This work was made possible by grants from the National Institute for General Medical Science (NIGMS) (5P20GM103427), a component of the National Institutes of Health (NIH)

1. Media Preparation
<ol>
	<li>Prepare minimal media (MM) with 0.25% glucose. To make 1 L of MM with 0.25% glucose, add 200 mL of sterile M9 salt solution, 2 mL of sterile 1 M MgSO4, 100 &#181;L of sterile 1 M CaCl2, and 12.5 mL of sterile 20% (w/v) glucose to water (dH2O) at a final volume of 1 L.</li>
	<li>Transfer required media to a sterile bottle using sterile techniques. Prepare the amount required depending on the number of channels being used. Typically, each channel requires 200 &#181;L for priming, 300 &#181;L for seeding and 1300 &#181;L for the 24 h experiment.</li>
	<li>Place the bottle in an incubator or water bath set at the experimental temperature. The media should be at the experiment temperature before use to avoid bubbles forming in the microchannels.</li>
</ol>
2. Preparation of an Overnight and Experimental Culture of PA01-EGFP.
<ol>
	<li>Place 15 mL of experimental media into a sterile sidearm flask and inoculate with one or two colonies from an agar plate streaked with PA01-EGFP. Expand the culture for 12-16 h on an incubator/shaker table at 37 &#176;C and 180-220 rpm.</li>
	<li>Measure the OD600 of the overnight culture. When the OD600 is greater than 0.80, dilute the overnight culture to the final OD of 0.8 using fresh MM. Place the experimental culture in an incubator or water bath at 37 &#176;C until needed for seeding the microfluidic channels. Check the OD600 again immediately before seeding to ensure that it has not changed significantly from the target OD600, 0.8 in this case.</li>
</ol>
3. Equipment Startup
<ol>
	<li>Set up the system station according to the user guide, similar to the setup in Figure 1. To avoid an error in the connection of the instrument to the software, turn on the instrument in the following order: 
	PC Workstation 
	Fluorescence Module. Make sure the fluorescence shutter is ON (blue light by shutter button on) 
	Hardware Controllers 
	Imaging system Controller (see Table of Materials) 
	CCD Camera 
	Imaging Station (Microscope) 
	NOTE: The temperature of the heated plate should be adjusted to the desired experimental temperature.</li>
	<li>Start the control application and enter the plate number located on the label on the side of the plate. 
	NOTE: After control application starts there will be two separate application windows, one for the software that controls the microscope/imaging software and one for the control module that controls the pump and the well-plate interface. This is shown in Figure S1, where the &#34;Multi Dimensional Acquisition&#34; menu is open on the top and the Control Module is set for an autorun on the bottom.</li>
</ol>
4. Priming and Seeding the Microfluidic Plate
NOTE: The priming, seeding, bacterial attachment and growth are illustrated in Figure 3.
<ol>
	<li>Remove the 48-well microfluidic plate from the packaging making sure to not touch the glass surface at the bottom of the plate. Clean the glass slide at the bottom of the plate well with a lens tissue, a lint free cloth, or a low-lint wipe.</li>
	<li>To prime the microfluidic channels, pipette 200 &#181;L of 37 &#176;C MM into the output well, being careful to avoid bubbles. Place the plate into the plate stage and wipe the interface with ethanol, allowing to dry, before sealing it onto the plate stage.</li>
	<li>In Manual Mode on the Control Module, set Fluid as LB at 37 &#176;C and Max Shear at 5.00 dyne/cm2. Click the output wells to activate flow from the output to the input well, priming the channels. After the 5 min priming, pause the flow to prepare for seeding. Carefully remove the plate from the stage and pipette residual media from the output but not removing any of the media from the inner circle that leads to the microfluidic channels.</li>
	<li>To seed the experimental channels, first pipette 300 &#181;L of MM into the input well followed by pipetting 300 &#181;L of the bacterial culture into the output well into the output well. Place the plate back into the plate stage making sure to wipe the interface before placing it on the plate.</li>
	<li>On the Control Module, focus on a single channel using the live camera feed after placing the plate stage onto the microscope stage. While visually monitoring by the live feed, resume flow at 1.00-2.00 dyne/cm2 for approximately 2-4 s to allow cells to enter the experimental channel but not in the serpentine channels. Leave the plate on the temperature controlled stage for 1 h to allow for cell attachment. During the 1 h incubation, the Control Module and Montage software can be set up for the autorun (Step 5). 
	NOTE: The amount of time needed for seeding will vary with media and organism, so it should be monitored closely by the live feed until optimized and used as a general time frame. Seeding throughout the plate may vary which would require more time of applied flow to certain columns of channels for complete seeding.</li>
	<li>After the attachment period, gently remove the plate from the stage and pipette the bacteria from the output first avoiding disturbing the channel. With a new pipette tip, remove the media from the input wells.</li>
</ol>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58926/58926fig3.jpg" /> 
Figure 3: Experimental overview of priming, seeding, and attachment of PA01-EGFP in the microfluidic channels. The priming, seeding, and attachment is outlined. The first step of priming requires fresh media introduced to the output. The seeding entails equal volumes of media and bacterial culture in the input and output wells, respectively. The culture should not pass the viewing segment of the experimental channel (red line) to avoid clogging the serpentine channels. After the incubation period is over, fresh media continuously flows from the input, to the viewing chamber and into the outlet. This initiates the attachment and growth of the bacterial biofilm. <a href="https://www.jove.com/files/ftp_upload/58926/58926fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
5. Setting the Software
<ol>
	<li>In the software, open &#34;Multiple Dimensional Acquisition&#34; to control the microscope image acquisition. Under the &#34;Main&#34; menu, select &#34;Timelapse&#34;, &#34;Multiple Stage Positions&#34;, and &#34;Multiple Wavelengths&#34; (Figure S1A).
	<ol>
		<li>Setting up the Saving settings, create a simple Base Name, making sure that Increment base name if file exists is checked. Click Select Directory to select the folder in which all files will be saved. Include essential details of the experiment in the Description.</li>
		<li>Under the Timelapse tab, adjust the Duration of the experimental time to 24 h. To control how often images are acquired, set the Time Interval so images are acquired every 5 min throughout the experiment. The Number of Time Points automatically adjusts with the set parameters.</li>
		<li>Set stage positions using the live camera feed in the bright field microscope, allowing for correct placement and focus. Starting with the 10X objective, focus on the center of the channel, located above or below the channel numbers engraved on the plate. Switch to the 20X objective, finding the optimal viewing area and focal plane within the channel. Add the position to the list or replace the preset channel position with the new settings.</li>
		<li>Under the Wavelengths menu, set the number of wavelengths to 3. For wavelength 1 (W1), select the FITC 100% CAM with a 10 ms exposure time. For wavelength 2 (W2), select the Brightfield 50% CAM 50% VIS with a minimal exposure time of 3 ms. For wavelength 3 (W3), set as the All Closed filter so no light remains on the last channel between acquisition times.</li>
	</ol>
	</li>
	<li>When setting up the Control Module, check to make sure that the manual run has been stopped.
	<ol>
		<li>In the Edit Autorun menu under the Protocol Setup tab, set a new protocol for 24 h of duration time, flowing in the forward direction at the desired shear rate. Shear rate is varied to study the effect of biofilm growth with shear rate. Click Add and Save As the protocol.</li>
		<li>Under the Sequence Setup tab, create a new sequence by selecting LB@37degrees as the default fluid for all channels. Under Step Iteration 1, for channels 1-12, select the protocol with the desired shear rate and enable all channels. For channels 13-24, select the protocol with the second desired shear rate and enable all channels. Select Apply and Save As the sequence.</li>
		<li>In the Autorun menu, select the saved sequence to be used for the autorun.</li>
	</ol>
	</li>
</ol>
6. Timed Biofilm Growth Experiment Setup
<ol>
	<li>Pipette a maximum of 1,300 &#181;L of sterile MM into the input well of the microfluidic plate. Place the plate back onto the plate stage and wipe the interface with ethanol, allowing ethanol to dry completely, before sealing the plate.&#160;</li>
	<li>Placing the plate stage onto the microscope stage, and be sure that the protocol(s) and sequence(s) are set up correctly. Select Start to start the autorun quickly followed by clicking Acquire to start the microscope image collection.</li>
	<li>Adjust the focus and placement of all stage positions between image acquirements. Select Pause and, using the live image mode in the bright field wavelength, select Go To to view each stage positions. Set new settings by selecting Set to Current. Click Resume before the next scheduled acquisition time.</li>
</ol>
7. Review and Analyze Image Sequences after the Timed Biofilm Growth Experiment
<ol>
	<li>To review each channel following the 24 h autorun, in the software, open Review Multidimensional Data, located under Analysis Tools. Click on Select Base File | Select Directory to navigate to the folder with image sequences for the experiment of interest. In the list of Data Sets in the folder that appears, select the base name of the data of interest.</li>
	<li>Once the data of interest is selected, click View to review this data. Select a wavelength to review that data under Wavelength window (Figure S2).</li>
	<li>Review the image sequence for each channel by selecting the channel number under Stage Position and using the video controls to analyze the data for the 289 time points (assuming that images are obtained every 5 min over a 24 h time frame). Take note of useable growth data. 
	NOTE: In each channel, watch for the development of air bubbles and clogs that will disturb the biofilm growth within the channel, affecting the data. However, if these occur later in the run, data prior to these circumstances may be found useful.</li>
	<li>After reviewing and selecting the desired data for analysis, create stacks of the images for each channel. Under the File menu, select Open Special | Build Stack | Numbered Names. In the Build Stack window, select the Select First Image button then select the first image for the stack in the file folder; select the Select Last Image button and then select the file corresponding to the last image in the stack. Click on OK to open the stack with all the images of the channel in chronological order. Stacks can be saved under the File menu, Save As as tiff files. 
	NOTE: Do not create stacks with the thumb images. These files are not very useful and can be deleted to save room on the hard drive. Also, image files are saved as the following: 
	 
