Introduction to MS-based proteomics andBioconductor infrastructure

Laurent Gatto

lg390@cam.ac.uk � @lgatt0

http://cpu.sysbiol.cam.ac.uk

CSAMA � 17 June 2015

Outline

Proteomics and MS data

Bioconductor infrastructure

Examples

Ranges infrastructure

Application: spatial proteomics

Mass-spectrometry � LC-MS/MS

Precursor iondissociation

Detector

Precursor ions MS1

Fragmented ions

MSMS - MS2

AnalyserSourceSeparation

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0Chromatogram: total intensity over time

Time (sec)

Tota

l ion

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TMT_Erwinia_1uLSike_Top10HCD_isol2_45stepped_60min_01−20141210.mzMLTIC: 7.22e+09

MS1 (and MS2) spectra

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m/z

Mass-spectrometry � LC-MS/MS

Precursor iondissociation

Detector

Precursor ions MS1

Fragmented ions

MSMS - MS2

AnalyserSourceSeparation

Fragmentation

Credit abrg.org

cid <- calculateFragments("AEGKLRFK",

type=c("b", "y"), z=2)

## Modifications used: C=160.030649

ht(cid, n = 3)

## mz ion type pos z seq

## 1 36.52583 b1 b 1 2 A

## 2 101.04713 b2 b 2 2 AE

## 3 129.55786 b3 b 3 2 AEG

## ...

## mz ion type pos z seq

## 31 357.7185 y6* y* 6 2 GKLRFK

## 32 422.2398 y7* y* 7 2 EGKLRFK

## 33 457.7583 y8* y* 8 2 AEGKLRFK

MS1 and MS2 spectra

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m/zretention time

Proteomics data

I raw data:MS1 and MS2 overretention time

I identi�cation:MS2

I quantitation:MS1 or MS2

I protein database(to match MS2 spectraagainst)

Status package

Raw (mz*ML) X mzR

mzTab X MSnbase

mgf X MSnbase

mzIdentML X mzID, mzRmzQuantML (?mzR)

Bioconductor infrastructure

biocViews: Proteomics, MassSpectrometry

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Bioconductor Versions

ProteomicsMassSpectrometry

Learning from Bioconductor

| genomics | proteomics |

|------------------+------------------------|

| eSet (past?) | *MSnSet (present) |

| Ranges (present) | *Pbase et al. (future) |

| | PPI |

| | *localisation (present)|

MSnSet

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)

Example

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126.080 126.591 127.102 127.613 128.124 128.635 129.146 129.657 130.168 130.679 131.190

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]])

Example

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prec scan: 6975, prec mass: 545.017, prec z: 3, # common: 34

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prec scan: 7198, prec mass: 545.017, prec z: 3, # common: 35

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])

Example

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3.0 3.5 4.0 4.5−

0.15

−0.

10−

0.05

0.00

0.05

0.10

0.15

A

M

Median: 0.000187IQR: 0.013

More

I RforProteomics package

library("RforProteomics")

RforProteomics()

RProtVis()

citation(package = "RforProteomics")

I Proteomics work�ow on the Bioc site

I Lab on Friday

I protein database

I raw dataI quantitationI identi�cation

Ranges infrastructure

gs ge

G

T1

esi ee

i

i = 1

esi ee

i

i = 2

esi ee

i

i = 3

esi ee

i

i = 4

T2i = 1 i = 3 i = 4

T3i = 1 i = 4

P

psj pe

j

j = 1

psj pe

j

j = 2

psj pe

j

j = 3

Pj = 1 j = 2 j = 31 LP

Π

πsk πe

k

k = 1

πsk πe

k

k = 2

πskπe

k

k = 3

πskπe

k

k = 4

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)

Along protein coordinates

100

200

300

400

500

600

700

800

NH COOH

pept

ides

230

240

250NH COOH

P13

639

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

pept

ides

Along genome coordinates

... using transcript models as GRangesList and Gviz for plotting.

Chromosome 1

156.11 mb

156.115 mb

156.12 mb

156.125 mb

156.13 mb

156.135 mb

pept

ides

SYLLGNSSPRTQSPQNCSIM

RATRSGAQASSTPLSPTR

SVGGSGGGSFGDNLVTR

RATRSGAQASSTPLSPTR ELEKTYSAKLDNAR

METPSQRRATR

DTSRRLLAEKEREMAEMR

LALDMEIHAYR

LALDMEIHAYRK

RQNGDDPLLTYRFPPK

SVGGSGGGSFGDNLVTR

SVGGSGGGSFGDNLVTR

From the Pbase mapping vignette.

