Metabolomics in Practice: Successful Strategies to Generate and Analyze Metabolic Data

431 Pages·2013·4.42 MB·English

Editedby
MichaelLa¨mmerhoferand
WolframWeckwerth
MetabolomicsinPractice
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Edited by Michael La¨mmerhofer and Wolfram Weckwerth
Metabolomics in Practice
Successful Strategies to Generate and
Analyze Metabolic Data
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V
Contents
ListofContributors XV
Preface XXIII
1 TheSamplingandSamplePreparationProbleminMicrobial
Metabolomics 1
WalterM.vanGulik,Andre´B.Canelas,RezaM.Seifar,and
JosephJ.Heijnen
1.1 Introduction 1
1.2 MicroorganismsandTheirProperties 1
1.3 SamplingMethods 2
1.3.1 TheNeedforRapidSampling 2
1.3.2 SamplingSystems 3
1.4 Quenching 4
1.4.1 QuenchingProceduresandTheirProperties 4
1.4.2 ValidationoftheQuenchingProcedureandMinimizationof
MetaboliteLeakage 5
1.4.3 QuenchingProcedureforDeterminationofIntracellularMetabolites
inthePresenceofExtracellularAbundance 6
1.4.4 QuenchingofBacteria 7
1.5 MetaboliteExtraction 9
1.5.1 ExtractionMethodsandTheirProperties 9
1.5.2 ValidationofExtractionMethodsforYeastMetabolomics 9
1.6 Applicationof13C-LabeledInternalStandards 13
1.7 Conclusions 17
References 18
2 TandemMassSpectrometryHyphenatedwithHPLCandUHPLCfor
TargetedMetabolomics 21
Ge´rardHopfgartnerandEmmanuelVaresio
2.1 Introduction 21
2.2 LC-MS-BasedTargetedMetabolomics 22
2.3 LiquidChromatography 22
VI Contents
2.4 MassSpectrometry 27
2.4.1 IonizationTechniques 27
2.4.2 MassAnalyzers 28
2.5 SamplePreparation 30
2.6 RelativeandAbsoluteQuantification 31
2.7 Applications 32
2.8 Synopsis 34
References 35
3 UncertaintyofMeasurementinQuantitativeMetabolomics 39
RaffaeleGuerrasio,ChristinaHaberhauer-Troyer,StefanNeubauer,
KristapsKlavins,MadeleineWerneth,GundaKoellensperger,and
StephenHann
3.1 Introduction 39
3.1.1 MS-BasedTechniquesinMetabolomics 39
3.1.2 UncertaintyofMeasurementinQuantitativeAnalysis 41
3.1.2.1 Definition 41
3.1.2.2 UncertaintyCalculationAccordingtotheBottom-UpApproach 42
3.2 UncertaintiesofQuantitativeMSExperiments 48
3.2.1 UncertaintiesinSamplePreparation 48
3.2.1.1 SamplingandSamplePreparationinMetaboliteProfilingin
Fermentations 49
3.2.1.2 CalculationofSamplePreparationUncertaintyforIntracellular
MetaboliteQuantitationinYeast:APracticalExample 53
3.2.1.3 LC-MS 59
3.2.2 UncertaintyofMassSpectrometricAssays(LC-MSandGC-MS
Measurements) 61
3.2.2.1 GC-MS 61
3.2.2.2 CalculationofUncertaintyforLC-MSMeasurementsofCellExtracts:
APracticalExample 63
3.3 ConcludingRemarks 66
Abbreviations 66
Acknowledgment 67
References 67
4 GasChromatographyandComprehensiveTwo-DimensionalGas
ChromatographyHyphenatedwithMassSpectrometryforTargeted
andNontargetedMetabolomics 69
SongYang,JaminC.Hoggard,MaryE.Lidstrom,and
RobertE.Synovec
4.1 IntroductionandScope 69
4.2 SamplePreparationforGC-BasedMetaboliteProfiling 71
4.3 GC–MSandGC×GC–TOFMSInstrumentationfor
Metabolomics 74
4.4 DataAnalysisStrategiesandSoftware 82
