The Nuts & Bolts of NMR Metabolomics: from sample preparation to spectral processing

Training Workshop on Metabolomics and NutritionDublin, Ireland

11th January 2005

Dr. Mark ViantSchool of Biosciences

University of Birmingham, U.K.

Overview of talk

1. Introduction to metabolomics, sample prep, NMR

2. Case study - Detection of muscle withering syndrome in shellfish

• Example of 1-D NMR metabolomics study• Optimized data processing

3. Case study - Detection of liver cancer in dab, a marine flatfish

• Limitations of 1-D NMR approach• Consistency between NMR and MS approaches

4. Case study – Embryogenesis and developmental toxicity in Japanese medaka eggs

• Advantages of 2-D NMR approach• Concept of “developmental metabolic trajectory”

Typical Metabolomics Experiment

Diseased or exposed

group

Control group

Tissue or biofluid sample

Measure metabolite profiles

Mass spectrometry1H NMR spectroscopy

Spectral pre-processing (ProMetab software)

Classification algorithms

Metabolic biomarker discovery

Mechanistic of action

Sample Preparation

ORGANISM

COLLECT SAMPLE IN MANNER TO PRESERVE METABOLITE CONCENTRATIONS (‘Quenching’)

SAMPLE EXTRACTION

1. Put sample into format compatible with analysis2. Retain metabolites you do want3. Remove biochemicals you don’t want

Metabolites you want:1. lipids2. carbohydrates3. amino acids4. other small metabolites

Biochemicals you don’t want:1. proteins2. DNA and RNA3. salts

SPECIFIC MODIFICATIONS TO SAMPLE FOR NMR OR MS ANALYSIS

Samples and Sample Preparation for NMR metabolomics

Animal or plant

Biological fluid(no cells)

Biological fluid(with cells)

Cell cultureTissue

e.g. urineor CSF

e.g. plasma e.g. livere.g. neural cells

• store -80ºC• anticoagulant = heparin• centrifuge to remove cells• store -80ºC

• freeze immediatelyin liquid nitrogen

• store -80ºC

• remove media• wash cells• freeze or extract

immediately (below)• store -80ºC

Tissue preparation

Biofluid preparation

• remove proteins?• analyze neat or dilute in

saline or buffer

• mechanically disrupt tissue• ‘hard’ vs. ‘soft’ tissue

Extraction of ground/homogenized or cellular sample

• Several options for extraction:1. Polar metabolites only

- perchloric acid (PCA)- acetonitrile:water- methanol:water

2. Polar and lipophilic metabolites- methanol:chloroform (2-phase)

LiquidSupernatant

Final preparation for NMR spectroscopy

• aqueous fraction (polar metabolites)- freeze dry- add phosphate buffer (D2O, pH 7.4)- add TMSP

• chloroform fraction (lipophilic metabolites)- remove solvent- add deuterated MeOD:CDCl3 solvent- add TMS

1H NMR Spectroscopy

NMR Metabolomics Experiments

1-D NMR - for measurement of metabolite fingerprints

• Single pulse 1H NMR sequence

• CPMG 1H spin-echo sequence

2-D NMR - for measurement of metabolite fingerprints

• projections from J-Resolved spectra

2-D NMR - for confirmation of peak assignments

• 1H-1H correlation spectroscopy (COSY)

• 1H-13C heteronuclear single quantum coherence (HSQC)

Typical 1-D 1H NMR spectrum of tissue extract

chemical shift (ppm)

123456789

Amino acids,e.g. tyrosine

Nucleotides,e.g. ATP

Carbohydrates,e.g. ribosyl moiety

Organic acids,e.g. succinate

Case study - Detection of muscle withering syndrome in shellfish

• Example of NMR-based metabolomics study, from

sample preparation to multivariate analysis.

• Optimized data processing.

Viant, Rosenblum, Tjeerdema, Env. Sci. Technol. 37, 4982-4989 (2003)

Rationale for study

• Classify disease status of abalone based upon the metabolite profile of the foot muscle.

• Attempt to identify biomarkers of the disease.

Healthy red Severe atrophy of abalone foot muscle

Experimental design: 19 age matched abalone

9 healthy abalone

5 “stunted” abalone

5 diseased abalone

digestive gland tissue

foot muscle tissue

hemolymph

Collect and freeze foot

muscle

Perchloric acid extraction of polar

metabolites

Overview of abalone study

1D 1H NMR spectroscopy

Raw data Spectral pre-processing and PCA

1-D 1H NMR spectra of foot muscle extract

012345Chemical shift (ppm)

Inte

nsity

Diseased abalone Healthy

abalone

zoomTMSPglycine-betaine

taurine

1.41.61.822.22.42.62.83Chemical shift (ppm)

Inte

nsity

dimethylglycine

alanine

acetate

expand2.462.502.542.582.62

Chemical shift (ppm)

Inte

nsity

carnitine argininehypotaurine

1-D 1H NMR spectrum of foot muscle extract

Expanded region

• extremely congested spectra

with hundredsof overlapping

peaks

Step 1 - Pre-processing of NMR spectral data

• Transform raw (Bruker) NMR data into matrix format for

multivariate analysis.

