An Introduction to NMR -Based Metabolomics at the UNC

An Introduction to NMR-Based Metabolomics at the UNC Metabolomics Laboratory

NONAME00

9 8 7 6 5 4 3 2 1Chemical Shift (ppm)

Tom O’Connell, Ph.D. Director & Associate Prof. Pharmacotherapy

The Definition of Metabonomics

The quantitative measurement of metabolic responses in biological fluids, cells or tissues to pathophysiological stimuli.

Adapted from Nicholson and Wilson, Nature Reviews in Drug Discovery, 2, 668, 2003

Metabolomics vs. Metabonomics?

Don’t’ worry about it.

Presenter

Presentation Notes

Analytical Coverage of the Metabolome

Adapted from Sumner, LW, et al., Phytochem, 62, 817,2003

NMR

LC/UV

GC/MS

LC/MS

µM (10-6)

nM (10-9)

pM (10-12)

fM (10-15)

Main Analytical Approaches to Metabolomics

MS

Chromatography

LC/MS

GC/MS

CE/MS

NMR

LC/NMR

Off-linehyphenation

Pros & Cons of NMR-Based Metabolomics

• Comprehensive profiling by a single non-destructive method

• Inherently quantitative signals

• Minimal sample preparation

• Very high information content for structure elucidation (stereo/regiochemistry)

• Very high long term intra/inter-lab reproducibility

• Relatively insensitive; concentrations in low µ-mole

• Spectral crowding necessitates advanced methods (2D, 1H-13C) on some samples

• NMR spectral data libraries are growing, but still relatively small

Pros Cons

Presenter

Presentation Notes

UNC Metabolomic Laboratory

600MHz Magnet Tube-based Automation Robot

Fluidic sample system & flow

probe

Presenter

Presentation Notes

The metabolomics lab brings together expertise in analytical sciences and statistics/informatics for collaborations with scientist and physician researchers at UNC.

Sensitivity & Resolution

950

• Sensitivity increases as B3/2• Resolution increases linearly• Cold probe adds ~ 3-4 fold sensitivity

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Presentation Notes

Micro-coil NMR System

10 - 20 µl>500 µl

• Sample volumes of 10 µl • Urine & serum analyzed in ~ 25

minutes w/out concentration• Sensitivity gains with

concentration• Fully automated data collection• Sample returned to vial/plate

after data collection

Protasis High-throughput Sample System

Temperature controlled sample stack for up to 6 plates

Automated syringe injection from vials

In-line filters and reverse rinse protocols minimize clogs from biofluids

Sampling format is customizable allowing the use of multiple types of vials or microplates

Magic Angle Spinning NMR for Profiling of Intact Tissue

Yang et al., J. Proteome Res. 6, 2605, 2007

HR-MASNMR

In vitroextracts

Bo

Presenter

Presentation Notes

Typical Urine NMR Spectrum

9 8 7 6 5 4 3 2 1 0Chemical Shift (ppm)

Hundreds/thousands of peaks corresponding to hundreds/thousands

of metabolites

Typical Urine NMR Spectrum

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5Chemical Shift (ppm)

What types of samples can we look at?


Human serum

Presenter

Presentation Notes

These are the three main types of samples that we’ll be looking at. Biofluids will come mainly from clinician scientists, many through the GCRC. The tissue/cell extracts and media will more likely come from basic researchers. As these are generally easier to come by I think they will be a larger fraction of our sample load. They will hopefully provide a significant revenue source for the lab.



Human bronchoalveolarlavage fluid

Presenter

Presentation Notes




Human CSF

Presenter

Presentation Notes


Metabolomics Involves Many Samples

Looking for subtle differences in many spectra requires some data reduction/simplification

NONAME00


Data Reduction

Variables (chemical shift bins)

Sam

ples

Each sample is described by ~ 200 variablesReduce the data by capturing the variance with combinations of variables

