The extraordinary cellular diversity and the complex connections established within different cells types render the nervous system of vertebrates one of the most sophisticated tissues found in living organisms. Such complexity is ensured by numerous regulatory mechanisms that provide tight spatiotemporal control, robustness and reliability. While the unusual abundance of long noncoding RNAs (lncRNAs) in nervous tissues was traditionally puzzling, it is becoming clear that these molecules have genuine regulatory functions in the brain and they are essential for neuronal physiology. The canonical view of RNA as predominantly a ‘coding molecule’ has been largely surpassed, together with the conception that lncRNAs only represent ‘waste material’ produced by cells as a side effect of pervasive transcription. Here we review a growing body of evidence showing that lncRNAs play key roles in several regulatory mechanisms of neurons and other brain cells. In particular, neuronal lncRNAs are crucial for orchestrating neurogenesis, for tuning neuronal differentiation and for the exact calibration of neuronal excitability. Moreover, their diversity and the association to neurodegenerative diseases render them particularly interesting as putative biomarkers for brain disease. Overall, we foresee that in the future a more systematic scrutiny of lncRNA functions will be instrumental for an exhaustive understanding of neuronal pathophysiology. Despite being initially considered as ‘transcriptional noise’, lncRNAs are currently recognized as modulators of cellular functions in every branch of life and have been implicated in a large number of biological processes including X-chromosome silencing, genetic imprinting, regulation of cell state, coordination of differentiation and possibly disease modulation While only a small portion (~3%) of the vertebrate genome encodes for proteins, it has been proposed that ~80% can lead to the production of a variety of noncoding RNAs (ncRNAs) ,15,16,17The brain of vertebrates, and in particular of birds and mammals, is one of the most complex systems in biology where an elaborate network established between different cell types allows advanced cognitive tasks . During The importance of lncRNAs in the central nervous system is exemplified by the high number of brain-specific transcripts compared to other tissues ,37, i.e.lacZ reporter mouse lines have revealed that lncRNAs are excellent markers for brain developmental stages, brain sub-region identity and even reliable markers for neuronal subtypes, further indicating that there is a tight regulation in their expression profile and that they are intentional transcription products [Results from in situ hybridization and products ,11,12,49products ,51.Despite the overall low level of conservation of lncRNAs, brain-specific lncRNAs display high sequence conservation when compared to lncRNAs from other tissues ,52. EvenAltogether, these observations remain correlative, raising the question of whether lncRNA expression is just an ‘epiphenomenon’, a casual correlate of other processes such as gene expression regulation, or if it plays a direct causative role in the regulation of brain morphogenesis and functioning. To answer this question, in the next sections we will summarize the molecular roles of the most relevant lncRNAs and their functions in neurons and other cells for the central nervous system.Enhancer lncRNAs (eRNAs), which are the product of bidirectional DNA transcription taking place in the region of active enhancers [Antisense lncRNAs  produced by antisense transcription of a protein-coding sequence which can be degraded following the formation of double strand RNAs [Cis-acting nuclear lncRNAs, thus acting in the same genome locus from which they originate and that interfere with and/or regulate the transcription of neighboring genes [Trans-acting nuclear lncRNAs involved in epigenetic regulation; (5) LncRNAs that modulate gene expression by post transcriptional mechanisms and affect mRNA stability, translation, or splicing [LncRNAs directly modulating protein functions that are generally functioning in association with RNA binding proteins (RBPs), in some cases also outside the nucleus. This classification is summarized in Among the numerous possible classifications for lncRNAs proposed ,59,60,61nhancers ; (2) Antand RNAs ; (3) Cisng genes ; (4) Trasplicing ,66,67 anEven though eRNAs are sometimes excluded from the class of lncRNAs because they can be shorter than 200 nucleotides, the vast majority are longer than 200 nucleotides. Moreover, these ncRNAs do not behave as most other shorter ncRNAs. For these reasons, and for their predominant abundance in the brain, we believe they should be discussed here.c-Fos and Nr4a2 [Several eRNAs are produced in the vicinity of genes as bidirectional transcripts, whose levels correlate with the local gene expression ,69. For nd Nr4a2 ,70,71.Arc), it was described that eRNAs can directly modulate in cis the elongation of the corresponding mRNA by acting as decoys for the negative elongation factor NELF, thus facilitating the progression of RNA polymerase II at the promoter of the target gene [utNgn1. eRNA utNgn1 is expressed in concomitance with Neurogenin-1 (NGN1), an essential transcription factor for neuronal differentiation [utNgn1 might control the levels of NGN1 [For the activity-regulated cytoskeleton-associated gene , while repressing another gene that is ~25 Mb distant (Akr1b8). Moreover, its overexpression activates Lsm8, also on the same chromosome but ~12 Mb apart [trans cannot be excluded, since it has been observed that Evf2 can also recruit transcriptional activators (such as DLX1/2) and repressors (such as MECP2) that can interfere with the regulatory regions of developmental genes such as Dlx5, Dlx6 and Gad1, explaining its effect on GABAergic differentiation [Altogether these findings point to a specific biological function of eRNAs, suggesting that their transcription is not just a side effect of the change in the local DNA conformation and epigenetic state, but that they might directly recruit protein complexes. They might even function as bridges between far DNA regions. This is exemplified by Mb apart . These entiation .Overall, a comprehensive study of the molecular functions and structural effects of eRNAs is largely missing. Remarkably, several single nucleotide polymorphisms on eRNAs near synaptic activity-regulated genes are associated with autism spectrum disorders, indicating that their sequences contain structural motifs required for a specific scaffolding function .Around 70% of genes are transcribed in the antisense direction, giving rise to AS-lncRNAs implicated in post-transcriptional regulation of the corresponding ‘sense’ transcript by degradation of the complementary mRNA molecules or by modulating their expression or stability ,79,80,81In the brain, several mRNAs are under the control of a corresponding AS-lncRNA, including for example the genes encoding the CAMK2N1 subunit of the calmodulin-dependent protein kinase and neurogranin (NRGN), which are both implicated in the regulation of postsynaptic signal transduction pathways and synaptic long-term potentiation .Kcna2-AS that targets the mRNA of the ‘shaker type’ voltage-dependent potassium channel (Kcna2), which is relevant for human pathology since its mutations are linked to ataxia and focal epilepsies [Kcna2-AS in experimental models inversely correlates with the levels of KCNA2 [Kcna2-AS as a result of transcription activation mediated by the myeloid zinc finger protein 1 (MZF1) that leads to a ~50% decrease of active channels [Kcna2-AS during nerve damage reduces the voltage-gated potassium current, and increases the excitability of dorsal root ganglia neurons producing neuropathic pain symptoms. While these observations hold great potential for therapeutic strategies aimed at decreasing neuropathic pain following injury, this does not entirely explain the physiological roles of Kcna2-AS in the regulation of neuronal excitability which will require further studies.Another notable example is ilepsies . Indeed,of KCNA2 , confirmchannels . As a coSox (SRY-related HMG-box) genes that encode high mobility groups that can bind and bend the DNA during the regulation of embryonic development and influence cell fate. In particular two members of the Sox family, Sox4 and Sox11, have corresponding AS-lncRNAs that are highly expressed during the development of the mouse cortex, suggesting they might have a specific function in cerebral corticogenesis and cell differentiation [During the development of the brain cortex, several AS-lncRNA are expressed to subtly regulate the amounts of transcription factors. Examples include ntiation .BDNF-AS, which controls the levels of the brain-derived neurotrophic factor (BDNF), a neurotrophin implicated in the regulation of several key processes including neurogenesis, precursor cell proliferation, neuronal differentiation, maturation, plasticity and with a role in pathophysiology of psychiatric disorders [BDNF-AS overexpression decreases the levels of BDNF, both in vitro and in vivo [BDNF-AS function are not entirely known, although it seems that its expression affects in cis the Bdnf gene by recruiting EZH2, an abundant histone methyltransferase that causes the inactivation of the BDNF promoter. As expected, BDNF-AS knockdown results in increased neuronal cell number, differentiation and more prominent neurite outgrowth [BDNF-AS is increased only in individuals who started to drink before 21 years of age, suggesting that this BDNF-AS might affect epigenetic reprogramming in an age-dependent manner [An eminent AS-lncRNAs in the brain is isorders ,87,88,89 in vivo . The exautgrowth ,90. The t manner . Deficitt manner .BACE1-AS which, in contrast to most AS-lncRNAs, stabilizes its related transcript Bace1 which encodes the β-secretase enzyme 1 implicated in Alzheimer’s disease (BACE1) [BACE1-AS lncRNA is thus involved in a feed-forward loop that is induced by high levels of the amyloid-beta 1–42 fragment itself. The molecular details of this unusual process are not known and, although it has been suggested that RNA duplex formation could alter the structure of Bace1 RNA and thus increase its stability, this possibility remains speculative. To reinforce its connection to the pathology, the concentration of BACE1-AS is higher in patients with Alzheimer’s disease than in matched controls [Among AS-lncRNAs associated with human pathologies, it is worth mentioning  (BACE1) . This encontrols  and corrcontrols .Ube3a-ATS, might become a possible target for therapeutic intervention. This transcript is nuclear, neurospecific and regulates the gene Ube3a [Ube3a codes for a ubiquitin ligase, that ubiquitinates and regulates the stability of several important proteins in neuronal function such as MAPK1 and β-catenin. The paternal Ube3a is usually repressed by Ube3a-ATS and the pathology manifests itself upon deletion of the maternal gene. As a consequence of paternal imprinting, cells that in principle contain a copy of the functioning gene, do not actually express the protein UBE3A. For this reason, downregulation of the paternal Ube3a-ATS could be a rescue strategy for this pathology [Angelman syndrome is a pathology where an AS-lncRNA, named