Transcript Standardising Analytical Metabonomics

“Standardising Analytical
Metabonomics”
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
Metabolic profiling-analytical metabolomics
Sample collection
Analytical procedure
Data extraction
Data mining
Tools
LC-MS
• Pros
• Sensitive, specific, accurate
• Widely available, several dif
ferent mass analysers and i
onisation possibilities
• Multitude of information
Cons
• Unstable, irreproducible, t
emperamental
• Several different mass analyse
rs and ionisation possibilities.
• Not really robust
Bottlenecks in analytical procedure
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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
Bottlenecks in data treatment
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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
day-to day precision?
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Need for standardization & harmonisation
Establishing guidelines/ SOPs
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Data quality (accuracy and precision of measurements)
QC procedures
Instrument performance and maintenance
Sample collection/storage
Sample treatment
Data acquisition protocols
Data manipulation
How can we validate a metabolic profiling method
when we don’t know the analytes in advance
Integration of classical analytical strategies with
modern unbiased data analysis
•Implementation of QC (pooled study sample analyzed at regular intervals)
•Synthetic mixtures injections
•Randomisation of injection order
•Technical replicates
QC pipeline
Gika et al J Proteome Res 2007
The project
“Standardising Analytical Metabonomics”
Co-funded by European social fund and national sources
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
study design
Project focus
sample collection
sample prep
analytical procedure
analysis
data extraction
data analysis
Data mining, chemometrics
biomarkers IDs
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. 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
Extraction
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
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.
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 (?)
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
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.
Summary
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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.
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