This protocol describes the development of a microfluidic device for investigating bacterial chemotaxis in stable concentration gradients of chemoeffectors.
1. Fabrication of silicon masters using standard SU-8 photolithography1(not shown in this video).
2. Replica molding of PDMS from SU-8 master: The fluidic layer and the control layer are made by replica molding of PDMS from SU-8 master.
3. Bonding of PDMS devices:
4. Assembling the PDMS device
5. Growth of highly motile E. coli
6. Monitoring chemotaxis
7. Data analysis: The following steps can be performed using any commercially available image analysis program (e.g., ImageJ, Metamorph) or using simple codes written in Matlab.
Representative Results3,4:
We have used the microfluidic device (Figure 1A) described here to investigate chemotaxis of E. coli in gradients of attractants (aspartic acid, autoinducer-2) and repellents (NiSO4, indole)3,4. The prototypical chemotaxis strain5 E. coli RP437 expressing GFP was used, along with kanamycin-killed E. coli TG1 cells expressing red fluorescent protein to monitor flow effects. The concentration gradient formed in the μFlow device is shown in Figure 1B. The cell inlet was capped so that there was no flow through it and a stable gradient of 0 100 ng/mL of fluoresscein was established in the device. The pixel intensity across the device was used to determine the concentration profile of fluorescein. The data show that bacteria encounter a linear gradient when they enter the chemotaxis chamber. Figure 2A and B shows fluorescence images of E. coli RP437 migrating in response to gradients of aspartic acid (0 100 μM) and NiSO4 (0 225 μM). The observed responses to these canonical attractant and repellent are as predicted. Figure 3A shows the quantified distribution of E. coli RP437 in the absence of a concentration gradient (i.e., null gradient of aspartic acid), while Figures 3B and C show the distribution in gradients of aspartic acid and NiSO4. The specificity of these responses (i.e., the bias in migration) is evident as a E. coli RP437 Δtar strain (i.e., strain lacking the Tar chemoreceptor that is used in E. coli to sense aspartic acid and nickel) does not demonstrate the bias in migration in the presence of a concentration gradient of aspartic acid or NiSO4. The trends evident in the spatial distribution profiles are also consistent with the calculated CPC and CMC values. The CPC values for the migration of E. coli RP437 in gradients of aspartic acid or NiSO4 are +0.33 and -0.33, respectively. The corresponding CMC values are +0.13 and -0.14, respectively. In contrast, the CPC and CMC values with the E. coli RP437 Δtar strain in gradients of aspartic acid or NiSO4 are negligible. In addition, the CPC and CMC in a null gradient of aspartic acid are +0.03 and +0.02, respectively, and indicate the lack of bias in migration in the absence of a gradient.
Figure 1. Device schematic and concentration gradient.
(A) Schematic representation of the microfluidic chemotaxis device. The device consists of a gradient-mixing module (20 x 100 x 18750 μm) and a chemotaxis observation module (20 x 1050 x 11500 μm). The widths of the two gradient channels entering the device are 500 μm and the width of the bacteria inlet is 50 μm. The inset depicts a gradient of signaling molecule (grey) and bacteria migrating in response to it. Live bacteria are depicted as solid ovals, whereas dead bacteria are shown as open ovals. (B) A concentration gradient between 0 and 100 ng/mL fluorescein was generated in the device and imaged using fluorescence microscopy. Fluorescence images were acquired after 30 min and quantified by image analysis.
Figure 2. Chemotaxis of E. coli RP437 in the microfluidic device.
E. coli RP437 was exposed to a gradient of (A) 0-100 μM L-aspartate or (B) 0-225 μM NiSO4 in the device and the migration of live bacteria (green) towards L-aspartate or away from nickel imaged every 2.5 sec for 30 min. Dead bacteria (red) served as the control for flow effects in the device. Images were quantified using an in-house developed analysis program3. Data shown are representative pseudo-colored images from three independent experiments.
Figure 3: Quantification of migration profiles to a canonical attractant and repellent.
Spatial distribution of RP437 in the microfluidic device in response to (A) uniform 100 μM L-aspartate (i.e., no gradient), (B) a 0 100 μM gradient of aspartate, and (C) a 0 225 μM gradient of NiSO4. The distribution of dead cells is shown as a solid line, the distribution of RP437 cells is shown as a dashed line, and the distribution of RP437 eda+ Δtar cells is shown as a dotted line. Data shown are averaged for three independent experiments.
The chemotaxis partition coefficient (CPC) and the chemotaxis migration coefficient (CMC) can be calculated as described in Mao et al5. If a cell is detected on the high concentration side, it is given a value of +1, whereas a cell detected on the low concentration side is given a value of -1. The values are summed up and divided by the total number of cells to generate the CPC. The sign of the CPC (positive or negative) gives the direction of migration (towards or away from a signal). Although the CPC indicates whether cells respond to a chemical as an attractant or repellent, it does not quantify the extent of the chemotaxis. For this, we calculate the CMC by dividing the spatial distribution profile of bacteria across the width of the device into 64 sections (32 on each side of the cell inlet), and assigning a weighting factor to cells in each section based on the distance of migration. The sections farthest from the center (sections 1 and 64) are multiplied by +1 (= 31.5/31.5) or -1 since they contain cells that have migrated the maximum distance. The next two sections (2 and 63) are multiplied by +0.968 (= 30.5/31.5) or -0.968. The last two sections (i.e., 32 and 33 that are closest to the cell inlet) are multiplied by + 0.015 (= 0.5/31.5) or -0.015. The weighted values are summed and normalized to the total number of cells to generate the CMC. A CMC of +1 indicates that all the bacteria moved completely up the gradient to the wall of the flow chamber. In the case of a mild attractive response, the CMC would still be a positive, but have a smaller value, whereas the CPC value would still be +1. Thus the CMC discriminates responsive cells based on the extent of migration.
Certain parameters need to be carefully controlled while performing chemotaxis experiments. The temperature at which the bacteria are cultivated is important. For E. coli, we have observed that the best growth temperature is 32°C and higher temperatures reduce motility. For certain receptors to be present in bacteria, it may be necessary to grow the bacteria in the presence of an inducing chemoeffector (i.e., to prime the cells for chemotaxis). For example, when investigating the response of bacteria to maltose, cells are grown with 0.1% (w/v) of the sugar. When resuspending the bacteria after centrifugation, it is important that gentle shaking/rolling of the tube is performed and the cells are not vortexed. Vigorous shaking can shear the flagella and reduce the motility of the cells being tested. The flow rates used in the device will need to be determined based on the motility of the bacterium being tested. Slower flow rates may be required for bacteria that are less motile, and the optimal flow rate must be determined empirically.
We anticipate that the μFlow device can be used for fundamental investigations on bacterial chemotaxis (e.g., competition between chemoeffectors for the same receptor) as well as in identifying bacteria in environmental samples that respond to particular chemicals as attractants or repellents.
This work was supported in part by the National Science Foundation (CBET 0846453).