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• W3124454906 abstract "•High-resolution single-cell transcriptome and 3D genome atlas of developing mouse brain•3D genome “structure types” behind transcriptional cell types•Major transformation of transcriptome and 3D genome after birth, unaffected by experience•Allele-specific 3D structure of imprinted genes, including one that spans a chromosome Both transcription and three-dimensional (3D) architecture of the mammalian genome play critical roles in neurodevelopment and its disorders. However, 3D genome structures of single brain cells have not been solved; little is known about the dynamics of single-cell transcriptome and 3D genome after birth. Here, we generated a transcriptome (3,517 cells) and 3D genome (3,646 cells) atlas of the developing mouse cortex and hippocampus by using our high-resolution multiple annealing and looping-based amplification cycles for digital transcriptomics (MALBAC-DT) and diploid chromatin conformation capture (Dip-C) methods and developing multi-omic analysis pipelines. In adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first post-natal month. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, we examine allele-specific structure of imprinted genes, revealing local and chromosome (chr)-wide differences. These findings uncover an unknown dimension of neurodevelopment. Both transcription and three-dimensional (3D) architecture of the mammalian genome play critical roles in neurodevelopment and its disorders. However, 3D genome structures of single brain cells have not been solved; little is known about the dynamics of single-cell transcriptome and 3D genome after birth. Here, we generated a transcriptome (3,517 cells) and 3D genome (3,646 cells) atlas of the developing mouse cortex and hippocampus by using our high-resolution multiple annealing and looping-based amplification cycles for digital transcriptomics (MALBAC-DT) and diploid chromatin conformation capture (Dip-C) methods and developing multi-omic analysis pipelines. In adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first post-natal month. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, we examine allele-specific structure of imprinted genes, revealing local and chromosome (chr)-wide differences. These findings uncover an unknown dimension of neurodevelopment. Two intimately related dimensions of the mammalian genome—gene transcription and three-dimensional (3D) genome architecture—are both crucial for the development of nervous systems. The dynamic interplay between cell-type-specific gene expression, chromatin structure, sensory experience, and other factors (e.g., epigenetic marks) underlies our brain’s immense plasticity and functions. Dysregulation of transcription and chromatin structure leads to debilitating neurodevelopmental disorders, such as autism (Satterstrom et al., 2020Satterstrom F.K. Kosmicki J.A. Wang J. Breen M.S. De Rubeis S. An J.Y. Peng M. Collins R. Grove J. Klei L. et al.Autism Sequencing ConsortiumiPSYCH-Broad ConsortiumLarge-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism.Cell. 2020; 180: 568-584.e23Abstract Full Text Full Text PDF PubMed Scopus (424) Google Scholar) and schizophrenia (Howrigan et al., 2020Howrigan D.P. Rose S.A. Samocha K.E. Fromer M. Cerrato F. Chen W.J. Churchhouse C. Chambert K. Chandler S.D. Daly M.J. et al.Exome sequencing in schizophrenia-affected parent-offspring trios reveals risk conferred by protein-coding de novo mutations.Nat. Neurosci. 