We want to acknowledge FONCYCYT of CONACYT (Mexico), the ministry of Foreign affairs of France and the Universit&#233; Paris 13, though the financial support of the international collaborative ECOS-NORD project named Nano-palpation for diagnosis, No. 263337 (Mexico) and MI5P02 (France). AMR would like to thank the financial support of SIP-IPN through the project No. 20195489. SPC is supported by a PhD fellowship from CONACYT (No. 288029) and IPN through the cotutelle agreement to obtain double PhD certificate (IPN-UPS). ED and CFD are researchers at Centre National de la Recherche Scientifique (CNRS).

1. Microbial cell culture
<ol>
	<li>Revivify cells from a glycerol stock. 
	NOTE: C. albicans are stored at -80 &#176;C in glycerol stocks, on marbles.
	<ol>
		<li>Pick a marble in the -80 &#176;C stock and rub it on yeast peptone dextrose (YPD) agar. Grow the cells for 2 days at 30 &#176;C, before liquid cultivation.</li>
	</ol>
	</li>
	<li>Prepare liquid cultures.
	<ol>
		<li>Fill a culture tube with 5 mL of sterile YPD broth and add a single colony of C. albicans cells, grown on the YPD agar plate.</li>
		<li>Grow the culture in static conditions at 30 &#176;C for 20 h before harvesting by centrifugation (4000 x g, 5 min). Discard the supernatant and eliminate as biohazard waste.</li>
		<li>Wash the pellets 2x with 10 mL of acetate buffer (8 mM sodium acetate, 1 mM CaCl2, 1 mM MnCl2, pH 5.2). Centrifuge (4000 x g, 5 min) in between washings.</li>
		<li>Resuspend the pellet in 2 mL of acetate buffer and use this solution for cell immobilization on the PDMS stamp. 
		NOTE: This suspension cannot be stored and should be prepared fresh for section 3.</li>
	</ol>
	</li>
</ol>
2. PDMS stamp preparation
<ol>
	<li>Silicon master mold preparation
	<ol>
		<li>Draw the desired microstructures using computer assisted design (CAD) software. 
		NOTE: The wells designed should be of similar size to the microbe to trap. The design should provide a large matrix of wells 100 x 100 at list. It is best to make several arrays with slightly different sizes around the average size of the microbe.</li>
		<li>If a clean room is available, follow steps 2 to 12 of the previously published protocol12. Otherwise, silicon master mold can be acquired from commercial clean room facilities.</li>
	</ol>
	</li>
	<li>PDMS stamp molding
	<ol>
		<li>Prepare 55 g of PDMS prepolymer solution containing a mixture of 10 to 1, mass ratio, of PDMS oligomers and curing agent (Table of Materials).</li>
		<li>Mix and degas this solution under vacuum (in the range of 10-1-10-2 bars) until all trapped bubbles are removed from the PDMS solution (5&#8722;10 min).</li>
		<li>Pour 20 g of the degassed solution on the silicon master mold and degas again (in the range of 10-1-10-2 bars). 
		NOTE: The stamp thickness should be around 2&#8722;3 mm.</li>
		<li>When all bubbles are removed, reticulate the PDMS at 80 &#176;C during 1 h.</li>
		<li>Cut the PDMS microstructured stamp with a scalpel (0.5 x 1.5 cm2) in a direction parallel to the visible microstructure arrays.</li>
		<li>Peel the stamp from the silicon master mold.</li>
		<li>Return the stamp to exhibit the microstructures on its upper side and deposit it on a glass slide. Make sure to have the microstructures facing up away from the glass slide. Align the microstructures that can be seen on the stamp with the side of the glass slide, which will later serve as a reference for the AFM automation procedure. 
		NOTE: At this stage, the PDMS stamp is ready for cell immobilization. The PDMS stamps can be stored on the silicon master mold for several months. When all the PDMS is removed from the master mold, a new PDMS stamp can be casted again on the master mold (to keep the master mold safe, it is possible to replicate it in polyurethane)14.</li>
	</ol>
	</li>
</ol>
3. Sample preparation
<ol>
	<li>Cell immobilization
	<ol>
		<li>Centrifuge (500 x g, 5 min) 600 &#181;L of the resuspended cell solution to separate the buffer from the cells.</li>
		<li>Pipet 200 &#181;L of the supernatant from step 3.1.1 onto the PDMS stamp, and degas under vacuum (in the range of 10-1-10-2 bars) for about 40 min. 
		NOTE: This step is important to improve the cell immobilization inside the wells. Molecules from the yeast cell wall, present in the supernatant, are probably deposited on the PDMS surface during this pre-wetting step. These molecules, most probably, enhance the adhesion of the cells and contribute to the increase in the stamp filling rate.</li>
		<li>After 40 min, with a pipette, remove the buffer from the PDMS surface and deposit, with a pipette, 200 &#181;L of the cell solution from step 1.2.4 for 15 min at room temperature.</li>
		<li>Place the cells into the microstructures of the stamp by convective/capillary assembly. For that, manually spread 200 &#181;L of cells suspension across the stamp using a glass slide in both directions with an angle between 30 and 50&#176;. It may be necessary to pass the glass slide several times on the stamp to achieve a high filling rate. 
		NOTE: A full description of this method is available13.</li>
		<li>Remove the cell suspension with a pipette. Wash the stamp 3x with 1 mL of acetate buffer, pH 5.2 to remove the cells that were not trapped.</li>
		<li>Dry the back of the stamp using nitrogen flow, in order to ensure that the stamp will adhere to the dry Petri dish.</li>
		<li>Finally deposit the PDMS stamp filled with cells in a Petri dish (Table of Materials) and fill it with 2 mL of acetate buffer to maintain the cells in liquid medium.</li>
	</ol>
	</li>
	<li>Setting the stamp on the AFM stage
	<ol>
		<li>Center the stage at 0:0 when starting AFM operations.</li>
		<li>Calibrate sensitivity and spring constant of the cantilever on glass and in water as described in Unsay et al.15</li>
		<li>Take the Petri dish with the stamp and place it in the AFM Petri dish holder.</li>
		<li>Align the stamp edge perpendicular to the Petri dish holder Y axis. 
		NOTE: An acceptable tilt angle is under 5&#176; as illustrated in Figure 1.</li>
		<li>Place the AFM head onto the stage and be careful that the stepper motors are sufficiently extended to avoid the tip to crash on the stamp.</li>
	</ol>
	</li>
</ol>
4. Running the AFM program
NOTE: The AFM program is provided as a Supplementary Material (AutomatipSoftware2019.pdf). It requires a JPK-Bruker AFM Nanowizard II or III equipped with a motorized stage and JPK desktop software version 4.3. The program has been developed under Jython (version based on python 2.7)
<ol>
	<li>Data acquisition
	<ol>
		<li>Center the AFM tip on top of the left corner of the 4.5 x 4.5 &#181;m2 wells (corresponding to the cell size) using the AFM optical microscope. If another well size is needed, center on top left corner of the desired wells.</li>
