Statistical mass spectrometry-based proteomics

Transcription

1 1 Statistical mass spectrometry-based proteomics Olga Vitek

2 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data

3 Proteomics: system-wide characterization of all proteins Sequence, structure, localization, abundance, PTMs, interactions More challenging than gene expression Complexity Human genome: ~0,000 protein coding genes Their translation+splicing+proteolysis: ~50, ,000 proteins Somatic DNA rearrangements and PTM: ~10 million Dynamic range > 10 orders of magnitude in plasma Unlike nucleotides, proteins cannot be amplified How to make progress? Goals.of.proteomics Sample preparation, separation, sensitive instruments Statistical experimental design and accurate analysis 3

4 Acquisi7on.of.mass.spectra Single Stage MS protein peptides peptides Digestion Ionization Mass Analysis MS m/z 4

5 Liquid.chromatography.coupled.with.mass. spectrometry.(lcams) '()%*+"%!"#$"%!#&,01,01,01,01 #!,$)-%*+")./ Hydrophobicity 5

6 Liquid.chromatography.coupled.with.mass. spectrometry.(lcams) $,#&,)$#/ %01 %01 %01 %01!" Hydrophobicity!"0!" #$%&'()*+,'-. 6

7 Ahrens et al., Nature Reviews, 010 Proteomics.is. increasingly. comprehensive a Number of proteins identified Number of publications Proteome coverage (%) b 3,000,500,000 1,500 1, Year Year 10,000 5,000,000 1, , ,738,013 Saccharomyces cerevisiae Aribadopsis thaliana Drosophila melanogaster Caenorhabditis elegans ,345, ,740 3, Assay technology Instrumentation Computational solutions 50 Year ICAT Mascot Shotgun proteomics SILAC LTQ LTQ-FT OrbiTrap MRM Peptide- Protein- Target Max- 7

8 Sample preparation Global LC-MS/MS Global.proteomic.workflows Käll and Vitek, PLoS Computational Biology, 7, 011 8

9 Sample preparation Targeted SRM Targeted.proteomic.workflows Käll and Vitek, PLoS Computational Biology, 7, 011 9

10 Varia7on.is.experimentAspecific LC-MS: Human diabetes SRM: yeast proteome (Picotti et al, 009) Difference of intensities Affy: ALL (Acute Leukemia) SRM: Human ovarian cancer (Hüttenhain et al, 01)

11 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data 11

12 Example:.labelAfree.LCAMS High/low.invasive.breast.cancer.cell.lines Goal:.proteinAlevel.conclusions Safia Thaminy ETHZ Average log(intensity) Average log(intensity) 6 4 hypoxia low invasive normoxia low invasive hypoxia high invasive normoxia high invasive low nm 6 hrs low nm 4 hrs low hyp 6 hrs low hyp 4 hrs high nm 6 hrs Condition high nm 4 hrs high hyp 6 hrs high hyp 4 hrs 0 6 hours 4 hours Time 6 hours 4 hours 1

13 Important.differences.with.microarrays Oligonucleotide microarrays RMA: Tukey Median Polish Robust averaging of all probes Fluorescent image into array-specific summary Proteomics Number and quality of features per protein vary widely Missing features introduce imbalance Label-based workflows combine multiple samples blocking structure Targeted workflows create a nested structure protein/peptide/transition Explicit probabilistic models best represent the data PM MM AGATCGATCAGTCAGTACTAGTACT AGATCGATCAGTGAGTACTAGTACT Perfect Match (PM) probe A probe pair consists of a PM and MM probe MisMatch (MM) probe Probe pair set Sebastiani et al, Statistical Science,

14 Linear.mixed.models.for.feature.intensi7es Deviation from the reference due to log( Expected Random peak = reference + LC-MS + condition + feature condition + biol. + meas. intensity) abundance feature interaction replicate error y ijkl = µ F i + C j + (F C) ij + S(C) k(j) + " ijkl where F 1 = C 1 =(F C) i1 =(F C) 1j =0 " ijkl iid N 0, Error, ijk and (a) reduced scope of biological replication: S(C) 1(1) =0 (b) expanded scope of biological replication: S(C) k(j) iid N 0, S T. Clough et al. JPR, 009 T. Clough et al. Meth. Mol. Biol., 011 T. Clough et al. BMC Bioinformatics., 01 C.-Y. Chang et al. Mol. Cel. Proteomics, 01 Average log(intensity) low nm 6 hrs low nm 4 hrs low hyp 6 hrs low hyp 4 hrs high nm 6 hrs high nm 4 hrs Condition high hyp 6 hrs high hyp 4 hrs hypoxia low invasive normoxia low invasive 6 hours 4 hours normoxia high invasive Time hypoxia high invasive 6 hours 4 hours

