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I n t r o d u ct i on t o M i c r o a r r ay D a t a A na l ys i s

I n t r o d u ct i on t o M i c r o a r r ay D a t a A na l ys i s. O u t l i n e. I n t r o d u c ti o n M i c r o a r rays T e c h n o l o g y T y p e s a n d Us es o f M ic r o a r rays M i c r o a r rays f o r t h e S t u dy o f G e n e E x p r e s si o n F a b r i c a ti o n

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I n t r o d u ct i on t o M i c r o a r r ay D a t a A na l ys i s

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  1. IntroductiontoMicroarrayDataAnalysis

  2. Outline • Introduction • MicroarraysTechnology • TypesandUsesofMicroarrays • MicroarraysfortheStudyofGeneExpression • Fabrication • Spottedmicroarrays • 2.Oligonucletidemicroarrays • ExperimentswithMicroarrays • Flowchartofaexperimentwithmicroarrays • SoftwareforMicroarrayDataAnalysis

  3. Introduction(1) Briefreviewofmolecularbiology... Mostlifeformsaremadeofcells.Eachindividualhasaverylargeindefinitenumberofcells.  Eachcell containschromosomes (e.g. human cells  contain23pairs ofchromosomes). These organized structuresofDNAandinheritedinformation. proteins arethecarriersof AchromosomeisasinglepieceofcoiledDNAcontainingmanygenes,regulatoryelementsandothernucleotidesequences. 

  4. Introduction(2) Whatelse? ThegenomeofanorganismisinscribedinDNAorRNAinsomevirus  AgeneisthebasicunitofheredityinalivingorganismandistheportionoftheDNAthatcodesforaproteinoranRNA Eachprotein-codinggeneisagenetranscribedintoRNAinsomemoleculesandinturnmRNAistranslatedintoatleastoneproteininsomecells  

  5. Introduction(3) TheCentralDogmaofMolecularBiology • Information flowfromDNAtoRNAto proteinoccursinfourstages Replication TheDNAreplicatesitsinformationinaprocessthatinvolvesmanyenzymes Transcription TheDNAcodesfortheproductionofmessengerRNA(mRNA) Splicing Ineucaryoticcells,themRNAisprocessedandmigratesfromthenucleustothecytoplasm. Translation MessengerRNAcarriescodedinformationtoribosomes.Theribosomesreadthisinformationanduseitforproteinsynthesis.

  6. Introduction(4) TechniquesinMolecularBiology • MolecularbiologyhasdevelopedmultipletechniquestomeasurelevelsofRNA,DNA,proteinsormetabolites,suchas – – – – – SouthernBlotNorthernBlotDifferentialdisplaySAGE … • Post-genomicseraisperformandtoanalyze characterized byitscapabilityto data sets from large-scale experimentssimultaneously

  7. Introduction(5) Theparadigmshift • Withthesameresourcesweobtainapicturewithlowerresolutionbutwithaviewofthewholecontext vs Basedon“Theparadigmshift”slidefromJ.Dopazo(CNIO)

  8. Introduction(6) Todrawananalogywithpre-genomicsera • Biologyusedto“spy”ongeneseverythingindeepandindividually(i.e.genebygene)

  9. Introduction(7) Todrawananalogywithpost-genomicsera Nowadays,alotofgenescanbe“spied”atthesametime...but...  …Howcanwesplitthewheatfromthechaff?

  10. MicroarraysTechnology(1) Broadlyspeaking... Microarraysareavarietyofplatforms  inwhichhighdensityassaysperformedinparallelonasupport. aresolid PublicationsinPubMedwithmicroarraywordinthetitle 10911080 1000 1000986 988 Thistechnologyhaschangedtheway  920 biologistsapproach problemsandnewchallengesfor hugeeach 800 introducesstatisticiansquantityofexperiment numberofpublications 747 becauseofthedatageneratedin 600 544 400 Theyhavebeenusedforallkindsofbiologicalproblems  259 200 171 83 24 5 0 Theliteraturecontainsalmost8000papersusingmicroarraywordinthetitle  1998 2000 2002 2004 2006 2008 2010 year

  11. MicroarraysTechnology(2) Thebiologicalprincipleofmicroarraysinvolvedin... • ItisthesameonethatallowsDNAdoublehelicesto providethebasisforheredity • SequencesofDNAorRNAmoleculescontainingcomplementarybasepairshaveanaturaltendencytobindtogether. ...AAAAAGCTAGTCGATGCTAG... ...TTTTTCGATCAGCTACGATC... • IfweknowthemRNAsequence,wecanbuildaprobeforitusingthecomplementarysequence.

