Ann M. Knolhoff, PhD FDA/CFSAN/ORS [email protected]
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Transcript of Ann M. Knolhoff, PhD FDA/CFSAN/ORS [email protected]
Metabolomics
Goal: complete small molecule characterization
Complementary to other chemical characterizations
Often used to link chemical content to phenotype
Genomics (genes)
Transcriptomics (mRNA)
Proteomics (proteins)
Metabolomics (metabolites)
The Metabolome Includes Diverse Analytes
Amino acids Lipids Steroids Small peptides Carbohydrates Exogenous drugs/metabolites Many others…
Metabolite Profiling vs. Metabolomics
Metabolite profiling– Targeted screening– Can be quantitative– Will miss compounds that
are not on list Metabolomics
– Non-targeted screening– All encompassing
Metabolomic Applications
Disease, injury, and disorders Model organisms Gene modifications Exposure effects
– Drug metabolism– Toxins
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Publications with “Metabolomics” in the Title
Many of these studies are comparative
Metabolomics in Plant Biology
Bigger genomes, bigger metabolome– >200,000 metabolites predicted1
Applications2
– Plant growth & development– Crop quality– Stress responses
o Abiotico Biotic
– Medicinal plantso Natural products
Most Commonly Used Analytical TechniquesNuclear Magnetic Resonance (NMR) SpectroscopyChemical shifts based on structure Structure elucidation Non-destructive Sample preparation is minimal Fast acquisition Can be quantitative Reproducible
Mass Spectrometry (MS)
Mass-to-charge (m/z) ratios of ions 1000s of compounds can be
detected in a single sample Better limits of detection Large dynamic range
Complementary
Chemical Separation Prior to Mass Spectrometry
Gas chromatography (GC)– Interaction with stationary phase– Volatile compounds– Reference spectra
Liquid chromatography (LC)– Interaction with stationary phase– Hydrophilic and hydrophobic
compounds Capillary electrophoresis (CE)
– Size and charge– Polar and ionic compounds
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dsMass Range Searched (Da)
Reference 4
High Resolution Mass Spectrometry (HR-MS)
Accurate mass Molecular formula generation
– 80-90% probability of correct formula with <3 ppm error & <5% absolute isotope ratio3
Resolution of compounds with similar m/z values
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m/z
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+ 3 ppm366.1107-366.1129
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Resolving Power
Mass Spectrometry Imaging
Reference 5
Considerations with Sample Preparation for LC/MS
Sampling and sample sizes Quenching metabolism Replicates Compounds of interest
– Matrix effects Sample and analyte stability Extraction blank Matrix spikes
LC/MS Data Analysis for Metabolite Profiling
Specified analytes Monitor m/z and
retention time Can be done with
a fairly large list of compounds
Retention Time
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Extracted Ion Chromatograme.g., m/z 366.1107-366.1129
LC/MS Data Analysis Challenges for Metabolomics
Hundreds to thousands of m/z values detected in one analysis
Many unknown analytes Background vs sample Different ion types
– Adducts and fragments– Multiply charged ions
Reproducibility (target vs suspect screening)
Non-Targeted Data Analysis Workflow
Componentization
Statistical Analysis, ID Sample Outliers and
Differentiating Compounds
Formula GenerationC16H18N2O4S
Database Searching
Interpretation of Ions
m/z?Adducts?
MS/MS Approaches
∆m/z
Putative ID, Confirm with Analytical Standard
Identification Strategies
QCs should be used to
confirm accuracy of
methods
Modified from Reference 5
Sample complexity– Ion suppression– Matrix effects
Dynamic range Chromatography Mass resolution Data analysis software
Factors That Can Influence Data Output and Quality
Componentization Interpretation of Ions
m/z?Adducts?
Reducing Number of Features is Critical
Feature = one detected compound with all associated ions
100s-1000s of features can be detected in a single sample
May not care about identifying everything
Multiple molecular formulae can be generated for a single feature
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Molecular Formula
SciFinder ChemSpider PubChem Metlin
Reference 4
C3H6N6 C16H18N2O4S C19H28O2
MS/MS Identification
Tandem mass spectrometry (MS/MS) can aid in identifying compounds Used in both targeted and non-targeted methods MS/MS libraries are available
m/z
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m/zIn
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Comparative Analyses Aid in Highlighting Relevant Features
Comparison# of features after
background subtract
# of features after processing
w/ QC*
# of features after processing
w/o QC*Spiked Rolled Oats vs. Same Brand 3024 13 8
Spiked Rolled Oats vs. All Brands 4703 13 1
* Median area >1e5, Adjusted p-value <0.05, Ratio >2
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Spiked Lot, Brand 1Unspiked Lot, Brand 1Brand 1Brand 2Brand 3Brand 4
Irish Oats
Principal Component Analysis (PCA)
Rolled Oats
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PCA, Types of Oats
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Steel Cut
Oat Bran
Oat Flour
Old Fashioned
Rolled
Brand 4Brand 3
PC1
PC2
Non-Targeted Data Analysis Workflow
Componentization
Statistical Analysis, ID Sample Outliers and
Differentiating Compounds
Formula GenerationC16H18N2O4S
Database Searching
Interpretation of Ions
m/z?Adducts?
MS/MS Approaches
∆m/z
Putative ID, Confirm with Analytical Standard
Identification Strategies
QCs should be used to
confirm accuracy of
methods
Modified from Reference 5
Major Points
Challenges in Metabolomics– Data mining– Throughput
Advantages of Metabolomics– Information-rich data sets– Determine molecular differences between control and altered states– Identify molecular targets that can be screened
Metabolomics of GE Foods, Selected Publications
“Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops”
“Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes”
“Metabolic changes in transgenic maize mature seeds over-expressing the Aspergillus niger phyA2”
“Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment”
“Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding”
“Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars”
“An integrated multi-omics analysis of the NK603 Roundup-tolerant GM maize reveals metabolism disturbances caused by the transformation process”
References
1. R.A. Dixon, D. Strack, Phytochemistry meets genome analysis, and beyond, Phytochemistry, 62 (2003) 815-816.
2. J. Hong, L. Yang, D. Zhang, J. Shi, Plant Metabolomics: An Indispensable System Biology Tool for Plant Science, International Journal of Molecular Sciences, 17 (2016) 767.
3. T. Kind, O. Fiehn, Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry, BMC Bioinformatics, 8 (2007) 105.
4. A.M. Knolhoff, T.R. Croley, Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry, Journal of Chromatography A, 1428 (2016) 86-96.
5. A.R. Korte, M.D. Yandeau-Nelson, B.J. Nikolau, Y.J. Lee, Subcellular-level resolution MALDI-MS imaging of maize leaf metabolites by MALDI-linear ion trap-Orbitrap mass spectrometer, Analytical and Bioanalytical Chemistry, 407 (2015) 2301-2309.
Acknowledgments
Tim Croley Christine Fisher Clark Ridge Steve Swatkoski