		<li>Perform a 64 x 64 force map (Z range = 4 &#181;m, tip velocity = 90 &#181;m&#183;s-1, applied force 3 to 5 nN) over a 100 x 100 &#181;m&#178; area. Select Force Mapping mode from the Measurement mode drop-down box. In the force control mapping panel input the following parameters: Rel. Setpoint = 3 to 5 nN; z length 4 &#181;m; Z movement: constant duration; extend time: 0.01s; ext. delay:0; Retr delay: 0, Delay mode: Constant Force, Sample rate 2048 Hz; Z closed loop uncheck; Grid: check Square image, Fast 100 &#181;m, slow: 100&#181;m, X offset: 0 &#181;m; Y offset: 0 &#181;m; grid angle: 0 degree; Pixels: 64x64; pixel ratio: 1:1 
		NOTE: A typical result is shown in Figure 2. This image will help measure and verify the pitch between two wells.</li>
		<li>Note the coordinates of the center of the top left well (W1) and of the bottom left well (referred as W2 on Figure 2). To do so, make a square box around the well. The coordinate of the center of the box appears on the left panel of the AFM software in x,y coordinates boxes.</li>
		<li>To open the automation software (Automatip_scan.py): in the JPK desktop software click on advance in the top bar menu and select open the script. In the window that opens select the path toward the script file provided in Supplementary Data (Automatip_scan.py).</li>
		<li>Implement W1 and W2 coordinate values in the Inputs box section of the Jython script (Figure&#160;3). Input the W1 coordinates in the P1 variable line 239 of the script and the W2 coordinates in the P2 variable line 241. 
		NOTE: The wells selected as initial coordinates (W1 and W2) should not be too close from the scanning area edge. Otherwise the centering algorithm would not execute correctly because it needs to measure the height on the PDMS surface on each side of the well. For an example, see Figure 4.</li>
		<li>Attribute the pitch value to the pitch variable line 245 of the script.</li>
		<li>Input the well dimension in the Ws variable line 248. This is known from the design of the well patterns and can be checked on the same image as the one used to verify the pitch (Figure 2).</li>
		<li>Write the path to the saving directory in line 251 to save the data at the desired place.</li>
		<li>Set the totalArea variable line 254 to the desire multiple &#34;n&#34; of 100 &#181;m (that is the maximum scan area of the AFM used). The total number of wells that will be probed can be calculated using this value and the pitch: maximum scan area/pitch*n2. 
		NOTE: In the example of Figure 3, 9 areas of 100 x 100 &#181;m2 will be analyzed.</li>
		<li>Set the force curves matrix, row and column (3, 3 or 4, 4), recorded per well in the numScans variable line 257. 
		NOTE: In the example of Figure&#160;3, a matrix of 3 x 3 = 9 FCs will be recorded for each well.</li>
		<li>Run the program. Click on the Start button. 
		NOTE: The program first automatically executes a centering algorithm to better determine the center of W1 and W2 wells (step 1). It then automatically acquires the Force Curves (FCs) matrix on each well of the first scanning area (step 2). When all the wells of that area are probed, the script automatically moves the AFM tip to the first well of the next scanning area. The tip is retracted, the microscope stage moves to the next area, the tip is again approached on the stamp and the centering algorithm is executed again to re-center automatically on the first well (1&#39;) of that area (step 3). The first area is defined by the user, the second one, is on the right etc. until n is reached. n+1 area is underneath n, n+2 on the left of n+1, etc. until 2n is reached. 2n+1 is underneath 2n, and 2n+2 is on the right on 2n, etc. Globally, the tip serpentines through the total area. Step 2 and 3 are repeated automatically until the total number &#34;n2&#34; of scanning areas have been probed. Figure 5 presents the flowchart of the program. It takes ~4 h to complete the program.</li>
	</ol>
	</li>
	<li>Data analysis
	<ol>
		<li>Execute the &#34;Copy files&#34; python script (Copy_files_L.py, provided in Supplementary Data) to organize the FCs files into one folder. This script was developed with Python 2.7 and the SciPy module. Use Visual&#160;Studio Code software to open the python script. Input path to the general folder (line 67&#160;of the script provided in supplementary data) and where it will be stored (line 73).</li>
		<li>Open the AFM manufacturer data processing software to analyze the force curves. In the top menu File, select open &#8216;batch of spectroscopy curves.</li>
		<li>In the batch processing window, select the process provided in Supplementary Data (StiffnessProcess.jpk-proc-force). Select the last step of the process and click on Keep and Apply to All. All force curves will receive the same treatment. 
		NOTE: The process uses the calibration from the FCs files to convert the deflection curves into force curves calibrated in Newton; a data smoothing algorithm is applied (average of 3 consecutives points); the baseline is translated to rest on the zero axis; the contact point is extrapolated and the FC is offset to place the contact point at coordinate (0,0); the bending of the cantilever is subtracted to the FCs, the retract slope is fitted. At the end of the data treatment, the software generates a file that contains a table giving for each FCs: its name, Young Modulus, contact point, adhesion force, slopes, etc.</li>
		<li>Repeat steps 4.2.1 to 4.2.3 for all experiments. Be careful to save the data in different folders (i.e.: &#8220;&#8230;\TREATED\&#8221; and &#8220;&#8230;\UNTREATED\&#8221;)</li>
		<li>Use the R script provided in Supplementary Data to plot histograms and box plots and perform ANOVA statistical treatments.
		<ol>
			<li>To open the R script (DataAnalisys.R), use R studio software and load the files containing the information extracted with the data processing software (.tsv).</li>
			<li>On the environment window use the Import Dataset button, from the list displayed select from text (readr) and in the new window select the Browser button and find the .tsv file.</li>
			<li>Once the file has loaded, select the columns (stiffness and adhesion) to be included for the analysis. To run all the code, press Ctrl+Alt+R. 
			NOTE: The script works with 4 datasets, consider two experiments both having untreated and treated cells. It is possible to execute blocks of the script and see how the variables change according to the functions executed.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The main improvement provided by this methodology is a significant increase in the number of measured cells in a determined amount of time. The counterpart is a reduction of the number of points measured per cell. It means that this method is not designed to provide a detailed analysis of a single cell. The method only applies to cells that can fit in the wells of the PDMS stamp. The stamp is quite versatile, while it contains wells of 1.5 x 1.5 &#181;m2 up to 6 x 6 &#181;m2. Still it is impossible ...

