The DOI matches its Crossref record; 216 of the 219 references it cites were found in a registry or an index; none of the 60 sources its claims cite is checked for support yet.
What is a cell type and how to define it?
Open accessAbstract
Cell types are the basic functional units of an organism. They exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize and understand. This Review provides an overview of the basic principles of cell types rooted in evolution and development, and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism’s function, using the mammalian brain as a primary example. I propose a roadmap towards a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.
Overview
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Written from the paper’s full text.
TL;DR
This review argues that cell types arise through evolution and have identities encoded in the genome, and it proposes a multilevel, iterative roadmap anchored in single-cell transcriptomic taxonomy and refined by epigenomic, spatial, anatomical, physiological, functional, developmental, and cross-species evidence. It illustrates these ideas mainly with the mammalian brain, especially mouse cortex.
Key findings
- Single-cell transcriptomic taxonomies in adult mammalian brain are consistently hierarchical: neuronal versus non-neuronal classes at the top, then major brain structures/regions, then subclasses and types; in mouse cortex, glutamatergic neurons divide into nine subclasses and GABAergic neurons into six, giving about 110 transcriptomic types per cortical area.
- Transcriptomic types correspond well with spatial organization and some multimodal properties: mouse retina studies converge on roughly 130 neuron types, cortical transcriptomic continua align with laminar depth, and Patch-seq/MERFISH link transcriptomic variation to morphology, electrophysiology, and layer position.
- Cell types must be distinguished from cell states; transcriptomic snapshots can mix transient states with stable identities, and non-neuronal cells such as astrocytes, oligodendrocytes, microglia, and vascular cells show pronounced state changes across development, aging, injury, and disease.
- The proposed roadmap is to use transcriptomic taxonomy as an anchor, add epigenomic and spatial transcriptomic data, integrate anatomy, physiology, connectivity, and function with molecular identification, study development and cross-species evolution, and organize results in a branching, tree-like classification with standardized ontology and nomenclature.
Methods
- Design: conceptual review and synthesis, not a primary empirical study; no new sample or dataset is reported.
- Evidence base: literature on cell-type characterization across molecular, morphological, physiological, functional, developmental, and evolutionary modalities, with the mammalian brain and mouse cortex as main examples.
- Analysis: narrative integration of single-cell transcriptomics, epigenomics, spatially resolved transcriptomics, Patch-seq, connectomics, lineage/trajectory studies, and cross-species comparisons; concludes with a proposed roadmap.
Limitations
- The paper states that no consistent standard definition of cell types exists; it is often unclear whether types defined by different phenotypic features agree, and transcriptomic clusters at the lowest hierarchical levels have not been fully validated.
- It notes technical and conceptual gaps: continuous variation makes exact type counts difficult; cross-species comparisons need well-annotated genomes, comparable ontologies, and high-quality data; single-cell proteomics and mammalian connectomics remain under development; links between transcriptomic types and projection or functional properties are unresolved.
- Evident from the text: as a review, it presents no new experimental tests of its roadmap, and its examples are heavily weighted toward mammalian brain and mouse cortex, so generality to other organs and species is asserted rather than demonstrated.
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In the paper
“Within each subclass, the study also finds extensive heterogeneity among individual neurons; this heterogeneity reflects three axes of variations: regional specificity, topographic specificity and individual (potentially stochastic) variation,”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Using the former approach, it has been shown that GABAergic transcriptomic types in mouse visual cortex differ in their response to behavioral states (e.g., running versus resting), whereas visual response properties (e.g., orientation or direction selectivity) only differ at subclass level (Bugeon et al., 2021);”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Bugeon et al., 2021
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Currently single-cell transcriptomics and connectomics (i.e., delineating the patterns of interconnections between individual neurons) are the two primary approaches that have the potential to meet the completeness requirement.”
§ What is a cell type and how to define it? > Approaches to characterize cell types
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Integration of transcriptomic, electrophysiological and morphological properties by Patch-seq reveals multimodal corresponding distinctions of mouse motor cortex cell types at subclass level (Scala et al., 2021).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Scala et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“which do not readily correlate with transcriptomic types within the subclass.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So far it has been shown that transcriptomic types have excellent correspondence with their spatial distribution patterns.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIn the paper
“These visual cortex GABAergic MET types show robust cross-modality concordance and mutual predictability.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“in somatosensory cortex, higher sensory response is seen in a specific L2/3 IT excitatory transcriptomic type (Condylis et al., 2022).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Condylis et al., 2022 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Another Patch-seq study of mouse visual cortical GABAergic neurons also reveals a relatively high degree of corresponding continuous variations of transcriptomic types with their anatomical distribution along the cortical depth and the variations of their morpho-electrical properties (Fig. 3B) (Gouwens et al., 2020).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Using the former approach, it has been shown that GABAergic transcriptomic types in mouse visual cortex differ in their response to behavioral states (e.g., running versus resting), whereas visual response properties (e.g., orientation or direction selectivity) only differ at subclass level (Bugeon et al., 2021);”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Bugeon et al., 2021
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 15
- Probably not a substantial claim. 17
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that single-cell transcriptomics alone can faithfully capture the overall cell type organization at class and subclass levels, although the validity of transcriptomic clusters at the lowest branch level has yet to be fully tested.”
§ What is a cell type and how to define it? > Hierarchical organization of transcriptomically-defined cell types
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Glutamatergic neuron types are distinct between visual and motor cortical areas whereas GABAergic neuron types are shared between the two areas (Tasic et al., 2018).”
§ What is a cell type and how to define it? > Hierarchical organization of transcriptomically-defined cell types
What the sources say
- Not checked yetcites Tasic et al., 2018 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, whether specific transcriptomic types (beyond the subclass level) have specific connectional or functional attributes or not is still unclear in many cases.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since a neuron type usually has multiple projection targets and a neuron within that type can choose a subset of those targets either specifically or randomly, a single-target-site Retro-seq assay is often insufficient to resolve the target specificity of a transcriptomic type except in special cases.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: A neuron type usually has multiple projection targets and individual neurons can choose a subset specifically or randomly.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it is also reasonable to propose that some connectional or functional properties should not be used to define cell types, because they may be emerging properties arising from the interaction of a network of cell types, or from experience and/or activity dependent processes that represent a cell “state” rather than a defining feature for a cell “type”.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“specific molecular signatures responsible for certain essential anatomical or functional features may be hidden below noise and will need to be brought out through supervised approaches and used to refine the classification of cell types towards more functional relevance.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the first and foremost principle is that cell types are the product of evolution and cell type identities are encoded in the genome.”
§ What is a cell type and how to define it? > Cell types are the product of evolution
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“since in theory transcriptomic clusters can be infinitely subdivided and the more cells profiled the more clusters can be obtained”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“A study using this approach in cortical excitatory neuron subclass-specific Cre driver lines (Peng et al., 2021) reveals distinct projection patterns between subclasses, e.g., not only between IT and ET but also between L2/3 IT and L5 IT neurons, confirming subclass level projection specificity (Fig. 3C).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“To overcome heterogeneity at individual type level, a set of triple-modality MET types are defined at an intermediate level of granularity (between transcriptomic subclasses and types).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Studies over the past century have revealed that cells within an organism can be grouped into types – cells within a type exhibit similar structure and function that are distinct from cells in other types (Arendt, 2008).”
§ What is a cell type and how to define it? > Abstract
What the sources say
- Not checked yetcites Arendt, 2008 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Oligodendrocyte pathology is evident in a range of disorders including multiple sclerosis, schizophrenia and Alzheimer’s disease (Kuhn et al., 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Kuhn et al., 2019 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Developmental trajectory also is not the same as developmental lineage, as a lineage is defined as all the cells descended from a single precursor/progenitor and it has been shown in multiple systems that a progenitor can produce cells belonging to several neuronal and non-neuronal types, ordered by developmental timing (Agathocleous and Harris, 2009; Sagner and Briscoe, 2019; Sulston et al., 1983”
§ What is a cell type and how to define it? > Cell type development
What the sources say
- Not checked yetcites Agathocleous and Harris, 2009
- Not checked yetcites Sagner and Briscoe, 2019
- Not checked yetcites Sulston et al., 1983 source
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The transcriptomic taxonomy contains evolutionarily rooted molecular signatures and allows effective label transfer and linking with all other modalities.”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conclusion, cell types are the product of evolution, and they are the basic functional units of an organism.”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, individual variations within a species (e.g., within the human population) that are driven by genetic or environmental factors may be manifested as a variety of cell type or cell state variations.”
§ What is a cell type and how to define it? > Cell types versus cell states
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Microglia display diverse and dynamic phenotypic states and play a plethora of roles in development, adulthood (homeostasis), aging and diseases (Butovsky and Weiner, 2018; Prinz et al., 2019; Thion and Garel, 2020).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The distinction between cell types and cell states is particularly challenging during development, as cells continually change their states and, at certain key time points, they may switch their cell type identities.”
§ What is a cell type and how to define it? > Cell types versus cell states
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“First, opposing morphogen gradients establish the basic plan for cortex (anterior-posterior) or spinal cord (dorsal-ventral) patterning and provide instructive signals for the expression of complementary sets of transcription factor activators and repressors, which in turn define distinct neural progenitor domains within cortex or spinal cord.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The generation and patterning of mouse cortex and spinal cord cell types are two example systems where extensive historical studies have uncovered several common principles (Cadwell et al., 2019; Catela et al., 2015; Jessell, 2000; O’Leary et al., 2007; Osseward and Pfaff, 2019; Sagner and Briscoe, 2019).”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Myelination process also exhibits activity-dependent plasticity (Monje, 2018).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Monje, 2018 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The transcriptomic taxonomy organizes cell types in a hierarchical manner, laying out different levels of descriptions from major divisions at class and subclass levels to more granular and fuzzier divisions at type and subtype levels (due to the more prevailing continuous variations at the latter levels).”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Astrocytes exhibit diverse morphological and physiological properties in different brain regions and contribute to essential functions in blood-brain barrier, synaptogenesis, neurotransmitter buffering, ion homeostasis, and secretion of neuroactive agents (Ben Haim and Rowitch, 2017; Khakh and Deneen, 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Ben Haim and Rowitch, 2017
- Not checked yetcites Khakh and Deneen, 2019
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The set of cell type-defining transcription factors in a progenitor or a daughter cell can change with time, such that different neuronal types emanate from the same progenitor in a precisely timed “birth order”.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Later in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Single-cell transcriptomic studies reveal a relatively homogeneous adult microglia population, and greater heterogeneity of microglia states at different developmental stages, during aging and in pathological conditions (Hammond et al., 2019; Li et al., 2019; Masuda et al., 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Reactive astrocytes undergo morphological, molecular, and functional changes in response to injury or CNS diseases; they may adopt multiple, heterogeneous states depending on context (Escartin et al., 2021).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Escartin et al., 2021 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Cell types are the basic functional units of an organism.
“Cell types are the basic functional units of an organism.”
The paper relies on this without support.
There has not been a consistent and standard definition of cell types.
“However, there has not been a consistent and standard definition of cell types even though it is critical for reproducible investigation.”
The paper relies on this without support.
It is often unclear whether cell types defined by different phenotypic features agree, nor which feature is the right one to define cell types.
“It is often unclear if cell types defined by different phenotypic features agree with each other, nor which feature is the “right” one to define cell types.”
The paper relies on this without support.
Cellular properties at individual cell level are highly heterogeneous, and variations in different modalities do not necessarily exhibit high concordance, making it often impossible to define exactly what is a cell type and draw clear boundaries between types.
“At the same time, cellular properties at individual cell level are highly heterogeneous, variations in different modalities do not necessarily exhibit high degrees of concordance, making it often impossible to define exactly what is a cell type and draw clear boundaries between “types”.”
The paper relies on this without support.
The vast majority of cortical and subcortical neuron types have long-range axon projections forming circuit networks throughout the brain.
“The vast majority of cortical and subcortical neuron types have long-range axon projections to form circuit networks throughout the brain.”
The paper relies on this without support.
Transcriptomes are rich in containing molecular correlates of all sorts of cellular properties.
“Since transcriptomes are rich in containing the molecular correlates of all sorts of cellular properties”
The paper relies on this without support.
Emergence of a new cell type during development is more often the consequence of cell division from which a daughter cell takes up a new cell type identity.
“More often, emergence of a new cell type during development is the consequence of cell division from which a daughter cell takes up a new cell type identity (Fig. 4).”
The paper relies on this without support.
Coordinated neuronal activities in brain circuits generate sensory perception and behavior.
“Coordinated neuronal activities in brain circuits generate sensory perception and behavior.”
The paper relies on this without support.
IEG activation leads to expression of downstream effectors, such as ion channels or synaptic proteins, that shape cell states and remodel neuronal connections.
“IEG activation leads to expression of downstream effectors, such as ion channels or synaptic proteins, that shape the cell states and remodel neuronal connections.”
The paper relies on this without support.
Cell state changes in the brain are closely related to neural plasticity.
“Cell state changes in the brain are closely related to neural plasticity.”
The paper relies on this without support.
Various diseased conditions can induce pathological changes in cell states in different brain regions or tissue organs.
“Similarly, various diseased conditions can induce pathological changes in cell states in different brain regions or tissue organs.”
The paper relies on this without support.
Numerous studies have revealed selective vulnerability of specific cell types for specific diseases.
“Numerous studies have revealed selective vulnerability of specific cell types for specific diseases.”
The paper relies on this without support.
Astrocytes become reactive under pathological conditions.
“Astrocytes become reactive under pathological conditions.”
The paper relies on this without support.
Oligodendrocytes are the myelinating cells of the CNS generated from OPCs throughout life.
“Oligodendrocytes are the myelinating cells of the central nervous system (CNS) that are generated from OPCs throughout life.”
The paper relies on this without support.
Microglia are the primary innate immune cells in the CNS and have a distinct developmental origin from peripheral immune cells.
“Finally, microglia are the primary innate immune cells in the CNS and have a distinct developmental origin from peripheral immune cells.”
The paper relies on this without support.
Microglia are generated from mesodermal progenitors that arise from the yolk sac and are among the earliest residential cell types in the brain.
“They are generated from mesodermal progenitors that arise from the yolk sac and are among the earliest residential cell types in the brain.”
The paper relies on this without support.
Microglia can be both responders to and inducers of various neurodegenerative, neuroinflammatory and neurodevelopmental diseases.
“In particular, microglia can be both responders to and inducers of various neurodegenerative, neuroinflammatory and neurodevelopmental diseases.”
The paper relies on this without support.
The entire repertoire of cell types is built through sequential and parallel developmental events starting from the zygote.
“The entire repertoire of cell types in the brain and the body is built through a sequential and parallel series of spatially and temporally coordinated developmental events starting from a single fertilized egg, the zygote.”
The paper relies on this without support.
Transcriptional and epigenetic regulatory programs are unfolded from genome sequences and drive proliferation and differentiation leading to diverse cellular phenotypes.
“Transcriptional and epigenetic regulatory programs are unfolded from the genome sequences and drive a cascading series of cell proliferation and differentiation processes leading to the manifestation of diverse cellular phenotypes.”
The paper relies on this without support.
Earlier-stage ancestral cell types are fewer and more multipotent, giving rise to more descendant cell types with increasingly restricted fates.
“In the developmental ontogeny of cell types, earlier-stage ancestral cell types are fewer and are more multipotent, they give rise to a larger number of descendant cell types with increasingly restricted fates.”
The paper relies on this without support.
The developmental program specifies a spatial plan, including the location of organs and the spatial organization of cell types.
“The developmental program rolls out not only a temporal but also an elaborate spatial plan, specifying the location of each tissue organ and the spatial organization of all the cell types within each.”
The paper relies on this without support.
Brain development includes patterning and regionalization, neurogenesis and neuronal migration, gliogenesis and glia differentiation, neuronal differentiation and circuit formation, and circuit refinement and plasticity.
“For the brain, the main series of events of brain development leading to the mature adult-stage cell types and circuits include: patterning and regionalization (laying out the master plan of brain architecture), neurogenesis and neuronal migration, gliogenesis and glia cell differentiation, neuronal differentiation and circuit formation (axonogenesis, dendritic arborization, synaptogenesis, myelin”
The authors state this as an assumption.
Transcriptomic changes tend to be more continuous during cell state transitions and more abrupt or discrete when cells switch their types.
“Although not absolute, it is reasonable to assume that transcriptomic changes tend to be more continuous during cell state transitions, and more abrupt or discrete when cells switch their types.”
The paper relies on this without support.
A neuron type usually has multiple projection targets and individual neurons can choose a subset specifically or randomly.
“Since a neuron type usually has multiple projection targets and a neuron within that type can choose a subset of those targets either specifically or randomly”
Claims other papers make about this one
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In the citing paper
“Recent studies define a cell type as a (continuous) set of cell states, which can be aligned with the range of cellular phenotypes resulting from the interaction between a cell type and its microenvironment4.”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [4]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Single-cell transcriptomics by single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) provides unprecedented profiling depth and scalability, enabling comprehensive quantitative analysis and classification of cell types at scale2,3,7–9.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [2]
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In the citing paper
“Consistent with the principle of hierarchical cell-type organization identified in previous studies2, here we have defined a hierarchical taxonomy of cell types across the entire mouse brain, with four levels of classification: class, subclass, supertype and cluster (or type).”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [2]
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
The structure of the nervous system of the nematode Caenorhabditis elegans
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
The embryonic cell lineage of the nematode Caenorhabditis elegans
Sulston, J. E., Schierenberg, E., White, J. G., & Thomson, J. N. (1983) · Developmental Biology · doi:10.1016/0012-1606(83)90201-4 · Cited by 4,199
Cell 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
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 2,842
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Reactive astrocyte nomenclature, definitions, and future directions
Escartin, C., Galea, E., Lakatos, A., O’Callaghan, J. P., Petzold, G. C., Serrano-Pozo, A., Steinhäuser, C., Volterra, A., Carmignoto, G., Agarwal, A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00783-4 · Cited by 2,513
Blood-Brain Barrier: From Physiology to Disease and Back
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R., & Zlokovic, B. V. (2019) · Physiological Reviews · doi:10.1152/physrev.00050.2017 · Cited by 2,312
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Hammond, T. R., Dufort, C., Dissing-Olesen, L., Giera, S., Young, A., Wysoker, A., Walker, A. J., Segel, M., Nemesh, J., Saunders, A., et al. (2018) · doi:10.1101/406140 · Cited by 2,225
Neuronal specification in the spinal cord: inductive signals and transcriptional codes
Jessell, T. M. (2000) · Nature Reviews Genetics · doi:10.1038/35049541 · Cited by 2,089
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033
A molecular atlas of cell types and zonation in the brain vasculature
Vanlandewijck, M., He, L., Mäe, M. A., Andrae, J., Ando, K., Del Gaudio, F., Nahar, K., Lebouvier, T., Laviña, B., Gouveia, L., et al. (2018) · Nature · doi:10.1038/nature25739 · Cited by 1,892
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.003 · Cited by 1,784
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775
Self-citationShared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714
