DSV/iBiTec-S/SPI/LEMM - TGE FT-ICR

Transcription

DSV/iBiTec-S/SPI/LEMM
Bases de données en métabolomique
Christophe Junot
CEA/Laboratoire d’Etude du Métabolisme des Médicaments
CEA-Saclay (iBiTec-S)
[email protected]
Metabolites and metabolome
Primary metabolites
Aminoacids
Secondary metabolites
Xenobiotics
Sugars
Drugs
Hormones
Pesticides
Organic acids
Neurotransmitters
Nucléotides
(…)
Pollutants
(…)
D:\439010\...\ara 0uM CdCl2 n1.1
(…)
12/07/02 15:50:20
RT: 0,00 - 150,02
100
Central metabolism
Metabolic fingerprint
90
Food / drinking
NL:
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
80
Gut microbiota
Relative Abundance
70
Environment :
xenobiotics
(pollutants, drugs…)
60
50
40
30
20
Pathology
10
0
0
20
40
60
80
Tim e (m in)
100
120
140
How to detect metabolites in biological media?
Metabolic fingerprint
D:\439010\...\ara 0uM CdCl2 n1.1
12/07/02 15:50:20
RT: 0,00 - 150,02
100
NL:
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
90
80
Relative Abundance
70
60
50
40
30
20
10
0
0
NMR
- Simple, non invasive
- Rapid
- Robust: analysis of large
series of samples
But:
- Limited sensitivity
20
40
60
80
Tim e (m in)
100
GC-EI-MS
- Sensitive
- Reproducible
- Spectral libraries
But:
- Chemical derivatization of
non volatile compounds
- Issue of thermolabile
compounds
120
140
LC-API-MS
- Molecular mass of intact
compounds
- Analysis of thermolabile
compounds
- sensitive
But:
Poor inter-platform
reproducibility
MS à haute résolution: détecter plus de
métabolites et les identifier plus facilement
Résolution en masse
Précision en masse
C10H15O4 (0.1 ppm)
Databases
Déroulement d’une analyse métabolomique
PREPARATION DES
ECHANTILONS
ACQUISITION DES EMPREINTES
TRAITEMENT DES DONNEES
VARIABLES
(RT-MASSES)
DETECTION AUTOMATIQUE
DES SIGNAUX
ECH.
EXTRACTION
DILUTION…
CONFIRMATION ET
QUANTIFICATION
IDENTIFICATION DES BIOMARQUEURS
Data Tri 22-5 NVol Q=1.M13 (OPLS), M1-par
DETECTION
AUTOMATIQUE
t[Comp. 1]/t[Comp.
2]
according to Obs ID (Primary)
DES Colored
SIGNAUX
BASES DES DONNEES, MS/MS …
URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5
F: FTMS + p ESI Full ms2 [email protected] [50.00-800.00]
116.07041
100
G1
G3
4000
3000
95
TEMOINS
MALADES
90
85
2000
80
75
1000
Relative Abundance
65
t[2]O
70
-C2H3ON
60
55
0
-1000
50
45
40
[M+H]+
-HCOOH
35
30
-3000
25
20
-H2O
15
10
5
70.06497
-4000
155.08136
169.67426
80.49458
86.06400
61.03990
102.71753
184.86218
143.04167
0
60
80
100
120
m/z
-2000
173.09190
127.08652
140
160
180
-4000 -3000 -2000 -1000
0
t[1]P
1000 2000 3000 4000
Les différents types de bases de données
en métabolomiques
Bases de données spectrales:
Bases de données
MS/MS: identification
biochimiques/métaboliques ESI/MS: annotation
annotation
12/07/02 15:50:20
RT: 0,00 - 150,02
100
12/07/02 15:50:20
RT: 0,00 - 150,02
80
100
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
90
80
50
100
140
120
140
30
20
10
0
100
120
140
20
-H2O
15
10
5
70.06497
155.08136
169.67426
143.04167
80.49458
86.06400
61.03990
102.71753
184.86218
0
60
80
100
120
m/z
140
160
180
Patients with unexplained encephalopathy
5.9
9.1
6.3
0.9
2.0
8.3
2.2
1.1
0.8
0.7
1.6
2.2
4.4
3.3
2.5
1.3
1.0
0.8
2.3
1.2
1.9
1.6
0.9
5.0
1.6
0.8
1.8
4.8
4.0
1.3
1.2
1.0
1.1
1.4
8.0
3.9
1.7
3.0
2.3
0.1 6.0
0.1
0.6
1.1
0.4
0.8
1.8
0.6
1.3
1.1
Threonic acid
0.8
1.1
0.7 0.6
0.9
2.5
0.5
1.1
Quinic acid
3.4
0.3
0.6
0.0
0.0
4.7 0.0
Succinyladenosine
0.8
0.6
0.7
0.5 0.8
1.7
0.4
Proline Betaine
0.7
0.5
2.5
0.5 0.8
2.1 0.4
2.7
3.6
1.5
0.8
0.6
1.2
0.5 0.8
0.9
0.7
1.2
0.7 0.8
0.7
1.2
0.7 0.8
1.2
5.0
0.9
0.7
0.5
1.0
0.4
0.7
0.5
0.7
1.7
0.5 0.6
0.7
1.3
0.5
0.5
0.7
3.0
0.7 0.6
2.1
1.0
1.0
0.6
0.6
1.2
0.5 0.6
0.8
1.2
0.7 0.6