This work provides a methodology to perform automatic force measurements on hundreds of living cells using an atomic force microscope (AFM). It also provides a method to immobilize microbes on a PDMS microstructured stamp that is compatible with AFM experiments conducted in a liquid environment.
Bio-AFM is a highly specialized technology conceived for applications in biology and then used to study living cells. It requires a trained engineer who can analyze one cell at the time. In these conditions, the number of different cells that can be analyzed is rather small, typical 5 to 10 cells in 4-5 hours. However, the quantity of force measurements recorded on a single cell are usually very high and can easily reach 1000. Thus, the current paradigm of AFM force measurements on living cells is to record hundreds of force curves (FCs) but on a limited number of cells.
Statistically, this approach is questionable, and raises the issue of the representativeness of the sample. Indeed, it is difficult, for example, to evaluate the heterogeneity of a cell population by measuring only a few cells, even if hundreds of measurements are recorded on these few cells. However, it is on the basis of this paradigm that major advances have been made in biophysics, microbiology and nanomedicine1,2,3. Indeed, nanometer analysis at the scale of single cells has provided new information on cellular nanomechanics, on the organization of transmembrane proteins, or the action mechanism of antimicrobial or anticancer drugs4,5,6,7. Recently however, several high-throughput biomechanical tests conducted on cells have emerged8, showing the scientific community&#8217;s interest in changing this paradigm and accessing the cell population heterogeneity. These tests all rely on microfluidic systems to deform cells and optically measure their deformation under stress to obtain an indirect measure of their overall surface elasticity8. However, an important issue with these methods is that they are mono-parametric: only cell elasticity can be probed. Moreover, they do not allow the measurement of the mechanical parameters of adherent cells, which can be limiting for the studies of noncirculating mammalian cells or biofilms for example.
Approaches involving AFM have been developed by the teams of S. Scheuring9 and M. Favre10. Scheuring et al. immobilized cells on fibronectin patterns9, forcing individual cells to take the shape of the pattern9. Then this team mapped the mechanical properties of a few cells to define average data, representative of 14 to 18 cells. The development carried out by Farve et al. aimed at multiplexing the measurements by parallelizing the AFM cantilevers10. To our knowledge, this work in the multiplexing direction has not led to measurements on living cells.
An interesting approach proposed by Dujardin&#8217;s team presents an automated AFM capable of identifying cells and imaging them at the bottom of custom-made wells. Although this method does not allow for the analysis of a large population of cells, it allows the automatic testing of different conditions in each well11.
Our objective in this work is more ambitious since we wanted to measure at least 1000 cells to access not an average cell, but, on the contrary, the heterogeneity between cells. The strategy that we developed here to access cell population heterogeneity using AFM is based on the analysis of hundreds of cells on which a limited number of force curves are recorded. Compared to the &#8220;classical&#8221; approach of recording a large number of force curves on a limited number of cells, this approach should be considered as complementary since it does not provide the same information. Indeed, while the typical method allows one to probe individual cell surface heterogeneity, using our approach, we are able to access the entire cell population heterogeneity. To achieve this objective, we have combined a method that directly immobilizes microbes (here the yeast species Candida albicans) into the wells of a PDMS microstructured stamp12, and develops an original program for moving the AFM tip, automatically, from cell to cell13 and measuring the mechanical properties of each cell.

