This application note explains how to study bacteria adaptation to stress and environmental changes with pressure-driven flow controlled microfluidics such as the effect of antibiotics on bacterial growth. It aims to introduce the reader to an experimental method allowing the study of the effect parameters on bacterial growth. This technical note was written in collaboration with Dr. Bianca Sclavi (LCQB, UMR 7238, CNRS, Sorbonne Université, Paris) and Ilaria Iuliani (ilaria.iuliani@gmail.com), PhD candidate.
Figure 1: A sample movie of typical mother machine experiment. This is a field of view (FOV), during an experiment up to 20 different FOV are acquired as a function of time. Growing and dividing bacteria can be seen filling the channels. The bacteria at the bottom of the channels are called mother bacteria. Courtesy of the authors.
Bacteria, such as Escherichia coli, can rapidly respond and adapt to changes in their environment affecting nutrient availability or to the presence of specific stressors such as the presence of sublethal concentrations of antibiotics.
These rapid response mechanisms result in changes in growth rate, cell size and gene expression.
However, sometimes there is a heterogeneous response in the population, where some of the cells respond differently than the others, which can result in a subpopulation that can better respond to renewed exposure to the stress at the cost of increased gene expression and/or slower growth rate.
This means that while some cells adapt quickly, within one or two hours, others have much longer times of adaptation that can last several hours. To study the dynamics of this kind of cellular response it is necessary to have a tight temporal and spatial control of the cells’ environment.
These experiments can last up to 72 hours in order to allow us to observe some of the slower adaptation mechanisms and in order to include several switches between different concentrations of nutrients.
Microfluidic chip resistance can be very different between one chip to another. Moreover, during the experiment the resistance can increase due to the formation of clogs. For this reason, it is very important to have a pressure-driven flow to have good reproducibility and long experiments.
Thanks to the efficient interface, we can easily check the experiments remotely. This can be very useful particularly for experiments that can last more than 1 day.
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If you wish to estimate the shear stress, microfluidic flow resistance applied in your system for a specific flow rate or chip design, please refer to Elveflow’s microfluidic calculator and its selection of application notes to help you perform your estimation:
The detailed experimental protocol presented here can be used for other biological applications such as:
Figure 2: Simplified schematic representation of the PDMS microfluidic chemostat device used for for the study of bacteria adaptation to stress and environmental changes [2]
To easily image the changes in single cell size and gene expression we have used a PDMS microfluidic device where the bacterial cells are growing in 2000 parallel channels that are about 20 µm long, 0.8 µm wide and 1.1 µm deep (Long et al., 2013, 2014) [1] .
These channels are flanked by larger channels (50 µm wide and 20 µm deep) where the growth medium flows and brings nutrients to the bacteria at the same time as washing away waste and metabolites.
This allows for the bacteria to grow in a constant environment to achieve what is called balanced growth.
Balanced growth is difficult to achieve in bacteria growing in flasks or on an agar pad, as the nutrients at a certain point run out and metabolites can accumulate decreasing the pH and slowing down growth.
Flow controller OB1 Mk3+
2x Mux-distributor
2x Flow sensors
Manifold 9 ports
5x Tubing, fittings and reservoirs
Figure 3: Schematic representation of the microfluidic setup employed for the study of bacteria adaptation to stress and environmental changes comprising an OB1 pressure-driven flow controller with two channels, flow sensor, Mux Distribution and microfluidic chip. Courtesy of the authors.
The above microfluidic setup composed of a pressure-driven flow controller (0 – 2 bar pressure range), two rotary valves (Mux Distributor), flow sensors and a microfluidic chip, ensures a constant environment for long time experiment and allows to quickly change between growth medium or temperature. Additionally, the PID feedback loop permits an effective control over the flow rate while keeping the stability and responsiveness of pressure-driven flows.
The following colour code was given to ease the protocole follow-up:
Figure 4: Picture of the microfluidic setup employed for the study of bacteria adaptation to stress and environmental changes comprising an OB1 pressure-driven flow controller, flow sensor, Mux Distribution and microfluidic chip. Courtesy of the authors.
Figure 5: Picture of the ESI microfluidic software interface employed for the study of bacteria adaptation to stress and environmental changes comprising an OB1 pressure-driven flow controller, flow sensor, Mux Distribution and microfluidic chip. Courtesy of the authors.
