Team:EPF Lausanne/Interlab
What are we talking about?
The characterization of new devices is as important as their conception. For iGEM, the importance of characterization is huge since thousands of new parts are registered each year. Last year the competition launched its first InterLab Study, inviting every participating team to measure previously existing devices. In addition to providing robust and statistically useful data, the InterLab Study aims at assessing how measurements vary between labs. If two teams use the same protocols, is the data similar? How does this differ if different measurement equipment is used? This year, these questions will be answered for three constructs that were sent to each team. Each contained a promoter from the widely used Anderson promoter collection that controlled the expression of 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. In addition to the study, we were also obtained data in from our project since our reporter plasmid has 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
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 (cf. Protocols) 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 (cf. Protocols). 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. 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 is available in our InterLab Protocol, InterLab Worksheet and in our Protocol section.
What did we get?
We plotted the constructs’ mean of fluorescence for the three measurements of the three biological replicates (Fig.1). We also expressed the ratio between the three promoters (Fig.2) which gives more relevant information about differences 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. We used a Flow-Cytometer that allows finer measurements of week constructs such as J23117, and it is possible 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.
We also decided to investigate variations among our own samples or, in other words, to lead an "IntraLab study". For that purpose, we did as much as possible to reproduce the same experimental conditions for the growth of our cultures to avoid unwanted variation due to different 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 . In order to determine if technical replicates were significantly different, we calculated confidence intervals (CI) 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 a large number of samples 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 between 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. We were surprised by how two colonies with the same plasmids could have very 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!
What did it bring to us?
Finally, these results bring us some insight for our project that aims at making cells possess the circuits of a machine. How could this information be integrated in the design of a system like ours? We can imagine for example applying a selection mechanism to organisms, somehow like it has been done in agriculture and for the cattle in man history. To comply with the rigid structure imposed by logic gates, bacteria must work in the most similar way and of course are expected to be the most efficient ones. This final comment underlines what we wanted to prove at the beginning: measuring is equally important as creating. A powerful system is the product of the strong and bilateral interaction between those two concepts.
