Already said. Already said. Outlook. Look at LC-MS data. A look at data for quantitative analysis using MSight and Phenyx. What data for quantitation?

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1 A look at data for quantitative analysis using MSight and Phenyx Pierre-Alain Binz Institut Suisse de Bioinformatique GeneBio SA Atelier Protéomique Quantitative Juin 2007 La Grande Motte Already said Importance of biological question, sample choice, experimental strategy Complexity of sample is a challenge for MS Peak capacity, concentration range, chemical properties, Many methods with goods and bads itraq, SILAC, ICAT, MRM, label-free, Many instrumental settings: heterogeneity of data type, amount, resolution Many bioinformatics tools Identification, signal detection, quantitation Validation methods Already said Importance of biological question, sample choice, experimental strategy Complexity of sample is a challenge for MS Peak capacity, concentration range, chemical properties, Many methods with goods and bads itraq, SILAC, ICAT, MRM, label-free, Many instrumental settings: heterogeneity of data type, amount, resolution Many bioinformatics tools Identification, signal detection, quantitation Outlook Visualise LC-MS data Detect signal Align LC-MS runs Match images (differential analysis) Add identification results Quantitation with search engine Validation methods What data for quantitation? Look at LC-MS data MS data: dimensions: Intensity Rt, pi, scan number Secondary data Sample (one, more than one) Molecular interpretation (peptide, protein) Quantitation method (label description, comparison method, thresholds, corrections) Raw MS traces or peaklists (spectrum view or gel view) Chromatographic profiles (TIC, XIC ) 2D images (LC-MS) Annotated spectra Overlapped spectra, head-to-head view Overlapped images 1

2 Visualise LC-MS data: spectrum view, gel view, chromatograms 2D representation 38x Rt I Rt Example: LC-ESI-Q-TOF kda extract of human BJAB B-cell line Time 0 Data display principle 10 Time min Image part to display 6000x400 Projection Screen size 800x Da mass time interval min sampling rate s measures 900 spectra measures (55 MB) 1200 MS data 32000x900 Full image Time 0 Zoom 256x 10 Time min s Da Da Less 3+ than % of the data displayed

3 MSight LC- MS data analysis tool It looks a bit like Melanie Developed by the Proteome Informatics Group of the Swiss Institute of Bioinformatics Based on Melanie 2D gel analysis software Why MSight? Generate and evaluate LC-MS images Import LC-MS and MS/MS runs from various MS instruments and formats Workspace to manage experiments and data Rich visualisation and annotation Visualise the complexity of a LC-MS run Detect contaminants, running aberations Perform peak detection from raw LC-MS data Improve Rt and accuracy using 2D Quantitation and comparison Alignment and matching of LC-MS images Quantitation reports for differential expression analysis Label-free quantitation, Generation of inclusion/exclusion list Integrate with identification tools (Phenyx) Annotate MS peaks with peptide identity labels Use the annotations to validate matching peaks across LC-MS experiments Import Visualisation Raw LC-MS and MS/MS data format Native format (yep, baf, fid, T2D, dat) mzxml, mzdata Ascii exports Handle big original files (100MB-1GB) Include profile LC-MS trace and MS/MS spectra Open multiple images Zoom in/out Chromatographic profile («XIC») Spectrum view Editable and searchable annotations landmarks, Rt,, peptide sequence, hyperlinks, others Synchronisation between views Superpose images in transparency mode and complementary colors 3D view Artefacts Artefacts 1 min 100 Da 3

4 Mass calibration Contaminants 44 Da Polymer PEG 30 s 2 Da 500 Da SDS-MALDI-TOF 0.15 interval sampling rate 5 min 100 Da 2 Da mass measures 90 spectra measures Contaminants (2) Redundancy: Peptide modifications 10 min 5 min 100 Da 100 Da Spot from 2DE gel Redundancy: Peptide modifications Redundancy: Peptide modifications 5.33 (3+) Oxidation 5.33 (3+) 10 min 2 min 100 Da 2+ 5 Da Oxidation 4

