Characterization and Classification of Adherent Cells in Monolayer Culture using Automated Tracking and Evolutionary Algorithms

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Zhang, Zhen, Bedder, Matthew, Smith, Stephen Leslie orcid.org/0000-0002-6885-2643 et

al. (3 more authors) (2016) Characterization and Classification of Adherent Cells in

Monolayer Culture using Automated Tracking and Evolutionary Algorithms. Biosystems.

pp. 110-121. ISSN 0303-2647

https://doi.org/10.1016/j.biosystems.2016.05.009

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ContentslistsavailableatScienceDirect

BioSystems

j ourna l h o m e pa g e : w w w . e l s e v i e r . c o m / l o c a t e / b i o s y s t e m s

Characterization

and

classification

of

adherent

cells

in

monolayer

culture

using

automated

tracking

and

evolutionary

algorithms

Zhen

Zhang

a

_,

_Matthew

_Bedder

b

_,

_Stephen

_L.

_Smith

a,∗

,

Dawn

Walker

c

,

Saqib

Shabir

d

,

Jennifer

Southgate

d

a_Department_of_Electronics,_University_of_York,_Heslington,_York_YO10_5DD,_UK b_Department_of_Computer_Science,_University_of_York,_Heslington,_York_YO10_5GW,_UK

c_Department_of_Computer_Science_&_Insigneo,_Institute_for_in_silico_Medicine,_University_of_Sheffield,_Sheffield_S1_4DP,_UK d_Jack_Birch_Unit,_Department_of_Biology,_University_of_York,_Heslington,_York_YO10_5DD,_UK

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received6May2016 Accepted17May2016 Availableonline3June2016

a

b

s

t

r

a

c

t

Thispaperpresentsanovelmethodfortrackingandcharacterizingadherentcellsinmonolayerculture. Asystemofcelltrackingemployingcomputervisiontechniqueswasappliedtotime-lapsevideosof replicatenormalhumanuro-epithelialcellculturesexposedtodifferentconcentrationsofadenosine triphosphate(ATP)andaselectivepurinergicP2Xantagonist(PPADS),acquiredovera24hperiod. Sub-sequentanalysisfollowingfeatureextractiondemonstratedtheabilityofthetechniquetosuccessfully separatethemodulatedclassesofcellusingevolutionaryalgorithms.Specifically,aCartesianGenetic Program(CGP)networkwasevolvedthatidentifiedaveragemigrationspeed,in-contactangular veloc-ity,cohesivityandaveragecellclumpsizeastheprincipalfeaturescontributingtotheseparation.Our approachnotonlyprovidesnon-biasedandparsimoniousinsightintomodulatedclassbehaviours,but canbeextractedasmathematicalformulaefortheparameterizationofcomputationalmodels.

1. Introduction

Urotheliumisaremarkableepithelialtissuethatlinesthe blad-derandassociatedurinarytracts formingthetightestandmost efficientself-repairingbarrierinthebody.Inresponsetophysical orotherdamage,theurotheliumswitchesrapidlyandtransiently fromastable,mitotically-quiescentbarrierintoahighly prolifera-tivestate.Themechanismsthatfacilitatethisswitcharecentralto thepathophysiologyofthebladder,butarepoorlyunderstood.

Theurotheliumisreportedtorespondtomechanicaland chem-icalstimulationbyreleasingsolublefactors,includingadenosine triphosphate(ATP),whichareproposedtoplayarolein mediat-ingneuronalsignalling(Birder,2011).Inaddition,theurothelium expressespurinergicP2XandP2Yreceptorsandchannelsthatare responsivetoATPreleasedfromautocrine orparacrine sources (Shabiretal.,2013).Theoutcomeofsuchsignallingisincompletely understood,asitcouldhaveafeedbackroleinmodulatingneuronal signalling,butalternativelycouldplayamoredirectrolein

urothe-∗_{Corresponding}_author.

E-mailaddresses:[email protected](Z.Zhang),[email protected](M.Bedder),

[email protected](S.L.Smith),[email protected](D.Walker),

[email protected](J.Southgate).

lialbarrierrepair(Shabiretal.,2013).Ithasbeenfurthersuggested thataberrantexpressionofreceptorsand/ormediatorreleaseby theurotheliumisinvolvedindysfunctionaldiseasesofthe blad-der,includingidiopathicdetrusorinstabilityandinterstitialcystitis (BirderanddeGroat,2007).

Despitethereportedexpressionofthesechannelsandreceptors bytheurothelium,consensushasbeenconfoundedby inconsisten-ciesinexperimentalapproaches,includingthespecies,specificity ofreagents, andthenatureof thetissue preparation(reviewed (YuandHill,2011)).Wehavedevelopedacellculturesystemfor investigatingnormalhumanurothelial(NHU)cellsandtissuesin vitro.Inpreviousworkusingthisculturesystem,weshowedthat stimulationofP2receptorswithexogenousATPenhancedscratch woundrepair,asdidadditionoftheecto-ATPaseinhibitor ARL-67156,whichpreventsthebreakdownofautocrine-producedATP. Bycontrast,blockadeofP2Xactivityinhibitedscratchwoundrepair ineitherthepresenceorabsenceofATP(Shabiretal.,2013).This indicatesthatATPisoneofthemajorfactorsreleasedupon urothe-lialdamageandthatitislikelytocontributetourothelialbarrier repair.

