Spatially Resolved Gene Expression Analysis... · Spatially Resolved Gene Expression Analysis Michaela Asp Doctoral Thesis in Biotechnology KTH Royal Institute of Technology School

SpatiallyResolvedGeneExpressionAnalysisMichaelaAsp

DoctoralThesisinBiotechnologyKTHRoyalInstituteofTechnologySchoolofEngineeringSciencesinChemistry,BiotechnologyandHealthDepartmentofGeneTechnologyScienceforLifeLaboratoryStockholm,Sweden2018

©MichaelaAspStockholm2018KTHRoyalInstituteofTechnologySchoolofEngineeringSciencesinChemistry,BiotechnologyandHealthDepartmentofGeneTechnologyScienceforLifeLaboratorySE-17165SolnaSwedenPrintedbyUniversitetsserviceUS-ABDrottningKristinasväg53BSE-10044StockholmSwedenISBN:978-91-7729-965-3TRITA-CBH-FOU-2018:43

PublicdefenseofdissertationThis thesis will be defended October 26th at 10:00 in Gardaulan, Nobels väg 18,Folkhälsomyndigheten,Solna,forthedegreeofDoctorofPhilosophy(PhD)inBiotechnology.RespondentMichaelaAspMSc.inBiotechnologyDepartmentofGeneTechnology,KTHRoyalInstituteofTechnology,ScienceforLifeLaboratory,Stockholm,SwedenFacultyopponentProf.ThierryVoetSangerInstitute–EBISingle-CellGenomicsCentre,WellcomeTrustSangerInstitute,Hinxton,UKDepartmentofHumanGenetics,UniversityofLeuven,Leuven,BelgiumEvaluationcommitteeDr.ÅsaBjörklundDepartmentforCellandMolecularBiology,NationalBioinformaticsInfrastructureSweden,ScienceforLifeLaboratory,UppsalaUniversity,Uppsala,SwedenAssoc.Prof.LarsFeukDepartmentofImmunology,GeneticsandPathology,UppsalaUniversity,Uppsala,SwedenAssoc.Prof.MarcFriedländerDepartmentofMolecularBiosciences,TheWenner-GrenInstitute,StockholmUniversity,ScienceforLifeLaboratory,Stockholm,SwedenChairmanProf.StefanStåhlDivisionofProteinTechnology,KTHRoyalInstituteofTechnology,AlbaNovaUniversityCenter,Stockholm,SwedenRespondent’smainsupervisorProf.JoakimLundebergDepartmentofGeneTechnology,KTHRoyalInstituteofTechnology,ScienceforLifeLaboratory,Stockholm,SwedenRespondent’sco-supervisorAsst.Prof.PatrikL.StåhlDepartmentofGeneTechnology,KTHRoyalInstituteofTechnology,ScienceforLifeLaboratory,Stockholm,Sweden

AbstractSpatially resolved transcriptomics has greatly expanded our knowledge of complexmulticellular biological systems. To date, several technologies have been developedthatcombinegeneexpressiondatawith informationabout itsspatial tissuecontext.There is as yet no single spatial method superior to all others, and the existingmethods have jointly contributed to progress in this field of technology. Somechallengespresentedbyexistingprotocolsincludehavingalimitednumberoftargets,being labor extensive, being tissue-type dependent and having low throughput orlimited resolution.Within thescopeof this thesis,manyaspectsof thesechallengeshave been taken into consideration, resulting in a detailed evaluation of a recentlydeveloped spatial transcriptome-wide method. This method, termed SpatialTranscriptomics(ST),enablesthespatiallocationofgeneactivitytobepreservedandvisually links it to itshistologicalpositionandanatomicalcontext.PaperIdescribesall thedetailsof theexperimentalprotocol,whichstartswhen intact tissuesectionsare placed onbarcodedmicroarrays and finisheswith high throughput sequencing.Here, spatially resolved transcriptome-wide data are obtained from both mouseolfactory bulb and breast cancer samples, demonstrating the broad tissueapplicabilityandrobustnessoftheapproach.InPaperII,theSTtechnologyisappliedto samples of human adult heart, a tissue type that contains large proportions offibrous tissue and thusmakes RNA extraction substantiallymore challenging. Newprotocol strategies are optimized in order to generate spatially resolvedtranscriptomedata fromheart failure patients. This demonstrates the advantage ofusing the technology for the identificationof lowlyexpressedbiomarkers thathavepreviouslybeenseentocorrelatewithdiseaseprogressioninpatientssufferingheartfailure. Paper III shows that, although the ST technology has limited resolutioncompared to other techniques, it can be combinedwith single-cell RNA-sequencingand hence allow the spatial positions of individual cells to be recovered. Thecombinedapproachisappliedtodevelopinghumanhearttissueandrevealscellularheterogeneity of distinct compartments within the complete organ. Since the STtechnology is based on the sequencing of mRNA tags, Paper IV describes a newversionofthemethod,inwhichspatiallyresolvedanalysisoffull-lengthtranscriptsisbeingdeveloped.Exploringthespatialdistributionoffull-lengthtranscriptsintissuesenables further insights intoalternativesplicingand fusion transcriptsandpossiblediscoveriesofnewgenes.KeywordsRNA,RNA-sequencing,transcriptomics,spatialtranscriptomics,singlecells

SammanfattningSpatialt upplöst transkriptomik har markant breddat vår kunskap om komplexamulticellulärabiologiskasystem.Idagharflertaletteknikerutvecklatsmedavsiktattlänkagenuttrycksdatameddessfysiskavävnadssammanhang.Ävenomdetännuintefinnsenendaenskildteknikmedöverlägsnafördelargentemotandrateknikersåharbefintligametodergemensamtbidragittillframstegavteknikutvecklingen.Befintligaprotokoll omfattar dock fortfarande vissa utmaningar, så som ett limiterat antalgener,tungtlaborativtarbete,beroendeavvävnadstyp,storskalighetellerbegränsadupplösning. Inomramen fördennaavhandlinghar aspekternaavdessautmaningarbeaktatsochresulterat ienomfattandeutvärderingavennyligenutveckladspatialtupplösthel-transkriptom-omfattandemetod.DennametodgårundernamnetSpatialTranscriptomics (ST) och möjliggör en bevarad lokalisering av genaktivitet, vilkensamtidigt kan kopplas visuellt till dess histologiska position och anatomiskasammanhang. Artikel I beskriver detaljerna kring det experimentella protokollet,vilket börjarmed att placera intakta vävnadssnitt på avkodningsbaramikroarrayerochavslutasmedstorskaligsekvensering.Härvisasspatialtupplösthel-transkriptomdata från såväl vävnad av musluktlob och bröstcancerprover, vilket visar på dessbredaapplicerbarhetochrobusthet.ArtikelIIapplicerarSTteknikenpåadulthumanahjärtprover, en vävnadstyp innehållande stora proportioner av fibrös vävnad vilketgör RNA extraheringen väsentligt mer utmanande. Nya strategier förprotokollutförandet optimerades för att kunna generera spatialt upplösttranskriptomikdatafrånpatientermedhjärtsvikt.Härdemonstrerasfördelenmedattanvändateknikenförattdetekteradelågtuttrycktabiomarkörersomvanligtviskanses i sjukdomsutvecklingen av hjärtsvikt. Trots att ST tekniken har en begränsadupplösningjämförtmedandrateknikervisarartikelIIIattmetodenkankombinerasmed RNA-sekvensering på enskilda celler, och på så sätt erhålla vävnadsspecifikapositioner för enskilda celler. Detta kombinerade tillvägagångssätt appliceras påhumanhjärtutvecklingochvisarpåcellulärheterogenitetinomdeolikaregionernaavorganet.EftersomSTteknikenärbaseradpåsekvenseringavmRNA-taggar,beskriverartikel IV en ny version av metoden, där spatialt upplöst analys av full-längdstranskriptutvecklas.Dettamöjliggörytterligareinsikteromalternativsplicing,fusionstranskriptochupptäcktenavnyagener.NyckelordRNA, RNA-sekvensering, transkriptomik, spatialt upplöst transkriptomik, enskildaceller

ListofpublicationsI. Patrik L. Ståhl*, Fredrik Salmén*, Sanja Vickovic**, Anna Lundmark**, José

FernándezNavarro,JensMagnusson,StefaniaGiacomello,MichaelaAsp,JakubO. Westholm, Mikael Huss, Annelie Mollbrink, Sten Linnarsson, SimoneCodeluppi, Åke Borg, Fredrik Pontén, Paul Igor Costea, Pelin Sahlén, JanMulder, Olaf Bergmann, Joakim Lundeberg, Jonas Frisén. Visualization andanalysisofgeneexpressionintissuesectionsbyspatialtranscriptomics.Science353:78-82(2016).doi:10.1126/science.aaf2403

II. MichaelaAsp,FredrikSalmén*,PatrikL.Ståhl*,SanjaVickovic*,UlrikaFelldin,Marie Löfling, José Fernandez Navarro, Jonas Maaskola, Maria J. Eriksson,Bengt Persson, Matthias Corbascio, Hans Persson, Cecilia Linde, JoakimLundeberg. Spatial detection of fetal marker genes expressed at low level inadulthumanhearttissue. Sci.Rep.7:1-10 (2017).doi:10.1038/s41598-017-13462-5

III. Michaela Asp, Stefania Giacomello, Daniel Fürth*, Johan Reimegård*, Eva

Wärdell*,JoaquinCustodio,FredrikSalmén,ErikSundström,ElisabetÅkesson,Patrik L. Ståhl, Magda Bienko, Agneta Månsson-Broberg, Christer Sylvén,Joakim Lundeberg. An organ-wide gene expression atlas of the developinghumanheart.Submitted.

IV. Michaela Asp, Erik Borgström*, Alex Stuckey*, Joel Gruselius, Konstantin

Carlberg, Zaneta Andrusivova, Fredrik Salmén, Max Käller, Patrik L. Ståhl,Joakim Lundeberg. Spatial isoform profiling within individual tissue sections.Manuscript.

*Theseauthorscontributedequallytothework.**Theseauthorscontributedequallytothework.

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Contents

INTRODUCTION........................................................................................................................11.Aspatialaspectofbiology............................................................................................................1

1.1Thebuildingblocksofanorganism.................................................................................................................11.2Thegeneticcode.......................................................................................................................................................11.3Thecentraldogmaofmolecularbiology........................................................................................................21.4Theimportanceofspatialcontextinbiology...............................................................................................3

2.Analyzinggeneexpression...........................................................................................................52.1Background.....................................................................................................................................................52.1.1Northernblot..........................................................................................................................................................52.1.2PolymeraseChainReaction(PCR)................................................................................................................52.1.3Quantitativereal-timePCR(qPCR)...............................................................................................................62.1.4Microarrays.............................................................................................................................................................62.1.5Sangersequencing,EST,SAGEandCAGE...................................................................................................6

2.2RNAsequencing(RNA-seq).....................................................................................................................72.2.1Massivelyparallelsequencing........................................................................................................................72.2.2Singlemoleculeandlongreadsequencing................................................................................................7

2.3SinglecellRNAsequencing(scRNA-seq)...........................................................................................82.3.1FirstRNA-seqofasinglecell...........................................................................................................................82.3.2PCRamplificationusingtheterminaltransferaseapproach.............................................................82.3.3PCRamplificationusingthetemplate-switchapproach......................................................................92.3.4Linearamplification............................................................................................................................................92.3.5ScalingupthemultiplexityofscRNA-seq..................................................................................................92.3.6Singlecellcombinedapproaches................................................................................................................10

3.Analyzingspatiallyresolvedgeneexpression....................................................................113.1Technologiesbasedonmicrodissectedgeneexpression........................................................113.1.1Lasercapturemicrodissection(LCM).......................................................................................................113.1.2Analyzingindividualcryosections.............................................................................................................113.1.3RNAtomography(tomo-seq).......................................................................................................................113.1.4Transcriptomeinvivoanalysis(TIVA)....................................................................................................123.1.5NICHE-seq.............................................................................................................................................................12

3.2Insituhybridizationtechnologies.....................................................................................................133.2.1Single-moleculeRNAfluorescenceinsituhybridization(smFISH)............................................133.2.2Sequentialhybridization(seqFISH)..........................................................................................................133.2.3Multiplexederror-robustFISH(MERFISH)............................................................................................13

3.3Insitusequencingtechnologies..........................................................................................................153.3.1Insitusequencingusingpadlockprobes................................................................................................153.3.2Barcodeinsitutargetedsequencing(BaristaSeq)..............................................................................163.3.3Spatially-resolvedTranscriptAmpliconReadoutMapping(STARmap)...................................163.3.4FluorescentinsituRNASequencing(FISSEQ)......................................................................................16

3.4Insitucapturingtechnologies..............................................................................................................193.4.1SpatialTranscriptomics(ST)........................................................................................................................19

3.5Insilicoreconstructionofspatialdata.............................................................................................203.5.1UsingISHasreferencemaps.........................................................................................................................203.5.2UsingSTasreferencemaps...........................................................................................................................20

PRESENTINVESTIGATION..................................................................................................22PaperI–Aspatiallyresolvedtranscriptome-wideapproachtostudywholetissuesections...............22PaperII–Detectinglowlyexpressedbiomarkersinheartfailurediseaseprogression...........................24PaperIII–Tracingcellularheterogeneitywithinthedevelopinghumanheart..........................................25PaperIV–Constructingspatiallyresolvedfull-lengthtranscriptomeswithinwholetissuesections26

Concludingremarks.............................................................................................................28Abbreviations.........................................................................................................................30

Acknowledgments.................................................................................................................31

References...............................................................................................................................34

Don'tkidyourselfthatyou’regoingtoliveagainafteryou’redead;you’renot.Makethemostoftheonelifeyou’vegot.Liveittothefull.

