Accurate, precise and reproducible measurement is a key component of all disciplines and synthetic biology is not an exception for that. However, the ability to reproduce measurements in different labs is a difficult task to achieve.
The goal of the Fifth International Interlab study was standardizing GFP expression per cell across all the world laboratories. GFP is widely used as a measurement tool in synthetic biology. Hence, it is of immense importance to standardize the GFP expression for all laboratories so that they can compare and build upon each other’s data. Last year’s iGEM Interlab study focused on standardizing GFP expression for a cell population calibrated against a known concentration of fluorescent molecule.
This year, the study has been extrapolated to calculate GFP expression per cell thus eliminating error caused due to cell-to-cell variability. Moreover, this study has been carried out by laboratories across the world using a standardized protocol provided by iGEM Measurement Committee to minimize lab-to-lab variability.
For the Fifth International Interlab study 2018, the absorbance and fluorescence of the cell populations were used to calculate cell concentration by Calibration Curve method. Moreover, absolute cell number of the samples at zero hour was obtained by performing Standard plate count. The above two approaches were used to determine the exact fluorescence value per cell.
We, Team Ruia-Mumbai performed both the plate reader as well as the Flow cytometry protocol as a part of the IGEM Interlab study 2018. We had a fruitful collaboration with National Institute for Research in Reproductive Health (NIRRH), Mumbai for the Interlab study.


We, Team Ruia-Mumbai tried our hands on working for an international collaborative experiment of publishable quality by participating in the IGEM Fifth International Interlab Measurement Study 2018. It is a great feeling to be part of a world-wide community working for standardizing measurements in synthetic biology. After going through the protocol for plate reader and flow cytometer for around 10-15 times, the next challenge for us was of executing the experiments.The execution of experiment required three to four days of rigorous planning and there we realized the importance of planning and patience. We got an opportunity to analyse our data using a flow cytometer in collaboration with NIRRH and it was a great experience of learning new techniques. The Excel sheets provided by IGEM measurement committee 2018 were explicit explaining all the calculations precisely. The sheets helped us immensely in our Interlab data analysis.Data is just information till we analyse it. Once analysed, it becomes a powerful tool for scientific study.
While analysing the data from the plate reader and the flow cytometer we were amazed by the readouts and trends within biological and technical replicates and the power of flow cytometric data analysis over the plate reader. We also got to know that the data from plate reader and that of the flow cytometer were matching with each other. We learnt team spirit and work ethics. Moreover, we experienced the sheer joy and satisfaction when our Interlab data got accepted in the very first attempt!!


Negative control Well 2D BBa_R0040 BBa_R0040 - -
Positive control Well 2B BBa_I20270 BBa_J23151 - BBa_B0032
Test Device-1 Well 2F BBa_J364000 BBa_J23101 0.70 BBa_B0032
Test Device-2 Well 2H BBa_J364001 BBa_J23106 0.47 BBa_B0034
Test Device-3 Well 2J BBa_J364002 BBa_J23117 0.06 BBa_B0034
Test Device-4 Well 2L BBa_J364007 BBa_J23100 1.00 BBa_B0034
Test Device-5 Well 2N BBa_J364008 BBa_J23104 0.72 BBa_B0034
Test Device-6 Well 2P BBa_J364009 BBa_J23116 0.16 BBa_B0034



  1. Low absorbance values were observed as expected for both LUDOX (~0.07) as well as ddH2O(~0.03).


  1. Microsphere beads settled in less than 10 mins (~10 secs)
  2. The values obtained did not give a perfect 1:1 plot as expected for both the graphs. However the calculated R2 values were approximately equal to 1.
  3. The microsphere calibration protocol gives us a standard plot of Absorbance (A600) vs Particle size, which is equivalent to the size of E.coli cells (DH5 alpha). The values obtained in the cell measurement protocol, can be extrapolated on this curve to get the corresponding cell number.

