The document discusses metabolomics analysis of wine. It describes how metabolomics uses targeted and untargeted analytical approaches to comprehensively characterize small molecule metabolites in biological systems like wine. Experimental design considerations for wine metabolomics studies are outlined, including using a clear question, minimizing variables, sufficient biological replicates, and collecting detailed metadata. Sample preparation and LC-MS acquisition parameters are also important to consider. The goal of wine metabolomics is to detect markers that can then be validated and identified to generate new hypotheses.

1. Οίνος και Μεταβολομική
Wine metabolomics
Agricultural University of Athens
April 2019

2. Metabolomics: www.scopus.com statistics
metabolomics

3. Metabolomics: www.scopus.com statistics
Wine metabolomics

4. From targeted to untargeted

5. Targeted versus Untargeted

6. Targeted versus Untargeted

7. Targeted versus Untargeted

8. χρόνος
Όγκοςδουλειάς
targeted analysis
untargeted analysis

9. Metabolomics: definitions
metabolomics is the "systematic study of the unique chemical fingerprints that
specific cellular processes leave behind", the study of their small-molecule
metabolite profiles (Daviss 2005)
metabolomics is a newly emerging field of "omics" research concerned with the
comprehensive characterization of the small molecule metabolites in biological
systems. (Metabolomics Society)
metabolomics is the comprehensive and holistic study of the metabolome
the complete set of small-molecule
metabolites to be found within a biological
sample
metabonome: the complete set of metabologically
regulated elements in cells

10. Metabolomics: facts
 Holistic approach: complementary platforms
 Multidisciplinary: chemistry + biology + physics + mathematics + informatics
 Untargeted: the metabolites are by definition not pre-defined
 Unfeasible validation: hundreds to thousands metabolites, many unknown
 Self-awareness: minimum reporting standard / levels of annotation
Mass Spectrometry (MS)
Direct infusion/Imaging
Gas Chromatography (GC)
Liquid Chromatography (LC)
Capillary Electrophoresis (EC)
Nuclear Magnetic Resonance (NMR) NMR: up to 100 metabolites
few hundreds metabolites
ESI-
ESI+
Reverse Phase (RP)
Normal Phase (NP)
few hundreds metabolites
few thousands metabolites
GCxGC
few hundreds metabolites
Derivatisation

11. Plant metabolome is estimated to cover
200 000 metabolites
#ofmetabolites5-21% ethanol
g/L
mg/L
µg/L
ng/L
pg/L
fg/L
How big is the wine metabolome?

12. Plant metabolome is estimated to cover
200 000 metabolites
How big is the wine metabolome?

13. Εφαρμογές στην Οινολογία

14. Metabolomics: workflow
Experimental
design
Experiment
Sampling
LC-MS
analysis
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Method
adaptationMetadata
Randomi-
zation
Clear
Question
few
variables
Simple
sample
preparation
Biological
variability
60-150
samples
Data
processing
XCMS
QC OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Internal
database
External
database
MS/MS
Arapitsas et al. JCA 2016

15. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Metabolomics: experimental design - sampling
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Clear question: How headspace O2 influences wine metabolome?
One variable: Oxygen level (low vs high)
Sample size: 96 samples
Metadata: Cultivar, vintage, closer, alcohol, winery,
titrated acidity, volatile acidity, TPO, HSO,
total and free SO2, ascorbic acid, etc.
Biological variability: 12 wines from 6 white cultivars
Conditions: realistic

16. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Metabolomics: experimental design - sampling
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery

17. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Metabolomics: experimental design - sampling
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
Chardonnay
Grillo
Inzolia
Muller Thurgau
Pinot gris
Traminer
9+9
3x(9+9)
9+9
9+9
5x(9+9)
9+9
1
3
1
1
5
1
cultivar wine
Initial
bottles
6 cultivars 12 216
1+1
3x(1+1)
1+1
1+1
5x(1+1)
1+1
Basic
analysis
bottles
24
O2
bottles
24
1+1
3x(1+1)
1+1
1+1
5x(1+1)
1+1
Sensorial
analysis
bottles
24
1+1
3x(1+1)
1+1
1+1
5x(1+1)
1+1
LC-MS
analysis
bottles
96
4+4
3x(4+4)
4+4
4+4
5x(4+4)
4+4

18. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Metabolomics: experimental design - sampling
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
LO (Low O2) HO (High O2)
2 months storage

19. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Metabolomics: meta-data
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
LO
(Low O2)
HO
(High O2)

20. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Nomasens O2 sensors
LO (Low O2) HO (High O2)
Metabolomics: meta-data

21. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
- 20 % - 40 %
Metabolomics: meta-data

22. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
- 5 % - 14 %
Metabolomics: meta-data

23. Experimental
design
Experiment
Sampling
LC-MS
analysis
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Mezzacorona winery
Metabolomics: meta-data
O
OH OH
O
OH
OH
O
O O
O
OH
OH
O2
- 23 % - 65 %

24. Metabolomics: experimental design - sampling
Experimental
design
Experiment
Sampling
LC-MS
analysis
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Metadata
Clear
Question
few
variables
Biological
variability
60-150
samples
Data
processing
untargeted vs. targeted approach
two different tools
development and validation analysis data analysis
untargeted metabolomics
targeted analysis

25. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Sumner et al. Metabolomics 2007
Metabolomics: experimental design - sampling
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Minimum reporting
standards
 Sample Preparation
o Sampling process and protocol
o Extraction protocol
 Chromatography-Separation
o Instrument description (manufacturer, model, software)
o Injection (auto injection, volume, wash cycle)
o Column and pre-column (manufacturer, model, parameters)
o Derivatization, Spiking (IS)
o Separation method
 Mass spectrometry
o Instrument description (manufacturer, model, software)
o Sample introduction (GC, LC, direct injection)
o Ionization source (ESI, EI), polarity, voltages, vacuum, gases)
o Mass Analyzer (TOF, FT-ICR) and acquisition mode (full scan, MSn)
o Data acquisition parameters (scan range, calibration, accuracy, resolution, lock mass)

26. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Metabolomics: MetaboLights
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #1: Organize your meta-data and raw-data

27. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
A
BSPE
no SPE
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Arapitsas et al. PLoSone 2013

28. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

29. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Arapitsas et al. Food Chem 2016

30. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Arapitsas et al. Food Chem 2016
1:9 wine:acn
Time
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
T007_QC10_NP_neg 1: TOF MS ES-
151.06 50PPM
92.6
T050_QC05_NP_neg 1: TOF MS ES-
151.06 50PPM
128
T072_QC03_NP_neg 1: TOF MS ES-
151.06 50PPM
184
T127_QC02_NP_neg 1: TOF MS ES-
151.06 50PPM
176
T152_QC01_NP_neg 1: TOF MS ES-
151.06 50PPM
93.1
1:9 (wine:ACN)
1:4 (wine:ACN)
1:2 (wine:ACN)
1:1 (wine:ACN)
no dilution

31. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Arapitsas et al. Food Chem 2016
wine
Time
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00
%
0
100
T007_QC10_NP_neg 1: TOF MS ES-
243.062 50PPM
849
T050_QC05_NP_neg 1: TOF MS ES-
243.062 50PPM
1.14e3
T072_QC03_NP_neg 1: TOF MS ES-
243.062 50PPM
959
T127_QC02_NP_neg 1: TOF MS ES-
243.062 50PPM
734
T152_QC01_NP_neg 1: TOF MS ES-
243.062 50PPM
279
1:9 (wine:ACN)
1:4 (wine:ACN)
1:2 (wine:ACN)
1:1 (wine:ACN)
no dilution

32. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Stability Test (1:1 H2O:Wine 10 uL inj)
0
1000
2000
3000
4000
5000
6000
7000
8000
2
6
10
15
19
24
28
32
37
41
46
50
54
59
injection number
numberoffeatures
IS stability test
0
20
40
60
80
100
120
140
47
51
55
60
64
69
73
77
82
86
91
95
99
104
injection number
area
indol proprionic
diOH-bezoic
IS or not IS

33. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
QC (Quality Control) samples
Your Best Friend
1. training
2. method development/adaptation
3. column equilibration
4. system control
5. data quality
6. marker quality

34. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

35. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
www.random.org/sequences/
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

36. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

37. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

38. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Noack et al. CellPress 2014

39. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #3: Randomize your samples*
*the instrumental analysis or better before the sample preparation
Tip #2: Keep your sample preparation simple

40. Experimental
design
Experiment
Sampling
LC-MS
analysis
Method
adaptation
Randomi-
zation
Simple
sample
preparation
Data
processing
Metabolomics: wet lab
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #4: QC samples are your best friend*
Tip #5: Understand/Know your instrument/method**
Tip #6: Blacks and Standard mixes are not good QC sample
Tip #7: Think if you really need IS(s)
*show my your friend and I will show you your future
**velocity, coverage, resolution, accuracy, robustness during the time of analysis
machines are limited

41. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

42. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
664 injections
111 QCs

43. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

44. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #4: QC samples are your best friend*
Tip #8: Choose the right QC
*show my your friend and I will show you your future

45. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
https://metlin.scripps.edu/xcms/
XCMS on-line
XCMS
metaXCMS
XCMS2
mzMine
OpenMS
MetaboAnalyst
Metabox
Progenesis (nonLinear)
MarkerLynx (Waters)
MassHunder Profiler (Agilent)
Compound Discoverer (Thermo)
ChromaTOF (Leco)
http://proteowizard.sourceforge.net/
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
https://xcmsonline.scripps.edu

46. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
peak picking
centWave
Tautenhahn et al. BMC Bioinformatics (2008)
Each sample separately
high resolution MS and centroid MS data

47. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

48. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Resolution (chromatography – MS)
Accuracy - Stability
High resolution avoid peak overlaps
High resolution makes peak picking easier
Cellar
Time
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
%
0
100
2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00
%
0
100
A009_2402_C_RP_pos 1: TOF MS ES+
215.016 50.00PPM
146
A009_2402_C_RP_pos 1: TOF MS ES+
261.145 50.00PPM
100
A009_2402_C_RP_pos 1: TOF MS ES+
213.074 50.00PPM
491
A009_2402_C_RP_pos 1: TOF MS ES+
493.135 50.00PPM
463
XCMS
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

49. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
grouping
alignment
Group of samples
Each ion is aligned across all samples

50. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #9: don’t underestimate peak picking and peak alignment

51. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Filling in
missing
peaks

52. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
XCMS
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
peak table
variables
samples

53. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Resolution (chromatography – MS)
Accuracy - Stability
High resolution avoid peak overlaps
High resolution makes peak picking easier
XCMS
QC
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Arapitsas et al. JCA 2016

54. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation

55. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Markers
detection
Markers
validation
Data
processing
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation
supervised multivariate analysis
unsupervised multivariate analysis

56. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Markers
detection
Markers
validation
Data
processing
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation
supervised multivariate analysis
unsupervised multivariate analysis

57. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
supervised multivariate analysis
unsupervised multivariate analysis
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation
Arapitsas et al. Metabolomics 2015; XCMS; Umetrics-Waters

58. -1,0
-0,8
-0,6
-0,4
-0,2
-0,0
0,2
0,4
0,6
0,8
1,0
-0,0005 -0,0004 -0,0003 -0,0002 -0,0001 0,0000 0,0001 0,0002 0,0003 0,0004 0,0005 0,0006 0,0007 0,0008 0,0009 0,0010 0,0011 0,0012 0,0013 0,0014
p(corr)[1]P(Correlation)
CoeffCS[2](Group) (X Effects)
S-Plot (Group 1 = -1, Group 2 = 1)
EZinf o 2 - dataSet (M4: OPLS-DA) - 2013-03-12 17:09:58 (UTC+1)
Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation
Arapitsas et al. Metabolomics 2015; XCMS; Umetrics-Waters
supervised multivariate analysis
unsupervised multivariate analysis

59. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation
Arapitsas et al. Metabolomics 2015; XCMS; Umetrics-Waters
supervised multivariate analysis
unsupervised multivariate analysis
false negatives false positive
true
positive
false
positive
relevant elements
selected elements

60. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation

61. Metabolomics: markers
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
C T QC C T QC
Markers
detection
Markers
validation
OPLS-DA Visual
control
TargetLynx
Statistics
Targeted
analysis
Markers
identification
Hypothesis
generation

62. Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Metabolomics: dry lab – data processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #10: don’t trust statistics – always turn to raw files

63. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Internal
database
External
database
MS/MS
Sumner et al. Metabolomics 2007

64. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Tip #11: annotation needs time, be patient!

65. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Internal
database
External
database
MS/MS
O2
OH NH
NH
O
NH2
O
S
O
O
OH
OH NH
NH
O
NH2
O
S
O
O
OH
OH NH
NH
O
NH2
O
SH
O
O
OH
O2, HSO3
-
OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
O
Arapitsas et al. J Chromatogr A 2016

66. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Internal
database
External
database
MS/MS
Arapitsas et al. J Chromatogr A 2016
OH NH
NH
O
NH2
O
SH
O
O
OH
OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
O
HO
LO
O2, HSO3
-
HO
LO

67. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Internal
database
External
database
MS/MS
O
NHNH
O
OH
OH S
S
O
O
OH
O
NHNH
O
OH
OH SH
N
H
OH
S
O
O
OH
N
H
OH
O2, HSO3
-
O2, HSO3
- O
NH2
S OH
S
O
O
OH
O
NH2
SH OH
O2, HSO3
-
Arapitsas et al. J Chromatogr A 2016

68. Metabolomics: identification/annotation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation
Internal
database
External
database
MS/MS
N
H
O
O
OH
O
OH
OH
OH
OH
N
H
O
O
OH
O
OH
OH
OH
OH
S O
OH
O
O2, HSO3
-
Arapitsas et al. J Chromatogr A 2016

69. Metabolomics: hypothesis generation
Experimental
design
Experiment
Sampling
LC-MS
analysis
Data
processing
Markers
detection
Markers
validation
Markers
identification
Hypothesis
generation

70. Metabolomics: hypothesis verification OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
O
Arapitsas et al. J Chromatogr A 2016
H.T. Clarke, The action of sulfite upon cystine, J. Biol. Chem. 97 (1932) 235–248.
S.G. Waley, Acidic peptides of the lens. 5. S-Sulphoglutathione, Biochem. J. 71 (1959) 132–137.

71. Metabolomics: hypothesis verification OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
O
GSSG GSSG/SO2
10/1
GSSG/SO2
1/1
GSSG/SO2
1/10
GSSG GSSG/SO2
10/1
GSSG/SO2
1/1
GSSG/SO2
1/10
OH NH
NH
O
NH2
O
S
O
O
OH
OH NH
NH
O
NH2
O
S
O
O
OH
GSSG
OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
OGSSO3H

72. Metabolomics: hypothesis verification OH NH
NH
O
NH2
O
S
O
O
OH
S
O OH
O
O2

73. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
OH
NH2
N
H
O
O
OH
N
H
OH
O
OH
N
H
OH
N
H
TOL
N
OH
O
OH
N
H
O
NH O
N
H
OH
NH2
shikimic
OH
OH
OH
OOH
OOH
NH2
O
NH2
O
O
N
H
OH
O
NH
N
H
O
O
O
NH2
N
H
O
tryptophan melatoninserotonin
tryptophan ethyl ester
N-acetyl-tryptophan ethyl ester
kynurenic
anthranillic
kynurenine
indole-3-pyruvic acid
indole-3-lactic acid
indole-3-acetic acid
tryptophol
2-aminoacetophenone
OOH
NH2
anthranillic

74. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
1 1 1
2
3
4
5 6
7
8
9
1 10
2
3
4
5 6
7
8
9
1 1 0
O
OH
N
H
OH
O
OH
N
H
OH
N
H
TOL
indole-3-lactic acid
indole-3-acetic acid
tryptophol
OH
N
H
S
O
O
OH
O
OH
N
H
OH
S
O
O
OH
TOL-SO3H
(tryptophol-SO3H)
ILA-SO3H
(indole-lactic acid-SO3H)
O
OH
N
H S O
O OH
IAA-SO3H
(indole-acetic acid-SO3H)
SO2 in water
indole in ethanol
+ 3 mol SO2
rt, 2 days
+ 18 mol SO2
rt, 14 days
+ 12 mol SO2
rt, 6 days
Arapitsas et al. Scientific Reports 2018

75. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
1 1 1
2
3
4
5 6
7
8
9
1 10
2
3
4
5 6
7
8
9
1 1 0
O
OH
N
H
OH
O
OH
N
H
OH
N
H
TOL
indole-3-lactic acid
indole-3-acetic acid
tryptophol
OH
N
H
S
O
O
OH
O
OH
N
H
OH
S
O
O
OH
TOL-SO3H
(tryptophol-SO3H)
ILA-SO3H
(indole-lactic acid-SO3H)
O
OH
N
H S O
O OH
IAA-SO3H
(indole-acetic acid-SO3H)
SO2 in water
indole in ethanol
+ 3 mol SO2
rt, 2 days
+ 18 mol SO2
rt, 14 days
+ 12 mol SO2
rt, 6 days
Arapitsas et al. Scientific Reports 2018

76. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
Arapitsas et al. Scientific Reports 2018

77. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
Arapitsas et al. Scientific Reports 2018
O
OH
N
H S O
O OH
OH
N
H
TOL
tryptophol
red
sparkling
white
O
OH
N
H
OH
indole-3-lactic acid
O
OH
N
H
OH
S
O
O
OH
red
sparkling
white
O
OH
N
H
indole-3-acetic acid
O
OH
N
H S O
O OH
red
sparkling
white

78. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
Arapitsas et al. Scientific Reports 2018
O
OH
N
H S O
O OH
OH
N
H
TOL
tryptophol
red
sparkling
white
O
OH
N
H
OH
indole-3-lactic acid
O
OH
N
H
OH
S
O
O
OH
red
sparkling
white
O
OH
N
H
indole-3-acetic acid
O
OH
N
H S O
O OH
red
sparkling
white
r= 0,62
Verdicchio
Age Age

79. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
Arapitsas et al. Scientific Reports 2018
O
OH
N
H S O
O OH
OH
N
H
TOL
tryptophol
red
sparkling
white
O
OH
N
H
OH
indole-3-lactic acid
O
OH
N
H
OH
S
O
O
OH
red
sparkling
white
O
OH
N
H
indole-3-acetic acid
O
OH
N
H S O
O OH
red
sparkling
white
r = 0,78
Verdicchio

80. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
N
H
O
O
OH
O
OH
OH
OH
OH

81. Metabolomics: hypothesis verification
N
H
OH
S
O
O
OH
N
H
O
O
OH
O
OH
OH
OH
OH
Top 30 red grape cultivars Top 30 white grape cultivars
271 entries 181 entries

82. Wine Metabolomics – public repositories
N
H
O
O
OH
O
OH
OH
OH
OH
http://www.ebi.ac.uk/metabolights/MTBLS137Franceschi et al. Frontiers 2014
indole-3-lactic glucoside
0
100
200
300
400
500
600
700
800
Germany Italy
area
Phoenix
Regent

