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Spatio-temporal transcriptome of the human brain
Open accessAbstract
Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
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“In Kang et al., 2011, the expression trajectories of the two largest modules comprising genes that are co-regulated across brain regions and time, indicate that many transcriptional processes are shared and coordinated across different regions as the brain develops (Figures 3C and D).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In particular, these approaches have implicated the projection neurons in the prefrontal and primary motor-somatosensory cortex during mid-fetal development in autism spectrum disorder and the frontal cortex during fetal development in schizophrenia (Gulsuner et al., 2013; Kang et al., 2011; Lin et al., 2015; Parikshak et al., 2013; State and Sestan, 2012; Willsey et al., 2013).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The expression trajectories of the modules, which can be summarized using the module eigengene or an intramodular hub gene, often exhibit dynamic expression patterns across development, consistent with their roles in distinct biological processes (Johnson et al., 2009; Kang et al., 2011; Miller et al., 2014; Parikshak et al., 2013; Willsey et al., 2013).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
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In the citing paper
“Surprisingly, it turned out that transcription profiles in the adult neocortex are relatively homogeneous throughout the cortex, although a certain level of heterogeneity still does exist (Hawrylycz et al., 2012; Kang et al., 2011; Pletikos et al., 2014)”
From Genetic Changes Shaping the Human Brain (2015) · cites this paper as Kang et al., 2011
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In the citing paper
“We chose dorsal frontal cortex (DFC) and CBC for our analyses because both regions have been implicated in DS neuropathology (Hartley et al., 2014; Haydar and Reeves, 2012; Letourneau et al., 2014; Lott, 2012) and exhibit diverse transcriptional profiles over the course of development (Kang et al., 2011; Tebbenkamp et al., 2014).”
From Down Syndrome Developmental Brain Transcriptome Reveals Defective Oligodendrocyte Differentiation and Myelination (2016) · cites this paper as Kang et al., 2011
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In the citing paper
“NaV1.2 is widely expressed throughout the human central nervous system, but not in peripheral tissues [7,8].”
From Progress in Understanding and Treating SCN2A-Mediated Disorders (2018) · cites this paper as [8]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“SCN2A expression begins early in gestation [8], and channel dysfunction may alter brain development.”
From Progress in Understanding and Treating SCN2A-Mediated Disorders (2018) · cites this paper as [8]
Experimental — accuracy not yet measuredIn the citing paper
“Developmental stages 5 and 6 represent mid-gestation, the period during which upper layer neurons are generated and neuronal differentiation including axonogenesis and dendritic arborization takes place12,13.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [12]
In the citing paper
“Studies of the human brain transcriptome have shown that genes associated with synaptic functions may be expressed early during intrauterine development (3-6 months gestational age) 3 .”
From Progress toward treatments for synaptic defects in autism (2013) · cites this paper as [3]
In the citing paper
“In contrast, in humans, the genes involved in dendrite and synapse formation are switched on very early in utero , and the amounts of transcripts reach a plateau at 6-9 months of fetal life 3 .”
From Progress toward treatments for synaptic defects in autism (2013) · cites this paper as [3]
In the citing paper
“Recent important advances include the evaluation of spatial and temporal transcriptomes by the Allen Brain Institute and BrainSpan8,11–13”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [13]
In the citing paper
“Network approaches are particularly useful for relating whole-tissue-level changes to data from these high-resolution experiments11–13,26 (FIG. 2a).”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [13]
In the citing paper
“Data was generated across eight studies (2, 19, 20), uniformly processed, and combined through a consolidated genomic data processing pipeline ((21); Fig S1), yielding a total of 2188 samples passing quality control (QC) for this analysis, representing frontal and temporal cerebral cortex from 1695 unique subjects across the lifespan, including 279 technical replicates (Fig S2).”
From Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder (2018) · cites this paper as [20]
In the citing paper
“The NMDA receptor changes in composition during prenatal to postnatal neurodevelopment with GRIN2A predominantly expressed during late childhood and adolescence, recapitulating expected epidemiological observations on schizophrenia age-ofonset (Supplementary Information, Figure 3B) 27 .”
From Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia (2020) · cites this paper as [27]
In the citing paper
“Developed from neuropharmacological and neuropathological observations, the glutamatergic hypothesis postulates that the hypofunction of glutamatergic signaling through NMDA receptors is a possible mechanism of disease28 (Supplementary Note).”
From Rare coding variants in ten genes confer substantial risk for schizophrenia (2022) · cites this paper as [28]
In the citing paper
“Several important recent studies have begun to analyse transcriptional dynamics during human brain development4, 5, although only in a small number of relatively coarse brain regions.”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [4]
In the citing paper
“this high degree of similarity is suggestive of a strong underlying common blueprint for the human brain transcriptome and is consistent with other recent studies of human neocortical gene expression4, 5”
From An anatomically comprehensive atlas of the adult human brain transcriptome (2012) · cites this paper as [4]
In the citing paper
“Several recent studies have assayed a limited set of brain structures1,3–5 and layers2 from prenatal human brain.”
From Transcriptional landscape of the prenatal human brain (2014) · cites this paper as [4]
In the citing paper
“The astroglial population is likely largely non-proliferative and fully differentiated by the end of the first postnatal year (Kang et al., 2011; Sanai et al., 2011)”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“Of these, nine out of ten genes were differentially expressed or spliced across brain regions and/or time (Kang et al., 2011; Mazin et al., 2013).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“This work has revealed that the great majority of protein coding genes (at least 86% according to Kang et al., 2011) and an ever-expanding number of non-coding genes (see Box 3) are used at some point in the building of the human CNS.”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“In particular, the module enriched for genes associated with neuronal specification is most highly expressed embryonically and early fetally (Figure 3C), while the module enriched for genes associated with synaptic function and ion channels begins to rise late fetally and plateaus in early childhood (Figure 3D).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“When compared with dorsal pallial structures (amygdala, hippocampus and neocortex), the cerebellum is the most transcriptionally distinctive region in the developing brain, followed by thalamus and striatum (Figure 3B) (Kang et al., 2011; Numata et al., 2012).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“Robust inter-areal transcriptional differences were particularly prominent during early and mid-fetal development and included specific transcriptional signatures associated with prospective prefrontal and perisylvian areas (Johnson et al., 2009; Kang et al., 2011; Miller et al., 2014; Pletikos et al., 2014), which are involved in some of the most distinctly human aspects of cognition and behavior”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“Interestingly, these regional/areal transcriptional profiles were largely bilaterally symmetrical across the two hemispheres at the population-level during fetal, early postnatal development and in adulthood (Johnson et al., 2009; Kang et al., 2011; Lambert et al., 2011; Miller et al., 2014; Pletikos et al., 2014).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“Recent transcriptome studies have identified substantial regional/areal differences in the developing human neocortex (Johnson et al., 2009; Kang et al., 2011; Lambert et al., 2011; Miller et al., 2014; Pletikos et al., 2014).”
From The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016) · cites this paper as Kang et al., 2011
In the citing paper
“The transcriptome of the developing, prenatal human neocortex, however, is quite heterogeneous across different cortical areas (Johnson et al., 2009; Kang et al., 2011; Miller et al., 2014).”
From Genetic Changes Shaping the Human Brain (2015) · cites this paper as Kang et al., 2011
In the citing paper
“RFX and GPR56 expression patterns are correlated (fig. S5, A and B) (15), with RFX3 and RFX7 most prominent in human ventrolateral prefrontal cortex, the region affected in perisylvian polymicrogyria”
From Evolutionarily Dynamic Alternative Splicing of GPR56 Regulates Regional Cerebral Cortical Patterning (2014) · cites this paper as [15]
In the citing paper
“Each have reinforced findings of significant differences in the transcriptome between neuronal cell-types, brain regions, developmental stages, and species.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [10]
In the citing paper
“Existing whole-tissue analyses10, even combined with careful manual dissection are not sensitive to inter- or intra-cellular variability and, as a result, functional genomic information is ‘diluted’ amongst different classes of intermixed neurons and the high proportion of glial cells.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [10]
In the citing paper
“This finding is opposite previously described patterns of mRNA expression, which has been shown to become more globally similar between brain regions over development (2).”
From Identification of differentially expressed microRNAs across the developing human brain (2014) · cites this paper as [2]
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Citations
Cites
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Bardo, M. T., & Hammer, R. P. (1991) · Neuroscience · doi:10.1016/0306-4522(91)90226-e · Cited by 3,229
Notch3 mutations in CADASIL, a hereditary adult-onset condition causing stroke and dementia
Joutel, A., Corpechot, C., Ducros, A., Vahedi, K., Chabriat, H., Mouton, P., Alamowitch, S., Domenga, V., Cécillion, M., Maréchal, E., et al. (1996) · Nature · doi:10.1038/383707a0 · Cited by 2,068
Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism
Study, P. A. R. I. S., Jamain, S., Quach, H., Betancur, C., Råstam, M., Colineaux, C., Gillberg, I. C., Soderstrom, H., Giros, B., Leboyer, M., et al. (2003) · Nature Genetics · doi:10.1038/ng1136 · Cited by 1,851
Neuroligins and neurexins link synaptic function to cognitive disease
Südhof, T. C. (2008) · Nature · doi:10.1038/nature07456 · Cited by 1,849
Insel, T. R. (2010) · Nature · doi:10.1038/nature09552 · Cited by 1,783
Common variants conferring risk of schizophrenia
†Genetic Risk and Outcome in Psychosis (GROUP), Stefansson, H., Ophoff, R. A., Steinberg, S., Andreassen, O. A., Cichon, S., Rujescu, D., Werge, T., Pietiläinen, O. P. H., Mors, O., et al. (2009) · Nature · doi:10.1038/nature08186 · Cited by 1,677
Evolution of the neocortex: a perspective from developmental biology
Rakic, P. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2719 · Cited by 1,565
Intermediate phenotypes and genetic mechanisms of psychiatric disorders
Meyer-Lindenberg, A., & Weinberger, D. R. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1993 · Cited by 1,216
Self-citationSchizophrenia as a Disorder of Neurodevelopment
Lewis, D. A., & Levitt, P. (2002) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.25.112701.142754 · Cited by 1,163
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Large-scale genomics unveil polygenic architecture of human cortical surface area
Chen, C., Peng, Q., Schork, A. J., Lo, M., Fan, C.-C., Wang, Y., Desikan, R. S., Bettella, F., Hagler, D. J., Pediatric Imaging, N. A. G. S., et al. (2015) · Nature Communications · doi:10.1038/ncomms8549 · Cited by 33
Genetic risk mechanisms of posttraumatic stress disorder in the human brain
Bharadwaj, R. A., Jaffe, A. E., Chen, Q., Deep‐Soboslay, A., Goldman, A. L., Mighdoll, M. I., Cotoia, J. A., Brandtjen, A. C., Shin, J. H., Hyde, T. M., et al. (2016) · Journal of Neuroscience Research · doi:10.1002/jnr.23957 · Cited by 33
Self-citationThe Timing of Brain Maturation, Early Experience, and the Human Social Niche
Finlay, B. L., & Uchiyama, R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-804042-3.00134-2 · Cited by 33
Scott, T. M., Guo, H., Eichler, E. E., Rosenfeld, J. A., Pang, K., Liu, Z., Lalani, S. R., Bi, W., Yang, Y., Bacino, C. A., et al. (2020) · Human Mutation · doi:10.1002/humu.23992 · Cited by 33
Liu, J. Y. W., Dzurova, N., Al-Kaaby, B., Mills, K. A., Sisodiya, S. M., & Thom, M. (2020) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2020.00053 · Cited by 33
Chen, S., Wang, J., Çiçek, A. E., Roeder, K., Yu, H., & Devlin, B. (2020) · Molecular Autism · doi:10.1186/s13229-020-00386-7 · Cited by 33
Suzuki, I., & Hirata, T. (2012) · Development Growth & Differentiation · doi:10.1111/dgd.12020 · Cited by 33
Kimura, H., Tsuboi, D., Wang, C., Kushima, I., Koide, T., Ikeda, M., Iwayama, Y., Toyota, T., Yamamoto, N., Kunimoto, S., et al. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu147 · Cited by 33
On Expression Patterns and Developmental Origin of Human Brain Regions
Lior, K., & Chechik, G. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005064 · Cited by 33
Specific gene expression patterns of 108 schizophrenia-associated loci in cortex
Ohi, K., Shimada, T., Nitta, Y., Kihara, H., Okubo, H., Uehara, T., & Kawasaki, Y. (2016) · Schizophrenia Research · doi:10.1016/j.schres.2016.03.032 · Cited by 32
Lü, P., Chen, X., Feng, Y., Zeng, Q., Jiang, C., Zhu, X., Fan, G., & Xue, Z. (2016) · Science China Life Sciences · doi:10.1007/s11427-016-0194-6 · Cited by 32
Komorowski, A., James, G. M., Philippe, C., Gryglewski, G., Bauer, A., Hienert, M., Spies, M., Kautzky, A., Vanicek, T., Hahn, A., et al. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw355 · Cited by 32
Zhou, H., Cheng, Z., Bass, N. J., Krystal, J. H., Farrer, L. A., Kranzler, H. R., & Gelernter, J. E. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0258-8 · Cited by 32
547 transcriptomes from 44 brain areas reveal features of the aging brain in non-human primates
Li, M., Wu, S., Zhang, J.-J., Tian, H.-Y., Shao, Y., Wang, Z., Irwin, D. M., Li, J., Hu, X., & Wu, D. (2019) · Genome biology · doi:10.1186/s13059-019-1866-1 · Cited by 32
Self-citationA neural stem cell paradigm of pediatric hydrocephalus
Duy, P. Q., RAKIC, P. T., Alper, S. L., Robert, S. M., Kundishora, A. J., Butler, W. E., Walsh, C. A., Šestan, N., Geschwind, D. H., Jin, S. C., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac341 · Cited by 32
Self-citationKikuchi, M., Ogishima, S., Miyamoto, T., Miyashita, A., Kuwano, R., Nakaya, J., & Tanaka, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076162 · Cited by 32
Localizing Genes to Cerebellar Layers by Classifying ISH Images
Lior, K., Liscovitch, N., & Chechik, G. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002790 · Cited by 32
Modular Development of Cortical Gray Matter Across Childhood and Adolescence
Krongold, M., Cooper, C., & Bray, S. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv307 · Cited by 32
Price, A. J., Hwang, T., Tao, R., Burke, E. E., Rajpurohit, A., Shin, J. H., Hyde, T. M., Kleinman, J. E., Jaffe, A. E., & Weinberger, D. R. (2019) · Genome Research · doi:10.1101/gr.250217.119 · Cited by 32
Self-citationWeigel, B., Tegethoff, J. F., Grieder, S. D., Lim, B., Nagarajan, B., Liu, Y.-C., Truberg, J., Papageorgiou, D., Adrian-Segarra, J. M., Schmidt, L. K., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01959-7 · Cited by 31
Homozygous ARHGEF2 mutation causes intellectual disability and midbrain-hindbrain malformation
Ravindran, E., Hu, H., Yuzwa, S. A., Hernandez-Miranda, L. R., Kraemer, N., Ninnemann, O., Musante, L., Boltshauser, E., Schindler, D., Hübner, A., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006746 · Cited by 31
Sanfilippo, C., Castrogiovanni, P., Imbesi, R., Tibullo, D., Volti, G. L., Barbagallo, I. A., Vicario, N., Musumeci, G., & Rosa, M. D. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.03.008 · Cited by 31
Borbély, É., Payrits, M., Hunyady, Á., Mező, G., & Pintér, E. (2019) · GeroScience · doi:10.1007/s11357-019-00083-1 · Cited by 31
Khan, S., Liu, Y., Siddique, R., Nabi, G., Xue, M., & Hou, H. (2019) · International Journal of Biological Sciences · doi:10.7150/ijbs.35520 · Cited by 31
Purcell, R. H., Sefik, E., Werner, E., King, A. T., Mosley, T. J., Merritt-Garza, M. E., Chopra, P., McEachin, Z. T., Karne, S., Raj, N., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh0558 · Cited by 31
Zemp, N., Tavares, R., & Widmer, A. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005536 · Cited by 31
XSAnno: a framework for building ortholog models in cross-species transcriptome comparisons
Zhu, Y., Li, M., Sousa, A. M. M., & Šestan, N. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-343 · Cited by 31
Self-citationWang, Z., Henn, B. C., Wang, C., Wei, Y., Su, L., Sun, R., Chen, H., Wagner, P. J., Lu, Q., Lin, X., et al. (2017) · Environmental Health · doi:10.1186/s12940-017-0288-3 · Cited by 31
Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information
Guelfi, S., D’Sa, K., Botía, J. A., Vandrovcová, J., Reynolds, R. H., Zhang, D., Trabzuni, D., Collado‐Torres, L., Thomason, A., Leyton, P. Q., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14483-x · Cited by 31
Kobow, K., Reid, C. A., van Vliet, E. A., Becker, A. J., Carvill, G. L., Goldman, A. M., Hirose, S., Lopes‐Cendes, Í., Khiari, H. M., Poduri, A., et al. (2020) · Epileptic Disorders · doi:10.1684/epd.2020.1143 · Cited by 31