ne Ube3a . Angelmane Ube3a . The genathology ,96.Six3OS, which is transcribed from the opposite strand of the Six3 gene, a homeodomain transcription factor involved in forebrain development and retinal cell specification [Six3, Six3OS also acts as a molecular scaffold for the transcriptional co-regulator of SIX3 (EYA) and the histone-lysine N-methyltransferase enzyme EZH2, both implicated in the regulation of SIX3 target genes [Six3OS with EZH2 possibly changes the chromatin structure by triggering the H3K27me3 modification, which is usually associated with decreased expression of nearby genes [Six3OS is implicated in the glial-neuronal lineage specification of adult neural stem cells in vivo [Six3). An additional confounding factor could be the several isoforms of Six3OS, some of which probably code for short ~100 amino acid proteins [cis in the vicinity of the gene, similar to cis-acting lncRNAs which will be discussed in the next section.A number of AS-lncRNAs appear to have acquired additional functions besides regulating the expression of their sense gene. One example is fication . Besideset genes . Interacby genes . Further in vivo . Due to proteins . Neverthcis has been observed and described in many cases. At the same time, in several cases as also summarized here, a more careful examination of their role has revealed that many lncRNAs also have additional roles, besides their activity in cis.The local regulation of gene expression exerted by lncRNAs can encompass different mechanisms, such as regulation of promoter activity but also local modulation of transcription and splicing ,101. Thecis-acting mechanism is Paupar (Pax6 upstream antisense RNA), which is transcribed upstream the Pax6 gene in the opposite direction [Paupar acts in cis regulating PAX6-dependent functions since its knockdown leads to an increase in PAX6 expression and interferes with eye development, progenitor cell potency and proliferation in the brain [Pax6 is not entirely known, although it could be speculated that its transcription affects the nearby promoter region of Pax6, either stabilizing transcription factor binding or inducing promoter-enhancer looping. In parallel, Paupar has probably evolved a more elaborate mechanism of action. In addition to regulating the expression of Pax6, it also regulates the binding of PAX6 to its neural target genes [trans by forming a ribonucleoprotein (RNP) complex together with PAX6 and KAP1 (coded by Trim28), which is involved in transcriptional control [Paupar promotes the association of KAP1 with the DNA resulting in H3K9me3 modifications on the promoter of genes involved in neural stem cell renewal and differentiation, inducing heterochromatin and inactivation [Paupar disrupts olfactory bulb neurogenesis [A relevant lncRNA with a irection . Paupar he brain ,105. The control ,106. It ogenesis .linc–Brn1b , has a cis-regulatory effect on Pou3f3. The level of linc–Brn1b usually is higher in neural stem cells and its expression declines during development. Mice where both copies of linc–Brn1b are ablated exhibit a ~50% reduction in POU3F3 levels and a subsequent decrease in the number of cortex intermediate progenitor cells and abnormal lamination in the cerebral cortex [Pou3f3 gene and the effect on its expression suggest that the linc–Brn1b acts as an eRNA, although the differential expression observed in other surrounding genes might suggest that linc–Brn1b also has additional functions that surpass its local effects on DNA conformation. Dali (DNMT1-Associated Long Intergenic RNA) is yet another lncRNA that interferes with the expression of Pou3f3, which is located ~50 Kb upstream. Additionally, similarly to Paupar, Dali has a set of supplementary functions and forms a complex with POU3F3 which, in trans, regulates a subset of genes involved in neural differentiation. Another mechanism by which Dali carries on its functions is achieved by its direct interaction with chromatin remodeling factors such as BRG1, SIN3A and DNMT1 [Dali affects the epigenetic reprogramming of gene promoters, as also shown by its knockdown which increases the methylation of CpG islands on the promoters of genes involved in neuronal differentiation [A key transcription factor in cerebral cortex development is POU3F3 . It has been reported that POU3F3 is under the control of several lncRNAs. In its close proximity, there are two lncRNAs that have been studied in detail by the laboratory of Rinn and collaborators . Deletiol cortex . While tnd DNMT1 . Throughntiation . There alncOL1 is a lncRNA with epigenetic functions, which is exclusively expressed in oligodendrocytes, a specific type of glial cells deputed to myelination in the central nervous system. lncOL1 promotes oligodendrocyte differentiation and speeds the onset of myelination forming a complex with SUZ12, a zinc finger that is part of the polycomb repressive complex 2. The lncOL1-SUZ12 complex silences genes that in undifferentiated cells repress oligodendrocyte genes promoting the formation of H3K27me3 modifications on their regulatory elements. As a result, lncOL1 overexpression promotes myelination and even more strikingly favors re-myelination following injury, suggesting a possible therapeutic use of this lncRNA in the treatment of demyelinating conditions [In several tissues, nuclear lncRNAs often act as molecular scaffolds that recruit chromatin modifiers such as the polycomb repressive complex (PRC2) and the DNA methyltransferase 3 (DNMT3) to regulatory genomic elements and modulate the expression of target genes . Innditions .HOXA Transcript Antisense RNA, Myeloid-Specific 1 (HOTAIRM1) and Tcl1 upstream neuron-associated lincRNA (Tuna). HOTAIRM1 modulates Neurogenin 2, a basic helix-loop-helix transcription factor that acts as a master regulator of neurogenesis [Tuna, a highly expressed and evolutionary conserved lncRNA that has been studied in the developing mouse brain, is sufficient to block neurogenesis in both mouse and human cell models [Regulating neuronal differentiation and neurogenesis, examples of lncRNAs include the Hox-encoded ogenesis . Downregl models .Tuna results in the deposition of the active histone modification H3K4me3 to the promoters of key genes in neuronal specification such as Nanog, Fgf4 and Sox2, indicating that the lncRNA might also modulate the activity of some histone methyltransferases [Tuna affects neuronal development by recruiting two heterogenous nuclear ribonucleoproteins (HNRNPK and PTBP1) and Nucleolin, which can regulate chromatin condensation by bringing histone H1 in the proximity of genes to be silenced . The ovesferases .GM12371. This lncRNA is nuclear and more abundantly expressed in the hippocampus. Its depletion in primary hippocampal neurons determines the reduction of spontaneous excitatory postsynaptic currents. Furthermore, decreasing GM12371 interferes with spine density and dendritic arborization. Mechanistically, GM12371 is regulated by cAMP-PKA signaling and, once produced, it binds to active chromatin in trans to regulate genes which are involved in neuronal growth and development. The expression of GM12371 is strictly required in neurons to induce changes in excitatory synaptic transmission mediated by cAMP-PKA signaling [Transcriptional regulation can take place independently from epigenetic remodeling as possibly observed in the case of the lncRNA ignaling .RMST, which was originally discovered as a rhabdomyosarcoma associated transcript. During neuronal differentiation, RMST is significantly upregulated in human embryonic stem cells and it is able to modulate the expression of neurogenic transcription factors [RMST is under the control of the RE1-silencing transcription factor (REST), which is a master repressor of thousands of neurospecific genes [RMST expression is released and RMST interacts with SOX2, the transcription factor that also interacts with Tuna and that has an important role in neural fate determination [RMST-SOX2 regulates a large number of downstream genes which are involved in neurogenesis, and RMST is particularly required for the binding activity of SOX2 to the promoter of target genes [Another example of a lncRNA involved in transcriptional regulation is  factors ,117. RMSic genes . During mination . The comMalat1 is expressed in various tissues and in the brain its levels are particularly high in the latest stages of development, when Malat1 is found within nuclear speckles [Malat1 is able to recruit pre-mRNA-splicing factors to active transcription sites [Malat1-depleted HeLa cells shows that this lncRNA can interfere with the splicing of pre-mRNAs including the RNA of the calcium/calmodulin dependent protein kinase II beta (Camk2b), which is crucial for postsynaptic signaling, plasticity and synaptogenesis [Malat1-depleted neuronal cells also showed that this lncRNA affects the expression of presynaptic proteins (such as SNAP25 and VAMP2) and other proteins relevant for neuronal activity and development, including the activity-regulated cytoskeleton-associated protein (ARC) and the axon guidance co-receptor Neuropilin-1. Additionally, Malat1 regulates synapse formation by altering transcript levels of the synaptic cell adhesion molecule-1 (SynCAM1) and the postsynaptic cell surface protein Neuroligin-1 [Malat1 is important for the regulation of mRNAs encoding synaptic proteins is further confirmed by the fact that synaptic density is reduced by Malat1 knock-down, while, when Malat1 is overexpressed, the density of presynaptic boutons increases [Malat1 knockout mice show no distinct phenotype or histological abnormalities [cis-regulatory effect of Malat1 was observed on the close locus of the lncRNA NEAT1 [Malat1 remain to be established.Another mechanism employed by lncRNAs to interfere with cellular functions is to regulate RNA splicing. The lncRNA speckles . Malat1 on sites . Gene exogenesis . DNA micoligin-1  and neuroligin-1 . The obsncreases . Puzzlinmalities ,123,124 Malat1, the lncRNA Gomafu localizes in the nucleus and is expressed in pyramidal neurons in the cerebral cortex and CA1 pyramidal neurons in the hippocampus [Gomafu in mammalian retinal development and oligodendrocyte lineage specification [Gomafu and subsequent release of splicing factors was observed in transcriptome analyses upon neuronal depolarization in both primary mouse neurons and induced pluripotent stem cell (iPSC)-derived neurons systems [Gomafu acts as a scaffold for the splicing proteins QKI and SRSF1, and interferes with the splicing of a set of genes involved in embryonic brain development and neural progenitor proliferation including Wnt7b, Disc1 and Erbb4 [Gomafu functions to balance proliferation and differentiation of neural progenitor cells through regulating splice variations of genes involved in neuronal cell fate determination [Similar to pocampus . Early sfication ,126. A s systems . Studiesnd Erbb4 ,50. In tmination .Pinky (Pnky) is also involved in the early stages of neuronal differentiation. This lncRNA is highly expressed in the ventricular and the subventricular zone of adult mice and it has high levels in the developing brain. Pnky regulates neural stem cell self-renewal and its expression is decreased during the differentiation of neural stem cells. Loss of function experiments showed that Pnky knockdown increased