The DOI matches its Crossref record; 216 of the 219 references it cites were found in a registry or an index; none of the 60 sources its claims cite is checked for support yet.
What is a cell type and how to define it?
Open accessAbstract
Cell types are the basic functional units of an organism. They exhibit diverse phenotypic properties at multiple levels, making them challenging to define, categorize and understand. This Review provides an overview of the basic principles of cell types rooted in evolution and development, and discusses approaches to characterize and classify cell types and investigate how they contribute to the organism’s function, using the mammalian brain as a primary example. I propose a roadmap towards a conceptual framework and knowledge base of cell types that will enable a deeper understanding of the dynamic changes of cellular function under healthy and diseased conditions.
Overview
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
Verification
Claims the paper makes
0 of 60 cited sources checked · 0 supported · 0 flagged
In the paper
“Within each subclass, the study also finds extensive heterogeneity among individual neurons; this heterogeneity reflects three axes of variations: regional specificity, topographic specificity and individual (potentially stochastic) variation,”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Using the former approach, it has been shown that GABAergic transcriptomic types in mouse visual cortex differ in their response to behavioral states (e.g., running versus resting), whereas visual response properties (e.g., orientation or direction selectivity) only differ at subclass level (Bugeon et al., 2021);”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Bugeon et al., 2021
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Currently single-cell transcriptomics and connectomics (i.e., delineating the patterns of interconnections between individual neurons) are the two primary approaches that have the potential to meet the completeness requirement.”
§ What is a cell type and how to define it? > Approaches to characterize cell types
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Integration of transcriptomic, electrophysiological and morphological properties by Patch-seq reveals multimodal corresponding distinctions of mouse motor cortex cell types at subclass level (Scala et al., 2021).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Scala et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“which do not readily correlate with transcriptomic types within the subclass.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So far it has been shown that transcriptomic types have excellent correspondence with their spatial distribution patterns.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIn the paper
“These visual cortex GABAergic MET types show robust cross-modality concordance and mutual predictability.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“in somatosensory cortex, higher sensory response is seen in a specific L2/3 IT excitatory transcriptomic type (Condylis et al., 2022).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Condylis et al., 2022 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Another Patch-seq study of mouse visual cortical GABAergic neurons also reveals a relatively high degree of corresponding continuous variations of transcriptomic types with their anatomical distribution along the cortical depth and the variations of their morpho-electrical properties (Fig. 3B) (Gouwens et al., 2020).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Using the former approach, it has been shown that GABAergic transcriptomic types in mouse visual cortex differ in their response to behavioral states (e.g., running versus resting), whereas visual response properties (e.g., orientation or direction selectivity) only differ at subclass level (Bugeon et al., 2021);”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Bugeon et al., 2021
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 15
- Probably not a substantial claim. 17
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that single-cell transcriptomics alone can faithfully capture the overall cell type organization at class and subclass levels, although the validity of transcriptomic clusters at the lowest branch level has yet to be fully tested.”
§ What is a cell type and how to define it? > Hierarchical organization of transcriptomically-defined cell types
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Glutamatergic neuron types are distinct between visual and motor cortical areas whereas GABAergic neuron types are shared between the two areas (Tasic et al., 2018).”
§ What is a cell type and how to define it? > Hierarchical organization of transcriptomically-defined cell types
What the sources say
- Not checked yetcites Tasic et al., 2018 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, whether specific transcriptomic types (beyond the subclass level) have specific connectional or functional attributes or not is still unclear in many cases.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since a neuron type usually has multiple projection targets and a neuron within that type can choose a subset of those targets either specifically or randomly, a single-target-site Retro-seq assay is often insufficient to resolve the target specificity of a transcriptomic type except in special cases.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: A neuron type usually has multiple projection targets and individual neurons can choose a subset specifically or randomly.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“it is also reasonable to propose that some connectional or functional properties should not be used to define cell types, because they may be emerging properties arising from the interaction of a network of cell types, or from experience and/or activity dependent processes that represent a cell “state” rather than a defining feature for a cell “type”.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“specific molecular signatures responsible for certain essential anatomical or functional features may be hidden below noise and will need to be brought out through supervised approaches and used to refine the classification of cell types towards more functional relevance.”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the first and foremost principle is that cell types are the product of evolution and cell type identities are encoded in the genome.”
