TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming

TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Confirmatory Results TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming View ORCID Profile Laura Lázaro-Carot , Esteban Jiménez-Villalba , Jordi Planells , View ORCID Profile Anna Lozano-Ureña , Jennifer Díaz-Moncho , Raquel Montalbán-Loro , Adela Lleches-Padilla , Martina Kirstein , Mitsuteru Ito , Elizabeth J. Radford , View ORCID Profile Sacri R. Ferrón doi: https://doi.org/10.1101/2025.02.13.638077 Laura Lázaro-Carot 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laura Lázaro-Carot Esteban Jiménez-Villalba 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jordi Planells 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Lozano-Ureña 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain 2 Department of Molecular, Cellular and Developmental Neurobiology, Cajal Institute , Madrid, 28002, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anna Lozano-Ureña Jennifer Díaz-Moncho 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Raquel Montalbán-Loro 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Adela Lleches-Padilla 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martina Kirstein 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mitsuteru Ito 3 Department of Genetics, University of Cambridge , CB2 3EH, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth J. Radford 4 Department of Pediatrics, University of Cambridge , CB2 0QQ, Cambridge, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sacri R. Ferrón 1 Instituto de Biotecnología y Biomedicina (BiotecMed)/Departamento de Biología Celular, Universidad de Valencia , 46100, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sacri R. Ferrón For correspondence: sacramento.rodriguez{at}uv.es Abstract Full Text Info/History Metrics Preview PDF Abstract Genomic imprinting is an epigenetic mechanism that drives monoallelic gene expression depending on parental origin. Loss of imprinting (LOI) is associated with human imprinting disorders, fetal development, and cancer progression. Imprinted genes, organized in clusters, are regulated by methylation at imprint control regions (ICRs), differentially methylated regions (DMRs) between parental chromosomes. Somatic cell reprogramming into induced pluripotent stem cells (iPSCs) is a valuable tool for studying pluripotency and holds promise for patient-specific therapies. Discerning whether genomic imprinting changes during reprogramming represent epigenetic abnormalities or essential adaptations linked to pluripotency is crucial. Here, we perform RNA-seq and MeDIP-seq analysis on mouse iPSCs derived from neural stem cells (NSCs). Our findings reveal that ICRs undergo DNA hypomethylation, confirming widespread LOI in pluripotent cells. However, the IG-DMR within the Dlk1-Dio3 imprinted cluster resists hypomethylation, a hallmark of successful pluripotency acquisition. We also identify a non-canonical role of TET3 in IG-DMR methylation protection through transcriptional regulation of Oct4 and Trim28 . These findings highlight genomic imprinting as a key mechanism of gene dosage control in pluripotency acquisition and maintenance. Introduction During mammalian development, the vast majority of genes are expressed or repressed from both alleles. However, there is a small number of genes, termed “ imprinted genes ” that are expressed monoallelicaly from either the maternally or the paternally inherited chromosomes ( Ferguson-Smith, 2011 ). Approximately 150 imprinted genes have been described in mammals and are generally organized in clusters, although examples of singleton imprinted genes do exist ( Barlow & Bartolomei, 2014 ; Lassi & Tucci, 2019 ; Ferguson-Smith, 2011 ). An imprinting cluster is usually under the control of a DNA element, called the imprinting control region (ICR) that consists of differentially DNA methylated regions (DMRs) between the two parental chromosomes ( Edwards & Ferguson-Smith, 2007 ; SanMiguel & Bartolomei, 2018 ; Tucci et al , 2019 ). Deletion or alteration of an ICR results in loss of imprinting (LOI) of multiple genes in the cluster and this has been associated to several human pathologies and imprinting disorders such as Prader Willi Syndrome (PWS) and Angelman Syndrome ( Barlow & Bartolomei, 2014 ; Ferguson-Smith, 2011 ; Lassi & Tucci, 2019 ). The parental specific marks at ICRs are established in the developing germline, giving as a result gametes bearing imprints according to the sex of the individual ( Bartolomei & Ferguson-Smith, 2011 ; Edwards & Ferguson-Smith, 2007 ; Ferguson-Smith, 2011 ). After fertilization, a rapid and extensive reprogramming of the parentally inherited genomes occurs, and most DNA methylation is lost ( Smallwood & Kelsey, 2012 ; SanMiguel & Bartolomei, 2018 ). However, the parental-specific imprints are maintained during this period and a memory of parental origin is propagated into daughter cells during somatic cell divisions ( Takahashi et al , 2015 ). At a molecular level, DNA methylation is a dynamic process where different enzymes are involved in actively methylating and demethylating cytosine residues of the DNA. Methylation is established and maintained by DNA methyltransferase (DNMT) family of enzymes, either de novo by DNMT3A and DNMT3B, or maintained by DNMT1. On the other hand, ten-eleven translocases (TET) enzymes family, integrated by TET1, TET2 and TET3, catalyse the conversion of 5-methylcytosine (5mC) into 5-hydroxymethylcytosine (5hmC) to remove methylation marks ( Wu & Zhang, 2017 ). TET proteins have been implicated in maintaining DNA methylation at ICRs during the germline resetting of genomic imprints in embryonic development ( Hackett et al , 2013 ). In addition to their well-characterized catalytic function, TET proteins bind to 5mC-free promoters and interact with key epigenetic regulators, including histone deacetylases, acetyltransferases and Polycomb repressive complex 2, to regulate gene expression ( Lian et al , 2016 ). This suggests additional functions for TET enzymes independent of their catalytic activity ( Montalbán-Loro et al , 2019 ). In vitro reprograming of somatic cells into induced pluripotent stem cells (iPSCs), has enormous therapeutic potential as it opened up the possibility of generating patient-specific pluripotent cell lines to study and treat different degenerative diseases. A variety of cell types have been reprogrammed into iPSCs including fibroblasts ( Takahashi & Yamanaka, 2006 ), hepatocytes, gastric epithelial cells, B cells ( Hanna et al , 2008 ), pancreatic β cells ( Stadtfeld et al , 2008 ), neural progenitor cells ( Kim et al , 2008a ), melanocytes or keratinocytes ( Aasen et al , 2008 ). Different combinations of reprogramming factors, such as Oct4 , Sox2 , Klf4 and c-Myc , have been used to convert somatic cells into iPSCs with comparable expression profiles to embryonic stem cells (ESCs) ( Eminli et al , 2008 ; Kim et al , 2008a ). The key determinants and the temporal sequence of epigenetic events that transition a differentiated cell to a pluripotent state during iPSC derivation remain incompletely understood. Evidence from multiple studies highlights the critical role of DNA methylation changes in successful reprogramming, particularly the necessity for demethylation at promoters of pluripotency-associated genes ( Takahashi & Yamanaka, 2006 ). Therefore, incomplete DNA demethylation can result in only partially reprogrammed cells ( Mikkelsen et al , 2008 ), as the erasure of differentiation-specific epigenetic marks is required for faithful reprogramming ( Apostolou et al , 2013 ). While genomic imprinting remains relatively stable in somatic cells, it is variably lost during iPSCs reprogramming, with some imprinted regions more severely affected than others ( Arez et al , 2022 ; Kim et al , 2013 ; Lee et al , 2016 ; Liu et al , 2010 ; Yagi et al , 2019 ; Takikawa et al , 2013 ; Perrera & Martello, 2019 ). Moreover, aberrant silencing of imprinted genes during iPSC generation has been linked to impaired tissue development ( Yagi et al , 2019 ; Li et al , 2019 ). Several studies have reported global hypomethylation of ICRs during reprogramming, although de novo methylation at later stages has also been observed ( Yagi et al , 2019 ). For instance, the Dlk1-Dio3 imprinted region on murine chromosome 12 has been shown to undergo hypermethylation in iPSCs, disrupting the expression of multiple genes within the cluster ( Stadtfeld et al , 2008 ; Liu et al , 2010 ; Stadtfeld et al , 2010 ; Pham et al , 2022 ). Additionally, imprinting defects in iPSCs appear to depend on both the sex of donor cells and culture conditions, and these defects cannot be rescued upon differentiation ( Arez et al , 2022 ; Nazor et al , 2012 ). Most of these studies have analysed only a limited number of imprinted genes and ICRs in iPSCs, providing a partial view of genomic imprinting alterations. Therefore, distinguishing between essential and undesirable imprinting changes in iPSCs is crucial for their therapeutic applications. Here, we present a comprehensive and unbiased analysis of transcriptome and methylome alterations in iPSCs derived from adult murine NSCs, using only the two reprogramming factors Klf4 and Oct4 . Genome-wide RNA-seq and MeDIP-seq analysis reveal a profoundly altered transcriptome in iPSCs, accompanied by extensive DNA hypomethylation. Studying DNA methylation across all described ICRs shows that most DMRs undergo hypomethylation, confirming a global loss of genomic imprinting in the iPSCs generated from adult NSCs. These methylation changes strongly correlate with transcriptional alterations in genes within affected imprinted clusters. However, the IG-DMR, which regulates the Dlk1-Dio3 imprinting cluster on chromosome 12, resists this widespread hypomethylation during reprogramming, preserving genomic imprinting at this locus in iPSCs. We propose a model in which TET3 transcriptionally regulates the expression of Trim28 and Oct4 to safeguard IG-DMR methylation. Results NSCs from the adult SVZ convert into a pluripotent state only with the transduction of Oct4 and Klf4 Previous studies have reported that neurosphere cultures obtained from postnatal day 5 mouse brain endogenously express Sox2 , c-Myc and Klf4 . Thus, these cultures can be reprogrammed with Oct4 alone, or with Oct4 and Klf4 at a similar efficiency to the reprogramming rate of murine fibroblast with the original four factors ( Kim et al , 2008b ). To assess the expression level of these transcription factors in NSCs derived from the adult subventricular zone (SVZ), quantitative PCR (qPCR) was performed using a cell line of pluripotent embryonic stem cells (ESCs) as a reference. We found that adult NSCs consistently expressed neural genes such as Pax6 or Olig2 , and also expressed high levels of Sox2 , Klf4 , and c-Myc ( Fig. 1A ). As expected, genes associated with pluripotency, such as Oct4 , Nanog or Zfp42 were not expressed in adult NSCs ( Fig. 1A ). Based on this gene expression profile, we developed a reprogramming protocol using retroviral vectors encoding only the transcription factors Oct4 and Klf4 (2 factors condition, 2F), along with a retrovirus encoding the red fluorescent protein mCherry to track the exogenous expression of the reprogramming factors ( Fig. 1B ). These retroviruses were produced by transfecting Plat-E packing cells with the retroviral plasmids ( Fig. 1B and S1A ). Post-infected NSCs (PI-NSCs) were grown on a feeder layer of mouse embryonic fibroblasts using a medium supplemented with the cytokine leukaemia inhibitory factor (LIF) ( Fig. 1B ). After 10 days, cultures started to form clone-like aggregates, which were large, with poorly defined edges and some of them expressed the pluripotency marker stage-specific embryonic antigen 1 (SSEA1) ( Fig. 1B and S1A ). Retroviral vectors are transcriptionally silent in pluripotent stem cells ( Hotta & Ellis, 2008 ). However, most of the clones formed were still positive for mCherry, indicating that despite expressing SSEA1, they were only partially reprogrammed ( Fig. S1A ). We considered this state of cells as pre-iPSCs ( Fig. 1B and S1A ). To promote a ground state of pluripotency of these pre-iPSCs, we applied molecularly defined conditions by neutralizing inductive differentiation stimuli with a dual inhibition (2i) of mitogen-activated protein kinase signalling (MEK) and glycogen synthase kinase-3 (GSK3) ( Silva et al , 2008 ). In this serum-free culture medium, LIF was also added, to maximize clonogenic self-renewal of pluripotent cells ( Ying et al , 2008 ) ( Fig. 1B ) . This reprogramming protocol was repeated in 6 independent cultures of adult NSCs. After 10 days in the new controlled 2i/LIF conditions, ESC-like colonies that did not express mCherry were obtained ( Fig. S1B ), suggesting that cells had been fully reprogrammed into iPSCs. At least 10 clones of each culture were isolated and expanded i n vitro ( Fig. 1B ). Download figure Open in new tab FIGURE 1. NSCs from the adult SVZ are reprogrammed into iPSCs by exogenous expression of Oct4 and Klf4 . (A) Quantitative PCR (qPCR) of the neural genes Pax6 and Olig2, and the pluripotency genes Nanog and Zfp42 in ESCs (blue) and adult NSCs (beige) (left and right panels). qPCR analysis of the endogenous expression of the reprogramming transcription factors Oct4 , Sox2 , Klf4 and c-Myc in ESCs and adult NSCs is also shown (middle panel). (B) Schematic representation of the protocol used to reprogram adult NSCs into iPSCs. NSCs are infected with retroviruses encoding Oct4 , Klf4 and the fluorescent protein mCherry. After 5 days in vitro (DIV) in NSCs medium, neurospheres formed by post-infected NSCs (PI-NSCs) are dissociated into single cells and plated on murine embryonic fibroblasts using ESC/LIF medium. 5 days after dissociation, mCherry + and SSEA1 + clone-like aggregates containing pre-iPSCs start to appear. Medium is then changed to 2i/LIF medium to complete the reprogramming process. After 10 more DIVs, cells have become full iPSCs and 10 single clones of each culture are picked and subcultured independently for further analysis. (C) qPCR expression analysis of retroviral Klf4 and Oct4 expression in adult NSCs, PI-NSCs, pre-iPSCs and iPSCs . (D) qPCR expression analysis of the neural genes Nestin and Olig2 (upper panel) and the pluripotency-related genes Oct4 , Nanog and Zfp42 (lower panel) in NSCs, pre-iPSCs and iPSCs. ESCs were used as a control of the pluripotent state. (E) Immunocytochemistry (ICC) images of SSEA1, OCT4 (green) and SOX2 (red) in ESCs (left panel) and adult NSCs (right panel). ICC for the pluripotency marker NANOG (red) for ESCs (left panel) and for the neural marker OLIG2 (blue) in NSCs (right panel) are also shown. (F) ICC images for SSEA1 and OCT4 (green) in pre-iPSCs (left panel) and iPSCs (middle panel). mCherry fluorescence for pre-iPSCs (left panel) and ICC for the pluripotency marker NANOG (red) and the neural marker OLIG2 (blue) for iPSCs (middle panel) are shown. Phase contrast images for fully reprogrammed iPSCs clones (right panel) are also shown. Gapdh was used as a housekeeping gene for qPCR normalization. DAPI was used to counterstain nuclei in immunofluorescence images. Scale bars for ICCs in e and f: 20 μm; and for phase contrast images in e 40 μm in upper panel and 5 μm in lower panel. P-values and number of samples are indicated. In box and whiskers plots, the mean is indicated as + and whiskers represent the maximum and minimum values. In bar plots, error bars show s.e.m. To further characterise the acquisition of a pluripotent state of these clones, we performed a qPCR analysis on the PI-NSCs, pre-iPSCs and the iPSCs generated. As expected, PI-NSCs expressed high levels of both Oct4 and Klf4 retroviral transgenes ( Fig. 1C ). Although lower, retroviral genes expression was still present in pre-iPSCs ( Fig. 1C ). The expression levels of the neural markers Nestin and Olig2 were downregulated in pre-iPSCs, although the expression of pluripotency markers such Oct4 , Nanog and Zfp42 remained practically undetectable in these cells ( Fig. 1D ). These results confirmed that pre-iPSCs were an intermediate state in which critical attributes of true pluripotency, including stable expression of endogenous Oct4 and Nanog, were not attained yet. Complete downregulation of retroviral transgenes, essential for full reprogramming, was corroborated in finally derived iPSCs compared to the original infected NSCs and to pre-iPSCs ( Fig. 1C ). This was accompanied by the stable induction of endogenous pluripotency-related genes Oct4, Nanog and Zfp42 ( Fig. 1D ) consistent with the acquisition of a pluripotent state. Moreover, the expression of neural specific genes such as Nestin and Olig2 , was completely absent in iPSCs ( Fig. 1D ). Immunocytochemistry studies confirmed the presence of mCherry in pre-iPSCs but not in iPSCs ( Fig. 1E,F and S1B ). Immunocytochemistry for OCT4 and NANOG, together with histochemistry against alkaline phosphatase activity confirmed the pluripotency of the generated iPSCs ( Fig. 1F and S1C ). We next corroborated the naive pluripotency of iPSCs by evaluating the reactivation of the inactive X chromosome in female iPSCs ( Janiszewski et al , 2019 ). RNA levels of Xist , responsible for X chromosome inactivation were significantly reduced in iPSCs together with an increase in Tsix expression ( Fig. S1D ) confirming the X chromosome reactivation and thus the acquisition of a fully pluripotent state in iPSCs. Moreover, genetic variations, such as aneuploidy or polyploidy, may be introduced during the generation of iPSCs ( Vaz et al , 2021 ). Karyotype analysis in iPSCs clones revealed that the majority of the analysed lines (93%) exhibited a normal karyotype with around 40 chromosomes per metaphase ( Fig. S1E ) and iPSCs lines with chromosomal abnormalities were excluded from further studies. iPSCs derived from adult NSCs differentiate in vitro and in vivo into cells from the three germ layers Pluripotent stem cells have the potential to differentiate into cells of the three germ layers: mesoderm, endoderm and ectoderm. This differentiation potential is typically confirmed by demonstrating the capacity of the iPSCs to form three-dimensional structures called embryoid bodies (EBs), comprising characteristic cell types of these germ layers ( Höpfl et al , 2004 ). EBs emulate the structure of developing embryos and serve as a model to obtain various cell lineages ( Spelke et al , 2010 ). Thus, to explore the developmental capacity of the iPSCs generated from adult NSCs, we induced EBs formation by subjecting cells to conditions that are adverse to pluripotency and proliferation, using the hanging drop method ( Höpfl et al , 2004 ) ( Fig. 2A ). Suspended iPSCs on the dish lid aggregate at the base of the drop consistently generating uniform EBs ( Fig. 2A ). ESCs and ESCs-derived EBs were utilized as a comparative condition of differentiation ( Fig. S2 ). Download figure Open in new tab FIGURE 2. iPSCs generated from NSCs are able to differentiate into cells of the three germ layers in vitro and in vivo . (A) Schematic representation of the embryoid bodies (EB) assay using the “ hanging drops ” method. iPSCs were dissociated and the cell suspension (30 cells/μL) was distributed in drops in a plate that was incubated upside-down for 3 days in vitro (DIVs) in EB medium. Then, the plate was inverted and culture media was added. Incipient EBs were incubated 4 more DIVs in floating conditions. Then, EBs were seeded in gelatine pre-treated plates to allow differentiation for 3 more DIVs before analysis. (B) qPCR expression analysis of pluripotency-related genes Oct4 , Nanog and Zfp42 genes in NSCs (beige), iPSCs (green) and iPSCs-derived EBs (brown) (left panel). Phase contrast image of an EB is included (right panel). (C) qPCR analysis of Kdr1 and Afp (mesoderm), Foxa2 and Meox1 (endoderm), and Zic1 and Cer1 (ectoderm) in iPSCs (green) and iPSCs-derived EBs (brown). (D) ICC detection of the pluripotency marker NANOG (red) and the different germ layer markers α-fetoprotein (green, endoderm) (upper panel), βIII-tubulin (green, ectoderm) and Brachyury (red, mesoderm) (middle panel), and α-SMA (green, mesoderm) and GATA4 (red, endoderm) (lower panel) in iPSCs-derived EBs. (E) Image of the dorsolateral area of immunocompromised Nude mice 2 weeks after the injection of iPSCs, including a detailed image of the formed teratoma after its extraction (left panel). Histological analysis of teratomas using haematoxylin-eosin staining (right panel). Muscle fibres derived from mesoderm, columnar epithelium derived from endoderm and epithelial cells derived from ectoderm are indicated with arrowheads. Gapdh was used as a housekeeping gene for qPCR normalization. DAPI was used to counterstain nuclei in immunofluorescence images. Scale bars in b: 10 μm; in d: 50 μm; and in e: 1 cm (left panel) and 20 μm (right panel). P-values and number of samples are indicated. In box and whiskers plots, the mean is indicated as + and whiskers represent the maximum and minimum values. In bar plots, error bars show s.e.m. Initially, we determined the gene expression levels of pluripotency and differentiation genes in iPSCs-derived EBs using qPCR analysis. The results showed a downregulation for the pluripotency markers Oct4 , Nanog and Zfp42 upon differentiation ( Fig. 2B ). Additionally, a significant increase for the mesoderm markers Kdr1 and Afp , the endoderm markers Foxa2 and Meox1, and the ectoderm markers Zic1 and Cer1 was observed, confirming the presence of cells from the three germ layers within the generated EBs ( Fig. 2C and S2A) . The presence of the differentiated cells from all three germ layers was further confirmed by immunocytochemistry with specific antibodies against α-fetoprotein and GATA4 (endoderm), βIII-tubulin (neuroectoderm), and Brachyury and α-SMA (mesoderm) ( Fig. 2D and S2B ). We next evaluated the in vivo pluripotent capacity of iPSCs derived from adult NSCs using the teratoma assay ( Prokhorova et al , 2009 ). Teratomas are non-malignant tumours that result from uncontrolled expansion and disorganized differentiation of pluripotent cells. When ESCs are transplanted into immunocompromised Nude mice, they trigger teratoma formation ( Prokhorova et al , 2009 ). In this study, adult NSCs-derived iPSCs were injected into the dorsolateral area in the subcutaneous space of Nude mice ( Fig. 2E ). Subsequent histological analysis of the teratomas, revealed disorganized tumoral cytoarchitecture and confirmed the presence of cells representing all three germ layers, such as ectodermal secretory epithelium, mesodermal cartilage and endodermal gut epithelium derivatives ( Fig. 2E ). Together these findings confirmed the in vitro and in vivo pluripotency of the iPSCs derived from adult NSCs. Expression of imprinted genes is altered during the reprogramming of NSCs into iPSCs To investigate the transcriptional changes associated to the reprogramming process, the transcriptome of both adult NSCs and derived iPSCs was profiled with RNA-seq (GSE282749). The principal component analysis (PCA) clearly distinguished iPSCs from NSCs ( Fig. 3A ). Comparison of NSCs versus iPSCs unveiled 11,986 differentially expressed genes, representing more than 37% of all genes expressed in NSCs ( Fig. 3B,C and S3A ). Among these alterations, 5,633 genes were downregulated (17,4% of all genes expressed in NSCs) while 6,353 were upregulated (19,6%) in iPSCs compared to adult NSCs ( Fig. 3C and S3A ). Gene Ontology (GO) terms analysis showed several biological processes altered in iPSCs compared to NSCs ( Fig. S3B ). Among upregulated genes, processes such as nucleotide metabolism, mitochondrial gene expression and translation or biogenesis of ribonucleoprotein complexes were enriched; while downregulated genes showed enrichment in categories such as forebrain development, neurogenesis and signal transduction (mainly Wnt signalling) ( Fig. S3B ). As expected, RNA-seq analysis confirmed the upregulation of key pluripotency-associated genes in iPSCs compared to NSCs, including Oct4, Zfp42, and Nanog ( Fig. 3D ). Conversely, neural-specific genes such as Nestin, Zic1, and Olig2 were downregulated ( Fig. 3D ). Download figure Open in new tab FIGURE 3. Expression of imprinted genes in adult NSCs is regulated during the reprogramming process. (A) Principal component analysis (PCA) generated with top 500 most variable genes obtained from RNA-seq of 3 NSCs and 2 iPSCs cultures, using normalized (variance stabilized transformation) gene counts. It shows principal components 1 and 2. (B) Heatmap showing the scaled expression (Z-score) of all differentially expressed genes (DEGs) obtained comparing gene expression levels of iPSC to adult NSCs cultures. (C) Volcano plot for all expressed genes based on RNA-seq data. The number of significantly downregulated (blue) and upregulated (red) genes in iPSCs compared to NSCs is indicated inside the circles. (D) RNA-seq log 2 (fold change) of gene expression in iPSCs compared to NSCs for the three pluripotency-related genes Oct4, Zfp42 and Nanog and for the three neural genes Nestin (Nes) , Zic1 and Olig2 . (E) Heatmap showing the scaled expression (Z-score) of the imprinted DEGs in iPSCs compared to NSCs. (F) Volcano plot for all expressed imprinted genes based on RNA-seq data. The number of significantly downregulated (blue) and upregulated (red) genes in iPSCs compared to NSCs is indicated inside the circles. (G) Representation of the log 2 (fold change) of all imprinted DEGs in iPSCs compared to NSCs. Maternally (left panel) and paternally (right panel) expressed genes are shown separately. Some alterations of genomic imprinting have been observed during reprogramming of somatic cells ( Perrera & Martello, 2019 ; Takikawa et al , 2013 ; Arez et al , 2022 ). These changes have an impact on stem cell plasticity suggesting that genomic imprinting may be a mechanism employed to modulate gene dosage to control stem cell potential ( Ferrón et al , 2011 ; Perez et al , 2016 ). Therefore, we next focused on the study of the regulation of imprinted genes during the reprogramming process using the RNA-seq data obtained in adult NSCs and iPSCs. We identified 78 imprinted genes that were differentially expressed between iPSCs and NSCs, representing 60% of all analysed imprinted genes ( Fig. 3E,F and S3A ). Among them, similar changes in both paternally and maternally expressed genes were observed ( Fig. 3G ). Imprinted genes expression changes were validated by qPCR in NSCs and iPSCs ( Fig. S3C ), confirming that the acquisition of a pluripotent state also associates with significant transcriptional changes of imprinted genes. Acquisition of pluripotency in adult NSCs requires global DNA hypomethylation DNA methylation represents one of several epigenetic mechanisms employed by cells to regulate gene expression during cell fate decisions ( Parry et al , 2021 ). Moreover, previous studies have shown that changes in DNA methylation patterns are essential for successful cell reprogramming ( Hochedlinger & Jaenisch, 2015 ; Lee et al , 2014 ; Takahashi & Yamanaka, 2006 ). In order to characterize methylation changes at a global level, immunoprecipitation of methylated DNA using an antibody against 5-methylcytosine (5mC) followed by high-throughput sequencing (MeDIP-seq) was performed in adult NSCs and NSC-derived iPSCs (GSE282748) ( Fig. S4A ). A PCA of the results showed clear segregation between NSCs and iPSCs ( Fig. 4A ). Previous studies have reported that iPSCs exhibit lower levels of DNA methylation than somatic cells, highlighting DNA demethylation as a crucial chromatin feature for achieving pluripotency ( Lee et al , 2014 ). Consistent with this, the MeDIP-seq analysis revealed genome-wide hypomethylation during the acquisition of the pluripotent state ( Fig. 4B,C and S4B ). Among the regions with altered methylation status, 97% displayed hypomethylation (11,865 regions), while only 3% were hypermethylated (362 regions) ( Fig. 4B ). This global hypomethylation was particularly pronounced at promoters and transcription start sites (TSS), as demonstrated by overlapping methylation changes with the 15 chromatin states model ( Vu & Ernst, 2023 ) ( Fig. S4C ). To explore the relationship between transcriptional changes and DNA methylation, RNA-seq and MeDIP-seq data were integrated ( Fig. 4C ). The