This work was funded in part by the National Institute of General Medical Sciences (NIGMS) T32 program (T32 GM133369) and the National Science Foundation (NSF 1454230). Additionally, WVU Shared Research Facilities and Applied Biophysics assistance and support are acknowledged.

1. ZIF-8 synthesis
<ol>
	<li>For the purpose of this example, use a 1:10:100 (metal:linker:solvent) mass ratio to synthesize the ZIF-8. For this, measure out zinc nitrate hexahydrate, and record the measurement. Utilize the example mass ratio to calculate the amount needed for the linker, 2-methylimidazole, and the solvent (i.e., methanol).</li>
	<li>Place the zinc nitrate hexahydrate and linker into two different glass vials. Add half of the calculated amount of methanol to the zinc nitrate hexahydrate and the other half to the linker. Allow each solution to dissolve.</li>
	<li>Combine the two solutions (i.e., solutions containing the metal and linker, respectively). Place the container with the combined solution for the reaction onto a stir plate and stir at 700 rpm for 24 h at room temperature (RT).</li>
</ol>
2. ZIF-8 collection
<ol>
	<li>Once the above reaction has concluded (i.e., after 24 h), place the solution into 50 mL centrifuge tubes and centrifuge at 3,075 x g for 5 min (use equal amounts and balance the rotor). Remove any of the supernatants from the centrifuged tubes.</li>
	<li>Add methanol (5-15 mL) to the centrifuge tubes and redisperse the particles.</li>
	<li>Repeat the centrifugation and washing steps at least two to four more times. After the last wash, discharge the supernatant. 
	NOTE: The washing steps remove any non-precipitated species.</li>
	<li>Remove the lids of the tubes and place parafilm tape over the top; subsequently, poke holes in the parafilm. Place the tube/s containing the sample in a vacuum chamber at RT to dry for 24 h.</li>
</ol>
3. ZIF-8 surface morphology (scanning electron microscopy [SEM])
<ol>
	<li>Mount the dry powders of ZIF-8 onto carbon tape and sputter for 120 s with gold-palladium to prevent any charging that could occur during the SEM analysis.</li>
	<li>Set the accelerating voltage of the microscope to 15 kV. Bring the particles into focus and adjust the magnification (magnifications of 500x, 1,000x, 1,500x, 4,000x, 10,000x, 20,000x, 30,000x, and 40,000x were used.)</li>
	<li>Image the particles under the magnification that allows both a clear particle size and morphology assessment (recommended to consider ranges for particles of 5 &#181;m, 10 &#181;m, and 20 &#181;m).</li>
	<li>Collect data from at least three different fields of view, and for each, consider different magnifications.</li>
</ol>
4. ZIF-8 elemental composition
<ol>
	<li>Follow the steps for SEM imaging.</li>
	<li>Using the energy-dispersive X-ray (EDX) spectroscopy unit of the SEM microscope, analyze the sample elemental composition.</li>
	<li>Make sure the SEM accelerating voltage and magnification match the EDX monitor. Turn off the infrared camera on the SEM microscope.</li>
	<li>On the EDX monitor, go to Collect Spectrum and input a collection time of 200 s. This is recommended to avoid particle charging and disintegration that could lead to false evaluations.</li>
	<li>Collect data from at least three different fields of view. Once the collection is finished, analyze the data, and identify the elements of interest.</li>
</ol>
5. Cell culture
<ol>
	<li>Culture immortalized BEAS-2B cells in media supplemented with 5% fetal bovine serum (FBS) and 0.2% penicillin/streptomycin. 
	NOTE: Passages between 5-10 should be used to ensure that no phenotypical changes have previously taken place in the cell batch being used37 (all reagents are listed in the Table of Materials).</li>
	<li>Take a 100 mm cell culture plate and place 10 mL of the media onto the plate. Add 1 mL of the BEAS-2B cell solution to the plate.</li>
	<li>Place the plate into an incubator (37 &#176;C, 5% CO2) and allow the cells to grow.</li>
	<li>Check the plate after 24 h to determine if the cells have reached 80% confluency. Herein, the 80% confluency refers to the percentage of the surface covered by adhered cells. If not, change the media and allow the cells to grow until the 80% confluency level is reached.</li>
</ol>
6. Cell counting
<ol>
	<li>Once the cells on the plate have reached a confluency of 80%, remove the media from the plate. Wash the plate with 5 mL of phosphate-buffered saline (PBS).</li>
	<li>Remove the PBS from the plate. Add 2 mL of trypsin to the plate and place it in the incubator (37 &#176;C, 5% CO2) for 2 min.</li>
	<li>Add 2 mL of media to the plate and pipet the media up and down to remove the cells from the plate. Transfer the solution to a 15 mL tube and centrifuge at 123 x g for 5 min.</li>
	<li>Remove the supernatant from the tube and add 2 mL of media. Pipet the media up and down to redisperse the cells.</li>
	<li>Using a 1.5 mL tube, add 20 &#181;L of trypan blue and then 20 &#181;L of the cell solution (1:1 ratio of trypan blue:cell solution).</li>
	<li>Place 10 &#181;L of the cell mixture onto a glass slide and place it onto an automatic cell counter. Wait until the screen comes into focus, then select capture.</li>
	<li>The screen displays the live cell numbers and cell viability. The measurement range extends from 1 x 104-1 x 107 cells.</li>
	<li>Alternatively, count the cells manually using a hemocytometer.
	<ol>
		<li>For this, add 15-20 &#181;L of the trypan blue-treated cell solution to the hemocytometer.</li>
		<li>Use an upright microscope with a 10x objective focus on the grid lines of the hemocytometer.</li>
		<li>Count the number of cells in the four outer squares and divide the number by four. Then, multiply by 10,000 to obtain the live cell count number.</li>
		<li>To calculate cell viability, add together the live and dead cell count to obtain the total cell count, and subsequently divide the live cell count by the total cell count.</li>
	</ol>
	</li>
</ol>
7. ZIF-8 dose preparation
<ol>
	<li>Make a ZIF-8 stock solution of 1 mg/mL. For this, measure the amount of ZIF-8 and add deionized (DI) water to the particles to reach a 1 mg/mL concentration.</li>
	<li>Sonicate (set the sonicator to sonics) the ZIF-8 stock solution for 2 min at 30 s intervals in a water bath.</li>
	<li>Make different doses of ZIF-8 for cellular exposures by calculating the amount of stock solution and Dulbecco&#39;s modified eagle medium (DMEM) needed to reach the desired doses using the equation C1V1 = C2V2. The doses will range from 0-950 &#181;g/mL. 
	NOTE: In this experiment, doses of 0 &#181;g/mL, 1 &#181;g/mL, 10 &#181;g/mL, 50 &#181;g/mL, 100 &#181;g/mL, 250 &#181;g/mL, 500 &#181;g/mL, 750 &#181;g/mL, and 950 &#181;g/mL were selected.</li>
</ol>
8. Half-maximal inhibitory concentration (IC&#160;&#160;50)
<ol>
	<li>After cell counting, seed the BEAS-2B cells at a density of 4 x 104 cells/mL in a 96-well plate, with a volume of 100 &#181;L/well.
	<ol>
		<li>To reach a density of 4 x 104 cells/mL, use C1V1 = C2V2 to calculate the amount of cell solution needed to be combined with the media.</li>
		<li>Ensure to maintain blank wells for each dose; leave these wells empty until ZIF-8 exposure.</li>
	</ol>
	</li>
	<li>Place the 96-well plate into the incubator at 37 &#176;C and 5% CO2 and allow the cells to grow to a confluent monolayer for 24 h.</li>
	<li>Remove the media from the wells and expose the BEAS-2B cells to ZIF-8 doses ranging from 0-950 &#181;g/mL. Add 100 &#181;L of ZIF-8 to their respective blank and cell wells.</li>
	<li>Place the 96-well plates back into the incubator for an exposure time of 48 h.</li>
	<li>After the exposure, add 10 &#181;L of 4-[3-(4-idophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate (WST-1) to each well (blank and cell wells) and incubate for 2 h. The media remains in the wells; the ratio is 1:10 (reagent:media).</li>
	<li>Read the changes in absorbance on a plate reader using a 485 nm absorbance.</li>