-RichardDawkins

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INTRODUCTION

1.AspatialaspectofbiologyTheword“spatial”,whichoriginatesfromtheLatinwordspatium,meaning“space”,canbeusedwhendescribinghowobjects relate to eachotherwith regard to theirrelativepositions.Thespatialconceptallowsabiologicalsystemtobedescribedasaglobal networkwhere each element is influenced by its surrounding environment.Singlecellsincloseproximityconstitutetissuesandindividualorgansarelinkedintoorgansystems.Studyingbiologythereforemeansthateverypieceofinformationhastobeputintocontextinordertofullyunderstandeachelement’sentirerepertoireofmechanisms.Herefollowsanintroductiontovariousbiologicalunitsandtheirspatialrelationstoeachother.

1.1ThebuildingblocksofanorganismOrganismsarelifeformsconsistingofoneormorecells:thebasicbiologicalunitoflife1. A unicellular organism, such as a member of the bacteria or archaea, is thesimplest formoforganismandabsorbs itsnutritiondirectly from theenvironmentthroughitscellularmembrane.Onceitisreadytoreproduce,itsimplydividesitselfinto two unicellular organisms. A multicellular organism, however, is a lot morecomplex.Animals,plantsandfungiareexamplesoforganismsthatcancontainmanycells.Forexample,thenumberofcellsinthehumanbodyhasbeenestimatedat~37trillion2,allofwhichhavedefinedroleswithinthebiologicalsystem.Similarcellsinmulticellular organisms are grouped into tissues (muscle-, nervous-, connectivetissue etc.) and different types of tissues together form organs (heart, brain, lungsetc.), which, in turn, make up whole organ systems (cardiovascular-, respiratory-,nervous system etc.) that constitute the entire organism. When it comes to thereproductionofamulticellularorganism,theprocessiswaymorecomplexthanjustdividingitself(andfarbeyondthescopeofthisthesis).

1.2ThegeneticcodeLookingmorecloselyintoacellularunit,nearlyeveryoneofthosethatconstituteahuman being contains a nucleus. The nucleus holds possibly the most famousbiological molecule in the world – deoxyribonucleic acid (DNA). DNA is a longmolecule,andifthecontentofallourcellstogetherwerestretchedoutitwouldreachto the sun and backmore than 80 times2,3. DNA contains the genetic code, or the“blueprint”,andcarriesall instructionsregardingcellular functions. Italsocontainsinformationdeterminingourindividualtraits,suchaseyecolor,whetherodorcanbe

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detected in the urine after eating asparagus and if we are susceptible to certaindiseases–someofthesetraitsarepassedontoouroffspringduringreproduction.Asan example, all blue-eyedpeople in theworld share a commonancestorwho lived~10,000 years ago. Originally, we all had brown eyes but that single commonancestor carried amutation affecting the production ofmelanin in the iris4,whichresultedinablueeyecolor.ZoominginevenfurtherdowntoaDNAmolecularunit,onecanseethatitsstructureis somewhat similar to a twisted ladder. The steps of the ladder are comprised ofevensmallerbuildingblockscallednucleotidesorbases.TherearefourtypesofDNAbases: adenine (A), thymine (T), guanine (G) and cytosine (C). It is the ordering ofthese bases (or the steps of the ladder) that makes up the personal blueprint foreveryindividual.ThepresenceofDNAhasbeenknownforover100yearsanditwasisolatedforthefirsttimein1869byaSwisschemistnamedFriedrichMiescher5.Henamedit“nuclein”asitwasfoundinsidethecells’nuclei,andin1944itwasshownthatDNAisthehereditarymaterial6.In1953,RosalindFranklinandMauriceWilkinsusedX-raydatatodemonstratethatDNAisassembledinarepeatedhelixstructure.JamesWatsonandFrancisCrickgotholdoftheunpublisheddata(withoutFranklin'spermission),andmadetheinferencethattheDNAmoleculeiscomposedofadoublestranded helix, where A always pairs with T and C always with G. All thesediscoverieswerepublishedinthesameissueofthejournalNature7–9.About40yearslater,in1990,aninternationalscientificresearchprojectsetthegoalofdeterminingtheorderofallbasesinthehumangenome,whichhasatotalsizeof~3billionbasepairs10. The project was named the Human Genome Project and as the projectprogressed,theprivatecompanyCeleraGenomicsjoinedtheracein1998.Boththepublicandtheprivateeffortpublishedtheirdraftgenomesin200111,12.

1.3ThecentraldogmaofmolecularbiologyCells inourbodycontainalmost thesameblueprint13,sohowcometheydonotalllook the same or have the same function? The answer lies in the process oftransferring genetic information from DNA into a final product within the cell, aprocess referred to as the central dogma ofmolecular biology. The central dogma,which was introduced by Francis Crick in 195814, concerns three mechanismsperformed by the cellularmachinery. Existing DNA can be copied to produce newDNA(replication)oraribonucleicacid(RNA)(transcription).TheRNAmoleculecanpossessdirectactivityandhave,forexample,aregulatoryfunctionwithinthecell.Itcanalsoactasan intermediatebetweenDNAand the finalproduct,aprotein.RNAcanbecopiedtomakenewRNA(replication)andusedasthetemplateforproteins(translation).Proteins, themainbuildingblocksof the cell, cannotbe replicatedor

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copied into DNA or RNA. The central dogma was refined in 1970 when it wasdiscoveredthatRNAcanbecopiedintoDNA(reversetranscription)15.TheDNAmoleculecontainsshortersegmentsreferredtoas“genes”.Thedefinitionofa gene varies within the scientific community but generally, one can explain theconceptof a geneas apieceofDNA,which canbe transcribed intoRNAandhas afunctionwithin thecell16.Differentgenesare turnedonoroff indifferentcellsandgenesthatareactivecanbesoatdifferentlevels.Therearedifferent"flavors"ofRNAspecies, however, this thesis will focus mainly on the messenger RNA molecule(mRNA – a single-stranded RNA molecule that is later translated to proteins). AsmRNAs are the intermediates between DNA and proteins, they can be used asindicatorsofaspecificcelltype.In2016,aninternationaleffortcalledtheHumanCellAtlasconsortiumsetthegoalofidentifyingallcelltypesinthehumanbody1.Withtheimplementation of this large-scale project, the number of cell types identified isexpectedtoincreasesignificantlyoverthenextfewyears.Acell’scomplementofRNAmolecules will hereafter be referred to as either its “transcriptome”, its “geneexpression”orsimplytoasits“contentofRNAmolecules”.

1.4TheimportanceofspatialcontextinbiologyLet us return to the spatial concept and how it describes biology. The molecularpropertiesofindividualcellswithinamulticellularorganismcanonlybecompletelydetermined once we know their physical locations17. Cells within distinct tissuemicroenvironments express specific sets of genes, both influenced by, andinfluencing, the cells around them. This phenomenon governs, for example, theformationofgeneexpressiongradientsalongthemainembryonicbodyaxesduringdifferentstagesofdevelopment.Thesegradientsdirecttheactivationofthecorrectdevelopmental gene programs, needed for the construction of specific organs18,19.Moreover,cellslocatedinthesameorganarenotalwaysuniform,astatereferredtoas cellular heterogeneity which is always present to some degree in any cellularpopulation.When it comes to tumormicroenvironments, thecellularenvironmentsinwhichtumorsexist,severalsub-populationsofcancercellsconstitutingthetumorcandiffer fromeachothercompletely intermsofbothstructural featuresandgeneexpression. Even down to a subcellular level, it is recognized that organelles andmolecules are spatially localized in certain positions in order to carry out allnecessary cellular functions. Proteins are spatially localized in certain structures20andsimilarly,DNAisfoundinboththenucleusandthemitochondriaand,inplants,theplastids. Some cells, such asmuscle fibers, evenhavemultiple nuclei21 and theoverall spatial distribution of DNA within the nucleus is arranged in a dynamicway22,23.RNAcanalsohavedifferentlocalizationswithinthecell;forexampleishalf


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oftheneuronalmRNAsthatarepresentinrathippocampusareenrichedintheaxonsordendritescomparedtotheirlevelsinthecellbody24.Earlier biological studies aiming to understand cellular heterogeneity withinbiological sampleswere carriedoutusinghistologicalmethods.Tissue sectionsarefirststainedtohighlightcellularstructuresortotagparticularmoleculesofinterest.This is still done extensively today by, for example, pathologists wishing todistinguish diseased tissue from healthy and particularly to discriminate betweencancer types25. Because it is based on a subjective visual evaluation, there areoccasionswhenpathologistsdisagreeon their findings26.Anobjectiveclassificationofcellswithinasamplecouldbeachievedbycombiningthehistologicalimagewithinformationaboutgeneexpressionprofiles.Theworkinthisthesisconcernshowwecanconnectgeneexpressionprofileswithmorphologicalpositions.

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2.AnalyzinggeneexpressionTheentiresetofanorganism’sDNAiscalleditsgenome.Itcontainsbothcodingparts(genes)andnon-codingparts.ThecodingpartsgiverisetoRNAmolecules,andthesumof theseRNAs isreferredtoas its transcriptome.Thehumangenomecontainsmore than 20,000 distinct genes, expressed at different levels in different tissues.DeterminingtheDNAsequenceofacell’sgenometellsuswhatthecelliscapableofdoing,whereasanalyzingitsRNAcontentinsteadgivesusanindicationofwhatthecell is doing at that particular point in time. A brief historical timeline oftranscriptomicsmethodsmentionedinthischapterisshowninFigure1.

2.1BackgroundAwiderangeoftechnologiesforanalyzingRNAmoleculeswithinabiologicalsamplehas preceded today’s techniques. These technologies have typically beenhybridization-,amplification-orsequencing-basedapproaches.

2.1.1NorthernblotEarly gene expression analysis was performed by means of the northern blottechnologydevelopedin197727.RNAisfirstextractedfromatissue-oracellculturesample, after which RNA molecules are separated by size using electrophoresis.Separated RNA molecules are then transferred to a blotting membrane, on whichdetection is achieved by hybridizing a labeled complementary DNA nucleotidesequence(aprobe)correspondingtothesequenceofaknownexpressedgene.

2.1.2PolymeraseChainReaction(PCR)Thepolymerasechainreaction(PCR)wasdeveloped in the1980sbyKaryMullis28,who received the Nobel Prize for it in 1993. The technique is one of the mostimportantscientificinventionsandisstillusedextensivelytodayinmanyprotocols.Itissometimesreferredtoas“molecularphotocopying”29,wheresmallpiecesofDNAareamplifiedtoproducealargenumberofcopies.Itconstitutesarepeatedseriesofheating cycles, where each cycle contains three separate steps: (i) Denaturing ofdoublestrandedDNAintosinglestrandedDNA,(ii)Hybridizationofspecificprimers(short DNA fragments), complementary to the flanking areas of theDNA region ofinterest,and(iii)Elongation,wheretheprimersareextendedovertheDNAtargetbyadditionoffreenucleotidesbyDNApolymerase.TheextendedprimersgeneratenewDNAtargetmolecules,usedastemplatesforthenextamplificationcycle.ThismeansthattheoreticallythenumberofDNAmoleculesisdoubledaftereachcycleandthatthe amplification rate is exponential. However, because some DNA fragments are


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more accessible for amplification than others, the reaction efficiency is less than100%30–32.