5 Outlook Peak detection Visualise LC-MS data Detect signal Align LC-MS runs Match images (differential analysis) Add identification results Quantitation with search engine Detect and quantify MS peaks in a 2D image Interactive use Manual validation via visualisation Export in centroid mode Peak detection variability Locating the source of noise High vs low resolution in axis Isotopic profile vs bump Sampling resolution (Rt and ) LC-MALDI < ESI-MS with MS/MS < ESI-MS (QTOF<LTQ) Noise (chemical, electronic) Shape (rectangle, circle, other) Intensity (max, sum, fit max, integrate) 5 min 5 Da 15 s And for quantitation: Detect individual sample and compare vs align and use one single shape per aligned feature Locating the source of noise Streak a b c d e f g h 2000 (5+) i j k L (2+) 28 min 3000 m n (2+) 2 min min 80 m i j n b c k d e L f g h 1 Da 1 Da

6 Peptide deconvolution Outlook time: 31.9 min min 4+ Visualise LC-MS data Detect signal Align LC-MS runs Match images (relative quantitation) Add identification results Quantitation with identification results 1 Da Alignment and comparison Alignment transformation Align images via landmarks (corrections for local deviations) 4 min Match images (pair peaks together) Report relative quantification information Migration variability Outlook A A - B B 1 min 2 Da 1 min 2 Da Visualise LC-MS data Detect signal Align LC-MS runs Match images (differential analysis) Add identification results Quantitation with identification results 6

7 Quantitation Quantitation 3+ 5 min Protein Mixture +26 fmol kda fraction of lysate from a culture of BSAa B-cell line ~ 1pmol up to 180 proteins detectable in this sample when analysed extensively (2+) by LC-MS/MS LGEYGFQNAL +83 fmol +520 fmol +26 fmol +83 fmol +520 fmol 2 min Da 2 Da Quantitation Differential (low resolution) +26 fmol +83 fmol +520 fmol 5 min 20 Da BSA BSA+Lyz Differential analysis Differential analysis A-B A A-B A 2 Da B 100 Da 7

8 Outlook Visualise LC-MS data Detect signal Align LC-MS runs Match images (differential analysis) Add identification results Quantitation with search engine Coupling with identification Sofar, quantitation without consideration of molecular interpretation To quantitate protein, need to select signals and to couple with peptide identification Phenyx A software platform dedicated to the identification and characterization of proteins and peptides from mass spectrometry data Developed by GeneBio, in collaboration with the Swiss Institute of Bioinformatics (SIB) Launched in September 2004 (version 1.8) Version 2.3 in April 2007 Rapid development and recognized tool Integration in a number of third-party software (Scaffold, TPP, MSight, ProteinScape, Proteus LIMS, ) Adopted by a number of large renowned Proteomics centres Some features Core calculation Robust and flexible scoring including log likelihood measures Conflict resolution algorithm Use of annotations in databases (PTMs, variants, AA modifs ) Flexible and interactive interface: the Phenyx Web Interface User and jobs properties (user privileges, job sharing) Manual validation functionality Import third party jobs (Mascot, Sequest, X!Tandem, Popitam, ) Many exports (native Phenyx, Excel, XML, text ) Results comparison functionality Integration of Phenyx into workflows: a job follows a suite of configurable events (pre-processing, processing and postprocessing) Submission The Phenyx Web Interface: Integrate MSight and Phenyx Desktop Results views Example: Annotate LC-MS images with peptide identifications Annotated images Results comparison Management console Raw LC-MS Exported peptide identifications Peaklists Excel, xml and text exports Phenyx interface 8

9 Phenyx results are stored as annotations in the images LC-MS and MS/MS: undersampling Time [min] LC-MS and LC-MS/MS on a QStar of KDa SDS separated and trypsin digested proteins, from a human B-cell line Focus on a small time x region (about 1/250 of the full run) LC-MS and MS/MS: undersampling Outlook Time [min] FFADLLDYIK LALDLEIATYR /40 peptides analysed 3/7 identified < 10% positively identified using stringent criteria SLDLDSIIAEVK Visualise LC-MS data Detect signal Align LC-MS runs Match images (relative quantitation) Add identification results Quantitation with search engine Quantitation with search engine Quantitation: needed information Use of MS/MS data Reporter ions: isobaric labeling (itraq, TMT) empai (~ratio observed/predicted peptides) Multiplex (SILAC, 18O) Use of MS raw traces Stable isotope labeling (ICAT, SILAC, AQUA, 18O, ICPL, ) Label-free Need identified peptides Need access to intensities (MS/MS and MS) Need quantitation method Labeling method (fixed, variable mode) Definition of pairs Intensity correction factors Thresholds for what peptides to consider (confidence levels, scores, #pep / protein) Create report, calculate ratios, evaluate outliers Include in search engine GUI 9