	BASE NAME_WAVELENGTH_sCHANNEL#_tIMAGE#. 
	 
	For example, 07062018_W1FITC 100_s12_t112 is for the channel 12, time point 112, image in the first wavelength-FITC 100% camera data with the base name &#34;07062018&#34;.</li>
	<li>Finally, select Save As from the File drop-down menu to save the sequence as a tiff stack. Stacks can also be overlaid and saved as a movie. This is addressed in step 9.</li>
	<li>Before quantifying the % surface coverage, calibrate the image distances. Under Analysis Tools, click on Calibrate Distances. Select the appropriate calibration measurement and click Apply to All Open Images.</li>
	<li>While the stack is on the first image of the sequence, click on the Threshold button. Use Auto Threshold for Light Objects to threshold for fluorescent signal (FITC wavelength) and Auto Threshold for Dark Objects to threshold in bright field. Adjust threshold to the coverage that represents the coverage of the image.
	<ol>
		<li>For fluorescent thresholds, utilize a channel with non-fluorescent cells to establish any background signal and set the minimum threshold value to exclude signal detected in channels containing these non-fluorescent cells to ensure the signal measurement does not overestimate the area of fluorescence. This may be different from run to run so it is a good idea to use at least one, if not more, channel(s) for non-fluorescent controls.</li>
		<li>For bright field thresholds, if all cells are not covered by the orange threshold signature, adjust the maximum threshold value either via the sliding toolbar or using the Threshold Image (found under the Analysis Tools) so that all cells are covered but any background is excluded. 
		NOTE: While these threshold values ideally would be used for all images within a stack and for all channels, threshold values selected for one image/channel may not be appropriate for other images or channels, therefore the user may need to adjust the threshold range periodically. For this reason, it is a good idea to always record the maximum and minimum threshold values used should the data need to be reviewed later.</li>
	</ol>
	</li>
	<li>To quantify the coverage, under Analysis Tools, click Show Region Statistics. Make sure that Use Threshold is checked, the Entire Image is selected, and under Obtained Data, make sure the desired measurements/settings are selected (threshold area, average, standard deviation, min, max, and % threshold area). Click on Open Log and make sure DDE file is selected before clicking OK. Select Microsoft Excel and click OK. In the spreadsheet that will open, click Log Data to automatically record the measurements of the image being analyzed.</li>
	<li>Leaving this spreadsheet open, use the same sheet to collect all image analysis data for a single stack. In the stack, go to the fourth image and threshold to the optimal settings. Click on Log Data in the Show Region Statistics screen and the values are logged into the spreadsheet. Repeat this for analysis of images every 15 min.</li>
</ol>
8. Other Analysis Including Morphology and Surface Coverage Measure Using Python scripts from the Biofilm Morphology Suite
<ol>
	<li>Install a distribution of Python 3.6 that includes standard scientific modules by using a standard scientific Python distribution such as Anaconda, available at https://www.anaconda.com/download.</li>
	<li>Obtain the Biofilm Morphology Suite from GitHub in a browser by navigating to https://github.com/cdwentworth/Biofilm-Morphology-Suite.git, then select Clone or download (green button) and select Download Zip. Unzip the file and move the code folders to a working directory.</li>
	<li>Measure percent thresholded coverage by copying the image stack into the &#34;Coverage&#34; folder. Use a terminal window to navigate to that folder and then execute the script with the command 
	 
	python bfCoverage.py tiffStackName.tif 
	 
	from the command line interface, where tiffStackName.tif is the name of the file containing the tiff stack of images. A text file containing the coverage measurements at each time point will be created with the name tiffStackName.txt and a graphics file with a plot of coverage as a function of time will be created with the name tiffStackName.png.</li>
	<li>Use the same procedure specified in step 8.3 to measure the other morphological characteristics: biofilm accumulation, roughness coefficient, and textural entropy. Use the bfAcc.py script for the accumulation measurement, the roughCoef.py script for the roughness coefficient measurement, and the TEvsTime.py script for the textural entropy measurement.</li>
</ol>
9. Other Software Applications - Making Overlays and Movies
<ol>
	<li>If multiple wavelengths are used, create overlay stacks including these wavelengths. Open all stacks of interest and calibrate the distance as done in step 7.6.
	<ol>
		<li>Under Analysis Tools, select Overlay Images. Set the sources as the stacks of interest. In the FITC stack, click on the rainbow circle and select green color filter to be added in order to highlight this wavelength.</li>
		<li>Use bal to adjust the overlay so that the FITC stack is apparent in the images. After all adjustments have been made, select All Planes for both stacks and click Apply. Save the overlays in the same way as stacks described in step 7.5 or as a movie described in step 9.2.</li>
	</ol>
	</li>
	<li>To save stacks or overlays as a timelapse movie, under MM Standard and Stack, click on Make Movie. Select the source as the stack or overlay that is desired. Click Save and save as a Microsoft Video 1 with a quality set between 70-80.</li>
</ol>

The authors have nothing to disclose.