2.1.3Quantitativereal-timePCR(qPCR)ThedevelopmentofPCRtechnologysubsequentlyenabledgeneexpressionstudiestobecarriedoutbymodifyingthetechniqueintoaversioncalledquantitativereal-timePCR(qPCR)33.InqPCR,theamplificationofDNAmoleculesismonitoredinreal-time,and not solely at the end of the reaction. This is done by including fluorescentmolecules in the PCR reaction mix (previously ethidium bromide was used butmodern protocols often use SYBR Green34,35), which then bind nonspecifically todouble-strandedDNA.Amorespecificmeasurementcanbeachievedbyusingtarget-complementary probes attached to a fluorophore35. As the fluorescent signal isproportionaltotheamountofDNAmoleculesitispossibletoquantifytheamountofmoleculespresentinthebeginningofthereaction.

2.1.4MicroarraysAnothermethod,whichexceeds the capacityofqPCR in termsofmultiplexing (theability to process and analyzing multiple samples at once), is the microarrayapproach,whichhasbeenusedextensivelyingeneexpressionstudiessince199536.Here, clusters containing short complementaryDNAnucleotide sequences (probes)corresponding to known gene sequences are attached to a solid-state surface.Extracted geneticmaterial from a tissue sample is then labeled and applied to themicroarray. If a gene (the target) is expressed, a hybridization-match between thearray-probesandthe targetwilloccur, resulting inasignal.Relativeabundancesoftarget sequences can then be determined and quantitative measurements of geneexpressionwithinthesamplecanbemade.However,microarrayssufferfromthreemajor limitations: (i) prior knowledge about gene sequences is required (ii) cross-hybridization can result in high background levels and (iii) the dynamic range ofdetectionislimited,resultinginbothbackgroundandsaturatedsignals37.

2.1.5Sangersequencing,EST,SAGEandCAGEIn1977,FrederickSangerandcolleaguesdevelopedasequencingmethodthatcoulddirectly determine the base sequences of nucleic acids38, for which he andWalterGilbertreceivedtheNobelPrizein1980.ItwasthemaintechnologyusedduringtheHumanGenomeProjectandisstillusedtodaytovalidatesequencingdataproducedfrom newer technologies39. The Sanger technology is able to read nucleotidesequences from cDNA and expressed-sequence-tags (EST) libraries (shortersubsequences of cDNA) without previous knowledge of the target sequence40,41.However, EST sequencing suffers from being low-throughput, generally notquantitative,andcostlyforlargerprojects.Tag-basedmethodssuchasserialanalysis

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of gene expression (SAGE)42 and cap analysis of gene expression (CAGE)43 werethereforedevelopedtoincreasethethroughputofsequencingdata.

2.2RNAsequencing(RNA-seq)With the development of massively parallel sequencing technologies, one couldprocessmillionsofDNAsequences inparallel, rather than96usingSanger44.High-throughput technologies allow us to explore the entire transcriptome in a parallelfashion,anapproachreferredtoasRNA-seq.

2.2.1MassivelyparallelsequencingThefirstcommercialmassivelyparallelsequencingapproachwasdevelopedin2005,whenpyrosequencing45wasmultiplexedby immobilizinghundredsof thousandsofunique DNA templates to nano-beads in a system named 45446. Individualnucleotidesareaddedonebyoneandeachincorporationeventisdetectedaslightisemitted through a cascade of enzymatic reactions. The principle of nucleotideadditionanddetectingtheincorporationofeachnucleotideiscalledsequencing-by-synthesis(SBS).IonTorrenttechnology47isanothersystemutilizingSBS,butinsteadofdetectingtheincorporationeventasalightsignal,itregistersthechangeinpHthatoccurs as a hydrogen ion is released at every base incorporation step. Anothercommercial massively parallel sequencing system released in 2007 is SOLiD(Sequencing by Oligo Ligation and Detection)44. In contrast to SBS, consecutiveroundsofligationoffluorescentlylabeledoligonucleotides(oligos)areusedtoreadout the sequences - a principle called sequencing-by-ligation (SBL).Many differenthigh-throughput technologies have followed but undoubtedly the most successfulone is the Solexa system, acquired by Illumina in 200748. The Illumina method isbasedonSBS,butusesfluorescentlylabelednucleotidesfordetection.

2.2.2SinglemoleculeandlongreadsequencingExistinghigh-throughputtechnologieshaveasequencingreadlengthofaround100-500 bases. Sincemost RNAmolecules aremuch longer49, shorter cDNA fragmentshave to be sequenced individually and pieced together computationally. In 2011,Pacific Biosciences (PacBio) released a commercially available platform able toperform single molecule sequencing on long DNA fragments50. The technology isbased on Single Molecule Real-Time (SMRT) sequencing, meaning that individualDNA sequences are recorded directly as fluorescently labeled nucleotides areincorporated. DNA molecules are attached to polymerases, which in turn areindividually attached to the bottom of zeptoliter-sized wells, called zero-modewaveguides (ZMW)51,52. Although the system has an average read length of about15,000 bases, it does not yield the same throughput as the previous massivelyparallelsequencingtechnologies53.ForRNAstudies,thePacBioIso-Seqprotocolhas


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beenusedtodirectlyprovidefull-lengthinformationabouthumancDNAmolecules,without the need to assemble them afterwards54. Another technology that can beused for transcriptome studies is the Oxford Nanopore platform. This is based onimmobilizedproteinnanoporesonapolymermembrane55.Asanelectricalpotentialisappliedoverthemembrane,moleculesarepushedthroughtheporesandbasesaredetected when changes in the potential are registered. Generally, in practice theconceptofsequencingRNAreallymeanssequencingthecDNA(reversetranscribedRNA). The process of reverse transcription (RT) and additional amplification mayintroduce errors and biases. In 2017, Oxford Nanopore launched a new RNAsequencing solutionmaking it possible to performdirect sequencing on full-lengthnativeRNAmolecules56,57.

2.3SinglecellRNAsequencing(scRNA-seq)InitiallyRNA-seqonlyallowedforanalyzinggeneexpressionfrombulksamplesdueto the large amount of RNA required for input. Whole pieces of tissue arehomogenized and thousands of cells are analyzed simultaneously. This creates anaveragepictureofgeneexpressionwithin thebulksample,potentiallymasking theproductsofgenes thatareexpressedonlybyaminorityof thecells.Measuring thegene activity of a single cell instead allows us to define gene expression profilesindividually. However, there was one major challenge to be overcome in orderaccomplishthis;onesinglecellcontainsroughly1-50pg58oftotalRNA,ofwhichonly1-5%(0.01-2.5pg)compriseits~300,000mRNAmolecules59.

2.3.1FirstRNA-seqofasinglecellIn2009,Tangetal.60 improvedexistingsingle-cellRNAamplificationprotocols61–63andwereable toapply theirmethod tohigh-throughputRNA-seqandexamine thewhole transcriptome of one single cell. From then on, the number of methodsavailable for analyzing single cells has exploded and todaywe are able to analyzehundredsofthousandsofsinglecellsinparallel.

2.3.2PCRamplificationusingtheterminaltransferaseapproachMostmethodstakeadvantageofthepoly-AtailofeukaryoticmRNAs.Byhybridizinga poly-Tprimer (a short single-stranded stretch ofDNAnucleotides) to thepoly-Atails,mRNA can be reverse transcribed to complementaryDNA (cDNA). The cDNAthenneedstobeamplified,whichcanonlybeachievedwhenamplificationadaptorsequencesareaddedtotheends.Thefirstadaptorcanbeincorporatedtogetherwiththepoly-Tprimer,whilethesecondadaptorisintroducedbydifferentstrategies.TheTang protocol enzymatically added another poly-A stretch to the cDNA usingterminaldeoxynucleotidyl transferase;asecondpoly-Tprimercontainingtheother

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amplificationadaptorcanbehybridizedtoit.Thisapproachwasupdatedin2013toamoresimplifiedprotocolnamedQuartz-Seq64.Here,thewholeprocedurewasmademoresensitivebycompletingallreactionstepsinasingletube,withoutanyneedforpurification. The protocol achieved highermultiplexing capacity in 201865,when acell barcode (a sequence unique for one specific cell) and a molecular barcode (asequenceuniquetoonespecifictranscript)wereintroduced.

2.3.3PCRamplificationusingthetemplate-switchapproachAnotherapproachto introducing thesecondamplificationadaptor is toemploy thetemplate-switchmechanism66.WhenthereversetranscriptasereachestheveryendofthemRNAmolecule,itaddsasmalladditionalnumberofnucleotides.Theresultisa short single-stranded overhanging sequence to which the second adaptor canhybridize. This has been applied in a couple of methods: in 2011 in STRT-seq67(Single-cell tagged reverse transcription) and in 2012 in Smart-seq49. In STRT-seq,cDNA is fragmented and enriched for its 5’ ends, while in Smart-Seq full-lengthinformation is retrieved enabling details about alternative RNA isoforms to beobtained.Bothprotocolshavebeenfurtherupdated:STRT-seqintroducedmolecularbarcodes68andSmart-seq2improveditssensitivityandfull-lengthcoverage69.

2.3.4LinearamplificationThe above-mentioned technologies are all based on exponential PCR amplification,which is prone to the risk of introducing sequence biases. One way to reduceamplification biases is by using invitro transcription (IVT) amplification70, a linearamplification-based approach. IVTwas implemented in 2012 in a protocol termedCEL-Seq (Cell Expression by Linear amplification and Sequencing)71 with furtheroptimizationstepsin201672.Upto2014,protocolswereabletoanalyze~100cellsin parallel73 but an automated IVT approach namedMARS-Seq (massively parallelsingle-cell RNA-seq)74 made it possible to analyze several thousands of cells inparallel.

2.3.5ScalingupthemultiplexityofscRNA-seqTofurtherincreasethemultiplexitycapacityofscRNA-seqexperimentsuptotensofthousands73ofcells,amethodnamedCytoSeq75,whichwasdescribedin2015,usesanapproachthatcombinesmultipleshorterbarcodes.Thisenabledthegenerationofagreaternumberofbarcodesandthusincreasesthenumberofcellsbeinganalyzed.Shortly afterwards, two papers published in the same issue of the journal Cellintroduced single cell droplet technology. The first of these, named InDrop (IndexDroplets)76, uses the previously described combinatorial indexing approach, whilethe second one, named Drop-seq77, instead employs longer random barcodes.Recently, newmultiplexing strategies have been developed andwe are reaching apoint where a number in the hundreds of thousands of cells can be analyzed


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together73.Here,cellsarenotisolatedandbarcodedindividually.Instead,barcodingtakes place inside the cell within pools of ~10-100 cells78,79. Several rounds ofpooling,mixing and re-pooling facilitates a decrease in the probability of two cellsendingupwiththesamebarcode.

2.3.6SinglecellcombinedapproachesEven thoughasnapshotofacell'sRNAcontent (whichresults fromtranscriptionalbursts) can give us an indication of what cell type we are looking at, the idealprocedure would be to combine it with other omics technologies80. Today,technologiescombiningsingle-celltranscriptomicswithinformationsuchasthecell'sgenome81,82, its proteome83–85 and its chromatin conformation23 and accessibility22havebeendeveloped.

Figure1.Briefhistoricaltimelineoftranscriptomics.

1977

Northern Blot

1991

ESTs

1992

qPCR

1995

cDNA microarrays

2003

CAGE

SAGE

2005

RNA-seq 454

2006

RNA-seq SBS

2015

2017

100,000-plexscRNA-seq

10,000-plexscRNA-seq

2014

1,000-plexscRNA-seq

2013

Iso-Seq

2009

scRNA-seq

Direct full-lengthRNA-seq

Sanger sequencing

2007

RNA-seq SBL

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3.AnalyzingspatiallyresolvedgeneexpressionThere has been extensive analysis of gene expression in both bulk and single cellsamples.However,understandingthecomplexnetworkofbiologicalfunctionswithintissuesandorgans requires the spatial contextof geneexpression tobepreserved.Today, several experimental and computational methods have been establishedwhich are designed to connect positional information with gene expression data.CharacteristicsoftheexperimentalmethodsmentionedinthischapterareshowninTable1.

3.1TechnologiesbasedonmicrodissectedgeneexpressionOne way of capturing spatial gene expression information is simply by isolatingregions of interest from within a sample. These regions can then be individuallyplacedintesttubesforRNAextractionandsubsequentgeneexpressionprofiling.

3.1.1Lasercapturemicrodissection(LCM)Lasercapturemicrodissection(LCM)isatechniqueinwhichalaserbeamisusedtocut out tissue regions identified under a microscope86,87. In 2017, an extendedversion of the LCM protocol, named Geo-seq (geographical position sequencing)88,combined LCM with scRNA-seq in order to profile the transcriptomes of tissueregions as small as ten single cells.AlthoughLCM is a robust technology, it is verylaborintensive,whichlimitsthethroughputofsamplestobeanalyzed.

