metabolite analysis – metabolomics non-plant (mostly bacterial & medical) plant-specific
TRANSCRIPT
Metabolite analysis – Metabolomics
non-plant (mostly bacterial & medical)
plant-specific
Metabolomics, spring 06
Hans BohnertERML 196
265-5475333-5574
http://www.life.uiuc.edu/bohnert/
(3/28/06)
(1) Some more oninstrumentationand basics –
technology in a nutshellwith a focuson GC/MS
- (2) Challenges(3) Literature
(4) Integration possibilities
Technology in a NutshellTechnology in a Nutshell (six steps)
• Extraction of metabolitescomprehensive, avoid degradation,avoid modification (Fiehn et al. 2000; Kopka et al. 2004)
• Derivatization make amenable for GC (volatile but temperature
stable) (Schmelz et al. 2004)
• Separation by GCstandardized gas flow, automation,temperature programming, capillarycolumn choice
• IonizationESI, MALDI, EI (electron impact) - most prevalent
(least susceptible to suppression, reproducible)
• Time resolved detection of fragments/molecules(dependent on analytical objective) (Ryan et al. 2004)
different mass detection devices (Mueller et al. 2002)
sector-field detectorquadrupole detector (QUAD) – routine workion trap detectors – allows for MS-MS, 2D detection
time-of-flight (TOF) – fast scans or precision mass> Ideal: GCxGC-TOF-MS
• Acquisition of data and evaluationthe real challenge
Extraction, Derivatization, ChromatographyExtraction, Derivatization, Chromatography
• Metabolite concentrations change rapidly, within seconds in primary metabolism – rapid sampling
• Metabolite composition changes during freeze storage –keep extracts, not tissues
• Metabolite amounts can be highly variable in individuals –large pools or many inividuals, lots of repeats
• Metabolites are highly dynamic – take samples diurnally,different leaf age, different developmental age
• Extracts from methanol-water/chloroform phases• Alkoxyamination – CH3-O-NH2 > stabilize C=O • Silylation (mono-/di-/tri-methyl-silyl), wide spectrum -
•Si(CH3)3 (TMS)• Alkylation – mostly methylations, transalkylation of ester-
bonds > efficient breakdown of complex metabolites• Acylation, less reactive – acetylation or trifluoro-acetylation
• Separation of volatiles in GC columns – choice of column
Mass detection and quantitative calibration techniquesMass detection and quantitative calibration techniques
Kopka J (2006) GC-MS. In: Plant Metabolomics (Saito, Dixon, Willmitzer, eds.), Springer, pp 3-20.
Mass spectral deconvolution of deuterated mass isotopomers Mass spectral deconvolution of deuterated mass isotopomers
Mass spectral deconvolution of deuterated mass isotopomers Mass spectral deconvolution of deuterated mass isotopomers
Compound Resolution - GC/MS instrumentsCompound Resolution - GC/MS instruments
polar phase (methanol/water)
glycerol malicglycine
glucose G1-Pinositol
sucrose oleic
stachyose
Reality of complexity Reality of complexity vs.vs. reality of knowledge reality of knowledge
Extraction schemeExtraction scheme Weckwerth, 2003.“Metabolomics inSystems Biology”
metabolites
proteins RNA
GC-MS for metabolite profilingGC-MS for metabolite profiling
Waters Micromass
GCT
Agilent 5975 inert MSD
Ionization techniques for GCIonization techniques for GC
• Electron Impact (EI) (-/+)
library searchable spectra, fragmentation, most versatile
• Chemical Ionisation (CI+/-)molecular weight information
• Desorption Chemical Ionisation (DCI)
thermally labile compounds, molecular weight information
• Field Ionisation (FI) / Field Desorption
soft ionisation, molecular weight information, reduced background
Ionisation MethodsIonisation Methods
Matrix AssistedLaser DesorptionIonisation
The sample is embedded in solid phase (MATRIX). MALDI is a mild ionisation that typically results in single charged ions, i.e. the m/z = m/1, and hence shows the true mass.
