We acknowledge the help of Antoine Wiedmer with the setup of the robotic dispenser and the dispenser.py script.

1. Bacterial culture conditions
<ol>
	<li>Working under a laminar flow hood, use 100 &#956;L of a glycerol stock of GFP-tagged Pseudomonas putida KT2440 (1 &#215; 107 mL-1, stored at -80 &#176;C) to inoculate 5 mL of Luria-Bertani (LB) medium. Incubate at 30 &#176;C while shaking at 250 rpm overnight.</li>
	<li>The next day, resuspend 100 &#956;L of the overnight culture in 5 mL LB medium and incubate under the same conditions for 5h (exponential phase). Sample a 1 mL aliquot into a 2 mL tube, allow to cool to room temperature (~15 min) and centrifuge (2300 x g for 5 min).</li>
	<li>Remove the supernatant and add 1 mL motility buffer to the pellet. Vortex briefly to homogenize the sample. Dilute to reach the desired cell concentration, e.g., 5 x 105 mL-1.</li>
	<li>For experiments involving natural communities, such as those derived from streams, prepare a non-selective cultivation medium. For instance, use sterile-filtered and autoclaved stream water or an artificial stream water medium amended with a complex carbon source (LB medium).</li>
</ol>
2. Preparation of a microfluidic device in polydimethylsiloxane (PDMS)
<ol>
	<li>Design the desired porous geometry by means of computer-aided drafting (CAD) software18, which consists of a matrix of circles (that is the impermeable obstacle to flow), described by radius size and center coordinates. 
	NOTE: An example of a porous geometry with randomized grain and pore sizes is provided in Figure 1A. An observation channel without obstacles close to the outlet facilitates the acquisition of BTCs.</li>
	<li>Based on the chosen geometry, prepare a mold using standard SU-8-photolithography18. 
	NOTE: Alternatively, molds can also be ordered from dedicated microfabrication facility. In order to obtain heterogeneous fluid flow in the horizontal plane, it is important to design the thickness of the microfluidics chamber of the same order of magnitude as the average pore throat size. However, make sure that the dimensions of the microfluidic device are suitable for observation under the microscope (e.g., work on microscope slides).</li>
	<li>Prepare 50 g of PDMS by adding 10% of cross linker (dimethyl, methylhydrogen siloxane copolymer) to 90% of elastomer by weight, using a syringe without needle. Work under clean conditions and avoid dust as much as possible. Mix the two reagents in a clean disposable container and apply vacuum (100 mbar) for 30 min to remove dissolved air and bubbles from the viscous PDMS.</li>
	<li>Place the mold into a Petri dish (100 mm in diameter, 15 mm high). Pour the PDMS onto the mold to the desired height (e.g., 2-5 mm). Cover the petri dish and keep it at 60 &#176;C for 4 h (overnight for thicker layers) to cure. 
	NOTE: For visualization purpose, light should be able to pass through the PDMS, thus, a thin layer between 2 mm to 5 mm is desirable. Thicker layers (&#62;5 mm) reduce the device transparency and thinner ones are subjected to deformations during application.</li>
	<li>Remove the mold from the oven and allow the microfluidic device to cool to room temperature. Once it is cooled, carefully remove the desired portion of PDMS with a scalpel. 
	NOTE: Strong pressures on the mold result in mold fractures. Do not touch the PDMS with bare hands, as fingerprints will affect optical transparency.</li>
	<li>Temporarily seal the bottom of the PDMS channel (where the desired geometry has been engraved) with tape. With a 0.5 mm diameter biopsy puncher, pierce microfluidic channel to create an inlet and an outlet fitting the 0.5 mm (inner diameter) tubing. 
	NOTE: The soft nature of PDMS will ensure tightness once the tubing will be inserted. Inlet and outlet channels cannot be made after the PDMS has been sealed to the glass.</li>