(ppm) File Name [0.50 .. 0.53] [0.53 .. 0.59] [0.59 .. 0.61] [0.61 .. 0.63] [0.63 .. 0.66] [0.66 .. 0.69] [0.69 .. 0.72] [0.72 .. 0.75] [0.75 .. 0.79] [0.79 .. 0.81] [0.81 .. 0.83] [0.83 .. 0.85] [0.85 .. 0.91 3 s01_d01 2.04 4.20 1.88 1.60 2.17 2.08 2.42 2.64 2.83 1.80 1.76 2.01 9.754 s01_d03 2.15 4.38 1.95 1.68 2.25 2.19 2.51 2.69 2.81 1.82 1.81 2.00 9.625 s01_d05 2.37 4.81 2.18 1.85 2.47 2.38 2.75 2.88 3.05 1.98 1.95 2.10 9.366 s01_d07 2.47 4.97 2.26 1.91 2.54 2.45 2.83 3.03 3.18 2.01 1.98 2.13 8.917 s02_d01 2.12 4.42 1.94 1.66 2.24 2.15 2.52 2.70 2.85 1.86 1.83 2.15 10.268 s02_d03 2.29 4.73 2.15 1.81 2.42 2.35 2.71 2.93 3.04 1.99 2.01 2.25 10.069 s02_d05 2.36 4.85 2.19 1.87 2.46 2.37 2.76 2.91 3.04 1.98 1.95 2.11 9.0510 s02_d07 2.41 4.86 2.20 1.89 2.50 2.40 2.80 2.94 3.07 1.99 1.97 2.14 8.8811 s03_d01 2.39 4.88 2.22 1.88 2.52 2.48 2.87 3.22 3.56 2.20 2.17 2.58 11.1112 s03_d03 2.41 4.88 2.22 1.89 2.58 2.48 2.89 3.19 3.45 2.15 2.12 2.47 10.6213 s03_d05 2.43 4.92 2.24 1.91 2.56 2.47 2.88 3.17 3.38 2.14 2.06 2.32 9.4114 s03_d07 2.38 4.83 2.20 1.87 2.50 2.43 2.83 3.18 3.41 2.09 2.02 2.26 9.4215 s04_d01 2.43 5.00 2.21 1.89 2.57 2.51 2.89 3.22 3.49 2.21 2.16 2.54 11.1816 s04_d03 2.53 5.20 2.35 2.00 2.67 2.57 3.05 3.36 3.61 2.27 2.21 2.51 10.7917 s04_d05 2.85 5.58 2.51 2.12 2.94 2.84 3.28 3.45 3.63 2.31 2.27 2.43 8.8418 s04_d07 2.74 5.49 2.45 2.11 2.83 2.79 3.17 3.45 3.63 2.32 2.24 2.46 9.8519 s05_d01 2.24 4.71 2.14 1.82 2.44 2.43 2.78 3.06 3.48 2.20 2.09 2.52 11.4320 s05_d03 2.35 4.85 2.22 1.89 2.51 2.57 2.96 3.25 3.56 2.26 2.19 2.52 10.6821 s05_d05 2.44 4.98 2.27 1.93 2.59 2.52 2.88 3.09 3.28 2.13 2.05 2.26 9.0422 s05_d07 2.42 4.91 2.21 1.92 2.55 2.49 2.88 3.11 3.35 2.14 2.12 2.19 9.4823 s06_d01 2.77 5.63 2.51 2.15 2.94 2.86 3.31 3.58 3.73 2.39 2.42 2.77 11.4924 s06_d03 2.89 5.92 2.66 2.30 3.04 2.98 3.47 3.63 3.82 2.50 2.50 2.71 10.8425 s06_d05 3.09 6.24 2.80 2.41 3.18 3.12 3.57 3.78 3.97 2.59 2.55 2.76 10.6026 s06_d07 3.01 6.05 2.71 2.34 3.11 3.01 3.50 3.69 3.89 2.53 2.50 2.75 10.6727 s07_d01 2.89 5.81 2.58 2.23 3.02 2.94 3.36 3.66 3.85 2.51 2.49 2.75 11.3828 s07_d03 2.79 5.71 2.53 2.22 2.91 2.83 3.29 3.55 3.78 2.43 2.42 2.70 11.0229 s07_d05 3.00 6.05 2.71 2.31 3.08 3.01 3.47 3.73 3.91 2.53 2.46 2.70 10.3830 s07_d07 2.87 5.85 2.62 2.27 2.99 2.89 3.39 3.60 3.73 2.46 2.46 2.57 10.2231 s08_d01 2.04 4.21 1.86 1.66 2.19 2.13 2.48 2.77 2.99 1.91 1.89 2.16 10.8632 s08_d03 2.35 4.82 2.19 1.89 2.55 2.49 2.89 3.30 3.54 2.22 2.23 2.48 11.5033 s08_d05 2.60 5.27 2.41 2.07 2.75 2.68 3.09 3.34 3.51 2.25 2.25 2.40 9.8034 s08_d07 2.39 4.88 2.24 1.89 2.54 2.48 2.87 3.11 3.35 2.15 2.12 2.27 9.9639 s10_d01 2.37 4.84 2.18 1.87 2.53 2.41 2.80 2.99 3.14 2.07 2.07 2.22 10.0140 s10_d03 2.44 5.02 2.30 1.94 2.63 2.55 2.94 3.11 3.26 2.12 2.13 2.21 10.0741 s10_d05 2.76 5.57 2.55 2.14 2.89 2.78 3.16 3.42 3.54 2.29 2.21 2.33 9.0542 s10_d07 2.70 5.46 2.49 2.10 2.79 2.73 3.19 3.38 3.50 2.23 2.19 2.33 9.0743 s11_d01 2.28 4.72 2.10 1.79 2.44 2.40 2.82 3.00 3.19 2.05 2.03 2.39 10.4544 s11_d03 2.54 5.20 2.39 1.98 2.66 2.62 3.09 3.25 3.44 2.21 2.18 2.46 10.20