Ionisation MethodsIonisation Methods
Electro-Spray
Ionisation
may be coupled with LC
++++
+++
+++++
++++
++
+ +
+ ++
+ +
+ ++
+ +
+ ++-
--
-
--
--
---
------
+
+
++
-----
pressure / potential gradient
+ kV
Taylor cone
1st generation droplets
++ + ++
+
++
++
+
2nd generation droplets
(15% charge, 2% mass)
+
++
+
++
+
++
+
++
[M+nH]n+
multiple droplet division
++++
+++
+++++
++++
++
+ +
+ ++
+ +
+ ++
+ +
+ ++-
--
-
--
--
---
------
+
+
++
-----
pressure / potential gradient
+ kV
Taylor cone
1st generation droplets
++ + ++
+
++
++
+
2nd generation droplets
(15% charge, 2% mass)
+
++
+
++
+
++
+
++
[M+nH]n+
multiple droplet division
The sample is in liquid phase and ESI typically results in multiple charged ions. This facilitates the analysis of high mass molecules. However, the true
mass depends on resolution
Ionisation MethodsIonisation Methods
EElectronlectron I Impactmpact
• Ionisation via bombardment of the sample with a
stream of high energy electrons
• Impact of the high energy electrons
with the vaporised sample molecules causes ejection of
(multiple) electrons from the analyte
and a radical cation M+• is formed
M + e- M+• + 2e-
Mass analyzersMass analyzers
Quadrupole
Consists of 4 metal rods to which an
electro-magnetic field is applied. The
modulation of the electromagnetic field only transmits ions that have a certain
m/z. Quadrupole is a low resolution mass filter often used with
ESI.
Best combined with an upstream separation device, e.g., liquid chromatography or capillary electrophoresis
Analyzers for MS/MS - Triple QuadrupoleAnalyzers for MS/MS - Triple Quadrupole
collision cell
Q2Q1
Time Of Flight
For GC or LC
The time needed for an accelerated ion to transverse a field-free drift zone is directly related to the mass of an ion / peptide. The longer the flight path the better the resolution.
Field free drift region
Ionisation of peptides
Detection of ions
Ion acceleration by high voltage
Mass analyzersMass analyzers
Mass analyzersMass analyzers
Magnetic SectorAnalytes deviate in their path based on mass in the magnetic field of the analyzer. The analyzer focuses a given m/z to the detector.
2D GC-ToFMS2D GC-ToFMS
Tandem MS (MS/MS)Tandem MS (MS/MS)
MS/MS instruments select a single ion from a spectrum obtained by MS1
58.2134.6
178.8
121.2
This ion is fragmented by collision with an inert gas
58.2134.6178.8121.2
daughter ion scan
The mass of the secondary fragment ions is measured by MS2. For peptides, the amino acid sequence is deduced from the mass differences of the ions
primary scan
Tandem Mass SpectrometryTandem Mass Spectrometry
RT: 0.01 - 80.02
5 10 15 20 253 035 40 45 50 55 60 65 70 75 80Time (min)
0
10
20
30
40
50
60
70
80
90
100
Relati
ve Ab
undanc
e
13891991
1409 21491615 1621
14112147
161119951655
15931387
21551435 19872001 21771445 1661
19372205
1779 21352017
1313 22071307 23291105 17071095
2331
NL:1.52E8
Base Peak F: + c Full ms [ 300.00 - 2000.00]
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
200 400 600 800 1000 1200 1400 1600 1800 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
850.3
687.3
588.1
851.4425.0
949.4
326.0524.9
589.2
1048.6397.1226.9
1049.6489.1
629.0
Scan 1708
LCLC
S#: 1707 RT: 54.44 AV: 1 NL: 2.41E7F: + c Full ms [ 300.00 - 2000.00]
200 400 600 800 1000 1200 1400 1600 1800 2000
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e Ab
unda
nce
638.0
801.0
638.9
1173.8872.3 1275.3
687.6944.7 1884.51742.1122.0783.3 1048.3 1413.9 1617.7
Scan 1707
MS1MS1
MS/MSMS/MSIon
Source
MS-1collision
cell MS-2
Analyzers: Quadrupole Analyzers: Quadrupole vs.vs. ToF ToF
Elemental Composition ReportMass Calc. Mass mDa ppm Formula29.0027 29.0027 0.0 -1.4 C H O 29.0140 -11.3 -388.7 H N2 29.0265 -23.8 -822.3 C H3 N 29.0391 -36.4 -1255.9 C2 H5
accurate mass
by ToF
ToF
- high resolution
- better peak separation
Quadrupole
- poor resolution
ToF:ToF: resolves co-eluting compounds resolves co-eluting compounds
Peak finding software
- mass spectral deconvolution
(further resolves coeluting and/or low abundant
analytes)