§ What is a cell type and how to define it? > Cell types are the product of evolution
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“since in theory transcriptomic clusters can be infinitely subdivided and the more cells profiled the more clusters can be obtained”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“A study using this approach in cortical excitatory neuron subclass-specific Cre driver lines (Peng et al., 2021) reveals distinct projection patterns between subclasses, e.g., not only between IT and ET but also between L2/3 IT and L5 IT neurons, confirming subclass level projection specificity (Fig. 3C).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Peng et al., 2021 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“To overcome heterogeneity at individual type level, a set of triple-modality MET types are defined at an intermediate level of granularity (between transcriptomic subclasses and types).”
§ What is a cell type and how to define it? > Correspondence between transcriptomic cell types and other cellular properties
What the sources say
- Not checked yetcites Gouwens et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Studies over the past century have revealed that cells within an organism can be grouped into types – cells within a type exhibit similar structure and function that are distinct from cells in other types (Arendt, 2008).”
§ What is a cell type and how to define it? > Abstract
What the sources say
- Not checked yetcites Arendt, 2008 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Oligodendrocyte pathology is evident in a range of disorders including multiple sclerosis, schizophrenia and Alzheimer’s disease (Kuhn et al., 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Kuhn et al., 2019 source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Developmental trajectory also is not the same as developmental lineage, as a lineage is defined as all the cells descended from a single precursor/progenitor and it has been shown in multiple systems that a progenitor can produce cells belonging to several neuronal and non-neuronal types, ordered by developmental timing (Agathocleous and Harris, 2009; Sagner and Briscoe, 2019; Sulston et al., 1983”
§ What is a cell type and how to define it? > Cell type development
What the sources say
- Not checked yetcites Agathocleous and Harris, 2009
- Not checked yetcites Sagner and Briscoe, 2019
- Not checked yetcites Sulston et al., 1983 source
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The transcriptomic taxonomy contains evolutionarily rooted molecular signatures and allows effective label transfer and linking with all other modalities.”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conclusion, cell types are the product of evolution, and they are the basic functional units of an organism.”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, individual variations within a species (e.g., within the human population) that are driven by genetic or environmental factors may be manifested as a variety of cell type or cell state variations.”
§ What is a cell type and how to define it? > Cell types versus cell states
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Microglia display diverse and dynamic phenotypic states and play a plethora of roles in development, adulthood (homeostasis), aging and diseases (Butovsky and Weiner, 2018; Prinz et al., 2019; Thion and Garel, 2020).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The distinction between cell types and cell states is particularly challenging during development, as cells continually change their states and, at certain key time points, they may switch their cell type identities.”
§ What is a cell type and how to define it? > Cell types versus cell states
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“First, opposing morphogen gradients establish the basic plan for cortex (anterior-posterior) or spinal cord (dorsal-ventral) patterning and provide instructive signals for the expression of complementary sets of transcription factor activators and repressors, which in turn define distinct neural progenitor domains within cortex or spinal cord.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The generation and patterning of mouse cortex and spinal cord cell types are two example systems where extensive historical studies have uncovered several common principles (Cadwell et al., 2019; Catela et al., 2015; Jessell, 2000; O’Leary et al., 2007; Osseward and Pfaff, 2019; Sagner and Briscoe, 2019).”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Myelination process also exhibits activity-dependent plasticity (Monje, 2018).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Monje, 2018 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The transcriptomic taxonomy organizes cell types in a hierarchical manner, laying out different levels of descriptions from major divisions at class and subclass levels to more granular and fuzzier divisions at type and subtype levels (due to the more prevailing continuous variations at the latter levels).”
§ What is a cell type and how to define it? > How to define cell types?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Astrocytes exhibit diverse morphological and physiological properties in different brain regions and contribute to essential functions in blood-brain barrier, synaptogenesis, neurotransmitter buffering, ion homeostasis, and secretion of neuroactive agents (Ben Haim and Rowitch, 2017; Khakh and Deneen, 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Ben Haim and Rowitch, 2017
- Not checked yetcites Khakh and Deneen, 2019
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The set of cell type-defining transcription factors in a progenitor or a daughter cell can change with time, such that different neuronal types emanate from the same progenitor in a precisely timed “birth order”.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Later in development, the same neural progenitors also generate non-neuronal cell types such as astrocytes and oligodendrocytes.”
