Team:IIT Delhi/Characterization


In order to functionally characterise our circuit, we designed a fluorometer based assay on the spectra Max M2e Spectrofluorometer. This is one of the three types of fluorometers commonly used for analysis of fluorescent reporters in biological circuits [18]. The principle of this device is that it excites a culture filled in a quartz glass cuvette, with light of a certain wavelength, and then measures the emission at a set wavelength, using a collector which is set at right angles to the plane in which the excitation light passes. The excitation and emission wavelengths can be set manually, depending on the absorption spectra of the fluorescent proteins.

It has been reported in literature that when the number of cells is higher than ~200 – 500, we do not see oscillations, but instead there is a burst of fluorescence emanating from the center of the culture, which then spreads out and dies. We decided to use this circuit to characterise the oscillations of two systems, the first being the Danino oscillator [21], and the second one being the iDanino (Module – 2, iGEM IIT Delhi 2016) in two separate experiments, in order to characterise this burst of fluorescence for the two reporters, yemGFP (sfGFP) and mRFP1 respectively. The typical spectra of fluorescence is however, not a sharp peak, but one that usually increases and decreases over a band of ~30 nanometers (figure below), allowing some variability in choosing the wavelengths, in case the peak emission and excitation spectra peaks lie too close to each other.

The wavelengths of the yepGFP and mRFP1 were set according to the absorption spectra [19, 20] and are listed as follows:

  1. mRFP1 – Excitation – 584 nm Emission – 605 nm
  2. sfGFP – Excitation – 485 nm Emission – 510 nm

Experimental Setup

The same experimental setup and procedure followed were the same for the testing of both the Danino oscillator and the iDanino oscillator, and is listed as follows:

  1. E.coli DH5α cultures for the positive control (containing the plasmid BBa_2173001) , negative control (containing the plasmid BBa_K2173000), and our iDanino circuit (containing both plasmids BBa_K2173000 and BBa_K2173001) were grown overnight to the stationary phase (O.D.600 ~ 2).
  2. 500 µl of each of these cultures was picked up and diluted 1/20th in 250ml conical flasks containing 10 ml LB medium with the respective antibiotics.
  3. Cultures were spun at extremely low speed (20 rpm), to provide an analogue of flow rate, appearing in the equation for external AHL in the modelling of the iDanino oscillator, found to be essential for maintaining oscillation/burst behavior of the cells.
  4. 1 ml sample from each of the cultures was taken and replaced with 1ml of fresh media, in order to maintain a near constant O.D. value of the cultures (this 1 ml replacement of media was found to be optimal, after calculating the growth curve (figure 2) of the iDanino circuit and it’s positive and negative controls).
  5. The sample was placed in a quartz glass fluorimetry cuvette, and emission at the wavelength given above was noted over time at the given value of excitation wavelength (along with the OD600 values).

The fluorescence values obtained were then normalized for the small changes in OD due to experimental errors using curve fitting through support vector regression in R, followed by plotting of the fluorescence value versus time for the root mean square value of the OD for each of the cultures, and the trends were analyzed.

Microfluidics and Microscopy

For the real time characterisation and validation of our programmable oscillator circuits, we decided to follow the same scheme as that by Danino et al[11], and work on microfluidic chambers. Recent technological advances now make it possible to track the dynamics of gene networks in single cells under various environmental conditions using these microfluidic ‘lab-on-a-chip’ devices, and researchers are using these new techniques to analyse cellular dynamics and discover regulatory mechanisms [22].

In the device, E. coli cells are loaded from the cell port while keeping the media port at sufficiently higher pressure than the waste port below to prevent contamination. Cells were loaded into the cell traps by manually applying pressure pulses to the lines to induce a momentary flow change. The flow was then reversed and allowed for cells to receive fresh media with 0.1% Tween20 which prevented cells from adhering to the main channels and waste ports.

We constructed several microfluidic chambers of similar design but different dimensions, with the cell loading and media ports at alternate opposite ends, with two waste ports, designed at similar positions as shown in figure 3. The chamber was constructed by designing the channels through laser etching on two acrylic sheets of 3mm thickness, using the Epilog LASER FUSION M2™. The Corel Draw models for the chambers designed by us are given in figure 3.

For imaging, we used the Nikon eclipse Ti – 5 fluorescence microscope using the filter for RFP and GFP. At 20x, fluorescent images were taken every 5-7 minutes which we found to be sufficient to prevent photobleaching (500ms exposure, 10% lamp setting). The images were then analysed using ImageJ, and the trends were plotted to show the mean fluorescence values oscillating over time.

Experimental Design

  1. E.coli DH5α cultures for the positive control (containing the plasmid BBa_K2173001), negative control (containing the plasmid BBa_K2173000), and our iDanino circuit (containing both plasmids BBa_K2173000 and BBa_K2173001) were grown overnight to the stationary phase (O.D.600 ~ 2).
  2. A small amount of the iDanino circuit was loaded onto the cell loading port, and pushed through by applying s positive pressure pulse over the port, while keeping the waste port on the cell loading side blocked.
  3. Cells were pushed into the trap, and the residual culture was washed out. Media containing 0.075% Tween20 was then loaded onto the media port as a reservoir, and was left as is, for flow through the channel due to capillary forces.
  4. Images were taken at different time intervals, and the fluorescence images were analysed using ImageJ for trends among mean fluorescence values.

This setup was first modelled in silico using MATLAB, with the assumption that the cells initially start their oscillations with random phases. We analysed how a system of ‘n’ cells would behave over time by integration of the behavior over an instantaneous time scale dt. The cells were all assumed to have sinusoidal oscillations of the type A(t)=Ao+A1 sin⁡〖(ωt+ θ)〗

The results from the oscillations showed that the oscillations should synchronise over time, and if each figure is broken down into 100 sample blocks, and the mean fluorescence in each of these individual blocks will give a variance of < 0.05*moving average of mean value (figure 5), which is what we decided to do.