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Metabolomics Technology Development LDI-MS (UK EPSRC/RSC); SERS (UK BBSRC) Imaging PowerPoint Presentation
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Metabolomics Technology Development LDI-MS (UK EPSRC/RSC); SERS (UK BBSRC) Imaging

Metabolomics Technology Development LDI-MS (UK EPSRC/RSC); SERS (UK BBSRC) Imaging

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Metabolomics Technology Development LDI-MS (UK EPSRC/RSC); SERS (UK BBSRC) Imaging

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  1. Surface-enhanced Raman Scattering for MetabolomicsRoger Jarvis & Roy School of Chemistry & Manchester Interdisciplinary Biocentre, The University of Manchester Levels of functional genomics • Metabolomics • E. coli stress (BBSRC & AZ); recombinant mammalian cells (BBSRC); Oral cancer (EPSRC); Psoriasis (Stiefel Labs); META-PHOR (EU FP6); Biotrace IP (EU FP6); Plants (BBSRC) • Systems Biology • STREPTOMICS (EU FP6); SYSMO (EU/BBSRC) • Metabolomics Technology Development • LDI-MS (UK EPSRC/RSC); SERS (UK BBSRC) • Imaging • MALDI imaging (Shimadzu); Raman, FT-IR imaging (ORS); SIMS (UK BBSRC) • Bacterial Identification • SERS (UK HOSDB) Metabolomics The analysis of metabolites (typically low molecular weight molecules) in a biological organism at a given time, with the aim of elucidating gene function and defining biochemical pathways. The Metabolome “The total biochemical composition of a cell, tissue or organisms at any given time (Oliver et al., 1998).” Laboratory for Bioanalytical Spectroscopy (

  2. Mainly E. coli, S. cerevisieae Current Knowledge of (most) fundamental metabolic processes Measure cell components with MS, FTIR, GCMS Develop understanding to investigate metabolic network regulation Need Grow mutant & WT cells under different conditions Develop understanding of responses to genetic or environmental influences Ultimate Goal Functional genomics Determine gene function (including Bioinformatics) Why study the metabolome? Functional Genomics aims to assign (new) functions to (uncharacterised) genes. “Genomics and proteomics tell you what might happen, but metabolomics tells you what actually did happen.” Bill Lasley, University of California, Davis.

  3. Metabolite analysis. Metabolic Fingerprinting Crude metabolite mixtures for classification. (FT-IR/Raman/DIMS) Selection of technology is a compromise between speed, selectivity and sensitivity. SER(R)S Metabolite target analysis Analysis of specific metabolites. Metabolic profiling Quantification of pre-defined targets. (GC-MS, LC-MS, NMR, HPLC, LC/MS/MS) Four Approaches SER(R)S ?? SER(R)S Particular interest in low molecular weightcompounds – the substrates and products in pathways. Metabolomics Unbiased identification of all metabolites in sample. (Fiehn, 2001)

  4. SERRS Reproducibilty • We want to use SERRS as a metabolic profiling and fingerprinting tool • We know that there is a question mark over reproducibilty • Metabolomics requires quantitatively accurate data • Therefore we have been looking at strategies for assessing objectively, the reproducibility of our SERRS experiments

  5. Colloidal Batch-Batch Reproducibility • 3 replicate absorbance measurements • l (absorption) max. - larger value equates to a larger particle size • FWHH (full width at half height), a larger FWHH indicates wider particle size distribution. • Extinction - lower value for the extinction indicates greater aggregation Colloids prepped by Emma Oleme and Arunkumar Paneerrselvam

  6. SERRS spectra of Cresyl Violet Mean SERRS spectra of cresyl violet acquired using the four colloidal substrates that were found to be SERRS active.

