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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 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 measured
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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 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
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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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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