The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A survey of human brain transcriptome diversity at the single cell level
Open accessAbstract
Significance: The brain comprises an immense number of cells and cellular connections. We describe the first, to our knowledge, single cell whole transcriptome analysis of human adult cortical samples. We have established an experimental and analytical framework with which the complexity of the human brain can be dissected on the single cell level. Using this approach, we were able to identify all major cell types of the brain and characterize subtypes of neuronal cells. We observed changes in neurons from early developmental to late differentiated stages in the adult. We found a subset of adult neurons which express major histocompatibility complex class I genes and thus are not immune privileged.
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In the citing paper
“The central nervous system of older mice is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over-or underrepresented (Darmanis et al., 2015;Grindberg et al., 2013;Krishnaswami et al., 2016;Lake et al., 2016;Lake et al., 2017;Tasic et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as Darmanis et al., 2015
Experimental — accuracy not yet measuredIn the citing paper
“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 [16]
In the citing paper
“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 [16]
In the citing paper
“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 Darmanis et al., 2015
In the citing paper
“The number of cells for scRNA-seq and the number of replicates for bulk RNA-seq were empirically determined based on previous studies (Darmanis et al., 2015; Tabula Muris et al., 2018; Zhang et al., 2014). The used sample-size in scRNA-seq allowed us to robustly identify cell populations as small as 20 cells.”
From Developmental Heterogeneity of Microglia and Brain Myeloid Cells Revealed by Deep Single-Cell RNA Sequencing (2019) · cites this paper as Darmanis et al., 2015
In the citing paper
“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 [10]
In the citing paper
“As expected, NeuN+ and NeuN− FIRE-associated genes were mainly enriched in neurons and glia, respectively (Fig. 1i, j).”
From Neuronal and glial 3D chromatin architecture informs the cellular etiology of brain disorders (2021) · cites this paper as [10]
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Cites
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · cited 2×
“As a complementary approach, which takes into account prior biological information, we used existing RNAseq data from purified cell types of the mouse brain (11).”
MethodSelf-citationCahoy, 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. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
Self-citationCell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons
Rudy, B., Fishell, G., Lee, S., & Hjerling‐Leffler, J. (2011) · Developmental Neurobiology · doi:10.1002/dneu.20853 · Cited by 1,420
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342
Self-citationQuantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183
In situ sequencing for RNA analysis in preserved tissue and cells
Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920
CNS immune privilege: hiding in plain sight
Carson, M. J., Doose, J. M., Melchior, B., Schmid, C. D., & Ploix, C. C. (2006) · Immunological Reviews · doi:10.1111/j.1600-065x.2006.00441.x · Cited by 838
Regulation of Class I MHC Gene Expression in the Developing and Mature CNS by Neural Activity
Corriveau, R. A., Huh, G. S., & Shatz, C. J. (1998) · Neuron · doi:10.1016/s0896-6273(00)80562-0 · Cited by 548
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Ciuculete, D. M., Voisin, S., Kular, L., Jonsson, J., Rask-Andersen, M., Mwinyi, J., & Schiöth, H. B. (2020) · Clinical Epigenetics · doi:10.1186/s13148-020-00884-8 · Cited by 35
AAV vectors trigger DNA damage responses and STING-dependent inflammation in human CNS cells
Costa-Verdera, H., Meneghini, V., Fitzpatrick, Z., Alezz, M. A., Fabyanic, E., Huang, X., Dzhashiashvili, Y., Ahiya, A., Mangiameli, E., Valeri, E., et al. (2024) · doi:10.21203/rs.3.rs-4171795/v1 · Cited by 32
Costa-Verdera, H., Meneghini, V., Fitzpatrick, Z., Abou Alezz, M., Fabyanic, E., Huang, X., Dzhashiashvili, Y., Ahiya, A., Mangiameli, E., Valeri, E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58778-3 · Cited by 32
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Wang, W., Penland, L., Gokce, O., Croote, D., & Quake, S. R. (2018) · BMC Genomics · doi:10.1186/s12864-018-4512-5 · Cited by 23
Self-citationPostmortem human brain genomics in neuropsychiatric disorders — how far can we go?
Jaffe, A. E. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.11.002 · Cited by 19
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“…a neuroinflammatory response, inducing a reactive cellular state with transcriptional signatures unique to reactive astrocyte subtypes (Clarke et al., 2018; Darmanis et al., 2015; Liddelow and Barres, 2017; Pekny and Nilsson, 2005; Sofroniew, 2009; Sofroniew and Vinters, 2010; Zhang et al., 2016).”
HMGCS2-Induced Autophagic Degradation of Tau Involves Ketone Body and ANKRD24
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BrainImageR: spatiotemporal gene set analysis referencing the human brain
Linker, S. B., Hsu, J. Y., Pfaff, A., Amatya, D., Ko, S.-M., Voter, S., Wong, Q., & Gage, F. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty618 · Cited by 7
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2019) · doi:10.1101/639054 · Cited by 7
Philtjens, S., Turnbull, M. T., Thedy, B. P., Moon, Y., & Kim, J. (2020) · doi:10.1101/2020.04.27.064881 · Cited by 7
Zhou, Y., Tao, L., & Zhu, Y. (2023) · Brain Sciences · doi:10.3390/brainsci13071070 · Cited by 7
Metadata
- Authors
- Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R.
- Affiliations
- Howard Hughes Medical Institute (US)
- Neurobiology, and
- Neurosurgery, and
- Corresponding author
- Barres, Ben A.
- Quake, Stephen R.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2015-05-18
- Volume
- 112
- Issue
- 23
- Pages
- 7285-7290
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4466750
- OpenAlex
- W1552434751
- Cited by
- 1,401
- References
- 28
- Citations by year
- 2026: 68 · 2025: 101 · 2024: 163 · 2023: 143 · 2022: 150 · 2021: 161 · 2020: 166 · 2019: 144 · 2018: 119 · 2017: 94 · 2016: 73 · 2015: 17
- FWCI
- 43.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- California Institute for Regenerative Medicine (GC1R-06673)
- Svenska Sällskapet för Medicinsk Forskning
- National Institutes of Health
- National Heart, Lung, and Blood Institute (U01HL099995, U01HL099999)
- National Institute of Mental Health (F30MH106261, R01MH099555)
- National Institute of General Medical Sciences (T32GM007365)
- National Institute of Neurological Disorders and Stroke (R00NS089780, R01NS081703, K99NS089780)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell transcriptome, neuronal cell types, interneuron subtypes, glial cell types, temporal lobe, hippocampus, cell type classification
- MeSH terms
- Adult, Brain, HLA Antigens, Humans, Neurons, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome
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