1 / 77

EMBO Practical Course 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.

merrill-jensen
Download Presentation

EMBO Practical Course on Metabolomics Bioinformatics for Life Scientists

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

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

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

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

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

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

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

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

  10. THE IMPORTANCE OF EXPERIMENTAL DESIGN

  11. BASIC DIAGRAM OF A MASS SPECTROMETER

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

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

  14. Watch out serum/plasma samples from biobanks!

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

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

  17. 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

  18. 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

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

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

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

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

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

  24. Ammonium fluoride Ammonium acetate F- Ammonium fluoride

  25. 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

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

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

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

  29. RAW METABOLOMICS DATA

  30. FROM RAW DATA TO METABOLITE IDs METABOLITE IDENTIFICATIONS STATISTICAL ANALYSIS PRE-PROCESSING RAW DATA CONVERSION

  31. 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

  32. LC-MS WORKFLOW 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

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

  34. 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

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

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

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

  38. 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

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

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

  41. 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

  42. #features=4704 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)

  43. 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

  44. 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

  45. 10M data points # mZRT=51908 (i) analytical variability # mZRT=38377 (ii) features intensity # mZRT=4704 (iii) hypothesis testing + fold change # mZRT=250 Annotation Data Base look-up Identification experiments 10-50 differential metabolites LC-MS WORKFLOW

  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. 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. 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

  49. Step 2: Search databases for accurate mass

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

More Related