average methylation signal at the TSS revealed that upregulated genes exhibited a sharper methylation drop in iPSCs compared to the downregulated ones ( Fig. 4D ). Furthermore, the overlap of DMRs with promoter regions showed significant greater hypomethylation in upregulated genes compared to downregulated and unaltered genes ( Fig. S4D ). Gene ontology (GO) analysis of the intersected data revealed that upregulated genes with hypomethylated promoters were enriched in biological processes related to cell division and metabolism, while downregulated genes with hypermethylated promoters were associated with neural development ( Fig. 4E ). Download figure Open in new tab FIGURE 4. A global hypomethylation is observed in iPSCs genome compared to adult NSCs. (A) PCA from MeDIP-seq of 3 NSCs and 2 iPSCs cultures based on normalized (variance stabilized transformation) counts. (B) Volcano plot showing the differential methylation signal between iPSCs and NSCs. The number of significantly hypomethylated (purple) and hypermethylated (yellow) regions in iPSCs compared to NSCs is also displayed. (C) Heatmap representing both normalized expression and methylation of the whole genome in NSCs and iPSCs. Differential expression (DE) is shown in red (upregulation) and blue (downregulation). Methylation changes (MC) are shown in yellow (hypermethylation) and purple (hypomethylation). Non-significant (NS) changes are shown in black. (D) Distribution of 5mC methylation signal around transcription start sites (TSS) of upregulated genes (upper panel) and dowregulated genes (lower panel). (E) Gene ontology (GO) analysis of biological processes for gene groups identified with RNA-seq and MeDIP-seq data intersection. Dot size denotes the fold enrichment of each ontology over the background. DMR: differentially methylated region. IG-DMR escapes global DNA hypomethylation during acquisition of pluripotency in adult NSCs Given the variability in the extent and nature of the methylation changes at ICRs reported during pluripotency, we analysed the methylation dynamics across all described imprinted clusters ( Santini et al , 2021 ) during adult NSCs reprogramming. Consistent with the genome-wide trends we previously observed, hypomethylation occurred in both germline and somatic ICRs in iPSCs compared to adult NSCs ( Fig. 5A ). Specifically, MeDIP-seq analysis revealed that 24 out of the 25 germline ICRs (96% of all described gDMRs) and 7 out of the 13 somatic ICRs (54% of all described sDMRs) were hypomethylated in iPSCs relative to NSCs ( Fig. 5A ; Tables S1 and S2 ). Notably, the IG-DMR previously reported to be hypermethylated in adult NSCs ( Ferrón et al , 2011 ), retained its hypermethylated status in iPSCs ( Fig. 5A ; Tables S1 and S2 ). To validate these findings, bisulfite DNA treatment followed by pyrosequencing was performed, confirming the hypomethylation of most ICRs and the associated loss of imprinting in these clusters ( Fig. 5B ). Furthermore, the hypermethylation of the IG-DMR in both NSCs and iPSCs was confirmed ( Fig. 5B ), demonstrating that this ICR resists the wave of hypomethylation occurring during iPSC reprogramming. Download figure Open in new tab FIGURE 5. Alterations of methylation and gene expression are observed in different imprinting clusters. (A) Volcano plot representing the methylation change between iPSCs and NSCs of different imprinting control regions (ICRs). The number of hypomethylated (purple) and hypermethylated (yellow) DMRs is shown. (B) MeDIP-seq methylation signal representation at ICR loci in NSCs and iPSCs (left panels). DNA methylation quantification by pyrosequencing at specific ICRs in NSCs and iPSCs are shown (right panels). Genomic positionss based on the Genome Reference Consortium Mouse Build 38 (GRCm38/mm10) are also indicated. (C) Heatmap representing both normalized expression (DE) and methylation (IMC) changes of imprinted clusters. Imprinted genes are cluster-organized, showing the methylation status of each ICR in iPSCs compared to NSCs. In bar plots, error bars show s.e.m. To investigate whether the observed changes in DNA methylation at ICRs were associated with alterations in imprinted genes expression, we compared transcriptomic data with the MeDIP-seq results in iPSCs and NSCs ( Fig. 5C ). We found that 25% of the changes in imprinted gene expression correlated with the loss of methylation at ICRs in iPSCs ( Fig. 5C ). Focusing on the IG-DMR, which regulates several genes within the Dlk1-Dio3 cluster ( Ferrón et al , 2011 ; Montalbán-Loro et al , 2021 ) ( Fig. S5A ), we observed that the expression levels of Rtl1 remained unchanged in iPSCs compared to adult NSCs, as expected ( Fig. 5C ). Surprisingly, other genes within the cluster, such as Begain , Dlk1, Meg3, and Dio3 were upregulated in iPSCs ( Fig. 5C ). The upregulation of Dlk1 and Meg3 could not be attributed to methylation changes at their somatic DMRs, as Dlk1DMR was hypomethylated and Meg3DMR remained hypermethylated in iPSCs ( Fig. 5C ). Interestingly, while the promoter region of some genes in the cluster ( Meg3 and Rtl1 ) showed no detectable methylation signal ( Fig. S5B ), significant hypomethylation was observed at the promoters of the upregulated genes Begain , Dlk1, and Dio3 ( Fig. S5B ). This suggests an additional transcriptional mechanism regulating gene dosage within this cluster. Together, these findings demonstrate that although the IG-DMR resists the widespread hypomethylation associated with reprogramming, the altered expression of genes within the Dlk1-Dio3 locus arises from transcriptional mechanisms independent of genomic imprinting. TET3-mediated transcriptional regulation of Trim28 preserves IG-DMR hypermethylation in iPSCs We have demonstrated that the IG-DMR resists the wave of hypomethylation occurring in most ICRs in during the acquisition of a pluripotent state. TET enzymes are dioxygenases that convert 5mC to 5hmC resulting in the removal of methylation marks ( Wu & Zhang, 2017 ) and their role has been demonstrated to be critical for iPSCs reprogramming ( Sardina et al , 2018 ). qPCR analysis showed that Tet3 is the most abundant member of the Tet dioxygenases in NSCs isolated from the adult SVZ ( Montalbán-Loro et al , 2019 ) ( Fig. 6A and S6A ). To investigate the potential role of TET3 in the demethylation process of ICRs during the acquisition of a pluripotent state, a murine genetic model with a conditional deletion of Tet3 in Gfap-expressing cells (mainly NSCs and astrocytes) was generated (see methods). Tet3 expression ablation was confirmed in NSCs isolated from the SVZ of Tet3-deficient ( Gfap-Tet3 KO ) compared to wild-type ( Gfap-Tet3 WT ) ( Fig. S6B ). To induce reprogramming, Gfap-Tet3 KO and Gfap-Tet3 WT derived NSCs were co-transduced with the combination of Oct4, Klf4 and mCherry-encoding retroviral supernatants as previously described ( Fig. 1B and S6C-E ). To confirm the full reprogramming of Tet3KO NSCs into iPSCs, pluripotency markers and silencing of neural genes were analysed by qPCR in several iPSCs clones ( Fig. 6B and S6F ). Although the expression levels of the pluripotency-related gene Zfp42 and the neural gene Olig2 were indistinguishable between wild-type and Tet3 -deficient iPSCs ( Fig. S6F ), an incomplete upregulation of Nanog and downregulation of the neural marker Nestin were observed in Tet3 -deficient iPSCs compared to wild-type ( Fig. 6B ). These results suggest that absence of TET3 impairs acquisition of full pluripotency of adult NSCs. Download figure Open in new tab FIGURE 6. TET3 mediates IG-DMR methylation protection by regulating Trim28 and Oct4 gene expression. (A) qPCR quantification of Tet1 , Tet2 and Tet3 in NSCs, pre-iPSCs and iPSCs. (B) qPCR quantification of the neural marker Nestin and the pluripotency marker Nanog in wild-type (WT) and Tet3 -deficient (Tet3KO) NSCs and iPSCs. (C) Schematic of the protocol used to differentiate iPSCs into neuroectoderm (NE). iPSCs are disaggregated and re-plated in gelatin-treated plates at a 1.5 ×10 4 cells/cm 2 density in N2B27 supplemented medium. Seven days after cells are analyzed. (D) Percentage of Nestin and βIII-tubulin positive cells in iPSCs and NE cultures of both genotypes (left panel). Immunocytochemistry images of Nestin (red) and βIII-tubulin (green) in NE cultures of both genotypes (right panel). (E) Quantification by pyrosequencing of the percentage of methylation Snurf-Snrpn DMR and IG-DMR in NSCs and iPSCs from both WT and Tet3KO cultures. Gray dashed line indicates the percentage of methylation in control brain samples. (F) Heatmap showing ChIP-seq peaks of transcription factors and other proteins in pluripotent stem cells (ChIP-Atlas). Colours correspond to the number of occurrences (number of peaks) each protein binds to different ICRs. (G) qPCR analysis of Trim28 , Oct4 and Zfp57 in NSCs and iPSCs from WT and Tet3KO mice. Gapdh was used as a housekeeping gene for qPCR analysis. DAPI was used to counterstain DNA. P-values and number of samples are indicated. In box and whiskers plots, the mean is indicated as + and whiskers represent the maximum and minimum values. In bar plots, error bars show s.e.m. To determine whether Tet3 deficiency affects the pluripotency of iPSCs, we induced EB formation using the hanging drop method ( Fig. 2A ). Expression analysis of differentiation genes in Tet3KO EBs revealed a significant upregulation of mesoderm ( Kdr1) , endoderm ( Foxa) and ectoderm ( Cer1) markers ( Fig. S6G ). Consistently, immunocytochemistry