Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
Open accessAbstract
Single-nucleus gene expression: Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest. Science , this issue p. 1586
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snRNA-seq overcomes the need for intact cell dissociation, thereby minimizing dissociation-associated challenges and providing a more physiologically accurate representation of the brain's cellular heterogeneity.
“snRNA-seq overcomes the need for intact cell dissociation, thereby minimizing dissociation-associated challenges and providing a more physiologically accurate representation of the brain’s cellular heterogeneity[12, 14, 15].”
From Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing (2026) · cites this paper as [14]
The Main Text (page unavailable) says isolating nuclei reduces contamination or degradation encountered with whole-neuron dissociation, and reports that the method identifies neuronal subtypes. It does not establish that snRNA-seq gives a more physiologically accurate representation of the brain’s overall cellular heterogeneity; this study examined neuronal nuclei from six cortical regions. (LLM, conf=0.86)
Experimental — accuracy not yet measuredsnRNA-seq has unlocked the ability to unbiasedly assess transcriptional states of the human brain at single-cell resolution.
“The advent of snRNA-seq has unlocked the ability to unbiasedly assess transcriptional states of the human brain at single-cell resolution10,11.”
From Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain (2022) · cites this paper as [10]
Single-cell RNA-seq data from PsychENCODE and other brain studies were uniformly processed with standard pipelines.
“we used standard pipelines to uniformly process single-cell RNA-seq data from PsychENCODE, in conjunction with other single-cell studies on the brain (14, 16, 20).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [14]
Hi-C and single-cell sequencing had not been fully integrated at scale with brain genomics data.
“new methodologies, such as Hi-C and single-cell sequencing, have yet to be fully integrated at scale with brain genomics data (13–16).”
From Comprehensive functional genomic resource and integrative model for the human brain (2018) · cites this paper as [14]
Single-nucleus sequencing of human cortical layer 1 identified 10 GABAergic neuron types, which is more than the 8 types previously described for the entire human cortex.
“The targeted application of single nucleus sequencing reported here has demonstrated a significantly higher degree of GABAergic neuron complexity in just one layer of the human cerebral cortex (10 types) than what has previously been described in all of the cortex (8 types22).”
From Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type (2018) · cites this paper as [22]
Single-nucleus RNA-seq was developed to analyze RNA in nuclei from fresh, frozen, or lightly fixed tissues.
“To address this challenge, we3 and others4–6 developed single nucleus RNA-seq (snRNA-seq) for analysis of RNA in single nuclei from fresh, frozen or lightly fixed tissues.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [4]
Existing snRNA-seq methods use FACS sorting into plates or C1 microfluidics, which are not scalable to tens of thousands of nuclei or many samples.
“However, these methods either rely on sorting nuclei by FACS into plates (96 or 384 wells)3,5 or on C1 microfluidics4, neither of which are scalable, precluding profiling tens of thousands of nuclei”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [4]
Human GABAergic sub-clusters showed good congruence with previously defined clusters in mouse hippocampus, mouse visual cortex, and human cortex, including the same canonical marker assignments.
“we found good congruence between our GABAergic sub-clusters and those previously defined3,4,17 in mouse and human”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [4]
Single-nucleus transcriptomes from multiple regions of human brain have established cell type taxonomies.
“Single-nucleus transcriptomes from multiple regions of the human brain have established cell type taxonomies (Fig. 2B–D) (Darmanis et al., 2015; Habib et al., 2017; Lake et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Lake et al., 2016
Cellular heterogeneity in the human brain poses significant challenges for unraveling the gene regulatory architecture because the brain comprises heterogeneous cell populations with distinct gene expression and chromatin accessibility profiles.
“However, cellular heterogeneity poses significant challenges for unraveling the complexity in the gene regulatory architecture of the human brain, as it is comprised of heterogeneous cell populations, mostly different types of neurons and glia, each of which display distinct gene expression3,8–10 and chromatin accessibility profiles11–14.”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [8]
Glu-BD genes show much stronger cell-type-specificity, with the highest expression signature in L2/3 cortical projection neurons (Ex1) and L5/6 corticothalamic projection neurons (Ex7-8).
“Glu-BD genes showed a much stronger cell-type-specificity, with the highest expression signature in L2/3 cortical projection neurons (Ex1) and L5/6 corticothalamic projection neurons (Ex7-8).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [8]
Neuronal subtype enrichment shows a clear distinction between risk genes identified by Glu and GABA H-MAGMA.
“Neuronal subtype enrichment showed a clear distinction between risk genes identified by Glu and GABA H-MAGMA, uncovering cellular and disease specificities that have not been described before (Fig. 5c).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [8]
In interneurons, GABA-SCZ genes and GABA-BD genes show similar enrichment for parvalbumin-expressing cells (In6).
“In interneurons, GABA-SCZ genes and GABA-BD genes showed similar enrichment for parvalbumin-expressing cells (In6).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [8]
Glu-SCZ genes display widespread expression among many Glu neuronal subclasses, with relatively higher expression signatures in L3/4 neurons (Ex2) and L5/6 corticothalamic projection neurons (Ex7).
“Glu-SCZ genes displayed widespread expression among many Glu neuronal subclasses, with relatively higher expression signatures in L3/4 neurons (Ex2) and L5/6 corticothalamic projection neurons (Ex7).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [8]
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- Authors
- Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., Vijayaraghavan, R., Wong, J., Chen, A., Sheng, X., Kaper, F., Shen, R., Ronaghi, M., Fan, J.-B., Wang, W., Chun, J., & Zhang, K.
- Affiliations
- University of California San Diego (US)
- Scripps Research Institute (US)
- Illumina (United States) (US)
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA.
- Corresponding author
- Lake, Blue B.
- Ai, Rizi
- Kaeser, Gwendolyn E.
- Salathia, Neeraj S.
- Fan, Jian-Bing
- Wang, Wei
- Chun, Jerold
- Zhang, Kun
- ORCID
- Venue
- Science
- Published
- 2016-06-24
- Volume
- 352
- Issue
- 6293
- Pages
- 1586-1590
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5038589
- OpenAlex
- W2471513101
- Cited by
- 957
- References
- 37
- Citations by year
- 2026: 48 · 2025: 71 · 2024: 82 · 2023: 133 · 2022: 85 · 2021: 117 · 2020: 126 · 2019: 105 · 2018: 115 · 2017: 68 · 2016: 7
- FWCI
- 51.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- NIH Common Fund Single Cell Analysis Program (1U01MH098977)
- Neuroplasticity of Aging (5T32AG000216-24)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- neuronal cell types, single-nucleus RNA sequencing, human cerebral cortex, neuronal heterogeneity, transcriptomic heterogeneity, cortical cytoarchitecture, neuron classification, postmortem brain
- MeSH terms
- Cell Nucleus, Cerebral Cortex, Humans, Neurons, Sequence Analysis, RNA, Gene Expression Profiling, Transcriptome
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