Ouyang Wei, Bowman Richard W, Wang Haoran, Bumke Kaspar E, Collins Joel T, Spjuth Ola, Carreras-Puigvert Jordi, Diederich Benedict
W. Ouyang, Science for Life Laboratory School of Engineering Sciences in Chemistry, Biotechnology and Health KTH - Royal Institute of Technology, Stockholm, 114 28, Sweden.
R. W. Bowman, K. E. Bumke, J. T. Collins, Department of Physics, University of Bath, Bath, BA2 7AY, UK.
Adv Biol (Weinh). 2022 Apr;6(4):e2101063. doi: 10.1002/adbi.202101063. Epub 2021 Oct 24.
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.
生物实验中的样本数量在不断增加,但复杂的实验方案以及许多情况下的人为误差导致数据质量欠佳,进而难以重现科学发现。实验室自动化可通过精确重现机器可读的实验方案来缓解诸多此类问题。这些仪器通常需要高额的前期投资,而且由于缺乏开放的应用程序编程接口(API),科学家们在供应商提供的软件之外对其进行定制和控制异常困难。在此,本文展示了用于生命科学跨学科研究的自动化高通量实验,该实验使用OpenFlexure、Opentrons、ImJoy和UC2等开放工具,通过组合这些工具以确保可重复性,从而能够以适度的预算进行复制。这种自动化样本制备和成像流程可以通过易于理解的算法和易于构建的显微镜,在许多实验室以及教育环境中轻松复制和建立。此外,通过根据对先前获取图像的分析进行后续移液或成像步骤来创建反馈回路,能够实现完全自主的“智能”显微镜实验。所有文档和源文件均公开可用,以证明使用廉价开放工具实现智能实验室自动化的概念。相信这将使人们更广泛地获得自动化实验的能力和可重复性。