	<li>Seal the microfluidic channel via oxygen plasma bonding using the high frequency generator (plasma bonder, Table of Materials). For this, clean a silicate glass slide (25 mm x 75 mm) with ethanol and let it dry. Remove tape from the PDMS channel and place the channel with the porous side facing up. Treat the silicate glass slide and PDMS surfaces with plasma for about 45 s at room temperature.</li>
	<li>Place the PDMS channel onto the silicate glass slide and heat at 100 &#176;C for 30 min on a hot plate.</li>
	<li>Remove the microfluidic device from the hot plate and cool it to room temperature. Connect the PDMS channel inlet with tubing. Apply vacuum for 30 min to remove air from PDMS, which is almost impermeable to fluids but permeable to gas.</li>
	<li>Prepare 100 mL of motility buffer (10 mM potassium phosphate, 0.1 mM EDTA, supplemented with 1% w/v glucose, pH 7.0) and inject 1 mL of it into the microfluidic device using a syringe pump operated at 10 &#181;L min-1. 
	NOTE: As the PDMS is under-saturated in gas (due to the previous vacuum step), bubbles will disappear within ~30 min.</li>
</ol>
3. Preparation of a fluidic device in poly (methyl methacrylate)
<ol>
	<li>Design the desired geometry with the CAD software. If applicable, make sure that the dimensions are suitable for observation under the microscope (e.g., dimensions of a standard 96 well plate in combination with an appropriate holder). The fluidic device is composed by a base (127 &#215; 127 &#215; 12 mm) and a lid (127 &#215; 127 &#215; 12 mm) of PMMA. 
	NOTE: Expertise with CAD software is recommended. An example technical drawing is supplied in Figure 1A.</li>
	<li>To produce the pore compartment, by means of high precision micromilling (WF31SA, Mikron), remove 0.5 mm from the bottom PMMA layer and mill a groove (1.1 &#215; 1.1 mm) for a rubber O-ring. Drill 12 threaded holes (M5). This will serve as the base of the fluidic device. 
	NOTE: The dimensions of the fluidic device need to be adjusted to fit the microscope stage and focal distance. A technical drawing is supplied in Figure S1.</li>
	<li>Drill two threaded holes (type 1/4-28UNF) for in- and outlet into the top part of the fluidic device, and 12 holes (5.5 mm) for the screws. This will serve as the lid of the fluidic device. 
	NOTE: Expertise in micro-milling is advisable; the authors use support from a specialized workshop.</li>
	<li>In order to clean and sterilize the fluidic device prior to and after each use, soak the base and lid of the fluidic device in HCl 7% and rinse three times with deionized water.</li>
	<li>Screw base and lid together using the 12 threaded holes.</li>
</ol>
4. Setup of automated dispenser
NOTE: Commercially available liquid dispensers are costly and often do not offer the flexibility to dispense directly from the outflow of the fluidic device. Therefore, assembling a cheap and flexible robotic dispenser system from an XY Plotter Robot (Table of Materials) is recommended.
<ol>
	<li>In order to dispense the outflow from the fluidic device into 96 well plates, mount the robotic dispenser onto a PMMA plate and mill cavities of 85.8 x 128 mm with a depth of 1 mm to hold several 96 well plates.</li>
	<li>Attach the outflow tube of the fluidic device to the robotic arm of the dispenser.</li>
	<li>To operate the robotic dispenser download bCNC from github:&#160;https://github.com/vlachoudis/bCNC and follow the instructions to install the program.</li>
	<li>Download dispenser.py from the supporting material of this article. 
	NOTE: This python code provides a plugin to bCNC for a simple robotic dispenser layout.</li>
	<li>Connect the robotic dispenser to the computer running bCNC and identify the correct COM port.</li>
	<li>In bCNC, click the home button to return the robotic dispenser to the home position. 
	NOTE: Homing returns the robotic dispenser to a known position (x=0, y=0) and therefore improves the accuracy of the dispenser.</li>
	<li>Prior to the experiment, prepare a sufficient number of 96 well plates, with wells containing an appropriate amount of fixative (e.g., final concentration 3.7% formaldehyde). 
	NOTE: For instance, at a flow rate of 0.2 mL min-1, 100 &#181;L are dispensed into each well every 30 s. Therefore, add 10 &#181;L of 37% formaldehyde to each well to reach a final concentration of Formaldehyde between 2 and 4%. Using eight 96 well plates will allow to operate the experiment for more than 6 h with a total of 768 data points. Moreover, note that GFP-tagged cells may lose their fluorescent signal after fixation using formaldehyde.</li>
</ol>
5. Analyze bacterial transport using PDMS microfluidic devices
<ol>
	<li>Place the PDMS microfluidic device previously saturated with the motility buffer on the microscope stage. Use tape to fix the tubing to minimize disturbance of the flow during stage movement.</li>
	<li>Move the microscope stage to the observation channel close to the outlet. Using bright field microscopy or phase contrast, focus to the center of the observation channel and adjust the magnification to visualize individual bacterial cells.</li>
	<li>Switch the light path settings to fluorescence microscopy and adjust the camera exposure time to resolve individual bacterial cells (e.g. 100 ms), or such that fluorescence signals of cells are at least 3x stronger than background noise.</li>
	<li>Next, insert the inlet tubing into a 2 mL tube containing the bacterial suspension. Reverse pump direction and start withdrawing the suspension at a flow rate of 1 &#181;L min-1.</li>
	<li>Scan the cross section of the entire observation channel recording a composite picture every 2 min, over the entire duration of the experiment.</li>
</ol>
6. Basic image processing
NOTE: The goal of these basic image processing routines is to count the number of bacteria in the recorded images. Optimal processing procedures depend on the technical specifications of the microscope and camera, as well as on the fluorescence properties of the bacterial strain used in the experiment and therefore need to be adjusted.
<ol>
	<li>First export images in .tiff format.</li>
	<li>Import images to a desired software platform (e.g., MATLAB, ImageJ, R or Python).</li>
	<li>Remove camera noise, which is a random variation of pixel intensity in images and correct for optical aberration. This can be done applying a Gaussian filter to each picture: the size of the filter depends on the quality of the camera sensor (e.g. CCD or CMOS). The optical aberration can be removed by normalizing each picture by a reference image collected in absence of the specimen with the same optical configuration.</li>
	<li>Crop the images to a region of interest.</li>
	<li>Identify a threshold value (pixel intensity), such that values above the threshold include bacterial cells. 
	NOTE: In case images are unevenly illuminated (because of optical aberration or noise) it may be useful to apply an adaptive threshold, which chooses a threshold value based on local mean intensities.</li>
	<li>Subtract from each picture the threshold defined above.</li>
	<li>Binarize the resulting image, such that bacterial cells take a value of 1, whereas background takes a value of 0.</li>
	<li>Remove clusters with an area smaller than the smallest bacterial cell size.</li>
	<li>Sum the binarized image to obtain the total number of pixels of the remaining clusters. Divide the number of pixels by the average size (in pixels) of a bacterial cell: this provides an estimate of the total number of cells.</li>
	<li>Knowing depth of view and area of investigation, transform counts into concentration (particles mL-1).</li>
	<li>It is fundamental to identify the concentration of the injected bacteria suspension. To do this, inject with a syringe 1 mL of bacteria culture suspension into the observation channel of a clean microfluidic device. Record the image and calculate the influent bacterial concentration (C0) as described above (6.1 to 6.10).</li>
	<li>Analyze BTCs by normalizing effluent bacterial concentration (C) with influent bacterial concentration (C0) and plot C/C0 versus time.</li>
</ol>
7. Analyze bacterial transport at the pore scale