TounderstandfurthertheeffectofATPandP2Xsignallingon urothelialcellphenotype,time-lapsevideoshavebeengenerated oflowdensityurothelialcellculturestowhichexogenousATPand selectiveantagonistsofP2Xhavebeenapplied.Thispaperdescribes http://dx.doi.org/10.1016/j.biosystems.2016.05.009

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Fig.1.SchematicmodelofWnt/␤-cateninsignallingcrosstalkwithEGFR/ERKandcell:cellcontact-mediated␤-cateninregulationinNHUcellproliferation. Reproducedwithpermissionfrom(Georgopoulosetal.,2014).

Fig.2. Sampleframefromtime-lapsevideoofNHUcellsinculture.

thedevelopmentofanautomatedmethodforobjective measure-mentofthesevideosusingcomputervisiontechniques,followed bytheextractionoffeatures,withtheaimofidentifyingkey char-acteristicsofcellbehaviourrelatedtodifferencesinthepopulation. Replicatecellculturesarepreparedinparallelandrecordedovera 24-hperiodusingstandardvideomicroscopy.Thedigitalvideosare thenprocessedusingcustomcelltrackingsoftwareimplemented usingarangeofcomputervisiontechniques.Theresultingtracking dataisthensubjectedtotwomethodsofanalysiswiththeaimof characterizingthebehaviourofthecellcultures.Thefirstisthe extractionofa setoffeatures informedfrom previousresearch andspecifiedbythebiologicalmotivationforthisstudy.The sec-ondapproach is theapplication ofa novelclassifier employing evolutionaryalgorithms−computerprogramswhoseoperationis

inspiredbytheprocessesofDarwinianevolution.Thesealgorithms havethepotentialtoprovidepowerclassifiers,aswellasrevealing thosebiologicalpropertiesthatcontributetotheclassification.

Section2ofthispaperdescribestheunderlyingbiological pro-cessesoftheurotheliumingreater depthandthen providesan

overviewofcurrentmodelling,alongwithanintroductionto evo-lutionaryalgorithms.Theprocessesandmethodologyadoptedin ourworkaredescribedinSection3,andresults,withstatistical analysis,arepresentedinSection4.Finally,conclusionsandfuture workareconsideredinSection5.

2. Background

2.1. Theurothelium–arelevanttissue-specificexperimentalcell system

Urothelium,thetransitionalepitheliumfoundliningthebladder and associated urinary tract functions as a stable, but self-repairing urinarybarrier.Thisbarrierfunctionis attributableto urothelium-specificspecialisationsacquiredduringdifferentiation ofthesuperficialcells,withsurfacemembraneplaquescomposed of uroplakins(Hu etal.,2002)and well-developedintercellular tightjunctions(Smithetal.,2015)Withintheurothelium, indi-vidualcellsaremaintainedinamitotically-quiescentstateuntil,

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Fig.3. Theprocessusedfordetectingcelllocationswithinavideo,andtrackingofdetectedcellsbetweenvideoframes.

Fig.4.Exampletrackingofasinglecellovera24-hvideo,sampledevery5min.Trackingbeganwhenthecellwasrightofcentre,andcontinuesuntilitapproachesthelower lefthandsideofthereferenceframe.

followingdamagetothebarrier,cellsfromalllayersswitchtoa pro-liferativephenotypeinordertoeffectefficientbarrierrepair(Varley etal.,2005).Thebalancebetweentheparadoxicalprocesses of regenerationanddifferentiationiscriticaltomaintainingan effec-tiveurinarybarrier,butthemechanismsthatregulateurothelial

tissuehomeostasisandtheswitchbetweenquiescentand regen-erative(self-repair)phenotypesarepoorlyunderstood.

Wehavedevelopedarobustandexperimentally-tractable cul-turesystemforisolatednormalhumanurothelial(NHU)cellsin whichregenerativeandfunctionally-differentiatedbarrierstates canbereplicated(Bakeretal.,2014).Inthesimplestcase,NHU

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Fig.5. Examplecalculationofcellmigrationspeed(pixels/frame).

Fig.6. Directionoftravelofcellmigration.

Fig.7. Examplecalculationofcellmigrationpersistenceovertwoconsecutiveframes.

cells maintainedinlow calcium,serum-freeconditionsadopt a “basal” epithelial cell phenotype, in which cells grow as non-stratified,highlyproliferativeandmigratoryculturesthatbecome contact-inhibitedatconfluence(Southgateetal.,1994).By mod-ifying E-cadherin (adherens) contacts (by switching exogenous calcium from low [0.09mM] to near-physiological [2mM], or by retroviral transduction of a dominant negative E-cadherin (H-2Kd_-E-cad) _construct), _we _have _shown _that _the _stability _of

E-cadherincell–cellcontactsisresponsiblefordifferentially reg-ulatingpopulationgrowththroughtheEpidermalGrowthFactor Receptor(EGFR)/ExtracellularSignal-RegulatedKinase(ERK)and Phosphatidylinositol 3-Kinase (PI3-K)/AKT signalling pathways (Georgopoulos et al., 2010). Stable adherens junctions down-regulatetheEGFR/ERKpathway,whilstinducingPI3-K/AKTactivity topromoteproliferationatlowcelldensity.Functional inactiva-tion ofE-cadherin interferes withthecapacity of NHU cells to formstablecalcium-mediatedcontactsandattenuates

