PT  - JOURNAL ARTICLE
AU  - Pavel Katunin
AU  - Ashley Cadby
AU  - Anton Nikolaev
TI  - An open-source experimental framework for automation of cell biology experiments
AID  - 10.1101/2020.07.02.185454
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.02.185454
4099  - http://biorxiv.org/content/early/2020/07/03/2020.07.02.185454.short
4100  - http://biorxiv.org/content/early/2020/07/03/2020.07.02.185454.full
AB  - Modern data analysis methods, such as deep learning, have been successfully applied to a number of biological and medical questions. For these methods to be efficient, a large number of high quality experiments need to be conducted, which requires a high degree of automation. Here we report an open-source hardware that allows for automatic high-throughput generation of large amounts of biological data. The hardware consists of an automatic XY-stage for moving multiwell plates containing growing cells; a perfusion manifold allowing application (perfusion) of up to 8 different solutions; and a small epifluorescent microscope. It is extremely cheap (£300 without and £2500 with a fluorescent microscope) and can be quickly customised for individual experimental needs.Key points- We present an open source framework for automation of cell biology experiments- The framework consists of an XY platform, application of up to 8 solutions and a small epifluorescent microscope- Very cheap (£300 without a fluorescent microscope and £2500 with a fluorescent microscope), customisable, 3D printable- Can be used in a variety of biological applications such as imaging of fluorescent reporters, optimisation of treatment conditions and immuno-labellingCompeting Interest StatementThe authors have declared no competing interest.