JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation

JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation | 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 New Results JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation Wentao Han , Hassan Bjeije , Hamza Celik , Michael Rettig , Nancy Issa , Andrew L. Young , Yanan Li , Infencia Xavier Raj , Christine R. Zhang , Aishwarya Krishnan , Tyler M. Parsons , Samantha C. Burkart , Jason Arand , Wei Yang , Jeffrey A. Magee , View ORCID Profile Grant A. Challen doi: https://doi.org/10.1101/2025.09.03.672211 Wentao Han 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hassan Bjeije 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hamza Celik 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael Rettig 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nancy Issa 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew L. Young 2 Division of Hematology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yanan Li 3 Department of Pediatrics, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Infencia Xavier Raj 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christine R. Zhang 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aishwarya Krishnan 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tyler M. Parsons 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samantha C. Burkart 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jason Arand 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wei Yang 3 Department of Pediatrics, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jeffrey A. Magee 3 Department of Pediatrics, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Grant A. Challen 1 Division of Oncology, Department of Medicine, Washington University School of Medicine , St. Louis, MO, USA , 63110 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Grant A. Challen For correspondence: grantchallen{at}wustl.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Hematopoietic stem cell transplantation is a common treatment for many blood disorders and can be a life-saving therapy for patients with leukemias, lymphomas and multiple myeloma. Umbilical cord blood (UCB) serves as a valuable source of hematopoietic stem and progenitor cells (HSPCs) for transplantation, particularly for patients lacking a matched donor. However, the limited number of repopulating cells in UCB units restricts its clinical utility. Our prior studies showed that genetic deletion of the polycomb repressive complex 2 (PRC2) co-factor Jarid2 in mouse multipotent progenitors (MPPs) conveyed ectopic self-renewal capacity. Here, we hypothesized that the function of human HSPCs could be enhanced through JARID2 inhibition. In this study, we demonstrate that both constitutive and transient knockdown of JARID2 increases the number and enhances the functionality of human HSPCs both in vitro and in vivo . This phenotype was distinct from inhibition of EZH2 in UCB cells, suggesting the mechanism was independent of PRC2 co-factor activity of JARID2. Mechanistically, JARID2 knockdown promotes a quiescent, long-term self-renewal gene expression program governed by upregulating STAT1 and characterized by an MHC class II immunophenotype. Analogous to mice, these mechanisms conferred HSC-like potential to human MPPs in vivo . Taken together, these findings highlight JARID2 inhibition as a novel and reversible approach to expand functional UCB-derived HSPCs ex vivo, potentially improving access to stem cell transplantation for a wider patient population. One Sentence Summary Genetic inhibition of JARID2 enhances repopulating activity of human hematopoietic stem and progenitor cells in vivo via STAT1 upregulation. INTRODUCTION Bone marrow transplantation (BMT) is a potentially curative therapy for a variety of hematopoietic malignancies including acute and chronic leukemias, Hodgkin and non-Hodgkin lymphoma, myelodysplastic syndromes, and Fanconi anemia ( 1 ). The functional units of BMT are hematopoietic stem cells (HSCs) contained within the donor graft. The effectiveness of BMT is limited by two primary factors: toxicity (most typically graft-versus-host disease [GvHD]) and donor availability ( 2 , 3 ). Choice of an allogeneic stem cell donor is determined by donor-recipient histocompatibility (based on human leukocyte antigens [HLA]), recipient disease type, recipient condition, and age ( 4 ). Current sources of stem cell donation from adults are matched siblings (preferred), matched unrelated donors, mismatched unrelated donors, or haploidentical donors. However, only about 30% of patients have a matched sibling available, and the likelihood of finding a matched unrelated donor is low for patients from diverse ethnic backgrounds ( 5 ). These challenges have led to the rise of umbilical cord blood (UCB) as a donor source. UCB transplantation is now a standard treatment for both children and adults who do not have a matched sibling or unrelated donor. As the hematopoietic cells from newborn babies are immunologically more naïve, there is less risk of GvHD after UCB transplantation and the patient and donor do not need to be as closely matched ( 6 – 8 ). UCB transplantation is especially useful for patients of racial and ethnic minorities, and UCB sources are readily available (>700,000 UCB units have been stored for transplantation world-wide)( 9 ). Although UCB transplantation has multiple advantages, it is associated with delayed engraftment and immune reconstitution, thereby leading to greater risks of infection and graft failure compared to adult bone marrow or peripheral blood grafts. This is primarily due to the lower content of HSCs within individual cord blood units. Each UCB unit contains a total HSC dose that is one to two logs lower than the dose contained in adult harvests ( 10 ). Double-UCB transplantation was implemented to increase the HSC dose and thereby allow adult patients without sufficient-sized single-cord unit to proceed to BMT. Double-UCB transplantation is associated with less graft failure in adult patients, but may produce graft-versus-graft immune complications ( 11 , 12 ). With the increasing use of UCB in BMT, strong efforts are being made to overcome these limitations. There are intense research efforts aiming to increase UCB-derived HSC numbers or enhance their potency ex vivo . The major challenge is to maintain HSC self-renewal potential while promoting the proliferation of cells with repopulation potential. Upon extraction from their native bone marrow (BM) environment, HSCs rapidly lose self-renewal capacity in culture, undergoing substantial changes at both transcriptomic and epigenomic levels. Various strategies have been developed to overcome this limitation in attempts to maintain or expand primary human HSCs ex vivo . Notable examples include aryl hydrocarbon receptor antagonists (e.g. StemRegenin-1)( 13 ), pyrimidoindole derivatives (e.g., UM171)( 14 ), PPAR-γ antagonists( 15 ) (e.g., GW9662), and Prostaglandin E2 (PGE2) derivatives ( 16 – 18 ), which have all produced mixed success in clinical trials ( 19 , 20 ). Despite these challenges, pursuit of this endeavor remains a worthy goal. During hematopoietic development, epigenetic modifications are crucial in programming lineage determination and cellular identity ( 21 ). As epigenetic modifications are reversible and dynamically remodeled during normal development, manipulation of the epigenetic machinery could provide a novel strategy to bias HSC fate decisions and expand populations with reconstituting potential. Recent work in our laboratory has identified a critical role for the Polycomb Repressive Complex 2 (PRC2) co-factor Jarid2 in regulating hematopoietic repopulating potential. Genetic deletion of Jarid2 in mice confers long-term self-renewal to multipotent progenitor cells (MPPs), the cell population immediately downstream of HSCs in the hematopoietic hierarchy, which possesses only transient repopulating capacity under normal circumstances. As a mechanism, it was found Jarid2 recruits PRC2 to epigenetically silence transcriptional programs associated with HSC cell identity in MPPs through deposition of the H3K27me3 repressive chromatin mark. In the absence of Jarid2 these genes were not epigenetically suppressed in MPPs and sustained expression of genes associated with HSC identity conferred ectopic self-renewal ( 22 ). A prior study suggested genetic inhibition of JARID2 may also enhance human HSC function ( 23 ). With these data in mind, we hypothesized that inhibition of JARID2 could be utilized to epigenetically reprogram human UCB-derived MPPs with HSC-like potential and enhance BMT. RESULTS Long-Term Deletion of JARID2 Produces Durable Hematopoiesis Without Transformation JARID2 is a tumor suppressor in chronic myeloid neoplasms that is lost by hemizygous deletions of the short arm of chromosome 6 during transformation of myeloproliferative neoplasm (MPN) and myelodysplastic syndromes (MDS) patients to secondary acute myeloid leukemia (sAML)( 24 – 26 ). Thus, any potential perturbation of JARID2 in hematopoietic stem and progenitor cells (HSPCs) could carry inherent risk of neoplastic transformation. To assess the safety of long-term JARID2 deletion in human HSPCs, we performed CRISPR/Cas9 targeting of JARID2 in UCB CD34+ cells. 2.5×10 5 edited cells were transplanted into NSG mice and maintained for one year. Targeting efficiencies were similar for two independent guide RNAs (gRNAs) targeting JARID2 and the AAVS1 negative control gRNA ( Fig. S1a ). Peripheral blood (PB) engraftment of human CD45+ (hCD45+) cells reached saturation by eight weeks post-transplantation and remained stable at high levels throughout the study period ( Fig. 1a ). The percentage of targeted cells for the two gRNAs targeting JARID2 was higher than the AAVS1 control, but all remained stable over one year ( Fig. 1b ). After one-year post-transplant, there were no significant difference in the peripheral blood lineage differentiation spectrum between the groups ( Fig. 1c ) in terms of proportions of myeloid cells (CD33+), B-cells (CD19+) and T-cells CD3+). Similarly, lineage output was observed in the BM ( Fig. S1b ) and complete blood count (CBC) analysis revealed no significant differences between the groups ( Fig. 1d ; Fig. S1c ). Histological analysis using hematoxylin and eosin (H&E) and reticulin staining showed no significant pathological changes in the bone marrow or spleens of transplanted mice ( Fig. 1e ). In summary, long-term JARID2 deficiency in human UCB CD34+ cells did not induce notable hematopoietic pathologies in vivo , suggesting that manipulating JARID2 could be a safe translational approach. This is consistent with the observation that JARID2 is not mutated in human clonal hematopoiesis (CH) or is deleted as a founding event in human blood cancers ( 27 ), suggesting that JARID2 loss-of-function cannot operate as an oncogenic event in isolation. Download figure Open in new tab Fig. 1: Long-Term Deletion of JARID2 Produces Durable Hematopoiesis Without Transformation (a) Longitudinal analysis of human CD45+ chimerism in peripheral