1. on Metabolomics Bioinformatics for Life Scientists
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. 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

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!
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.

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 )

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

18. Requisite for untargeted metabolomics
Maximize ionization efficiency
over the whole mass range (e.g., m/z )
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??
time
mass
intensity
Feature: molecular entity with a unique m/z and retention time value

20. 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.

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):

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

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
3. Sample amount injected

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 29

30. 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

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 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

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
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), , 2012

35. LC-MS WORKFLOW XCMS PRE-PROCESSING 1. FEATURE DETECTION
[BMC Bioinformatics, :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. LC-MS WORK-FLOW FEATURES RANKING CRITERIA (I) ANALYTICAL VARIABILITY
-RANDOMIZE
-USE QCs TO CHECK ANALYTICAL VARIATION
WORKLIST

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

43. 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

44. 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

45. 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

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 46

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 47 47

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 48 48

49. Step 2: Search databases for accurate mass

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

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

52. 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

53. 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 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

54. 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

55. 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

56. 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

57. 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

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

59. 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

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

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

62. 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

63. 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

64. 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)

65. 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

66. 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…

67. 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…

68. 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…

69. 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…

70. 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

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

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

73. Chemical synthesis of hypothesized structure is required73

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

75. 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

76. 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
…..

77. Thank you email: oscar.yanes@urv.cat
web: