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.
AI snapshot
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.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 60 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Within each cortical excitatory subclass, individual neurons show extensive heterogeneity along regional, topographic, and individual (potentially stochastic) axes.
“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,”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Peng et al., 2021 source
Not checked in the free run.
- Premisecitation
In mouse visual cortex, visual response properties such as orientation or direction selectivity differ only at subclass level.
“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);”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Bugeon et al., 2021
Not checked in the free run.
- Interpretationreasoning
Single-cell transcriptomics and connectomics are the two main approaches that could achieve complete cell type definition.
“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.”
Quote in the paperNot a substantial claim - Premisecitation
Patch-seq data integrating transcriptome, electrophysiology and morphology indicate that mouse motor cortex cell types have corresponding multimodal distinctions at the subclass level.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Scala et al., 2021 source
Not checked in the free run.
- Premisecitation
These three axes of variation do not readily correlate with transcriptomic types within the subclass.
“which do not readily correlate with transcriptomic types within the subclass.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Peng et al., 2021 source
Not checked in the free run.
- Interpretationnone given
Transcriptomic types have excellent correspondence with their spatial distribution patterns.
“So far it has been shown that transcriptomic types have excellent correspondence with their spatial distribution patterns.”
Quote in the paperNot a substantial claim - Premisecitation
These visual cortex GABAergic MET types show strong cross-modality concordance and mutual predictability.
“These visual cortex GABAergic MET types show robust cross-modality concordance and mutual predictability.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gouwens et al., 2020 source
Not checked in the free run.
- Premisecitation
In mouse somatosensory cortex, higher sensory response is seen in a specific L2/3 IT excitatory transcriptomic type.
“in somatosensory cortex, higher sensory response is seen in a specific L2/3 IT excitatory transcriptomic type (Condylis et al., 2022).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Condylis et al., 2022 source
Not checked in the free run.
- Premisecitation
A Patch-seq study of mouse visual cortical GABAergic neurons found relatively high corresponding continuous variation of transcriptomic types with cortical-depth anatomical distribution and morpho-electrical properties.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gouwens et al., 2020 source
Not checked in the free run.
- Premisecitation
In mouse visual cortex, GABAergic transcriptomic types differ in their response to behavioral states such as running versus resting.
“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);”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bugeon et al., 2021
Not checked in the free run.
- Premisecitation
In mouse hypothalamus, activated neurons during a specific behavioral state are often distributed across a range of transcriptomic cell types.
“In hypothalamus, several studies using either of the two approaches in mice demonstrate that activated neurons during a specific behavioral state are often distributed across a range of transcriptomic cell types (Kim et al., 2019; Moffitt et al., 2018; Xu et al., 2020).”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
Cell type-specific gene expression changes associated with different cell states may be seen during circadian cycles, variable metabolic states, development, aging, or under behavioral, pharmacological or diseased conditions.
“Cell type-specific gene expression changes associated with different cell states may be seen during circadian cycles, variable metabolic states, development, aging, or under behavioral, pharmacological or diseased conditions (Fig. 4) (Mayr et al., 2019; Morris, 2019).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Some transcriptomic types show extra heterogeneity in morpho-electrical properties.
“There is also additional heterogeneity of morpho-electrical properties within some transcriptomic types, indicating a more complex picture.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Scala et al., 2021 source
Not checked in the free run.
- Premisecitation
Brain-wide complete reconstruction of single neuron morphology is currently the only method to capture the full extent of a neuron's axon projection pattern and define projection neuron types.
“Brain-wide complete reconstruction of single neuron morphology is currently the only approach to capture the full extent of a neuron’s axon projection pattern and define projection neuron types (Gao et al., 2022; Peng et al., 2021; Winnubst et al., 2019).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
In each mouse motor cortex subclass, morpho-electrical properties change continuously together with transcriptomic types.
“Within each subclass the morpho-electrical properties vary continuously along with the transcriptomic types.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Scala et al., 2021 source
Not checked in the free run.
- Interpretationreasoning
Because spatial distribution patterns are defined during development, transcriptomes may retain the developmental plan.
“Since the spatial distribution pattern is defined during development, this suggests that transcriptomes may retain the developmental plan.”
Quote in the paperSubstantial claim - Premisecitation
Transcriptomically derived taxonomies in adult mammalian brain consistently show a hierarchical organization of cell types.
“Transcriptomically derived cell type taxonomies in the adult mammalian brain, with majority of the studies conducted in mouse, have consistently revealed a hierarchical organization of cell types (Fig. 2) (Brain Initiative Cell Census Network, 2021; Macosko et al., 2015; Romanov et al., 2017; Russ et al., 2021; Saunders et al., 2018; Shekhar et al., 2016; Tasic et al., 2016; Tasic et al., 2018;”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisenone given
Most phenotypic measurements, including single-cell transcriptome, are only a one-time snapshot of the cell.
“This is a difficult question since most of the phenotypic measurements including the single-cell transcriptome are only a one-time snapshot of the cell.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Transcriptomic and epigenomic classification is a good proxy for cell type classification because regulatory mechanisms for cell identity are embedded in these data.
“Like phylogenomics for species classification, transcriptomic (and epigenomic) classification is a good proxy of cell type classification as the gene regulatory mechanisms that encode and maintain cell type identities are embedded in the transcriptomes and epigenomes.”
Quote in the paperSubstantial claim - Interpretationcitation
Continuous variations among cortical neuron types in transcriptomic space correspond strongly to their continuous spatial distribution along cortical depth.
“Thus, a strong correspondence is demonstrated here between the continuous variations among cortical neuron types in the transcriptomic space and their continuous and directed spatial distribution patterns.”
Quote in the paperSubstantial claim - Premisecitation
Specific sets of cell adhesion molecules provide guidance cues for axon path finding and synapse formation, leading to assembly of region- and cell type-specific local and global circuit networks.
“Third, specific sets of cell adhesion molecules provide guidance cues for axon path finding and synapse formation, leading to the assembly of region- and cell type-specific local and global circuit networks.”
