Introduction to MS-based proteomics and Bioconductor infrastructure

HTML
DOWNLOAD

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "Introduction to MS-based proteomics and Bioconductor infrastructure"

Αἴολος Παπάζογλου
6 χρόνια πριν
Προβολές:

1 Introduction to MS-based proteomics and Bioconductor infrastructure Laurent CSAMA 17 June 2015

2 Outline Proteomics and MS data Bioconductor infrastructure Examples Ranges infrastructure Application: spatial proteomics

3 Mass-spectrometry LC-MS/MS Separation Source Analyser Precursor ions MS1 Detector Fragmented ions MSMS - MS2 Precursor ion dissociation

4 Chromatogram: total intensity over time Total ion current TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_ mzML TIC: 7.22e Time (sec)

5 MS1 (and MS2) spectra MS1 21:3 min MS2 scan, precursor m/z intensity 0.0e e e e e+08 0e+00 2e+06 4e+06 6e+06 8e+06 1e+07 0e+00 1e+06 2e+06 3e+06 4e+06 5e+06 6e MS2 scan, precursor m/z m/z m/z

6 Mass-spectrometry LC-MS/MS Separation Source Analyser Precursor ions MS1 Detector Fragmented ions MSMS - MS2 Precursor ion dissociation

7 Fragmentation Credit abrg.org

8 cid <- calculatefragments("aegklrfk", type=c("b", "y"), z=2) ## Modifications used: C= ht(cid, n = 3) ## mz ion type pos z seq ## b1 b 1 2 A ## b2 b 2 2 AE ## b3 b 3 2 AEG ##... ## mz ion type pos z seq ## y6* y* 6 2 GKLRFK ## y7* y* 7 2 EGKLRFK ## y8* y* 8 2 AEGKLRFK

9 MS1 and MS2 spectra MS1 21:3 min MS2 scan, precursor m/z intensity 0.0e e e e e+08 0e+00 2e+06 4e+06 6e+06 8e+06 1e+07 0e+00 1e+06 2e+06 3e+06 4e+06 5e+06 6e MS2 scan, precursor m/z m/z m/z

10 MS1 and MS2 spectra 2.0e e e e e e e e e e retention time m/z retention time m/z

11 Proteomics data raw data: MS1 and MS2 over retention time identication: MS2 quantitation: MS1 or MS2 protein database (to match MS2 spectra against) Status package Raw (mz*ml) mzr mztab MSnbase mgf MSnbase mzidentml mzid, mzr mzquantml (?mzr)

12 Bioconductor infrastructure biocviews: Proteomics, MassSpectrometry Number of Bioconductor packages Proteomics MassSpectrometry MassSpectrometryData Package downloads Proteomics MassSpectrometry Bioconductor Versions Bioconductor Versions

13 Learning from Bioconductor genomics proteomics eset (past?) *MSnSet (present) Ranges (present) *Pbase et al. (future) PPI *localisation (present)

14 MSnSet

15 Example library("msnbase") rx <- readmsdata("rawdata.mzml") ## raw data rx <- addidentificationdata(rx, "identification.mzid") rx <- rx[!is.na(fdata(rx)$pepseq)] plot(rx[[10]], reporters = TMT6, full=true)

16 Example Precursor M/Z e+05 8e+05 6e+05 4e+05 6e+05 2e+05 0e Intensity 4e+05 2e+05 0e M/Z

17 Example library("msnbase") rx <- readmsdata(f, centroided = TRUE) rx <- addidentificationdata(rx, g) rx <- rx[!is.na(fdata(rx)$pepseq)] plot(rx[[10]], reporters = TMT6, full=true) plot(rx[[4730]], rx[[4929]])

18 Example intensity prec scan: 6975, prec mass: , prec z: 3, # common: 34 prec scan: 7198, prec mass: , prec z: 3, # common: m/z

19 Example library("msnbase") rx <- readmsdata(f, centroided = TRUE) rx <- addidentificationdata(rx, g) rx <- rx[!is.na(fdata(rx)$pepseq)] plot(rx[[10]], reporters = TMT6, full=true) plot(rx[[4730]], rx[[4929]]) qt <- quantify(rx, reporters = TMT6) ## qt <- readmsnset("quantdata.csv", ecols = 5:11) nqt <- normalise(qt, method = "vsn") boxplot(exprs(nqt)) MAplot(nqt[, 1:2])