blood (PB) of NSG mice transplanted with cord blood CD34+ cells targeted with guide RNAs for AAVS1 (n=4), JARID2 #1 (n=5) or JARID2 #2 (n=5). (b) Longitudinal analysis of modified alleles from CRISPR edits in human CD45+ PB cells in transplanted NSG mice. (c) Lineage composition of human CD45+ PB cells one-year post-transplant (B-cells = CD19+, T-cells = CD3+, myeloid cells = CD33+). (d) Complete blood count (CBC) analysis of white blood cells (WBC), red blood cells (RBC), hemoglobin (Hb), hematocrit (HCT) and platelets (PLT) of recipient mice one-year post-transplant. Dashed lines represent normal range. (e) Representative histology of PB smear, bone marrow (BM) cytospins, and spleen (SPL) harvested one-year after transplantation of CRISPR-edited human CD34+ cells into NSG mice. Scale bars - 10µm (PB) and 25µm (BM and SPL). JARID2 Inhibition Enhances Functional Output of Human HSPCs in vivo To facilitate genetic inhibition of JARID2 in human CD34+ cells, lentiviral vectors expressing short hairpin RNAs (shRNAs) and a GFP reporter were constructed ( 28 ). The two most effective shRNAs targeting JARID2 ( Fig. S2a ) relative to the negative control Renilla shRNA (shRen) were chosen for experimentation. UCB CD34+ cells were transduced with GFP-expressing lentiviruses and 48-hours post-transduction 4.0×10 4 GFP+CD34+ cells were transplanted into sublethally irradiated NSG mice. Additionally, excess cells were cultured for eight-days ex vivo to observe effects of reprogramming mediated by JARID2 inhibition. Transduction with JARID2 shRNAs produced more overall cellular output ( Fig. S2b ), increased numbers of CD34+ cells ( Fig. S2c ), and increased colony forming unit (CFU) activity from a fixed volume of cell culture equivalent ( Fig. S2d ). Genetic inhibition by JARID2 -knockdown ( JARID2 -KD) significantly improved peripheral blood engraftment of UCB CD34+ cells relative to the sh Ren control group ( Fig. 2a ) without altering lineage differentiation bias ( Fig. 2b ) or overall blood counts ( Fig. S2e ). At 20-weeks post-transplant, JARID2 inhibition was sustained in targeted BM CD34+ cells ( Fig. S2f ) and engraftment of JARID2 -KD cells was significantly increased in the BM ( Fig. 2c ). Moreover, flow cytometric analysis ( Fig. S2g ) revealed increased numbers of GFP+ HSPCs including hematopoietic stem cells (HSCs; CD34+ CD38- CD90+ CD45RA-), multipotent progenitors (MPPs; CD34+ CD38- CD90- CD45RA-) and multilymphoid progenitors (MLPs; CD34+ CD38- CD90- CD45RA+) resulting from JARID2 -KD ( Fig. 2d ). These results show constitutive JARID2 suppression can increase functionality from transplantation of a limited number of human HSPCs. Download figure Open in new tab Fig. 2: JARID2 Inhibition Enhances Functional Output of Human HSPCs in vivo (a) Peripheral blood (PB) engraftment of cord blood cells transduced with indicated constitutive lentiviral shRNAs. (b) Lineage composition of GFP+ PB cells 16-weeks post-transplant. (c) Engraftment of GFP+ human cells in the BM of recipient mice 20-weeks post-transplant. (d) Quantification of GFP+ human HSPC populations in the BM of recipient mice 20-weeks post-transplant (sh Ren n=16, sh JARID2 #1 n=14, sh JARID2 #3 n=17). (e) PB engraftment of cord blood cells transduced with indicated constitutive lentiviral shRNAs in secondary recipients. (f) Lineage composition of GFP+ PB cells 16-weeks post-secondary transplant. (g) Engraftment of GFP+ human cells in the BM of secondary recipient mice 20-weeks post-transplant. (h) Quantification of GFP+ human HSPC populations in the BM of recipient mice 20-weeks post-transplant (sh Ren n=8, sh JARID2 #1 n=6, sh JARID2 #3 n=6). To more stringently assess long-term HSC self-renewal, 1.0×10 6 CD34+ cells from primary recipients were transplanted into secondary NSG mice. JARID2 -KD cells maintained higher peripheral blood engraftment in secondary recipients ( Fig. 2e ) with normal differentiation ( Fig. 2f ). JARID2 -KD cells showed increased overall engraftment in the BM of secondary recipients ( Fig. 2g ) as well as increased numbers of HSPC populations ( Fig. 2h ). Notably, sh JARID2 #3 produced more pronounced functional enhancement than sh JARID2 #1, which was likely due to dose-dependent JARID2 inhibition. Cumulatively, these data show JARID2 inhibition enhances both the lineage reconstitution and self-renewal capacity of human HSPCs, highlighting therapeutic potential for clinical applications. JARID2 Inhibition Enhances HSC Function Without Altering Lineage Specification or Gene Expression Programs We performed spectral antibody immunophenotypic analysis of primary transplant recipients to more comprehensively characterize the spectrum of differentiated cells produced after JARID2 -KD in UCB CD34+ cells. Representative t-SNE plots ( Fig. 3a ; Fig. S3a ) and heatmaps ( Fig. S3b ) reflecting cell distributions demonstrated comparable cell populations between JARID2 -KD and sh Ren control groups. In a parallel experiment, transduced UCB CD34+ cells were transplanted into NSGS mice to better assess myeloid output ( Fig. S3c ). No differences in myeloid differentiation potential were observed between the groups ( Fig. 3b ). Collectively, these results further demonstrate that long-term JARID2 inhibition does not impair multilineage differentiation. Download figure Open in new tab Fig. 3: JARID2 Inhibition Enhances HSC Function Without Altering Lineage Specification or Gene Expression Programs (a) tSNE plots of lineage distribution of human CD45+ cells in BM of recipient mice 20-weeks post-transplant by flow cytometry. (b) Frequency of maturing myeloid cells in NSGS mice transplanted with cord blood cells transduced with indicated constitutive lentiviral shRNAs 20-weeks post-transplant. (c) JARID2 expression across indicated cell populations showing knockdown efficiency. (d) UMAP of scRNA-seq data for hCD34+ cells isolated 20-weeks post-transplantation. Cell populations are identified by marker genes. (e) Numbers of cells of indicated genotypes per cluster based on Azimuth cell type annotations. To evaluate stem and progenitor cell composition in more granularity, human CD34+ cells were isolated from the BM of primary NSG recipients and subject to single-cell RNA sequencing. Cell identities were assigned based on selected signature genes ( Fig. S3d ) and unsupervised uniform manifold approximation and projection (UMAP) clustering identified distinct hematopoietic lineages ( Fig. S3e ). Efficient knockdown of JARID2 was observed across all sh JARID2 populations ( Fig. 3c ). Quantification of cell numbers within each population between each group ( Fig. 3d ) using Azimuth reference dataset within Seurat showed no significant differences in lineage distribution between groups ( Fig. 3e )( 29 , 30 ). These data suggest that JARID2 inhibition enhances functional capacity of human HSPCs without dramatically altering transcriptional networks or lineage specification programs. Transient JARID2 Inhibition Imparts Long-Term But Reversible Functional Benefits to Human HSPCs We next aimed to more closely simulate clinical stem cell collection and transplantation protocols where UCB CD34+ cells would be transiently exposed to JARID2 inhibition ex vivo prior to transplantation, after which the exposure would be ceased. To facilitate this, doxycycline-inducible lentiviruses were engineered to constitutively express a reverse tetracycline-controlled transactivator (rtTA3) protein from a PGK promoter with separate co-expression of shRNA of interest and blue fluorescent protein (BFP) driven by a Tet-On 3G (T3G) promoter in the presence of doxycycline ( Fig. S4a ). Dose titration experiments identified 200 ng/mL doxycycline as optimal, achieving effective knockdown with minimal cytotoxicity. Vector functionality was validated in HEL cells by Western blot, confirming efficient JARID2 suppression upon doxycycline exposure which was reversed when doxycycline was withdrawn ( Fig. S4b ). The kinetics of vector activity loss after doxycycline withdrawal was confirmed in both the HEL cell line ( Fig. S4c ) and UCB CD34+ cells ( Fig. S4d ). Given that JARID2 is a PRC2 co-factor and prior studies reported 90% of promoters bound by JARID2 in human stem cells were also associated with PCR2 binding (EZH2 / SUZ12)( 31 , 32 ), we generated EZH2 -targeting shRNAs in this inducible lentivirus system ( Fig. S4e ) to determine if similar effects would be recapitulated by PRC2 inhibition. This would begin to determine if the mechanism of functional enhancement of human HSPCs following JARID2 inhibition was PCR2-dependent. UCB CD34+ cells per well were transduced with inducible vectors carrying shRNAs targeting Renilla (negative control), JARID2 or EZH2 . After 48-hour exposure to doxycycline, 4.0×10 4 transduced (BFP+) CD34+ cells were purified and cultured for eight days with doxycycline. The entire progeny of these cultures were transplanted into NSG mice. BFP+ hCD45+ cells became undetectable in the peripheral blood of recipient mice by four-weeks post-transplant, suggesting that shRNA expression was no longer active in vivo in the absence of doxycycline ( Fig. S4f ). Even with only transient inhibition ex vivo , JARID2 -KD enhanced the peripheral blood engraftment of UCB CD34+ cells compared to the sh Ren control group ( Fig. 4a ) with normal lineage differentiation ( Fig. 4b ). In stark contrast, this effect was not observed from the sh EZH2 groups ( Fig. 4a ). The divergent effects observed between JARID2 and EZH2 inhibition suggest the function of JARID2 in HSCs may not be completely tied to its PRC2 co-factor activity, in contrast to what has been reported in human embryonic stem (ES) cells ( 32 ). BM engraftment of human cells was more variable but significantly lower in the sh EZH2 groups ( Fig. 4c ). But human HSPC numbers were consistently increased in the group that received transient inhibition from the more potent JARID2 shRNA ( Fig. 4d ). Download figure Open in new tab Fig. 4: Transient JARID2 Inhibition Imparts Long-Term But Reversible Functional Benefits to Human HSPCs (a) Peripheral blood (PB) engraftment of cord blood cells transduced with indicated inducible lentiviral shRNAs induced for eight-days ex vivo prior to transplant. (b) Lineage composition of hD45+ PB cells 16-weeks post-transplant. (c) Engraftment of hCD45+ cells in the BM of recipient mice 20-weeks post-transplant. (d) Quantification of hCD45+ HSPC populations in the BM of recipient mice 20-weeks post-transplant (sh Ren n=9, sh JARID2 #1 n=6, sh JARID2 #3 n=8, sh EZH2 n=7). (e) PB engraftment of cord blood cells transduced with indicated inducible shRNAs induced in vivo at 14-weeks post-transplant (normalized to sh Ren ). (f) Engraftment of BFP+ hCD45+ cells in the BM of recipient mice (shRNAs for JARID2 and EZH2 are combined for statistical comparison). (g) Quantification of BFP+ hCD45+ HSPC populations in the BM of recipient mice (sh Ren n=3, sh JARID2 #1+3 n=9, sh EZH2 #1+2 n=8). To evaluate long-term HSC self-renewal, 4.0×10 5 CD34+ cells were isolated from the BM of primary recipients and transplanted into secondary NSG mice. In this setting, PB engraftment of human cells was comparable amongst all groups ( Fig. S4g ). Western blot of human CD34+ cells from the BM of secondary recipients confirmed restoration of JARID2 protein levels following doxycycline withdrawal ( Fig. S4h ), further demonstrating the reversibility of JARID2 system in vivo . These data suggest that transient JARID2 inhibition can provide an immediate benefit to human HSPCs that enhances initial reconstitution of human cells, but this phenotype is lost when JARID2 protein levels are restored in the absence of doxycycline as long-term self-renewal in secondary recipients was comparable to the sh Ren control group. Thus, any functional reprogramming resulting from JARID2 inhibition is not permanent and can be reset in vivo . We leveraged this inducible system to determine if JARID2 inhibition could directly reprogram human HSPCs in vivo . To test this, UCB CD34+ cells were transduced with inducible lentiviral shRNAs in the absence of doxycycline and transplanted into NSG mice. In the absence of induction, peripheral blood engraftment of human cells was comparable amongst all groups. 