• Custom written MATLAB code (ProMetab Release 1.1).

(a) segment spectra into frequency ‘bins’ or ‘buckets’.

• bin width = 0.005 ppm (user defined)

• 10x higher resolution than most studies

(b) remove TMSP and water peaks.

(c) various normalization options.

(d) generalized log transformation.

1750150012501000

12345Chemical shift (ppm)

Inte

nsity

diseasedabalone

healthyabalone

2000Bin #

segment into frequency “bins”

waterremoved

TMSPremoved

ProMetabsoftware

Step 1 - Pre-processing of NMR spectral data

Step 2 - Multivariate analysis of pre-processed data

Examples:

• principal components analysis (PCA)

- unsupervised

• partial least squares regression (PLS)

- supervised

Goal is often to summarize and visualize the

similarities and differences between the NMR spectra

using simple 2-dimensional plots.

PCA scores plot of abalone foot muscle

-5 0 5 10-3

-2

-1

0

1

2

3

4

5

-WS

?WS

PC 1 (68.08%)

PC

2 (2

0.00

%)

+WS

+WS+WS

+WS

?WS?WS

?WS

-WS

-WS-WS-WS

-WS-WS

-WS-WS

+WS

“stunted”

?WS

healthy diseased

Generalized log transformation

Before

Multiple spectra…

After

Purohit, Rocke, Viant and Woodruff, OMICS 8, 118-130 (2004)

PCA scores plots of foot muscle

metabolite profiles

withouttransformation

healthy

“stunted”

diseased

healthy

“stunted”

diseased

with generalized log transformation

200 300 400 500 600

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

healthy abalone

diseased abalone

Chemical shift axis (bin #)

tryptophan

tyrosinephenylalanine

ATP

homarine

formateAMP

Load

ings

on

PC

1 (6

8.08

%)

Section of PCA loads plot - Diagnostic biomarker profile for withering syndrome

Conclusions from abalone study

• NMR approach could distinguish disease status of muscle samples based upon metabolic profiles.

• Metabolomics is a powerful approach for biomarker discovery.

• Optimised spectral pre-processing is crucial:

- generalized log transformation

- ‘binning’ at high resolution (0.005 ppm)

Case study - Detection of liver cancer in dab, a marine flatfish

• Illustrate limitations of 1-D NMR approach, in

terms of peak congestion.

• Show consistency between NMR and mass

spectrometry based metabolomics.

Rationale for study

• Collaboration with CEFAS.

• Use disease status, parasite

loads and liver pathology of dab

(Limanda limanda) as indicators

of environmental stress.

• Prevalence of liver tumours in

over 10% of fish at hotspots.

• Current methods based upon

histopathology.

Experimental design – preliminary study

N=9 dab with liver tumours

‘healthy’ liver sample

macroscopic liver tumour

Dissect pairedliver samples Overview of

dab study

Extract tissue usingMeOH:chloroform

1-D 1H NMR spectroscopy and mass spectrometry

Raw data Spectral pre-processing followed by PCA and PLS

PLS scores plot of NMR dataConsistent metabolic change induced by cancer in every fish

-30

-20

-10

0

10

20

30

40

-50 -40 -30 -20 -10 0 10 20 30 40 50

LV1 axis (32.1%)

LV2

axis

(15.

3%)

Cancer

‘Normal’

Average metabolic change

vector

one fis

h

PLS weightings plot of NMR dataPotential biomarker profile for dab liver cancer

Wei

ghtin

gs fo

r ave

rage

met

abol

ic c

hang

e ve

ctor

0246810

Higher concentration in cancer

Higher concentration in ‘normals’

Formate*

Succinate**

Acetate*

Lactate

Glycine**Phosphocholine*

*p<0.05**p<0.01

Chemical shift (ppm)

Good news… metabolic fingerprinting identified

several significant metabolic differences

between normal and cancerous tissues.

Challenges…

1. relatively low sensitivity of NMR imposes

restraints on fraction of the metabolome

observed. mass spectrometry can provide

increased sensitivity

2. peak congestion in 1-D NMR spectra limits

ability to resolve metabolites (to uniquely

identify and quantify). 2-D NMR

Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry

9.4 T FT-ICR mass spectrometer.

National High Magnetic Field Laboratory, Florida State Univ.

• Highest performance mass spectrometry technique.

• Ultra-high mass resolution and accuracy.

• Facilitates metabolite identification in complex mixtures.