<table>
	<tbody>
		<tr>
			<td>AFM cantilever</td>
			<td>Bruker AFM probes</td>
			<td>MLCT</td>
			<td>The cantilevers used were the labeled &#8220;C&#8221; with resonant frequency of 7 to 10 kHz and k: 0.01 N/m</td>
		</tr>
		<tr>
			<td>AFM data analysis</td>
			<td>JPK-Bruker</td>
			<td>JPK Data processing version minimum 5.1.8</td>
			<td>Can be downloaded from a JPK-Bruker user acount</td>
		</tr>
		<tr>
			<td>AFM Petri dishes</td>
			<td>WPI</td>
			<td>FluoroDish FD35-100</td>
			<td>The heater was used to monitor the temperature changes during the experiment</td>
		</tr>
		<tr>
			<td>Atomic force Microscope (AFM)</td>
			<td>JPK-Bruker</td>
			<td>Nanowizard II or III</td>
			<td>the AFM should be mounted on an inverted optical microscope with a motorized stage</td>
		</tr>
		<tr>
			<td>Caspofungin</td>
			<td>Sigma-Aldrich</td>
			<td>SML0425-5MG</td>
			<td>Caspofungin was used with a concentration of 4 MIC (Minimum Inhibitor Concentration)</td>
		</tr>
		<tr>
			<td>Code editor</td>
			<td>Microsoft</td>
			<td>Visual Studio Code version 1.40.1</td>
			<td>https://code.visualstudio.com/</td>
		</tr>
		<tr>
			<td>Cryobeads</td>
			<td>IFU</td>
			<td>CB12</td>
			<td></td>
		</tr>
		<tr>
			<td>Dessicator/Degassing chamber</td>
			<td>Fisherbrand</td>
			<td>15594635</td>
			<td>The equipment is used to degassing the PDMS stamps for about 50 minutes any dessicator coupled with a vaccum pump will do.</td>
		</tr>
		<tr>
			<td>Petri dish heater</td>
			<td>JPK-Bruker</td>
			<td>PetriDishHeater</td>
			<td>This is an add-on to the JPK/Bruker AFM. The heater was used to monitor the temperature changes during the experiment</td>
		</tr>
		<tr>
			<td>Sodium acetate buffer pH 5.2</td>
			<td>Sigma-Aldrich</td>
			<td>S7899</td>
			<td>The solution contains 18 mM sodium acetate, 1 mM CaCl2, and 1 mM MnCl2. Adjust the pH with glacial acetic acid. The solution can be stored at 4 &#176;C for 2 months</td>
		</tr>
		<tr>
			<td>Statistical analysis language</td>
			<td>https://www.r-project.org</td>
			<td>R version 3.6.1</td>
			<td>R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment</td>
		</tr>
		<tr>
			<td>Statistical analysis software</td>
			<td>https://rstudio.com</td>
			<td>R studio version 1.1.463</td>
			<td>collaboration between the R Foundation, RStudio, Microsoft, TIBCO, Google, Oracle, HP and others. RStudio and Shiny are affiliated projects of the Foundation for Open Access Statistics</td>
		</tr>
		<tr>
			<td>Sylgard 184</td>
			<td>Sigma-Aldrich</td>
			<td>761028</td>
			<td>Polydimethylsiloxane (PDMS) and curing agent in one set</td>
		</tr>
		<tr>
			<td>Yeast Peptone D Broth</td>
			<td>Difco</td>
			<td>242820</td>
			<td></td>
		</tr>
		<tr>
			<td>YPD Agar</td>
			<td>Difco</td>
			<td>DF0427-17-6</td>
			<td></td>
		</tr>
	</tbody>
</table>