  4. For ex: If we get values of absorbance 600 of a cell sample as 0.5 Abs units. Following calculations can be performed to determine the cell no. of the sample.
     y = 3E-09x + 0.0392
     0.5 = 3E-09 * cell number + 0.0392
     Cell number = 0.5 – 0.0392 / 3E-09
    cell number = 1.536 * 10^8 cfu/100 uL

  5. We obtained almost constant Abs600 values for higher dilutions which we suspect is because of:
    • Pipetting errors
    • Microspheres at higher dilutions might be in negligible concentration causing their Abs600 values to tend to the values of blank.
  6. Abs600 values of the 4th replicate were observed to be low compared to the previous replicates which may be due to the settling of microsphere between the reading frames of the instrument.


  • Fluorescein calibration protocol passed all the common sense tests i.e. the fluorescence readings reduced 1/2 times with every dilution and the blank showed less fluorescence than the test.
  • The fluorescence readings gave an almost perfect 1:1 plot on both linear and log scale. The drifting of higher dilution readings from the trendline may be attributed to the pipetting error.
  • The fluorescence readings of the test devices could be extrapolated on the fluorescein calibration curve obtained by this protocol using the above equations to get an estimate cell number of the respective test devices.


  1. The competent E.coli DH5α cells were transformed with 8 devices provided in the iGEM distribution kit. The transformed cultures were viewed under fluorescence microscope as 7 out of 8 devices express GFP.
  2. We always faced a time lag between setting up OD600 of starting sample and taking measurements after pipetting on 96 well plate reader due to the sheer enormity of pipetting such a huge number of samples.
  3. The net fluorescence in a.u.values increased for all 7 devices excluding negative control after 6 hours. However, the fluorescence per OD and fluorescence per particle values for 6 hours for all the devices decreased when compared to their O hour readings inspite of increase in Abs600 unlike the proportionate increase in fluorescence compared to increase in Abs600 as expected. We hypothesize that it might be happening because all the increased cell population might not be fluorescing to the same extent.
  4. The test device 3, 5 and 6 are the devices showing maximum fluorescence. The relative order of fluorescence shown by these three devices is as follows:-


  1. The overnight culture was diluted and the OD600 was measured. The culture was set to 0.1OD600 after doing relevant mathematical calculations using the diluted culture.
  2. We found it difficult to prepare 1:20 dilutions in 2ml eppendorrf tubes as the solution was filled up to the brim making it difficult to aliquot for further dilutions and effective vortexing.
  3. The results obtained for cfu count were as follows:-

  4. All the replicates of both positive and negative control devices showed Too numerous to count (TNTC) colonies in lowest dilution plated and too less colonies in highest dilution plated. In the dilution having countable number of colonies, the replicates did not show much uniformity.

    Series 1 – Colony 1
    Series 2 – Colony 2

    The cfu count/ml for 0.1OD600 cultures did not show much congruency between the two biological replicates – colony 1 and colony 2 for both positive and negative control devices.

    However, we got an estimate cfu count for both the biological replicates of positive and negative control devices as follows:-


What is flow cytometer?
Flow cytometer as the name suggest is a device that measures cell concentration by analysing the interaction between light and a single cell.

How single cell is analysed?
Sample used for flow cytometric analysis is generally a population of cells. This cell sample is acquired by the flow cytometer using a capillary and is converted into a single stream of cells by hydrodynamic focusing using sheath fluid. By varying the amount of sheath fluid the stream of cells can be adjusted such that a single cell leaves the nozzle at a time and comes in contact with the incident light which in case of flow cytometer is a laser. Lasers of different wavelengths can be used for measuring properties like fluorescence.
This light incident on the cell is scattered in all directions and is detected by the detectors. Detectors are placed such that one detects forward scattered light which indicates size of the cell and the other, placed at 90 degrees to the forward detector captures side scattered light which gives us the measure of granularity or the complexity of cells. Thus flow cytometer enables us to analyse individual cells, their interactions and trends in population over a time period.

How a flow cytometer data is analysed?
Flow cytometric data is analysed using a computer software wherein the data readout is in the form of scatter plot. This plot can be further analysed to determine concentration of cells, percentage of population positive for the desired characteristic, differentiation of cells having different granularity, singlets and doublets, correlation data, gated and non gated populations, histograms etc. These different types of analyses are explained below using Interlab devices as samples.

Flow cytometric analysis of Interlab devices: Sample used: 0 hour and 6 hour cell suspensions of Interlab devices.
Machine model: BD C6 Accuri
Number of events acquired: 20,000 events
Threshold used: 300
Characteristics measured: Fluorescence

Pictorial representation of different types of data analysis done using Interlab test device 6 as an example.

Scatter plot without gating:
It is the basic readout obtained indicating the density of the cell population with respect to its size and complexity. The X-axis represents the forward scatter while the Y-axis represents the side scatter of the population. More denser the region more number of cells showing the same electrical pulse. Increase in scatter after 6 hours of incubation indicate that cells are growing.

Histogram plot:
The scatter plot can be further represented in the form of histogram wherein cell count is plotted for the selected characteristics i.e. fluorescence in this case. This plot can be gated into two parts.Gating enables the selection of population actually fluorescing as against the background and autofluorescence.
Here the gate V1L represents autofluorescing population and gate V1R represents the percentage of population fluorescing in the fluorescent channel. The gate was set using the Interlab negative control device at corresponding time points so as to eliminate the autofluorescence.

Scatter plot with gating:
The V1L and V1R gates can be plotted in the form of scatter plot for further differentiation. Doublet singlet discrimination plot: Being a biological system it may happen that cell remain stick together while growing.This can be clearly indicated by the doublet singlet discrimination plot where FSC-H is plotted against FSC-A i.e. forward scatter height against forward scatter area. Cells showing linear trend i.e. linear height to area ratio are the singlet population whereas the plateau indicates doublets i.e. more height to area ratio.
Cells showing different linear trends are the different sets of population within the same sample.

Correlation between Colony 1 and Colony 2 of Interlab Devices for different parameters:
Y-axis represents the 6 hour/ 0 hour readings for colony 2 for the respective parameter.
X-axis represents the 6 hour/ 0 hour readings for colony 1 for the respective parameter.

Linearity indicates higher correlation between colony 1 and colony 2 for the considered parameter. From the graphs it can be concluded that both the biological replicates are showing correlation i.e. similar trends for almost all parameters. The extent of correlation for colonies varies for different parameters. This variation can be attributed to probable loss of plasmids from some cells, increased granularity due to protein over-expression, rise of possibly two different populations after 6 hours of incubation, etc. We used median values of replicates of Colony 1 and Colony 2 for getting a better estimate of the data.

Comparison between 0 hour and 6 hours for colony 1 and colony 2:
Concentration indicates the number of events per volume of sample acquired. More the concentration, the same number of events will be acquired in lesser volume. Here there is increase in concentration after 6 hours of incubation which clearly indicates that cells are growing but the extent of increase is not similar although all devices are transformed into same type of cell i.e. DH5 alpha. Increase in forward scatter clearly indicates increase in size and hence growth of cells. Increase in side scatter indicates increase in granularity which might be because of overexpression of proteins. Same trend can be observed in FSC and SSC median at 6 hours for all devices.
FL1 concentration indicates the fluorescence given by test devices. Very low fluorescence values at 0 hour can be the basal level expression whereas negative control doesn't show any fluorescence which is as per the expected results. After 6 hours of incubation there is increase in fluorescence value for all devices except negative control and test device 3. The extent of increase in fluorescence values is not identical which clearly reflects on the strength of promoter expressing the GFP. Whereas in Median FSC and SSC the larger value for negative control and test device 3 is because of gating as the negative population have a longer spread thus higher median values giving us false positive results in forward and side scatter.
Conclusion/Inference :
Although the trends for different parameters considered in flow cytometer data analysis is not identical we can clearly say that the test device 1, test device 5 and test device 6 have maximum fluorescence as compared to other devices.


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