neurogenesis in both the developing embryonic and postnatal cortex [Pnky interacts with a splicing factor, PTBP1, known as a repressor of neuronal differentiation and its deletion in vivo has no effect on the expression of the nearby Pou3f2 transcript, advocating for a bona fide trans mechanism of activity [The lncRNA l cortex . Functioactivity .LncND’ is expressed in neuron progenitor cells and its levels decrease during the differentiation of neurons. LncND works as a sponge for miR-143-3p and is particularly efficient since it contains 16 complementary elements for this miRNA. As an example, by blocking miR-143-3p, LncND regulates the levels of the Notch signaling receptors NOTCH-1 and NOTCH-2. Although LncND is absent in mice, its overexpression in mouse radial glia cells (RGC) resulted in the expansion of PAX6-positive RGC by interacting with the highly conserved miRNA-143-3p in the developing mouse cortex. These results imply the role of LncND in cerebral cortex expansion through miR-mediated regulation of Notch signaling [lncRNA-1604. This lncRNA is necessary for ectoderm differentiation of mouse ESCs. Functional analyses reveal that lncRNA-1604 is a decoy for miR-200c, and it reduces the degradative potential of miR-200c on the mRNAs of the transcription factor ZEB1/2, which promotes neural differentiation [Synage, which regulates synapse development in the cerebellum by titrating miR-325-3p [Synage increases the expression of the Cerebellin 1 Precursor transcript (Cbln1), which is essential for maintaining climbing fiber-Purkinje cell synapses. The KO of Synage in mice leads to cerebellar atrophy and neuron loss. Synage is one of the few examples of predominantly cytosolic lncRNAs, and it has been suggested to have an additional function as organizer of the synaptic scaffold [Arrl1, which acts as a sponge of miR-761 that would decrease the levels of Cdkn2b and thus inhibit neurite outgrowth after neuronal injury [LncRNAs can also indirectly increase the expression levels of target mRNAs by acting as molecular sponges or decoys for microRNAs (miRNAs) that, if not blocked, would lead to mRNA degradation. For instance, the neurodevelopment lncRNA ‘ignaling . Anotherntiation . AnotherR-325-3p . Throughscaffold . Among ol injury .NEAT1, which is also found in paraspeckles with Malat1 [NEAT1 expression is activity-dependent and its levels are high at rest while they decrease upon neuronal depolarization. Outside the nucleus, NEAT1 binds two potassium channel-interacting proteins, KCNAB2 and KCNIP1. Downregulation of NEAT1 combined with neuronal depolarization results in the increase of the cytoplasmic concentration of these channel-interacting proteins, thus modulating neuronal excitability. As observed in other cases, the function of lncRNAs seems to be pleiotropic, as exemplified in this case by the observation that NEAT1 controls the expression of ion channel related genes [It is more and more recognized that some lncRNAs have evolved the ability to sequester proteins and interfere with their function ,134. In h Malat1 . NEAT1 eed genes .neuroLNC, a nuclear lncRNA that is highly neuron-specific and that is conserved from rodents to humans [NeuroLNC influences several aspects of neuronal physiology such as calcium influx, neurite elongation and neuronal migration in the developing brain [neuroLNC functions by interacting with the RNA-binding protein TAR DNA binding protein-43 (TDP-43), leading to the specific stabilization of mRNAs encoding for presynaptic proteins. Mutating the UG-repeats which are essential for the binding of neuroLNC to TDP-43 ablates the ability of neuroLNC to promote synaptic vesicle release when overexpressed. Likewise, the down-regulation of TDP-43 abolishes the effects of neuroLNC, reveling a clear dependency between the function of neuroLNC and TDP-43. Another recent example of lncRNAs associated to neuronal activity modulation is ADEPTR, which can be quickly produced and transported to spines in a KIF2A-dependent mechanism requiring cAMP/PKA signaling [ADEPTR interacts with spectrin 1 and ankyrin B to regulate their localization and tune spine dynamics [During the characterization of lncRNAs implicated in neurotransmitter and synaptic vesicle release, our group has identified o humans . NeuroLNng brain . Of noteignaling . ADEPTR dynamics .BC1 measures 152 nucleotides and BC200 200 nucleotides, and although often referred to as lncRNAs, since lncRNAs are defined as molecules > 200 nucleotides, we refer to these as ‘non-protein coding RNAs’. These molecules were initially described as taking part in a dendritic ribonucleoprotein (RNP) complex in, regulating translation initiation in response to synaptic stimuli by interacting with SRP9/14, eIF4A, eIF4B, PABP, FMRP and SYNCRIP that interfere with 48S initiation complex formation [BC1/BC200 RNA can inhibit translation of dendritic protein synthesis affecting ARC and CaMKII levels [BC1/BC200 has been clearly confuted by several subsequent works, indicating that FMRP and BC1/BC200 act independently and do not in fact bind each other specifically, neither in vitro nor in vivo [BC200 and SYNCRIP has also been argued [BC1/BC200 seems to be implicated in neuronal function since the knockout mice show hyperexcitability and neuronal plasticity changes which can probably be reconducted to altered translation of group I metabotropic glutamate receptors (mGluRs) [In neurons, the tuning of local translation is associated with activity-dependent synaptic plasticity to regulate the expression of mRNAs encoding synaptic proteins . Deregulormation ,147,148.I levels . It is i in vivo ,150,151.(mGluRs) .In all these cases showcasing examples of direct interaction between lncRNAs and proteins, only few direct examples of extranuclear interactions of lncRNAs and proteins are found. It is tantalizing to speculate that some lncRNAs might be implicated in the direct regulation of phase separation and condensate formation at the synapse , in a siConsidering the importance of lncRNAs in neuronal physiology it is not surprising that dysregulation of lncRNAs in the brain might be linked to neurodevelopmental disorders ,161, autBiomarker signatures based on lncRNAs could be used for detecting neurodegenerative processes very early, when clinical symptoms are not yet apparent, and degenerative processes are not yet irreversible. Moreover, due to the difficult accessibility of brain tissue in patients, monitoring the expression of biomarker lncRNAs from peripheral fluids would allow to more reliably follow the progression of the pathology and possibly even understand which cell types are more actively contributing to the neurodegenerative process. Overall, for these reasons, lncRNAs hold great potential as biomarkers for neurodegenerative diseases. At the same time, one aspect that still requires thorough examination, is whether the expression of lncRNAs during neurodegenerative processes just correlates with brain alterations or if in some cases it might also actively contribute to the pathogenesis. In other words, if lncRNAs have a functional role in the pathology in some instances, they could be exploited as possible therapeutic targets. We will summarize here observations suggesting that only a few lncRNAs show a direct involvement in neurodegenerative alterations.BACE1-AS in vivo drives the reduction of β-amyloid synthesis and aggregation in the brain by decreasing the level of Bace1. As previously mentioned, BACE1-AS stabilizes BACE1 which is the enzyme cleaving amyloid precursor protein (APP) and producing the amyloid β precursors. It seems that BACE1-AS is involved in a positive feedback loop with β-amyloid peptides, so that high levels of BACE1-AS in AD results in amyloid beta-peptide synthesis and, subsequently, amyloid beta-peptide induces elevated BACE1-AS expression [BC200, another non-protein coding RNA, negatively regulated over aging, is significantly upregulated in AD [BC200 and its delocalization in perikaryal structures was also correlated with AD severity [lncRNA-17A, 51A, and Sox2OT in AD are also reported in other studies [BACE1-AS, in all other examples, the correlations between lncRNA levels and the presence of pathological alterations cannot be considered sufficient indication for a direct involvement in AD and targeted studies are required to clarify if the modulation of these lncRNAs has a direct role in the modulation of the pathology.The most common neurodegenerative disease, accounting for around 80% of dementia cases in the elderly population, is Alzheimer’s Disease (AD) . In the ed in AD . The incseverity . The dys studies ,176,177.AS-Uchl1 is an antisense RNA that seems to increase the translation of UCHL1, a deubiquitinating enzyme whose reduction is associated with neurodegenerative diseases and mutations in the Uchl1 gene are found in familial Parkinson’s disease (PD) [AS-Uchl1 was significantly downregulated in dopaminergic neurons indicating a possible direct role in the pathology [lincRNA-p21, Malat1, SNHG1, HOTAIR and TncRNA are upregulated in PD, while the two lncRNAs upstream of the lncRNA H19 (Huc 1 and 2) are downregulated [Malat1 in this context has been proposed to have a detrimental role and promote inflammasome activation and reactive oxygen species (ROS) production by interfering with the expression of NRF2, a transcription factor that controls the expression of antioxidant proteins during inflammation [HOTAIR might act as a regulator of miR-126-5p which in turn targets the RAB3A interacting protein RAB3IP in PD cells and in a PD mouse model where the pathology is induced with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) [LINC-PINT was recently found to correlate with neuronal maturation and increase in levels in several brain regions of neurodegenerative diseases [The lncRNA ase (PD) ,180,181.egulated ,183. Malammation . A recene (MPTP) . RAB3IP e (MPTP) . Among tdiseases . Its depdiseases .NEAT1 upregulation which is potentially induced by ubiquitin-proteasome system impairment in HD [NEAT1 upregulation under stress has implications in cell survival pathways and might represent a neuroprotective mechanism against neuronal damage in HD, as also observed in relation to its ability to meliorate the proteostasis stress caused by TDP-43 [Tuna expression is potentially negatively associated with the severity of HD as zebrafish treated with Tuna antisense showed severe locomotor defects [HAR1F and HAR1R, which are decreased in the striatum of HD patients [DGCR5 and MEG3 have REST binding sites and are downregulated in HD [Huntington disease (HD) is an inherited neurodegenerative disorder with progressive chorea and cognitive impairment. Patient postmortem HD brains exhibited dysregulated lncRNA expression such as nt in HD . NEAT1 uy TDP-43 . In cont defects . Of notepatients . Furthered in HD .NEAT1 isoforms and excessive paraspeckle formation which may contribute to ALS severity [ATXN2-AS in spinocerebellar ataxia type 2 which is associated with an increased risk for ALS [lncRNAs in ALS [LncRNAs have been implicated in neuronal degeneration during amyotrophic lateral sclerosis (ALS) and in motoneuron pathology ,191. Stuseverity ,193. Add for ALS . Screenis in ALS . In all Evf2, as described above, is a crucial lncRNA in GABAergic synaptic transmission of