2020; 23: 185-193Crossref PubMed Scopus (39) Google Scholar). Genome topology also determines the distribution of somatic DNA damage and mutations in human neurons (Zhu et al., 2019Zhu Q. Niu Y. Gundry M. Sheng K. Niu M. Zong C. Topologically Dependent Abundance of Spontaneous DNA Damage in Single Human Cells.bioRxiv. 2019; https://doi.org/10.1101/859686Crossref Scopus (0) Google Scholar). Recent progress has been made to characterize transcriptome and 3D genome of the brain; however, existing studies have 3 limitations. First, 3D genome structures of single brain cells have not been resolved. For example, bulk chromatin conformation capture (3C/Hi-C) assays can only measure ensemble-averaged maps of 3D genome. They also lump together the diverse sub-types of neurons (and/or glia), masking cell-type-specific features (Lu et al., 2020Lu L. Liu X. Huang W.K. Giusti-Rodríguez P. Cui J. Zhang S. Xu W. Wen Z. Ma S. Rosen J.D. et al.Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.Mol. Cell. 2020; 79: 521-534.e15Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar; Won et al., 2016Won H. de la Torre-Ubieta L. Stein J.L. Parikshak N.N. Huang J. Opland C.K. Gandal M.J. Sutton G.J. Hormozdiari F. Lu D. et al.Chromosome conformation elucidates regulatory relationships in developing human brain.Nature. 2016; 538: 523-527Crossref PubMed Scopus (252) Google Scholar). So far, bulk Hi-C only revealed 3D genome refolding in neurons that are either differentiated in vitro (Bonev et al., 2017Bonev B. Mendelson Cohen N. Szabo Q. Fritsch L. Papadopoulos G.L. Lubling Y. Xu X. Lv X. Hugnot J.P. Tanay A. Cavalli G. Multiscale 3D Genome Rewiring during Mouse Neural Development.Cell. 2017; 171: 557-572.e24Abstract Full Text Full Text PDF PubMed Scopus (462) Google Scholar; Lu et al., 2020Lu L. Liu X. Huang W.K. Giusti-Rodríguez P. Cui J. Zhang S. Xu W. Wen Z. Ma S. Rosen J.D. et al.Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases.Mol. Cell. 2020; 79: 521-534.e15Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar; Rajarajan et al., 2018Rajarajan P. Borrman T. Liao W. Schrode N. Flaherty E. Casiño C. Powell S. Yashaswini C. LaMarca E.A. Kassim B. et al.Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk.Science. 2018; 362: eaat4311Crossref PubMed Scopus (76) Google Scholar) or isolated at a single time point from the embryonic mouse brain (Bonev et al., 2017Bonev B. Mendelson Cohen N. Szabo Q. Fritsch L. Papadopoulos G.L. Lubling Y. Xu X. Lv X. Hugnot J.P. Tanay A. Cavalli G. Multiscale 3D Genome Rewiring during Mouse Neural Development.Cell. 2017; 171: 557-572.e24Abstract Full Text Full Text PDF PubMed Scopus (462) Google Scholar). Single-cell 3C/Hi-C has been achieved in adult human brains (Lee et al., 2019Lee D.S. Luo C. Zhou J. Chandran S. Rivkin A. Bartlett A. Nery J.R. Fitzpatrick C. O’Connor C. Dixon J.R. Ecker J.R. Simultaneous profiling of 3D genome structure and DNA methylation in single human cells.Nat. Methods. 2019; 16: 999-1006Crossref PubMed Scopus (53) Google Scholar) but had low resolution, yielded no 3D structures, and could not distinguish different neuronal sub-types based on structural information alone. Second, the post-natal dynamics of single-cell transcriptome has not been comprehensively studied in the mammalian brain. Although transcriptional diversity has been characterized in adult and embryonic brains (Di Bella et al., 2020Di Bella D.J. Habibi E. Yang S.-M. Stickels R.R. Brown J. Yadollahpour P. Chen F. Macosko E.Z. Regev A. Arlotta P. Molecular Logic of Cellular Diversification in the Mammalian Cerebral Cortex.bioRxiv. 