell adhesions. Tissue Barriers 5(2): e1327839.
  7. Kakiashvili, E., Dan, Q., Vandermeer, M., Zhang, Y., Waheed, F., Pham, M. and Szászi, K. (2011). The epidermal growth factor receptor mediates tumor necrosis factor-α-induced activation of the ERK/GEF-H1/RhoA pathway in tubular epithelium. J Biol Chem 286(11): 9268-9279.
  8. Láng, O., Kőhidai, L. and Wegener, J. (2017). Label-free profiling of cell dynamics: A sequence of impedance-based assays to estimate tumor cell invasiveness in vitro. Exp Cell Res 359(1): 243-250.
  9. McClatchey, A. I. and Yap, A. S. (2012). Contact inhibition (of proliferation) redux. Curr Opin Cell Biol 24(5): 685-694. 
  10. Stolwijk, J. A., Matrougui, K., Renken, C. W. and Trebak, M. (2015). Impedance analysis of GPCR-mediated changes in endothelial barrier function: overview and fundamental considerations for stable and reproducible measurements. Pflugers Arch 467(10): 2193-2218.
  11. Szaszi, K., Vandermeer, M. and Amoozadeh, Y. (2012). Epithelial Wound Healing and the Effects of Cytokines Investigated by ECIS. Jiang, W. (Ed.). In: Electric Cell-Substrate Impedance Sensing and Cancer Metastasis. Cancer Metastasis - Biology and Treatment, vol 17. Springer, Dordrecht. 131-175.
  12. Tsukita, S., Tanaka, H. and Tamura, A. (2019). The Claudins: From Tight Junctions to Biological Systems. Trends Biochem Sci 44(2): 141-152.
  13. Venugopal, S., Anwer, S. and Szaszi, K. (2019). Claudin-2: Roles beyond Permeability Functions. Int J Mol Sci 20(22): 5655.
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简介

[摘要 ] 电动细胞-基底阻抗感测(ECIS)是一个自动化的方法,该方法可用于定量如细胞附着,生长,迁移和屏障功能的处理(即, 紧密连接的特性)。该方法通过检测在电极上生长的细胞的电参数,提供有关细胞数量和紧密连接功能的同时信息。用小的交流电(AC)在一定频率范围内探测样品,并随时间测量电容和阻抗的变化。电容反映了细胞对电极的覆盖程度,与细胞数量相关,可用于评估细胞增殖或迁移。阻抗值告知屏障功能。获取有关这些参数的实时同步信息对于该系统而言是独一无二的,对于解决诸如紧密连接蛋白在细胞生长和迁移中的作用等基本问题具有重要价值。该协议描述了使用ECIS来跟踪肾小管上皮细胞中的细胞生长和紧密连接依赖性屏障生成。我们使用这种方法来探索如何耗尽紧密连接蛋白claudin-2如何影响肾小管细胞的生长和屏障功能。在此过程中,将细胞用对照或claudin-2特异性siRNA转染,然后在24小时后铺在电极上。随着单层细胞的发育,ECIS自动收集有关细胞生长和屏障的信息。最初使用ECIS软件对数据进行分析,然后将其导出到图形软件中以进行进一步处理。