automation of bio-atomic force microscope measurements on hundreds of c. albicans cells

The method presented in this paper aims to automate Bio-AFM experiments and the recording of force curves. Using this method, it is possible to record forces curves on 1000 cells in 4 hours automatically. To maintain a 4 hour analysis time, the number of force curves per cell is reduced to 9 or 16. The method combines a Jython based program and a strategy for assembling cells on defined patterns. The program, implemented on a commercial Bio-AFM, can center the tip on the first cell of the array and then move, automatically, from cell to cell while recording force curves on each cell. Using this methodology, it is possible to access the biophysical parameters of the cells such as their rigidity, their adhesive properties, etc. With the automation and the large number of cells analyzed, one can access the behavior of the cell population. This is a breakthrough in the Bio-AFM field where data have, so far, been recorded on only a few tens of cells.

We used the described protocol to analyze the effect of caspofungin on the biophysical properties of the opportunistic human pathogen C. albicans in its yeast form. Caspofungin is a last chance antifungal molecule used when other drugs are ineffective because of the resistance mechanisms cells develop towards antifungals. Its mechanism of action is based on the inhibition of the subunit Fks2 of the complex fks1/Fks2 responsible for the &#223; glucan synthesis. As &#223; glucans are a major component of the funga...

Watch this Scientific Journal Video about Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells at JoVE.com

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

This protocol aims to automate AFM measurements on hundreds of microbial cells. First, microbes are immobilized into PDMS stamp microstructures and then force spectroscopy measurements are performed automatically on hundreds of immobilized cells.

Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

The method presented in this paper aims to automate Bio-AFM experiments and the recording of force curves. Using this method, it is possible to record ...

automation-bio-atomic-force-microscope-measurements-on-hundreds-c

Research

JoVE Journal

Bioengineering

3.8K Views.  Université de Toulouse, CNRS, Toulouse, France. This protocol aims to automate AFM measurements on hundreds of microbial cells. First, microbes are immobilized into PDMS stamp microstructures and then force spectroscopy measurements are performed automatically on hundreds of immobilized cells.

Video: Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. albicans Cells

Multiplexed Single-molecule Force Proteolysis Measurements Using Magnetic Tweezers

The generation and detection of mechanical forces is a ubiquitous aspect of cell physiology, with direct relevance to cancer metastasis1, atherogenesis2 and wound healing3. In each of these examples, cells both exert force on their surroundings and simultaneously enzymatically remodel the extracellular matrix (ECM). The effect of forces on ECM has thus become an area of considerable interest due to its likely biological and medical importance4-7.
	Single molecule techniques such as optical trapping8, atomic force microscopy9, and magnetic tweezers10,11 allow researchers to probe the function of enzymes at a molecular level by exerting forces on individual proteins. Of these techniques, magnetic tweezers (MT) are notable for their low cost and high throughput. MT exert forces in the range of ~1-100 pN and can provide millisecond temporal resolution, qualities that are well matched to the study of enzyme mechanism at the single-molecule level12. Here we report a highly parallelizable MT assay to study the effect of force on the proteolysis of single protein molecules. We present the specific example of the proteolysis of a trimeric collagen peptide by matrix metalloproteinase 1 (MMP-1); however, this assay can be easily adapted to study other substrates and proteases.

In this article we describe the use of magnetic tweezers to study the effect of force on enzymatic proteolysis at the single molecule level in a highly parallelizable manner.