  12. MicroarraysTechnology(3) But...Whatisamicroarray? Itconsistofalargeset(thousandstotenofthousands)ofspecificsequences(knownasprobesorfeatures)attachedinorder(array)tomicroscopicspotsonasolidsupport(nylonorsiliconglass,...).  ... moleculesample1 moleculesample2 moleculesampler Aprobe(thatcanbeagene,aprotein,ametabolite,...)isusedtohybridizeamoleculeofanucleicacidsample(calledtarget)underhigh-stringencyconditions.  probeprobeprobeprobe gene1gene2gene3gene4 1 2 3 4 probeprobeprobeprobe5678 spots probeprobeprobeprobe9101112 Probe-Target determinetherelative hybridizationis usedtoof  abundance ... nucleicacidsequencesinthetargets(e.g. todeterminesequences,to detect variationsingenesequences,levels,genemapping,...). expression probek-3 probeprobeprobek-2k-1k Microarray

  13. TypesandUsesofMicroarrays(1) Typesofmicroarrays Microarraysspatiallyarrangedonasolidsurfacearemostwidelyused.`  Beadarraysarecreatedby  • eitherimpregnatingbeadswithdifferentconcentrationsoffluorescentdye, • orsometypeofbarcodingtechnology. Thebeadsareaddressableandusedtobindingeventsthatoccurontheirsurface. identify specific 

  14. TypesandUsesofMicroarrays(2) UsesofMicroarrays(1) Expressionanalysis  –TheprocessofmeasuringgeneexpressionviaRNA(orcDNAafterreversetranscription)iscalledexpressionanalysisorexpressionprofiling. Inthisexperimentstheexpressionlevelsofthousandsofgenesaresimultaneouslymonitoredtostudytheeffectsofcertaintreatments,diseases,anddevelopmentalstagesongeneexpression. – ComparativeGenomicHybridization  – Comparativegenomichybridization(CGH)orChromosomalMicroarrayAnalysis(CMA)isusedfortheanalysisofcopynumberchanges(increasesordecreases)oftheimportantchromosomalfragmentsharboringgenesinvolvedindiseases. Mutationanalysis  –AsinglebasedifferencebetweentwosequencesisknownasSingleNucleotidePolymorphism(SNP)anddetectingthemisknownasSNPdetection. WithgDNAthiskindofarraystrytodetectgenesthatmightdifferfromeachotherbyaslessasasinglenucleotidebase. –

  15. TypesandUsesofMicroarrays(2) UsesofMicroarrays(2) ProteinArray TissueArray CGHArrays SNPArrayAffymetrix CNVArrayIllumina ExpressionArrays cDNANylonMembraneArray GeneChipAffymetrixArray cDNAAgilentArray

  16. TypesandUsesofMicroarrays(3) ApplicationofMicroarrays Genediscovery  Identificationofnewgenes,knowabouttheirfunctioningandexpressionlevelsunderdifferentconditions. Molecularclassificationofcomplexdiseases  Toclassifythetypesofcanceronthebasisofthepatternsofgeneactivityinthetumorcells,todevelopmoreeffectivedrugs. Drugdiscovery  Comparativeanalysisofthegenesfromadiseasedandanormalcellhelptheidentificationofthebiochemicalconstitutionoftheproteinssynthesizedbythediseasedgenes.Thisinformationcanbeusedtosynthesizedrugsthatcombatwiththeseproteinsandreducetheireffect. Toxicologicalresearch  Microarraytechnologyprovidesarobustplatformfortheresearchoftheimpactoftoxinsonthecellsandtheirpassingontotheprogeny.

  17. MicroarraysfortheStudyofGeneExpression(1) Whatisthegeneexpression? • Thegeneexpressionisthepresenceofthegeneproductsofagene,intheformofmRNA(orprotein),inacell • Toputitstraight:Sincecellscontainthesamegeneticinformation,whatmakesdifferentbraincellsfromheartcellsisthegeneexpression.

