High Resolution GC-MS Application: Metabolomics

Vladimir Tolstikov, PhD

Eli Lilly and Company

Sample Harvest and Storage

Biological Metadata

Sample Extraction Extraction Metadata

Sample Preparation RI internal standards, Derivatization

Sample Analysis QC, randomization

Standard Operational Procedure

Raw Data

Chromatography Metadata Mass Spectrometry Metadata

Metabolite Peak Annotation

Data normalization, background subtraction, detection limit

Analytical Protocols

Processed Data Collection and Organization Statistical Analysis Pathway Analysis

Experiment Submission

A

Volatiles Alchohols Organic acids

Essential oils Amino acids Organic amines

Esters Catecholamines Nucleosides

Perfumes Fatty acids Nucleotides

Terpenes Phenolics Oligosaccharides

Carotenoids Prostanglandins Peptides

Flavanoids Steroids Co-factors

Perfumes Sugar phosphates Polar Lipids

LC/MS GC/MS

PEGASUS GC-HRT accurate mass TOF Gerstel ALEX/CIS MultiPurpose Autosampler

Triple TOF 5600 accurate mass Triple quad 5500

Lilly Metabolomics Platform

Lilly Metabolomics Platform Data Analysis and Visualization

• Statistical analysis: An array of commonly used statistical and machine learning methods :

• univariate -fold change analysis, t-tests, volcano plot, and one-way ANOVA, correlation analysis;

• multivariate - principal component analysis (PCA), partial least squares - discriminant analysis (PLS-DA) and PCA-DA;

• clustering - dendrogram, heatmap, K-means, and self organizing map (SOM));

• supervised classification - random forests and support vector machine (SVM).

• Functional enrichment analysis: The analysis is based on several libraries containing ~6300 groups of biologically meaningful metabolite sets collected primarily from human studies;

• Metabolic pathway analysis: Pathway analysis (including pathway enrichment analysis and pathway topology analysis) and visualization for Human metabolic pathways with a total collection of 1173 pathways;

• Pathway analysis : MetPA, Ingenuity, GeneGo

Human urine GC/MS profiling

Throughput Quality

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Time (min:sec)AIC

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Time (min:sec)AIC

Mouse CSF

Sample volume - 2uL

Methoxyamine, MSTFA 2% TMSCI

1 uL splitless, CIS C4 injector

Detector EI 70ev

>60% probability score

>3000 peaks deconvoluted

>1200 names assigned

~ 75 metabolites identified

Metabolomics study requirements for

GC/MS instruments

GC-HRT

1 Sensitivity √

2 Fast acquisition √

3 Robustness √

4 Reproducibility √

Unique features

1 Routine stable high resolution √

2 Routine stable high mass accuracy √

3 True peak deconvolution √

4 Elemental composition assignment √

High Resolution, High Mass Accuracy: YES or NO ID

High Resolution, High Mass Accuracy: YES or NO ID

High Resolution, High Mass Accuracy: YES or NO ID

Case study Pancreatic Cancer

• PDAC patients - 119 Group 1 • Healthy volunteers – 55 Group 2 • Benign cyst – 41 Group 2a • Chronic pancreatitis – 32 Group 3 • Other cancers – 19 Group 4 Unpaired samples. Blood plasma analysis.

GC/TOF/MS - 70 polar metabolites,

LC/MS/MS (MRM) – panel: Eicosanoids, LPA, SP1, SPA1, Bile acids, PC. 30 non-polar metabolites

Study performed in UC Davis Genome Center, Davis CA, USA

Cohort Study Design

PLS-DA Random Forest

Cross platform data integration: Metabolomics data obtained from current study: 95 metabolites Transcriptomics data was retrieved from Pancreatic Expression Database: 255 genes

Experimental Data

Prediction

Carbohydrate Metabolism, Energy production, Small Molecule Biochemistry

Experimental Data

Carbohydrate Metabolism, Energy production, Small Molecule Biochemistry

Prediction

Small molecule biomarkers

Current study

Univariate Classic ROC analysis for selected metabolite ratios

100 cross validation (CV) were performed and the results were averaged to generate the plot with threshold averaging.

Multivariate ROC analysis (PLS-DA)

The prediction model was composed of 15 features. 21 random samples from each group were allocated as hold-out data for validation.

Group 0 – PDAC patients; Group 1 - controls red circles - predicted scores for hold-out samples Numbers – samples classified to the wrong group

The average accuracy based on 100 cross validations is 0.907. The accuracy for hold out data prediction is 0.905(38/42).

Performance Measure: Area under ROC curve Permutation Times: 100

Multivariate ROC analysis (PLS-DA)

AUC, sensitivity, specificity, and accuracy were 0.965, 95.0%, 95.0%, and 90.0%, respectively, according to the training set data.

• Screening a panel of biomarkers might be effective by embracing the idea that pancreatic adenocarcinoma has vast genetic heterogeneity, meaning no single biomarker exists that is strongly correlated with its diagnosis across the population of people who develop the disease.

• Using a statistical model, it is possible to determine that many of so called weak biomarkers, having 95 percent specificity for the disease, on average, have only a 32 percent sensitivity.

• Increasing number of weak biomarkers it would be possible to achieve required 99 percent sensitivity.

• There is hope for developing a panel that would have greater than 99 percent accuracy.

American Association for Cancer Research, Press Release 2012

Acknowledgments

Prof. Shiro Urayama, MD, UC Davis, Department of Gastroenterology and

Hepatology, Davis, CA, USA Dr. Jean-Noel Billaud, PhD, INGENUITY SYSTEMS, Redwood City, CA, USA Dr. Wei Zou, PhD, Kindra Brooks, BS, UC Davis, Genome Center, Davis, CA, USA