Along genome coordinates (with raw data)

Chromosome X

78.11 mb

78.115 mb

78.12 mb

78.125 mb

5' 3'3' 5'

MS

2 sp

ectr

aE

NS

T00

0003

7331

6

From the Pbase mapping vignette.

With RNA-Seq readsChromosome 17

30.12 mb

30.13 mb

30.14 mb

30.15 mb

30.16 mb

30.17 mb

30.18 mb

EN

ST

0000

0612

959

0

20

40

60

80

Alig

nmen

tsTr

ack

From https://github.com/ComputationalProteomicsUnit/Intro-Integ-Omics-Prot

Spatial proteomics

I The cellular sub-division

allows cells to establish a

range of distinct

microenvironments, each

favouring di�erent

biochemical reactions and

interactions and, therefore,

allowing each compartment

to ful�l a particular

functional role.

I 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.

Figure : Immuno�uorescence:ZFPL1, Golgi (left) and FHL2,mainly localized to actin �lamentsand focal adhesion sites. Alsodetected in the nucleus (right).(from the Human Protein Atlas)

Cell lysatefraction

centrifugation

iTRAQMS/MS

LOPIT(PCA,

PLS-DA)

label-freeMS/MS

(χ )2PCP

Pure fraction

catalogue

Invariantrich

fraction(clustering)

Subtractiveproteomics

(enrichment)

Figure : Mass spectrometry-basedapproaches based on densitygradient subcellular fractionation.

Cell membrane lysis

Mechanical or bu�er-induced lysis of the plasma membrane with

minimal disruption to intracellular organelles followed by subcellular

fractionation.

Density gradient separation

Quantitation by LC-MSMS

Data

Fraction1 Fraction2 . . . Fractionm markers

p1 q1,1 q1,2 . . . q1, m unknown

p2 q2,1 q2,2 . . . q2, m loc1

p3 q3,1 q3,2 . . . q3, m unknown

p4 q4,1 q4,2 . . . q4, m lock...

......

......

...

pn qn,1 qn,2 . . . qn, m unknown

Data analysis

MSnbase for data manipulation, pRoloc for clustering, classi�cation

and plotting, and pRolocGUI for interactive exploration.

0.2

0.3

0.4

0.5

Correlation profile − ER

Fractions

1 2 4 5 7 81112

0.1

0.2

0.3

0.4

Correlation profile − Golgi

Fractions

1 2 4 5 7 81112

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Correlation profile − mit/plastid

Fractions

1 2 4 5 7 81112

0.15

0.20

0.25

0.30

0.35

Correlation profile − PM

Fractions

1 2 4 5 7 81112

0.1

0.2

0.3

0.4

0.5

0.6

Correlation profile − Vacuole

Fractions

1 2 4 5 7 81112

●●

●●

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●

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●●

●●

−10 −5 0 5

−5

05

Principal component analysis

PC1

PC

2

●

ERGolgimit/plastidPM

vacuolemarkerPLS−DAunknown

Figure : From Gatto et al. (2010), data from Dunkley et al. (2006).

2009 vs 2013

−3 −2 −1 0 1 2 3

−3

−2

−1

01

23

PC1 (58.53%)

PC

2 (2

9.96

%)

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ER/GolgimitochondrionPMunknown

−3 −2 −1 0 1 2 3

−3

−2

−1

01

23

PC1 (58.53%)

PC

2 (2

9.96

%)

CytoskeletonERGolgiLysosomemitochondrionNucleus

PeroxisomePMProteasomeRibosome 40SRibosome 60S

Figure : Semi-supervised approach Breckels et al. (2013). Data from Tanet al (2009).