0.7
8.1
1.3
0.8
0.6
0.9
0.6
1.0
0.6
0.7
1.0
0.5 0.4
1.5
1.7
0.6
0.6
3.3
3.3
1.1
0.7
1.0
1.7
0.8
0.8
0.7 0.6
1.0
0.5 0.9
1.1
1.2
1.4
0.6
1.1
1.5
2.3
3.2
5.3
1.1
0.4
9.3
2.3
3.1
0.1
1.1
0.3
0.0
0.6
1.2
0.7 0.0
1.1
0.0
6.2
4.1 2.3
1.5
0.9
0.7
1.3
0.6
1.0
0.7 0.8
1.2
0.8
0.9
0.7
1.4
0.6
0.6
1.3
0.1 0.9
1.9
4.3
0.6
1.0
0.9
0.9
0.1 2.3
0.8
0.7 3.2
0.3
2.1
0.9
0.1 4.1 0.8
0.5 0.9
1.5
0.4
1.2
1.1
0.8
0.8
0.8
0.6
0.9
1.4
0.4
1.1
1.2
0.8
0.8
0.5
0.7
0.5
1.0
1.4
1.0
2.8
1.2
0.8
0.8
0.6
0.5
1.0
0.7
1.0
1.5
0.5
1.4
1.2
1.0
1.1
Dihydroorotic acid
0.9
0.7 0.8
0.4
0.9
2.2
0.4
1.3
0.9
0.8
1.0
2.1
Acetyl-carnitine
1.8
0.7 0.6
1.1
0.5 0.9
0.6
0.9
2.7
1.0
1.5
2.7
1.2
2.1
1.5 2.0
Propionyl-carnitine
1.9
0.5
0.5
1.0
0.5
1.6
0.8
0.8
2.3
1.3
1.5
2.5
0.8
3.7
1.2
Butyryl-carnitine
0.9
0.5
0.5
0.7 0.8
1.6
0.6
0.6
1.4
1.6
1.5
2.4
1.0
3.3
1.3 2.2
1.3
Methylbutyroyl-carnitine
1.3
0.4
0.6
0.9
1.9
0.5
0.9
1.5
1.4
1.3
3.0
1.0
1.1 2.4
0.7
0.0
0.0
11.0
0.0
0.0
0.0
16.0
0.0
0.8
Glycochenodeoxycholic acid
Bile acids
Glycocholic acid
4.4
0.1
1.0
0.2
0.2
2.6
0.6
0.1
1.0
0.9
0.0
1.7
0.0
1.0
0.2
0.2
5.5
0.1
0.0
0.6
0.6
0.0
1.3
3.4
0.5 0.9
0.6
0.5
0.7
2.2
2.2
1.1
48.6
0.6
11.9
2.1
2.8
1.3
2.3
2.3
2.8
2.6
1.8
0.9
0.8
1.2
0.7 0.9
0.7 0.8
1.1
0.8
0.9
0.7
1.2
0.8
2.9
1.7
0.9
0.8
1.2
0.7 0.9
0.7 0.9
1.2
0.6
1.0
0.8
1.5
0.7 0.8
1.1
0.7 2.0
0.8
1.2
1.4
1.0
0.4
0.9
0.7
1.6
1.3
1.3
0.8
0.4
0.8
1.3
1.3
1.2
1.1
1.2
2.0
1.7
0.9
1.2
1.2
0.6
0.7
0.7
1.0
1.1
0.8
0.8
0.8
1.1
0.9
1.0
1.4
2.2
5.0
1.1
3.2
1.8
1.6
0.2
1140_68
5.0
4.1 2.0
0.8
N-Acetyl-D-allo-isoleucine
Acylcarnitines
5.7
7.8 4.4
8.1 4.0
0.6
Am inoadipic acid
N-acetyl-L-glutam ic acid
Pyrim idine derivated
7.7
1166_117
2.4
0.7 2.3
1.0
1161_90
0.9
0.8
1.7
1157_79
1.0
1.0
1.1
0.9
Glutam ic acid
Aminoacid and derivatives
1.2
10.2
0.3
1148_77
Bases de données de profils
métaboliques
1.0
0.3
1.1
0.6
1142_125
nucleoside derivative
0.3
1.5
3.9
0.5
1138_89
Hydroxy acids
1.4
0.5 0.6
0.4
1136_39
0.5
0.7 0.6
1135_131
1.0
0.4
1.4
Mannitol or isom ers
1134_43
0.5
0.6
Pentose
1124_67
0.9
Deoxyribose
1114_50
N-acetylneuram inic acid
1113_28
Carbohydrates
1087_27
sugar acid
1086_114
80
Time (min)
25
1074_80
60
173.09190
127.08652
1199_121
40
30
1073_29
20
[M+H]+
-HCOOH
35
LM8
0
40
XC96
80
Time (min)
120
45
469
60
100
50
48
40
80
Time (min)
55
1067_74
20
60
-C2H3ON
60
1061_32
10
50
0
40
0
40
65
1058_107
20
70
1057.B_133
30
70
20
0
60
75
1057.A_88
0
80
1052_46
10
50
90
40
80
85
1050_71
20
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
90
1018_55
30
12/07/02 15:50:20
60 0,00 - 150,02
RT:
100
95
1048_95
Relative Abundance
70 0\...\ara 0uM CdCl2 n1.1
D:\43901
40
Variables (Rt-mass)
60
Relative Abundance
Relative Abundance
70
URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5
F: FTMS + p ESI Full ms2 [email protected] [50.00-800.00]
116.07041
100
1047_127
90
?
?
?
?
?
?
?
?
?
1043_31
D:\439010\...\ara 0uM CdCl2 n1.1
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara
NL:0uM
CdCl2
n1.1
1,63E7
Relative Abundance
D:\439010\...\ara 0uM CdCl2 n1.1
Samples
5.5
2.5
2.7
1.3
0.5
0.7
1.1
0.8
1.0
0.7
0.5
1.4
0.6
0.7
1.4
0.5 0.6
1.3
1.0
0.4
1.5
1.2
0.9
0.6
1.5
0.9
0.9
0.3
0.7
1.5
0.8
0.8
0.8
0.7
1.3
1.2
0.8
0.6
1.9
0.8
1.0
0.2
0.4
1.8
1.1
0.9
0.9
0.8
1.2
1.1
0.9
0.6
1.2
1.0
0.9
0.3
0.6
1.6
0.7 0.9
1.1
1.0
1.3
0.9
0.7 0.6
1.1
0.8
0.9
0.3
0.4
1.7
0.9
0.8
1.0
0.0
0.6
2.8
1.3
0.0
1.2
1.3
0.1 0.0
0.0
0.6
0.1
0.7 0.2
0.1 0.4
0.4
1.9
2.2
0.1
0.5
1.8
0.2
0.5 0.0
0.2
0.2
1.2
1.1
1.5
2.1 0.8
0.8
1.0
0.6
0.8
0.7 0.8
11.1 6.7
7.4
0.3
9.6
6.2
4.8
0.9
Spectral databases
database
NIST
thematic
Conception / URL
Instrument
general
National institute for standard and technology (USA)
GC/MS