	<li>Calculate the cell viability through the software to determine the IC50 value (see section 10).</li>
</ol>
9. Electrical cell-substrate impedance sensing (ECIS)
<ol>
	<li>Use a 96-well plate (96W10idf) to perform ECIS analysis. The plate28 contains inter-digitated finger connection electrodes covering an area of about 4 mm2.</li>
	<li>First, test that all the wells are being read by the sensors. For this, place the 96-well plate onto the well station and click on the Setup button.</li>
	<li>If the wells are connected properly, green will appear; any unconnected wells will be identified as red. If such a case arises, remove and reinsert the well plate and click the Setup button again, until all the wells offer viable green readings.</li>
	<li>Stabilize the electrodes for 40 min with 200 &#181;L of media per well to prevent potential drift. Add 200 &#181;L of media to each well being used and then place the 96-well plate onto the ECIS station. Click on Setup/Check to ensure the plate is properly connected. Click on Stabilize.</li>
	<li>Once stabilization is complete, remove the plate from the station and remove the media from each well.</li>
	<li>Seed the BEAS-2B cells at a density of 4 x 104 cells/mL, with a volume of 100 &#181;L per well, into the wells containing the cells and place the plate onto the ECIS station in the incubator.</li>
	<li>On the monitor, click on Check to determine that all the electrodes are read by the sensors on the station.</li>
	<li>Set up the time course measurement by selecting Multiple Freq/Time (MFT). The program will measure each well at seven different frequencies.</li>
	<li>Click Start and allow it to run for 24 h so the cells can form a confluent monolayer.</li>
	<li>After 24 h, the wells containing cells will show increased resistance on the monitor. This is an indicator that cells have attached to the electrodes.</li>
	<li>Press Pause on the monitor to stop the data collection.</li>
	<li>Make the ZIF-8 doses at, above, and below the calculated IC50 value by following the steps outlined in section 7 above.</li>
	<li>Expose the ZIF-8 nanoparticles to their respective blank and cell wells at a volume of 100 &#181;L per well and place the 96-well plate back onto the station.</li>
	<li>Click on Check again on the monitor to ensure the sensors are reading all the electrodes. Click on Resume and allow the system to run for 72 h to monitor the cellular behavior and attachment.</li>
	<li>Once data collection has finished, click on Finish on the monitor. To save the file, click File &#62; Save As. Save the data as a spreadsheet file.</li>
	<li>To get the alpha parameters, click on Model. On the screen pop-up, click on Media-Only Well, then select the well that has only media.</li>
	<li>Click on Set Ref. On the monitor, click on Find.</li>
	<li>If the well shown in the system is not the same as what was chosen, repeat step 9.16.</li>
	<li>Click on Set. Click on Fit T-Range to determine the time range desired for data collection.</li>
	<li>While it is processing, click on Alpha. When the system finishes the analysis, click on Save RbA and save it as a spreadsheet file. 
	NOTE: Refer to the ECIS manual, which can be found on the manufacturer&#39;s website38.</li>
</ol>
10. Data analysis
<ol>
	<li>SEM
	<ol>
		<li>Open the imaging software to analyze the SEM images. Click File &#62; Open, and then select the image to analyze.</li>
		<li>Click on Image &#62; Type &#62; 8 Bits to convert the image to grayscale to perform the threshold operation.</li>
		<li>Calibrate the distance measurement of pixels to microns. Click on the Straight-Line tool and trace the length of each particle aimed to be measured.</li>
		<li>Click Analyze and then Set Scale. The known distance will be set to the length of the size of the scale bar on the SEM image. The known unit of length will be set to microns. Check Global and press Ok.</li>
		<li>Click on the Straight-Line tool and trace the diameter of the particle, then select Analyze and Measure. Record the length result.</li>
		<li>Repeat for all the collected images. Average all the diameters from at least 60 particles for a suitable statistics evaluation.</li>
	</ol>
	</li>
	<li>EDX
	<ol>
		<li>Average all the weight percentages of each element together from all the collected images (see EDX section above).</li>
		<li>Using a spreadsheet, plot a bar graph of each element on the x-axis and their average elemental weight percentage on the y-axis.</li>
	</ol>
	</li>
	<li>IC50&#8203;
	<ol>
		<li>Average the blank wells and average the dose wells for each replicate.</li>
		<li>Calculate the adjusted blank by subtracting the control blank average from the dose blank average. Calculate the true cell value for each dose by subtracting the adjusted blank from the average dose value.</li>
		<li>Subtract the adjusted blank value for each dose from the average control value. Calculate the cell viability of each dose utilizing the equation: cell viability = (true cell value/(control value - adjusted blank value)) x 100.</li>
		<li>Repeat this for all other replicates.</li>
		<li>Average the cell viability for each replicate together to get the final cell viability for each dose.</li>
		<li>In the software, choose the XY tab on the screen.</li>
		<li>Enter the concentration (dose) values into the X column and the cell viability (%) from each dose into the Y column.</li>
		<li>Transform the X values by clicking Analyze &#62; Transform &#62; Ok, and Transform X = Log(X).</li>
		<li>Normalize the Y values by selecting the Transformed Data sheet, clicking on Analyze, and then selecting Normalize.</li>
		<li>Select the Normalize of Transform data sheet, click Analyze, and select Nonlinear Regression (Curve Fit). Select Dose-Response-Inhibition and click Log(Inhibitor) Vs. Response-Variable Slope (Four Parameters). Click on OK to view the results.</li>
	</ol>
	</li>
	<li>ECIS
	<ol>
		<li>From the spreadsheet, take the resistance (ohm) columns for each replicate (blank wells and dose wells) and average them together from the 4,000 Hz frequency. This frequency allows cell-cell contact evaluation38.</li>
		<li>Calculate the corrected resistance by subtracting the averaged blank from the averaged dose for each replicate. Average the corrected resistance for the replicates for each dose.</li>
		<li>Repeat the same steps for the alpha values to calculate the average alpha parameter. Plot the x-axis as time (h) and the y-axis as the average resistance/alpha values for each dose value.</li>
	</ol>
	</li>
</ol>
11. Statistical analysis
<ol>
	<li>SEM
	<ol>
		<li>Perform a standard deviation in the spreadsheet file where the average diameters of the ZIF-8 particles were calculated.</li>
		<li>Utilizing the STDEV function in the spreadsheet, highlight the data of the 60 particles and click Enter. Graph the standard deviation for each average diameter calculated.</li>
	</ol>
	</li>
	<li>EDX
	<ol>
		<li>Similar to SEM (steps 11.1.1-11.1.2), calculate the standard deviation for each average elemental composition using the STDEV function in the spreadsheet. Graph the standard deviations for each average elemental composition.</li>
	</ol>
	</li>
	<li>IC50&#8203;
	<ol>
		<li>Open the software utilized to calculate the IC50 and click on Grouped Data Sheet.</li>
		<li>Enter the ZIF-8 doses in the rows labeled Group A, Group B, etc. Enter the average cell viability for each dose and replicate in the column corresponding with the ZIF-8 dose.</li>
		<li>Click on Analyze, and under Column Analyses, select One-way ANOVA (and nonparametric or mixed).</li>
		<li>In the Experimental Design tab, select No Matching Or Pairing. Under Gaussian Distribution Of Residuals, select Yes use ANOVA. Finally, select Yes Use Ordinary ANOVA Test under Assume Equal SDs.</li>
		<li>Under the Multiple Comparisons tab, select Compare the Mean of Each Column with the Mean of a Control Column. Click on OK to view the results.</li>
	</ol>
	</li>
</ol>