  18. MicroarraysfortheStudyofGeneExpression(2) FindingDifferentiallyExpressedGenes(DEG) Tofindgenesthatdisplayalargedifferenceingeneexpressionbetweentwoconditionsandarehomogeneouswithinthem  – Typicallystatisticaltests(t-test,Wilcoxontest)areused Iftherearemorethantwoconditions,orifconditionsarenested,theappropriatestatisticalmethodisANOVA  pvaluesfromthesetestshavetobecorrectedformultipletesting 

  19. MicroarraysfortheStudyofGeneExpression(3) Exploratorydataanalysis(1) Tofindgroupsthatarenotdefinedyet(e.g.noveldiseasesubtypes)Methods   – – – – fromthisfieldwerethefirsttobeusedformicroarraydata shouldbeusedonlyifnopriorknowledgeexiststhatcouldbeincorporated findpatternsinthedata,butanypatterns,whethertheyaremeaningfulornotinclude • • Clustering(hierarchicalandpartitioning)Projection(PCA,MDS) Alizadehetal.Nature403:503–511(2000)

  20. MicroarraysfortheStudyofGeneExpression(4) Timeseries,partitioningclusteringandcorrelation • Usuallyusedtofindpatternsofco-expressedgenesThemeaningoftimeseriesisdifferentfor • Biologists:2-10timepoints • statisticians:>200timepoints • “Non-optimal”solution:touseclusteringmethodstofindsuchpatterns    Notethattheyarebynomeansexhaustive,andthatnosignificancemeasurecanbeattachedtothem IncontrasttoEstimationofDistribuitonMethods(EDA),partitioningclustermethodsaremorepopular(e.g.K-meansorSelf-organizingmaps)   Toseekgeneswhoseexpressionprofileissimilartothatofaparadigmaticgene,correlationscanbecalculated,andsortbythem.Thereisnoneedforclustering.  Specialmethodsexistforperiodicchanges(⇒cellcycle),e.g.Fourieranalysis 

  21. MicroarraysfortheStudyofGeneExpression(5) Classification Wheninformationaboutgroupingofthesamplesisavailable,itcan(andshould)beusedtogetimprovedresults  Groupingsmaybe:  – – – – – – – – TreatmentandControlDiseaseandNormalDiseasestage1,2,3MutantandWildTypeGoodandPoorOutcome,Therapysuccessorfailure ... Onelearnscharacteristicpatternsfromatrainingsetandevaluatebypredictingclassesofatestset 

  22. MicroarraysfortheStudyofGeneExpression(6) SurvivalAnalysis Tofindpatternsthatareassociatedwithprolongedpatients’survivaltime  Insteadoftreatingoutcomeasabinaryvariable,canbeused  – – TheoverallsurvivaltimeorTheeventfreesurvivaltime ascontinuousvariables,andtrytoestimateitbyregression Sincetherisktosufferfromrelapseisdecreasingwithtime,linearregressionmodelsarealmostalwaysinappropriatespecializedmodelswouldbebetter  – – CoxregressionRegressiontrees

  23. MicroarraysfortheStudyofGeneExpression(7) Pharmacogenomics Tofindmolecularpredictorsthattellaboutprobablesuccess(orfailure)ofacertaintherapy.e.g.  – – estrogenreceptorstatusfortamoxifen(antihormone)therapyHER2/NEUstatusforherceptintherapyinbreastcancer Onemayregardtreatmentoutcomeasadiscretevariableanduseclassificationmethods  Sometimes,it’sconvenientnottowaitforthefinalendpoint(whichmaybeyearsaway),buttousesurrogatevariables,e.g.  – – thedropofthebloodlevelofacertainproteinreductionintumorvolume

  24. Fabrication Twomaintechnologies Therearemanytypesoftechnologies,butprinciplesarethesame  ThemostusedarespottedarraysandInsituarrays  Spottedarrays(akacDNAarraysorStanfordarrays)  – PreviouslysynthesizedcDNAsoroligonucleotidesaredepositedonthechip Basedon“printing-like”technologies – Insituarrays(akaoligoarraysorAffyarrays)  – – – ProbesaresynthesizeddirectlyonthechipBasedonphotolithographictechniques Affymetrixarraysarethebest-known...butnottheonlyone!

