AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting
Journal article, 2024

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at


Gabriel Reder

Chalmers, Life Sciences, Systems and Synthetic Biology

Royal Institute of Technology (KTH)

Erik Bjurström

Chalmers, Life Sciences, Infrastructures

Daniel Brunnsåker

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Filip Kronström

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

Praphapan Lasin

Chalmers, Life Sciences, Infrastructures

Ievgeniia Tiukova

Chalmers, Life Sciences, Infrastructures

Otto Savolainen

Chalmers, Life Sciences, Systems and Synthetic Biology

University of Eastern Finland

James N. Dodds

The University of North Carolina System

Jody C. May

Vanderbilt University

John P. Wikswo

Vanderbilt University

John A. McLean

Vanderbilt University

Ross King

Alan Turing Institute

University of Cambridge

Chalmers, Life Sciences, Systems and Synthetic Biology

Journal of the American Society for Mass Spectrometry

1044-0305 (ISSN) 18791123 (eISSN)

Vol. 35 3 542-550

Subject Categories

Computer and Information Science

Biological Sciences

Chemical Sciences





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