Transcript Document

Canadian Bioinformatics
Workshops
www.bioinformatics.ca
Module #: Title of Module
2
Module 1
Introduction to Metabolomics
David Wishart
Environmental Influence
The Pyramid of Life
Metabolomics
8000
Chemicals
Proteomics
7500 Enzymes
Genomics
25,000 Genes
What is Metabolomics?
• Genomics - A field of life science research
that uses High Throughput (HT) technologies
to identify and/or characterize all the genes
in a given cell, tissue or organism (i.e. the
genome).
• Metabolomics - A field of life science
research that uses High Throughput (HT)
technologies to identify and/or characterize
all the small molecules or metabolites in a
given cell, tissue or organism (i.e. the
metabolome).
What is a Metabolite?
• Any organic molecule detectable in the
body with a MW < 1500 Da
• Includes peptides, oligonucleotides,
sugars, nucelosides, organic acids,
ketones, aldehydes, amines, amino acids,
lipids, steroids, alkaloids, foods, food
additives, toxins, pollutants, drugs and
drug metabolites
• Includes human & microbial products
• Concentration > detectable (1 pM)
What is a Metabolome?
• The complete collection of small
molecule metabolites in a cell, organ,
tissue or organism
• Includes endogenous and exogenous
molecules as well as transient or even
theoretical molecules
• Defined by the detection technology
• Metabolome size is always ill-defined
Different Metabolomes
All Mammals
8000
Chemicals
20,000
Chemicals
200,000
Chemicals
The Pyramid of Life
All Microbes
All Plants
Human Metabolomes
3100 (T3DB)
Toxins/Env. Chemicals
1000 (DrugBank)
Drug metabolites
30000 (FooDB)
Food additives/Phytochemicals
1450 (DrugBank)
8500 (HMDB)
M
Drugs
Endogenous metabolites
mM
M
nM
pM
fM
Theoretical Human
Metabolomes
100,000 (Lipidome)
Lipids/Lipid derivatives
10,000 (Drug metabolome)
Secondary drug metabolites
100,000 (Food metabolome)
10,000 (Secondome)
M
mM
Secondary food metabolites
Secondary endogenous metabolites
M
nM
pM
fM
Why is Metabolomics
Important?
Small Molecules Count…
• >95% of all diagnostic clinical assays test
for small molecules
• 89% of all known drugs are small
molecules
• 50% of all drugs are derived from preexisting metabolites
• 30% of identified genetic disorders involve
diseases of small molecule metabolism
• Small molecules serve as cofactors and
signaling molecules to 1000’s of proteins
Metabolites Are the
Canaries of the Genome
A single base change can lead to a 10,000X change in metabolite levels
Response
Metabolomics
Response
Proteomics
Response
Metabolomics is More Time
Sensitive Than Other “Omics”
Genomics
Time
Metabolism is “Understood”
The Metabolome is Connected
to all other “Omes”
8000
Chemicals
7500 Enzymes
25,000 Genes
The Pyramid of Life
The Metabolome is Connected to
All Other “Omes”
• Small molecules (i.e. AMP, CMP, GMP, TMP) are the
primary constituents of the genome & transcriptome
• Small molecules (i.e. the 20 amino acids) are the
primary constituents of the proteome
• Small molecules (i.e. lipids) give cells their shape,
form, integrity and structure
• Small molecules (sugars, lipids, AAs, ATP) are the
source of all cellular energy
• Small molecules serve as cofactors and signaling
molecules for both the proteome and the genome
• The genome & proteome largely evolved to catalyze
the chemistry of small molecules
Metabolomics Enables
Systems Biology
Bioinformatics
Meta
bolomics
Cheminformatics
Proteomics
Genomics
Systems
Biology
Metabolomics Applications
• Toxicology Testing
• Genetic Disease Tests
• Clinical Trial Testing
• Nutritional Analysis
• Fermentation Monitoring • Clinical Blood Analysis
• Food & Beverage Tests
• Clinical Urinalysis
• Nutraceutical Analysis
• Cholesterol Testing
• Drug Phenotyping
• Drug Compliance
• Water Quality Testing
• Transplant Monitoring
• Petrochemical Analysis
• MRS and CS imaging
