2. AUTh bioAnalytical group
Fundamental/Developmental work
‘’Standardising Metabolomics’’ Excellence Grant GSRT
• Validation
• New Methods (Targeted, Untargeted) DOES IT WORK?
• New Chromatographic Materials
Clinical Studies
• Rheumatoid Arthritis Fleming Institute Prof. G. Kollias
• Physical Exercise Prof. V. Mougios AUTh
• Frailty/Ageing Prof. V. Mougios AUTh
• EmbryoMetabolomics http://www.embryometabolomics.eu/
• Sepsis/NEC newborns with Hippokrateion Hosp. Intens. Care Unit
3. Systems Biology and Metabolomics
3
‘’the systematic study of the unique chemical fingerprints
that specific cellular processes leave behind’’
Holistic Analysis of small molecules
4. Source: Considerations in the design of clinical and epidemiological metabolic phenotyping studies
G Theodoridis et al 2013, ebook Metabolic profiling in clinical applications. doi:10.4155/EBO.13.487
5. analytical procedure
sample collection
data extraction
data analysis
study design
Data mining, chemometrics
biomarkers IDs
sample prep
analysis
Analytical focus
Develop specific
assay
6. Bottlenecks in analytical procedure
• Wide spectrum of analytes (unlike genomics)
• Huge span in concentration: 7 orders of magnitude
• MS: Different instrumentation architecture
• Need for long analytical batches
• clean up steps : when? Can I combine data?
• Instrument calibration along the run: DISASTER !
• LC-MS instrumentation variability: Drifts in Rt, mass, sensitivity
• Ionisation in Mass Spectrometry not controlled
• Lack of LC-MS spectral libraries
7. Bottlenecks in data treatment
• Big datasets
• Impractical to correlate-combine data
• Various peak picking and treatment algorithms
• data repositories and databases still immature
• metID (>4 years trying to identify candidate ma
rkers, G. Patti, Bioanalysis 2012)
8. Major Problems
• Analytical Chemists, Informaticians, Chemometricians,
Biochemists still speak different language
• Fragmentation of research
• Genomics labs can split tasks /Metabolomics labs can’t
trust other peoples results
9. Way to go?
Standardization & Harmonisation
Establishing SOPs
• Data quality, QC procedures
• Instrument performance and maintenance
• Sample collection/storage
• Sample treatment
• Data acquisition protocols
• Data manipulation
• Reporting
10. QC procedures
• How can we validate a metabolic profiling method
when we don’t know the analytes that will be analysed?
• How can precision and reproducibility be assessed when we
don’t know what we are measuring?
• How can we report data quality?
• What analytical protocols should be adapted ?
• Which method is good?
11. QC procedures
Integration of “classical” analytical strategies
with unbiased data analysis
• Implementation of QC
Pooled sample, Injected in-between samples
• Synthetic mixtures injections
• Randomisation of injection order
• Technical replicates and other measures…
12. QC strategy: example 1 Raw data, TIC across all samples
QC samples
Sensitivity drift
15. Aim 1: New analytical methodologies
• Profiling methods with complementary/orthogonal selectivities
Sampsonidis P2-04
• Protocols for sample extraction
Optimization studies on extraction of samples, (e.g. different
pH values, organic solvent composition, mass to volume ratio)
method robustness
extraction efficiency
metabolome coverage
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
21. Aim 2: Data extraction
• Evaluation of various data extraction software free and commercial:
XCMS, MarkerLynx, MarkerView, Profiler and others in metabonomics studies
• Spiking experiments (comparison of sensitivity and reliability of the data
treatment software) A. Pechlivanis, MSc Study 2009, AUTh
• Intranet platform for the extraction of information from MS-profiling data
(rules for monitoring and reporting the various alterations and parameter
selection to improve standardization in data extraction and reporting)
22. Aim 3: Quality Control and standardisation protocols
• Scripts for QC in holistic MS data
• Examine data in depth and applying rules by automated scripts
(Matlab and R)
• Correction for sensitivity loss (?scaling?)
• Correction for retention time drift to improve peak alignment
in feature detection Zelena et al Anal Chem 2009
23. Aim 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 (?)
24. Aim 5: 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 searches in local and internet-
based spectral/biochemistry libraries.
• Compare isotope patterns between peaks in samples
and standards
25. Aim 6 : 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
Gika et al Anal Bioanal Chem 2012, Gika et al J. Sep. Sci 2011
Fasoula OP12, P2-03
• software to organise the necessary analyses and data
treatment for metID within an easy to use platform.
26. What do metabolomics offer ?
Biochemistry insight
Bio-Markers
Time frame
Physiology
Healthy stage
Diseased no treatment
Diseased with treatment
Diseased non respondant
healthy treated
Drug efficacy
Disease
Toxicity
Onset of disease
Clinical
symptoms
Diagnosis/
therapeutic
intervention
Potential for the discovery of biomarkers
Additional knowledge of the biochemical pathways
27. Perspective
• Metabolites downstream the biochemical pathway compared
to genes, proteins, closer to phenotype
• Can describe effects of xenobiotics (e.g. pharmaceuticals) and
host-guest interactions (e.g mammals with gut microflora)
• Describes ongoing phenomena
29. Call
• Metabolomics is analytically dependent
• Metabolomis grows and provides openings for analytical chemists
30. Auth
• Dr. G. Theodoridis
• Dr. H. Gika
• Prof. A. Papa
• Dr. N. Raikos
• C. Virgiliou MSc
• O. Deda MSc
• Dr. C. Zisi
• S. Fasoula MSc
• A. C. Hatzioannou MSc
• D. Palachanis 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
Funding