LC-METASPACE Introduction

Transcript

1. LC-METASPACE METASPACE technology for liquid chromatography @METASPACE2020 Ivan Protsyuk (EMBL) Andy Palmer (EMBL) Vitaly Kovalev (EMBL) Theodore Alexandrov (EMBL, UCSD)
2. Where to find it? http://lc.metaspace2020.eu
3. Analogy with imaging MS Imaging MS LC-MS
4. Analogy with imaging MS Imaging MS LC-MS LC-MS data ~ 1 acquisition row of imaging MS data
5. Generalization of bioinformatics Imaging Liquid Chromatography Spectral isotope measure: Spatial isotope measure: m/z-value: Measure of spatial chaos: Ion image Extracted Ion Chromatogram (XIC) Test isotope pattern Test isotope pattern Isotopic images are co-localized Isotopic XICs are similar Principal isotope peak is structured Principal XIC is peak-shaped
6. Infrastructure reuse http://metaspace2020.eu mzML, metadata upload explore annotations 10 minutes Amazon Web Services
7. User interface highlights
8. Data submission Centroided data
9. Metadata LC-specific metadata Adducts to screen for Adducts for negative polarity
10. Annotation visualization Elution profile for selected annotation
11. Updated MSM details Combined XIC for isotopic mass traces
12. Updated MSM details Plot can be zoomed in using click&drag
13. Adduct details Zooming in...
14. Adduct details
15. Evaluation of results
16. Evaluation Mix of standards, ng/µL Medium, ng/µL 1100 460 1200 230 110 50 20 10 470 240 120 50 0 • Mix of standards: • 64 metabolites (amino acids, nucleotides, bile acids, drugs) • Medium • McCoy cell culture medium • 65 compounds and nutrients • 2 replicates • In total • 106 samples (replicates, QC, blanks) • Detection • ESI(-) LC-MS/MS Orbitrap • Res power 70K @200 m/z • Out of 64, 47 metabolites were MS/MS validated manually
17. MS1 annotations, FDR 10%, [M-H]- No medium, mix 1100 ng/µL 46 193 63 17 Replicate 1 Replicate 2 Mix of standards 1 43 177 30 10 Replicate 1 Replicate 2 Mix of standards 1 Medium 1200 ng/uL, mix 1100 ng/µL 3
18. Export to mzML MSConvert tool: free Bulk conversion is possible Enable centroiding Select mzML as output format
19. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement № 634402. Acknowledgments EMBL METASPACE R&D team Theodore Alexandrov Vitaly Kovalev Renat Nigmetzianov Andrew Palmer Ivan Protsyuk Dominik Fay Sergey Nikolenko Artem Tarasov METASPACE Consortium Imperial College London University of Rennes 1 SCiLS VIB University California San Diego METASPACE data contributors