[背景 ] 上皮细胞产生以成熟的细胞间连接为特征的单层细胞。来自连接的输入确定细胞行为,包括增殖和迁移。随着连接的发展,由于接触抑制,细胞增殖和迁移减慢,然后停止(McClatchey and Yap,2012)。尽管过去的十年迅速增加了我们对增殖和迁移的分子机制的了解,但这些过程的许多方面以及接触抑制的确切性质仍未完全定义。随着细胞建立汇合层,包括紧密连接的细胞间连接发展并成熟,从而确保良好控制的副细胞通透性(屏障功能)(Zihni 等人,2016)。细胞旁密封和通透性途径由紧密连接蛋白的claudin家族建立(Tsukita 等人,2019)。有趣的是,越来越多的证据表明,除了其作为渗透性调节剂的作用外,许多claudins还可以调节增殖和迁移(Hagen,2017; Venugopal 等,2019)。但是,这些角色仍未完全理解。随时间推移同时跟踪屏障功能和细胞生长或迁移的方法对于获得新见解至关重要。Applied BioPhysics 开发的电池基底阻抗感测(ECIS)提供了这种可能性。ECIS是一种基于阻抗的自动化方法,可通过检测电特性来量化细胞行为的各个方面(Wegener 等,2000)。细胞在包含小金电极(称为阵列)的小室中生长,并且系统使用小的生物惰性交流电连续探测电极。62.5-64,000 Hz范围内的交流频率用于测量一段时间内电极两端的阻抗和电容。测量的基础是电流可以流过电池或电池下面或电池之间,并且这些隔室的电阻大小取决于所施加的交流电流的频率。因此,特定的频率可以用来探测不同的参数,并且可以用来追踪同一样本中细胞行为的各个方面(Stolwijk 等,2015; Wegener 等,2000)。在测量期间,系统会在整个频率范围内测量电容和阻抗,用户可以在分析期间选择所需的参数。Ç 使用高交流频率反映了电极的覆盖范围,与来自其他参数的影响小apacitance检测。在收集的电容数据典型的实验显示在图URE 1A(俯视图)。这表明随后的晶种电池会在电极上产生绝缘层,该绝缘层会被电容的下降所反射。Ç apacitance达到其最小值,并产生完全汇合层时稳定。初始下降速率反映了细胞附着,在3h内完成。在这段时间之后,下降速度反映了细胞的生长。图URE 1A底部图显示了在平行改变阻抗(Z)值在较低AC频率测量。在具有紧密连接的发达的上皮细胞中,在500 Hz或更低频率下测得的阻抗主要反映了旁细胞间隙的特性。随着细胞达到汇合和紧密连接的发展,阻抗增加。许多上皮细胞显示Z值增加,然后随着连接的成熟而减少(Amoozadeh 等人,2017)。有趣的是,同时测量电容和阻抗表明紧密的连接发展和成熟不会随着达到汇合而停止(Amoozadeh et al。,2018)。更详细的分析,分离阻抗的不同的组件可以使用数学模型来进行,内置于软件,它可以在与更弱的细胞旁电阻(细胞是有利的例如,内皮细胞)(韦格纳等人,2000 ; Stolwijk 。等人, 2015; García 等人,2019 )。尽管ECIS中的数学建模理论很复杂, 测量本身是相对简单的,简单的分析可以提供有价值的信息。ECIS还配备了一个Ë levated字段模块的层的创伤,和电容值,可用于自动细胞迁移测定(在该协议不讨论)(Szaszi 等人,2012) 。

ECIS的优点如下。它是一种自动,连续,实时,无标签的检测方法。由于将阵列放置在组织培养箱中,因此可以控制环境,并消除了因温度和CO 2 水平变化而产生的任何测量伪影。在与后面的细胞生长或迁移其他方法相比,ECIS的主要好处是同时检测跨上皮电阻(TER)的能力,即,屏障功能和细胞生长或迁移(Kakiashvili 等人,2011; Amoozadeh 等。,2015年和2017年; Stolwijk 。等人,2015;郎。等人,2017) (图URE 1)。这对于关联各种治疗对这些高度相互依赖的参数的影响可能很有价值,从而为基础细胞生物学问题提供新的见解。

该协议描述了使用ECIS跟踪肾小管上皮细胞的生长,以及通过形成紧密连接形成屏障。我们使用该方案评估细胞融合的发展(Amoozadeh 等,2018),并追踪紧密连接蛋白,claudin-2速率对增殖速率和屏障功能的沉默效应(Dan 等,2019)。沉默claudin-2既改变屏障功能(导致抗性升高)(Amoozadeh 等人,2018)又改变细胞生长(减少增殖)(Dan 等人,2019)。同时检测这些参数可以揭示这两种效果。我们发现claudin-2沉默减慢了融合的发展,但没有阻止它,并且claudin-2耗尽的细胞产生了一个屏障,该屏障比由于缺乏形成claudin-2的通道所致的控制更为严格。使该方法适应其他细胞类型和蛋白质需要优化接种条件,电极包被和转染参数。我们提供有关这些问题的说明,以指导读者。



