Projects per year
Abstract
The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous “smart” microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.
Original language | English |
---|---|
Article number | 2101063 |
Journal | Advanced Biology |
Volume | 6 |
Issue number | 4 |
Early online date | 24 Oct 2021 |
DOIs | |
Publication status | Published - 30 Apr 2022 |
Bibliographical note
Funding Information:The authors thank Rainer Heintzmann for reviewing the draft of this manuscript. The authors want to thank Opentrons for supporting this project. The authors thank the Free State of Thuringia for funding BD. The authors acknowledge the ZIM project ZF4006820DF9 for funding H.W. Additionally the authors thank the Royal Society (URF\R1\180153, RGF\EA\181034), EPSRC (EP/R013969/1, EP/R011443/1) for funding R.B., K.B., and J.C.
Keywords
- high-throughput
- lab automation
- machine learning
- open source
- smart microscopy
ASJC Scopus subject areas
- General Biochemistry,Genetics and Molecular Biology
- Biomedical Engineering
- Biomaterials
Fingerprint
Dive into the research topics of 'An Open-Source Modular Framework for Automated Pipetting and Imaging Applications'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Detailed Malaria Diagnostics with Intelligent Microscopy
Bowman, R. (PI) & Campbell, N. (CoI)
Engineering and Physical Sciences Research Council
1/02/18 → 31/01/22
Project: Research council