• Direct injection, ESI, positive ion mode.

-3 -2 -1 0 1 2 3 4-8

-6

-4

-2

0

2

4

Scores on LV 1 (20.18%)

Sco

res

on L

V 2

(26.

52%

)

‘normal’

tumour

PLS scores plot of FT-ICR MS dataConsistent metabolic change induced by cancer in every fish

Conclusions from dab liver study

• Although fish-to-fish variability was large, both NMR and MS identified significant metabolic changes associated with carcinogenesis.

• NMR approach is rapid and robust, but provides less metabolic data (partly due to peak congestion).

• FT-ICR MS provides unprecedented resolution of a larger number of metabolites, but technically more challenging.

• Choice of tool depends upon the biological question being asked.

Case study – Embryogenesis and developmental toxicity in Japanese

medaka eggs

• Illustrate advantages of 2-D NMR approach for

metabolomics

• Show concept of “developmental metabolic

trajectory”

Rationale for study

• Develop tools for screening changes in molecularphenotype in response to developmental toxicants.

• Model organism: Japanese medaka

• Developmental toxicant: trichloroethylene (TCE)

Experimental design

• Expose developing medaka embryos to trichloroethylene throughout 8 day period of embryogenesis.

• Follow metabolic changes as fish develops.

Exposure period (TCE = 0, 800 ppb, 8 ppm)

Day 1 Day 8

Metabolomics

Freeze groups of 100 eggs

Overview of medaka study

Extract eggs using:1. perchloric acid2. acetonitrile:H2O

Raw dataMeasure metabolite

profiles by 1D and 2D NMR spectroscopy

Spectral pre-processing and PCA

NMR spectra of medakaReduced peak congestion using 2-D NMR

STANDARD 1-D 1H NMR spectrum(7 min)

NEWProjection of

2-D J-resolved spectrum(20 min)

Viant, Biochem. Biophys. Res. Comm. 310, 943-948 (2003).

-20 -15 -10 -5 0 5 10 15 20 25-10

-8

-6

-4

-2

0

2

4

6

8Sc

ores

on

PC 2

(10.

16%

)

Day 1

Day 2

Day 3

Day 4Day 5

Day 6

Day 7

Day 8

Developmental metabolic trajectory

Controls

PCA scores plotChanges in metabolome during embryogenesis

Scores on PC 1 (75.18%)

-20 -15 -10 -5 0 5 10 15 20 25-10

-8

-6

-4

-2

0

2

4

6

8Sc

ores

on

PC 2

(10.

16%

)

Day 8

Controls800 ppb TCE8 ppm TCE

PCA scores plotEffects of TCE on developmental trajectory

Scores on PC 1 (75.18%)

p-JRES approach facilitates extraction of more reliable metabolic information

Viant, Biochem. Biophys. Res. Comm. 310, 943-948 (2003).

Conclusions from medaka study

• 2-D NMR (p-JRES approach) and generalized log transformation significantly improved peak resolution.

• Demonstrated “metabolic trajectories” through development of organism, and perturbation induced by external stressor.

• Equivalent approach could be used to map aging of humans, and effects of nutrition on “ideal”metabolic condition.

Strengths and weaknesses of NMR-based metabolomics approach

Minimal sample preparation.

High throughput analysis (200 samples per day?).

Inexpensive per-sample cost.

Robust, semi-quantitative (fully?) analysis.

Non-destructive analysis.

Unbiased identification of 1H-containing metabolites.

Limited sensitivity.

Overall, ideal for screening samples followed by more in-depth analysis of selected samples by mass spectrometry (or…).

Future work….

• Continued development of NMR and MS analytical methods and associated bioinformatics.

• Standardization of data acquisition, data processing and reporting structures (ArMet, SMRS).

• Construction of public-domain metabolite librariesto facilitate biomarker identification???

• Intelligent integration and interrogation of multi-omic datasets recorded from the same samples.

Acknowledgements

Abalone studies (UC Davis)Eric RosenblumRon Tjeerdema

Dab studies (Birmingham)Grant Stentiford (CEFAS)Brett Lyons (CEFAS)Steve Feist (CEFAS)Andy Southam

Medaka studies (UC Davis)Jake Bundy (Cambridge)Chris PincetichRon Tjeerdema

Bioinformatics (UC Davis)David RockeDavid WoodruffParul Purohit

NMR (UC Davis)Jeff de Ropp

FT-ICR MS (Birmingham)Helen CooperAlan Marshall (Florida State)

Funding – U.K.NERC, BBSRC, EU 6th Framework

Funding – U.S.California Sea Grant (NOAA)

UC Toxic Substances Research and Teaching ProgramCalifornia Oiled Wildlife Care Network

NIEHS Center for Environmental Health at UC DavisUC Davis NMR Facility