Single-cell dynamics of genome-nucleolus interactions captured by nucleolar laser microdissection (NoLMseq)

Domain: biology
Published: 2025-12-17
Editorial outcome: minor_revision

Abstract
Genepositioninginnuclearspaceiscentraltoregulation,withrepressivechromatindomainsoftencontactingthenuclearlaminaornucleoli.Thenucleolusundergoesstructuralchangesindifferentcellularstates,potentiallyalteringgenomeorganization.Yet,hownucleolarstatesinfluence3D-genomearchitectureremainsunderexplored,largelyduetothelackofmethodsabletomapnucleolar-associateddomains(NADs)insinglecellsandstressednucleoli.WedevelopedNoLMseq,atechniquecombininglaser-capturemicrodissectionandDNAsequencingtomapNADsinsinglecells.NoLMsequncoveredunexploredfeaturesofchromosomeorganizationaroundnucleoli,includingNADheterogeneityinmouseembryonicstemcells,yieldingtwomajorpopulationswithdistinctchromatinanddevelopmentalstates.NADspredominantlycontactnucleolimonoallelically,withcontactfrequencycorrelatingwithgeneexpressionandchromatinstates.Undernucleolarstress,NoLMseqrevealedextensivechromosomereorganization,highlightingtheimportanceofnucleolusintegrityingenomeorganization.Thus,NoLMseqprovidesacriticaltooltostudy3D-genomeresponsestonucleolarstatesinhealthanddisease.

Introduction
Thethree-dimensional(3D)organizationofchromosomesinthecell’snucleusplaysanimportantroleintheregulationofgeneexpressionprogramsandcellfate.Oneimportantaspectofthisregulationisthepositionofgenesinthenuclearspace.Thisisexemplifiedbythefrequentlocationofrepressivechromatindomainsatthenuclearperiphery(i.e.,lamina-associateddomains,LADs)oraroundnucleoli(nucleolar-associateddomains,NADs)1–3. However, while LADs have beenextensivelystudiedinmanycelltypesandinsinglecells,NADshaveremainedunderinvestigatedmainlyduetotechnicallimitationsinidentifyingDNAsequencesassociatedwithacompartmentlackingamembrane,suchasthenucleolus.

Thenucleolusexhibitsahighlydynamicstructurethatchangesinresponsetoexternalstimuliaffectingribosomebiogenesis,acriticalnucleolarprocessthatistightlyregulatedaccordingtocellstate4.Thesenucleolarstructuralalterations(i.e.,size,number,orphysico-chemicalproperties)mightsignificantlyimpacttheorganizationofthesurroundinggenome,therebyaffectinggeneexpressionandchromatinstates.RibosomebiogenesisisinitiatedinthenucleolusbytheRNApolymeraseI(PolI)-driventranscriptionofhundredsofribosomalRNA(rRNA)genes(rDNA)thatgenerate47Spre-rRNA.Thesescriptsarethenmodified,processed,andassembledwithribosomalproteins(RPs)inthenucleolus,formingpre-ribosomalparticles5.rRNAgenesaredistributedamongdifferentchromosomes;acrocentricchromosomesinhumancellsand,inmousecells,dependingonstrain,theycangenerallybefoundatchromosomes12,16,18,and19(rDNA-chromosomes)andclosetocentromericregions3.Dueetoribosomebiogenesis,thenucleolusstructureisinfluencedbycellstatessuchthathighlyproliferativecellshavelargernucleolicomparedtoquiescentcells6–9.Accordingly,alterationsinnucleolaractivitiesandstructurehavebeenwelldocumentedundermanycellularstressconditionsandinseveraldiseases,suchascancer,neurodegenerativedisorders,andprematureageing10.Forexample,nucleolarstressisdefinedasaconditioninwhichabnormalitiesinnucleolarstructureandfunctiondrivenbydiversecellularinsults,suchasnutrientstarvationorDNAdamage,ultimatelyleadtoactivationofstresssignalingpathwaysthatdownregulatoribosomebiogenesis,includingrRNAgenetranscription,andalternucleolarstructure11,12.However,whetherthesestructuralalterationsmight have animpacton the genomeorganizationaroundnucleoliremainsunexplored,mainlyduetothetechnicallimitationsofcurrentmethodstomapNADs.

Currently,thereare severalmethodstoidentifyNADs: thebiochemicalpurification ofnucleoli followed by sequencing and,more recently, the Nucleolar-DamID that relies on the expression of an engineerednucleolarhistoneH2BfusedtoDamthatservestomarkNADswithadeninemethylation(H2B-Dam-NoLS),nucleolar-TSAseq,andO-MAP13–15. Although based on different methodologies, these methodsrevealedthatNADsarecomposedofrepressivechromatindomains16–20. However, these methods also have some limitations. First, they cannot be applied to study how genome organizationaroundnucleoliisaffectedundernucleolarstress21,22.Indeed,undertheseconditions,nucleolibecomefrailandcannotbeefficientlybiochemicallypurified.Ontheotherside,theconsequentdownregulationofribosomebiogenesisdecreasesproteintranslation,therebyaffectingtheexpressionofH2B-Dam-NoLS.Moreover,thephysico-chemicalalterationsofnucleoliuponrDNAdownregulationcausetheexpulsionoftheH2B-Dam-NoLSfromnucleoli,similarlytoothernucleolarcomponents20.Finally,thesemethodshaveonlybeenappliedincellpopulations, and consequently, they have provided an average nucleolarcontact frequency,which might not accurately reflectthe structureofthegenomearoundsinglenucleoli.Thus,sofar,thereisnoMETHODOLOGYabletoidentifyNADsundernucleolarstressconditionsandinsinglecells.

Toovercomethesechallenges,wedevelopedNoLMseq,anovelapproachthatcombineslaser-capturemicrodissection(LCM)andDNAsequencingtoidentifyNADsinsinglecells.LCMisatechniquetraditionallyusedforisolatingsinglecellsorspecifictissueregions23–27,andwehaveexpandeditfortheorganelle-levelisolationofsinglenucleoli.TraditionaltechniquestostudygenomeorganizationpredominantlyrelyoneithermicroscopyorDNAsequencing.NoLMseqsynergisticallyintegratesthestrengthsofboththeseapproaches,enablingpreciseexaminationofthegenomearchitecturesurroundingsinglenucleoli.

Results
NoLMseqidentifiesNADsinsinglecells
To map NADs at a single-nucleoli resolution, we combined laser-capturemicrodissection(LCM)ofnucleoliandDNAsequencing.We named this method nucleolus laser microdissection sequencing (NoLMseq). We applied NoLMseq to ESCs, enabling comparisons with NADs recently identified in ESC populations using Nucleolar-DamID20.Moreover,themajorityofESCsdisplayasinglenucleolus28, allowingforonlyonemicrodissectionpercell.ESCsweregrownonathinpolyestermembranethatcanbecutwithafinelaser(Fig.1a,b,Supplementary Fig. 1a). In brightfield microscopy, the nucleolus appears as a distinct, dark, and dense region within the nucleus, allowing its visualization and excision using laser capture microdissection(LCM).TheisolatednucleoluswasthencollectedintothecapofamicrocentrifugetubeusingMMICapSuretechnology.TheDNAcontentofeachnucleoluswaspurifiedandamplifiedviaquasilinearwhole-genomeamplification(WGA)29.TopreventanylossofDNA, WGA was carried out in the same tube in which the microdissected nucleolus was collected. Before proceeding with library preparationandDNAsequencing,thepurityofnucleolarDNAwasassessedbyPCRtodeterminethepresenceofrRNAgenes,whicharelocatedwithinnucleoli,andtheabsenceofTuba1,anactivegenethatpreviousstudieshaveshownnottocontactnucleoli20(SupplementaryFig.1b).

Wegenerated53high-qualitysingle-nucleoluscontactmapsfromESCs,withamedianof5×105uniquelymappedreadspernucleolus(Fig.1c,SupplementaryFigs.1cand2).Webinnedthegenomeinto100kbsegmentsandcalledNADsusingSICER230ata100kbresolutionusinggenomicDNAasacontrol.Theidentifiedreadsoverthereferencegenomeindicatethatlociareeitherina“contact”or“nocontact”statewithnucleoli(Fig.1c,SupplementaryFig.1c).Imarisanalysisof3Dimmunofluorescenceimagesrevealedthatapproximately65%ofthevolumewithinthemicrodissectedcylinderisoccupiedbythenucleolus,markedbythenucleolarproteinNPM1,andthesurroundingNADs,whichwererecentlyshowntoforma~0.5–0.6μm-wideringaroundthenucleolusmarkedbyH3K9me231(SupplementaryFig.1d).ToexcludetheinevitablepresenceofgenomicsequencesaboveandbelownucleoliandNADs,weanalyzedthedatabycalculatingthenucleolarcontactfrequency(CF)thatwedefinedastheproportionofmicrodissectednucleolicontainingadefinedNADsequence(Fig.1d).Foralldownstreamanalyses,weselectedNADswithCF>20%.Byapplyingthiscut-offinCFtodefineNADsandconsideringthenucleolusandNADvolumeineachmicrodissectedcell,theprobabilitythatanon-NADregioncanbefalselyclassifiedasaNADisonly0.000144%(FDR1.44×10−6)(seeMethod).

We found thatNADs fromrDNA-chromosomes have higher CF than chromosomes not containing rRNA genes, underscoring the specificityofNoLMseqinidentifyingNADs.Moreover,chromosomeX,whichisactiveintheanalyzedmaleESCs,showedthelowestnucleolarCF.Thispatterncouldnotbeobtainedusing53randomsampleswithgenomecoveragematchingthegenomecoverageofeachofthe53ESCnucleolimeasuredbyNoLMseq,supportingthespecificityofouranalysis(SupplementaryFigs.1eand2).

The analysis of NAD coverage/chromosome of all 53 nucleoli further supported the high propensity of rDNA-chromosomes to contactnucleolirelativetotheotherchromosomes(Fig.1e).Furthermore,theanalysisof53microdissectednucleoliappearstobesufficientforrobustNADidentification.Calculationofnucleolarcontactfrequenciesandgenomecoverageusingsubsetsof20,30,and40randomlyselectednucleoliindicatesthatNADsaturationisreachedby40nucleoli(SupplementaryFig.1f,g).

TheaverageNADcoverageinsinglenucleoliwasabout15%,muchlowerthanthe30%NADcoverageobtainedfrompreviousNADstudiesinESCpopulation18,20(Fig.1e,f).ThedataalsoshowedthattheaveragelengthofNADsofrDNA-chromosomeswas1.85MbandsignificantlyhigherthantheaverageNADsizefromtherestofthechromosomes(1.30Mb, Fig. 1g). To validate the data, we used an orthogonal approachbycomputingtheCFofNADsfromNoLMseqgenomicdataandtheirdistancetoFibrillarinmarkednucleoliobtainedbyseqFISH+microscopy32(Fig.1h).WeobservedthatnucleolarCFsmeasuredbyNoLMseqwereinverselyproportionaltothedistanceofNADstothenucleolarmarkerFibrillarin,indicatingthatNoLMseqaccuratelymeasuresgenomicdistancetonucleoli.

TofurthersupporttheefficacyofNoLMseq,weanalyzedknownNADfeaturesoftheidentifiedNADs.Itiswellknownthatcentromerestendtobepositionedclosetothenucleolusinvariousmodelsystems.Sinceinmousecellsallchromosomesareacrocentric,wemeasuredtheaverageNADcoverageatcentromere-proximalregionsatthe5’-quintileportionofallchromosomesandfoundthatitincreaseswithnucleolarCF,furthersupportingtheaccuracyofNoLMseqtoidentifysequencesincontactwithnucleoli(Fig.2a).Consistentwithpreviousresults in ESC populations obtained with Nucleolar-DamID20, NADs identified byNoLMseq were significantly depleted of active histone marksrelativetosequenceswithintheactiveAcompartment,showedasignificantenrichmentfortherepressiveH3K9me2butnotfortheotherrepressivemarksH3K27me3andH3K9me3,anddisplayedlowgeneexpressionlevels(Fig.2b,c).AlthoughNADsgenerallyappeartohavearepressivechromatinandgeneexpressionstate,wecouldalsofindcaseswherehighlyexpressedgeneshavehighnucleolarCFs.Inparticular, we found high nucleolar CFs for ribosomal protein (RP) genes,whichencodeproteinsforribosomeassemblyinnucleoliandaretranscribedbyRNAPolII,andtRNAgenes,whicharetranscribedbyPolIII(Fig. 2d).In contrast, snoRNA genes, which express RNAs playinganimportantroleinrRNAmodificationsinnucleoli,didnotshowanypreferentiallocationtonucleoli.ThenucleolarcontactsofRP and tRNA genes were not due to their location at rDNA-chromosomes since most of them were located at chromosomes noncontainingrRNAgenes(92%and90%,respectively;Supplementary Fig. 3a). Consistent with this result, we reanalyzed previous Nucleolar-DamIDdatainESCpopulation20andfoundthat31%ofRPscouldbedetectedatNADs.Finally,theCFsofribosomalproteingeneswithnucleolifromseqFISH+ data32 highly correlate with the correspondingNoLMseqCFvalues(SupplementaryFig.3b).TheseresultsraisethepossibilitythatnucleolusproximitycouldplayaroleinregulatingRPgenesandribosomebiogenesis,aswellastRNA,potentiallyinfluencingproteintranslationonabroaderscale.Finally,thesedataalsosuggestedthatnotallNADsarenecessarilytranscriptionallyrepressed.

NADshavebeenpreviouslycharacterizedintwocategories:NAD-only(orType-1),whichcorrespondtosequencescontactingnucleoli but notthenuclearlamina(NL)andNAD/LAD(orType-2),whichareregionsthatcanbelocatedbothatnucleoliandNL13,18–20.TheanalysisofsinglenucleolidatarevealedthatNAD-onlysequenceshavehigherCFvaluesthanNAD/LAD(Fig.2e).ThisisconsistentwiththefactthatNAD-only regions are not associated with the NL and are therefore more frequently localized to nucleoli thanNAD/LAD regions, which canalsoassociatewiththeNL.ThisresultunderscorestheaccuracyofNoLMseqinidentifyingNADs.Moreover,thedatasuggestthatNAD/LAD domains can contact the nucleolus in one cell and the NL in another cell. We also observed a small but statistically significant inverse correlation between NAD-only contact frequency (CF) and H3K27aclevels,particularlyforregionswithCF>50%,indicatingthatstrong NADs tend to exhibitlow levelsof active chromatin (Fig. 2f, Supplementary Fig. 3c). Similarly, NAD-only domains with high nucleolar CF have high H3K9me2 levels (Fig. 2g, Supplementary Fig.3d).However,whileNAD/LADregionsshowedsignificantlylowergeneexpressionlevelsrelativetogeneswithintheactiveAcompartment,theexpressionofgeneswithinNAD-onlyregionswasnotsignificantlyreduced,albeittherewasatendencytobelowexpressedwhenlocatedatregionswiththehighestnucleolarCF(Fig.2h,SupplementaryFig.3e).Theseresultsareconsistentwiththedatadescribedaboveshowingthatsomeactivegenescanalsocontactnucleoli.

DetachmentofNADsupondifferentiationintoNPCscorrelateswithgeneactivation
TodeterminewhetherNoLMseqcouldbeappliedtocellsotherthanESCs,wemicrodissectednucleoliofneuralprogenitors(NPCs)derivedfrom the differentiation of ESCs, also taking advantage of recent Nucleolar-DamIDanalysesofNPCsfordatavalidation20.AsinthecaseofESCs,inNPCs,NADsfromrDNAchromosomesconsistentlyexhibited higher nucleolar CF than regions from other chromosomes (Fig.3a).However,NPCsdisplayedfewerNADsthanESCs,withalargeproportion(75%)beingthesameNADsasfoundinESCs(Fig.3b–d).ToassesswhetherthelowerNADcoverageinNPCsisaresultofincreasednoise, we performed an autocorrelation analysis on single nucleoli fromESCsandNPCs.TheanalysisrevealedthatautocorrelationdecaypatternsofESCs(initialautocorrelationa=0.74,decayconstantb=0.23)andNPCs(a=0.64,b=0.34)substantiallydifferfromvaluesgeneratedwithrandomsequencesforESCsandNPCs.TheseresultsindicatedthattheobserveddifferencesinNADcoveragebetweenESCsandNPCsarenotdrivenbyincreasednoiseinNPCsbutinsteadreflectbiologicallyrelevant changes innucleolarorganization (SupplementaryFig.4a).Accordingly,thereducedNADdetectioninsingleNPCnucleoliobtainedwithNoLMseqisconsistentwithpreviousNucleolar-DamIDandHiC-rDNAstudiesperformedinNPCpopulations,indicatingthatthearchitectureofchromosomessurroundingthenucleolusisinamorecompactandrigidformwhereasinESCsitappearsmoreflexibleandvariable20.Moreover,thesedataareconsistentwithpreviousreportsindicatingthatESCsharboramoreopenanddynamicchromatinthan differentiated cells33,34. Consistent with this, we observed more variability in nucleolus association in ESCs than in NPCs,asindicatedbyYule’sQcoefficient,whichprovidesameasureofsimilaritybetweensingle-cellNADs(Fig.3e).

Next,we investigatedwhethergenescontactingnucleoliinESCsbutdetachingfromnucleoliinNPCs(ESC-specificNADs,ESCsp-NADs)could change their expression state in NPCs. We applied stringent criteria for defining ESCsp-NADs as NADs with nucleolarCF>25% in ESCs and <10% in NPCs. The analysis of published RNAseq data20 revealed that genes located at ESCsp-NADs are significantly less expressed in ESCs than in NPCs (Fig. 3f,g), suggesting that the detachment fromnucleolimightfacilitategeneactivation.Moreover,theabsolutefoldchangeingeneexpressioninNPCscomparedtoESCswashigherfortheupregulatedgenes,withalargefraction(60%)notexpressedinESCs(Fig.3h,i).Incontrast,genesinNPCsp-NADs,common NADs, or A compartment show no significant changes in expression(SupplementaryFig.4b).Notably,previouscellpopulationstudies using Nucleolar-DamID20 failed to detect gene activation at ESCsp-NADs, highlighting the improved resolution of NoLMseq in identifyingNADscomparedtocellpopulation-basedmethods(Supplementary Fig. 4c). Accordingly, GO terms for these upregulated genesweresignificantlylinkedtodevelopmentalprocesses,inparticullarlynervoussystemdevelopment,whereasdownregulatedgenesweremainlyimplicatedinmetabolicprocesses(SupplementaryFig.4d,SupplementaryData1).TheseresultsshowthatthedetachmentofgenesfromnucleoliduringESCdifferentiationcorrelateswithgeneactivation,suggestingaregulatoryroleofthenucleolusfortheestablishmentormaintenanceofrepressivechromatinstates.

