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Proteomics
Laurent Gatto1
http://cpu.sysbiol.cam.ac.uk
CSAMA � 27 June 2014
Outline
Proteomics and MS data
Ranges infrastructure
Application: spatial proteomics
Mass-spectrometry (LC-MSMS)
Precursor iondissociation
Detector
Precursor ions MS1
Fragmented ions
MSMS - MS2
AnalyserSourceSeparation
MS1 and MS2 spectra
400 500 600 700 800
0.0e
+00
5.0e
+07
1.0e
+08
1.5e
+08
2.0e
+08
MS1 scan @ 21:3 min
m/z
inte
nsity 200 400 600 800
0e+
002e
+06
4e+
066e
+06
8e+
061e
+07
MS2 scan, precursor m/z 460.79
200 400 600 800 1000
0e+
001e
+06
2e+
063e
+06
4e+
065e
+06
6e+
06
MS2 scan, precursor m/z 488.8
m/z
MS1 and MS2 spectra
400
600
800
1000
1200
21.10
21.15
21.20
21.25
21.30
21.35
0.0e+00
5.0e+07
1.0e+08
1.5e+08
2.0e+08
m/zretention time200
400
600
800
1000
1200
21.06
21.07
21.08
21.09
21.10
21.11
0.0e+00
5.0e+07
1.0e+08
1.5e+08
2.0e+08
m/zretention time
Proteomics data
I raw dataI quantitationI identi�cation
I protein database
Status package
Raw (mz*ML) X mzRmzTab X MSnbase
mgf X MSnbasemzIdentML X mzID (mzR)mzQuantML (?mzR)
Proteomics data
I raw dataI quantitationI identi�cation
I protein database
Status package
Raw (mz*ML) X mzRmzTab X MSnbase
mgf X MSnbasemzIdentML X mzID (mzR)mzQuantML (?mzR)
Example
library("MSnbase")
rx <- readMSData(f, centroided = TRUE)
rx <- addIdentificationData(rx, g)
rx <- rx[!is.na(fData(rx)$pepseq)]
plot(rx[[10]], reporters = TMT6, full=TRUE)
Example
0e+00
2e+05
4e+05
6e+05
8e+05
300 600 900 1200M/Z
Inte
nsity
Precursor M/Z 600.36
0e+00
2e+05
4e+05
6e+05
8e+05
126.080 126.591 127.102 127.613 128.124 128.635 129.146 129.657 130.168 130.679 131.190
Example
library("MSnbase")
rx <- readMSData(f, centroided = TRUE)
rx <- addIdentificationData(rx, g)
rx <- rx[!is.na(fData(rx)$pepseq)]
plot(rx[[10]], reporters = TMT6, full=TRUE)
plot(rx[[4730]], rx[[4929]])
Example
0 500 1000 1500
−1.
0−
0.5
0.0
0.5
1.0
m/z
inte
nsity
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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, method = "max")
## qt <- readMSnSet(f2)
nqt <- normalise(qt, method = "vsn")
boxplot(exprs(nqt))
Example
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TMT6.126 TMT6.127 TMT6.128 TMT6.129 TMT6.130 TMT6.131
1015
2025
More
library("BiocInstaller")
biocLite("RforProteomics")
I raw dataI quantitationI identi�cation
I protein database
Ranges infrastructure
Pbase package
library("Pbase")
p <- Proteins(db)
p <- addIdentificationData(p, id)
aa(p) ## AAStringSet
pranges(p) ## IRangesList
i <- which(acols(p)[, "EntryName"] == "EF2_HUMAN")
plot(p[i])
plot(p[i], from = 155, to = 185)
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
Spatial proteomics
I The cellular sub-divisionallows cells to establish arange of distinctmicroenvironments, eachfavouring di�erentbiochemical reactions andinteractions and, therefore,allowing each compartmentto ful�l a particularfunctional role.
I Localisation andsequestration of proteinswithin subcellular niches is afundamental mechanism forthe post-translationalregulation of proteinfunction. Spatial proteomics is the systematic
study of protein localisations.
Spatial proteomics
Disruption of thetargeting/tra�cking process altersproper sub-cellular localisation,which in turn perturb the cellularfunctions of the proteins.
I Abnormal proteinlocalisation leading to theloss of functional e�ects indiseases (Laurila et al.2009)
I Disruption of thenuclear/cytoplasmictransport (nuclear pores)have been detected in manytypes of carcinoma cells(Kau et al. 2004).
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
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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−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: pRoloc package. Semi-supervised approach Breckels et al.(2013). Data from Tan et 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
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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
Dynamic
Figure: pRolocGUI package.
Dual localisation
0.0
0.1
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0.6
Fraction
Inte
nsity
1 1 2 2 4 4 5 5 7 7 8 8 11 11
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AT5G61790 (ER membrane)AT4G32400 (Plastid)AT2G45470 (PM)
−6 −4 −2 0 2 4 6
−4
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02
4PC1 (64.36%)
PC
2 (2
2.34
%)
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ER lumenER membraneGolgiMitochondrionPlastidPMRibosomeTGNunknownvacuole
●●●● ●● ● ●● ● ●
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Figure: Proteins may be present simultaneously in several organelles(dual localisation, tra�cking) vs. no man's land. (Gatto et al. 2014)
Acknowledgement
CPU
I Lisa Breckels
I Sebastien Gibb
I Thomas Naake
I Kathryn Lilley (CCP) Computational Proteomics UnitCambridge Centre for ProteomicsCambridge System Biology CentreDepartment of BiochemistryUniversity of Cambridgehttp://cpu.sysbiol.cam.ac.uk
@lgatt0
Software Sustainability Institutehttp://software.ac.uk
![[AION-Scan] Akuma no Riddle - Capítulo 06](https://img.pdfslide.us/doc/110x75/577ccf3c1a28ab9e788f3aa3/aion-scan-akuma-no-riddle-capitulo-06.jpg)