The microfluidic system and image analysis procedure discussed here focuses on the execution of microfluidic biofilm experiments to determine morphological properties that do not require the full three-dimensional information typically found from confocal microscope studies. These included microcolony substratum coverage (percent coverage), surface roughness measured by the roughness coefficient, and textural entropy. A method for estimating total relative biofilm cell accumulation is also presented from which growth rates in log phase can be calculated.
There are several small but significant steps that should be highlighted in this method. Wiping the interface with alcohol helps avoid contamination of other bacteria in going from experiment to experiment but also from well to well in one experiment. Priming and seeding are also very important because priming allows the user to determine which channels are allowing the media to flow through the channels without any disturbances or clogging. Channels should also not be disturbed (i.e. they should be constantly full of media) after priming to increase the chances of a successful experiment with no air bubbles or clogging. The seeding step can be varied according the type of bacteria and so should be optimized for cell attachment. For example, if cells do not seem to attach, surface modifications may have to occur on the microfluidic plate prior to seeding or longer incubations times may be needed. It is also critical to make sure that the microscope is correctly set up to obtain an in-focus image and should be monitored periodically throughout the experiment to assure that quality images are obtained. If the focus is off, the microscope can and should be adjusted as the experiment continues. During the image acquisition time, the last wavelength should be set to All Closed in order to avoid the exposure of filters and lighting to only one channel during the waiting time that occurs in between the image acquisitions. Also, the image analysis that determines the % surface coverage was designed in house because the Montage software manual did not explicitly describe the procedure. Furthermore, in order to expand on the image analysis and determine other characteristics such as surface roughness, etc., open source Python-based code45 was developed in house and shared on the gitHub repository. There are also limitations on how much data can be stored and managed on the local hard drive, so an external hard drive or online data sharing is needed such as CyVerse46.
Conventional bioreactors, such as the CDC reactor and the drip flow reactor34, require a lot media, provide fewer sample sizes, and demand a high amount of sterilization of equipment. In contrast, the advantages of this higher throughput platform include the ability to control shear, flow rates, and the assumption that the in vitro experiments closely resemble in vivo conditions. Disadvantages of the system include the multiple accessories and software that require a meticulous setup that must be performed in the correct order of events. Furthermore, the manual that is provided for the equipment does not fully explain each step of the experiments, and the software commands, and consequently, many mistakes occur during the experiments, including clogging of channels, lack of growth or attachment, or the lack high quality microscopy images or movies. The instrument itself and consumables, such as the microfluidic plates, are also relatively expensive with a price tag of over $200 per plate and are not reusable. Thus, while the technique lends powerful results, the technical expertise required for its use is relatively high and requires repeated training by experts in the field. This report attempts to resolve this issue by providing a guide to new users of these bioreactors to study biofilms characteristics.
The microfluidic system, which is able to perform cellular analysis, has gained considerable attention for various scientific modalities, such as in microbiology, immunology, hematology, oncology, and stem cell research. More specifically, the technology has resulted in many publications describing topics that are highly relevant to medical applications37,47, including microbial oral adhesion48, determining the effects of biosurfactants on Pseudomonas aeruginosa and Staphylococcus aureus49,50, host pathogen interactions in E. coli51, Streptococcus adherence52, and treatment of cystic fibrosis53. Given the fact that this microfluidic system is very versatile, it is anticipated that more and more systems will be distributed throughout the world.
Some specific protocol steps should be carefully considered. Media can be diluted to 50% with dH2O to help prevent bubbles and clogging but was not required in this case. The specific value of OD600 used for seeding must be determined using trial runs of a growth experiment to see what works best for the particular set of conditions used. Bubbles in the wells prior to sealing can lead to bubbles in the microfluidic channels and should be removed by either popped or sucked out with a pipette tip. It is important to keep bacteria out of the small serpentine channels. By having equal volumes of media in the input and output during the seeding process, flow due to pressure from the liquid volume will be controlled so flow is only due to the applied pressure from the system. The calibration distances should be set up during installation by the company representative. These settings are specific for each camera.
There are several challenges that occur when finding the most representative threshold for an image. Setting maximum threshold values can be difficult if the average pixel intensity across regions of the background are not consistent caused by either selecting a stage position that is not at the center of the channel or from debris on the plate. Under the MM Standard, click on Process, and then select Background and Shading Correction tool to correction for these inconsistencies. However, this tool is generally only helpful if the user has taken images of the channels before seeding that they can use as reference images. Or, if background/shading reference images are unavailable, the user will need to use their judgment to set a threshold value that covers the most cell area without including background for the entire image. Alternatively, select representative areas to measure that exclude regions of inconsistency by click Rectangular Region, Ellipse Region, or Trace Region to select a region and select Active Region rather than Entire Image on the Show Region Statistics window (under Analysis Tools). If a representative region is utilized for thresholding the bright field image, the same region should be used for measurement of the corresponding FITC image. It is helpful to record the specific spatial statistics (Left, Top, Width, Height, Area, Perimeter) associated with that representative region so the same region will be found and measured on the corresponding FITC image.
To prevent a buildup of data on the hard drive that will cause the computer to slow down, an external hard drive can be purchased for data storage. Another option for data storage and facilitating data sharing is the CyVerse bioinformatics platform. Create an account on the CyVerse system by going to http://www.cyverse.org/. Once logged in, launch the Discovery Environment then select &#34;Log in CyVerse&#34;. Select &#34;Data&#34; and navigate to your the folder. If the image stack is on the local computer then select &#34;Upload&#34; then &#34;Simple Upload from Desktop&#34;. Find the image stack file and select for upload. The file or a folder can be shared with collaborators if they have a CyVerse account and are granted permission. Sharing of the data folder to the general public requires that metadata be added for each file using CyVerse approved standards. This procedure will not be discussed here because this is not within the scope of this work.