Window 1: [ORANGE] Main window
Window 2: [RED] MUX top and bottom window
Window 3: [GREEN] OB1 window
Window 4: [BLUE] ESI Sequence Scheduler window
Window 5: [PURPLE] Microscope window (Ti-Eclipse Nikon inverted microscope)
a) Grow the bacteria overnight in filtered growth medium. NOTE: Always use a growth medium filtered at 0.2 µm in order to avoid clogging the microfluidic device.
b) In the morning, dilute the bacteria (ex 20 µl in 1.5ml). Put them at 30°C for around 3 hours (we want them to be in exponential phase when they go into the device).
Figure 6 : Image analysis allows for the detection of the bacteria in each time frame in order to carry out segmentation and tracking using a script created by the author’s lab and their collaborators. Courtesy of the authors.
a) Start the flow of the medium
b) Start the acquisition
ESI software
Use the ESI sequence scheduler to automate your microfluidic experiment. In this protocole, the following sequence was created and performed:
STEP 1: Start the acquisition of pressure and or/ flow rate measurements.
STEP 2 – 3: Set both mux distributors (DIST) to valve 1 (slow growth medium)
STEP 4: Start pumping (OB1) with a constant flow rate in both channels but with different flow rates (we can save our OB1 personalized settings in a text file to be loaded for each kind of experiment)
STEP 5: Maintain these conditions for 2h 10ms
STEP 6 – 7: Switch both the mux distributors to valve 2 (fast growth medium)
STEP 8: Maintain these conditions for 2h 10ms
Figure 7: Picture of the ESI software interface focusing on the Sequence Scheduler employed for the study of bacteria adaptation to stress and environmental changes comprising an OB1 pressure-driven flow controller, flow sensor, Mux Distribution and microfluidic chip. Courtesy of the authors.
/!\The flow sensors are fragile. A drop of liquid can cause malfunctioning for several hours. Keep them dry /!\
Image acquisition takes place on over 20 fields of view, each one with 10 channels, and each channel with about 10 bacteria. This can result in up to 10 000 cells in a 7 hour experiment. Automated image segmentation and tracking are therefore important steps in the analysis process.
Movies obtained from the microscope are in the .nd2 format and can be opened with the Fiji software. Background subtraction is performed using a 50-pixel rolling ball technique and different positions are stored separately as a set of tiff image files. Channels are selected manually and stored in different folders. In order to perform segmentation and tracking on mother machine dataset, we use the codes developed by Mia Panlilio, Cambridge University [3]. Necessary modifications were made based on our experimental setup.
Figure 8: Microfluidic study of the growth medium evolution over 30 minutes illustrated by the evolution of the fluorescence for Luria-Bertani growth medium (blue curve) and pure water (red curve). Courtesy of the authors.
To measure how quickly the growth medium changes within the device, the fluorescence of the LB (Luria-Bertani) growth medium was measured.
Here, we switched from water to LB. Time 0 is the time at which the growth medium enters the device. This data was acquired in the last channels before the exit.
The time necessary to completely fill the device with the new growth medium is negligible in the timescale of our experiments.
Figure 9 : Image analysis allows for the detection of the bacteria in each time frame in order to carry out segmentation and tracking using a script created by the author’s lab and their collaborators. Courtesy of the authors.
The figure 10 shows the average values for all the bacteria in the fields of view of the experiments.
There are changes in division time, cell volume and fluorescence upon an adaptation to a switch from a growth medium containing glucose to one containing both glucose and amino acids (GluCaa).
In the first growth medium, the cells spend a lot of energy synthetizing their own amino acids and therefore grow more slowly.
Once the amino acids are added, the glucose is used mostly for energy production and the growth rate can increase.
The fluorescence comes from the expression of the green fluorescent protein (GFP) under control of a promoter chosen by us to report on the activity of a specific regulatory pathway. One can see that the cell division time becomes shorter and cell volume increases as the bacteria adapt to a faster growth rate. In addition, in this case the fluorescence also increases soon after the change in growth medium, indicating that gene expression has been induced as part of the cellular response.
Figure 10: Population average change in division time (min), volume and fluorescence as the bacteria adapt from a poor to a rich growth medium over an 8 hour experiment obtained with the microfluidic setup. Courtesy of the authors.
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