14-weeks post-transplant, expression of shRNAs was induced through administration of doxycycline in rodent chow. Immediately following induction, sh JARID2 groups displayed a significant increase in peripheral blood engraftment relative to the sh Ren control group, whereas the sh EZH2 group showed a decline in engraftment levels ( Fig. 4e ). After 12-weeks of doxycycline induction, there was increased engraftment of human cells in the BM ( Fig. 4f ) and increased numbers of BFP+ HSPCs ( Fig. 4g ) from the JARID2 -KD groups. This experiment shows human HSPCs can be directly reprogrammed in vivo by JARID2 inhibition to enhance functional output. Cell Culture Changes the Phenotypic Identify of Functional Human Repopulating Cells After ex vivo culture, the CD34+ cell population contains a mixture of HSPCs, most of which lack long-term repopulating potential. Our prior data in mouse models showed that genetic inhibition of Jarid2 conveyed long-term repopulating potential to MPPs ( 22 ), a cell population that possesses only transient engraftment potential under normal circumstances. It is possible a similar mechanism operates in human cells and the enhanced functional output of UCB CD34+ cells following JARID2 -KD is due to differential effects on specific HSPC subpopulations. To examine this, UCB CD34+ cells were transduced with GFP-expression lentiviral vectors constitutively expressing shRNAs targeting JARID2 or Renilla negative control. In this case, following ex vivo culture 1250 HSCs (CD34+ CD38- CD90+ CD45RA-) or 1500 MPPs (CD34+ CD38- CD90- CD45RA-) were purified based on canonical human cell surface markers and transplanted into NSG mice. At 12-weeks post-transplant, peripheral blood engraftment of HSCs receiving JARID2 -KD exhibited higher engraftment than the sh Ren control group ( Fig. S5a ). However, MPPs from all groups failed to engraft ( Fig. S5b ) and overall engraftment levels were markedly lower than that observed in prior experiments. This suggested that the immunophenotype used to identify native human HSPC population may be influenced by ex vivo culture and/or lentiviral transduction. Colony forming unit (CFU) assays were performed to define the phenotype of functional HSCs after ex vivo culture. While used as a marker of long-term human HSCs in vivo ( 33 – 35 ), CD49f was no longer a reliable marker of functional HSCs after ex vivo culture as all CD49f+ cells failed to generate colonies ( Fig. S5c ). Rather, the CD34+EPCR+CD90+CD49f-phenotype enriched for most robust CFU activity ( Fig. S5c,d ). Eight days post-transduction, JARID2 -KD increased the number of CD34+EPCR+CD90+CD49f-cells in culture ( Fig. S5e ) and enhanced CFU activity from this population on a per cell basis, which was enhanced in replating experiments ( Fig. S5f ). CITE-seq was performed to integrate single cell transcriptomics with immunophenotype of cells after ex vivo culture ( Fig. S5g ). As CD49f+ cells lacked CFU potential after culture, this population ( Fig. S5h ) was excluded from the final UMAP ( Fig. 5a ). HSCs were identified by specific expression of canonical human HSC-defining genes ( Fig. S5i ). However, in addition to a cluster of cells expressing well-described human HSC-defining genes such as HLF , expression of other HSC-specific genes such as MECOM and MEIS1 spread to another cluster of cells after ex vivo culture ( Fig. 5b ). This emergent population was expanded after JARID2 -KD and also marked by cell surface expression of EPCR (CD201) and CD90 ( Fig. 5c ). This emergent population was annotated as megakaryocyte-erythroid progenitors (MEPs) based on transcriptional signature ( Fig. 5a ), with expression of CD71 demarcating this population from HSCs ( Fig. 5c ). However, CD71 has also been noted as a marker of activated HSCs ( 36 ) and given the ectopic expression of some HSC-specific genes in this population and it’s expansion after JARID2 -KD, we hypothesized this may represent the cell population that is functionally augmented after JARID2 inhibition. Download figure Open in new tab Fig. 5: Cell Culture Changes the Phenotypic Identify of Functional Human Repopulating Cells (a) UMAP of CITE-seq data from GFP+CD34+ cells after eight-days culture (CD49f+ cells excluded). (b) Feature plots showing expression of HSC marker genes HLF , MEIS1 and MECOM . (c) Feature plots showing expression of antibody-derived tags (adt) for surface proteins CD90, CD201 (EPCR) and CD71. (d) PB engraftment in recipient mice transplanted with 1000 cells of indicated HSC (GFP+CD34+CD90+ EPCR+ CD49f-) phenotype (CD71+ / CD71-) transduced with indicated lentiviral shRNAs. (e) Engraftment of GFP+ human cells in the BM of recipient mice 20-weeks post-transplant. To analyze the functional potential of this emergent population after JARID2 -KD, UCB CD34+ cells were transduced with constitutive lentiviral shRNA vectors and after eight days of culture, equal numbers of CD34+EPCR+CD90+CD49f-cells were purified from each group with additional separation based on CD71 expression ( Fig. S5j ) and transplanted into NSG mice. Only the CD71-cell fraction displayed long-term engraftment potential which was enhanced by JARID2 -KD ( Fig. 5d ). JARID2 -KD in CD34+EPCR+CD90+CD49f-CD71-cells also resulted in higher human cell chimerism in the BM of recipient mice ( Fig. 5e ) and increased numbers of HSPCs ( Fig. 5f ). Similar results were observed from serial CFU replating of these presumptive HSC populations ( Fig. S5k ). Although JARID2 -KD expanded a population of cells with HSC transcriptional and phenotypic characteristics in culture, ultimately this CD71+ cell fraction possessed no reconstitution capacity in vivo . Our data reveal that functional repopulating activity was contained within the CD34+EPCR+CD90+CD49f-CD71-cell fraction after culture manipulation. JARID2 Knockdown Produces Subtle Molecular Effects in HSPCs that are Distinct From PRC2 Inhibition To further investigate the molecular mechanisms underlying the enhanced functionality of HSPCs upon JARID2 inhibition, epigenetic profiles of UCB CD34+ cells following JARID2 and EZH2 knockdown were compared. Western blot analysis after ex vivo culture confirmed that EZH2 knockdown led to significant depletion of H3K27me3 in HSPCs, consistent with its central role in PRC2-mediated histone methylation ( Fig. 6a ). In contrast, JARID2 knockdown did not significantly alter global H3K27me3 levels ( Fig. 6a ), suggesting a distinct role from the PRC2 core complex in human HSPCs. To assess if specific loci were affected, CUT&RUN and CUT&TAG was performed for a variety of epigenetic modifications on purified GFP+CD34+EPCR+CD90+CD49f-CD71-cells after eight days of culture. Experiments were performed on three independent UCB samples as biological replicates and compared to the freshly isolated UCB CD34+ cells as a reference control. Consistent with Western blot results, H3K27me3 peaks were largely maintained in JARID2 -KD samples, but markedly reduced in EZH2 -KD samples ( Fig. 6b,c ). Despite these epigenetic distinctions, the inherent biological variation of individual UCB donors was the strongest driver of sample variability ( Fig. 6d ). After removing the effects of individual biological replicates, pathway analysis of H3K27me3-enriched regions ( Table S1 ) did not identify significantly enriched terms ( Fig. S6a ). This effect was consistent across multiple epigenetic marks profiled such as H3K4me3, H2AK119ub and CTCF ( Fig. S6b ). Transcriptional profiling by bulk RNA-seq of the same cell populations revealed a similar effect ( Fig. 6e ). The variability of genetic background between different donors resulted in identification of very few differentially expressed genes (DEGs) between comparisons ( Table S2 ), none of which seemed to explain the observed functional results ( Fig. S6c ). Download figure Open in new tab Fig. 6: JARID2 Knockdown Produces Subtle Molecular Effects in HSPCs that are Distinct From PRC2 Inhibition (a) Western blot analysis of cord blood CD34+ cells transduced with indicated shRNAs after eight-days of ex vivo culture. (b) H3K27me3 peak counts from CUT&Tag analysis of GFP+CD49f-CD90+CD34+EPCR+CD71- cells transduced with indicated shRNAs after eight-days of ex vivo culture. (c) Heatmaps of H3K27me3 signal intensity centered at transcription start sites (±5 kb) for genes identified in all four experimental conditions. Data represent combined results from three biological replicates per condition. Heatmaps are sorted by decreasing H3K27me3 signal intensity in naïve UCB CD34+ reference sample. (d) Principal component analysis (PCA) of H3K27me3 peak profiles from CUT&Tag analysis of GFP+CD49f-CD90+CD34+EPCR+CD71- cells transduced with indicated shRNAs after eight-days of ex vivo culture. (e) PCA of RNA-seq gene expression profiling of GFP+CD49f-CD90+CD34+EPCR+CD71- cells transduced with indicated shRNAs after eight-days of ex vivo culture. JARID2 Knockdown Reinforces Stem Cell Gene Expression Programs and Reprograms MPPs in vivo As cell culture conditions modified transcriptomic and immunophenotypic properties of HSCs, to uncover true effects of JARID2 -KD in HSPCs we revisited scRNA-seq data generated from GFP+CD34+ cells isolated from recipient mice after transplant, reasoning this would provide sufficient time to re-establish native cell surface markers. Within the annotated HSC cell cluster ( Fig. 7a ), JARID2 -KD increased expression of well-annotated HSC-defining genes ( 37 , 38 ) such as HLF , MLLT3 , MYCT1 , and MEIS1 relative to control sh Ren HSCs ( Fig. 7b ). Due to the limited dataset size, only a few individual genes reached statistical significance across conditions. To address this, we incorporated a curated list of HSC-defining genes from a recent study to compute a HSC module score ( 38 ). This HSC module score was significantly elevated in sh JARID2 HSCs relative to control sh Ren HSCs ( Fig. 7c ). Looking across other cell populations, while as expected the HSC module score peaked in the HSC cluster, elevated scores were also detected