ESCsarecomposedoftwopopulationswithdistinctnucleolarcontacts
AfterhavingvalidatedtheapplicationofNoLMseqtoidentifyNADsinsinglecellsatmultiplelevels,westartedtoanalyzeunexploredfeatures of NADs, such as the degree of cell heterogeneity in genome organization around nucleoli. We calculated the NAD coverage for eachchromosomeinsinglecellsandperformedhierarchicalclustering(Fig.4a).WefoundtwomainESCpopulationsthatdifferaccordingtothelevelofnucleolarcontactsamongchromosomes.Themajordifferences between these two groups were in the level of rDNA-chromosomecontactswiththenucleolus,whichshowedamutuallyexclusiverelationship.LargeportionsofrDNA-chromosomes12and19had high levels of nucleolar contacts in population 1 but not in population2(Fig.4b).Incontrast,rDNA-chromosomes16and18displayedhigherNADcoverageinpopulation2thaninpopulation1.InthecaseofchromosomesnotbearingrRNAgenes,chromosomes7,11,15, and 2 showed more nucleolar contacts in population 2 than in population 1, whereas chromosomes 3 and 5 preferentially contactnucleoli of population 1. Although ESCs are predominantly in the S phase,thepotentialinfluenceofcellcyclefactorscannotbeentirelyruledout.Nonetheless,itisnotablethattheanalysisofhistonemodifications revealed that NADs enriched in population 1 were significantlydepletedofactivehistonemarksandenrichedinH3K9me2relativetoNADsofpopulation2,suggestingdifferentchromatinstatesbetweenthesetwoESCpopulations(Fig.4d).Toassesswhethertheseenrichments were specific and not due to random fluctuations, we performed 1000 bootstrap randomizations of the NADs of populations 1 and 2. In none of these iterations did we observe enrichment valuesforH3K9me2,H3K4me1,orH3K27acthatmatchedorexceededthose observed inthe actualdata,supporting the specificity ofour results(SupplementaryFig.5a).

GOandpathwayanalysesshowedthatthespecificNAD-genesofpopulation 2 have significant enrichments in pathways linked to developmentand pluripotency,whereasNAD-genesofpopulation1are linked to metabolism and signaling pathways (SupplementaryData 2). Thus, the presence of these two major populations could reflectthe well-documentedheterogeneityofESCsculturedinserum, theconditionsusedinthisstudy,characterizedbyawidevarietyofmorphologiesanddifferentialexpressionofpluripotencyregulatorsandlineagespecifyingfactors35,36.Inparticular,theexpressionofthepluripotencyfactorNanogisheterogeneousinESCs,givingrisetotwo major populations: Nanog-high and Nanog-low37,38. The Nanog-high populationexpressesmarkersforpluripotencyandisinamorepluripotentstatethantheNanog-low population.TodeterminewhetherthetwoESCpopulationsidentifiedfortheirNADprofilescorrelatewithNanog expression levels, we performed single-cell (sc)RNAseq. Uniform manifold approximation and projection (UMAP) displayed 7 distinctclustersofESCpopulations(Fig.4e,SupplementaryFig.5b,c).Nanog-highpopulationcorrespondstoclusters0,1,2,and3.Next,we examined the top 30 genes, selected from a total of 282 genes of population1and126genesofpopulation2,thatshowedthehighestinteractionswithnucleoliandthatwerespecificforoneofthe twopopulations (i.e., NAD-genes of population 1 that have CF>30% in population 1 and <5%CFinpopulation2andviceversaforgenesofpopulation2).Notably,alargefractionofNADgenesinpopulation2(22)co-expresswithNanogandSox2inclusters0–3,whereasonlya few genes (12)from population 1 exhibit this patternwith 13 genes mainlyexpressedincluster7(Fig.4f).Consistently,theUMAPanalysisofthetopfivenucleolus-interactinggenesrevealedthatfouroutoffivegenesfrompopulation2shareanexpressionprofilecharacteristicofNanog-highandSox2-positivecells,whileonlyonegenefrompopulation1displaysasimilarpattern(Fig.4g).

These results suggest that the ESC populations 1 and 2 might possess different pluripotency states. Taken together, the data revealedstructuralheterogeneityintheESCgenome,resultingintwomajorpopulationsdistinguishedbytheirgenomeorganizationaroundnucleoli,eachcharacterizedbydistinctchromatinstates and eventuallydifferentdevelopmentalpotentials.

Nucleolarintegrityisrequiredforgenomiccontactswithnucleoli
Nucleolus structure is highly dynamic and promptly responds to externalstimulithataffectribosomebiogenesisandnucleolarstructure.Forexample,downregulationofrDNAtranscriptionbytreatmentwithlowdosesofActinomycinD(ActD)inducesaspatialreorganizationofthenucleolarstructure,includingthemigrationofrRNAgenes tothenucleolarperiphery,formingtheso-callednucleolarcaps,and the release of the granular component (GC) protein NPM1 fromnucleoli(Fig.5a)39.However,untilnow,itcouldnotbeinvestigatedwhetherthesestructuralchangescanimpacttheorganizationofthegenomearoundnucleoli,mainlyduetothetechnicallimitationsofthecurrent technologies to map NADs under conditions in which nucleolarintegrityisaffected.SincenucleoliinESCstreatedwithActD(ESCs+ActD)arestilldetectablewithbrightfieldmicroscopy,wereasonedthat NoLMseq could be applied to determine how nucleolus integrity affects chromosome contacts with nucleoli. Compared to untreatedcells,theaverageNADcoverageinESC+ActDsignificantlydecreased from 15% to 10% (Fig. 5b, c, Supplementary Fig. 6a). AlthoughNoLMseqrevealedasubstantiallossofnucleolarcontactsacrossallchromosomes, the majority of NADs retained upon ActD treatmentwerefromtherDNA-chromosomes(Fig.5d).WevalidatedthislossofcontactswithnucleoliinESCs+ActDbyquantitativeDNA-FISH,usingprobestargetingfourNADsatchromosomes1,2,5,and19 that were previously identified and validated by Nucleolar-DamID20(Fig.5e,f,SupplementaryFig.6b).ThesearestrongNADssincetheycontactnucleoliin70–95%ofESCs(Fig.5f).TheDNA-FISHprobefortherDNA-chromosome19targetedaNADsequenceproximaltotherDNA locus. Accordingly, this NAD remained anchored to nucleoli upon ActD treatment, an expected result since rRNA genes remain closetonucleolibyformingnucleolarcaps(Fig.5f).Incontrast,DNA-FISH probes targeting NADs at chromosomes 1, 2, and 5 showed a significant detachment of NADs fromthe nucleoli upon ActD treatment,indicatingthatnucleolarintegrityiscrucialforNADassociationwith nucleoli and supporting the results obtained with NoLMseq.

BecauseoftheretentionofrDNA-chromosomestonucleoliinESC+ActD,wereasonedthatregionsfromrDNA-chromosomesstillabletocontact nucleoli in ESC+ActD should correspond to centromere-proximalsequences,whicharelinearlylocatedclosetorRNAgenes.Accordingly, NAD coverage of centromere-proximal sequences of rDNA-chromosomeswashigherthanthecorrespondingNADcoverageinuntreatedESCs,suggestingareorganizationofrDNA-chromosomesaroundnucleoli(Fig.5g).Incontrast,NADcoverageofcentromere-proximal sequences of chromosomes not bearing rDNA repeats showedasignificantreductioncomparedtoNADsofuntreatedcells,furtherindicatingthattheanchoringofthesesequencestonucleoli dependsonnucleoli’sintegrity.Alltheseresultsindicatethatnucleolarintegrityisanimportantdeterminantforchromosomeorganizationaround nucleoli. To assess whether the detachment of NADs fromnucleoliuponnucleolarstressaffectsgeneexpression,weperformedquantitative RNAseq spiked-in with ERCC control RNAs (Fig. 5h). TreatmentwithActDfor24hsignificantlyalteredtheexpressionof5567genes(log foldchange≥1;P<0.05),ofwhich3235geneswereupregulatedand2332weredownregulatedcomparedtocontrolcells.TodeterminewhetherActDtreatmentaffectstheexpressionofgenesatNADs,weanalyzedgenesinhigh-confidenceNAD-onlyregionsinESCs(CF>30%)(Fig.5i).Wefoundthat17%oftheseNAD-genes(507)wereupregulateduponActDtreatment,whereasonly9%(260)weredownregulated.Incontrast,genesatnon-NADs(CF<20%)showedasimilarnumberofup-anddownregulatedgenes.TheseresultssuggestthatthelossofnucleolarintegritymightplayasignificantroleinthederepressionofNAD-genes.TheupregulatedNAD-geneswerelinkedtosignalingpathwaysinvolvingcalcium,PI3K-Akt,andp53,thelatterbeingwellknowntobeactivatedbyRNAPolIinhibitionuponActDtreatment40,41(SupplementaryFig.6c,SupplementaryData3).WealsofoundthatabouthalfoftheupregulatedNAD-genesremainincontactwithnucleoli(Fig.5i)and,accordingly,themajorityofthem(71%)werelocated at rDNA-chromosomes. These results suggest that the nucleolusmayalsoloseitsrepressivepropertiesunderthesenucleolarstress conditions, although further investigation is required to fully understandtheseproperties.

TogetherwiththereactivationofNADgenesduringESCdifferentiationintoNPCsuponlossofnucleolarcontact,theseresultsareconsistentwiththepossibilitythatthenucleolusislinkedtogenerepression,althoughitsdirectregulatoryroleremainstobefullyestablished(Fig.5j).Moreover,thedatademonstratethatNoLMseqis auniqueandonlygenomicmethodforstudyingtheroleofnucleolusingenomeorganizationundernucleolarstressconditionsthataffectnucleolarintegrity.

Genomiccontactswithnucleoliarepredominantlymono-allelic
Recentdatahavestartedtodetectallelicasymmetryingeneexpressionand3D-genomeorganization42,43.Sofar,however,allstudiesonNADswereperformedusingcellswhosematernalandpaternalchromosomescouldnotbedistinguishedfromeachother,asinthecaseoftheE14ESClinethatderivesfromafullyinbredhomozygousmousestrain. Consequently, the information on NAD coverage has beenbasedontheassumptionthatbothallelescontactnucleoli.TodeterminetheallelicdistributionofNADsinsinglenucleoli,weappliedNoLMseqtothemalemouseF123-ESClinethatwasderivedfromtheF1generationoftwofullyinbredhomozygousmousestrains:Musmusculus castaneus (CAST, paternal chromosomes) and Mus musculus domesticus S129S4/SvJae (S129, maternal chromosomes)44. The F123 DNAsequencehasbeenwellcharacterizedandcontainsahighdensityofsingle-nucleotidevariants(SNVs),withanaverageof1SNVevery124 nucleotides across autosomes45. We analyzed NAD profiles from 50 microdissectednucleoliofF123-ESCsbyaligningthemagainsttheF123genome to identify allele-specific NADs (i.e., NADS129 and NADCAST) (Fig.6a).Thevastmajority(93%)oftotalNADsidentifiedinF123-ESCs correspondedtoNADsidentifiedinE14-ESCs,indicatingthatgenomiccontactswithnucleoliaregenerallyconservedbetweencelllineswiththesamedevelopmentalstagebutfromdifferentstrains(SupplementaryFig.7a).NADsofF123-ESCsshowedaslightlylowergenomecoverage(11.3%,calculatedtohaploid genome)relativeto15%NADcoveragemeasuredinE14-ESCs(Fig.6B).Surprisingly,wefoundthata largefractionofNADs(86%)aremono-allelic(Fig.6a–c).MonoallelicNADswereequallydistributedamongS129andCASTchromosomes(SupplementaryFig.7b).Notably,rDNA-chromosomeshaveabout10-fold higher proportion of biallelic NADs compared to non-rDNA chromosomes (Supplementary Fig. 6c). The distinction between monoallelic and biallelic NADs was also evident from nucleolus CF analysis, which showed that biallelic NADs have a higher CF than monoallelic NADs whereas NAD-only and NAD/LAD regions display similarcontent(SupplementaryFig.7d).WevalidatedtheseresultsbyDNA–FISHfor twostrong NADs,one located at chromosome 5 and anotherlocatedattherDNA-chromosome19andclosetotherDNAlocus(Fig.6d).TheNADofchromosome5showedmonoalleliccontactsinalmostallthecells.Remarkably,eventheNADofchromosome19,locatedclosetotherDNAlocus,showedmono-alleliccontactswithnucleoliin50%ofthecells,supportingtheresultsofNoLMseq.Thisprevalentmono-allelicNADdistributionresultedinamuchlowerNADcoveragepercell(5.6%,297Mbp)comparedtothepreviouslyassumedbi-allelicdistribution(Fig.6e).Todeterminewhethermono-andbi-allelicNADsreflectdifferencesinchromatinmodifications,weperformedChIPseq analysis for H3K9me2, H3K27ac, and H3K4me3 in F123-ESCs (Fig. 6f). We found that bi-allelic NADs are in a more repressedchromatinstatesthanmono-allelicNADssincetheydisplayreduced levels of H3K27ac and H3K4me3 and higher levels of H3K9me2,thelattertherepressivehistonemarkcharacterizingNADsin ESCs20. Similarly, RNAseq analyses showed a significantly lower expressionofgeneslocatedatbi-allelicNADs(Fig.6g).Amonggeneslocated at bi-allelic NADs, we found cell type specific transcription factors,suchasGata6andTAF4b,aswellasgeneslinkedtometabolicpathways and neuroactive ligand-receptor interactions (SupplementaryFig.7e).Theseresultsfurtherindicatethathigh-frequencycontactwith nucleoli, even at the allelic levels, results in a more repressive state.

Next,we asked whether themono-allelic distribution of NADs might be parent-specific. We applied stringent criteria to assign parent-specific NADs, considering, for example, that a specific NAD shouldhave>25%nucleolarCFwiththeS129genomeand<10%nucleolarCFwiththeCASTgenome(Fig.6h).However,veryfewNADs(1%onbothalleles)showedaparent-specificdistribution.This indicates that, generally, in one cell, it is the paternal allele contacting nucleoli while in another cell it is the maternal allele(Fig.6h).

Methods
Celllines
Onehundredandtwenty-ninemouseembryonicstemcells(E14line)were cultured in serum medium containing Dulbecco’s modified Eagle’smedium+Glutamax(LifeTechnologies),15%FCS(LifeTechnologies;Catno.10270106FBSSouthAmerican),1×MEMNEAA(LifeTechnologies), 100μM β-mercaptoethanol, recombinant leukemia inhibitory factor, LIF (Polygene, 1000U/ml), 1× penicillin/streptomycin (Life Technologies). ESCs were seeded at a density of 50,000cells/cm2inculturedishes(CorningCellBINDsurface)coatedwith 0.1% gelatin without a feeder layer. Propagation of cells was carriedoutevery2daysusingenzymaticcelldissociation.ESCswere treatedwithActinomycinD(10ng/ml)for24hbeforedownstreamanalyses.

NeuralprogenitorcellsweregeneratedfromESCs,accordingtoa previously established protocol53. In brief, differentiation used a suspension-based embryoid bodies formation (Bacteriological Petri Dishes,Bio-onewithvents,Greiner).Theneuraldifferentiationmedia (DMEM,10%fetalcalfserum,1×MEMNEAA,2mMPen/Strep,β-mercaptoethanol, and sodium pyruvate) were filtered through 0.22μm filtersandstoredat4°C.Duringthe8-daydifferentiationprocedure, themediawereexchangedevery2days.Inthelast4daysofdifferentiation, the media were supplemented with 2μM retinoic acid to generateneuralprecursorsthatarePax-6-positiveradialglialcells.

F123-ESCs (mouse male, hybrid cell line, F1 S129/Jae and Cast mouse cross)44 were cultured on a layer of mitotically inactivated feeder murine embryonic fibroblasts under standard conditions (DMEM, supplemented with 15% KSR, 1× Glutamax, 10mM non-essentialamino acids, 50µM beta-mercaptoethanol, 1000U/mlLIF). Mediawas changed every24h.Beforeharvesting,mESCs werepassagedontofeeder-freeESGRO-gelatin(EMDMilliporeSF008).coated platesforatleast2passagestoremovefeedercells.Cellswereharvestedafterapproximately48hat70–80%confluency.F123-ESCswere agiftfromBingRen,UniversityofCalifornia,SanDiego.

NoLMseq
Forlaser-microdissection,cellsweregrownonESGRO-gelatin-coated and UV-irradiated PET membrane slides (1.4μm thick, RNase-free, MMI,50102)for24h.Cellswerewashedtwicewith1×PBSandfixed withEM-gradefreshly-depolymerized3%PFAin1×PBSfor30min.The slides werethen placed in 70% ethanol for 1minand airdried. The slideswerethenwashedwithfilteredultra-pureH2O(3×1mineach) andairdried.SinglenucleoliwerelasermicrodissectedwiththeMMI Laser Capture Microdissection Nikon microscope (Molecular Machines&Industries)intosingle0.2mltubes(transparentMMIcaps, 50208).Nucleoliwereidentifiedbybright-fieldimaging.A0.2mlcap filledwithtransparentadhesivematerialwasloweredontothecells.A laserwasusedtocutthePETmembranesurroundingeachnucleolus andthecutPETmembranesectionalongwiththesinglenucleoluswas liftedoffbythestickycap.Eachsinglenucleoluswascollectedina separate cap and stored for further processing. Nucleoli identified usingbright-fieldmicroscopywereinitiallyfocusedusingKöhlerilluminationtoachieveoptimalcontrastforaccuratevisualizationofthe nucleoli. To ensure precision, the stage was adjusted with camera-stagealignment,maintaininganinclinationoflessthan±0.1°.Paraxial lens offset was employed to align the optical axes of all objectives. Laseralignmentwascalibratedbyfiringasingleshot,adjustingthe pixel center ofthe imaging system to coincide with the laser beam center.Lasercuttingparameters,suchascutvelocity,laserfocus,and laserpower,werethenoptimized.Thecutvelocitywassetat20μm/s, laserpowerat0.001%ofmaximumcapacity,andthefocuswascalibratedforeachcuttoachieveacutdiameterof0.25μmthickness, ensuring reproducibility. Prior to nucleolus excision, the laser was refocusedonanemptyareaoftheslide,whereacutsimilarinsizeto thenucleoluswasdrawn.Thelaser’sinnerringwaspositioned0.25μm fromtheedgeofthedrawnshape.Whenthecapwasloweredontothe membranewiththesample,anymembranedisplacement(typicallya fewmicrons)wascorrectedusingtheCapZoffset.Tomaintainconsistentfocusduringslidemovement,theslidesurfacefocusingfunction of the microscope was employed to account for potential topologicaldifferencesinthemembrane.Uponidentifyingacellwitha distinctnucleolus,a0.2mLcapcontainingatransparentadhesivewas loweredontothesample.TheinnerringofthelaserusedtocutthePET membraneispositionedapproximately0.25μmfromtheouteredge ofthe nucleolus. TheexcisedPETmembrane,alongwiththenucleolus, wasliftedoffusingtheadhesivecap.Thecapwasloweredontoan emptyareaofthemembranetocheckthatthesamplewascorrectly excised.Anycapswithimpropercutsorthoselackingaclearlyidentifiablenucleoluswerediscarded.Onlycapscontainingcorrectlyisolatednucleoliwereretainedforfurtherprocessingandanalysis.After collection,anewcapwasadded,andtheentirefocusingandcutting process was repeated to ensure consistency across samples. Each nucleoluswascollectedinaseparatecapforfurtherprocessing.