medial prefrontal cortex and hippocampal neurons. In vivo analyses of Evf2 showed that its downregulation increases seizure susceptibility and severity in the adult mouse brain [NEAT1, which regulates potassium channel function, is upregulated in human epilepsy samples and increases seizure susceptibility [KCNA2-AS induction, as also discussed earlier. Notably, hypersensitivity and neuropathic pain could be decreased by blocking KCNA2-AS expression, opening a new therapeutic window in relieving neuropathic pain [H19 has also been implicated in epilepsy, since its expression is strongly upregulated in a rat model of temporal lobe epilepsy [Dysregulation of lncRNAs has been associated with synapse function and neurological disorders such as epilepsy. se brain . In conttibility . Ion chahic pain . The impepilepsy . Its funepilepsy ,197.lnc-NR2F1 is a conserved lncRNA that was recently found to be associated with autism spectrum disorder and intellectual disability in children and it has been observed that the lnc-NR2F1 locus is frequently mutated in these patients [lnc-NR2F1 revealed that this lncRNA binds to a genomic region enriched with basic helix-loop-helix motifs to regulate the transcription of genes involved in neuron maturation [The lncRNA patients . Importaturation .Disc1 and Errb4 [Gomafu knockdown through an interaction with the splicing proteins SF1, SRSF1 and QKI [Gomafu is also downregulated in post-mortem cortex of patients with schizophrenia [Gomafu is embedded in a schizophrenia linked chromosomal region [Gomafu dysregulation with schizophrenia pathogenesis.Emerging evidence suggests that lncRNAs are involved in various other psychiatric disorders including schizophrenia. Schizophrenia is a complex psychiatric disease with aberrant splicing of genes associated with this pathology, including nd Errb4 ,199. It  and QKI ,157. Gomophrenia . In addil region ,201 and l region , which aXIST can be used to trigger silencing of chromosome 21 in Down syndrome patient-derived cells, and correct a differentiation delay observed in this model [The study of lncRNAs in neurological disorders using animal models and in vitro strategies is promising for defining the etiology of neuropathologies, and in the future will eventually contribute to the diagnosis and the treatment of brain diseases . A recenis model , openingis model , will alis model  is also is model , providiis model , and brois model , renderiOne important aspect is to characterize lncRNAs in a broad and unbiased manner. Along these lines, omics studies combining lncRNA knockdown or knockout with molecular phenotyping (such as deep-sequencing libraries) are becoming available for human cell lines , and, alCyrano, which promotes the degradation of miR-7 and thus favors the accumulation of Cdr1as, a circular RNA that modulates neuronal activity [Cyrano, Cdr1as is depleted in neurons, in concomitance with the help of a second miRNA, miR-671 [In the brain, a notable recent example of the fact that lncRNAs can be part of subtle regulation of neuronal function is activity . In the  miR-671 . This is miR-671 . Of note miR-671 ,208. Mor miR-671 ,210,211,Malat1 KO mice which show no obvious differences [One aspect that very often remains obscure in lncRNA biology is whether these molecules only show correlations with a specific pathology or phenotype or whether they are active players. This can only be addressed if their ablation or overexpression can recapitulate some of the observed changes that do correlate with their expression levels. In order to exclude that their effect is overestimated, direct KO in mice or cells with CRISPR approaches is necessary, although in some cases it leads to puzzling results, as in the case of ferences ,123,124.ferences , and morFor the examination of neurodegenerative diseases in clinical settings, lncRNAs hold great potential since brain lncRNAs can be detected in peripheral fluids  and can Overall, we foresee that a more systematic study of lncRNAs in the pathophysiology of the brain will provide a more comprehensive representation of the complexity of brain function.


The power of embryonic stem cells (ESCs) lies in their ability to self-renew and differentiate. Behind these two unique capabilities is a fine-tuned molecular network that shapes the genetic, epigenetic, and epitranscriptomic ESC plasticity. Although RNA has been shown to be functionally important in only a small minority of long non-coding RNA genes, a growing body of evidence has highlighted the pivotal and intricate role of lncRNAs in chromatin remodeling. Due to their multifaceted nature, lncRNAs interact with DNA, RNA, and proteins, and are emerging as new modulators of extensive gene expression programs through their participation in ESC-specific regulatory circuitries. Here, we review the tight cooperation between lncRNAs and Polycomb repressive complex 2 (PRC2), which is intimately involved in determining and maintaining the ESC epigenetic landscape. The lncRNA-PRC2 partnership is fundamental in securing the fully pluripotent state of ESCs, which must be primed to differentiate properly. We also reflect on the advantages brought to this field of research by the advent of single-cell analysis. Hotair . A . A cis-sand PRC2 .Linc-HOXA1  is located approximately 50 kb downstream of the Hoxa gene cluster. By single-cell transcript counting performed in mESCs, there emerged a considerable heterogeneity of linc-HOXA1 and Hoxa1 RNA in pluripotent cultures at single-cell resolution and an inverse correlation between linc-HOXA1 RNA and Hoxa1 mRNA abundance  staining, as well as immunofluorescence and RT-qPCR analysis of the pluripotency markers SSEA1 and OCT4, confirmed that pluripotency of mESCs was not disrupted by linc-HOXA1 knockdown, and excluded the possibility that the increased Hoxa gene expression was due to mESC differentiation [Hoxa gene promoters. However, no significant changes in H3K4me3 were observed at Hoxa gene promoters in KO ESCs [At the molecular level, n in cis . PRC2 isand PRC2 . Specifintiation . In cont KO ESCs .T-UCstem1 was recently identified and its role in ESC self-renewal maintenance was elucidated. T-UCstem1 exerts a dual but distinct function in the nucleus and in the cytosol. In the nucleus, as demonstrated by RIP, T-UCstem1 directly binds PRC2, recruiting the complex to bivalent chromatin-associated genes. In T-UCstem1-depleted cells, PRC2 displacement leads to an increased H3K4me3/H3K27me3 ratio at bivalent chromatin domains, committing ESCs to exit from pluripotency and to differentiation [T-UCstem1 determines a cellular phenotype similar to that resulting from the withdrawal of PRC2 activity from ESCs, and a global gene derepression of bivalent chromatin-associated genes   , a mastemmitment . Of noteFendrr, critical for cardiac mesoderm specification, positively modulates chromatin state to activate a subset of developmental regulators by binding both PRC2 and Trithorax group/MLL protein complexes (TrxG/MLL) [HOTTIP and Mistral through the interaction with WDR5, a specific subunit of the MLL complex, or the lncRNA HoxBlinc, which is associated with active chromatin states by regulating cardiac/hematopoietic differentiation [Of note, LncRNAs can also influence the interaction between PRC2 and other epigenetic modifying complexes, working as a scaffold to bind multiple chromatin-modifier structures. For instance, the lateral mesoderm-specific lncRNA rxG/MLL) . Recent ntiation .LncRNAs participate in a wide variety of stem cells and developmental physiological processes such as transcriptional regulation, chromatin modification, imprinting, DNA methylation, and splicing, introducing a novel layer of biological regulation. The functional role of lncRNAs in chromatin remodeling in a temporal and cell type-specific manner has become a hot topic. LncRNAs act as important cofactors in mediating PRC2 recruitment to specific target loci, thus determining specific chromatin states corresponding to different stages of embryonic development and stem cell differentiation. Although promiscuous RNA binding to PRC2 is described , severalSeveral methods have recently been developed to map lncRNAs on chromatin, shedding considerable light on their biological importance in epigenetic regulation. Similar to the well-established ChIP technique, RIP has proved to be a powerful tool to study the direct binding between individual proteins and RNA molecules in close proximity ,131. UsiNascent NGS technologies at single-cell resolution could provide valuable insights into the complex role of lncRNA in stem cell differentiation. Single-cell RNA sequencing (scRNAseq) correlates the transcriptional profile of individual cells to the complexity of biological systems within a population ,133,134.Currently, the use of bisulfite sequencing to study nucleosome occupancy and methylome at the single-cell level limits the capture rates and does not provide extensive and functional mapping of chromatin states at single-cell resolution. In contrast, the assay for transposase-accessible chromatin sequencing (ATAC-seq) will likely become a routinely used strategy in single-cell analysis and represents an important step forward in assessing epigenetic diversity. ATAC-seq identifies genome-wide chromatin accessible regions by using a transposase enzyme to insert and tag open chromatin. Via a microfluidic approach, this technique uses barcoding to successfully process tens of thousands of single cells in parallel, providing a regulatory landscape of the genome at the epigenetic level. A common drawback in both single-cell transcriptomics and epigenomics is technical variability. Although the level of technical noise in scRNAseq could be estimated and normalized, such strategies have not yet been developed in epigenome sequencing .Their extremely tissue-specific expression and exclusive mode of action further complicate our understanding of the role of lncRNA in physiological processes. Ongoing studies are, therefore, combining genome-wide transcriptional analysis of single cells with the complexity and heterogeneity of tissue structures by using in situ transcriptomics technology to map lncRNAs based on their localization. Thanks to their cell type- and organ-specific expression patterns, lncRNAs might be employed as candidate targets for stem cell-based therapies and other clinical applications such as the screening of new drugs to introduce chromatin changes. While epigenetic drugs produced to date function mainly as enzyme inhibitors by attenuating their activity and inducing drastic chromatin changes in the genome, manipulating lncRNA expression that normally targets one gene or a small set of genes may reduce the aberrant expression of off-target genes and facilitate the development of small-molecule drugs with more specific modes of action. Loss-of-function studies using both small interfering RNA and antisense oligonucleotides, as well as gain-of-function experiments describe only local alterations without detecting changes in overall chromatin configuration. Moreover, deciphering the ‘epigenetic code’ of lncRNA could unravel the intricate network of these different molecule species, elucidating the molecular mechanisms controlling stem cell differentiation and their potential involvement in tissue regeneration.