Quote in the paperNot a substantial claim0 of 3 sources checked - Interpretationreasoning
Taken together, non-neuronal cell types actively respond to and contribute to different physiological and pathological changes in the brain.
“Taken together, these studies paint a collective picture on how the variety of non-neuronal cell types actively respond to and contribute to different physiological and pathological changes in the brain.”
Quote in the paperSubstantial claim - Premisecitation
Driven by transcription factor networks, each type of neural progenitor generates a series of neuronal cell types.
“Second, driven by the transcription factor network, each type of neural progenitors generates a series of neuronal cell types.”
Quote in the paperNot a substantial claim0 of 3 sources checked - Premisecitation
There are transient cell types and circuits that mainly exist during development and have developmental stage-specific functions.
“Finally, there are transient cell types and circuits that mainly exist during development and have developmental stage-specific functions (Cossart and Garel, 2022; Molnar et al., 2020).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Cossart and Garel, 2022
Not checked in the free run.
- cites Molnar et al., 2020 source
Not checked in the free run.
- Interpretationcitation
Many non-neuronal cell types in the brain, despite lower diversity than neurons in baseline adult state, undergo pronounced changes and exhibit many different cell states under different physiological or diseased conditions.
“Here I highlight a prominent feature of the non-neuronal cell types in the brain, which is that despite having lower diversity than neurons in baseline adult state, many non-neuronal cell types undergo pronounced changes, i.e., they exhibit many different cell states, under different physiological or diseased conditions.”
Quote in the paperSubstantial claim - Interpretationreasoning
Cell type development is not a simple linear process but a highly multifaceted process leading to a complex cell type landscape that underlies the richness of cell type function.
“All these observations, and more to be discovered, contribute to a nuanced understanding that cell type development is not a simple linear process, but a highly multifaceted process leading to the complex cell type landscape described in the above sections, which underlies the richness of cell type function.”
Quote in the paperSubstantial claim - Premisecitation
The development of a cell type may not follow a simple trajectory but involves multiple steps of divergent or convergent differentiation.
“The development of a cell type may not follow a simple trajectory but involves multiple steps of divergent or convergent differentiation (Shekhar et al., 2022), the former due to the multipotency of progenitors or transitional cell types and the latter due to convergence of different transitional types.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Shekhar et al., 2022 source
Not checked in the free run.
- May combine two claims. 15
- Classified as probably not a substantial claim. 17
May combine two claims.
InterpretationreasoningSingle-cell transcriptomics alone can faithfully capture overall cell type organization at class and subclass levels, but validity at the lowest branch level remains untested.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationGlutamatergic neuron types differ between visual and motor cortical areas, while GABAergic neuron types are shared between these areas.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Tasic et al., 2018 source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenWhether specific transcriptomic types beyond the subclass level have specific connectional or functional attributes is still unclear in many cases.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningA single-target-site Retro-seq assay is often insufficient to resolve a transcriptomic type's target specificity except in special cases, because a neuron type usually has multiple projection targets and a neuron within that type 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, a single-target-site Retro-seq assay is often insufficient to resolve the target specificity of a transcriptomic type except in special cases.”
Quote in the paperSubstantial claim- Rests on an assumption: A neuron type usually has multiple projection targets and individual neurons can choose a subset specifically or randomly.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningSome connectional or functional properties should not be used to define cell types because they may be emerging properties from network interactions or experience/activity-dependent processes representing cell state rather than type.
“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”.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningSpecific molecular signatures responsible for certain essential anatomical or functional features may be hidden below noise and need supervised approaches to be brought out.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenCell types are products of evolution, and their identities are encoded in the genome.
“Therefore, the first and foremost principle is that cell types are the product of evolution and cell type identities are encoded in the genome.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenTranscriptomic clusters can in theory be infinitely subdivided, and profiling more cells yields more clusters.
“since in theory transcriptomic clusters can be infinitely subdivided and the more cells profiled the more clusters can be obtained”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationA study using complete reconstruction in cortical excitatory neuron subclass-specific Cre driver lines found distinct projection patterns between subclasses, including between IT and ET and between L2/3 IT and L5 IT neurons, confirming subclass level projection specificity.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Peng et al., 2021 source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationTo reduce heterogeneity at the individual type level, triple-modality MET types were defined at a granularity between transcriptomic subclasses and types.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gouwens et al., 2020 source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationCells within an organism can be grouped into types, with cells of a type sharing similar structure and function distinct from other types.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Arendt, 2008 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOligodendrocyte pathology is evident in multiple sclerosis, schizophrenia, and Alzheimer's disease.
“Oligodendrocyte pathology is evident in a range of disorders including multiple sclerosis, schizophrenia and Alzheimer’s disease (Kuhn et al., 2019).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kuhn et al., 2019 source
Not checked in the free run.
May combine two claims.
PremisecitationDevelopmental trajectory is not the same as developmental lineage; a progenitor can produce cells belonging to several neuronal and non-neuronal types, ordered by developmental timing.
“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”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Agathocleous and Harris, 2009
Not checked in the free run.
- cites Sagner and Briscoe, 2019
Not checked in the free run.
- cites Sulston et al., 1983 source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenThe transcriptomic taxonomy contains evolutionarily rooted molecular signatures and allows effective label transfer and linking with all other modalities.
“The transcriptomic taxonomy contains evolutionarily rooted molecular signatures and allows effective label transfer and linking with all other modalities.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenCell types are the product of evolution and are the basic functional units of an organism.
“In conclusion, cell types are the product of evolution, and they are the basic functional units of an organism.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIndividual variations within a species driven by genetic or environmental factors may manifest as a variety of cell type or cell state variations.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMicroglia display diverse and dynamic phenotypic states and play roles in development, adulthood, aging, and diseases.
“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).”
Quote in the paperNot a substantial claim0 of 3 sources checkedClassified as probably not a substantial claim.
Premisenone givenThe distinction between cell types and cell states is particularly challenging during development, as cells continually change their states and may switch their cell type identities at certain key time points.