83. Εφαρμογές στην Οινολογία

84. Wine storage
Arapitsas et al. Metabolomics 2015

85. 1 2 3 4 5
6 7 8 9 10
11 12 13 14 15
16 17 18 19 20
Wine storage
Arapitsas et al. Metabolomics 2015

86. 1
2010
Tempo 0
~4 o
C Cellar DomesticWine storage
Arapitsas et al. Metabolomics 2015

87. 2010
~4 o
C Cellar Domestic
6 mesi
Wine storage
Arapitsas et al. Metabolomics 2015

88. 2010
~4 o
C Cellar Domestic
12 mesi
2011
Wine storage
Arapitsas et al. Metabolomics 2015

89. ~4 o
C Cellar Domestic
2011
18 mesi
Wine storage
Arapitsas et al. Metabolomics 2015

90. ~4 o
C Cellar Domestic
2012
24 mesi
2011
Wine storage
Arapitsas et al. Metabolomics 2015

91. Wine storage
Storage
Cellar
Domestic
Arapitsas et al. Metabolomics 2015

92. Wine storage
O
+
O
OH
OH
O
OH
O
OH
O
OH
OH
OH
OH
R
O
+
OH
OH
OH
O R
O
O
O
+
O
OH
OH
O
O
OH
OH
O
R
cellar
domestic
cellar
domestic

93. Wine storage
cellardomestic
time zero
cellar
domestic
*SO2 bleaching, Glories index
cellar
domestic
cellar
domestic

94. O
OH
OH
OH
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
O
OH
OH
OH
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
O
+
OH
O
OH
O
O
O
C6H5
O OH
O
+
OH
O
OH
O
O
O
C6H5
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
Wine storage

95. O
OH
OH
OH
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
O
OH
OH
OH
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
O
+
OH
O
OH
O
O
O
C6H5
O OH
O
+
OH
O
OH
O
O
O
C6H5
OH
OH
O
+
OH
O
OH
OH
O
O
C6H5
Wine storage

96. Wine storage
cellardomestic
time zero
cellar
domestic
O
+
OH
OH
OH
O R
O
OO
+
O
OH
OH
O
O
OH
O
O
R

97. Wine storage
O
+
O
OH
OH
O
OH
O
OH
O
OH
OH
OH
OH
R
O
+
OH
OH
OH
O R
O
O
O
+
O
OH
OH
O
O
OH
OH
O
R
cellar
domestic
Domestic storage  loss of red color
Pinotins  possible markers of bad storage
O
+
O
OH
OH
O
OH
O
R
OH
O
OH
OH
OH
OH
CH3
O
+
O
OH
OH
O
O
R
O CH3

98. Wine storage
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
cellar
domestic
cellar
domestic

99. Wine storage
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
-
O
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
O
-
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
cellar
domestic
cellar
domestic

100. Wine storage
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
O
-
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
Free SO2
Total SO2
cellar
domestic
cellar
domestic

101. Wine storage
OH
R
OOH
OH
R
OO
OH
OH
OH
O
O
O
OH
OH
OH
OH
OH
O
O
OH
OH
OH
OH
O
O
glu
Domestic
Cellar
domestic
domestic
O
ONH
OH
OH
CH3
CH3
OH

102. O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
epicatechin
procyanidin B2
ECAT-SO3H
epicatechin-SO3H
PROC-B2-SO3H
procyanidin B2-SO3H
O
OH
OH
OH
OH
OH
S
O
O
OH
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
OH

103. O
OH
OH
OH
OH
OH O
OH
OH
OH
OH
OH
(+)-catechin (-)-epicatechin
O
OH
OH
OH
OH
OH
S
O
O
OH
O
OH
OH
OH
OH
OH
S
O
O
OH
O
OH
OH
OH
OH
OH
(-)-catechin
O
OH
OH
OH
OH
OH S
O
O
OH
O
OH
OH
OH
OH
OH
(+)-epicatechin
O
OH
OH
OH
OH
OH
S
O
O OH
OH
OH
OH
OH
OH
OHS
O
OOH
OH
OH
OH
OH
OH
OHS
O
OOH
Flavanols isomerisation