E-cadherin-mediatedPI3-K/AKTinduction,butenhancesNHUcellproliferation by promoting the autocrine-driven EGFR/ERK pathway, which (viaGSK3␤phosphorylationand inactivationof thedestruction complex)activates␤-catenin-TCFsignalling.Furthermore,ifEGFR activity is blocked, then NHU cells are seen to be responsive to canonical Wnt signalling – either provided by addition of exogenousWntligandorendogenouslyifNHUcellsarecultured withpalmitic acidto enablepost-translational palmitylation of autocrine-producedWntligands(Georgopoulosetal.,2014).This hasrevealedacomplex,contextualinterrelationshipwhereinthe inherentcapacityforself-repairiscarriedviaatleast3 autocrine-regulatedintracellularpathwaysthatinteract(“crosstalk”)through regulationoftheactivity/availabilityofkeypathwaycomponents (minimallyE-cadherin,EGFR,pERK,␤-catenin,andpAkt),as sum-marizedinFig.1.

The E-cadherin-definedadherensjunctioneffectively actsas a master regulator through which urothelial cells individually

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Fig.8. ExampleCGPnetworkwith10inputsI0-I9,threecolumnsofprocessing nodesandoneoutputO1.

“sense” and effect control over their neighbours to regulate populationgrowth.Thesystemishighlycontext-specificand self-regulating,withthelocaldensityofcells inthepopulationand thepropensityfor cells toform cell:cell bondsaffecting recep-toravailabilityandthedownstreampathwaysthatareactivated. Thereis furtherregulation through specific (both positive and negative)feedbackbetweenthesignallingpathways. Disruption of population homeostasis (eg by scratch-wounding a conflu-entcontact-inhibited culture) results in cells of thepopulation respondinglocally.Theseinteractionsarecomplexand,aswehave shownpreviouslywhenwerevealedthePI3Kpathway contribu-tion(Georgopoulosetal.,2010),canbeinformedbyinsightfrom modelling.

Theobservationthat NHUcell culturesare proliferative,but reversiblycontact-inhibitedatconfluencewasusedasthebasisto developtheagent-basedcomputationalframeworkcalled Epithe-liome (Walker et al., 2004b). The non-deterministic nature of theEpitheliomewasshowntopredictregenerativebehaviourin scratch-woundedmonolayers(Walkeretal.,2004a)andpredicted anunidentified growthactivationpathwaypresent atlow den-sityinphysiological[2mM]calciumcultures.Thisobservationled directlytotheidentificationofthecellcontact-mediatedPI3-K/Akt signallingpathwaydescribedabove(Georgopoulosetal.,2010).

Thecurrentstudyhasbeeninpartmotivatedbytheneed iden-tifiedin ourpreviousmodellingworktoaccuratelyclassifythe behaviourofinvitrocellculturesunderdifferentexperimental con-ditionsandultimately,carriesthegoalofautomatedextractionof rulesandparameterstoreliablyinformcomputationalmodels.

2.2. Thestateoftheartinthemodellingofbiologicaltissues Previously,wehaveusedagent-basedmodels(ABMs)inorder tosimulateNHUcellsattheleveloftheindividualcell,andmake predictionsabouttheemergentpopulation-levelbehaviourunder differentcultureconditions. Briefly, inan ABM,each individual realworldentity(inthiscase, biologicalcell)isrepresentedby anequivalentvirtualentity,or“softwareagent”whichperforms simplebehavioursandinteractswithitslocalneighboursusing pre-programmedrules.Wehaveexploredthecontact-mediatedeffects oncellpopulationgrowth(Walkeretal.,2004b;Walkeretal.,2010) andwoundhealing(Walkeretal.,2004a;WalkerandSouthgate, 2013)usinganagent-basedrepresentationofNHUcellsinlowand physiologicalcalciumconditions.

Thesemodelsincludedrulesforcellmigration,adhesionand proliferationaccordingtotheimmediateneighboursand extracel-lularconditionsofindividualagents.Theserules,asisthecasefor otherABMsofcellularsystems,werederived fromthe observa-tionofsmallnumbersofindividualcellsincultureusingtime-lapse microscopy.Wheresuchdatawereunavailable,ruleswerebased onassumptionsorhypothesesrelatingtohowcellswould reason-ablybeexpectedtobehaveundervariousconditions:thelatter

Fig.9.Automatedcalculationofcellmigrationpersistence.AveragemigrationspeedsareshowninF-distributionformforControl,10␮MATPand50␮MATPVideos:small circlesmarkthemeanofthegroupandthebarsthe95%confidenceinterval.

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Control 10uM ATP 50uM ATP

0.0

0.2

0.4

0.6

0.8

1.0

1.2 Migr

at

ion

S

pee

d

Fig.10.Calculationofcellmigrationpersistenceusingmanualtracking.

1.50 2.00 2.50 3.00 3.50 4.00

Fig.11. Averagemigrationspeed(pixelsperframe).

beingdrawnfromboththeliteratureand“domainknowledge”of researchersworkingwiththeexperimentalsystem.

