These slides present the bioinformatics for metabolite annotation of HR imaging MS. The bioinformatics was developed in the framework of the METASPACE project. METASPACE is a European Horizon2020 project on Bioinformatics for Spatial Metabolomics. Specifically, it aims at developing an engine for metabolite annotation of HR imaging mass spectrometry data. The project was funded in the Personalizing Health and Care program for 3 years (2015-2018) and is coordinated by the European Molecular Biology Laboratory. The presentation was given at the METASPACE Training Course at OurCon'17 on 25.10.2017. For more information on METASPACE, please visit the project website http://metaspace2020.eu, twitter @metaspace2020, or email us at contact@metaspace2020.eu.

1. Theodore alexandrov
EMBL / UCSD
@thalexandrov
METASPACE TRAINING COURSE
PART 1

2. Metaspace project

3. metabolite
annotation
engine
HR imagingMS
(LC-MS/MS)
molecular
annotations
metabolic
images
pathways
analysis
Cell cultures
Microbial agar plates
Tissue sections
Metabolomics resources
Imaging software
repositories
LAPTOP, WORKSTATION, CLUSTER, CLOUD
Open-source, big data tech
Metaspace
EU horizon2020 Project
@METASPACE2020
http:|| METASPACE2020.eu

4. Project overview
Aims
Solving the problem of molecular annotation of HR imaging MS data
Providing online engine
Developing new algorithms
Using modern big data technologies
Project partners
Theodore Alexandrov, EMBL (coordinator) Claire O’Donovan, EMBL-EBI
Lennart Martens, VIB Charles Pineau, URennes
Zoltan Takats, Kirill Veselkov, ICL Pieter Dorrestein, UCSD
Dennis Trede, SCiLS Oliver Panzer, ERS
funding
European program Horizon 2020, Personalising Health and Care
2015-2018

5. Advisory board
General
Metabolomics:
Amanda Hummon, U Notre Dame
Kristina Schwamborn, TU Munich
High-resolution mass spectrometry:
Ljiljana Pasa-Tolic, PNNL
Yury Tsybin, SpectroSwiss
Imaging mass spectrometry:
Ron Heeren, Maastricht University
Andreas Roempp, U Bayreuth
Axel Walch, Helmholtz Center München
Bioinformatics:
Sebastian Boecker, U Jena
Michael MacCoss, U Washington
Industry
Vendors:
James Langridge, Waters
Alexander Makarov, Thermo Fisher Scientific
Ann-Christin Niehoff, Shimadzu
Rohan Thakur, Bruker Daltonics
Pharma:
Michael Becker, Boeringer Ingelheim
Steve Castellino, GSK
Mathieu Gaudin, Servier
Richard Goodwin, AstraZeneca
Stacey Oppenheimer, Pfizer
Sheerin K. Shahidi-Latham, Genentech
Markus Stoeckli, Novartis
Jens Riedel, Sanofi
Standardization
Journal editors:
Richard Caprioli, Ed.-in-Chief J Mass Spectrom
Roy Goodacre, Ed.-in-Chief Metabolomics
Joseph A. Loo, Associate Ed. JASMS
Jonathan Sweedler, Ed.-in-Chief Anal Chem
Dietrich Volmer, Ed. Rapid Commun Mass Spec
Open access:
Peter Murray-Rust, University of Cambridge

6. bioinformatics

7. m/z 718.54
m/z 204.12
Microbial interactions
with Alex Koumoutsi, Nassos Typas (EMBL)
65umpixelsize
C. albicans
P. aeruginosa
Challenge:
metabolite identification

8. Challenge:
metabolite identificationDark matter
m/z A m/z B m/z C
Glucose
MW 180.063
[M+H]+
=181.070
in-source
fragmentation
ion adducts
isotopologues
dataset
100 GB / 100.000 spectra / 10.000.000 images
Metabolome
100.000 molecules
For 100K
moleculaR
structures

9. Targeted metabolite imaging
howto
1. Consider possible adducts
– +H, +Na, +K or –H, –Cl
2. Calculate m/z of each adduct
– principal or monoisotopic
3. Examine ion images
4. Examine the potential isotopic pattern
5. Estimate the ambiguity (any isomers? isobars?)
6. Validate with in situ MS/MS
– On a region of high intensity

10. DEVELOPED solution
Palmer et al., Nature Methods, 2017
Bioinformatics
– Metabolite ó score
– False Discovery Rate
METASPACE
– Engine, 10 min / dataset
– Graphical interface
– Knowledgebase of public results

11. Palmer et al., Nature Methods, 2017
Molecular annotation
Fdr calculation

12. Molecular annotation
Fdr calculation
1. Consider possible adducts
2. Calculate m/z of each adduct
3. Examine images
4. Examine the potential isotopic pattern
5. Estimate the ambiguity (any isomers? isobars?)
1
3
4
3
2
5

13. Measure of Spatial chaos
Structured
informative
Chaotic
non-informative

14. Spectral & spatial isotope scores
14
monoisotopic image structured? ok
fine isotope structure matching theor? ok
isotopic images co-localized? not
è doesn‘t pass the filters

15. Molecular annotation
Fdr calculation
Ok, all ions are scored by their likelihood
... But how to choose the cutoff?

16. how to select parameters
in proteomics
Database
Data
Molecular
Identification
List of molecules
1. How to quantify correctness?
2. False Discovery Rate
FDR = ratio of false positives
3. Don’t know false positives è
cannotcalculate FDR
4. Can we estimate it?
true positives
false positives