Evaluating MOF-Induced Cell Behavior Changes by Electric Cell-Substrate Sensing

The authors report no conflicts of interest in this work.

Previous analysis showed that ECIS could be used to assess the behavior of cells exposed to analytes (i.e., carbon nanotubes35, drugs43, or nanoclays16). Furthermore, Stueckle et al. used ECIS to evaluate the toxicity of BEAS-2B cells exposed to nanoclays and their byproducts and found that the cellular behavior and attachment were dependent on the physicochemical characteristics of such materials42. Herein, we proposed to det...

Metal-organic frameworks (MOFs) are hybrids formed through the combination of metal ions and organic linkers1,2 in organic solvents. Due to the variety of such combinations, MOFs possess structural diversity3, tunable porosity, high thermal stability, and high surface areas4,5. Such characteristics make them attractive candidates in a variety of applications, from gas storage6,7 to catalysis8,9, and from contrast agents10,11 to drug delivery units12,13. However, the implementation of MOFs into such applications has raised concerns relative to their safety to both the users and the environment. Preliminary studies have shown, for instance, that cellular function and growth change upon the exposure of cells to metal ions or linkers used for MOF synthesis1,14,15. For instance, Tamames-Tabar et al. demonstrated that ZIF-8 MOF, a Zn-based MOF, was leading to more cellular changes in a human cervical cancer cell line (HeLa) and a mouse macrophage cell line (J774) relative to Zr-based and Fe-based MOFs. Such effects were&#160;presumably due to the metal component of ZIF-8 (i.e., Zn), which could potentially induce cell apoptosis upon framework disintegration and Zn ion release1. Similarly, Gandara-Loe et al. demonstrated that HKUST-1, a Cu-based MOF, caused the highest reduction in mouse retinoblastoma cell viability when used at concentrations of 10 &#181;g/mL or greater. This was presumably due to the Cu metal ion incorporated during the synthesis of this framework, which, once released, could induce oxidative stress in the exposed cells15.
Moreover, analysis showed that the exposure&#160;to MOFs with different physicochemical characteristics could lead to varying responses of exposed cells. For instance, Wagner et al. demonstrated that ZIF-8 and MIL-160 (an Al-based framework), used in the exposure of an immortalized human bronchial epithelial cell, led to cellular responses dependent on frameworks&#39; physicochemical properties, namely hydrophobicity, size, and structural characteristics16. Complementarily, Chen et al. demonstrated that a concentration of 160 &#181;g/mL MIL-100(Fe) exposed to human normal liver cells (HL-7702) caused the largest loss in cellular viability, presumably due to the metal component of this specific framework (i.e., Fe17).
While these studies categorize MOFs&#39; deleterious effects on cellular systems based on their physicochemical characteristics and exposure concentrations, thus raising potential concerns with framework implementation, especially in biomedical fields, most of these evaluations are based on single time point colorimetric assays. For instance, it was shown that when (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) tetrazolium (MTT) and water-soluble tetrazolium salt (WST-1) assays were used, these biochemical reagents could lead to false positives upon&#160;their&#160;interactions with the particles that the cells were also exposed to18. The tetrazolium salt and neutral red reagents were&#160;shown to possess a high adsorption or binding affinity onto the surfaces of the particles, resulting to agent signal interference19. Moreover, for other types of assays, such as flow cytometry, which was previously shown to be used for assessing changes in cells exposed to MOFs20,21, it was shown that major issues have to be circumvented if a viable analysis of particles&#39; deleterious effects is to be considered. In particular, detection ranges of the particles&#39; sizes, especially in mixed populations like the ones offered by MOFs or references of the particles used for calibration before cellular changes, have to be addressed22. It was also shown that the dye used during cell labeling for such cytometry assays could also interfere with the nanoparticles that the cells were exposed to23.
The goal of this study was to use a real-time, high-throughput evaluation assay to assess changes in cell behavior upon exposure to a select MOF. Real-time evaluations can help provide insights into time-dependent effects, as related to the windows of exposures16. Further, they provide information on changes in cell-substrate interactions, cell morphology, and cell-cell interactions, as well as how such changes depend on the physicochemical properties of the materials of interest and exposure times24,25 respectively.
To demonstrate the validity and applicability of the proposed approach, human bronchial epithelial (BEAS-2B) cells, ZIF-8 (a hydrophobic framework of zeolitic imidazolate16), and electric cell-substrate impedance sensing (ECIS) were used. BEAS-2B cells represent a model for lung exposure26 and have been previously used to evaluate changes upon the exposure of cells to nanoclays and their thermally degraded byproducts26,27,28,&#160;as well as assess the toxicity of nanomaterials, such as single-walled carbon nanotubes (SWCNTs)18. Furthermore, such cells have been used for more than 30 years as a model for pulmonary epithelial function29. ZIF-8 was chosen due to its wide implementation&#160;in catalysis30 and as contrast agents31 for bioimaging and drug delivery32, and thus for the extended potential for lung exposure during such applications. Lastly, ECIS, the noninvasive, real-time technique, was previously used to evaluate changes in cell adherence, proliferation, motility, and morphology16,26 as a result of a variety of interactions between analytes (both materials and drugs) and exposed cells in real-time16,18,28. ECIS uses an alternating current (AC) to measure the impedance of cells immobilized on gold electrodes, with the impedance changes giving insights into changes in resistance and capacitance at the&#160;cell-gold substrate interface, barrier function as induced by cell-cell interactions, and over-cell layer coverage of such gold electrodes33,34. Using ECIS allows quantitative measurements at a nanoscale resolution in a noninvasive, real-time manner26,34.