www.nist.gov/srd/nist1a.htm
Fiehn Library
general
Fiehn Laboratory Univ California Davis – Genome center
GC/MS

http://fiehnlab.ucdavis.edu/Metabolite-Library-2007
Golm

plant
Max Planck Institute for Molecular Plant Physiology (Germany)
GC/MS
csbdb.mpimp-golm.mpg.de
HMDB
human metabolites
Department of Computing Science, University of Alberta (Canada)
NMR, API/MS/MS

www.hmdb.ca/extrIndex.htm
Lipidmaps
lipidomics
LIPID MAPS Bioinformatics Core (USA)
API-MS/MS

www.lipidmaps.org/data/index.html.
Massbank
general

Keio university, university of Tokyo, Kyoto university, RIKEN plant Science center (Japan)
and others
API-MS/MS
www.massbank.jp
Metlin
human metabolites
Scripps Center for Mass Spectrometry
API-MS/MS

metlin.scripps.edu
Brucker
Madison Metabolomic
Consortium database

general
general
NMRS
http://mmcd.nmrfam.wisc.edu/

 free access,  partially free access,  licenced
NMRS
Annotation of peak lists is required
to help for metabolite identification
Samples
D:\439010\...\ara 0uM CdCl2 n1.1
12/07/02 15:50:20
RT: 0,00 - 150,02
100
D:\439010\...\ara 0uM CdCl2 n1.1
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara
NL:0uM
CdCl2
n1.1
1,63E7
12/07/02 15:50:20
90
RT: 0,00 - 150,02
80
100
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
90
80
50
Relative Abundance
70 0\...\ara 0uM CdCl2 n1.1
D:\43901
40
30
20
10
0
12/07/02 15:50:20
60 0,00 - 150,02
RT:
100
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
50
90
40
80
30
70
20
0
60
20
10
50
0
40
0
40
20
60
40
80
Time (min)
60
80
Time (min)
100
120
100
140
120
140
30
20
10
0
0
Variables (Rt-mass)
60
Relative Abundance
Relative Abundance
70
20
40
60
80
Time (min)
100
120
140
Few thousands of
variables…
?
?
?
?
?
?
?
?
?
…Few hundreds of
metabolites ??
 Chemical and biochemical databases: KEGG (www.genome.jp/kegg),
Metlin (www.metlin.scripps.edu), HMDB (www.hmdb.ca)
 spectral databases
The relevance of a spectral database
One molecule = several ions
Pic of interest
Automated detection of ions, list of annotated features
M/Z
RT
Formula
Compound Attribution
188.0709
5.28 C11H10NO2
Tryptophan
[(M+H)-(NH3)]+
189.0757
190.0787
5.28 C10[13C]H10NO2
5.28 C9[13C]2H10NO2
Tryptophan
Tryptophan
[(M+H)-(NH3)]+ (13C)
[(M+H)-(NH3)]+ (13C2)
205.0975
5.28 C11H13N2O2
Tryptophan
206.1010
5.28 C10[13C]H13N2O2 Tryptophan
[(M+H)]+ (13C)
207.1051
5.28 C9[13C]2H13N2O2 Tryptophan
[(M+H)]+ (13C2)
409.1902
5.28 C22H25N4O4
[(2M+H)]+
410.1938
5.28 C21[13C]H25N4O4 Tryptophan
Tryptophan
[(M+H)]+
[(2M+H)]+ (13C)
Annotations
(HMDB, KEGG, METLIN)
Deethylatrazine
3-amino-2-naphthoic acid
Indoleacrylic acid
Ethyl Oxalacetate
Tryptophan
ethotoin
Vasicinol
Idazoxan
Nirvanol
N-Acetyl-D-fucosamine
N-Acetyl-D-quinovosamine
Gly Trp Phe (and isomers)
Lys Met Met (and isomers)
Tyr Leu Asp (and isomers)
Ile Tyr Asp (and isomers)
Val Tyr Glu (and isomers)
(Roux et al., PhD work, 2008-2011, Roux et al., Anal. Chem. 2012)
Development of an ESI-mass spectral
database for metabolomics : methodology
Chemical library
(reference compounds)
ESI mass spectral
database
12/07/02 15:50:20
RT: 0,00 - 150,02
100
D:\439010\...\ara 0uM CdCl2 n1.1
90
D:\439010\...\ara 0uM CdCl2 n1.1
90
50
D:\439010\...\ara 0uM CdCl2 n1.1
70
20
10
20
0
RT: 0,00 - 150,02
100
80
50
70
40
30
0
12/07/02 15:50:20
90
60
Relative Abundance
40
30
12/07/02 15:50:20
RT: 0,00 - 150,02
100
80
20
10
90
60
80
50
70
40
40
30
0
20
0
20
10
Relative Abundance
60
Relative Abundance
Relative Abundance
NL:
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM NL:
1,63E7
CdCl2 n1.1
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM NL:
1,63E7
CdCl2 n1.1
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
NL:
MS ara 0uM
1,63E7
CdCl2 n1.1
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
12/07/02 15:50:20
RT: 0,00 - 150,02
100
80
70
60
60
80
Tim e (m in)
40
40
60
30
0
100
120
80
Tim e (m in)
100
120
140
20
0
20
40
60
80
Tim e (m in)
100
120
140
0
20
40
60
80
Tim e (m in)
100
120
10
0
12/07/02 15:50:20
RT: 0,00 - 150,02
100
D:\439010\...\ara 0uM CdCl2 n1.1
12/07/02 15:50:20
90
RT: 0,00 - 150,02
100
90
80
50
70
30
20
D:\439010\...\ara 0uM CdCl2 n1
.1
D:\43901
0\...\ara 0uM CdCl2 n1.1
RT: 0,00 - 150,02
100
90
20
0
0
20
10
60
50
4040
20
20
40
10
0
0
NL:
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00] NL:
MS ara 0uM
1,63E7
CdCl2 n1.1
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
NL:
CdCl2 n1.1
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
NL:
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
NL:
CdCl2 n1.1
1,63E7
Bas e Peak F: + c
ESI Full m s [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
90
60
80
50
70
80
40
Tim e (m in)
60
30
60
30
0
0
12/07/02 15:50:20
12/07/02 15:50:20
80 - 150,02
RT: 0,00
100
70
70
30
10
12/07/02 15:50:20
90
80
50
40
RT: 0,00 - 150,02
100
D:\439010\...\ara 0uM CdCl2 n1.1
60
Relative Abundance
40
Relative Abundance
Relative Abundance
70
60
Relative Abundance
Relative Abundance
80
140
Control
D:\439010\...\ara 0uM CdCl2 n1.1
50
20
100
60
40
10
30
0
20 20 0
40
120
80
Tim e (m in)
20
60
Samples
140
50
Structure dataset with
multivariable analysis
140
100
80
40
Tim e (m in)
Variables
(Tr-masse)
D:\439010\...\ara 0uM CdCl2 n1.1
- Gather together signals
from the same metabolite
- MSn confirmation
