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METASPACE TRAINING COURSE
PART 1. THEORY
Metabolite annotation In HR imaging MS
Theodore alexandrov
EMBL / UCSD / SCILS
@thalexandrov
m/z	203.103	m/z	212.852	 m/z	223.075	
C. albicans
E. coli
with Alex Koumoutsi, Nassos Typas @ EMBL
C. albicans
E. coli
metabolite identification
dataset
100 GB
100.000 spectra
10.000.000 images
metabolome
50.000-100.000
molecular structures
Metabolite annotation
Of 10.000.000 ion images
in-source
fragmentation
ion adducts
m/z A m/z B m/z C
AMP
MW 347.063
[M+H]=348.071
For 50K
moleculaR
structures
Dark matter
isotopologues
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
our bioinformatics solution
Palmer et al., Nature Methods, accepted
Molecular annotation
Fdr calculation
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
Measure of Spatial chaos
Structured
informative
Chaotic
non-informative
Spectral & spatial isotope scores
9	
monoisotopic image structured? ok
fine isotope structure matching theor? ok
isotopic images co-localized? not
è doesn‘t pass the filters
Molecular annotation
Fdr calculation
Ok, all ions are scored by their likelihood
... But how to choose the cutoff?
How to choose msm cutoff
how to select parameters
in proteomics
Database	
Data	
Molecular	
Iden=fica=on	
List	of	molecules	
Correct?
1.  How to quantify correctness?
2.  False Discovery Rate
FDR = ratio of false positives
3.  Don’t know false positives è
cannot calculate FDR
4.  Can we estimate it?
true positives
false positives
How to estimate fdr
In proteomics
Database	
Data	
Molecular	
Iden=fica=on	
Molecular	IDs	
Fake	
database	 Molecular	IDs	
“Decoy”	
“Target”	
FDR
# false positives for target
# identifications for target
estimated FDR
# target FPs
# target IDs
= ≈
# decoy FPs
# target IDs
true positives
false positives
false positives
positives:
=
=
Target	
similar	to	
Decoy	
defini3on	
=
Explain	me	
like	I’m	five!
you won tickets to chile!
which cities are sunny?
Whom
to
believe?
1.  Let’s take cities which we know answers for (always rainy) = decoy
2.  Ask to predict what the weather will be like there
“sunny” they say è false positive (# decoy FPs)
FDR
FDR
FDR
FDR
FDR
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FDRFDRFDR
FDRFDR
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FDRFDRFDR
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FDRFDRFDR
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FDRFDRFDR
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FDRFDRFDR
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FDR
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FDR
DR
DR
DR
DR
DR
DR
DRFDRFDR
FDRFDR
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FDRFDRFDR
FDRFDR
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FDR
FDR
HM... How is it related to imaging ms?!
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDRFDR
FDRFDR
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FDR
FDR
FDR
FDR
FDR
FDR
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FDRFDRFDR
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FDR
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FDRFDRFDR
FDRFDR
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FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDRFDR
FDRFDR
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FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
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FDRFDRFDR
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FDR
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FDR
FDR
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FDRFDRFDR
FDRFDR
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FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDR
FDR
FDR
FDR
FDR FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDRFDR
FDRFDR
FDR
FDR
FDR
FDR
FDR
FDR
FDRFDR
FDRF
FDR for imaging ms
Fdr calculation
Palmer et al., Nature Methods, accepted
Fdr-controlled
metabolite annotation
Palmer et al., Nature Methods, accepted
Does it really work?
Test case
mouse brain sections
Reference H&E images from Allen Brain Atlas
Bregma 1.42mm
3 serial sections
Bregma -1.46mm
1 section
Bregma -3.88mm
1 section
animal 1 animal 2
Imaging ms
DHB matrix
ImagePrep
50 μm pixel size
10K spectra
100-1.200 m/z
20-30 GB
► ►
solarix XR 7T
Paracell
130K @ m/z 400
with Regis Lavigne, Charles Pineau @ UR1, France
FDR(10%)-controlled annotation
OVERVIEW
FDR(10%)-controlled annotation
DETECTED METABOLITES
LC-MS/MS validation
XIC
LC-MS/MS validation
Standard available
LC-MS/MS validation
no standard
@ EMBL
Andy Palmer, Prasad Phapale
Vitaly Kovalev, Sergey Nikolenko
Artem Tarasov, Dominik Fay
Luca Rappez
Thank you
METASPACE Consortium
Christoph Steinbeck, EMBL-EBI
Lennart Martens, VIB
Charles Pineau, Regis Lavigne,
URennes
Pieter Dorrestein, UCSD
Zoltan Takats, Kirill Veselkov, ICL
Dennis Trede, SCiLS
Oliver Panzer, ERS
and their team members
Bruker
Michæl Becker, Jens Fuchser
@alexandrovteam
European Horizon2020
HEALTH programme

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ARCHIVED: new version available. 2016 - Bioinformatics of METASPACE