Presenter

Presentation Notes

Data processing is generally a simple step in the project. Most of this is quite readily automated using the ACD software. This step simply gets the data optimized and ready for the multivariate statistical analysis stage. Chenomx analysis requires more manual interaction and is thus more labor intensive. But, the results may be more valuable in terms of determining biomarkers.

Serum Metabolomics Analysis from Binned Data

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tmoc_101_a.M4 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M4

R2X[1] = 0.378319 R2X[2] = 0.267475 Ellipse: Hotelling T2 (0.95)

Class 1Class 2Class 3Class 4

SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

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SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

MD

Effects of methyl donor rich diet (choline, betaine, folic acid) on high dose ethanol consuption

Tools to Identify Biomarkers

Set of 1D1H spectra

2D spectra1H & 13C

NMRDatabase

1H & 13CPrediction

KEGG Analysis

MetaboliteID

Quantitative Fitting with NMR Database

The Human Metabolome Database

http://www.metabolomics.ca/

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Metabolite ID with 2D Datasets1H-1H or 1H-13C correlation spectra on selected samples

1 = terminal methyl groups of low density (LDL) and very low density lipoproteins (VLDL). 2 = valine. 3 = leucine. 4 = 3-hydroxybutyrate. 5 = lactate. 6 = methylene protons of LDL and VLDL. 7 = alanine. 8 = methylene protons of C3 of VLDL lipoproteins. 9 = allylic methylenes of lipoproteins. 10 = acetate. 11 = N-acetylated glyoproteins. 12 = methylene protons of C2 of VLDL. 13 = methylene protons between olefinic groups of lipoproteins. 14 = albumin lysyl methylene groups. 15 = phospholipid choline headgroups. 16 = taurine. 17 = glucose. 18 = glycerol. 19 = amino acid Ca protons. 20 = choline. 21 = methylene groups of phosphatidylethanolamines.

Targetted Metabolomics

Perform quantitative fitting on all critical metabolites and use this data for statistical analysis

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All_conc_targetted.M3 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M3



SIMCA-P+ 11.5 - 2/4/2008 1:26:37 PM

HFD+MD

HFD+MD+EtOH

HFD

HFD+EtOH

Serum Metabolomics Analysis from Targetted Metabolite Profiles

MD

EtOH

Moving from Global to Targeted Metabolomics

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SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

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SIMCA-P+ 11.5 - 11/21/2007 3:43:14 AM

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p[2]

p[1]

