1. Outline 1 1.INTRODUCTION 2. METABOLOMICS WORKFLOW 3. CHALLENGES AND LIMITATIONS

2. 1. INTRODUCTION  Sensitive and specific methods, but… … targeted approaches focus on particular compounds / activities  Increasing need for methods allowing a global characterisation whatever the situation Objective: to address, in a comprehensive manner, complex situations that were previously dealt as piecemeal We only search for the “known” & we only find what is search Concept of biological fingerprint From targeted to untargeted approaches 2

3. Different levels of investigation 1. INTRODUCTION 3 Genomics Transcriptomics Proteomics Metabolomics GENOTYPE PHENOTYPE What may happen What makes happen What happens ADN & ARN Proteins & Peptides Metabolites GLOBAL APPROACHES without any A PRIORI Different levels of investigation but similar objectives

4. Large-scale analysis of biological systems ( cells, tissues, complex matrices) Transcriptomics: large-scale analysis of mRNA transcripts Proteomics: large-scale analysis of proteins Metabolomics: large-scale analysis of metabolites other “…omics” Markers = genes, proteins, metabolites, … 1. INTRODUCTION Different levels of investigation 4

5. METABONOMICS “…measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification…” Nicholson et al., 1999. METABOLOMICS “...the complete set of metabolites/low-molecular-weight intermediates, which are context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism…” Oliver, 1998 1. INTRODUCTION Définitions 5 In practice, same final objective : compare patterns, signatures or ‘‘fingerprints’’ of metabolites that change in response to external stimuli using a differential analysis of samples collected from two (or more) populations, namely ‘case’ and ‘control’.

6. Large diversity of Molecular Weight (from 50 to 1500 Da) Organic acids- sugars- fatty acids - lipids- aminoacids- peptides - vitamins - … M ETABOLOME Multiple classes of compounds => ≠ chemical properties => ≠ physical properties Extented range of concentrations: pmol  mmol What is the metabolome? Metabolites come from: ● catabolism (Degradation of organic matter) ● anabolism (synthesis of components by the cell) ● external sources: diet, medication, … (xenometabolome) 1. INTRODUCTION 6

7. The metabolome size: ● Vegetal : more than 200 000 metabolites ● Human : unknown but estimated to 1000’s metabolites and even larger (if we consider the external sources, diet, medicines…) 1. INTRODUCTION 7

8. Organisms Biological FluidsFood matrices  « Controls » « Cases » 2/ Generation of fingerprints and search for differences in the metabolic profiles (= potential biomarkers) 3/ Biological interpretation of observations 1/ Collection and preparation of the samples (2 or more sub-groups to be compared) 1. INTRODUCTION Concept 8 Plants REGULATION

9. 9 1. INTRODUCTION 3 different approaches ● Metabolite targeted analysis: detection and precise quantification of a single or small set of target compounds ● Metabolic profiling: analysis of a group of metabolites either related to a specific metabolic pathway or a class of compounds ● Metabolic fingerprinting Scope Accuracy http://manet.illinois.edu/pathways.php

10. Outline 10 1.INTRODUCTION 2. METABOLOMICS WORKFLOW 3. CHALLENGES AND LIMITATIONS

11. 2. METABOLOMICS WORKFLOW DEFINITION AND STUDY DESIGN SAMPLE PREPARATION DATA PROCESSING EXPERIMENT / SAMPLE COLLECTION  STRUCTURAL ELUCIDATION / BIOLOGICAL INTERPRETATION METABOLOMICS PROFILES GENERATION DATA ANALYSIS 11

12. 2.1 Study Design – Sample Collection Study Design is a step which needs for multidisciplinary skills - statisticians - biologists / clinicians - chemists Different questions have to be answered at this stage: ● A priori knowledge of the subject? ● Confusing factors? ● Statistical power? 12

13. 13 ● Sample selection ? -for mammalian species: urine, plasma, serum, tissue…? -for plant: root, leave, flower…? ● Feasibility of collection? ● Single collection protocol? ● Interruption of the metabolism? (e.g. quenching with liquid nitrogen or organic solvents ) ● Sample storage? -80°C? -20°C 2.1 Study Design – Sample Collection

14. 2.2 Sample preparation Liquid matrices Filtering Solid matrices Protein Removal 10 Kda cut-off - homogenization Freeze-drying for better extraction capabilities - Freeze-drying for urine samples to normalize the dilution factor - Dilution Matrices Protocols MeOH ACN/Acetone Ethyl Acetate Chloroforme Polarity + - Different solvents may give access to different parts of the metabolome Further protocols - Solid Phase extraction (for better selectivity) - Derivatization 14 - Liquid/liquid partitioning