in other JARID2 -KD HSPC populations ( Fig. S7a ), suggesting JARID2 inhibition may reprogram some of these populations with HSC-like potential in vivo . To test this, HSCs, MPPs and MLPs were isolated from the BM of secondary recipients of constitutive shRNA transduced UCB CD34+ cells 20-weeks post-transplant and subject to in vitro functional analysis via CFU assay. JARID2 -KD significantly enhanced serial replating of both HSC and MPP cell fractions, but not MLPs ( Fig. 7d ; Fig. S7b ). Thus, in human HSPCs, JARID2 inhibition not only enhances function of HSCs, but also conveys HSC-like potential to the MPPs which lacks self-renewal capacity under normal conditions, analogous to our prior results from murine models ( 22 ). Download figure Open in new tab Fig. 7: JARID2 Knockdown Reinforces Stem Cell Gene Expression Programs and Reprograms MPPs in vivo (a) UMAP of scRNA-seq data for hCD34+ cells sorted from NSG mice transplanted with cord blood cells transduced with constitutive lentiviral shRNAs 20-weeks post-transplant. Cell populations are identified by marker genes. sh Ren , sh JARID2 #1 and sh JARID2 #3 samples are combined, the HSC population is highlighted. (b) Dot plot highlighting differential expression of a curated HSC signature gene set (Aguadé-Gorgorió et al., 2024, Nature) amongst shRNA genotypes within the HSC population. (c) HSC module scores calculated using the Seurat package based on the curated HSC signature gene list. (d) Serial replating showing CFU efficiency of indicated HSPC populations isolated from BM of recipient mice 20-weeks post-transplant. (e) Volcano plot showing differentially expressed genes between sh JARID2 and sh Ren cells within the HSC population. (f) Representative flow cytometry plots showing the proportion of MHCII-high (red boxes) HSCs from each genotype from the BM of recipient mice 20-weeks post-transplant. (g) Serial replating showing CFU efficiency of indicated HSPC genotypes separated by MHCII levels isolated from BM of recipient mice 20-weeks post-transplant. (h) Cell cycle analysis by flow cytometry showing proportion of quiescent (G 0 ) HSCs of each genotype from the BM of recipient mice 20-weeks post-transplant. To investigate the molecular mechanisms underlying the enhanced function observed following JARID2 inhibition, differential gene expression analysis was compared specifically within the HSC population. While most downregulated genes were related to ribosome biogenesis ( Fig. S7c ), the most significantly upregulated genes in JARID2 -KD HSCs were largely related to the major histocompatibility complex class II (MHCII; Fig. 7e ). Recent studies have suggested a role for MHCII in HSC regulation through antigen presentation as a mechanism of safeguarding the integrity of the HSC pool ( 39 , 40 ). Flow cytometry validated the scRNA-seq data and confirmed higher expression of MHCII proteins on the surface of JARID2 -KD HSCs ( Fig. 7f ). As MHCII has been suggested to mark long-term HSCs in mice ( 39 ), to assess if increased MHCII marked more functional human cells after JARID2 -KD, HSCs from recipient mice transplanted with UCB CD34+ cells transduced with constitutive shRNA vectors were sorted based on MHCII expression and subject to CFU assay. Serial replating showed that JARID2 -KD enhanced functionality of both MHCII-negative and MHCII-high HSCs, but most of the CFU potential was contained in the MHCII-high fraction which was dramatically enhanced following JARID2 inhibition ( Fig. 7g ). In both mice and human, MHCII-high HSCs show reduced cycling and apoptosis and are resistant to stress-induced proliferation, linked to MHCII-high HSCs residing in a deeper quiescent state ( 39 , 40 ). We performed cell cycle analysis of post-transplant transduced HSCs ( Fig. S7d ) which confirmed that JARID2 -KD HSCs contained a significantly higher proportion of cells in the quiescent G 0 phase ( Fig. 7h ). As deeper quiescence is a hallmark of more functional HSCs, this presents one potential mechanism through which JARID2 inhibition can enhance the long-term reconstitution of human HSCs. JARID2 Inhibition Upregulates STAT1 to Enhance Function of Human HSPCs A recent study showed STAT1 is essential for the maintenance of MHCII-high HSCs ( 40 ). In addition to MCHII components, STAT1 was one of the most significantly upregulated genes in JARID2 -KD HSCs ( Fig. 7e ; Fig. 8a ). From this data, scatterplot analysis using Seurat revealed a correlation between cells with the highest STAT1 expression levels in JARID2 -KD HSCs also having the highest expression of MHCII ( Fig. 8b ) and western blot analysis confirmed upregulation of STAT1 protein in JARID2 -KD CD34+ cells ( Fig. 8c ). We hypothesized if the mechanism of augmented HSPC function resulting from JARID2 inhibition was through STAT1 upregulation, that inhibiting STAT1 in JARID2 -KD cells may ameliorate their functional enhancement. shRNAs were designed and validated to target STAT1 and cloned into a BFP-expressing lentiviral vector ( Fig. S8a ). GFP+ CD34+ cells from primary transplant recipients of constitutive shRNA lentiviruses were transduced with STAT1 shRNA lentiviruses and subject to functional assessment by CFU assay. Results showed that STAT1 inhibition was able to rescue the increased CFU activity of JARID2 -KD HSPCs ( Fig. 8d ). While there were no obvious epigenetic changes at the STAT1 locus in JARID2 -KD HSCs that would explain the increased expression ( Fig. S8b,c ), collectively these findings suggest that JARID2 inhibition enhances human HSPC functionality at least in part by upregulating STAT1 and MHCII while promoting a quiescent gene expression signature characteristic of long-term self-renewing HSCs. Download figure Open in new tab Fig. 8: JARID2 Inhibition Upregulates STAT1 to Enhance Function of Human HSPCs (a) Dot plot showing expression of STAT1 and MHCII components in HSCs of indicated genotypes derived from scRNA-seq data. (b) Scatterplot showing the correlation between STAT1 mRNA levels and MHCII gene module scores in individual HSCs. Each dot represents a single cell within the defined HSC population. (c) Western blot showing STAT1 protein levels in hCD34+ cells isolated from recipient mice of cord blood cells transduced with indicated lentiviral shRNAs 20-weeks post-transplant. (d) CFU analysis of indicated HSC genotypes isolated from primary recipient mice transduced with indicated BFP+ lentiviral shRNAs. DISCUSSION The ability to effectively propagate functional human HSCs ex vivo would have tremendous benefit for BMT and also open new avenues for autologous gene therapy / gene editing applications for human blood disorders. Efforts to propagate transplantable human HSCs ex vivo have largely failed due to the repression of self-renewal molecular programs resulting from exposure to cytokines and cell division outside the bone marrow niche. The approach we undertook to tackle this problem was to use acute epigenetic manipulation during ex vivo expansion culture to prevent repression of self-renewal genetic networks. Each step of hematopoietic differentiation is associated with modulation of the epigenome as new cell fates are adopted. As epigenetic modifications are reversible and dynamically remodeled during normal development, we reasoned manipulation of the epigenetic machinery could provide a novel approach to biasing HSC fate decisions towards self-renewal and expand functional HSCs. Prior work from our lab identified a crucial role for the PRC2 co-factor Jarid2 in epigenetic repression of self-renewal pathways in developmental hematopoiesis ( 22 ). In mice, Jarid2 is responsible for recruiting PRC2 to epigenetically repress self-renewal genes in MPPs during HSC differentiation via establishment of the repressive H3K27me3. The goal of this study was to inhibit this function of JARID2 during hematopoietic fate decisions during ex vivo culture of UCB CD34+ cells to circumvent epigenetic silencing of self-renewal pathways as a mechanism to expand transplantable human cells while maintaining self-renewal potential. Our data show that JARID2 inhibition enhances output of human UCB cells through multiple mechanisms. In human HSCs, JARID2 inhibition promotes a quiescent gene expression signature characteristic of long-term self-renewing HSCs, at least partly driven by upregulation of STAT1 and marked by increased MHCII expression. Inhibition of JARID2 also appears to reprogram human MPPs in vivo to endow this population with some measure of self-renewal potential. This finding is analogous to prior murine studies ( 22 ) and extends previous observations regarding the role of JARID2 in human hematopoiesis ( 23 ). Importantly for potential translational approaches, transient JARID2 inhibition either through ex vivo or in vivo inducible shRNA expression, was sufficient to reprogram human HSPCs with enhanced functional potential. This effect was reversible in secondary transplant experiments showing that transient JARID2 inhibition does not permanently reprogram human HSPCs, which could alleviate concerns about future malignant potential while providing an initial boost of blood count recovery short-term post-transplant. The observed mechanism of functional enhancement resulting from JARID2 inhibition in human HSPCs appears to be at least partly attributed to upregulation of STAT1 . This aligns with recent data from mouse models that show HSCs from STAT1 -deficient mice are functionally impaired in competitive transplantation assays and STAT1 is required to maintain protective transcriptional programs in homeostatic HSCs, including inhibition of cell cycling( 40 ). In mice, STAT1 is also required to maintain MHCII-high HSCs as this population is lost upon genetic deletion of STAT1 ( 40 ). Although epigenomic analysis did not identify conclusive insight into how JARID2 inhibition leads to STAT1 upregulation, this appears to be a mechanism that augments HSC function that is conserved between mouse and human. Another major caveat of this study is that the exact identity of the human HSPCs that obtain functional benefit from JARID2 inhibition could not be definitively identified because the nature of ex vivo culture conditions alters the immunphenotypic profile of repopulating cells. Nevertheless, results from these studies position JARID2 inhibition as a novel method to enhance human HSPC function from limiting donor stem cell sources which could be implemented to improve clinical BMT applications. MATERIALS AND METHODS Study Design This study was designed to determine if genetic inhibition of JARID2 was sufficient to enhance the functionality of human HSPCs. This could broaden the clinical utility of UCB as a donor source for stem cell transplantation to treat a variety of blood disorders. UCB CD34+ cells were transduced with lentiviruses expressing constitutive or inducible shRNAs targeting JARID2 and subject to in vitro and in vivo functional assays. Sample sizes were not predetermined by a statistical method but informed by historical studies from our laboratory. Investigators were not blinded to sample identity and no data were excluded. Animals All animal