Self-citationExploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Chan, K. Y., Jang, M. J., Yoo, B. B., Greenbaum, A., Ravi, N., Wu, W.-L., Sánchez-Guardado, L., Lois, C., Mazmanian, S. K., Deverman, B. E., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4593 · Cited by 1,578
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,531
Microglia Biology: One Century of Evolving Concepts
Prinz, M., Jung, S., & Priller, J. (2019) · Cell · doi:10.1016/j.cell.2019.08.053 · Cited by 1,525
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
A GAL4-Driver Line Resource for Drosophila Neurobiology
Jenett, A., Rubin, G. M., Ngo, T.-T. B., Shepherd, D., Murphy, C., Dionne, H., Pfeiffer, B. D., Cavallaro, A., Hall, D., Jeter, J., et al. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.09.011 · Cited by 1,449
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365
Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution
Masuda, T., Sankowski, R., Staszewski, O., Böttcher, C., Amann, L., Sagar, Scheiwe, C., Nessler, S., Kunz, P., van Loo, G., et al. (2019) · Nature · doi:10.1038/s41586-019-0924-x · Cited by 1,310
Museum of spatial transcriptomics
Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255
The Neuronal Organization of the Retina
Masland, R. H. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.002 · Cited by 1,245
The neocortical circuit: themes and variations
Harris, K. D., & Shepherd, G. M. G. (2015) · Nature Neuroscience · doi:10.1038/nn.3917 · Cited by 1,215
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176
Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system
Marques, S., Zeisel, A., Codeluppi, S., van Bruggen, D., Mendanha Falcão, A., Xiao, L., Li, H., Häring, M., Hochgerner, H., Romanov, R. A., et al. (2016) · Science · doi:10.1126/science.aaf6463 · Cited by 1,132
Li, Q., Cheng, Z., Zhou, L., Darmanis, S., Neff, N. F., Okamoto, J., Gulati, G., Bennett, M. L., Sun, L. O., Clarke, L. E., et al. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.006 · Cited by 1,129
A connectome and analysis of the adult Drosophila central brain
Scheffer, L. K., Xu, C. S., Januszewski, M., Lu, Z., Takemura, S.-Y., Hayworth, K. J., Huang, G. B., Shinomiya, K., Maitlin-Shepard, J., Berg, S., et al. (2020) · eLife · doi:10.7554/elife.57443 · Cited by 1,128
Van Hove, H., Martens, L., Scheyltjens, I., De Vlaminck, K., Pombo Antunes, A. R., De Prijck, S., Vandamme, N., De Schepper, S., Van Isterdael, G., Scott, C. L., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0393-4 · Cited by 1,074
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
Yao, Z., van Velthoven, C. T. J., Nguyen, T. N., Goldy, J., Sedeno-Cortes, A. E., Baftizadeh, F., Bertagnolli, D., Casper, T., Chiang, M., Crichton, K., et al. (2021) · Cell · doi:10.1016/j.cell.2021.04.021 · Cited by 1,065
Self-citationThe Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster
Zheng, Z., Lauritzen, J. S., Perlman, E., Robinson, C. G., Nichols, M., Milkie, D., Torrens, O., Price, J., Fisher, C. B., Sharifi, N., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.019 · Cited by 1,050
Microglial signatures and their role in health and disease
Butovsky, O., & Weiner, H. L. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0057-5 · Cited by 1,046
Connectomic reconstruction of the inner plexiform layer in the mouse retina
Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013) · Nature · doi:10.1038/nature12346 · Cited by 997
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983
Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
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., et al. (2016) · Science · doi:10.1126/science.aaf1204 · Cited by 957
Defined types of cortical interneurone structure space and spike timing in the hippocampus
Somogyi, P., & Klausberger, T. (2005) · The Journal of Physiology · doi:10.1113/jphysiol.2004.078915 · Cited by 927
Neuronal cell-type classification: challenges, opportunities and the path forward
Zeng, H., & Sanes, J. R. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2017.85 · Cited by 841
Self-citationFly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly
Li, H., Janssens, J., Waegeneer, M. D., Kolluru, S. S., Davie, K., Gardeux, V., Saelens, W., David, F. P. A., Brbić, M., Spanier, K. I., et al. (2022) · Science · doi:10.1126/science.abk2432 · Cited by 826
Daigle, T. L., Madisen, L., Hage, T. A., Valley, M. T., Knoblich, U., Larsen, R. S., Takeno, M. M., Huang, L., Gu, H., Larsen, R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.035 · Cited by 817
Self-citationComparative cellular analysis of motor cortex in human, marmoset and mouse
Bakken, T. E., Jorstad, N. L., Hu, Q., Lake, B. B., Tian, W., Kalmbach, B. E., Crow, M., Hodge, R. D., Krienen, F. M., Sorensen, S. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03465-8 · Cited by 812
A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
The Types of Retinal Ganglion Cells: Current Status and Implications for Neuronal Classification
Sanes, J. R., & Masland, R. H. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034120 · Cited by 789
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777
The origin and evolution of cell types
Arendt, D., Musser, J. M., Baker, C. V. H., Bergman, A., Cepko, C., Erwin, D. H., Pavlicev, M., Schlosser, G., Widder, S., Laubichler, M. D., et al. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.127 · Cited by 764
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759
Development and Functional Diversification of Cortical Interneurons
Lim, L., Mi, D., Llorca, A., & Marín, O. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.009 · Cited by 714
Revisiting the neurovascular unit
Schaeffer, S. M., & Iadecola, C. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00904-7 · Cited by 699
Molecular topography of an entire nervous system
Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., Varol, E., Oikonomou, P., Glenwinkel, L., McWhirter, R. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.023 · Cited by 696
Oligodendrocytes in development, myelin generation and beyond
Kuhn, S., Gritti, L., Crooks, D., & Dombrowski, Y. (2019) · Cells · doi:10.3390/cells8111424 · Cited by 625
Functional diversity of astrocytes in neural circuit regulation
Haim, L. B., & Rowitch, D. H. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.159 · Cited by 618
Lineage tracing meets single-cell omics: opportunities and challenges
Wagner, D. E., & Klein, A. M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0223-2 · Cited by 617
The Human and Mouse Enteric Nervous System at Single-Cell Resolution
Drokhlyansky, E., Smillie, C. S., Wittenberghe, N. V., Ericsson, M., Griffin, G. K., Eraslan, G., Dionne, D. A., Cuoco, M. S., Goder-Reiser, M. N., Sharova, T. Y., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.003 · Cited by 606
Area Patterning of the Mammalian Cortex
O’Leary, D. D. M., Chou, S., & Sahara, S. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.010 · Cited by 593
Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3445 · Cited by 581
OpenCell: Endogenous tagging for the cartography of human cellular organization
Cho, N. H., Cheveralls, K. C., Brunner, A.-D., Kim, K., Michaelis, A. C., Raghavan, P., Kobayashi, H., Savy, L., Li, J. Y., Canaj, H., et al. (2022) · Science · doi:10.1126/science.abi6983 · Cited by 567
Molecular architecture of the developing mouse brain
La Manno, G., Siletti, K., Furlan, A., Gyllborg, D., Vinsland, E., Mossi Albiach, A., Mattsson Langseth, C., Khven, I., Lederer, A. R., Dratva, L. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03775-x · Cited by 563
Kamath, T., Abdulraouf, A., Burris, S. J., Langlieb, J., Gazestani, V., Nadaf, N. M., Balderrama, K., Vanderburg, C., & Macosko, E. Z. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01061-1 · Cited by 552
Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545
A viral strategy for targeting and manipulating interneurons across vertebrate species
Dimidschstein, J., Chen, Q., Tremblay, R., Rogers, S. L., Saldi, G.-A., Guo, L., Xu, Q., Liu, R., Lu, C., Chu, J., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4430 · Cited by 541
Winnubst, J., Bas, E., Ferreira, T. A., Wu, Z., Economo, M. N., Edson, P., Arthur, B. J., Bruns, C., Rokicki, K., Schauder, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.042 · Cited by 538
Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538
Self-citationThe evolution of cell types in animals: emerging principles from molecular studies
Arendt, D. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2416 · Cited by 525
A multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522
Self-citationThe Emerging Nature of Astrocyte Diversity
Khakh, B. S., & Deneen, B. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070918-050443 · Cited by 512
Tosches, M. A., Yamawaki, T. M., Naumann, R. K., Jacobi, A. A., Tushev, G., & Laurent, G. (2018) · Science · doi:10.1126/science.aar4237 · Cited by 506
Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes
Romanov, R. A., Zeisel, A., Bakker, J., Girach, F., Hellysaz, A., Tomer, R., Alpár, A., Mulder, J., Clotman, F., Keimpema, E., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4462 · Cited by 502
Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498
Self-citationSingle-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Hrvatin, S., Hochbaum, D. R., Nagy, M. A., Cicconet, M., Robertson, K., Cheadle, L., Zilionis, R., Ratner, A., Borges-Monroy, R., Klein, A. M., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0029-5 · Cited by 497
The emergence of transcriptional identity in somatosensory neurons
Sharma, N., Flaherty, K., Lezgiyeva, K., Wagner, D. E., Klein, A. M., & Ginty, D. D. (2020) · Nature · doi:10.1038/s41586-019-1900-1 · Cited by 490
Molecular logic of cellular diversification in the mouse cerebral cortex
Di Bella, D. J., Habibi, E., Stickels, R. R., Scalia, G., Brown, J., Yadollahpour, P., Yang, S. M., Abbate, C., Biancalani, T., Macosko, E. Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03670-5 · Cited by 464
Detecting Activated Cell Populations Using Single-Cell RNA-Seq
Wu, Y. E., Pan, L., Zuo, Y., Li, X., & Hong, W. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.026 · Cited by 438
Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity
Paul, A., Crow, M., Raudales, R., He, M., Gillis, J., & Huang, Z. J. (2017) · Cell · doi:10.1016/j.cell.2017.08.032 · Cited by 421
Morphological diversity of single neurons in molecularly defined cell types
Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03941-1 · Cited by 417
Self-citationSpatiotemporal transcriptomic divergence across human and macaque brain development
Zhu, Y., Sousa, A. M. M., Gao, T., Skarica, M., Li, M., Santpere, G., Esteller-Cucala, P., Juan, D., Ferrández-Peral, L., Gulden, F. O., et al. (2018) · Science · doi:10.1126/science.aat8077 · Cited by 414
Development and arealization of the cerebral cortex
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398
Self-citationA transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
Yao, Z., Liu, H., Xie, F., Fischer, S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Bartlett, A., Behrens, M. M., Van den Berge, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03500-8 · Cited by 398
Self-citationConnectomes across development reveal principles of brain maturation
Witvliet, D., Mulcahy, B., Mitchell, J. K., Meirovitch, Y., Berger, D. R., Wu, Y., Liu, Y., Koh, W. X., Parvathala, R., Holmyard, D., et al. (2021) · Nature · doi:10.1038/s41586-021-03778-8 · Cited by 393
Hulse, B. K., Haberkern, H., Franconville, R., Turner-Evans, D., Takemura, S.-Y., Wolff, T., Noorman, M., Dreher, M., Dan, C., Parekh, R., et al. (2021) · eLife · doi:10.7554/elife.66039 · Cited by 392
Hulse, B. K., Haberkern, H., Franconville, R., Turner-Evans, D. B., Takemura, S., Wolff, T., Noorman, M., Dreher, M., Dan, C., Parekh, R., et al. (2020) · doi:10.1101/2020.12.08.413955 · Cited by 392
Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3443 · Cited by 386
Single-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386
The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383
Innovations present in the primate interneuron repertoire
Krienen, F. M., Goldman, M., Zhang, Q., C. H. del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2781-z · Cited by 381
A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types
Kozareva, V., Martin, C., Osorno, T., Rudolph, S., Guo, C., Vanderburg, C. R., Nadaf, N. M., Regev, A., Regehr, W. G., & Macosko, E. Z. (2021) · Nature · doi:10.1038/s41586-021-03220-z · Cited by 380
High-throughput mapping of single-neuron projections by sequencing of barcoded RNA
Kebschull, J. M., Garcia da Silva, P., Reid, A. P., Peikon, I. D., Albeanu, D. F., & Zador, A. M. (2016) · Neuron · doi:10.1016/j.neuron.2016.07.036 · Cited by 376
Whole-brain serial-section electron microscopy in larval zebrafish
Hildebrand, D. G. C., Cicconet, M., Torres, R., Choi, W., Quan, T. M., Moon, J., Wetzel, A. W., Champion, A. S., Graham, B. J., Randlett, O., et al. (2017) · Nature · doi:10.1038/nature22356 · Cited by 372
Innovations in Primate Interneuron Repertoire
Krienen, F. M., Goldman, M., Zhang, Q., del Rosario, R., Florio, M., Machold, R., Saunders, A., Levandowski, K., Zaniewski, H., Schuman, B., et al. (2019) · doi:10.1101/709501 · Cited by 372
Sathyamurthy, A., Johnson, K. R., Matson, K. J. E., Dobrott, C. I., Li, L., Ryba, A. R., Bergman, T. B., Kelly, M. C., Kelley, M. W., & Levine, A. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.003 · Cited by 370
Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling
Poulin, J.-F., Zou, J., Drouin-Ouellet, J., Kim, K.-Y. A., Cicchetti, F., & Awatramani, R. B. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.008 · Cited by 352
Understanding the retinal basis of vision across species
Baden, T., Euler, T., & Berens, P. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0242-1 · Cited by 350
Preissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332
A unifying concept: the history of cell theory
Mazzarello, P. (1999) · Nature Cell Biology · doi:10.1038/8964 · Cited by 326
Spatially resolved transcriptomics adds a new dimension to genomics
Larsson, L., Frisén, J., & Lundeberg, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01038-7 · Cited by 325
A single-cell atlas of the normal and malformed human brain vasculature
Winkler, E. A., Kim, C. N., Ross, J. M., Garcia, J. H., Gil, E., Oh, I., Chen, L. Q., Wu, D., Catapano, J. S., Raygor, K. P., et al. (2022) · Science · doi:10.1126/science.abi7377 · Cited by 324
The logic of single-cell projections from visual cortex
Han, Y., Kebschull, J. M., Campbell, R. A. A., Cowan, D., Imhof, F., Zador, A. M., & Mrsic‐Flogel, T. D. (2018) · Nature · doi:10.1038/nature26159 · Cited by 319
Single-cell dissection of the human brain vasculature
García, F., Sun, N., Lee, H., Godlewski, B. R., Mathys, H., Galani, K., Zhou, B., Jiang, X., Ng, A. P., Mantero, J. C., et al. (2022) · Nature · doi:10.1038/s41586-022-04521-7 · Cited by 313
Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304
DNA methylation atlas of the mouse brain at single-cell resolution
Liu, H., Zhou, J., Tian, W., Luo, C., Bartlett, A., Aldridge, A., Lucero, J., Osteen, J. K., Nery, J. R., Chen, H., et al. (2021) · Nature · doi:10.1038/s41586-020-03182-8 · Cited by 289
Establishing neuronal diversity in the spinal cord: a time and a place
Sagner, A., & Briscoe, J. (2019) · Development · doi:10.1242/dev.182154 · Cited by 287
Molecular atlas of the adult mouse brain
Ortiz, C., Navarro, J. F., Jurek, A., Märtin, A., Lundeberg, J., & Meletis, K. (2020) · Science Advances · doi:10.1126/sciadv.abb3446 · Cited by 286
Barcoded viral tracing of single-cell interactions in central nervous system inflammation
Clark, I. C., Gutiérrez-Vázquez, C., Wheeler, M. A., Li, Z., Rothhammer, V., Linnerbauer, M., Sanmarco, L. M., Guo, L., Blain, M., Zandee, S. E. J., et al. (2021) · Science · doi:10.1126/science.abf1230 · Cited by 285
Human neocortical expansion involves glutamatergic neuron diversification
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285
Transient cortical circuits match spontaneous and sensory-driven activity during development
Molnár, Z., Luhmann, H. J., & Kanold, P. O. (2020) · Science · doi:10.1126/science.abb2153 · Cited by 280
Tiklová, K., Björklund, Å. K., Lahti, L., Fiorenzano, A., Nolbrant, S., Gillberg, L., Volakakis, N., Yokota, C., Hilscher, M. M., Hauling, T., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08453-1 · Cited by 276
Myelin plasticity and nervous system function
Monje, M. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061853 · Cited by 274
High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing
Chen, X., Sun, Y.-C., Zhan, H., Kebschull, J. M., Fischer, S., Matho, K. S., Huang, Z. J., Gillis, J., & Zador, A. M. (2019) · Cell · doi:10.1016/j.cell.2019.09.023 · Cited by 274
Brain Architecture: Understanding the Basic Plan
Swanson, L. W. (2002) · Cited by 273
The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus
Morgan, J., Berger, D. R., Wetzel, A. W., & Lichtman, J. W. (2016) · Cell · doi:10.1016/j.cell.2016.02.033 · Cited by 270
An atlas of cortical arealization identifies dynamic molecular signatures
Bhaduri, A., Sandoval-Espinosa, C., Otero‐García, M., Oh, I., Yin, R. H., Eze, U. C., Nowakowski, T. J., & Kriegstein, A. R. (2021) · Nature · doi:10.1038/s41586-021-03910-8 · Cited by 267
Single-neuron projectome of mouse prefrontal cortex
Gao, L., Liu, S., Gou, L., Hu, Y., Liu, Y., Deng, L., Ma, D., Wang, H., Yang, Q., Chen, Z., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01041-5 · Cited by 264
Cerebellar nuclei evolved by repeatedly duplicating a conserved cell-type set
Kebschull, J. M., Richman, E. B., Ringach, N., Friedmann, D., Albarran, E., Kolluru, S. S., Jones, R. C., Allen, W. E., Wang, Y., Cho, S. W., et al. (2020) · Science · doi:10.1126/science.abd5059 · Cited by 262
Multimodal analysis of cell types in a hypothalamic node controlling social behavior
Kim, D.-W., Yao, Z., Graybuck, L. T., Kim, T. K., Nguyen, T. N., Smith, K. A., Fong, O., Yi, L., Koulena, N., Pierson, N., et al. (2019) · Cell · doi:10.1016/j.cell.2019.09.020 · Cited by 259
Self-citationUnbiased spatial proteomics with single-cell resolution in tissues
Mund, A., Brunner, A.-D., & Mann, M. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.05.022 · Cited by 259
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 258
Self-citationEvolution of prefrontal cortex
Preuss, T. M., & Wise, S. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01076-5 · Cited by 257
A harmonized atlas of mouse spinal cord cell types and their spatial organization
Russ, D. E., Cross, R. B. P., Li, L., Koch, S. C., Matson, K. J. E., Yadav, A., Alkaslasi, M. R., Lee, D. I., Le Pichon, C. E., Menon, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25125-1 · Cited by 255
From Progenitors to Differentiated Cells in the Vertebrate Retina
Agathocleous, M., & Harris, W. A. (2009) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.042308.113259 · Cited by 249
Spatially resolved single-cell genomics and transcriptomics by imaging
Zhuang, X. (2021) · Nature Methods · doi:10.1038/s41592-020-01037-8 · Cited by 243
Concepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 242
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241
Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans
Vormstein-Schneider, D., Lin, J. D., Pelkey, K. A., Chittajallu, R., Guo, B., Arias-Garcia, M. A., Allaway, K., Sakopoulos, S., Schneider, G., Stevenson, O., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0692-9 · Cited by 235
Diversity of Interneurons in the Dorsal Striatum Revealed by Single-Cell RNA Sequencing and PatchSeq
Muñoz-Manchado, A. B., Bengtsson Gonzales, C., Zeisel, A., Munguba, H., Bekkouche, B., Skene, N. G., Lönnerberg, P., Ryge, J., Harris, K. D., Linnarsson, S., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.053 · Cited by 229
A curated database reveals trends in single-cell transcriptomics
Svensson, V., da Veiga Beltrame, E., & Pachter, L. (2020) · Database · doi:10.1093/database/baaa073 · Cited by 223
Comprehensive single-cell transcriptome lineages of a proto-vertebrate
Chen, C., Lemaire, L. A., Wang, W., Yoon, P. H., Choi, Y. A., Parsons, L. R., Matese, J. C., Wang, W., Levine, M., & Chen, K. (2019) · Nature · doi:10.1038/s41586-019-1385-y · Cited by 221
EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization
Wang, Y., Eddison, M., Fleishman, G. M., WEIGERT, M. G., Xu, S., Wang, T., Rokicki, K., Goina, C., Henry, F. E., Lemire, A. L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.024 · Cited by 213
Individual human cortical progenitors can produce excitatory and inhibitory neurons
Delgado, R. N., Allen, D. E., Keefe, M. G., Mancia, W., Ziffra, R. S., Crouch, E. E., Álvarez-Buylla, A., & Nowakowski, T. J. (2021) · Nature · doi:10.1038/s41586-021-04230-7 · Cited by 211
Cell transcriptomic atlas of the non-human primate Macaca fascicularis
Han, L., Wei, X., Liu, C., Volpe, G., Zhuang, Z., Zou, X., Wang, Z., Pan, T., Yuan, Y., Zhang, X., et al. (2022) · Nature · doi:10.1038/s41586-022-04587-3 · Cited by 211
Single-cell delineation of lineage and genetic identity in the mouse brain
Bandler, R. C., Vitali, I., Delgado, R. N., Ho, M. C., Dvoretskova, E., Molinas, J. S. I., Frazel, P. W., Mohammadkhani, M., Machold, R. P., Mädler, S. C., et al. (2021) · Nature · doi:10.1038/s41586-021-04237-0 · Cited by 206
Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity
Turner, N. L., Macrina, T., Bae, J. A., Yang, R., Wilson, A. M., Schneider-Mizell, C., Lee, K., Lu, R., Wu, J., Bodor, A. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.023 · Cited by 206
The diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201
Behavioral state coding by molecularly defined paraventricular hypothalamic cell type ensembles
Xu, S., Yang, H., Menon, V., Lemire, A. L., Wang, L., Henry, F. E., Turaga, S. C., & Sternson, S. M. (2020) · Science · doi:10.1126/science.abb2494 · Cited by 201
Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex
Mich, J. K., Graybuck, L. T., Hess, E. E., Mahoney, J. T., Kojima, Y., Ding, Y., Somasundaram, S., Miller, J. A., Kalmbach, B. E., Radaelli, C., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108754 · Cited by 193
Self-citationA roadmap for the Human Developmental Cell Atlas
Haniffa, M. A., Taylor, D. M., Linnarsson, S., Aronow, B. J., Bader, G. D., Barker, R. A., Cámara, P. G., Camp, J. G., Chédotal, A., Copp, A. J., et al. (2021) · Nature · doi:10.1038/s41586-021-03620-1 · Cited by 193
A repeated molecular architecture across thalamic pathways
Phillips, J. W., Schulmann, A., Hara, E., Winnubst, J., Liu, C., Valakh, V., Wang, L., Shields, B. C., Korff, W. L., Chandrashekar, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0483-3 · Cited by 192
Decoding molecular and cellular heterogeneity of mouse nucleus accumbens
Chen, R., Blosser, T. R., Djekidel, M. N., Hao, J., Bhattacherjee, A., Chen, W., Tuesta, L. M., Zhuang, X., & Zhang, Y. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00938-x · Cited by 191
Cortical interneuron development: a tale of time and space
Hu, J. S., Vogt, D., Sandberg, M., & Rubenstein, J. L. (2017) · Development · doi:10.1242/dev.132852 · Cited by 189
Enhancer viruses for combinatorial cell-subclass-specific labeling
Graybuck, L. T., Daigle, T. L., Sedeño-Cortés, A. E., Walker, M., Kalmbach, B., Lenz, G. H., Morin, E., Nguyen, T. N., Garren, E., Bendrick, J. L., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.011 · Cited by 187
Self-citationAn atlas of gene regulatory elements in adult mouse cerebrum
Li, Y. E., Preissl, S., Hou, X., Zhang, Z., Zhang, K., Qiu, Y., Poirion, O. B., Li, B., Chiou, J., Liu, H., et al. (2021) · Nature · doi:10.1038/s41586-021-03604-1 · Cited by 185
A transcriptomic axis predicts state modulation of cortical interneurons
Bugeon, S., Duffield, J. A. M., Dipoppa, M., Ritoux, A., Prankerd, I., Nicoloutsopoulos, D., Orme, D., Shinn, M., Peng, H., Forrest, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04915-7 · Cited by 185
Molecular design of hypothalamus development
Romanov, R. A., Tretiakov, E. O., Kastriti, M. E., Zupančič, M., Häring, M., Korchynska, S., Popadin, K. Y., Benevento, M., Rebernik, P., Lallemend, F., et al. (2020) · Nature · doi:10.1038/s41586-020-2266-0 · Cited by 178
Abbott, L. F., Bock, D. D., Callaway, E. M., Denk, W., Dulac, C., Fairhall, A. L., Fiete, I., Harris, K. M., Helmstaedter, M., Jain, V., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.010 · Cited by 172
Neuronal identity control by terminal selectors in worms, flies, and chordates
Hobert, O., & Kratsios, P. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.006 · Cited by 169
Recording development with single cell dynamic lineage tracing
McKenna, A., & Gagnon, J. A. (2019) · Development · doi:10.1242/dev.169730 · Cited by 169
Transcriptional and spatial resolution of cell types in the mammalian habenula
Hashikawa, Y., Hashikawa, K., Rossi, M. A., Basiri, M. L., Liu, Y., Johnston, N. L., Ahmad, O. R., & Stuber, G. D. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.011 · Cited by 167
Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits
Colquitt, B. M., Merullo, D. P., Konopka, G., Roberts, T. F., & Brainard, M. S. (2021) · Science · doi:10.1126/science.abd9704 · Cited by 167
Single-cell protein analysis by mass spectrometry
Slavov, N. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2020.04.018 · Cited by 164
Continuous and Discrete Neuron Types of the Adult Murine Striatum
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C., & Quake, S. R. (2020) · Neuron · doi:10.1016/j.neuron.2019.11.004 · Cited by 158
Circuit and molecular architecture of a ventral hippocampal network
Gergues, M. M., Han, K. J., Choi, H. S., Brown, B., Clausing, K. J., Turner, V. S., Vainchtein, I. D., Molofsky, A. V., & Kheirbek, M. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0705-8 · Cited by 158
The problem of neuronal cell types: a physiological genomics approach
Nelson, S. B., Sugino, K., & Hempel, C. M. (2006) · Trends in Neurosciences · doi:10.1016/j.tins.2006.05.004 · Cited by 157
Unique homeobox codes delineate all the neuron classes of C. elegans
Reilly, M. B., Cros, C., Varol, E., Yemini, E., & Hobert, O. (2020) · Nature · doi:10.1038/s41586-020-2618-9 · Cited by 157
Wolff, T., & Rubin, G. M. (2018) · Journal of Comparative Neurology · doi:10.1002/cne.24512 · Cited by 156
Genetic dissection of the glutamatergic neuron system in cerebral cortex
Matho, K. S., Huilgol, D., Galbavy, W., He, M., Kim, G., An, X., Lu, J., Wu, P., Di Bella, D. J., Shetty, A. S., et al. (2021) · Nature · doi:10.1038/s41586-021-03955-9 · Cited by 155
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154
Genetic strategies to access activated neurons
DeNardo, L. A., & Luo, L. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.05.014 · Cited by 148
Neuronal Cell Types and Connectivity: Lessons from the Retina
Seung, H. S., & Sümbül, U. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.054 · Cited by 143
A cell atlas of the chick retina based on single-cell transcriptomics
Yamagata, M., Yan, W., & Sanes, J. R. (2021) · eLife · doi:10.7554/elife.63907 · Cited by 141
Álvarez, G., Daclin, M., Shihavuddin, A., Lansade, P., Fortoul, A., Faucourt, M., Clavreul, S., Lalioti, M., Taraviras, S. L., Hippenmeyer, S., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.051 · Cited by 134
Redmond, S., Figueres‐Oñate, M., Obernier, K., Nascimento, M. A., Parraguez, J. I., López‐Mascaraque, L., Fuentealba, L. C., & Álvarez-Buylla, A. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.088 · Cited by 128
Sun, Y.-C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J., & Zador, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00842-4 · Cited by 124
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118
Genetic and epigenetic coordination of cortical interneuron development
Allaway, K. C., Gabitto, M. I., Wapinski, O. L., Saldi, G.-A., Wang, C., Bandler, R. C., Wu, S. J., Bonneau, R., & Fishell, G. (2021) · Nature · doi:10.1038/s41586-021-03933-1 · Cited by 117
Multiple convergent hypothalamus–brainstem circuits drive defensive behavior
Lovett-Barron, M., Chen, R., Bradbury, S., Andalman, A. S., Wagle, M. D., Guo, S., & Deisseroth, K. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0655-1 · Cited by 116
The Neuron Identity Problem: Form Meets Function
Fishell, G., & Heintz, N. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.035 · Cited by 115
Structure and function of axo-axonic inhibition
Schneider-Mizell, C. M., Bodor, A. L., Collman, F., Brittain, D., Bleckert, A. A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2021) · eLife · doi:10.7554/elife.73783 · Cited by 115
The development and evolution of inhibitory neurons in primate cerebrum
Schmitz, M. T., Sandoval, K., Chen, C. P., Mostajo-Radji, M. A., Seeley, W. W., Nowakowski, T. J., Ye, C. J., Paredes, M. F., & Pollen, A. A. (2022) · Nature · doi:10.1038/s41586-022-04510-w · Cited by 111
Extraction of Distinct Neuronal Cell Types from within a Genetically Continuous Population
Kim, E. J., Zhang, Z., Huang, L., Ito-Cole, T., Jacobs, M. W., Juavinett, A. L., Senturk, G., Hu, M., Ku, M., Ecker, J. R., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.018 · Cited by 103
The subiculum is a patchwork of discrete subregions
Cembrowski, M. S., Wang, L., Lemire, A. L., Copeland, M., DiLisio, S. F., Clements, J., & Spruston, N. (2018) · eLife · doi:10.7554/elife.37701 · Cited by 98
Spatially resolved transcriptomics in neuroscience
Close, J. L., Long, B. R., & Zeng, H. (2021) · Nature Methods · doi:10.1038/s41592-020-01040-z · Cited by 94
Self-citationCell type and circuit modules in the spinal cord
Osseward, P. J., & Pfaff, S. L. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.03.003 · Cited by 93
The cellular basis of distinct thirst modalities
Pool, A., Wang, T., Stafford, D. A., Chance, R. K., Lee, S., Ngai, J., & Oka, Y. (2020) · Nature · doi:10.1038/s41586-020-2821-8 · Cited by 92
Decoding Cellular Mechanisms for Mechanosensory Discrimination
von Buchholtz, L. J., Ghitani, N., Lam, R. M., Licholai, J. A., Chesler, A. T., & Ryba, N. J. P. (2020) · Neuron · doi:10.1016/j.neuron.2020.10.028 · Cited by 91
Postnatal connectomic development of inhibition in mouse barrel cortex
Gour, A., Boergens, K. M., Heike, N., Hua, Y., Laserstein, P., Song, K., & Helmstaedter, M. N. (2020) · Science · doi:10.1126/science.abb4534 · Cited by 91
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Stadler, T., Pybus, O. G., & Stumpf, M. P. H. (2021) · Science · doi:10.1126/science.aah6266 · Cited by 91
Towards a Human Cell Atlas: Taking Notes from the Past
Lindeboom, R. G. H., Regev, A., & Teichmann, S. A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.03.007 · Cited by 87
Diversity of oligodendrocytes and their progenitors
Dimou, L., & Simons, M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.09.015 · Cited by 86
Isoform cell-type specificity in the mouse primary motor cortex
Booeshaghi, A. S., Yao, Z., van Velthoven, C. T. J., Smith, K. A., Tasic, B., Zeng, H., & Pachter, L. S. (2021) · Nature · doi:10.1038/s41586-021-03969-3 · Cited by 81
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Condylis, C., Ghanbari, A., Manjrekar, N., Bistrong, K., Yao, S., Yao, Z., Nguyen, T. N., Zeng, H., Tasic, B., & Chen, J. L. (2022) · Science · doi:10.1126/science.abl5981 · Cited by 81
Self-citationAssembly and function of spinal circuits for motor control
Catela, C., Shin, M. M., & Dasen, J. S. (2015) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100814-125155 · Cited by 77
BRAIN 2.0: Transforming neuroscience
Ngai, J. (2022) · Cell · doi:10.1016/j.cell.2021.11.037 · Cited by 77
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Tanay, A., & Sebé-Pedrós, A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.008 · Cited by 76
A periodic table of cell types
Xia, B., & Yanai, I. (2019) · Development · doi:10.1242/dev.169854 · Cited by 73
Epigenomic diversity of cortical projection neurons in the mouse brain
Zhang, Z., Zhou, J., Tan, P., Pang, Y., Rivkin, A. C., Kirchgessner, M. A., Williams, E., Lee, C.-T., Liu, H., Franklin, A. D., et al. (2021) · Nature · doi:10.1038/s41586-021-03223-w · Cited by 73
Temporal controls over inter-areal cortical projection neuron fate diversity
Klingler, E., Tomasello, U., Prados, J., Kebschull, J. M., Contestabile, A., Galiñanes, G. L., Fièvre, S., Santinha, A. J., Platt, R. J., Huber, D., et al. (2021) · Nature · doi:10.1038/s41586-021-04048-3 · Cited by 71
Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics
Bates, A. S., Janssens, J., Jefferis, G. S., & Aerts, S. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.12.012 · Cited by 70
Flagged first, then by citations
3 of the 219 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
Yao, Z., van Velthoven, C. T. J., Kunst, M., Zhang, M., McMillen, D., Lee, C., Jung, W., Goldy, J., Abdelhak, A., Aitken, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06812-z · Cited by 1,203
Self-citationEvolution of neuronal cell classes and types in the vertebrate retina
Hahn, J., Monavarfeshani, A., Qiao, M., Kao, A. H., Kölsch, Y., Kumar, A., Kunze, V. P., Rasys, A. M., Richardson, R., Wekselblatt, J. B., et al. (2023) · Nature · doi:10.1038/s41586-023-06638-9 · Cited by 218
“Advances in high-throughput single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have enabled related activity focused on determining the extent to which cell types, the functional units of complex tissues 6,7 , are conserved among species.”
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 253
“Defining a cell type is not a trivial question [41].”
BackgroundThe molecular cytoarchitecture of the adult mouse brain
Langlieb, J., Sachdev, N., Balderrama, K. S., Nadaf, N. M., Raj, M., Murray, E. M., Webber, J. T., Vanderburg, C. R., Gazestani, V., Tward, D. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06818-7 · Cited by 252
Qi, L., Iskols, M., Shi, D., Reddy, P., Walker, C., Lezgiyeva, K., Voisin, T., Pawlak, M., Kuchroo, V. K., Chiu, I. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.02.006 · Cited by 211
Metadata
- Authors
- Zeng, H.
- Affiliations
- Allen Institute for Brain Science (US)
- Corresponding author
- Zeng, Hongkui
- ORCID
- Zeng, Hongkui
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 185
- Issue
- 15
- Pages
- 2739-2755
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC9342916
- OpenAlex
- W4286449197
- Semantic Scholar
- fcb96c79ea7a1b1e583da762c3405f5296bcb8d6
- Cited by
- 439
- References
- 219
- Citations by year
- 2026: 95 · 2025: 126 · 2024: 121 · 2023: 83 · 2022: 13
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 385 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIMH (U19MH114830)
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- cell types, cell type classification, mammalian brain, cell function, cellular heterogeneity
- MeSH terms
- Animals, Brain, Humans, Mammals, Single-Cell Analysis
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.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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.
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