Metabolomics Methods
Metabolomics Workflow
Biological or Tissue Samples
pp
m
7
6
5
4
3
Data Analysis
2
Extraction
Biofluids or Extracts
1
Chemical Analysis
Why Metabolomics is Difficult
Chemical Diversity
Metabolomics
2x105
Chemicals
Proteomics
20 Amino acids
Genomics
4 Bases
The Pyramid of Life
Metabolomics Technologies
•
•
•
•
•
•
•
•
•
UPLC, HPLC
CE/microfluidics
LC-MS
FT-MS
QqQ-MS
NMR spectroscopy
X-ray crystallography
GC-MS
LIF detection
Chromatography
Chromatography
• The separation of components in a
mixture that involves passing the mixture
dissolved in a "mobile phase" through a
stationary phase, which separates the
analyte to be measured from other
molecules in the mixture based on
differential partitioning between the
mobile and stationary phases
• Column, thin layer, liquid, gas, affinity, ion
exchange, size exclusion, reverse phase,
normal phase, gravity, high pressure
High Pressure (Performance)
Liquid Chromatography - HPLC
• Developed in 1970’s
• Uses high pressures (6000 psi) and
smaller (5 m), pressure-stable particles
• Allows compounds to be detected at
ppt (parts per trillion) level
• Allows separation of many types of
polar and nonpolar compounds
HPLC Modalities
• Reversed phase – for separation of nonpolar molecules (non-polar stationary
phase, polar mobile phase)
• Normal phase – for separation of nonpolar molecules (polar stationary phase,
non-polar/organic mobile phase)
• HILIC – hydrophilic interaction liquid
chromatography for separation of polar
molecules (polar stationary phase,
mixed polar/nonpolar mobile phase)
HPLC Columns
Reverse Phase Column
HPLC Separation Efficiency
HPLC Schematic
Gradient HPLC Schematic
HPLC of a Biological
Mixture
Gas Chromatography
Gas Chromatography
• Involves a sample being vaporized to a
gas and injected into a column
• Sample is transported through the column
by an inert gas mobile phase
• Column has a liquid or polymer stationary
phase that is adsorbed to the surface of a
metal tube
• Columns are 1.5-10 m in length and 2-4
mm in internal diameter
• Samples are usually derivatized with TMS
to make them volatile
TMS Derivatization
Gas Chromatography
GC-Columns
Polysiloxane
Retention Time/Index
• Retention time (RT) is the time taken by an
analyte to pass through a column
• RT is affected by compound, column
(dimensions and stationary phase), flow
rate, pressure, carrier, temp.
• Comparing RT from a standard sample to
an unknown allows compound ID
• Retention index (RI) is the retention time
normalized to the retention times of
adjacently eluting n-alkanes
Compound Identification
and Quantification
GC-MS Chromatogram of a
Biological Mixture
Mass Spectrometry
• Analytical method to measure the
molecular or atomic weight of samples
Typical Mass Spectrometer
MS Principles
• Different compounds can be
uniquely identified by their mass
Butorphanol
L-dopa
N -CH2OH
Ethanol
COOH
HO
-CH2CH-NH2
CH3CH2OH
HO
HO
MW = 327.1
MW = 197.2
MW = 46.1
Mass Spectrometry
• For small organic molecules the MW can be
determined to within 1 ppm or 0.0001% which
is sufficiently accurate to confirm the
molecular formula from mass alone
• For large biomolecules the MW can be
routinely determined within an accuracy of
0.002% (i.e. within 1 Da for a 40 kD protein)
• Recall 1 dalton = 1 atomic mass unit (1 amu)
Different Types of MS
• GC-MS - Gas Chromatography MS
– separates volatile compounds in gas column
and ID’s by mass
• LC-MS - Liquid Chromatography MS
– separates delicate compounds in HPLC
column and ID’s by mass
• MS-MS - Tandem Mass Spectrometry
– separates compound fragments by magnetic
or electric fields and ID’s by mass fragment
patterns
Masses in MS
• Monoisotopic
mass is the mass
determined using
the masses of the
most abundant
isotopes
• Average mass is
the abundance
weighted mass of
all isotopic
