The Nuts & Bolts of NMR Metabolomics:

Transcription

1 The Nuts & Bolts of NMR Metabolomics: from sample preparation to spectral processing Training Workshop on Metabolomics and Nutrition Dublin, Ireland 11 th January 2005 Dr. Mark Viant School of Biosciences University of Birmingham, U.K.

2 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

3 Typical Metabolomics Experiment

4 Control group Diseased or exposed group Tissue or biofluid sample Measure metabolite profiles Mass spectrometry 1 H NMR spectroscopy Spectral pre-processing (ProMetab software) Classification algorithms Metabolic biomarker discovery Mechanistic of action

5 Sample Preparation

6 ORGANISM COLLECT SAMPLE IN MANNER TO PRESERVE METABOLITE CONCENTRATIONS ( Quenching ) SAMPLE EXTRACTION 1. Put sample into format compatible with analysis 2. Retain metabolites you do want 3. Remove biochemicals you don t want Metabolites you want: 1. lipids 2. carbohydrates 3. amino acids 4. other small metabolites Biochemicals you don t want: 1. proteins 2. DNA and RNA 3. salts SPECIFIC MODIFICATIONS TO SAMPLE FOR NMR OR MS ANALYSIS

7 Samples and Sample Preparation for NMR metabolomics Animal or plant Biological fluid (no cells) e.g. urine or CSF store -80ºC Biological fluid (with cells) e.g. plasma anticoagulant = heparin centrifuge to remove cells store -80ºC Tissue e.g. liver freeze immediately in liquid nitrogen store -80ºC Cell culture e.g. neural cells remove media wash cells freeze or extract immediately (below) store -80ºC Biofluid preparation remove proteins? analyze neat or dilute in saline or buffer Liquid Tissue preparation mechanically disrupt tissue hard vs. soft tissue Final preparation for NMR spectroscopy Supernatant 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) aqueous fraction (polar metabolites) - freeze dry - add phosphate buffer (D 2 O, ph 7.4) - add TMSP chloroform fraction (lipophilic metabolites) - remove solvent - add deuterated MeOD:CDCl 3 solvent - add TMS

8 1 H NMR Spectroscopy

9 NMR Metabolomics Experiments 1-D NMR - for measurement of metabolite fingerprints Single pulse 1 H NMR sequence CPMG 1 H spin-echo sequence 2-D NMR - for measurement of metabolite fingerprints projections from J-Resolved spectra 2-D NMR - for confirmation of peak assignments 1 H- 1 H correlation spectroscopy (COSY) 1 H- 13 C heteronuclear single quantum coherence (HSQC)

10 Typical 1-D 1 H NMR spectrum of tissue extract Nucleotides, e.g. ATP Carbohydrates, e.g. ribosyl moiety Organic acids, e.g. succinate Amino acids, e.g. tyrosine chemical shift (ppm)

11 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, (2003)

12 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 abalone Severe atrophy of foot muscle

13 Experimental design: 19 age matched abalone 9 healthy abalone 5 diseased abalone 5 stunted abalone foot muscle tissue digestive gland tissue hemolymph

14 Collect and freeze foot muscle Overview of abalone study Perchloric acid extraction of polar metabolites 1D 1 H NMR spectroscopy Raw data Spectral preprocessing and PCA

15 1-D 1 H NMR spectra of foot muscle extract Diseased abalone taurine Healthy abalone Intensity glycine-betaine zoom TMSP Chemical shift (ppm) 1 0

16 1-D 1 H NMR spectrum of foot muscle extract Intensity Expanded region dimethylglycine acetate expand alanine Chemical shift (ppm) 2.46 Intensity hypotaurine carnitine arginine extremely congested spectra with hundreds of overlapping peaks Chemical shift (ppm)

17 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 = ppm (user defined) 10x higher resolution than most studies (b) remove TMSP and water peaks. (c) various normalization options. (d) generalized log transformation.