Whole-genome amplification (WGA) was carried out using the MALBACsingle-cellWGAkit(YK001A) following the manufacturer’s recommendationswithminormodifications.TheentireWGAreaction was carried out in the same 0.2ml tube with a single nucleolus. Anothertube,withanemptysection,wasusedasanegativecontrol, whereas30pgofwholecellDNAwasusedasapositivecontrolforthe WGAreaction.Thecelllysisreactionwasfirstcarriedoutbyusinga heated lidat50°C for 50min, and then samples werecollected by briefly spinning and proceeding with the cell lysisreactionagain at 50°C for 50min, followed by inactivation at 80°C for 10min. The reactionis then cooled to 4°C. The lysisstep is followed by a pre-amplificationstep,whichinvolvesthequasilinearamplificationusing randomprimersat20°Cprogressivelywith10°Cstepstill70°C.After cooling,thesamplesweresubjectedtoafinalamplificationstepby20 cyclesofPCR.

WGA-amplifiedDNAwaspurifiedusingaQiagenMinElutePCR PurificationKitandelutedin140μlultrapurelibrary-gradeH2O.The amplifiednucleolarDNAwascheckedona2%agarosegeltoselect positive samples showing smears corresponding to amplified DNA similar to the positive control. The positive samples were further checked for thepresenceofrDNAand the absenceofthe Tubulin genebyPCRonanagarosegel.Thesamplesthatpassedthisquality controlcheckwerethenusedforlibrarypreparationandsequencing. Intotal,120μlofthereactionwasfirstsonicatedinsplit-captubesby CovarisE220toobtainfragmentsaround300–500ntinlength(Peak power—75,Dutyfactor—20,cycle/bursts—1000,duration—35s).The sampleswerethenpurifiedandconcentratedwithAMPureXPbeads (Beckman Coulter,A63881)and usedforIlluminalibraryusingthe NEBNext ultra II kit (NEB E7600). Briefly, NoLMseq samples (10–100ng)wereend-repairedandpolyadenylatedbeforetheligationofIllumina-compatibleadapters.Theadapterscontaintheindex for multiplexing. Library concentrations were estimated using a Qubit® 4 fluorometer (Thermo Fisher Scientific) and the 4200 TapeStationsystem,and librarieswere pooledtogetherinbatches averaging40samples.Eachlibrarypoolwassequencedinsingle-end 100bpinonelaneofNovaseq6000togetanaverageof10million readsperlibrary.

For data analysis, NoLM-seq reads were aligned to the mouse mm10referencegenomeusingBowtie2(version2.5.054)withdefault parameters.PCRduplicateswereremovedusingsamtools55.Positive NADbinswerecalledusingSICER230,usingthesequencedgDNAfileas the control file and the parameters w=100,000, g=100,000, fdr= 0.0001. Bigwig files were created from the bam files using bamCoveragefromdeeptools57.ForF123-ESCs,anN-maskedgenomewas createdusingthemm10referencegenomeandtheVariantCallFormat (VCF)filefromthemousegenomeprojectcontainingtheSNPinformationforallstrains.TheSNPsplitpackage58wasusedtoreadtheVCF file and extract the SNP information for the S129/SvJae and CAST strains and N-mask for the mm10 genome. The reads were aligned using Bowtie2 to this N-masked reference genome using default parameters.SNPsplitwasthenusedtotagtheSNPsforS129/SvJaeand CASTallelesinthebamfileandsortthehaplotype-specificbamfiles forbothalleles.bamCoverageandSicer2positiveNADcallingwere performedindependentlyontheallele-specificbamfiles.Integrative GenomeViewer(IGV,version2.15.4)59wasusedtovisualizeandextract representativesingle-nucleolusNoLMseqbigwigandbedtracks.

Nucleolarcontactfrequencywascalculatedata100kbresolution. We defined NADs as sequences with a >20% nucleolar contact frequency. Our sequencing results show that ~15% of the genome is recovered per single nucleus.Given the NADvolume,this coverage value indicates that one-third of the recovered sequences upon microdissectionofonesinglecellmayarisefromnon-NADs.Thus,in each single microdissected sample, approximately one-third of the capturedsequenceswouldoriginatefromnon-NADregions.Byintegratingthedatafrom53microdissectedcellsusingaCFthresholdof 20%,theprobabilitythatanon-NADregionisfalselyclassifiedasaNAD isonly0.000144%.Sincethemousegenomecomprises27,904100kb bins,theexpectednumberoffalse-positivecallsinthedatais0.04,less thana singlebin.CFthreshold was cPalculatedusingthecumulative distributionfunction(CDF)P(X≤k)= i=0k(in)pi(1-p)n-1where “n”isthenumberofsinglenucleolisamples,“p”istheprobabilitythata false-positive region is presentin thesample,“k”is the contactfrequencythreshold.ForthehierarchicalclusteringofNADs,NADchromosomecoveragesinsinglenucleoliwereusedforclusteringsingle nucleolibasedondivisiveclustering(Diana)usingthe“cluster”package(version2.1.6)inR.

Forrandomization,wegenerated53randomizedcontroldatasets, eachmatchedingenome-widecoveragetooneofthe53ESCNoLM-seqsamples.Foreachdataset,werandomlyassigned100kbgenomic bins aseither “NAD”or “non-NAD,”matching the total number and proportion of NAD-classified bins as those observed in the correspondingexperimentalsamples.The contactfrequencydistribution was calculated similarly to ESC NoLM-seq samples. Proportions of regions from rDNA-chromosomes, non-rDNA chromsomes and XY-chromosomesovercontactfrequencieswereplottedusingGraphpad Prism(version10.5.0).Randomizedcontroldatasetsweregenerated forNPCsinasimilarmanner.

BootstrappinganalysiswasperformedinRusing1000iterations to randomly sample NADs from the NADs of populations 1 and 2. Enrichment for each histone mark (H3K27ac, H3K4me1, H3K4me3, H3K9me2,H3K9me3,andH3K27me3)wascalculatedandtestedfor significance.Theempiricalp-valuewascomputedastheproportionof bootstrapiterationsinwhichtheenrichmentdifferencewasequalto or greater than the observed difference between the two subpopulations.

AutocorrelationanalysiswasperformedusingacustomPython script ontheNADprofilesfrom single nucleoli ofembryonicstem cells(ESCs)andneuralprogenitorcells(NPCs).Theautocorrelation function(ACF)wascomputedforeachsampleusingPearsoncorrelationcoefficientsatincreasinglagdistances(1–50bins).Toquantify therateofautocorrelationdecay,wefittedanexponentialfunction of the form ACF(x)=a*e-bx, where a represents the initial autocorrelationandbisthedecayconstant,whichreflectshowrapidly NAD associations are lost over increasing genomic distances. Data visualization was carried out using Matplotlib (https://github.com/matplotlib/matplotlib). ACFs and their corresponding exponential fits were plotted for both ESCs and NPCs, with fitted parameters (amplitudeaanddecayconstantb).

RNAseq
TotalRNAwaspurifiedwithTRIzolreagent(LifeTechnologies).The quality of the isolated RNA was determined with a Qubit® 4 fluorometer (Life Technologies, California, USA) and 4200 TapeStation system(Agilent,SantaClara,California,USA).Onlythosesampleswith a260nm/280nmratiobetween1.8and2.1anda28S/18Sratiowithin 1.5–2werefurtherprocessed.TheTruSeqStrandedmRNA(Illumina, Inc.,California,USA)wasusedinthesucceedingsteps.Briefly,total RNAsamples (100–1000ng) were polyA-enriched and then reverse-transcribed into double-stranded cDNA. The cDNA samples were fragmented, end-repaired and adenylated before ligation of TruSeq adapterscontaininguniquedualindices(UDI)formultiplexing.Fragments containing TruSeq adapters on both ends were selectively enrichedwithPCR.Thequalityandquantityoftheenrichedlibraries werevalidatedusingtheQubit®4fluorometer.Theproductisasmear withanaveragefragmentsizeofapproximately260bp.Librarieswere normalizedto10nMinTris-Cl10mM,pH8.5,with0.1%Tween20.The NovaSeqX(Illumina,Inc.,California,USA)wasusedforclustergenerationandsequencingaccordingtostandardprotocol.Sequencing waspaired-endat2×150bp.

ThequalityofthereadswascheckedbyFastQC,aqualitycontrol toolforhigh-throughputsequencedata60.Thequalityofthereadswas increasedbyapplying:a)SortMeRNA61(version2.1)tooltofilterribosomalRNA;b)Trimmomatic62(version0.40)softwarepackagetotrim thesorted(a)reads.N-maskedgenomewascreatedusingthemm10 referencegenomeandtheVCFfilefromthemousegenomeproject containingtheSNPinformationforallstrains.TheSNPsplitpackage58 wasusedtoreadintheVCFfileandextracttheSNPinformationforthe S129/SvJae and CAST strains and N-mask the mm10 genome. The sorted (a), trimmed (b) reads were mapped against the Nmasked mouse genome (mm10) using the default parameters of the STAR (SplicedTranscriptsAlignmenttoaReference,version2.663).SNPsplit wasthenusedtotagtheSNPsforS129/SvJaeandCASTallelesinthe bamfileandsortthehaplotype-specificbamfilesforbothalleles.For eachgene,readsperkilobasepermillion(RPKM)werecalculatedas RPKM=(109*C)/(N*L),whereCisthenumberofreadsmappedtoa gene,Nisthetotalmappedreadsintheexperiment,andListhegene lengthinbase-pairsforagene64.

CellsweretreatedwithActinomycinD(10ng/mL)for24h,after which1×10⁶cellswerecollectedforRNApurificationusingtheZymo RNAextractionkit(ZymoResearch).Threebiologicalreplicateswere preparedforbothActD-treatedanduntreatedconditions.RNAisolationwasperformedaccordingtothemanufacturer’sinstructions.All samples were then spiked with ERCC control RNAs (Thermo Fisher Scientific),followingthemanufacturer’srecommendations. Library preparation wascarried out using the TruSeq Stranded mRNAkit(Illumina,Inc.,California,USA)asdescribedpreviously.The absolute abundance of mRNA transcripts was assessed using the ERCC92 RNA spike-in control65. Most of the 92 ERCC transcripts showedconsistentcountsbetweenuntreatedandActD-treatedsamples.ReadswerealignedtothemousegenomeusingSTARandRPKM valuesasdescribedabove.

Single-cellRNAseq
ESCswereculturedasdescribedabove,andasingle-cellsuspension waspreparedbydissociatingthecellsbytreatmentwithAccutaseat 37°C for 3min. The reaction was quenched with serum-containing medium, and cells were washed twice with PBS supplemented with 0.04%bovineserumalbumin(BSA).Theconcentrationofthesingle cellsuspensionwasassessedusinganautomatedcellcounter(LUNA-FX7,Logos)witha fluorescentdye(AO/PI).Librarypreparationwas conductedusingthe10×ChromiumNextGEMSingleCell3’Kitv3.1. Approximately 10,000 cells were loaded onto the Chromium Next GEMChipG.Librarypreparationfollowedthemanufacturer’sguidelinesoutlinedintheUserGuide(CG000315,RevF).Forsequencing, the resulting libraries were processed on one lane of an Illumina NovaSeq X Plus 10 billion flow cell with a 150bp paired-end read configuration.ReadalignmentwasperformedusingSTARagainstthe mouseGRCm39 reference genome. Count matrices were generated withCellRanger(10×Genomics,v8.0.0)usingtheGRCm39reference genome and GENCODE M31 gene annotations. Analysis yielded an averageofapproximately55,544readsand30,658uniquemolecular identifiers(UMIs)percell,acrossatotalof7304recoveredcells.All downstream single-cell analyses were conducted using the Seurat framework(v5.1).DatavisualizationwasperformedusingthescRNA shinyapp(https://github.com/ucdavis-bioinformatics/scRNA_shiny).

ChIPseq
ChIP analysis was performed as previously described66. Briefly, 1% formaldehyde was added to cultured cells to cross-link proteins to DNA.Isolatednucleiwerethenlysedwithlysisbuffer(50mMTris-HCl, pH 8.1, 10mM EDTA, pH 8, 1% SDS, 1× protease inhibitor complete EDTA-freecocktail,Roche).NucleiweresonicatedusingaBioruptor ultrasonic cell disruptor (Diagenode) to shear genomic DNA to an averagefragmentsizeof200bp.20μgofchromatinwasdilutedtoa total volume of 500μl with ChIP buffer (16.7mM Tris-HCl, pH 8.1, 167mMNaCl,1.2mMEDTA,0.01%SDS,1.1%TritonX-100)andincubated overnight with the ChIP-grade antibodies against H3K9me2, H3K9me3, H3K27me3, H3K4me1, H3K4me3 and H3K27ac. After washing,boundchromatinwaselutedwiththeelutionbuffer(1%SDS, 100mM NaHCO3). Upon proteinase K digestion (50°C for 3h) and reversion of cross-linking (65°C, overnight), DNA was purified with phenol/chloroformandethanolprecipitated.

The quality and quantity of the isolated DNA were determined withaQubit®4Fluorometer(LifeTechnologies,California,USA)and the4200TapeStationsystem.Briefly,ChIPsamples(1ng)wereend-repaired and polyadenylated before the ligation of Illumina-compatible adapters. The adapters contain the index for multiplexing.ThequalityandquantityoftheenrichedlibrarieswerevalidatedusingQubit®4Fluorometerandthe4200TapeStationsystem. Thelibrarieswerepooledto10nMinTris-Cl10mM,pH8.5,with0.1% Tween 20. Sequencing was performed on the NovaSeq 6000 for 100bpsingle-endreads(Illumina,Inc.,California,USA).

ForF123-ESCs,anN-maskedgenomewascreatedusingthemm10 reference genome and the Variant Call Format (VCF) file from the mousegenomeprojectcontainingtheSNPinformationforallstrains. TheSNPsplitpackage58wasusedtoreadtheVCFfileandextractthe SNPinformationfortheS129/SvJaeandCASTstrainsandN-maskfor themm10genome.ChIPseqreadswerealignedtotheN-maskedmm10 referencegenome with Bowtie2 (version 2.5.054) using default parameters.PCRduplicateswereremovedusingsamtools55.SNPsplitwas thenusedtotagtheSNPsforS129/SvJaeandCASTallelesinthebam fileandsortthehaplotype-specificbamfilesforbothalleles.Bigwig files were created from the bam files using bamCoverage from deeptools57usingdefaultparameters.IntegrativeGenomeViewer(IGV, version 2.15.4)59 was used to visualize and extract representative ChIPseqtracks.

Geneontology(GO)enrichment
GOenrichmentanalysisofgeneswasperformedwithDAVID6.867.All genesinMusmusculuswereusedasthebackground.Datavisualization wasperformedonGraphpadPrism(version10.5.0).

DNA–FISH
DNA–FISHprobesforchromosomes1,2,5,and19weregeneratedas previously described20. The probes were obtained with oligopaint librariesthatwereconstructedusingthePaintSHOPinterfacecreated bytheBeliveaulabandwereorderedfromCustomArray/Genscriptin the12KOligopoolformat.Eachlibrarycontainsauniversalprimerpair used to amplify all the probes in the library, followed by a specific primerpairhookedtothe40–46-mergenomicsequences,foratotal probeofaround124–130-mers.Oligopaintlibrarieswereproducedby emulsionPCRfromthepool,followedbya“two-stepPCR”andlambda exonuclease as described before20,68. Specifically, the emulsion PCR withtheuniversalprimersallowedamplifyingallpoolofprobesinthe library.The“two-stepPCR”ledtotheadditionofatailtothespecific probesboundtotheAlexaFluor488fluorochrome.AlloligonucleotideswerepurchasedfromIntegratedDNATechnologies(IDT,Leuven, Belgium).Alloligonucleotide sequences and the mm10 coordinates fortheprobelibrariesarelistedinSupplementaryData4.

DNA-FISHwasadaptedfromtheprotocolofCavalli’slab69.Cells fixedwith4%PFAfor10minatroomtemperature,24hafterseeding. Cellswerewashedthreetimeswith1×PBSandthenpermeabilizedwith 0.5%Triton-X100/PBSfor10minonice,followedby10minatroom temperature. After three additional washes with 1× PBS, cells were incubated with 0.1M HCl for 10min at room temperature, washed twice with 2× SCCT, and twice with 50% Formamide-2× SCCT for 20min,firstlyatroomtemperatureandthenat60°C.Probemixture containedaround45pmolofOligopaintprobe,0.8=µLofribonuclease A (ThermoScientific, 10mg/mL), and FISH hybridization buffer for a totalmixturevolumeof20µL,whichwasdirectlyaddedtotheslide. Cell DNA was denatured at 83°C for 8min, and hybridization was performed in a humid dark chamber overnight at42°C. Cells were washed with 2× SCCT for 15min once at 60°C and once at room temperature, once with 0.2× SCC for 10min, twice with 1× PBS for 2min,andthreetimeswith1×PBTfor2min.Slideswerethenincubatedfor1hatroomtemperaturewithblockingsolutioninadark, humidchamber,andovernightat4°Cwithprimaryantibodyagainst Nucleolin(AbcamCat#ab22758).Cellswerewashedfourtimeswith1× PBTatincreasingincubationtimes(1×2min,1×3min,2×5min)and thenincubatedfor2hatroomtemperaturewithgoatanti-mouseIgG (H + L)AlexaFluor546(ThermoFischerCat#A11035)inadark,humid chamber. Slides were washed four times with 1× PBT at increasing incubation times (1×2min, 1×3min, 2×5min), three times with 1× PBS for 2min. Then, slides were mounted with Vectashield DAPI mountingmedia(Vector,H-1200)and,afterdrying,storedat4°C.