2020; https://doi.org/10.1101/2020.07.02.185439Crossref Scopus (0) Google Scholar; La Manno et al., 2020La Manno G. Siletti K. Furlan A. Gyllborg D. Vinsland E. Langseth C.M. Khven I. Johnsson A. Nilsson M. Lönnerberg P. et al.Molecular architecture of the developing mouse brain.bioRxiv. 2020; https://doi.org/10.1101/2020.07.02.184051Crossref Scopus (0) Google Scholar; Lake et al., 2016Lake B.B. Ai R. Kaeser G.E. Salathia N.S. Yung Y.C. Liu R. Wildberg A. Gao D. Fung H.L. Chen S. et al.Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain.Science. 2016; 352: 1586-1590Crossref PubMed Scopus (403) Google Scholar; Tasic et al., 2016Tasic B. Menon V. Nguyen T.N. Kim T.K. Jarsky T. Yao Z. Levi B. Gray L.T. Sorensen S.A. Dolbeare T. et al.Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.Nat. Neurosci. 2016; 19: 335-346Crossref PubMed Scopus (763) Google Scholar, Tasic et al., 2018Tasic B. Yao Z. Graybuck L.T. Smith K.A. Nguyen T.N. Bertagnolli D. Goldy J. Garren E. Economo M.N. Viswanathan S. et al.Shared and distinct transcriptomic cell types across neocortical areas.Nature. 2018; 563: 72-78Crossref PubMed Scopus (442) Google Scholar; Zeisel et al., 2018Zeisel A. Hochgerner H. Lönnerberg P. Johnsson A. Memic F. van der Zwan J. Häring M. Braun E. Borm L.E. La Manno G. et al.Molecular Architecture of the Mouse Nervous System.Cell. 2018; 174: 999-1014.e22Abstract Full Text Full Text PDF PubMed Scopus (763) Google Scholar), only a few studies probed its post-natal dynamics, each with a limited set of time points sampled (Carter et al., 2018Carter R.A. Bihannic L. Rosencrance C. Hadley J.L. Tong Y. Phoenix T.N. Natarajan S. Easton J. Northcott P.A. Gawad C. A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum.Curr. Biol. 2018; 28: 2910-2920.e2Abstract Full Text Full Text PDF PubMed Scopus (76) Google Scholar; Kalish et al., 2020Kalish B.T. Barkat T.R. Diel E.E. Zhang E.J. Greenberg M.E. Hensch T.K. Single-nucleus RNA sequencing of mouse auditory cortex reveals critical period triggers and brakes.Proc. Natl. Acad. Sci. USA. 2020; 117: 11744-11752Crossref PubMed Scopus (14) Google Scholar; Kim et al., 2020Kim D.W. Washington P.W. Wang Z.Q. Lin S.H. Sun C. Ismail B.T. Wang H. Jiang L. Blackshaw S. The cellular and molecular landscape of hypothalamic patterning and differentiation from embryonic to late postnatal development.Nat. Commun. 2020; 11: 4360Crossref PubMed Scopus (18) Google Scholar; Li et al., 2018Li M. Santpere G. Imamura Kawasawa Y. Evgrafov O.V. Gulden F.O. Pochareddy S. Sunkin S.M. Li Z. Shin Y. Zhu Y. et al.BrainSpan ConsortiumPsychENCODE ConsortiumPsychENCODE Developmental SubgroupIntegrative functional genomic analysis of human brain development and neuropsychiatric risks.Science. 2018; 362: 362Crossref Scopus (206) Google Scholar; Rosenberg et al., 2018Rosenberg A.B. Roco C.M. Muscat R.A. Kuchina A. Sample P. Yao Z. Graybuck L.T. Peeler D.J. Mukherjee S. Chen W. et al.Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.Science. 2018; 360: 176-182Crossref PubMed Scopus (400) Google Scholar; Tiklová et al., 2019Tiklová K. Björklund A.K. Lahti L. Fiorenzano A. Nolbrant S. Gillberg L. Volakakis N. Yokota C. Hilscher M.M. Hauling T. et al.Single-cell RNA sequencing reveals midbrain dopamine neuron diversity emerging during mouse brain development.Nat. Commun. 