3.1.2AnalyzingindividualcryosectionsAnotherwayofproducingregionalizedgeneexpressiondataistoslicetissuesamplesintothincryosectionsandprepareindividualsequencinglibrariesfromeachpieceoftissue.In2013thistranscriptome-wideapproachwasappliedtoDrosophilaembryos,where cryosectioning and sequencing was performed along the anterior-posteriorbodyaxis,revealingbothknownandnovelspatialgeneexpressionpatterns89.Highquantitiesof inputRNAwereneeded for thepreparationof sequencing libraries, aproblemwhichwas solvedby adding large amountsof carrierRNA.This approachrequiresalargenumberofsequencingreadsinordertoobtainasufficientnumberofthesequencesoriginatingfromtheindividualtissuesections.

3.1.3RNAtomography(tomo-seq)Acryosectioningapproachdescribedin2014,calledRNAtomography(tomo-seq)90,avoidedtheneedofcarrierRNAbylinearlyamplifying71thecDNAofindividualtissuesections. Here, the authors systematically cryosectioned three identical zebrafishembryos, each along one of the threemain body axes: anterior-posterior, ventral-


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dorsal and left-right. A 3D transcriptional profile of the embryo was thenreconstructed computationally by overlapping RNA-seq information from allcryosections. This method is superior for both RNA quantification and spatialresolution compared to the earlier cryosectioning protocol89. However,transcriptome-wide maps using tomo-seq can only be constructed using identicalbiologicalsamplesandcannotbeappliedtoclinicalsamples.

3.1.4Transcriptomeinvivoanalysis(TIVA)In order to accurately explore gene expression in cells within their naturalenvironment,thecellsmustbealive.OnetechnologythatenablesspatiallyresolvedtranscriptomicsoflivingcellsintheirnaturalenvironmentisTIVA(transcriptomeinvivoanalysis)91,publishedin2014.IntheTIVAprotocol,intactlivetissuesectionsareexposedtoTIVAtags(eachtagisaphotoactivatablemRNAcapturemolecule).Thesemultifunctional tags are attached to a cell-penetrating peptide, allowing them toenterthecellcytosol.Next,TIVAtagscanselectivelybeactivatedincellsofinterestby laser photoactivation, afterwhich themRNA capturemolecule can hybridize tomRNAswithinthecell.TIVAtag-mRNAhybridscanthenbepurifiedandthecapturedmRNAscanbefurtheranalyzedbyRNA-seq.Althoughitiscurrentlytheonlymethodthatcanbeappliedtolivingtissue,itsmainlimitationisitslowthroughput,asonlyafew single cells can be analyzed at a time. Furthermore, as TIVA is applied to livetissue,itsusefortheanalysisofclinicalsamplesislimited.

3.1.5NICHE-seqAn alternative technology published in 2017, also using photoactivation, isNICHE-seq92.Asthenameimplies,transcriptomesofindividualcellswithinaspecificniche(anareawithina tissue thatholdsa specificmicroenvironment)areprofiled.First,specific niches are visualized by intravenously transferring labeled landmark-cells(e.g. T and B cells) into transgenic mice expressing photoactivatable greenfluorescent protein (GFP). Niches of interest are then photoactivated, followed bytissuedissociation,cellsortingofGFP+activatedcellsandscRNA-seq.Thistechnologyisveryhighthroughputandcananalyzethousandsofcellswithinaniche,buttheirexactpositionswithinthephotoactivatedareaarenotknown.Asthecurrentprotocoldependsongeneticallyengineeredmodelorganisms, itcannotbeappliedtohumanclinicalsamples.

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3.2InsituhybridizationtechnologiesInsteadofextractingRNAmoleculesfromindividualparts(orcells)withinatissue,onecanvisualizethemdirectlyintheiroriginalenvironment.Thiscanbeachievedbyhybridizingalabeledprobecomplementarytothetargetofinterest.Anoverviewofinsituhybridization(ISH)technologiesmentionedhereisshowninFigure2.

3.2.1Single-moleculeRNAfluorescenceinsituhybridization(smFISH)The ISH techniquehas existed since the 1960s93 andhas beenused for visualizinggene expression from the early 1980s94 onwards. Fluorescently labeledprobes areindividually hybridized to predefined RNA targets in order to visualize geneexpression in fixed tissue. A further development of the fluorescence in situhybridization (FISH) protocol uses instead many short probes to target differentregions of the same transcript. This approach, named single-molecule RNAfluorescence in situ hybridization (smFISH), enables quantitativemeasurements oftranscriptssinceonefluorescentspotindicatesasingleRNAmolecule.Initially,asetof five probes (50 bases in length) each coupled to five fluorophoreswas used totarget each transcript95. However, labeling probeswith a large set of fluorophoreshas two main problems: (i) They are difficult to synthesize and purify (ii)Fluorophores on the same probe can potentially interact with each other causingaltered hybridization characteristics and self-quenching. An alternative smFISHmethod was developed in 2008, using instead a set of ~40 probes (20 bases inlength),eachcoupledtoasinglefluorophore96.AlthoughsmFISHhashighsensitivityandsubcellularspatialresolution,itcanonlytargetafewgenesatatime.

3.2.2Sequentialhybridization(seqFISH)AmultiplexsmFISHapproachthatusessequentialhybridizations(seqFISH)97,98wasdevelopedin2014.Individualtranscriptsaredetectedseveraltimesbyserialroundsof hybridization, imaging andprobe stripping. In everyhybridization round, a pre-defined colored set of 24 encoding probes is used for each target. However, anincreasednumberofhybridizationroundsrequiresanincreasednumberofsmFISHprobes,whichmakesseqFISHbothexpensiveandtimeconsuming.

3.2.3Multiplexederror-robustFISH(MERFISH)Another sequential hybridization method is multiplexed error-robust FISH(MERFISH)99. Here, ~192 smFISH probes are hybridized to each RNA target, eachprobe carrying two flanking regions used for the readout. Fluorescent readout-probesarethenhybridizedtoidentifythetargetsinseveralroundsofhybridization,imaging,andsignalextinguishing.Computationalerror-correctionisperformedafterreadouttoaccountforimperfecthybridizations.SincetheRNAtargetsonlyneedtobe hybridized with the smFISH probes once (a procedure taking ~10 hours), the


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experimental time is reduced significantly. The following hybridization of thereadout-probestakes~15minutes. In2018, theMERFISHtechniquewascombinedwithexpansionmicroscopy100(anapproachthatphysicallyenlargestissues101)soasto increase the distance between overlapping RNA molecules and so increase thenumber of molecules that can be measured. Although no super-resolutionmicroscopy equipment is required, the large volumes of image processing can besomewhatcomputationallydemanding.

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Figure2.(Figureappearsonprecedingpage)OverviewofISHtechnologies.smFISH:IntheoriginalsmFISHapproach,asetoffiveprobes labeled with five fluorophores each are hybridized to individual targets95.Alternatively,asetof~40probescoupledtoasinglefluorophoreisused96.seqFISH:smFISHprobesarehybridizedconsecutivelytothetargetandstrippedoffbyDNaseIinbetweeneach round.The sameprobe sequences areused indifferent roundsofhybridization, but probes are connected to different fluorophores97. seqFISH(smHCR):seqFISHincombinationwithsingle-moleculehybridizationchainreaction(smHCR)toamplifythesignal98.MERFISH:Aprobesetwithuniqueflankingreadoutregionsisusedforindividualtargets.Readoutprobesarehybridizedsequentiallyinbetweenroundsofsignalextinguishing99.

3.3InsitusequencingtechnologiesMassivelyparallelsequencingcanbeperformeddirectlyontheRNAcontentofacellwhile it remains in its tissue context.This is referred toas insitu sequencing (ISS)and several protocols have been developed. An overview of ISS technologiesmentionedhereisshowninFigure3.

3.3.1InsitusequencingusingpadlockprobesIn 2013, the first in situ sequencing approach was published that used padlockprobes to targetknowngenes102.First,mRNAmoleculeswithina tissuesectionarereversetranscribedtocDNAs,afterwhichtheoriginalmRNAsaredegradedinorderto make the cDNAs accessible to the padlock probes. Padlock probes are single-stranded DNAmolecules, in which each end contains regions complementary to aparticularcDNAsequence.TheprobescanbindtothecDNAtargeteitherwithagapbetweentheends,orwiththeendsadjacenttoeachother.Inthefirstcase,thegapisfilledbyDNApolymerizationandthen,inbothcases,theendsareligatedtocreateacircle of DNA. The targets are then amplified so that sequencing signals can bedistinguished. This is done by amplifying theDNA circles, a process called rolling-circleamplification(RCA),resultinginmicrometer-sizedRCAproducts(RCPs)withinthe cells. Each RCP contains numerous repeats of the original padlock probesequence.RCPsarethensubjectedtoSBL,whereeitherthegap-filledsequence,orafour-base-longbarcodewithintheprobewithadjacentends,isdecoded.ByapplyingISStoabreastcancertissuesection102,31targetswerespatiallylocalized.AlthoughISSwith padlock probes facilitates subcellular resolution, the number of targets islimiteddue to the short sequencing read-length (of fourbases) and the size of theRCPs.


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3.3.2Barcodeinsitutargetedsequencing(BaristaSeq)A recently developed method called Barcode in situ targeted sequencing(BaristaSeq)103 has an increased read-length of 15 bases. Instead of using SBL, itappliestheIlluminasequencingchemistry(SBS),whichhasahighersignal-to-noiseratio than SBL. The Illumina chemistry requires repeated heat cycles, which ispossible in BaristaSeq since RCPs are cross-linked using the same procedure as inFISSEQ104(describedbelow).

3.3.3Spatially-resolvedTranscriptAmpliconReadoutMapping(STARmap)Another recently developed approach to ISS is Spatially-resolved TranscriptAmpliconReadoutMapping(STARmap)105.STARmapusesbarcodedpadlockprobesthat hybridize to the targets, but avoid the RT step by using a second primer,targeting the site next to the padlock probe. This duplex process is termed SNAIL(Specific Amplification of Nucleic Acids via Intramolecular Ligation). Both probesneedtohybridizetothesametargetinorderforthepadlockprobetobecircularized;it is then amplified by RCA to generate nanometer-sized single-stranded DNAproducts callednanoballs. Consequently, the SNAIL strategy reduces thenoise thatcould otherwise occur from non-specific hybridization events when only a singlepadlockprobe isused.Amine-modifiedbasesare incorporatedduringtheRCA,andused for embedding thenanoballs in a3D scaffoldbyapplyinga specifichydrogel-tissue chemistry. The tissue-hydrogel complex is subsequently cleared of unboundproteins and lipids, enhancing the transparencyof the tissue.Next, amodified SBLapproach is applied to decode the five-base-long barcode. By using the hydrogel-tissue chemistry in combinationwith themodified sequencingprotocol,more than1,000genesweretargetedinmousebraintissuesections.

3.3.4FluorescentinsituRNASequencing(FISSEQ)An untargeted ISSmethod named Fluorescent in situ RNA Sequencing (FISSEQ)104waspublishedin2014.ThistechnologyissimilartothepadlockapproachinthatitalsoutilizesRCAtoamplifythesequencingtargetandsubsequentSBL.However,theydifferfromeachotheratseveralsteps.First,inFISSEQcDNAsynthesisisperformedusing a mix of regular and modified amine-bases, together with tagged random-hexamerRTprimers.Becauseoftheincorporatedamine-bases,cDNAiscross-linkedto its cellular environment and thereafter circularizedby ligation. SubsequentRCAcreates single-stranded DNA nanoballs, whose positions are maintained via cross-linkingtothecellularproteinmatrix.Lastly,sequencingispreformedusingSBLwitha read-length of 30 bases. However, sequencing every nanoball within a cell isproblematic because of overcrowding; overlapping fluorescent nanoballs will beimpossible to distinguish from each other. Instead, by extending the sequencingprimers (whichare complementary to the tag attached to the random-hexamerRT

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primers), nanoballs are randomly sampled so that only a subset is sequenced. ByapplyingFISSEQ to cultured fibroblasts,more than8,000 targets couldbedetectedwithsubcellularresolution.Although ISS technologies offer subcellular resolution, they all usemicrometer- ornanometer-sizedDNAballstoamplifythesignal.Duetospacelimitationinthecell,thenumberoftranscriptsthatcanbedetectedislimited.Allmethods,exceptFISSEQ,aretargetedapproaches,andthisfurtherlimitsunbiasedtranscriptome-widestudiesofintacttissuesections.