Here, we will demonstrate a method to measure the effect of shear stress on the formation of fluorescent Pseudomonas aeruginosa (P. aeruginosa) PA01 biofilms using an automated higher-throughput microfluidic system.
Biofilms are communities of microorganisms, such as bacteria, organized by an extracellular polymeric substance that are attached to a support, and are typically found at the interface between a liquid and a solid surface1. These biofilm communities can be beneficial to the environment, such as improving water quality in water supply lines and in bioremediation of recalcitrant compounds2,3. However, biofilms can also be highly harmful to human health with undesirable consequences. For example, medical devices, such as hip and knee implants, are one type of surface where biofilm accumulation has been a challenge and causes severe medical complications4,5. Biofilms can also enter natural water systems, such as rivers and lakes, and infiltrate water supply pipes leading to bacteria contamination in drinking water resulting in infections6,7,8. Biofilms formed in marine environments adhere to ships and other man-made substrates and present a major economic and environmental problem as increased friction leads to increase fuel consumption9,10. Antimicrobial coatings, such as Tributyltin, have been developed to prevent these problems but are toxic to marine life11.
P. aeruginosa is a Gram-negative bacterium with high thriving capabilities in a variety of environmental and nutrimental conditions12. P. aeruginosa is a common cause of community- and hospital-acquired infections and found to be closely associated to injuries, such as severe burns, and immunocompromised hosts, such as in cystic fibrosis (CF)5,12,13, AIDS, and cancer patients5,13. The formation of P. aeruginosa biofilms has been most seriously connected to CF, where chronic lung infections are the leading cause of death for this disease5.
A reference strain of P. aeruginosa, PA01, is used in this report and is genetically modified to express an enhanced green fluorescent protein (PA-EGFP). EGFP represents a mutant form of GFP with greater fluorescence properties which allows for in situ biofilm analysis using fluorescence microscopy14,15,16. This type of fluorescence analysis is advantageous for the study of biofilms because GFP does not interfere significantly with cell growth and function17. For example, Escherichia coli cells that were tagged with GFP grew well and continuously without having suffered any toxic effects in comparison to the control bacteria17. Other reports substantiate this claim18,19,20. Furthermore, the use of a fluorescent reporter such as EGFP is quick and simple, yet only live cells will be measured because dead cells quickly cease to fluoresce21.
Biofilms can grow under various environmental conditions including those with different flow rates. For example, films can grow in high shear stress, such as in rivers, where the high water flow conditions lead to a greater microbial diversity22. Contrarily, standing water in ponds or oral biofilms experience a much lower shear force23. In addition to flow rate, there are other factors that influence biofilm adhesion, including surface roughness and hydrophobicity, media composition, and even the bacterial cell surface1,4,7,24. Conditions can also cause variation in the spatial structure or morphology of a biofilm. This includes environmental conditions such as shear stress exerted by a moving fluid or gradients in nutrient availability and biological factors such as the species present in the system, motility of cells, and the specific proteins present in the extracellular polymeric substance25,26,27. Under some conditions, the biofilm will be lawn-like (smooth and flat), while under other conditions the biofilm will be rough, fluffy, or even mushroom-like28. While the qualitative difference between biofilm lawns and mushroom structures can be clearly seen in microscopic images, understanding the relationship between film structure and biological processes within the film requires systematic and quantitative methods of describing the morphology. Morphological properties suggested for study by researchers include porosity, fractal dimension, diffusional length, microcolony area at the substratum, microcolony volume, roughness coefficient, and textural entropy29,30.
Bioreactors are used in the study of biofilms to mimic real life conditions31. Drip flow reactors (DFR) represent a low-shear environment where nutrients in media flow slowly across cells that are attaching to a surface over time to form a biofilm with a high cell density32. CDC reactors are bioreactors that create a high shear stress fluid environment by control of a stirring rod that is continuously rotating within the media filled tank33. These types of bioreactors are simple to set up, but they are limited in scope because of the relatively low sample size, the high consumption of media, large amounts of biohazardous waste produced from media drip flows ranging from 125 &#181;L/minute for drip flow reactors to more than 1 mL/min for CDC reactors, and the need to autoclave large amounts of glassware and waste media34. Biofilms do not grow evenly across the surface in a drip flow reactor because the low shear of media causes trailing along the larger conglomerates of P. aeruginosa bacteria therefore, the biofilm growth is not very smooth and the uneven samples cannot be easily analyzed with fluorescence microscopy35,36 .
Some common bioreactor limitations are overcome by using a medium-throughput microfluidic bioreactor, where only milliliters of media are required, and the reaction plates are small and readily disposable after autoclaving37. Furthermore, depending on the number of wells, many replications can be performed in just one reactor run, which provides sufficient amount of data to conduct meaningful statistical analysis. In Figure 1, the different components of the microfluidic-microscopy system that allow for controlled conditions, including temperature and flow rate38,39,40, are shown. The bioreactor is coupled with fluorescence microscopy to visualize the fluorescence of the EGFP tag in PA01 under applied low through high shear conditions that will mimic more realistic scenarios that are encountered in the environment or in the biomedical field.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58926/58926fig1.jpg" /> 
Figure 1: Individual components of the Microfluidic System. The individual components are listed from left to right: 1. CCD Camera, 2. High Resolution Inverted Microscope with Automated Stage, Automated Fluorescence Module, and Autofocus Module, 3. Plate Stage, 4: Imaging system Interface, 5: Manual Microscope Stage Control, 6: Vapor Trap, 7: Imaging system Controller (including Temperature Controller), 8: Hardware Controllers, 9: Fluorescence Controller, 10: Uninterruptible Power Supply, 11: External Hard Drive for Image Storage, 12: PC Workstation. <a href="https://www.jove.com/files/ftp_upload/58926/58926fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
An excerpt of the microfluidic plate is shown in Figure 2. The most commonly used plates consist of 48 wells. One experiment requires one inlet and one outlet wells, a total of 2 wells. This allows for 24 simultaneous experiments that can be performed with various experimental conditions, such as bacterial strains, antimicrobial treatments, and media varied from channel to channel and controlled shear flow for each column of six channels. The experimental temperature is also controlled with one temperature setting throughout the plate. The microfluidic channels show that each channel has a serpentine region to provide sufficient back pressure and controlled shear.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58926/58926fig2.jpg" /> 
Figure 2:&#160;Visualization of the microfluidic channels and viewing window. Two inlet and outlet wells with the microfluidic channels connecting them are highlighted with red and green dyes. The dye makes visible a serpentine region in each channel which creates sufficient back pressure and controlled shear during fluid flow. Each viewing channel (within the red circle) can be imaged with desired wavelengths. Shown are bright field (top) and fluorescent (bottom) microscopy images of a single channel with a PA01-EGFP biofilm using a 20X objective. Scale bar = 80 &#181;m. <a href="https://www.jove.com/files/ftp_upload/58926/58926fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A step by step guide is provided to allow users of microfluidic bioreactors that are coupled with fluorescence microscopy to conduct novel biofilm experiments using different shear environments. This method will allow for the expansion of experiments involving other microorganisms besides bacteria, such as fungi and algae, that have medical and environmental applications41,42,43. The detailed approach describes how to culture PA01-EGFP, inoculate a 48-well plate and set up the microfluidic device and software, set up the fluorescent microscope, and demonstrate the software analysis to obtain the biofilm coverage, growth rate, and morphological properties such as surface roughness.

