1. Metabolomics Data Analysis
PCB 5530
Tom Niehaus
Fall 2016

2. Data Analysis Goals • Huge data files • Identify all peaks
In practice this is very difficult if not impossible
Time [min]
Normalized
Intensity
or TIC
m/z
Chromatogram (GC-MS)
100 50 75 25
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
22.00 30 40 60 70 80 90
110
120
130
140
150
160
170
166 97
129 83 61 47 35
119
112
Mass spectrum
Peak selector

3. Data Analysis Identifying peaks
• MS libraries can identify peaks (mostly GC/MS), especially when combined with RT information (GC/MS only):
e.g. NIST library

4. Data Analysis Goals • Huge data files • Identify all peaks
In practice this is very difficult if not impossible
• quantification or semi-quantification of compounds
Standards are needed to quantify a metabolite. For rigorous quantification, samples must be spiked with an isotopically labeled standard.
• Generally relative abundance of metabolites (e.g. –fold change in KO vs WT)
• Various statistical tests to look for differences in the treatment groups
e.g. PCA, MCA, ANOVA

5. Data Analysis Example PCA analysis
Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency (PMID:  )
Figure 2: Multivariate statistical analysis results of serum metabolites in the pCPA-treated mice (n = 40) and the control mice (n = 40).

6. Data Analysis Identifying peaks
• -Fold changes in metabolites can be visualized by a heat map.
PMID: 
Figure 3: Heat map denoting fold changes (over normalized means) of the 21 biomarkers in mice injected with increasing dosages of pCPA and the control mice (n = 10).

7. Example output of a metabolomics experiment
Data Analysis
Example output of a metabolomics experiment
• Open GC-TOF-MS dataset from class website:
-How many compounds identified? How many significant -fold changes
-Pathway analysis at
-enter compound names or KEGG IDs for significant -fold changes
-choose organism ‘E. coli’ and submit
Which pathways are affected in this dataset?
• Open HILIC-TOF-MS dataset from class website:
-How many compounds identified? How many significant -fold changes
-How many unidentified peaks?
-Can you identify an unknown peak with a significant fold change

8. Activity 1: Identifying peaks
Data Analysis
Activity 1: Identifying peaks
• Can you find sucrose in a MS dataset?
Example: sucrose (C12H22O11)

9. Why is resolution important?
Mass Spectrometry
Why is resolution important?
• High resolution is needed to determine the accurate mass
• High resolution is also needed to determine accurate isotopic patterns
• Note:
-monoisotopic vs ave mass

10. Adduct formation – expect the unexpected
…around 290 different adducts
Statistics: Adducts in NIST12 MS/MS DB (80,000 spectra)
Most common adducts for LC-MS ([M+H]+ [M+Na]+ [M+NH4]+ [M+acetate]+)

11. Activity 1: Identifying peaks
Data Analysis
Activity 1: Identifying peaks
• Accurate mass can help determine the chemical formula:
Example: sucrose (C12H22O11)
-Determine monoisotopic mass at
( Da)
-Determine M+H from MS adduct excel sheet (class website)
( Da)
Lets say you find that mass in the dataset, but is it really sucrose?
-Download Molecular weight calculator at
-Open formula finder under tools
-enter molecular weight target:
-how many isobars are at 2 ppm? 0.1 ppm
-enter at chemspider, how many isomers?

12. Heavy-acetate labeled metabolite
Data Analysis
Using stable isotopes
• Unwanted metabolites can be acetylated to target them for breakdown or disposal
Unwanted
metabolite
acetyl-CoA O
CH3
Targeted for
secretion / breakdown
• Labeling cells with heavy-acetate (13C2,2H3) can produce heavy-acetylated compounds
13C
metabolite
heavy-acetyl-CoA O
13CD3
Heavy-acetate labeled metabolite
shift

13. Data Analysis Using stable isotopes
• Experiment: find acetyl-maltose in E. coli
acetyl maltose
KEGG ID C02130
chemspider ID:
monoisotopic mass =
• Samples given either natural-acetate or heavy-acetate

14. Homework
Use what you have learned about mass spectroscopy to answer the following questions:
You find a parent ion with m/z = How do you determine its identity?
How would you search for an expected compound in a MS dataset (e.g. sucrose)?