components
Isotopic Distributions
1H
= 99.9%
2H = 0.02%
12C
= 98.9%
13C = 1.1%
35Cl
= 68.1%
37Cl = 31.9%
Isotopic Distributions
1H
12C
= 99.9%
2H = 0.02%
= 98.9%
13C = 1.1%
100
32.1
6.6
2.1
m/z
0.06 0.00
35Cl
= 68.1%
37Cl = 31.9%
Mass Spec Principles
Sample
+
_
Ionizer
Mass Analyzer
Detector
Typical Mass Spectrum
aspirin
Typical Mass Spectrum
• Characterized by sharp, narrow peaks
• X-axis position indicates the m/z ratio of a
given ion (for singly charged ions this
corresponds to the mass of the ion)
• Height of peak indicates the relative
abundance of a given ion (not reliable for
quantitation)
• Peak intensity indicates the ion’s ability to
desorb or “fly” (some fly better than others)
Resolution & Resolving Power
• Width of peak indicates the resolution of the
MS instrument
• The better the resolution or resolving
power, the better the instrument and the
better the mass accuracy
DM
• Resolving power is defined as:
M
• M is the mass number of the observed mass
(DM) is the difference between two masses
that can be separated
Resolution in MS
Low resolution Instrument
(Ion trap)
High resolution Instrument
(TOF)
2847
Resolution in MS
Resolution/Resolving Power
MW(mono) = 3482.7473
MW(ave) = 3484
Blue DM/M = 1000
Red DM/M = 3000
Green DM/M = 10000
Black DM/M = 30000
Mass Spectrometer Schematic
Turbo pumps
Diffusion pumps
Rough pumps
Rotary pumps
High Vacuum System
Inlet
Sample Plate
Target
HPLC
GC
Solids probe
Ion
Source
Mass
Analyzer
MALDI
ESI
IonSpray
API
LSIMS
EI/CI
TOF
Quadrupole
Ion Trap
Orbitrap
QTrap
Mag. Sector
FTMS
Detector
Microch plate
Electron Mult.
Hybrid Detec.
Data
System
PC’s
UNIX
Mac
Different Ionization Methods
• Electron Ionization (EI - Hard method)
– Small molecules, 1-1000 Daltons, structure
• Chemical Ionization (CI – Semi-hard)
– Small molecules, 1-1000 Daltons, simple spectra
• Electrospray Ionization (ESI - Soft)
– Small molecules, peptides, proteins, up to 200,000
Daltons
• Matrix Assisted Laser Desorption (MALDISoft)
– Smallish molecules, peptides, proteins, DNA, up to
500 kD
Electron Impact Ionization
• Sample introduced into instrument
by heating it until it evaporates
• Gas phase sample is bombarded
with electrons coming from rhenium
or tungsten filament (energy = 70 eV)
• Molecule is “shattered” into
fragments (70 eV >> 5 eV bonds)
• Fragments sent to mass analyzer
• Most commonly used in GC-MS
EI Fragmentation of CH3OH
CH3OH
CH3OH+
CH3OH
CH2O=H+
CH3OH
+
CH2O=H+
+ H
CH3 + OH
CHO=H+ + H
Electron Impact MS of CH3OH
Molecular ion
EI Breaks up Molecules in Predictable Ways
Soft Ionization Methods
337 nm UV laser
Fluid (no salt)
+
_
Lecture 2.1
cyano-hydroxy
cinnamic acid
Gold tip needle
MALDI
ESI
63
Electrospray (Detail)
Electrospray (Detail)
Electrospray Ionization
• Sample dissolved in polar, volatile buffer
(no salts) and pumped through a stainless
steel capillary (70 - 150 m) at a rate of 10100 L/min
• Strong voltage (3-4 kV) applied at tip along
with flow of nebulizing gas causes the
sample to “nebulize” or aerosolize
• Aerosol is directed through regions of
higher vacuum until droplets evaporate to
near atomic size (still carrying charges)
Electrospray Ionization
5%H2O/95%CH3CN
95%H2O/5%CH3CN
100 V
1000 V
3000 V
Electrospray Ionization
• Can be modified to “nanospray” system
with flow < 1 L/min
• Very sensitive technique, requires less
than a picomole of material
• Strongly affected by salts & detergents
• Positive ion mode measures (M + H)+ (add
formic acid to solvent)
• Negative ion mode measures (M - H)- (add
ammonia to solvent)
Mass Spectrometer Schematic
Turbo pumps
Diffusion pumps
Rough pumps
Rotary pumps
High Vacuum System
Inlet
Sample Plate
Target
HPLC
GC
Solids probe
Ion
Source
Mass
Analyzer
MALDI
ESI
IonSpray
FAB
LSIMS
EI/CI
TOF
Quadrupole
Ion Trap
Mag. Sector
FTMS
Detector
Microch plate
Electron Mult.
