An Open‐Source Modular Framework for Automated Pipetting and Imaging Applications. Issue 4 (24th October 2021)

• Record Type: Journal Article
• Authors: Ouyang, Wei; Bowman, Richard W.; Wang, Haoran; Bumke, Kaspar E.; Collins, Joel T.; Spjuth, Ola; Carreras‐Puigvert, Jordi; Diederich, Benedict
• Is Part Of: Advanced biology. Volume 6:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-24
• Subjects: high‐throughput -- lab automation -- machine learning -- open source -- smart microscopy; Molecular biology -- Periodicals; Systems biology -- Periodicals; Biological systems -- Periodicals; Biotechnology -- Periodicals; Bioengineering -- Periodicals; Biomedical engineering -- Periodicals; 660.6
• DOI: 10.1002/adbi.202101063
• Languages: English
• ISSNs: 2701-0198

Abstract

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. Abstract : This study describes how the combination of different open‐source hard‐ and software projects can form a cutting‐edge lab automation setup with high‐throughput robotic microscopy (UC2 and OpenFlexure), automated sample preparation (Opentrons), and image processing (Imjoy). Publicly available documentation guides the user through the process of replicating the setup.