DNAFISH/IFsampleswereimagedusingaZeissLSM980Airyscan, withaz-stackcollectedforeachchannel(stepsize,0.3μm),usingtheoil objective Plan-Apochromat/1.40. Images were processed by ImageJ (version2.14.0/1.54F).TheindividualcellswereidentifiedbyHoechst/ DAPIstaining,andcellscontainingasignalfortheDNA–FISHchannel were identified manually on the corresponding fluorescent channel. ThedistancebetweentheDNA–FISHsignalandthenucleolarmarker immunofluorescencesignalwascalculatedusingImageJ(version2.14.0/ 1.54F)andusedtocountthenumberoffocicontactingthenucleolus andthenumberofcellswithatleastonecontactwiththenucleolus.

Immunofluorescence
Cellswereplatedoncoverslips(VWR,ECN631–1577)andpre-coated with Matrigel (Corning, 356238) for 1h at 37°C. After 48–72h, dependingonthetreatment,cellswerefixedusing3.7%formaldehyde (Sigma-Aldrich,47608)inPBSfor10minatroomtemperature.After two washes inPBS, cells werepermeabilized with 0.5%TritonX-100 (Sigma-Aldrich,T8787)inPBSfor10minoniceand10minatroom temperature.CellswerethenwashedtwotimesinPBSandblocked with 1% Bovine Serum Albumin (Sigma-Aldrich, A2153) in PBS-0.1% Tween20(Tween20:Merck,P9416)for1hatroomtemperatureina humidifiedchamber.Thecoverslipswerethenincubatedwitha1:500 dilutionofprimaryantibodies(NPM1,Sigma-AldrichCat#B0556;UBF, ThermoFischer Cat# H00007343-M02; Nucleolin, Abcam Cat# ab22758) resuspended in blocking solution overnight at 4°C in a humidified chamber. After 3 washes in 1× PBS, the coverslips were incubatedwith1:500dilutedsecondaryAlexaFluor®antibodyresuspendedinBlockingSolutionfor1hatroomtemperature.Afterthree washesin1×PBS,thecoverslipsweremountedwithVECTASHIELD® Antifade Mounting Medium with DAPI (H-1200-10), sealed with nail polishandimagedwithinoneweek.