15. What is an optimal sample preparation? Elimination of interferences (limit matrix effects)Repeatable and reproducibleSimple and fast Maximisation of the information to answer the biological question Fingerprint as exhaustive as possible 2.2 Sample preparation 15

16. 16 m/z reading 2.3 Metabolomics profiles generation Mass Measurment principle m/z

17. 17 2.3 Metabolomics profiles generation Mass Measurment principle IONISATION FRAGMENTATION Metabolite Intact Metabolite = precursor ion Fragmented Metabolite Each part is a product ion

18. 18 2.3 Metabolomics profiles generation Mass Measurment principle Abundance m/z Largest Fragment Smallest Fragment

19. CO m/z = 27.99491 N 2 m/z = 28.00614 R = 1000 R = 5000 28.0 27.9949128.00614 resolution : ability to distinguish two peaks of slightly different mass-to-charge ratios ΔM, in a mass spectrum M MM ● Resolution = 19 With increased resolving power, increased information is obtained from the acquisition 2.3 Metabolomics profiles generation m/z Abundance m/z Abundance Mass Measurment principle

20. LOW < 1000 MIDDLE > 5000 HIGH Up to 100000 ULTRA-HIGH Up to 2000000 RESOLVING POWER QUADRUPOLE ION TRAP TOF ORBITRAP FT-ICR MASS SPECTROMETER ZOOM 20 Mass spectrometry Different analysers 2.3 Metabolomics profiles generation

21. 21 2.3 Metabolomics profiles generation Mass spectrometry Different acquisition modes ScanDissociationSelect Precursor Ion SetProduct Ion Set ScanDissociationScan SelectionDissociationSelection Scan Full Scan Precursor ion scanning Neutral Loss scanning Multiple Reaction Monitoring (a) (b) (c) (d)

22.  Full scan mode ● When there is no presupposed hypothesis on the subject… be as open as possible 22 More information to process  less visibility of the differences between groups « Control » Sample « Test » Sample 2.3 Metabolomics profiles generation Mass spectrometry Different acquisition modes

23. 23 Product ion scan : Example of estradiol-17-sulfate ● When there is a presupposed hypothesis on the subject… more specificity can be useful  precursor ion scan mode: When prior knowledge is known, relevant information can be made easier to find with appropriate acquisition parameters 2.3 Metabolomics profiles generation Mass spectrometry Different acquisition modes ScanDissociationSelect Precursor Ion SetProduct Ion Set

24. 24 ● Coupling mass spectrometry with GC or LC Gas ChromatographyLiquid Chromatography Volatile compounds EI (CI)ESI (APCI, APPI) Polar and ionic compounds Apolar columns (polar columns)Reverse phase HPLC (HILIC ) Many choices have to be made to reach the most relevant part of the metabolome (to answer the biological question) Often you do with the configuration that you have in the lab… 2.3 Metabolomics profiles generation

25. 25 1 sample = 3D matrix Time m/z Intensity 1 Sample = 1 total ionic current Temps (min) Intensity 1 sample = x HR mass spectra typically 2 scans [m/z 60-1000] per second m/z Intensity Complexity of a metabolic fingerprint  Need for a well- defined peaklist 2.4 Data processing Problematic

26. Metabolic profiles to be compared Initial informationAnalysable information 26 2.4 Data processing Need for tools to convert the 3D matrix into a table of descriptors Problematic

27. Raw data Filtration Peak picking Alignment Reporting Statistical Analysis DATA PROCESSING SIEVE Many tools 2.4 Data processing 27

28. Dibutylphthalate FiltrationPeak pickingAlignmentReporting Analytical background noise 2.4 Data processing 28

29. Time Peak Picking: report the signal abundance observed for each ion [m/z;rt] i in all the analyzed samples: various methods largely influenced by multiple end-user parameters m/z Extracted ion chromatogram Integrated peak areas rt (min) Signal intensity m/z Extracted mass spectrum Ion signal intensities Signal intensity FiltrationPeak pickingAlignmentReporting 2.4 Data processing 29

30. Samples One injection : 10’s min Stability/reproducibility of retention times ? Alignment required FiltrationPeak pickingAlignmentReporting Peak Alignment M x T y +/-  tr Int tr Int MxTyMxTy 2.4 Data processing 30

31. FiltrationPeak pickingAlignmentReporting Generation of final report 2.4 Data processing Peak list which can be analyzed to extract relevant information (differences in intensity for defined ions between groups) 31 Detected Ions Characterisitics  m/z  TR Signal Intensities in samples Sample 1Sample 2 …

32. Need for tools to explore the inherent structure and meaning of the data set 2.5 Statistical Analysis In metabolomics K >> N K variables N samples XY Exploratory methods (unsupervised) Modeling methods (supervised) Exploratory data analysis (exploring similarities and differences among samples and data variables, e.g. PCA) Data modeling (construction of classification / discrimination models, e.g. PLS-DA)

33. 2.5 Statistical Analysis Principal Component Analysis t[3] PCA : Process that transforms a number of possibly correlated variables into a fewer number of uncorrelated latent variables or principal components Reduces 1000’s of variables into 2-3 key features Score plot v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 vkvk Need for the definition of new axis to project the observations from the original space (k dimensions) to 2D - 3D surfaces with maximal information and minimal deformation.