20 Example TMT6.126 TMT6.128 TMT6.130 M Median: IQR: A

21 More RforProteomics package library("rforproteomics") RforProteomics() RProtVis() citation(package = "RforProteomics") Proteomics workow on the Bioc site Lab on Friday

22 protein database raw data quantitation identication

23 Ranges infrastructure g s g e G e s i e e i e s i e e i e s i e e i e s i e e i T1 i = 1 i = 2 i = 3 i = 4 T2 i = 1 i = 3 i = 4 T3 i = 1 i = 4 p s j p e j p s j p e j p s j p e j P j = 1 j = 2 j = 3 P 1 L P j = 1 j = 2 j = 3 π s k π e k π s k π e k π s k π e k π s k π e k Π k = 1 k = 2 k = 3 k = 4

24 Pbase package library("pbase") p <- Proteins("uniprot.fasta") p <- addidentificationdata(p, "identification.mzid") aa(p) ## peptides sequences as a AAStringSet pranges(p) ## peptide ranges as IRangesList i <- which(acols(p)[, "EntryName"] == "EF2_HUMAN") plot(p[i]) plot(p[i], from = 155, to = 185)

25 Along protein coordinates NH COOH NH COOH P13639 W A F T L K Q F A E M Y V A K F A A K G E G Q L G P A E R A K K V E peptides peptides

26 Along genome coordinates... using transcript models as GRangesList and Gviz for plotting. Chromosome mb mb mb mb mb mb RATRSGAQASSTPLSPTR DTSRRLLAEKEREMAEMR RATRSGAQASSTPLSPTR ELEKTYSAKLDNAR METPSQRRATR RQNGDDPLLTYRFPPK SYLLGNSSPRTQSPQNCSIM peptides LALDMEIHAYRK LALDMEIHAYR SVGGSGGGSFGDNLVTR SVGGSGGGSFGDNLVTR SVGGSGGGSFGDNLVTR From the Pbase mapping vignette.

27 Along genome coordinates (with raw data) Chromosome X mb mb 5' 3' 3' 5' mb mb ENST MS2 spectra From the Pbase mapping vignette.

28 With RNA-Seq reads Chromosome mb mb mb mb mb mb mb AlignmentsTrack ENST From

Spatial proteomics The cellular sub-division allows cells to establish a range of distinct microenvironments, each favouring dierent biochemical reactions and interactions and, therefore, allowing

29 Spatial proteomics The cellular sub-division allows cells to establish a range of distinct microenvironments, each favouring dierent biochemical reactions and interactions and, therefore, allowing each compartment to full a particular functional role. Localisation and sequestration of proteins within subcellular niches is a fundamental mechanism for the post-translational regulation of protein function. Spatial proteomics is the systematic study of protein localisations.

$fraction Cell lysate centrifugation Pure fraction catalogue Subtractive proteomics (enrichment) Figure : Immuno$ $(from the Human Protein Atlas) label-free MS/MS Invariant rich fraction (clustering) PCP (χ2) itraq MS/MS LOPIT (PCA,$

30 fraction Cell lysate centrifugation Pure fraction catalogue Subtractive proteomics (enrichment) Figure : Immuno uorescence: ZFPL1, Golgi (left) and FHL2, mainly localized to actin laments and focal adhesion sites. Also detected in the nucleus (right). (from the Human Protein Atlas) label-free MS/MS Invariant rich fraction (clustering) PCP (χ2) itraq MS/MS LOPIT (PCA, PLS-DA) Figure : Mass spectrometry-based approaches based on density gradient subcellular fractionation.

31 Cell membrane lysis Mechanical or buer-induced lysis of the plasma membrane with minimal disruption to intracellular organelles followed by subcellular fractionation.