Hybrid Detec.
Data
System
PC’s
UNIX
Mac
Different Types of Mass
Analyzers
• Magnetic Sector Analyzer (MSA)
– High resolution, exact mass, original MA
• Quadrupole Analyzer (Q or Q*)
– Low (1 amu) resolution, fast, cheap
• Time-of-Flight Analyzer (TOF)
– No upper m/z limit, high throughput
• Ion Cyclotron Resonance (FT-ICR)
– Highest resolution, exact mass, costly
MS Mass Accuracy
Type
Mass Accuracy
FT-ICR-MS
0.1 - 1 ppm
Orbitrap
0.5 - 1 ppm
Magnetic Sector
1 - 2 ppm
TOF-MS
3 - 5 ppm
Q-TOF
3 - 5 ppm
Triple Quad
3 - 5 ppm
Linear IonTrap
50-200 ppm
(10 ppm in Ultra-Zoom)
ppm  (
mexp - mcalc
)  1 E 6
mexp
Mass Chromatograms
• Standard “output” from an LC-MS or GC-MS
experiment
• X-axis is retention time, Y-axis is signal intensity
• Total Ion Current (TIC) chromatogram is summed
intensity across the entire range of masses being
detected at every point in the analysis
• Base Peak chromatogram (BPC) is like a TIC but
displays only the most intense peak in each spectrum
• Extracted Ion chromatogram (EIC) contains one or
more analytes extracted from the TIC or BPC
Mass Chromatograms of
Biological Mixtures
Tomato Extract
Arabidopsis Extract
NMR Spectroscopy
Explaining NMR
Principles of NMR
• Measures nuclear magnetism or changes
in nuclear magnetism in a molecule
• NMR spectroscopy measures the
absorption of light (radio waves) due to
changes in nuclear spin orientation
• NMR only occurs when a sample is in a
strong magnetic field
• Different nuclei absorb at different
energies (frequencies)
Protons (and other
nucleons) Have Spin
Spin up
Spin down
Each Spinning Proton is
Like a “Mini-Magnet”
S
N
N
S
Spin up
Spin down
Principles of NMR
N
N
S
S
Low Energy
High Energy
hn
hn
Bigger Magnets are Better
Increasing magnetic field strength
low frequency
high frequency
A Modern NMR Instrument
Radio Wave
Transceiver
NMR Magnet
NMR Magnet Cross-Section
Magnet Legs
An NMR Probe
NMR Sample & Probe Coil
1H
NMR Spectra Exhibit...