<table border="0" cellpadding="0" cellspacing="0" style="width:800px;" width="801">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Ammonium Chloride, ACS</td>
			<td style="width:115px;">VWR</td>
			<td style="width:119px;">BDH9208-500G</td>
			<td style="width:304px;">Part of the minimal media composition</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:263px;">BioFlux 1000 48 Well Low Shear Plate</td>
			<td style="width:115px;">Fluxion Biosciences</td>
			<td>910-0047</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:263px;">BioFlux 1000Z Microfluidic Imaging System</td>
			<td style="width:115px;">Fluxion Biosciences</td>
			<td>BF 1000Z</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Calcium Chloride Dihydride, ACD Grade</td>
			<td style="width:115px;">VWR</td>
			<td>97061-904</td>
			<td>Part of the minimal media composition</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Dextrose, Anhydrous, ACS</td>
			<td style="width:115px;">VWR</td>
			<td>BDH9230-500G</td>
			<td>Part of the minimal media composition</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Magnesium Sulfate ACS Grade</td>
			<td style="width:115px;">VWR</td>
			<td>EM-MX0070-1</td>
			<td>Part of the minimal media composition</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:263px;">Potassium Phosphate Monobasic, ACS Grade</td>
			<td style="width:115px;">VWR</td>
			<td>BDH9268-500G</td>
			<td>Part of the minimal media composition</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:263px;">Pseudomonas Aeurginosa GFP</td>
			<td style="width:115px;">ATCC</td>
			<td style="width:119px;">15692GFP</td>
			<td style="width:304px;">Pseudomonas aeurinosa bacterial strain PA01 with GFP modification used for this study.</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:263px;">Sodium Chloride, ACS</td>
			<td style="width:115px;">VWR</td>
			<td style="width:119px;">BDH9286-500G</td>
			<td>Part of the minimal media composition</td>
		</tr>
		<tr height="43">
			<td height="43" style="height:43px;width:263px;">Sodium Phosphate, Monobasic, Anhydrous, Reagent Grade</td>
			<td style="width:115px;">VWR</td>
			<td>97061-942</td>
			<td>Part of the minimal media composition</td>
		</tr>
	</tbody>
</table>

live cell analysis of shear stress on pseudomonas aeruginosa using an automated higher-throughput microfluidic system

A higher-throughput microfluidic in vitro bioreactor coupled with fluorescence microscopy has been used to study bacterial biofilm growth and morphology, including Pseudomonas aeruginosa (P. aeruginosa). Here, we will describe how the system can be used to study the growth kinetics and the morphological properties such as the surface roughness and textural entropy of P. aeruginosa strain PA01 that expresses an enhanced green fluorescent protein (PA01-EGFP). A detailed protocol will describe how to grow and seed PA01-EGFP cultures, how to set up the microscope and autorun, and conduct the image analysis to determine growth rate and morphological properties using a variety of shear forces that are controlled by the microfluidic device. This article will provide a detailed description of a technique to improve the study of PA01-EGFP biofilms which eventually can be applied towards other strains of bacteria, fungi, or algae biofilms using the microfluidic platform.

Figure 4a demonstrates the percent thresholded area over time from a 24 h run at shear flows of 0.2, 0.5, and 1.0 dyne/cm2. The biofilm coverage or percent threshold surface area (C [%]) was different for all three shear settings. The biofilm coverage was fastest at 1.0 dyne/cm2 shear, where the threshold area increased from 2-5% to 100% after 200 min of growth and reached a stationary phase after 400 min. At 0.5 dyne/cm2, the biofilm coverage was delayed and began to increase at 400 min reaching 100% coverage after 800 min. The lowest shear at 0.2 dyne/cm2 clearly demonstrated the slowest increase in biofilm coverage, where percent coverage started to increase at 500 min but never reached beyond the 65% threshold area. These results indicated shear had a direct influence in biofilm surface coverage. The higher shear seemed to be a more optimal condition for biofilm growth, possibly due to the fact that the media provided the bacteria with more nutrients so that the biofilm can proliferate quicker.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58926/58926fig4.jpg" /> 
Figure 4:&#160;Percent threshold area, total biofilm accumulation, roughness coefficient, and textural entropy. a) Percent threshold area (C [%]) over time using 0.2, 0.5, and 1.0 dyne/cm2 over a 24 h time period. Data was obtained from one channel for each shear condition. b) Total biofilm accumulation (relative measure) as a function of time using 0.2, 0.5, and 1.0 dyne/cm2 over a 24 h time period. The black lines are least-squares fits to an exponential model. Data was obtained from one channel for each shear condition. c) Roughness coefficient of PA01-EGFP using shear stress values of 0.2, 0.5, and 1.0 dyne/cm2 monitored over 24 h. Data was obtained from one channel for each shear condition. d) Textural entropy of PA01-EGFP using shear stress values of 0.2, 0.5, and 1.0 dyne/cm2. Data was obtained from one channel for each shear condition (Valquier-Flynn, H., Sutlief, A.L., Wentworth, C.D., 2018). <a href="https://www.jove.com/files/ftp_upload/58926/58926fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 4b is consistent with the findings shown in Figure 4a. The biofilm accumulation (N[rel]) was measured based on the assumption that the GFP signal at a point in an image is proportional to the live cell density at that position. It demonstrates that total relative measurement biofilm accumulation increased as a function of time using 0.2, 0.5, and 1.0 dyne/cm2 over a 24 h time period, and the growth rate declined from highest shear stress to lowest shear stress. There is a clear time period giving exponential growth from which a quantitative growth rate can be calculated.
Figure 4c demonstrates the roughness coefficient (Ra) at shear flows of 0.2, 0.5, and 1.0 dyne/cm2. Surface roughness, quantified by the roughness coefficient, measures the variance in the thickness profile of the film. The formal definition is
<img alt="Equation 1" src="/files/ftp_upload/58926/58926eq1.jpg" />
where Ti&#160;is the i-th thickness measurement, Ta is the average thickness, and N is the number of thickness measurement30. The procedure described in this investigation measures the thickness associated with living cells. A flat arrangement of cells will yield a roughness coefficient of zero while significant variations of thickness from the average will yield a roughness coefficient greater than one. Similar to the shear influence on the growth rate and the percent threshold coverage of the film, the biofilms exhibited different topographies over time. Overall, Ra decreased over time for all shear conditions indicating that all surfaces became smoother. However, in comparison to the lowest shear of 0.2 dyne/cm, the higher shear settings of 0.5 and 1.0 dyne/cm2 resulted in a smoother surface over time indicating that a faster shear flow contributed to smoother and more even surface, and the highest shear of 1.0 dyne/cm2 reaching below 0.2 Ra.
The smoothness, regularity, or coarseness of a surface can also be expressed in textural entropy (TE). TE is a property used in image analysis to measure the degree of randomness in a two-dimensional image. Its calculation is based on the gray level co-occurrence matrix, defined by Haralick et al., which looks at whether pixel values at one location are correlated with pixel values at another location44. A high degree of correlation will lead to low entropy. Figure 4d depicts the TE at shear flows of 0.2, 0.5, and 1.0 dyne/cm2. The TE increased over time for all shear conditions but the highest shear stress at 1.0 dyne/cm reached the maximum TE (1.0) earlier than the lower shear stresses at 900 min. The lowest shear of 0.2 dyne/cm2 had the lowest TE (0.8) reaching its maximum after 1,000 min. However, the intermediate shear stress of 0.5 dyne/cm2 reached its maximum TE (1.2) much later than the high or low shear stress conditions.
The roughness coefficient and TE measure different features. While a flat film would have a low roughness coefficient and low entropy, a film with significant variation in thickness would have a high roughness coefficient but could still be low entropy if the variation is sinusoidal rather than random. In this case, Ra decreased with increased shear stress and time while the TE trends cannot be directly related to shear stress applied to biofilm formation over time.
<img alt="Supplemental Figure 1" src="/files/ftp_upload/58926/58926figS1.jpg" /> 
Figure S1: Image capture of the control software windows. Software window with Multi Dimensional Acquisition menu open (Top). Control Module set for an AutoRun (Bottom). <a href="https://www.jove.com/files/ftp_upload/58926/58926figS1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 2" src="/files/ftp_upload/58926/58926figS2.jpg" /> 
Figure S2Image capture of the software for reviewing data. Application window after selecting the Data Set of interest using the Review Multi Dimensional Data tool from the Analysis Tools menu. <a href="https://www.jove.com/files/ftp_upload/58926/58926figS2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Live Cell Analysis of Shear Stress on Pseudomonas aeruginosa Using an Automated Higher-Throughput Microfluidic System at JoVE.com

