PNNL - ABF_IMS_SyntheticBio

PeakDecoder enables machine learning-1 based metabolite annotation and accurate profiling in multidimensional mass spectrometry measurements
  • Organism: Pseudomonas putida KT2440
  • Instrument: TSQ Altis
  • SpikeIn: Yes
  • Keywords: metabolomics, mass spectrometry, ion mobility spectrometry, data-independent acquisition, synthetic biology, HILIC, algorithm, machine learning.
  • Lab head: Yuqian Gao Submitter: Yuqian Gao
Multidimensional measurements using state-of-the-art separations and mass spectrometry provide great advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive, 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 apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards.
Created on 2/6/23, 4:06 PM
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