Exploratory Wine Study Using SIEVE 2.0. Michael Athanas, Ph.D. VAST SCIENTIFIC

Transcription

1 Exploratory Wine Study Using SIEVE 2.0 Michael Athanas, Ph.D. VAST SCIENTIFIC

2 BRIMS Biomarker Research Initiative in Mass Spectrometry Amol Prakash Jennifer Sutton Informatics Center of Excellence Project Manager Assoc. Director Informatics Center of Excellence David Sarracino Manager, Biomarker Workflows Mary Lopez Director Bryan Krastins Leader Biomarker Translational Center Michael Athanas Assoc. Director Informatics Center of Excellence brims.center

3 SIEVE 2.0 New Features Beta release now available Component Elucidator Algorithm 64 bit Enhanced multi-threading Interoperability with Protein Center New hierarchal component view Dynamic framing PerfectPair wizard Integrated raw file explorer Enhanced frame target handling Much more.

4 2008 Wildfires and Wine Over 2790 individual wild fires Weather conditions: 3 years of below normal rainfall Lightning Poor air quality

5 13 Data Samples # Blend Location 1 zinfandel Lake 10 petite sirah Lake 13 zinfandel Lake 36 cabernet sauvignon Mendocino 37 petite sirah Mendocino 2 cabernet franc Napa 3 cabernet franc Napa 20 petite verdot Napa 21 cabernet franc Sonoma 25 cabernet sauvignon Sonoma 33 merlot Sonoma 35 merlot Sonoma 44 cabernet sauvignon Sonoma Small and diverse samples

6 Sample Processing 13 samples direct LC injection LC/MS using Thermo Q-Exactive Open Accela 1250 Triplicate measurements interspersed by single matrix blank measurements Data analysis with SIEVE 2.0

7 SIEVE Analysis Platform Statistically rigorous automated label-free LC/MS differential analysis platform State 1 Raw file State 2 raw file State raw file Workflow Align Detect Identify Reports: Components Identification Relative Quantitation Statistical Analysis Trend information Applied to: peptide, protein, small molecule data

8 Align Detect Identify SIEVE WORKFLOW

9 SIEVE Workflow Alignment 1 1. Full scan spectra are typically acquired at 1Hz to 100Hz with high mass accuracy (<5ppm). Intensity Data File 1 Reference M/Z Intensity Data File 2 M/Z

10 SIEVE Workflow Alignment 2 1. Full scan spectra are typically acquired at 1Hz to 20Hz with high mass accuracy (<5ppm). Intensity Data File 1 Reference 2. The spectra are binned. M/Z Intensity Data File 2 M/Z

11 SIEVE Workflow Alignment 3 1. Full scan spectra are typically acquired at 1Hz to 20Hz with high mass accuracy (<5ppm). 2. The spectra are binned. 3. A dot product correlation is calculated between each pair of spectra X Data File 1 Reference Data File 2

12 SIEVE Workflow Alignment 4 Scan # data file 1 Scan-to-scan correlation: Red High Green Low Scan # data file 2

13 SIEVE Workflow Alignment 5 An overlapping tile is constructed from the next region starting from the middle of the optimal path.

14 SIEVE Workflow Alignment 6 An overlapping tile is constructed from the next region starting from the middle of the optimal path. The full plane is tiled and a final alignment score is calculated. Overlapping measurements are averaged

15 SIEVE Workflow Alignment 7 Unaligned basepeaks Wine

16 SIEVE Workflow Alignment 8 Aligned basepeaks Wine Alignment scores

17 Background Subtraction Sample - Solvent blank = Analyte signals

18 Component Detection Adducts, fragments and multimers , z=1, I=4.2E+08, 100% , z=1, I=1.0E+08, 24.6% , z=1, I=1.1E+06, 0.3% [M+H]+ [M+Na]+ [M+K]+ Isotopic peaks A , I=1.2E+08, 28.9% A , I=2.3E+07, 5.5% A+3 A , I=3.0E+06, 0.7% , I=3.9E+05, 0.1% A+1 Isotopic peaks A , I=2.9E+07, 27.8% , I=5.6E+06, 5.4% A , I=9.0E+05, 0.9% Constituents are represented by base component

19 Component Efficiency Spiked standards in 9 out of the 10 compounds were identified using default settings

20 Frame / Feature Frame: a well defined rectangular region in the M/Z versus Retention Time plane. L-Epicatechin MW =

21 L-Epicatechin # Blend Location 1 zinfandel Lake 10 petite sirah Lake 13 zinfandel Lake 36 cabernet sauvignon Mendocino 37 petite sirah Mendocino 2 cabernet franc Napa 3 cabernet franc Napa 20 petite verdot Napa 21 cabernet franc Sonoma 25 cabernet sauvignon Sonoma 33 merlot Sonoma 35 merlot Sonoma 44 cabernet sauvignon Sonoma L-Epicatechin MW =

22 Dynamic Framing Myoglobin Protein ratio = Protein ratio = SIEVE 2.0 SIEVE 1.3 SP2 VEADIAGHGQEVLIR LFTGHPETLEK SIEVE 2.0 Reference: ABRF 2007 Study Jennifer Thermo

23 Complementary Signal Detection Algorithms Peak detection Pattern Recognition Basepeak Framing Fully unbiased based upon signal intensity, MS/MS, or Frame Targets Component Extraction Automated peak shape on full scan Isotope clusters Charge state + adduct + isotope induction Performance Fast Faster Signal to noise Background subtraction Application Identification Many noise frames use Frames Filter Use Frame Targets to build background list Proteomics, Small Molecule PD 1.x, Mascot, built in SEQUEST, DBLookup Only identified components Completely automated Small Molecule Only ChemSpider, DBLookup, (Mass Frontier coming soon)

24 SIEVE Frames vs. Components Frame ID Component 17 Components were identified from 224 frames. Shown in this plot are how the frames were grouped into the different components.

25 Accurate Mass Identification Component MW Mass accuracy 2.9 ppm using background ion chemspider web service Local database List of candidates

26 Identification Results 1224 components 255 identified components Flavonoid accurate mass database MolWt Expression Name L-Epicatechin Epigallocatechin D-glycoside of vanillin Vellokaempferol 3-5-dimethyl ether Velloquercetin 4 -methyl ether Epigallocatechin 3-O-(4-hydroxybenzoate) Epigallocatechin 3-O-cinnamate Quercetin 4 -galactoside Epigallocatechin 3-O-caffeate Epigallocatechin 3-O-(3-O-methylgallate) Isorhamnetin 7-alpha-D-Glucosamine;Quercetin 3 -methyl ether 7-alpha-D-Glucosam Quercetin 7-glucuronide Epigallocatechin 3-O-(3-5-di-O-methylgallate) Myricetin 3-glucuronide Hydroxykaempferol tetramethyl ether 6-rhamnoside;6-[(6-Deoxy-alpha-L Kaempferol 3-(3-4 -diacetylrhamnoside) Hydroxymyricetin tetramethyl ether 7-glucoside Caohuoside D;8-(3 -Hydroxy-3 -methylbutyl)kaempferol 4 -methyl ether 7-glucoside Quercetin 3-xylosyl-(1->2)-rhamnoside Quercetin 3-(2 -galloylrhamnoside) Quercetin 3-(3 -p-coumarylglucoside) Rutin; Pentahydroxyflavone 3-rutinoside;3-Rutinosylquercetin;Birutan;Qu Kaempferol 3-(4 -acetyl-6 -p-coumarylglucoside) Quercetin 3-glucuronide-7-glucoside Tamarixetin 3-glucosyl-(1->2)-galactoside;Quercetin 4 -methyl ether 3-glucosyl-( Kaempferol 3-(2-3 -diacetyl-4 -p-coumarylrhamnoside Euphorbianin;Quercetin 3-(6 -acetylglucosyl)-(1->3)-galactoside;3-[[3-o-(6-o-ace Platanoside;Kaempferol 3-(2-3 -di-(e)-p-coumaroylrhamnoside);3-[[6-deoxy-2-3-bi Quercetin 3-(3-6 -di-p-coumarylglucoside) Kaempferol 3-(3 -p-coumaryl-6 -ferulylglucoside) Quercetin 3-O-beta-(6 -O-E-p-coumaroylglucoside)-7-O-beta-glucoside.

27 Cluster Wine Flavonoids Clustering based upon signal intensity profile Flavonoid database

28 Absolute Quantitation Calibration Curve Method Standards Epicatechin Catechin Orientin Luteolin Ellagic Acid Quercetagetin Chrysin Genistein Rhamnetin Tamarixetin Dilution series at four concentrations (.02,.04,.2,.4) mg/ml in 10% methanol LC/MS using Thermo Q-Exactive Data analysis with SIEVE 2.0

29 Linear Regression Observed value in Zinfandel from Lake (16ug/ml)

30 Linear Regression Observed value in Cabernet Sauvignon from Mendocino (74ug/ml)

31 Absolute Quantitation mg/ml Quercetagetin L-Epicatechin

32 Contaminant Response Possible smoke related contaminants MolWt FORMULA NAME C7H5Cl3O 2,4,6-Trichloroanisole C9H10O2 2-Methoxy-4-vinylphenol (4-Vinylguaiacol) C11H20O 2-Methylisoborneol C8H10O 4- ethylphenol C9H12O2 4-ethylguaiacol C8H10O2 4-methylguaiacol C7H8O Cresol C10H12O2 Eugenol C5H4O2 Furfural C12H22O Geosmin C7H8O2 Guaiacol C13H18O7 ß-D-glycoside of Guaiacol C14H18O8 ß-D-glycoside of vanillin C8H10O3 Syringol

33 Contaminant Response Methoxy-4-vinylphenol (4- Vinylguaiacol) 4- ethylphenol Eugenol Furfural Syringol D-glycoside of Guaiacol D-glycoside of vanillin

34 Summary Deep appreciation for superior expert laboratory craftsmanship to: Mark Dreyer, Applications Specialist, ThermoFisher Thomas Collins, Ph.D, Senior Manager Research and Development, Treasury Wine Estates For more information: Team SEIVE Mary Lopez Amy Zumwalt Jennifer Sutton Mark Sanders David Peakes Gouri Vadali Michael Athanas