Assumptionsandapproximationsrelatingtothequalitativeand quantitativenatureofcellbehaviourarethusinherentinABMs (as withother modelling approaches), giving rise to epistemic uncertaintyinthemodelpredictions.Recentresearchefforthas focusedonthedevelopmentofuncertaintyanalysistechniquesin ordertoattempttoquantifytheeffectsofepistemicandaleatory (stochastic-based)uncertaintyinpredictivemodels(Aldenetal., 2013).Thoughrobust,suchmethodshavethedisadvantageofbeing computationallyintensive,requiringlargenumbersofsimulations exploringapotentiallylargeparameterspace.However,the auto-matedtracking and evolutionaryanalysis techniques described here offer an alternative approach to reducing theproblem of uncertaintyinmodels.Ifagentrulescanbedirectlyinformedby statistically-sound and objective observations of cell behaviour extracteddirectlyfrommicroscopyimagesets,thiswillincrease confidenceintheaccuracyofmodelpredictionsandsubstantially reducethesearchspaceofanysubsequentexplorationofmodel uncertainty. 1.00 1.50 2.00 2.50 3.00 3.50

Fig.12.Post-contactmigrationspeed(pixelsperframe).

2.00 2.20 2.40 2.60 2.80 3.00 3.20 3.40 3.60 3.80 4.00

Fig.13.In-contactmigrationspeed(pixelsperframe).

1.20 1.30 1.40 1.50 1.60 1.70 1.80 1.90 2.00

Fig.14. Averageangularvelocity(migratorypersistence)(radiansperframe).

2.3. Celltrackingandcharacterization

A range of cell tracking and characterization methods is described in the literature. Meijering et al. (Meijering et al., 2012)reviewedvariouscomputationalapproachesand

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quantita-1.20 1.40 1.60 1.80 2.00 2.20 2.40

Fig.15. Post-ContactAngularVelocity(radiansperframe).

1.40 1.50 1.60 1.70 1.80 1.90 2.00 2.10 2.20 2.30 2.40

Fig.16. In-ContactAngularVelocity(radiansperframe).

1.40 1.50 1.60 1.70 1.80 1.90 2.00 2.10

Fig.17.Cohesivity(averagenumberofcontactspercell).

tivemeasuresfortrackingandcharacterizingcellsusingtime-lapse microscopy.Whilstexploringtherequirementsfortrackingcells, itisclearthatanumberofstages,includingpre-processingofthe sourceimages,appropriate tool selection andverification, need

6.40 6.90 7.40 7.90 8.40 8.90 9.40 9.90 10.40

Fig.18.Averagecellclumpsize(numberofcells).

6.00 8.00 10.00 12.00 14.00 16.00

Fig.19. Averagecontactduration(inframes).

100.00 120.00 140.00 160.00 180.00 200.00 220.00 240.00 260.00 280.00

Fig.20.Cellcount.

tobeconsideredcarefullytoensurethattheapproachadoptedis optimalfortheparticularapplicationunderconsideration.Thisis reflectedbytherangeofotherapproaches presentedinthe lit-erature:Srinivasandcolleaguesdescribedamultimodalimaging approachtocelltrackingusingMRI,fluorescence,SPECT,PETand

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bioluminescence(Srinivasetal.,2013),andalthougheffective,is resourceintensiveandtimeconsumingtothepointwhereitcan becomelogisticallyprohibitive.Morerecentmethodshavebeen proposedwhich claim efficient trackingperformances (such as (SeetoandLipke,2016),(Amatetal.,2015)and(Paintdakhietal., 2016)).Otherfactors,suchasthereliabilityoftrackingcellsasthey leaveandenterthefieldanddepthofviewarealsoofmajor con-sideration;Chatterjeeaetal.proposedusingmatchingandlinking ofbipartitegraphs,whichtheyclaimdoesnotrequireanexplicit motionmodel,ishighly scalableandcaneffectively handlethe entryandexitofcellsfromthefieldofview(Chatterjeeetal.,2013). Having reviewed these existing techniques,there remains a strongmotivationfordeveloping bespoketoolsthatcanbe tai-loredtomatchtheimagequalityand characteristicsofthecell culturesunderconsideration.Suchanapproachalsopermitsthe tuningoftoolstoensurethatperformanceisadequatetofacilitate analysisofcellcultureswithinapracticaltimescale.Finally,itis importantthattheresultsofthisprocessingarecompatiblewith othernovelcharacterisationandclassificationapproaches,suchas evolutionaryalgorithms.

