RMassBank: Automatic Recalibration and Processing of Tandem HR-MS Spectra for MassBank

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1 RMassBank: Automatic Recalibration and Processing of Tandem HR-MS Spectra for MassBank Eawag: Swiss Federal Institute of Aquatic Science and Technology Presenting: Emma Schymanski Coauthors: Michael Stravs, Heinz Singer and Juliane Hollender Eawag Department of Environmental Chemistry Dübendorf (Zurich), Switzerland RMassBank Questions:

2 Presentation Overview Contents o Introduction and Problem Formulation o Spectral clean-up (garbage in => garbage out!) o Spectral annotation (compound-spectrum link) o The RMassBank Workflow o Data processing o Record creation o Results of RMassBank Processing o Proof-of-concept on 70 pesticide spectra o Spectral Interpretation

3 Spectral Clean-up Garbage in = garbage out! The General Problem o No time: many want to add spectra in principle, no-one has time to do it o Manual data entry is tedious, repetitive, prone to errors o Full manual quality control of spectra is tedious and resource-consuming The Result* Database becomes a spectral dump ** o We ll just use a noise cut-off, that ll be fine e.g. WA o Cut-off at 5 (MassBank reporting can go down to 1 ) o We ll just use the peak list from our software e.g. CE o Many low intensity noise peaks, including peaks above [M+H] + o Annotation is tedious e.g. JEL00007 o Minimum amount of information provided only * These examples are demonstrations only and no offense intended! ** Thanks to Oliver Fiehn for this apt description!

4 Spectral Clean-up * These examples are demonstrations only and no offense intended!

5 Spectral Clean-up These examples are demonstrations only and no offense intended!

6 Spectral Clean-up Example of Orbitrap Spectra Garbage in = garbage out! Measurement artefacts include o Systematic increase in ppm error with low m/z values o 5 ppm accuracy => 15 ppm at m/z < 100 o Satellite or shoulder peaks result of FT instrument processing o Consistent electronic or measurement noise peaks o Noise peaks at a ~fixed level hiding real peaks of lower intensity o A strict noise cut-off will result in a loss of information High mass accuracy standard spectra have advantages: o Meringer et al. (2011) showed a missing subformula indicates instrument noise or interfering peaks o Use subformula assignment to perform spectral clean-up! Meringer et al. 2011, MATCH Commun. Math. Comput. Chem. 65,

7 Spectral Annotation Matching Spectrum and Compound Information User needs to contribute a bare minimum of information o Only the user knows what compound has been measured o At least one form of unambiguous compound identifier is required o e.g. internal ID, name, SMILES and retention time o Measurement parameters / methods / settings are relatively consistent o These can be added in batch form, not individually Internet Services: Let search engines do the work for you! o CACTUS Chemical Identifier Resolver 1 o SMILES (c1ccccc1) to InChI Key (UHOVQNZJYSORNB-UHFFFAOYSA-N) o Chemical Translation Service (CTS) 2 to do the rest o Names, CAS #, InChI and Identifiers (IDs, if available): PubChem CID, ChemSpider, ChEBI, HMDB, KEGG, LipidMaps

8 RMassBank Workflow Simple Form Automatic MS and MS/MS Recalibration and Clean-up Remove interfering peaks Spectral Annotation with - Experimental Details - Compound Information Stravs, Schymanski, Singer and Hollender, 2012, Journal of Mass Spectrometry, accepted. DOI: /jms.3131

9 RMassBank Clean-up and Recalibration LC-MS/MS raw data Extract MS2 spectra Automated extraction by accurate m/z and RT. FT-satellite removal (± 0.5 Da; I < 5 % of peak) Subformula assignment: large tolerance Elements of molecular formula ± adduct ppm 15 (m/z < 120) or 10 (m/z > 120) compound list Recalibrate raw data with assigned fragments Use only peaks with unique formula calculated Subformula reassignment: 5 ppm tolerance Formulas recalculated post calibration. For "Fail Peaks", recalculate with +2N & O noise peaks Multiplicity filtering Peaks are retained if they occur 2x per compound (in e.g. 14 recorded spectra) discarded noise intense unmatched peaks filtered peaks Filtered peaks go through to Records Intense unmatched peaks for manual check

10 RMassBank Example Clean-up C 10 H 13 O Da 2.1 ppm Verapamil C 27 H 39 N 2 O 4 + [M+H]+ = Da 0e+00 2e+06 4e+06 O O N N O O

11 RMassBank Spectrum Annotation compound list: SMILES, name annotation list online resources: CTS, CACTUS Chemical annotation: InChI, ChemSpiderID, PubChem CID... manual curation Experimental annotation: CE, ionization, RT... filtered peaks Record generation user-defined additional peaks MassBank records structure files - Recalibrated, cleaned-up - Annotated - Ready for upload!