120
60
140
100
80
Tim e (m in)
120
100
140
120
140
10
0
0
20
40
60
80
Tim e (m in)
100
120
140
Disease
Metabolomics relational
database:
Annotated peaklist
- Kind of biofluid
- Physiological Factors
- Disease, toxicology
Analysis of reference compounds
One molecule = many ions :
- Pseudo-molecular and isotopes
- Adducts
- Fragments
Annot.
FIA*
M/Z
Intensity
Relative
M/Z (theo) Delta (ppm) RDBE
72.04431
7.7E+04
0.82
72.04439
-1.11
1.5
90.05482
3.6E+05
3.78
90.05496
-1.55
0.5
116.034
2.7E+04
0.29 116.03422
-0.22
2.5
142.08585
1.0E+03
0.01 142.08626
-2.85
2.5
174.11217
1.6E+04
0.17 174.11247
-0.3
1.5
184.09687
2.4E+03
0.03 184.09682
0.26
3.5
202.10729
1.5E+04
0.16 202.10738
-0.46
2.5
220.11799
9.69E+06
100 220.11795
0.18
1.5
221.12135
6.84E+05
7.06 221.121305
0.2
1.5
222.12166
4.59E+04
0.47 222.122194
-2.4
1.5
223.12604
2.26E+03
0.02 223.125549
2.2
1.5
238.12923
1.76E+03
0.02 238.128515
3
0.5
242.09992
6.61E+06
68.15 242.099895
0.1
1.5
243.10331
4.44E+05
4.58 243.10325
0.25
1.5
244.10363
1.79E+04
0.18 244.104139
-2.09
1.5
258.07386
1.29E+05
1.33 258.073833
0.1
1.5
259.07731
1.49E+03
0.02 259.077188
0.47
1.5
264.082
1.68E+05
1.74 264.08184
0.61
1.5
265.08498
3.08E+03
0.03 265.085195
-0.81
1.5
310.08654
1.10E+03
0.01 310.08732
-2.52
1.5
461.21113
6.45E+05
6.65 461.210569
1.22
2.5
462.21438
1.08E+05
1.11 462.213924
0.99
2.5
463.2174
9.76E+03
0.1 463.217279
0.26
2.5
483.19256
4.21E+04
0.43 483.192514
0.1
2.5
One molecule = one
retention time
obtained by LC/MS
Composition
C3 H6 O N
C3 H8 O2 N
C7 H10 O N
C7 H12 O2 N
C8 H16 O3 N
C9 H14 O3 N
C9 H16 O4 N
C9 H18 N O5
C8 13C H18 N O5
C9 H18 N O4 18O
C8 13C H18 N O4 18O
C9 H20 N O6
C9 H17 N Na O5
C8 13C H17 N Na O5
C9 H17 N Na O4 18O
C9 H17 K N O5
C8 13C H17 K N O5
C9 H16 N Na2 O5
C8 13C H16 N Na2 O5
C10 H18 N Na2 O7
C18 H34 N2 Na O10
C17 13C H34 N2 Na O10
C16 13C2 H34 N2 Na O10
C18 H33 N2 Na2 O10
Attribution
FIA UPLC (C8)
[(M+H)-(C6H10O3)-(H2O)]+
0
1.80
[(M+H)-(C6H10O3)]+
0
1.80
[(M+H)-(C2H6O3)-(H2O)]+
0
1.80
[(M+H)-(C2H6O3)]+
0
1.80
[(M+H)-(HCOOH)]+
0
1.80
[(M+H)-2(H2O)]+
0
1.80
[(M+H)-(H2O)]+
0
1.80
[(M+H)]+
0
1.80
[(M+H)]+ (13C)
0
1.80
[(M+H)]+ (18O)
0
1.80
[(M+H)]+ (13C+18O)
0
1.80
[(M+H)+(H2O)]+
0
1.80
[(M+Na)]+
0
1.80
[(M+Na)]+ (13C)
0
1.80
[(M+Na)]+ (18O)
0
1.80
[(M+K)]+
0
1.80
[(M+K)]+ (13C)
0
1.80
[(M-H+2Na)]+
0
1.80
[(M-H+2Na)]+ (13C)
0
1.80
[(M+Na)+(HCOONa)]+
0
1.80
[(2M+Na)]+
0
1.80
[(2M+Na)]+ (13C)
0
1.80
[(2M+Na)]+ (13C2)
0
1.80
[(2M-H+2Na)]+
0
1.80
UPLC (C18) HSF5
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
ESI-mass spectral database
→ Annot. SPI*
* Tools of SPI-LIMS web Interface developed at CEA (CEA/DSV/GIPSI)
Reference compounds analysis :
FIA spectrum annotation
« Annotation FIA »: to calculate precise m/z of potential ions for a
given mass formula and compare them to experimental m/z :
- Isotopes: 13C, 34S, 18O…
- Adducts: Na, K, Cl…
- Fragments (in source CID)
Reference compounds analysis
Pantothenic acid [M+H]+
M/Z
Intensity
Relative
72.04431
7.7E+04
0.82
72.04439
-1.11
1.5
90.05482
3.6E+05
3.78
90.05496
-1.55
0.5
116.034
2.7E+04
0.29 116.03422
-0.22
2.5
142.08585
1.0E+03
0.01 142.08626
-2.85
2.5
174.11217
1.6E+04
0.17 174.11247
-0.3
1.5
184.09687
2.4E+03
0.03 184.09682
0.26
3.5
202.10729
1.5E+04
0.16 202.10738
-0.46
2.5
220.11799
9.69E+06
100 220.11795
0.18
1.5
221.12135
6.84E+05
7.06 221.121305
0.2
1.5
222.12166
4.59E+04
0.47 222.122194
-2.4
1.5
223.12604
2.26E+03
0.02 223.125549
2.2
1.5
238.12923
1.76E+03
0.02 238.128515
3
0.5
242.09992
6.61E+06
68.15 242.099895
0.1
1.5
243.10331
4.44E+05
4.58 243.10325
0.25
1.5
244.10363
1.79E+04
0.18 244.104139
-2.09
1.5
258.07386
1.29E+05
1.33 258.073833
0.1
1.5
259.07731
1.49E+03
0.02 259.077188
0.47
1.5
264.082
1.68E+05
1.74 264.08184
0.61
1.5
265.08498
3.08E+03
0.03 265.085195
-0.81
1.5
310.08654
1.10E+03
0.01 310.08732
-2.52
1.5
461.21113
6.45E+05
6.65 461.210569
1.22
2.5
462.21438
1.08E+05
1.11 462.213924
0.99
2.5
463.2174
9.76E+03
0.1 463.217279
0.26
2.5
483.19256
4.21E+04
0.43 483.192514
0.1
2.5
Fragments ions and
their isotopes
Pseudo-molecular ion
and its isotopes
Adducts ions and
their isotopes
Retention time
Composition
C3 H6 O N
C3 H8 O2 N
C7 H10 O N
C7 H12 O2 N
C8 H16 O3 N
C9 H14 O3 N
C9 H16 O4 N
C9 H18 N O5
C8 13C H18 N O5
C9 H18 N O4 18O
C8 13C H18 N O4 18O
C9 H20 N O6
C9 H17 N Na O5
C8 13C H17 N Na O5
C9 H17 N Na O4 18O
C9 H17 K N O5
C8 13C H17 K N O5
C9 H16 N Na2 O5
C8 13C H16 N Na2 O5
C10 H18 N Na2 O7
C18 H34 N2 Na O10
C17 13C H34 N2 Na O10
C16 13C2 H34 N2 Na O10
C18 H33 N2 Na2 O10
Attribution
UPLC (C8)
[(M+H)-(C6H10O3)-(H2O)]+
1.80
[(M+H)-(C6H10O3)]+
1.80
[(M+H)-(C2H6O3)-(H2O)]+
1.80
[(M+H)-(C2H6O3)]+
1.80
[(M+H)-(HCOOH)]+
1.80
[(M+H)-2(H2O)]+
1.80
[(M+H)-(H2O)]+
1.80
[(M+H)]+
1.80
[(M+H)]+ (13C)
1.80
[(M+H)]+ (18O)
1.80
[(M+H)]+ (13C+18O)
1.80
[(M+H)+(H2O)]+
1.80
[(M+Na)]+
1.80
[(M+Na)]+ (13C)
1.80
[(M+Na)]+ (18O)
1.80
[(M+K)]+
1.80
[(M+K)]+ (13C)
1.80
[(M-H+2Na)]+
1.80
[(M-H+2Na)]+ (13C)
1.80
[(M+Na)+(HCOONa)]+
1.80
[(2M+Na)]+
1.80
[(2M+Na)]+ (13C)
1.80
[(2M+Na)]+ (13C2)
1.80
[(2M-H+2Na)]+
1.80
UPLC (C18) HSF5
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
Reference compounds analysis :
Mass spectrum annotation
Reference compounds analysis:
annotation using CEA spectral database