<ol>
	<li>In order to analyze local velocities and trajectories of bacteria transported through the porous matrix, move the microscope stage to the region of interest and adjust the focus to the center of the microfluidic device.</li>
	<li>Set microscope to bright field or phase contrast. 
	NOTE: This only in case the fluorescence signal of bacterial cell does not allow recording images at exposure time shorter than the average bacterial time, otherwise use fluorescence microscopy.</li>
	<li>Record time-lapse images, at exposure time that captures bacteria displacement (shorter than the average displacement over a number of pixels smaller than the object size), and that optimizes bacterial cell detection (e.g. exposure of 20 ms and images recorded every 50 ms). Record pictures over a sufficient amount of time in order to record enough (to be statistically representative) of the slowest trajectories (e.g. 3 min). 
	NOTE: Make sure the computer has enough disk space.</li>
	<li>To remove background noise, subtract from each image the average of all recorded images. To do that, create a matrix whose result is the sum of intensity of all the images, for each pixel, and divide it by the number of images.</li>
	<li>From the processed image (Im), determine the modulus of the numerical gradient <img alt="Equation 1" src="/files/ftp_upload/60701/60701eq1v2.jpg" style="opacity: 0.9;" />&#160;and normalize it by its maximum value (max), as defined below. 
	<img alt="Equation 2" src="/files/ftp_upload/60701/60701eq2v2.jpg" />&#160; 
	<img alt="Equation 3" src="/files/ftp_upload/60701/60701eq3v2.jpg" /></li>
	<li>Binarize the matrix B via intensity thresholding, see steps 6.7-6.8.</li>
	<li>For each time point, record bacteria coordinates (x, y in pixel or mm) and time of image acquisition into a three-column file.</li>
	<li>Finally, apply a particle tracking script to process the recorded data and compute the trajectories of the bacteria. For instance use the established protocol19, and the freely available Matlab Particle Tracking Code: (http://site.physics.georgetown.edu/matlab/).&#160;</li>
</ol>
8. Study bacterial filtration by means of deposition profiles
<ol>
	<li>To obtain deposition profiles of GFP-tagged P. putida KT2440 cells by fluorescence microscopy, record a composite image of the entire porous channel before, that is the background, and after injection of bacterial suspension through the microfluidic device. Use an exposure time that allows acquiring bacterial fluorescent signal (e.g. 100 ms), without bleaching the signal.</li>
	<li>Export images and import in desired software platform (see 6.1-6.2).</li>
	<li>Remove background from the images recorded after bacterial injection.</li>
	<li>Integrate the total fluorescence signal of retained bacteria along transversal sections of the porous channel.</li>
	<li>In order to compute the deposition profile, plot the integrated florescence signal versus porous channel length.</li>
</ol>
9. Analyze bacterial transport using PMMA fluidic devices and flow cytometry
<ol>
	<li>Connect peristaltic pump with the inlet using 50 cm (1 mm inner diameter) tubing, and the outflow with the automated dispenser using the same tubing (50 cm, see section 4). 
	NOTE: Use the pump to dispense cultivation media, and to inject bacterial cells. Use Luer-lock connectors and three-way valves to shift between medium and bacterial suspension during the constant-rate release.</li>
	<li>Pump cultivation medium in the fluidic device. Note the arrival of medium at the outlet tubing fixed to the robotic dispenser.</li>
	<li>Start injecting the bacterial suspension through the PMMA fluidic device at a flow rate of 0.2 mL min-1,</li>
	<li>When bacteria reach the fluidic device inlet turn on the automated dispenser.</li>
	<li>In order to make a pulse injection, inject bacterial suspension for some pore volumes (e.g. 30), and then switch injection to cultivation media until experiment end. 
	NOTE: pore volume of the fluidic device is approximately 0.2 mL, thus at the prosed flow rate every minute one entire pore volume is exchanged.</li>
	<li>Once a 96 well plate is completed, cover the plate to reduce evaporation and store it at 4 &#176;C.</li>
	<li>Analyze bacterial abundance via flow cytometry, following established protocols20. 
	NOTE: For instance, add 25 &#181;L of the green-fluorescent nucleic acid stain (Table of Materials) at 0.025 mM (in ultrapure water) to each well. It stains bacterial DNA, thus allowing to quantify by flow cytometry the bacterial abundance. Incubate samples for 15 min in the dark and then analyze using a flow cytometer equipped with a 488 nm laser and detectors at 515 nm.</li>
	<li>Prior to BTC analysis, consider the dilution of the sample due to the addition of fixative and stain. Correct bacterial abundance by a factor of 1.35 to account for fixative and stain.</li>
</ol>

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The authors declare that there is no conflict of interest.

Here we suggest two means to study the transport of microbes through porous systems at the single-cell and population level. While the study of transport phenomena using BTC modeling has provided valuable insights into the spread of pathogens or contaminants at the ecosystem scales, difficulties to scale from laboratory experiments to field conditions still exist. The tools described here allow researchers to experimentally resolve the spatial and temporal scales in order to better understand the ecological strategies of...

Studies aiming to understand the transport of microbes through porous media have mainly been driven by concerns of contamination1, the transmission of disease2 and bioremediation3. In this regard, bacteria have mostly been treated as particles in transport models4 and processes such as filtration, straining, gravitational settling or remobilization from biofilms have been identified as drivers of retention or transport of microbes5. However, studying the transport of bacteria through porous landscapes can also inform us on the ecological strategies underpinning their success in these complex environments. Yet, this requires novel experiments and mathematical models operating at the single cell, population or microbial community level.
Natural porous environments, such as those found in the hyporheic zone of streams and rivers, are densely colonized by diverse communities of biofilm-forming microbes6. Biofilms form structures that modify the flow and thus the transport and dispersal of bacteria in the liquid phase7,8. The transport of bacteria at pore scale depends to the constrained space availability in the porous matrix and motility-related dispersal may be an effective way to increase the individual fitness through reduced competition for resources in less densely populated areas. On the other hand, motile bacteria can also reach more isolated regions of the porous matrix and the extended exploration of such areas may provide ecological opportunities to motile populations10. At larger spatial scales, biofilm growth diverts the flow paths also leading to (partial) clogging of pores and, thus, to the establishment of even more channelized and heterogeneous flow conditions11. This has consequences for nutrient supply and dispersal capacity, frequency and distance. Preferential flow, for instance, can generate so-called &#34;fast-tracks&#34; and motile bacteria can attain even higher velocities than the local flow along these tracks12. This is an effective way to increase the exploration of novel habitats.
A variety of tools avail themselves for the study of transport of motile and non-motile bacteria (and particles) in porous media. Numerical models have great predictive capacities important for applications, however are often limited by inherent assumptions4. Laboratory-scale experiments13, 14 combined with breakthrough curve (BTC) modeling have provided important insights in the importance of bacterial cell surface properties for sticking efficiency15. Typically, BTCs (i.e., times series of particle concentration at a fixed location) are obtained via constant-rate releases and measurement of cell numbers at the outflow of the experimental device. In this context, BTCs reflect the advection-dispersion dynamics of bacteria in the porous matrix and can be extended by a sink term accounting for attachment. However, modeling of BTCs alone does not resolve the role of spatial organization of the porous substrate or biofilm for transport processes. Other macroscopic observables like dispersivity or deposition profiles have been proven to provide important information about the spatial distribution or the retained particles or growing communities. Microfluidics is a technology that allows studying transport in porous media by microscopy investigation9,12,16, and except a recent work10, experimental systems are typically constrained to a single length scale of resolution, that is, the pore scale or the entire fluidic device scale.
Here, we introduce a suite of combined methods to study the transport of motile and non-motile bacteria in porous landscapes at different scales. We combine observations of bacterial transport at the pore scale with information at larger scale, by means of BTC analysis. Microfluidic devices built from soft lithography using polydimethylsiloxane (PDMS) are bio-compatible, resistant to a range of chemicals, allow replicability at low costs and provide excellent optical transparency as well as low autofluorescence critical for microscopic observation. Microfluidics based on PDMS has been previously used to study the transport of microbes in simple channels17 or in more complex geometries12. However, typically microfluidics experiments focus on short-term horizons and epi-fluorescence microscopic observation of living cells is commonly restricted to genetically-modified strains (e.g., GFP-tagged strains). Here we present tools to study bacterial transport using PDMS-based microfluidic devices in combination with microscopy and larger devices fabricated from poly(methyl methacrylate) (PMMA, also known as plexiglass) in combination with flow cytometry. PDMS and PMMA differ in gas permeability and surface properties, thus providing complementary opportunities to study bacterial transport. While the microfluidic device provides a more controlled environment, the larger device allows for experiments over extended periods of time or using natural bacterial communities. Microscopy counting at high temporal resolution in a dedicated area is used to obtain BTC in the PDMS-based microfluidic device. To obtain cell counts for BTC modeling from the PMMA-based device, we introduce a self-constructed automated liquid dispenser in combination with flow cytometry. In this setup, cells pass the fluidic device and are consecutively dispensed into 96 well plates. The temporal resolution is restricted by the minimum volume that can be accurately dispensed and thus the medium flow rate through the fluidic device. Fixative in the wells prevents growth and facilitates DNA staining for downstream flow-cytometric enumeration. To prevent bacterial growth during transport experiments we use a minimal medium (termed motility buffer).
Since protocols for the preparation of fluidic devices at different scales are readily available, we only briefly introduce the techniques to produce such devices and rather focus on the experimental procedures to record BTCs. Similarly, various routines exist for the flow cytometric enumeration of microbes and users require expert knowledge to interpret results obtained by flow cytometry. We report the novel use of microfluidic devices in combination with microscopic imaging to record BTCs of fluorescently-tagged cells. At the pore scale, local velocities and trajectories are obtained by means of image processing. Further, we demonstrate the use of a PMMA-based fluidic device in combination with flow-cytometric counting to observe bacterial transport of motile and non-motile cells in porous environments colonized by a native stream biofilm.