2019; 10: 581Crossref PubMed Scopus (70) Google Scholar). Furthermore, most of these studies used low-sensitivity methods, detecting few transcripts per cell. Third, no existing methods offer in-depth, integrative analysis of single-cell 3D genome data in conjunction with other omic datasets. For the above-mentioned reasons, existing studies can neither address the true relationship between the brain’s diverse transcriptional cell types and their underlying 3D genome “structure types” nor trace their interplay during development in vivo. Here, we fill in this long-standing technological and knowledge gap. Using our highly sensitive and accurate multiple annealing and looping-based amplification cycles for digital transcriptomics (MALBAC-DT) method (Chapman et al., 2020Chapman A.R. Lee D.F. Cai W. Ma W. Li X. Sun W. Xie X.S. Correlated Gene Modules Uncovered by Single-Cell Transcriptomics with High Detectability and Accuracy.bioRxiv. 2020; https://doi.org/10.1101/2019.12.31.892190Crossref Scopus (0) Google Scholar), we created a transcriptome atlas of the developing mouse forebrain, encompassing ∼3,500 single cells from 2 brain regions and 7 time points. Using a streamlined version of our diploid chromatin conformation capture (Dip-C) method (Tan et al., 2018Tan L. Xing D. Chang C.H. Li H. Xie X.S. Three-dimensional genome structures of single diploid human cells.Science. 2018; 361: 924-928Crossref PubMed Scopus (148) Google Scholar, Tan et al., 2019Tan L. Xing D. Daley N. Xie X.S. Three-dimensional genome structures of single sensory neurons in mouse visual and olfactory systems.Nat. Struct. Mol. Biol. 2019; 26: 297-307Crossref PubMed Scopus (33) Google Scholar), we created a 3D genome atlas, encompassing ∼2,000 single-cell contact maps and ∼800 3D structures from 2 brain regions and 6 time points. We further analyzed 3D genome in the visual cortex of sensory-deprived mice, with ∼1,700 single cells from 5 time points. Finally, we developed multi-omic data visualization and analysis methods. We directly addressed several fundamental questions in developmental biology. On the transcriptome side, we observed a major transformation around post-natal day (P) 14 in all 3 main cell lineages: neurons, astrocytes, and oligodendrocytes. In neurons, the primary source of transcriptome variation was 2 developmentally regulated, correlated gene modules. On the 3D genome side, we found that cell-type identity was encoded in the 3D wiring of the genome, because 3D genome structure alone could separate single cells into 13 “structure types.” Between structure types, differential chromatin A/B compartmentalization correlated well with cell-type-specific gene expression. We observed a major 3D genome transformation between P7 and P28 in all 3 cell lineages. The 6 adult neuron sub-types only emerged during this transition; neonatal neurons, in contrast, adopted a more primordial 3D genome state that resembled embryonic neurons and neurons cultured/differentiated in vitro. We gained mechanistic insights into this transformation. We revealed large-scale, neuron-specific radial reconfiguration, which could be linked to global non-CpG DNA methylation (Lister et al., 2013Lister R. Mukamel E.A. Nery J.R. Urich M. Puddifoot C.A. Johnson N.D. Lucero J. Huang Y. Dwork A.J. Schultz M.D. et al.Global epigenomic reconfiguration during mammalian brain development.Science. 2013; 341: 1237905Crossref PubMed Scopus (1158) Google Scholar) and shared striking similarity with 3D remodeling of a peripheral nervous system (Bashkirova et al., 2020Bashkirova E. Monahan K. Campbell C.E. Osinski J.M. Tan L. Schieren I. Barnea G. Xie X.S. Gronostajski R.M. Lomvardas S. Homeotic Regulation of Olfactory Receptor Choice via NFI-dependent Heterochromatic Silencing and Genomic Compartmentalization.bioRxiv. 