Team:NUS Singapore/InterLab
Interlab Study
InterLab Measurement Study is a component under the Bronze Medal criteria of the iGEM competition. One major challenge in the field of synthetic biology and engineering discipline is the ability to standardize measurements and data processing across different labs. This enables scientists to trouble-shoot the flaw in the engineered biological constructs, to share constructs between labs, and to be able to make meaningful analysis of experimental results across different labs. In order to address this challenge, the iGEM committee provides researchers with detailed protocols and requires data analysis on the measurement of GFP. Based on the experimental data gathered from iGEM teams all over the world, the iGEM InterLab Measurement Committee will be able to modify and improve the protocol as well as the data analysis process accordingly.
Materials
- 2017 InterLab parts
- Positive control (BBa_I20270)
- Negative control (BBa_R0040)
- Test Device 1 (BBa_J364000) J23101 + I13504
- Test Device 2 (BBa_J364001) J23106 + I13504
- Test Device 3 (BBa_J364002) J23117 + I13504
- Test Device 4 (BBa_J364003) J23101.BCD2.E0040.B0015
- Test Device 5 (BBa_J364004) J23106.BCD2.E0040.B0015
- Test Device 6 (BBa_J364005) J23117.BCD2.E0040.B0015
- Competent Escherichia coli K-12 DH5-alpha cell
- Plate reader: Synergy H1
- 96 well plate
- InterLab Measurement Kit
- Relevant laboratory equipment
Experiment
The InterLab Measurement Study has two components: calibration and cell measurement. The calibration component aims to identify the optimal settings for the plate reader to read the Abs600nm and fluorescence value from the E. coli K-12 DH5-alpha cells transformed with different plasmids. All the experimental procedures provided by the iGEM committee are faithfully adhered to ensure consistent and accurate results for easy comparison with other teams.
OD600 Reference Point
OD600 Reference point was measured with LUDOX-S40 to find out the correction factor to convert the absorbance value, Abs600nm to the optical density value, OD600.
Plate reader setting:
- Wavelength of 600 nm
- Turn off path length correction
Fluorescein Fluorescence Standard Curve
The fluorescein fluorescence standard curve will be used to find out the corresponding fluorescein and GFP concentration based on the cell-based readings. We varied the experiment setting that affects the sensitivity (i.e. gain at 100, 80, 60, 55, 50, 45) to identify the optimal setting.
Optimal plate reading setting:
- Turn off pathlength correction
- Excitation: 485 nm, Emission: 528 nm, Optics: Top, Gain: 45
Cell Measurement
E. coli K-12 DH5-alpha cells were transformed with different plasmids provided by the InterLab Measurement Kit. We followed the Transformation Protocol provided by iGEM InterLab page.
Plate reader setting for cell measurement:
- OD reading: Excitation: 485 nm, Emission: 528 nm, Optics: Top, Gain: 45
- Fluorescence reading: 600 nm
Result
Discussion
Similar trends were observed in the OD, fluorescence and fluorescein/OD graphs (Fig 1-6) as seen in each of the 2 colonies for all the devices. This shows that the experimental procedures were faithfully adhered to and executed. Therefore, the differences observed in the OD and fluorescence among the devices could be attributed to the nature of the plasmid constructs with different promoters, RBS or GFP-encoding genes. From the device description, devices 1-3 could be highly similar constructs with varying promoters only, and the same reasoning could be applied to devices 4-6.
From the OD data (Fig 1 and Fig 4), all devices had experienced an exponential population growth and reached a plateau after 6 hours, apart from device 1 which was reported to have close to zero-growth over the course of 6 hours. Comparing between device 2 and device 3, the former has slower exponential growth as compared to the latter. Comparing among devices 4-6, device 6 has the highest exponential growth, followed by device 5 and lastly, device 4.
From the fluorescence data (Fig 2 and Fig 5), comparing among devices 1-3, device 1 containing promoter J23101 had yield non-exponential fluorescence production. Device 3 containing promoter J23117 did not yield any fluorescence production. Device 2 containing promoter J23106 was observed to have strong exponential increase in fluorescence production, suggesting that the promoter J23106 had the strongest affinity for initial RNA polymerase binding when compared to other devices housing promoter J23117 and J23101. Such strong affinity had ensued strong downstream expression of GFP encoding gene E0040 resulting in higher fluorescence output. In devices 4-6, promoter J23101 (device 4) had elucidated strong GFP expression downstream of construct. Device 5 (J23106) had elucidated moderate fluorescence readings while device 6 (J23117) had no fluorescence output.
To gauge which device is the best GFP producer, the fluorescein is normalized over their innate OD, which is equivalent to finding the amount of fluorescein released from one unit of cell mass. Comparing among devices 1-3, from the result (Fig 3 and Fig 6), device 1 appeared to be the best GFP producer, and this is no surprise, as explained from earlier paragraphs stating that device 1 had minimal growth but having moderate GFP production over the course of 6 hours. Device 3 appeared to be the non-ideal GFP producer, with high exponential growth and minimal GFP production. Among devices 4-6, device 4 appeared to be the best GFP producer, with high GFP production stemming from a lower concentration of cells. The converse is true for device 6, which is a low GFP producer, with minimal GFP production and high growth rate.
Conclusion
All in all, promoter J23106 is the most efficient in elucidating strong GFP expression for the constructs in devices 1-3. While promoter J23101 had elucidated highest GFP expression in devices 4-6, there appears to be an inverse relationship between the rate of cell proliferation and GFP production. This suggests that externally infused genetic processes such as GFP expression had imposed metabolic stresses on the bacteria such that it limits other innate cellular processes like cell division from proceeding, thereby limiting the growth rate of the bacteria. Lastly, screening assays as depicted in this experiment are nevertheless important in characterizing the most ideal GFP producer with varying engineered promoter and RBS sites.