Nucleolar volumes were quantified using Imaris (version 10.2) basedonimmunofluorescencestainingofthenucleolarmarkerNPM1 (Sigma-AldrichCat#B0556),whilenuclearvolumesweredefinedusing DAPI.Thevolumeofnucleolar-associatedchromatinwasestimatedby adding0.6μmtothemeasurednucleolarradius,correspondingtothe averagewidthoftheperinucleolarshellmarkedbyH3K9me2,which delineates NADs31. Based on this spatial definition, the nucleolar-associatedvolumeaccountedfor~65%ofthetotalnuclearvolumeof the laser-microdissected samples.Using the cumulative distribution function for nucleolar contact frequency threshold of >20% in 53 microdissected ESCs, the false positive rate was estimated to be 0.000144%.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript describes a novel approach (NoLMseq) to identify the genomic regions that are proximal to nucleoli. The method is clever and conceptually simple. While several other mapping methods have been reported to map nucleolus-genome proximity/interactions, NoLMseq stands out because it is a single-cell method. Setting this up is an impressive accomplishment. The only single-cell method that comes close is seqFISH+, but this seems to have a current resolution of ~1Mb, compared to ~100kb for NoLMSeq.\nBoth the text and the figures are generally clear. That said, in my view substantially more evidence is necessary to document the quality of the data. See below.\nBecause this is a new mapping technology, the results are largely descriptive and not particularly surprising considering what the previous nucleolus-mapping methods had already revealed. Nevertheless, the data still uncover some interesting findings, such as changes upon ActD treatment, proximity of ribosomal protein genes, and potential mono/bi-allelic proximity to nucleoli.\nMajor points:\n1. Figure 1b shows only one example of a laser capture \"hole\". Please provide a more extensive set of images to document how this was done. A potential limitation of the NoLMseq method would be that it may be only feasible in cells with one large nucleolus, while many cell types have multiple smaller nucleoli (including some cells shown in figure 1b, and in figure 6d). Could this create a bias in the current study? Or is it possible to capture multiple nucleoli from one cell?\n2. The single-cell NAD patterns shown in figure 1c seem almost random, with the exception of the higher density on a subset of chr19 . Similar for Extended Data figure 1b. This is very different from single-cell lamina interactions, which, albeit stochastic, show clearly recurrent domain patterns (see papers by Jop Kind). Likewise, it is difficult to tell whether the contact frequencies in Extended figure 2 are very different from random. The low signals of the X chromosome and a slight enrichment on chromosomes with rDNA are encouraging, but overall the patterns seem much noisier than previously reported by other methods - although these methods also may suffer from some limitations. I am missing a critical assessment of this. It is natural that single-cell data are noisy, but it is important that the reader gets a good sense of this. Below I suggest some scatterplots that could offer some more insights into the noise levels.\n3. As mentioned by the authors, one possible concern with NoLMseq is that (parts of) chromosomes above and below the nucleolus may also be captured, thus contaminating the data. It would be useful to have a quantitative estimate of this contamination. How much of the total nuclear volume is above and below the nucleolus? This could be estimated by confocal or super-resolution microscopy. And what do the single-cell maps look like for a set of control captures (capturing a similar sized nuclear region outside the nucleolus)?\n4. The overlap with SPRITE and nucleolar DamID (Figure 1h) seems improbably high. It is unclear how this analysis was done; how is overlap defined? Much better (and more transparent) would be to plot NoLMseq contact frequency NoLMseq versus nucleolus interaction scores according to the other two assays in conventional scatter plots (so continuous rather than binarized data). What do these plots look like, and what is the correlation coefficient?\n5. I find the claim of 2 populations (Figure 3) not entirely compelling. Hierarchical clustering by definition yields two populations at the highest bifurcation, so even random data would generate two populations; it may be good to randomize/bootstrap the data 1,000 times and see if the mild enrichment of some (but not all) histone marks are true, or just random fluctuations. Furthermore, the two populations remain enigmatic: what is their biological meaning? What two different states of the cells may it reflect?\n6. Section starting on line 212: \"Detachment of NADs upon differentiation into NPCs results in gene activation\" incorrectly claims a causal relationship. It is only a correlation. The final sentence of this section also incorrectly claims a causal relationship. Moreover, the differences in gene activity shown in Figure 4f are tiny (0.1 log2 unit ~ 15%). I am puzzled about figure 4g: usually in such fish-tail distributions, genes with lower expression are less reliable and hence show fewer significant changes. But in this plot there are many blue and red dots in this unreliable range. The Methods do not specify how this analysis was done. If it was not done so, I strongly recommend to use DESeq2 or similar state-of-the-art package to do this RNAseq analysis. Please also specify the number of independent biological replicates (in the figure legend), and confirm that the ESC and NPC RNAseq data were generated side-by-side.\n7. The preferential association of ribosomal protein genes with nucleoli is very surprising, considering their very high transcriptional activity. Could this be an artefact? Are they perhaps speckle-associated, and does NoLMseq pick up some speckle-associated DNA? Were the RP genes also detected by any of the other nucleolus-mapping methods? It may be useful to highlight these genes in the scatterplots suggested under point 4.\n8. The bi-versus-mono-allelic analysis 6c seems tricky to me. If the data are noisy (particularly due to a high rate of false-negatives), then obviously the chance of capturing bi-allelic interactions becomes small. One would at least expect that rDNA loci show a high bi-allelic frequency; is this indeed the case?\nMinor points:\n9. It is incorrect to state that NoLMseq maps nucleolus *interactions* (title), which would suggest molecular contacts. It maps spatial proximity.\n10. The lower NAD coverage in NPCs could be due to higher noise levels in the data. Autocorrelation analysis may help to assess this. Were the ESC and NPC data generated in parallel?\n11. Please comment on the success rate of the method. Which proportion of captured nucleoli passed the QC criteria of Extended Figure 1a? And of these, did they all yield useful sequencing information, or were some datasets discarded? How robust is the method from experiment to experiment?\n12. p6: \"The identified reads over the reference genome showed a bimodal distribution, indicating that loci are either in a “contact” or “no contact” state with nucleoli (Fig. 1c, Extended Data Fig. 1b)\": Figure 1c seems to show binarized data, and from Extended Data Fig 1b it is not obvious that this is a biomodal distribution. Please add a figure panel showing a histogram / density plot that directly demonstrates the bimodal distribution.\n13. Figure 2fgh: it does not make a lot of sense to do all these pairwise t-tests between different bins (because so many comparisons were done, it would require multiple-testing correction). The binning also may obscure fine patterns in the overall relationships. Please replace by scatterplots that directly show the correlations between CF and RPKM, and provide correlation coefficients and associated p-values.\n14. Figure 3b, right-hand panel: label at top should be \"non-rDNA chromosomes\"?\n15. Are the genes that detach from the nucleolus in NPCs preferentially relocated to the lamina? This could be easily analysed using available DamID data (e.g. Peric Hupkes et al, Mol Cell 2013).\n16. Figure 2a may be a case of circular reasoning? If the contact frequency is very high, then naturally the LAD coverage will be high too?", "reviewer_2": "The authors employ a laser microdissection tool to isolate mouse ES nucleoli for DNA sequencing, enhancing the accuracy of NAD (NoLMseq) identification. This method enables single-cell resolution of nucleolar-associated DNA, providing a more precise representation of associated DNA domains. The study offers new insights into the role of nucleoli in genome organization, particularly the spatial association of ribosome synthesis-related genes with nucleoli. Furthermore, nucleolar stress disrupts NAD organization, correlating with changes in gene expression. The discovery of two distinct NAD populations—one more transcriptionally active than the other—in mES cells is particularly interesting. Overall, this study introduces a more accurate approach to analyzing nucleolar-associated genome domains and uncovers novel insights into their functional significance, making it suitable for publication in this journal. The following are specific points for consideration:\n1) It would be insightful to include an analysis of NAD dynamics across the two identified populations during the transition from ES cells to NPCs, providing a deeper understanding of nucleoli and NADs organization during differentiation.\n2) The authors could further discuss the differences in cell cycle length and ribosome biogenesis between NPCs and ES cells examined in this study, particularly in relation to potential alterations in NAD organization.\n3) A elaborated comparison of NADs identified in ES and NPC cells using the improved method with those reported in the authors’ 2022 paper would strengthen the discussion.", "reviewer_3": "The genome organization around nucleoli and its functional implications remains a crucial question in nuclear architecture. Several methods, including sequencing of chromatin associated with purified nucleoli, nucleolar-DamID, and TSA-Seq, have been employed to map nucleolus-associated domains (NADs). In this study, Walavalkar et al. developed NoLMseq, a new method that employs laser-capture microdissection to directly isolate the chromatin near nucleoli for mapping NADs at single-cell resolution. This approach offers a unique advantage in that it does not rely on direct molecular interactions between nucleolar components and chromatin, making it well-suited to study NADs under stress conditions, where nucleolar composition and properties may change. The authors show that the NADs identified by NoLMseq are consistent with previous studies, but also reveal a significant degree of heterogeneity at the single-cell level. Furthermore, they demonstrate that NADs undergo dynamic changes during ES cell differentiation and upon nucleolar stress, with the detachment of genomic regions from nucleoli being associated with gene expression changes. Overall, these data suggest that NoLMseq is a valid method for mapping NADs and may provide information that is unattainable using other methods.\nWhile the study is interesting from a methodological standpoint, my main concern is that it provides limited novel biological insights at the current stage. The observation that a large fraction of NADs detach from nucleoli during the differentiation of ESCs into NPCs or upon nucleolar disruption has been reported previously (Bersaglieri et al. 2022, Vertii et al. 2019). Although the authors correlate NAD detachment with gene activation through bulk RNA-Seq, the direct role of nucleolar association in gene regulation remains unproven. One of the key advantages of NoLMseq is its ability to resolve NAD heterogeneity at the single-cell level. The authors uncover several intriguing findings, such as the classification of ESCs into two populations based on their NAD organization and the observation that most NADs associate with nucleoli in a monoallelic manner. However, the functional implications of these findings are not explored. These and other suggestions are discussed in greater detail below.\n1. Lines 121-125: It is common for a single ES cell nucleus to harbor multiple nucleoli. Did the authors dissect only one nucleolus from each nucleus, or did they dissect all nucleoli from the same nucleus and combine them in one tube for later analysis? While this may not affect NAD identification, it could influence interpretations of NAD heterogeneity across cells. For instance, when the authors classify ESCs into two populations based on nucleolar contacts, could this be due to the capture of different nucleoli rather than true biological variability?\n2. Lines 182-185: The authors found that the NAD/LAD (Type-2) regions exhibit overall lower CF values compared to NAD-only (Type-1) regions and conclude that “NAD/LAD domains rarely contact both nucleolus and NL in the same cell”. I find this logic unclear. It seems plausible that Type-2 regions represent a different type of heterochromatin than Type-1, and therefore associate with the nucleoli at a lower frequency.\n3. The observation that ESCs can be classified into two populations, with chromosomes 12 and 19 preferentially associated with nucleoli in one group and chromosomes 16 and 18 in the other, is compelling. I suggest the authors conduct DNA-FISH to further validate this result.\n4. Related to the last point, what are the functional implications of these distinct patterns of NAD organization in different ESCs? The authors should explore the relationship between NAD heterogeneity and gene expression by performing RNA-FISH. Additionally, the authors could analyze available scRNA-Seq data to determine whether gene expression from NADs in different ESCs can also be categorized into two populations.\n5. Lines 226-228: The authors observe that genes within domains that detach from nucleoli upon ESC differentiation to NPCs (ESCsp-NADs) are more highly expressed in NPCs, leading them to conclude that detachment facilitates gene activation. However, the authors should include appropriate controls for the analysis in Figure 4f-i, such as genes within common NADs, NPCsp-NADs, and non-NADs within Compartment A, to support this conclusion. Ideally, RNA-FISH should also be performed to validate this finding.\n6. While this study demonstrates that the ESCsp-NADs genes are activated in NPCs, a previous study from the same group (Bersaglieri et al. 2022) stated that the ESCsp-NADs genes do not exhibit significant gene expression changes and that the detachment from nucleoli unlocks these genes for activation in later stages. What is the reason for this discrepancy? The NoLMseq method likely revealed the real changes in NAD organization. However, the authors should discuss the differences between the two studies in more detail.\n7. In Figures 5h and 5i, the authors show that genes within NADs that detach upon ActD treatment are enriched in upregulated genes versus downregulated genes. While this finding is intriguing, I recommend that the authors compare the fraction of upregulated, downregulated, and unchanged genes across three groups: detached NADs, NADs that remain attached, and non-NAD regions. Statistical analyses, such as the Chi-square test, should be performed to help assess the significance of these observations.\n8. Lines 277 to 279: The authors stated, “The detachment of genes from nucleoli, when nucleolar integrity is compromised, leads to their transcriptional activation”. I think this is an overstatement, as the authors have not conducted experiments to prove causality. Additionally, even within detached NADs, while 906 genes show upregulation, 705 genes are downregulated (Figure 5i). Thus, the effect of nucleolar association on gene expression appears to be facilitative and context-dependent. The authors should also exercise caution when making similar claims in the discussion.\n9. The finding that most NADs associate with nucleoli in a monoallelic manner is also intriguing. As with my previous comment (point 4), I suggest the authors either perform RNA-FISH or analyze available scRNA-Seq data to investigate how the allelic association with nucleoli affects gene expression."}, "author_response": "We thank all the Reviewer’s for the positive and constructive comments. In this revised manuscript, we have provided additional data addressing all the Reviewer’s requests and modified the text to clarify points that were not sufficiently clear. Furthermore, to more clearly differentiate the validation results of NoLMseq from the novel discoveries revealed by this method, we have reorganized the figures order by swapping Figures 3 and 4 and modified the text accordingly. The additional data strengthen the conclusions of our work.\nList of changes\nModifications in the text were highlighted in red.\nThe new data are listed here below.\nRevised Previous Description Reviewer\nmanuscript version\nQuantification and statistical analysis of up- and\nESC+ActD.\nInclusion of CF of randomized sequences for 53\nnucleoli\nScatterplot of SeqFISH+ and NoLMseq CF with\nribosomal protein genes\nScatterplot of histone PTM and gene expression levels\nvs nucleolar CF\nComparisons of gene expression levels between\nNPCs\nrDNA\nAutocorrelation decay patterns using randomized ESC\nRev. 1\nand NPC datasets\nthe genomic regions that are proximal to nucleoli. The method is clever and conceptually simple. While\nseveral other mapping methods have been reported to map nucleolus-genome proximity/interactions,\nNoLMseq stands out because it is a single-cell method. Setting this up is an impressive accomplishment.\nThe only single-cell method that comes close is seqFISH+, but this seems to have a current resolution of\n~1Mb, compared to ~100kb for NoLMSeq. Both the text and the figures are generally clear. That said, in\nmy view substantially more evidence is necessary to document the quality of the data. See\nbelow. Because this is a new mapping technology, the results are largely descriptive and not particularly\nsurprising considering what the previous nucleolus-mapping methods had already revealed. Nevertheless,\nthe data still uncover some interesting findings, such as changes upon ActD treatment, proximity of\nribosomal protein genes, and potential mono/bi-allelic proximity to nucleoli.\nAuthor. We thank the Reviewer for the appreciation of our study and the constructive input for the\ncomputational and statistical analyses of our data. Below are clarifications and additional analyses that\ndemonstrate the specificity and quality of our data.\nWe would like to clarify that the 'non particularly surprising' results presented in the initial part of this work\nserved for the validation of this first single cell/nucleolus method to identify NADs, as recognized by the\nReviewer. As explicitly stated in the manuscript, in the initial part of this work, we used established\nknowledge to validate NoLMseq. This includes the expected frequent association of rDNA chromosomes\nwith nucleoli, the known nucleolar association of centromeric proximal sequences, the known repressive\nchromatin signature of NADs, and the known NAD reorganization during ESC differentiation into NPCs.\nThese results are not a limitation of this study. Instead, they demonstrate the validity of NoLMseq. The\nsurprising and “interesting findings” are shown after the validation part of this manuscript. These results\ninclude (1) the presence of ribosomal protein genes at nucleoli, (2) genome remodelling upon ActD\ntreatment, (3) the mono-allelic features of NADs, (4) the presence of two ESC populations with distinct NAD\nprofiles and chromatin states. These important results could only be obtained by a single cell technology\nlike the NoLMseq and could not be captured by any other existing technologies. To clearly separate the\nvalidation experiments from the exploratory application of NoLMseq, we reorganized the order of the figures\nand modified the text accordingly.\nMajor points:\n1. Figure 1b shows only one example of a laser capture \"hole\". Please provide a more extensive set of\nimages to document how this was done. A potential limitation of the NoLMseq method would be that it may\nbe only feasible in cells with one large nucleolus, while many cell types have multiple smaller nucleoli (including some cells shown in figure 1b, and in figure 6d). Could this create a bias in the current study? Or is it possible to\ncut two nucleoli, however, we did not include this in our analysis since the large majority of ESCs have one\nlarge nucleolus.\n2. The single-cell NAD patterns shown in figure 1c seem almost random, with the exception of the higher\ndensity on a subset of chr19 . Similar for Extended Data figure 1b. This is very different from single-cell\nlamina interactions, which, albeit stochastic, show clearly recurrent domain patterns (see papers by Jop\nKind). Likewise, it is difficult to tell whether the contact frequencies in Extended figure 2 are very different\nfrom random. The low signals of the X chromosome and a slight enrichment on chromosomes with rDNA\nare encouraging, but overall the patterns seem much noisier than previously reported by other methods -\nalthough these methods also may suffer from some limitations. I am missing a critical assessment of this.\nIt is natural that single-cell data are noisy, but it is important that the reader gets a good sense of this. Below\nI suggest some scatterplots that could offer some more insights into the noise levels.\nAuthor. To assess whether the observed patterns arise from noise, we generated 53 “random samples”\nensuring that each had genome coverage comparable to our single-nucleolus data (15%, Fig. 1f). In these\nrandom datasets, less than 0.5% of the genome exhibited contact frequencies above 30%, whereas in ESC\nNADs more than 10% of the genome exceeded this (new Extended Data Fig. 1c). Additionally, in the\nrandom datasets, there is almost no genomic region at 0% contact frequency, whereas in our single-\nnucleolus data, nearly 10% of the genome was absent from all 53 single-nucleolus datasets. This suggests\nthat specific regions are preferentially associated with or excluded from nucleolar interactions, rather than\nthe data being dominated by noise. Furthermore, when analysing the distribution of rDNA-chromosomes,\nnon-rDNA chromosomes, and sex chromosomes in the random datasets, we found that their proportions\nremained similar across all contact frequencies, and as expected reflect their genomic sizes (new\nExtended Data Fig. 1c). This is in stark contrast to our experimental data, where rDNA-chromosome\nproportions increase with contact frequency, while proportions of X and Y chromosome decrease (Fig. 1d).\nFinally, in the new Extended Data Fig. 2, we included the contact frequencies over the chromosome from\nthe random samples. If the experimental data were entirely random, we would expect relatively flat curves\nas seen when we plot contact frequencies over chromosome lengths from random samples. Instead, our\nresults reveal clear enrichments and depletions, reinforcing the notion that the observed patterns are non-\nrandom. Finally, it is important to clarify that these NAD profiles were validated using orthogonal single cell\napproaches, such the comparison of CF between high-throughput DNA-FISH analyses (seqFISH+) and\nNoLMseq CF which show a high correlation (Fig. 1i). Taken together, these analyses indicate that our\nsingle-cell NAD data, while inherently stochastic, exhibit structured and reproducible patterns that\ndistinguish them from random noise.\n3. As mentioned by the authors, one possible concern with NoLMseq is that (parts of) chromosomes above\nand below the nucleolus may also be captured, thus contaminating the data. It would be useful to have a\n4\nquantitative estimate of this contamination. How much of the total nuclear volume is above and below the\nnucleolus? This could be estimated by confocal or super-resolution microscopy. And what do the single-\ncell maps look like for a set of control captures (capturing a similar sized nuclear region outside the\nnucleolus)?\nAuthor. We performed confocal microscopy followed by 3D-reconstruction, which revealed that, on\naverage, 66% of the microdissected cylindrical volume corresponds to the non-nucleolar region (i.e.,\nnucleus + cytoplasm). This information has been included in the Method section. Importantly, since NADs\ntypically surround the nucleolus, it is expected that a portion of this non-nucleolar space contains NADs.\nWe think that the sequencing of control captures will not be informative as these would have random DNA\nbut would still have some NADs due to their monoallelic nature. Moreover, since the nucleolus is mostly\ncentral in ESCs and ESCs are spherical, a similar sized 2D cut will lead to the capture of lesser genomic\nmaterial and adjusting for it further complicates any realistic comparison to the nucleolar microdissection.\nInstead, simulated random collection of similar genomic contents as the nucleolus as described above\naddresses these concerns better.\n4. The overlap with SPRITE and nucleolar DamID (Figure 1h) seems improbably high. It is unclear how this\nanalysis was done; how is overlap defined? Much better (and more transparent) would be to plot NoLMseq\ncontact frequency NoLMseq versus nucleolus interaction scores according to the other two assays in\nconventional scatter plots (so continuous rather than binarized data). What do these plots look like, and\nwhat is the correlation coefficient?\nAuthor. We apologize to not have clearly explained this data. We calculated the proportion of NADs\nidentified by NoLMseq that were defined as NADs by Nucleolar-DamID (Bersaglieri et al. 2022) or genomic\ndomains interacting with rRNA as defined by SPRITE (Quidonoz et al., 2018). We modified the text to\nincrease clarity.\nWe believe that plotting NoLMseq CF values together with Nucleolar-DamID scores from cell populations\nis not informative. The reason is that the score value in Nucleolar-DamID is the enrichment of Dam\nmethylation in ESCs expressing the nucleolar histone H2B fused to Dam relative to ESCs expressing H2B-\nDam. Thus, both Dam-fused proteins are very similar to each other since they are histones (i.e., they both\nbind DNA, but H2B-Dam-NoLS binds more DNA at nucleoli than H2B-Dam), explaining why the score\nvalues of the identified NADs, while statistically significant and reproducible, are not as high and are\ngenerally similar to each other. We do not doubt this DamID method since the identification of NADs has\nbeen validated with several orthogonal approaches like multiple DNA-FISH analyses and HiC-rDNA. Thus,\nNucleolar-DamID is more a qualitative method as it cannot clearly determine if a NAD sequence is more\nfrequently located at the nucleolus than another one as in the case of NoLMseq. This is different from the\nLAD scores since in LaminB1-DamID in cell population values are calculated relative to control cells\nexpressing GFP-Dam, a protein completely different from LMNB1, which has also a certain preference for\nactive chromatin. Finally, the data of Fig. 1h, showing that NADs identified by NoLMseq are similar to the\nNADs identified by previous methods in cell populations, is not a major result of this work as it represents\none of the many validations presented in the manuscript (Figs. 1-3).