procedures were approved by the Institutional Animal Care and Use Committee at Washington University. NOD-scid IL2Rgamma null (NSG) mice were obtained from The Jackson Laboratory (#005557). Male and female mice aged 8-12 weeks were used as recipients and were transplanted via retro-orbital injection after 200 rads of gamma irradiation (Cesium-137). Equal numbers of male and female mice were included in the experiments with no gender bias observed. Human Cord Blood Samples Anonymous cord blood specimens were obtained from the St. Louis Cord Blood Bank, St. Louis Barnes-Jewish Hospital, and the Cleveland Cord Blood Center. All cord blood samples were collected and transported with written consent, in accordance with a protocol approved by the Washington University Human Studies Committee and following the Declaration of Helsinki. Mononuclear cells were isolated from the cord blood samples using Ficoll gradient centrifugation. CD34+ cells were then enriched by magnetic selection (Miltenyi Biotec, #130-100-453) and cultured in SFEMII medium (StemCell Technologies, #09605) supplemented with Penicillin-Streptomycin (50 U/mL), human SCF (100 ng/mL), human TPO (100 ng/mL), and human FLT3L (100 ng/mL). Cell Sorting and Flow Cytometry All cells for flow cytometry were stained for >20 minutes at 4°C in Hank’s Balanced Salt Solution (HBSS; Corning, #21021CV) supplemented with Penicillin-Streptomycin (100 U/mL; Fisher Scientific, #MT30002CI), HEPES (10 μM; Life Technologies, #15630080), and fetal bovine serum (2%; Sigma, #14009C). Antibodies were diluted at 1:100. The following fluorophore-conjugated antibodies were used: anti-human CD11b (BUV737, BD Biosciences, #612800; FITC, BioLegend, #101205), CD11c (PE-Cy7, BD Biosciences, #561356), CD123 (BV510, BioLegend, #306022), CD14 (APC-Cy7, BioLegend, #367108; BV650, BioLegend, #301836), CD15 (BV421, BioLegend, #323039; FITC, BD Biosciences, #555401), CD16 (PE, BD Biosciences, #555407; R718, BD Biosciences, #566969), CD19 (Biotin, BioLegend, #328106; BV605, BioLegend, #302244; PE-Cy7, BioLegend, #302215), CD201/EPCR (BV421, BD Biosciences, #743552), CD27 (BV421, BioLegend, #356418), CD3 (AF488, BD Biosciences, #557694; PE, Invitrogen, #12-0038-42), CD33 (APC, BD Biosciences, #561817; BV421, BD Horizon, #565949; FITC, BioLegend, #366620; PE-CF594, BD Biosciences, #562492), CD34 (APC, BioLegend, #343608; Biotin, BioLegend, #343524; PE, BioLegend, #343606), CD38 (APC, BioLegend, #356606; BV605, BioLegend, #356642), CD4 (BV711, BD Biosciences, #563028), CD45 (APC, BioLegend, #368512; BUV395, BD Biosciences, #563792; Biotin, BioLegend, #304004), CD45RA (APC, BioLegend, #304112; APC-Cy7, BioLegend, #304128; BV421, BioLegend, #304130; FITC, BioLegend, #304106; PerCP/Cy5.5, BioLegend, #304121), CD45RO (PacBlue, BioLegend, #304215), CD49f (APC, BioLegend, #313616; APC-Cy7, BioLegend, #313628; Biotin, BioLegend, #313604), CD56 (PE, BD Biosciences, #340363), CD71 (APC, BioLegend, #334108; FITC, BioLegend, #334103), CD8 (BUV496, BD Biosciences, #612942), CD90 (APC, BioLegend, #328114; PE-Cy7, BioLegend, #328124), CD303 (APC-Vio770, Miltenyi Biotec, #130-113-652), γδ TCR (BB700, BD Biosciences, #745944), HLA-DR (BV786, BD Biosciences, #564041), HLA-DR, DP, DQ (BV605, BD Biosciences, #740408), IgD (APC, BD Biosciences, #561303), and Ki67 (PE, BioLegend, #350503; PE-Cy7, BioLegend, #350525). Mouse-specific antibodies included anti-mouse CD45 (BV605, BioLegend, #103140; PE-Cy5, BioLegend, #103110). Streptavidin-BV605 (BioLegend, #405229) was used for detection of biotin-conjugated antibodies. For cell viability staining, Live/Dead Blue (Thermo Fisher, #L23105) or 7-AAD (BioLegend, #420404) was added at 1:100 dilution. After antibody staining, cells were washed and resuspended in HBSS+ containing 7-AAD for cell viability staining prior to flow cytometry analysis. Flow cytometry was performed using either an Attune NxT Flow Cytometer or a Yeti Flow Analyzer provided by the Siteman Flow Cytometry Core. Cell sorting was carried out on a Beckman Coulter MoFlo or BD Aria Ilu SORP sorter. Western Blot Protein samples were separated on pre-cast 4%–15% gradient SDS-PAGE gels (Bio-Rad, #456– 1084) and transferred to nitrocellulose membranes (Millipore, #IPVH00010). Membranes were blocked with 5% BSA and incubated with primary antibodies targeting specific proteins of interest. The following primary antibodies were used: anti-β-actin (Santa Cruz, #sc-47778), EZH2 (Abcam, #ab3748; CST, #5246S), histone H3 (CST, #4499S), H3K27me3 (CST, #9733S), H3K4me3 (CST, #9751S; EpiCypher, #13-0060), HDAC2 (CST, #2540S), JARID2 (Asp1114) (CST, #13594S), and STAT1 (CST, #9172S). After washing with TBS-T, membranes were incubated with HRP-conjugated secondary antibodies specific to either mouse or rabbit IgG, and detection was carried out using a chemiluminescent HRP substrate (Millipore, #WBKLS0100). Imaging was performed using a Bio-Rad imaging system CRISPR/Cas9 mediated gene knock out We designed multiple small guide RNAs (gRNAs) targeting exon 3 of the human JARID2 gene using the CRISPR design tool available via the UCSC Genome Browser ( https://genome.ucsc.edu/ ). Synthetic gRNAs were synthesized by Synthego and included two sequences targeting exon 3 of JARID2 : CTTGGTAATGACCAGTCTAA (anti- JARID2 #1) and GTTGCTAGTGGAGGACACTT (anti- JARID2 #2). A gRNA targeting the AAVS1 locus (GGGGCCACTAGGGACAGGAT) was used as a negative control. CD34+ cells were pre-cultured for 12–24 hours before nucleofection with Cas9 ribonucleoprotein (RNP) complexes (IDT, #1074181) assembled with these synthetic gRNAs, following previously published protocols( 41 ). Twenty-four hours post-nucleofection, approximately 250,000 cells were transplanted into sublethally irradiated (200 rads) NSGS mice via X-ray-guided intra-tibial injection. A small aliquot of cells was retained for PCR amplicon-based sequencing to assess the initial CRISPR/Cas9 editing efficiency. Immunohistochemistry Mouse tibias were fixed in 10% neutral buffered formalin (Fisher Scientific, #SF100-4) overnight at 4°C. Decalcification was performed using 14% EDTA for 14 days at 4°C. Following fixation and decalcification, tibia samples were submitted to the Washington University Musculoskeletal Histology and Morphometry Core for hematoxylin and eosin (H&E) and reticulin staining. Mouse spleens were collected, fixed in 10% neutral buffered formalin (Fisher Scientific, #SF100-4) overnight at 4°C, and then submitted to the same core facility for processing. For cytospin cell preparation, 1 million cells enriched from either bone marrow or spleen were filtered through a 40 μm cell strainer and resuspended in 100 μL of Hanks+ media. Cells were then centrifuged at 500 rpm for 5 minutes, air-dried for 45 minutes, and subsequently stained with H&E. HSPC Culture and Expansion Assays Enriched human cord blood CD34+ cells were cultured in SFEM II medium (StemCell Technologies, #09605) supplemented with Penicillin-Streptomycin (50 U/mL), human SCF (100 ng/mL), human TPO (100 ng/mL), and human FLT3L (100 ng/mL). For doxycycline-inducible shRNA lentiviral vectors, doxycycline (200 ng/mL) was added to induce shRNA expression for eight days, after which the entire cell culture was transplanted into NSG mice. For mice receiving doxycycline chow, 1250 ppm doxycycline-containing chow was used. Lentiviral vectors for shRNAs We used an optimized microRNA lentiviral backbone to carry our shRNAs, as recommended ( 28 ). The backbone information is available from Addgene Plasmid #111170. The sequences of the shRNAs inserted into this vector are as follows: for JARID2 , the shRNAs included shJARID2#1 (CCAGCATTAATCTTTCTTTTA), shJARID2#2 (AAGCATTAATCTTTCTTTTAA), shJARID2#3 (CGCAGTTATTTTTGAATGTGA), and shJARID2#4 (ATGAACTGTTTTGGAATATTT); for EZH2 , we used shEZH2#1 (CCAGGATGGTACTTTCATTGA), shEZH2#2 (CCAGCAGAAGAACTAAAGGAA), shEZH2#3 (ATCGGTGTCAAACACCAATAA), and shEZH2#4 (AACCAATAAAGATGAAGCCAA); for STAT1 , the shRNAs were shSTAT1#1 (CAAGGAAGTAGTTCACAAAAT), shSTAT1#2 (CCCAGATGTCTATGATCATTT), shSTAT1#3 (CGAGCACAGTGATGTTAGACA), and shSTAT1#4 (CCAGAAAGAGCTTGACAGTAA). A shRNA targeting Renilla luciferase (CAGGAATTATAATGCTTATCT) was used as a non-targeting control. For inducible shRNA vectors, we modified Addgene Plasmid #111176 by replacing the DsRed reporter with BFP and removing both the YFP and IRES fragments to streamline vector design for our system Lentivirus Production For constitutive expression, lentiviral particles were produced by co-transfecting 293T cells with the packaging plasmids pMD.G, psPAX2, and pSFFV-eGFP-miR-E, in which the miR-E cassette was engineered to target Renilla , JARID2 , EZH2 , or STAT1 . All plasmids were mixed at a 1:1:1 molar ratio. For inducible expression systems, 293T cells were co-transfected with pMD.G, psPAX2, and pT3G-BFP-miR-E-PGK-rtTA3. Transfections were carried out in 150 × 25 mm tissue culture dishes (Falcon #353025) when 293T cells reached 70–80% confluency. Six to eight hours post-transfection, the culture media were replaced to remove residual polyethyleneimine (PEI). Supernatants containing lentiviral particles were harvested 48–72 hours post-transfection, filtered through 0.2 µm PES Nalgene Rapid-Flow filters (Thermo Fisher, Cat. #564-0020), and concentrated by ultracentrifugation at 25,000 rpm for 90 minutes. The viral pellets were collected from the bottom of the tubes after carefully discarding the supernatant. Colony-Forming Assays Colony-forming assays were performed using MethoCult™ H4035 Optimum Without EPO (Stemcell Technologies, Cat. #04035). After eight days of culture, an equivalent volume of 200-300 cord blood CD34+ cells from the sh Ren group were plated on 6-well plates, each containing 2 mL of MethoCult. Colonies were counted and visualized after 12 days. Following the first round of colony-forming assays, cells were collected from the initial plates and 10,000 cells were replated onto new 6-well plates containing 2 mL of MethoCult. Colonies were counted and visualized again after 12 days. Human HSPC Transplantation UCB CD34+ cells were transduced with constitutive lentiviral vectors carrying shRNAs, using LentiBOOST (1:100, SIRION Biotech, Cat No. SB-P-LV-101-12) and protamine sulfate (1:1000) to enhance transduction efficiency, as described( 42 ). Transduction efficiency was typically ∼40%. For inducible shRNA transplantation experiments, enriched hCD34+ cells were transduced with inducible lentiviral vectors, using LentiBOOST (1:100) and protamine sulfate (1:1000)( 42 ). Doxycycline treatment (200 ng/mL) was initiated 18–24 hours post-transduction. After 24–48 hours of doxycycline treatment, BFP+ cells were sorted, and 40,000 starting cells were cultured in the presence of doxycycline. Following eight days of ex vivo culture, the entire progeny of 40,000 cells were transplanted into NSG mice via retro-orbital injection after sublethal irradiation (200 rads). Peripheral blood (PB) engraftment was evaluated every four-weeks up to 16-weeks post-transplant to monitor human cell engraftment (hCD45 versus mCD45). Multilineage engraftment was assessed by detecting human myeloid (CD33), B lymphoid (CD19), and T lymphoid (CD3) cells in the PB. After 20-weeks of engraftment, BM was harvested (femur, tibia, and fibula) for flow cytometric analysis of HSPCs. Single cell RNA-sequencing (scRNA-seq) After 20-weeks of engraftment, hCD34+ cells were sorted for scRNA-seq. Live cells were harvested in cold, sterile PBS containing 0.05% RNase-free BSA, and resuspended at a concentration of 1,200 cells/μL in 1.5 mL EP tubes. cDNA was synthesized following GEM (Gel Bead in Emulsion) generation and barcoding, followed by the GEM-RT reaction and bead cleanup steps. The purified cDNA was then amplified for 11 to 13 cycles and cleaned up using SPRIselect beads. The concentration of the cDNA was measured using a Bioanalyzer. GEX libraries were prepared according to the 10x Genomics Chromium Single Cell 3′ Reagent Kits v3 user guide, with modifications to the PCR cycles based on the calculated cDNA concentration. For library preparation on the 10x Genomics platform, the following kits were used: Chromium Single Cell 3′ GEM Library and Gel Bead Kit v3 (PN-1000075), Chromium Chip B Single Cell Kit (10× Genomics, PN-10000153), and Chromium Dual Index Kit TT Set A (PN-1000215). The concentration of each library was quantified via qPCR using the KAPA Library Quantification Kit (KAPA Biosystems/Roche) to ensure proper cluster generation on the Illumina NovaSeq6000 platform. The normalized libraries were sequenced on the NovaSeq6000 S4 Flow Cell (Illumina) using the XP workflow with a 28 × 10 × 10 × 150 sequencing recipe. A median sequencing depth of 50,000 reads per cell was targeted for each library. scRNA-seq data analysis Single-cell RNA-seq data were demultiplexed and aligned to the Genome Reference Consortium Human Genome, GRCh38. The aligned data were annotated and UMI-collapsed using Cell Ranger (v3.1.0, 10x Genomics). Cells with more than 10% mitochondrial gene expression or with the top 5% of unique feature counts were excluded from the analysis. Principal component analysis (PCA) was performed to reduce dimensionality using Seurat 4.0( 43 ). Clusters were identified through dimensionality reduction techniques, including tSNE and UMAP, using the same package( 44 ). Conventional cell markers were used to annotate the clusters based on expression levels. Differentially expressed genes were identified within the clusters of interest. The fgsea package was utilized to identify enriched signaling pathways. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) UCB CD34+ cells were transduced with constitutive lentiviral shRNA vectors (sh Ren , sh JARID2 #1, sh JARID2 #3, sh EZH2 #1, and sh EZH2 #2). For each vector, two units of cord blood as biological replicates were transduced and used for experiments. After 36-hours of transduction, GFP+ cells were sorted and cultured for eight days in complete SFEM media. Following the 8-day culture, cord blood cells were stained with PE-CD34 antibody (561 epitope, 1:100 dilution, 200 μL per sample) for 30 minutes on ice. We then sorted 250,000 CD34+ cells into Hanks+ media and stained them with the CITE-seq TotalSeq B antibody combination for 30 minutes on ice. The panel included antibodies targeting CD201 (clone RCR-401, barcode GTTTCCTTGACCAAG), CD90 (clone 5E10, GCATTGTACGATTCA), CD133 (clone S16016B, GTAAGACGCCTATGC), CD34 (clone 581, GCAGAAATCTCCCTT), CD45RA (clone HI100, TCAATCCTTCCGCTT), CD38 (clone HB-7, CCTATTCCGATTCCG), CD49f (clone GoH3, TTCCGAGGATGATCT), CD135 (clone BV10A4H2, CAGTAGATGGAGCAT), CD370 (clone 8F9, CTGCATTTCAGTAAG), CD71 (clone CY1G4, CCGTGTTCCTCATTA), CD41 (clone HIP8, ACGTTGTGGCCTTGT), CD115 (clone 9-4D2-1E4, AATCACGGTCCTTGT), and CD15 (clone W6D3, TCACCAGTACCTAGT). After two washes with Hanks+ media, the cells were sorted a second time into cold, sterile PBS with 0.05% RNase-free BSA in EP tubes. Cells were spun down, and the supernatant was removed to adjust the cell concentration to 1000 cells/μL. The cells were then processed using the 10x 3’ kit protocol (Chromium Single Cell 3’ Reagent Kits User Guide, v3.1 Chemistry Dual Index, with Feature Barcoding Technology for Cell Surface Protein, User Guide CG000317). Reads were collected at a depth of 500 million per library using the Illumina NovaSeq 6000 sequencing system. CITE-seq data analysis CITE-seq data were processed to jointly analyze transcriptomic and cell surface protein expression profiles. Raw sequencing data (FASTQ files) were processed using Cell Ranger (10x Genomics). The cellranger count pipeline was run with Feature Barcode support enabled to quantify both gene expression and cell surface protein tags (antibody-derived tags, ADTs). After processing individual samples, outputs were merged using the cellranger aggr function to generate a combined gene-barcode matrix for downstream analysis. The merged dataset was analyzed using the Seurat (v4) R package. Cells with a mitochondrial gene content greater than 5% were excluded to remove potentially stressed or dying cells. The expression data were normalized and scaled, followed by Principal Component Analysis (PCA). Both transcriptomic (RNA) and surface protein (ADT) data were respectively used for dimensionality reduction and clustering, including PCA and Uniform Manifold Approximation and Projection (UMAP) for visualization. Cell type annotation was performed using the Azimuth reference mapping framework, enabling transfer of labels from reference atlases to query datasets. Differential expression analysis was conducted using Seurat’s FindMarkers function, applying the Wilcoxon rank-sum test to identify cell type-specific marker genes. RNA sequencing (RNA-seq) UCB CD34+ cells were transduced with constitutive lentiviral vectors. 36-hours post-transduction, GFP+ cells were sorted and cultured for eight days in complete SFEM media. After culture, cells were stained with the flow cytometry antibodies 30 minutes on ice. Following staining, 5,000–10,000 HSCs (CD34+CD90+EPCR+CD49f-CD71-) were sorted for RNA extraction. RNA was isolated using the NucleoSpin RNA XS kit (Macherey-Nagel #740902.250). RNA libraries for sequencing were prepared using the SMARTer Ultra Low RNA Kit (Clontech), following the manufacturer’s instructions. Sequencing was performed on an Illumina NovaSeq X plus sequencing system, using the S4 2 × 150 sequencing setup. RNA-seq analysis RNA-seq libraries were prepared using the manufacturer’s protocol, indexed, pooled, and sequenced on an Illumina NovaSeq X Plus. Base calling and demultiplexing were performed using DRAGEN and BCLconvert (v4.2.4). Reads were aligned to the Ensembl release 101 (hg38) using STAR (v2.7.9a)( 45 ), and gene-level counts were obtained using featureCounts (v2.0.3). Salmon (v1.5.2) was used to quantify isoform expression ( 46 ). Quality metrics such as alignment rates, ribosomal content, and read distribution were assessed using RSeQC (v4.0) ( 47 ). Gene counts were imported into EdgeR for TMM normalization, and low-expressed or ribosomal genes were filtered. Differential expression analysis was performed using Limma-voomWithQualityWeights, incorporating an additive model without intercept, where batch numbers were included as blocking factors to adjust for technical variation( 48 ). Batch effects were also regressed out in PCA, MDS, and heatmap visualizations, ensuring clearer sample clustering. Surrogate Variable Analysis (SVA) was additionally applied to account for latent sources of variation. Genes were considered differentially expressed at p-value ≤ 0.05 and |log2 fold change| ≥ 2, although no gene passed FDR correction under these criteria. Heatmaps based on the additive model demonstrated coherent sample clustering with minimal noise and were used for downstream interpretation. Functional enrichment analysis of differentially expressed genes was performed using GAGE for Gene Ontology (GO), KEGG, and MSigDb pathways( 49 , 50 ). Visualization was carried out using heatmap3 and Pathview( 51 ). Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules of co-expressed genes, followed by clusterProfiler for enrichment of GO terms( 52 ). Significant terms (adjusted p ≤ 0.05) were summarized into category network plots to infer biological functions of each gene module. Cut&Tag and Cut&Run The cell transduction and culture procedure described for RNA sequencing was similarly applied for Cut&Tag and Cut&Run experiments. Functional HSCs (CD34+CD90+EPCR+CD49f-CD71-) were sorted for these experiments, with 10,000–20,000 HSCs used as input. The Cut&Tag protocol was followed according to the Bench top CUT&Tag V.3 from Dr. Steven Henikoff’s lab (DOI: dx.doi.org /10.17504/protocols.io.bcuhiwt6). For Cut&Run experiments, we utilized the CUTANA™ ChIC/CUT&RUN Kit (Epicypher 14-1048) and the CUTANA™ CUT&RUN Library Prep Kit (Epicypher SKU: 14-1001). Libraries were prepared and then submitted for sequencing on an Illumina NovaSeq 6000 sequencing system using the S4 2 × 150 setup. The following antibodies were used for profiling chromatin-bound factors: anti-CTCF (Cell Signaling Technology, #2899S), EZH2 (Abcam, #ab3748; CST, #5246S), H2AK119ub1 (CST, #8240S), histone H3 (CST, #4499S), H3K27ac (CST, #8173S), H3K27me3 (CST, #9733S), H3K4me3 (CST, #9751S; EpiCypher, #13-0060), and JARID2 (Asp1114) (CST, #13594S). Normal rabbit IgG (Millipore Sigma, #12-370) was used as an isotype control, and anti-β-actin (Santa Cruz, #sc-47778) was included as a specificity control. Cut&Tag and Cut&Run data analysis CUT&Run and CUT&Tag sequencing data were processed using the CUT&RUNTools 2.0 pipeline, which supports both assay types and provides integrated steps for alignment, peak calling, and quality control( 53 ). Sequencing reads were aligned to the human genome (hg38) using Bowtie2, as implemented in the pipeline( 54 ). Peak calling was performed within the pipeline using both MACS2 (for narrow and broad peaks) and SEACR (Sparse Enrichment Analysis for CUT&RUN)( 55 , 56 ). To assess differential enrichment between experimental conditions, we employed DiffBind, allowing for comprehensive differential peak analysis across replicates and conditions. For genome-wide visualization of read coverage and peak profiles, we used the WashU Epigenome Browser, enabling intuitive inspection of signal tracks and region-specific enrichment. Statistical Analysis Statistical analyses were performed using Student’s t-test, Mann–Whitney U test, one-way ANOVA, and two-way ANOVA, as appropriate. p -values are stated where comparisons reach statistical significance. All data are presented as the mean ± S.E.M. List of Supplementary Materials Fig. S1 to S8. Tables S1 and S2. Funding This publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. G.A.C was supported by the National Institutes of Health (NIH; HL147978, DK119231 and DK124883), Gabrielle’s Angel Foundation, the Edward P. Evans Foundation, the Leukemia and Lymphoma Society (6667– 23 ) and the American Cancer Society (CSCC-RSG-23-991417-01-CSCC). H.B. was supported by the Edward P. Evans Center for MDS at Washington University in St. Louis. A.L.Y. was supported by NIH T32HL007088, the American Society of Hematology (Research Training Award for Fellows), the Edward P. Evans Foundation (Young Investigator