§ What is a cell type and how to define it? > Cell type development
What the sources say
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Single-cell transcriptomic studies reveal a relatively homogeneous adult microglia population, and greater heterogeneity of microglia states at different developmental stages, during aging and in pathological conditions (Hammond et al., 2019; Li et al., 2019; Masuda et al., 2019).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Reactive astrocytes undergo morphological, molecular, and functional changes in response to injury or CNS diseases; they may adopt multiple, heterogeneous states depending on context (Escartin et al., 2021).”
§ What is a cell type and how to define it? > Cell types versus cell states
What the sources say
- Not checked yetcites Escartin et al., 2021 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper relies on this without support.
Cell types are the basic functional units of an organism.
“Cell types are the basic functional units of an organism.”
The paper relies on this without support.
There has not been a consistent and standard definition of cell types.
“However, there has not been a consistent and standard definition of cell types even though it is critical for reproducible investigation.”
The paper relies on this without support.
It is often unclear whether cell types defined by different phenotypic features agree, nor which feature is the right one to define cell types.
“It is often unclear if cell types defined by different phenotypic features agree with each other, nor which feature is the “right” one to define cell types.”
The paper relies on this without support.
Cellular properties at individual cell level are highly heterogeneous, and variations in different modalities do not necessarily exhibit high concordance, making it often impossible to define exactly what is a cell type and draw clear boundaries between types.
“At the same time, cellular properties at individual cell level are highly heterogeneous, variations in different modalities do not necessarily exhibit high degrees of concordance, making it often impossible to define exactly what is a cell type and draw clear boundaries between “types”.”
The paper relies on this without support.
The vast majority of cortical and subcortical neuron types have long-range axon projections forming circuit networks throughout the brain.
“The vast majority of cortical and subcortical neuron types have long-range axon projections to form circuit networks throughout the brain.”
The paper relies on this without support.
Transcriptomes are rich in containing molecular correlates of all sorts of cellular properties.
“Since transcriptomes are rich in containing the molecular correlates of all sorts of cellular properties”
The paper relies on this without support.
Emergence of a new cell type during development is more often the consequence of cell division from which a daughter cell takes up a new cell type identity.
“More often, emergence of a new cell type during development is the consequence of cell division from which a daughter cell takes up a new cell type identity (Fig. 4).”
The paper relies on this without support.
Coordinated neuronal activities in brain circuits generate sensory perception and behavior.
“Coordinated neuronal activities in brain circuits generate sensory perception and behavior.”
The paper relies on this without support.
IEG activation leads to expression of downstream effectors, such as ion channels or synaptic proteins, that shape cell states and remodel neuronal connections.
“IEG activation leads to expression of downstream effectors, such as ion channels or synaptic proteins, that shape the cell states and remodel neuronal connections.”
The paper relies on this without support.
Cell state changes in the brain are closely related to neural plasticity.
“Cell state changes in the brain are closely related to neural plasticity.”
The paper relies on this without support.
Various diseased conditions can induce pathological changes in cell states in different brain regions or tissue organs.
“Similarly, various diseased conditions can induce pathological changes in cell states in different brain regions or tissue organs.”
The paper relies on this without support.
Numerous studies have revealed selective vulnerability of specific cell types for specific diseases.
“Numerous studies have revealed selective vulnerability of specific cell types for specific diseases.”
The paper relies on this without support.
Astrocytes become reactive under pathological conditions.
“Astrocytes become reactive under pathological conditions.”
The paper relies on this without support.
Oligodendrocytes are the myelinating cells of the CNS generated from OPCs throughout life.
“Oligodendrocytes are the myelinating cells of the central nervous system (CNS) that are generated from OPCs throughout life.”
The paper relies on this without support.
Microglia are the primary innate immune cells in the CNS and have a distinct developmental origin from peripheral immune cells.
“Finally, microglia are the primary innate immune cells in the CNS and have a distinct developmental origin from peripheral immune cells.”
The paper relies on this without support.
Microglia are generated from mesodermal progenitors that arise from the yolk sac and are among the earliest residential cell types in the brain.
“They are generated from mesodermal progenitors that arise from the yolk sac and are among the earliest residential cell types in the brain.”
