MetabolomicsMetabolomicsMetabolome Reflects the State of the Cell, Organ or OrganismMetabolome Reflects the State of the Cell, Organ or Organism

Change in the metabolome is a direct consequence of protein activity changes • Not necessarily true for genomic, proteomic or transcriptomic changes

Disease, environmental factors, Drugs, etc., perturbs the state of the metabolome • Provides a system-wide view of the organism or cell’s response

NMR Metabolomics OverviewNMR Metabolomics Overview

Prepare the cells, tissue or biofluids

Harvest the metabolome

Collect the NMR data

Analyze the NMR data

Analyze the metabolite changes

NMR Metabolomics DataNMR Metabolomics Data

One-dimensional 1H NMR spectrum

Two-dimensional NMR spectra

2D 1H-13C HSQC Experiment – workhorse of metabolomics

Correlates all directly bonded 13C-1H pairs

generally requires 13C-labeling (1.1% natural abundance)

2D 1H-1H TOCSY Experiment – workhorse of metabolomics

Correlates all 3-bonded 1H-1H pairs in a molecules

NMR Metabolomics ProcessNMR Metabolomics Process

Monitor in vivo protein and drug activity

Forgue et al. (2006) J. Proteome Res. 5(8):1916-1923Halouska & Powers (2006) J. Mag. Res. 178:88-95

Inactive Inactive DrugDrug

Active & Selective Active & Selective DrugDrug

Active & Not Active & Not SelectiveSelective

Active Against Wrong Active Against Wrong ProteinProtein

Differential NMR MetabolomicsDifferential NMR Metabolomics

NMR and Multivariate StatisticsNMR and Multivariate StatisticsExtreme Sensitivity to Experimental Differences

Want PCA Clustering to Result from Metabolome Change NOT Experimental Variability

EVERYTHING should be a CONSTANT between samples or the study is invalid

NMRexperimental parameters

temperature Buffer (pH) shimming Tuning & matching

lock 90o pulse

acquisition parameters

Spectral width

Data points Recycle time

Acquisition time

Solvent removal

Receiver gain

processing parameter

Zero filling Baseline correction

Window function

Linear prediction

Solvent removal

phasing

Negative Impact of Noise in NMR PCA Clustering

ATPATP-glucose

ATP-glucoseATP

Remove NoiseRemove Noise

ATP #2

ATP #9

Single NMR Sample Single NMR Sample with repeat data with repeat data collectioncollection

ATP #2

ATP #9

Higher PC2 dispersion (-10 to 10) and an outlier

lower PC2 dispersion (-4 to 2)

Differential NMR MetabolomicsDifferential NMR Metabolomics

Differential NMR MetabolomicsDifferential NMR MetabolomicsThe Role of NMR Signal-to-Noise in PCA Clustering

Increasing Number of NMR Scans Increasing Number of NMR Scans (S/N)(S/N)

Differential NMR MetabolomicsDifferential NMR MetabolomicsHow to Quantify the Statistical Significance of Cluster Separations?

Analyze Metabolomic Data Using Tree Diagrams• Calculate distances between cluster centers distance matrix

Apply Standard Boot-Strapping Methods• Randomize selection of cluster members to determine cluster center• Generate 100 different distance matrices 100 different trees consensus tree• Bootstrap number -> how many times the consensus node appears in the set of 100 trees

Differential NMR MetabolomicsDifferential NMR MetabolomicsBootstrap Number and Statistical Significance of Cluster Separations

Larger the Distance Between Clusters More Significant

• Larger bootstrap or smaller p-value• > 50% is significant

More Data Points Easier to Distinguish Between Clusters

• more data points (solid line)

Sample Replicates Affects Class DistinctionSample Replicates Affects Class Distinction

Increasing number of replicates

6

810

Significant increase in statistical significance of cluster from a modest increase in number or replicates

Ellipses and Tree Diagrams Define ClassesEllipses and Tree Diagrams Define Classes

P-value on each node identifies statistical significance (< 0.001) of cluster Ellipses represent 95% confidence limits from a normal distribution

Metabolite IdentificationDifferential NMR MetabolomicsDifferential NMR Metabolomics

Orthogonal partial least squares discriminant analysis (OPLS-DA)• a non-linear variant of PCA that minimizes class (group) variations• S-plots and loadings identify which “bins” (NMR chemical shifts – metabolites) are strongly correlated with class separation

S-plotsS-plotsloadingsloadings

Metabolite Identification

Overlay of 2D 1H-13C HSQC spectra for wild-type (red) and aconitase mutant (black)

Differential NMR MetabolomicsDifferential NMR Metabolomics

Grow cells in the presence of a 13C-labeled metabolite

Only observe metabolites derived from the 13C-labeled

metabolite provided to the cells

O

OH

OH

OH

OH

HO

NH2

O

OH

Hu et al. (2011) J. Am. Chem. Soc. 133:1662-1665

Convert Peak Intensities to Convert Peak Intensities to Concentrations (HSQC0)Concentrations (HSQC0)

Our 2D 1H-13C HSQC calibration curve

Convert Peak Intensities to Convert Peak Intensities to Concentrations (HSQC0)Concentrations (HSQC0)

Can now compare changes between metabolites

Convert Concentrations to HeatmapConvert Concentrations to Heatmap

Provides two-levels of hierarchal clustering• Identifies replicates with same overall changes• Identifies metabolites with correlated changes between replicates

Provides a simple view of a large amount of dataCalculated with a statistical package, like R

http://www.r-project.org/

Differential NMR MetabolomicsDifferential NMR MetabolomicsMetabolite Network Mapping (Cytoscape)

Metabolites increased (red), decreased (green) or unperturbed/undetected (grey)

Differential NMR MetabolomicsDifferential NMR MetabolomicsTraditional Metabolic Pathway

Some Final thoughtsSome Final thoughts A number of different analytical methods can be used to analyze the metabolome

• NMR, GC-MS, LC-MS, CE-MS, FTIR, etc.

A variety of statistical techniques can be used to analyze metabolomics data•PCA, PLS, OPLS-DA, HCM, SOM, SVN, etc.

Can combine multiple datasets (NMR and MS) for multivariate statistical analysis

Can incorporate proteomics, genomics and any other data source with metabolomics data to generate system-wide view of the organism or cell response