2.4. Anintroductiontoevolutionaryalgorithms

Theevolutionaryanalysistechniquesemployedinthis work, EvolutionaryAlgorithms(EAs)(Chiongetal.,2012)aremembersof theartificialintelligencefamily,ormorepreciselycomputational intelligence,astheydependonaformoflearninginspiredby Dar-winianevolution.Theyareineffecta number(orpopulation)of candidatesolutions(individuals)toaclassificationproblemthatare repeatedlyrefined(orevolved)overanumberofiterations( gen-erations)untilasuitablyaccurateclassifieralgorithmisobtained, orthecomputationalresourceshavebeenexhausted.The proce-dureforfindingaclassifier,forexampletodiscriminateonecell culturefromanother,canbesummarizedasfollows:A popula-tionofindividuals(candidatesolutions)israndomlyinitialized.The effectivenessorfitnessofeachindividualtocorrectlyclassifydata previouslyobtainedfromthecellculturesisdeterminedusinga fitnessfunction.Thefittestindividual(theonewiththehighest fit-nessscoredeterminedbythefitnessfunction)isretainedandthe othersdiscarded.Copies(orclones)ofthisfittestindividualarethen generatedandsubtlymodified(ormutated)toformanew popula-tionofindividuals.Thefitnessofthisnewpopulationofindividuals isthenevaluatedinthesamewayusingthefitnessfunction,and theprocessisrepeatedoveranumberofgenerationsuntila suf-ficientlyfitclassifierisobtained,orthenumberofpredetermined generationshasbeenreached.Manydifferenttypesofevolutionary algorithmhavebeendevelopedwhichspecify,notonlythe char-acteristicsoftheevolutionaryprocess,butalsotherepresentation oftheindividualcandidatesolutions.

3. Methods

Theautomatedanalysisofcellmotioncomprisesthefollowing sequenceofanalysis:capturingimagesofcellsincultureatregular intervalsbyvideomicroscopy,trackingcellswithinthevideoona frame-by-framebasisusingcustom-writtensoftwarefollowedby characterizationofcellmovementthroughtheextractionof specifi-callydesignedfeatures.Eachofthesestagesisconsideredinfurther detailinthefollowingsections.

3.1. Cellcultureandvideomicroscopy

Normalhumanurothelial(NHU)cellswereestablishedin cul-ture as finite (non-immortalized) cell lines and maintained as detailedelsewhere(Southgateetal.,1994).Cultureswereseededin

12wellplatesandexposedto100␮MPPADs (pyridoxalphosphate-6-azophenyl-2′_,4′_-disulphonic _acid) _or _0.1% _DMSO _(as _vehicle control) for 10min, before addition of 0, 10 or 50␮M ATP in replicatesoffour.Cultureswereobservedusing×4objectiveby

differentialinterferencecontrastvideomicroscopy(OlympusIX81 microscope) in an environmental chamber with an automated mechanicalstage.Time-lapsevideoswerecompiledfrom individ-ualimagescaptureddigitallyevery5minovera24htimeperiod. AsampleframefromonesuchvideoisillustratedinFig.2.

3.2. Celltracking

Custom-writtensoftware was developed toundertake auto-mated cell tracking using the OpenCV computer vision pro-gramming library(Bradski,2000).In orderto tracktherelative movementofcellswithinavideo,eachframeundergoesprocessing toidentifythelikelylocationsofcells.Thisprocesstakestheraw videosasaninput,performscommonpre-processingtoeachframe, andtheneithertriestoidentifythelikelylocationofcells,ortrack thelocationofpreviouslylocatedcells.Thesestepsareexplainedin furtherdetailbelow,aspreviouslyreportedbytheauthors(Zhang etal.,2015).

Each video frame initially undergoes Gaussian blurring to removenoise, followedbysimplethresholdingagainsta prede-terminedfixedvalue,resultingina binaryimageseparatingthe foregroundand background(i.e.thecells fromtheframe back-ground).Furtherprocessing(intheformofadistancetransform) isthenappliedtothisbinaryimage,resultinginframeswherethe centresoflargecells(orgroupsofcells)areassignedalargevalue, theedgeofcellsalowervalue,andthebackgroundavalueof0.In ordertoefficientlyestimatethelocationsofthecentresofcells,the localmaximaofthedistanceimagesarecomputed.Localmaxima arethenselectedfromthehighesttolowestscoring,withasmall areaaroundeachselectedmaximabeingfilteredouttoreducethe numberofselectionsmadewithinthebodyofacell.The(x,y) coor-dinatesoftheselectedmaximaarethenusedasestimationsofthe locationsofcellswithintheframe.

Toestimatethelocationofacellwithinaframe,giventhe loca-tion withintheprevious frame,thedistance imagearound the previouscelllocationisfirstmultipliedbyasimpleGaussianfilter. Themaximalpixelvalueinthisregioncanthenbeusedto esti-matethenewcelllocation.Thisapproach,althoughsimplistic,is demonstratedtobeeffective.Theusageofthedistanceimage pro-motesmatcheswiththecentreofcells,whilsttheapplicationof theGaussianfiltermeansthatmatchesarepreferredthatareclose totheoriginallocationofthecell.

Althoughtheprocessfortrackingcellsworkswell,itisunableto consistentlyidentifyandtrackcelllocationsforthedurationofthe videos.Inordertodetectasmanycellsaspossible,alargenumberof potentialcelllocationsareinitiallycalculated,withmanyofthese quicklyconvergingtothesamelocations.Similarly,thecelltracking processcanoccasionallyfailtotrackthelocationofcells within frames,meaningthatifcelldetectionwereonlytobeperformedon thefirstframeofthevideothenmanycellswouldnotbetracked inthelatterpartsofthevideo.Thesedifficultiesassociatedwith trackingcellscanbeduetocellproliferation(givingrisetonew cells),celldeath(thelossofcells),andcellsmovinginand/orout ofthefieldofview.