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Figure3.(Figureappearsonprecedingpage)Overview of ISS technologies. ISS with padlock probes: Padlock probe-based ISSusingRCAandsequencing-by-ligation(thegapfillingapproachisnotshownhere)102.BaristaSeq: Utilizes the padlock probe-approach but the RCP is cross-linked to thecellular matrix. Sequencing is performed using sequencing-by-synthesis103.STARmap:ASNAILprobecomplexbindsdirectlytoRNA.RCAisperformedandtheRCP is embedded within a hydrogel, which is thereafter cleared from unboundproteinsandlipids.Amodifiedversionofsequencing-by-ligationisperformedontheRCP105.FISSEQ:cDNAiscross-linkedtoitscellularenvironment.RCAisperformedoncirculatedcDNAandtheRCPisagaincross-linkedtothecellularmatrix.Sequencingisperformedusingsequencing-by-ligation104.

3.4InsitucapturingtechnologiesThe spatial techniques described so far have been based on either isolation ofalready-known tissue regions of interest, or in situ visualization of RNAmoleculesusing hybridization or sequencing. Another approach is to capture and barcodewholetranscriptomesinsitu,thenperformsequencingexsitu.

3.4.1SpatialTranscriptomics(ST)The Spatial Transcriptomics (ST)106 technology, published in 2016, constitutes thefundamental base upon which this thesis is built. It is described in detail in thePresentInvestigationchapter.Inshort,athintissuesectionisplacedonamicroarrayglass slide printed with 1,007 spots, where each spot contains ~200 millionimmobilizedRTprimers.Eachprimerwithinaspotbearsauniquebarcodesequencecorresponding to its position within the array. Individual spots are 100 µm indiameter, with a center-to-center distance of 200 µm. After the tissue section hasbeenplacedonthemicroarray,itisfixed,stained,imagedandpermeabilized.Duringthe permeabilization process,mRNAmolecules diffuse vertically down to the solidsurface andhybridize locally to theRTprimers. cDNA synthesis is thenperformeddirectly on themicroarray, afterwhich the tissue is removed and the cDNA-mRNAcomplexesarecleavedoff.Subsequently,moleculesarepooledintoasinglereaction,where amplification and library preparation take place. Massively parallelsequencingisthenperformedandthebarcodedreadsaresuperimposedbackontothetissueimage.STisapplicabletoawiderangeoftissuetypes106–112andallowsfortranscriptome-wide studies. However, the current spot size limits the spatialresolutionto~10-40cells.


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3.5InsilicoreconstructionofspatialdataAs well as the experimental approaches described in this chapter, there are alsocomputational ways of constructing spatially resolved gene expression datasets.These technologies start from dissociated single cells and aim to computationallyassignthemtoaspatiallocationbyusingageneexpressionreferencemap.

3.5.1UsingISHasreferencemapsIn 2015, two similar computational approaches using preexisting in situhybridization (ISH) reference databases were published. The first (named Seurat)integratedscRNA-seqdataintothespatialcontextofazebrafishembryo113,whiletheother focused on the brain of themarine annelid P. dumerilii114. In bothmethods,reference maps are created by computationally dividing the tissue into smallerregions(bins).EachbinisgivenascorebasedonwhethergenesintheISHdatabasearepresentorabsentinthatparticularregion.Singlecellsarethenplacedonthetissue map by using their individual algorithms. The Seurat method applied tozebrafish tracedback851singlecellsbasedonareferencemapconstructedof ISHdatafor47genes.IntheP.dumeriliistudy,theauthorsusedalargerreferencesetof72 genes, mapping back 112 single cells. Another algorithm published in 2017 isDistMap115.Here, the researchgroupusedscRNA-seqdata fromover10,000singlecells of dissociated Drosophila embryos to reconstruct a 6,000-cellular virtualembryo.ByusingaISHreferencesetcomprising84ISHgenes,theydiscoveredthatmostofthecellswithintheembryohaveauniquetranscriptionalidentity.Allthesecomputational methods are able to give single cells a relatively precise locationwithinatissue.

3.5.2UsingSTasreferencemapsUsing existing ISHdatabases to construct spatial referencemaps limits analyses tothose tissue types forwhich ISHatlases alreadyexist.Accordingly this approach isnot applicable to clinical samples. A way to overcome this limitation could be toconstruct a reference map based on transcriptomics-wide data, for example dataobtained fromST technology. By combining scRNA-seq and ST on the same tissue,onecouldrefinespatialmapsoftheoreticallyanytypeoftissue.ThisapproachwasappliedtoatumorsamplewhereonehalfwassubjectedtoscRNA-seq,andtheotherhalftoST116.MarkergenesfoundinthescRNA-seqdatawereusedtodeconvolutethecell type compositions of different tissue regions. Another approach to this isdescribed in Paper III in the Present Investigation chapter. Briefly, expressionpatterns found in the ST data are used to generate a spatial gene expressionreferencemap,onwhichsinglecellsarepositionedbasedongeneprofilematches.

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Table1.Comparisonbetweenspatiallyresolvedtranscriptomicstechnologies.Single-cellRNA-seqisnotaspatialtechnique,butisshownhereforreference.

Method Spatialresolution Approach Detection

efficiency

scRNA-seq NA Transcriptome-wide 5-40%117

LCM CellularTargetedor

Transcriptome-wide

NA

Geo-seq 10cells Transcriptome-wide NA

tomo-seq Anatomicalfeatures

Transcriptome-wide NA

TIVA Cellular Transcriptome-wide NA

NICHE-seq Cellular Transcriptome-wide NA

smFISH Subcellular Targeted Nearly100%118

seqFISH Subcellular Targeted 84%98

MERFISH Subcellular Targeted 80%99

ISSusingbarcodedpadlockprobes Subcellular Targeted 30%119

BaristaSeq Subcellular Targeted 30%103

STARmap Subcellular Targeted NolessthanscRNA-seq105

FISSEQ Subcellular Transcriptome-wide <0.005%120

SpatialTranscriptomics

Anatomicalfeatures

Transcriptome-wide 6.9%106


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PRESENTINVESTIGATIONThisthesisisbasedonfourresearchpapers,allofwhichaddressthefieldofspatialtranscriptomics.Paper I is the landmark paper describing a novel technology forspatially resolved transcriptomics, whilePaper II describes the application of thetechnologytoadulthumancardiactissue. InPaper III, thetechnology isappliedtodevelopinghumanhearttissueincombinationwithscRNA-seqtofacilitateincreasedspatial resolution. Finally,Paper IV extends the technology described inPaper I,makingitalsoapplicabletofull-lengthgeneexpressionanalyses.

PaperI–Aspatiallyresolvedtranscriptome-wideapproachtostudywholetissuesectionsSpatiallyresolvedtranscriptomicsprovidesuswithnewinsights intothemoleculardiversity of complex multicellular organisms. Several approaches have beenestablishedinordertopreservegeneexpressioninformationtogetherwithitstissuelocalization.However, existing challenges formanyspatial technologies include theextent of existing knowledge about the targets, the labor-intensive nature of themethods or the fact that they are not applicable to clinical samples. This paperdescribesamethodwherebywholeintacttissuesectionscanbestudiedinaspatialwhole-transcriptomemanner.Firstly,freshfrozentissueiscryosectionedintoathinnearly-unicellular layer and placed on a barcoded microarray. The microarraycontainssix6,200x6,600μmareas,eachcovering1,007individuallyprintedspotswithadiameterof100μm.Eachspotcontainsaclusterof~200millionimmobilizedpoly-T oligos. All oligos within the same spot carry a unique positional barcodesequence,specifyingthexandycoordinateofthespotinthearray.Thetissuesectionisthenfixedandimaged,andsubsequentlysubjectedtopermeabilization.Duringthepermeabilizationstep,mRNAmoleculeswilldiffusedowntothemicroarraysurfaceand hybridize to the oligos underneath it. Reverse transcription then takes placedirectly on the surface. Next, the tissue section is removed and the mRNA-cDNAhybrids are detached from the microarray for library preparation and high-throughput sequencing. Barcoded sequencing reads can then be overlaid on thehistologicalimageofthetissue.Hence,individualtranscriptomesobtainedfromeachspot will provide spatial gene expression profiles. The technology was applied tomouse olfactory bulb tissue and the resulting spatial gene expression profilescorrelated well with the ISH based Allen Brain Atlas. Additionally, the technologyshowedadetectionefficiencyof~6.9%whencomparedtosmFISH.Onekeystepintheprotocol is thepermeabilization. If thetissue ispermeabilizedtoomuch,mRNAmoleculeswilldiffusehorizontallyandtheirspatialcontextswillbelost.Ontheother

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hand,ifthetissueispermeabilizedtoolittle,mRNAsmaynotbeextractedfromthecells at all. In order to investigate the amount of transcript diffusion, microarraysuniformly coated with non-barcoded poly-T oligos were used together with amodified reverse transcription mixture containing Cy3-labeled nucleotides. Theprotocolwas performed as described above up to tissue removal. The intensity ofsignalsgenerated from theCy3-cDNA footprintwascomparedwith thehistologicalstainingofthetissue,revealingadiffusiondistanceof~1.7μm.Lastly,thetechnologyis applied to explore tumor heterogeneity within human breast cancer biopsies,showingitsapplicabilitytoclinicalsamples.Althoughthistechnologyprovidesalesslabor-intensive,transcriptome-wideapproachapplicabletoawiderangeofsamples,itdoesnothavesingle-cellresolution. Furthermore,theexperimentalprocedureofthecurrentprotocolrestrictsanalysesoffull-lengthtranscripts.

Figure4.OverviewoftheSpatialTranscriptomicstechnology.


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PaperII–DetectinglowlyexpressedbiomarkersinheartfailurediseaseprogressionStudying human adult heart biopsies has previously only been carried out usingmicroarraysorbulkRNA-seqapproaches.However,thisgeneratesanaverageprofileof gene expression across the sample and the signal from lowly expressed genesrelatedtosubpopulationsofcellscanbelost.Identifyingweaklyexpressedgenescanbe of importance when investigating heart failure progression within clinicalsamples.Earlierstudieshaveshownthatgenesnormallyonlyexpressedduringthefetalstagearereactivatedduringheartfailurepathogenesis,andthuscanbeusedasbiomarkersfordiseaseprogression.Here,thetechnologydescribedinPaperI(ST)isappliedtoheartfailureclinicalsamples.Thepaperrepresentsapilottechnicalstudyinwhich adult heart biopsies from three individual patients are investigated. Thiswasperformedinordertoexploretissuehandling,permeabilizationproceduresandthesensitivityofthemethodbeforeapplyingittofuturelargerclinicalstudies.OneofthemainchallengeswastoretainsufficientamountsofhighqualityRNA.Adulthearttissue contains a large proportion of fibrous tissue and the cellular density is low,meaningthatRNAextractioncanbeproblematic.Initially,theexperimentalprotocolwas modified in order to make adult cardiac RNA accessible. The modificationsincludedbothastrongerpermeabilizationtreatmentaswellasaharsherprocedurefortissueremoval.Investigatingtheexpressionofsixpotentialbiomarkersacrossallsamples revealed that theSTmethodprovides ahigher sensitivity than thatwhichcanbeachievedthroughbulkdata.AlthoughthestudyshowstheadvantageofusingST technology for detecting biomarkers expressed at low levels, it covers only alimitedsetofpatientsandthereforenoconclusionsaboutHFdiseaseprogressioncanbemade.

Figure5.ApplyingSpatialTranscriptomicstechnologytohumanadulthearttissue.

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PaperIII–TracingcellularheterogeneitywithinthedevelopinghumanheartLittle isknownabouttheprocessofhumancardiacmorphogenesisasawhole.Theearly structure of the primitive heart tube will eventually transform into a fullyfunctionaladultheart.Duringthisprocess,variousstem-cellpopulationsfurnishtheheart with its entire repertoire of cell types, a procedure not yet completelyunderstood. In this paper, the ST technology described in Paper I is applied todeveloping human heart tissue in combinationwith scRNA-seq to investigate geneexpression programs during human heart development. Initially, two heart tissuesamples of the same age (~7 weeks-old) were processed either through the STprotocol or by scRNA-seq. Spatial gene expression profiles were linked to distinctanatomical structures within the heart and one of the regions, called the outflowtract, deviated particularly strongly from the rest. Based on gene profile matchesbetween cardiac regions (ST data) and single cells (scRNA-seq data), cellularheterogeneityacrosstheregionscouldbetracked.Byusingthecombinedapproach,theoutflowtractregioncouldbecharacterizedashavingarelativelyhighercellulardiversitycomparedtotheotherregions.Furthermore,twoadditionalheartsamplesfromanearlier(~5weeks-old)andalater(~9weeks-old)developmentaltimepointwere processed by the ST protocol. Comparisons between the three time pointsshowed low temporal differences in gene expression (fromweek 5 to week 9) incontrast to the differences seenwithin the heart.Here, a framework is set up thatcombines scRNA-seq data with ST technology in order to trace single cells to alocation in situ. This paper is a first step towards a complete organ-wide geneexpressionatlasofthedevelopinghumanheart.However,additionalsampleswouldprovide a more detailed map of cardiac structures and facilitate more in-depthinvestigationsregardingcell-to-cellinteractions.