2020; https://doi.org/10.1101/2020.08.30.274035Crossref Scopus (0) Google Scholar; Clowney et al., 2012Clowney E.J. LeGros M.A. Mosley C.P. Clowney F.G. Markenskoff-Papadimitriou E.C. Myllys M. Barnea G. Larabell C.A. Lomvardas S. Nuclear aggregation of olfactory receptor genes governs their monogenic expression.Cell. 2012; 151: 724-737Abstract Full Text Full Text PDF PubMed Scopus (211) Google Scholar; Monahan et al., 2019Monahan K. Horta A. Lomvardas S. LHX2- and LDB1-mediated trans interactions regulate olfactory receptor choice.Nature. 2019; 565: 448-453Crossref PubMed Scopus (105) Google Scholar; Tan et al., 2019Tan L. Xing D. Daley N. Xie X.S. Three-dimensional genome structures of single sensory neurons in mouse visual and olfactory systems.Nat. Struct. Mol. Biol. 2019; 26: 297-307Crossref PubMed Scopus (33) Google Scholar). We also observed local and long-range 3D rewiring of developmental and/or disease-implicated genes and their enhancers. Changes in single-cell chromatin A/B (scA/B) compartments correlated with developmentally regulated gene modules, although many genes displayed discordant or temporally shifted changes in expression and 3D structure. Furthermore, sensory deprivation had little effect on 3D genome in the visual cortex, suggesting that 3D transformation was genetically predetermined. Finally, we report a genome-wide, allele-specific survey of imprinted genes. We found parent-of-origin-specific structure for at least 7 of the 29 known imprinted loci (Perez et al., 2015Perez J.D. Rubinstein N.D. Fernandez D.E. Santoro S.W. Needleman L.A. Ho-Shing O. Choi J.J. Zirlinger M. Chen S.K. Liu J.S. Dulac C. Quantitative and functional interrogation of parent-of-origin allelic expression biases in the brain.eLife. 2015; 4: e07860Crossref PubMed Scopus (41) Google Scholar). In an extreme case, allelic difference could be seen extending tens of Mbs from the Prader-Willi/Angelman syndrome (PWS/AS) locus. We performed MALBAC-DT on 3,517 single cells from the mouse cortex and hippocampus at 7 different ages: P1, P7, P14, P21, P28, P56, and P180 (Figures 1A and 1B , top). Dense sampling ensured weekly resolution in the first post-natal month—a critical period for cognitive plasticity. To minimize stress (Lacar et al., 2016Lacar B. Linker S.B. Jaeger B.N. Krishnaswami S.R. Barron J.J. Kelder M.J.E. Parylak S.L. Paquola A.C.M. Venepally P. Novotny M. et al.Nuclear RNA-seq of single neurons reveals molecular signatures of activation.Nat. Commun. 2016; 7: 11022Crossref PubMed Scopus (179) Google Scholar), cells were rapidly isolated from dissected tissues as individual nuclei (Krishnaswami et al., 2016Krishnaswami S.R. Grindberg R.V. Novotny M. Venepally P. Lacar B. Bhutani K. Linker S.B. Pham S. Erwin J.A. Miller J.A. et al.Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons.Nat. Protoc. 2016; 11: 499-524Crossref PubMed Scopus (158) Google Scholar) (STAR methods). Note that we still referred to them as “cells.” MALBAC-DT allowed us to measure single-cell transcriptome with high sensitivity and accuracy (Chapman et al., 2020Chapman A.R. Lee D.F. Cai W. Ma W. Li X. Sun W. Xie X.S. Correlated Gene Modules Uncovered by Single-Cell Transcriptomics with High Detectability and Accuracy.bioRxiv. 2020; https://doi.org/10.1101/2019.12.31.892190Crossref Scopus (0) Google Scholar). We detected an average of 12.8 k mRNA molecules (also known as unique molecule identifiers [UMIs]; SD = 8.4 k, minimum = 1.7 k, maximum = 49.8 k) and 3.4 k genes (SD = 1.3 k, minimum = 1.0 k, maximum = 7.8 k) per cell. This was an order of magnitude higher than previous studies that probed post-natal cortical development in mice (Kalish et al., 2020Kalish B.T. Barkat T.R. Diel E.E. Zhang E.J. Greenberg M.E. Hensch T.K. Single-nucleus RNA sequencing of mouse auditory cortex reveals critical period triggers and brakes.Proc. Natl. Acad. Sci. USA. 2020; 117: 11744-11752Crossref PubMed Scopus (14) Google Scholar; Rosenberg et al., 2018Rosenberg A.B. Roco C.M. Muscat R.A. Kuchina A. Sample P. Yao Z. Graybuck L.T. Peeler D.J. Mukherjee S. Chen W. et al.Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding.Science. 2018; 360: 176-182Crossref PubMed Scopus (400) Google Scholar). We visualized transcriptional diversity with uniform manifold approximation and projection (UMAP) (Hafemeister and Satija, 2019Hafemeister C. Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.Genome Biol. 2019; 20: 296Crossref PubMed Scopus (422) Google Scholar; Stuart et al., 2019Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck 3rd, W.M. Hao Y. Stoeckius M. Smibert P. Satija R. Comprehensive Integration of Single-Cell Data.Cell. 2019; 177: 1888-1902.e1821Abstract Full Text Full Text PDF PubMed Scopus (2364) Google Scholar). Cells clearly separated into clusters, each with distinct age and tissue patterns (Figure 1C). We referred to these clusters as “transcriptome types” to distinguish from 3D genome analysis in later sections. Louvain clustering identified 4 major cell types: neurons, astrocytes, oligodendrocytes, and other cells (“microglia, etc.”: microglia and a small number of vascular cells) (Figure 1C, left; Figures 1D and 1E, top), each expressing specific marker genes (Figure S1; Table S1). A fundamental question in developmental biology is to what extent post-natal cells continue to mature in their gene expression profile. This is especially important for neurons in the brain—most of which have become postmitotic at this point, and must last a lifetime. On our UMAP plot, within each major type, cells segregated primarily by age, with the clearest visual distinction between P7 and P14 (Figure 1C, middle). This suggested a major transcriptome transformation after birth. We first examined oligodendrocytes and astrocytes. Oligodendrocytes segregated into 3 distinct sub-types: oligodendrocyte progenitors (Pdgfra+), newly formed oligodendrocytes (Tns3+), and mature oligodendrocytes (Mal+) (Figure 1C, left), consistent with previous studies (Cahoy et al., 2008Cahoy J.D. Emery B. Kaushal A. Foo L.C. Zamanian J.L. Christopherson K.S. Xing Y. Lubischer J.L. Krieg P.A. Krupenko S.A. et al.A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function.J. Neurosci. 2008; 28: 264-278Crossref PubMed Scopus (2078) Google Scholar; Tasic et al., 2016Tasic B. Menon V. Nguyen T.N. Kim T.K. Jarsky T. Yao Z. Levi B. Gray L.T. Sorensen S.A. Dolbeare T. et al.Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.Nat. Neurosci. 2016; 19: 335-346Crossref PubMed Scopus (763) Google Scholar). Starting from a single transcriptome type (oligodendrocyte progenitors) on P1, post-natal development led to the emergence of 2 new types over time: newly formed oligodendrocytes on P7 and mature oligodendrocytes on P21 (Figure 1E, bottom). Similarly, transcriptome transformation in astrocytes led to the emergence of a more mature type (“adult astrocytes”; Gjb6+) on P14 in addition to the neonatal type (Tnc+) (Figure 1E, middle), consistent a previous study (Cahoy et al., 2008Cahoy J.D. Emery B. Kaushal A. Foo L.C. Zamanian J.L. Christopherson K.S. Xing Y. Lubischer J.L. Krieg P.A. Krupenko S.A. et al.A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function.J. Neurosci. 