Although polyploid plants have larger leaves than their diploid counterparts, the molecular mechanisms underlying this difference (or trait) remain elusive.PpnCKX1. The interactions between PpnGRF5‐1 and growth‐regulating factor (GRF)‐interacting factors (GIFs) were experimentally validated and a multilayered hierarchical regulatory network (ML‐hGRN)‐mediated by PpnGRF5‐1 was constructed with top‐down graphic Gaussian model (GGM) algorithm by combining RNA‐sequencing data from its overexpression lines and DAP‐sequencing data.Differentially expressed genes (DEGs) between triploid and full‐sib diploid poplar trees were identified from two transcriptomic data sets followed by a gene association study among DEGs to identify key leaf growth regulators. Yeast one‐hybrid system, electrophoretic mobility shift assay, and dual‐luciferase assay were employed to substantiate that PpnGRF5‐1 directly regulated PpnCKX1. Overexpression of PpnGRF5‐1 in diploid transgenic lines resulted in larger leaves resembling those of triploids, and significantly increased zeatin and isopentenyladenine in the apical buds and third leaves. PpnGRF5‐1 also interacted with GIFs to increase its regulatory diversity and capacity. An ML‐hGRN‐mediated by PpnGRF5‐1 was obtained and could largely elucidate larger leaves.PpnGRF5‐1 is a negative regulator of PpnGRF5‐1 and the ML‐hGRN‐mediated by PpnGRF5‐1 were underlying the leaf growth and development. GRF proteins generally harbor conserved QLQ and WRC domains in their N‐terminal regions  starting from a TF after perturbating it Wei, . Our prePpnGRF5‐1 were particularly high in the apical buds and young leaves of triploids compared to those in the full‐sib diploids. PpnGRF5‐1 inhibited PpnCKX1 expression, resulting in the accumulation of cytokinins to promote the meristematic potential of the proliferative and formative cells during leaf development. PpnGRF5‐1 overexpression transgenic lines in diploids had larger leaves resembling those of triploids. We generated RNA‐sequencing (RNA‐seq) data from these transgenic lines and then reconstructed PpnGRF5‐1‐mediated ML‐hGRN by integration of the PpnGRF5‐1 and target gene relationships acquired from DNA affinity purification sequencing (DAP‐seq) experiment. The fact that the larger leaves resulting from PpnGRF5‐1 overexpression in transgenic diploids and significantly high expression of PpnGRF5‐1 in triploids implies that PpnGRF5‐1‐mediated ML‐hGRN contributed to leaf growth and development, resulting in much larger leaves.In this study, we attempted to dissect the molecular mechanisms underlying larger leaf size in poplar triploids with the full‐sib diploids as a comparison. The expression levels of n = 3x = 57) of Populus section Tacamahaca  and full‐sib diploid (2n = 2x = 38) population were generated in our previous study ) as described , and cleaned with chloral solution  of the apical buds, third and fifth leaves of the same triploid and the full‐sib diploid population (4‐month‐old) as mentioned earlier. There were two biological replicates, each was mixed from five plants. Furthermore, 2 μg RNA per sample was used for library preparation with RNA‐NEBNext Ultra RNA Library Prep Kit. RNA‐seq was performed by Novogene Co. Ltd  using Illumina HiSeq 4000 platform, and approximately 6 GB of 150‐bp paired‐end reads were generated from each library. The clean reads mapped to et al., P. trichocarpa genome 4.1 using Burrows Wheeler Aligner (BWA)  generated in our earlier study , the internode between the first and second leaves (internode 2), root tips (root 1), the long white roots (without side roots) (root 2), and small side roots (root 3) were harvested from 3‐month‐old poplars for extracting total RNA with TRIzol reagent according to the manufacturer’s instructions . The more detailed information for complementary DNA (cDNA) synthesis and reverse transcriptase quantitative polymerase chain reaction (RT‐qPCR) was provided in Methods S1–S10 of the Supporting Information. Three independent biological replicates were used in the analysis. Primers are listed in the Supporting Information, Table PpnGRF5‐1 was amplified from the cDNA of allotriploid and inserted into the pBI121‐eGFP vector containing the CaMV35S promoter (35S) to generate the 35S::PpnGRF5‐1‐eGFP‐nos construct. Primers are listed in Table Agrobacterium tumefaciens‐mediated transformation of 84K clone  that is transformation‐amenable for gene transformation following the protocol as described earlier , and propagated them for leaf phenotypic studies, RNA‐seq experiment, and cytokinin measurement.The coding sequence of PpnGRF5‐1 overexpression lines were harvested. For each line, the apical buds of five plants with similar growth were selected and mixed. The 15 apical buds of wild‐type plants were collected and divided into three groups as controls. RNA extraction, library construction and sequencing were done at Annoroad Corp.  using the same protocols and approximately 4GB of 150‐bp paired‐end reads were generated from each library. The clean reads were mapped to the 84K polar genome using TopHat2  were identified using the Pop’s Pipes pipeline . The R module clusterprofiler available at Bioconductor (http://bioconductor.org) was used to identify the enriched GO terms associated with a DEG list via hypergeometric probability. We applied a multiple testing correction using the Benjamini & Hochberg  with at least one mutually overlapping gene were connected with each other.The DEGs were used for gene ontology (GO) analysis using AmiGO's Term Enrichment tool  and two of the combined PpnGRF5‐1‐responsive DEGs, x and y. If the PpnGRF5‐1 significantly interfered with the two responsive genes, namely d=rxy‐rxy|z is significant after being tested with the multivariate delta method  were recorded. When PpnGRF5‐1 and all combinations of all responsive genes were evaluated, the interference frequency between PpnGRF5‐1 and each responsive candidate target gene was calculated. Then, the PpnGRF5‐1‐responsive candidate target genes having the highest frequency and PpnGRF5‐1‐binding motif were retained in this middle layer (directly targets). The next layer (bottom) was generated in top‐down fashion only from each TF present in the middle layer by recursively calling top‐down GGM algorithm. Additional details can be found in Methods We used the top‐down GGM algorithm . The most striking advantages are greater height, larger breast diameter, larger leaf area, augmented stress resistance, and lower fertility , which were upregulated and downregulated in the leaves of triploids, respectively, had a negative correlation in both data sets  gametes and their full‐sib diploid population. We performed gene association studies using two transcriptomic data sets from the triploids and their full‐sib diploids, and found that ets Fig. . Based oPpnGRF5 genes, PpnGRF5‐1 (Potri.013G077500) and PpnGRF5‐2 (Potri.018G065400), expressed in apical buds in poplar triploids  gametes) triploids. The expression levels of PpnGRF5‐1 in apical buds, the third and fifth leaves of each genotype were measured by RT‐qPCR , Potri.014G103900 (PtrGIF2), and Potri.019G013100 (PtrGIF3), in P. trichocarpa, respectively. We tested the physical interactions between PpnGRF5‐1 and each of these three PpnGIFs using a Y2H system. However, because strong autoactivation was observed for PpnGRF5‐1 when fused to the Gal4 DNA binding domain, the assay was performed only in one direction. The interaction between PpnGRF5‐1 and PpnGIF2 was explicitly evidenced by gradient dilution  alone, bound to PpnGIF2‐His6 and PpnGIF3‐His6  transgenic lines (35S::PpnGRF5‐1‐eGFP‐nos vector) in diploid poplar. We used the 84K poplar clone as our gene transformation system and obtained 10 independent transgenic lines overexpressing PpnGRF5‐1  were significantly increased , zeatin, and isopentenyladenine (IPA), in the apical buds and the third leaves of both PpnGRF5‐1‐OE and WT plants. In both young and old leaves, zeatin and IPA concentrations were significantly increased in overexpression compared to WT leaves  of GRF‐binding sites is present at two locations: one is 1710 bp upstream and the other is 60 bp downstream from the CKX1 start codon. We amplified c. 150 bp flanking regions of each motif  and named these PagCKX1p‐1 (−1710 bp site) and PagCKX1p‐2 (+60 bp site)  Fig. . We thentic Fig. . To furtPpnGRF5‐1 could directly regulate the transcription of the target PagCKX1 gene using a dual‐LUC reporter assay. GV3101, harboring the 35S::PpnGRF5‐1 effector and PagCKX1p::LUC reporter plasmids, was injected into N. benthamiana leaf epidermal cells. We found that the PagCKX1p::LUC reporter was activated in N. benthamiana leaves in the absence of PpnGRF5‐1, indicating that some endogenous factors in N. benthamiana activate the PagCKX1p::LUC reporter gene in mature tobacco leaves  and three WT 84K poplar replicates. The sequence reads yielded from these lines were aligned to the 84K poplar genome  by comparing the transgenic and WT 84K poplar lines  were located in the proximal upstream regions including the 5′ untranslated region (5′UTR) and around the transcription start site . We found that many binding sites (TGTCAG) of PpnGRF5‐1 in the 5′ proximal regions (including parts of 5′ coding regions) of some target genes are corresponding precisely to the peaks of read density  and a probability method, as described earlier  and triploids where PpnGRF5‐1 expression levels were elevated. The mechanism underlying the elevated expression of PpnGRF5‐1 in triploids is unknown, but, one review ; but, the relationships between OsGRF4 and the two CKXs were not characterized in the study where the elevated levels of cytokinins (iPR and cis‐zeatin) were observed  that function as cytokinin receptors    genes