• Chemical Shifts (peaks at different
frequencies or ppm values)
• Splitting Patterns (from spin coupling)
• Different Peak Intensities (# 1H)
8.0
7.0
6.0
5.0 4.0
3.0 2.0 1.0
0.0
Chemical Shifts
• Key to the utility of NMR in chemistry
• Different 1H in different molecules exhibit
different absorption frequencies
• Each compound can be defined by a
unique pattern of chemical shifts (a
fingerprint)
• Chemical shifts are mostly affected by
electronegativity of neighbouring atoms,
bonds or groups
Characteristic Chemical Shifts
Assigning Simple NMR
Spectra
TMS
Assigning Simple NMR
Spectra
NMR Spectra Need “Fixin’”
Before
After
Baseline
correction
Shimming
Water
suppression
Referencing
Phasing
NMR Spectra Need “Fixin’”
• Chemical shift referencing (TMS, DSS)
– Calibrates/normalizes chemical shifts
• Shimming
– Fixes line shape to look Lorentzian
• Phasing
– Fixes line shape to look “absorptive”
• Water suppression/removal
– Removes large water signal
• Baseline correction
– Makes spectrum look flat – not wobbly
NMR Spectrum of a
Biological Mixture
# Metabolites or Features detected (Log10)
Technology & Sensitivity
Unknowns
4
LC-MS or
DI-MS
3
GC-MS
TOF
2
NMR
1
Knowns
GC-MS
Quad
0
M
mM
M
nM
Sensitivity or LDL
pM
fM
Comparison
NMR (with cold
probe)
GC-MS
DI-MS
Techniques
Metabolites
Water-soluble
(amino acids,
organic acids,
sugars)
mainly watersoluble (some
hydrophobic)
Mainly
hydrophobic
(some watersoluble)
Types of samples
Biofluids, plant,
bacterial,
animal tissue
extracts, Food
Biofluids, plant,
Mainly biofluids
bacterial, animal
tissue extracts, Food
Sample Volume
100 µL (min)
30-50 µL (min)
10 µL
Comparison
NMR
GC-MS
DI-MS
Sample prep time
30 -120
min/20
samples
30 -120 min/20
samples
3-4 h for 96
samples
Run time
20 -90
min/sample
30-60 min/sample
7 min/sample
Data Analysis
30-60 min /
sample
30-60 min / sample
1-2 h for 96
samples
Limit of Detection
~ 5 µM
~ 100 nM
~ 5 nM
No. of metabolites
~ 20 - 50
~20 -50
~ 100-180
Overlapping
Metabolites
10-15
10-15
10-15
Cross-checking
10-30 %
10-30 %
10-30 %
What’s Possible
• NMR-based metabolomics (~50 metabolites
identified/quantified, M sensitivity)
• GC-MS based metabolomics (~70 metabolites
identified/quantified, <M sensitivity)
• DI-MS based metabolomics (160 metabolites
identified/quantified, nM sensitivity)
• LC-MS based metabolomics (300 metabolites
identified/quantified, nM sensitivity)
• Lipidomics (3000 lipids identified and semiquantified, nM sensitivity)
• Specialty phytochemical, nutrient, drug and
pesticide analysis (mostly HPLC, nM sensitivity)
2 Routes to Metabolomics
ppm
7
6
5
4
Quantitative (Targeted)
Methods
3
2
Chemometric (Profiling)
Methods
25
TMAO
hippurate
allantoin creatinine taurine
1
PC2
20
creatinine
15
10
citrate
ANIT
5
hippurate
urea
2-oxoglutarate
water
succinate
fumarate
0
-5
-10
ppm
7
6
5
4
3
2
1
Control
-15
PAP
-20
-25
-30
PC1
-20
-10
0
10
Profiling (Untargeted)
Data Reduction
Data Collection
25
PC2
20
15
10
5
ANIT
0
-5
-10
-15
-20
-25
-30
Sample Prep
Metabolite Identification
Control
PAP
-20
-10
PC1
0
10
Quantitative (Targeted)
Sample Prep
25
PC2
20
Biological Interpretation
15
10
5
ANIT
0
-5
-10
-15
-20
-25
-30
Control
PAP
-20
-10
PC1
0
Data Reduction
Metabolite Identification & Quantification
10
From Spectra to Lists
Table 1 . List of 121 dansyl derivative standards
identified and quantified in a 5
7
6
5
4
3
2
1
-day pooled hu man urine sample
(in bold face)
by using fast LC -FTICR MS.
Retention
Time
(min)
Conc. in
Urine
(µM)
Dns -o-phospho -L-serine
0.92
<D.L. *
Dns-Ile
6.35
25
Dns -o-phospho -L-tyrosine
0.95
<D.L.
Dns-3-aminosalicylic acid
6.44
0.5
Dns -adnosine monophosphate
0.99
<D.L.
Dns-pipecolic acid
6.50
0.5
Dns-o-phosphoethanolamine
Dns-glucosamine
1.06
16
6.54
54
Dns -o-phospho -L-threonine
1.06
1.09
22
<D.L.
Dns-Leu
Dns-cystathionine
Dns-Leu -Pro
6.54
6.60
0.3
0.4
Dns -6-dimet hylamine purine
1.20
<D.L.
