Team:BostonU/Data

Using BBN Technologies TASBE tools under the guidance of Dr. Jacob Beal, we analyzed our library of constitutively expressed RFP Level 1 MoClo devices.

Below, you can see more details of our experimental design and the controls we used, which are required by the TASBE tools in order to convert the arbitrary fluorescence units obtained from the flow cytometer into absolute units in the form of molecules of equivalent fluorescein (MEFL). This allows the user to show their data in absolute units that then allow scientists to compare experiments across labs and machines.

The Cytometer Setup and Tracking beads offered by BD Biosciences were utilized to set the laser delay and optimize the cytometer settings prior to running any samples through the Fortessa.

We also used Spherotech's 8-peak particles (RCP-30-5A) in order to obtain standard MEFL units for the FITC channel. They are also used to measure the long term performance of the flow cytometer and should be included in every experiment run through the flow cytometer.

Below is a subset of our results that demonstrate the strength of the TASBE tools for analyzing fluorescence data. (We will be showing the full set of data at the Jamboree.)

Here we are showing RFP fluorescence as both arbitrary units (top graph) and in MEFLs (middle and bottom graphs). All of the devices shown were made using the MoClo method. However, we converted a handful of our Level 1 devices into a BioBricks compatible format for the iGEM competition requirements. Our original MoClo parts (BBa_K1114500, BBa_K1114502-4) are in the pSB1K3 backbone while the BioBrick switches (BBa_K1114701-4) are in the pSB1C3 backbone, as required for the competition submissions.

At first glance, the top graph showing arbitrary units suggests that the expression levels between the two backbones varies. However, when we convert our data into MEFLs using the TASBE tools, the data is binned into subpopulations based on fluorescence expression amounts. This binning allows us to see that these subpopulations within each sample that have different levels of expression. If we only look at the geometric mean of the entire population, then we are missing parts of the data that can help explain unexpected results.

We will continue to characterize more parts prior to the Jamboree and look forward to showing more complex devices in our presentation.

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