is to promote cell expansion  in PpnGRF5‐1 overexpression lines were significantly upregulated   Fig. . These EPagGRF2, PagGRF4, and PagGRF9, PagGIF1, PagTCP4, PagMYB105, and PagLBD38, function in developmental, metabolic, and growth processes. The GRF‐GIF duo associated with the SWI2/SNF2 complex regulates transcription of target genes, including a mutual auto‐activation between GRFs and GIFs through a positive feedback loop in Arabidopsis thaliana, rice, and maize  were activated to some degree , whereas two GRFs, PagGRF1 and PagGRF7, and two GIFs, PagGIF1 and PagGIF3, decreased  in the PpnGRF5‐1‐OE lines . Furthermore, our computational analysis with psRNATarget . We thus hypothesize that PpnGRF5‐1 delays cell differentiation and increases cell division in the leaf marginal and basal domains by directly suppressing the expression of PpnTCP4, indicating the presence of a negative feedback loop between GRFs and TCPs  represses many other known N‐responsive genes including key genes, for example, nitrate transporters (NRTs), required for nitrate uptake and assimilation , and genes involved in nitrate transport, including PagNRT1.5 and PagNRT1.7, were upregulated  in apical buds. These results give an inkling that PpnGRF5‐1 can regulate nutritional supplements through PagLBD38 during leaf development, and further research is needed to verify this   was classified into the 21st subfamily of the myeloblastosis oncoprotein (MYB) family. AtMYB105/AtLOF2 and AtMYB117/AtLOF1 (subgroup 21) function redundantly to control lateral organ separation and axillary meristem formation .Click here for additional data file.Dataset S3 List of PpnGRF5‐1 direct target genes.Click here for additional data file.Fig. S1 Leaf area and cell area of fifth, sixth, and seventh leaves from 5‐month‐old triploid and diploid poplars.Fig. S2 PpnGRF5 and PpnCKX1 expression levels and correlation in two transcriptomic data sets from triploid (generated by first division restitution (FDR) gametes) and full‐sib diploid poplars.Fig. S3 Phylogenetic analysis of growth‐regulating factor (GRF) genes in Populus trichocarpa and Arabidopsis thaliana.Fig. S4 PpnGRF5‐1 protein domains and potential to act as a transcription factor.Fig. S5 Subcellular localization of PpnGRF5‐1‐GFP induced fluorescence in the 84K poplar leaf mesophyll protoplasts.Fig. S6 The various phenotypes and the expression levels of PpnGRF5‐1 in its overexpression (OE) transgenic lines.Fig. S7 Enriched gene ontology (GO) in the differentially expressed genes identified from the apical buds of the 3‐month‐old PpnGRF5‐1 overexpression lines as compared with the 84K wild‐type (WT).Fig. S8 The top 30 biological processes resulting from gene ontology (GO) enrichment analysis on the differentially expressed genes (DEGs) identified from PpnGRF5‐1‐overexpression transgenic lines  in comparison with the 84K wild type (WT).Fig. S9 The distribution of PpnGRF5‐1 DNA affinity purification sequencing (DAP‐seq) reads in different genic and intergenic regions.Fig. S10 Identification of overrepresented variants of PpnGRF5‐1 binding motifs from PpnGRF5‐1 DNA affinity purification sequencing (DAP‐seq) data using Homer software (v.4.11).Fig. S11 Mapping the genome‐wide binding sites of PpnGRF5‐1 in the 84K poplar genome using DNA affinity purification sequencing (DAP‐seq).Methods S1 PpnGRF5‐1 using top‐down graphic Gaussian model (top‐down GGM) algorithm.Methods S2 RNA isolation, RT‐PCR and qRT‐PCR.Methods S3 Transcriptional activation analysis in yeast cells.Methods S4 Yeast one‐hybrid assays.Methods S5 Electrophoretic mobility shift assay (EMSA).Methods S6 DNA affinity purification sequencing (DAP‐seq) and data analysis.Methods S7 Dual‐luciferase assay.Methods S8 Yeast two‐hybrid assays.Methods S9 GST (glutathione‐S‐transferase)‐fusion protein pull‐down assay and western blotting.Methods S10 Split luciferase complementation assay.Table S1 All primer sequences used in this study.Table S2 The height, diameter and the fifth leaf area of 5‐month‐old PpnGRF5‐1 overexpression transgenic lines.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Click here for additional data file.


The gene activity underlying cell differentiation is regulated by a diverse set of transcription factors (TFs), histone modifications, chromatin structures and more. Although definitive hematopoietic stem cells (HSCs) are known to emerge via endothelial-to-hematopoietic transition (EHT), how the multi-layered epigenome is sequentially unfolded in a small portion of endothelial cells (ECs) transitioning into the hematopoietic fate remains elusive. With optimized low-input itChIP-seq and Hi-C assays, we performed multi-omics dissection of the HSC ontogeny trajectory across early arterial ECs (eAECs), hemogenic endothelial cells (HECs), pre-HSCs and long-term HSCs (LT-HSCs) in mouse embryos. Interestingly, HSC regulatory regions are already pre-configurated with active histone modifications as early as eAECs, preceding chromatin looping dynamics within topologically associating domains. Chromatin looping structures between enhancers and promoters only become gradually strengthened over time. Notably, RUNX1, a master TF for hematopoiesis, enriched at half of these loops is observed early from eAECs through pre-HSCs but its enrichment further increases in HSCs. RUNX1 and co-TFs together constitute a central, progressively intensified enhancer-promoter interactions. Thus, our study provides a framework to decipher how temporal epigenomic configurations fulfill cell lineage specification during development. Here, the authors employed Hi-C and low-input itChIP-seq in four rare populations of the hematopoietic stem cell (HSC) ontogeny trajectory across early arterial endothelial cells (eAECs), hemogenic endothelial cells (HECs), pre-HSCs, and long-term HSCs (LT-HSCs) from mouse embryos to show that active histone modifications are largely set up in eAECs and 3D genome is then subsequently configured. LT-HSCs gradually emerge through the process of endothelial-to-hematopoietic transition (EHT), acquiring the ability of self-renewal and reconstruction of all blood lineages6. Aortic progenitor cells may differentiate into aortic endothelium or definitive hemogenic endothelium, revealed by genetic reporter studies in zebrafish and in vitro cell culture9. In murine, a subpopulation of early arterial endothelial cells (eAECs) has been identified to contribute to hemogenic endothelial cells (HECs), subsequently pre-hematopoietic stem cells (pre-HSCs) and finally LT-HSCs based on single-cell RNA-seq (scRNA-seq)12. Nevertheless, how and at which stage a selected fraction of endothelial cells take on the path of EHT remains unresolved.Studying the cellular origin and molecular regulation of hematopoiesis are of paramount importance. There are three waves of hematopoiesis during mouse embryo development, with definitive long-term hematopoietic stem cells (LT-HSCs) arising last at the aorta–gonad–mesonephros (AGM) region14. The 3D genome re-organization shapes the transcriptional landscape and vice versa to some extent at different scales. Some regulatory events are not required for TAD boundary formation or higher-level compartmentalization but are important for sub-TAD connections15. Apart from the structural proteins cohesin and CTCF, transcription factors (TFs), chromatin regulators, and histone modifications also play crucial roles in shaping multiscale genome architecture during development and diseases18. Direct E-P looping is one of the mechanisms, by which enhancers target promoters and control the gene transcriptional activity and specificity19. These interactions are predicted to most likely occur within TADs21. It has attracted great attention to study how these multi-layer regulatory inputs are hierarchically coordinated over time during cell fate specification.A better understanding of gene regulation and cell fate decisions requires comprehensive knowledge of the spatial 3D genome organization and its influence on transcriptional output. During cell differentiation, the 3D genome structure is dynamically arranged at multiple scales, including large-scale compartments (A/B), topologically associating domains (TADs), and chromatin loops22. Tan and colleagues also approached the dynamic transcriptional network underlying HSC ontogeny by performing RNA-seq and histone mark ChIP-seq on five cell populations from endothelium through fetal liver to adult LT-HSCs, nominating a few TFs with potential roles in HEC formations23. To examine which subsets of HECs undertake the differentiation trajectory to HSCs, they performed both scRNA-seq and scATAC-seq and reconstructed a continuous developmental path. An intermediate cell state termed pre-HEC with great fate potential to ultimately lead to HSCs was identified24. RUNX1 is a master TF implicated in many stages of hematopoiesis, including EHT in early embryos as well as leukemia development25. The germline deletion of Runx1 resulted in the LT-HSC elimination and the embryonic lethality by E12.527. RUNX1 together with other hematopoiesis-related TFs, GFI1, SPI1, and FOSB (FGRS), has been shown to in vitro reprogram endothelial cells into HSCs28. Several studies have further characterized RUNX1 binding target genes in cell cultures or bulk samples of adults31. However, thus far, none of them has examined how RUNX1 regulates gene transcription in the context of defined chromatin states in embryos. Further, an integrated knowledge of epigenomic configurations of the 3D genome structure, histone modifications, and TFs in transcriptional regulation to promote EHT is still lacking. This is notably challenging in part due to the extremely low number of cells available in early embryos for ChIP-seq with histone marks and TFs