QTOF-based proteomics and metabolomics for the agro-food chain.
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1 QTOF-based proteomics and metabolomics for the agro-food chain
2 Metabolomics Two scenarios identification of known unknowns and unknown unknowns For known unknowns use spectral or metabolite libraries to ID and quantify via spectral deconvolution For unknown unknowns (truly novel) use computer-aided structure elucidation methods (CASE) there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns -- the ones we don't know we don't know.
3 The Pyramid of Life Physiological Influence Environmental Influence Metabolome Proteome Metabolomics Proteomics Genomics Genome
4 Metabolomics Workflow Biological or Tissue Samples Extraction Biofluids or Extracts ppm Data Analysis Chemical Analysis
5 # Metabolites or Features detected (Log 10 ) Knowns Technology & Sensitivity NMR GC-MS TOF GC-MS Quad M mm µm nm pm fm Sensitivity or LDL Unknowns LC-MS or DI-MS
6 QTOF-based METABOLOMICS
7 High Resolution MS Metabolomic profile Physiology Reaceability/adulteration Biomarkers
8 Identification / screening Scoring based on: Monoisotopic mass (varies in ppm) Mass Match + Isotope distribution (varies in %) Isotope spacing (varies in ppm) Abund. Match + Spacing Match = Overall Score 8
9 Identification / screening Peak volume: - Extracted ion current from total signal Isotopic profile: - Accurate mass - Accurate isotope spacing - Isotopes ratio
10
11 From meaningless to meaningfull data Raw data Huge amount of information Redundancy? False positive? dataset Data gathering Alignment and filtering Interpret. Statistics Chemometrics & bioinformatics Answer(s) to a biological problem?
12 Alignment and filtering in Mass Profiler Professional 1. Alignment (optionally using internal standards) - Retention time tolerance 2. Filter by mass tolerance 3. Remove irreproducible compounds Filter by Frequency 4. Remove weak compounds Filter by Abundance 5. Remove highly variable compounds Filter by Sample Variability
13 SPECTRAL LIBRARIES BASED METABOLOMICS (GC-MS)
14 Electron impact MS spectra
15 Need for derivatization
16 Metabolite ID by GC-MS GC -MS total Ion chromatogram
17 Spectral comparison
18 PROTEOMICS
19 Bottom-up proteomics
20 nanolc-ms-ms Ionized parent peptide Fragmentation
21 Protein ID (database search)
22 DATA REDUCTION and INTERPRETATION
23 Data analysis Data collected represented in a matrix Chemometric Approach Principle Component Analysis (PCA) Partial Least-Squares (PLS) Method Orthogonal PLS (OPLS) Targeted Profiling
24 Unsupervised cluster analysis
25 Volcano analysis ANOVA (p=0.01) Fold-change = -3 Fold-change = +3
26 PCA Unsupervised Multivariate analysis based on projection methods Extract and display the systematic variation in the data Each Principle Component (PC) is a linear combination of the original data parameters PCs Orthogonal to each other Conversion of original data leads to two matrices, known as scores and loadings The scores(t) represent a low-dimensional plane that closely approximates X. Linear combinations of the original variables. Each point represents a single sample spectrum. A loading plot/scatter plot(p) shows the influence (weight) of the individual X-variables in the model. Each point represents a different spectral intensity. The part of X that is not explained by the model forms the residuals(e) X = TP T = t 1 p 1 T + t 2 p 2 T E
27 OPLS OPLS method is a recent modification of the PLS method to help overcome pitfalls Main idea to seperate systematic variation in X into two parts, one linearly related to Y and one unrelated (orthogonal). Comprises two modeled variations, the Y-predictive (T p P T p ) and the Y- orthogonal (T o P T o ) compononents. Only Y-predictive variation used for modeling of Y. OPLS-DA compared to PLS-DA
28 APPLICATIONS
29 Effect of abiotic stress on plant metabolome (Zn vs salinity vs control) Fold-change analysis let to perform: Unsupervised cluster analysis Venn diagrams Front. Plant Sci. 9:472. doi: /fpls
30 Effect of abiotic stress on plant proteome (Zn vs salinity vs control) Front. Plant Sci. 6:240. doi: /fpls
31 Mycorrhiza and mitigation of Cd stress in plant (metabolomics) Front. Plant Sci. 6:477. doi: doi: /fpls
32 Mycorrhiza and mitigation of drought in plant (proteomics) J Prot. 169, 21-32; DOI: doi.org/ /j.jprot
33 Discrimination of different processing technologies Unsupervised cluster analysis Pulp Puree Pastes Partial Least Square Discriminant Analysis (PLS-DA) Pastes Pulp Puree Lucini, Rocchetti, Kane, & Trevisan. Food Control, 2017 Model accuracy: 100%
34 Traceability of POD grana cheese Altri DOP bactofugation
35 Authenticity: use of palm oil
36 Food Res. Int., 97 (2017). doi: /j.foodres Fermentation patterns of gluten free flours 4 3 Amaranth Flavanone s 2 1 Flavanols logfc h 8h time Flavonols -3-4
37 Different biostimulants and plant stress
38 Effect of diet restriction on blood metabolome in lambs
39 Front. Plant Sci. 9:472. doi: /fpls hormone-like activity of biomass-derived extracts The mechanism towards modulation of melon root growth by a biopolymer was elucidated through metabolomics
40 Molecular Structure Correlator Systematic bondbreaking approach [Hill and Mortishire- Smith, 2005] to correlate tandem MS data with chemical structures Scores are generated from each product ion, the mass accuracy of the fragments, and the overall percentage of ions intensity being plausibly explained with sub-structures
41 Structural elucidation to discriminate isobaric compounds Structure elucidation & metabolite ID (pesticide degradation)
42 Structural confirmation Confirm structure of selected differential metabolites
43 Identification of compounds in inks for food packaging material has been possible using MSC interfaced to ChemSpider From totally unknown to structures
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