The WPI-2016 iGEM team developed its unique method of measurement through technological constraints. We lacked proper access to a flow cytometer at our university. However, measuring fluorescence was still required to analyze our experiments. We devised a new way to measure fluorescence of a sample using microscopy and image analysis. By using the program ImageJ, the images taken of fluorescence microscopy were analyzed for brightness on a pixel basis. Through taking images of the same sample of cells at the same exposure times, data was collected and averaged to derive fluorescence data. This data was then checked through a collaboration with the Boston University Weblab, using their flow cytometer. When compared, the flow cytometry data from BU confirmed the legitimacy of the microscopy image analysis method. This technique was also used for the validation of another BioBrick part that focused on arsenic absorption, shown below. It is through this ingenuity that we believe that our team qualifies for the measurement prize.

Quantifying Fluorescence via Microscopy

All images used for analysis were taken under the same magnification. The ones taken under the same channel (green/red fluorescence) were taken at the same exposure to reduce variability.

ImageJ Analysis:

• Open the image files in ImageJ
• In the menu, select “Analyze” → “Set Measurement” → select “Area”, “Integrated density”, “Mean gray value” → “OK”
• For each image, “Analyze” → “Measure” (Results should include “Area”, “Mean”, “IntDen”, and “RawIntDen”)
• Save “Measure” results

The Excel analysis was performed as follows:

The results obtained by this method can be viewed here.

Arsenic Absorption Experiment

The efficiency of the arsenic absorbing ability was measured by testing the concentration of the remaining arsenic in the solution. A sample image of the testing result is shown below:

The yellow color indicates the arsenic concentration, with a lower concentration producing a lighter color. A visual scale with corresponding concentration is shown below:

The concentration decided by this method is usually estimated visually; however, we used ImageJ to obtain a numeric value for each shade of the yellow color (in the visual scale above), from which we were able to create two functions that approximate the relationship between color and concentration.