<table>
	<tbody>
		<tr>
			<td>EDTA</td>
			<td>Sigma</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Elastomer Sylgard 184</td>
			<td>Dowsil</td>
			<td>101697</td>
			<td></td>
		</tr>
		<tr>
			<td>Flow cytometer NovoCyte</td>
			<td>Acea</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Glucose</td>
			<td>Sigma</td>
			<td></td>
			<td>https://www.makeblock.com/project/xy-plotter-robot-kit</td>
		</tr>
		<tr>
			<td>LB broth</td>
			<td>BD</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Liquid dispenser, XY Plotter Robot Kit</td>
			<td>makeblock</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope Axio Imager</td>
			<td>Zeiss</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope AxioZoom v16</td>
			<td>Zeiss</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope slides, 75 mm &#215; 25 mm</td>
			<td>Corning</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Minipuls 3 peristaltic pump</td>
			<td>Gilson</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Plasma bonder Corona SB</td>
			<td>BlackHole Lab</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium phosphate</td>
			<td>Sigma</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Syringe pump New Era NE 4000</td>
			<td>New Era</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Syto 13 Green Fluorescent Nucleic Acid Stain</td>
			<td>Molecular Probes, Invitrogen</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Tygon tubing</td>
			<td>Ismatec</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>WF31SA universal milling machine</td>
			<td>Mikron</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

combining fluidic devices with microscopy and flow cytometry to study microbial transport in porous media across spatial scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

To illustrate the functionality of the presented workflow, we performed experiments using genetically modified Pseudomonas putida KT2440, a gram negative motile bacterium important for bioremediation and biotechnology. Genetically modified versions of this strain that express GFP production are commercially available. A non-motile strain of P. putida KT2440 which lacks the relevant structural and regulatory genes for motility is also available. Using both, motile and non...

Watch this Scientific Journal Video about Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales at JoVE.com

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

combining-fluidic-devices-with-microscopy-flow-cytometry-to-study

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JoVE Journal

Environment

6.3K Views.  École Polytechnique Fédérale de Lausanne. Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Video: Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Preparation and Testing of Impedance-based Fluidic Biochips with RTgill-W1 Cells for Rapid Evaluation of Drinking Water Samples for Toxicity

This manuscript describes how to prepare fluidic biochips with Rainbow trout gill epithelial (RTgill-W1) cells for use in a field portable water toxicity sensor. A monolayer of RTgill-W1 cells forms on the sensing electrodes enclosed within the biochips. The biochips are then used for testing in a field portable electric cell-substrate impedance sensing (ECIS) device designed for rapid toxicity testing of drinking water. The manuscript further describes how to run a toxicity test using the prepared biochips. A control water sample and the test water sample are mixed with pre-measured powdered media and injected into separate channels of the biochip. Impedance readings from the sensing electrodes in each of the biochip channels are measured and compared by an automated statistical software program. The screen on the ECIS instrument will indicate either "Contamination Detected" or "No Contamination Detected" within an hour of sample injection. Advantages are ease of use and rapid response to a broad spectrum of inorganic and organic chemicals at concentrations that are relevant to human health concerns, as well as the long-term stability of stored biochips in a ready state for testing. Limitations are the requirement for cold storage of the biochips and limited sensitivity to cholinesterase-inhibiting pesticides. Applications for this toxicity detector are for rapid field-portable testing of drinking water supplies by Army Preventative Medicine personnel or for use at municipal water treatment facilities.

This manuscript describes how to prepare fluidic biochips with Rainbow trout gill epithelial cells for use in a field portable electric cell-substrate impedance sensor. The protocol for running a rapid drinking water toxicity test with the sensor is also described.

This manuscript describes how to prepare fluidic biochips with Rainbow trout gill epithelial (RTgill-W1) cells for use in a field portable water toxicity ...

Implementation of Portable Emissions Measurement Systems (PEMS) for the Real-driving Emissions (RDE) Regulation in Europe

Vehicles are tested in controlled and relatively narrow laboratory conditions to determine their official emission values and reference fuel consumption. However, on the road, ambient and driving conditions can vary over a wide range, sometimes causing emissions to be higher than those measured in the laboratory. For this reason, the European Commission has developed a complementary Real-Driving Emissions (RDE) test procedure using the Portable Emissions Measurement Systems (PEMS) to verify gaseous pollutant and particle number emissions during a wide range of normal operating conditions on the road. This paper presents the newly-adopted RDE test procedure, differentiating six steps: 1) vehicle selection, 2) vehicle preparation, 3) trip design, 4) trip execution, 5) trip verification, and 6) calculation of emissions. Of these steps, vehicle preparation and trip execution are described in greater detail. Examples of trip verification and the calculations of emissions are given.

