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Cited by 209Single-cell and spatial transcriptomicsNeuroscience and Neuropharmacology ResearchNeurogenesis and neuroplasticity mechanisms

Transcriptomic cytoarchitecture reveals principles of human neocortex organization

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Jorstad, Nikolas L., Close, Jennie, Johansen, Nelson, Yanny, Anna Marie, Barkan, Eliza R., Travaglini, Kyle J., Bertagnolli, Darren, Campos, Jazmin +35 more·2023·Science
DOI 10.1126/science.adf6812PMC PMC11687949

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Abstract

Abstract Variation in cortical cytoarchitecture is the basis for histology-based definition of cortical areas, such as Brodmann areas. Single cell transcriptomics enables higher-resolution characterization of cell types in human cortex, which we used to revisit the idea of the canonical cortical microcircuit and to understand functional areal specialization. Deeply sampled single nucleus RNA-sequencing of eight cortical areas spanning cortical structural variation showed highly consistent cellular makeup for 24 coarse cell subclasses. However, proportions of excitatory neuron subclasses varied strikingly, reflecting differences in intra- and extracortical connectivity across primary sensorimotor and association cortices. Astrocytes and oligodendrocytes also showed differences in laminar organization across areas. Primary visual cortex showed dramatically different organization, including major differences in the ratios of excitatory to inhibitory neurons, expansion of layer 4 excitatory neuron types and specialized inhibitory neurons. Finally, gene expression variation in conserved neuron subclasses predicts differences in synaptic function across areas. Together these results provide a refined cellular and molecular characterization of human cortical cytoarchitecture that reflects functional connectivity and predicts areal specialization.

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  • BICCN and BICAN reference classifications provide a foundational reference to understand the cellular, molecular and epigenomic underpinnings of AD.

    “These reference classifications provide an extremely powerful foundational reference to understand the cellular, molecular and epigenomic underpinnings of AD.”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [17]

    The Discussion describes BICCN’s cortical cell-classification framework, and the Main Text reports a transcriptomic taxonomy of human neocortical cells. That supports the classification-as-reference aspect, but the paper does not establish that these classifications explain the cellular, molecular, and epigenomic underpinnings of Alzheimer’s disease; its donor criteria excluded known neuropathology (Materials and Methods, Postmortem tissue donors). (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Brains were collected using highly optimized brain preparation methods with a mean postmortem interval of 7.0 h that enable exceptionally high-quality snRNA-seq, snATAC-seq and MERFISH profiling.

    “Brains were collected using highly optimized brain preparation methods (mean postmortem interval = 7.0 h; Extended Data Fig. 1b) that enable exceptionally high-quality snRNA-seq, snATAC–seq and MERFISH profiling17–19,23,36,37.”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [17]

    The Materials and Methods sections on postmortem tissue donors and specimen processing describe brain preparation for snRNA-seq and MERFISH. They report a mean postmortem interval of 12.8 hours for the snRNA-seq donors, not 7.0 hours; the cited paper also does not establish that its preparation enabled exceptionally high-quality snATAC-seq profiling. The citing sentence assigns the 7.0-hour figure to its own Extended Data figure, rather than specifically to this source. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • After mapping each cell in the spatial transcriptomic dataset to subclasses and supertypes, there was concordance between expected and mapped spatial distributions; for example, excitatory IT subclasses were restricted to expected cortical layers and matched proportions observed in previous studies of neurotypical MTG tissue.

    “After mapping each cell in the spatial transcriptomic dataset to subclasses and supertypes, we found concordance between expected and mapped spatial distributions; for example, excitatory intratelencephalic (IT) subclasses were restricted to expected cortical layers and matched proportions observed in previous studies of neurotypical MTG tissue17,50 (Extended Data Fig. 7f,g).”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [17]

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Previous BICCN efforts identified 151 transcriptionally distinct cell types and states in the MTG from neurotypical adult reference donors, hierarchically organized into 24 subclasses within three main classes.

    “Previous BICCN efforts identified 151 transcriptionally distinct cell types and states in the MTG from neurotypical adult reference donors17, hierarchically organized into 24 subclasses (for example, L2/3 intratelencephalic-projecting excitatory neurons or L2/3 IT) within three main classes (excitatory neurons, inhibitory neurons and nonneuronal cells).”

    From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [17]

    The Main Text section “Within-area cell taxonomies demonstrate common subclass architecture” places MTG nuclei within 24 subclasses, but reports 120–142 cell types per area, not 151 in MTG. The “Cross-areal consensus taxonomy” section reports 153 types across areas. Our copy therefore supports the subclass organization but not the attributed MTG count; page numbers are unavailable. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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References 75 · Cited by 209

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Authors
Jorstad, N. L., Close, J., Johansen, N., Yanny, A. M., Barkan, E. R., Travaglini, K. J., Bertagnolli, D., Campos, J., Casper, T., Crichton, K., Dee, N., Ding, S.-L., Gelfand, E., Goldy, J., Hirschstein, D., Kiick, K., Kroll, M., Kunst, M., Lathia, K., Long, B., Martin, N., McMillen, D., Pham, T., Rimorin, C., Ruiz, A., Shapovalova, N., Shehata, S., Siletti, K., Somasundaram, S., Sulc, J., Tieu, M., Torkelson, A., Tung, H., Callaway, E. M., Hof, P. R., Keene, C. D., Levi, B. P., Linnarsson, S., Mitra, P. P., Smith, K., Hodge, R. D., Bakken, T. E., & Lein, E. S.
Affiliations
  • Allen Institute for Brain Science (US)
  • Allen Institute for Brain Science, Seattle, WA 98109, USA.
  • Salk Institute for Biological Studies (US)
  • Icahn School of Medicine at Mount Sinai (US)
  • University of Washington (US)
  • Karolinska Institutet (SE)
  • Cold Spring Harbor Laboratory (US)
Corresponding author
  • Hodge, Rebecca D.
  • Bakken, Trygve E.
  • Lein, Ed S
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2023-10-13
Volume
382
Issue
6667
Pages
eadf6812-eadf6812
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.adf6812
PMC
PMC11687949
OpenAlex
W4387568575
Cited by
209
References
75
Citations by year
2026: 70 · 2025: 70 · 2024: 59 · 2023: 10
FWCI
18.62
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Washington University in St. Louis
  • Nancy and Buster Alvord Endowment
  • Knut och Alice Wallenbergs Stiftelse (2018.0220)
  • National Institutes of Health (5u01mh114812-03, U01MH114812)
  • National Institute of Mental Health (U01MH114812, U19MH114830, U01MH117023)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
Keywords
human neocortex, cortical cytoarchitecture, single-nucleus RNA sequencing, excitatory neurons, inhibitory neurons, primary visual cortex, primary sensorimotor cortex, association cortex, cortical laminar organization, astrocytes, oligodendrocytes
MeSH terms
Single-Cell Gene Expression Analysis, Humans, Neurons, Phylogeny, Neocortex, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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