on Metabolomics Bioinformatics for Life Scientists EMBO Practical Course on Metabolomics Bioinformatics for Life Scientists “Dissecting an untargeted metabolomic workflow” Oscar Yanes, PhD

Untargeted metabolomics workflow Sample preparation Experimental design Sample analysis by MS and NMR Pre-processing data analysis Metabolite identification Experimental validation Hypothesis

Untargeted metabolomics workflow Sample preparation Experimental design Sample analysis by MS and NMR Pre-processing data analysis EMBO Course Metabolite identification Experimental validation Hypothesis

List of metabolites differentially Ultimate goal of metabolomics List of metabolites differentially regulated Biomarker discovery Pathway analysis Model construction Scientific literature Disease vs. control Mechanism Validation Hypothesis

Untargeted metabolomics workflow Sample preparation Experimental design Sample analysis by MS and NMR Pre-processing data analysis Metabolite identification Experimental validation Hypothesis

THE IMPORTANCE OF EXPERIMENTAL DESIGN I want to do metabolomics ME COLLABORATOR

THE IMPORTANCE OF EXPERIMENTAL DESIGN … I want to do metabolomics ME COLLABORATOR

THE IMPORTANCE OF EXPERIMENTAL DESIGN I have many samples at -80°C. Could you do metabolomics and find out something? ME COLLABORATOR

THE IMPORTANCE OF EXPERIMENTAL DESIGN I have many samples at -80°C. Could you do metabolomics and find out something? !! ME COLLABORATOR

THE IMPORTANCE OF EXPERIMENTAL DESIGN

BASIC DIAGRAM OF A MASS SPECTROMETER

BASIC DIAGRAM OF A MASS SPECTROMETER Gas-phase: Gas chromatography Liquid-phase: Liquid chromatography Capillary electrophoresis Solid-phase: Surface-based

BASIC DIAGRAM OF A MASS SPECTROMETER Electron ionization (EI) Chemical ionization (CI) Atmospheric pressure chemical ionization (APCI) Electrospray ionization (ESI) Laser desorption ionization (LDI)

Watch out serum/plasma samples from biobanks! This slide shows a real example of the effect observed in urine samples after being left on the bench in our lab for different time periods. In this study we tracked the concentration of several metabolites along different time periods. As example, here we show variation along the time for four of them this effect common to other metabolites. As you can see, concentration of these 4 compounds varied along the time meaning that, measurement of samples collected at different time periods give differences (if the samples have not been properly stored) and the differences observed are not related to any pathological state but are due to different storing conditions instead. So, tinny mistakes in collecting the samples might ruin the whole experiment and the huge effort made to collect sample, specially for large datasets, might be in vain.

Untargeted metabolomics workflow Sample preparation Experimental design Sample analysis by MS Pre-processing data analysis Metabolite identification Experimental validation Hypothesis

Requisite for untargeted metabolomics Maximize ionization efficiency over the whole mass range (e.g., m/z 80-1500)

Requisite for untargeted metabolomics Maximize ionization efficiency over the whole mass range (e.g., m/z 80-1500) Number of features Intensity of the features

Requisite for untargeted metabolomics Maximize ionization efficiency over the whole mass range (e.g., m/z 80-1500) Number of features Intensity of the features Coverage of the metabolome Accurate quantification and identification of metabolites

How do we increase the number of features and their intensity?? time mass intensity Feature: molecular entity with a unique m/z and retention time value

How do we increase the number of features and their intensity?? time mass intensity Sample preparation: - Extraction method Chromatography: - Stationary-phase - Mobile-phase Ion Funnel Technology etc.

