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Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer

Nature Methods volume 22, pages 1008–1016 (2025)Cite this article

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Abstract

Chemical exposures may affect human metabolism and contribute to the etiology of neurodegenerative disorders such as Alzheimer’s disease. Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can substantially degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with a greater than 150-fold higher intensity of noise ions than true fragment ions. For human plasma samples from patients with Alzheimer’s disease that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.5-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides.

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Fig. 1: Flowchart for Spectral Denoising.
Fig. 2: Developing, validating and benchmarking the Spectral Denoising algorithm.
Fig. 3: Probability distributions of MS/MS entropy similarities before (‘raw’) and after applying three benchmarking methods against the Spectral Denoising algorithm, under varying levels of artificially added chemical and electronic noises.
Fig. 4: Density distributions for MS/MS similarity improvements after Spectral Denoising for all MS/MS spectra from 240 injected standards between 0.02 and 200 pmol, using the 500-pmol spectra as reference.
Fig. 5: Denoising Search results for positive ESI mode hydrophilic interaction LC–MS/MS data acquired on an Exploris 240 Orbitrap instrument and the Astral mass spectrometer, using 20 plasma samples of patients with Alzheimer’s disease.

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Data availability

NIST Tandem Mass Spectral Library, 2023 release (NIST23) spectra are commercially available and can be purchased from multiple vendors. MassBank of North America database (Massbank.us) spectra can be freely downloaded from Massbank.us (https://massbank.us/). The metabolome dataset of Alzheimer’s disease samples and the experimental data from the chemical dilution series are available via Zenodo at https://zenodo.org/records/14920689 (ref. 43). Source data are provided with this paper.

Code availability

The code for using spectral denoising and denoising search is available via GitHub at https://github.com/FanzhouKong/spectral_denoising and via Zenodo at https://zenodo.org/records/14920689 (ref. 43).

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Acknowledgements

Samples were provided by the Alzheimer’s Disease Metabolomics Consortium (ADMC) funded wholly or in part by the following grants and supplements thereto. However, recipients of these awards were not authors of the report presented here: grant nos. NIA R01AG046171, RF1AG051550, RF1AG057452, R01AG059093, RF1AG058942, U01AG061359 and U19AG063744 and FNIH grant no. DAOU16AMPA were awarded to R. Kaddurah-Daouk at Duke University in partnership with a large number of academic institutions. A complete listing of ADMC investigators can be found at https://sites.duke.edu/adnimetab/team/. T.S., Y.L., F.K. and O.F. were supported by grant nos. R01 GM155383 (to O.F.), R01 HL157535 (to O.F.) and U01 AG08862 (to O.F.).

Author information

Authors and Affiliations

  1. Chemistry Department, University of California Davis, Davis, CA, USA

    Fanzhou Kong

  2. West Coast Metabolomics Center, University of California Davis, Davis, CA, USA

    Fanzhou Kong, Tong Shen, Yuanyue Li & Oliver Fiehn

  3. Thermo Fisher Scientific, San Jose, CA, USA

    Amer Bashar & Susan S. Bird

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Contributions

O.F. supervised and directed this project. F.K. developed and performed the analysis. T.S. acquired experimental data. Y.L. curated validation data. A.B. and S.S.B. provided instruments and collected experimental data. F.K. and O.F. wrote the manuscript with comments from all the other authors.

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Correspondence to Oliver Fiehn.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Distribution of ion counts in NIST 23 Orbitrap MS/MS spectra.

When analyzing NIST 23 Orbitrap MS/MS spectra, the plot shows the distribution of the total counts of ion frequencies per spectrum that share identical intensity values within a tolerance of ±0.1%. The vertical dashed line at 4 counts marks the 99.5th percentile. We therefore used this threshold as rule for electronic denoising.

Source data

Extended Data Fig. 2 Entropy similarity distribution between denoised and raw NIST 23 Orbitrap MS/MS spectra.

This histogram illustrates the distribution of entropy similarities between 10,000 randomly sampled denoised NIST 23 Orbitrap MS/MS spectra, against their corresponding raw spectra. The mode, mean and median spectral entropy similarity are all above 0.99. This means spectra that have already high quality (like NIST23 spectra) are not significantly affected by electronic and chemical denoising.

Source data

Extended Data Fig. 3 Count of experimental MS/MS spectra generated in dilution series for 240 chemical standards for positive and negative electrospray ionization (ESI) modes for an Orbitrap mass analyzer.

Due to the number of different adducts, more than 240 MS/MS spectra were generated by injection of reference standards at high levels of compounds. However, at lower amounts injected on the column, a decreasing number of MS/MS spectra were recorded, reflecting the ionization efficiencies of different compounds at different concentrations (0.02–500 pmol injected on column) for both positive (blue) and negative (orange) electrospray ionization (ESI) modes.

Source data

Extended Data Fig. 4 Performance benchmarking of different denoising methods on experimental spectra.

Probability density estimates of entropy similarities comparing raw spectra and spectra denoised by various denoising methods: DNL denoising, MS Reduce, 1% base peak (bp) thresholding, and Spectral Denoising. The analysis performed different denoising methods on spectra acquired at 0.02–200 pmol, using the 500 pmol spectra as reference.

Source data

Extended Data Fig. 5 MS/MS spectral similarity improvement from Spectral Denoising across different levels of noise addition and spectral entropy.

Probability density estimates of entropy similarity improvements after applying Spectral Denoising to MS/MS spectra from 240 injected standards (0.02–200 pmol), using spectra acquired at 500 pmol as references. The analysis includes nine conditions with varying levels of artificially introduced electronic and chemical noise. Spectra are further stratified into five groups based on the spectral entropy of reference spectra.

Source data

Extended Data Fig. 6 Benchmarking false discovery rates between Denoising Search and Entropy Search.

False-discovery rate (FDR) benchmarking of 1,247 positive ESI-mode Orbitrap MS/MS spectra from human plasma metabolites, manually annotated using MassBank.us, GNPS, and NIST23 libraries. The plots compare FDR values across spectral similarity scores between Denoising Search and Entropy Search using experimental spectra. (a) FDR calculated based on the top hit only. (b) FDR calculated using the top 3 hits.

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Kong, F., Shen, T., Li, Y. et al. Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer. Nat Methods 22, 1008–1016 (2025). https://doi.org/10.1038/s41592-025-02646-x

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