Self-citationGenome-wide association analysis identifies common variants influencing infant brain volumes
Xia, K., Zhang, J., Ahn, M., Jha, S., Crowley, J. J., Szatkiewicz, J., Li, T., Zou, F., Zhu, H., Hibar, D., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.159 · Cited by 30
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Apte, M., & Kumar, A. B. (2023) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2023.03.011 · Cited by 30
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Hackett, T. A., Guo, Y. P., Clause, A. R., Hackett, N., Garbett, K. A., Zhang, P., Polley, D. B., & Mirnics, K. (2015) · BMC Genomics · doi:10.1186/s12864-015-1709-8 · Cited by 30
A comparative study of the genetic components of three subcategories of autism spectrum disorder
Li, J., Hu, S., Zhang, K., Shi, L., Zhang, Y., Zhao, T., Wang, L., He, X., Xia, K., Liu, C., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0081-x · Cited by 30
Zouini, B., Sfendla, A., Ahlström, B. H., Senhaji, M., & Kerekes, N. (2019) · Annals of General Psychiatry · doi:10.1186/s12991-019-0251-5 · Cited by 30
Vasung, L., Rollins, C. K., Velasco‐Annis, C., Yun, H. J., Zhang, J., Warfield, S. K., Feldman, H. A., Gholipour, A., & Grant, P. E. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa033 · Cited by 30
Yan, W., Yue, H., Ji, X., Li, G., & Sang, N. (2020) · Environment International · doi:10.1016/j.envint.2020.105659 · Cited by 30
Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder
Xie, Y., Zhang, X., Liu, F., Qin, W., Fu, J., Xue, K., & Yu, C. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108137 · Cited by 29
Lin, G. N., Song, W., Wang, W., Wang, P., Yu, H., Cai, W., Jiang, X., Huang, W., Qian, W., Chen, Y., et al. (2022) · Science Advances · doi:10.1126/sciadv.abi6180 · Cited by 30
Transcriptomic immaturity of the hippocampus and prefrontal cortex in patients with alcoholism
Murano, T., Koshimizu, H., Hagihara, H., & Miyakawa, T. (2017) · Scientific Reports · doi:10.1038/srep44531 · Cited by 30
The R213G polymorphism in SOD3 protects against allergic airway inflammation
Gaurav, R., Varasteh, J., Weaver, M. R., Jacobson, S. R., Hernandez‐Lagunas, L., Liu, Q. L., Nozik‐Grayck, E., Chu, H. W., Alam, R., Nordestgaard, B. G., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.95072 · Cited by 30
Duarte, R., & Morrison, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006781 · Cited by 30
Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression
Wang, J., Devlin, B., & Roeder, K. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz619 · Cited by 30
Dystrophin deficiency leads to dysfunctional glutamate clearance in iPSC derived astrocytes
Patel, A. M., Wierda, K. D. B., Thorrez, L., van Putten, M., Smedt, J. D., Ribeiro, L. F., Tricot, T., Gajjar, M. K., Duelen, R., Damme, P. V., et al. (2019) · Translational Psychiatry · doi:10.1038/s41398-019-0535-1 · Cited by 30
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Liu, L., Lei, J., & Roeder, K. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas844 · Cited by 30
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Ji, P., Wang, N., Yu, Y., Zhu, J., Zuo, Z., Zhang, B., & Zhao, F. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100768 · Cited by 29
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Barr, C. L., & Misener, V. L. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12269 · Cited by 29
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PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids
Jourdon, A., Scuderi, S., Capauto, D., Abyzov, A. S., & Vaccarino, F. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0763-3 · Cited by 29
Fang, X., Sun, W., Jeon, J. H., Azain, M. J., Kinder, H. A., Ahn, J., Chung, H. C., Mote, R. S., Filipov, N. M., Zhao, Q. C., et al. (2020) · Nutrients · doi:10.3390/nu12072090 · Cited by 29
Afridi, R., Seol, S., Kang, H. J., & Suk, K. (2021) · Biochemical Pharmacology · doi:10.1016/j.bcp.2021.114532 · Cited by 29