Live Cell Analysis of Shear Stress on Pseudomonas aeruginosa Using an Automated Higher-Throughput Microfluidic System

Here, we describe the use of a higher-throughput microfluidic bioreactor coupled with a fluorescent microscope for the analysis of shear stress effects on Pseudomonas aeruginosa biofilms expressing green fluorescent proteins, including instrument set up, the determination of biofilm coverage, growth rate, and morphological properties.

Live Cell Analysis of Shear Stress on Pseudomonas aeruginosa Using an Automated Higher-Throughput Microfluidic System

A higher-throughput microfluidic in vitro bioreactor coupled with fluorescence microscopy has been used to study bacterial biofilm growth and morphology, ...

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7.4K Views.  Doane University. Here, we describe the use of a higher-throughput microfluidic bioreactor coupled with a fluorescent microscope for the analysis of shear stress effects on Pseudomonas aeruginosa biofilms expressing green fluorescent proteins, including instrument set up, the determination of biofilm coverage, growth rate, and morphological properties.

Video: Live Cell Analysis of Shear Stress on Pseudomonas aeruginosa Using an Automated Higher-Throughput Microfluidic System

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of Caenorhabditis elegans' Locomotion

The nematode Caenorhabditis elegans is a versatile model organism for biomedical research because of its conservation of disease-related genes and pathways as well as its ease of cultivation. Several C. elegans disease models have been reported, including neurodegenerative disorders such as Parkinson's disease (PD), which involves the degeneration of dopaminergic (DA) neurons 1. Both transgenes and neurotoxic chemicals have been used to induce DA neurodegeneration and consequent movement defects in worms, allowing for investigations into the basis of neurodegeneration and screens for neuroprotective genes and compounds 2,3.
Screens in lower eukaryotes like C. elegans provide an efficient and economical means to identify compounds and genes affecting neuronal signaling. Conventional screens are typically performed manually and scored by visual inspection; consequently, they are time-consuming and prone to human errors. Additionally, most focus on cellular level analysis while ignoring locomotion, which is an especially important parameter for movement disorders.
We have developed a novel microfluidic screening system (Figure 1) that controls and quantifies C. elegans' locomotion using electric field stimuli inside microchannels. We have shown that a Direct Current (DC) field can robustly induce on-demand locomotion towards the cathode ("electrotaxis") 4. Reversing the field's polarity causes the worm to quickly reverse its direction as well. We have also shown that defects in dopaminergic and other sensory neurons alter the swimming response 5. Therefore, abnormalities in neuronal signaling can be determined using locomotion as a read-out. The movement response can be accurately quantified using a range of parameters such as swimming speed, body bending frequency and reversal time.
Our work has revealed that the electrotactic response varies with age. Specifically, young adults respond to a lower range of electric fields and move faster compared to larvae 4. These findings led us to design a new microfluidic device to passively sort worms by age and phenotype 6.
We have also tested the response of worms to pulsed DC and Alternating Current (AC) electric fields. Pulsed DC fields of various duty cycles effectively generated electrotaxis in both C. elegans and its cousin C. briggsae 7. In another experiment, symmetrical AC fields with frequencies ranging from 1 Hz to 3 KHz immobilized worms inside the channel 8.
Implementation of the electric field in a microfluidic environment enables rapid and automated execution of the electrotaxis assay. This approach promises to facilitate high-throughput genetic and chemical screens for factors affecting neuronal function and viability.

Microfluidic-based Electrotaxis for On-demand Quantitative Analysis of Caenorhabditis elegans&#039; Locomotion

A semi-automated micro-electro-fluidic method to induce on-demand locomotion in Caenorhabditis elegans is described. This method is based on the neurophysiologic phenomenon of worms responding to mild electric fields (&#x201C;electrotaxis&#x201D;) inside microfluidic channels. Microfluidic electrotaxis serves as a rapid, sensitive, low-cost, and scalable technique to screen for factors affecting neuronal health.

The nematode Caenorhabditis elegans is a versatile model organism for biomedical research because of its conservation of disease-related genes and ...

Microfluidic Picoliter Bioreactor for Microbial Single-cell Analysis: Fabrication, System Setup, and Operation

In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described in detail. The PLBR can be utilized for the analysis of single bacteria and microcolonies to investigate biotechnological and microbiological related questions concerning, e.g.&#160;cell growth, morphology, stress response, and metabolite or protein production on single-cell level. The device features continuous media flow enabling constant environmental conditions for perturbation studies, but in addition allows fast medium changes as well as oscillating conditions to mimic any desired environmental situation. To fabricate the single use devices, a silicon wafer containing sub micrometer sized SU-8 structures served as the replication mold for rapid polydimethylsiloxane casting. Chips were cut, assembled, connected, and set up onto a high resolution and fully automated microscope suited for time-lapse imaging, a powerful tool for spatio-temporal cell analysis. Here, the biotechnological platform organism Corynebacterium glutamicum&#160;was seeded into the PLBR and cell growth and intracellular fluorescence were followed over several hours unraveling time dependent population heterogeneity on single-cell level, not possible with conventional analysis methods such as flow cytometry. Besides insights into device fabrication, furthermore, the preparation of the preculture, loading, trapping of bacteria, and the PLBR cultivation of single cells and colonies is demonstrated. These devices will add a new dimension in microbiological research to analyze time dependent phenomena of single bacteria under tight environmental control. Due to the simple and relatively short fabrication process the technology can be easily adapted at any microfluidics lab and simply tailored towards specific needs.

