Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering
Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering
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DOI：
10.1101/194324
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发表时间：
2017-09
期刊：
影响因子：
3.7
通讯作者：
Isaac Núñez;Tamara Matúte;Roberto Herrera;J. Keymer;T. Marzullo;T. Rudge;Fernán Federici
中科院分区：
文献类型：
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作者：
Isaac Núñez;Tamara Matúte;Roberto Herrera;J. Keymer;T. Marzullo;T. Rudge;Fernán Federici
易于使用的开源微控制器、现成的电子产品和可定制的制造技术的出现促进了廉价科学设备和实验室设备的开发。在这项研究中，我们描述了一个成像系统，集成了低成本和开源的硬件，软件和遗传资源。多荧光成像系统由现成的470 nm LED，Raspberry Pi相机和一组低成本丙烯酸树脂制成的滤光片组成。该设备允许在从单个菌落到整个平板的范围内进行成像。我们开发了一套遗传组件（如启动子，编码序列，终止子）和载体的标准框架的金门，这使得制造的遗传构建体的组合，低成本和强大的方式。为了提供多波长信号的同时成像，我们筛选了一系列可以与青色/绿色荧光蛋白结合的长斯托克斯位移荧光蛋白。我们发现CyOFP 1、mBeRFP和sfGFP是3通道荧光成像的最兼容组。我们开发了开源Python代码来操作硬件，通过自动控制照明和相机来运行延时实验，并使用Python模块来分析数据并提取有意义的生物信息。为了证明这种集成系统的潜在应用，我们在微生物生态学，微生物学和合成生物学等学科中经常使用的各种成像分析上测试了其性能。我们还评估了它在高中环境中进行STEM教学的潜力，使用它来教授生物学，硬件设计，光学和编程。总之，这些结果证明了开源硬件，软件，遗传资源和可定制制造的成功整合，为STEM教育，科学研究和生物工程提供了强大，低成本和强大的系统。这里开发的所有资源都可以在开源许可证下使用。
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 licenses.