“Your rewards in life are always in direct proportion to your contribution – your service.”
– Earl Nightingale
Repeatable and comparable measurements in Science and Engineering is the key to a successful experiment. Many of us would have worried about getting different results each time we perform the same experiment. And this is common in the microbial cultures where we find high variability in the cell numbers in the sample. And since there is high variability, the cell numbers estimation is the most common measurement that is needed to be carried out. The iGEM Measurement Committee has been designing experiments to improve the reproducibility of an experiment across the globe to make it the biggest interlaboratory study ever done in synthetic biology.
The interlaboratory study had important elements to consider for our project “SpecifiCAR”, so we performed the Interlab studies, which helped us achieve reproducible results in our experiments.
Previous Interlaboratory studies were designed to focus on the measurement procedures of Optical Density (OD600) and the fluorescence measurements of Green Fluorescent Protein (GFP) for quantifying the cell number and the bacterial fluorescence by using distinct calibrators.
The measurement committee has extended the one-color calibrator system to multiple-color calibrators (Green-Red-Blue) for validation of dual colors along with the single-colored devices.
Here we describe the setup of the plate reader
| Device | Part Number | Coordinates |
|---|---|---|
| Negative control | BBa_J428100 | Kit Plate 1 Well 12M |
| Positive control | BBa_I20270 | Kit Plate 1 Well 1A |
| Device 1 | BBa_J364000 | Kit Plate 1 Well 1C |
| Device 2 | BBa_J364001 | Kit Plate 1 Well 1E |
| Device 3 | BBa_J364002 | Kit Plate 1 Well 1G |
| Device 4 | BBa_J364007 | Kit Plate 1 Well 1I |
| Device 5 | BBa_J364008 | Kit Plate 1 Well 1K |
| Device 6 | BBa_J364009 | Kit Plate 1 Well 1M |
We performed the calibration experiments (Three color calibrants with silica nanoparticles) to make the absolute fluorescent values comparable to values obtained from different instruments involved in this study. We had a similar problem to other iGEM teams in calibrating the silica beads (Error in production).
After the successful completion of the Calibration experiment, we were reading through the Interlab experiments. We then found the interesting Experiment 3 challenge: Plate reader Culturing and green fluorescence development over time. We then decided to take up this challenge for the Interlab study. The primary aim of the experiment was to compare the differences in the fluorescence measurement of the constructs vary when cultured in the plate reader instead of the test tubes.
Fluorescence
Absorbance
Shake
In this experiment we performed a time-dependent measurement of the fluorescence of six devices that encodes for a green fluorescent protein cultured in the 96-well plate and subsequently in the culture tubes (test tubes) to calibrate the fluorescence of these devices to the calibrant dyes, Optical density of the culture to the cell density calibrant. We followed the cell measurement protocol without any modifications except that we used the highest bandpass of 10.0nm in the instrument (Experiment 3 challenge).
We were able to successfully transform the test devices and the measurements were taken within the accepted limits to avoid any bias. We obtained several colonies in each transformation which we used for the Inter-lab studies. We also repeated the measurements twice to make sure there are no outliers or differences in the measurements.
Fluorescence of samples cultured in Plate reader:
The fluorescence measurements of samples in Plate reader with the orbital shaker showed a
similar
pattern with highest fluorescence levels in device 1, followed by device 2 and 4. We did detect
minimum
fluorescence in the samples with devices 3 and 6.
Fluorescence of samples cultured in test tubes:
From Figure 4 and Figure 5, it is evident that the test device 1 showed the highest fluorescence
levels
in culture tubes followed by device 4 and 5. All the devices showed maximum fluorescence at 6hr,
and we
saw similar bacterial growth pattern across all the devices. Yet, the devices 3 and 6 showed minimum
or no
fluorescence. This could be because of the construct itself or might be due to problems with the
plasmid
sequence.
All the transformed devices showed growth and fluorescence with device 1 with highest fluorescence across both culture conditions. In comparison, the samples with test devices cultured in the test tube (Culture tubes) showed higher fluorescence levels when compared to samples cultured in the plate reader with shaking.
As part of our partnership with iGEM team Edinburgh-UHAS_Ghana, we collaboratively designed a tutorial for molecular docking simulations. We experienced ourselves that at first, this field can be overwhelming. By providing a detailed tutorial, we help future iGEM teams to quickly get over this first phase so that they can produce their own docking simulations as fast as possible. Our guide explains step by step how to investigate protein-ligand interactions. We give an overview over the tools required in order to perform the simulations as well as provide the code necessary for reproduce our example docking. Furthermore, we contribute a detailed explanation on how to perform an in silico mutagenesis of a protein as well as a reference on how to analyse the output of molecular docking simulations.
We believe our tutorial enables everyone to computationally model molecular structures and thus profit from the insights, those simulations give into the functionality of a molecular system.
From the interviews with research experts who are active in the field of CAR T-cell therapy, it became apparent that the lack of databases for CAR systems was a constraint hampering the entire community. Since we also struggled with the problem of cataloging and sharing our CARs all the time, we decided to simplify the work for future researchers, including iGEM teams and us.
Throughout our journey through the CAR-focused academic community, we noticed the lack of a dedicated database. This need we set out to fill with OSCAR (open-source CAR) - a database that stores available CARs in one place. By performing extensive literature research, we managed to add over 100 CARs. Our modular database schema supports the separate storage of CAR parts, for instance, the receptor, the linker, or the transmembrane domain. By breaking down the data, the creation of new CARs with new functionalities is enabled, and sharing discoveries is effortless. Due to OSCAR’s open-source nature, iGEM teams can adapt and use our tools in the future.
For a more detailed description of OSCAR, view our Software page.
You can view the data as an exported PDF.