15 All.inference.is.based.on.problemAspecific. linear.combina7ons.of.model.terms Quantity of interest: H 0 : L = µ [high, nm, 6] µ [low, nm, 6] =0 Model-based estimate and test statistic: IP ˆL = Ĉ[high, nm, 6] + 1 I ( F d C) i, [high, nm, 6] + 1 K i=1 IP - Ĉ [low, nm, 6] + 1 I ( F d C) i, [low, nm, 6] + 1 K t = ˆL SE{ ˆL} i=1 Student distribution KP k=1 KP k=1 S(C) d k([high, nm, 6]) S(C) d k([low, nm, 6]) In balanced datasets: ˆL = Ȳ [high, nm, 6] Ȳ [low, nm, 6] t = ˆL p IKL ˆ Error Student IJK(L 1)+(I 1)J(K 1) distribution Also.methods.for.model.diagnos7cs,.data.visualiza7on.etc 15

16 Tools.for.quan7ta7ve.proteomics Veavi&Chang Meena&Choi Tim&Clough A#variety#of#quan0ta0ve#workflow 4 Global,'targeted,'data-independent - Label-free'and'label-based Accounts#for#experimental#designs - Group'comparison,'7me'course Data#visualiza0on#&#QC Model4based#analysis - Model'fi:ng'and'inference Planning#future#experiments - Sample'size,'resource'alloca7ons Collaboration: Since 010: extensive documentation published case studies protocols for typical analyses 13 tutorials and workshops Since 011: 370 unique visitors over 50 unique downloads over 50 mailing list members Ruedi.Aebersold Michael. MacCoss 16

17 Outline What is proteomics? Biological questions and technologies Protein quantification in label-free workflows Joint analysis of multiple features and conditions Protein quantification in label-based workflows Appropriately account for the labeling structure Mass spectrometry-based imaging Account for the spacial heterogeneity of spectral data 17

18 LabelAbased.workflows.help.separate.the. biological.and.the.technological.varia7on Healthy Disease run 1 run T transition log (intensity) T1 T T3 ence enous T1 T T3 H L H L rence enous average of log-intensities of paired log-intensities of L and H difference of log-intensities of L and H = log-ratios of L over H run Group 1 Group I Run 1 Run M Subject 1 Subject J Subject 1 Subject J Peptide 1 Transition Endogenous: Transition L light labeled.. peptide Peptide K Transition Transition L Transitions Peptide 1 Transition Reference: Transition L heavy labeled.. peptide Peptide K Transition Transition L Legend : Label Feature: Transition/Peptide Group Run Subject 18