Inordertocopewiththeseissues,anapproachwasadopted whereduplicatedcelllocationsareremovedfromthetracking pro-cessandcell detectionisperformedatregular intervalstofind newcandidatelocations.Thisapproachisfoundtobeeffectiveand resultsinlocationdataforasufficient numberofcellsover the durationofthevideotoadequatelydescribethecellpopulation. TheentirecelltrackingprocessissummarizedinFig.3.

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Table1

Summaryoffeaturesextractedfromcellculturevideos.

FeatureNo. FeatureName Description

1 AverageMigrationSpeed Averagespeedofcellsovertheperiodoftracking

2 Post-ContactMigrationSpeed Averagemigrationspeedofcellsoverthreeframesafter

leavingaclumpofcells

3 In-ContactMigrationSpeed Averagemigrationspeedofcellswhenincontactwithfive

ormoreothercells(aclump)

4 AverageAngularVelocity(or

MigratoryPersistence)

Rateofchangeinmigrationdirectionofcellsbetweentwo

videoframes

5 Post-ContactAngularVelocity Rateofchangeinmigrationdirectionofcellsafterleaving

aclump

6 In-ContactAngularVelocity Rateofchangeinmigrationdirectionofcellswhenina

clump

7 Cohesivity Averagenumberofcontactspercell

8 AverageCellClumpSize Averagesizeofclumpinnumberofcells

9 AverageContactDuration Averagedurationofcellcontactwithclump

10 CellCount Differencebetweenthemaximumnumberofcellstracked

duringthevideofromthenumbertrackedatthebeginning

Fig.21. EvolvedCGPnetworksolutionthatachieved94%classificationaccuracybetweencellcultureswithandwithoutPPADS.Inputs(0)–(9)representfeatures1–10as

definedinTable1.Theboldnodesandpathsrepresenttheactivenetwork(whilstgreyed-outnodesandpathsarenotused,butmayhavebeeninstrumentalintheevolution

oftheclassifierinpreviousgenerations).

3.3. Featureextraction

Oncethelocationofindividualcellshasbeenidentifiedforeach frameofthevideointheformof(x,y)coordinatepairs,itispossible

toextractfeatureswiththeaimofdescribingthecellpopulation behaviour.ThiswasundertakenusingtheMATLABprogramming

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environmentandtoillustratetheprocessingapplied,asinglecell fromavideoanalysedistakenasanexampletodemonstratehow featuresofinterestarecalculated.Theselectedcellwastracked fromavideoofNHUcellculturewith50␮MexogenousATP.As thiscellwassuccessfullytrackedfromthebeginningtotheendof thevideo,itspathcanbeshowngraphicallyasdepictedinFig.4.It isinterestingtonotethesignificantchangeincellbehaviourfrom thestartoftracking(nearthecentreofthegraph)toendof track-ing(whenthecellleavesthelowerlefthandsideofthereference frame);initially,thecell’scourseiserratic,butsubsequently sta-bilizes.Theextractionofotherfeaturesfromthetrackingdatais anticipatedtohelpusrelatesuchchangesinbehaviourtofactors intheenvironment,suchasinteractionswithothercells,including intercellularadhesionorsubsequentseparation.

3.3.1. Choiceoffeatures

Thechoiceof features toextractfrom thevideoswasmade on the basis of characteristics previously associated with cell behaviourand the computationalfeasibility of theirextraction. Featurescanbroadlybeconsideredintwogroups:(i)thosethat describeacell’sbehaviourovertheentiretimeitwassuccessfully tracked,and(ii)intermsofbehaviourdelimitedbyinteraction,or morespecifically,contactwithothercells–describedhereaseither in-contactorpost-contact.Thenumberofcellssharingthecontact, orclumpsize,isalsoofinterest.Fromvisualinspectionofthevideos andforthepurposeofthisinvestigation,aclumphasbeendefined asagroupoffiveormorecells.Thefeaturesaresummarizedin Table1;alldynamicfeaturesareexpressedinunitsofpixelsper frame.

FurtherexplanationofhowthefeaturesAverageMigrationSpeed andAverageAngularVelocityhavebeendefinedisgivenbelow. 3.3.2. Averagemigrationspeed

Thespeedofanobjectistherateofchangeofitsposition.In thiscase,theaimistoobtainthemigrationspeedofacellfroma video,whichcanbedeterminedbycalculatingthenumberofpixels travelledoveracertaintimeinterval.Thetimeintervalapplicable inthiscontextisthatbetweentwoconsecutivevideoframes,at aframerateofoneevery5min.Themigrationspeedistherefore simplyobtainedbycalculatingtheEuclideandistancebetweenthe twopairsofcoordinatesforthecellbetweenconsecutiveframes. ThisisshowngraphicallyinFig.5wheretheinitialpositionofthe cellisatcoordinates(74,32)andinthesubsequentframe, coordi-nates(75,33).Hence,thedistancetravelledbythiscellovertimedt (5min),andsubsequently,itsspeed,canbecalculated.The migra-tionspeedofallcellstrackedduringtheentirevideowascalculated inthesameway.

