An open-source experimental framework for automation of cell biology experiments
Preprint posted on 3 July 2020 https://www.biorxiv.org/content/10.1101/2020.07.02.185454v1
Deep learning and artificial intelligence-based methods have been explored over the last decade, and have proved to be extremely useful for high throughput image analysis, optimization tasks, and robotics. Moreover, platforms have been designed with the purpose of providing a user-friendly interface for users with little or no experience using deep learning and artificial intelligence-based methods. While in terms of image analysis these advances have been revolutionary, large training datasets need to be generated, and this is often not feasible in biological experiments, as they often take a long time to conduct. Therefore, there is a demand for automated systems that would allow hundreds or thousands of experiments to be performed simultaneously. Various currently available systems have various limitations, the main ones being that they are cost-prohibitive, non-flexible in terms of setup, or lack integrated solutions (for example image acquisition and simultaneous analysis). To address this gap, Katunin et al (1) present here a novel open-source hardware and software that allows for automatic high-throughput generation of large amounts of cell-biological data.
Key findings and developments
The system consists of a hardware part, based on a 3D-printed modular design. The modules include a X-Y stage capable of moving a multiwell plate horizontally; a perfusion manifold, capable of applying up to 8 solution into individual wells; and a small autofocusing epifluorescent microscope. The authors make available the 3D printing files at https://github.com/frescolabs/FrescoM
The perfusion system is based on syringe pumps, and allows solution addition to individual wells, as well as solution changes. The microscope is a standard inverted microscope system including a non-infinity corrected objective, a 100mm camera lens, and no-tube lens; blue and green Throlabs filters, and a dichroic mirror. The objective moves separately in a Z-stage.
The authors also share details of the automatic control and electric circuitry; and assembly instructions for the hardware (XY platform, the perfusion manifold, the Z-stage, and a mini-microscope).
A further advantage of this design, is that it is very cheap (between 300 and 2500 GBP based on the components required); it is customizable towards user needs; and it is fully 3D printable, which further reduces costs of repairs if needed.
Software for operation
The software for operation is written in Python 3 and allows key functions including starting live fluorescence imaging without recording; moving the platform in X and Y directions (forward, backward, left and right), moving the application manifold up and down, returning to zero position, setting top-right and bottom-right positions for a multiwell plate; moving the pump forwards and backwards; and starting an experiment. The software is available for download at https://github.com/frescolabs/FrescoM/blob/master/software
Proof of principle and applicability.
As proof of principle, the authors imaged calcium dynamics in response to ATP, and show that the resulting images were of sufficient quality to allow identification of individual cells and their response to calcium. The system allows imaging of fluorescent reporters in live cells, or fixed samples labeled with antibodies. Altogether, this experimental framework would make generation of large scale biological data quicker and more accurate.
The authors discuss the potential applications of their design, including high-throughput screening experiments allowing exploring chemicals affecting a range of biological functions; optimization of experiments in an automatic manner for a large range of conditions, with the output being fluorescence-based; large-scale characterization of cells derived from individual patients; and routine cell biology experiments.
What I like about this preprint
I firmly believe in the value of open-science and this work is based on this principle. Beyond the uses in general labs, I think this will allow performing the experiments discussed in this work, in remote areas too.
- Katunin P, et al, An open-source experimental framework for automation of cell biology experiments, bioRxiv, 2020.
Posted on: 15 July 2020
doi: https://doi.org/10.1242/prelights.23132Read preprint
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