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Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering

last modified Oct 05, 2017 01:57 AM
Former OpenPlant Fellow Dr Fernan Federici, former OpenPlant PDRA Dr Tim Rudge and colleagues have recently published a pre-print for their low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering, supported by the OpenPlant Fund.

Nuñez, Isaac, Tamara Matute, Roberto Herrera, Juan Keymer, Tim Marzullo, Tim Rudge, and Fernan Federici. "Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering." bioRxiv (2017): 194324


Examples of images of bacterial colonies and cell-free systems using the microscope. Credit: Federici Lab


The advent of easy-to-use open source microcontrollers, off-the-shelf electronics and customizable manufacturing technologies has facilitated the development of inexpensive scientific devices and laboratory equipment. In this study, we describe an imaging system that integrates low-cost and open-source hardware, software and genetic resources. The multi-fluorescence imaging system consists of readily available 470 nm LEDs, a Raspberry Pi camera and a set of filters made with low cost acrylics. This device allows imaging in scales ranging from single colonies to entire plates.

We developed a set of genetic components (e.g. promoters, coding sequences, terminators) and vectors following the standard framework of Golden Gate, which allowed the fabrication of genetic constructs in a combinatorial, low cost and robust manner. In order to provide simultaneous imaging of multiple wavelength signals, we screened a series of long stokes shift fluorescent proteins that could be combined with cyan/green fluorescent proteins. We found CyOFP1, mBeRFP and sfGFP to be the most compatible set for 3-channel fluorescent imaging. We developed open source Python code to operate the hardware to run time-lapse experiments with automated control of illumination and camera and a Python module to analyze data and extract meaningful biological information.

To demonstrate the potential application of this integral system, we tested its performance on a diverse range of imaging assays often used in disciplines such as microbial ecology, microbiology and synthetic biology. We also assessed its potential for STEM teaching in a high school environment, using it to teach biology, hardware design, optics, and programming. Together, these results demonstrate the successful integration of open source hardware, software, genetic resources and customizable manufacturing to obtain a powerful, low cost and robust system for STEM education, scientific research and bioengineering. All the resources developed here are available under open source license


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