This study assesses and compares the simplicity and ease of evaluation of MOF-induced changes in cellular behavior in real-time with single-point assay evaluations. Such a study could be further extrapolated for evaluating cell profiles in response to exposure to other particles of interest, thus allowing for safe-by-design particle testing and subsequent helping with&#160;implementation. Moreover, this study could complement genetic and cellular assays that are single-point evaluations. This could lead to a more informed analysis of the deleterious effects of particles on the cellular population and could be used for screening such particles&#39; toxicity in a high-throughput manner16,35,36.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>&#160;4-[3-(4-idophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate (WST-1 assay)&#160;</td>
			<td>Roche</td>
			<td>5015944001</td>
			<td></td>
		</tr>
		<tr>
			<td>0.25% Trypsin-EDTA (1x)</td>
			<td>Gibco</td>
			<td>25255-056</td>
			<td></td>
		</tr>
		<tr>
			<td>100 mm plates</td>
			<td>Corning</td>
			<td>430167</td>
			<td></td>
		</tr>
		<tr>
			<td>1300 Series A2 biofume hood</td>
			<td>Thermo Scientific</td>
			<td>323TS</td>
			<td></td>
		</tr>
		<tr>
			<td>2510 Branson bath sonicator</td>
			<td>Process Equipment &#38; Supply, Inc.&#160;</td>
			<td>251OR-DTH</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylimidazole, 97%</td>
			<td>Alfa Aesar</td>
			<td>693-98-1</td>
			<td></td>
		</tr>
		<tr>
			<td>5 mL sterile microtube</td>
			<td>Argos Technologies</td>
			<td>T2076S-CA</td>
			<td></td>
		</tr>
		<tr>
			<td>50 mL&#160; tubes&#160;</td>
			<td>Falcon</td>
			<td>352098</td>
			<td></td>
		</tr>
		<tr>
			<td>96W10idf well plates</td>
			<td>Applied Biophysics&#160;</td>
			<td>96W10idf PET</td>
			<td></td>
		</tr>
		<tr>
			<td>96-well plates</td>
			<td>Fisherbrand</td>
			<td>FB012931</td>
			<td></td>
		</tr>
		<tr>
			<td>Biorender</td>
			<td>Biorender</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess cell counting chamber slides</td>
			<td>Invitrogen</td>
			<td>C10283</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess II FL automated cell counter</td>
			<td>Life Technologies</td>
			<td>C0916-186A-0303</td>
			<td></td>
		</tr>
		<tr>
			<td>Denton Desk V sputter and carbon coater</td>
			<td>Denton Vacuum</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethly sulfoxide&#160;</td>
			<td>Corning</td>
			<td>25-950-CQC</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS/Modified</td>
			<td>Cytiva</td>
			<td>SH30028.02</td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s modified Eagle medium</td>
			<td>Corning</td>
			<td>10-014-CV</td>
			<td></td>
		</tr>
		<tr>
			<td>ECIS-Z&#920;</td>
			<td>Applied Biophysics&#160;</td>
			<td>ABP 1129</td>
			<td></td>
		</tr>
		<tr>
			<td>Excel</td>
			<td>Microsoft</td>
			<td>Version 2301</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon tubes (15 mL)</td>
			<td>Corning</td>
			<td>352196</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal bovine serum</td>
			<td>Gibco</td>
			<td>16140-071</td>
			<td></td>
		</tr>
		<tr>
			<td>FLUOstar OPTIMA plate reader</td>
			<td>BMG LABTECH</td>
			<td>413-2132</td>
			<td></td>
		</tr>
		<tr>
			<td>GraphPad Prism Software (9.0.0)</td>
			<td>GraphPad Software, LLC</td>
			<td>Version 9.0.0</td>
			<td></td>
		</tr>
		<tr>
			<td>HERAcell 150i CO2 Incubator</td>
			<td>Thermo Scientific</td>
			<td>50116047</td>
			<td></td>
		</tr>
		<tr>
			<td>Hitachi S-4700 Field emission scanning electron microscope equipped with energy dispersive X-ray&#160;</td>
			<td>Hitachi High-Technologies Corporation</td>
			<td>S4700 and EDAX TEAM analysis software</td>
			<td></td>
		</tr>
		<tr>
			<td>ImageJ software</td>
			<td>National Institutes of Health</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Immortalized human bronchial epithelial cells</td>
			<td>American Type Culture Collection</td>
			<td>CRL-9609</td>
			<td></td>
		</tr>
		<tr>
			<td>Isotemp freezer</td>
			<td>Fisher Scientific&#160;</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Methanol, 99%</td>
			<td>Fisher Chemical</td>
			<td>67-56-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Parafilm sealing film</td>
			<td>The Lab Depot</td>
			<td>HS234526A</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin/Steptomycin</td>
			<td>Gibco</td>
			<td>15140-122</td>
			<td></td>
		</tr>
		<tr>
			<td>Sorvall Legend X1R Centrifuge&#160;</td>
			<td>Thermo Scientific</td>
			<td>75004220</td>
			<td></td>
		</tr>
		<tr>
			<td>Sorvall T 6000B</td>
			<td>DU PONT</td>
			<td>&#160;T6000B</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan blue, 0.4% solution in PBS</td>
			<td>MP Biomedicals, LLC</td>
			<td>1691049</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum Chamber</td>
			<td>Belart</td>
			<td>999320237</td>
			<td></td>
		</tr>
		<tr>
			<td>Zinc Nitrate Hexahydrate, 98% extra pure</td>
			<td>Acros Organic</td>
			<td>101-96-18-9</td>
			<td></td>
		</tr>
	</tbody>
</table>