Pantothenic acid [M+H]+
M/Z
Intensity
Relative
72.04431
7.7E+04
0.82
72.04439
-1.11
1.5
90.05482
3.6E+05
3.78
90.05496
-1.55
0.5
116.034
2.7E+04
0.29 116.03422
-0.22
2.5
142.08585
1.0E+03
0.01 142.08626
-2.85
2.5
174.11217
1.6E+04
0.17 174.11247
-0.3
1.5
184.09687
2.4E+03
0.03 184.09682
0.26
3.5
202.10729
1.5E+04
0.16 202.10738
-0.46
2.5
220.11799
9.69E+06
100 220.11795
0.18
1.5
221.12135
6.84E+05
7.06 221.121305
0.2
1.5
222.12166
4.59E+04
0.47 222.122194
-2.4
1.5
223.12604
2.26E+03
0.02 223.125549
2.2
1.5
238.12923
1.76E+03
0.02 238.128515
3
0.5
242.09992
6.61E+06
68.15 242.099895
0.1
1.5
243.10331
4.44E+05
4.58 243.10325
0.25
1.5
244.10363
1.79E+04
0.18 244.104139
-2.09
1.5
258.07386
1.29E+05
1.33 258.073833
0.1
1.5
259.07731
1.49E+03
0.02 259.077188
0.47
1.5
264.082
1.68E+05
1.74 264.08184
0.61
1.5
265.08498
3.08E+03
0.03 265.085195
-0.81
1.5
310.08654
1.10E+03
0.01 310.08732
-2.52
1.5
461.21113
6.45E+05
6.65 461.210569
1.22
2.5
462.21438
1.08E+05
1.11 462.213924
0.99
2.5
463.2174
9.76E+03
0.1 463.217279
0.26
2.5
483.19256
4.21E+04
0.43 483.192514
0.1
2.5
RT
Human urines
4.72
4.73
4.74
4.74
4.74
4.74
4.74
MZ
202.107624
90.054915
220.117542
221.12168
222.122088
242.100637
258.075314
#
Composition
C3 H6 O N
C3 H8 O2 N
C7 H10 O N
C7 H12 O2 N
C8 H16 O3 N
C9 H14 O3 N
C9 H16 O4 N
C9 H18 N O5
C8 13C H18 N O5
C9 H18 N O4 18O
C8 13C H18 N O4 18O
C9 H20 N O6
C9 H17 N Na O5
C8 13C H17 N Na O5
C9 H17 N Na O4 18O
C9 H17 K N O5
C8 13C H17 K N O5
C9 H16 N Na2 O5
C8 13C H16 N Na2 O5
C10 H18 N Na2 O7
C18 H34 N2 Na O10
C17 13C H34 N2 Na O10
C16 13C2 H34 N2 Na O10
C18 H33 N2 Na2 O10
Masse
1
1
1
1
1
1
1
202.10738
90.05496
220.11795
221.121305
222.122194
242.099895
258.073833
Composé
pantothenic
pantothenic
pantothenic
pantothenic
pantothenic
pantothenic
pantothenic
acid
acid
acid
acid
acid
acid
acid
Attribution
FIA UPLC (C8)
[(M+H)-(C6H10O3)-(H2O)]+
0
1.80
[(M+H)-(C6H10O3)]+
0
1.80
[(M+H)-(C2H6O3)-(H2O)]+
0
1.80
[(M+H)-(C2H6O3)]+
0
1.80
[(M+H)-(HCOOH)]+
0
1.80
[(M+H)-2(H2O)]+
0
1.80
[(M+H)-(H2O)]+
0
1.80
[(M+H)]+
0
1.80
[(M+H)]+ (13C)
0
1.80
[(M+H)]+ (18O)
0
1.80
[(M+H)]+ (13C+18O)
0
1.80
[(M+H)+(H2O)]+
0
1.80
[(M+Na)]+
0
1.80
[(M+Na)]+ (13C)
0
1.80
[(M+Na)]+ (18O)
0
1.80
[(M+K)]+
0
1.80
[(M+K)]+ (13C)
0
1.80
[(M-H+2Na)]+
0
1.80
[(M-H+2Na)]+ (13C)
0
1.80
[(M+Na)+(HCOONa)]+
0
1.80
[(2M+Na)]+
0
1.80
[(2M+Na)]+ (13C)
0
1.80
[(2M+Na)]+ (13C2)
0
1.80
[(2M-H+2Na)]+
0
1.80
Composition
C9 H16 O4 N
C3 H8 O2 N
C9 H18 N O5
C8 13C H18 N O5
C9 H18 N O4 18O
C9 H17 N Na O5
C9 H17 K N O5
Attribution
[(M+H)-(H2O)]+
[(M+H)-(C6H10O3)]+
[(M+H)]+
[(M+H)]+ (13C)
[(M+H)]+ (18O)
[(M+Na)]+
[(M+K)]+
UPLC (C18) HSF5
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
4.77
11.17
UPLC (C18)
4.77
4.77
4.77
4.77
4.77
4.77
4.77
Tools for annotation and metabolite identification,
and data on biofluids begin to be published
Biochemical data on ~ 40000 metabolites
Spectral data (RMN, MS)
Formal identification of metabolites often requires several
complementary analytical tools
NMR spectroscopy
Mass spectrometry
C:\Documents and Settings\...\MTA-CID1
eau/MeCM, HCOOH 0.1%, col 15%
12/02/2007 18:17:20
5 ug/ml
MTA-CID1 #1 RT: 0.01 AV: 1 NL: 3.22E7
T: + p Full m s 2 [email protected] [50.00-300.00]
136.0
100
95
90
85
80
75
70
m/z
65
60
55
Molecular mass
50
45
40
35
30
297.9
25
20
15
10
5
163.1
145.1
61.1
0
60
75.1 93.8
80
97.1
100
119.2
120
159.6
140
160
178.4 191.5
180
m /z
208.2 221.2
200
220
238.4
256.9
240
260
280.1
280
300
MS/MS, MSn
061017-CIDPB-PT-UPLC #2447-2474 RT: 44.30-44.76 AV: 10 NL: 2.60E7
F: FTMS - c ESI d Full ms2 [email protected] [ 55.00-260.00]
x100
x20
x20
247.0721
100
204.0667
80
176.0720
218.0333
60
161.0612
20
0
198.8961
133.0664
40
60.9031
77.0776 85.4235
60
80
100.6693 114.1821 126.9277 137.0212 149.5655
100
120
140
160
m/z
220.7238
179.1933
242.8735
191.3928
165.7842
180
229.1446
214.9476
200
220
257.4338
240
Structural information
260
To discriminate between isomers
L’identification des métabolites:
1. Spectre de masse
- Isoler [M+H]+ ou [M-H]- composition élémentaire (CxHyOz)
- Isotopes (13C, 18O, 34S…)
2. Bases de données
3. Spectres de fragmentation (CID)
4. Expériences complémentaires (échange H/D, RMN)
5. Confirmation
- Synthèse chimique
- Analyse LC/MS comparative
Annotation
RT: 0.00 - 60.00 SM: 7G
16.69
100
100
80
70
60
50
40
30
20
Relative Abundance
Metabolite identification
90
NL: 1.03E7
x5
Base Peak m/z=
220.10669-220.12871 F:
FTMS + c ESI Full ms
[75.00-1000.00] MS
070829-pos-05-urines
U
80
Formal Identification
142.08651
156.10193
40
90.05494 116.03427
20
5.40
15.67
16.52
22.57
72.04433
23.40 30.84 39.26
45.02
53.68
NL: 2.43E7
Base Peak m/z=
220.10669-220.12871 F:
[75.00-1000.00] MS
070829-POS-04-melstd
0
100
90
Relative Abundance
80
80
70
60
201.85446
184.09723
60
50
40
STD
30
142.08658
156.10229
40
20
90.05491 116.03442
20
72.04417
10
3.19
0
100
6.65
18.35
16.53
31.12 34.74
0
44.12
201.86206
56.09
50
100
90
80
70
NL: 2.17E7
Base Peak m/z= 150
220.10669-220.12871
F:
m/z
[75.00-1000.00] MS
070829-pos-06-urinesurch
???
60
50
40
30
20
10
5.01
0
0
NL
070
877
F: F
220
[50
60
10
0
100
184.09719
15.62
10
22.54 23.36
20
30
Time (min)
38.84 43.29
40
52.83
50
200
NL
070
844
F: F
220
[50
XCMS output
CAMERA output
pcgroup