Award) and is a Fellow of the Leukemia and Lymphoma Society. I.X.R was supported by NIH P30 CA091842. C.R.Z. was supported by the NIH (K99HL165103), the American Society of Hematology, the Edward P. Evans Foundation and is a Special Fellow of the Leukemia and Lymphoma Society. A.K. was supported by the American Society of Hematology. T.M.P. was supported by NIH K99CA296777 and is a Fellow of the Leukemia and Lymphoma Society. J.A.M. was supported by the NIH (HL152180) and the Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital. J.A.M. is a Scholar of the Leukemia and Lymphoma Society. Author contributions Conceptualization and study design – W.H., G.A.C. Experimentation and data acquisition – W.H., H.B., H.C., M.R., N.I., Y.L., I.X.R., C.R.Z., A.K., T.M.P., S.C.B., J.A. Data analysis – W.H., A.L.Y., C.R.Z., W.Y., J.A.M., G.A.C. Funding acquisition – G.A.C. Project administration and supervision – G.A.C. Manuscript preparation – W.H., G.A.C. Competing interests The authors declare the following competing interests (unrelated to this work): T.M.P has performed consulting for Pillar Patient Advocates, Silence Therapeutics, PharmaEssentia and the MPNRF. A.L.Y. has performed consulting for BioGenerator is a co-founder, CEO, and shareholder of Pairidex, Inc. G.A.C. has performed consulting and received research funding from Incyte, Ajax Therapeutics and ReNAgade Therapeutics Management, and is a co-founder, member of the scientific advisory board and shareholder of Pairidex, Inc. Data and materials availability All raw read data (FASTQ files) for RNA sequencing and whole genome bisulfite sequencing data are publicly available at the Gene Expression Omnibus database ( http://www.ncbi.nlm.nih.gov/geo/ ) under accession number GSE303350. No original code was generated in the study. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Acknowledgments We thank all members of the Challen laboratory. We thank the Alvin J. Siteman Cancer Center for flow cytometry support. Funder Information Declared National Institutes of Health , DK124883 , HL147978 , DK119231 , T32HL007088 , K99CA296777 , K99HL165103 Gabrielle’s Angel Foundation for Cancer Research, https://ror.org/04aj0cy60 Edward P. Evans Foundation, https://ror.org/03h22gm35 Leukemia and Lymphoma Society , 6667-23 American Cancer Society, https://ror.org/02e463172 , CSCC-RSG-23-991417-01-CSCC American Society of Hematology, https://ror.org/02nw48b86 Children’s Discovery Institute, https://ror.org/02swjdp46 St. Louis Children’s Hospital Footnotes https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE303350 References and Notes 1. ↵ N. S. Majhail , S. H. Farnia , P. A. Carpenter , R. E. Champlin , S. Crawford , D. I. Marks , J. L. Omel , P. J. Orchard , J. Palmer , W. Saber , B. N. Savani , P. A. Veys , C. N. Bredeson , S. A. Giralt , C. F. LeMaistre , Indications for Autologous and Allogeneic Hematopoietic Cell Transplantation: Guidelines from the American Society for Blood and Marrow Transplantation . Biology of Blood and Marrow Transplantation 21 , 1863 – 1869 ( 2015 ). OpenUrl CrossRef PubMed 2. ↵ N. Singh , A. W. Loren , Overview of Hematopoietic Cell Transplantation for the Treatment of Hematologic Malignancies . Clin Chest Med 38 , 575 – 593 ( 2017 ). OpenUrl PubMed 3. ↵ M. Imamura , S. Hashino , J. Tanaka , Graft-versus-leukemia effect and its clinical implications . Leuk Lymphoma 23 , 477 – 492 ( 1996 ). OpenUrl PubMed 4. ↵ J. Dehn , S. Spellman , C. K. Hurley , B. E. Shaw , J. N. Barker , L. J. Burns , D. L. Confer , M. Eapen , M. Fernandez-Vina , R. Hartzman , M. Maiers , S. R. Marino , C. Mueller , M. A. Perales , R. Rajalingam , J. Pidala , Selection of unrelated donors and cord blood units for hematopoietic cell transplantation: guidelines from the NMDP/CIBMTR . Blood 134 , 924 – 934 ( 2019 ). OpenUrl Abstract / FREE Full Text 5. ↵ L. Gragert , M. Eapen , E. Williams , J. Freeman , S. Spellman , R. Baitty , R. Hartzman , J. D. Rizzo , M. Horowitz , D. Confer , M. Maiers , HLA Match Likelihoods for Hematopoietic Stem-Cell Grafts in the US Registry . New England Journal of Medicine 371 , 339 – 348 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 6. ↵ M. Eapen , P. Rubinstein , M. J. Zhang , C. Stevens , J. Kurtzberg , A. Scaradavou , F. R. Loberiza , R. E. Champlin , J. P. Klein , M. M. Horowitz , J. E. Wagner , Outcomes of transplantation of unrelated donor umbilical cord blood and bone marrow in children with acute leukaemia: a comparison study . Lancet 369 , 1947 – 1954 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 7. V. Rocha , J. E. Wagner , Jr. , K. A. Sobocinski , J. P. Klein , M. J. Zhang , M. M. Horowitz , E. Gluckman , Graft-versus-host disease in children who have received a cord-blood or bone marrow transplant from an HLA-identical sibling. Eurocord and International Bone Marrow Transplant Registry Working Committee on Alternative Donor and Stem Cell Sources . N Engl J Med 342 , 1846 – 1854 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 8. ↵ J. N. Barker , J. E. Wagner , Umbilical cord blood transplantation: current practice and future innovations . Crit Rev Oncol Hematol 48 , 35 – 43 ( 2003 ). OpenUrl PubMed 9. ↵ J. N. Barker , C. E. Byam , N. A. Kernan , S. S. Lee , R. M. Hawke , K. A. Doshi , D. S. Wells , G. Heller , E. B. Papadopoulos , A. Scaradavou , J. W. Young , M. R. M. van den Brink , Availability of Cord Blood Extends Allogeneic Hematopoietic Stem Cell Transplant Access to Racial and Ethnic Minorities . Biology of Blood and Marrow Transplantation 16 , 1541 – 1548 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 10. ↵ D. K. Kim , Y. Fujiki , T. Fukushima , H. Ema , A. Shibuya , H. Nakauchi , Comparison of hematopoietic activities of human bone marrow and umbilical cord blood CD34 positive and negative cells . Stem Cells 17 , 286 – 294 ( 1999 ). OpenUrl CrossRef PubMed Web of Science 11. ↵ J. Versluis , B. Kalin , W. Zeijlemaker , J. Passweg , C. Graux , M. Manz , M. C. Vekemans , B. Biemond , M. C. Legdeur , M. V. Kooy , J. Janssen , T. Pabst , B. Lowenberg , M. Jongen-Lavrencic , G. J. Schuurhuis , G. Ossenkoppele , J. Cornelissen , Graft Versus Leukemia Effect of Allogeneic Stem Cell Transplantation and Minimal Residual Disease in Patients with Aml in First Complete Remission . Haematologica 102 , 7 – 7 ( 2017 ). OpenUrl Abstract / FREE Full Text 12. ↵ L. Balligand , C. Galambrun , A. Sirvent , C. Roux , C. Pochon , B. Bruno , C. Jubert , A. Loundou , S. Esmiol , I. Yakoub-Agha , E. Forcade , C. Paillard , A. Marie-Cardine , D. Plantaz , V. Gandemer , D. Blaise , F. Rialland , C. Renard , M. Seux , K. Baumstarck , M. Mohty , J. H. Dalle , G. Michel , Single-Unit versus Double-Unit Umbilical Cord Blood Transplantation in Children and Young Adults with Residual Leukemic Disease . Biol Blood Marrow Transplant 25 , 734 – 742 ( 2019 ). OpenUrl PubMed 13. ↵ A. E. Boitano , J. A. Wang , R. Romeo , L. C. Bouchez , A. E. Parker , S. E. Sutton , J. R. Walker , C. A. Flaveny , G. H. Perdew , M. S. Denison , P. G. Schultz , M. P. Cooke , Aryl Hydrocarbon Receptor Antagonists Promote the Expansion of Human Hematopoietic Stem Cells . Science 329 , 1345 – 1348 ( 2010 ). OpenUrl Abstract / FREE Full Text 14. ↵ I. Fares , J. Chagraoui , Y. Gareau , S. Gingras , R. Ruel , N. Mayotte , E. Csaszar , D. J. Knapp , P. Miller , M. Ngom , S. Imren , D. C. Roy , K. L. Watts , H. P. Kiem , R. Herrington , N. N. Iscove , R. K. Humphries , C. J. Eaves , S. Cohen , A. Marinier , P. W. Zandstra , G. Sauvageau , Cord blood expansion. Pyrimidoindole derivatives are agonists of human hematopoietic stem cell self-renewal . Science 345 , 1509 – 1512 ( 2014 ). OpenUrl Abstract / FREE Full Text 15. ↵ B. Guo , X. Huang , M. R. Lee , S. A. Lee , H. E. Broxmeyer , Antagonism of PPAR-gamma signaling expands human hematopoietic stem and progenitor cells by enhancing glycolysis . Nat Med 24 , 360 – 367 ( 2018 ). OpenUrl CrossRef PubMed 16. ↵ J. Hoggatt , P. Singh , J. Sampath , L. M. Pelus , Prostaglandin E-2 enhances hematopoietic stem cell homing, survival, and proliferation . Blood 113 , 5444 – 5455 ( 2009 ). OpenUrl Abstract / FREE Full Text 17. T. E. North , W. Goessling , K. R. Kopani , E. Mayhall , I. H. Jang , C. Lengerke , C. E. Burns , A. Lord , T. Grosser , G. A. FitzGerald , G. Q. Daley , L. I. Zon , A high-throughput chemical genetic screen in zebrafish establishes that prostaglandin E2 is a potent regulator of hematopoietic stem cell formation . Blood Cells Molecules and Diseases 38 , 191 – 191 ( 2007 ). OpenUrl 18. ↵ T. E. North , W. Goessling , C. R. Walkley , C. Lengerke , K. R. Kopani , A. M. Lord , G. J. Weber , T. V. Bowman , I. H. Jang , T. Grosser , G. A. Fitzgerald , G. Q. Daley , S. H. Orkin , L. I. Zon , Prostaglandin E2 regulates vertebrate haematopoietic stem cell homeostasis . Nature 447 , 1007 – 1011 ( 2007 ). OpenUrl CrossRef PubMed Web of Science 19. ↵ J. E. Wagner , C. G. Brunstein , A. E. Boitano , T. E. DeFor , D. McKenna , D. Sumstad , B. R. Blazar , J. Tolar , C. Le , J. Jones , M. P. Cooke , C. C. Bleul , Phase I/II Trial of StemRegenin-1 Expanded Umbilical Cord Blood Hematopoietic Stem Cells Supports Testing as a Stand-Alone Graft . Cell Stem Cell 18 , 144 – 155 ( 2016 ). OpenUrl CrossRef PubMed 20. ↵ C. Cutler , P. Multani , D. Robbins , H. T. Kim , T. Le , J. Hoggatt , L. M. Pelus , C. Desponts , Y. B. Chen , B. Rezner , P. Armand , J. Koreth , B. Glotzbecker , V. T. Ho , E. Alyea , M. Isom , G. Kao , M. Armant , L. Silberstein , P. R. Hu , R. J. Soiffer , D. T. Scadden , J. Ritz , W. Goessling , T. E. North , J. Mendlein , K. Ballen , L. I. Zon , J. H. Antin , D. D. Shoemaker , Prostaglandin-modulated umbilical cord blood hematopoietic stem cell transplantation . Blood 122 , 3074 – 3081 ( 2013 ). OpenUrl Abstract / FREE Full Text 21. ↵ A. Zhao , H. Zhou , J. Yang , M. Li , T. Niu , Epigenetic regulation in hematopoiesis and its implications in the targeted therapy of hematologic malignancies . Signal Transduct Target Ther 8 , 71 ( 2023 ). OpenUrl PubMed 22. ↵ H. Celik , W. K. Koh , A. C. Kramer , E. L. Ostrander , C. Mallaney , D. A. C. Fisher , J. Xiang , W. C. Wilson , A. Martens , A. Kothari , G. Fishberger , E. Tycksen , D. Karpova , E. J. Duncavage , Y. Lee , S. T. Oh , G. A. Challen , JARID2 Functions as a Tumor Suppressor in Myeloid Neoplasms by Repressing Self-Renewal in Hematopoietic Progenitor Cells . Cancer Cell 34 , 741 – 756 e748 ( 2018 ). OpenUrl CrossRef PubMed 23. ↵ S. A. Kinkel , R. Galeev , C. Flensburg , A. Keniry , K. Breslin , O. Gilan , S. Lee , J. Liu , K. L. Chen , L. J. Gearing , D. L. Moore , W. S. Alexander , M. Dawson , I. J. Majewski , A. Oshlack , J. Larsson , M. E. Blewitt , Jarid2 regulates hematopoietic stem cell function by acting with polycomb repressive complex 2 . Blood 125 , 1890 – 1900 ( 2015 ). OpenUrl Abstract / FREE Full Text 24. ↵ A. Puda , J. D. Milosevic , T. Berg , T. Klampfl , A. S. Harutyunyan , B. Gisslinger , E. Rumi , D. Pietra , L. Malcovati , C. Elena , M. Doubek , M. Steurer , N. Tosic , S. Pavlovic , P. Guglielmelli , L. Pieri , A. M. Vannucchi , H. Gisslinger , M. Cazzola , R. Kralovics , Frequent deletions of JARID2 