“ Standardising Analytical Metabonomics ”

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 Sample collection Data mining Analytical procedure Data extraction

4. Tools LC-MS Cons • Unstable, irreproducible,temperamental • Several different mass analysers and ionisation possibilities. • Not really robust • Pros • Sensitive, specific, accurate • Widely available, several different mass analysers and ionisation possibilities • Multitude of information

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

10. QC pipelineGika 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 forMS-based metabolic profiling) • To develop informatics tools to improve the quality of the extracted information

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

14. WP1: Development of Analytical Methodologies Method robustness Extraction efficiency Metabolome coverage • Profiling methods with complementary/orthogonal selectivities -HILIC/MS-MS for quantitative determination of ca. 140primary 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 tovolume 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

15. Extraction

16. WP 2: Data extraction • Evaluationof various dataextractionsoftware(free and commercial: XCMS, MarkerLynx, MarkerView, Profiler and others) in realmetabonomicsstudies. • Spikingexperiments (comparisonof sensitivity and reliability of thedata treatmentsoftware) • Development ofintranetplatformfor the extraction ofinformationfrom MS-profilingdata(rules for monitoring and reporting thevariousalterations and parameterselectiontoimprovestandardization indataextraction 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 retentiontimedrifttoimprovepeakalignment in featuredetection. • Unifyingtheseutilities in oneprogramtostandardizepromote and consolidatequalitycontroland minimizeerrorpossibilities.

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 database). • Scripts for automated searchesin local and internet-based spectral/biochemistry libraries. • Compare isotope patterns between peaks in samples and standards

20. WP6 : Retention Time Prediction • IncorporatingRtdatato assistsMetID • Use of data fromorthogonalchromatographic systems: chemicalinformation (polarity, LogPetc) • Rule out candidate IDs Retentiontimepredictionalgorithm in HILIC • softwareto organise the necessaryanalyses and data treatmentfor metIDwithinaneasytouseplatform.

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. The group 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