Figure6.CombiningscRNA-seqandSpatialTranscriptomics.


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PaperIV–Constructingspatiallyresolvedfull-lengthtranscriptomeswithinwholetissuesectionsIn the protocol described in Paper I, tissue sections are placed on a barcodedmicroarray and fixedwith formaldehyde, a chemical agent responsible for creatingcovalent cross-links between the mRNAs and their surrounding molecules. Thereverse transcription is therefore restricted by the site of a cross-link, resulting inshort tags of mRNA data. This approach is very valuable for quantitative geneexpression studies, but it doesnotprovide full-length transcript information.Moredetailedinvestigationsintoalternativesplicing,fusiontranscriptsandmutationsaretherefore largely precluded. This paper describes an alternative protocol using STbarcoded microarrays to study complete transcript structures within individualtissue sections. The protocol differs from the standard procedure in the followingsteps; (i) fixation, (ii) reverse transcription, (iii), amplification and (iv) librarypreparation.Firstly, thetissue is fixedwithmethanoltokeepentireRNAmoleculesintactandaccessibletothereversetranscriptase.Secondly,thereversetranscriptionreaction is performed with an enzyme possessing terminal transferase activity,resultingintheadditionofacoupleofnon-templatednucleotidestothe3’endofthecDNA. A template-switch oligo (designed with an amplification handle) can thenhybridize to the extended part of the cDNA, and the RT enzyme can continue toextend over the template-switch oligo. The cDNA constructs will consequentlycontain amplification handles on both sides and can be subsequently amplified byPCR.Next,thedilutedcDNAlibraryisplacedin10xGenomicsdropletsandasecondbarcodeis incorporatedintothecDNAmolecules.Thisproceduregeneratesshorterbarcoded cDNA fragments suitable for massively parallel sequencing. Dropletbarcodes and spatial barcodes are then linked computationally to reconstruct full-lengthtranscripts.Thispaperdemonstratesthatspatiallybarcodedlibrariescanbecombinedwiththe10xGenomicsdropletstationtoidentifyspatialpositionsoffull-lengthcDNAswithina tissuecontext.However, futurestudiesbasedonredesignedbarcoded surface-probes and/or the droplet barcodes would provide a moreseamlesssystemforspatialisoformprofiling.

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Figure7.Spatiallyresolvedfull-lengthtranscriptomics.


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ConcludingremarksMorethantenyearsago,massivelyparallelsequencingandtheintroductionofRNA-seqrevolutionizedthefieldoftranscriptomics.Shortlyafter, itwasevenpossibletoanalyze the individual transcriptome of a single cell. Going from analyzing bulksamples to single cells has truly openedourminds to the cellular diversity in, e.g.,complextissuesystemsanddevelopmentalprocesses.Byaddingaspatialdimensionto the analysis of these heterogeneous tissue structures, we are beginning tounderstandbiologicalnetworksandinteractionswithinthesystem.Today, there isawiderangeofspatial technologiesbutnoneofthemissuperior ineveryrespecttotheothers.Dissectionapproaches,suchasGeo-seqandTIVA,canbecombinedwithscRNA-seqtoretaincompletetranscriptome-wideinformation.TIVAcan also be applied to living tissue, a unique feature no other spatial techniquepossesses.However,boththesemethodssufferfromlowthroughputwhenitcomestoanalyzinganumberof regions/cells inparallel.NICHE-seqand tomo-seq,on theother hand, offer high throughput, but are not applicable to clinical samples. Thetranscriptome-wide ST technology can be applied to a variety of tissue types,including clinical samples, but it does not achieve single cell resolution. In situapproaches, incontrast,haveexceptional spatial resolution, reachingdowneven tothesubcellularlevel,butarerestrictedregardedthefactthattargetedgenesneedtobe definedapriori. An exception is FISSEQ, but thismethod has a lower detectionefficiencycomparedtotheothers.Amajorobstacleformanyspatialtechnologiestodayistheefficiencyofdetection.Inorder to increase the efficiency, RNA losses throughout the workflow need to beminimized. This canbe achievedbyusingminimumnumber of experimental stepsand ideally without performing any wash steps. However, RT may be the mostefficiency-limiting step, inwhich not all transcripts are converted to cDNA. Futureprotocolswill likely haveworkflowswith substantially fewer steps andmanywillprobablyexcludetheRTstepbydirectlytargetingnativeRNA.Further implementation of spatial techniques in the clinical setting would benefitfrom protocols compatible with formaldehyde fixed-paraffin embedded tissue.Althoughpossibleby somepresent-daymethods, it ismore laborious and it is stillchallenging to achieve sufficient quality.Whether or not spatial techniqueswill beroutinelyusedinfutureapplicationswithinclinics,theycouldcertainlybeapowerfulscreeningtoolforthedetectionofdiseasebiomarkers.

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Foratechnologytobewidelyaccessible,itmaybebeneficialeithertocommercializethemethodintheformofakitoraservice,ortomaketheprotocolprocedurecostefficient and compatible with regular lab equipment. LCM, ST and padlock-probebasedISSstudieshaveallbeenpublishedoutsidetheirlabofcreation.Whichoneofthe existing spatial methods will reach the widest future community of scientists,eitherinitscurrentformorwithcomplementaryimprovements,remainstobeseen.Beyondanalyzingspatially resolvedgeneexpression lies thechallengeofanalyzingspatially resolved multiomics. This integration of transcriptomics, genomics,proteomicsandcertainlymanyothertypesofomicscouldadvanceourknowledgeofcell heterogeneity and interactions between cells. Existing protocols for combinedsingle cell omics could surely be applied to spatial techniques using dissectionapproaches.Intermsofanalyzingindividualtissuesections,usingtechnologiessuchas ISH, ISS and ST, it would currently be possible to process consecutive tissuesections inorder to integrate transcriptomicsandproteomics forexample.Anotherfutureprospect,beyondspatiallyresolvedtranscriptomics,wouldbetomonitorreal-timetemporalchangesingeneexpressioninlivingtissue,apossibilitythatmaynotbe too far away considering the technological progression made during the lastdecadealone.One thing is for sure; teamwork is fundamental to technological development andscientific discoveries, and it is oftenmost effectivewhen it brings together peopleacross disciplinary divides. One excellent example is the global Human Cell Atlasinitiative, which is gathering the efforts ofmany researchers to profile all the celltypesinthehumanbody.Bythetimeitiscompleted,thisinternational,openaccessprojectwillhaveproducedanenormouslibraryofdescriptionsofatleast10billioncells1.Asthisprojectproceeds,newtechnologiesandcollaborationswilladvanceinarapid manner. These are technologies that will produce a tremendous amount ofdata,inneedofanalysisandinterpretationwithnew,standardizedprocedures.Thefieldofspatiallyresolvedtranscriptomicsisundoubtedlyonlybeginningtoshowuswhatispossible.


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AbbreviationsA AdenineBaristaSeq BarcodeinsitutargetedsequencingC CytosineCAGE CapanalysisofgeneexpressioncDNA ComplementaryDNACEL-Seq CellexpressionbylinearamplificationandsequencingDNA DeoxyribonucleicacidEST Expressed-sequence-tagsFISH Fluorescenceinsituhybridization FISSEQ FluorescentinsituRNASequencingG GuanineGeo-seq GeographicalpositionsequencingGFP GreenfluorescentproteinInDrop IndexdropletsISH InsituhybridizationISS InsitusequencingIVT InvitrotranscriptionLCM LasercapturemicrodissectionMARS-Seq Massivelyparallelsingle-cellRNA-sequencingMERFISH Multiplexederror-robustFISHmRNA messengerRNAPacBio PacificBiosciencesPCR PolymerasechainreactionqPCR Quantitativereal-timePCRRCA Rolling-circleamplificationRCP Rolling-circleamplificationproductsRNA RibonucleicacidRNA-seq RNAsequencingRT ReversetranscriptionSAGE SerialanalysisofgeneexpressionSBL Sequencing-by-ligationSBS Sequencing-by-synthesisscRNA-seq SinglecellRNAsequencingseqFISH SequentialhybridizationsmFISH Single-moleculeRNAfluorescenceinsituhybridizationsmHCR Single-moleculehybridizationchainreactionSMRT Singlemoleculereal-timeSNAIL SpecificAmplificationofNucleicAcidsviaIntramolecularLigationSOLiD SequencingbyoligoligationanddetectionST SpatialTranscriptomicsSTARmap Spatially-resolvedtranscriptampliconreadoutmappingSTRT-seq Single-celltaggedreversetranscriptionT ThymineTIVA TranscriptomeinvivoanalysisTomo-seq RNAtomographyZMW Zero-modewaveguides

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AcknowledgmentsIstartedmyPhDinAugust2013bysettinguptwogoals:(i)developingmyabilitytocritically analyze andmanage challenges and (ii) improvingmy presentation skillsand not shy away from opportunities to speak aboutmy research. I did not knowthen that this journeywouldgivemesomuchmore than just that.DuringmyPhDyears,IhaveonlynotjustachievedthegoalsthatIinitiallysetup,butalsodevelopedprofessionally and personally on so many levels. All of this would not have beenpossiblewithoutabunchofamazing, intelligent,drivenandinspiringpeopleIhavehadthefortuneofhavingaroundme:Joakim!Myfavoriteprofessor!;)ThankyouforgivingmetheopportunitytodomyPhD in your research group. And Thank you for supporting me in so manyconferencesandcoursesaroundtheworld,makingitpossibleformetobuildmyownsolid foundation of networks. It has been such an experience and I have certainlylearnt a lot during the process. During times of difficulties, I have very muchappreciatedthechocolateplacedonmydesk.Myco-supervisorPatrik–Thankyouforallscientificadvicesandgreatsmall talks.Youaretrulyoneofmygreatestrolemodels! My unofficial co-supervisor, and also very dear friend, Stefania – a truesourceofinspiration!Thankyouforbothguidingandpushingmescientifically,andformanymemorableadventures:NewYork,Malta,Cyprus,theSwedishmountains…(andno,thereisnopossibilitytoflushaSwedishoutdoortoilet(“dass”)).ToAfshin–Thankyou foryouropennessand for alwayshaving time for scientificdiscussions,andforcriticallyreviewingthecontentofthisthesis.IalsowanttohighlightAnders,Kim, Joseph andKostas here for fantastic feedback on the early versions of thisthesis.Toallmyco-authors andcollaborators,especially to:Matthias – forprovidingmethe experience of taking part of several heart surgeries. Eva W – for your greatknowledge of handling tissue samples and for devoting a lot of your time to thedevelopmental heart project. Christer – All PhD students should have their ownChrister!Ithasbeen,andstillis,atruepleasuretoworkwithyou.JohanR–foryourcalmandpatiencedespitemyonethousandsquestionsaboutSIMCAandGit.Fornotcancellinga telephonemeetingeven thoughabunchofscreamingkidsarehangingaroundyourlegs.TomyDearUppsalateam,whereitallbegunin2011.ThankyouUlfandIngerforintroducingme to theworldofsequencing technologies.To“flocken”:Linnéa– forthebestmentorship,Ida–myscience-partner-in-crimeanddevotedtravel-partner,