The  generation and detection of mechanical forces is a ubiquitous aspect of cell physiology,  with direct relevance to cancer metastasis1, atherogenesis2 ...

Candida albicans Biofilm Chip (CaBChip) for High-throughput Antifungal Drug Screening

Candida albicans remains the main etiological agent of candidiasis, which currently represents the fourth most common nosocomial bloodstream infection in US hospitals1. These opportunistic infections pose a growing threat for an increasing number of compromised individuals, and carry unacceptably high mortality rates. This is in part due to the limited arsenal of antifungal drugs, but also to the emergence of resistance against the most commonly used antifungal agents. Further complicating treatment is the fact that a majority of manifestations of candidiasis are associated with the formation of biofilms, and cells within these biofilms show increased levels of resistance to most clinically-used antifungal agents2. Here we describe the development of a high-density microarray that consists of C. albicans nano-biofilms, which we have named CaBChip3. Briefly, a robotic microarrayer is used to print yeast cells of C. albicans onto a solid substrate. During printing, the yeast cells are enclosed in a three dimensional matrix using a volume as low as 50 nL and immobilized on a glass substrate with a suitable coating. After initial printing, the slides are incubated at 37 &deg;C for 24 hours to allow for biofilm development. During this period the spots grow into fully developed "nano-biofilms" that display typical structural and phenotypic characteristics associated with mature C. albicans biofilms (i.e. morphological complexity, three dimensional architecture and drug resistance)4. Overall, the CaBChip is composed of ~750 equivalent and spatially distinct biofilms; with the additional advantage that multiple chips can be printed and processed simultaneously. Cell viability is estimated by measuring the fluorescent intensity of FUN1 metabolic stain using a microarray scanner. This fungal chip is ideally suited for use in true high-throughput screening for antifungal drug discovery. Compared to current standards (i.e. the 96-well microtiter plate model of biofilm formation5), the main advantages of the fungal biofilm chip are automation, miniaturization, savings in amount and cost of reagents and analyses time, as well as the elimination of labor intensive steps. We believe that such chip will significantly speed up the antifungal drug discovery process.

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening

We have developed a high-density microarray platform consisting of 3D nano-biofilms of C. albicans called CaBChip. The susceptibility profile of drugs tested on a CaBChip is comparable to the conventional 96-well plate model, suggesting that the fungal chip is ideally suited for true high-throughput screening of antifungal drugs.

Candida  albicans remains the main etiological agent of candidiasis, which  currently represents the fourth most common nosocomial bloodstream infection ...

Biosensor for Detection of Antibiotic Resistant Staphylococcus Bacteria

A structurally transformed lytic bacteriophage having a broad host range of Staphylococcus aureus strains and a penicillin-binding protein (PBP 2a) antibody conjugated latex beads have been utilized to create a biosensor designed for discrimination of methicillin resistant (MRSA) and sensitive (MSSA) S. aureus species 1,2. The lytic phages have been converted into phage spheroids by contact with water-chloroform interface. Phage spheroid monolayers have been moved onto a biosensor surface by Langmuir-Blodgett (LB) technique 3. The created biosensors have been examined by a quartz crystal microbalance with dissipation tracking (QCM-D) to evaluate bacteria-phage interactions. Bacteria-spheroid interactions led to reduced resonance frequency and a rise in dissipation energy for both MRSA and MSSA strains. After the bacterial binding, these sensors have been further exposed to the penicillin-binding protein antibody latex beads. Sensors analyzed with MRSA responded to PBP 2a antibody beads; although sensors inspected with MSSA gave no response. This experimental distinction determines an unambiguous discrimination between methicillin resistant and sensitive S. aureus strains. Equally bound and unbound bacteriophages suppress bacterial growth on surfaces and in water suspensions. Once lytic phages are changed into spheroids, they retain their strong lytic activity and show high bacterial capture capability. The phage and phage spheroids can be utilized for testing and sterilization of antibiotic resistant microorganisms. Other applications may include use in bacteriophage therapy and antimicrobial surfaces.

Lytic phage biosensors and antibody beads are able to discriminate between methicillin resistant (MRSA) and sensitive staphylococcus bacteria. The phages were immobilized by a Langmuir-Blodgett method onto a surface of a quartz crystal microbalance sensor and worked as broad range staphylococcus probes. Antibody beads recognize MRSA.

A structurally transformed lytic bacteriophage having a broad host range of Staphylococcus aureus strains and a penicillin-binding protein (PBP 2a) ...