  7. Signal-to-noise ratios (S/N) observed in the median SERRS spectra of cresyl violet

  8. MANOVA on the S/N ratios calculated from the SERRS bands identified in spectra of cresyl violet, from four active substrates

  9. Quantification of Cresyl Violet using SERRS • Bootstrapped correlation analysis for the log-log relationship to area under the cresyl violet SERRS band at 930 cm-1 • Dilution series from 5 x 10-6 M to 5 x 10-2 M, using the • PVP capped colloidal silver substrate.

  10. Next question – we can find colloids that give statistically reproducible batch to batch SERS – but what happens when we start playing with chemistry? Potassium choride Sodium chloride Potassium nitrate Sodium nitrate Optimisation of cytosine SERS

  11. Cytosine

  12. Optimisation of surface-enhanced Raman scattering (SERS) experiments Roger Jarvis, William Rowe, Nicola Yaffe, Sven Evans, Joshua Knowles, Ewan Blanch & Roy Goodacre

  13. Experimental Pseudo Full-Factorial Experiment • 3 colloidal silver preps at 25, 50 & 75% v/v • hydroxylamine, citrate, borohydride • 6 aggregating agents at 1, 10 & 100 mM • NaCl, KCl, Na2SO4, K2SO4, NaNO3, KNO3 • 785 nm NIR Raman probe, 3 s integrations with ~ (Goodness knows what!!) mW power a source, spectral range (150 - 2900 cm-1) • Single analyte – L-cysteine (100 mM) • Total of 162 experiments,5 replicate measurements for each giving 810 SERS spectra This allows us to determine the “optimal” experimental conditions

  14. Cont… Multiobjective optimisation • Questions • Can we use this experiment to determine the utility of an directed search algorithm for optimising these conditions more rapidly? • Could some form of interpolation be used to derive further experiments that yield superior results? • Objective functions • Reproducibility: standard deviation of the Mahalanobis distance between principal component scores recovered from replicate spectra • Signal intensity: peaks areas calculated for 4 major bands and meaned across replicates

  15. Published results: GC-TOF mass spectrometer optimization via PESA-II O’Hagan,S., Dunn, W.B., Brown, M., Knowles, J.D. and Kell, D.B. (2005)Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Analytical Chemistry 77(1): 290-303. PESA-II used to optimize the settings of a mass-spectrometer to improve the chromatograms. • Optimized: • - Number of true peaks • - Signal-to-noise ratio • - Sample analysis time - throughput

  16. Typical SERS spectrum of L-cysteine and Raman bands for which peak areas were calculated C-S Red – shifted due to binding at silver surface

  17. Summary of metrics calculated to quantify signal reproducibility and intensity of enhancement Homogeneous distribution Skewed Distribution

  18. Exp. Colloid Amount (% v/v) Agg. Agent Conc. (mM) Enhancement M. dist. 45 Hydroxylamine 75 K2SO4 100 662.0311 0.8539 36 Hydroxylamine 75 NaNO3 100 779.4253 0.7642 54 Hydroxylamine 75 KNO3 100 675.0239 0.6618 Summary of Results Pareto front

  19. Multiobjective Pareto optimisation using the PESA II algorithm • Find solutions which give best trade-off between 2 objectives • PESA II is a region based Pareto selection algorithm • Select a region or hypercube • Randomly select individual from this subset • Problem!! Our solution space is quite sparse and disperse!! • Analysis to be completed, however: • Directed search optimises experimental conditions in 60 iterations • Interpolation attempted but hasn’t improved SERS

  20. Group Leader: Professor Roy Goodacre Postdocs: Dr Will Allwood, Dr Robert Cormell, Dr Elon Correa, Dr Roger Jarvis, Dr Yankuba Kassama, Dr Iggi Shadi, Dr Catherine Winder, Dr Yun Xu.With Collabs: SERS (4), Metabolomics (2), ToF-SIMS (2) Research Technicians: Steffi Schuler, Richard O’Connor PhD Students: Felicity Currie, Katherine Hollywood, Nicoletta Nicolaou, Soyab Patel, Ketan Patel, Emma Wharfe, Nicola Wood, Dong Hyun Kim, Will Cheung, Robert Coe.