Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Guillevic, Myriam; Guillevic, Aurore; Vollmer, Martin K.; Schlauri, Paul; Hill, Matthias; Emmenegger, Lukas; Reimann, Stefan

doi:10.1186/s13321-021-00544-w

Cited by 4 publications

(3 citation statements)

References 63 publications

(106 reference statements)

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“…Metabolite annotation remains the main bottleneck in untargeted metabolomics [ 1 , 2 ], with the vast majority of metabolites being left as unidentified [ 3 ]. Beyond the molecule’s mass, other molecule’s properties such as Retention Time (RT), collision cross section, or the fragmentation spectrum can be very valuable during the metabolite annotation process [ 4 , 5 ]. The most common approach to annotate metabolites is to query a metabolomics database for compounds that have a mass compatible with the experimental masses.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

et al. 2022

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Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by the lack of reproducibility between different chromatographic methods. Accurate prediction of retention time for a given chromatographic method would be a valuable support for metabolite annotation. We have trained state-of-the-art machine learning regressors using the 80, 038 experimental retention times from the METLIN Small Molecule Retention Tim (SMRT) dataset. The models included deep neural networks, deep kernel learning, several gradient boosting models, and a blending approach. 5, 666 molecular descriptors and 2, 214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software. The models were trained using only the descriptors, only the fingerprints, and both types of features simultaneously. Bayesian hyperparameter search was used for parameter tuning. To avoid data-leakage when reporting the performance metrics, nested cross-validation was employed. The best results were obtained by a heavily regularized deep neural network trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of $$39.2 \pm 1.2\; s$$ 39.2 ± 1.2 s and $$17.2 \pm 0.9\;s$$ 17.2 ± 0.9 s , respectively. To the best of our knowledge, these are the most accurate predictions published up to date over the SMRT dataset. To project retention times between chromatographic methods, a novel Bayesian meta-learning approach that can learn from just a few molecules is proposed. By applying this projection between the deep neural network retention time predictions and a given chromatographic method, our approach can be integrated into a metabolite annotation workflow to obtain z-scores for the candidate annotations. To this end, it is enough that just as few as 10 molecules of a given experiment have been identified (probably by using pure metabolite standards). The use of z-scores permits considering the uncertainty in the projection when ranking candidates, and not only the accuracy. In this scenario, our results show that in 68% of the cases the correct molecule was among the top three candidates filtered by mass and ranked according to z-scores. This shows the usefulness of this information to support metabolite annotation. Python code is available on GitHub at https://github.com/constantino-garcia/cmmrt.

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Section: Introductionmentioning

confidence: 99%

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

et al. 2022

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“…However, EI fragment spectra are often preferred over CID due to better reproducibility, platform independence, and availability of comprehensive libraries. Though examples for nontargeted analysis with GC-EI-MS are available in the literature, identification of the molecular ion, greatly facilitating compound assignment, is often not feasible due to extensive fragmentation immediately following the 70 eV ionization process; a complementary ionization mechanism yielding the molecular ion would therefore be desirable. , Numerous examples for “softer” ionization processes in a low-pressure ionization GC–MS system, for example, chemical- (CI), , photo- (PI), , field- (FI), , and cold electron ionization, , have been introduced. All these techniques generate intact molecular ions.…”

Section: Introductionmentioning

confidence: 99%

Parallel Operation of Electron Ionization and Chemical Ionization for GC–MS Using a Single TOF Mass Analyzer

et al. 2022

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This work describes a novel mass spectrometer coupled to gas chromatography (GC–MS) that simultaneously displays the mass spectral information of electron (EI)- and chemical ionization (CI)-generated ion populations for a single chromatographic peak. After GC separation, the eluent is equally split and supplied in parallel to an EI and a novel CI source, both operating continuously. Precise switching of the ion optics provides the exact timing to consecutively extract the respective ion population from both sources and transfer them into a time-of-flight (TOF) mass analyzer. This technique enables the acquisition of complementary information from both ion populations (EI and CI) within a single chromatographic run and with sufficient data points to retain the chromatographic fidelity. The carefully designed GC transfer setup, fast ion optical switching, and synchronized TOF data acquisition system provide an automatic and straightforward spectral alignment of two ion populations. With an eluent split ratio of about 50% between the two ion sources, instrument detection limits of <40 fg on the column (octafluoronaphthalene) for the EI and <2 pg (benzophenone) for the CI source were obtained. The system performance and the additional analytical value for compound identification are demonstrated by means of different common GC standard mixtures and a commercial perfume sample of unknown composition.

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“…[ 97 ] Thus, machine learning is being integrated into the data analysis of atmospheric mass spectrometry, but little attention is currently devoted to compound identification. GC‐MS machine learning models for molecular formula annotation of atmospheric, halogenated compounds, [ 98 ] or for molecular property and quantification factor prediction, [ 69 ] are two notable exceptions.…”

Section: Compound Identification With Mass Spectrometrymentioning

confidence: 99%

Data‐Driven Compound Identification in Atmospheric Mass Spectrometry

Sandström,

Rissanen,

Rousu

et al. 2023

Advanced Science

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Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time‐of‐flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data‐driven compound identification approaches could alleviate the problem, yet remain rare to non‐existent in atmospheric science. In this perspective, the authors review the current state of data‐driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry.

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Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

Cited by 4 publications

References 63 publications

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

Parallel Operation of Electron Ionization and Chemical Ionization for GC–MS Using a Single TOF Mass Analyzer

Data‐Driven Compound Identification in Atmospheric Mass Spectrometry

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