Measuring the Mechanical Properties of Living Cells Using Atomic Force Microscopy

Mechanical properties of cells and extracellular matrix (ECM) play important roles in many biological processes including stem cell differentiation, tumor formation, and wound healing. Changes in stiffness of cells and ECM are often signs of changes in cell physiology or diseases in tissues. Hence, cell stiffness is an index to evaluate the status of cell cultures. Among the multitude of methods applied to measure the stiffness of cells and tissues, micro-indentation using an Atomic Force Microscope (AFM) provides a way to reliably measure the stiffness of living cells. This method has been widely applied to characterize the micro-scale stiffness for a variety of materials ranging from metal surfaces to soft biological tissues and cells. The basic principle of this method is to indent a cell with an AFM tip of selected geometry and measure the applied force from the bending of the AFM cantilever. Fitting the force-indentation curve to the Hertz model for the corresponding tip geometry can give quantitative measurements of material stiffness. This paper demonstrates the procedure to characterize the stiffness of living cells using AFM. Key steps including the process of AFM calibration, force-curve acquisition, and data analysis using a MATLAB routine are demonstrated. Limitations of this method are also discussed.

This paper demonstrates a protocol to characterize the mechanical properties of living cells by means of microindentation using an Atomic Force Microscope (AFM).

Mechanical  properties of cells and extracellular matrix (ECM) play important roles in many  biological processes including stem cell differentiation, ...

Cell Patterning on Photolithographically Defined 
Parylene-C: SiO2 Substrates

Cell patterning platforms support broad research goals, such as construction of predefined in vitro neuronal networks and the exploration of certain central aspects of cellular physiology. To easily combine cell patterning with Multi-Electrode Arrays (MEAs) and silicon-based &#8216;lab on a chip&#8217; technologies, a microfabrication-compatible protocol is required. We describe a method that utilizes deposition of the polymer parylene-C on SiO2&#160;wafers. Photolithography enables accurate and reliable patterning of parylene-C at micron-level resolution. Subsequent activation by immersion in fetal bovine serum (or another specific activation solution) results in a substrate in which cultured cells adhere to, or are repulsed by, parylene or SiO2 regions respectively. This technique has allowed patterning of a broad range of cell types (including primary murine hippocampal cells, HEK 293 cell line, human neuron-like teratocarcinoma cell line, primary murine cerebellar granule cells, and primary human glioma-derived stem-like cells). Interestingly, however, the platform is not universal; reflecting the importance of cell-specific adhesion molecules. This cell patterning process is cost effective, reliable, and importantly can be incorporated into standard microfabrication (chip manufacturing) protocols, paving the way for integration of microelectronic technology.

Cell Patterning on Photolithographically Defined 
Parylene-C: SiO2 Substrates

This protocol describes a microfabrication-compatible method for cell patterning on SiO2. A predefined parylene-C design is photolithographically printed on SiO2 wafers. Following incubation with serum (or other activation solution) cells adhere specifically to (and grow according to the conformity of) underlying parylene-C, whilst being repulsed by SiO2 regions.

Cell patterning platforms support broad research goals, such as construction of predefined in vitro neuronal networks and the exploration of certain ...

From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn&#8217;t need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on &#181;m-sized membrane regions in the micro-to-milli-second time range.

Spatial distribution and temporal dynamics of plasma membrane proteins and lipids is a hot topic in biology. Here this issue is addressed by a spatio-temporal image fluctuation analysis that provides conceptually the same physical quantities of single particle tracking, but it uses small molecular labels and standard microscopy setups.

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the ...

Atomic Force Microscopy Imaging and Force Spectroscopy of Supported Lipid Bilayers

Atomic force microscopy (AFM) is a versatile, high-resolution imaging technique that allows visualization of biological membranes. It has sufficient magnification to examine membrane substructures and even individual molecules. AFM can act as a force probe to measure interactions and mechanical properties of membranes. Supported lipid bilayers are conventionally used as membrane models in AFM studies. In this protocol, we demonstrate how to prepare supported bilayers and characterize their structure and mechanical properties using AFM. These include bilayer thickness and breakthrough force. 
The information provided by AFM imaging and force spectroscopy help define mechanical and chemical properties of membranes. These properties play an important role in cellular processes such as maintaining cell hemostasis from environmental stress, bringing membrane proteins together, and stabilizing protein complexes.

We describe a protocol for preparation of supported lipid bilayers and its characterization using atomic force microscopy and force spectroscopy.

Atomic force microscopy (AFM) is a versatile, high-resolution imaging technique that allows visualization of biological membranes. It has sufficient ...