Rusyn_all_integ.M1 (PCA-X)p[Comp. 1]/p[Comp. 2]Colored according to model terms

R2X[1] = 0.903587 R2X[2] = 0.0421382

[9.40 .. 9[9.38 .. 9[9.35 .. 9[9.33 .. 9[9.31 .. 9[9.25 .. 9[9.23 .. 9[9.21 .. 9[9.19 .. 9[9.13 .. 9[9.11 .. 9[9.09 .. 9[9.03 .. 9[8.99 .. 9[8.93 .. 8[8.90 .. 8

[8.85 .. 8[8.83 .. 8[8.78 .. 8[8.74 .. 8[8.70 .. 8[8.68 .. 8[8.63 .. 8

[8.60 .. 8[8.58 .. 8[8.52 .. 8[8.47 .. 8[8.44 .. 8[8.42 .. 8[8.40 .. 8[8.37 .. 8[8.32 .. 8[8.26 .. 8[8.23 .. 8[8.18 .. 8

[8.16 .. 8[8.13 .. 8

[8.07 .. 8[8.01 .. 8[7.95 .. 8[7.93 .. 7[7.91 .. 7[7.85 .. 7[7.81 .. 7[7.79 .. 7[7.76 .. 7[7.71 .. 7

[7.66 .. 7[7.61 .. 7

[7.55 .. 7[7.49 .. 7[7.46 .. 7

[7.40 .. 7[7.34 .. 7[7.29 .. 7

[7.24 .. 7[7.20 .. 7

[7.14 .. 7[7.12 .. 7

[7.10 .. 7[7.07 .. 7[7.02 .. 7[6.99 .. 7

[6.93 .. 6

[6.88 .. 6[6.83 .. 6

[6.80 .. 6[6.77 .. 6[6.75 .. 6[6.73 .. 6[6.70 .. 6[6.64 .. 6[6.62 .. 6[6.60 .. 6[6.58 .. 6[6.55 .. 6[6.51 .. 6[6.47 .. 6[6.45 .. 6[6.40 .. 6[4.25 .. 4

[4.21 .. 4[4.18 .. 4[4.13 .. 4

[4.07 .. 4 [4.02 .. 4

[3.97 .. 4

[3.92 .. 3

[3.86 .. 3

[3.82 .. 3

[3.79 .. 3

[3.74 .. 3

[3.69 .. 3

[3.67 .. 3[3.65 .. 3[3.63 .. 3

[3.61 .. 3[3.59 .. 3

[3.56 .. 3

[3.54 .. 3[3.48 .. 3

[3.46 .. 3

[3.40 .. 3

[3.37 .. 3

[3.31 .. 3

[3.26 .. 3

[3.24 .. 3

[3.22 .. 3

[3.19 .. 3[3.13 .. 3[3.09 .. 3[3.07 .. 3

[3.02 .. 3

[3.00 .. 3[2.98 .. 3[2.96 .. 2[2.94 .. 2[2.92 .. 2

[2.87 .. 2 [2.85 .. 2[2.81 .. 2

[2.77 .. 2[2.71 .. 2

[2.69 .. 2[2.66 .. 2

[2.60 .. 2

[2.55 .. 2[2.51 .. 2[2.46 .. 2[2.43 .. 2

[2.37 .. 2[2.32 .. 2[2.27 .. 2

[2.25 .. 2[2.20 .. 2[2.18 .. 2

[2.16 .. 2

[2.11 .. 2[2.08 .. 2

[2.02 .. 2

[2.00 .. 2[1.98 .. 2[1.95 .. 1

[1.90 .. 1[1.86 .. 1[1.80 .. 1

[1.75 .. 1[1.70 .. 1

[1.66 .. 1

[1.60 .. 1

[1.58 .. 1[1.56 .. 1[1.54 .. 1[1.52 .. 1

[1.47 .. 1

[1.45 .. 1[1.40 .. 1[1.35 .. 1

[1.31 .. 1

[1.27 .. 1

[1.25 .. 1

[1.20 .. 1[1.18 .. [1.15 .. 1[1.13 .. 1

[1.10 .. 1[1.07 .. 1[1.03 .. 1

[0.97 .. 1[0.91 .. 0

[0.87 .. 0

[0.84 .. 0[0.82 .. 0[0.80 .. 0

[0.76 .. 0[0.74 .. 0

[0.68 .. 0

[0.66 .. 0[0.64 .. 0[0.62 .. 0[0.60 .. 0[0.58 .. 0[0.56 .. 0[0.54 .. 0[0.51 .. 0[0.46 .. 0