electric cell-substrate sensing for real-time evaluation of metal-organic framework toxicological profiles

Metal-organic frameworks (MOFs) are hybrids formed through the coordination of metal ions and organic linkers in organic solvents. The implementation of MOFs in biomedical and industrial applications has led to concerns regarding their safety. Herein, the profile of a selected MOF, a zeolitic imidazole framework, was evaluated upon exposure to human lung epithelial cells. The platform for evaluation was a real-time technique (i.e., electric cell-substrate impedance sensing [ECIS]). This study identifies and discusses some of the deleterious effects of the selected MOF on the exposed cells. Furthermore, this study demonstrates the benefits of using the real-time method versus other biochemical assays for comprehensive cell evaluations. The study concludes that observed changes in cell behavior could hint at possible toxicity induced upon exposure&#160;to MOFs of different physicochemical characteristics and the dosage of those frameworks being used. By understanding changes in cell behavior, one foresees the ability to improve safe-by-design strategies of MOFs to be used for biomedical applications by specifically tailoring their physicochemical characteristics.

Using a common in vitro model cell line39 (BEAS-2B), this study aimed to demonstrate the feasibility and applicability of ECIS to assess changes in cell behavior upon exposure to a lab-synthesized MOF. These changes assessment was&#160;complemented by analysis through&#160;conventional colorimetric assays.
The physicochemical characteristics of the framework were first evaluated to ensure the reproducibility of the methods employed, the validity of the obtained...