2008; 28: 264-278Crossref PubMed Scopus (2078) Google Scholar). We now focus on neurons. Different functional sub-types of neurons, such as excitatory and inhibitory neurons, are known to carry out different computations. Neurons in different brain regions, and/or at different developmental stages, can also exhibit different properties. To investigate the transcriptomic basis of such diversity, we separately analyzed the 2,277 neurons. On the UMAP plot, neurons formed complex clusters according to their ages, brain regions, and functions (Figure 2A). For most sub-types, a prominent age gradient could be seen—usually with the clearest visual separation between P7 and P14 (Figure 2A, second to the right). Louvain clustering classified neurons into 20 transcriptome types (Figure 2A, second to the left; Figure 2B). Here, we merged different developmental stages of the same sub-type when possible, leading to 16 main types and 4 immature types. Among the 16 main types, 8 were excitatory: 4 cortical pyramidal cell types based on their cortical layers (L2/3: Camk2d+; L4: Rorb+; L5: Tshz2+; L6: Tle4+, among which a small sub-cluster of Ctgf+ L6b cells were visible), 2 hippocampal pyramidal cell types of the cornu ammonis (CA) region (CA1: Gm2115+; CA3: Grik4+), hippocampal granule cells of the dentate gyrus (DG) region (Prox1+), and Cajal-Retzius (CR) cells (Nhlh2+) (Figures 1A and 1B; Figure S1; Table S1). The other 8 were inhibitory: 5 known interneuron types (SST: Sst+; PV: Pvalb+; VIP: Vip+; LAMP5: Lamp5+ [Tasic et al., 2018Tasic B. Yao Z. Graybuck L.T. Smith K.A. Nguyen T.N. Bertagnolli D. Goldy J. Garren E. Economo M.N. Viswanathan S. et al.Shared and distinct transcriptomic cell types across neocortical areas.Nature. 2018; 563: 72-78Crossref PubMed Scopus (442) Google Scholar], some of which are neuron-derived neurotrophic factor [NDNF] interneurons [Tasic et al., 2016Tasic B. Menon V. Nguyen T.N. Kim T.K. Jarsky T. Yao Z. Levi B. Gray L.T. Sorensen S.A. Dolbeare T. et al.Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.Nat. Neurosci. 2016; 19: 335-346Crossref PubMed Scopus (763) Google Scholar]; MEIS2: high in Meis2 [Di Bella et al., 2020Di Bella D.J. Habibi E. Yang S.-M. Stickels R.R. Brown J. Yadollahpour P. Chen F. Macosko E.Z. Regev A. Arlotta P. Molecular Logic of Cellular Diversification in the Mammalian Cerebral Cortex.bioRxiv. 2020; https://doi.org/10.1101/2020.07.02.185439Crossref Scopus (0) Google Scholar; Jin et al., 2020Jin X. Simmons S.K. Guo A. Shetty A.S. Ko M. Nguyen L. Jokhi V. Robinson E. Oyler P. Curry N. et al.In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes.Science. 2020; 370: eaaz6063Crossref PubMed Scopus (22) Google Scholar; Tasic et al., 2018Tasic B. Yao Z. Graybuck L.T. Smith K.A. Nguyen T.N. Bertagnolli D. Goldy J. Garren E. Economo M.N. Viswanathan S. et al.Shared and distinct transcriptomic cell types across neocortical areas.Nature. 2018; 563: 72-78Crossref PubMed Scopus (442) Google Scholar]), medium spiny neurons (MSNs: Tac1+, also Meis2+ but low [Jin et al., 2020Jin X. Simmons S.K. Guo A. Shetty A.S. Ko M. Nguyen L. Jokhi V. Robinson E. Oyler P. Curry N. et al.In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes.Science. 2020; 370: eaaz6063Crossref PubMed Scopus (22) Google Scholar]), and 2 unknown interneuron types (Figures 1A and 1B; Figure S1, Table S1). The remaining 4 types were immature neurons that corresponded to multiple adult types: neonatal cortical L2–L5 pyramidal cells (mostly P1–P7), intermediate cortical L2–L4 pyramidal cells (P14), neonatal hippocampal pyramidal cells (P1–P7), and neonatal SST/PV interneurons (P1–P7) (Figures 1A and 1B; Figure S1). We used principal-component analysis (PCA) to dissect the distinct sources of cell-to-cell heterogeneity (Figure 2C). Despite neurons’ well-known transcriptional specialization based on functions and regions, the first