12 Example MassBank Record

13 RMassBank with 70 Eawag Pesticide Spectra Experimental o Individual Injection; routine chromatography; Orbitrap XL with ESI + / - o Daily vendor-recommended calibration MS, MS/MS CID 35%, HCD 15, 30, 45, 60, 75, 90 % R=7500 & Screened for [M+H] + precursor within RT ± 0.3 min MS/MS retrieved from MS with highest intensity

14 RMassBank with 70 Eawag Pesticide Spectra Processing: The Numbers o 68 of 70 pesticides with sufficient [M+H] + for processing o 55,594 peaks present following satellite removal o 14,699 with at least one subformula post-calibration o 13,305 of these present in two or more spectra per compound o 454 peaks with subformula Formation only when of adding N 2 and 2N H 2 + O adducts o 256 of these occurred at least is relevant twice per in compound MS/MS! => reproducible o Only 44 peaks remained for manual inspection o No difference observed between spectra with different resolutions Additional modes (results not shown here) o M +, [M+Na] +, [M-H] -, M -, [M+FA] -, Effect of Recalibration o Shown in the next few slides ~76 % of peaks are noise!

15 RMassBank with 70 Eawag Pesticide Spectra Recalibration Curve: Relative mass deviation over m/z before recalibration after recalibration δ ppm m/z m/z

16 RMassBank with 70 Eawag Pesticide Spectra Recalibration: Relative Mass Deviation Distribution before recalibration after recalibration

17 RMassBank with 70 Eawag Pesticide Spectra Frequency of Occurrence of Peaks by Intensity: Multiplicity Filtering Peaks with no formula Single occurrence peaks with a formula Multiple occurrence peaks with a formula

18 RMassBank with 70 Eawag Pesticide Spectra 944 MS/MS spectra from [M+H] + of 70 pesticides o o Total Number of RMassBank Spectra o RMassBank records in Uchem-MassBank: o 5,312 records (374 compounds) LTQ Orbitrap XL o 153 records (12 compounds) Orbitrap Adducts o 1,262 records (151 compounds) Q Exactive Orbitrap o RMassBank records in NORMAN MassBank: o 3,193 records (158 compounds) from UFZ Orbitrap XL o 3,102 records (226 compounds) from Eawag Orbitrap XL

19 RMassBank with 70 Eawag Pesticide Spectra For those online: pull up MassBank record EA Intensity 0e+00 2e+05 4e+05 6e+05 N H 2 O S O NH O O CH 3 C 5 H 5 + Asulam C 6 H 6 N +C 6H 6 NO + C 6 H 8 NO + C 6 H 6 N 3 + N + N NH C 6 H 6 NO 2 S +

20 Conclusions: RMassBank The RMassBank Workflow o Reduces much manual work associated with bulk creation of many records o Creates high quality MS/MS spectra o Annotation with formula value-adds the spectra o Works very well for the spectra it was developed on (Orbitrap) o BUT: Every mass spectrometer is different: o Processing and measurement steps will probably need adjusting Benefit for Contributors o We have learnt a lot about our spectra and compounds (e.g. MS/MS adducts!) o MassBank is being using within our department o If you want to know more about what recalibration can do for your data: o th November: Emerging Pollutants Workshop, Non-target session o Stravs et al. 2012, J. Mass Spectrom., DOI: /jms.3131

21 Acknowledgements o Marie-Curie Post Doctoral Fellowship (E. Schymanski), o Eawag Discretionary Funds (M. Stravs) o Coauthors: Michael Stravs, Heinz Singer, Juliane Hollender o R & MassBank help: Steffen Neumann, Michael Gerlich, Carsten Kuhl, (IPB) o Discussions: Matthias Ruff, Martin Loos (Eawag); Tobias Schulze, Martin Krauss, Werner Brack (UFZ) o Programs: Markus Meringer (MOLGEN-MS/MS), Florian Rasche (Sirius), Miguel Rojas-Cherto & Egon Willighagen (MEF) o MassBank & Naming Rights: Prof. Takaaki Nishioka o NORMAN Association

22 Any Questions? Emma Schymanski, Michael Stravs, Heinz Singer, Juliane Hollender (ES, MS) DOI: /jms.3131 All Details Contained Within!

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