Dns-5-hydroxylysine
6.65
1.6
Dns-3-methyl -histidine
1.22
80
Dns-Cystine
6.73
160
Dns-taurine
Dns-carnosine
1.25
834
Dns-N-norleucine
6.81
0.1
Dns-Arg
1.34
1.53
28
36
Dns -5-hydroxydopamine
Dns-dimethylamine
7.17
7.33
<D.L.
293
Dns-Asn
1.55
133
Dns-5-HIAA
7.46
18
Dns-hypotaurine
1.58
10
Dns-umbelliferone
7.47
1.9
Dns-homocarnosine
1.61
3.9
<D.L.
1.62
1.72
<D.L.
633
Dns -2,3 -diaminoproprionic acid
Dns-L-ornithine
7.63
Dns -guanidine
Dns-Gln
Dns-4-acetyamidophenol
7.70
7.73
15
51
Dns-allantoin
1.83
3.8
Dns-procaine
7.73
8.9
Dns-L-citrulline
1.87
2.9
Dns-homocystine
7.76
3.3
Dns-1 (or 3 -)-methylhistamine
Dns-adenosine
1.94
1.9
7.97
82
Dns -methylguanidine
2.06
2.20
2.6
<D.L.
Dns-acetaminophen
Dns-Phe-Phe
Dns-5-methyo xysalicylic acid
8.03
8.04
0.4
2.1
Dns-Ser
2.24
511
Dns-Lys
8.16
184
Dns-aspartic acid amide
2.44
26
Dns -aniline
8.17
<D.L.
Dns-4-hydroxy -proline
Dns-Glu
2.56
2.3
0.3
21
Dns-leu -Phe
Dns-His
8.22
2.57
8.35
1550
Dns-Asp
Dns-Thr
2.60
3.03
90
157
Dns -4-thialysine
Dns -benzylamine
8.37
8.38
<D.L.
<D.L.
Dns -epinephrine
3.05
<D.L.
Dns-1-ephedrine
8.50
0.6
Dns-ethanolamine
3.11
471
Dns-tryptamine
8.63
0.4
Dns-aminoadipic acid
Dns-Gly
3.17
70
Dns -pyrydoxamine
8.94
<D.L.
Dns-Ala
3.43
3.88
2510
593
Dns -2-methyl -benzylamine
Dns-5-hydroxytrptophan
9.24
9.25
<D.L.
0.12
Dns-aminolevulinic acid
3.97
30
Dns-1,3 -diaminopropane
9.44
0.23
Dns-r-amino -butyric acid
3.98
4.6
Dns-putrescine
9.60
0.5
Dns-p-amino -hippuric acid
Dns-5-hydro xymethyluricil
3.98
2.9
9.66
0.1
Dns-tryptophanamide
4.58
4.70
1.9
5.5
Dns-1,2 -diaminopropane
Dns-tyrosinamide
Dns-dopamine
9.79
10.08
29
140
Dns -isoguanine
4.75
<D.L.
Dns-cadaverine
10.08
0.08
Dns-5-aminopentanoic acid
4.79
1.6
Dns-histamine
10.19
0.4
Dns-sarcosine
Dns-3-amino -isobutyrate
4.81
7.2
10.19
9.2
4.81
4.91
85
17
Dns-3-methoxy -tyr amine
Dns-Tyr
10.28
10.44
321
<D.L.
Compound
ppm
and the corresponding metabolites
Dns-2-aminobutyric acid
Compound
Dns -cysteamine
Retention
Time
(min)
Conc. in
Urine
(µM)
From Lists to Pathways
Table 1 . List of 121 dansyl derivative standards
identified and quantified in a 5
and the corresponding metabolites
-day pooled hu man urine sample
(in bold face)
by using fast LC -FTICR MS.
Retention
Time
(min)
Conc. in
Urine
(µM)
Dns -o-phospho -L-serine
0.92
<D.L. *
Dns-Ile
6.35
25
Dns -o-phospho -L-tyrosine
0.95
<D.L.
Dns-3-aminosalicylic acid
6.44
0.5
Dns -adnosine monophosphate
0.99
<D.L.
Dns-pipecolic acid
6.50
0.5
Dns-o-phosphoethanolamine
Dns-glucosamine
1.06
16
6.54
54
Dns -o-phospho -L-threonine
1.06
1.09
22
<D.L.