as well as Hi-C in measuring chromatin interactions.Efforts have been focused on deciphering the molecular basis in HSC formation through EHT. A recent study compared 3D genome organization in fetal and adult HSCs, showing that large-scaled compartments and TADs are relatively conserved while intra-TAD interactions are much more dynamic and stage-specific32 and sisHi-C 33 in low-input samples to profile histone modifications, RUNX1 binding, and the 3D genome, for integrated analysis with gene transcription. Different from tedious steps involving the sonication and ligation in traditional ChIP-seq, itChIP-seq adopts Tn5-transposase-based tagmentation as the strategy for simultaneous chromatin fragmentation and barcoded adapter ligation for the one-step PCR enrichment. itChIP-seq allows for highly sensitive profiling of histone modifications and TFs in low input samples as few as 100 cells32. Four purified populations, early AECs, HECs, pre-HSCs, and LT-HSCs, are based on combined immunophenotypes we recently defined in mouse embryos36. We quantify the dynamic trajectories in different scales of the 3D genome structure until the scale correlates well with dynamic histone modifications in biological processes. HSC regulatory elements are already pre-configured with active histone modifications as early as eAECs, preceding small-scale chromatin structure dynamics. Developmental stage-specific TFs are identified in partnering with RUNX1 to drive the formation of HSCs. Together, our data support a model in illustrating how temporal epigenomic configurations fulfill cell lineage specification during development.Here, we improved and used itChIP-seq (indexing and tagmentation-based ChIP-seq)10. The eAECs are termed with regard to lateAECs as we previously reported4. Thus, eAECs represent a specific subpopulation of early progenitors with differentiation potential toward HECs or lateAECs, verified by the previous CFU-C assay4. We performed Hi-C and itChIP-seq experiments to delineate the dynamic trend in the 3D genome re-organization in association with several feature histone modifications using 200–500 cells per experiment33  to fetal liver at E14.5 Fig. , as we r37. Having confirmed the reproducibility and data quality of ChIP-seq data  and long-range contacts (2–10 MB) may indicate differential regulatory mechanisms. Our genome-wide analysis presented significantly up-regulated short-range intra-TADs contacts in HECs, compared to eAECs Fig. . We alsolue Fig. . This idals Fig. .Taken together, our data indicated that HSC-activated regulatory elements are primed with active histone modifications at early AECs, and acquire high chromatin contacts to locate cognate target genes during HSC development.Ets1, an EC marker gene in C1 cluster, loop loss over time was accompanied by a reduced enhancer activity and subsequent down-regulated gene expression. At Cebpg, an immune marker gene in C2 cluster, the gain of loops was followed by increased enhancer activity. At Meis1, a hematopoiesis marker gene, increasingly emerged loops were observed in association with a slight increase in the enhancer activity and a marked increase in gene expression  and promoter regions (within TSS ± 5 kb). Most RUNX1 peaks were distributed in proximal promoters . We focused on a few top significant TF families whose expression was also significant in either population or in an increasing trend Fig. . TFs ide56.In summary, by integrating multi-omics datasets generated from Hi-C and itChIP-seq for histone modifications and RUNX1 as well as scRNA-seq in low-input newly defined cell populations, we approached how the dynamics in multi-layered chromatin states shaped the path of HSC ontogeny. In particular, we followed the continuum of HSC formation starting from a population of early AECs with a small fraction expressing RUNX1, defined by a combination of cell surface markers+ immunophenotype58 in eAECs and HECs. This indicates that early AECs may be already specified to differentiate toward HECs and ultimately HSCs, likely explaining that the change in the large-scale 3D genome re-organization is not directly linked to HSC development-related genes. However, we concur that the clear differences observed between E10.0 eAECs and the E10.0/E11.0 EHT populations may not solely reflect an important transition in EHT, but also be influenced by at least part of the E10.0 eAECs being on a different fate trajectory from the E10.0 HE/pre-HSCs. Nevertheless, the identities of HECs and eAECs can be readily distinguished by their distinct transcriptome and domain score features, indicating the main component in each population. This finding still provides the epigenetic basis to delineate cell fates as previously inferred by the transcriptome from scRNA-seq56.Our study provides a biological annotation of the dynamic 3D genome structure in combination with histone modifications, TF binding, and transcriptome during early HSC development. We observed the dynamic TAD boundaries on large scale, but unlikely associated with hematopoiesis. Instead, intra-TAD dynamics were largely linked to HSC development. By focusing on differential enhancer–promoter interactions within these TADs, we found that about 40.9% of E–P interactions were bound by RUNX1 along with the HSC development. Further analysis identified both known and unknown TFs as RUNX1 interacting regulators to enhance hematopoiesis-related interactions. Importantly, our data support that HSC-specific chromatin loops are stepwise reinforced while active histone modifications among these regions are pre-established before EHT and later enhanced in LT-HSCs. The pre-configured active histone modifications, H3K27ac and H3K4me1 as reporting enhancer activation, are likely attributed to the arterial program with the CD44−CD31+ ECs to bone marrow HSCs23. However, computational prediction of their target genes can be inevitably inaccurate to define the molecular basis of HSC ontogeny. Our work capitalized on the high-resolution Hi-C data to firstly determine the dynamic genome interaction associated with hematopoiesis enriched functional terms. This set of chromatin loops offers the high-precision map to understand how RUNX1 and co-TFs identified cooperate to guide HSC development. It may also inform the better design to reprogram or differentiate embryonic stem cells to HSCs for regenerative medicine.Rather than focusing on the identification of stage-specific enhancers, we examined how spatial interactions in HSC are established between enhancers and their target genes, mediated by TFs. A recent report focused on enhancer dynamics across from RUNX160. Our study presents a time-course mapping of genome-wide RUNX1 occupancy using low-input itChIP-seq33 in rare and purified cell populations around EHT in mouse embryos. Most RUNX1 binding sites in HSCs were found to be largely evident in early AECs. Strikingly, early AECs purified by CD31+CD44+ already exhibited RUNX1-binding signals. Although this is in accordance with increasing Runx1 expression during development, we cannot rule out the relative heterogeneity in early AECs, a mixed population of RUNX1− and RUNX1+ corresponding to a small number of HECs in eAECs purified by the current FACS gating strategy. Further single-cell ChIP-seq for RUNX1 would be crucial to study its molecular functions during cell fate decisions, especially before and at the start of EHT. Notwithstanding this limitation, our work not only provides a rich source for the RUNX1-binding dynamics but also supports the prerequisite of RUNX1 chromatin engagement for HSC formation.RUNX1 is essential for both primitive and definitive hematopoietic development. To date, all studies profiled RUNX1-binding sites by ChIP-seq using a large number of cells from either in vitro cell culture or immortalized cell lines derived from patientsThe integrated multi-omics dissection of HSC development in mouse embryos using relatively purified cell populations reveals that the pre-configured chromatin modifications are established prior to the formation of the mature 3D genome structure in HSCs. This finding informs a reprogramming strategy to derive cell types of interest from epigenetically neighboring cell lineages by manipulating key chromatin interactions as designed. Together with a previously established combination of TFs in an attempt to reprogram ECs to acquire HSCs, we are closing to the successful era of generating off-the-shelf HSCs for therapeutic treatments of blood diseases.61. Primary embryonic single-cell suspension was acquired using the type I collagenase for dissociation at 37 °C and PBS/5% FBS for washes.All animal experiments were performed according to protocols approved by the Institutional Animal Care and Use Committees of Peking University and Academy of Military Medical Sciences. All animal experiments were approved by the Animal Care and Use Committee of the Institute. All cell populations were collected from fetuses of 8–10 weeks old wild-type female mice maintained on C57BL/6 background. Early embryos were staged by somite pair (sp) countings, such as 31–35 sp for E10.0 and 41–45 sp for E11.0. E14.5 embryos were staged by morphologic features. AGM region was dissected as reported−CD43−CD45−CD31+CD44+Kit−. Surface markers for E10.0 HECs in AGM regions were CD41−CD43−CD45−CD31+CD44+Kit+CD201+. Surface markers of E11.0 AGM pre-HSCs were CD31+Kit+CD201+. Surface markers for E14.5 fetal liver LT-HSCs were CD45+CD201+CD150+CD48−. Cells were stained using the following antibodies (1:100 diluted): Anti-CD31 , Anti-CD41 , Anti-CD43 , Anti-CD44 , Anti-CD45 , Anti-CD48 , Anti-CD201 , Anti-Kit , and Anti-CD150 . 