Inter-sample
correlation
**
531
NA
**
**
512
NA
[681][M+1]+
**
512
NA
L-Urobilin
**
[650][M]+
[M-H]-
394
C-Curarine / L-Urobilinogen
9.40
[650][M+1]+
**
394
631.3256
9.40
**
[M+Cl]-
394
1.00
0.98
0.96
3797
481.2789
9.46
**
**
552
NA
GPCho(10:0/4:0) / GPCho(12:0/2:0)
2763
381.1910
9.53
**
**
627
NA
**
3834
485.1792
9.61
**
**
544
NA
Rutaevin / Nafenopinglucuronide
1255
253.1440
9.67
**
**
556
NA
**
Variable
number
m/z
Retention
time (min)
1806
303.1443
9.33
**
4663
593.3312
9.34
[681][M]+
4668
594.3368
9.34
4679
595.3463
9.40
4682
596.3514
4878
isotopes
adduct
Public database annotation
L-Urobilinogen
**
**
C-Curarine
Automated analysis of multistage MS
(MSn): Spectral trees
(Kasper PT, Rapid Commun. Mass Spectrom., 2012)
(Rojas-Cherto M., Anal. Chem., 2012)
(Rasche F, Anal. Chem., 2012)
(Peironcely J, Anal. Chem., 2013)
Mise en place d'une méthode d’étalonnage pour
la construction d’une base de données MS/MS en
science métabolomique
F. Ichou, D. Lesage, C. Junot, J-C. Tabet
Travail actuellement poursuivi et coordonné par R. Cole
dans le cadre de l’infrastructure MetaboHUB
Les bases de données
o Majoritairement en ionisation par électron (E.I)
 En E.I : beaucoup de fragmentations, absence parfois de l’ion
moléculaire M+. et spectres de masse reproductibles
 Nombreuses bases de données et de tailles importantes
 Exemple :
220.000
spectres E.I
o En API : Peu de fragmentations, peu reproductible et interférence de la
matrice
 Conséquence : bases de données MS peu fiables
 Généralement bases de données MS/MS
 Problème de reproductibilité en CID à un régime de basse énergie
 2 approches utilisées
Les différentes approches de base de données MS/MS:
1 - Approche non-standardisée
2 - Approche standardisée
Principe reposant sur l’enregistrement de multiples empreintes CID
 H.Oberacher et M. Pavlic7
‘MSFor ID library’
 Analyses à 10 énergies de collision
 Elimination de l’ion parent
• Grande variation de l’abondance
 Autres exemples :
 Amélioration de l’algorithme de
matching
Test
7H.Oberacher,
M.Pavlic. J. Mass Spectrom. 2009, 44, 485
27
Les différentes approches de bases de données MS/MS:
1 - Approche non-standardisée
2 - Approche standardisée
Etalonnage avec un composé
 P. Marquet et al8
 Glafenine
 C.Hopley and T. Bristow9
 Reserpine
8P.
Marquet et al, Clin. biochem. 2005, 38, 362
T. Bristow, Rap. Com. Mass Spectrom. 2008, 22, 1779
9C.Hopley,
Type d’excitation
Instruments à piégeage d’ions (Piège LTQ, 3D)
Abondance relative
Chauffage lent
Ion fils
- Rupture compétitive
MH+
B+
D+
Ion père
A+
m/z
Excitation résonante
Type d’excitation
Instruments à faisceau d’ions (TQ, QTOF,…)
Abondance relative
Chauffage rapide
Ion fils
- Rupture consécutive
- Rupture compétitive
MH+
C+
Ion petit-fils
B+
….
E+
A+
D+
Excitation non-résonante
m/z
Procédure d’étalonnage
 Ajuster les ratios des abondances relatives des ions produits
par la fragmentation du Polyéthylène glycol (PEG) pour avoir
des spectres CID comparables à un spectre de référence
 Instruments à piégeage d’ions (Résonant) :
Choisir la même énergie de collision normalisée (15%, 20% et 25%)
Ajuster le temps d’activation
 Instruments à faisceau d’ions (Non-résonant) :
 Choisir trois énergies de collision
 Ajuster la pression de la cellule de collision
Objectifs
Evaluation de la procédure :
Reproductibilité inter-laboratoire sur le même type d’instruments
Est-il possible d’utiliser des spectres de référence enregistrés dans d’autres
laboratoires pour identifier les métabolites ?
Comparaison inter-instrumentale
Les spectres CID provenant de différents instruments donnent-ils les mêmes
informations ?
Quelles sont les limites de la méthode d’étalonnage ?
Doit-on construire une base de données MS/MS spécifique à chaque type
d’instruments (tandems à faisceau d’ions ou à piégeage d’ions)?
Validation de la procédure
 19 composés :
 Panel représentatif de la diversité des problèmes rencontrés en
métabolomique
 9 laboratoires partenaires
 13 instruments (QTOF, LTQ, Orbitrap)
 Mise en situation avec un logiciel commercial (SmileMS)
Panel de composés
Composé
Caféine
Myricetine
Xanthosine
Inosine
5-Methoxyindoleacetate
Acide Indoleacrylique
Acide 12hydroxydodecanoïque
Acide Xanthurenique
Dimethoate
Naringénine
Tyrosine
Panthenol
Acide Cis-aconitique
Acide Trans-aconitique
Cystathionine
Acetamiprid
Arginine
Glutathion
Carnosine
Masse molaire
(g/mol)
194
318
284
268
205
187
Pos
Pos&Neg
Pos&Neg
Pos&Neg
Pos
Neg
216
Neg
205
229
272
181
205
174
174
222
222
174
307
226
Neg
Pos
Pos
Pos&Neg
Pos&Neg
Neg
Neg
Pos&Neg
Pos&Neg
Pos&Neg
Pos&Neg
Pos&Neg
Polarité
Caractéristique des composés
Groupe A.
Peu de voies de dissociations compétitives
(< 2 ions fragments dans les pièges à ions)
Groupe B.
2-3 ions fragments
Groupe C.
Beaucoup de voies de dissociations
(> 3 ions fragments)
Logiciel SmileMS
 Originalité de l’algorithme X-Rank
 Comparaison par rapport aux m/z et
indirectement aux abondances relatives des ions
 Classification des fragments par ordre
d’intensité des pics
 Compatibilité Inter-instrumentale
• Extraction par protéowizards des différents
formats constructeurs
Convertion sous .mzXML, .mgf
Pierre-Alain Binz and co-workers, Anal. Chem. 2009; 81, 7604
Principe de l’expérience
Comparaison :