The paper relies on this without support.
Microglia can be both responders to and inducers of various neurodegenerative, neuroinflammatory and neurodevelopmental diseases.
“In particular, microglia can be both responders to and inducers of various neurodegenerative, neuroinflammatory and neurodevelopmental diseases.”
The paper relies on this without support.
The entire repertoire of cell types is built through sequential and parallel developmental events starting from the zygote.
“The entire repertoire of cell types in the brain and the body is built through a sequential and parallel series of spatially and temporally coordinated developmental events starting from a single fertilized egg, the zygote.”
The paper relies on this without support.
Transcriptional and epigenetic regulatory programs are unfolded from genome sequences and drive proliferation and differentiation leading to diverse cellular phenotypes.
“Transcriptional and epigenetic regulatory programs are unfolded from the genome sequences and drive a cascading series of cell proliferation and differentiation processes leading to the manifestation of diverse cellular phenotypes.”
The paper relies on this without support.
Earlier-stage ancestral cell types are fewer and more multipotent, giving rise to more descendant cell types with increasingly restricted fates.
“In the developmental ontogeny of cell types, earlier-stage ancestral cell types are fewer and are more multipotent, they give rise to a larger number of descendant cell types with increasingly restricted fates.”
The paper relies on this without support.
The developmental program specifies a spatial plan, including the location of organs and the spatial organization of cell types.
“The developmental program rolls out not only a temporal but also an elaborate spatial plan, specifying the location of each tissue organ and the spatial organization of all the cell types within each.”
The paper relies on this without support.
Brain development includes patterning and regionalization, neurogenesis and neuronal migration, gliogenesis and glia differentiation, neuronal differentiation and circuit formation, and circuit refinement and plasticity.
“For the brain, the main series of events of brain development leading to the mature adult-stage cell types and circuits include: patterning and regionalization (laying out the master plan of brain architecture), neurogenesis and neuronal migration, gliogenesis and glia cell differentiation, neuronal differentiation and circuit formation (axonogenesis, dendritic arborization, synaptogenesis, myelin”
The authors state this as an assumption.
Transcriptomic changes tend to be more continuous during cell state transitions and more abrupt or discrete when cells switch their types.
“Although not absolute, it is reasonable to assume that transcriptomic changes tend to be more continuous during cell state transitions, and more abrupt or discrete when cells switch their types.”
The paper relies on this without support.
A neuron type usually has multiple projection targets and individual neurons can choose a subset specifically or randomly.
“Since a neuron type usually has multiple projection targets and a neuron within that type can choose a subset of those targets either specifically or randomly”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Recent studies define a cell type as a (continuous) set of cell states, which can be aligned with the range of cellular phenotypes resulting from the interaction between a cell type and its microenvironment4.”
From Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation (2024) · cites this paper as [4]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Single-cell transcriptomics by single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) provides unprecedented profiling depth and scalability, enabling comprehensive quantitative analysis and classification of cell types at scale2,3,7–9.”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [2]
The cited source says what the claim attributes to it.
In the citing paper
“Consistent with the principle of hierarchical cell-type organization identified in previous studies2, here we have defined a hierarchical taxonomy of cell types across the entire mouse brain, with four levels of classification: class, subclass, supertype and cluster (or type).”