[0.44 .. 0[0.42 .. 0[0.40 .. 0[0.38 .. 0[0.35 .. 0[0.33 .. 0[0.29 .. 0[0.25 .. 0

SIMCA-P+ 11.5 - 2/7/2008 9:25:21 AM

Higher inEtOH

Higher in Controls

Global models show separation

Loadings guide metabolite ID

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All_conc_targetted.M3 (PCA-X)t[Comp. 1]/t[Comp. 2]Colored according to classes in M3



SIMCA-P+ 11.5 - 2/4/2008 1:26:37 PM

HFD+MD

HFD+MD+EtOH

HFD

HFD+EtOH

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p[2]

p[1]

All_conc_targetted.M3 (PCA-X)p[Comp. 1]/p[Comp. 2]Colored according to model terms

R2X[1] = 0.453432 R2X[2] = 0.246049

Choline

O-Phosphoc

N,N-Dimeth

Carnitine

CitrateTrimethyla

Creatine

CreatinineAlanine

GlutamateGlutamine

Glycine

LysineThreonine

Valine

glyco-prot

glyceryl/c

LDL &VLDL

lipidslipids1

SIMCA-P+ 11.5 - 2/4/2008 1:32:39 PM

HFD+EtOHHFD+MD+EtOH

HFD+MD HFD

Targeted model improves separation

Targeted loadings guide interpretation

2.02.53.03.54.04.5 2.12.22.32.42.62.72.82.93.13.23.33.43.63.73.83.94.14.24.34.4

129S1-SvImJ vs DBA-2J TCE

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129-SvImJ

Lactate

Choline phospho-choline betaine

Glutathione

Glutamate

Sugar Region

7.07.17.27.37.47.57.67.77.87.98.08.18.28.38.48.58.68.78.88.99.0


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129-SvImJ

Formate

Adenosine

Adenine PheNiacinamidenicotinate

UridineTyr

0.80.91.01.1 0.850.951.05


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DBA-2J

129-SvImJ

Val Leu

Ileu

OPLS Loadings Plot for Metabolite ID

Peak intensity relates to importance in discriminating the groups

Color relates to the confidence in the model

DBA/2J

129-SvlmJ

Combining Global Metabolomics with Targeted Metabolite Assays

non-responders phenotype 1 phenotype 2

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DC

A C

onc.

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16 strain inbred mouse panel of acute trichloroethylene (TCE) dosing

Global metabolomics identifies two responder “metabotypes” (global NMR)

Responder metabotypes have distinct levels of DCA metabolite (targetted MS)

Heat map of NMR spectra DCA Levels by Targeted LC/MS


NMR spectra4.0 3.5 3.0 2.5 2.0 1.5 1.0

Chemical Shift (ppm)

High throughput collection

1 [0.50 .. 0.52] 1.1 1.0 1.1 1.0 1.1 1.0 1.1 1.0 1.0 1.1 1.1 1.0 1.1 1.1 1.1 1.1 1.4 1.12 [0.52 .. 0.55] 1.1 1.0 1.1 0.9 1.1 1.0 1.1 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1.0 1.4 1.13 [0.55 .. 0.59] 1.9 1.7 1.9 1.7 1.9 1.8 1.9 1.8 1.7 1.9 2.0 1.8 1.9 1.9 1.9 1.9 2.4 1.84 [0.59 .. 0.61] 1.1 0.9 1.0 0.9 1.1 1.0 1.0 1.0 0.9 1.1 1.1 1.0 1.0 1.0 1.1 1.0 1.3 1.05 [0.61 .. 0.64] 1.2 1.0 1.2 1.0 1.2 1.1 1.2 1.1 1.1 1.2 1.2 1.1 1.2 1.2 1.2 1.1 1.5 1.16 [0.64 .. 0.66] 1.0 0.9 1.0 0.9 1.0 1.0 1.0 0.9 0.9 1.0 1.0 0.9 1.0 1.0 1.0 1.0 1.2 1.07 [0.66 .. 0.68] 1.1 0.9 1.1 0.9 1.1 1.0 1.0 1.0 0.9 1.1 1.1 1.0 1.1 1.0 1.1 1.0 1.3 1.08 [0.68 .. 0.70] 1.1 0.9 1.0 0.9 1.1 1.0 1.0 1.0 0.9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 1.09 [0.70 .. 0.72] 1.2 1.0 1.1 1.0 1.2 1.1 1.1 1.1 1.0 1.1 1.2 1.1 1.1 1.1 1.2 1.3 1.4 1.1