Contents VII
4.5 IllustrativeExamplesandConcludingRemarks 88
References 89
5 LC-MS-BasedNontargetedMetabolomics 93
GeorgiosA.Theodoridis,HelenG.Gika,andIanD.Wilson
5.1 Introduction 93
5.2 LC-MS-BasedUntargetedMetabolomics 94
5.2.1 LCIssues 94
5.2.2 MassSpectrometry 97
5.3 StudyDesign 98
5.4 SamplePreparation 100
5.5 AnalyticalStrategies 103
5.6 DataAnalysis 104
5.7 MetaboliteIdentification 107
5.8 Applications 109
5.9 Synopsis 112
References 113
6 ThePotentialofUltrahighResolutionMS(FTICR-MS)in
Metabolomics 117
FrancoMoritz,SaraForcisi,MouradHarir,BasemKanawati,
MariannaLucio,DimitriosTziotis,andPhilippeSchmitt-Kopplin
6.1 Introduction 117
6.2 MetabolomicsTechnologies 118
6.3 PrinciplesofFTICR-MS 121
6.3.1 NaturalIonMovementInsideanICRCellSubjectedtoMagneticand
ElectricFields 121
6.3.2 AppliedPhysicalTechniquesinFTICR-MS 123
6.3.3 PracticalAdvantagesofFTICR-MS 124
6.4 ProceedinginMetabolomics 126
6.4.1 NetworkAnalysisandNetCalcCompositionAssignment 126
6.4.2 StatisticsonFTICR-MSDatasets 127
6.5 ApplicationExampleinMetabolomicsUsingFTICR-MSExhaled
BreathCondensate 128
6.5.1 TheExperiment 128
6.5.2 FT-ICR/MSMeasurement 129
6.5.3 DataPreprocessing 129
6.5.4 C–H–N–O–S–PFormulaAnnotation 130
6.5.5 StatisticalAnalysis 130
6.5.5.1 StatisticalPreprocessing 130
6.5.6 SynthesisofBiochemicalMassDifferenceNetworkingandStatistical
Results 131
6.6 ConclusionandRemarks 134
References 134
VIII Contents
7 TheArtandPracticeofLipidomics 137
KoenSandra,Rubent’Kindt,LucieJorge,andPatSandra
Abbreviations 137
7.1 Introduction 139
7.2 LipidDiversity 140
7.3 TacklingtheLipidome:State-of-the-Art 141
7.4 LC-MS-BasedLipidomics 146
7.4.1 LipidExtraction 146
7.4.1.1 BiologicalFluidsandCellularMaterial 146
7.4.1.2 Skin(StratumCorneum) 148
7.4.1.3 Solid-PhaseExtraction(SPE) 148
7.4.2 LC–MS(/MS) 151
7.4.2.1 RetentionTimeCharacteristics 151
7.4.2.2 IonizationCharacteristics 155
7.4.2.3 IdentificationofLipids 156
7.4.3 DataProcessingandAnalysis 161
7.5 GC-MS-BasedLipidomics 165
7.5.1 SamplePreparation 165
7.5.2 GC–MS 166
7.5.3 DataProcessingandAnalysis 170
7.6 Conclusion 172
References 173
8 TheRoleofCE–MSinMetabolomics 177
RawiRamautar,GovertW.Somsen,andGerhardusJ.deJong
Abbreviations 177
8.1 Introduction 177
8.2 CE–MS 179
8.2.1 CESeparationConditions 179
8.2.2 CE–MSCoupling 180
8.2.2.1 Interfacing 180
8.2.2.2 MassAnalyzers 184
8.3 SamplePretreatment 185
8.4 DataAnalysis 187
8.5 Applications 190
8.5.1 TargetedApproaches 190
8.5.2 NontargetedApproaches 202
8.6 ConclusionsandPerspectives 203
References 206
9 NMR-BasedMetabolomicsAnalysis 209
AndreaLubbe,KashifAli,RobertVerpoorte,andYoungHaeChoi
9.1 Introduction 209
9.2 PlatformsforMetabolomics 210
9.2.1 MassSpectrometry(MS) 210
Contents IX
9.2.1.1 GasChromatography–MassSpectrometry(GC-MS) 210
9.2.1.2 LiquidChromatography–MassSpectrometry
(LC–MS) 211
9.2.1.3 CapillaryElectrophoresis–MassSpectrometry
(CE–MS) 211
9.2.1.4 FourierTransform-IonCyclotronResonance-MassSpectrometry
(FT-ICR-MS) 212