“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.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationOpposing morphogen gradients establish the basic plan for cortex (anterior-posterior) or spinal cord (dorsal-ventral) patterning and provide instructive signals for expression of complementary transcription factor activators and repressors, which define distinct neural progenitor domains.
“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.”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationExtensive historical studies of mouse cortex and spinal cord cell types have uncovered several common principles.
“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).”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMyelination exhibits activity-dependent plasticity.
“Myelination process also exhibits activity-dependent plasticity (Monje, 2018).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Monje, 2018 source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisereasoningThe transcriptomic taxonomy organizes cell types hierarchically, from major divisions at class and subclass levels to more granular and fuzzier divisions at type and subtype levels due to prevailing continuous variations at the latter levels.
“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).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationAstrocytes 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.
“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).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Ben Haim and Rowitch, 2017
Not checked in the free run.
- cites Khakh and Deneen, 2019
Not checked in the free run.
May combine two claims.
PremisecitationThe set of cell type-defining transcription factors in a progenitor or daughter cell can change with time, so different neuronal types emanate from the same progenitor in a precisely timed birth order.
“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”.”
Quote in the paperNot a substantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationLater in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.
“Later in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationSingle-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.
“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).”
Quote in the paperNot a substantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationReactive astrocytes undergo morphological, molecular, and functional changes in response to injury or CNS diseases and may adopt multiple heterogeneous states depending on context.
“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).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Escartin et al., 2021 source
Not checked in the free run.
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
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent studies define a cell type as a continuous set of cell states aligned with the range of cellular phenotypes resulting from interaction between the cell type and its microenvironment.
“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]
In “Cell types versus cell states” (page unavailable), the paper says a cell type can exist in different states, that states can respond to context, and that state transitions tend to be continuous. It does not define a cell type as a continuous set of states or equate that set with the range of phenotypes produced by interaction with its microenvironment. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSingle-cell transcriptomics provides unprecedented profiling depth and scalability, enabling comprehensive quantitative analysis and classification of cell types at scale.
“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]
Consistent with the principle of hierarchical cell-type organization identified in previous studies, the study defined a hierarchical taxonomy of cell types across the entire mouse brain with four levels: class, subclass, supertype and cluster.
“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]
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 · see citing works
The structure of the nervous system of the nematode <i>Caenorhabditis elegans</i>
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 · see citing works
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 · see citing works
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 · see citing works
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 3,326 · see citing works
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 · see citing works
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 2,995 · see citing works
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 · see citing works
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 2,533 · see citing works
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 2,294 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2,074 · see citing works
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 · see citing works
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 · see citing works
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 1,847 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Museum of spatial transcriptomics
Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255 · see citing works
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 1,246 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,160 · see citing works
The Neuronal Organization of the Retina
Masland, R. H. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.002 · Cited by 1,155 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Fly 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 847 · see citing works
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 · see citing works
Self-citationDaigle, 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 751 · see citing works
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 · see citing works
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 681 · see citing works
Revisiting the neurovascular unit
Schaeffer, S. M., & Iadecola, C. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00904-7 · Cited by 654 · see citing works
Oligodendrocytes in development, myelin generation and beyond
Kuhn, S., Gritti, L., Crooks, D., & Dombrowski, Y. (2019) · Cells · doi:10.3390/cells8111424 · Cited by 625 · see citing works
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 · see citing works
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 594 · see citing works
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 585 · see citing works
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 · see citing works
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 574 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationTosches, 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 · see citing works
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 · see citing works
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 498 · see citing works
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 · see citing works
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 · see citing works
The Emerging Nature of Astrocyte Diversity
Khakh, B. S., & Deneen, B. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070918-050443 · Cited by 485 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 378 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 370 · see citing works
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 · see citing works
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 334 · see citing works
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 · see citing works
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 318 · see citing works
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 317 · see citing works
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 313 · see citing works
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 310 · see citing works
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 310 · see citing works
A unifying concept: the history of cell theory
Mazzarello, P. (1999) · Nature Cell Biology · doi:10.1038/8964 · Cited by 307 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 282 · see citing works
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 · see citing works
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 · see citing works
Myelin plasticity and nervous system function
Monje, M. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061853 · Cited by 274 · see citing works
Brain Architecture: Understanding the Basic Plan
Swanson, L. W. (2002) · Cited by 273 · see citing works
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. A., & Zador, A. M. (2019) · Cell · doi:10.1016/j.cell.2019.09.023 · Cited by 273 · see citing works
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 · see citing works
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 264 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 254 · see citing works
Self-citationCerebellar 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 253 · see citing works
Evolution of prefrontal cortex
Preuss, T. M., & Wise, S. P. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01076-5 · Cited by 244 · see citing works
Spatially resolved single-cell genomics and transcriptomics by imaging
Zhuang, X. (2021) · Nature Methods · doi:10.1038/s41592-020-01037-8 · Cited by 243 · see citing works
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 · see citing works
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 240 · see citing works
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 · see citing works
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 233 · see citing works
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 · see citing works
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 · see citing works
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 218 · see citing works
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 212 · see citing works