104. winesAn update on wine ageing and sulfonations
195 wines
vintage: 1986-2016
93 white wines
35 Chardonnay
32 Pinot gris
24 Verdicchio
…
(2001-2016)
37 sparking wines
Chardonnay, Pinot noir
White, Rosé, Riserva
5 rosé still wines 60 red wines
18 Sagrantino
13 Tannat
12 Sangiovese
11 Amarone
…
(1986-2015)

105. MethodAn update on wine ageing and sulfonations
Metabolites
ILA indole 3-lactic acid
ILA-GLU indole 3-lactic acid glucoside
ILA-SO3H indole 3-lactic acid 2-sulfonate
IAA indole 3-acetic acid
IAA-ASP indole 3-acetic acid aspartic acid
IAA-SO3H indole 3-acetic acid 2-sulfonate
IPA indole 3-pyruvic acid
ICA indole 3-carboxaldehyde
2AAP 2-aminoacetophenone
TRP tryptophan
N-TRP-EE N-acetyl-tryptophan ethyl ester
TRP-EE tryptophan ethyl ester
MEL melatonine
SER serotonine
N-SER N-acetyl serotonine
KYNA kynurenic acid
KYN kynurenine
TOL tryptophol
TOL-SO3H tryptophol sulfonate
TYR tyrosine
TYR-EE tyrosine-ethyl ester
N-TYR-EE N-acetyl-tyrosine-ethyl ester
TYL tyrosol
PHE phenylalanine
ABA abscisic acid
ABA-GLU abscisic acid glucoside
CAT catechin
ECAT epicatechin
PROC-B1 procyanidin B1
PROC-B2 procyanidin B2
ECAT-SO3H epicatechin 4-sulfonate
PROC-B2-
SO3H procyanidin B2 -sulfonate
O
NH2
N
H
OH
O
OH
OH
OH
OH
OH
O
NH2
OH
O
NH2
OH
OH
O
OH
O OH

106. An update on wine ageing and sulfonations
red
Results, flavanols
sparkling
white
sparkling
white
red
ECAT-SO3H
epicatechin-SO3H
PROC-B2-SO3H
procyanidin B2-SO3H
O
OH
OH
OH
OH
OH
S
O
O
OH
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
OH

107. Results, flavanolsAn update on wine ageing and sulfonations
Amarone
6 8 10 1416 19 24 26 31
Age
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
epicatechin
procyanidin B2
6 8 10 14 16 19 24 26 31
Age
Amarone
ECAT-SO3H
epicatechin-SO3H
PROC-B2-SO3H
procyanidin B2-SO3H
O
OH
OH
OH
OH
OH
S
O
O
OH
O
OH
OH
OH
OH
OH
O
OH
OH
OH
OH
OH
S
O
O
OH

108. Results, flavanolsAn update on wine ageing and sulfonations
Amarone Tannat Sagrantino
% PROC-B2
% PROC-B2-SO3H
% ECAT
% ECAT-SO3H

109. Results: overviewAn update on wine ageing and sulfonations

110. Consorzio
Brunello di Montalcino
Tomas Roman
Mario Malacarne
Giorgio Nicolini
Fulvio Mattivi
Urska Vrhovsek
Silvia Carlin
Daniele Perenzoni
Andrea Angeli
Giuseppe Speri
Ron Wherens
Pietro Franceshi
Luca Narduzzi
Anna Della Corte
Domenico Masuero
Winery
Fondazione E. Mach
CREA-NUT
Paolo Pangrazzi
Maurizio Ugliano
Graziano Guella
Joana Oliveira
Carolin Ehrhardt
Gerhard Flick
Georgios Theodoridis
Helen Gika