Dns-Leu
Dns-cystathionine
Dns-Leu -Pro
6.54
6.60
0.3
0.4
Dns -6-dimet hylamine purine
1.20
<D.L.
Dns-5-hydroxylysine
6.65
1.6
Dns-3-methyl -histidine
1.22
80
Dns-Cystine
6.73
160
Dns-taurine
Dns-carnosine
1.25
834
Dns-N-norleucine
6.81
0.1
Dns-Arg
1.34
1.53
28
36
Dns -5-hydroxydopamine
Dns-dimethylamine
7.17
7.33
<D.L.
293
Dns-Asn
1.55
133
Dns-5-HIAA
7.46
18
Dns-hypotaurine
1.58
10
Dns-umbelliferone
7.47
1.9
Dns-homocarnosine
1.61
3.9
<D.L.
1.62
1.72
<D.L.
633
Dns -2,3 -diaminoproprionic acid
Dns-L-ornithine
7.63
Dns -guanidine
Dns-Gln
Dns-4-acetyamidophenol
7.70
7.73
15
51
Dns-allantoin
1.83
3.8
Dns-procaine
7.73
8.9
Dns-L-citrulline
1.87
2.9
Dns-homocystine
7.76
3.3
Dns-1 (or 3 -)-methylhistamine
Dns-adenosine
1.94
1.9
7.97
82
Dns -methylguanidine
2.06
2.20
2.6
<D.L.
Dns-acetaminophen
Dns-Phe-Phe
Dns-5-methyo xysalicylic acid
8.03
8.04
0.4
2.1
Dns-Ser
2.24
511
Dns-Lys
8.16
184
Dns-aspartic acid amide
2.44
26
Dns -aniline
8.17
<D.L.
Dns-4-hydroxy -proline
Dns-Glu
2.56
2.3
0.3
21
Dns-leu -Phe
Dns-His
8.22
2.57
8.35
1550
Dns-Asp
Dns-Thr
2.60
3.03
90
157
Dns -4-thialysine
Dns -benzylamine
8.37
8.38
<D.L.
<D.L.
Dns -epinephrine
3.05
<D.L.
Dns-1-ephedrine
8.50
0.6
Dns-ethanolamine
3.11
471
Dns-tryptamine
8.63
0.4
Dns-aminoadipic acid
Dns-Gly
3.17
70
Dns -pyrydoxamine
8.94
<D.L.
Dns-Ala
3.43
3.88
2510
593
Dns -2-methyl -benzylamine
Dns-5-hydroxytrptophan
9.24
9.25
<D.L.
0.12
Dns-aminolevulinic acid
3.97
30
Dns-1,3 -diaminopropane
9.44
0.23
Dns-r-amino -butyric acid
3.98
4.6
Dns-putrescine
9.60
0.5
Dns-p-amino -hippuric acid
Dns-5-hydro xymethyluricil
3.98
2.9
9.66
0.1
Dns-tryptophanamide
4.58
4.70
1.9
5.5
Dns-1,2 -diaminopropane
Dns-tyrosinamide
Dns-dopamine
9.79
10.08
29
140
Dns -isoguanine
4.75
<D.L.
Dns-cadaverine
10.08
0.08
Dns-5-aminopentanoic acid
4.79
1.6
Dns-histamine
10.19
0.4
Dns-sarcosine
Dns-3-amino -isobutyrate
4.81
7.2
10.19
9.2
4.81
4.91
85
17
Dns-3-methoxy -tyr amine
Dns-Tyr
10.28
10.44
321
<D.L.
Compound
Dns-2-aminobutyric acid
Compound
Dns -cysteamine
Retention
Time
(min)
Conc. in
Urine
(µM)
From Pathways & Lists to
Models & Biomarkers
Table 1 . List of 121 dansyl derivative standards
identified and quantified in a 5
and the corresponding metabolites
-day pooled hu man urine sample
(in bold face)
by using fast LC -FTICR MS.
Retention
Time
(min)
Conc. in
Urine
(µM)
Dns -o-phospho -L-serine
0.92
<D.L. *
Dns-Ile
6.35
25
Dns -o-phospho -L-tyrosine
0.95
<D.L.
Dns-3-aminosalicylic acid
6.44
0.5
Dns -adnosine monophosphate
0.99
<D.L.