in leukemic transformation of chronic myeloid malignancies . Am J Hematol 87 , 245 – 250 ( 2012 ). OpenUrl CrossRef PubMed 25. J. D. Milosevic , A. Puda , L. Malcovati , T. Berg , M. Hofbauer , A. Stukalov , T. Klampfl , A. S. Harutyunyan , H. Gisslinger , B. Gisslinger , T. Burjanivova , E. Rumi , D. Pietra , C. Elena , A. M. Vannucchi , M. Doubek , D. Dvorakova , B. Robesova , R. Wieser , E. Koller , N. Suvajdzic , D. Tomin , N. Tosic , J. Colinge , Z. Racil , M. Steurer , S. Pavlovic , M. Cazzola , R. Kralovics , Clinical significance of genetic aberrations in secondary acute myeloid leukemia . Am J Hematol 87 , 1010 – 1016 ( 2012 ). OpenUrl CrossRef PubMed 26. ↵ R. Rampal , J. Ahn , O. Abdel-Wahab , M. Nahas , K. Wang , D. Lipson , G. A. Otto , R. Yelensky , T. Hricik , A. S. McKenney , G. Chiosis , Y. R. Chung , S. Pandey , M. R. van den Brink , S. A. Armstrong , A. Dogan , A. Intlekofer , T. Manshouri , C. Y. Park , S. Verstovsek , F. Rapaport , P. J. Stephens , V. A. Miller , R. L. Levine , Genomic and functional analysis of leukemic transformation of myeloproliferative neoplasms . Proc Natl Acad Sci U S A 111 , E5401 – 5410 ( 2014 ). OpenUrl Abstract / FREE Full Text 27. ↵ N. Cancer Genome Atlas Research , Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia . N Engl J Med 368 , 2059 – 2074 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 28. ↵ C. Fellmann , T. Hoffmann , V. Sridhar , B. Hopfgartner , M. Muhar , M. Roth , D. Y. Lai , I. A. Barbosa , J. S. Kwon , Y. Guan , N. Sinha , J. Zuber , An optimized microRNA backbone for effective single-copy RNAi . Cell Rep 5 , 1704 – 1713 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 29. ↵ K. A. Oetjen , K. E. Lindblad , M. Goswami , G. Gui , P. K. Dagur , C. Lai , L. W. Dillon , J. P. McCoy , C. S. Hourigan , Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry . JCI Insight 3 , ( 2018 ). 30. ↵ J. M. Granja , S. Klemm , L. M. McGinnis , A. S. Kathiria , A. Mezger , M. R. Corces , B. Parks , E. Gars , M. Liedtke , G. X. Y. Zheng , H. Y. Chang , R. Majeti , W. J. Greenleaf , Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia . Nat Biotechnol 37 , 1458 – 1465 ( 2019 ). OpenUrl CrossRef PubMed 31. ↵ D. Landeira , S. Sauer , R. Poot , M. Dvorkina , L. Mazzarella , H. F. Jorgensen , C. F. Pereira , M. Leleu , F. M. Piccolo , M. Spivakov , E. Brookes , A. Pombo , C. Fisher , W. C. Skarnes , T. Snoek , K. Bezstarosti , J. Demmers , R. J. Klose , M. Casanova , L. Tavares , N. Brockdorff , M. Merkenschlager , A. G. Fisher , Jarid2 is a PRC2 component in embryonic stem cells required for multi-lineage differentiation and recruitment of PRC1 and RNA Polymerase II to developmental regulators . Nat Cell Biol 12 , 618 – 624 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 32. ↵ D. Pasini , P. A. Cloos , J. Walfridsson , L. Olsson , J. P. Bukowski , J. V. Johansen , M. Bak , N. Tommerup , J. Rappsilber , K. Helin , JARID2 regulates binding of the Polycomb repressive complex 2 to target genes in ES cells . Nature 464 , 306 – 310 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 33. ↵ F. Notta , S. Doulatov , E. Laurenti , A. Poeppl , I. Jurisica , J. E. Dick , Isolation of Single Human Hematopoietic Stem Cells Capable of Long-Term Multilineage Engraftment . Science 333 , 218 – 221 ( 2011 ). OpenUrl Abstract / FREE Full Text 34. H. D. Huntsman , T. Bat , H. Cheng , A. Cash , P. S. Cheruku , J. F. Fu , K. Keyvanfar , R. W. Childs , C. E. Dunbar , A. Larochelle , Human hematopoietic stem cells from mobilized peripheral blood can be purified based on CD49f integrin expression . Blood 126 , 1631 – 1633 ( 2015 ). OpenUrl FREE Full Text 35. ↵ M. E. MacAldaz , J. Shu , G. Edin , M. Hale , C. J. Eaves , Hematopoietic stem cells in human fetal liver selectively express CD49f . Exp Hematol , 104788 ( 2025 ). 36. ↵ S. Gross , K. Helm , J. J. Gruntmeir , W. S. Stillman , D. W. Pyatt , R. D. Irons , Characterization and phenotypic analysis of differentiating CD34+ human bone marrow cells in liquid culture . Eur J Haematol 59 , 318 – 326 ( 1997 ). OpenUrl PubMed Web of Science 37. ↵ V. Calvanese , A. T. Nguyen , T. J. Bolan , A. Vavilina , T. Su , L. K. Lee , Y. Wang , F. D. Lay , M. Magnusson , G. M. Crooks , S. K. Kurdistani , H. K. A. Mikkola , MLLT3 governs human haematopoietic stem-cell self-renewal and engraftment . Nature 576 , 281 – 286 ( 2019 ). OpenUrl CrossRef PubMed 38. ↵ J. Aguade-Gorgorio , Y. Jami-Alahmadi , V. Calvanese , M. Kardouh , I. Fares , H. Johnson , V. Rezek , F. Ma , M. Magnusson , Y. Wang , J. E. Shin , K. J. Nance , H. S. Goodridge , S. Liebscher , K. Schenke-Layland , G. M. Crooks , J. A. Wohlschlegel , H. K. A. Mikkola , MYCT1 controls environmental sensing in human haematopoietic stem cells . Nature 630 , 412 – 420 ( 2024 ). OpenUrl PubMed 39. ↵ P. Hernandez-Malmierca , D. Vonficht , A. Schnell , H. J. Uckelmann , A. Bollhagen , M. A. A. Mahmoud , S. L. Landua , E. van der Salm , C. L. Trautmann , S. Raffel , F. Grunschlager , R. Lutz , M. Ghosh , S. Renders , N. Correia , E. Donato , K. O. Dixon , C. Hirche , C. Andresen , C. Robens , P. S. Werner , T. Boch , D. Eisel , W. Osen , F. Pilz , A. Przybylla , C. Klein , F. Buchholz , M. D. Milsom , M. A. G. Essers , S. B. Eichmuller , W. K. Hofmann , D. Nowak , D. Hubschmann , M. Hundemer , C. Thiede , L. Bullinger , C. Muller-Tidow , S. A. Armstrong , A. Trumpp , V. K. Kuchroo , S. Haas , Antigen presentation safeguards the integrity of the hematopoietic stem cell pool . Cell Stem Cell 29 , 760 – 775 e710 ( 2022 ). OpenUrl CrossRef PubMed 40. ↵ J. Li , M. J. Williams , H. J. Park , H. P. Bastos , X. Wang , D. Prins , N. K. Wilson , C. Johnson , K. Sham , M. Wantoch , S. Watcham , S. J. Kinston , D. C. Pask , T. L. Hamilton , R. Sneade , A. K. Waller , C. Ghevaert , G. S. Vassiliou , E. Laurenti , D. G. Kent , B. Gottgens , A. R. Green , STAT1 is essential for HSC function and maintains MHCIIhi stem cells that resist myeloablation and neoplastic expansion . Blood 140 , 1592 – 1606 ( 2022 ). OpenUrl PubMed 41. ↵ M. C. Gundry , L. Brunetti , A. Lin , A. E. Mayle , A. Kitano , D. Wagner , J. I. Hsu , K. A. Hoegenauer , C. M. Rooney , M. A. Goodell , D. Nakada , Highly Efficient Genome Editing of Murine and Human Hematopoietic Progenitor Cells by CRISPR/Cas9 . Cell Rep 17 , 1453 – 1461 ( 2016 ). OpenUrl CrossRef PubMed 42. ↵ J. W. Schott , D. Leon-Rico , C. B. Ferreira , K. F. Buckland , G. Santilli , M. A. Armant , A. Schambach , A. Cavazza , A. J. Thrasher , Enhancing Lentiviral and Alpharetroviral Transduction of Human Hematopoietic Stem Cells for Clinical Application . Mol Ther Methods Clin Dev 14 , 134 – 147 ( 2019 ). OpenUrl PubMed 43. ↵ Y. Hao , S. Hao , E. Andersen-Nissen , W. M. Mauck , 3rd . , S. Zheng , A. Butler , M. J. Lee , A. J. Wilk , C. Darby , M. Zager , P. Hoffman , M. Stoeckius , E. Papalexi , E. P. Mimitou , J. Jain , A. Srivastava , T. Stuart , L. M. Fleming , B. Yeung , A. J. Rogers , J. M. McElrath , C. A. Blish , R. Gottardo , P. Smibert , R. Satija , Integrated analysis of multimodal single-cell data . Cell 184 , 3573 – 3587 e3529 ( 2021 ). OpenUrl CrossRef PubMed 44. ↵ E. Becht , L. McInnes , J. Healy , C. A. Dutertre , I. W. H. Kwok , L. G. Ng , F. Ginhoux , E. W. Newell , Dimensionality reduction for visualizing single-cell data using UMAP . Nat Biotechnol , ( 2018 ). 45. ↵ A. Dobin , C. A. Davis , F. Schlesinger , J. Drenkow , C. Zaleski , S. Jha , P. Batut , M. Chaisson , T. R. Gingeras , STAR: ultrafast universal RNA-seq aligner . Bioinformatics 29 , 15 – 21 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 46. ↵ Y. Liao , G. K. Smyth , W. Shi , featureCounts: an efficient general purpose program for assigning sequence reads to genomic features . Bioinformatics 30 , 923 – 930 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 47. ↵ L. Wang , S. Wang , W. Li , RSeQC: quality control of RNA-seq experiments . Bioinformatics 28 , 2184 – 2185 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 48. ↵ R. Liu , A. Z. Holik , S. Su , N. Jansz , K. Chen , H. S. Leong , M. E. Blewitt , M. L. Asselin-Labat , G. K. Smyth , M. E. Ritchie , Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses . Nucleic Acids Res 43 , e97 ( 2015 ). OpenUrl CrossRef PubMed 49. ↵ W. Luo , M. S. Friedman , K. Shedden , K. D. Hankenson , P. J. Woolf , GAGE: generally applicable gene set enrichment for pathway analysis . BMC Bioinformatics 10 , 161 ( 2009 ). OpenUrl CrossRef PubMed 50. ↵ W. Luo , C. Brouwer , Pathview: an R/Bioconductor package for pathway-based data integration and visualization . Bioinformatics 29 , 1830 – 1831 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 51. ↵ S. Zhao , Y. Guo , Q. Sheng , Y. Shyr , Advanced heat map and clustering analysis using heatmap3 . Biomed Res Int 2014 , 986048 ( 2014 ). OpenUrl PubMed 52. ↵ P. Langfelder , S. Horvath , WGCNA: an R package for weighted correlation network analysis . BMC Bioinformatics 9 , 559 ( 2008 ). OpenUrl CrossRef PubMed 53. ↵ F. Yu , V. G. Sankaran , G. C. Yuan , CUT&RUNTools 2.0: a pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis . Bioinformatics 38 , 252 – 254 ( 2021 ). OpenUrl CrossRef PubMed 54. ↵ B. Langmead , S. L. Salzberg , Fast gapped-read alignment with Bowtie 2 . Nat Methods 9 , 357 – 359 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 55. ↵ Y. Zhang , T. Liu , C. A. Meyer , J. Eeckhoute , D. S. Johnson , B. E. Bernstein , C. Nusbaum , R. M. Myers , M. Brown , W. Li , X. S. Liu , Model-based analysis of ChIP-Seq (MACS) . Genome Biol 9 , R137 ( 2008 ). 56. ↵ M. P. Meers , D. Tenenbaum , S. Henikoff , Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling . Epigenetics Chromatin 12 , 42 ( 2019 ). View the discussion thread. Back to top Previous Next Posted September 04, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation 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 JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation Wentao Han , Hassan Bjeije , Hamza Celik , Michael Rettig , Nancy Issa , Andrew L. Young , Yanan Li , Infencia Xavier Raj , Christine R. Zhang , Aishwarya Krishnan , Tyler M. Parsons , Samantha C. Burkart , Jason Arand , Wei Yang , Jeffrey A. Magee , Grant A. Challen bioRxiv 2025.09.03.672211; doi: https://doi.org/10.1101/2025.09.03.672211 Share This Article: Copy Citation Tools JARID2 Inhibition Reprograms Human Hematopoietic Progenitor Cells To Enhance Bone Marrow Transplantation Wentao Han , Hassan Bjeije , Hamza Celik , Michael Rettig , Nancy Issa , Andrew L. Young , Yanan Li , Infencia Xavier Raj , Christine R. Zhang , Aishwarya Krishnan , Tyler M. Parsons , Samantha C. Burkart , Jason Arand , Wei Yang , Jeffrey A. Magee , Grant A. Challen bioRxiv 2025.09.03.672211; doi: https://doi.org/10.1101/2025.09.03.672211 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Developmental Biology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17690) Bioengineering (13892) Bioinformatics (41936) Biophysics (21451) Cancer Biology (18588) Cell Biology (25499) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88603) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15152) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)