9.2.2 FourierTransform–InfraredSpectroscopy(FT–IR) 212
9.2.3 NuclearMagneticResonanceSpectroscopy:Principlesand
Techniques 212
9.2.3.1 One-DimensionalNuclearMagneticResonance(1Hand13C
NMR) 213
9.2.3.2 J-ResolvedSpectroscopy(JRES) 215
9.2.3.3 CorrelationSpectroscopy(COSY) 215
9.2.3.4 TotalCorrelationSpectroscopy(TOCSY) 217
9.2.3.5 HeteronuclearTwo-DimensionalMethods 217
9.2.3.6 CombinedTwo-DimensionalMethods 217
9.3 NMRforMetabolomics 219
9.3.1 SamplePreparation 220
9.3.2 MetaboliteIdentification 221
9.3.3 DataAnalysis:TurningDataintoInformation,Possibly
Knowledge 223
9.3.3.1 DataPreprocessing 223
9.3.3.2 PrincipalComponentAnalysis(PCA) 225
9.3.3.3 PartialLeastSquares(PLS)ProjectionstoLatentStructures 226
9.3.3.4 BidirectionalOrthogonal-PLS(O2PLS) 227
9.3.3.5 Validation 227
9.4 ApplicationsofNMR-BasedMetabolomics 228
9.4.1 UnderstandingStressResponse 228
9.4.2 ApplicationtoBioactivityScreening 230
9.4.3 QualityControlofHerbalMedicines 231
9.4.4 Chemotaxonomy 232
9.4.5 AgriculturalApplications 233
9.5 FutureProspectsandConclusions 233
References 234
10 PotentialofMicrofluidicsandSingleCellAnalysisinMetabolomics
(Micrometabolomics) 239
MeghanM.Mensack,RyanE.Holcomb,andCharlesS.Henry
10.1 Introduction 239
10.2 SampleProcessingforMetabolomics 240
10.2.1 SolidPhaseExtraction 240
10.2.2 LaminarDiffusion 241
10.2.3 FluidicPumpingforOn-ChipMixing 242
10.3 MicrofluidicSeparationsforMetabolicAnalysis 243
X Contents
10.3.1 MicrochipCapillaryElectrophoresis 243
10.3.1.1 MCESystems 243
10.3.1.2 SampleInjection 244
10.3.1.3 ElectrophoreticSeparations 244
10.3.2 AnalyteDetection 245
10.3.2.1 OpticalDetection 245
10.3.2.2 ElectrochemicalDetection 246
10.4 MicrofluidicsforCellularAnalysis 247
10.4.1 RequirementsforSingleCellMetabolomics 247
10.4.2 TypesofMicrofluidicInstrumentation 248
10.4.3 BiologicalQuestions 249
10.4.3.1 MonitoringMetabolicResponsetoStimulationandCell-to-Cell
Signaling 249
10.4.3.2 Pharmacokinetics/Pharmacodynamics 252
10.4.3.3 ClinicalDiagnostics 254
10.5 ALookForward 254
References 256
11 DataProcessinginMetabolomics 261
AgeK.Smilde,MargrietM.W.B.Hendriks,JohanA.Westerhuis,and
HuubC.J.Hoefsloot
11.1 IntroductionandScope 261
11.2 CharacteristicsofMetabolomicsData 261
11.2.1 CorrelationStructureofMetabolomicsData 261
11.2.2 InformativeversusNoninformativeVariation 262
11.2.3 LowSamples-to-VariablesRatio 263
11.2.4 MeasurementError 263
11.2.5 Dynamics 263
11.2.6 NonlinearRelations 264
11.3 TypesofBiologicalQuestionsAsked 264
11.3.1 MethodsShouldFollowtheQuestions 264
11.3.2 Biomarkers 264
11.3.3 TreatmentEffects 264
11.3.4 NetworksandMechanisticInsight 265
11.4 Validation 265
11.4.1 SeveralLevelsofValidation 265
11.4.2 CurseofDimensionality 266
11.4.3 Cross-ValidationandPermutations 267
11.5 OverviewofMethods 272
11.5.1 ExploratoryAnalysis 272
11.5.2 ANOVAandOtherUnivariateMethods 273
11.5.3 AdvancedExploratoryAnalysis 275
11.5.4 RegressionMethods 277
11.5.5 DiscriminantAnalysis 280
11.5.6 MultilevelApproaches 281