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A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Author

Listed:
  • Mingdu Luo

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yandong Yin

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences)

  • Zhiwei Zhou

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences)

  • Haosong Zhang

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xi Chen

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Hongmiao Wang

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zheng-Jiang Zhu

    (Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences
    Shanghai Key Laboratory of Aging Studies)

Abstract

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Suggested Citation

  • Mingdu Luo & Yandong Yin & Zhiwei Zhou & Haosong Zhang & Xi Chen & Hongmiao Wang & Zheng-Jiang Zhu, 2023. "A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37539-0
    DOI: 10.1038/s41467-023-37539-0
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    References listed on IDEAS

    as
    1. Zhiwei Zhou & Mingdu Luo & Xi Chen & Yandong Yin & Xin Xiong & Ruohong Wang & Zheng-Jiang Zhu, 2020. "Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    2. Zhiwei Zhou & Mingdu Luo & Haosong Zhang & Yandong Yin & Yuping Cai & Zheng-Jiang Zhu, 2022. "Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    3. Oliver Alka & Premy Shanthamoorthy & Michael Witting & Karin Kleigrewe & Oliver Kohlbacher & Hannes L. Röst, 2022. "DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Xavier Domingo-Almenara & Carlos Guijas & Elizabeth Billings & J. Rafael Montenegro-Burke & Winnie Uritboonthai & Aries E. Aisporna & Emily Chen & H. Paul Benton & Gary Siuzdak, 2019. "The METLIN small molecule dataset for machine learning-based retention time prediction," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    5. Xiaotao Shen & Ruohong Wang & Xin Xiong & Yandong Yin & Yuping Cai & Zaijun Ma & Nan Liu & Zheng-Jiang Zhu, 2019. "Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    6. Tongzhou Li & Yandong Yin & Zhiwei Zhou & Jiaqian Qiu & Wenbin Liu & Xueting Zhang & Kaiwen He & Yuping Cai & Zheng-Jiang Zhu, 2021. "Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    7. Catherine G. Vasilopoulou & Karolina Sulek & Andreas-David Brunner & Ningombam Sanjib Meitei & Ulrike Schweiger-Hufnagel & Sven W. Meyer & Aiko Barsch & Matthias Mann & Florian Meier, 2020. "Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    8. Vadim Demichev & Lukasz Szyrwiel & Fengchao Yu & Guo Ci Teo & George Rosenberger & Agathe Niewienda & Daniela Ludwig & Jens Decker & Stephanie Kaspar-Schoenefeld & Kathryn S. Lilley & Michael Mülleder, 2022. "dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
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