19 G STATISTICAL PROTEOMICS i =0 S(G) i=0 j=0 AND j(i) =0 F k,l=1 BIOINFORMATICS kl =0 Rm =0 m=1 ect: F; Run: F ect: R;Run: F ect: F; Run: R log (intensity) More.complex.but.similar.linear. T i (T F ) ikl mixed.effects.models Systematic deviations from the mean Statistical interactions iid ect: R;Run: R Observed Systematic deviations from the mean Statistical interactions log(int of peak) = Overall mean + Group N(0, iid S ) N(0, iid R ) N(0, Time by Group by Run by + Subject +Feature+Run+ + + Random RF ) Observed or time Systematic log(int deviations of peak) from = subject the Overall mean meant i + (T Group Ti F ) ikl feature Statistical + Subject or time feature interactions +Feature+Run+ error su me Observed log(int of peak) = Overall mean + Group Time by Group Run by (a) course: case-control: Observed + Subject +Feature+Run+ + Random log(int of peak) = Overall or time mean + Group (a) case-control: Time by Group by Run by + Subject +Feature+Run+ subject feature + + or time subject feature feature error y (a) case-control: ijklm = µ + TG i (T+S(G) F j(i) ) ikl + F kl y ijklm + R m + = µ T i + (G (T G i F ) ikl +S(G) +(R j(i) F + ) klm F+ kl + ijklm R m + (a) case-control: Fixed/Random F F F/R Fixed/Random F F/R F/R IX F F IX F/R F/R MX F R: N(0, F/R y ijklm = µ + G i +S(G) j(i) + kl + R m + (G F ) ikl +(R klm + ) (T S) IX JX KL ij =0 (T F ) ikl =0 (R F ) ijklm =0 y ijklm = µ + G Xi +S(G) j(i) MX + F kl + R ect: F; Run: F T i =0 S j =0 F kl =0 Rm =0 i=0 m + (G F ) i=0 m=1 ikl +(R F ) klm + iid JX KL (b) time course: X KL i=0 j=0 k,l=1 (b) time course: X m=1 (T S) Fixed/Random F F F/R F F/R ij =0 (T F ) ikl =0 (R F ) F/R R: N(0, klm =0 Fixed/Random F F F/R F ) j=0 F/RT i (T F ) k,l=1 ikl F k,l=1 F/R Reference yeast: measurement y Healthy Disease (b) time ijklm = µ + T course: i (T+ SF j ) ikl + F kl y ijklm Endogenous + R m + (T = S) µ ij + (T T i F ) ikl + +(RS j F + ) klm F+ kl + ijklm R m + (T iid ect: R;Run: F (b) time course: 3,800 N(0, iid Idp Timepoint S ) Peptide 1 1 N(0, Light TS ) run 1 run T1 T T3 T4 T5 T6 T7 T8 T9 T10 Fixed/Random F F F/R Fixed/Random F F/R F/R T7F T8 T9 T10 F F/R F/R F R: N(0, F/R y ijklm = µ + T i + S j + kl + R 6.56 m + (T S) ij (T F ) ikl +(R klm + ) iid ect: F; Run: R y ijklm = µ + T i + S j ijklm N(0, + F iid R ) kl + R m + (T S) ij + (T F ) ikl N(0, +(R F ) RF ) klm e5 Intensity, cps Idp Peptide 1 Heavy 6.56 Timepoint Time, min IX run 1.6e5 Intensity, cps Idp Peptide 1 Heavy JX T i (T F ) ikl 6.55 iid N(0, S ) T i (T F ) ikl log (intensity) T Time, min i (T F ) ikl KL X Intensity, cps Time, min Fixed/Random Fixed/Random F F F F/R F F F/R F/R F F/R F/R F/R F F/R F R: N(0, F/R iid T1 T T3 T4 T5 ect: R;Run: R N(0, S ) iid N(0, iid R ) N(0, iid TS ) N(0, RF ) MX iid N(0, R ) T6 run 1.3e5 Intensity, cps Idp Peptide (G F ) ikl =0 i=0 KL X (G F ) ikl =0 k,l=1 log (intensity) Time, min T i (T F ) ikl T1 T T3 H L H L (H) reference (L) endogenous (R F ) klm =0 m=1 KL X (R F ) klm =0 k,l=1 iid N(0, RF ) y ijklm = µ + T i + S j + F kl + R m + (T S) ij + (T F ) ikl +(R F ) klm + ijklm C.-Y. Chang et al. MCP, 01 T i (T F ) ikl T i (T F ) ikl Systematic deviations from the mean T i (T F ) ikl T i (T F ) ikl T (T F ) T1 T T3 19 (H) reference (L) endogenous ) iid N + N

20 STATISTICAL PROTEOMICS AND BIOINFORMATICS Purdue University Kyle Bemis Meena Choi Tim Clough Veavi Chang Robert Ness Danni Yu Cheng Zheng s ETH Zürich Ruedi Aebersold Ruth Hüttenhain and lab Thaminy SilviaSurinova Surinova ga VitekSafia Robert Ness Silvia Purdue Graham Cooks and lab Stanford Mark Stolowtiz CGR, Spain Parag Mallick NSF-SI-SSE Eduard Sabidó NSF-BIO-DBI 36