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 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 µm), 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.
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 "fast-tracks" 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.
1. Bacterial culture conditions
2. Preparation of a microfluidic device in polydimethylsiloxane (PDMS)
3. Preparation of a fluidic device in poly (methyl methacrylate)
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.
5. Analyze bacterial transport using PDMS microfluidic devices
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.
7. Analyze bacterial transport at the pore scale
8. Study bacterial filtration by means of deposition profiles
9. Analyze bacterial transport using PMMA fluidic devices and flow cytometry
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-motile GFP tagged P. putida KT2440, we performed sequential experiments in PDMS microfluidic devices with a random array of pillars (Figure 1B) and recorded BTCs (Figure 2A). BTCs have been normalized to the concentration of injected cells (C0). Simultaneously, bacterial trajectories at the pore scale were visualized via image processing and particle tracking as described above (Figure 2B).
Next, we performed experiments with large-scale fluidic devices milled from PMMA (Figure 1A). Motile and non-motile P. putida KT2440 (non-fluorescent) were injected into a regularly spaced porous matrix and BTCs were obtained using the liquid dispenser and flow cytometry counting as described above (Figure 3A). Strikingly, in a porous environment devoid of biofilm, motile and non-motile P. putida KT2440 showed a markedly different transport behavior. In a porous matrix colonized for 48h with a complex stream biofilm community, these differences in BTC between motile and non-motile P. putida KT2440 vanished (Figure 3B.)
Figure 1: Fluidic devices to study microbial transport in porous media (A) Illustration of a fluidic device milled from PMMA. The porous matrix is milled into the base layer of the device, the lid is closed using screws. A cross section shows the arrangement of the pillars within the fluidic device. The insert shows a porous matrix with a regular spaced grid of pillars and the respective velocity flow field. (B) The PDMS device is mounted onto a microscopy glass slide. Shown are the in- and outflow, connected to the medium reservoir and the syringe pump, respectively. The observation chamber for microscopic counting is placed as a separate chamber without a porous matrix onto the same microscope slide. The insert shows a porous matrix with a random array of pillars (in diameter and spacing). Please click here to view a larger version of this figure.
Figure 2: Bacterial transport at channel and pore scale in the PDMS fluidic device (A) BTCs of motile and non-motile P. putida KT2440 (GFP tagged) obtained with a PDMS microfluidic device and microscopic counting. (B) Trajectories of non-motile cells at the pore scale. Colors are chosen to enhance differentiation of trajectories. Please click here to view a larger version of this figure.
Figure 3: Bacterial transport at channel and pore scale in the PMMA fluidic device (A) BTCs of motile and non-motile P. putida KT2440 (non tagged) obtained using a PMMA fluidic device and flow-cytometry counting. (B) The fluidic device was colonized by a natural stream community for 2 days. Please click here to view a larger version of this figure.
Supplementary Figure 1: Technical drawings of the PMMA fluidic device. The device is composed of a base unit containing the porous matrix and a lid unit featuring the holes for the inlet and outlet. The device is sealed using 12 screws and an O-ring. Please click here to download this figure.
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 microbes relevant for transport in porous environments. Experimenters may use or modify these systems to study other microbial traits than motility, such as chemotaxis or quorum sensing or modify the geometry or other habitat characteristics of the porous matrix. Moreover, using these systems the bacterial transport behavior can be readily coupled to deposition profiles, which provide important insights into colonization patterns and are critical to understand how biofilms modify local flow fields. We anticipate that a better understanding of microbial strategies to disperse and colonize porous media will improve model predictions and thus contribute to the management of pathogen spread or contaminant containment. Further modifications of the system may also contribute to the development of novel filtration devices or biotechnology tools in which cells need to be physically separated.
We recommend PMMA-based devices for large and long-term experiments and PDMS based devices for smaller, shorter term experiments or when high temporal resolution is critical. It has to be kept in mind that the two materials have different properties. For instance PDMS is permeable to gas like oxygen, while PMMA is gas tight. This difference might be used to study gas consumption in the PMMA scenario, while PDMS might be more suitable for experiment where oxygen limitations related to bacterial respiration are undesired.
In general, the protocols described here are easily reproducible and data obtained using these tools consistently reveals differences in the transport of motile and non-motile bacteria. The self-made liquid dispenser may be replaced by a commercially available alternative. However, for reasons of versatility and cost-effectiveness we recommend the one described here. Critical steps in the protocol mainly concern the handling of the fluidic devices and experience with image processing. The quality of data obtained through image analysis critically depends on image quality (mainly determined by focus and exposure time) and an appropriate thresholding strategy. Data quality obtained by flow-cytometric counting critically depends on effective fixing and staining of the cells and expertise in the interpretation of flow-cytometry results.
The authors have nothing to disclose.
We acknowledge the help of Antoine Wiedmer with the setup of the robotic dispenser and the dispenser.py script.
EDTA | Sigma | ||
Elastomer Sylgard 184 | Dowsil | 101697 | |
Flow cytometer NovoCyte | Acea | ||
Glucose | Sigma | https://www.makeblock.com/project/xy-plotter-robot-kit | |
LB broth | BD | ||
Liquid dispenser, XY Plotter Robot Kit | makeblock | ||
Microscope Axio Imager | Zeiss | ||
Microscope AxioZoom v16 | Zeiss | ||
Microscope slides, 75 mm × 25 mm | Corning | ||
Minipuls 3 peristaltic pump | Gilson | ||
Plasma bonder Corona SB | BlackHole Lab | ||
Potassium phosphate | Sigma | ||
Syringe pump New Era NE 4000 | New Era | ||
Syto 13 Green Fluorescent Nucleic Acid Stain | Molecular Probes, Invitrogen | ||
Tygon tubing | Ismatec | ||
WF31SA universal milling machine | Mikron |