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

The European Commission has developed a Real-Driving Emissions (RDE) test procedure to verify pollutant emissions during real-world vehicle operation using the Portable Emissions Measurement Systems (PEMS). This paper presents the experimental procedures required by the newly-adopted RDE test.

Vehicles are tested in controlled and relatively narrow laboratory conditions to determine their official emission values and reference fuel consumption. ...

Using Pharmacological Manipulation and High-precision Radio Telemetry to Study the Spatial Cognition in Free-ranging Animals

An animal&#39;s ability to perceive and learn about its environment plays a key role in many behavioral processes, including navigation, migration, dispersal and foraging. However, the understanding of the role of cognition in the development of navigation strategies and the mechanisms underlying these strategies is limited by the methodological difficulties involved in monitoring, manipulating the cognition of, and tracking wild animals. This study describes a protocol for addressing the role of cognition in navigation that combines pharmacological manipulation of behavior with high-precision radio telemetry. The approach uses scopolamine, a muscarinic acetylcholine receptor antagonist, to manipulate cognitive spatial abilities. Treated animals are then monitored with high frequency and high spatial resolution via remote triangulation. This protocol was applied within a population of Eastern painted turtles (Chrysemys picta) that has inhabited seasonally ephemeral water sources for ~100 years, moving between far-off sources using precise (&#177; 3.5 m), complex (i.e., non-linear with high tortuosity that traverse multiple habitats), and predictable routes learned before 4 years of age. This study showed that the processes used by these turtles are consistent with spatial memory formation and recall. Together, these results are consistent with a role of spatial cognition in complex navigation and highlight the integration of ecological and pharmacological techniques in the study of cognition and navigation.

This paper describes a novel protocol that combines the pharmacological manipulation of memory and radio telemetry to document and quantify the role of cognition in navigation.

An animal&#39;s ability to perceive and learn about its environment plays a key role in many behavioral processes, including navigation, migration, ...

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and stability of the suspension. In this study, a systematic step-wise approach is carried out to identify optimal sonication conditions in order to achieve a stable dispersion. This approach has been adopted and shown to be suitable for several nanomaterials (cerium oxide, zinc oxide, and carbon nanotubes) dispersed in deionized (DI) water. However, with any change in either the nanomaterial type or dispersing medium, there needs to be optimization of the basic protocol by adjusting various factors such as sonication time, power, and sonicator type as well as temperature rise during the process. The approach records the dispersion process in detail. This is necessary to identify the time points as well as other above-mentioned conditions during the sonication process in which there may be undesirable changes, such as damage to the particle surface thus affecting surface properties. Our goal is to offer a harmonized approach that can control the quality of the final, produced dispersion. Such a guideline is instrumental in ensuring dispersion quality repeatability in the nanoscience community, particularly in the field of nanotoxicology.

Here, we present a step-wise protocol for the dispersion of nanomaterials in aqueous media with real-time characterization to identify the optimal sonication conditions, intensity, and duration for improved stability and uniformity of nanoparticle dispersions without impacting the sample integrity.

The sonication process is commonly used for de-agglomerating and dispersing nanomaterials in aqueous based media, necessary to improve homogeneity and ...

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Microfluidic devices are versatile tools for studying transport processes at a microscopic scale. A demand exists for microfluidic devices that are resistant to low molecular-weight oil components, unlike traditional polydimethylsiloxane (PDMS) devices. Here, we demonstrate a facile method for making a device with this property, and we use the product of this protocol for examining the pore-scale mechanisms by which foam recovers crude oil. A pattern is first designed using computer-aided design (CAD) software and printed on a transparency with a high-resolution printer. This pattern is then transferred to a photoresist via a lithography procedure. PDMS is cast on the pattern, cured in an oven, and removed to obtain a mold. A thiol-ene crosslinking polymer, commonly used as an optical adhesive (OA), is then poured onto the mold and cured under UV light. The PDMS mold is peeled away from the optical adhesive cast. A glass substrate is then prepared, and the two halves of the device are bonded together. Optical adhesive-based devices are more robust than traditional PDMS microfluidic devices. The epoxy structure is resistant to swelling by many organic solvents, which opens new possibilities for experiments involving light organic liquids. Additionally, the surface wettability behavior of these devices is more stable than that of PDMS. The construction of optical adhesive microfluidic devices is simple, yet requires incrementally more effort than the making of PDMS-based devices. Also, though optical adhesive devices are stable in organic liquids, they may exhibit reduced bond-strength after a long time. Optical adhesive microfluidic devices can be made in geometries that act as 2-D micromodels for porous media. These devices are applied in the study of oil displacement to improve our understanding of the pore-scale mechanisms involved in enhanced oil recovery and aquifer remediation.