10 A quantitation module for Phenyx A quantitation module for Phenyx Generic Quantitation methods (Phenyx) result file API InSilicoSpectro PhenyxPerl + Prediction of Co-peptides Extraction of Intensities: MS level Extraction of Intensities: MS/MS level Labeling config file (xml) Quantitation module Quantitation Result file (text) Calculation of ratios; exportation InSilicoDef definition file (xml) External statistics ( R ) One possible integration with MSight (label-free) Phenyx: generate reports from identification results Perl scripts to generate many kinds of exports Annotated images Raw LC-MS Exported peptide identifications Peaklists Align, compare Annotated peptide ratios Raw LC-MS Exported peptide identifications Peaklists Example for itraq Examples of filters and search parameters that alter quantitation results Minimal number of peptides per protein Minimal number of proteotypic peptides Minimal score for each peptide Filter on redundancy same sequence (same or different charge states) same exact primary structure, Imbedded sequences (missed-cleavages, etc.) Remove outliers (quant values > threshold CV) Number of missed cleavages allowed Semi-tryptic peptides and fully unspecific cleavages Number of queried modifications 10

11 Only valid peptides: 6 proteins, 22 peptides Min. 3 valid peptides: 4 proteins, 19 peptides Min. 3 valid peptides, Intensities >10 000: 4 proteins, 15 peptides Min. 3 valid peptides, Intensities >10 000, CV<20%: 2 proteins, 7 peptides Effect of filters False discovery rate export # peptide in decoy database # peptide in forward database = f(z-score and p-value) Filter # peptides # proteins Number of valid hits as fct of zscore 8000 Z-score 22 6 # hits True hits Hits in reverse peptides z-score FDR (hits in rev / hits in fw d) + Intensity + CV FDR (hits in rev / hits in fwd) 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% z-score 11

12 Calibration status of instrument (3 datasets) Calibration status of instrument Effect of the search parameters 1rnd, Only 3 fixed mods 131 valid, 75% cov zscore delta 2rnd, Add variable mods 205 valid, 84% cov. 2rnd, With all mods And half cleaved 348 valid, 90% cov. Import jobs into Phenyx Results comparison tool Sequest What protein in what job? What peptide in what protein/job? X!Tandem Mascot Phenyx Manual validation and then quantitation as if Phenyx job Concatenate results from different runs/search engine And then go to quantitation Summary Take-home messages LC-MS data and 2D image analysis (MSight) Rich source of information Detect strange behaviors (discontuity, contaminations, QC issues) Use of 2 dimensions efficient for signal detection Alignment of multiple MS runs: consider local aberrations Quantitation possible for pairs and for groups (statistics) Quantitation with protein identification tool only (Phenyx) Quantitation methods limited to information in peaklists (isobaric labeling, empai, Multiplex) Quantitation with MSight and Phenyx Get access to raw data information Full panel of quantitation methods Need tight integration (annotation, statistics, filters) Thanks to import functionality, access to other search engines Biological variability Experimental variability Quantitation method tolerance Error to appreciate Many tools available, make your choice according to: biological question capacity to analyse data from the chosen quantitation method capacity to analyse data from your instruments possibility to validate generated data (interactivity) Understand, evaluate 12

13 Aknowledgements Thank you for your attention! Phenyx devel team Alexandre Masselot Nicolas Budin Anne Niknejad Olivier Evalet PIG group Ron Appel Daniel Walther Gerard Bouchet Sébastien Catherinet Stéphane Pelhâtre Patricia Palagi BPRG Ali Vaezzadeh PAF Manfredo Quadroni University Bern Manfred Heller IPBS David Bouyssié MSight: Phenyx: 13