3.3.3. Averageangularvelocity(ormigratorypersistence)

Incellmigration, persistenceisoneofthefeaturesin which biologistsaremostinterestedandcanbedescribedasthetendency ofcellstochangedirection.Hence,obtainingthedirectionoftravel ofthecellineachframeofthevideoisessentialforcalculating migrationpersistence.

Fig.6showshowtheangleofthevectorformedfromthe coor-dinatesofthecellinconsecutiveframesofthevideocanbeusedto determinethedirectionoftravel.AngularVelocityisdefinedhere astherateofchangeofthedirectionoftravelofacellover sub-sequentframes.Fig.7illustratesanexamplecalculationovertwo consecutiveframes.

3.4. Cellcultureclassification

Theaimofextractingfeaturessuchasthosedescribedin Sec-tion3.3istopermitcharacterizationand,hence,classificationof a cell culture. Thiscan be achieved tosome degreeby simply

ExamplefunctionsetprovidingalookuptableforfunctionsF1toF5usedinthe networkillustratedinFig.8.

Function ArithmeticOperation

F1 + F2 – F3 * F4 / F5 mean Table3

Automatedaveragemigrationspeedandaverageangularvelocityvaluesfora

con-trolculturewithnoATP,aculturewith10␮MATPandaculturewith50␮MATP.

CellCultureVideo CellCultureDescription

1–4 Control 5–8 10␮MATP 9–12 50␮MATP 13–16 Control+PPADS 17–20 10␮MATP+PPADS 21–24 50␮MATP+PPADS

observingthedifferencesinmeasurementsobtainedby extract-ingthesefeaturesfromtrackingdataobtainedfromtherespective cellvideos.However,therearesituationsinwhichsuchasimple approachisinsufficienttoprovideafullunderstandingofthe dif-ferencesbetweencellcultures(egfollowingdrugtreatment).The relationshipbetweenthefeaturesdefinedinTable1iscomplexand notwellunderstood.Insuchsituations,conventional, statistically-basedclassifiers donot alwaysprovide thebestresultsandfor thisreasonacomputationalintelligenceapproachwasapplied,in thiscaseanevolutionaryalgorithm.Suchapproachesalsohavethe advantageofbeingabletoprovideaninsighttothecharacteristics ofthecellculturethatleadstothisclassification.

3.4.1. Applicationofevolutionaryalgorithms

Fortheworkdescribedhere,Cartesian GeneticProgramming (CGP)(MillerandTurner,2015)wasused:aformofGenetic Pro-gramming that comprisesa fixed,non-cyclic directed graph,as showninFig.8.Thisgraphiseffectivelyanetworkofprocessing elementsthatcanbereconfiguredduringtheevolutionary pro-cessintwofundamentalways:byselectingthefunctionforeach nodefromapredefinedlist,andspecifyingtowhichothernodes each inputand outputof thenodeareconnected.Inthe exam-pleshown in Fig.8,thefeatures 1–10extracted inTable 1are presentedtoinputsI0-I9.Thesearethenprocessedbythe follow-ingthreecolumnsofnodes,eachexecutinganarithmeticfunction takenfromasetF1...F5,typicallyprimitivearithmeticoperations suchasillustratedinTable2;theresultofthenetworkispresented atoutputO1.

There are two ways in which CGP provides an advantage over otherclassification techniques.Firstof all,for highly non-linearcomplexdatasets, suchasthose foundinmeasurements ofdynamicbehaviours,CGPhasbeenshowntoevolvehigh per-formance classifiers(Lones etal., 2014).Secondly, unlike other machinelearningtechniques,onceahighperformingclassifierhas beenevolved,amathematicalexpressiondefining thisclassifier canbeeasilyobtainedbydecodingtheresultingCGPnetwork.As previouslymentioned,thiscanprovidevaluableinsightintothose featuresobtainedfromthedynamicsofthecellculturethatplaya definingroleinitsclassification.

4. Results

Intotal24time-lapsevideosweregeneratedfromsixclasses ofNHUcellculturescomprisingcombinationsofcontrolcultures,

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Table4

AutomatedaveragemigrationspeedandaverageangularvelocityvaluesforacontrolculturewithnoATP,aculturewith10␮MATPandaculturewith50␮MATP.

CellCultureVideo AverageMigrationSpeed(pixels/frame) AverageAngularVelocity(rads/frame)

Control1 3.52 1.31 Control2 3.75 1.35 Control3 3.45 1.37 Control4 3.56 1.36 10␮MATP1 3.04 1.52 10␮MATP2 3.09 1.39 10␮MATP3 3.08 1.52 10␮MATP4 3.01 1.46 50␮MATP1 2.00 1.79 50␮MATP2 2.22 1.74 50␮MATP3 2.06 1.77 50␮MATP4 1.83 1.85

withandwithoutATPandPPADS,asdetailedinTable3,andwere analysedusingthemethodsdescribedaboveinSection3. 4.1. Evaluationofresults

Aninitialevaluationoftheresultsobtainedconsideredasubset ofthreeclassesofNHUcellcultures:(i)acontrolculturewithno ATP;(ii)aculturewith10␮MATP;and(iii)aculturewith50␮M ATP.Theaveragecellmigrationspeedsandaverageangularvelocity foreachvideowerecalculatedandarepresentedinTable4.