The goal of this procedure is to easily and rapidly produce a microfluidic device with customizable geometry and resistance to swelling by organic fluids for oil recovery studies. A polydimethylsiloxane mold is first generated, and then used to cast the epoxy-based device. A representative displacement study is reported.

Microfluidic devices are versatile tools for studying transport processes at a microscopic scale. A demand exists for microfluidic devices that are ...

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

Metal Corrosion and the Efficiency of Corrosion Inhibitors in Less Conductive Media

Material corrosion can be a limiting factor for different materials in many applications. Thus, it is necessary to better understand corrosion processes, prevent them and minimize the damages associated with them. One of the most important characteristics of corrosion processes is the corrosion rate. The measurement of corrosion rates is often very difficult or even impossible especially in less conductive, non-aqueous environments such as biofuels. Here, we present five different methods for the determination of corrosion rates and the efficiency of anti-corrosion protection in biofuels: (i) a static test, (ii) a dynamic test, (iii) a static test with a reflux cooler and electrochemical measurements (iv) in a two-electrode arrangement and (v) in a three-electrode arrangement. The static test is advantageous due to its low demands on material and instrumental equipment. The dynamic test allows for the testing of corrosion rates of metallic materials at more severe conditions. The static test with a reflux cooler allows for the testing in environments with higher viscosity (e.g., engine oils) at higher temperatures in the presence of oxidation or an inert atmosphere. The electrochemical measurements provide a more comprehensive view on corrosion processes. The presented cell geometries and arrangements (the two-electrode and three-electrode systems) make it possible to perform measurements in biofuel environments without base electrolytes that could have a negative impact on the results and load them with measurement errors. The presented methods make it possible to study the corrosion aggressiveness of an environment, the corrosion resistance of metallic materials, and the efficiency of corrosion inhibitors with representative and reproducible results. The results obtained using these methods can help to understand corrosion processes in more detail to minimize the damages caused by corrosion.

The testing of processes associated with material corrosion can often be difficult especially in non-aqueous environments. Here, we present different methods for short-term and long-term testing of corrosion behavior of non-aqueous environments such as biofuels, especially those containing bioethanol.

Material corrosion can be a limiting factor for different materials in many applications. Thus, it is necessary to better understand corrosion processes, ...

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Characterizing Microbiome Dynamics &amp;#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Biofilms are a most successful microbial lifestyle and prevail in a multitude of environmental and engineered settings. Understanding biofilm morphogenesis, that is the structural diversification of biofilms during community assembly, represents a remarkable challenge across spatial and temporal scales. Here, we present an automated biofilm imaging system based on optical coherence tomography (OCT). OCT is an emerging imaging technique in biofilm research. However, the amount of data that currently can be acquired and processed hampers the statistical inference of large scale patterns in biofilm morphology. The automated OCT imaging system allows covering large spatial and extended temporal scales of biofilm growth. It combines a commercially available OCT system with a robotic positioning platform and a suite of software solutions to control the positioning of the OCT scanning probe, as well as the acquisition and processing of 3D biofilm imaging datasets. This setup allows the in situ and non-invasive automated&#160;monitoring of biofilm development and may be further developed to couple OCT imaging with macrophotography and microsensor profiling.

Microbial biofilms form complex architectures at interphases and develop into highly scale-dependent spatial patterns. Here, we introduce an experimental system (hard- and software) for the automated acquisition of 3D optical coherence tomography (OCT) datasets. This toolset allows the non-invasive and multi-scale characterization of biofilm morphogenesis in space and time.

Biofilms are a most successful microbial lifestyle and prevail in a multitude of environmental and engineered settings. Understanding biofilm ...

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial communities has many limitations, and determining a predator, prey, and predatory rates is often difficult. Presented here is an optimized method based on the addition of fluorescently labelled prey as a tracer, which allows for reliable quantitation of the grazing rates in aquatic predatory eukaryotes and estimation of nutrient transfer to higher trophic levels.

Presented here is a protocol for a single-cell, epifluorescence microscopy-based technique to quantify grazing rates in aquatic predatory eukaryotes with high precision and taxonomic resolution.