for Brachyury (mesoderm), α-fetoprotein (endoderm) and βIII-tubulin (neuroectoderm) confirmed that Tet3KO iPSCs could give rise to cells from all three germ layers ( Fig. S6H ). To further evaluate the differentiation potential of Tet3- deficient iPSCs, we directed their differentiation into neuroectoderm (NE) using LIF-free medium with N2 and B27 serum-free supplements ( Fig. 6C ). qPCR analysis of neural markers Nestin and Tubb3 showed that Tet3KO iPSCs failed to achieve physiological expression levels of these genes ( Fig. S7A ). Additionally, immunocytochemistry of Tet3KO NE cultures showed a significant reduction in the percentage of Nestin + and βIII-tubulin + cells compared to wild-type iPSCs ( Fig. 6D ). These findings demonstrate that TET3 plays a critical role in the acquisition of naive pluripotency during the reprogramming of adult NSCs and is essential for proper neural differentiation. To investigate whether the loss of differentiation capacity in Tet3-deficient iPSCs correlates with altered methylation levels at ICRs, we performed bisulfite conversion followed by pyrosequencing of the Snrpn-DMR in wild-type and Tet3KO iPSCs. NSCs from both genotypes were also analysed. As previously shown, wild-type iPSCs exhibited hypomethylation at the Snrpn-DMR compared to NSCs ( Fig. 6E ). Similarly, Tet3 KO iPSCs also showed hypomethylation of this DMR ( Fig. 6E ), indicating that TET3 does not regulate the methylation status of this specific DMR in iPSCs. Notably, the hypermethylation pattern observed at the IG-DMR in wild-type NSCs and iPSCs confirmed its protection during reprogramming ( Fig. 6E ). However, a significant loss of methylation was observed in Tet3KO iPSCs ( Fig. 6E ), demonstrating that TET3 is crucial for maintaining IG-DMR methylation levels throughout the reprogramming process. To elucidate the specific role of TET3 in safeguarding IG-DMR methylation during reprogramming, we performed an in silico analysis of TET3 binding using chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) data from the ChIP-Atlas database ( Zou et al , 2024 ). The analysis showed that TET3 does not directly bind to the IG-DMR ( Fig. S7B ). This finding led us to hypothesize that TET3 might protect IG-DMR methylation in iPSCs by regulating the expression of genes encoding proteins that bind this ICR. To test this hypothesis, we used the ChIP-Atlas database to identify proteins binding to the IG-DMR ( Zou et al , 2024 ). Among these, TRIM28 (also known as KAP1 or TIF1-beta) was identified as a protein with high binding occurrences at the IG-DMR ( Fig. 6F ). TRIM28 interacts with the KRAB domain zinc finger protein ZFP57, which serves as a scaffold for recruiting multiple epigenetic factors ( Messerschmidt, 2012 ). Consistent with this, our analysis also identified ZFP57 as an IG-DMR-binding protein ( Fig. 6F ). Additionally, OCT4, which has been implicated in driving ICR hypomethylation in post-implantation embryos ( Zimmerman et al , 2013 ), was also found to bind the IG-DMR ( Fig. 6F ). qPCR analysis revealed upregulation of Trim28 , Oct4 and Zfp57 in wild-type iPSCs compared to NSCs ( Fig. 6G and S7C ). Notably, Trim28 upregulation was not observed in Tet3KO iPSCs ( Fig. 6G ), while Oct4 levels were significantly higher in Tet3KO iPSCs than in wild-type iPSCs ( Fig. 6G ) and remained elevated even after differentiation into neuroectoderm ( Fig. S7D ). In contrast, Zfp57 expression was unaffected in Tet3KO iPSCs ( Fig. 6G ). These observations align with ChIP-seq data showing TET3 binding at the promoters of Trim28 and Oct4 ( Fig. S7E ), suggesting that TET3 regulates these genes. Together, these findings reveal a non-canonical role for TET3 in protecting the IG-DMR from global hypomethylation during reprogramming potentially modulating Trim28 and Oct4 expression ( Fig. S7F ). Discussion Our study uncovers a novel mechanism in which the dioxygenase TET3 plays a pivotal role in maintaining methylation levels during the reprogramming of adult NSCs to iPSCs. Reprogramming with the transcription factors Oct4 and Klf4 induces global gene expression changes and a widespread loss of DNA methylation, including at ICRs. Remarkably, we found that the IG-DMR, the ICR regulating the imprinted Dlk1-Dio3 cluster on mouse chromosome 12, is uniquely protected from this demethylation wave. This protection depends critically on TET3, which acts indirectly through transcriptional regulation of Trim28 and Oct4 . Our model suggests that TRIM28 protein binds to the IG-DMR and recruits DNMT enzymes to preserve DNA methylation during reprogramming. However, in the absence of TET3, TRIM28 is not expressed at sufficient levels to protect the IG-DMR, allowing OCT4 to bind and promote its demethylation. Importantly, the loss of TET3 not only disrupts the maintenance of methylation at the IG-DMR but also impairs the differentiation potential of the resulting iPSCs. This suggests that the epigenetic instability caused by TET3 deficiency compromises the ability of iPSCs to properly execute lineage-specific differentiation programs. These findings highlight a previously unrecognized role of TET3 in preserving the epigenetic status of the Dlk1-Dio3 cluster and ensuring the functional integrity of pluripotent stem cells, emphasizing its importance for successful reprogramming and differentiation. Through genome-wide expression analysis using next-generation sequencing, we demonstrate that various markers are activated or repressed following reprogramming induction, both in iPSCs and in the adult NSCs of origin. Specifically, as expected, the repression of neural markers such as Nestin and Olig2 correlates with the activation of pluripotency genes like Oct4 , Nanog , and Zfp42 in iPSCs. Furthermore, a GO analysis reveals that multiple gene sets, associated with key biological pathways, are significantly upregulated or downregulated in iPSCs compared to adult NSCs, confirming that the transition to pluripotency involves broad transcriptomic changes. Our study also highlights that epigenetic remodelling during the reprogramming of NSCs into iPSCs is critical for pluripotency induction. A global change in the DNA methylation is necessary for achieving true pluripotency. Detailed analysis of the methylation profiles of pluripotency-associated genes showed DNA hypomethylation, particularly at the promoter regions, while neural-associated genes exhibited increased methylation, consistent with their repression. Imprinted genes, which are expressed monoallelically from either the maternal or the paternal chromosome ( Tucci et al , 2019 ), represent a unique epigenetic subset that poses additional challenges during reprogramming. Consistently, our RNA-seq data show that approximately 60% of all imprinted genes analyzed undergo transcriptional changes in iPSCs derived from NSCs, suggesting that the regulation of imprinted gene expression plays a critical role in both NSC behaviour and the reprogramming process. DNA methylation at ICRs is essential for both the establishment and maintenance of genomic imprinting during development ( Bartolomei & Ferguson-Smith, 2011 ). Germline DMRs are crucial for initiating monoallelic expression during development, while somatic DMRs play pivotal roles in maintaining imprinting in somatic cells ( Ferguson-Smith, 2011 ). Naïve iPSCs exhibit several features similar to early mammalian embryos, including global genome hypomethylation, which is accompanied by a widespread loss of DNA methylation at imprinted loci ( Perrera & Martello, 2019 ). In line with this, our findings reveal that the majority of ICRs in imprinted clusters are also hypomethylated in iPSCs. These epigenetic alterations correlate with changes in the expression levels of several imprinted genes within these clusters, as observed by RNA-seq, suggesting that the reprogramming process contributes to the regulation of genomic imprinting during the acquisition of pluripotency. Previous studies from our group have revealed that differential DNA methylation at the IG-DMR on chromosome 12 is not maintained in adult NSCs and niche astrocytes in the adult mouse brain ( Ferrón et al , 2011 ). In these cells, the IG-DMR becomes hypermethylated, leading to bi-allelic expression of the Dlk1 gene, in contrast to the mono-allelic expression observed in embryos ( Ferrón et al , 2011 ; Montalbán-Loro et al , 2021 ). Studies from other groups have proposed the Dlk1-Dio3 cluster as a potential exclusive marker for iPSCs, noting that iPSCs with normal Dlk1-Dio3 expression generate high-grade chimeras, while those with silenced expression contribute poorly to chimeras ( Benetatos et al , 2014 ). Contradictory findings suggest that this locus may not be critical for reprogramming, as they report no significant role for Dlk1-Dio3 in iPSC formation ( Stadtfeld et al , 2010 ; Li et al , 2011 ; Pham et al , 2022 ). We reveal here that the IG-DMR resists the wave of hypomethylation occurring during adult NSC reprogramming, thereby maintaining the original hypermethylated status at this locus in iPSCs. We postulate that gain of methylation in NSCs and further maintenance of IG-DMR hypermethylation in iPSCs may be essential for achieving full pluripotency during reprogramming. We also propose that the methylation level of the Dlk1-Dio3 cluster may be a more reliable pluripotency marker, suggesting a clear influence of the Dlk1-Dio3 locus in iPSCs pluripotency. Many questions remain regarding how mechanistically genomic