18 Step 1 - Pre-processing of NMR spectral data diseased abalone healthy abalone ProMetab software Intensity segment into frequency bins Chemical shift (ppm) 1 water removed TMSP removed Bin #

19 Step 2 - Multivariate analysis of preprocessed 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.

20 PCA scores plot of abalone foot muscle 5 4?WS stunted 3?WS?WS +WS PC 2 (20.00%) healthy -WS -WS -WS -WS -WS -WS -WS -WS -WS?WS?WS +WS diseased +WS +WS +WS PC 1 (68.08%)

21 Generalized log transformation Multiple spectra Before After Purohit, Rocke, Viant and Woodruff, OMICS 8, (2004)

22 PCA scores plots of foot muscle metabolite profiles without transformation stunted diseased healthy diseased with generalized log transformation stunted healthy

23 Section of PCA loads plot - Diagnostic biomarker profile for withering syndrome 0.03 diseased abalone homarine Loadings on PC 1 (68.08%) AMP healthy abalone ATP formate tryptophan phenylalanine tyrosine Chemical shift axis (bin #)

24 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)

25 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.

26 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.

27 Experimental design preliminary study N=9 dab with liver tumours healthy liver sample macroscopic liver tumour

28 Dissect paired liver samples Overview of dab study Extract tissue using MeOH:chloroform 1-D 1 H NMR spectroscopy and mass spectrometry Raw data Spectral pre-processing followed by PCA and PLS

29 PLS scores plot of NMR data Consistent metabolic change induced by cancer in every fish 40 LV2 axis (15.3%) one fish Average metabolic change vector Cancer Normal LV1 axis (32.1%)

30 PLS weightings plot of NMR data Potential biomarker profile for dab liver cancer Weightings for average metabolic change vector 10 Higher concentration in cancer Formate* Higher concentration in normals 8 6 Succinate** Glycine** 4 Acetate* 2 Lactate Phosphocholine* *p<0.05 **p< Chemical shift (ppm)

31 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

32 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.

33 PLS scores plot of FT-ICR MS data Consistent metabolic change induced by cancer in every fish 4 Scores on LV 2 (26.52%) normal tumour Scores on LV 1 (20.18%)

34 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.

35 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

36 Rationale for study Develop tools for screening changes in molecular phenotype in response to developmental toxicants. Model organism: Japanese medaka Developmental toxicant: trichloroethylene (TCE)

37 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

38 Freeze groups of 100 eggs Overview of medaka study Extract eggs using: 1. perchloric acid 2. acetonitrile:h 2 O Measure metabolite profiles by 1D and 2D NMR spectroscopy Raw data Spectral preprocessing and PCA

39 NMR spectra of medaka Reduced peak congestion using 2-D NMR STANDARD 1-D 1 H NMR spectrum (7 min) NEW Projection of 2-D J-resolved spectrum (20 min) Viant, Biochem. Biophys. Res. Comm. 310, (2003).

40 PCA scores plot Changes in metabolome during embryogenesis 8 6 Developmental metabolic trajectory 4 Day 5 Scores on PC 2 (10.16%) Day 3 Day 4 Day 6 Day 7-6 Day 2 Controls Day 8-8 Day Scores on PC 1 (75.18%)

41 PCA scores plot Effects of TCE on developmental trajectory Scores on PC 2 (10.16%) Day Controls 800 ppb TCE 8 ppm TCE Scores on PC 1 (75.18%)

42 p-jres approach facilitates extraction of more reliable metabolic information Viant, Biochem. Biophys. Res. Comm. 310, (2003).

43 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.

44 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 1 H-containing metabolites. Limited sensitivity. Overall, ideal for screening samples followed by more in-depth analysis of selected samples by mass spectrometry (or ).

45 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 libraries to facilitate biomarker identification??? Intelligent integration and interrogation of multiomic datasets recorded from the same samples.

46 Acknowledgements Abalone studies (UC Davis) Eric Rosenblum Ron Tjeerdema Dab studies (Birmingham) Grant Stentiford (CEFAS) Brett Lyons (CEFAS) Steve Feist (CEFAS) Andy Southam Medaka studies (UC Davis) Jake Bundy (Cambridge) Chris Pincetich Ron Tjeerdema Bioinformatics (UC Davis) David Rocke David Woodruff Parul Purohit NMR (UC Davis) Jeff de Ropp FT-ICR MS (Birmingham) Helen Cooper Alan Marshall (Florida State) Funding U.K. NERC, BBSRC, EU 6 th Framework Funding U.S. California Sea Grant (NOAA) UC Toxic Substances Research and Teaching Program California Oiled Wildlife Care Network NIEHS Center for Environmental Health at UC Davis UC Davis NMR Facility