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 209 · see citing works
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 · see citing works
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 · see citing works
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 200 · see citing works
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 197 · see citing works
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 197 · see citing works
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 · see citing works
Self-citationA 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. V., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0483-3 · Cited by 191 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
A 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 184 · see citing works
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 177 · see citing works
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 172 · see citing works
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 · see citing works
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 · see citing works
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 169 · see citing works
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 · see citing works
Recording development with single cell dynamic lineage tracing
McKenna, A., & Gagnon, J. A. (2019) · Development · doi:10.1242/dev.169730 · Cited by 164 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Wolff, T., & Rubin, G. M. (2018) · Journal of Comparative Neurology · doi:10.1002/cne.24512 · Cited by 156 · see citing works
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 · see citing works
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154 · see citing works
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 · see citing works
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 140 · see citing works
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 139 · see citing works
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 · see citing works
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 · see citing works
Sun, Y.-C., Chen, X., Fischer, S., Lu, S., Zhan, H., Gillis, J. A., & Zador, A. M. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00842-4 · Cited by 124 · see citing works
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 122 · see citing works
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 · see citing works
The Neuron Identity Problem: Form Meets Function
Fishell, G., & Heintz, N. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.035 · Cited by 115 · see citing works
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 111 · see citing works
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 · see citing works
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 110 · see citing works
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 · see citing works
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 · see citing works
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 97 · see citing works
Phylodynamics for cell biologists
Stadler, T., Pybus, O. G., & Stumpf, M. P. H. (2021) · Science · doi:10.1126/science.aah6266 · Cited by 97 · see citing works
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 · see citing works
Self-citationThe 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 91 · see citing works
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 89 · see citing works
Cell 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 87 · see citing works
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 86 · see citing works
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 · see citing works
Self-citationDense functional and molecular readout of a circuit hub in sensory cortex
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 · see citing works
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 · see citing works
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 77 · see citing works
BRAIN 2.0: Transforming neuroscience
Ngai, J. (2022) · Cell · doi:10.1016/j.cell.2021.11.037 · Cited by 77 · see citing works
Evolutionary cell type mapping with single-cell genomics
Tanay, A., & Sebé-Pedrós, A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.008 · Cited by 75 · see citing works
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 71 · see citing works
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 · see citing works
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 · see citing works
Shekhar, K., Whitney, I. E., Butrus, S., Peng, Y.-R., & Sanes, J. R. (2022) · eLife · doi:10.7554/elife.73809 · Cited by 68 · see citing works
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 1,194 · see citing works
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 · see citing works
“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 237 · see citing works
“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 240 · see citing works
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 186 · see citing works
Genetics of human brain development
Zhou, Y. F., Song, H., & Ming, G. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00626-5 · Cited by 173 · see citing works
Molecular and anatomical characterization of parabrachial neurons and their axonal projections
Pauli, J. L., Chen, J., Basiri, M. L., Park, S., Carter, M. E., Sanz, E., McKnight, G. S., Stuber, G. D., & Palmiter, R. D. (2022) · eLife · doi:10.7554/elife.81868 · Cited by 162 · see citing works
Single-cell genomics and regulatory networks for 388 human brains
Emani, P. S., Liu, J. J., Clarke, D., Jensen, M., Warrell, J., Gupta, C., Meng, R., Lee, C.-Y., Xu, S., Dursun, C., et al. (2024) · Science · doi:10.1126/science.adi5199 · Cited by 168 · see citing works
Insights into Alzheimer’s disease from single-cell genomic approaches
Murdock, M. H., & Tsai, L.-H. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01222-2 · Cited by 148 · see citing works
Hippocampal GABAergic interneurons and memory
Tzilivaki, A., Tukker, J. J., Maier, N., Poirazi, P., Sammons, R. P., & Schmitz, D. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.016 · Cited by 145 · see citing works
Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain
Liu, H., Zeng, Q., Zhou, J., Bartlett, A., Wang, B.-A., Berube, P., Tian, W., Kenworthy, M., Altshul, J., Nery, J. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06805-y · Cited by 139 · see citing works
“Developing a brain cell-type taxonomy requires integrating various molecular modalities, verifying cell clusters on the basis of multiple molecular information and applying a uniform nomenclature 29 .”
Self-citationPurification and characterization of human neural stem and progenitor cells
Liu, D. D., He, J. Q., Sinha, R., Eastman, A. E., Toland, A. M. S., Morri, M., Neff, N. F., Vogel, H. S., Uchida, N., & Weissman, I. L. (2023) · Cell · doi:10.1016/j.cell.2023.02.017 · Cited by 124 · see citing works
A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brainPreprint
Yao, Z., van Velthoven, C. T. J., Kunst, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.06.531121 · Cited by 138 · see citing works
Self-citationCancer cell states: Lessons from ten years of single-cell RNA-sequencing of human tumors
Tirosh, I., & Suvà, M. L. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.08.005 · Cited by 131 · see citing works
Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution
Lu, T., Brbić, M., Park, Y.-J., Jackson, T., Chen, J., Kolluru, S. S., Qi, Y., Katheder, N. S., Cai, X. T., Lee, S., et al. (2023) · Science · doi:10.1126/science.adg0934 · Cited by 118 · see citing works
A reference cell tree will serve science better than a reference cell atlas
Domcke, S., & Shendure, J. (2023) · Cell · doi:10.1016/j.cell.2023.02.016 · Cited by 110 · see citing works
Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
Song, Y., Miao, Z., Brāzma, A., & Papatheodorou, I. (2023) · Nature Communications · doi:10.1038/s41467-023-41855-w · Cited by 106 · see citing works
Neuroscience Needs Network Science
Barabási, D. L., Ginestra Bianconi, A., Bullmore, E. T., Burgess, M., Chung, S., Eliassi‐Rad, T., Dileep George, A., Kovács, I. A., Makse, H. A., Nichols, T. E., et al. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.1014-23.2023 · Cited by 95 · see citing works
“Single-cell transcriptomics has also enabled rapid and diverse profiling of cellular identity in various animals, developmental stages, and brain health patterns (Zeng, 2022).”
BackgroundSelf-citationBravo González-Blas, C., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01316-4 · Cited by 85 · see citing works
“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 microenvironment 4 .”
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortexPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525290 · Cited by 70 · see citing works
Molecular taxonomy of nociceptors and pruriceptors
Kupari, J., & Ernfors, P. (2023) · Pain · doi:10.1097/j.pain.0000000000002831 · Cited by 70 · see citing works
Inhibitory specificity from a connectomic census of mouse visual cortex
Schneider-Mizell, C. M., Bodor, Á. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Elabbady, L., Gamlin, C. R., Kapner, D., Kinn, S., Mahalingam, G., et al. (2025) · Nature · doi:10.1038/s41586-024-07780-8 · Cited by 141 · see citing works
Su, Y., Zhou, Y., Bennett, M. L., Li, S., Carceles-Cordon, M., Lu, L., Huh, S., Jimenez-Cyrus, D., Kennedy, B. C., Kessler, S. K., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.09.010 · Cited by 78 · see citing works
A marmoset brain cell census reveals regional specialization of cellular identities
Krienen, F. M., Levandowski, K. M., Zaniewski, H., del Rosario, R. C. H., Schroeder, M. E., Goldman, M., Wienisch, M., Lutservitz, A., Beja-Glasser, V. F., Chen, C., et al. (2023) · Science Advances · doi:10.1126/sciadv.adk3986 · Cited by 66 · see citing works
“Cell types are identified based on many factors including their function, morphology, developmental origin, and regional context ( 36 – 38 ).”
BackgroundVirtual Fly Brain—An interactive atlas of the Drosophila nervous system
Court, R., Costa, M., Pilgrim, C., Millburn, G., Holmes, A., McLachlan, A. D., Larkin, A., Matentzoglu, N., Kır, H., Parkinson, H., et al. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1076533 · Cited by 51 · see citing works
“The concept of cell type is even more controversial (Bates et al., 2019; Zeng, 2022), but neurobiologists typically group cells sharing morphological, connectomic, functional and developmental properties together under a common name, and generally refer to such groupings as types.”
BackgroundAdvances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types
Ben‐Simon, Y., Hooper, M. J., Narayan, S., Daigle, T. L., Dwivedi, D., Way, S. W., Oster, A., Stafford, D. A., Mich, J. K., Taormina, M. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.002 · Cited by 81 · see citing works
“1,3,11,83 We utilized single-cell transcriptomic and epigenomic data to identify marker genes for the generation of transgenic lines, as well as putative enhancers for the creation of enhancer AAVs, and evaluated their specificity for experimental access to cortical cell types.”
Mapping visual functions onto molecular cell types in the mouse superior colliculus
Liu, Y., Savier, E. L., DePiero, V. J., Chen, C., Schwalbe, D. C., Abraham‐Fan, R., Chen, H., Campbell, J. N., & Cang, J. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.036 · Cited by 59 · see citing works
“These studies have generated rich information regarding neuronal diversity and identified genetic access points to specific neuronal populations for mechanistic investigations.(1) However, even though the connection between morphology and function has been firmly established, the link between functional and molecular…”
A guide to the BRAIN Initiative Cell Census Network data ecosystem
Hawrylycz, M., Martone, M. E., Ascoli, G. A., Bjaalie, J. G., Dong, H., Ghosh, S., Gillis, J. A., Hertzano, R. P., Haynor, D. R., Hof, P. R., et al. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002133 · Cited by 55 · see citing works
Self-citationThe cell-type underpinnings of the human functional cortical connectome
Zhang, X.-H., Anderson, K. M., Dong, H.-M., Chopra, S., Dhamala, E., Emani, P. S., Gerstein, M., Margulies, D. S., & Holmes, A. J. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01812-2 · Cited by 62 · see citing works
“Although these multidimensional features do not necessarily vary concurrently, recent developments in high-throughput single-nucleus RNA-sequencing (snRNA-seq) data have played a key role in characterizing broad profiles of cellular gene expression 34 .”
Voyager: exploratory single-cell genomics data analysis with geospatial statisticsPreprint
Moses, L., Einarsson, P. H., Jackson, K. C., Luebbert, L., Booeshaghi, A. S., Antonsson, S. E., Bray, N., Melsted, P., & Pachter, L. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.20.549945 · Cited by 57 · see citing works
“However, with more considerations on the nature of cell types 72,73 and ESDA of categorical variables, categorical methods may be added in future versions.”
Causal evidence of a line attractor encoding an affective state
Vinograd, A., Nair, A., Kim, J. H., Linderman, S. W., & Anderson, D. J. (2024) · Nature · doi:10.1038/s41586-024-07915-x · Cited by 63 · see citing works
“One focuses on understanding behaviourally specialized neuron types and their functional connectivity 8–10 , whereas the other investigates emergent properties of neural networks, such as attractors 1,3,11 .”
Methods and Insights from Single-Cell Expression Quantitative Trait Loci
Kang, J. B., Raveane, A., Nathan, A., Soranzo, N., & Raychaudhuri, S. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101422-100437 · Cited by 53 · see citing works
“While the concept of cell types is nuanced (168), they can be thought of as biologically functional units playing distinct roles—for example, the pancreas contains hormone-producing types, like alpha and beta cells, as well as supporting cell types, like endothelial cells and fibroblasts.”
BackgroundFourth Report on Chicken Genes and Chromosomes 2022
Smith, J., Alfieri, J. M., Anthony, N., Arensburger, P., Athrey, G., Balacco, J., Balic, A., Bardou, P., Barela, P., Bigot, Y., et al. (2022) · Cytogenetic and Genome Research · doi:10.1159/000529376 · Cited by 52 · see citing works
Transcriptomic neuron types vary topographically in function and morphology
Shainer, I., Kappel, J. M., Laurell, E., Donovan, J. C., Schneider, M., Kuehn, E., Arnold-Ammer, I., Stemmer, M., Larsch, J., & Baier, H. (2025) · Nature · doi:10.1038/s41586-024-08518-2 · Cited by 54 · see citing works
A transcriptomic taxonomy of mouse brain-wide spinal projecting neurons
Winter, C. C., Jacobi, A., Su, J., Chung, L., van Velthoven, C. T. J., Yao, Z., Lee, C., Zhang, Z., Yu, S., Gao, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06817-8 · Cited by 42 · see citing works
Self-citationHoffman, G. E., Lee, D., Bendl, J. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.17.533005 · Cited by 48 · see citing works
“Conversely, control samples needed to be classified by (1) CERAD protocol as “no AD” or “possible AD” and (2) Braak AD staging protocol within the stage 0-2.”
Ning, K., Lu, B., Wang, X., Zhang, X., Nie, S., Jiang, T., Li, A., Fan, G., Wang, X., Luo, Q., et al. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01230-2 · Cited by 48 · see citing works
The heterocellular heart: identities, interactions, and implications for cardiology
Lother, A., & Kohl, P. K. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-01000-6 · Cited by 43 · see citing works
“The rise of single-cell RNA-seq (scRNA-seq) technologies and the accompanying possibility of making ever more subtle distinctions, but also the user-dependence of RNA-seq data analysis, presentation and interpretation, have refuelled the ongoing debate on how to define a ‘cell type’ [123, 195, 218].”