−6 −4 −2 0 2 4

−4

−2

02

4

PC1 (50.05%)

PC

2 (2

4.61

%)

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Actin cytoskeletonCytosolEndosomeER/GAExtracellular matrixLysosomeMitochondriaNucleus − ChromatinNucleus − NucleolusPeroxisomePlasma MembraneProteasomeRibosome 40SRibosome 60Sunknown

●●●●●●

111111

●●●●●●222222 ●

●●●3333

●●●●●

●●●4 4

4444

44

●●

●

●●

● ●

55

5

55

5 5

●● ●●●

● ●

●

● 66 666

6 6

6

6

●●●●●●●●●

● ●● 777 7 77 7

777 7

7

●●●●● ●●●

88888 8

88

●●●● ●●●

●

9999 999

9

1 Dynein2 Vesicles − Clathrin 3 13S condensin4 T complex5 Nucleus lamina

6 Vesicles − COPI/II7 eIF3 complex8 ARP2/3 complex9 COP9 signalosome

From Betschinger et al. (2013)

−6 −4 −2 0 2 4

−4

−2

02

4

Mouse ESC (E14TG2a) in serum LIF

PC1 (50.05%)

PC

2 (2

4.61

%)

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Actin cytoskeletonCytosolEndosomeER/GAExtracellular matrixLysosomeMitochondriaNucleus − ChromatinNucleus − NucleolusPeroxisomePlasma MembraneProteasomeRibosome 40SRibosome 60Sunknown

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Acknowledgement

I Lisa Breckels

I Sebastien Gibb

I Kathryn Lilley

(CCP)

## R version 3.2.0 Patched (2015-04-22 r68234)## Platform: x86_64-unknown-linux-gnu (64-bit)## Running under: Ubuntu 14.04.2 LTS#### attached base packages:## [1] stats graphics grDevices utils datasets methods base#### loaded via a namespace (and not attached):## [1] Biobase_2.29.1 vsn_3.37.1## [3] splines_3.2.0 foreach_1.4.2## [5] Formula_1.2-1 affy_1.47.1## [7] Pbase_0.9.0 highr_0.5## [9] stats4_3.2.0 latticeExtra_0.6-26## [11] BSgenome_1.37.1 Rsamtools_1.21.8## [13] impute_1.43.0 RSQLite_1.0.0## [15] lattice_0.20-31 biovizBase_1.17.1## [17] limma_3.25.9 chron_2.3-45## [19] digest_0.6.8 GenomicRanges_1.21.15## [21] RColorBrewer_1.1-2 XVector_0.9.1## [23] colorspace_1.2-6 preprocessCore_1.31.0## [25] plyr_1.8.2 MALDIquant_1.12## [27] XML_3.98-1.2 biomaRt_2.25.1## [29] zlibbioc_1.15.0 scales_0.2.4## [31] affyio_1.37.0 cleaver_1.7.0## [33] BiocParallel_1.3.25 IRanges_2.3.11## [35] ggplot2_1.0.1 SummarizedExperiment_0.1.5## [37] GenomicFeatures_1.21.13 nnet_7.3-9## [39] Gviz_1.13.2 BiocGenerics_0.15.2## [41] proto_0.3-10 survival_2.38-1## [43] magrittr_1.5 evaluate_0.7## [45] doParallel_1.0.8 MASS_7.3-40## [47] foreign_0.8-63 mzR_2.3.1## [49] BiocInstaller_1.19.6 Pviz_1.3.0## [51] tools_3.2.0 data.table_1.9.4## [53] formatR_1.2 matrixStats_0.14.0## [55] stringr_1.0.0 MSnbase_1.17.5## [57] S4Vectors_0.7.5 munsell_0.4.2## [59] cluster_2.0.1 AnnotationDbi_1.31.16## [61] lambda.r_1.1.7 Biostrings_2.37.2## [63] pcaMethods_1.59.0 GenomeInfoDb_1.5.7## [65] mzID_1.7.0 futile.logger_1.4.1## [67] grid_3.2.0 RCurl_1.95-4.6## [69] dichromat_2.0-0 iterators_1.0.7## [71] VariantAnnotation_1.15.13 bitops_1.0-6## [73] gtable_0.1.2 codetools_0.2-11## [75] DBI_0.3.1 reshape2_1.4.1## [77] GenomicAlignments_1.5.9 gridExtra_0.9.1## [79] knitr_1.10.5 rtracklayer_1.29.10## [81] Hmisc_3.16-0 ProtGenerics_1.1.0## [83] futile.options_1.0.0 stringi_0.4-1## [85] parallel_3.2.0 Rcpp_0.11.6## [87] rpart_4.1-9 acepack_1.3-3.3