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图1. Ç apacitance和阻抗测量的LLC-PK1细胞用或不用紧密连接蛋白2沉默。一。导出的图表显示了同时检测到的32 kHz电容(反映细胞生长)和500 kHz(阻抗屏障)的阻抗(z)的变化。LLC-PK1细胞进行转染ü š 荷兰国际集团控制或紧密连接蛋白2特异性siRNA和使用ECIS测定的电性能,如在协议中所述。乙。在两个不同的实验中,在接种细胞后72小时测定阻抗(z)。来自同一实验的数据以黑色和红色表示实验#1,以蓝色和绿色表示实验#2(分别为对照和claudin-2 siRNA)。

关键字:阻抗传感, 上皮细胞, 细胞生长, 上皮细胞跨膜阻抗, 紧密连接发育

材料和试剂


 


1. 清除组织培养处理的6个-Well微孔板(Costar公司康宁® ,Millipore公司-Σ,C atalog号:CLS3513 )      


2. 微量离心管(1.5 米升)(FISHERBRAND ,目录号:2043-05408129)      


3. Countess TM 细胞计数室载玻片(Thermo Fisher,Invitrogen,Thermo Fisher ,目录号:C10228)      


4. 细胞系:LLC-PK 1 肾小管上皮细胞系(欧洲认证细胞培养物保藏中心,目录号:86121112)      


5. 细胞培养基和试剂:      


Dulbecco 改良的Eagle培养基(D-MEM),低葡萄糖,含L-谷氨酰胺和110 mg / L 丙酮酸钠(Gibco,Thermo Fisher,目录号:11885084)


胎牛血清(Gibco,Thermo Fisher,目录号:12483-020)


青霉素-链霉素,100 x ,无菌过滤,细胞培养测试(Sigma - Aldrich,目录号:P- 4333。25300062)


胰蛋白酶-EDTA(0.05%胰蛋白酶与EDTA 4Na)(Thermo Fisher,Gibco,目录号25300062)


用于转染的Opti-MEM TM I还原血清培养基(Thermo Fisher,Gibco,目录号:31985088)


6. 用于Countess TM 自动细胞计数器(Invitrogen,Thermo Fisher,目录号T10282)的台盼蓝染色剂(0.4%)(Gibco,目录号:15250061)。      


7. Lipofectamine TM RNAiMAX 转染试剂(Invitrogen,Thermo Fisher,目录号13778150)      


8. 两个针对猪claudin-2的定制设计的siRNA(Thermo Fisher / Dharmacon )(序列1,CCAGAACTCTCGCGCCAAA和序列2 CCCTGATAGCTGGGATCAT)      


9. Silencer TM 阴性对照1号siRNA(非相关(NR))siRNA(Applied Biosystems / Ambion ,Thermo Fisher,目录号:AM4611)      


10. ECIS电极阵列,类型:8W10E或8W10E + PET(应用生物物理学,目录号:8W10E或8W10E +)   


11. L-半胱氨酸(Millipore-Sigma-Aldrich产品目录号:C7 352)   


12. 用于电极稳定的10 mM L-半胱氨酸溶液(请参阅食谱)   


 


设备


 


ECIS ® Z-西塔仪器与容纳在培养箱16孔阵列模块(应用生物物理学)
Countess TM 自动细胞计数器(Thermo Fisher,Invitrogen,Thermo Fisher ,目录号C10228)
组织培养设备(无菌罩,培养箱,离心机)




软件


 


ECIS ž θ 软件
图形软件(例如Excel或GraphPad)
 


程序


 


半胱氨酸预处理可稳定电极
用200μl 过滤的10 mM L-半胱氨酸在室温下预处理电极15分钟。
吸出半胱氨酸溶液,并用200μl 高压灭菌的dH 2 O 洗涤两次。不要用PBS清洗电极。
播种细胞之前,先风干电极。或者,可以将已处理和干燥的电极在室温下保存直至失效。
 


分裂上皮细胞以转染siRNA
在37 ° C,5%CO 2 的培养箱中,在补充有10%FBS和1%Pen-Strep的低葡萄糖DMEM中培养LLC-PK 1 细胞。
当细胞长到约90%汇合时,将其拆分。
使用标准细胞培养程序将种子细胞转染到6孔板中。
每孔使用2 ml不含抗生素,含血清的DM EM接种种子细胞。旨在第二天获得30-40%的汇合度。
 