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Li, S., Li, J., Liu, J., Wang, J., Li, X., Huo, Y., Li, Y., Liu, Y., Li, M., Xiao, X., et al. (2021) · Brain · doi:10.1093/brain/awab357 · Cited by 29
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Marečková, K., Miles, A. E., Liao, Z., Andrýsková, L., Brázdil, M., Paus, T., & Nikolova, Y. S. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.102976 · Cited by 29
Xu, X., Li, Q., Qian, Y., Cai, H., Zhang, C., Zhao, W., Zhu, J., & Yu, Y. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac211 · Cited by 29
Self-citationXu, Z., Xia, M., Wang, X., Liao, X., Zhao, T., & Yong He, A. (2022) · Communications Biology · doi:10.1038/s42003-022-04028-x · Cited by 29
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Tsujimura, K., Shiohama, T., & Takahashi, E. (2022) · Brain Sciences · doi:10.3390/brainsci12101366 · Cited by 29
Annamneedi, A., Gora, C., Dudas, A., Leray, X., Bozon, V., Crépieux, P., & Pellissier, L. P. (2023) · British Journal of Pharmacology · doi:10.1111/bph.16216 · Cited by 29
van der Voorn, B., Wit, J. M., van der Pal, S. M., Rotteveel, J., & Finken, M. J. J. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-2843 · Cited by 29
Matsunaga, E., Nambu, S., Oka, M., & Iriki, A. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.10.032 · Cited by 29
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Shi, L., Chang, X., Zhang, P., Coba, M. P., Lu, W., & Wang, K. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt226 · Cited by 29
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Bettencourt, C., Forabosco, P., Wiethoff, S., Heidari, M., Johnstone, D. M., Botía, J. A., Collingwood, J. F., Hardy, J., Milward, E. A., Ryten, M., et al. (2015) · Neurobiology of Disease · doi:10.1016/j.nbd.2015.12.004 · Cited by 29
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Zirra, A., Wiethoff, S., & Patani, R. (2016) · Stem Cells International · doi:10.1155/2016/8291260 · Cited by 29
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Berto, S., Wang, G.-Z., Germi, J., Lega, B. C., & Konopka, G. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx083 · Cited by 29
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Gupta, A. R., Westphal, A., Yang, D. Y. ‐., Sullivan, C. A. W., Eilbott, J. A., Zaidi, S., Voos, A. C., Wyk, B. C. V., Ventola, P., Waqar, Z., et al. (2017) · Molecular Autism · doi:10.1186/s13229-017-0133-0 · Cited by 29
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Breen, M. S., Özcan, S., Ramsey, J. M., Wang, Z., Ma’ayan, A., Rustogi, N., Gottschalk, M. G., Webster, M. J., Weickert, C. S., Buxbaum, J. D., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0306-4 · Cited by 29
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Kaur, N., Han, W., Li, Z., Madrigal, M. P., Shim, S., Pochareddy, S., Gulden, F. O., Li, M., Xu, X., Xing, X., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.035 · Cited by 29
Self-citationSanfilippo, C., Castrogiovanni, P., Imbesi, R., Nunnari, G., & Rosa, M. D. (2020) · Brain Research · doi:10.1016/j.brainres.2020.147127 · Cited by 29
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Reiche, L., Küry, P., & Göttle, P. (2019) · Cells · doi:10.3390/cells8121591 · Cited by 28
Contracted functional connectivity profiles in autism
Weber, C. F., Kebets, V., Benkarim, O. M., Larivière, S., Wang, Y., Ngo, A., Jiang, H., Chai, X. J., Park, B., Milham, M. P., et al. (2024) · Molecular Autism · doi:10.1186/s13229-024-00616-2 · Cited by 28
Molecular signatures of cortical expansion in the human foetal brain
Ball, G., Oldham, S., Kyriakopoulou, V., Williams, L. Z. J., Karolis, V. R., Price, A. N., Hutter, J. M., Seal, M. L., Alexander‐Bloch, A. F., Hajnal, J. V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54034-2 · Cited by 28
Histone H4 acetylation regulates behavioral inter-individual variability in zebrafish
Román, Á., Vicente-Page, J., Pérez‐Escudero, A., Carvajal-González, J. M., Fernández‐Salguero, P. M., & de Polavieja, G. G. (2018) · Genome biology · doi:10.1186/s13059-018-1428-y · Cited by 28