From A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain (2023) · cites this paper as [2]
Citations
Cites
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
The structure of the nervous system of the nematode Caenorhabditis elegans
White, J. G., Southgate, E., Thomson, J. N., & Brenner, S. (1986) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1986.0056 · Cited by 5,589
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
The embryonic cell lineage of the nematode Caenorhabditis elegans
Sulston, J. E., Schierenberg, E., White, J. G., & Thomson, J. N. (1983) · Developmental Biology · doi:10.1016/0012-1606(83)90201-4 · Cited by 4,199
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 2,842
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Reactive astrocyte nomenclature, definitions, and future directions
Escartin, C., Galea, E., Lakatos, A., O’Callaghan, J. P., Petzold, G. C., Serrano-Pozo, A., Steinhäuser, C., Volterra, A., Carmignoto, G., Agarwal, A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00783-4 · Cited by 2,513
Blood-Brain Barrier: From Physiology to Disease and Back
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R., & Zlokovic, B. V. (2019) · Physiological Reviews · doi:10.1152/physrev.00050.2017 · Cited by 2,312
Flagged first, then by citations
3 of the 219 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–300 of 479
A variegated model of transcription factor function in the immune system
Chowdhary, K., & Benoist, C. O. (2023) · Trends in Immunology · doi:10.1016/j.it.2023.05.001 · Cited by 5
Mapping the Topography of Spatial Gene Expression with Interpretable Deep Learning
Chitra, U., Arnold, B. J., Sarkar, H., Ma, C., Lopez-Darwin, S. L., Sanno, K., & Raphael, B. J. (2024) · Lecture notes in computer science · doi:10.1007/978-1-0716-3989-4_33 · Cited by 4
A mosaic of new and old cell types
Faltine-Gonzalez, D. Z., & Kebschull, J. M. (2022) · Science · doi:10.1126/science.add9465 · Cited by 7
Enhancer grammar of liver cell types and hepatocyte zonation states
González-Blas, C. B., Matetovici, I., Hillen, H., Taskiran, I. I., Vandepoel, R., Christiaens, V., Sansores-García, L., Verboven, E., Hulselmans, G., Poovathingal, S., et al. (2022) · doi:10.1101/2022.12.08.519575 · Cited by 5
Mapping Cell Identity from scRNA-seq: A primer on computational methods
Traversa, D., & Chiara, M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.03.051 · Cited by 5
Condensates and cell states: A new paradigm for understanding tumor biology
Nussinov, R., & Jang, H. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.01.052 · Cited by 5
Cell-free genomics: transcription factor interactions in reconstituted naïve embryonic chromatin
Becker, P. B. (2024) · Biochemical Society Transactions · doi:10.1042/bst20230878 · Cited by 3
Walsh, J. T., Junker, I. P., Chen, Y., Chen, Y., Gifford, H., Chen, D. S., & Ding, Y. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516083122 · Cited by 5
A mouse brain atlas based on dendritic microenvironments
Liu, Y., Zhao, S., Yun, Z., Xiong, F., & Peng, H. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02119-6 · Cited by 9
scATAcat: cell-type annotation for scATAC-seq data
Altay, A., & Vingron, M. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae135 · Cited by 5
Cell type census in cerebral cortex reveals species-specific brain function and connectivity
Onishi, K., & Shimogori, T. (2024) · Neuroscience Research · doi:10.1016/j.neures.2024.11.008 · Cited by 4
Let’s Talk About Sex…Cell Lineages
MacCord, K. (2025) · Biological Theory · doi:10.1007/s13752-024-00488-y · Cited by 4
The Computational Evolution of Cognitive Architectures
Kotseruba, I., & Tsotsos, J. K. (2025) · doi:10.1093/oso/9780192844835.001.0001 · Cited by 4
Network inference applicable to both synchronous and desynchronous systems from oscillatory signals
Matsuki, A., Kori, H., & Kobayashi, R. (2025) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000063 · Cited by 6
Strategies for the Establishment of Fibroblastic Lines for the Conservation of Wild Mammals
Pereira, A. F., de Oliveira, L. R. M., de Aquino, L. V. C., Viana, J. V. D. S., & Rodrigues, L. L. V. (2023) · Veterinary medicine and science · doi:10.5772/intechopen.114028 · Cited by 4
Wong, K. H., Andrade-Rodriguez, N., & Traylor‐Knowles, N. (2024) · Integrative and Comparative Biology · doi:10.1093/icb/icae112 · Cited by 6
Przytycki, P. F., & Pollard, K. S. (2024) · Genome biology · doi:10.1186/s13059-024-03440-2 · Cited by 3
Predictive and robust gene selection for spatial transcriptomicsPreprint
Covert, I., Gala, R., Wang, T., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.13.491738 · Cited by 4
Enriched Single-Nucleus RNA-Sequencing reveals unique attributes of distal convoluted tubule cellsPreprint
Su, X., Reyes, J. V., Lackey, A. E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.05.555810 · Cited by 4
Wang, M., Pietro-Torres, A. D., Feregrino, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.26.586769 · Cited by 4
Rebboah, E., Rezaie, N., Williams, B. A., Weimer, A. K. K., Shi, M., Yang, X., Liang, H. Y., Dionne, L. A., Reese, F., Trout, D. E., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598567 · Cited by 3
Gradients of Cell Recognition Molecules Wire Visuomotor TransformationPreprint
Dombrovski, M., Zang, Y., Frighetto, G., Vaccari, A., Jang, H., Mirshahidi, P., Xie, F., Sanfilippo, P., Hina, B. W., Rehan, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.04.610846 · Cited by 4
Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?