\n5. I find the claim of 2 populations (Figure 3) not entirely compelling. Hierarchical clustering by definition\nyields two populations at the highest bifurcation, so even random data would generate two populations; it\nmay be good to randomize/bootstrap the data 1,000 times and see if the mild enrichment of some (but not\nall) histone marks are true, or just random fluctuations. Furthermore, the two populations remain enigmatic:\nwhat is their biological meaning? What two different states of the cells may it reflect?\nAuthor. We want to thank the Reviewer for this important suggestion. As requested, to assess whether the\ndifferences in histone modifications arise from random fluctuations, we conducted a bootstrap analysis with\n1,000 random permutations of the NADs of populations 1 and 2 (Fig. 3d). We then quantified how many\ntimes the two populations obtained from these bootstrap randomisations showed similar observed\n5\nenrichments. In none of these iterations did we observe enrichment values for H3K9me2, H3K4me1, or\nH3K27ac that matched or exceeded those observed in the actual data, supporting the specificity of these\nenrichments. These results strongly support that the observed enrichments for H3K9me2, H3K4me1, and\nH3K27ac in the two populations do not result from random fluctuations. We have discussed these data in\nthe revised manuscript and included empirical and observed p-values in the new Extended data Fig. 5a.\nWe performed GO and pathway analyses showing that the specific NAD-genes of population 2 have\nsignificant enrichments in pathways linked to development and pluripotency whereas NAD-genes of\npopulation 1 are linked to metabolism and signalling pathways (new Supplementary Table 2). Thus, the\npresence of these two major populations could reflect the well-documented heterogeneity of ESCs cultured\nin serum, the conditions used in this study. ESCs are characterized by high or low expression of the\npluripotency marker Nanog, which reflect distinct developmental potentials. To test this, we performed\nscRNAseq. Nanog-high and Nanog-low ESCs can be visualized in the UMAP of the new Fig. 4e. We then\nexamined the top 5 genes with the highest nucleolar interactions with specifically one of the two populations\n(i.e., genes of population 1 that have CF >30% in population 1 and <5% CF in population 2 and vice versa\nfor genes of population 2). We found that 4 out of 5 genes of population 2 show a similar expression profiles\nof Nanog-high cells and Sox2 positive ESCs while only 1 gene of population 1 shows such a pattern (new\nFig. 4f, Extended data Fig. 5c,d). Thus, populations 1 and 2 might reflect populations with distinct\ndevelopmental potential. Thus, the data revealed structural heterogeneity in the ESC genome, resulting in\ntwo major populations distinguished by their genome organization around nucleoli, each characterized by\ndistinct chromatin states and eventually different developmental potentials.\n6. Section starting on line 212: \"Detachment of NADs upon differentiation into NPCs results in gene\nactivation\" incorrectly claims a causal relationship. It is only a correlation. The final sentence of this section\nalso incorrectly claims a causal relationship. Moreover, the differences in gene activity shown in Figure 4f\nare tiny (0.1 log2 unit ~ 15%). I am puzzled about figure 4g: usually in such fish-tail distributions, genes with\nlower expression are less reliable and hence show fewer significant changes. But in this plot there are many\nblue and red dots in this unreliable range. The Methods do not specify how this analysis was done. If it was\nnot done so, I strongly recommend to use DESeq2 or similar state-of-the-art package to do this RNAseq\nanalysis. Please also specify the number of independent biological replicates (in the figure legend), and\nconfirm that the ESC and NPC RNAseq data were generated side-by-side.\nAuthor. We revised the text to eliminate any unintended implication of a causal relationship.\nAs stated in the paragraph “Published datasets”, the RNAseq of ESCs vs NPC was from our previous work\n(PMID: 35304483, GSE150822). We made this point clear in Result section. We confirm that we used\nDESeq2, each condition was in triplicate, and that ESC and NPC RNAseq data were generated side-by-\nside.\nFig. 5f showed the log (RPKM+1) for all the genes that are at NADs specifically in ESCs but not in NPCs\n10\n(ESCsp-NAD genes, up, down and unchanged). This means that the difference between all genes is >50%.\nThe ESCsp-NAD genes significantly affected are shown in Fig. 4g,h.\n7. The preferential association of ribosomal protein genes with nucleoli is very surprising, considering their\nvery high transcriptional activity. Could this be an artefact? Are they perhaps speckle-associated, and does\nNoLMseq pick up some speckle-associated DNA? Were the RP genes also detected by any of the other\nnucleolus-mapping methods? It may be useful to highlight these genes in the scatterplots suggested under\npoint 4.\nAuthor. As stated in the Discussion section (now lanes 398-399), our data are consistent with previous\nstudies based on imaging analyses in living or fixed yeast cells showing the proximity of ribosomal protein\ngenes and tRNA genes to nucleoli. As requested, we re-analysed the published Nucleolar-DamID and found\nthat 31% of ribosomal protein genes are at NADs and included this information in the revised manuscript.\nMoreover, we have included a scatter plot showing that the CFs of ribosomal protein genes with nucleoli\n6\nfrom seqFISH+ data highly correlate with the corresponding NoLMseq CF values (new Extended Data\nFig. 3b). Taken together, these data allow us to confidently exclude the possibility that the nucleolar\nassociation of ribosomal protein genes is an artifact.\n8. The bi-versus-mono-allelic analysis 6c seems tricky to me. If the data are noisy (particularly due to a high\nrate of false-negatives), then obviously the chance of capturing bi-allelic interactions becomes small. One\nwould at least expect that rDNA loci show a high bi-allelic frequency; is this indeed the case?\nAuthor. We analysed the frequency of bi-allelic NADs on rDNA- and non-rDNA chromosomes and found\nthat rDNA-chromosomes have about 10-fold higher proportion of biallelic NADs compared to non-rDNA\nchromosomes. We included this information in the revised manuscript (new Extended Data Fig. 7c). We\ncan not analyse the rDNA loci since these are repetitive sequences and were not mapped for the SNVs.\nWe would like to strength that several data supported the mono-allelicity of NADs like DNA-FISH (Fig. 6d)\nand the higher CF of biallelic NADs compared to monoallelic NADs (Extended data Fig. 7d). Furthermore,\nanalyses of histone modifications and gene expression between mono- and bi-allelic NADs indicate that bi-\nallelic NADs are in a more repressed chromatin and gene expression states (Figs. 6f,g). As described in\nthe responses above, the false-negative rates do not seem to be leading to such specific differences\nbetween mono- and biallelic NADs. These findings collectively support our observations and argue against\nnoise as the primary driver of bi- versus mono-allelic NAD patterns.\nMinor points:\n9. It is incorrect to state that NoLMseq maps nucleolus *interactions* (title), which would suggest molecular\ncontacts. It maps spatial proximity.\nAuthor. We agree. Accordingly, the term “interactions” was not used in the rest of the manuscript. However,\nwe think that for a title the term “interactions” would be much clearer for a broad readership as done in the\npast for many LADs papers.\n10. The lower NAD coverage in NPCs could be due to higher noise levels in the data. Autocorrelation\nanalysis may help to assess this. Were the ESC and NPC data generated in parallel?\nAuthor. The reduction of contacts with nucleoli in NPCs compared to ESCs is consistent with previous work\nusing Nucleolar-DamID and rDNA-HiC (PMID: 35304483).\nWe performed the autocorrelation analysis as suggested by the Reviewer. The autocorrelation and decay\nbetween ESCs and NPCs seem very comparable, suggesting similar levels of signal over noise ratios\n(SNRs). We included this data in the revised manuscript (new Extended data Fig. 4a).\nTo assess whether the observed autocorrelation decay patterns were distinct from random noise, we\nperformed the same analysis using randomized ESC and NPC datasets while preserving their respective\ngenome coverage in single cells (see image below). As expected, the randomized samples exhibited a\ncompletely random decay pattern, confirming that the autocorrelation observed in the actual data reflects\nbiologically relevant nucleolar associations rather than noise.\nNPCs were generated in parallel to ESCs but were dissected and thus processed at different times. This is\nalso true for samples themselves. No batch effects were seen from any of the sequencing runs.\n7\n11. Please comment on the success rate of the method. Which proportion of captured nucleoli passed the\nQC criteria of Extended Figure 1a? And of these, did they all yield useful sequencing information, or were\nsome datasets discarded? How robust is the method from experiment to experiment?\nAuthor. For ESCs, the success rate of the captured nucleoli to pass the QC criteria was 95% and of these\nabout 90% yielded useful sequencing information. The method is very robust among different experiments.\nSamples with little to no alignment to the mm10 genome after sequencing were discarded.\n12. p6: \"The identified reads over the reference genome showed a bimodal distribution, indicating that loci\nare either in a “contact” or “no contact” state with nucleoli (Fig. 1c, Extended Data Fig. 1b)\": Figure 1c\nseems to show binarized data, and from Extended Data Fig 1b it is not obvious that this is a biomodal\ndistribution. Please add a figure panel showing a histogram / density plot that directly demonstrates the\nbimodal distribution.\nAuthor. We apologise for the confusion. The term “bimodal distribution” was not used as a statistical\nmeaning. It was a way to describe that the data show that loci are either in a “contact” or “no contact” state\nwith nucleoli. We modified the text to avoid confusion.\nIn Fig. 1b, each black bar denotes that the region contacts the nucleolus and the white region indicates no\ncontact. We clarified this in the corresponding figure legend. Extended Data Fig. 1b shows how the data\nlook like after alignment to the mm10 genome. There are some regions that contain reads and some other\nregions, and sometime an entire chromosome, that do not.\n13. Figure 2fgh: it does not make a lot of sense to do all these pairwise t-tests between different bins\n(because so many comparisons were done, it would require multiple-testing correction). The binning also\nmay obscure fine patterns in the overall relationships. Please replace by scatterplots that directly show the\ncorrelations between CF and RPKM, and provide correlation coefficients and associated p-values.\nAuthor. According to the request of the Reviewer, we calculated again the statistical significance (Q-values)\nusing multiple-testing correction by the Benjamini and Hochberg method. The results remain the same. We\nhave included these data in the revised manuscript.\nWe have also provided scatterplots. However, we decided to place them in the Extended data Figs. 3c-e\nsince they are less informative than the boxplots showed in Figs. 2f-h. Indeed, the boxplot representation\nof the data allows comparisons with genomic domains within the A compartment, the NAD-only, and\nNAD/LAD regions.\n14. Figure 3b, right-hand panel: label at top should be \"non-rDNA chromosomes\"?\nAuthor. Thank you! We corrected it.\n15. Are the genes that detach from the nucleolus in NPCs preferentially relocated to the lamina? This could\nbe easily analysed using available DamID data (e.g. Peric Hupkes et al, Mol Cell 2013).\nAuthor. We performed this analysis using the LAD profile of Peric Hupkes et al. We found that 21.56% of\nthe NADs and 11.83% of the genes that detach from nucleoli relocate to the lamina in NPC. Notably, a large\nfraction of these genes (95%) do not show changes in gene expression between ESC and NPCs. We did\nnot include these data in the revised manuscript, as the analysis of NADs and LADs during ESC\ndifferentiation into NPCs was extensively addressed in our previous work using Nucleolar-DamID. In this\nstudy, this analysis served primarily to validate the application of NoLMseq in a cell type other than ESCs.\n16. Figure 2a may be a case of circular reasoning? If the contact frequency is very high, then naturally the\nLAD coverage will be high too?\nAuthor. We think the Reviewer means Fig. 2e.\nNAD/LAD regions. This makes sense, as NAD-only regions do not contact the nuclear lamina and therefore\ncan be found more often in nucleoli, whereas NAD/LAD regions can also be located at the nuclear lamina.\nThe important point here is that these measurements could only be achieved by single cell analyses.\nMoreover, these results further confirm that NoLMseq accurately identified NADs. We clarified this point in\nthe manuscript.\n===============Reviewed by Bas van Steensel, review task accepted: 19 Feb 2025; completed 24\nFeb 2025. It is my standard policy to sign and date *all* of my manuscript review reports, regardless of my\ncomments and recommendations. All correspondence about this manuscript should go via the editor.\nPLEASE DO NOT REMOVE THIS NOTE ===========\nThe authors employ a laser microdissection tool to isolate mouse ES nucleoli for DNA sequencing,\nenhancing the accuracy of NAD (NoLMseq) identification. This method enables single-cell resolution of\nnucleolar-associated DNA, providing a more precise representation of associated DNA domains. The study\noffers new insights into the role of nucleoli in genome organization, particularly the spatial association of\nribosome synthesis-related genes with nucleoli. Furthermore, nucleolar stress disrupts NAD organization,\ncorrelating with changes in gene expression. The discovery of two distinct NAD populations—one more\ntranscriptionally active than the other—in mES cells is particularly interesting. Overall, this study introduces\na more accurate approach to analyzing nucleolar-associated genome domains and uncovers novel insights\ninto their functional significance, making it suitable for publication in this journal.\nAuthor. We thank the Reviewer for the appreciation of our study.\nThe following are specific points for consideration:\n1) It would be insightful to include an analysis of NAD dynamics across the two identified populations during\nthe transition from ES cells to NPCs, providing a deeper understanding of nucleoli and NADs organization\nduring differentiation.\nAuthor. This is a very interesting question; however, we believe that NoLMseq is not suitable for this\nanalysis. The differentiation of ESCs into NPCs was performed using a previously established protocol\n(PMID:15332090) that yields a homogeneous Pax-6-positive NPC population after 8 days. However, the\nprocess itself involves a transiently heterogeneous cell population, with some cells differentiating faster\nthan others. NoLMseq is well suited for homogeneous cell populations with few or one single nucleoli, such\nas ESCs and NPCs. However, its application to heterogeneous populations would be extremely\nchallenging, as it would also require a substantial increase in the number of microdissected nucleoli (well\nover 50 for each time points) to distinguish subpopulations. We think the point raised by the Reviewer is\nvery important and we are currently developing methods to analyse NADs in heterogeneous biological\npopulations.\n2) The authors could further discuss the differences in cell cycle length and ribosome biogenesis between\nNPCs and ES cells examined in this study, particularly in relation to potential alterations in NAD\norganization.\nAuthor. These are very complex topics. To our knowledge, there are no comprehensive studies directly\ncomparing the cell cycle dynamics between ESCs and NPCs in the context of nucleolar organization. With\nregard to ribosome biogenesis, it is very well known that ESCs display high levels of rRNA transcription and\nelevated ribosome biogenesis compared to differentiated cells, which also accounts for their\ncharacteristically large nucleoli (Reviewed in PMID: 32976768). However, it remains unclear whether a\nhyperactive nucleolus is responsible for changes in NADs between ESCs and NPCs. This is primarily\nbecause the molecular mechanisms underlying the anchoring of chromatin to the nucleolus are still not fully\nunderstood. As discussed in the Result section, we believe that these differences mainly stem from changes\nin global genome organization and chromatin composition between ESCs and NPCs, with ESCs harbouring\na more open and dynamic chromatin than differentiated cells.\n3) A elaborated comparison of NADs identified in ES and NPC cells using the improved method with those\nreported in the authors’ 2022 paper would strengthen the discussion.\nAuthor. We thank the Reviewer for this valuable suggestion.\nThe NADs identified in ESCs and NPCs using NoLMseq provide a more quantitative assessment of\nnucleolar associations compared to our previous approach using Nucleolar-DamID (Bersaglieri et al.,\n2022). While nucleolar DamID allows to classify genomic regions broadly as associated or not associated\nwith the nucleolus, and these data were validated by multiple DNA-FISH analyses and rDNA-HiC, it lacked\n10\nthe sensitivity to quantify the degree of association. In contrast, NoLMseq enables the quantification of\nnucleolar CF and the use of stringent criteria to robustly define NADs, as in the case of ESC -NADs that\nsp\nwere defined as high-confidence NADs in ESCs that were consistently excluded from the nucleolus (>25%\nCF in ESCs, <10% CF in NPCs). Thus, this quantitative resolution provides a more nuanced view of\nnucleolar genome organization and enabled us to detect changes that were only partially captured with\nNucleolar-DamID. We have included this information in the Discussion of the revised manuscript.\nThe genome organization around nucleoli and its functional implications remains a crucial question in\nnuclear architecture. Several methods, including sequencing of chromatin associated with purified nucleoli,\nnucleolar-DamID, and TSA-Seq, have been employed to map nucleolus-associated domains (NADs). In\nthis study, Walavalkar et al. developed NoLMseq, a new method that employs laser-capture microdissection\nto directly isolate the chromatin near nucleoli for mapping NADs at single-cell resolution. This approach\noffers a unique advantage in that it does not rely on direct molecular interactions between nucleolar\ncomponents and chromatin, making it well-suited to study NADs under stress conditions, where nucleolar\ncomposition and properties may change. The authors show that the NADs identified by NoLMseq are\nconsistent with previous studies, but also reveal a significant degree of heterogeneity at the single-cell level.\nFurthermore, they demonstrate that NADs undergo dynamic changes during ES cell differentiation and\nupon nucleolar stress, with the detachment of genomic regions from nucleoli being associated with gene\nexpression changes. Overall, these data suggest that NoLMseq is a valid method for mapping NADs and\nmay provide information that is unattainable using other methods. While the study is interesting from a\nmethodological standpoint, my main concern is that it provides limited novel biological insights at the current\nstage. The observation that a large fraction of NADs detach from nucleoli during the differentiation of ESCs\ninto NPCs or upon nucleolar disruption has been reported previously (Bersaglieri et al. 2022, Vertii et al.\n2019). Although the authors correlate NAD detachment with gene activation through bulk RNA-Seq, the\ndirect role of nucleolar association in gene regulation remains unproven. One of the key advantages of\nNoLMseq is its ability to resolve NAD heterogeneity at the single-cell level. The authors uncover several\nintriguing findings, such as the classification of ESCs into two populations based on their NAD organization\nand the observation that most NADs associate with nucleoli in a monoallelic manner. However, the\nfunctional implications of these findings are not explored. These and other suggestions are discussed in\ngreater detail below.\nAuthor. We thank the Reviewer for the appreciation of our method.\nRegarding the “limited novel biological insights” of our study and, conversely, the uncovering of novel and\n“intriguing findings”, we would like to clarify the following points. As explicitly stated, in the first part of\nmanuscript, we validated the novel NoLMseq method using established knowledge, which naturally\ngenerated expected results. Thus, these results are not a limitation of this study. Instead, they demonstrated\nthe validity of NoLMseq, so far, the first and unique genomic method to detect NADs in single nucleoli. To\nclearly separate the validation experiments from the exploratory application of NoLMseq, we reorganized\nthe order of the figures and modified the text accordingly. The validation data of NoLMseq against\nestablished knowledge has been placed in Figs. 1-3, which also include some novel and intriguing data\nlike the nucleolar proximity of ribosomal protein genes, followed by Figs. 4-6 that uncovered novel and\nimportant results (i.e., genome remodelling upon ActD treatment, the mono-allelic features of NADs, the\npresence of two ESC populations with distinct NAD profiles and chromatin states, etc.). These are novel\nand important results that could only be obtained by a single cell technology like the NoLMseq and could\nnot be captured by any other existing technologies.\nRegarding the comment ”that a large fraction of NADs detach from nucleoli during the differentiation of\nESCs into NPCs or upon nucleolar disruption has been reported previously by Bersaglieri et al. 2022 and\nVertii et al. 2019 ”, we would like to clarify two important points. The ESC to NPC differentiation previously\nanalysed by Nucleolar-DamID in cell populations by Bersaglieri et al. was used to validate NoLMseq in\nanother cell type than ESCs. We clarified this point in the revised manuscript. Moreover, this analysis added\nnew information about the gene expression state upon detachment from nucleoli that was not reported\npreviously. Second, our work is the first to demonstrate at both the genomic and single-cell levels that\nconditions disrupting nucleolar integrity such as the treatment with ActD causes NAD detachment from the\nnucleolus. Verti et al did not provide genomic NAD data upon nucleolar disruption, an analysis that so far\nonly NoLMseq could perform. Verti et al. presented only three DNA-FISH results, one of which was not\n12\nstatistically significant, from cells treated with hexanediol, without data showing that the nucleolus was\naffected/disrupted by this unusual condition to study nucleolus integrity.\nRegarding the comment that “The direct role of nucleolar association in gene regulation remains unproven”,\nwe would like to clarify that this was not the aim of this study. To prove this, it requires the use of other\ntechnologies, which we are currently establishing in our lab. Any unintended implication of a causal\nrelationship has been corrected in this revised manuscript. However, we cannot disregard the data showing\nthe high correlation between the derepression of genes at NADs upon disruption of nucleolar integrity and\nduring NPC differentiation as well as the observation that biallelic NADs are in a more repressive state than\nmonoallelic ones.\nWe respectfully disagree with the comment that “the functional implications of these findings are not\nexplored ”as the data of ESC to NPC differentiation and the disruption of nucleolar integrity have already\nprovided functional significance (activated NAD-genes in NPC are linked to neural pathway and the ones\nactivated upon treatment with ActD are implicated in p53 pathways). In this manuscript, we included\nscRNAseq data of ESCs showing that the two ESC populations not only differ in NAD profiles and chromatin\nfeatures but also they might represent two distinct developmental states.\n1. Lines 121-125: It is common for a single ES cell nucleus to harbour multiple nucleoli. Did the authors\ndissect only one nucleolus from each nucleus, or did they dissect all nucleoli from the same nucleus and\ncombine them in one tube for later analysis? While this may not affect NAD identification, it could influence\ninterpretations of NAD heterogeneity across cells. For instance, when the authors classify ESCs into two\npopulations based on nucleolar contacts, could this be due to the capture of different nucleoli rather than\ntrue biological variability?\nAuthor. We apologize to not have provided more details on the nucleolus structure of mouse ESCs. The\nlarge majority of mouse ESCs display one single nucleoli (see image below and also new Fig. 1b). During\nthe microdissection, we only selected cells with one nucleolus cells (Fig. 1b). We clarified this point in the\nrevised manuscript.\n2. Lines 182-185: The authors found that the NAD/LAD (Type-2) regions exhibit overall lower CF values\ncompared to NAD-only (Type-1) regions and conclude that “NAD/LAD domains rarely contact both\nnucleolus and NL in the same cell”. I find this logic unclear. It seems plausible that Type-2 regions represent\na different type of heterochromatin than Type-1, and therefore associate with the nucleoli at a lower\nfrequency.