7-aminoactinomycin D  was used to exclude dead cells. The FACS Diva 8 “index sorting” function was activated and sorting was performed in the single-cell mode.The dissociated cells were incubated with antibody madoptixture at 4 °C for 30 min, followed by incubation with 7-AAD antibody at room temperature for 5 min. Cells were sorted and analyzed by flow cytometers FACS Aria II (BD Biosciences) and MoFlo XDP (Beckman Coulter) in the purity model. The FACS data were analyzed with FlowJo software . Surface markers for E10.0 early AECs in AGM regions were CD4132 was performed with a few modifications. Briefly, after collection by FACS, cells were fixed by 1% formaldehyde solution at room temperature for 3 min followed by 1 × PBS washing and centrifugation at 4 °C. Samples could be stored at −80 °C or used immediately. Fixed cells were incubated in hypotonic buffer at 62 °C for 10 min (histone modification ChIP) or 37 °C for 1 h (TF ChIP), to relax chromatin. Cells were slightly sheared and then quenched with Triton X-100. Chromatin was digested by Tn5 assembled with MEA/B adapters at 37 °C for 1 h. After tagmentation reaction was stopped, the sample was sheared to release chromatin out of nuclei. By centrifugation at 4 °C, the soluble supernatant was collected and used for incubation with antibodies overnight. Dynabeads protein A  beads were used to pull down chromatin fragments coupled with antibodies. Adapter-ligated fragments were eluted from beads and treated with proteinase K. DNA was purified and extracted by phenol–chloroform, then used for library preparation with Q5 polymerase (NEB) and Illumina Nextera index primers. After the size selection for 200–1000 bp fragments, libraries were quantified with Qubit for concentrations. Samples were mixed and sequenced on Nova 6000 (Illumina) for paired-end 150 bp reads. The itChIP-seq experiments were performed with the following antibodies: Anti-H3K4me3 , Anti-H3K27ac , Anti-H3K27me3 , Anti-H3K4me1 , and Anti-RUNX1 .The itChIP-seq33. Different cell populations were sorted by FACS and fixed with 1% formaldehyde solution at room temperature for 10 min. MboI enzyme  was used to digest chromatin. After biotinylation, ligation, and proteinase K treatment, DNA was purified and sheared to 300–500 bp with Covaris M220, followed by Dynabeads Myone Streptavidin C1 bead pulling down. The Hi-C libraries were amplified for 12–15 cycles with Extaq (Takara) and Illumina Nextera index primers. Size selection was carried out by first 0.5× AMPure beads to remove >1 kb fragments, and second 0.5× AMPure beads to the supernatant to obtain 200–1000 bp fragments for sequencing. Libraries were then quantified and sequenced with paired-end 150 bp reads on NovaSeq 6000 platform (Illumina).The generation of sisHi-C library was performed as reported with a few modificationsThe quality of sequencing was evaluated by fastqc (v.0.11.8). Raw reads were trimmed and removed for adapter sequences by cutadapt (v.2.6) with parameters “-q 20 -O 10 –trim-n -m 30 –max-n 0.1”. Trimmed reads were mapped to mm10 mouse genome by Bowtie2 (v.2.3.5) with the parameter “-N 1”. Mapped reads with MAPQ ≥ 30 were considered as uniquely mapped reads, sorted with samtools (v.1.9) for the subsequent analyses. Duplicates were removed with picard MarkDuplicates (v.2.2.4). Only non-duplicated reads were used for further analysis. Reads of replicates were sampled to the same level for comparisons by samtools (v.1.9). Bigwig files were generated with bamCoverage (v.3.3.1), normalized to 1M reads. Peaks were called with non-duplicated bam files by MACS2 callpeak (v.2.2.5).62 in “bins” or “BED-file” modes. Correlation heatmaps were generated by plotCorrelation in deepTools software.To evaluate the correlations between replicates of itChIP data, we calculated Pearson correlation coefficients with normalized average scores in global bins or peak regions by multiBigwigSummary (v.3.3.1) from deepTools software62.Non-duplicated reads on ChIP-seq peaks were normalized and used for calculation of histone modification or TF signals on TAD boundaries, within TADs, or on looping enhancers and promoters. Normalized bigwig files were used as input for calculation by “computeMatrix reference-point” function in deepTools software64 was used for the processing of Hi-C data. Only uniquely mapped read pairs were saved for further trimming of dangling end reads, the same fragment reads, self-circled reads, self-ligated reads, and other invalid Hi-C reads. Non-duplicated reads were used to generate Hi-C matrix at 5, 10, and 40 kb resolution with cooler (v.0.8.5)65. After filtering bins with low counts, the matrices were balanced by Knight–Ruiz (KR) with hicCorrectMatrix in HiCExplorer. Replicates of Hi-C matrices were normalized to the smallest read count by hicNormalize in HiCExplorer with “–normalize smallest” parameter.HiCExplorer (v.3.4.2)Contact heatmaps were generated with matrices at 5 kb resolution by hicPlotMatrix in HiCExplorer. Curves showing count distribution with genomic distances were performed by hicPlotDistVsCounts in HiCExplorer. PyGenomeTracks (v.1.0) was used to perform and visualize loops and interactions at different regions. Aggregate peak analysis was processed with hicAggregateContacts was used with parameter “–transform obs/exp”, which generated aggregate heatmaps and average contact signals.21. Besides, we judge the transcription activity based on pubic scRNA-seq data of the same cell populations identified by surface markers. That facilitates judgment of Type A or B compartments.Compartments were predicted with matrices at 100 kb resolution and regarded as A or B according to the gene density. The compartments with higher gene density were selected as type A, while the compartments with lower gene density were selected as type Bz-score of the Hi-C matrix and is defined as the mean z-score of all the matrix contacts between the left and right regions64. The minus value of the insulation score was boundary strength. We calculated the standard deviations of the strength of each TAD boundary across four-cell stages and sorted boundaries by standard deviations. Then the top 1000 variable TAD boundaries were selected based on the ranking order.TADs were identified with hicFindTADs at 40 kb resolution. TAD insulation scores  were generated when identifying TAD boundaries with hicFindTADs tool of HiCExplorer software. According to hicFindTADs tool of HiCExplorer software, an insulation score is used to identify the degree of separation between the left and right regions of each Hi-C matrix bin. The insulation score is measured using the 67. In brief, a domain score was defined as a ratio of the number of intra-TAD paired-end tags to that of all TAD paired-end tags. Domain scores were further normalized by dividing against average domain scores from 1000 simulated TADs with the same size randomly distributed in the same chromosome. Next, the scores were further normalized by subtracting the mean score of all TADs and were treated for quantile-normalization to facilitate comparisons among all Hi-C libraries. The top 1000 variable TADs were chosen by ranking variances of domain scores across four stages, from 2068 significantly changing TADs identified by ANOVA test (with the cutoff FDR < 0.001).Domain scores were calculated to reflect the connectivity within TADsLoops and interactions were detected with hicDetectLoops at 10 kb resolution. To quantify interaction counts between any two lists of genomic regions (loop strength), hicAggregateContacts was used with parameter “–transform obs/exp”.Loops involving enhancers or promoters were those loops with at least one anchor overlapped with enhancer regions  or promoter regions (TSS ± 5 kb). Enhancer–promoter (E–P), promoter–promoter (P–P), and enhancer–enhancer (E–E) interactions were classified by overlapping anchor regions with enhancers and promoters. RUNX1-engaged E–P interactions were those with RUNX1 occupancy on anchor regions. RUNX1-engaged E–P interactions were further separated into three types, with RUNX1 occupancy on promoters, enhancers, or both.The scRNA-seq data is existing data, which have been deposited in the NCBI Gene Expression Omnibus under accession number GSE139389 and GSE67120. The public data of four stages were analyzed together after normalization by log2(TPM/10 + 1).Pie chart showing genomic annotations for function regions were performed with R package ChIPseeker (v.1.20.0) and clusterProfiler (v.3.12.0). We set TSS ± 5 kb regions as the promoter regions. Genes near to peaks or interacting regions were annotated with annotatePeaks.pl in Homer. Only genes whose distances to anchor regions were ≤20 kb were treated as genes influenced by this loop or interaction.P-values were smaller than 10e−2 were treated as significantly enriched motifs.De novo motifs and known motifs were identified with enhancers  and promoters (TSS ± 5 kb regions) by findMotifsGenome.pl in Homer. The parameters were set as “-mask -size given -len 8,10,12”. Only those motifs whose P-value < 0.05 was referred as a statistically significant value (if not specified for stricter 0.01 or 0.001). We applied Benjamini–Hochberg correction to correct P-values in multiple-testing procedures. FDR < 0.05 after correction was referred as a statistically significant value (if not specified for stricter 0.01 or 0.001). Gene Ontology analysis about the biological process was performed using Metascape (http://metascape.org)69 and GREAT (http://great.stanford.edu/public/html/)70, with Benjamini–Hochberg P-value correction.All statistical analysis was conducted with R version 3.5.1. ANOVA and two-sample Wilcoxon Rank Sum test was used for comparisons of domain scores, histone modification signals, and gene expressions between two adjacent cell populations or replicates. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary