Proteomic Analysis using Accurate Mass Tags. Gordon Anderson PNNL January 4-5, 2005

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1 Proteomic Analysis using Accurate Mass Tags Gordon Anderson PNNL January 4-5, 2005

2 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 2

3 Approach for Proteome Analysis Cell lysis Protein extraction/fractionation Optional protein stable-isotope labeling Proteolytic digestion Capillary separation on-line with ESI-FTICR Data processing and display 3

4 PNNL Accurate Mass and Time Tag Approach Given the constraint of a sequenced genome, high accuracy mass measurements by FTICR provide unique biomarkers for nearly all proteins : Accurate Mass and Time Tags (AMTs) Use of AMTs for protein identification - Subsequent MS/MS not required - Enables high throughput proteomics 4

5 Overall Methodology Stage 1 Stage 2 Protein Extract Tryptic Digestion Control Perturbed LC-MS/MS LC-FTICR Accurate Mass Measurements Validate Accurate Mass Tags Create AMT database LC-FTICR Identify proteins using AMT database Determine abundance ratios 5

6 ...APEEKARGITINTAHVEYQTETRHYSHVDCPGHADYVK... Fragment from Elongation factor Tu Tryptic digestion APEEKAR GITINTAHVEYQTETR HYSHVDCPGHADYVK Capillary LC- FTICR GITINTAHVEYQTETR Capillary LC-MS/MS (e.g. with ion trap) y m/ ,000 1,250 1,500 m/ Accurate mass observed: Calculated PMT mass ( ) and elution time y4 b6 y5 b7 b9 b ,000 1,400 1,800 m/ Validated AMT (GITINTAHVEYQTETR) y6 y7 y8 y10 b11 y11 b10 b12 y12 b13 b14 y13 PMT identified from D. radiodurans: GITINTAHVEYQTETR 6

7 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 7

8 FTICR/TOF Mass Spectrometers Raw data formats FTICR Bruker Finnigan odyssey LTQ-FT TOF Agilent Waters Data analysis De-isotoping In house developed software Large data volumes Standards needs Raw spectrum Instrument settings Analysis results Analysis parameters 8

9 ION Trap Mass Spectrometer Raw data formats Thermo LCQ Thermo LTQ Agilent ION trap Data analysis Peptide ID SEQUEST MASCOT In house developed tools Standards needs Raw spectrum Instrument settings Analysis results Score normaliation Analysis parameters 9

10 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 10

11 Proteomics Analysis Pipeline 11

12 High resolution mass spectrum analysis CS Abundance m/ Fit Average MW Monoisotopic MW Most abundant MW E

13 High Resolution MS Data 13

14 Overlapping Isotopic Distributions CS Abundance m/ Fit Average MW Monoisotopic MW Most abundant MW E E E

15 Isotopic signature Identification, N14/N15 CS Abundance m/ Fit Average MW Monoisotopic MW Most abundant MW Isotope E N E N15 15

16 Capillary LC-FTICR 2-D Display of Peptides from a Yeast Soluble Protein Digest 2,500 >160,000 isotopic distributions corresponding to thousands of peptides 2,243 1,987 M r /Da 1,731 1,475 1, minutes

17 Unique Mass Classes Generation 1,280 M r /u 1, UMC 1,230 1,205 1, Spectrum number (Time) 17

18 Q Rollups Summary Sheet Quantitation ID SampleName Comment Experiments Jobs Threshold for Inclusion (%Max) 153 MGYMO_007a, 4 replicates Jobs: 33775, 33777, 33779, MGYMO_007a 33775, 33777, 33779, % Unique Mass Tag Count UMC Count NetAdj NET Min NetAdj NET Max MMA Tolerance PPM NET Tolerance Refine Mass Cal PPM Shift Results Folder Path \\pogo\mtd_peak_matching\results\mt_yeast_p51\ 11T\MT_Yeast_P51_Job33775_auto_pm_92 18

19 Q Rollups Peptide Sheet Mass Tag Abundance StDev Reference Abundance Average Mass Tag ID Mass Tag Abundance Peptide Monoisotopic Mass UMC Match Count Avg Q APDFVESNTIFNLNTVK Q SSSIEFLLTSPPAVHSFNTPAVQS Q WLISQEAIYDTIMNMTK Q DVHNGYILR Q DVHNGYILR Q DVHNGYILR Q DVHNGYILR Q FVVTAADVIHDFAIPSLGIK Q LLDTDTSMVVPVDTHIR Q LNQVSALIQR Q AIGYQWYWK Q IEAVSLPK UMC Mass Tag Hit Count Avg Reference Mass Tag ID Scan Minimum Scan Maximum Mass Error PPM Avg Replicate Count Avg Replicate Count Min Replicate Count Max Used For Abundance Computation Q Q Q Q Q Q Q Q Q Q Q Q

20 Q Rollups Protein Sheet Mass Tag Count Unique Observed Mass Tag Count Used For Abundance Avg Mass Tag Matching Ion Count Fraction Scans Matching Single Mass Tag Reference Abundance Average Abundance StDev Mass Error PPM Avg YNL055C % YBL030C % YBR085W % YJR077C % YGL008C % YPL036W % YMR056C % YBL104C % Q % Q % YNL309W % YOR065W % YBR054W % Reference Replicate Count Avg Replicate Count StDev Replicate Count Max YNL055C YBL030C YBR085W YJR077C YGL008C YPL036W YMR056C YBL104C Q Q YNL309W YOR065W YBR054W

21 Q Rollups Crosstab Sheets Protein Crosstab Reference Job (QID147) Job (QID148) Job (QID149) Job (QID150) Avg Abu YNL055C YBL030C YBR085W YJR077C YGL008C YPL036W YMR056C YBL104C Q Q YNL309W YOR065W YBR054W Proteins with Peptides Crosstab Reference Mass Tag ID Job (QID147) Job (QID148) Job (QID149) Job (QID150) Avg Abu Q Q Q Q Q Q Q Q Q Q Q Q

22 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 22

23 Create information about the proteins in biological samples using mass spectrometer data 36 Organisms 129 Research Campaigns 10,950 Prepared Samples 22,341 MS Instrument Runs 76,543 Automated Analyses 30 TB Data / results files 210 Databases 350 GB Data in databases 23

24 PRISM Functions Collect Information Capture spectra files from MS instruments Gather metadata (biomaterial, sample prep., etc.) Store Information Raw spectra files Analysis results files Lab information Process Information Analye raw spectra with multiple tools Identify mass tags, peptides and proteins Formulate reports Present Information Web interface for general user access Database and file access for external tools and systems Utility programs for special purpose data access 24

25 PRISM Hardware Primary Servers Databases Web User Interfaces Data Management System (DMS) Mass Tag System (MTS) Storage Servers Bulk file storage and automated file handling Add more to handle more MS instruments Auxiliary Processors Automated data analysis Add more to address increasing sample volume 25

26 Lab Information / Data Analysis Campaign Line of research Cell Culture Biomaterial source Experiment Prepared sample Dataset MS Instrument run Analysis Job Peptide Identification Peak Identification Total Ion Current (TIC) 26

27 Mass Tag Creation Mass Tag System (MTS) Main Database Centralie references to DMS metadata Manage automatic update cycle Peptide Database Select Analysis Jobs from DMS Based on metadata in DMS tracking DB Extract peptide identifications From DMS analysis results files Based on selection criteria Mass Tag Database Select analysis jobs from peptide database Import selected peptides and make PMTs Extract and match monoisotopic peaks From DMS analysis results files Match to PMTs by mass and elution time 27

28 Oct-01 Dec-01 Feb-02 Apr-02 Jun-02 Aug-02 Oct-02 Dec-02 Feb-03 Apr-03 Jun-03 Aug-03 Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04 PRISM Operation Phased Improvements Phase 1 (March 2000) Basic Dataset Capture Phase 2 (March 2001) Automated Analyses Phase 3 (March 2002) Mass Tag System Datasets Phase 4 (April 2003) High-capacity storage servers Phase 5 (June 2004) Quantitation Automation, More tools, Enhanced Filtering UMC Automation Experiments Feb-03 Apr-03 Jun-03 Aug-03 Oct-03 Dec-03 Feb-04 Apr-04 0 Jun-04 Aug-04 Steady operation for several years ~99% Uptime Analysis Jobs 28 Apr-00 Jun-00 Aug-00 Oct-00 Dec-00 Feb-01 Apr-01 Jun-01 Aug-01 Oct-01 Dec-01 Feb-02 Apr-02 Jun-02 Aug-02 Oct-02 Dec-02 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov-02 Jan-03 Mar-03 May-03 Jul-03 Sep-03 Nov-03 Jan-04 Mar-04 May-04 Jul-04 Apr-01 Aug-01 Jun-01

29 Outline Accurate Mass and Time Tag (AMT) based proteomics Instrumentation Data analysis Data management Challenges 29

30 Challenges Multiple data standards are required Experiment description Raw mass spec data Capture the instrument configuration Spectral analysis results De-isotoping Peptide ID Protein expression results Common dictionary is needed to support interoperability Proteomics method description (analysis workflow) Analyses tool meta data capture Tools used, executables available Versions Parameter files Current efforts HUPO PSI mxml intergraded into one PNNL development 30

31 Enabling Actions Small working groups Develop a roadmap Dictionary Multi-institution programs targeted at interoperability Funded to develop and implement standards Samples analyed at multiple sites Data exchanged and analyed at multiple sites Development of a unified web based dissemination of data and methods Efforts integrated with HUPO PSI Publication requirements for data access 31

32 Team Members Proteomics Software Team Gordon Anderson (Team leader) Ken Auberry (Scientific liaison) Dave Clark (Process automation SW development) Lars Kangas (Artificial intelligence SW development) Gary Kiebel (System architecture, database, web interface) Matt Monroe (Analytic tool development) Nikola Tolic (Peak matching, visualiation SW development) Eric Strittmatter (Analytic tool development) Contributors Edward Ellis Dick Smith and his research group Nathan Trimble Kyle Littlefield John Sandoval Funding DOE, OBER For more details 32

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