BackgroundThe cell type composition of the adult mouse brain revealed by single cell and spatial genomicsPreprint
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) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.06.531307 · Cited by 44 · see citing works
“Although the precise number of distinct cell types present in the brain is unknown, the number is presumed to be in the thousands 1,2 .”
BackgroundInterneuron diversity in the human dorsal striatum
Garma, L. D., Harder, L., Barba-Reyes, J. M., Salas, S. M., Díez-Salguero, M., Nilsson, M. F., Serrano‐Pozo, A., Hyman, B. T., & Muñoz‐Manchado, A. B. (2024) · Nature Communications · doi:10.1038/s41467-024-50414-w · Cited by 48 · see citing works
Mapping the topography of spatial gene expression with interpretable deep learning
Chitra, U., Arnold, B. J., Sarkar, H., Sanno, K., Ma, C., Lopez-Darwin, S. L., & Raphael, B. J. (2025) · Nature Methods · doi:10.1038/s41592-024-02503-3 · Cited by 63 · see citing works
Li, C., Shao, X., Zhang, S., Wang, Y., Jin, K., Yang, P., Lu, X., Fan, X., & Wang, Y. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101568 · Cited by 46 · see citing works
Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex
Bella, D. J. D., Domıńguez-Iturza, N., Brown, J. R., & Arlotta, P. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.021 · Cited by 37 · see citing works
The brain is not mental! coupling neuronal and immune cellular processing in human organisms
Ciaunica, A., Shmeleva, E. V., & Levin, M. G. (2023) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2023.1057622 · Cited by 60 · see citing works
“Studies going back to Ramon y Cajal revealed that cell types in the brain and body display several properties in many modalities (e.g., molecular, morphological, physiological, and functional) (Zeng and Sanes, 2017; see Zeng, 2022 for a recent review).”
BackgroundPredictive and robust gene selection for spatial transcriptomics
Covert, I., Gala, R., Wang, T., Svoboda, K., Sümbül, U., & Lee, S. (2023) · Nature Communications · doi:10.1038/s41467-023-37392-1 · Cited by 40 · see citing works
The landscape of transcription factor promoter activity during vegetative development in Marchantia
Romani, F., Sauret-Gueto, S., Rebmann, M., et al. (2024) · The Plant Cell · doi:10.1093/plcell/koae053 · Cited by 41 · see citing works
Frank, M. M., Sitko, A. A., Suthakar, K., Cadenas, L. T., Hunt, M., Yuk, M. C., Weisz, C. J. C., & Goodrich, L. V. (2023) · eLife · doi:10.7554/elife.83855 · Cited by 35 · see citing works
Brain wiring determinants uncovered by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · Current Biology · doi:10.1016/j.cub.2023.08.020 · Cited by 37 · see citing works
Spotiphy enables single-cell spatial whole transcriptomics across an entire section
Yang, J., Zheng, Z., Jiao, Y., Yu, K., Bhatara, S., Yang, X., Natarajan, S., Zhang, J., Pan, Q., Easton, J. B., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02622-5 · Cited by 39 · see citing works
Single-cell multi-omics and lineage tracing to dissect cell fate decision-making
Haghverdi, L., & Ludwig, L. S. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.003 · Cited by 35 · see citing works
“The blueprint that underlies the development of a human body, consisting of over 30 trillion cells and organized into a myriad of organ systems each composed of diverse cell types and states, is orchestrated by information encoded in our genomes (Zeng, 2022).”
BackgroundHarmonized cross-species cell atlases of trigeminal and dorsal root gangliaPreprint
Bhuiyan, S. A., Xu, M., Yang, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.04.547740 · Cited by 38 · see citing works
Large-scale neurophysiology and single-cell profiling in human neuroscience
Lee, A. T., Chang, E. F., Paredes, M. F., & Nowakowski, T. J. (2024) · Nature · doi:10.1038/s41586-024-07405-0 · Cited by 33 · see citing works
“We must also embrace the fact that a consensus definition of a cell type is currently lacking 40 , but even if the question of the absolute number of cell types in the brain cannot be fully addressed, we can be successful in defining their transcriptomic marker genes.”
Stimulus novelty uncovers coding diversity in survey of visual cortexPreprint
Garrett, M., Groblewski, P. A., Piet, A. T., Ollerenshaw, D. R., Najafi, F., Yavorska, I., Amster, A., Bennett, C., Buice, M. A., Caldejon, S., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.14.528085 · Cited by 29 · see citing works
Self-citationIntrinsic neural diversity quenches the dynamic volatility of neural networks
Hutt, A., Rich, S., Valiante, T. A., & Lefebvre, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2218841120 · Cited by 33 · see citing works
Evolution of neuronal cell classes and types in the vertebrate retinaPreprint
Hahn, J., Monavarfeshani, A., Qiao, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.07.536039 · Cited by 33 · see citing works
Principles of neural stem cell lineage progression: Insights from developing cerebral cortex
Hippenmeyer, S. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102695 · Cited by 31 · see citing works
Zheng, Z., Liu, Y., Mu, R., Guo, X., Feng, Y., Chen, G., Yang, L., Qiu, W., Zhang, Q., Yang, W., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.012 · Cited by 37 · see citing works
Functional analysis of cell plasticity using single-cell technologies
Qin, X., & Tape, C. J. (2024) · Trends in Cell Biology · doi:10.1016/j.tcb.2024.01.006 · Cited by 32 · see citing works
Multimodal cortical neuronal cell type classification
Mao, X., & Staiger, J. F. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-02923-2 · Cited by 34 · see citing works
“This approach enables the identification of previously unknown cell types, revealing their unique genetic signatures and paving the way for a more comprehensive and nuanced classification of cortical neuronal cell types [40, 41].”