Linear dynamic range: 104-106
2D GC-MS
2D GC- separates coeluting peaks in 2nd dimension
1D GC- Analytes Coelute in
complex samples
Spectral comparison with librariesSpectral comparison with libraries
chromatogram
Mass-spectrum
Library hits
Selected peak
Spectral match
NIST, Wiley
Comparison of EI and FI spectraComparison of EI and FI spectra
60 80 100 120 140 160 180 200 220 240 260 280 300m/z0
100
%
0
100
%
74.04
55.05
87.05
75.04
298.29255.23143.11
129.09101.06
199.17
185.16157.12 213.19 241.22267.27
269.25299.29
298.29
299.30
300.31
EI+EI+
FI+FI+Methyl StearateMethyl Stearate
Fragmentation
Intact ion
56
56
56
43
12
13
31
det
ecti
ve w
ork
CH3(CH2)16COOCH3
GC/MS – a routine technology - ChallengesChallenges
(1) Automation of sample preparation, wet chemistry, data processing after
an increasing number of data is obtained,
(2) Extension of the analytical scope – e.g., combined analyses of a sample
using multiple platforms,
(3) Combined analyses with proteome and transcriptome studies
(4) Profiling trace compounds, or signaling molecules in the presence of (very) abundant ‘bulk’ metabolites,
(5) Increasing accuracy in multi-parallel metabolite quantification
(6) Combining metabolite and flux analyses
(7) Establishing quantitative repeatability, arrive with an unambiguous nomenclature,
(8) Comparability between analytical platforms, and of work done by different labs.
ReferencesReferences (see slide 1-2)
Birkemeyer et al. (2005) Metabolome analysis: the potential of in vivo labeling with stableisotopes for metabolite profiling. Trends Biotechnol. 23, 28-33.
Fiehn et al. (2000a) Identification of uncommon plant metabolites based on calculationof elemental compositions using GC and quadrupole MS. Analyt. Chem. 72, 3573-3580.
Fiehn et al. (2000b) Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157-1161.
Fiehn (2003) Metabolic networks of Cucurbita maxima phloem. Phytochem. 62, 875-886.Kopka et al. (2004) Metabolite profiling in plant biology- platforms and destinations.
Genome Biol. 5, 109-117. Mueller et al. (2002) A multiplex GC-MS/MS technique for the sensitive and quantitative
single-run analysis of acidic phytohormones and related compounds, and itsapplication to Arabidopsis thaliana. Planta 216, 44-56.
Roessner-Tunali et al. (2004) Metabolic profiling of transgenic tomato plants over-expressing hexokinase reveals that the influence of hexokinase phosphory-lation diminishes fruit development. Plant Physiol. 133, 84-99.
Ryan et al. (2004) Analysis of roasted coffee bean volatiles by comprehensive two-dimensional GC-TOF-MS. J. Chromatogr. A 1054, 57-65.
Schauer et al. (2005) GC-MS libraries for the rapid identification of metabolites in complexbiological samples. FEBS Lett. 579, 1332-1337.
Schmelz et al. (2004) The use of vapor phase extraction in metabolic profiling of phytohormones and other metabolites. Plant J. 39, 790-808.
Weckwerth et al. (2004) Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulationin biochemical networks. Proteomics 4, 78-83.
(a) Typical ES- mass spectrum for polar extract green tomato (L. esculentum) fruit. Major identifiable peaks: 179 (hexose sugars, [M)H])), 191 (citric/iso-citric acid, [M)H])), 215 (hexose sugars, [M+Cl])), 237 (HEPES buffer, [M)H])), 475 (HEPES buffer, [2M)H])).
(b) Typical ES+ mass spectrum for polar extract of green tomato (L. esculentum) fruit. Major identifiable peaks: 147 (glutamic acid, [M+H]+),203 (hexose sugars [M+Na]+), 219 (hexose sugars, [M+K]+), 239 (HEPES buffer, [M+H]+), 261 (HEPES buffer, [M+Na]+), 277 (HEPES buffer, [M+K]+).
Dunn et al. (2005) Evaluation of automated electrospray-TOF MS for metabolic fingerprinting of the plant metabolome. Metabolomics 1, 137.
Some metabolites are very abundant – how to quantify, and how to analyze low abundance
Quantification Relationship between concentration of metabolite standard added to a plant extract and molecular ion intensity.
(b) ES+; open circle - alanine, open diamond - proline, closed triangle - GABA, closed diamond - aspartate, closed square - leucine.