Dns-pipecolic acid
6.50
0.5
Dns-o-phosphoethanolamine
Dns-glucosamine
Dns-Leu
Dns-cystathionine
Compound
1.06
16
Dns -o-phospho -L-threonine
1.06
1.09
22
<D.L.
Dns -6-dimet hylamine purine
1.20
<D.L.
Dns-3-methyl -histidine
1.22
Dns-taurine
Dns-carnosine
Compound
Retention
Time
(min)
Conc. in
Urine
(µM)
6.54
54
Dns-Leu -Pro
6.54
6.60
0.3
0.4
Dns-5-hydroxylysine
6.65
1.6
80
Dns-Cystine
6.73
1.25
834
Dns-N-norleucine
6.81
0.1
Dns-Arg
1.34
1.53
28
36
Dns -5-hydroxydopamine
Dns-dimethylamine
7.17
7.33
<D.L.
293
Dns-Asn
1.55
133
Dns-5-HIAA
7.46
Dns-hypotaurine
1.58
10
Dns-umbelliferone
7.47
1.9
Dns-homocarnosine
1.61
3.9
7.63
<D.L.
Dns -guanidine
Dns-Gln
1.62
1.72
<D.L.
633
Dns -2,3 -diaminoproprionic acid
Dns-L-ornithine
Dns-4-acetyamidophenol
7.70
7.73
15
51
Dns-allantoin
1.83
3.8
Dns-procaine
7.73
8.9
Dns-L-citrulline
1.87
2.9
Dns-homocystine
7.76
3.3
Dns-1 (or 3 -)-methylhistamine
Dns-adenosine
Dns-acetaminophen
Dns-Phe-Phe
7.97
160
18
1.94
1.9
Dns -methylguanidine
2.06
2.20
2.6
<D.L.
Dns-5-methyo xysalicylic acid
8.03
8.04
0.4
2.1
Dns-Ser
2.24
511
Dns-Lys
8.16
184
Dns-aspartic acid amide
2.44
26
Dns -aniline
8.17
<D.L.
Dns-4-hydroxy -proline
Dns-Glu
2.56
2.3
0.3
21
Dns-leu -Phe
Dns-His
8.22
2.57
8.35
1550
Dns-Asp
Dns-Thr
2.60
3.03
90
157
Dns -4-thialysine
Dns -benzylamine
8.37
8.38
<D.L.
<D.L.
Dns -epinephrine
3.05
<D.L.
Dns-1-ephedrine
8.50
Dns-ethanolamine
3.11
471
Dns-tryptamine
8.63
0.4
Dns-aminoadipic acid
Dns-Gly
3.17
70
Dns -pyrydoxamine
8.94
<D.L.
Dns-Ala
3.43
3.88
2510
593
Dns -2-methyl -benzylamine
Dns-5-hydroxytrptophan
9.24
9.25
<D.L.
0.12
Dns-aminolevulinic acid
3.97
30
Dns-1,3 -diaminopropane
9.44
0.23
Dns-r-amino -butyric acid
3.98
4.6
Dns-putrescine
9.60
0.5
Dns-p-amino -hippuric acid
Dns-5-hydro xymethyluricil
3.98
2.9
9.66
Dns-tryptophanamide
4.58
4.70
1.9
5.5
Dns-1,2 -diaminopropane
Dns-tyrosinamide
Dns -isoguanine
4.75
<D.L.
Dns-5-aminopentanoic acid
4.79
Dns-sarcosine
Dns-3-amino -isobutyrate
4.81
4.81
4.91
85
17
Dns-2-aminobutyric acid
82
0.6
0.1
Dns-dopamine
9.79
10.08
29
140
Dns-cadaverine
10.08
0.08
1.6
Dns-histamine
10.19
7.2
Dns-3-methoxy -tyr amine
Dns-Tyr
10.19
9.2
10.28
10.44
321
<D.L.
Dns -cysteamine
0.4
Key Informatics Challenges
in Metabolomics
• Spectra -> Lists
– Data integrity and quality
– Data alignment and normalization
– Data reduction and classification
– Assessment of significance
– Metabolite identification/quantification
• Lists -> Pathways & Biomarkers
– Pathway mapping and identification
– Biological interpretation