BackgroundHarris, N., Bates, S. G., Zhuang, Z., Bernstein, M., Stonemetz, J. M., Hill, T. J., Yu, Y. V., Calarco, J. A., & Sengupta, P. (2023) · Current Biology · doi:10.1016/j.cub.2023.02.073 · Cited by 29 · see citing works
Hua-wen, Z., Han, W., Gómez-Cabrero, D., Tegner, J. N., Gao, X., Cui, G., & Aranda, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1316 · Cited by 31 · see citing works
Continuous cell-type diversification in mouse visual cortex development
Gao, Y., van Velthoven, C. T. J., Lee, C., Thomas, E. D., Mathieu, R., Ayala, A. P., Barta, S., Bertagnolli, D., Campos, J., Cardenas, T., et al. (2025) · Nature · doi:10.1038/s41586-025-09644-1 · Cited by 43 · see citing works
Self-citationKupferschmidt, D. A., Cummings, K. A., Joffe, M. E., MacAskill, A. F., Malik, R., Sánchez-Bellot, C., Tejeda, H. A., & Yarur, H. E. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1136-22.2022 · Cited by 29 · see citing works
“…intersectional genetic strategies, and expansion of the toolset to tag populations based on dynamic cellular processes, the decade of neuroscience research will see remarkable advances in the parsing of these important cells into increasingly refined sub-classes (Bugeon et al., 2022; Zeng, 2022).”
BackgroundA multi-tissue single-cell expression atlas in cattle
Han, B., Li, H., Zheng, W., Zhang, Q., Chen, A., Zhu, S., Shi, T., Wang, F., Zou, D., Yu, S., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02329-5 · Cited by 36 · see citing works
Hernández‐Frausto, M., Bilash, O. M., Masurkar, A. V., & Basu, J. (2023) · Frontiers in Neural Circuits · doi:10.3389/fncir.2023.1223891 · Cited by 33 · see citing works
Molecular and functional diversity of the autonomic nervous system
Wang, T., Tufenkjian, A., Ajijola, O., & Oka, Y. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-025-00941-2 · Cited by 35 · see citing works
“The subsequent maturation of molecular and genetic tools allowed different neuron classes to be further defined 136–139 .”
The Central Extended Amygdala Guides Survival-Relevant Tradeoffs for Action SelectionPreprint
Holley, D., & Fox, A. S. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104879 · Cited by 23 · see citing works
“It is also possible, however, that this appearance of multifunctionality could arise as a product of within-cell-type heterogeneity, and that intermingled groups of ostensibly specific EAc neurons might be further functionally dissociable (e.g., see Zeng, 2022).”
BackgroundA dynamic and multimodal framework to define microglial states
Sankowski, R., & Prinz, M. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01978-3 · Cited by 29 · see citing works
Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks
Sui, X., Lo, J. A., Luo, S., He, Y., Tang, Z., Lin, Z., Barabási, D. L., Zhou, Y., Wang, W. X., Jia Liu, B., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02867-0 · Cited by 57 · see citing works
“This approach allowed us to examine further the morphological diversity of several major subclasses of GABAergic neurons 65 , classified by their transcriptomic profiles, within the mouse cerebral cortex.”
Suarez, A. A. R., Plissonnier, M., Grand, X., Michelet, M., Giraud, G., Sàez‐Palma, M., Dubois, A., Heintz, S., Diederichs, A., Renne, N. V., et al. (2024) · Gut · doi:10.1136/gutjnl-2023-331396 · Cited by 27 · see citing works
“50 This type of question could be explored through the access of sequential samples of CHB patients using fine-needle aspirates, 51 52 and the use of in vivo and ex vivo models for the characterisation of inflammatory responses.”
BackgroundEmbryonic temporal-spatial delineation of excitatory spinal V3 interneuron diversity
Deska‐Gauthier, D., Borowska-Fielding, J., Jones, C., Zhang, H., Mackay, C., Michail, R., Bennett, L. A., Bikoff, J. B., & Zhang, Y. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113635 · Cited by 21 · see citing works
Cellular and Synaptic Diversity of Layer 2-3 Pyramidal Neurons in Human IndividualsPreprint
Planert, H., Mittermaier, F. X., Grosser, S., Fidzinski, P., Schneider, U. C., Radbruch, H., Onken, J., Holtkamp, M., Schmitz, D., Alle, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.08.467668 · Cited by 21 · see citing works
Qi, L., Iskols, M., Shi, D., Reddy, P., Walker, C. J., Lezgiyeva, K., Voisin, T., Pawlak, M., Kuchroo, V. K., Chiu, I. M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.22.537932 · Cited by 22 · see citing works
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis
Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30 · see citing works
“Cell-type annotation is a foundational problem in the single-cell analysis [41].”
BackgroundXu, P., Peng, J., Yuan, T., Chen, Z., He, H., Wu, Z., Li, T., Li, X., Wang, L., Gao, L., et al. (2024) · eLife · doi:10.7554/elife.85419 · Cited by 23 · see citing works
“Given the complexity of brain circuits, neuronal subtypes must be characterized from multiple view-points ( Zeng, 2022 ).”
BackgroundGenetically defined neuron types underlying visuomotor transformation in the superior colliculus
Cang, J., Chen, C., Li, C., & Liu, Y. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00856-4 · Cited by 22 · see citing works
Dissecting the cellular architecture and genetic circuitry of the soybean seed
Pelletier, J. M., Chen, M., Lin, J.-Y., Le, B. H., Kirkbride, R. C., Hur, J., Wang, T., Chang, S.-H., Olson, A., Nikolov, L. A., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2416987121 · Cited by 26 · see citing works
A standardized nomenclature for the rods and cones of the vertebrate retina
Baden, T., Angueyra, J. M., Bosten, J. M., Collin, S. P., Conway, B. R., Cortesi, F., Dedek, K., Euler, T., Flamarique, I. N., Franklin, A., et al. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003157 · Cited by 32 · see citing works
“Rods (PR0) are omitted for simplicity.”