In this protocol the fabrication, setup and basic operation of a microfluidic picoliter bioreactor (PLBR) for single-cell analysis of prokaryotic microorganisms is introduced. Industrially relevant microorganisms were analyzed as proof of principle allowing insights into growth rate, morphology, and phenotypic heterogeneity over certain time periods, hardly possible with conventional methods.

In this protocol the fabrication, experimental setup and basic operation of the recently introduced microfluidic picoliter bioreactor (PLBR) is described ...

Systematic Analysis of In Vitro Cell Rolling Using a Multi-well Plate Microfluidic System

A major challenge for cell-based therapy is the inability to systemically target a large quantity of viable cells with high efficiency to tissues of interest following intravenous or intraarterial infusion. Consequently, increasing cell homing is currently studied as a strategy to improve cell therapy. Cell rolling on the vascular endothelium is an important step in the process of cell homing and can be probed in-vitro using a parallel plate flow chamber (PPFC). However, this is an extremely tedious, low throughput assay, with poorly controlled flow conditions. Instead, we used a multi-well plate microfluidic system that enables study of cellular rolling properties in a higher throughput under precisely controlled, physiologically relevant shear flow1,2. In this paper, we show how the rolling properties of HL-60 (human promyelocytic leukemia) cells on P- and E-selectin-coated surfaces as well as on cell monolayer-coated surfaces can be readily examined. To better simulate inflammatory conditions, the microfluidic channel surface was coated with endothelial cells (ECs), which were then activated with tumor necrosis factor-&#945; (TNF-&#945;), significantly increasing interactions with HL-60 cells under dynamic conditions. The enhanced throughput and integrated multi-parameter software analysis platform, that permits rapid analysis of parameters such as rolling velocities and rolling path, are important advantages for assessing cell rolling properties in-vitro. Allowing rapid and accurate analysis of engineering approaches designed to impact cell rolling and homing, this platform may help advance exogenous cell-based therapy.

Systematic Analysis of In Vitro Cell Rolling Using a Multi-well Plate Microfluidic System

This study used a multi-well plate microfluidic system, significantly increasing throughput of cell rolling studies under physiologically relevant shear flow. Given the importance of cell rolling in the multi-step cell homing cascade and the importance of cell homing following systemic delivery of exogenous populations of cells in patients, this system offers potential as a screening platform to improve cell-based therapy.

A major challenge for cell-based therapy is the inability to systemically target a large quantity of viable cells with high efficiency to tissues of ...

Designing Microfluidic Devices for Studying Cellular Responses Under Single or Coexisting Chemical/Electrical/Shear Stress Stimuli

Microfluidic devices are capable of creating a precise and controllable cellular micro-environment of pH, temperature, salt concentration, and other physical or chemical stimuli. They have been commonly used for in vitro cell studies by providing in vivo like surroundings. Especially, how cells response to chemical gradients, electrical fields, and shear stresses has drawn many interests since these phenomena are important in understanding cellular properties and functions. These microfluidic chips can be made of glass substrates, silicon wafers, polydimethylsiloxane (PDMS) polymers, polymethylmethacrylate (PMMA) substrates, or polyethyleneterephthalate (PET) substrates. Out of these materials, PMMA substrates are cheap and can be easily processed using laser ablation and writing. Although a few microfluidic devices have been designed and fabricated for generating multiple, coexisting chemical and electrical stimuli, none of them was considered efficient enough in reducing experimental repeats, particular for screening purposes. In this report, we describe our design and fabrication of two PMMA-based microfluidic chips for investigating cellular responses, in the production of reactive oxygen species and the migration, under single or coexisting chemical/electrical/shear stress stimuli. The first chip generates five relative concentrations of 0, 1/8, 1/2, 7/8, and 1 in the culture regions, together with a shear stress gradient produced inside each of these areas. The second chip generates the same relative concentrations, but with five different electric field strengths created within each culture area. These devices not only provide cells with a precise, controllable micro-environment but also greatly increase the experimental throughput.

Micro-fabricated devices integrated with fluidic components provide an in vitro platform for cell studies mimicking the in vivo micro-environment. We developed polymethylmethacrylate-based microfluidic chips for studying cellular responses under single or coexisting chemical/electrical/shear stress stimuli.

Microfluidic devices are capable of creating a precise and controllable cellular micro-environment of pH, temperature, salt concentration, and other ...

Rapid Subtractive Patterning of Live Cell Layers with a Microfluidic Probe

The microfluidic probe (MFP) facilitates performing local chemistry on biological substrates by confining nanoliter volumes of liquids. Using one particular implementation of the MFP, the hierarchical hydrodynamic flow confinement (hHFC), multiple liquids are simultaneously brought in contact with a substrate. Local chemical action and liquid shaping using the hHFC, is exploited to create cell patterns by locally lysing and removing cells. By utilizing the scanning ability of the MFP, user-defined patterns of cell monolayers are created. This protocol enables rapid, real-time and spatially controlled cell patterning, which can allow selective cell-cell and cell-matrix interaction studies.

We present a protocol to perform subtractive patterning of live cell monolayers on a surface. This is achieved by local and selective lysis of adherent cells using a microfluidic probe (MFP). The cell lysate retrieved from local regions can be used for downstream analysis, enabling molecular profiling studies.

The microfluidic probe (MFP) facilitates performing local chemistry on biological substrates by confining nanoliter volumes of liquids. Using one ...

Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. Unfortunately, many researchers spend large amounts of time and resources developing and optimizing workflows that are only relevant to their own experiments. In this article, we describe a protocol that can be used by those with access to a widefield high-content analysis system (WHCAS) to image any slide-mounted tissue, with options for customization within pre-built modules found in the associated software. Not originally intended for slide scanning, the steps detailed in this article make it possible to acquire slide scanning images in the WHCAS which can be imported into the associated software. In this example, the automated segmentation of brain tumor slides is demonstrated, but the automated segmentation of any fluorescently-labeled nuclear or cytoplasmic marker is possible. Furthermore, there are a variety of other quantitative software modules including assays for protein localization/translocation, cellular proliferation/viability/apoptosis, and angiogenesis that can be run. This technique will save researchers time and effort and create an automated protocol for slide analysis.

Here we describe a protocol for the automated segmentation of fluorescently labeled tissues on slides using a widefield high-content analysis system (WHCAS). This protocol has wide-ranging applications in any field which involves the quantitation of fluorescent markers in biological tissues including the biological sciences, medical engineering, and health sciences.

Automated slide scanning and segmentation of fluorescently-labeled tissues is the most efficient way to analyze whole slides or large tissue sections. ...

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of cellular heterogeneity in biological systems. However, single-cell sequencing of large populations has been hampered by limitations in processing genomes for sequencing. In this paper, we describe a method for single-cell genome sequencing (SiC-seq) which uses droplet microfluidics to isolate, amplify, and barcode the genomes of single cells. Cell encapsulation in microgels allows the compartmentalized purification and tagmentation of DNA, while a microfluidic merger efficiently pairs each genome with a unique single-cell oligonucleotide barcode, allowing &gt;50,000 single cells to be sequenced per run. The sequencing data is demultiplexed by barcode, generating groups of reads originating from single cells. As a high-throughput and low-bias method of single-cell sequencing, SiC-seq will enable a broader range of genomic studies targeted at diverse cell populations.

Single-cell sequencing reveals genotypic heterogeneity in biological systems, but current technologies lack the throughput necessary for the deep profiling of community composition and function. Here, we describe a microfluidic workflow for sequencing &gt;50,000 single-cell genomes from diverse cell populations.

Sequencing technologies have undergone a paradigm shift from bulk to single-cell resolution in response to an evolving understanding of the role of ...

Light-sheet Fluorescence Microscopy to Capture 4-Dimensional Images of the Effects of Modulating Shear Stress on the Developing Zebrafish Heart

The hemodynamic forces experienced by the heart influence cardiac development, especially trabeculation, which forms a network of branching outgrowths from the myocardium. Genetic program defects in the Notch signaling cascade are involved in ventricular defects such as Left Ventricular Non-Compaction Cardiomyopathy or Hypoplastic Left Heart Syndrome. Using this protocol, it can be determined that shear stress driven trabeculation and Notch signaling are related to one another. Using Light-sheet Fluorescence Microscopy, visualization of the developing zebrafish heart was possible. In this manuscript, it was assessed whether hemodynamic forces modulate the initiation of trabeculation via Notch signaling and thus, influence contractile function occurs. For qualitative and quantitative shear stress analysis, 4-D (3-D+time) images were acquired during zebrafish cardiac morphogenesis, and integrated light-sheet fluorescence microscopy with 4-D synchronization captured the ventricular motion. Blood viscosity was reduced via gata1a-morpholino oligonucleotides (MO) micro-injection to decrease shear stress, thereby, down-regulating Notch signaling and attenuating trabeculation. Co-injection of Nrg1 mRNA with gata1a MO rescued Notch-related genes to restore trabeculation. To confirm shear stress driven Notch signaling influences trabeculation, cardiomyocyte contraction was further arrested via tnnt2a-MO to reduce hemodynamic forces, thereby, down-regulating Notch target genes to develop a non-trabeculated myocardium. Finally, corroboration of the expression patterns of shear stress-responsive Notch genes was conducted by subjecting endothelial cells to pulsatile flow. Thus, the 4-D light-sheet microscopy uncovered hemodynamic forces underlying Notch signaling and trabeculation with clinical relevance to non-compaction cardiomyopathy.

Here, we present a protocol to visualize developing hearts in zebrafish in 4-Dimensions (4-D). 4-D imaging, via light-sheet fluorescence microscopy (LSFM), takes 3-Dimensional (3-D) images over time, to reconstruct developing hearts. We show qualitatively and quantitatively that shear stress activates endocardial Notch signaling during chamber development, which promotes cardiac trabeculation.

The hemodynamic forces experienced by the heart influence cardiac development, especially trabeculation, which forms a network of branching outgrowths ...

Gene Expression Analysis of Endothelial Cells Exposed to Shear Stress Using Multiple Parallel-plate Flow Chambers

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate flow chambers. Endothelial cells form the inner cellular lining of blood vessels and are chronically exposed to the frictional force of blood flow called shear stress. Under physiological conditions, endothelial cells function in the presence of various shear stress conditions. Thus, the application of shear stress conditions in in vitro models can provide greater insight into endothelial responses in vivo. The parallel-plate flow chamber previously published by Lane et al.9 is adapted to study endothelial gene regulation in the presence and absence of steady (non-pulsatile) laminar flow. Key adaptations in the set-up for laminar flow as presented here include a large, dedicated environment to house concurrent flow circuits, the monitoring of flow rates in real-time, and the inclusion of an exogenous reference RNA for the normalization of quantitative real-time PCR data. To assess multiple treatments/conditions with the application of shear stress, multiple flow circuits and pumps are used simultaneously within the same heated and humidified incubator. The flow rate of each flow circuit is measured continuously in real-time to standardize shear stress conditions throughout the experiments. Because these experiments have multiple conditions, we also use an exogenous reference RNA that is spiked-in at the time of RNA extraction for the normalization of RNA extraction and first-strand cDNA synthesis efficiencies. These steps minimize the variability between samples. This strategy is employed in our pipeline for the gene expression analysis with shear stress experiments using the parallel-plate flow chamber, but parts of this strategy, such as the exogenous reference RNA spike-in, can easily and cost-effectively be used for other applications.

Here, a workflow for the culture and gene expression analysis of endothelial cells under fluid shear stress is presented. Included is a physical arrangement for simultaneously housing and monitoring multiple flow chambers in a controlled environment and the use of an exogenous reference RNA for quantitative PCR.

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate ...

Purification and Analytics of a Monoclonal Antibody from Chinese Hamster Ovary Cells Using an Automated Microbioreactor System

Monoclonal antibodies (mAbs) are one of the most popular and well-characterized biological products manufactured today. Most commonly produced using Chinese hamster ovary (CHO) cells, culture and process conditions must be optimized to maximize antibody titers and achieve target quality profiles. Typically, this optimization uses automated microscale bioreactors (15 mL) to screen multiple process conditions in parallel. Optimization criteria include culture performance and the critical quality attributes (CQAs) of the monoclonal antibody (mAb) product, which may impact its efficacy and safety. Culture performance metrics include cell growth and nutrient consumption, while the CQAs include the mAb's N-glycosylation and aggregation profiles, charge variants, and molecular weight. This detailed protocol describes how to purify and subsequently analyze HCCF samples produced by an automated microbioreactor system to gain valuable performance metrics and outputs. First, an automated protein A fast protein liquid chromatography (FPLC) method is used to purify the mAb from harvested cell culture samples. Once concentrated, the glycan profiles are analyzed by mass spectrometry using a specific platform (refer to the Table of Materials). Antibody molecular weights and aggregation profiles are determined using size exclusion chromatography-multiple angle light scattering (SEC-MALS), while charge variants are analyzed using microchip capillary zone electrophoresis (mCZE). In addition to the culture performance metrics captured during the bioreactor process (i.e., culture viability, cell counts, and common metabolites including glutamine, glucose, lactate, and ammonia), spent media is analyzed to identify limiting nutrients to improve the feeding strategies and overall process design. Therefore, a detailed protocol for the absolute quantification of amino acids by liquid chromatography-mass spectrometry (LC-MS) of spent media is also described. The methods used in this protocol take advantage of high-throughput platforms that are compatible for large numbers of small-volume samples.

A detailed protocol for the purification and subsequent analysis of a monoclonal antibody from harvested cell culture fluid (HCCF) of automated microbioreactors has been described. Use of analytics to determine critical quality attributes (CQAs) and maximizing limited sample volume to extract vital information is also presented.

Monoclonal antibodies (mAbs) are one of the most popular and well-characterized biological products manufactured today. Most commonly produced using ...