An Open‐Source Modular Framework for Automated Pipetting and Imaging Applications

TLDR

Biological experiments increasingly involve large sample numbers, yet complex protocols and human error compromise data quality and reproducibility, and while laboratory automation can mitigate these issues, existing instruments are costly and lack open APIs for customization. The study demonstrates that high-throughput, reproducible life‑science experiments can be performed on a modest budget by integrating open‑source tools such as OpenFlexure, Opentrons, ImJoy, and UC2. The authors present a modular, open‑source pipeline that automates sample preparation and imaging, supports feedback loops for autonomous experiments, and is easily replicated in laboratories and educational settings with publicly available algorithms and microscope designs. The framework is expected to democratize access to automated, reproducible experiments.

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.