17. How to estimate fdr
In proteomics
Database
Data
Molecular
Identification
Molecular IDs
Fake
database Molecular IDs
“Decoy”
“Target”
FDR
# false positives for target
# identifications for target
estimated FDR
# target FPs
# target IDs
= ≈
# decoyFPs
# target IDs
true positives
false positives
false positives
positives:
=
=
Target
similar to
Decoy
definition
=

18. FDR for imaging ms
Fdr calculation
Palmer et al., Nature Methods, 2017

19. Fdr-controlled
metabolite annotation
Palmer et al., Nature Methods, accepted

20. Big data computing
Upload
Metabolite images
Cloud computing
10 min
Dominik
Fay
Andy
Palmer
Artem
Tarasov
Vitaly
Kovalev
Metabolite imaging
knowledgebase
http ://annotate.Metaspace2020 .eu

21. Metaspace software development
• Open-source
• By Alexandrov team, EMBL, Heidelberg, DE
• Modern software engineering tech
• 2 software developers (Vitaly, Artem) +ex-contributors, 1 scientist (Andy)
https://github.com/METASPACE2020

22. Metaspace releases 2017
• Version 0.6, April
– New webapp
– User authentication
– New backend
• Services, API for programmatic access
• Version 0.7, September
– Deletion of datasets by users
– General search
– Annotation against multiple databases
– Dynamic summary viz

23. Web app
http ://annotate.Metaspace2020 .eu

24. Contribution from the community
1700 datasets
50 labs
50 TB of raw data

25. Represented organisms

26. Represented technologies

27. Histogram of numbers of annotations
FDR10%
Phospholipids
Di-/Triglycerides
Ceramides
Fatty acids
Arachidonic acid
Eicosanoids, prostaglandins
Gangliosides
Inositolphosphates
Amino acids
Di-peptides
Saccarides
Heme
Drugs
Antibiotics
Secondary metabolites

28. Drug imaging
Olanzapine 10 mg/kg,
moxifloxacin 25 mg/kg,
erlotinib 10 mg/kg,
terfenadine 25 mg/kg
Olanzapine 10 mg/kg
Swales et al. (2016) Sci Reports
Data submitted by Nicole Strittmatter, Richard Goodwin (AstraZeneca)
Calibration
standards, nmol/g

29. Erlotinib Olanzapine
7-hydroxyolanzapine/
2-hydroxymethylolanzapine
Swalesetal.(2016)METASPACE
Not reported
Olanzapine only

30. Near-Future plans
• Optical images
• Private space
• METASPACE Kidney (NIH)
• Your lab space?

31. Alexandrov team
AndyPalmer,Prasad Phapale
VitalyKovalev,SergeyNikolenko
Artem Tarasov, DominikFay
SergioTriana,Ivan Protsyuk
Luca Rappez,Katya Ovchinnikova
CristinaGonzalezLopez
Thank you
METASPACE Consortium
CharlesPineau,Regis Lavigne(URennes)
ZoltanTakats, James McKenzie (ICL)
DennisTrede (SCiLS)
METASPACEdata contributors
Adam Pruska, Alexandrov Theodore, Andreas Roempp, Andrew Palmer, Annabelle Fülöp, Anne Mette Handler, Benedikt Geier, Berin Boughton,
Bernhard Spengler, Buck Achim, Carina Ramallo-Guevara, Charles Pineau, Chris Anderton, Christian Janfelt, Christina Burr, Claire Carter, Corinna
Henkel, Cristina Gonzalez Lopez, Cristine Quiason, David Muddiman, Denis Sammour, Dhaka Bhandari, Dinaiz Thinagaran, Dirk Hoelscher, Don
Nguyen, Dušan Velickovic, Eike Ulrich Brockmann, Emilia Sogin , Emrys Jones, Eric Weaver, Erin Gemperline, Guanshi Zhang, Gus Grey, Heath
Patterson, Hidenobu Miyazawa, József Pánczél, James Langridge, James McKenzie, Jan-Hinrich Rabe, Janfelt Christian, Jens Soltwisch, Jialing
Zhang, Josephine Bunch, Julian Griffin, Julien Delecolle, Kaija Schaepe, Klaus Dreisewerd, Konstantin Nagornov, Ksenija Radic, Kumar Sharma,
Kyana Garza, Lavigne Regis, Lennart Huizing, Liebeke Manuel, Lingjun Li, Livia Eberlin, Logan Mackay, Luca Rappez, Marina Reuter, Mario
Kompauer, Mark Bokhart, Marta Sans, Marty Paine, Mathieu Gaudin, Maureen Kane, Max Müller, Michael Becker, Michael Linscheid, Mikhail Belov,
Na Sun, Neha Garg, Nicolas Desbenoit, Nicole Strittmatter, Oliver Lechtenfeld, Pegah Khamehgir-Silz, Rappez Luca, Renata Soares, Richard
Caprioli, Richard Goodwin, Rima Ait-Belkacem, Ron Heeren, Samantha Walker, Sandra Schulz, Sarah Aboulmagd, Sergio Triana, Shane Ellis,
Sheerin Latham, Sophie Jacobsen, Spencer Thomas, Stefanie Gerbig, Steve Castellino, Veronika Saharuka, Vitaly Kovalev, Yury Tsybin, Zoe Hall,
Zoltan Takats
European Horizon2020 HEALTH
Amazon Cloud Creditsfor Research
NIH KidneyPrecision Medicine
Surfing photo: Russel Ord