32 Density gradient separation

33 Quantitation by LC-MSMS

34 Data Fraction 1 Fraction 2... Fraction m markers p 1 q 1,1 q 1,2... q 1, m unknown p 2 q 2,1 q 2,2... q 2, m loc1 p 3 q 3,1 q 3,2... q 3, m unknown p 4 q 4,1 q 4,2... q 4, m loc k p n q n,1 q n,2... q n, m unknown Data analysis MSnbase for data manipulation, proloc for clustering, classication and plotting, and prolocgui for interactive exploration.

35 Correlation profile ER Correlation profile Golgi Correlation profile mit/plastid Fractions Fractions Fractions Principal component analysis Correlation profile PM PC PC1 ER Golgi mit/plastid PM vacuole marker PLS DA unknown Fractions Correlation profile Vacuole Fractions Figure : From Gatto et al. (2010), data from Dunkley et al. (2006).

36 2009 vs PC1 (58.53%) PC2 (29.96%) ER/Golgi mitochondrion PM unknown PC1 (58.53%) PC2 (29.96%) Cytoskeleton ER Golgi Lysosome mitochondrion Nucleus Peroxisome PM Proteasome Ribosome 40S Ribosome 60S Figure : Semi-supervised approach Breckels et al. (2013). Data from Tan et al (2009).

37 PC1 (50.05%) PC2 (24.61%) Actin cytoskeleton Cytosol Endosome ER/GA Extracellular matrix Lysosome Mitochondria Nucleus Chromatin Nucleus Nucleolus Peroxisome Plasma Membrane Proteasome Ribosome 40S Ribosome 60S unknown Dynein 2 Vesicles Clathrin 3 13S condensin 4 T complex 5 Nucleus lamina 6 Vesicles COPI/II 7 eif3 complex 8 ARP2/3 complex 9 COP9 signalosome From Betschinger et al. (2013) Mouse ESC (E14TG2a) in serum LIF PC1 (50.05%) PC2 (24.61%) Actin cytoskeleton Cytosol Endosome ER/GA Extracellular matrix Lysosome Mitochondria Nucleus Chromatin Nucleus Nucleolus Peroxisome Plasma Membrane Proteasome Ribosome 40S Ribosome 60S unknown Tfe3

38 Acknowledgement Lisa Breckels Sebastien Gibb Kathryn Lilley (CCP) ## R version Patched ( r68234) ## Platform: x86_64-unknown-linux-gnu (64-bit) ## Running under: Ubuntu LTS ## ## attached base packages: ## [1] stats graphics grdevices utils datasets methods base ## ## loaded via a namespace (and not attached): ## [1] Biobase_ vsn_ ## [3] splines_3.2.0 foreach_1.4.2 ## [5] Formula_1.2-1 affy_ ## [7] Pbase_0.9.0 highr_0.5 ## [9] stats4_3.2.0 latticeextra_ ## [11] BSgenome_ Rsamtools_ ## [13] impute_ RSQLite_1.0.0 ## [15] lattice_ biovizbase_ ## [17] limma_ chron_ ## [19] digest_0.6.8 GenomicRanges_ ## [21] RColorBrewer_1.1-2 XVector_0.9.1 ## [23] colorspace_1.2-6 preprocesscore_ ## [25] plyr_1.8.2 MALDIquant_1.12 ## [27] XML_ biomart_ ## [29] zlibbioc_ scales_0.2.4 ## [31] affyio_ cleaver_1.7.0 ## [33] BiocParallel_ IRanges_ ## [35] ggplot2_1.0.1 SummarizedExperiment_0.1.5 ## [37] GenomicFeatures_ nnet_7.3-9 ## [39] Gviz_ BiocGenerics_ ## [41] proto_ survival_ ## [43] magrittr_1.5 evaluate_0.7 ## [45] doparallel_1.0.8 MASS_ ## [47] foreign_ mzr_2.3.1

Σχετικά έγγραφα

Proteomics. Laurent Gatto 1 CSAMA 27 June

Proteomics. Laurent Gatto 1 CSAMA 27 June Proteomics Laurent Gatto 1 http://cpu.sysbiol.cam.ac.uk CSAMA 27 June 2014 1 lg390@cam.ac.uk Outline Proteomics and MS data Ranges infrastructure Application: spatial proteomics Mass-spectrometry (LC-MSMS)