10 [0.72 .. 0.74] 1.1 0.9 1.1 1.1 1.2 1.3 1.0 1.0 0.9 1.1 1.1 1.0 1.1 1.0 1.1 1.4 1.3 1.111 [0.74 .. 0.76] 1.1 0.9 1.1 1.1 1.3 1.5 1.1 1.1 0.9 1.1 1.1 1.0 1.1 1.1 1.1 1.2 1.4 1.112 [0.76 .. 0.78] 1.3 1.1 1.3 1.1 1.4 1.5 1.2 1.2 1.1 1.3 1.3 1.2 1.3 1.2 1.3 1.2 1.5 1.213 [0.78 .. 0.80] 1.3 1.0 1.2 1.1 1.4 1.4 1.1 1.1 1.0 1.2 1.2 1.1 1.3 1.2 1.2 1.2 1.5 1.214 [0.80 .. 0.82] 1.6 1.2 1.4 1.9 2.0 2.2 1.3 1.4 1.1 1.5 1.3 1.2 1.6 1.3 1.4 1.6 1.7 1.415 [0.82 .. 0.88] 12.9 12.0 11.3 16.9 21.4 20.2 9.5 13.1 10.2 11.9 9.6 10.8 12.5 11.1 9.8 8.6 13.2 10.616 [0.88 .. 0.94] 6.9 8.4 6.2 6.7 8.5 6.7 5.4 5.3 5.3 8.2 5.8 6.2 6.4 5.3 6.3 5.4 8.3 6.217 [0.94 .. 0.99] 6.5 7.3 6.3 5.5 6.0 6.2 5.9 5.6 5.8 7.4 6.9 6.9 6.2 5.8 7.3 6.2 9.2 6.418 [0.99 .. 1.05] 5.1 4.9 4.9 3.8 4.4 4.5 4.6 4.3 4.2 5.3 5.0 4.8 4.7 4.5 5.2 4.2 6.4 4.719 [1.05 .. 1.07] 2.1 2.0 2.1 1.4 1.8 1.7 1.9 1.7 1.7 2.3 2.2 2.0 2.0 1.9 2.2 1.6 2.7 1.920 [1.07 .. 1.10] 1.6 1.2 1.5 1.1 1.5 1.5 1.4 1.3 1.1 1.5 1.5 1.2 1.5 1.3 1.6 1.3 1.8 1.421 [1.10 .. 1.12] 1.5 0.9 1.5 1.1 1.5 1.4 1.3 1.3 0.9 1.3 1.3 1.1 1.4 1.2 1.4 1.2 1.6 1.322 [1.12 .. 1.15] 5.0 2.9 4.5 5.1 6.0 8.2 4.2 4.8 3.3 3.7 3.8 3.2 4.4 4.2 4.2 5.1 4.7 4.423 [1.15 .. 1.20] 137.8 84.6 126.5 83.3 76.6 113.7 110.6 128.4 110.0 80.4 83.7 84.8 98.2 110.6 101.1 99.6 107.2 113.424 [1.20 .. 1.22] 6.5 5.4 5.0 2.8 2.8 2.2 4.7 4.2 10.4 6.7 5.6 5.3 4.2 4.9 6.2 2.1 6.9 6.625 [1.22 .. 1.24] 1.9 2.2 1.8 2.8 3.1 3.2 1.7 1.8 2.0 2.0 1.6 2.1 2.0 1.7 1.6 1.6 2.3 1.926 [1.24 .. 1.26] 2.6 2.7 2.3 5.0 6.1 5.8 2.0 2.2 2.0 2.7 2.0 2.3 2.7 2.0 2.0 2.0 2.8 2.327 [1.26 .. 1.30] 6.8 9.1 5.5 12.9 19.8 12.0 4.7 5.8 4.6 9.0 4.8 5.7 7.2 4.9 5.4 4.7 7.7 5.628 [1.30 .. 1.35] 59.0 80.7 57.1 56.6 60.6 59.9 65.7 75.5 61.4 70.6 73.8 74.9 63.7 67.7 68.6 54.6 82.2 60.029 [1.35 .. 1.39] 2.2 4.0 2.2 3.0 2.9 2.5 2.4 2.5 3.2 2.8 2.4 3.4 2.3 2.4 2.1 2.5 3.4 2.430 [1.39 .. 1.43] 2.4 3.1 2.4 2.8 2.5 2.8 2.5 2.7 2.7 2.6 2.5 3.0 2.5 2.5 2.3 2.9 3.4 2.531 [1.43 .. 1.49] 3.8 5.5 3.7 5.6 5.2 5.7 4.0 4.0 4.3 5.1 4.6 5.3 4.3 4.2 4.8 4.0 6.3 4.432 [1.49 .. 1.51] 1.3 1.9 1.3 1.5 1.5 1.8 1.4 1.6 1.5 2.1 1.8 2.4 1.5 1.5 1.5 1.3 2.0 1.433 [1.51 .. 1.56] 5.2 5.6 4.7 7.2 7.9 10.0 5.4 7.9 5.8 5.4 5.3 5.9 5.9 6.5 4.8 4.7 7.0 5.534 [1.56 .. 1.62] 2.8 3.7 2.8 3.3 3.7 3.2 3.1 3.3 3.6 3.3 3.2 3.6 3.0 3.3 3.1 2.8 4.2 3.035 [1.62 .. 