  25. SpottedArrays(1) Fromthechipstotheimages ChipDesignandProduction SamplePreparation Hybridization ScanningandCapturingImages ImageAnalysis Quantification

  26. SpottedArrays(2) Chipdesignandconstruction • Productionbeginswiththeselectionofthe"probes"tobeprintedonthearrayIngeneral:chosenfrom • GenBank(http://www.ncbi.nlm.nih.gov/) • dbEST(http://www.ncbi.nlm.nih.gov/UniGene/index.html) • cDNA’sareprintedonthearray • Eachspotcancontainuniquesequences • Printing”meansadheringsequencestothespots    Amovieoftheprintingprocessisavailablehere

  27. SpottedArrays(3) Samplepreparation RNAisextractedfromthesamples ThisRNAisconvertedtofluorescentlylabeledcDNAbyreversetranscriptioninpresenceoffluorescentlylabelednucleotideprecursors RNAfromeachsamplesare labelledfluorescentCy-5)to withdyes different(e.g.Cy-3, allowdirect comparison 4.Afterlabeling,theyaremixed andhybridizedsequencesonthe(probes) witharray

  28. SpottedArrays(4) Hybridizationwithprobes Targetslabeledandcombined Amovieofthehybridizationprocessisavailablehere

  29. SpottedArrays(5) Scanningandcapturingimage AfterhybridizationeachDNAspotisilluminatedandfluorescencemeasurestakenforeachdyeseparately Thesemeasurementswillbeused,aftertheappropriatequalitycontrols,todeterminetherelativeabundance,ofthesequenceofeachspecificgeneinthetwomRNAorDNAsamples  

  30. SpottedArrays(6) Imageanalysis(1) TIFFimagesareprocessedbyimageanalysisprograms  – – – SPOT, GenePix ... toacquireintensityvaluesforeachspot Thesemeasureswillbeused,aftertheappropriatequalitycontrols,todeterminetherelativeabundance,ofthesequenceofeachspecificgeneinthetwomRNAorDNAsamples 

  31. SpottedArrays(7) Imageanalysis(2) StepsinImageProcessing  Addressing:Estimatelocationofspotcenters Segmentation:Classifyeachspota foreground(signal)background(noise) ● ● 3.Informationextraction(quantification) Foreachspotonthearray,andeachdyeobtain  Signalmeasurements(R,G) – gg Backgroundmeasurements(bgR,bgG) gg – – Qualityindicators

  32. SpottedArrays(8) Quantification Genemeasuredmeasures expressionis ● fromintensityasthe relative (corrected) intensityofonedyevsthe(corrected)relativeintensityoftheother M=Rg,M Rg−bgRg = Corrected Gg Gg−bgGg Background correction ● maybeaccordingquality needed, ornot,array tothe

  33. SpottedArrays(8) Overviewoftheprocess Amovieofthewholeprocessisavailablehere

  34. InsituChips(1) Fromthechipstotheimages MainConcepts SynthesisofOligosontheChip SamplePreparation HybridizationProcess ScanningImages OutputImages QuantificationandExpressionMeasures

  35. InsituChips(1) Mainconcepts(1) MoreadvanceddesignthanspottedcDNAarrays  – – TheyareNOTbasedoncompetitivehybridization.Thatis,onechip,onesampleTheyareNOTaddedonthechipafterbeingsynthesizedinvitro Mainidea:Probesaresynthesizedinsitu(onthechip)  Sequencesarebuiltuponthechipsurfacebysequentiallyelongatingagrowingchainwithasinglenucleotideusingphotolithography  Chemicalyieldofthestepwiseelongationislimited  – SequencescanNOTgrowtomorethan25merslength(oligo) – Need16-20different25mersequencestouniquelycharacterizeagene • • Probe=Individual25mersequence Probeset=Setof25merscorrespondingtoaparticulargene/EST