Extraction method Hot EtOH/Amm. Acetate Cold Acetone/MeOH Only 45% of the metabolites are detected with Acetone/MeOH MS/MS threshold

Extraction method Yanes O., et al. Anal. Chem. 2011; 83(6):2152-61

Liquid Chromatography: mobile-phase Ammonium Fluoride Ammonium acetate Formic acid Yanes O et al. Anal. Chem. 2011; 83(6):2152-61

Ammonium fluoride Ammonium acetate F- Ammonium fluoride

Chromatography: stationary phase HILIC RP C18/C8 Effect of pH; ammonium salts; ion pairs (e.g. TBA) LC flow rate and pressure: UPLC vs. HPLC vs. nanoLC (vs. GC!) HPLC UPLC Minutes Minutes

BASIC DIAGRAM OF A MASS SPECTROMETER Electron ionization (EI) Chemical ionization (CI) Atmospheric pressure chemical ionization (APCI) Electrospray ionization (ESI) Laser desorption ionization (LDI)

PRACTICAL ASPECTS Number of scans/second Implications in LC/MS and GC/MS: Quantification Maximum intensity or integrated area Instrument resolution Implications: Detector saturation 3. Sample amount injected

Untargeted metabolomics workflow Sample preparation Experimental design Sample analysis by MS and NMR Pre-processing data analysis EMBO Course Metabolite identification Experimental validation Hypothesis

RAW METABOLOMICS DATA 29

FROM RAW DATA TO METABOLITE IDs METABOLITE IDENTIFICATIONS STATISTICAL ANALYSIS PRE-PROCESSING RAW DATA CONVERSION Intermediate step between recording of raw spectra and applying data analysis and modeling methods. Makes raw data amenable to subsequent analyses and modeling. It depends on the analytical platform used

FROM RAW DATA TO METABOLITES IDs METABOLITE IDENTIFICATIONS LC/MS GC/MS RAW DATA CONVERSION PRE-PROCESSING STATISTICAL ANALYSIS LC/MS GC/MS PATHWAY ANALYSIS

LC-MS WORKFLOW IDENTIFICATION LC-MS RAW DATA PROTEOWIZARD mZDATA PREPROCESSING mZRT Features Table Feature: individual ions with a unique mass-to-charge ratio and a unique retention time STATISTICAL ANALYSIS IDENTIFICATION

LC-MS WORKFLOW RAW LC-MS DATA TO mZXML: PROTEOWIZARD [Nature Biotechnology, 30 (918–920) (2012)]

LC-MS WORK-FLOW XCMS PRE-PROCESSING http://metlin.scripps.edu/download/ Free & Open Source Based on R On-line version Suitable for: -GC-MS -LC-MS Analytical Chemistry, 78(3), 779–787, 2006 Analytical Chemistry, 84(11), 5035-5039, 2012

LC-MS WORKFLOW XCMS PRE-PROCESSING 1. FEATURE DETECTION [BMC Bioinformatics, 2008 9:504]

LC-MS WORKFLOW XCMS PRE-PROCESSING 1. FEATURE DETECTION 1. Dense regions in m/z space 2. Gaussian peak shape in chromatogram

LC-MS WORK-FLOW XCMS PRE-PROCESSING 2. RETENTION TIME CORRECTION

LC-MS WORKFLOW 103-104 mZRT features  IDENTIFICATION NOT FEASIBLE! features redundancy: -adducts: [M+H+], [M+Na+], [M+NH4+], [M+H+-H2O]… -isotopes: [M+1], [M+2], [M+3] Many mZRT features are noisy in nature and irrelevant to our phenomea STATISTICAL ANALYSIS FEATURES RANKING Those features varying according to our phenomena are retained to further identification experiments

LC-MS WORK-FLOW FEATURES RANKING CRITERIA (I) ANALYTICAL VARIABILITY -RANDOMIZE -USE QCs TO CHECK ANALYTICAL VARIATION WORKLIST

LC-MS WORK-FLOW FEATURES RANKING CRITERIA (I) ANALYTICAL VARIABILITY

USEFUL PLOTS IN EXPLORATORY DATA ANALYSIS NEURONAL CELL CULTURES KO (N=15) vs WT (N=11) #mZRT=6831 RETINAS Hypoxia (N=12) vs Normoxia (N=13) #mZRT=7654

