Post on 21-Oct-2014
description
PSLID,theProteinSubcellularLoca4onImageDatabase:
Subcellularloca4onassignments,annotatedimagecollec4ons,image
analysistools,andgenera4vemodelsofproteindistribu4ons
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EstelleGlory,Jus.nNewberg,TaoPeng,IvanCao‐Berg,andRobertF.Murphy
DepartmentsofBiologicalSciences,BiomedicalEngineeringandMachineLearningand
Contributors• MichaelBoland• MiaMarkey• GregoryPorreca• MeelVelliste• KaiHuang• XiangChen• YanhuaHu• JuchangHua• TingZhao• Shann‐ChingChen• ElviraOsunaHighley• Jus4nNewberg• EstelleGlory• TaoPeng• LuisCoelho• IvanCao‐Berg
• DavidCasasent• SimonWatkins• JonJarvik,PeterBerget• JackRohrer• TomMitchell• ChristosFaloutsos• JelenaKovacevic• GeoffGordon• B.S.Manjunath,AmbujSingh• LesLoew,IonMoraru,JimSchaff• GustavoRohde• GhislainBonamy,SumitChanda,
DanRines
Overview
• SLIC– SubcellularLoca.onImageClassifica.on,Clustering,Comparison
• PUnMix– SubcellularPaVernUnmixing
• SLMLTools– Genera.veModelsofCellsandSubcellularOrganelles
• PSLID– ProteinSubcellularLoca.onImageDatabase
TheChallenge
Comparisonofcellimagespixel‐by‐pixelorregion‐by‐regionmatchingdoesnotworkforcellpaVernsbecausedifferentcellshavedifferentshapes,sizes,orienta4ons
Organelles/structureswithincellsarenotfoundinfixedloca4ons
Instead,describeeachimagenumericallyandoperateonthedescriptors(“SLF”‐SubcellularLoca=onFeatures)
SLICtoolcategories
• Segmenta.on• Featurecalcula.on• Classifica.on• Clustering• Comparison
Featurelevelsandgranularity
Object features
Single Object
Single Cell
Single Field
Cell features
Field features
Granularity: 2D, 3D, 2Dt, 3Dt
Aggregate/averageoperator
ER
Tubulin DNATfRAc.n
NucleolinMitoLAMP
gpp130gian.n
2DImagesofHeLacells
40
50
60
70
80
90
100
40 50 60 70 80 90 100
Computer Accuracy
Hu
man
Acc
ura
cySubcellularPaVernClassifica.on:
Computervs.Human
EvenbeVerresultsusingmul.resolu.onmethods
EvenbeVerresultsfor3Dimages
SLICversions–Sourcecode
• Matlab• Python• C++/ITK(subset;fromBadriRoysam’sgroup)
DecomposingmixturepaVerns
• Proteinscanbeinmorethanonestructure• ClusteringorclassifyingwholecellpaVernswillconsidereachcombina.onoftwoormore“basic”paVernsasauniquenewpaVern
• Desirabletohaveawaytodecomposemixturesinstead
• Ourapproach:assumethateachbasicpaVernhasarecognizablecombina.onofdifferenttypesofobjects
PUnMix
• Learnunmixingmodelinstance• Unmiximagesusingmodelinstance
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ExamplesofObjectTypes
Learnthetypesbyclusteringusingobjectfeatures
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34
56
78
Nuclear class
Lysosomal class
Golgi class0
0.1
0.2
0.3
0.4
0.5
Amt fluor.
Object type
12
34
56
78
Nuclear class
Lysosomal class
Golgi class0
0.1
0.2
0.3
0.4
0.5
Amt fluor.
Object type
1 2 34
56
78
Nuclear class
Lysosomal class
Golgi class
All0
0.05
0.1
0.15
0.2
0.25
Amt fluor.
Object type
PureGolgiPaRern
Pure Lysosomal Pattern
Testsamples
• HowdowetestasubcellularpaVernunmixingalgorithm?
• NeedimagesofknownmixturesofpurepaVerns–difficulttoobtain“naturally”
• Createdtestsetbymixingdifferentpropor.onsoftwoprobesthatlocalizetodifferentcellparts(lysosomesandmitochondria)
• Lysotracker
Tao Peng, Ghislain Bonamy, Estelle Glory, Sumit Chanda, Dan Rines (Genome Research Institute of Novartis Foundation)
• Mitotracker
• MixtureofLysotrackerandMitotracker
PaVernunmixingresults
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PUnMixversions
• Opensource–MatlabincludingC++• Compiledversions(notrequiringMatlablicense)forMacOS,Windows,Linux
SLMLTools‐Genera.vemodelsofsubcellularpaVerns
• Buildmodelinstancefromimagecollec.on• Generateimagesfrommodelinstance
• Viewmul.‐paVernimages
LAMP2paVern
Nucleus
Cell membrane
Protein
NuclearShape‐MedialAxisModel
Rotate
Medial axis Represented by two curves
the medial axis width along the medial axis
width
Synthe.cNuclearShapes
Withaddednucleartexture
CellShapeDescrip.on:DistanceRa.o
d1
d2 2
21
dddr +
=
Genera.on
Modelsforprotein‐containingobjects
• MixtureofGaussianobjects
• Learndistribu.onsfornumberofobjectsandobjectsize
• Learnprobabilitydensityfunc.onforobjectsrela.vetonucleusandcell
r:normalizeddistance,a:angletomajoraxis
SynthesizedImages
Lysosomes Endosomes
HaveXMLdesignforcapturingmodelparameters Haveportabletoolforgenera.ngimagesfrommodel
SLMLtoolbox‐IvanCao‐Berg,TaoPeng,TingZhao
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ModelDistribu.on
• Genera.vemodelsprovidebeVerwayofdistribu.ngwhatisknownabout“subcellularloca.onfamilies”(orotherimagingresults,suchasillustra.ngchangeduetodrugaddi.on)
• HaveXMLdesignforcapturingthemodelsfordistribu.on
• Haveportabletoolforgenera.ngimagesfromthemodel
CombiningModelsforCellSimula.ons
Protein 1 Cell Shape
Nuclear Model
Protein 2 Cell Shape
Nuclear Model
Protein 3 Cell Shape
Nuclear Model
XML
Simulation
Shared Nuclear and Cell
Shape
Examplecombina.on
Red=nuclearmembrane,plasmamembraneBlue=GolgiGreen=LysosomesCyan=Endosomes
SLMLToolsversions
• Opensource–MatlabincludingC++• Compiledversions(notrequiringMatlablicense)forMacOS,Windows,Linux
PSLID
• Loadingpipelinedrivenbyscript– Calculatesthumbnailimages,features,segmenta.on– Createsdatabaserecordsandlinks– Createspredefinedsets
• Webapplica.on– Createsetsbysearchingoncontextorcontent– AnalyzesetswithanySLICtool– Fulldisplayorsummary
– SOAP/XMLinterface
PSLID
• Opensource• Linuxonly:tomcat,postgres
AnnotatedDatasets
• 2Dand3Dimagesof9majorsubcellularpaVernsinHeLacells
• 3Dimagesof~300proteinsin3T3cells
• 2Dimagesof~3000proteinsin3T3cells
• 2Dand3DimagesforpaVernunmixing
• Datasetsfromotherinves.gators
• hVp://murphylab.web.cmu.edu/sooware• hVp://murphylab.web.cmu.edu/data
• PastmajorsupportfromNSF
• CurrentsupportfromNIHNIGMSandNCRR– Na.onalCenterforNetworksandPathways:MolecularBiosensorsandImagingCenter(AlanWaggoner)