Xu, J., Li, Q., Qin, W., Li, M. J., Zhuo, C., Liu, H., Liu, F., Wang, J., Schumann, G., & Yu, C. (2018) · Brain · doi:10.1093/brain/awy277 · Cited by 28
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Nevue, A. A., Lovell, P. V., Wirthlin, M. E., & Mello, C. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-75773-4 · Cited by 28
Gailus, B., Naundorf, H., Welzel, L., Johne, M., Römermann, K., Kaila, K., & Löscher, W. (2021) · Epilepsia · doi:10.1111/epi.17050 · Cited by 28
Monfared, R. V., Alhassen, W., Truong, T. M., Gonzales, M. A. M., Vachirakorntong, V., Chen, S., Baldi, P., Civelli, O., & Alachkar, A. (2021) · Cells · doi:10.3390/cells10112967 · Cited by 28
Ma, L., Yuan, T., Li, W., Guo, L., Zhu, D., Wang, Z., Liu, Z., Xue, K., Wang, Y., Liu, J., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.794151 · Cited by 28
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Klarić, T., Gudelj, I., Santpere, G., Novokmet, M., Vučković, F., Ma, S., Doll, H. M., Risgaard, R. D., Bathla, S., Karger, A., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg2615 · Cited by 28
Self-citationCameron, D. A., Middleton, F. A., Chenn, A., & Olson, E. C. (2012) · BMC Neuroscience · doi:10.1186/1471-2202-13-90 · Cited by 28
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Radonjić, N. V., Memi, F., Ortega, J. A., Glidden, N. C., Zhan, H., & Zečević, N. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu183 · Cited by 28
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Al‐Harazi, O., Insaif, S. A., Al-Ajlan, M., Kaya, N., Dzimiri, N., & Çolak, D. (2015) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2015.11.002 · Cited by 28
Li, S., Qu, Z., Haas, M., Ngo, L., Heo, Y. J., Kang, H. J., Britto, J. M., Cullen, H. D., Vanyai, H. K., Tan, S., et al. (2016) · Scientific Reports · doi:10.1038/srep29514 · Cited by 28
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Mitchell, C., & Silver, D. L. (2017) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2017.08.045 · Cited by 28
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Liu, J., Li, M., Luo, X., & Su, B. (2018) · Schizophrenia Research · doi:10.1016/j.schres.2018.05.015 · Cited by 28
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Estill, M. S., Hauser, R. B., & Krawetz, S. A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1223 · Cited by 28
Multimodality evaluation of the pediatric brain: DTI and its competitors
Vasung, L., Fischi-Gómez, E., & Hüppi, P. S. (2013) · Pediatric Radiology · doi:10.1007/s00247-012-2515-y · Cited by 27
Yan, P., Qiao, X., Wu, H., Yin, F., Zhang, J., Ji, Y., Wei, S., & Lai, J. (2016) · Journal of Molecular Neuroscience · doi:10.1007/s12031-016-0751-6 · Cited by 27
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 27
Yan, Y., Zhao, A., Qui, Y., Li, Y., Yan, R., Wang, Y., Xu, W. L., & Deng, Y. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00016 · Cited by 27
Al-Mubarak, B. R., Omar, A., Baz, B., Al-Abdulaziz, B., Magrashi, A. I., Alyemni, E. A., Jabaan, A., Monies, D., Abouelhoda, M. I., Abebe, D., et al. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-0619-7 · Cited by 27
Individual Variation of Human Cortical Structure Is Established in the First Year of Life
Gilmore, J. H., Langworthy, B., Girault, J. B., Fine, J. P., Jha, S. C., Kim, S. H., Cornea, E. A., & Styner, M. A. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.05.012 · Cited by 27
Yousaf, A., Waltes, R., Haslinger, D., Klauck, S. M., Duketis, E., Sachse, M., Voran, A., Biscaldi, M., Schulte‐Rüther, M., Cichon, S., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00906-2 · Cited by 27
A missense variant in NDUFA6 confers schizophrenia risk by affecting YY1 binding and NAGA expression
Li, Y., Ma, C., Li, W., Yang, Y., Li, X., Liu, J., Wang, J., Li, S., Liu, Y., Li, K., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01125-x · Cited by 27
Self-citationNon-coding de novo mutations in chromatin interactions are implicated in autism spectrum disorder
Kim, I. B., Lee, T., Lee, J., Kim, J., Lee, S., Koh, I. G., Kim, J. H., An, J., Lee, H., Kim, W. K., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01697-2 · Cited by 27
A 3D atlas of functional human brain energetic connectome based on neuropil distribution
Yu, Y., Akif, A., Hermán, P., Cao, M., Rothman, D. L., Carson, R. E., Agarwal, D., Evans, A. C., & Hyder, F. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac322 · Cited by 27
Functional genomics of human brain development and implications for autism spectrum disorders
Ziats, M. N., Grosvenor, L. P., & Rennert, O. M. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.153 · Cited by 27
Identity-by-Descent Mapping to Detect Rare Variants Conferring Susceptibility to Multiple Sclerosis
Lin, R., Charlesworth, J., Stankovich, J. M., Perreau, V. M., Brown, M. A., & Taylor, B. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056379 · Cited by 27
Homozygous microdeletion of exon 5 in ZNF277 in a girl with specific language impairment
IMGSAC, Ceroni, F., Simpson, N. H., Francks, C., Baird, G., Conti‐Ramsden, G., Clark, A., Bolton, P., Hennessy, E. R., Donnelly, P., et al. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.4 · Cited by 27
The Effects of anAPOEPromoter Polymorphism on Human Cortical Morphology during Nondemented Aging
Chen, Y., Li, P., Gu, B., Liu, Z., Li, X., Evans, A. C., Gong, G., & Zhang, Z. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1946-14.2015 · Cited by 27
Prilutsky, D., Kho, A. T., Palmer, N., Bhakar, A. L., Smedemark-Margulies, N., Kong, S. W., Margulies, D. M., Bear, M. F., & Kohane, I. S. (2015) · Molecular Autism · doi:10.1186/s13229-015-0061-9 · Cited by 27
Metadata
- Authors
- Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., Guennel, T., Shin, Y., Johnson, M. B., Krsnik, Ž., Mayer, S., Fertuzinhos, S., Umlauf, S., Lisgo, S. N., Vortmeyer, A., Weinberger, D. R., Mane, S., Hyde, T. M., Huttner, A., Reimers, M., Kleinman, J. E., & Šestan, N.
- Affiliations
- Yale University (US)
- Universidade do Porto (PT)
- University of Zagreb (HR)
- Virginia Commonwealth University (US)
- Centre for Life (GB)
- National Institutes of Health (US)
- Corresponding author
- Šestan, Nenad
- ORCID
- Kang, Hyo Jung
- Kawasawa, Yuka Imamura
- Cheng, Feng
- Zhu, Ying
- Xu, Xuming
- Li, Mingfeng
- Sousa, André M. M.
- Pletikos, Mihovil
- Sedmak, Goran
- Shin, Yurae
- Johnson, Matthew B.
- Krsnik, Željka
- Mayer, Simone
- Fertuzinhos, Sofia
- Umlauf, Sheila
- Lisgo, Steven N.
- Vortmeyer, Alexander
- Weinberger, Daniel R.
- Mane, Shrikant
- Hyde, Thomas M.
- Huttner, Anita
- Kleinman, Joel E.
- Šestan, Nenad
- Venue
- Nature
- Published
- 2011-10
- Volume
- 478
- Issue
- 7370
- Pages
- 483-489
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3566780
- OpenAlex
- W2022689242
- Cited by
- 2,084
- References
- 53
- Citations by year
- 2026: 115 · 2025: 128 · 2024: 156 · 2023: 146 · 2022: 149 · 2021: 181 · 2020: 175 · 2019: 163 · 2018: 135 · 2017: 133 · 2016: 144 · 2015: 141 · 2014: 126 · 2013: 104 · 2012: 82
- FWCI
- 40.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Wellcome Trust (GR082557)
- Medical Research Council
- National Institute of Mental Health (RC2MH089929, MH081896, MH089929, U01MH081896)
- National Institute on Drug Abuse (DA-026119, R25DA026119)
- National Institute of Neurological Disorders and Stroke (R01NS054273, U24NS051869, NS 054273)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD000836, R24HD000836)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- human brain transcriptome, spatiotemporal gene expression, brain development, postmortem brain, sex-biased gene expression, exon usage, co-expression network, single nucleotide polymorphisms, gene expression trajectories, prefrontal cortex, neocortex, brain aging
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Brain, Child, Child, Preschool, Exons, Female, Fetus, Humans, Infant, Male, Middle Aged, Quality Control, Sex Characteristics, Time Factors, Gene Expression Regulation, Developmental, Gene Expression Profiling, Quantitative Trait Loci, Gene Regulatory Networks, Young Adult, Transcriptome
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