Inter-QTOF
Inter-LTQ
Inter-Orbitrap
Inter-instrumentale
Deux principaux résultats :
Score d’identification
 Score dépendant de la quantité d’informations contenues dans le spectre CID
 Nette baisse du score en négatif sauf pour les molécules ayant beaucoup d’ions
fragments
• Mode négatif plus pauvre en fragments
Ecart-type
Résultats inter-instrumentaux
Avant standardisation
Après standardisation
Division
Division
• LMCO
• Consécutifs
37
Glutathion
Quantité d’informations
Positif
QTOF
LTQ
Négatif
Grp A
Grp A
Grp B
Grp B
Grp C
Grp C
Grp A
Grp A
Grp B
Grp B
Grp C
Grp C
Grp A
Grp A
Grp B
Grp B
Grp C
Grp C
Orbitrap
Résultats inter-Orbitrap
Score moyen d’identification et écart-types proches de 0
En mode positif
CH3
N
N
N
H3C
O
N
CH3
O
Caféine
Conclusion
 Procédure de standardisation
 Meilleure reproductibilité inter-laboratoires et inter-instrumentale
 Intérêts de la procédure d’étalonnage
 Contrôle de l’énergie interne
 Réduction des ruptures consécutives pour les tandems à faisceau d’ions
 Chaque instrument peut être une référence
 2 Sources de déviation dans les scores
 Les molécules avec peu de fragmentations
 Les tandems à faisceau d’ions (contrôle énergétique plus difficile)
 Perspectives
 Validation et construction de la librairie
 Projet MétaboHUB
Standards commerciaux
Composés issus de matrices biologiques,…
Several methods have to implemented in order
to achieve an optimal metabolome coverage
270 metabolites identified in
human plasma
3 LCs/HRMS
RP-C8
93 metabolites
16
2
17
70
ZICpHILIC
141 metabolites
52
58
24
PFPP
151 metabolites
(Boudah et al., J. Chromatogr. B, 2014)
The is a need to add other separative dimensions
to HRMS
Reverse Phase Liquid Chromatography
Classic
Retention mechanism :
- Hydrophobic interactions
Alkyl bound silica
C8,
C18…
…. Alternative selectivity
Bile acids, Steroid hormones…
-π–π interactions for phenylbased compounds
-dipole–dipole interactions
for halo/polar compounds
Most widely used columns:
-aqueous and organic solvents
compatibility
-robustness
-well defined retention mechnisms
-continuous manufacturer improvement
PFPP
PentaFluoroPhenylPropyl bound
silica
Amino acids , cyclic compounds…
Hydrophilic Interaction Liquid Chromatography
-hydrophilic partitioning
from the eluent to the
enriched-water layer
-electrostatic interactions
with either positive and
negative charges
ZIC HILIC Zwitterionic sulfobetaine bound
silica
Carboxylic acids, Sugars, Nucleotides…
Sandrine Aros-Calt
François Fenaille
RP-C8
To discriminate between
isomer species
Isoleucine
β Leucine
PFPP
Norleucine
ZICpHILIC
LC/MS analyses of human plasma
LC/MS/MS analyses
ZICpHILIC
[(M-H)]-
LC/MS/MS analyses
[(M-H)]-
[(M-H)]-
[(M-H)]-
RP-C8
Hypoxanthine reference spectra
Threonic acid reference spectra
[(M-H)](C5H3ON4)
[(M-H)](-C2H4O3)
(C4H7O5)
(-C2H4O2)
(-NHCO)
(-H2O)
(-CH2O)
(-H2O)
(-CO)
(-H2O)
(-H2O)
(-H2O)
Annotation of the human serum metabolome
using LC/HRMS
RP-C8
175 metabolites
6
20
21
128
52
17
19
ZICpHILIC
PFPP
219 metabolites
185 metabolites
-266 metabolites were distributed over 209 distinct accurate masses
-ESI(-): HILIC extended metabolome coverage 50% and the number of retained metabolites
(k>1) 75%.
-ESI(+): PFPP conditions improved the retention up to almost 200% of metabolites detected.
-using HILIC (ESI-) and PFPP (ESI+) ensure the detection of 243 out of 266 metabolites in
serum samples.
(Boudah et al., J. Chromatogr. B, 2014)
Annotation of the human serum metabolome
using LC/HRMS
Alkyl RP vs PFPP
Multiplatform strategy: toward a comprehensive
assessment of metabolomic profiles
Valine ?
Betaine ?
Both ?
**
Extracted Ion Chromatogram
of m/z= 118.086 obtained in
RP-LC
*
***
Betaine
Valine
Extracted Ion Chromatogram
of m/z= 118.086 obtained in
PFPP-LC
Proteinogenic
amino acids
Detoxification
reaction : liver,
kidney
Toward databases of metabolic profiles: it is required
to control analytical biais through design of experiment
- Randomization is required
- Batches of ~ 100 injections
- Blanck and QC samples
Some «reference» protocols are available:
Human plasma: Dunn WB, Nat. Protocols, 2011
Human urines: Want E., Nat. Protocols, 2010
Acide 4-methyl-2-oxovalerique ESI(-)
5.00E+06
QCa
Samples
QCb
Linéaire (QCa)
Linéaire (QCb)
4.50E+06
4.00E+06
3.50E+06
Aire
3.00E+06
2.50E+06
2.00E+06
1.50E+06
1.00E+06
5.00E+05
0.00E+00
0
100
200
300
Ordre de passage des échantillons
400
Toward databases of metabolicfusion.M1