Watch this Scientific Journal Video about Advances in Evaluating Human Lung Epithelial Cells' Response to Metal-Organic Frameworks at JoVE.com

Author Spotlight: Advances in Evaluating Human Lung Epithelial Cells' Response to Metal-Organic Frameworks 

The following study evaluates the toxicological profile of a selected metal-organic framework utilizing electric cell-substrate impedance sensing (ECIS), a real-time, high-throughput screening technique.

Electric Cell-Substrate Sensing for Real-Time Evaluation of Metal-Organic Framework Toxicological Profiles

Metal-organic frameworks (MOFs) are hybrids formed through the coordination of metal ions and organic linkers in organic solvents. The implementation of ...

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752 Views.  West Virginia University. The following study evaluates the toxicological profile of a selected metal-organic framework utilizing electric cell-substrate impedance sensing (ECIS), a real-time, high-throughput screening technique. 

Video: Author Spotlight: Advances in Evaluating Human Lung Epithelial Cells' Response to Metal-Organic Frameworks 

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

The development of a minimally invasive multiplexed monitoring system for rapid analysis of biologically-relevant molecules could offer individuals suffering from chronic medical conditions facile assessment of their immediate physiological state. Furthermore, it could serve as a research tool for analysis of complex, multifactorial medical conditions. In order for such a multianalyte sensor to be realized, it must be minimally invasive, sampling of interstitial fluid must occur without pain or harm to the user, and analysis must be rapid as well as selective.
Initially developed for pain-free drug delivery, microneedles have been used to deliver vaccines and pharmacologic agents (e.g., insulin) through the skin.1-2 Since these devices access the interstitial space, microneedles that are integrated with microelectrodes can be used as transdermal electrochemical sensors. Selective detection of glucose, glutamate, lactate, hydrogen peroxide, and ascorbic acid has been demonstrated using integrated microneedle-electrode devices with carbon fibers, modified carbon pastes, and platinum-coated polymer microneedles serving as transducing elements.3-7,8
This microneedle sensor technology has enabled a novel and sophisticated analytical approach for in situ and simultaneous detection of multiple analytes. Multiplexing offers the possibility of monitoring complex microenvironments, which are otherwise difficult to characterize in a rapid and minimally invasive manner. For example, this technology could be utilized for simultaneous monitoring of extracellular levels of, glucose, lactate and pH,9 which are important metabolic indicators of disease states7,10-14 (e.g., cancer proliferation) and exercise-induced acidosis.15

This article details the construction of a multiplexed microneedle-based sensor. The device is being developed for in situ sampling and electrochemical analysis of multiple analytes in a rapid and selective manner. We envision clinical medicine and biomedical research uses for these microneedle-based sensors.

The development of a minimally invasive multiplexed monitoring system for rapid analysis of biologically-relevant molecules could offer individuals ...

Electric Cell-substrate Impedance Sensing for the Quantification of Endothelial Proliferation, Barrier Function, and Motility

Electric Cell-substrate Impedance Sensing (ECIS) is an in vitro impedance measuring system to quantify the behavior of cells within adherent cell layers. To this end, cells are grown in special culture chambers on top of opposing, circular gold electrodes. A constant small alternating current is applied between the electrodes and the potential across is measured. The insulating properties of the cell membrane create a resistance towards the electrical current flow resulting in an increased electrical potential between the electrodes. Measuring cellular impedance in this manner allows the automated study of cell attachment, growth, morphology, function, and motility. Although the ECIS measurement itself is straightforward and easy to learn, the underlying theory is complex and selection of the right settings and correct analysis and interpretation of the data is not self-evident. Yet, a clear protocol describing the individual steps from the experimental design to preparation, realization, and analysis of the experiment is not available. In this article the basic measurement principle as well as possible applications, experimental considerations, advantages and limitations of the ECIS system are discussed. A guide is provided for the study of cell attachment, spreading and proliferation; quantification of cell behavior in a confluent layer, with regard to barrier function, cell motility, quality of cell-cell and cell-substrate adhesions; and quantification of wound healing and cellular responses to vasoactive stimuli. Representative results are discussed based on human microvascular (MVEC) and human umbilical vein endothelial cells (HUVEC), but are applicable to all adherent growing cells.

This protocol reviews Electric Cell-substrate Impedance Sensing, a method to record and analyze the impedance spectrum of adherent cells for the quantification of cell attachment, proliferation, motility, and cellular responses to pharmacological and toxic stimuli. Detection of endothelial permeability and assessment of cell-cell and cell-substrate contacts are emphasized.

Electric Cell-substrate Impedance Sensing (ECIS) is an in vitro impedance measuring system to quantify the behavior of cells within adherent cell layers. ...

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously attaining anatomical, functional and molecular contrast in deep optically opaque tissues. While enormous potential has been recently demonstrated in the application of optoacoustics for small animal research, vast efforts have also been undertaken in translating this imaging technology into clinical practice. We present here a newly developed optoacoustic tomography approach capable of delivering high resolution and spectrally enriched volumetric images of tissue morphology and function in real time. A detailed description of the experimental protocol for operating with the imaging system in both hand-held and stationary modes is provided and showcased for different potential scenarios involving functional and molecular studies in murine models and humans. The possibility for real time visualization in three dimensions along with the versatile handheld design of the imaging probe make the newly developed approach unique among the pantheon of imaging modalities used in today&#8217;s preclinical research and clinical practice.

We provide herein a detailed description of the experimental protocol for imaging with a newly developed hand-held optoacoustic (photoacoustic) system for three-dimensional functional and molecular imaging in real time. The demonstrated powerful performance and versatility may define new application areas of the optoacoustic technology in preclinical research and clinical practice.

The exclusive combination of high optical contrast and excellent spatial resolution makes optoacoustics (photoacoustics) ideal for simultaneously ...