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Recent advances in modular DNA assembly techniques have enabled synthetic biologists to test significantly more of the available &#34;design space&#34; represented by &#34;devices&#34; created as combinations of individual genetic components. However, manual assembly of such large numbers of devices is time-intensive, error-prone, and costly. The increasing sophistication and scale of synthetic biology research necessitates an efficient, reproducible way to accommodate large-scale, complex, and high throughput device construction.
Here, a DNA assembly protocol using the Type-IIS restriction endonuclease based Modular Cloning (MoClo) technique is automated on two liquid-handling robotic platforms. Automated liquid-handling robots require careful, often times tedious optimization of pipetting parameters for liquids of different viscosities (e.g. enzymes, DNA, water, buffers), as well as explicit programming to ensure correct aspiration and dispensing of DNA parts and reagents. This makes manual script writing for complex assemblies just as problematic as manual DNA assembly, and necessitates a software tool that can automate script generation. To this end, we have developed a web-based software tool, http://mocloassembly.com, for generating combinatorial DNA device libraries from basic DNA parts uploaded as Genbank files. We provide access to the tool, and an export file from our liquid handler software which includes optimized liquid classes, labware parameters, and deck layout. All DNA parts used are available through Addgene, and their digital maps can be accessed via the Boston University BDC ICE Registry. Together, these elements provide a foundation for other organizations to automate modular cloning experiments and similar protocols.
The automated DNA assembly workflow presented here enables the repeatable, automated, high-throughput production of DNA devices, and reduces the risk of human error arising from repetitive manual pipetting. Sequencing data show the automated DNA assembly reactions generated from this workflow are ~95% correct and require as little as 4% as much hands-on time, compared to manual reaction preparation.

Here, an automated workflow to perform modular DNA &#34;device&#34; assembly using a modular cloning DNA assembly method on liquid-handling robots is presented. The protocol uses an intuitive software tool for generating liquid handler picklists for combinatorial DNA device library generation, which we demonstrate using two liquid handling platforms.

Recent advances in modular DNA assembly techniques have enabled synthetic biologists to test significantly more of the available &#34;design space&#34; ...

Power Input Measurements in Stirred Bioreactors at Laboratory Scale

The power input in stirred bioreactors is an important scaling-up parameter and can be measured through the torque that acts on the impeller shaft during rotation. However, the experimental determination of the power input in small-scale vessels is still challenging due to relatively high friction losses inside typically used bushings, bearings and/or shaft seals and the accuracy of commercially available torque meters. Thus, only limited data for small-scale bioreactors, in particular single-use systems, is available in the literature, making comparisons among different single-use systems and their conventional counterparts difficult.
This manuscript provides a protocol on how to measure power inputs in benchtop scale bioreactors over a wide range of turbulence conditions, which can be described by the dimensionless Reynolds number (Re). The aforementioned friction losses are effectively reduced by the use of an air bearing. The procedure on how to set up, conduct and evaluate a torque-based power input measurement, with special focus on cell culture typical agitation conditions with low to moderate turbulence (100 &#60; Re &#60; 2&#183;104), is described in detail. The power input of several multi-use and single-use bioreactors is provided by the dimensionless power number (also called Newton number, P0), which is determined to be in the range of P0 &#8776; 0.3 and P0 &#8776; 4.5 for the maximum Reynolds numbers in the different bioreactors.

The power input in stirred bioreactors can be measured through the torque that acts on the impeller shaft during rotation. This manuscript describes how an air bearing can be used to effectively reduce friction losses observed in mechanical seals and improve the accuracy of power input measurements in small-scale vessels.

The power input in stirred bioreactors is an important scaling-up parameter and can be measured through the torque that acts on the impeller shaft during ...

Measurement of Force-Sensitive Protein Dynamics in Living Cells Using a Combination of Fluorescent Techniques

Cells sense and respond to physical cues in their environment by converting mechanical stimuli into biochemically-detectable signals in a process called mechanotransduction. A crucial step in mechanotransduction is the transmission of forces between the external and internal environments. To transmit forces, there must be a sustained, unbroken physical linkage created by a series of protein-protein interactions. For a given protein-protein interaction, mechanical load can either have no effect on the interaction, lead to faster disassociation of the interaction, or even stabilize the interaction. Understanding how molecular load dictates protein turnover in living cells can provide valuable information about the mechanical state of a protein, in turn elucidating its role in mechanotransduction. Existing techniques for measuring force-sensitive protein dynamics either lack direct measurements of protein load or rely on the measurements performed outside of the cellular context. Here, we describe a protocol for the F&#246;rster resonance energy transfer-fluorescence recovery after photobleaching (FRET-FRAP) technique, which enables the measurement of force-sensitive protein dynamics within living cells. This technique is potentially applicable to any FRET-based tension sensor, facilitating the study of force-sensitive protein dynamics in variety of subcellular structures and in different cell types.

Here, we present a protocol for the simultaneous use of F&#246;rster resonance energy transfer-based tension sensors to measure protein load and fluorescence recovery after photobleaching to measure protein dynamics enabling the measurement of force-sensitive protein dynamics within living cells.

Cells sense and respond to physical cues in their environment by converting mechanical stimuli into biochemically-detectable signals in a process called ...