profiles:
it is required
(PCA-X), ACP fusion UV neg 107 variables
t[Comp. 1]/t[Comp.
2]
to control analytical biais through
design
of experiment
Colored according to Obs ID (Exp)
LOESS: Low Order non linear locally
Estimated Smoothing Function
Cleveland, J. Am. Stat. Assoc. 1979
Dunn WB, Nat. Protoc. 2011
8
AA1_N
HL53_
HS63_SM61_
AC2_N
GC3_N
BS6_N
YM87_
LM95_
GB26_
KG20_
GJ97_
PM85_
LE39_
MR6_N
SM67_
MC3_NPP9_N
PO67_
YB72_
PD22_
CM40_
MM17_
AG16_
LM8_N
ME99_
CC5_N
PY63_
886_n 993_n SM83_
XC96_
2001c
HA96_
1313_
_48_N
1201. 1140_
801_n LC58_
1276_
1191_
1047_
914.A
968_n
1157_
1399_
1335_
492_n 692_n
OA4_N
1050_
1287_
1271_
1367_
1231_
1086_
1043_
870_n
717_n
1057.
1087_
986_n
1185_
1134_
956_n
906_n
1263_
1168.
1210_
1260.
1305_
1186_
1161_
715_n
_1073
1343_
1260.
462_n
1218_
999_n
1241.
1124_
1114_
1260.
914.B
1238_
900.B
1342_1168.
1361_
892_n
1135_
1235_
1205_
1074_ 900.A
1048_
1136_
1317_
938_n
1579_
1412_
1201.
837_n
1142_
1166_
835_n
1138_
1193_
1061_
1476_
1067_1148_
1245_
1058_
820_n
1173_
1316_
1018_
1419_
1273_1113_ 1057.
1052_
1382_
1435_ 712_n
1199_
896_n
1264_
1286_
1073_
6
4
t[2]
2
0
-2
-4
-6
-8
-14
-12
-10
-8
-6
R2X[1] = 0.233239
-2
0
2
4
6
8
10
12
R2X[2] = 0.096216
14
16
_1199
RM70_
_469_
18
20
0
-5
22
3
4
Ellipse: Hotelling T2 (0.95)
896_n HA96_ SIMCA-P+ 12 - 2013-05-26 23:59:50 (UTC+1)
1286_
1435_
1073_
_1073
OA4_N
1235_LC58_ 1382_
1052_
1018_
1113_
1142_
712_n
1273_
1316_
892_n
1058_
XC96_
SM83_
1245_
914.B
1201.
1067_
1173_
820_n
1476_1057. _48_N
1138_
1193_
715_n
1061_
1238_
PY63_
1264_
1260.
835_n
1419_
1199_ _1199
1271_
1114_
YB72_
1148_
LE39_
1276_
1048_
1218_
1191_
1057.
1342_
1367_
1241.
1135_
1317_
1260.
999_n
462_n
1043_
CM40_
837_n
1335_
1168.
1168.
1412_
717_n
938_n
900.B
1210_
1086_
KG20_
1343_
1136_
1579_
1134_
1361_
1124_
MM17_
1161_
1399_
ME99_
SM67_
1074_
1157_
968_n
692_n
AG16_
956_n
1166_
1186_
GJ97_
1263_
1087_
801_n
1047_
MC3_N
1260.
993_n870_n
1140_
1201.
1231_
1305_
HL53_
906_n
GC3_N CC5_N
HS63_
986_n
GB26_
2001c
BS6_N
PP9_N
1205_
1185_
886_n 1287_
914.A
SM61_
PO67_
1050_
MR6_N
LM8_N
YM87_
492_n
AA1_N
PM85_
1313_
900.A
LM95_ PD22_
AC2_N
5
t[2]
-4
fusion2.M1 (PCA-X), ACP UV neg fusion 86 variables
t[1]
t[Comp. 1]/t[Comp. 2]
Colored according to Obs ID (Exp)
3
4
_469_
-10
-15
RM70_
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
t[1]
R2X[1] = 0.258492
R2X[2] = 0.0890685
Ellipse: Hotelling T2 (0.95)
14
16
18
20
22
24
Quantitative metabolite profiling for large
scale studies
Absolute quantification of metabolites
Internal
standards (ISs)
Calibrants
Quantitative measurement of 363
metabolites in 284 serum samples
«Genetically determined metabotypes»
Biological
matrix
Samples
A/AIS
Calibration curve
Concentration
Molarity unit
Polymorphism in the FADS1
(fatty acid delta 5 desaturase) gene
Conclusion
Apport significatif de la FT-MS dans la détection des métabolites
(séparation des ions isobares) et dans leur identification
(annotation/élucidation structurale).
Disponibilité de différents types de bases de données (bases
spectrales de HRMS, de MS/MS, bases de données biochimiques et
métabolomique). Les outils de normalisation et de quantification
rendent possible la construction de bases de données de profils
métaboliques.
Un des principaux enjeux: le partage des données concernant les
composés inconnus. Pour cela, nécessité de standardisation des
spectres MS/MS.
Très haute résolution (Orbitrap, FTICR) versus efficacité des
acquisitions données dépendantes avec Q-TOF??
Remerciements
CEA/LEMM
CEA/iRCM
Jean-François Heilier
Alexandra Lafaye
Céline Ducruix
Erwan Werner
Geoffrey Madalinski
Emmanuel Godat
Jérôme Cotton
Ying Xu
Aurélie Roux
Eric Ezan
Benoit Colsch
Samia Boudah
Paul-Henri Roméo
Dhouha Darghouth
Lydie Oliveira
Bérengère Koehl
Marie-Françoise Olivier
G.H. Pitié-Salpétrière
Frédéric Sedel
Maria del Mar Amador
Fanny Mochel
Foudil Lamari
Profilomic
Laboratoire de Chimie
Structurale Organique et
Biologique (UPMC)
Jean-Claude Tabet,
Richard Cole
Denis Lesage, Sandra Alves
Estelle Paris, Farid Ichou
Céline Ducruix, Jérôme Cotton, Simon
Broudin, Fanny Leroux, Bruno Corman,
Stéphanie Oursel, Victor Sabarly, Denis
Desoubzdanne
CEA/Programmes Transversaaux de
Technologies pour la santé et Tox Nuc
Jacques Grassi, Marie-Thérèse Ménager
CEA/DRT/LIST/LOAD
Antoine Souloumiac, Etienne
Thévenot,

DSV/iBiTec-S/SPI/LEMM - TGE FT-ICR

Transcription

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