Salimando, G. J., Tremblay, S., Kimmey, B. A., Li, J., Rogers, S. A., Wojick, J. A., McCall, N. M., Wooldridge, L. M., Rodrigues, A., Borner, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41407-2 · Cited by 19 · see citing works
Lee, D., Shahandeh, M. P., Abuin, L., & Benton, R. (2025) · PLoS Biology · doi:10.1371/journal.pbio.3003120 · Cited by 24 · see citing works
“Encompassing neurons and glia, these cells can be categorized into a large number of different types—based upon developmental, anatomical, molecular, and functional properties [1,2]—with diverse roles.”
Caicedo, A., Benavides-Almeida, A., Haro-Vinueza, A., Peña-Cisneros, J., Pérez-Meza, Á. A., Michelson, J., Peñaherrera, S., & Picard, M. A. (2024) · Mitochondrion · doi:10.1016/j.mito.2024.101848 · Cited by 23 · see citing works
A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell typesPreprint
Ben‐Simon, Y., Hooper, M. J., Narayan, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.10.597244 · Cited by 23 · see citing works
“The definition of cell types and their taxonomy in the central nervous system and genetic access to each one are essential for our understanding of how they contribute to nervous system function and dysfunction 2,58,59 .”
Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocksPreprint
Sui, X., Lo, J. A., Luo, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.05.606553 · Cited by 23 · see citing works
“In this study, we also elucidate the morphological diversity of several major subclasses of GABAergic neurons 64 , classified by their transcriptomic profiles, within the mouse cerebral cortex.”
BackgroundGene panel selection for targeted spatial transcriptomics
Zhang, Y., Petukhov, V., Biederstedt, E., Que, R. A., Zhang, K., & Kharchenko, P. V. (2024) · Genome biology · doi:10.1186/s13059-024-03174-1 · Cited by 21 · see citing works
Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse BrainPreprint
Liu, H., Zeng, Q., Zhou, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.16.536509 · Cited by 22 · see citing works
Self-citationMendieta, J. P., Sangra, A., Yan, H., Minow, M. A. A., & Schmitz, R. J. (2023) · The Plant Journal · doi:10.1111/tpj.16351 · Cited by 20 · see citing works
Regulation of alternative splicing and polyadenylation in neurons
Lee, S., Aubee, J. I., & Lai, E. C. (2023) · Life Science Alliance · doi:10.26508/lsa.202302000 · Cited by 21 · see citing works
“Although all researchers can debate what the most interesting cell types are, it is without question that neurons comprise one of the most diverse subclasses of cell types (Zeng, 2022).”
BackgroundEnriched Single-Nucleus RNA-Sequencing Reveals Unique Attributes of Distal Convoluted Tubule Cells
Su, X., Reyes, J. V., Lackey, A. E., Demirci, H., Bachmann, S., Maeoka, Y., Cornelius, R. J., McCormick, J. A., Yang, C., Jung, H. J., et al. (2024) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000297 · Cited by 22 · see citing works
Chinese Immune Multi-Omics Atlas
Yin, J., Zheng, Y., Huang, Z., Zhou, W., Yuan, Y., Cai, P., Bai, Y., Yang, S., Gao, Y., Duan, S., et al. (2026) · Science · doi:10.1126/science.adt3130 · Cited by 34 · see citing works
A roadmap for delivering a human musculoskeletal cell atlas
Baldwin, M. J., Buckley, C. D., Guilak, F., Hulley, P. A., Cribbs, A. P., & Snelling, S. J. B. (2023) · Nature Reviews Rheumatology · doi:10.1038/s41584-023-01031-2 · Cited by 17 · see citing works
“This variability complicates efforts in developing a cell atlas, and it is important to capture, annotate and categorize the information that defines a particular cell type (or state) to enable the generation of tractable, suitably resolved cellular maps 9 .”
BackgroundThe energetics of cellular life transitions
Monzel, A. S., Levin, M. G., & Picard, M. A. (2023) · Life Metabolism · doi:10.1093/lifemeta/load051 · Cited by 20 · see citing works
“A cell’s hardware includes the proteins encoded by the sequences of its genome that are epigenetically “open” and active or “closed” and repressed, plus the complex set of organelles, proteins, and molecules it contains at any given time [19].”
Multimodal classification of neurons in the lateral septumPreprint
Reid, C. M., Ren, Y., Simon, R., Xie, Y., Garcίa, M. T., Tran, D., Vu, S., Liu, J., Adam, M. A., Hanson, S. M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.15.580381 · Cited by 20 · see citing works
Molecular gradients shape synaptic specificity of a visuomotor transformation
Dombrovski, M., Zang, Y., Frighetto, G., Vaccari, A., Jang, H., Mirshahidi, P., Xie, F., Sanfilippo, P., Hina, B. W., Rehan, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09037-4 · Cited by 21 · see citing works
Biophysically interpretable inference of cell types from multimodal sequencing data
Chari, T., Gorin, G., & Pachter, L. S. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00689-2 · Cited by 20 · see citing works
“The definition of type, and whether designations should be discrete or continuous, is a matter of debate 10,11 (and often investigation-dependent).”
Hu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 18 · see citing works
Implementation and validation of single-cell genomics experiments in neuroscience
Colonna, M., Konopka, G., Liddelow, S. A., Nowakowski, T. J., Awatramani, R. B., Bateup, H. S., Cadwell, C. R., Caglayan, E., Chen, J. L., Gillis, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01814-0 · Cited by 22 · see citing works
“While the definition of a cell type typically invokes other features (e.g., connectivity, function, and morphology), these features can be difficult or impossible to acquire in many species or in high throughput 15,96 .”
In Situ Synthesis and Assembly of Functional Materials and Devices in Living Systems
Wang, W., Sessler, C. D., Wang, X., & Jia Liu, B. (2024) · Accounts of Chemical Research · doi:10.1021/acs.accounts.4c00049 · Cited by 18 · see citing works
Identifying neuron types and circuit mechanisms in the auditory midbrain
Drotos, A. C., & Roberts, M. T. (2023) · Hearing Research · doi:10.1016/j.heares.2023.108938 · Cited by 17 · see citing works
- 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 · 2021: 1
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-07.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-07.
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 ›
Are you an author, or do you see a problem with this page? Request a review
