AbstractMultidimensional measurements using state-of-the-art separations and mass spectrometry, provide great advantages in untargeted metabolomics analyses for studying bio-chemical processes in environmental and biological research. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we developed and evaluated a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder™, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We applied PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results were validated manually and against selected reaction monitoring and gas-chromatography platforms, showing that 2350 metabolite features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
Published: May 12, 2023