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

12

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

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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)