LC-MS WORK-FLOW FEATURES RANKING CRITERIA (IV) HYPOTHESIS TESTING+FDR =0.05 (235 features significantly varied by chance, 26% out of 900) FDR=0.0074 (20 features varied by chance, 5% out of 404) #features=4704

USEFUL PLOTS IN EXPLORATORY DATA ANALYSIS NEURONAL CELL CULTURES KO (N=15) vs WT (N=11) #mZRT=6831 RETINAS Hypoxia (N=12) vs Normoxia (N=13) #mZRT=7654

USEFUL PLOTS IN EXPLORATORY DATA ANALYSIS NEURONAL CELL CULTURES KO (N=15) vs WT (N=11) #mZRT=6831 RETINAS Hypoxia (N=12) vs Normoxia (N=13) #mZRT=7654

Identification experiments 10-50 differential metabolites LC-MS WORKFLOW (i) analytical variability (ii) features intensity # mZRT=51908 # mZRT=38377 # mZRT=4704 # mZRT=250 (iii) hypothesis testing + fold change 10M data points Annotation Data Base look-up Identification experiments 10-50 differential metabolites

Workflow for Metabolite Identification Step 1: Select interesting features Step 2: Search databases for accurate mass Step 3: Filter “putative” identification list Step 4: Compare RT and MS/MS of standards 46

Workflow for Metabolite Identification Step 1: Select interesting features Step 2: Search databases for accurate mass Step 3: Filter “putative” identification list Step 4: Compare RT and MS/MS of standards 47 47

Workflow for Metabolite Identification Step 1: Select interesting features Step 2: Search databases for accurate mass Step 3: Filter “putative” identification list Step 4: Compare RT and MS/MS of standards 48 48

Step 2: Search databases for accurate mass

Step 2: Search databases for accurate mass Each feature returns many hits. Metlin HMDB 50

Step 2: Search databases for accurate mass Common adducts Na+, NH4+, K+, Cl-, and H2O loss Adducts increase number of hits returned! 51

Workflow for Metabolite Identification Step 1: Select interesting features Step 2: Search databases for accurate mass Step 3: Filter “putative” identification list Step 4: Compare RT and MS/MS of standards 52 52

Step 3: Filter “putative” identification list Eliminate drugs? intensity in the mass spectrum adducts? matches with obviously inconsistent retention times Example: feature with m/z 733.56 is unlikely to be a phospholipid if it has a 1-min RT with reverse-phase chromatography. Look for hits that implicate the same pathway, give those features priority. Standards can be expensive, your intuition will save you money and time! 53

Workflow for Metabolite Identification Step 1: Select interesting features Step 2: Search databases for accurate mass Step 3: Filter “putative” identification list Step 4: Compare RT and MS/MS of standards 54 54

What experimental data should be required to constitute a metabolite identification? Accurate mass? Retention time? MS/MS data? Unlike proteomics, no journals have requirements or guidelines for publication of metabolite identifications. 55

accurate mass and retention time “The identification of certain metabolites as their exact masses in their given biological context was strategic in the context of searching for biomarkers for CD.” accurate mass and retention time “…this method enables untargeted profiling of metabolites using accurate mass-retention time (AMRT) identifiers.” accurate mass, retention time, and MS/MS “Metabolites were putatively identified on the basis of accurate mass and retention time, and confirmed by comparing MS/MS data of unknowns to model compounds.” 56

accurate mass “The identification of certain metabolites as their exact masses in their given biological context was strategic in the context of searching for biomarkers for CD.” 57