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Sanna–thebestmud-runnerevertoteamupwith.Allthreeofyou:Thankyoufortrue friendshipreaching farbeyond theborderof science.ToAdam – theguywhokillsinnetworking!Thanksforgivingmethefirstintroductiontobioinformaticsandforbeingagreatcolleagueandfriend.Lars,JockeandAmmar–Alwayssomuchfuntohangoutwithyouguys!Bothscientificallyandnon-scientifically…SomanygreatmomentsatASHGinSanDiego!Many thanks to former and present people at the premium Alfa 3 floor: Bahram,Christian,Nemo,PärL,Max,Carsten,Francesco,Aleksandra,Florian,MikaelH,Jacke,Verena,Beata, Kicki, Guillermo,2xMattiasO,Remi,Rapolas, Emanuela,Simon,Mengxiao,Magdalena, Fanny, Lina, Chuan, Anna K, Tobias, Sailendra,Luisa,Niyaz,Erik,Anna,SáraandAlma.Sanja–forintroducingmeearlyoninthelabandformanypubnights.PerK–forgivinggreatfeedbackafterseveralDNAclubpresentations. Phil – for hiking and climbing gear discussions aswell aswhere totravel next. Joel – for uncountable numbers of concerts, pub sessions, fun runsessions… Conny and Olov – forbeingmybest ski-competitors!Sverker – for thebestlaughandformanylatenightwalksthroughthecity.Yue–foryourhappyspiritand energy! Johannes – for great co-organization of numerous Wild-Orsa-Ski-BiggestDalahorse-AppleglöggeventsdeepdowninthedarkestofDalarna.Benji–forswitchingmyworkinthelabwithyourbioinformaticsduringaperiod.Webothwerecompletelylostandtheprojectendedupinnothing,althoughIlearnthowtomanagetheterminal!Anddon'tworry,Ipromiseneverevertobecome“mogen”. Fredrik–for always giving me answers to my restricted number of 5-questions-per-dayquestions.Thanksforteachingmeeverythingyouknow,exceptshooting,whichyouareembarrassinglybadat.Anders–Inallhonestly,Thanksforbeingatruefriend.ToinspiringPIsandresearchersatSciLifeLab:Pelin,AndersAandPeterS.MarcFand Wenjing for educating me in miRNAs. Paul H for fun times during GeneTechnologycourses.TotheamazingSpatialTranscriptomicsgroup:Kim–MaSistá!Anamazingwomanseeingthegoodsideofallpeople.Foryourcaringandopenness!I<3U.Joseph–forkeepingusupdatedonthelatesthappeningsinthebusinessworld.Linnéa–foryourconstant smile and endless laughs around the lab.Emelie –An adorable andhardcoreVärmländska.Annelie –Ourverybeloved lab-mammy!Lab-mummy?Linda –foryournever-endingpositivity,energyand fightingspirit. Jose–MyverySpanishfriendalwaysup forarelió!Andno, Idon'thaveacat…Kostas– foralwaysbeinghelpfulandsupportive,bothinandoutsidethelab.Maja–thepositivegirlalwaysupforaMackmyraevent!LudvigL–fornerdyhiking-triptalks.Sami,EvaandZaneta–thenewstarsoftheSTteam.ThankyoualsoJonasM,LudvigB,BrittaandSidra.

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You have all contributed to the amazing atmosphere. Thank you David – Sort ofactuallypartofSTalthoughnotofficially.Myperfectyinandyangmatchingpartnerinthelabaswellasorganizingpokertournaments.To former premium’ers who left significant footprints in the SciLifeLab building:Mario–MyawesomefriendwhomIsharethesamehairstylewith.Noonecanthrowtogetherbettermega-,giga-andpeta-partieslikeyouandspreadingtheneedofa4thof July celebration around SciLifeLab. Move back to Sweden. Now. Sebbe – MypartnerthroughoutKTH,UppsalaandSciLifeLab.Whereisthenextadventuretakingus?Thankyouforbeingoneofmyverybestfriendsandforallthehulahulainthelab.Toallmysupportingandamazingfriendsoutsideacademia:NowyouknowwhatIhavebeendoingthepast5years;)Andfinally,tomyverybelovedandwonderfulFamily:Mamma,Pappa,Millan, Josh andWille –Thankyou for always supportingme inwhatever reasonableandunreasonabledecisions Imake in life.ToAndré –Thankyouforfeedingmewithaninfiniteamountoflove.Andfood.Jagälskarer.Thankyouall!Tackalla!


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References1. Regev,A.ScienceForum:TheHumanCellAtlas.Elife6:e27041,(2017).

2. Bianconi,E.etal.Anestimationofthenumberofcellsinthehumanbody.Ann.Hum.Biol.40,463–471(2013).

3. NASAScience.RetrievedJuly26,2018,fromhttps://solarsystem.nasa.gov/planets/earth/overview/

4. Eiberg,H.etal.BlueeyecolorinhumansmaybecausedbyaperfectlyassociatedfoundermutationinaregulatoryelementlocatedwithintheHERC2geneinhibitingOCA2expression.Hum.Genet.123,177–187(2008).

5. Dahm,R.FriedrichMiescherandthediscoveryofDNA.Dev.Biol.278,274–288(2005).

6. Avery,O.T.,MacLeod,C.M.&McCarty,M.Studiesonthechemicalnatureofthesunstanceinducingtransformationofpneumococcaltypes:InductionoftransformationbyadesoxyribonucleicacidfractionisolatedfrompneumococcustypeIII.J.Exp.Med.79,137–158(1944).

7. Franklin,R.E.&Gosling,R.G.MolecularConfigurationinSodiumThymonucleate.Nature171,740(1953).

8. Wilkins,M.H.F.,Stokes,A.R.&Wilson,H.R.MolecularStructureofNucleicAcids:MolecularStructureofDeoxypentoseNucleicAcids.Nature171,738(1953).

9. Watson,J.D.&Crick,F.H.C.Molecularstructureofnucleicaids:Astructurefordeoxyribosenucleicacid.Nature171,737–738(1953).

10. GRCh38.p12.RetrievedAugust9,2018,fromhttps://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.38

11. InternationalHumanGenomeSequencingConsortium.Initialsequencingandanalysisofthehumangenome.Nature409,860–921(2001).

12. Venter,J.C.etal.TheSequenceoftheHumanGenome.Science.291,1304–1351(2001).

13. Macaulay,I.C.&Voet,T.SingleCellGenomics:AdvancesandFuturePerspectives.PLOSGenet.10,e1004126(2014).

14. Crick,F.OnProteinSynthesis.Symp.Soc.Exp.Biol.NumberXIIBiol.ReplicationMacromol.CambridgeUniv.Press.138–163(1958).

15. Crick,F.Centraldogmaofmolecularbiology.Nature227,561–563(1970).

16. GeneticsHomeReference.inNationalInstitutesofHealth.U.S.NationalLibraryofMedicine.DepartmentofHealth&HumanServices11–12(2018).

17. Moor,A.E.&Itzkovitz,S.Spatialtranscriptomics:pavingthewayfortissue-levelsystemsbiology.Curr.Opin.Biotechnol.46,126–133(2017).

18. Ramel,M.-C.&Hill,C.S.TheventraltodorsalBMPactivitygradientintheearlyzebrafish

MichaelaAsp

35

embryoisdeterminedbygradedexpressionofBMPligands.Dev.Biol.378,170–182(2013).

19. Reeves,G.T.etal.Dorsal-ventralgeneexpressionintheDrosophilaembryoreflectsthedynamicsandprecisionofthedorsalnucleargradient.Dev.Cell22,544–557(2012).

20. Thul,P.J.etal.Asubcellularmapofthehumanproteome.Science.356,eaal3321(2017).

21. Bruusgaard,J.C.,Liestøl,K.,Ekmark,M.,Kollstad,K.&Gundersen,K.Numberandspatialdistributionofnucleiinthemusclefibresofnormalmicestudiedinvivo.J.Physiol.551,467–478(2003).

22. Cusanovich,D.A.etal.Multiplexsingle-cellprofilingofchromatinaccessibilitybycombinatorialcellularindexing.Science.348,910–914(2015).

23. Ramani,V.etal.Massivelymultiplexsingle-cellHi-C.Nat.Methods14,263–266(2017).

24. Buxbaum,A.R.,Haimovich,G.&Singer,R.H.Intherightplaceattherighttime:visualizingandunderstandingmRNAlocalization.Nat.Rev.Mol.CellBiol.16,95–109(2015).

25. Crosetto,N.,Bienko,M.&vanOudenaarden,A.Spatiallyresolvedtranscriptomicsandbeyond.Nat.Genet.16,57–66(2015).

26. Elmore,J.G.etal.Diagnosticconcordanceamongpathologistsinterpretingbreastbiopsyspecimens.JAMA313,1122–1132(2015).

27. Alwine,J.C.,Kemp,D.J.&Stark,G.R.MethodfordetectionofspecificRNAsinagarosegelsbytransfertodiazobenzyloxymethyl-paperandhybridizationwithDNAprobes.Proc.Natl.Acad.Sci.U.S.A.74,5350–5354(1977).

28. Mullis,K.Faloona,F.Scharf,S.Saiki,R.SpecificEnzymaticAmplificationofDNAinVitro:ThePolymeraseChainReaction.ColdSpringHarb.Symp.Quant.Biol.51,263–273(1986).

29. NationalHumanGenomeResearchInstitite.RetrievedAugust9,2018,fromhttps://www.genome.gov/10000207/polymerase-chain-reaction-pcr-fact-sheet/

30. Taylor,S.,Wakem,M.,Dijkman,G.,Alsarraj,M.&Nguyen,M.ApracticalapproachtoRT-qPCR—PublishingdatathatconformtotheMIQEguidelines.Methods50,S1–S5(2010).

31. Aird,D.etal.AnalyzingandminimizingPCRamplificationbiasinIlluminasequencinglibraries.GenomeBiol.12,(2011).

32. Kanagawa,T.Biasandartifactsinmultitemplatepolymerasechainreactions(PCR).J.Biosci.Bioeng.96,317–323(2003).

33. Higuchi,R.,Dollinger,G.,SeanWalsh,P.&Griffith,R.SimultaneousamplificationanddetectionofspecificDNAsequences.Biotechnol.10,413–417(1992).

34. Morrison,T.,Weis,J.&Wittwer,C.Quantificationoflow-copytranscriptsbycontinuousSYBRGreenImonitoringduringamplification.Biotechniques24,954–962(1998).

35. Huggett,J.&Bustin,S.A.Standardisationandreportingfornucleicacidquantification.Accredit.Qual.Assur.16,399(2011).

36. Schena,M.,Shalon,D.,Davis,R.W.&Brown,P.O.Quantitativemonitoringofgeneexpression


36

patternswithacomplementaryDNAmicroarray.Science.270,467–470(1995).

37. Okoniewski,M.&Miller,C.J.Hybridizationinteractionsbetweenprobesetsinshortoligomicroarraysleadtospuriouscorrelations.BMCBioinformatics7,276(2006).

38. Sanger,F.,Nicklen,S.&Coulson,A.R.DNAsequencingwithchain-terminatinginhibitors.Proc.Natl.Acad.Sci.U.S.A.74,5463–5467(1977).

39. Hagemann,I.S.inClinicalGenomics(eds.Kulkarni,S.&Pfeifer,J.B.T.-C.G.)3–19(AcademicPress,2015).doi:https://doi.org/10.1016/B978-0-12-404748-8.00001-0

40. Adams,M.D.etal.ComplementaryDNAsequencing:expressedsequencetagsandhumangenomeproject.Science.252,1651–1656(1991).

41. Boguski,M.S.,Tolstoshev,C.M.&Bassett,D.E.GenediscoveryindbEST.Science.265,1993–1994(1994).

42. Velculescu,V.E.,Zhang,L.,Vogelstein,B.&Kinzler,K.W.SerialAnalysisofGeneExpression.Science.270,484–487(1995).

43. Shiraki,T.etal.Capanalysisgeneexpressionforhigh-throughputanalysisoftranscriptionalstartingpointandidentificationofpromoterusage.Proc.Natl.Acad.Sci.100,15776–15781(2003).

44. Mardis,E.R.Theimpactofnext-generationsequencingtechnologyongenetics.TrendsGenet.24,133–141(2008).

45. Ronaghi,M.,Uhlén,M.&Nyrén,P.ASequencingMethodBasedonReal-TimePyrophosphate.Science.281,363–365(1998).

46. Margulies,M.etal.Genomesequencinginmicrofabricatedhigh-densitypicolitrereactors.Nature437,376–380(2005).

47. Rothberg,J.M.etal.Anintegratedsemiconductordeviceenablingnon-opticalgenomesequencing.Nature475,348–352(2011).

48. Illumina.Historyofsequencingbysynthesis.RetrievedAugust18,2018,fromhttps://emea.illumina.com/science/technology/next-generation-sequencing/illumina-sequencing-history.html?langsel=/it/

49. Ramsköld,D.etal.Full-LengthmRNA-SeqfromsinglecelllevelsofRNAandindividualcirculatingtumorcells.Nat.Biotechnol.30,777–782(2012).

50. genomeweb.RetrievedAugust18,2018,fromhttps://www.genomeweb.com/sequencing/pacbio-ships-first-two-commercial-systems-order-backlog-grows-44#.W3ghgS2B3-Z

51. Levene,M.J.etal.Zero-ModeWaveguidesforSingle-MoleculeAnalysisatHighConcentrations.Science.299,682–686(2003).

52. Eid,J.etal.Real-TimeDNASequencingfromSinglePolymeraseMolecules.Science.323,133–138(2009).

53. PacBio.SMRTSequencing:ReadLengths.RetrievedAugust18,2018,fromhttps://www.pacb.com/smrt-science/smrt-sequencing/read-lengths/

MichaelaAsp

37

54. Sharon,D.,Tilgner,H.,Grubert,F.&Snyder,M.Asingle-moleculelong-readsurveyofthehumantranscriptome.Nat.Biotechnol.31,1009–1014(2013).