1.64] 1.3 1.5 1.3 1.4 1.4 1.5 1.4 1.5 1.5 1.5 1.5 1.6 1.4 1.5 1.4 1.5 1.9 1.436 [1.64 .. 1.66] 1.1 1.2 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.2 1.3 1.2 1.2 1.2 1.2 1.6 1.237 [1.66 .. 1.68] 1.1 1.2 1.1 1.2 1.1 1.2 1.2 1.2 1.2 1.2 1.2 1.3 1.2 1.2 1.2 1.3 1.6 1.238 [1.68 .. 1.74] 3.8 4.4 3.8 3.7 3.7 4.0 4.0 3.9 3.9 4.5 4.3 4.6 4.1 4.0 4.3 4.1 5.8 4.039 [1.74 .. 1.80] 3.4 3.8 3.4 3.2 3.3 3.4 3.6 3.5 3.6 3.8 3.8 4.0 3.6 3.6 3.7 3.5 4.9 3.540 [1.80 .. 1.82] 1.1 1.2 1.1 1.1 1.1 1.1 1.2 1.2 1.1 1.1 1.2 1.2 1.2 1.2 1.1 1.2 1.5 1.241 [1.82 .. 1.84] 1.0 1.1 1.1 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.2 1.1 1.1 1.1 1.2 1.5 1.142 [1.84 .. 1.86] 1.1 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.5 1.6 1.243 [1.86 .. 1.88] 1.1 1.2 1.2 1.3 1.2 1.2 1.4 1.3 1.2 1.3 1.2 1.3 1.2 1.3 1.3 1.5 1.7 1.344 [1.88 .. 1.94] 4.9 5.1 4.7 4.2 4.4 5.0 4.8 4.3 4.9 5.4 5.2 5.6 5.1 5.0 5.5 4.0 7.2 4.745 [1.94 .. 1.96] 1.5 1.6 1.4 1.3 1.3 1.4 1.3 1.3 1.4 1.5 1.5 1.6 1.4 1.4 1.3 1.3 1.8 1.346 [1.96 .. 1.98] 1.3 1.4 1.3 1.5 1.6 1.6 1.2 1.2 1.2 1.4 1.3 1.4 1.3 1.2 1.2 1.3 1.6 1.247 [1.98 .. 2.00] 1.7 1.8 1.6 2.2 2.6 2.0 1.5 1.5 1.5 1.8 1.5 1.7 1.7 1.5 1.5 1.6 2.0 1.648 [2.00 .. 2.06] 5.2 6.3 4.9 6.3 8.0 5.3 4.5 4.4 4.6 6.0 4.7 5.2 5.0 4.4 4.9 4.6 6.6 4.949 [2.06 .. 2.10] 3.0 3.6 3.0 3.2 3.4 3.3 3.3 3.2 3.1 3.7 3.3 3.5 3.1 3.1 3.5 3.5 4.5 3.550 [2.10 .. 2.13] 2.4 2.8 2.6 2.4 2.5 2.7 2.8 2.7 2.7 3.0 3.0 3.1 2.7 2.7 3.3 2.1 4.0 2.951 [2.13 .. 2.18] 6.0 6.5 5.5 7.3 8.1 11.1 5.9 8.1 6.8 6.4 6.7 7.1 6.5 7.2 6.0 5.2 8.3 5.952 [2.18 .. 2.21] 2.0 2.1 1.8 1.7 1.9 1.8 2.1 2.6 2.4 2.1 2.1 2.3 2.1 2.4 2.0 1.6 2.7 2.153 [2.21 .. 2.26] 4.5 5.4 4.6 4.3 5.0 4.0 4.4 4.1 4.5 5.5 4.6 5.4 4.6 4.6 4.7 3.7 6.4 4.354 [2.26 .. 2.31] 2.9 3.1 2.8 2.6 2.8 2.7 2.8 2.9 3.2 3.1 3.1 3.3 2.8 2.8 2.9 2.7 3.9 3.055 [2.31 .. 2.34] 2.4 2.4 2.2 2.1 2.1 2.2 2.4 2.6 2.8 2.3 2.6 2.6 2.2 2.3 2.2 2.2 3.2 2.656 [2.34 .. 2.36] 1.3 1.4 1.2 1.1 1.2 1.2 1.3 1.3 1.3 1.4 1.4 1.4 1.3 1.3 1.3 1.2 1.8 1.357 [2.36 .. 2.41] 3.4 3.5 3.3 3.2 3.2 3.4 3.4 3.6 3.9 3.5 3.8 3.9 3.2 3.4 3.4 3.5 4.6 3.658 [2.41 .. 2.47] 4.1 4.4 4.2 3.9 4.0 4.4 4.5 4.6 4.6 4.5 4.9 5.0 4.3 4.5 4.9 3.8 6.2 4.659 [2.47 .. 2.52] 3.0 3.2 3.0 2.7 2.8 2.7 3.0 2.9 3.4 3.2 3.4 3.6 2.9 3.2 3.1 2.8 4.0 2.960 [2.52 .. 2.58] 3.0 3.0 3.0 2.8 2.9 2.8 3.1 3.0 3.2 3.2 3.1 3.4 2.9 3.1 3.0 2.9 3.8 3.1