Accurate mass identifications are putative All structures have a neutral mass of 146.0691 Mass error (even if small) and adducts add more possibilities! 58

accurate mass and retention time “The identification of certain metabolites as their exact masses in their given biological context was strategic in the context of searching for biomarkers for CD.” accurate mass and retention time “…this method enables untargeted profiling of metabolites using accurate mass-retention time (AMRT) identfiers.” accurate mass, retention time, and MS/MS “Metabolites were putatively identified on the basis of accurate mass and retention time, and confirmed by comparing MS/MS data of unknowns to model compounds.” 59

accurate mass and retention time “…this method enables untargeted profiling of metabolites using accurate mass-retention time (AMRT) identfiers.” 60

Many structural isomers have the retention time citrate Citrate and isocitrate have the same retention time but different MS/MS patterns. isocitrate 61

accurate mass and retention time “The identification of certain metabolites as their exact masses in their given biological context was strategic in the context of searching for biomarkers for CD.” accurate mass and retention time “…this method enables untargeted profiling of metabolites using accurate mass-retention time (AMRT) identfiers.” accurate mass, retention time, and MS/MS “Metabolites were putatively identified on the basis of accurate mass and retention time, and confirmed by comparing MS/MS data of unknowns to model compounds.” 62

accurate mass, retention time, and MS/MS “Metabolites were putatively identified on the basis of accurate mass and retention time, and confirmed by comparing MS/MS data of unknowns to model compounds.” 63

Step 4: Compare RT and MS/MS of standards Standard7α-hydroxy-cholesterol 367.33 Q-TOF 367.33 Biological sample 60 100 140 180 220 260 300 340 380 420 Mass-to-Charge (m/z)

Step 4: Compare RT and MS/MS of standards Retention time will be available from the profiling experiment, however, to obtain MS/MS data for the feature of interest in the research sample typically another experiment is required. Note: Only need to perform MS/MS on one research sample. Pick a sample from the group for which the feature is up-regulated! Do not pick this group 65

What if feature of interest is not in the database? (or model compound is not commercially available) FT-ICR MS can be used to limit chemical formulas MS/MS can be insightful to reveal structural insight (MS/MS library, bioinformatic approaches) NMR can provide structural details When a chemist is your best friend…

What if feature of interest is not in the database? (or model compound is not commercially available) FT-ICR MS can be used to limit chemical formulas MS/MS can be insightful to reveal structural insight (MS/MS library, bioinformatic approaches) NMR can provide structural details When a chemist is your best friend…

What if feature of interest is not in the database? (or model compound is not commercially available) FT-ICR MS can be used to limit chemical formulas MS/MS can be insightful to reveal structural insight (MS/MS library, bioinformatic approaches) NMR can provide structural details When a chemist is your best friend…

What if feature of interest is not in the database? (or model compound is not commercially available) FT-ICR MS can be used to limit chemical formulas MS/MS can be insightful to reveal structural insight (MS/MS library, bioinformatic approaches) NMR can provide structural details When a chemist is your best friend…

Thermophile organism adapted to live at high temperatures. Organisms challenged with cold temperature (72 º C) and compared to high-temperature (95 º C) controls. 70

Feature up-regulated at cold temperature Natural product * N1-Acetylthermospermine Identification??? * 71

Feature up-regulated at cold temperature Natural product * N1-Acetylthermospermine Intensity of m/z 112 fragment is significantly different. NOT A MATCH! * 72

Chemical synthesis of hypothesized structure is required 73

Synthesized metabolite produces comparable MS/MS data as natural product from Pyrococcusfuriosus. N4(N-Acetylaminopropyl)spermidine N1-Acetylthermospermine 74

List of metabolites differentially Ultimate goal of metabolomics List of metabolites differentially regulated Biomarker discovery Pathway analysis Model construction Scientific literature Disease vs. control Mechanism Validation Hypothesis

Validate your metabolites!! Targeted metabolomics Molecular biology techniques LC and GC-Triple quadrupole MS Immunohistochemistry Reverse Transcription-PCR Gene expression array Cell cultures Animal experimentation …..

Thank you email: oscar.yanes@urv.cat web: www.yaneslab.com Twitter: @yaneslab