\nAuthor. Our conclusions perfectly match with the ones of the Reviewer. The data in Fig. 2e simply showed\nthat NAD-only regions are more frequently found in microdissected nucleoli than NAD/LAD regions. This\nmakes sense, as NAD-only regions do not contact the nuclear lamina and therefore can be found more\noften in nucleoli, whereas NAD/LAD regions can also be located at the nuclear lamina. Because of this, we\nsuggested that NAD/LAD might contact the nucleolus in one cell and the nuclear lamina in another cell. We\nmodified the text to avoid confusion on this point. The important point here is that these measurements\ncould only be achieved by single cell analyses. Moreover, these results further confirm that NoLMseq\naccurately identified NADs. We clarified this point in the manuscript.\n13\n3. The observation that ESCs can be classified into two populations, with chromosomes 12 and 19\npreferentially associated with nucleoli in one group and chromosomes 16 and 18 in the other, is compelling.\nI suggest the authors conduct DNA-FISH to further validate this result.\nAuthor. We have included additional computational data following the request of Reviewer 1 (point 5) that\nsupport the conclusion of our work. We performed bootstrap analysis with 1,000 random permutations of\nthe data and confirmed that these two cell populations do not result from random fluctuations but they\nrepresent two populations with distinct chromatin features (Extended Data Fig. 5a).\nTo further validate the data and provide biological and functional significance of this finding, we performed\nscRNAseq of ESCs, as also suggested by the Reviewer in point 4. The ESC used in our study were cultured\nin the presence of serum. These conditions are well known to generate a certain heterogeneity. In\nparticularly, the expression of the pluripotency factor Nanog is heterogeneous in ESCs, giving rise to two\nmajor populations with distinct developmental potential: Nanog-high and Nanog-low. These two populations\ncan be clearly visualized in the UMAP shown in new Fig. 4e. Notably, 4 out of 5 top genes with the highest\nnucleolar interactions and that were specific for population 2 show a similar expression profiles of Nanog-\nhigh cells and Sox2 positive ESCs while only 1 gene of population 1 show a similar profile (new Fig. 4f,\nExtended Data Figs. 5b,c). Notably, GO and pathway analyses showed that the specific NAD-genes of\npopulation 2 have significant enrichments in pathways linked to development and pluripotency whereas\nNAD-genes of population 1 are linked to metabolism and signalling pathways (new Supplementary Table\n2). Thus, the two populations that were identified for a distinct NAD profile might represent two distinct\ndevelopmental states of ESCs. The data together revealed structural heterogeneity in the ESC genome,\nresulting in two major populations distinguished by their genome organization around nucleoli, each\ncharacterized by distinct chromatin states and eventually different developmental states.\nFor the validation by DNA-FISH suggested by the Reviewer, we will require the simultaneous use of four\ndistinct probes, each labelled with different colours. 2 probes for population 1 to demonstrate that these two\nNADs contact nucleoli in the same cells and 2 probes for population 2 to show that they contact nucleoli in\nthe other fraction of cells. However, such a complex experiment is not feasible for us. Accordingly, all\nscLamin-DamID studies published so far have not shown validation by DNA-FISH. We believe the\nadditional computational and experimental data we provided in this revised manuscript sufficiently validate\nthe findings obtained through NoLMseq\n4. Related to the last point, what are the functional implications of these distinct patterns of NAD\norganization in different ESCs? The authors should explore the relationship between NAD heterogeneity\nand gene expression by performing RNA-FISH. Additionally, the authors could analyze available scRNA-\nSeq data to determine whether gene expression from NADs in different ESCs can also be categorized into\ntwo populations.\nAuthor. As described above, we performed the scRNAseq showing that the two populations might reflect\ntwo different developmental potentials.\nRNA-FISH for nascent transcripts (targeting of introns) are usually performed for highly transcribed genes\nor reporter constructs whose signal can be easily detected. NAD genes are generally low expressed. This\nis also evident in the image below comparing Nanog and Sox2 expression with the other NAD genes of\npopulations 1 and 2. We have looked very carefully in the literature and no RNA-FISH has been performed\nfor genes at LADs. Therefore, we believe that RNA-FISH of NAD genes may not be feasible, as the low\nexpression of NADs is unlikely to produce a detectable signal. Moreover, we think that addition of new\ncomputational (i.e., bootstrap analysis with 1,000 random permutations) and experimental data (i.e.\nscRNAseq) have clearly demonstrated the presence of these two ESC subpopulations and their potential\nfunctional significance.\n14\n5. Lines 226-228: The authors observe that genes within domains that detach from nucleoli upon ESC\ndifferentiation to NPCs (ESCsp-NADs) are more highly expressed in NPCs, leading them to conclude that\ndetachment facilitates gene activation. However, the authors should include appropriate controls for the\nanalysis in Figure 4f-i, such as genes within common NADs, NPCsp-NADs, and non-NADs within\nCompartment A, to support this conclusion. Ideally, RNA-FISH should also be performed to validate this\nfinding.\nAuthor. We have included this analysis in the revised manuscript (new Extended data Fig. 4b). The data\nconfirmed the previous results by showing that ESC -NADs are more expressed in NPCs. Genes at NPC -\nsp sp\nNADs are very few (29) and showed a certain tendency to be more expressed in ESCs than in NPCs,\nalthough this difference was not significant. Genes at common NADs show only minor changes between\nESCs and NPCs as well as non-NADs within A compartment.\n6. While this study demonstrates that the ESCsp-NADs genes are activated in NPCs, a previous study from\nthe same group (Bersaglieri et al. 2022) stated that the ESCsp-NADs genes do not exhibit significant gene\nexpression changes and that the detachment from nucleoli unlocks these genes for activation in later\nstages. What is the reason for this discrepancy? The NoLMseq method likely revealed the real changes in\nNAD organization. However, the authors should discuss the differences between the two studies in more\ndetail.\nAuthor. This important point was introduced in the Discussion section (now lanes 374-376). “The accuracy\nof NoLMseq in detecting NADs relative to cell population studies is also shown by the identification of\nESC -NADs that contain genes activated in NPCs, which could not be detected in previous cell population\nsp\nstudies 19. We extended this part by explaining the major difference between the two methods.\nThe NADs identified in ESCs and NPCs using NoLMseq provide a more quantitative assessment of\nnucleolar associations compared to our previous approach using Nucleolar-DamID (Bersaglieri et al.,\n2022). While nucleolar DamID allows to classify genomic regions broadly as associated or not associated\nwith the nucleolus, and these data were validated by DNA-FISH and rDNA-HiC, it lacked the sensitivity to\nquantify the degree of association. In contrast, NoLMseq enables the quantification of nucleolar CF and the\nuse of stringent criteria to robustly define NADs, as in the case of ESC -NADs, defined as high-confidence\nsp\nNADs in ESCs that were consistently excluded from the nucleolus (>25% CF in ESCs, <10% CF in NPCs).\nThus, this quantitative resolution provides a more nuanced view of nucleolar genome organization and\nenabled us to detect changes that were only partially captured with Nucleolar-DamID. We included this\ninformation in the Discussion of the revised manuscript.\n15\n7. In Figures 5h and 5i, the authors show that genes within NADs that detach upon ActD treatment are\nenriched in upregulated genes versus downregulated genes. While this finding is intriguing, I recommend\nthat the authors compare the fraction of upregulated, downregulated, and unchanged genes across three\ngroups: detached NADs, NADs that remain attached, and non-NAD regions. Statistical analyses, such as\nthe Chi-square test, should be performed to help assess the significance of these observations.\n8. Lines 277 to 279: The authors stated, “The detachment of genes from nucleoli, when nucleolar integrity is\ncompromised, leads to their transcriptional activation”. I think this is an overstatement, as the authors\nhave not conducted experiments to prove causality. Additionally, even within detached NADs, while 906\ngenes show upregulation, 705 genes are downregulated (Figure 5i). Thus, the e(cid:431)ect of nucleolar association on\ngene expression appears to be facilitative and context-dependent. The authors should also exercise caution\nwhen making similar claims in the discussion.\nAuthor. We revised the text to eliminate any unintended implication of a causal relationship.\nWe would like to thank the Reviewer for the insightful question, which prompted us to reanalyse the data\nusing other criteria, which this time aim to determine how many genes at NADs are significantly regulated\nby ActD treatment, rather than how many regulated genes are located at NADs, as shown in the previous\nversion. We believe this new analysis provides a more precise measurement and more directly addresses\nthe question of how NAD-associated genes are regulated following ActD treatment. First, we analysed\ngenes located in high confident NAD-only regions (CF > 30%) in ESCs and found that 17% of these NAD-\ngenes (507) were upregulated upon ActD treatment whereas only 9% (260) were downregulated (new\nFig.5i). In contrast, genes in non-NADs (CF < 20%) showed similar number of up- and downregulated\ngenes upon ActD treatment. These results suggest that the loss of nucleolar integrity might play a significant\nrole in the derepression of NAD-genes. Moreover, we also found that about half of the upregulated NAD-\ngenes remain in contact with nucleoli in ESC+ActD and, accordingly, the majority of them (71%) were\nlocated at rDNA-chromosomes. These results suggest that the nucleolus may also lose its repressive\nproperties under these nucleolar stress conditions, although further investigation is required to fully\nunderstand these properties, but this goes outside the aim of this work.\nIt is important to note that not all genes at NADs are expected to be reactivated within 24 hours of ActD\ntreatment, as also key transcription factors may still be absent. Conversely, the downregulated genes may\nrepresent a subset that requires nucleolar association for their activity, which is consistent with our results\nshowing the presence of some actively transcribed genes at the nucleolus. However, this investigation is\nout of the scope of this work and will be fully addressed in our future studies.\n9. The finding that most NADs associate with nucleoli in a monoallelic manner is also intriguing. As with my\nprevious comment (point 4), I suggest the authors either perform RNA-FISH or analyze available scRNA-\nSeq data to investigate how the allelic association with nucleoli a(cid:431)ects gene expression.\nAuthor. We share the Reviewer’s interest in the relationship between allelic nucleolar association and gene\nexpression. We would like to strength that several data have supported the mono-allelicity of NADs like\nDNA-FISH (Fig. 6d) and the higher CF of biallelic NADs compared to monoallelic NADs (Extended data\nFig. 7d). Furthermore, analyses of histone modifications and gene expression between mono- and bi-allelic\nNADs indicate that bi-allelic NADs are in a more repressed chromatin and gene expression states (Figs.\n6f,g).\nWe followed the suggestion of the Reviewer and generated high-depth, allele-resolved scRNAseq data\nfrom the hybrid F123-ESCs (~30,658 UMIs per cell, compared to the current ~1,600 UMIs per cell in Bonora\net al., 2021). Despite this increased depth, the resolution remains insufficient to reliably distinguish\nmonoallelic expression, as we were only able to identify 26 genes with reliable allele-specific expression,\ndefined as having greater than one read per allele per cell on average (this number was zero in publicly\navailable F123 scRNAseq datasets). These genes are all highly expressed, do not localize to nucleoli, and\nexhibit the expected biallelic expression patterns across most cells. In contrast, genes NADs are generally\nlowly expressed, with average expression levels below one read per allele per cell. Consequently,\n16\nexpression at the single-cell level was often binary (i.e., “0” or “1”), which complicates robust inference of\nallelic expression states. Although a monoallelic expression pattern can superficially appear in such sparse\ndata, we believe this primarily reflects limitations of detection sensitivity rather than true regulatory bias. For\nthese reasons, we believe that current scRNAseq approaches do not have the resolution necessary to\nreliably assess whether allelic association with nucleoli influences gene expression. Similarly, due to the\ngenerally low expression levels of NAD-associated genes, RNA-FISH is unlikely to yield interpretable allelic\nexpression patterns for the majority of targets. Nevertheless, we share the Reviewer’s enthusiasm for this\nintriguing observation and believe that future developments in spatial transcriptomics or more sensitive\nallele-resolved methods may help address this question more definitively.\n17"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have further addressed my remaining points, but I think some more work is to be done for point 3, which I think provides a critical assessment of the data quality. The analysis that I suggest is highly feasible - the data are already there.\nGENERAL:\nThe authors now list a few external code packages that they used for their analysis, but still do not make any of their own code available. A lot of data processing was done, and custom R and Python scripts are mentioned for specific analyses. All code should be publicly shared, so others can reproduce and check the results.\n1. OK. I had missed that the images showed a cluster of cells, not a single cell. Figure 6d shows nuclei with 2 or 3 nucleoli, which further led me to think that multi-nucleolar cells are common.\n2. OK. As mentioned above, please make code of these simulations available.\n3. Thank you for the clarification. What the reader needs to understand is: which proportion of the captured DNA in each single-cell is truly NAD, and which proportion comes from chromosome chunks that happen to be below and above the nucleoli (and thus are not NADs, but \"bycatch\" DNA)? In our dialog we are gradually getting closer to this estimate, but I think we are not entirely there yet. A more precise calculation is warranted. Here is what I mean:\nThe new text now states that the nucleolus + 0.5µm NAD rim together occupy on average 65% of the excised cylinder. For the sake of my argument, let us assume that one-third of this volume consists of the rim, and two-thirds of the actual nucleolus. Thus, the rim (= NADs) occupies 65/3 = ~22% of the cylinder volume. Note that the nucleolus itself contains barely any DNA. The remaining 35% (100%-65%) of the captured cylinder consists of nucleoplasm and cytoplasm. The nucleoplasm contains non-NAD DNA, so it is important to know which part of this 35% is actually nucleoplasm. Let us assume that this number is 20%. If all of these numbers are correct, then the captured DNA from a single cell was derived from 22% (NADs) + 20% (nucleoplasmic DNA) of the cylinder volume. Thus, assuming similar DNA concentrations in NADs and nucleoplasm, nearly half (20/42) of all sequenced DNA would be nucleoplasmic DNA, and a bit more than half (22/42) would be NADs.\nOf course, I had to make a couple assumptions because the provided data was incomplete. But this rough calculation illustrates that as much as half of the captured DNA in each cell might NOT be from NADs.\nBecause the authors already have the obtained Imaris images, it should now be feasible to repeat this calculation with the real numbers, for each of the 6 cells (more cells would give a better view of cell-to-cell variability, which is so prominent in figure 1c). It is quite possible that the estimated proportion of non-NAD DNA turns out to be much lower than my back-of-the-envelope-calculation, which would take away my concern, and it would be great to share this information with the readers. But if the calculations indicate that a significant proportion of the data from each cell are non-LAD, then the readers need to know this too.\nFinally, I fully agree that \"pseudo-bulk\" analysis of the 53 cells will largely average out the randomly captured nucleoplasmic chunks. But NoLM-seq is claimed to be a single-cell method (e.g. in the Title), so it is important to determine the single-cell accuracy as carefully as possible.\n4. The new figure 1h that is mentioned in the rebuttal seems to be missing in Figure 1.\n9. \"Proximity\" would be more accurate, but I leave it up to the Editor to decide whether \"Interactions\" is OK in the Title.\n10. The ACF analysis is now adequate. However, from the main text it is unclear why the authors favour the interpretation that the difference in decay is reflecting biological differences, rather than differences in noise levels. It seems to me that higher noise levels in NPCs cannot be ruled out. Also, the main text could explain how the fitted a and b should be interpreted.\n11. What I meant is that it is not effective to only provide information in the rebuttal without adding it to the manuscript. Readers may have the same question.", "reviewer_3": "The authors have adequately addressed my previous questions in this revised manuscript. Overall, I find the study suitable for publication in Nature Communications."}, "author_response": "Single-cell dynamics of genome-nucleolus interactions captured by nucleolar laser\nmicrodissection (NoLMseq)\nWalavalkar et al.\nResponse to the Reviewers\nWe thank the Reviewers for the positive and constructive comments. In this revised manuscript, we\nhave provided additional data addressing all the requests of Reviewers 1 and 3, included additional\ninformation in the method section, and modified the text to clarify points that were not sufficiently clear.\nThe additional data strengthen the conclusions of our work.\nList of changes\nModifications in the text were highlighted in red.\nThe new data are listed here below.\nRevised Previous Description Reviewer\nmanuscript version\nCumulative histogram of genome-wide nucleolar\nCF values for randomized sequences for 53\nnucleoli matching the genome coverage of each\nof the 53 ESC nucleoli measured by NoLMseq\nExt. Data Fig. 3c-\n6 --- Calculation of R and P values of scatter plots Rev. 1\nd\nAutocorrelation analysis of ESCs and NPCs with\nrandom samples\nReviewer #1 (Remarks to the Author):\nGENERAL:\nThe authors have addressed several of my concerns (see below; numbering as in the rebuttal). But my\nmajor points 1-4 need additional attention. Point 3 is a very difficult one: from the responses and new\nanalyses, it seems to me that NoLM-seq has an intrinsic limitation that causes it to be quite noisy - it\noften captures chromosomal regions that are not nucleolus-associated, but happen to be above or\nbelow the nucleolus. This substantially dampens my enthusiasm for the technique. Perhaps the method\nwill be only really useful when applied to very flat cells with \"pancake\" nuclei such as fibroblasts? At\nthe very least, the authors should be very explicit about this limitation in the main text (instead of tucking it\naway in the Methods), so readers can decide for themselves. Points 2 and 4 are also relevant in this\ncontext.\nAuthor. Below we provide our detailed clarifications. We believe that several of the points raised may\nstem from misunderstandings of the data and interpretations presented. We appreciate the reviewer’s\ninitial enthusiasm for NoLMseq and hope that our responses will help to clarify these aspects and\nrestore confidence in the strength and novelty of our approach.\nImportant general remark: all code for data processing and data analysis (ideally a separate script or\nmarkdown file for each figure) should be made available, preferably on Github. Currently this is missing.\nCombined with the very cursory description of the computational analyses this is not acceptable. It\nseems misleading that the Reporting Summary states: \"No software was used for data collection\" -\nobviously a lot of computational work was needed for the processing and visualization of the data.\nAuthor. We would like to clarify that the statement in the Reporting Summary “No software was used\nfor data collection” refers specifically to the experimental phase. Thus, our statement is correct since\n1\nno automated or instrument-specific software was employed to collect the data (i.e., nucleoli\nmicrodissection) as clearly described in the method section.\nAll computational work was conducted during the analysis phase. No new software or pipelines were\ndeveloped for this study; all analyses were performed using publicly available and well-established\ntools. All software and tools were cited in the Methods section of the manuscript and were used followed\nstandard guidelines as per their documentation, all of which are available on their associated Github\nrepositories. Most other graphs were generated using Graphpad Prism. In this revised version, we\nincluded additional details in the methods.\nMAJOR POINTS:\n1. The new figure 1b still shows only one nucleolus. This is not sufficient for giving the readers a good\nview of how the microdissections are done. Please add a set of ~ 10 representative images collected\nthroughout this project.\nFurthermore, the authors only address the issue of multiple nucleoli *in their rebuttal*, but do not discuss\nthis *in the manuscript*: how often did the cells have multiple nucleoli for the collected data, and how\nwas this issue handled (were all nucleoli, including very small ones, captured; or was only one big\nnucleolus captured?). Bottom line: precise documentation and transparency in the manuscript is\nneeded.\nMinor point: It is puzzling that the authors respond in their rebuttal: \"It is certainly possible to cut two\nnucleoli, however, we did not include this in our analysis since the large majority of ESCs have one\nlarge nucleolus.\" Then why did they show in their rebuttal two images of nuclei in which multiple nucleoli\nare captured?\nAuthor: This request was addressed in our previous response, showing a series of images of\nmicrodissection. We have now included these images in the manuscript (new extended data Fig. 1a).\nMoreover, we would like to clarify that the laser-microdissection of nucleoli has reliably worked\nthroughout the study and has never posed any problems.\nAs clearly stated in the manuscript (Results, line 118; Discussion, line 438), the vast majority of ESCs\ncontain a single large nucleolus, which makes this cell type an ideal model for NoLMseq. We did not\nanalyse the few cells with multiple nucleoli and therefore did not discuss handling multiple nucleoli in\nthe manuscript, as it is not relevant to the data presented. Moreover, we have clearly stated that\nNoLMseq is a method for cells with single nucleoli (Discussion, lines 453-458).\nMinor point. The images in the rebuttal show multiple cells with one nucleolus each rather than one cell\nwith multiple nucleoli, as the Reviewer’s comment seems to imply. This point was perhaps\nmisunderstood.\nFinally, we kindly disagree with the comment of the reviewer that “precise documentation and\ntransparency in the manuscript is needed.” Our manuscript has provided transparent and precise\ndocumentation of our methodology (i.e., 4 pages were dedicated to describing NoLMseq).\nNevertheless, we included additional information in the Method to improve clarity.\n2. I appreciate the newly added random simulations. But there seems to be a flaw in this: I understand\nthat the 53 random datasets all were taken to have 15% coverage. The real data however show a broad\ndistribution of coverages (figure 1f). The random datasets should have the same distribution of\ncoverages. I could not find a description of the randomization in the Methods section, so it is not clear\nhow this was exactly done.\nAuthor: We performed the requested analysis with the random datasets with the same distribution of\ncoverages (new Fig. S1e). As expected, there is no difference. We included additional details in the\nmethod section of this revised manuscript how this analysis was performed.\n3. The new estimate that only 34% of the captured volume is nucleolar puts the results in a very different\nlight. If two-thirds of the captured volume is above and below the nucleoli, and considering that nucleoli\n(one-third of the volume) contain very little DNA, then this method primarily captures non-nucleolar\nDNA! This reduces my enthusiasm for the method; most of the domains visible in figure 1c may be\nsimply chunks of chromosomes that happen to be above or below the captured nucleolus. This explains\nwhy there seems so little concordance between individual nuclei, unlike Jop Kind's single-cell maps of\nlamina interactions.\nFurthermore, proper documentation is needed of the 34%: the readers need a figure showing how the\nmeasurement was exactly done, plus a plot of the distribution of percentages across a set of ~50 nuclei.\nAuthor: We think that there has been a misunderstanding.\nThe Reviewer requested the nucleolus volume of the microdissection. We provided this value (34%)\nusing Imaris on immunofluorescence images stained with the nucleolar marker NPM1, while nuclear\n2\nvolumes were defined using DAPI. Since the reason of this request was not clear to us, we did not\ndiscuss it in the results but included it in the method section, since as such it was not informative to\nexplain the NoLMseq method. Indeed, NADs are located around the nucleolus rather than residing\nwithin the nucleolus. Now that we understand the rationale behind the reviewer request, we would like\nto clarify that the relevant information for understanding what is captured by NoLMseq is the\ncombined volume of the nucleolus and surrounding NADs, not the nucleolus alone. As NADs\nwere recently reported to form a ring around nucleoli marked by H3K9me2 in ESCs with a ~0.5-0.6 μm\nwidth, we used this spatial definition and estimated that nucleolus+NAD occupies ~65% of the volume\nin the laser microdissections.