Data Processing/Reduction

Pathway Analysis

-0.1

0.0

0.1

0.2

[3.5

6 ..

3

[7.1

7 ..

7

[2.4

4 ..

2

[8.1

2 ..

8

[6.9

0 ..

6

[1.8

3 ..

1

[3.2

1 ..

3

[7.2

2 ..

7

[2.4

0 ..

2

[1.4

6 ..

1

[1.9

1 ..

1

[6.9

5 ..

7

[8.4

1 ..

8

Co

effC

S[2

](re

spo

nd

er

(<1

.5, >

2))

Var ID (Primary)

2 week APAP phar metab- 100 scaled.M8 (OPLS), OPLSDA- Day 5-6, 1.5 > ALT > 2.0CoeffCS[Last comp.](responder (<1.5, >2))

SIMCA-P+ 11.5 - 7/18/2007 3:46:31 PM

Identify Critical Metabolites

-10

-5

0

5

10

-40 -30 -20 -10 0 10 20 30 40

t[2]

t[1]SIMCA-P+ 11.5 - 7/11/2007 2:57:58 PM

Multivariate Statistics

Overall Process of Metabolomics Investigations

Acknowledgements

Metabolomics LabJohn Grimes

Wimal PathmasiriYi Shuai

Hamner-UNC Institute for Drug Safety Sciences

Paul Watkins

Documents

An Introduction to NMR -Based Metabolomics at the UNC