Team:EPF Lausanne/Interlab
What are we talking about?
In science, and in the field of synthetic biology in particular, characterizing new devices turns out to be as important as conceiving them. This not only provides a “how to use” guide for future users of your part but also allows the discovery of biologically relevant information about how it functions. When it comes to iGEM, the importance of characterization reaches huge proportion since thousands of new parts are registered each year. As a matter of fact, last year the competition launched its first InterLab Study, inviting every participating team to collaborate to measure previously existing devices. In addition to providing robust and statistically useful data, the InterLab Study aims at assessing how those measurements vary between labs. How similar are data from two teams using the same protocol? How well are the ratios conserved using two different measurement equipment? This year, these questions will be answered for the three constructs each team was given. They each contained a promoter from the widely used Anderson promoter collection that controlled the expression of a GFP (see description below). We contributed this year by measuring the three constructs in biological triplicates with a flow cytometer, which allowed us to assess the cell-to-cell variability of our samples. As part of the extra-credit assignment, we also provided technical triplicates of our data, thus determining the precision of the measurements. Beside the study, we were also able to integrate obtained data in our project since our reporter plasmid had its GFP controlled by J23117.
Tested constructs
BBa_J23101
+
BBa_I13504
in
pSB1C3
Sequencing can be found here
BBa_J23101
+
BBa_I13504
in
pSB1C3
Sequencing can be found here
How did we achieve this?
The construction of the three devices was achieved using the BioBrick cloning system. Plasmids pSB1C3 containing promoters J23101, J23106 or J23117 were opened using SpeI and PstI enzymes while plasmid pSB1A2 containing I13504 was digested with XbaI and PstI. pSB1C3-J23101, pSB1C3-J23106 and pSB1C3-J23117 were dephosphorylated with antarctic phosphatase in order to prevent their self-ligation. I13504 was finally ligated with each of the open promoter-containing pSB1C3 plasmids using T4 ligase. Constructs were run on a 1.2% agarose gel, purified and transformed in DH5alpha high-efficient bacteria to be finally plated on chloramphenicol LB agar plates. Three colonies per plate were cultured overnight as biological replicates in 5mL LB medium with chloramphenicol in test tubes. Cultures were spun down and pellets resuspended in 1mL PBS. Samples were measured by Accuri c6 Flow-Cytometer (BD) and data were acquired three times in arbitrary units.
More information are available in our InterLab Protocol, InterLab Worksheet and in our
Protocol
section.
What did we get?
We plotted the constructs’ mean of fluorescence across the three measurements of the three biological replicates (Fig.1). As units were arbitrary, we also expressed the ratio between the three promoters (Fig.2) which gives a more relevant information about difference in GFP expression. As showed in the figure, we compared our results with the measured strength of those promoters from the Anderson collection (lien vers la page). While the J23101/J23106 ratio is quite similar to the one measured by Anderson himself (only 1.67 fold difference), J23101/J23117 and J23106/J23117 ratios vary from 27 and 16 fold respectively between our results and Anderson’s. A plausible explanation for such discrepancy could be the variation between measurement instruments. While we used a Flow-Cytometer that allows finer measurements of week constructs such as J23117, it is likely that Anderson used a plate reader or another instrument. Also, the different chassis or protocol used to prepare samples could also impact on GFP expression. Those differences are precisely what the InterLab Study intends to shed light on and we are curious to see the results that other iGEM teams will obtain.
Nevertheless, while waiting for the InterLab results to come out, we found interesting to investigate variations among our own samples or, in other words, to lead an IntraLab study. For that purpose, we reproduced as much as possible the same experimental conditions while making our cultures grow, this way avoiding some unwanted variation due to sample preparation. We first compared technical replicates from a same colony. Each of the three technical replicates had 100’000 individuals measured. After eliminating the noise from our data, we approximated the fluorescence to be normally distributed across single cell measurements due to their large number. In order to determine if technical replicates were significantly different, we calculated confidence intervals for each one of them. Based on the Bonferroni correction (as we are comparing three measurements), we set the confidence level at 99.2% instead of the commonly used 95% level. Despite large samples number per measurement, we were surprised to see that about two third of the technical replicates were significantly different. Figure 3 displays a typical example where we can clearly see how the first two measurements are contained in each other’s CI while the third is clearly out. Unless samples were not sufficiently homogeneous, meaning bacteria were agglomerated in population clusters expressing different fluorescence levels and which seems quite unlikely, we can state that despite their great accuracy, measurements had a significant difference with each other. Increasing the statistical robustness could then be achieved by doing more than three technical replicates and consider each median as a part of a normal distribution itself. This will also allow measuring the proper variation due to the flow cytometer and obtaining a more precise mean of fluorescence for each construct.
Along with the technical variation, we also compared the different biological replicates. And we were blown away by how two colonies with the same exact plasmids could have so different fluorescence expression. Figure 4 shows how wide the gap is between colonies. Unlike Figure 3, it is not possible to clearly see CI bars on this figure since they are extremely small compared to median differences. This result is nevertheless reassuring in a way since it proves technical difference is less than biological one, which should appear as obvious due to the stochastic basis of a living organism. This huge biological variation can also be used to explain technical variation. 100’000 organisms are maybe not sufficient to have a comprehensive sample of the population. This explanation could be assessed by increasing the number of bacteria sampled per measure, for example by a 10x factor. So if the biological variance is so important, what can we expect from the comparison of fluorescence from two different chassis? That’s what we are eager to learn during the Giant Jamboree!