转染细胞
第二天(16-24小时后)使用Lipofectamine RNAiMAX 转染试剂以100 nM阴性对照(非相关性,NR)或claudin-2特异性siRNA转染细胞,如下所示:


混合试剂:
试管A:对于每种要转染的条件(孔),准备siRNA:在无菌微量离心管中,在无血清的200μlOpti-MEM培养基中稀释2.4μl100 nM储备siRNA双链体。轻轻混合。对于更多的井,请根据需要扩大数量。


管B:对于每个条件(井)制备的Lipofectamine TM :加入3 微升的Lipofectamine TM RNAIMAX 至200μl的Opti-MEM培养基。轻轻混合。对于更多的井,请根据需要扩大数量。


将试管在室温下孵育5分钟。
混合管A和B的内容物以生成siRNA-Lipofectamine复合物。孵育15分钟。
在孵育时间结束时,将400μlsiRNA-Lipofectamine混合物添加到相应的孔中。轻轻摇晃以混合,然后返回培养箱。
生长细胞24小时。




在电极阵列上播种细胞
第二天,准备播种的电极阵列:
将dd 400μl预热的完全培养基(带有血清和抗生素的生长培养基)预热至孔中,并在接种细胞之前放入培养箱中15分钟。这将用血清覆盖表面并加热阵列,以减少由于热差引起的培养基/细胞悬液的对流,这会影响电极上细胞的均匀沉降。


用对照siRNA和claudin-2特异性siRNA转染的胰蛋白酶消化细胞。当细胞漂浮时,加入温热的培养基,收集细胞,然后以300 xg的速度离心下来。重悬于新鲜培养基中。进行细胞计数时要注意保暖。
使用Countess自动细胞计数器对细胞进行计数:
使用无菌尖端除去10μl细胞悬液,并在微量离心管中与10μl锥虫蓝混合。将其添加到Countess TM 细胞计数室中,并使用自动细胞计数器对细胞进行计数以获得活细胞数。


注意:可以使用手动计数代替伯爵夫人。


使用温暖的培养基调节体积,以使最终体积为400μl的电极总数达到0.5 x 10 5 个。
从培养箱中移出阵列,并从孔中吸出培养基。向每个孔中添加400μl细胞悬液。
将电极插入培养箱中的ECIS阵列支架,并拧紧支架。
 


软件设置和获取数据
打开电脑和ECIS上ž θ 机。
打开ECIS软件。点击“设置”按钮。系统将检查连接。如果连接良好,则孔显示为绿色。如果连接不良(红色孔),请重新插入电极,然后单击“检查”以使用“检查”按钮确认所有电极的连通性。
选择电极的类型。选择“多个频率/时间(MFT)”选项(可以在分析过程中选择所需的频率)。
点击“开始”。输入文件名和要保存的任何注释。收集数据24-48小时。
随着细胞的生长,C在高频率(32或64千赫)降低(参见Ñ OTE 6对频率的选择的评论)。当C最小稳定时,达到融合。此时,可以停止测量。
 


数据分析


 


准备数据进行分析
我n个ECIS软件在右上角点击“分析”选项卡。图2显示了具有典型测量值的软件屏幕截图,并标有相应的命令。您可以选择要导出的参数和曲线。这限制了导出的数据量。或者,您可以导出所有数据(见下文)并在图形软件中进行处理。但是,在导出之前选择分析所需的数据可以简化数据集的管理。


选择显示参数:在“孔配置”中,选择要使用的孔(图2)。在顶部,选择以显示要使用的参数,在图表下方选择以显示频率。对于细胞生长,请使用高频(32或64 kHz)的电容(C)。对于跨上皮电阻,请在低频下选择Z(我们使用500 Hz)。
标准化数据:这对于比较不同日期的测量值或起始值的可变性使比较困难时很有用。在“时间序列”选项中,单击“标准化”。使用0 h点进行归一化。这将确保可以比较不同日期收集的数据。图3A和3 乙š 测量之前和归一化后如何。
结合并行测量:Ÿ OU必须结合所有平行测量,并将其导出为一个单一的数据集,以限制出口数据集的大小的选项。为此,在“井配置”中选择代表平行测量的井。单击Grp(组)。这将计算测量值的平均值并将其显示为一条曲线。计算每种条件的平均值(图3C)。
对导出数据进行任何其他调整:调整图形以反映您要导出的数据:在“井配置”(左下方)中,选择要导出的孔(单个测量值或不同条件的分组值)。在图形下,调整X轴以显示所需的时间范围(图2)。