Using Cell-substrate Impedance and Live Cell Imaging to Measure Real-time Changes in Cellular Adhesion and De-adhesion Induced by Matrix Modification

Cell-matrix adhesion plays a key role in controlling cell morphology and signaling. Stimuli that disrupt cell-matrix adhesion (e.g., myeloperoxidase and other matrix-modifying oxidants/enzymes released during inflammation) are implicated in triggering pathological changes in cellular function, phenotype and viability in a number of diseases. Here, we describe how cell-substrate impedance and live cell imaging approaches can be readily employed to accurately quantify real-time changes in cell adhesion and de-adhesion induced by matrix modification (using endothelial cells and myeloperoxidase as a pathophysiological matrix-modifying stimulus) with high temporal resolution and in a non-invasive manner. The xCELLigence cell-substrate impedance system continuously quantifies the area of cell-matrix adhesion by measuring the electrical impedance at the cell-substrate interface in cells grown on gold microelectrode arrays. Image analysis of time-lapse differential interference contrast movies quantifies changes in the projected area of individual cells over time, representing changes in the area of cell-matrix contact. Both techniques accurately quantify rapid changes to cellular adhesion and de-adhesion processes. Cell-substrate impedance on microelectrode biosensor arrays provides a platform for robust, high-throughput measurements. Live cell imaging analyses provide additional detail regarding the nature and dynamics of the morphological changes quantified by cell-substrate impedance measurements. These complementary approaches provide valuable new insights into how myeloperoxidase-catalyzed oxidative modification of subcellular extracellular matrix components triggers rapid changes in cell adhesion, morphology and signaling in endothelial cells. These approaches are also applicable for studying cellular adhesion dynamics in response to other matrix-modifying stimuli and in related adherent cells (e.g., epithelial cells).

Here, we present a protocol to continuously quantify cell adhesion and de-adhesion processes with high temporal resolution in a non-invasive manner by cell-substrate impedance and live cell imaging analyses. These approaches reveal the dynamics of cell adhesion/de-adhesion processes triggered by matrix modification and their temporal relationship to adhesion-dependent signaling events.

Cell-matrix adhesion plays a key role in controlling cell morphology and signaling. Stimuli that disrupt cell-matrix adhesion (e.g., myeloperoxidase and ...

Real Time Monitoring of Intracellular Bile Acid Dynamics Using a Genetically Encoded FRET-based Bile Acid Sensor

F&#246;rster Resonance Energy Transfer (FRET) has become a powerful tool for monitoring protein folding, interaction and localization in single cells. Biosensors relying on the principle of FRET have enabled real-time visualization of subcellular signaling events in live cells with high temporal and spatial resolution. Here, we describe the application of a genetically encoded Bile Acid Sensor (BAS) that consists of two fluorophores fused to the farnesoid X receptor ligand binding domain (FXR-LBD), thereby forming a bile acid sensor that can be activated by a large number of bile acids species and other (synthetic) FXR ligands. This sensor can be targeted to different cellular compartments including the nucleus (NucleoBAS) and cytosol (CytoBAS) to measure bile acid concentrations locally. It allows rapid and simple quantitation of cellular bile acid influx, efflux and subcellular distribution of endogenous bile acids without the need for labeling with fluorescent tags or radionuclei. Furthermore, the BAS FRET sensors can be useful for monitoring FXR ligand binding. Finally, we show that this FRET biosensor can be combined with imaging of other spectrally distinct fluorophores. This allows for combined analysis of intracellular bile acid dynamics and i) localization and/or abundance of proteins of interest, or ii) intracellular signaling in a single cell.

We provide a detailed protocol to study bile acid dynamics in living cells using a genetically encoded BAS FRET sensor. This Bile Acid Sensor represents a unique tool to study (regulation of) bile acid transport and FXR activation in a wide range of cell types.

F&#246;rster Resonance Energy Transfer (FRET) has become a powerful tool for monitoring protein folding, interaction and localization in single cells. ...

Hand-held Clinical Photoacoustic Imaging System for Real-time Non-invasive Small Animal Imaging

Translation of photoacoustic imaging into the clinic is a major challenge. Handheld real-time clinical photoacoustic imaging systems are very rare. Here, we report a combined photoacoustic and clinical ultrasound imaging system by integrating an ultrasound probe with light delivery for small animal imaging. We demonstrate this by showing sentinel lymph node imaging in small animals along with minimally invasive real-time needle guidance. A clinical ultrasound platform with access to raw channel data allows the integration of photoacoustic imaging leading to a handheld real-time clinical photoacoustic imaging system. Methylene blue was used for sentinel lymph node imaging at 675 nm wavelength. Additionally, needle guidance with dual modal ultrasound and photoacoustic imaging was shown using the imaging system. Depth imaging of up to 1.5 cm was demonstrated with a 10 Hz laser at a photoacoustic imaging frame rate of 5 frames per second.

A clinical handheld photoacoustic imaging system will be demonstrated for real-time non-invasive small animal imaging.

Translation of photoacoustic imaging into the clinic is a major challenge. Handheld real-time clinical photoacoustic imaging systems are very rare. Here, ...

A Protocol for Real-time 3D Single Particle Tracking

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although various RT-3D-SPT methods have been put forward in recent years, tracking high speed 3D diffusing particles at low photon count rates remains a challenge. Moreover, RT-3D-SPT setups are generally complex and difficult to implement, limiting their widespread application to biological problems. This protocol presents a RT-3D-SPT system named 3D Dynamic Photon Localization Tracking (3D-DyPLoT), which can track particles with high diffusive speed (up to 20 &#181;m2/s) at low photon count rates (down to 10 kHz). 3D-DyPLoT employs a 2D electro-optic deflector (2D-EOD) and a tunable acoustic gradient (TAG) lens to drive a single focused laser spot dynamically in 3D. Combined with an optimized position estimation algorithm, 3D-DyPLoT can lock onto single particles with high tracking speed and high localization precision. Owing to the single excitation and single detection path layout, 3D-DyPLoT is robust and easy to set up. This protocol discusses how to build 3D-DyPLoT step by step. First, the optical layout is described. Next, the system is calibrated and optimized by raster scanning a 190 nm fluorescent bead with the piezoelectric nanopositioner. Finally, to demonstrate real-time 3D tracking ability, 110 nm fluorescent beads are tracked in water.