imprinting is established, maintained and then erased during reprogramming. It is well known that DNA demethylation proceeds through one of two distinct mechanisms: passive loss of 5mC during DNA replication via suppression of DNMT activity or active demethylation by TET ( Hill et al , 2014 ; Tahiliani et al , 2009 ). Active demethylation is initiated through the progressive oxidation of 5mC to 5hmC ( Ito et al , 2011 ), after which demethylation is achieved ( Hashimoto et al , 2012 ). While Tet1 and Tet2 expression is induced during iPSC generation, a higher level of expression of Tet3 in adult NSCs is observed ( Montalbán-Loro et al , 2019 ). Here we address the potential role of TET3 in preserving IG-DMR methylation during iPSCs reprogramming by using a murine model with conditional Tet3 deletion in Gfap -expressing cells. Our results confirm that Tet3 -deficient NSCs showed incomplete reprogramming into iPSCs, with reduced upregulation of Nanog and diminished downregulation of Nestin , indicating defective pluripotency acquisition. Tet3KO iPSCs maintain their ability to differentiate into all three germ layers, however, when directed towards neuroectoderm differentiation, they also fail to achieve proper neural differentiation, suggesting a critical role for TET3 for the acquisition of naïve pluripotency during reprogramming. Further methylation analysis using pyrosequencing of the Snrpn-DMR revealed no significant differences in methylation between wild-type and Tet3KO iPSCs. However, Tet3KO iPSCs exhibited a marked loss of methylation at the IG-DMR, indicating that TET3 is necessary to maintain methylation of this ICR during reprogramming. In silico analysis of TET3 binding revealed that TET3 does not directly bind to the IG-DMR, but it likely regulates genes encoding proteins that bind this region. In line with this, we identify TRIM28 and ZFP57 as key proteins with significant binding activity at the IG-DMR in iPSCs. These two proteins have been reported to interact to protect methylation imprints at the IG-DMR from both active and passive demethylation during preimplantation development ( Alexander et al , 2015 ). Gene expression analysis confirmed that genes encoding for these two proteins are upregulated in iPSCs, whereas Trim28 expression was not induced in Tet3KO iPSCs indicating a direct regulation of this gene by TET3. Finally, we demonstrate that OCT4, a key pluripotency factor known to promote hypomethylation of ICRs in post-implantation embryos ( Zimmerman et al , 2013 ), also binds the IG-DMR. Oct4 levels were upregulated during reprogramming and were significantly higher in Tet3 -deficient compared to wild-type iPSCs, suggesting that TET3 also regulates the Oct4 expression. ChIP-seq data showing TET3 binding at the promoters of Trim28 and Oct4 indicate that TET3 may transcriptionally regulate these genes, which, in turn, play opposing roles in regulating IG-DMR methylation. In conclusion, our study reveals that loss of imprinting is observed in iPSCs generated from adult NSCs. This is often regarded as a dysfunctional state that conflicts with the establishment of pluripotency. However, the hypermethylation observed at the IG-DMR in iPSCs already exists in NSCs and is essential for their proper function ( Ferrón et al , 2011 ; Montalbán-Loro et al , 2021 ). Therefore, changes in the methylation at some ICRs could be a pivotal feature in transitioning to pluripotency, indicating that its presence is not inherently detrimental but may reflect a necessary step in the epigenetic reorganization of reprogramming. Moreover, our findings uncover a non-canonical role for TET3 in modulating key pluripotency genes like Oct4 and Trim28 . We propose that the recruitment of TRIM28 to the IG-DMR contributes to the maintenance of IG-DMR methylation in iPSCs. In this context, where the balance between methylation and demethylation is pivotal, the absence of TET3 may facilitate the aberrant binding of OCT4 to the IG-DMR. This would promote the demethylation of this region, disrupting its protective methylation marks and potentially impairing the proper establishment of pluripotency. Therefore, the role of TET3 in protecting IG-DMR methylation by repressing Oct4 and thus restricting OCT4 access to this ICR represents a critical aspect of the epigenetic regulation required for successful reprogramming. Methods Animals and in vivo manipulations The experiments were conducted in C57BL/6 wild-type mice. For the teratoma formation assays, homozygous immunosuppressed Nude (NU/J) mice obtained from the Jackson Laboratory were used. For Tet3 delection in GFAP + NSCs, heterozygous GFAP-cre transgenic animals (6.Cg-Tg(Gfap-cre)73.12Mvs/J) from the Jackson Laboratory were bred to Tet3 loxp/loxp ( Montalbán-Loro et al , 2019 ). Expression of Cre-recombinase under the Gfap promoter results in a deletion of exon 5 of Tet3 gene, causing a frame-shift from exon 6 and a premature stop codon in exon 7 of the gene. Animals were genotyped by PCR analysis of DNA as described ( Montalbán-Loro et al , 2019 ) and littermates lacking GFAP-Cre were used as control mice. All mice were maintained on a C57BL6 background and in a 12 h light/dark cycle with free access to food and water ad libitum and according to the Animal Care and Ethics committee of the University of Valencia. Neurosphere cultures Adult 2- to 4-month-old mice were euthanized by cervical dislocation. To initiate each independent culture, the brains of two different animals were dissected and the regions containing the SVZ were isolated from each hemisphere and washed in Earle’s balanced salt solution (EBSS; Gibco). Tissues were transferred to EBSS containing 12 U mL −1 papain (Worthington DBA), 0.2 mg mL −1 L-cystein (Sigma), 0.2 mg mL −1 EDTA (Sigma) and incubated for 20 min at 37°C. Tissue was then rinsed in EBSS, transferred to Dulbecco’s modified Eagle’s medium (DMEM)/F12 medium (1:1 v/v; Life Technologies) and carefully triturated with a fire-polished Pasteur pipette to a single cell suspension. Isolated cells were collected by centrifugation, resuspended in DMEM/F12 medium containing 2 mM L-glutamine (Gibco), 0.6% glucose (Panreac), 9.6 g mL −1 putrescine (Sigma), 6.3 ng mL −1 progesterone (Sigma), 5.2 ng mL −1 sodium selenite (Sigma), 0.025 mg mL −1 insulin (Sigma), 0.1 mg mL −1 transferrin (Sigma), 2 μg mL −1 heparin (sodium salt, grade II; Sigma) and supplemented with 20 ng mL −1 epidermal growth factor (EGF; Invitrogen) and 10 ng mL −1 fibroblast growth factor ( Belenguer et al , 2016 ) (FGF; Sigma). Neurospheres were allowed to develop for 6 days in a 95% air-5% CO 2 humidified atmosphere at 37 °C. For culture expansion, cells were plated at a relatively high density (75 cell/μl) and maintained for several passages. Reprogramming of adult NSCs into iPSCs To generate iPSCs from adult NSCs, exogenous Oct4 together with Klf4 (2F) were used for reprogramming as previously described ( Kim et al , 2008b ). To produce retroviruses expressing Oct4 and Klf4 , Platinum-E (Plat-E) retroviral packing cells (Cell Biolabs) were transfected with a plasmid solution containing 1 mL of Opti-MEM TM (Gibco), 60 μL of 1mg mL −1 polyethylenimine (PEI, Polysciences) and 20 µg of the retroviral vectors pMXs- Oct4 (#13366, Addgene), pMXs- Klf4 (#13370, Addgene) and pMXs- mCherry (pMX-2A-CH, designed and kindly provided by Dr. Jose Manuel Torres). After 24 hours, Plat-E culture medium (high glucose DMEM containing 10% foetal bovine serum FBS, 2 mM L-glutamine, 1 μg mL −1 Puromycin and 10 μg mL −1 Blasticidin) was replaced by NSCs complete medium. Transfection efficiency was checked by mCherry fluorescence in Plat-E cells ( Fig. S1A ). The day after, retrovirus-containing supernatants were collected and filtered with a 0.45 μm nitrocellulose filter. Neurospheres from female mice were grown for two days were transduced with a mixture of these supernatant (SN) as follows (volume per plate): 3 mL of Oct4 SN, 3 mL of Klf4 SN, 1 mL of mCherry SN and 3 ml of fresh NSCs complete medium. A control of infection was made with a mixture containing 7 ml of mCherry retrovirus containing medium and 3 mL of fresh complete medium. In order to enhance the efficiency of retroviral infection, retrovirus mixture was supplemented with 4 μg mL −1 of polybrene (Sigma). NSCs were then incubated for 14-18 hours at 37°C in a humidified incubator. Infected NSC medium was then replaced with fresh complete medium and neurospheres were allowed to develop for 5 days ( Fig. 1B ). The mouse fibroblast cell line SNL (Cell Biolabs) was used as feeder cells during the reprogramming process. SNL feeder cells were first mitotically inactivated by treatment with 4 μg mL −1 of Mitomycin C (Sigma) for 2-4 hours. Plates were treated with 0.1% of gelatin (Sigma) at 37°C for at least 20 min and then mitomyzed SNLs were plated at high density (2.5×10 6 cell/plate) in gelatine-treated plates (day 7). Five days after transduction, neurospheres were dissociated with Accutase® and 1.5×10 5 of infected NSCs were re-plated on SNL feeder cells with ESC/LIF medium: Glasgow Minimum Essential Medium (GMEM) containing 15% FBS, 2 mM L-glutamine, 1 mM Sodium pyruvate (Gibco) and 1 μM Leukaemia Inhibitory Factor (LIF). ESC/LIF medium was changed every other day until Stage-Specific Embryonic Antigen-1 (SSEA-1; also known as CD15) positive colonies appeared (pre-iPSCs), checked by staining with StainAlive SSEA-1 Antibody (DyLight 488) (Stemgent®, 1:100 dilution) ( Fig. 1F and Fig. S1A ). ESC/LIF medium was replaced with 2i/LIF Neurobasal medium containing B27 supplement; Gibco, 2 mM L-glutamine, 1 mM Sodium pyruvate, 1 mg mL −1 Transferrin, 50 μM Insulin, 16 μg mL −1 Putrescine, 60 ng mL −1 Progesterone, 0.3 μM Sodium selenite, 50 μg mL −1 Bovine serum albumin, 1 μM LIF, 1 μM iMEK (Millipore, PD03259Platinum and 3 μM iGSK3 (Millipore, CHIR99021) which is based on dual inhibition (2i) of mitogen-activated protein kinase (MAPK) signalling and glycogen synthase kinase-3 (GSK3) combined with LIF ( Silva et al , 2008 ). 