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图2. ECIS软件的屏幕截图,显示了协议中描述的典型测量结果。使细胞在置于Holder A中的8W10E +阵列中生长。条件如下:1-4(左侧):NR siRNA转染;5-8(右侧)Cldn-2 siRNA转染。显示的电容设置为32 kHz。协议中描述的软件中的各种选项均标有绿色箭头,蓝色文本框表示各种选项。


 


 D:\ Reformatting \ 2020-7-1 \ 1903017--1492 KatalinSzászi861628 \ Figs jpg \ figure 3.jpg


图3. 使用ECIS软件导出的数据,经过或未经过各种处理。AC。如协议中所述,导出了具有各种分析步骤的图形。A.所有数据示(从图图表URE 1)。B.将数据标准化到测量的第一点。C.根据条件将数据分组。黑色:NR siRNA;粉色:Claudin-2 siRNA。D. 确定恢复时间的一半。数据已导出到Excel,并使用带有线选项的散点图进行了绘制。蓝色:NR siRNA;红色:Claudin-2 siRNA。绿色水平线表示0.5 C值(Y轴),蓝色和红色虚线表示相应的时间(X轴值)。


 


导出图形图像和数据
可以使用“文件”,“导出图形”命令将图形导出为图像。打开要导出的图像后,选择顶部的保存图标,然后设置您要使用的文件名和格式。但是,此图像在使用小字体时质量较低。另一个选择是从图形中导出数据并使用图形软件进行处理。对于此打开的“文件”,选择“导出数据”,然后选择“图形数据”。这种导出模式只会将图表上显示的数据保存为* .csv格式。您也可以在“导出数据”命令中使用相应选项导出所有数据。这将导致导出在所有频率收集的所有数据。


使用绘图软件进行处理
尽管可以导出图形图像,并且生成的图片具有所有标签,但是这些标签无法更改,并且图形和标签的质量较低。因此,我们通常将数据导出到用于进一步处理的绘图程序和用于产生出版质量的曲线图(图URE 3D)。


分析TER
比较两种情况下的势垒的一种模式是在结形成后的给定时间点比较Z。在图URE 1B我们从两个测量(与图中所示的绘制Z值URE 1A底部图,并从不同的一天的类似系列实验的)。


分析细胞生长
为了比较在不同实验中获得的数据,我们为每次测量计算电池覆盖电极一半所需的时间(该时间对应于0.5 C值)。对于这种分析,我们使用该数据从所述电容(C)值在32千赫,归一化到第一测量点(一个或多个EE 步骤2)(图URE 3D)。收集所有条件的时间值,并计算每个条件的平均值±SD。


统计
必须至少在3个不同的实验中执行测量。对于每个数组的每个条件,我们至少使用重复项。数组之间的直接比较可能会带来挑战,因为原始值在不同的实验中显示出可变性。例如,从图1B所示的不同日期获得的两个阵列组合而来的TER数据显示,来自同一实验的数据更靠近在一起,但是其值不同于在另一个阵列上测得的值。尽管在这两种情况下,claudin-2 siRNA转染的细胞均显示出升高的TER,但组合数据集上未配对的t 检验显示变化并不显着。如C值所述,将数据标准化并表示为倍数变化可以解决此问题。如果选择该方法,则应使用非参数统计检验。


 


笔记


 


阵列类型:可用的电极阵列类型多种多样,但电极数量不同。阵列具有的电极越多,要测量和平均的单元就越多,从而可以降低噪声,但灵敏度也较小。根据实验类型选择合适的电极很重要。有关每种ECIS 文化软件的详细说明,请访问Applied BioPhysics 网站:https : //www.biophysics.com/cultureware.php 。
为了检测细胞生长和连接发育,我们使用了8W10E 或8W10E +。10E +具有更多的电极来收集信息。重要的是,对于给定的一系列实验,请始终使用相同的数组。