This protocol details the construction and operation of a real-time 3D single particle tracking microscope capable of tracking nanoscale fluorescent probes at high diffusive speeds and low photon count rates.

Real-time three-dimensional single particle tracking (RT-3D-SPT) has the potential to shed light on fast, 3D processes in cellular systems. Although ...

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved oxygen concentration). Nevertheless, the morphology of cells can be a suitable indicator for optimal conditions, since it changes with dependence on the growth state, product accumulation and cell stress. Furthermore, the single-cell size distribution provides not only information about the cultivation conditions, but also about the population heterogeneity. To gain such information, a photo-optical in situ microscopy device1 was developed to enable the monitoring of the single-cell size distribution directly in the cell suspension in bioreactors. An automated image analysis is coupled to the microscopy based on a neural network model, which is trained with user-annotated images. Several parameters, which are gained from the captures of the microscope, are correlated to process relevant features of the cells, like their metabolic activity. Until now, the presented in situ microscopy probe series was applied to measure the pellet size in filamentous fungi suspensions. It was used to distinguish the single-cell size in microalgae cultivation and relate it to lipid accumulation. The shape of cellular particles was related to budding in yeast cultures. The microscopy analysis can be generally split into three steps: (i) image acquisition, (ii) particle identification, and (iii) data analysis, respectively. All steps have to be adapted to the organism, and therefore specific annotated information is required in order to achieve reliable results. The ability to monitor changes in cell morphology directly in line or on line (in a by-pass) enables real-time values for monitoring and control, in process development as well as in production scale. If the off line data correlates with the real-time data, the current tedious off line measurements with unknown influences on the cell size become needless.

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

A photo-optical in situ microscopy device was developed to monitor the size of single cells directly in the cell suspension. The real-time measurement is conducted by coupling the photo-optical sterilizable probe to an automated image analysis. Morphological changes appear with dependence on the growth state and cultivation conditions.

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved ...

Manufacturing of a Nafion-coated, Reduced Graphene Oxide/Polyaniline Chemiresistive Sensor to Monitor pH in Real-time During Microbial Fermentation

Here, we report the engineering of a solid-state micro pH sensor based on polyaniline-functionalized, electrochemically reduced graphene oxide (ERGO-PA). Electrochemically reduced graphene oxide acts as the conducting layer and polyaniline acts as a pH-sensitive layer. The pH-dependent conductivity of polyaniline occurs by doping of holes during protonation and by the dedoping of holes during deprotonation. We found that an ERGO-PA solid-state electrode was not functional as such in fermentation processes. The electrochemically active species that the bacteria produce during the fermentation process interfere with the electrode response. We successfully applied Nafion as a proton-conducting layer over ERGO-PA. The Nafion-coated electrodes (ERGO-PA-NA) show a good sensitivity of 1.71 &#937;/pH (pH 4 - 9) for chemiresistive sensor measurements. We tested the ERGO-PA-NA electrode in real-time in the fermentation of Lactococcus lactis. During the growth of L. lactis, the pH of the medium changed from pH 7.2 to pH 4.8 and the resistance of the ERGO-PA-NA solid-state electrode changed from 294.5 &#937; to 288.6 &#937; (5.9 &#937; per 2.4 pH unit). The pH response of the ERGO-PA-NA electrode compared with the response of a conventional glass-based pH electrode shows that reference-less solid-state microsensor arrays operate successfully in a microbiological fermentation.

Here, we report the protocol for the fabrication of a Nafion-coated, polyaniline-functionalized, electrochemically reduced graphene oxide chemiresistive micro pH sensor. This chemiresistor-based, solid-state micro pH sensor can detect pH changes in real-time during a Lactococcus lactis fermentation process.

Here, we report the engineering of a solid-state micro pH sensor based on polyaniline-functionalized, electrochemically reduced graphene oxide (ERGO-PA). ...

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Conventional cell culture remains the most frequently used preclinical model, despite its proven limited ability to predict clinical results in cancer. Microfluidic cancer-on-chip models have been proposed to bridge the gap between the oversimplified conventional 2D cultures and more complicated animal models, which have limited ability to produce reliable and reproducible quantitative results. Here, we present a microfluidic cancer-on-chip model that reproduces key components of a complex tumor microenvironment in a comprehensive manner, yet is simple enough to provide robust quantitative descriptions of cancer dynamics. This microfluidic cancer-on-chip model, the &#34;Evolution Accelerator,&#34; breaks down a large population of cancer cells into an interconnected array of tumor microenvironments while generating a heterogeneous chemotherapeutic stress landscape. The progression and the evolutionary dynamics of cancer in response to drug gradient can be monitored for weeks in real time, and numerous downstream experiments can be performed complementary to the time-lapse images taken through the course of the experiments.

We present a microfluidic cancer-on-chip model, the &#34;Evolution Accelerator&#34; technology, which provides a controllable platform for long-term real-time quantitative studies of cancer dynamics within well-defined environmental conditions at the single-cell level. This technology is expected to work as an in vitro model for fundamental research or pre-clinical drug development.

Conventional cell culture remains the most frequently used preclinical model, despite its proven limited ability to predict clinical results in cancer. ...