(a) ES-; open circle - pyruvate, open triangle - oxalate, closed circle - fumarate, open triangle - oxalate, closed square - malate, open diamond - ascorbate.
Analytical and Biological Variations
Lycopersicon esculentum - white fill; L. pennellii - grey fill;
1 malic acid, 2 citric acid, 3 GABA, 4 C4 sugars, 5 hexoses, 6 pyruvic acid, 7 fumaric acid, 8 ascorbic acid, 9 valine, 10 leucine/isoleucine, 11 asparagine, 12 glutamine, 13 tyrosine.
For clarity, the responses for 3–8 are increased by a factor of 10, andthose for 9–13 increased by a factor of 50. Values are ion intensity (cps), calculations employed the summed ion intensity for 180 scans and arepresented as the means of three replicate extracts ± standard deviation.
Peak intensity for 13 selected metabolite ions measured in each of three fruit extracts of two tomato species
FTICR-MS (or FT-MS)FTICR-MS (or FT-MS)
Ultra-high resolution - Ultra-high mass accuracy
the
go
ld s
tan
dar
d
Metabolomics as a component of “Systems Biology” (SB)Metabolomics as a component of “Systems Biology” (SB)
The next 2 slides indicate that not even in yeast SB metabolomics is included. The slides are from this website for which the library has a trial period:http://www.mrw.interscience.wiley.com/ggpb/articles/g204307/frame.html
Wang QZ, Wu CY, Chen T, Chen X, Zhao XM. (2006) Integrating metabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms. Appl Microbiol Biotechnol. 70: 151-161.
Glinski M, Weckwerth W. (2006) The role of mass spectrometry in plant systems biology. Mass Spectrom Rev. 2006 Mar-Apr;25(2):173-214.
Oksman-Caldentey KM, Saito K. (2005) Integrating genomics and metabolomics for engineering plant metabolic pathways. Curr Opin Biotechnol. 16: 174-179.
Goodacre R. (2005) Making sense of the metabolome using evolutionary computation: seeing the wood with the trees. J Exp Bot. 56: 245-254.
Nikiforova VJ, Gakiere B, Kempa S, Adamik M, Willmitzer L, Hesse H, Hoefgen R. (2004) Towards dissecting nutrient metabolism in plants: a systems biology case study on sulphur metabolism. J Exp Bot. 55: 1861-1870.
Kell DB. (2004) Metabolomics and systems biology: making sense of the soup. Curr Opin Microbiol. 7: 296-307.
A structure for the Bayesian network in MAGIC. The network is instantiated with evidence (at the bottom nodes) for each pair of genes in the yeast genome, and the final confidence level is produced on the basis of the evidence for biological relationship available for each pair of genes and on the prior probabilities encoded in the network conditional probability tables (Troyanskaya et al. 2003).
No MetabolitesNo MetabolitesFunctional Genomics in Saccharomyces cerevisiae
Dolinski & Troyanskaya, 2006
Sources of functional genomics data collections for Sources of functional genomics data collections for S. cerevisiaeS. cerevisiae
GRID Breitkreutz et al. (2003) Genet./phys. Interactions http://biodata.mshri.on.ca/yeast_grid/servlet/SearchPage\BIND Bader et al. (2003) Genet. interact., pathwys http://www.blueprint.org/bind/bind.phpDIP Xenarios et al. (2002) Physical interactions http://dip.doe-mbi.ucla.edu/dip/Main.cgiMINT Zanzoni et al. (2002) Physical interactions http://160.80.34.4/mint/IntAct Hermjakob et al. (2004b) Physical interactions http://www.ebi.ac.uk/intact/index.htmlDeletion Consortium Winzeler et al. (1999); Giaever et al. (2002) Large-scale phenotype analysis http://www-sequence.stanford.edu/group/yeast_deletion_project/data_sets.htmlGEO Edgar et al. (2002); Brazma et al. (2003) MicroArray http://www.ncbi.nlm.nih.gov/geo/ArrayExpress MicroArray http://www.ebi.ac.uk/arrayexpress/
YMGV MicroArray Marc et al. (2001) http://www.transcriptome.ens.fr/ymgv/SMD MicroArray Gollub et al. (2003) http://smd.stanford.edu/
OPD Prince et al. (2004) Mass Spec/Proteomics http://bioinformatics.icmb.utexas.edu/OPD/