2i/LIF medium was changed every two days until well-defined iPSCs colonies appeared ( Fig. 1B ). To establish and expand clonal lines of iPSCs, individual colonies were isolated and plated on gelatine treated plates with 2i/LIF medium. The embryonic stem cell (ESC) line E14Tg2a was used as a pluripotency positive control in the different experiments. ESCs were cultured on gelatine-treated plates and 2 days after plating, cells were treated with Trypsin/EDTA and re-plated following a dilution of 1:5 in ESC/LIF medium. Embryoid bodies assays Embryoid bodies were obtained using the “ hanging drops ” method. iPSCs were treated with Accumax® (Millipore) and resuspended in EB medium: GMEM containing 10% FBS, 2 mM L-glutamine and 1 mM Sodium pyruvate. Several rows of 20 μl drops of a cell suspension at 30 cells/ μL were plated using a multichannel pipet ( Fig. 2A ). Plates were incubated upside-down for 3 days at 37°C in a 5% CO 2 humidified incubator. Plates were then inverted and EB medium was added. To avoid EB attachment plates were previously treated with 0.4% poly (2-HEMA) solution (Sigma) prepared in Ethanol:Acetone (1:1). EBs were incubated for 4 more days and then plated on gelatine-treated plates for 3 more days before analysis ( Fig. 2A ). Neuroectoderm differentiation Neuroectoderm (NE) differentiation was performed as previously described( Ying et al , 2003 ). Briefly, iPSCs were treated with Accumax® and resuspended in NE culture medium: DMEM/F12 GlutaMAX™ (containing 25 μg/mL insulin, 100 μg mL −1 transferrin, 6 ng mL −1 progesterone, 16 μg mL −1 putrescine, 30 nM sodium selenite, and 50 μg mL −1 BSA) and Neurobasal (containing 2% B27, 2 mM GlutaMAX™, 0.1% 2-mercaptoethanol, and 1.45% sterile glucose) ( Fig. 6C ). Cells were counted and plated at a density of 1.5 ×10 4 cells/cm 2 in gelatine pre-treated plates ( Fig. 6C ). Cultures were maintained in NE culture medium and the medium replenished every other day for a week ( Fig. 6C ). Teratoma formation and analysis To evaluate the capacity of iPSCs to generate teratomas, mouse iPSCs cultures were collected by treatment with Accumax®. iPSCs were washed in PBS and resuspended in PBS supplemented with 30% Matrigel® (Corning®) ( Prokhorova et al , 2009 ). Cells were kept on ice and drawn into a 1mL syringe immediately before injection. Approximately 1.5×10 6 cells resuspended in 200 μL of solution were injected in the dorsolateral area of the subcutaneous space on both sides of the mice back. Teratomas were allowed to develop for 15-20 days when the size of the teratomas was approximately 1.5-2 cm. Mice were sacrificed by cervical dislocation and teratomas were extracted for analysis. For teratoma analysis, samples were fixed in 4% paraformaldehyde (PFA) overnight at 4°C with shaking. Samples were embedded in paraffin and teratoma samples were serially sectioned into 7 μm sections using a microtome (Leica). Slices were stained with haematoxylin and eosin and cell types from the three embryonic layers were identified under the optic microscope (Nikon Eclipse Ni). Karyotype of iPSCs To perform the karyotype analysis, cell division was inhibited using 0.6 μg mL −1 of KarioMAX® Colcemid (Gibco) at 37°C. After 2 hours, culture medium was removed and 0.85% sodium citrate, previously warmed at 37 °C, was added. A cell scraper (Biofil®) was used to raise the cells. Cell suspension was transferred to a 15 mL conical tube and incubated at 37°C for 15 min. After that, 10 drops of cold Carnoy fixative (methanol-acetic acid, 3:1) were added to the suspension and softly mixed using a Pasteur pipette. Samples were washed several times with 5 ml of cold Carnoy solution and, after centrifugation (10 min, 300 g ), pellets were resuspended in 2 drops of Carnoy fixative. Cells extensions were made in microscope slides followed by heat fixation. Samples were stained with Leishmańs stain (Sigma). The number of chromosomes was determined under the optic microscope (Nikon Eclipse Ni). Immunocytochemistry and alkaline phosphatase (AP) staining NSCs, iPSCs and EBs were fixed for staining with 4% PFA in 0.1M PBS for 15 min and immunocytochemistry performed as previously described ( Belenguer et al , 2016 ). Primary and secondary antibodies and dilutions used are listed in Table S3 and Table S4 respectively. DAPI (1 μg mL −1 ) was used to counterstain DNA. Samples were photographed and analysed using an FV10i confocal microscope (Olympus). Alkaline phosphatase detection method was used in reprogrammed cells to check the presence of iPSCs after one month in 2i/LIF medium on feeders . Cells were fixed with cold methanol for 2 min and washed three times with 0.1M tris-HCl pH 8.5 buffer. Samples were incubated with the “ staining solution ” which contained 0.1 mg mL −1 naphtol phosphate (Sigma), 0.5% Dimethylformamide (Sigma) and 0.6 mg mL −1 Fast Red Salt (Sigma) in 0.1M tris-HCl pH 8.5. When red precipitate appeared, cells were washed with 0.1M tris-HCl and distilled water. Finally, the different plates were photographed using a dissection microscope. Expression studies RNAs were extracted with RNAeasy Mini Kit (Qiagen) including DNase treatment, following the manufacturer’s guidelines. For qPCR, 1 μg of total RNA was reverse transcribed using random primers and RevertAid H Minus First Strand cDNA Synthesis kit (Thermo Scientific), following standard procedures. Thermocycling was performed in a final volume of 10 μl, containing 4-10 ng of cDNA sample and the reverse transcribed RNA was amplified by PCR with appropriate Taqman probes ( Table S5 ). qPCR was used to measure gene expression levels relative to Gapdh , which expression did not differ among the groups. qPCR reactions were performed in a Step One Plus cycler with Taqman Fast Advanced Master Mix (Applied Biosystems). In case of using SYBR green, thermocycling was also performed in a final volume of 10 μL, containing 4-10 ng of cDNA sample, 0.2 μM of each primer ( Table S6 ) and SYBR® Premix ExTaq TM (Takara) according to the manufacture instructions, using ROX as a reference dye. A standard curve made up of doubling dilutions of pooled cDNA from the samples being assessed was run on each plate, and quantification was performed relative to the standard curve. RNA-seq RNA was isolated using TRIzol® following manufacturer’s instructions. Briefly, 1 mL of reagent was added per 5-10×10 6 cells for lysis during 20 min at room temperature (RT). Then, 100 μL of chloroform was added to samples and incubated at RT for 10 min and centrifuged at 12,000 g centrifugation at 4°C for 10 min. For RNA precipitation, aqueous phase was mixed with 500 μL of isopropanol and incubated for 5 min. Samples were centrifuged 8 min at 12,000 g at 4 °C. RNA pellet was washed in 1 mL of 75% ethanol, vortexed and centrifuged 5 min at 7500 g at 4 °C. Then, RNA pellet was resuspended in RNase-free water and stored at −80°C until use. Library preparation and high-throughput sequencing were performed by the Central Service for Experimental Research (SCSIE) at the University of Valencia. Libraries were generated from triplicated biological samples per condition using the Illumina TruSeq stranded mRNA Sample Preparation Kit v2 following the manufacturer’s protocol and sequenced using Illumina NextSeq 500. Read quality was assessed with FastQC . Expression was quantified at gene level with salmon ( Patro et al , 2017 ) in pseudomapping mode, with automatic library detection (-l A) and sequence bias correction (--seqBias) using the Gencode release M23 as reference ( Frankish et al , 2020 ). Gene expression quantification was imported into R with package tximeta ( Love et al , 2020 ) and differential expression analysis was performed with DESeq2 ( Love et al , 2014 ). Gene ontology (GO) analysis was conducted with R package clusterProfiler ( Wu et al , 2021 ). Ggplot2 was used for visualizations and dplyr , tibbl e and tidyr packages for data wrangling ( Wickham et al , 2019 ). Heatmap visualizations were performed with Complex Heatmap package ( Gu et al , 2016 ) and scaled variance stabilized counts. MeDIP-seq DNA was extracted with DNeasy Blood and Tissue Kit (Qiagen) following the manufacturer’s instructions. Samples were eluted in 100 μLof elution buffer and DNA concentration was measured using a Nanodrop 1000. MeDIP-seq protocol was adapted from Taiwo et al ., 2012 ( Taiwo et al , 2012 ). For immunoprecipitation, 3 μg of DNA were sonicated to obtain 150-200 bp fragments. Sonication efficiency was checked by capillary electrophoresis (Bioanalyzer, Agilent). DNA libraries were prepared using NEBNext® Ultra™ II FS DNA Library Prep Kit for Illumina (New England Biolabs). For MeDIP, 1.5 μg of DNA was diluted in TE buffer (10 mM tris-HCl, 1 mM EDTA, pH 7.5) and denatured for 10 min at 99°C. Non-specific interactions were blocked by adding 20 μl of 10x IP buffer (100 mM Na-Phosphate pH 7.0, 0.5% tritonX-100) and 100 μL of 5% skimmed milk buffer in 2 M NaCl. Then 2 μg of anti-5mC antibody (Diagenode, cat. no. C15200006) were added and incubated for 2 h at 4°C with rotation. In parallel, 11 μl per sample of Dynabeads® M-280 sheep anti-mouse IgG (Thermo Fisher, cat. no. 11201D) were blocked with 500 µl PBS-BSA (1 mg mL −1 BSA in 0.1 M PBS) for 2 hours at 4°C with rotation. After incubations, beads were collected in a magnetic rack, re-suspended in the original volume (11 μL) with 1x IP buffer (10 mM Na-Phosphate pH 7.0, 0.05% tritonX-100, 1 M NaCl) and added to the DNA samples, which were incubated overnight at 4°C with rotation. The day after, beads were collected using a magnetic rack and the supernatant (unbound fraction) was transferred to a fresh tube. Beads were washed three times with 500 µl of 1x IP buffer for 10 min with rotation at 4°C. After the final wash, bound and unbound fractions were treated with 0.3 mg/mL of Proteinase K (Roche) in digestion buffer (50 mM Tris-HCl pH 8.0, 10 mM EDTA, 0.5% SDS) and incubated at 55 °C for 30 min on a shaking heating block. Samples were purified using MiniElute PCR purification kit (Qiagen) and eluted in 10 μl of elution buffer. To calculate 5mC enrichment in the bound fraction, quantitative PCRs for unmethylated and methylated regions were done from bound and unbound fractions ( Fig. S4A ). Enrichment should be of at least 25x, specificity should be more than 95% and unmethylated recovery should be less than 1% ( Fig. S4A ). Samples were sequenced in a HiSeq2000 (Illumina, Inc) instrument. Primers used to evaluate MeDIP efficiency and specificity are provided in Supplementary Table S7. MeDIP-seq reads were pre-processed using MEDUSA pipeline ( Wilson et al , 2012 ) with default settings and the mm10 UCSC reference genome. Signal tracks were obtained with Deeptools suite ( Ramírez et al , 2014 ), with the bamCoverage function, applying the following parameters: --scaleFactor X --binSize 1 --blackListFileName -- minMappingQuality 20. The blacklist file was sourced from Boyle Lab Github repository ( Amemiya et al , 2019 ). The scaling factor was determined using the normalization factor obtained from counting reads in 10Kb bins, followed by TMM normalization using the edgeR::calcNormFactors function ( Robinson et al , 2009 ). Biological replicates were combined using the bigwigAverage function from Deeptools, averaging the methylation signal. Read counting, CpG density normalization, and differential methylation analysis were performed using the MEDIPS R package ( Lienhard et al , 2014 ). A sliding window of 100 bp was applied, and a minimum read depth of 10 across all samples was required for inclusion in the analysis. Differential methylation was modeled with the edgeR ( Robinson et al , 2009 ) implementation in the MEDIPS package, using TMM normalization and multiple testing correction via the Benjamini-Hochberg method. Principal component analyisis (PCA) was performed using the DESeq2 package ( Love et al , 2014 ). To ensure robust signal detection, we retained the top 5% percentile windows with the highest signal and discarded those that did not overlap a CpG island or a CpG shore (obtained from UCSC mm10 repository). Adjacent windows were merged by calculating the harmonic mean of p-values using the extraChIPs::mergeByHMP with the following parameters: merge_within = 1, p_adj_method = fdr, alpha = 0.01. Finally, windows were annotated to genes using the ChIPseeker package ( Yu et al , 2015 ). For ICR analysis, we used previously defined ICRs ( Santini et al , 2021 ), completed with additional somatic ICR locations identify in our laboratory ( Tables S1 and S2 ). We extracted windows overlapping ICR genomic regions prior to CpG filtering using the R package plyranges and the join_overlap_inner function ( Lee et al , 2019a ). Adjacent windows were merged, as described previously, with the merge_within parameter set to 300. Gene ontology (GO) analysis, data wrangling and visualization were performed with the aforementioned packages. Metagenes were generated by combining the ComputeMatrix reference-point and plotProfile functions from the Deeptools suite, which represent the methylation signal at nucleotide resolution (--bin Size 1) within a −5000/1500 bp window (-a 1500, -b 5000) around the TSS (--reference Point TSS). Signal tracks were visualized using SparK ( Kurtenbach & Harbour, 2019 ), with averaged the scaled methylation signal across biological replicates over the selected ICR genomic regions. Chromatin states were obtained from Vu and Ernst 2023 ( Vu & Ernst, 2023 ) and overlapped using plyranges and join_overlap_inner function ( Lee et al , 2019b ). DNA methylation analysis by pyrosequencing DNA methylation level was quantified using bisulfite conversion and pyrosequencing. The DNA was bisulfite-converted using EZ DNA Methylation-GoldTM kit (Zymo research) in accordance with the manufacturés protocol. Specifically, for the different DMRs, bisulfite-converted DNA was amplified by PCR with specific primer pairs ( Table S8 ). PCRs were carried out in 20 μL, with 2U HotStar Taq polymerase (Qiagen), PCR Buffer 10x (Qiagen), 0.2 mM dNTPs and 400 mM primers. PCR conditions were: 96 °C for 5 min, followed by 39 cycles of 94 °C for 30 s, 54 °C for 30 s and 72 °C for 1 min. For pyrosequencing analysis, a biotin-labelled primer was used to purify the final PCR product using sepharose beads. The PCR product was bound to Streptavidin Sepharose High Performance (GE Healthcare), purified, washed with 70% ethanol, denatured with 0.2 N NaOH and washed again with 10 mM tris-acetate. Pyrosequencing primer (400 mM) was then annealed to the purified single-stranded PCR product and pyrosequencing was performed using the PyroMark Q96MD pyrosequencing system using PyroMark® reactives (Qiagen). Statistical analysis All statistical tests were performed using the GraphPad Prism Software, version 7.00 for Windows. Data were first tested for normality using Shapiro-Wilk test. The significance of differences between groups was evaluated using appropriate statistical tests for each comparison. For data that passed normality tests: a paired t-test was used when comparing only two groups (applying Welch’s correction in case the standard deviation of groups is different); and one-way ANOVA followed by Šídák’s post-hoc test was applied for comparing three or more groups. For data groups that did not pass normality: Mann–Whitney nonparametric test was performed when comparing only two groups and Kruskal-Wallis test followed by Dunn’s post-hoc test when more than two groups were analysed. When comparisons were performed with relative values (percentages), data were previously normalized by using arcsin root transformation. Values of P<0.05 were considered statistically significant. Data are presented as the mean ± standard error of the mean (s.e.m.) and the number of experiments performed with independent cultures or animals ( n ) and P-values are indicated in the figures. In box and whiskers plots, horizontal lines of the box represent Q 3 , median and Q 1 , + represents the mean, and whiskers represent maximum and minimum values. Author Contribution LLC, EJV, ALU, RML, JDM and ALP carried out most of the experiments. LLC performed gene expression and statistical analysis. EJV performed DNA methylation assays, helped by MI. ALU, RML and JDM performed NSC reprogramming. ALP contributed to develop the reprogramming protocol. JP performed the bioinformatic analysis of transcriptome and methylome data. EJR helped with MeDIP-seq bioinformatic analysis. SRF initiated, designed and led the study, and wrote the manuscript. All authors contributed to experimental design, data analysis, discussion and writing of the paper. Competing financial interest statement The authors declare no competing financial interests. Data availability All relevant data can be found within the article and its supplementary information. Both methylation and expression data supporting the findings of this study have been deposited in Gene Expression Omnibus (GEO) with the accession numbers GSE282749 and GSE282748 respectively. RNA-seq raw counts and scaled MeDIP-seq signal tracks are provided as processed information in the GEO accession. A detailed list of tools used in the analysis of the present study can be found in Supplementary Table S9. Code developed to support current study can be provided upon request. Acknowledgements We firstly would like to thank Dr. Isabel Fariñas, Dr. Anne Ferguson-Smith and Dr. Ángel Raya and their groups for technical support and discussion of the data. This work was supported by grants from Ministerio de Ciencia e Innovación/AEI (PID2019-110045GB-I00, PID2022-142734OB-I00 and EUR2023-143479), Generalitat Valenciana (AICO/2020/367) and Fundación BBVA to SRF. 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Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming Laura Lázaro-Carot , Esteban Jiménez-Villalba , Jordi Planells , Anna Lozano-Ureña , Jennifer Díaz-Moncho , Raquel Montalbán-Loro , Adela Lleches-Padilla , Martina Kirstein , Mitsuteru Ito , Elizabeth J. Radford , Sacri R. Ferrón bioRxiv 2025.02.13.638077; doi: https://doi.org/10.1101/2025.02.13.638077 Share This Article: Copy Citation Tools TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming Laura Lázaro-Carot , Esteban Jiménez-Villalba , Jordi Planells , Anna Lozano-Ureña , Jennifer Díaz-Moncho , Raquel Montalbán-Loro , Adela Lleches-Padilla , Martina Kirstein , Mitsuteru Ito , Elizabeth J. Radford , Sacri R. 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