principal component (PC)—explaining 3.6% of the total variance—corresponded to developmental progression at different ages. By comparison, the next 2 PCs, which distinguished major sub-types (granule and pyramidal cells), explained 2.1% and 1.7%, respectively. Neurons were positioned along the first PC primarily based on age (Figure 2D). Note that the hippocampus harbored more adult cells on the negative (neonatal) side, partly because of adult neurogenesis of DG granule cells (Figure 2A). Genes underlying transcriptome maturation were identified from PC 1 loadings (Figure 2E; Tables S2 and S3) (Figure 2E). As an independent confirmation, we identified correlated gene modules (Langfelder and Horvath, 2008Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics. 2008; 9: 559Crossref PubMed Scopus (7397) Google Scholar). The 2 largest modules—termed “neonatal” and “adult”—corresponded well with PC 1 loadings. Among the 3,000 genes in PCA, the 75 genes in the neonatal module ranked between the 1st and the 123rd most negative (mean rank = 44), while the 72 genes in the adult module ranked between the 1st and the 148th most positive (mean rank = 53) (Figure 2E, left; Table S2). A less stringent list from the top 10,000 (rather than 3,000) variable genes yielded 122 genes in the neonatal module and 151 in the adult module (Table S3). We visualized the expression patterns of the 2 modules with their eigengenes (Langfelder and Horvath, 2008Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics. 2008; 9: 559Crossref PubMed Scopus (7397) Google Scholar). The neonatal eigengene was highly expressed on P1 and P7, sharply declined on P14, and remained low from P21 onward (Figure 2F, top). By contrast, the adult eigengene steadily increased over time until plateauing around P56 (Figure 2F, bottom). The 2 modules therefore represented 2 distinct modes of transcriptome transformation. Gene Ontology (GO) analysis (Thomas et al., 2003Thomas P.D. Campbell M.J. Kejariwal A. Mi H. Karlak B. Daverman R. Diemer K. Muruganujan A. Narechania A. PANTHER: a library of protein families and subfamilies indexed by function.Genome Res. 2003; 13: 2129-2141Crossref PubMed Scopus (1988) Google Scholar) revealed enriched gene functions among the 2 modules. Genes in the neonatal modules were enriched in neurogenesis (36 genes; false discovery rate [FDR] = 6 × 10−17), cellular developmental process (43; 4 × 10−13), neuron projection development (22; 7 × 10−13), axon development (17; 8 × 10−12), structural constituent of cytoskeleton (5; 4 × 10−3), and polymeric cytoskeletal fiber (12; 4 × 10−4) (Tables S2 and S3). Neonatal genes included cytoskeleton genes (e.g., Tuba1a/b2a/b2b/b3/b5, Stmn1/2, Syne2), axon guidance molecules (e.g., Gap43, Nrp1, Dpysl3/5, Plxnb1, Sema3c), and transcription factors (e.g., Lhx2, Sox4/11, Tcf4, Nfib, Nrep, Rcor2, Ctnnb1). Some functional enrichments were consistent with a bulk microarray study (Lyckman et al., 2008Lyckman A.W. Horng S. Leamey C.A. Tropea D. Watakabe A. Van Wart A. McCurry C. Yamamori T. Sur M. Gene expression patterns in visual cortex during the critical period: synaptic stabilization and reversal by visual deprivation.Proc. Natl. Acad. Sci. USA." @default.
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• W3124454906 title "Changes in genome architecture and transcriptional dynamics progress independently of sensory experience during post-natal brain development" @default.
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• W3124454906 doi "https://doi.org/10.1016/j.cell.2020.12.032" @default.
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