34. 34 v1v1 v2v2 v3v3 v4v4 v5v5 v6v6 v7v7 v8v8 v9v9 vkvk 2.5 Statistical Analysis Partial Least Square Discriminant Analysis Need for the definition of new axis to project the observations from the original space (p dimensions) from 2D surfaces with maximal distances between the pre-determined groups PLS-DA: Data reduction as for PCA but… Process that uses multiple linear regression technique to find the direction of maximum covariance between a data set (X) and a class membership (Y) Extracted features are in the form of latent variables t[3] t[2] Score plot

35. PLS-DA Males / Females Example of PCA/ PLS-DA performed on metabolomics profiles acquired from urine samples collected on control versus treated animals with an anabolic substance A commonly encountered problematic : the biological variability. Example: in metabolomics, the studied factor is commonly associated with a discrete signature difficult to be revealed by non supervised analyses. Supervised analysis PCA 2.5 Statistical Analysis 35 Controls/ Treated PC1 PC2 PC1 PC2

36. Molecular Ion = radical cation is produced from the neutral molecule that has lost one electron M +  (mass M) ● First step = identification of the MS molecular ion Electron Ionization (EI) Pseudomolecular Ion : In positive mode: - adducts with a proton: (M+H) + (mass : M+1) - adducts with reageant gaz (M+NH 4 + )(mass : M+18) In negative mode: - radical cation: M -  (mass : M) - loss of a proton : (M-H) - (mass : M-1) Chemical Ionization (CI) Atmospheric Pressure Ionization sources (ESI, APCI…) Pseudomolecular Ion : In positive mode : - adducts with a proton : (M+H) + (mass : M+1) - other adducts: (M+Na) +, (M+K) + … (mass : M+23 or M+39 …) In negative mode : - loss of proton: (M-H) - (mass : M-1) - acetate adduct: (M+CH 3 COO) - (mass : M+59) 36 2.6 Structure Elucidation

37. Application example in positive Electrospray (ESI) 86 132 149 154 263 170 (Fragment) (M+H) + (M+NH 4 ) + (M+Na) + (M+K) + (2M+H) + Leucine: What is the molecular ion? 37 2.6 Structure Elucidation

38. When the molecular ion has been identified… ● Second step: determination of the elemental composition thanks to: - High resolution - Nitrogen rule - Isotopic pattern - Atom valence ● Third step: from the elemental composition to chemical structure - Database searching - MS/MS experiment - molecule polarity - Use of other instruments (NMR, IR, …) - … 38 2.6 Structure Elucidation Confirmation of a chemical structure can only be made by the comparative analysis of the corresponding authentic reference standard

39. 39 REGULATION 2.7 Biological interpretation Give biological sense to the observations made

40. 1.INTRODUCTION 2. METABOLOMICS WORKFLOW 3. CHALLENGES AND LIMITATIONS Outline 40

41. 3. CHALLENGES AND LIMITATIONS 41 ● Challenge 1: be able to characterize discrete signature Limitation 1: it may be hidden by other sources of variability ● Challenge 2: be able to characterize one system through the generation of a unique metabolic profile Limitation 2: the metabolome is a dynamic system ( for example diurnal and seasonal variation in human studies…) ●Challenge 3: be able to connect genome and metabolome (systems biology) Limitation 3: difficulties to collect both informations On a biological point of view… … these biological challenges correspond to future directions in research

42. 42 3. CHALLENGES AND LIMITATIONS On an analytical point of view… … these analytical challenges correspond to future directions in research ● Challenge 1: be able to characterize the whole metabolome Limitation 1: at the moment, there is no such versatile instrument allowing to analyze such chemical diversity ● Challenge 2: long term repeatability of analytical sequences (when 100’s to 1000’s samples are analyzed) Limitation 2: still insufficient stability of the MS-instrument acknowledged, need for efficient way of normalization with Quality Controls ●Challenge 3: be reproducible between analytical platforms to allow comparison Limitation 3: Used protocols are different, need for standardization procedures