55. Clarke,J.etal.Continuousbaseidentificationforsingle-moleculenanoporeDNAsequencing.Nat.Nanotechnol.4,265–270(2009).

56. OxfordNanopore.Companyhistory.RetrievedAugust18,2018,fromhttps://nanoporetech.com/about-us/history

57. Smith,A.M.,Jain,M.,Mulroney,L.,Garalde,D.R.&Akeson,M.Readingcanonicalandmodifiednucleotidesin16SribosomalRNAusingnanoporedirectRNAsequencing.bioRxiv[PREPRINT](2017).doi:https://doi.org/10.1101/132274

58. Boon,W.C.etal.IncreasingcDNAYieldsfromSingle-cellQuantitiesofmRNAinStandardLaboratoryReverseTranscriptaseReactionsusingAcousticMicrostreaming.J.Vis.Exp.53,3144(2011).

59. Velculescu,V.E.etal.Analysisofhumantranscriptomes.Nat.Genet.23,387–388(1999).

60. Tang,F.etal.mRNA-Seqwhole-transcriptomeanalysisofasinglecell.Nat.Methods6,377–382(2009).

61. Tietjen,I.etal.Single-CellTranscriptionalAnalysisofNeuronalProgenitors.Neuron38,161–175(2003).

62. Kurimoto,K.etal.Animprovedsingle-cellcDNAamplificationmethodforefficienthigh-densityoligonucleotidemicroarrayanalysis.NucleicAcidsRes.34,e42(2006).

63. Kurimoto,K.,Yabuta,Y.,Ohinata,Y.&Saitou,M.Globalsingle-cellcDNAamplificationtoprovideatemplateforrepresentativehigh-densityoligonucleotidemicroarrayanalysis.Nat.Protoc.2,739–752(2007).

64. Sasagawa,Y.etal.Quartz-Seq:ahighlyreproducibleandsensitivesingle-cellRNAsequencingmethod,revealsnon-geneticgene-expressionheterogeneity.GenomeBiol.14,3097(2013).

65. Sasagawa,Y.etal.Quartz-Seq2:ahigh-throughputsingle-cellRNA-sequencingmethodthateffectivelyuseslimitedsequencereads.GenomeBiol.19,29(2018).

66. Schmidt,W.M.&Mueller,M.W.CapSelect:ahighlysensitivemethodfor5’CAP-dependentenrichmentoffull-lengthcDNAinPCR-mediatedanalysisofmRNAs.NucleicAcidsRes.27,e31(1999).

67. Islam,S.etal.Characterizationofthesingle-celltranscriptionallandscapebyhighlymultiplexRNA-seq.GenomeRes.21,1160–1167(2011).

68. Islam,S.etal.Quantitativesingle-cellRNA-seqwithuniquemolecularidentifiers.Nat.Methods11,163–166(2014).

69. Picelli,S.etal.Smart-seq2forsensitivefull-lengthtranscriptomeprofilinginsinglecells.Naturemethods10,1096–1098(2013).

70. Eberwine,J.etal.Analysisofgeneexpressioninsingleliveneurons.Proc.Natl.Acad.Sci.89,3010–3014(1992).


38

71. Hashimshony,T.,Wagner,F.,Sher,N.&Yanai,I.CEL-Seq:single-cellRNA-seqbymultiplexedlinearamplification.CellRep.2,666–673(2012).

72. Hashimshony,T.etal.CEL-Seq2:Sensitivehighly-multiplexedsingle-cellRNA-Seq.GenomeBiol.17,77(2016).

73. Svensson,V.,Vento-Tormo,R.&Teichmann,S.A.Exponentialscalingofsingle-cellRNA-seqinthepastdecade.Nat.Protoc.13,599–604(2018).

74. Jaitin,D.A.etal.Massivelyparallelsingle-cellRNA-seqformarker-freedecompositionoftissuesintocelltypes.Science.343,776–779(2014).

75. Fan,H.C.,Fu,G.K.&Fodor,S.P.A.Combinatoriallabelingofsinglecellsforgeneexpressioncytometry.Science.347,1258367(2015).

76. Klein,A.M.etal.Dropletbarcodingforsingle-celltranscriptomicsappliedtoembryonicstemcells.Cell161,1187–1201(2015).

77. Macosko,E.Z.etal.HighlyParallelGenome-wideExpressionProfilingofIndividualCellsUsingNanoliterDroplets.Cell161,1202–1214(2015).

78. Cao,J.etal.Comprehensivesingle-celltranscriptionalprofilingofamulticellularorganism.Science.357,661–667(2017).

79. Rosenberg,A.B.etal.Single-cellprofilingofthedevelopingmousebrainandspinalcordwithsplit-poolbarcoding.Science.360,176–182(2018).

80. Macaulay,I.C.,Ponting,C.P.&Voet,T.Single-CellMultiomics:MultipleMeasurementsfromSingleCells.TrendsinGenetics33,155–168(2017).

81. Macaulay,I.C.etal.G&T-seq :parallelsequencingofsingle-cellgenomesandtranscriptomes.Nat.Methods12,519–522(2015).

82. Dey,S.S.,Kester,L.,Spanjaard,B.,Bienko,M.&vanOudenaarden,A.Integratedgenomeandtranscriptomesequencingofthesamecell.Nat.Biotechnol.33,285–289(2015).

83. Darmanis,S.etal.SimultaneousMultiplexedMeasurementofRNAandProteinsinSingleCells.CellRep.14,380–389(2016).

84. Stoeckius,M.etal.Simultaneousepitopeandtranscriptomemeasurementinsinglecells.Nat.Methods14,865–868(2017).

85. Frei,A.P.etal.HighlymultiplexedsimultaneousdetectionofRNAsandproteinsinsinglecells.Nat.Methods13,269–275(2016).

86. Emmert-Buck,M.R.etal.LaserCaptureMicrodissection.Science.274,998–1001(1996).

87. Simone,N.L.,Bonner,R.F.,Gillespie,J.W.,Emmert-Buck,M.R.&Liotta,L.A.Laser-capturemicrodissection:openingthemicroscopicfrontiertomolecularanalysis.TrendsGenet.14,272–276(1998).

88. Chen,J.etal.SpatialtranscriptomicanalysisofcryosectionedtissuesampleswithGeo-seq.Nat.Protoc.12,566–580(2017).

MichaelaAsp

39

89. Combs,P.A.&Eisen,M.B.SequencingmRNAfromCryo-SlicedDrosophilaEmbryostoDetermineGenome-WideSpatialPatternsofGeneExpression.PLoSOne8,2–8(2013).

90. Junker,J.P.etal.Genome-wideRNATomographyintheZebrafishEmbryo.Cell159,662–675(2014).

91. Lovatt,D.etal.Transcriptomeinvivoanalysis(TIVA)ofspatiallydefinedsinglecellsinlivetissue.Nat.Methods11,190–6(2014).

92. Medaglia,C.etal.SpatialreconstructionofimmunenichesbycombiningphotoactivatablereportersandscRNA-seq.Science.358,1622–1626(2017).

93. Gall,J.G.&Pardue,M.Lou.FormationanddetectionofRNA-DNAhybridmoleculesincytologicalpreparations.Proc.Natl.Acad.Sci.63,378–383(1969).

94. Singer,R.H.&Ward,D.C.Actingeneexpressionvisualizedinchickenmuscletissueculturebyusinginsituhybridizationwithabiotinatednucleotideanalog.Proc.Natl.Acad.Sci.79,7331–7335(1982).

95. Femino,A.M.,Fay,F.S.,Fogarty,K.&Singer,R.H.VisualizationofSingleRNATranscriptsinSitu.Science.280,585–590(1998).

96. Raj,A.,vandenBogaard,P.,Rifkin,S.A.,vanOudenaarden,A.&Tyagi,S.ImagingindividualmRNAmoleculesusingmultiplesinglylabeledprobes.Nat.Methods5,877–879(2008).

97. Lubeck,E.,Coskun,A.F.,Zhiyentayev,T.,Ahmad,M.&Cai,L.Single-cellinsituRNAprofilingbysequentialhybridization.Nat.Methods11,360–361(2014).

98. Shah,S.,Lubeck,E.,Zhou,W.&Cai,L.InSituTranscriptionProfilingofSingleCellsRevealsSpatialOrganizationofCellsintheMouseHippocampus.Neuron92,342–357(2016).

99. Chen,K.H.,Boettiger,A.N.,Moffitt,J.R.,Wang,S.&Zhuang,X.Spatiallyresolved,highlymultiplexedRNAprofilinginsinglecells.Science.348,1360–1363(2015).

100. Wang,G.,Moffitt,J.R.&Zhuang,X.Multiplexedimagingofhigh-densitylibrariesofRNAswithMERFISHandexpansionmicroscopy.Sci.Rep.8,1–13(2018).

101. Chen,F.etal.NanoscaleimagingofRNAwithexpansionmicroscopy.Nat.Methods13,679–684(2016).

102. Ke,R.etal.InsitusequencingforRNAanalysisinpreservedtissueandcells.Nat.Methods10,857–860(2013).

103. Chen,X.,Sun,Y.C.,Church,G.M.,Lee,J.H.&Zador,A.M.Efficientinsitubarcodesequencingusingpadlockprobe-basedBaristaSeq.NucleicAcidsRes.46,e22(2018).

104. Lee,J.H.etal.HighlyMultiplexedSubcellularRNASequencinginSitu.Science.343,1360–1363(2014).

105. Wang,X.etal.Three-dimensionalintact-tissuesequencingofsingle-celltranscriptionalstates.Science.eaat5691(2018).doi:10.1126/science.aat5691

106. Ståhl,P.L.etal.Visualizationandanalysisofgeneexpressionintissuesectionsbyspatialtranscriptomics.Science.353,78–82(2016).


40

107. Salmén,F.etal.MultidimensionaltranscriptomicsprovidesdetailedinformationaboutimmunecelldistributionandidentityinHER2+breasttumors.bioRxiv[PREPRINT](2018).doi:https://doi.org/10.1101/358937

108. Berglund,E.etal.Spatialmapsofprostatecancertranscriptomesrevealanunexploredlandscapeofheterogeneity.Nat.Commun.9,1–13(2018).

109. Giacomello,S.etal.Spatiallyresolvedtranscriptomeprofilinginmodelplantspecies.Nat.Plants3,1–11(2017).

110. Lundmark,A.etal.Geneexpressionprofilingofperiodontitis-affectedgingivaltissuebyspatialtranscriptomics.Sci.Rep.8,(2018).

111. Asp,M.etal.Spatialdetectionoffetalmarkergenesexpressedatlowlevelinadulthumanhearttissue.Sci.Rep.7,(2017).

112. Maniatis,S.etal.Spatiotemporaldynamicsofmolecularpathologyinamyotrophiclateralsclerosis.bioRxiv[PREPRINT](2018).doi:https://doi.org/10.1101/389270

113. Satija,R.,Farrell,J.A.,Gennert,D.,Schier,A.F.&Regev,A.Spatialreconstructionofsingle-cellgeneexpressiondata.Nat.Biotechnol.33,495–502(2015).

114. Achim,K.etal.High-throughputspatialmappingofsingle-cellRNA-seqdatatotissueoforigin.Nat.Biotechnol.33,503–509(2015).

115. Karaiskos,N.etal.TheDrosophilaembryoatsingle-celltranscriptomeresolution.Science.358,194–199(2017).

116. Moncada,R.etal.Buildingatumoratlas:integratingsingle-cellRNA-Seqdatawithspatialtranscriptomicsinpancreaticductaladenocarcinoma.bioRxiv[PREPRINT](2018).doi:https://doi.org/10.1101/254375

117. Grün,D.&VanOudenaarden,A.DesignandAnalysisofSingle-CellSequencingExperiments.Cell163,799–810(2015).

118. Lein,E.,Borm,L.E.&Linnarsson,S.Thepromiseofspatialtranscriptomicsforneuroscienceintheeraofmolecularcelltyping.Science.358,64–69(2017).

119. Larsson,C.,Grundberg,I.,Söderberg,O.&Nilsson,M.InsitudetectionandgenotypingofindividualmRNAmolecules.Nat.Methods7,395–397(2010).

120. Lee,J.H.Quantitativeapproachesforinvestigatingthespatialcontextofgeneexpression.WileyInterdiscip.Rev.Syst.Biol.Med.9,e1369(2017).

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Spatially Resolved Gene Expression Analysis... · Spatially Resolved Gene Expression Analysis Michaela Asp Doctoral Thesis in Biotechnology KTH Royal Institute of Technology School