电极包被:根据所用细胞类型的特性,可以在植入细胞之前使用胶原蛋白,纤连蛋白,层粘连蛋白或其他细胞外基质蛋白包被电极。细胞外基质蛋白有助于细胞粘附并调节多种细胞功能,例如细胞增殖,分化,迁移和存活。用于包被的蛋白质类型也可能影响增殖和屏障的形成,尽管必须针对特定细胞进行系统研究。因此,重要的是在整个实验过程中标准化涂层条件。我们发现LLC-PK 1 细胞不需要特殊的涂层,因为它们在金电极上生长良好。
细胞数量和接种:接种技术是孔之间可比性和重现性的关键。孔之间的高度可变性可以掩盖增殖的差异。这通常是由于接种不一致和细胞数量变化所致。为了进行准确的测量,所有孔中的细胞数必须保持恒定。准备均匀的细胞悬液用于播种是关键。彻底胰蛋白酶消化细胞并混合悬浮液以消除细胞簇。当使用来自不同条件(例如转染)的细胞时,准确计数细胞悬液以获得可比数量也很重要。最后,对阵列和细胞悬液进行预热以消除孔底部和悬液之间的温差也至关重要。对于随后的细胞生长,必须使用低细胞数以使细胞在粘附后增殖。如果细胞数太高,则细胞在粘附后会融合,并且不会发生增殖。通常在头3小时内细胞粘附和扩散就完成了,在此时间范围后,细胞会通过增殖建立融合层。
应常规检测细胞株的支原体污染。
转染参数:转染条件取决于所使用的细胞二系和靶蛋白,因此可能需要优化条件。靶蛋白的敲低效率应通过蛋白质印迹或qPCR并行确认。转染细胞中汇合的变化应使用电容测量进行评估。阵列上的细胞可以使用显微镜观察。另外,对各个紧密连接蛋白的作用也是有益的,可以通过免疫荧光评估。
电容和阻抗测量的频率选择:设置系统时,建议使用在所有可用频率下收集数据的设置。由于对通过电池以及电池之间的电流的抵抗力不同,因此在不同交流频率下测得的参数将反映电池特性的不同方面。在分析期间,用户可以为感兴趣的参数选择频率。高频下确定的电容反映了电极的覆盖范围,而受其他参数(例如势垒功能)的影响最小。该软件中用于分析电容值的当前建议和默认值为64 kHz。值得注意的是,该软件的较早版本具有最高和默认的C值32 kHz,因此我们在较早的研究中使用了此设置。在此频率下,C还反映出电极覆盖率。相比之下,旁细胞间隙的电阻(称为紧密功能产生的屏障功能)最好由低AC频率反映出来。进出口edance(ż )在500Hz的值和下面反映细胞旁空间的显性抗性。该软件还能够执行数学建模,从而可以进行进一步的分析,并更好地区分细胞旁空间和细胞下方空间的抵抗力(细胞-基质附着)。有关详细信息,请参阅Stolwijk 等。(2015)。




菜谱


 


10 mM L- 半胱氨酸溶液,用于稳定电极
向50 ml dH 2 O中添加0.06 g L-半胱氨酸
过滤消毒
半胱氨酸溶于水后很容易氧化,因此必须重新配制该溶液
 


致谢


 


资金来源:加拿大肾脏基金会;加拿大卫生研究院(CIHR)授予PJT-149058和MOP-142409。


  该协议源自:Amoozadeh 等。,2015;Dan 等。,2019 。


 


利益争夺


 


作者声明他们没有财务或非财务利益冲突。


 


参考文献


 


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引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Anwer, S. and Szászi, K. (2020). Measuring Cell Growth and Junction Development in Epithelial Cells Using Electric Cell-Substrate Impedance Sensing (ECIS). Bio-protocol 10(16): e3729. DOI: 10.21769/BioProtoc.3729.
  2. Dan, Q., Shi, Y., Rabani, R., Venugopal, S., Xiao, J., Anwer, S., Ding, M., Speight, P., Pan, W., Alexander, R. T., Kapus, A. and Szászi, K. (2019). Claudin-2 suppresses GEF-H1, RHOA, and MRTF, thereby impacting proliferation and profibrotic phenotype of tubular cells. J Biol Chem 294(42): 15446-15465.
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