As a contribution to the iGEM community, we have conducted single cell characterization, determining the relative expression levels of three constitutive promoters from the Anderson Family (Designed by John Anderson in iGEM2006) from the iGEM parts registry: BBa_J23104 [1], BBa_J23106 and BBa_J23115. These have previously been characterized, but the relative characterization on individual cell level has not been done yet and it allows us to evaluate variance within the cell samples. The strength of these three specific promotors had not been characterized previously. Furthermore, BBa_J23106 was later used in the vector design of our project.
For the characterization, the promoters were introduced into the plasmid pSB1C3 upstream of a gene coding for the fluorescent protein RFP, with a ribosome binding site (RBS) from the parts registry (Bba_B0034). This was achieved through PCR with overhanging primers including the sequence of the promoter of interest. The vector was transformed into the E. coli strain DH5α, which will express the RFP at the levels induced by the specific promoter. Details about the methods used and partial results can be found in our Notebook.
Figure 1. Schematic illustration of the establish new plasmid.
To establish the plasmid with new promoter BBa_J23104, BBa_J23106 and BBa_J23115, respectively. It
was introduced upstream of a gene coding for RFP in pSB1C3 by PCR amplification. The figure on the
left shows the four primers that were used. The primers are positioned and directed so that the
amplification gives two segments, each containing half of the new promoter and half of the
chloramphenicol resistance gene. The two segments can then be assembled again, as shown in the
picture to the right.
Results
For RFP expression evaluation, three positive colonies of each transformation (plasmid with promoter J23104, J23106 and J23115, respectively) were cultivated to exponential phase (OD=0.3). The growth curve of each cell sample is presented in Figure 2, together with a negative control (non-transformed DH5 α bacteria).
Figure 2. Growth curves of the positive transformants with pSB1C3 expressing RFP
under the promoter J23104, J23106 and J23115, respectively. Three colonies with each
promoter were cultivated, as well as a negative control (without transformed plasmids) and OD600 was
measured over time. The graph shows the mean OD600 value for each set of colonies with the same
promoter.
Once the exponential phase was reached, the cell samples were spun down immediately and diluted to appropriate concentrations for fluorescence microscopy and plate reading.
Plate reading: characterization of the promotor strength in the bulk population
The average intensities of the fluorescence signals for the whole sample, measured using a plate reader, are presented in Figure 3. Cell samples expressing RFP under the control of promoter J23104 and J23106 clearly give a stronger fluorescent signal than those with promoter J23115. The intensity levels for each promoter are based on an average of the three cell samples studied.
Figure 3. Average intensity from each promotor. This graph shows the
intensity of three different promoters, J23104, J23106 and J23115. They were measured by the
intensity of the fluorescence signal which is measured by plate reader since there is RFP gene
downstream of each promoter. Numbers indicate the exact intensity value. Both J23104 and J23106 show
quite high intensity whereas J23115 shows extremely lower values than other two promoters. Error
bars show standard deviation. *: p <= 0.05.
Fluorescence microscope: characterization of the promotor strength using single cell data
The cells were imaged using a Leica fluorescent microscope and analysed based on the relative fluorescence intensity. The intensity measured for each promoter was then compiled in a box plot, as seen in figure 4 below.
Figure 4. Average intensity from each promotor.
This box chart shows the average intensity of each promotor by measuring fluorescence
intensity of single cell. Fluorescence intensity is calculated by measuring the intensity
under the fluorescence microscope. The dots correspond to the intensity levels of single
bacteria. **** indicates p value <= 0.0001, which means that there is a significant
difference in the fluorescence measured from the different promoters.
The data from the fluorescence microscope is single cell data, and approach that has been underexplored to characterize promotor strength so far, especially within iGEM projects. Each dot in the plot corresponds to a single bacterium under the microscope. The intensity was measured using the imaging processing package Fiji [2] and normalized subtracting the background intensity. We can see that the variability of the data is larger than for the plate reading results of the bulk population.
The intensity of the fluorescence can be correlated to the amount of protein that was expressed and therefore with the strength of the promotors that we introduced. From the results, we can infer that promotor J23104 is the strongest one, closely followed by J23106. Both are significantly stronger than J23115.
Figure 5. The distribution of the single cell fluorescence measurements for the different promotors.
The histogram showing the dashed lines represent the median for the set of measurements for each promotor. We chose the median over the mean because it is less sensitive to outliers.
To further study the intensity distribution, we build a histogram from the single cell data. From the histogram, we can see that the fluorescence intensities do not follow a normal distribution. There may be several factors contributing to the distribution of the intensity in the single cell data, one of which is likely human error during the measurements.
However, if we consider that we are dealing with single cell data, high variability is not unexpected: there can be many differences between individuals in a population that may reflect in the amount of protein expressed. In fact, this approach probably reflects better what really occurs within a population and can help understand the behaviour of the promotor better, which is important if we consider the large influence that the choice of promotor has on protein expression. Most projects will need a robust and reliable promotor for heterologous protein expression. In our case, the results point out that J23115 may behave more erratically than the others, which may be something to consider if a team wants to use this promotor
Although there are clear differences between the results from the bulk population and the single cell data, both results show that J23104 is the strongest promotor, followed by J23106, and J23115 being the weakest one.
Open source code
Our modelling team performed an extensive analysis on both the presence of enzymes related to plastic degradation in different environments and how the metabolism of E. coli would adapt to growing in plastic substrates.
We think that our analysis is easily applicable to many other iGEM projects. To give an example, if one would want to grow E. coli in compostable waste, one could just try to replicate our analysis changing the carbon source. As biologists, we know the struggle of getting familiar with coding, getting to know what tools can be used and how to make sure that your results are relevant. Therefore, we decided to share our Github with the iGEM community so that future teams can benefit from it either to get some inspiration for their projects, to try to replicate our analysis in the way that best fits their objectives, or simply to get examples on how to run certain functions.
- [1] “Part:BBa J23104 - parts.igem.org.” http://parts.igem.org/Part:BBa_J23104 (accessed Oct. 18, 2020).
- [2] J. Schindelin et al., “Fiji: An open-source platform for biological-image analysis,” Nature Methods, vol. 9, no. 7. Nature Publishing Group, pp. 676–682, Jul. 28, 2012, doi: 10.1038/nmeth.2019.
