Measurement Resources
Thinking about how your team might approach measurement? Check out some of the resources below to help get you started, some of which have been developed specifically for iGEM teams. We also encourage you to look at examples from past teams to get inspired.
Have a resource to contribute?
Please email the measurement committee at measurement [AT] igem [DOT] org and provide links to material with a short description. We’ll test out the material and if we believe it will be helpful, we’ll add it to this page!
Measuring Fluorescence
Below are some tools that can help you transform arbitrary unit fluorescence measurements into standard comparable units.
Plate Reader
iGEM Measurement Kit
The Measurement Kit is included in your iGEM Distribution Kit and is intended to help your team measure green fluorescent protein (GFP) reliably in a plate reader. The kit includes 9 tubes: 5 tubes of Fluorescein Sodium Salt, 3 tubes of LUDOX CL-X, and 1 tube of silica beads.
Fluorescein has a similar range of excitation and emission as most GFPs. Because of the overlap of the excitation and emission spectrums, we can utilize fluorescein to create a standard curve to compare GFP measures against in plate readers. LUDOX CL-X is a suspension of silica in water that can be used for calibrating optical density (OD) measurements.
- Protocol for using the iGEM Measurement Kit to calibrate fluorescence and OD
- iGEM Measurement Kit calculation spreadsheet
Storage: The Measurement Kit should be stored at room temperature or at least higher than 4°C.
Flow Cytometry
Flow cytometers allow high-throughput measurement of fluorescence from hundreds of thousands of individual cells. Calibration beads and appropriate controls allow you to turn raw “arbitrary unit” measurements into precise and replicable units.
Sources of calibration beads:
- SpheroTech Rainbow Calibration Particles (Recommended: URCP-38-2K) (Product Link)
- ClonTech EGFP and mCherry Calibration Beads (Product Link)
Free and open data analysis software for calibrated flow cytometry:
- TASBE Flow Analytics (Matlab/Octave library) (TASBE link)
TASBE Flow Analytics is a software tool that analyzes flow cytometry data, including bead-based conversion to standard units. Experiment templates support automated processing, comparison, and plotting of data. TASBE Flow Analytics was developed as Matlab and Octave compatible software. - CytoFlow (Python library + graphical interface) (CytoFlow link)
CytoFlow is a collection of Python tools for quantitative, reproducible flow cytometry analysis, including bead-based conversion to standard units and a Jupyter notebook interface. - FlowCal (Python library + Excel interface) (FlowCal link)
FlowCal is a library for reading, analyzing, and calibrating flow cytometry data in Python, including bead-based conversion to standard units and an Excel worksheet interface for simple data entry.
Analyzing and Plotting Data
Below are some tools that can help you analyze your data and create useful plots to explain your results.
DNAplotlib (link)
Visually integrating graphs of your data with a schematic representation of the parts and circuits which generated that data is an important aspect of scientific communication in synthetic biology. There are many ways to achieve this goal, but for teams with proficiency in the Python programming language, DNAplotlib is an excellent tool developed by the authors of Der and Glassey et al., 2016, ACS Synthetic Biology for this purpose. Even for teams without coding experience, we recommend looking at some of DNAplotlib’s sample graphs as an example of good data visualization practices in synthetic biology.
WebPlotDigitizer (link)
Often, published data (whether in scientific papers or in the BioBrick Registry) exists only in graphical form, which prevents you from being able to make quantitative comparisons between your results and existing work. WebPlotDigitizer, developed by Ankit Rohatgi, is an open-source web-based tool that solves this problem by allowing you to input an image of a graph or plot and returning numerical values for the data depicted in the image. No coding experience is required-- just upload an image, define values along the axes, and click on points within the graph to generate a table of data that you can analyze!