\nAn important clarification, already described in the manuscript, is that NAD identification does not rely\non sequencing from a single microdissected nucleolus. Instead, it results from the integration of data\nfrom 53 individual microdissections. With the new information of the nucleolus+NAD volume in the\nmicrodissection, we can now provide additional quantitative measurements demonstrating the high\nconfidence of NAD identification by NoLMseq. Specifically, when integrating across 53 microdissected\ncells using a 20% CF threshold, the measured ~15% NAD coverage per cell, and accounting for the\nnucleolus+NAD volume, the probability of incorrectly classifying a non-NAD region as a NAD is\njust 0.000144%. Moreover, since the mouse genome comprises of 27,904 100kb-bins, the number of\nfalse-positives in the data would be 0.04, thus less than a single bin! All of this updated analysis has\nbeen included in the results and method sections of the revised manuscript.\nIn any case, we believe that the volume captured in the microdissection does not play a critical\nrole in NAD identification. Even when using the 35% nucleolus-only volume, the CF approach we\nemployed shows that the probability of incorrectly classifying a non-NAD region as a NAD remains very\nlow, at just 0.072% (i.e., 20 100kb-bins).\nFinally, we believe the reviewer may have overlooked our stringent CF thresholding criteria for defining\nNADs, which were based on 53 microdissected nucleoli. As discussed in the manuscript, the predictable\ninclusion of genomic regions located below and above nucleoli, without applying any thresholding,\nwould indeed result in a high number of false positives. However, this does not reflect our methodology\nto NAD definition\nESCs exhibit a highly homogeneous nuclear structure. Accordingly, our Imaris measurements of the\nnucleolus+NAD volume from six microdissected cells have a margin of error of only ±5%. Given this\nlow variability, there is no need to measure 50 cells. Moreover, as indicated above, the estimated\nvolume has minimal impact on the CF-based method we applied to identify NADs using NoLMseq.\nJop Kind’s maps of LADs are irrelevant to this study since it is biologically and methodologically a totally\ndifferent system.\n4. I respectfully disagree. Binary analyses always require arbitrary cutoffs, which can greatly affect the\noutcome. Visualization of continuous data is much more informative. For the sake of transparency,\nscatterplots should really be shown. Of course the authors may then discuss their interpretation of\npotential caveats, but handwaving in a rebuttal only is not acceptable. Furthermore, the new scatterplot\nin Extended Fig 3b (comparing the results to SeqFISH, another method) shows the value of scatterplots:\na clear forked distribution is visible, pointing to a systematic bias in at least one of the two methods. It\nseems to me that systematic analysis of scatterplots comparing NoLMseq to DamID, SPRITE and\nSeqFISH could help to find out which method deviates from the others.\nMoreover, I still do not understand how the overlap in figure 1h was calculated. Please provide a\ndescription including formula in the Methods. Considering point 3 above, how can the overlap be close\nto 100%? This really does not make sense.\nAuthor: As described extensively in both our previous response and the Discussion section of the\nmanuscript, Nucleolar-DamID is inherently a qualitative method, not a quantitative one. Therefore,\ndirectly comparing the non-quantitative NAD scores from Nucleolar DamID, which tend to be very\nsimilar across regions, with the quantitative CF values derived from single-nucleolus data would not be\nscientifically appropriate and could lead to misinterpretation.\nRegarding SPRITE, the scores are not publicly available and reproducing them would require\nconsiderable expertise in the SPRITE data processing pipeline, which is beyond the scope of this study.\nFor this reason, we initially opted for a broader genomic coverage comparison of common NAD regions\n(previously shown in Fig. 1h). However, we have now removed this panel, as it appeared to create\nmore confusion than clarity.\nImportantly, the core conclusions of our study remain well supported by robust and quantitative\ncomparisons, particularly those between NoLMseq and seqFISH+ (now presented in Fig. 1h and\nExtended Data Fig. 3b). This represents an orthogonal and more meaningful comparison, as they both\nare quantitative single-cell technologies. Finally, the deviation of the scatter plot in Extended Data Fig.\n3\n3b occurs at CF >20%, which is the threshold used to define NADs. Additionally, the two datasets not\nonly differ in methodology but also in resolution, NoLMseq at 100 kb and seqFISH+ at 1 Mb, explaining\nwhy the results are not identical.\n5. OK, thanks for adding these new analyses à ESC population based on NADs (Fig. 4)\n6. OK. à ESC and NPC (Fig. 3)\n7. Thanks for the scatterplot, which boosts confidence in the ribosomal genes observation.\n8. OK. à Bi-allelic and mono-allelic NADs (Fig. 6)\nMINOR POINTS:\n9. I still think that the word \"interactions\" in the title is inappropriate. It is odd that the authors agree with\nme that this word should be avoided, but that they keep it in the title. Proximity is better, particularly\nconsidering point 3 above.\nAuthor. We appreciate the reviewer’s continued feedback on this point. While we understand the\nconcern regarding the term \"interactions\", we respectfully maintain our choice to use it in the title for the\nfollowing reasons. As indicated in our previous response (quoted below), we have deliberately avoided\nusing the term \"interactions\" throughout the main text of the manuscript to avoid any potential\noverstatement. However, we believe that in the context of the title, \"interactions\" remains the clearest\nand most accessible term for a broad readership. This usage aligns with established conventions in the\nfield, for example, numerous studies on LADs have used \"interactions\" in their titles to convey nuclear\nspatial associations, even when referring to proximity-based measurements.\n“We agree. Accordingly, the term ‘interactions’ was not used in the rest of the manuscript. However, we\nthink that for a title the term ‘interactions’ would be much clearer for a broad readership, as has been\ndone in the past for many LADs papers.”\nWe hope the reviewer will understand our rationale and accept this terminology in the interest of clarity\nand consistency with prior literature.\n10. I disagree that the ACFs are similar. Clearly, the autocorrelation is worse in NPCs (shifted\nsubstantially to the left!), and hence the data are probably more noisy. I do not understand how the\nPearson correlation was calculated, but it seems not the right measure to determine the shift in ACF.\nAuthor. We agree with the reviewer that the ACF curves for NPCs are shifted to the left, indicating\nreduced autocorrelation. However, as described in the manuscript, we believe this reflects underlying\nbiological differences between the two cell types (i.e., NPC has less NADs than ESCs). As described\nin details in our previous response to this point, these data are consistent with previous work “the\nreduction of contacts with nucleoli in NPCs compared to ESCs is consistent with previous work using\nNucleolar-DamID and rDNA-HiC (PMID: 35304483)”.\nTo further assess whether this shift could be attributed to technical noise, we generated random\ndatasets for both ESCs and NPCs, matched for genome coverage per sample. As expected, these\nrandom datasets showed substantially lower autocorrelation values closer to zero, supporting the\ninterpretation that the observed ACF patterns in actual nucleolar samples are biologically meaningful\nrather than artifacts. The random NPC datasets also displayed a slight leftward shift compared to\nrandom ESC datasets, likely due to lower overall NAD coverage in NPCs compared to ESCs. We have\nremoved the Pearson correlation analysis from the manuscript and describe values only for the\nautocorrelation and decay constants. We modified the text to clarify these points.\n11. (i.e., success rate of NoLMseq) OK, but please document this in the manuscript, ideally with small\ntable or figure, not just in a response in the rebuttal.\nAuthor. We respectfully disagree with the need to include this information in the main manuscript. The\nrelevant details have already been provided in our rebuttal, which will be publicly available and offers\nsufficient context for readers interested in the technical aspects. Our aim is to present a scientific\nmethod, not to market a product. The robustness and utility of our approach are clearly demonstrated\nthrough data quality, reproducibility, and comparative analyses with established methods.\nIncluding a table of “success rates” would not add meaningful scientific value.\n12. OK. à “bimodal” term\n4\n13. The new scatterplots shed new light on the links with H3K27ac and H3K9me2. The\ncorrelations turn out to be extremely weak! Correlation coefficients are not provided, but by eye they seem <0.2. This\nshould be discussed explicitly, and I think it is somewhat misleading to have these scatterplots tucked\naway in the Extended data.\nAuthor. First, we would like to clarify that all relevant data have been presented transparently, and we\nrespectfully disagree with the statement that any data were tucked. As explicitly stated in our previous\nresponse “we decided to place them (i.e., scatter plots) in the Extended data Figs. 3c-e since they are\nless informative than the boxplots showed in Figs. 2f-h. Indeed, the boxplot representation of the data\nallows comparisons with genomic domains within the A compartment, the NAD-only, and\nNAD/LAD regions.” Moreover, the box plots demonstrate that NAD-only regions with CF >50% have significantly\nlower H3K27ac and higher H3K9me2 levels compared to regions with lower CF, an insight that the\nscatter plots cannot convey as effectively. Therefore, we believe that the box plots provide a more\ninformative visualization and are appropriately placed in the main figure.\nWe have now included the requested r and P values in the scatter plots shown in Extended Data Figs.\n3c-e. As expected, the R values are modest; however, the decrease in H3K27ac levels and the increase\nin H3K9me2 content relative to nucleolar contact frequency are statistically significant and align well\nwith the box plot representations in Figs. 2f-h, as well as the corresponding descriptions in the Results\nsection. We described these results in the manuscript.\n14. OK. à legend correction, Fig. 3b\n15. OK. à NAD and LAD in ESC vs NPC\n16. OK. à NAD-only and NAD/LAD, Fig. 2e\n===============Reviewed by Bas van Steensel, review task received: 12 May 2025; completed 24\nMay 2025. It is my standard policy to sign and date *all* of my manuscript review reports, regardless of\nmy comments and recommendations. All correspondence about this manuscript should go via the\neditor. PLEASE DO NOT REMOVE THIS NOTE ===========\nThe revised manuscript has addressed the reviewer’s comments adequately. It is suitable for\npublication.\nAuthor. We thank the Reviewer for the appreciation of our study.\nReviewer #3 (Remarks to the Author):\nIn this revised manuscript, Walavalkar et al. have incorporated new experiments and data analyses that\nclarify the physiological implications of the NAD organization revealed by NoLMseq. In particular, by\nperforming scRNA-Seq in ESCs, the authors show that distinct nucleolar-associated domain (NAD)\norganization patterns in two ESC populations may correlate with the expression of genes within different\nNADs. Moreover, the authors performed additional analyses to further support the conclusion that\ndissociation of NADs from the nucleolus during ESC-to-NPC differentiation, as well as upon\nActinomycin D treatment, is associated with derepression of genes. The authors have also made textual\nedits that improve clarity and corrected some overstatements.\nOverall, I find that these revisions have significantly strengthened the manuscript. Most of my earlier\nconcerns have been addressed; however, I do have a few remaining points that require further attention\nby the authors.\nAuthor. We thank the Reviewer for the appreciation of our study. Below you will find the description of\nthe changes according to Reviewer’s request.\nMajor points:\n1. I appreciate the authors’ efforts to integrate scRNA-Seq with NAD mapping to illustrate that distinct\nnucleolar contact patterns in ESCs at the single-cell level may relate to differential gene expression and\ncellular states. By examining the expression of genes within NADs specific to the two ESC populations,\nthe authors show that genes in population 1 NADs tend to co-express with pluripotency markers Nanog\nand Sox2, while those in population 2 NADs do not. This is an interesting observation. However, the\ncurrent analysis is based on only five genes per NAD group. I suggest that the authors perform more\ncomprehensive statistical analyses across a broader set of genes with detectable expression in the\nscRNA-Seq dataset to rigorously assess whether genes within the same NAD group show higher\nexpression correlation compared to genes from different NAD groups.\nAuthor. We thank the Reviewer for this insightful suggestion. We expanded our analysis to include the\ntop 30 genes, selected from a total of 282 genes of population 1 and 126 genes of population 2, that\nshowed the highest nucleolar interactions and were specific to either population 1 or population 2 (now\nshown in the new Fig. 3f). This broader analysis confirmed our initial observation: a substantial fraction\nof population 2 NAD genes (22) co-express with pluripotency markers such as Nanog and Sox2 in\nclusters 0-3, whereas only few NAD genes of population 1 (12) exhibit this pattern with 13 genes mainly\nexpressed in cluster 7. These results are consistent with our previous findings based on UMAP and dot\nplots of the top 5 nucleoli-interacting genes.\nMinor points:\n2. Regarding lines 187 and 193 and my previous point #2: While I agree with the authors that it is\nplausible NAD/LAD regions may interact with the nucleolus in one cell and the nuclear lamina (NL) in\nanother, the low CF values alone do not provide direct evidence for or against simultaneous nucleolar\nand NL contacts within the same nucleus. For instance, one could imagine a scenario where a\nNAD/LAD region contacts the nucleolus in 15% of cells, and in all such cases, it also contacts the NL.\nTo avoid overinterpretation, I recommend removing the sentence in lines 186–188: “Since this\nclassification of NADs was obtained from ESC population, it has remained elusive whether NAD/LAD\nregions contact both nucleoli as well as the NL in the same or different cells.”\nAuthor. We removed the sentence\n3. In line 224, the authors show that by combining the more quantitative NoLMseq data and more\nstringent criteria, they were able to more accurately define the ESCsp-NADs and reveal the correlation\nbetween NAD changes and gene derepression. I suggest the authors include a panel in the extended\ndata figure to illustrate the differences between ESCsp-NADs identified by NoLMseq and those\npreviously defined using nucleolar DamID.\nAuthor. To further clarify the improved resolution of NoLMseq to detect NADs relative to the previous\ncell population based Nucleolar-DamID, we included new data showing the non-significant upregulation\nof genes located at ESC -NADs identified by Nucleolar-DamID between ESCs and NPCs (new\nsp\nextended data Fig. 4c).\n6\n4. Lines 225–227: It would be helpful to mention here that genes located in NPCsp-NADs, common\nNADs, or in the A compartment do not exhibit significant expression changes.\nAuthor. As suggested, we included a more detailed description of the Extended Data Fig. 4b.\n5. Line 278: The authors suggest that ESC populations with distinct nucleolar contact patterns may\nrepresent “different developmental potentials.” However, these populations could simply reflect\ndynamic, fluctuating states of ESCs under in vitro culture conditions. I recommend softening this\nstatement to avoid overinterpretation.\nAuthor. We would like to clarify that our statement is grounded in extensive literature describing the\ndistinction between ESCs cultured in 2i conditions and those maintained in serum/LIF. The latter are\nwidely recognized as representing a more heterogeneous and developmentally advanced or \"primed\"\npluripotent state. The terminology we used, referring to different developmental potentials, accurately\nreflects these established differences. Therefore, we believe the current wording appropriately reflects\nthe existing knowledge without overinterpretation.\nmicrodissection (NoLMseq)\nWalavalkar et al\nThird response to Reviewers\nWe would like to thank Reviewer #3 for the positive and constructive comments and for supporting the\npublication of our work.\nHere below, our response to the additional request of Reviewer 1. Changes in the text are highlighted\nin red.\nReviewer #1 (Remarks to the Author):\nThe authors have further addressed my remaining points, but I think some more work is to be done for\npoint 3, which I think provides a critical assessment of the data quality. The analysis that I suggest is\nhighly feasible - the data are already there.\nGENERAL:\nThe authors now list a few external code packages that they used for their analysis, but still do not make\nany of their own code available. A lot of data processing was done, and custom R and Python scripts\nare mentioned for specific analyses. All code should be publicly shared, so others can reproduce and\ncheck the results.\nAuthor. To clarify, our work did not develop any new software. At request of the Reviewer, we have\nnow included the very simple command-line instructions used in our work on GitHub.\n1. OK. I had missed that the images showed a cluster of cells, not a single cell. Figure 6d shows nuclei\nwith 2 or 3 nucleoli, which further led me to think that multi-nucleolar cells are common.\nAuthor. We find it difficult to accept this justification, as this mistake could have been avoided with a\nmore careful evaluation of our work. We repeatedly documented in the original manuscript, the first\nrevised version, and the initial rebuttal letter that the vast majority of ESCs contain a single large\nnucleolus. Unfortunately, this misinterpretation was taken as evidence to suggest that our\nmicrodissections were flawed, which had an unjustifiable negative impact on the evaluation of our work\nat NSMB.\n2. OK. As mentioned above, please make code of these simulations available.\nAuthor. These simple command-line instructions are now deposited on GitHub\n3. Thank you for the clarification. What the reader needs to understand is: which proportion of the\ncaptured DNA in each single-cell is truly NAD, and which proportion comes from chromosome chunks\nthat happen to be below and above the nucleoli (and thus are not NADs, but \"bycatch\" DNA)? In our\ndialog we are gradually getting closer to this estimate, but I think we are not entirely there yet. A more\nprecise calculation is warranted. Here is what I mean:\nThe new text now states that the nucleolus + 0.5µm NAD rim together occupy on average 65% of the\nexcised cylinder. For the sake of my argument, let us assume that one-third of this volume consists of\nthe rim, and two-thirds of the actual nucleolus. Thus, the rim (= NADs) occupies 65/3 = ~22% of the\ncylinder volume. Note that the nucleolus itself contains barely any DNA. The remaining 35% (100%-\n65%) of the captured cylinder consists of nucleoplasm and cytoplasm. The nucleoplasm contains non-\nNAD DNA, so it is important to know which part of this 35% is actually nucleoplasm. Let us assume that\nthis number is 20%. If all of these numbers are correct, then the captured DNA from a single cell was\nderived from 22% (NADs) + 20% (nucleoplasmic DNA) of the cylinder volume. Thus, assuming similar\nDNA concentrations in NADs and nucleoplasm, nearly half (20/42) of all sequenced DNA would be\nnucleoplasmic DNA, and a bit more than half (22/42) would be NADs.\nOf course, I had to make a couple assumptions because the provided data was incomplete. But this\nrough calculation illustrates that as much as half of the captured DNA in each cell might NOT be from\nNADs.\nBecause the authors already have the obtained Imaris images, it should now be feasible to repeat this\ncalculation with the real numbers, for each of the 6 cells (more cells would give a better view of cell-to-\ncell variability, which is so prominent in figure 1c). It is quite possible that the estimated proportion of\nnon-NAD DNA turns out to be much lower than my back-of-the-envelope-calculation, which would take\naway my concern, and it would be great to share this information with the readers. But if the calculations\n1\nindicate that a significant proportion of the data from each cell are non-LAD, then the readers need to\nknow this too.\nFinally, I fully agree that \"pseudo-bulk\" analysis of the 53 cells will largely average out the randomly\ncaptured nucleoplasmic chunks. But NoLM-seq is claimed to be a single-cell method (e.g. in the Title),\nso it is important to determine the single-cell accuracy as carefully as possible.\nAuthor.\nWe would like to clarify what the readers will understand and what the Reviewer needs to understand.\nAs clearly described in the manuscript, readers will understand that the microdissections will inevitably\ninclude non-NAD sequences located above and below nucleoli, and to exclude them we applied a\nthreshold in nucleolar contact frequency (20%) across the 53 microdissected nucleoli, which was then\nused to identify NADs in individual cells. They will also appreciate that the NADs obtained with this\napproach were validated using multiple orthogonal methods, which this Reviewer has previously\nacknowledged and accepted.\nAs explained in our previous response, the Reviewer needs to understand that the proportions of\nnucleolus volume (35%), nucleolus+NAD volume (65%), or, as now requested, NAD vs. non-NAD\nproportions within each microdissected cylinder are not critical for NAD identification by NoLMseq. It is\nthe nucleolar CF threshold that distinguishes NADs from non-NADs in each single microdissection.\nWhile the Reviewer has referred to this as a “pseudo-bulk” approach, we would like to stress that this\nis not a shortcoming of NoLMseq but rather a fundamental aspect of single-cell analyses. Indeed,\nvirtually all single-cell methods rely on some form of “pseudo-bulk” treatment, such as thresholds,\naggregation, or modelling, to extract accurate biological signals from inherently sparse and noisy data.\nWe find that the assumptions suggested by the Reviewer for his new request to calculate NAD vs. non-\nNAD proportions within the microdissected cylinder to be flawed and likely to produce misleading results\n(see below).\n• Cylinder definition: As clearly described in the Figure, Figure legend and Methods, the volume of\nmicrodissected cylinder used in our measurements includes only nucleoplasm and nucleolus, not\ncytoplasm.\n• DNA distribution: The assumption that DNA is homogeneously distributed in the nucleoplasm is\nconceptually incorrect. Chromatin is more condensed around nucleoli than in the nucleoplasm. Since\nDNA concentration is not uniform and is higher around nucleoli, calculating NAD and non-NAD\nproportions under this assumption is conceptually incorrect.\n• NADs in nucleoli: The assumption that there are no NADs within nucleoli is also inaccurate. While it\nis true that nucleoli inside contain less DNA overall, some NADs can be found within nucleoli or at the\ninner outer layer of the nucleolus (granular component).\nFor the sake of curiosity, we performed this calculation using the simplified assumptions of the Reviewer\nand obtained a 50% NAD proportion within the microdissected cylinder. However, this value would likely\nbe higher, given the higher DNA concentration around nucleoli relative to the nucleoplasm. Thus, we\nwill not include this value in the manuscript, as it is based on incorrect assumptions, would be\nscientifically misleading, and does not accurately reflect NAD biology.\nWe will not modify the current description of this section of the manuscript, as it accurately reflects the\ndata and does not require further changes. Readers will understand.\n4. The new figure 1h that is mentioned in the rebuttal seems to be missing in Figure 1.\nAuthor. There is no new Fig. 1h.\n9. \"Proximity\" would be more accurate, but I leave it up to the Editor to decide whether \"Interactions\" is\nOK in the Title.\nAuthor. This point was extensively explained in all our previous responses.\n10. The ACF analysis is now adequate. However, from the main text it is unclear why the authors favour\nthe interpretation that the difference in decay is reflecting biological differences, rather than differences\nin noise levels. It seems to me that higher noise levels in NPCs cannot be ruled out. Also, the main text\ncould explain how the fitted a and b should be interpreted.\nAuthor. This point, including the noise, was extensively explained in all our previous responses and in\nthe manuscript and the data are consistent with published work. The a and b values were defined in the\nM&M section. We have now included their definition in main text, as requested.\n2\n11. What I meant is that it is not effective to only provide information in the rebuttal without adding it to\nthe manuscript. Readers may have the same question.\nAuthor. This point (success rate) was extensively explained in all our previous responses.\nAs it happens for all experiments, the success rate mainly depends on the expertise of the researcher.\nThus, including a table of “success rates” would not add meaningful scientific value. The robustness\nand utility of our approach are clearly demonstrated through data quality, reproducibility, and\ncomparative analyses with established methods.\nReaders interested in further details can either contact us directly or consult our rebuttal letters, which\nwill be publicly accessible.\n===============Reviewed by Bas van Steensel, review request received: 08 August 2025;\ncompleted 13 August 2025. It is my standard policy to sign and date *all* of my manuscript review\nreports, regardless of my comments and recommendations. All correspondence about this manuscript\nshould go via the editor. PLEASE DO NOT REMOVE THIS NOTE ===========\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed my previous questions in this revised manuscript. Overall, I\nfind the study suitable for publication in Nature Communications.\nAuthor. We would like to thank Reviewer #3 for the positive and constructive comments and its support\nfor publication of our work.\n3"}]

Decision letter