Byapplyinganalysisofvariance(ANOVA),itcanbeseeninFig.9 thattheseparationbetweenthethreeclassesformigrationspeed wasstatisticallysignificant.Verificationoftheseresultswas con-firmedbycomparingwithmanualtrackingof15randomcellsfor eachexperimentalconditionasshowninFig.10.Similarly,results forangularvelocity,showninFig.10,alsodemonstratedgood sep-arationbetweenthethreesetsofcultureconditions.

4.2. Fullfeaturesetresults

Inadditiontocellmigrationspeedsandmigratorypersistence, weareparticularlyinterestedinthenatureofthecontactbetween cells.Thisrelatestothephysicalextentofthecontactthatforms betweenthemandtowhatextentinteractingcellsmaketransient ormore sustained contacts.Thefeatures, previously defined in Table1,thatcharacterizethebehaviourofcellswhilstincontact andpost-contactthroughoutthevideosareshowninFigs.11–20. 4.3. Applicationofevolutionaryalgorithms

ItcanbeseenintheresultspresentedinSection4.2thatcell cul-turescontainingPPADScannotbeclearlydistinguishedfromthose cultureswithoutPPADSineverycase.Astherelationshipbetween thefeaturesiscomplexandnotwellunderstood,anevolutionary algorithmwasusedinanattempttogenerateaneffectiveclassifier andprovidesomeinsightontheroleofthefeaturesinthe classifi-cation.ACGPnetworkwasused,describedbytheparameterslisted inTable5.Intotal,fiverunsof10separateexperimentswere under-taken,achievinganaverageaccuracyof91.4%onindependenttest setdata.

AnevolvedCGPnetworkthatsuccessfullyclassifiescellcultures withandwithoutPPADStoanaccuracyof94%isshowninFig.21. ThefeaturesdefinedinTable1werepresentedtotheinputsofthe networkandthevalueobtainedattheoutputisusedtoobtainthe classificationresult.

Theaddedadvantageofapplyingevolutionaryalgorithmsinthis wayisthattheevolvednetworkcaneasilybedescribedasa con-ventionalmathematicalexpression.Thisprovidesvaluableinsight intothefeaturesmostsignificantintheclassificationprocessand theirrelationship.Forexample,intheexampleprovidedinFig.21, itcanbeseenthatthisparticularclassifierisdependentoninputs:

Table5

ParametersspecifyingtheCGPnetworkusedtoevolveclassifierstodiscriminate betweencellcultureswithandwithoutPPADS.

Parameter Value Numberofinputs 9 Numberofoutputs 1 Numberofcolumns 70 Numberofrows 1 Mutationrate 1.0% Populationsize 10 Functionset

Arithmeticoperators: +-*/SQRSQRTCUBE

Constants: 01

Logicaloperators: ANDORNANDNORNOT

Numberofgenerations 10,000

(0),(5),(6)and(7),which,withreferencetoTable1,canbeequated tofeatures:averagemigrationspeed,in-contactangularvelocity, cohesivityandaveragecellclumpsize,respectively.

5. Discussionandconclusions

Thispaperhasdescribeda novelapproach tothe character-ization and classification of replicate cell cultures through the applicationofcelltrackingtotime-lapsevideos.Anumberof fea-tureshavebeenproposedthatprovideameansofdescribingcell behaviourintermsofmigrationspeedandmigrationpersistence, bothwhileincontactwithothercellsandpost-contact.This pro-videsauniqueopportunitytoinferbehaviourwithrespecttocell contactinafullyautomatedway.Ithasalsobeendemonstrated howevolutionaryalgorithmscanbeusedtosuccessfullyclassify cellculturesevenwhenthisisnotclearlyindicatedbyconsidering theextractedfeaturesalone.

InthespecificexampleofNHUcellsstudiedhere,wepreviously reportedthatinscratchassays,wheretherepairofdamageinflicted onaconfluentpopulationofcellsismonitoredtotheclosureofthe wound,theeffectofexogenousATPwastoenhancewoundrepair. In support of these observations, inhibition of ATP breakdown wasshowntoenhancetherateofrepair,whereasPPADs,a selec-tiveantagonistoftheATP-activatedP2Xreceptor,wasinhibitory (Shabiretal.,2013).TheseobservationsledustopredictthatATP hadapositiveeffectoncellmigrationanditwasunexpectedhere thatinsparsecellcultures,theeffectsofexogenousATPwasto reducemigrationspeed.Theseobservations,alongwiththe equiv-ocaleffectoftheP2XantagonistPPADS,suggeststhattheresponse ofNHUcellstoATPmaybemorecontextdependentthanhitherto thoughtandotherurothelial-expressedATP-modulatedreceptors, suchtheP2YGprotein-coupledreceptors(Shabiretal.,2013)may berelevant.Thenovelmethodofanalysispresentedinthispaper providesthemeansbywhichpredominantaffectedparametersand themechanismsresponsiblecanbefullyexaminedinanautomated andobjectiveway.

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Acknowledgements

This work was supported by the Wellcome Trust

[090577/Z/09/Z] and York against Cancer. Data associated with this publication can be accessed via the following DOI: 10.15124/383e4704-98d4-42bf-8836-55c8b1ab44fa.

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