  36. InsituChips(2) Mainconcepts(2) Affymetrix(http://www.affymetrix.com)istheleadercompanyofthesekindsofchips.TheycallthemGeneChips  Eachgeneisrepresentedbyasetofshortsequences  Someofthesechipscontainwholegenomes,thatis>50.000probesets  Aprobeset(usuallydenotedprobeset)isusedtomeasurethemRNAlevelsofauniquegene  Eachprobesetismadeupofmultipleprobecells  – – withmillonsofcopiesofoneoligodecopiasdeunoligo(25bp)Organizedinprobepairswith • • aPerfectMatch(PM):matchperfectlywithapieceofagene aMismatch(MM):itisthesametoPMbutwiththecentralnucleotidechangebythecomplementary

  37. InsituChips(3) Mainconcepts(1) MoreadvanceddesignthanspottedcDNAarrays  – – TheyareNOTbasedoncompetitivehybridization.Thatis,onechip,onesampleTheyareNOTaddedonthechipafterbeingsynthesizedinvitro Mainidea:Probesaresynthesizedinsitu(onthechip)  Sequencesarebuiltuponthechipsurfacebysequentiallyelongatingagrowingchainwithasinglenucleotideusingphotolithography  Chemicalyieldofthestepwiseelongationislimited  – SequencescanNOTgrowtomorethan25merslength(oligo) – Need16-20different25mersequencestouniquelycharacterizeagene • • Probe=Individual25mersequence Probeset=Setof25merscorrespondingtoaparticulargene/EST

  38. InsituChips(4) GeneChip®expressionarraydesign

  39. InsituChips(5) Onegene,oneprobeset Probesareselectedtobespecificoftherepresentedgene  Themusthavegoodpropertiesofhybridization  genesequence

  40. InsituChips(6) Synthesisofoligosonthechip(1) GeneChip®probearraysaremanufacturedthroughauniqueandrobustprocess,acombinationofphotolithographyandcombinationalchemistry  ImagecourtesyofAffymetrix

  41. InsituChips(7) Synthesisofoligosonthechip(2) mask mask mask mask mask mask mask C A T C mask T T T A C GA TC AG A GeneChip ImagefromacourseofDanNettleton

  42. InsituChips(8) Synthesisofoligosonthechip(3) Severalcopiesofasinglefeaturearedepositedineachcell  ImagecourtesyofAffymetrix

  43. InsituChips(9) Samplepreparation

  44. InsituChips(8) Hybridizationprocess OncetheoligoshavebeensynthesizedhybridizationisperformedbyaddingmRNAfromthetissuetoanalyzeonthechip  ImagecourtesyofAffymetrix

  45. InsituChips(9) ScanningImages Scanningoftaggedandun-taggedprobesonanAffymetrixGeneChip®microarray  ImagecourtesyofAffymetrix

  46. InsituChips(10) OutputImage DatafromanexperimentshowingtheexpressionofthousandsofgenesonasingleGeneChip®probearray  ImagecourtesyofAffymetrix

  47. InsituChips(11) Quantification Intensitiesfromeachelementareextracted  QuantitativeanalysisofthehybridizationresultsisperformedbyanalyzingthehybridizationpatternofthesetofPMandMMprobesofeverygene  Incontrastwithspottedchipsexpressionmeasuresusedhereareabsoluteones.Thatis,eachchipishybridizedwithonlyonetissueatatime 

  48. InsituChips(12) Absoluteexpressionmeasures MeasurestodeterminethequantitativeRNAabundance,i.e.theexpressionlevelbasedontheaverageofthedifferencesPMminusMMforeachprobefamily  Avg.Diff=1¿j∈APM−MM ∣A∣ Manyalternativeshavebeenintroduced 

  49. SpottedvsInsituArrays PRO'sandCON's cDNAmicroarraysOligomicroarrays PRO's PRO's • • • • • • Cheaper Flexibilitywiththeexperimentaldesign Highsignalintensity(largesequences) Quickmanufacture(automated)Highreproducibility Highspecificity Alotofprobes/genes • CON's CON's • Requiresmorespecializedequipment ExpensivesLowflexibility • Lowreproducibility • Cross-hybridization(lowspecificity) • Highmanupulation(ssibilityofcontamination) • •

  50. InsituChips(13) Overviewoftheprocess Amovieofthewholeprocessisavailablehere ImagecourtesyofAffymetrix

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