1. “ Standardising Analytical Metabonomics ”

2. AUTh bioAnalytical group Metabonomics Fundamental / Developmental work New Methods (Targeted, Untargeted) New Materials Validation Clinical Studies Rheumatoid Arthritis Physical Exercise Frailty EmbryoMetabolomics Sepsis/NEC newborns

3. Metabolic profiling-analytical metabolomics Analytical procedure Sample collection Data extraction Data mining

4. Cons Unstable, irreproducible, te mperamental Several different mass analyse rs and ionisation possibilities. Not really robust Pros Sensitive, specific, accurate Widely available, several dif ferent mass analysers and i onisation possibilities Multitude of information LC-MS Tools

5. Bottlenecks in analytical procedure LC-MS instrumentation variability: Drifts in Rt, mass, sensitivity Need for long analytical batches Unknown trends /unknown components-analytes Instrument calibration along the run Different instrumentations/architecture Wide spectrum of analytes Huge span in concentration: 7 orders of magnitude Full scan mode aqcuisition

6. Bottlenecks in data treatment Big datasets Impractical to correlate-combine data Various pick picking and treatment algorithms Filtering of noise analytically-oriented lack of data repositories and databases lack of commercial or wide-use LC-MS spectra libraries metID Analytical Chemists, Informaticians, Chemometricians, biochemists still speak different language

7. - day-to day precision?

8. Need for standardization & harmonisation Establishing guidelines/ SOPs Data quality (accuracy and precision of measurements) QC procedures Instrument performance and maintenance Sample collection/storage Sample treatment Data acquisition protocols Data manipulation

9. How can we validate a metabolic profiling method when we don’t know the analytes in advance Implementation of QC (pooled study sample analyzed at regular intervals) Synthetic mixtures injections Randomisation of injection order Technical replicates Integration of classical analytical strategies with modern unbiased data analysis

10. QC pipeline Gika et al J Proteome Res 2007

11. The project “Standardising Analytical Metabonomics” Co-funded by European social fund and national sources

12. Scope To promote standardization and quality control To address major bottlenecks in analytical practice (development of advanced analytical methodologies for MS-based metabolic profiling) To develop informatics tools to improve the quality of the extracted information

13. analytical procedure sample collection data extraction data analysis study design Data mining, chemometrics biomarkers IDs sample prep analysis Project focus

14. WP1: Development of Analytical Methodologies Profiling methods with complementary/orthogonal selectivities - HILIC/MS-MS for quantitative determination of ca. 140 primary metabolites -Implementation of other HILIC chemistries eg zwitterionic, diol, RP-WAX - Computational approach for column selection for metabolic profiling Protocols for sample extraction - Optimization studies on extraction of feces samples, tissue etc (e.g. different pH values, organic solvent composition, mass to volu me ratio) -Liquid and Solid Phase Extraction (SPE) assays for the fractionation of the extract minimising ion suppression effects/compatibility with MS Derivatisation conditions optimization for GC-MS Method robustness Extraction efficiency Metabolome coverage

15. Extraction

16. WP 2: Data extraction Evaluation of various data extraction software (free and commercial: XCMS, MarkerLynx, MarkerView, Profiler and others) in real metabonomics studies. Spiking experiments (comparison of sensitivity and reliability of the data treatment software) Development of intranet platform for the extraction of information from MS-profil ing data (rules for monitoring and reporting the various alterations and parameter selection to improve standardization in data extraction and reporting

17. WP3: Quality Control and standardisation protocols Scripts for QC in holistic MS data Examine data in depth and applying rules by automated scripts Correction for retention time drift to improve peak alignment in feature detection. Unifying these utilities in one program to standardize promote and consolidate quality control and minimize error possibilities.

18. WP 4: Data fusion Software tools to fuse data from different methods LC-MS/MS + GC-MS LC-MS/MS + NMR HILIC-MS + RPLC-MS +evi ESi/ -evi ESI link data combine into one table of features or metabolites (?)

19. WP5: Metabolite Identification MetID the major bottleneck in LC-MS metabonomics scripts for adduct identification to reduce the number of detected features : +Na +, + NH4 +, dimers etc MS spectra by analysis of standards (in-house MS databa se). Scripts for automated searches in local and internet-bas ed spectral/biochemistry libraries. Compare isotope patterns between peaks in samples an d standards

20. WP6 : Retention Time Prediction Incorporating Rt data to assists MetID Use of data from orthogonal chromatographic systems: chemical information (polarity, LogP etc) Rule out candidate IDs Retention time prediction algorithm in HILIC software to organise the necessary analyses and data treatment for metID within an easy to use platform.

21. Summary Strong need for Standardisation LC-MS is a major part of the solution (and the problem!) Metabolomics is analytically dependent Intelligent tools are needed to go through data and efficiently check data quality The major aim is to find biomarkers – when you’ve found them the real work begins.

22. Auth Dr. H. Gika Dr. G. Theodoridis Prof. A. Papa Dr. N. Raikos Dr. C. Zisi Dr. C. Liambas O. Deda MSc S. Fasoula MSc A. C. Hatzioannou MSc D. Palachanis MSc C. Virgiliou MSc I. Sampsonidis MSc External collaborators I. D. Wilson Imperial college London UK P. Vorkas Imperial college London UK P. Francheshi IASMA Trento Italy The group