Bongrand, P. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413371 · Cited by 3
The epipliancy journey: Tumor initiation at the mercy of identity crisis and epigenetic drift
Benhassoun, R., Morel, A., Jacquot, V., Puisieux, A., & Ouzounova, M. (2025) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2025.189307 · Cited by 4
Escaping ageing through Cell Annealing—a phenomenological model
Memczak, S., Belmonte, J. C. I., & Graepel, T. (2025) · Cell Research · doi:10.1038/s41422-025-01138-z · Cited by 3
GASTON-Mix: a unified model of spatial gradients and domains using spatial mixture-of-experts
Chitra, U., Dan, S., Krienen, F. M., & Raphael, B. J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf254 · Cited by 7
Designed optogenetic tool for bridging single-neuronal multimodal information in intact animals
Tao, R., Sun, L., Yu, Q., Huang, Y., Chen, Y.-H., Guan, C.-Y., Wang, M.-C., Sun, Y., & Du, J. (2025) · Nature Communications · doi:10.1038/s41467-025-62938-w · Cited by 7
Flexible and robust cell-type annotation for highly multiplexed tissue images
Sun, H., Yu, S., Casals, A. M., Bäckström, A., Lu, Y., Lindskog, C., Ruffalo, M., Lundberg, E. K., & Murphy, R. F. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101374 · Cited by 3
Whole-organ and whole-body 3D atlases enable cellome-wide profiling
Yoshida, S. Y., Matsumoto, K., Takagi, S., Kinoshita, F. L., Yamashita, K., Shigeta, D., Yoshioka, Y., Ushiku, T., Morii, E., Susaki, E. A., et al. (2026) · Cell · doi:10.1016/j.cell.2025.12.057 · Cited by 6
Fast, flexible analysis of differences in cellular composition with crumblr
Hoffman, G. E., & Roussos, P. (2026) · Nature Communications · doi:10.1038/s41467-026-75681-7 · Cited by 15
Evo-devo applied to sleep research: an approach whose time has come
Brown, R. E. (2024) · SLEEP Advances · doi:10.1093/sleepadvances/zpae040 · Cited by 3
Cellular and laminar architecture: A short history and commentary
Rockland, K. S. (2023) · The Journal of Comparative Neurology · doi:10.1002/cne.25553 · Cited by 3
Zhang, Q., Liu, X., Gong, L., & He, M. (2023) · Development Growth & Differentiation · doi:10.1111/dgd.12902 · Cited by 2
Cahill, R., Wang, Y., Xian, R. P., Lee, A. J., Zeng, H., Yu, B., Tasic, B., & Abbasi-Asl, R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319804121 · Cited by 4
Self-citationCao, L., Zhang, W., Yang, F., Chen, S., Huang, X., Zeng, F., & Wang, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf013 · Cited by 2
Automated Classification of Cell Shapes: A Comparative Evaluation of Shape Descriptors
Vadori, V., Peruffo, A., Graïe, J.-M., Finos, L., & Grisan, E. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980998 · Cited by 3
From Cells to Selves and Back: Human Neurodiversity at Single-Cell Resolution
Lessi, M., Caporale, N., & Testa, G. (2025) · Physiology · doi:10.1152/physiol.00010.2025 · Cited by 3
Maximum entropy models for patterns of gene expression
Sarra, C., Sarra, L., Carlo, L. D., GrandPre, T., Zhang, Y., Callan, C. G., & Bialek, W. (2025) · Physical review. E · doi:10.1103/wjcn-l4ms · Cited by 5
Dendritome mapping reveals the spatial organization of striatal neuron morphology
Park, C. S., Yan, M., Zhu, M., Akram, M. A., Foster, N. N., Bennecke, A., Choi, C., Marrett, K., Moradi, K., Zhang, J. Y., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02085-z · Cited by 4
Gene panel selection for targeted spatial transcriptomicsPreprint
Zhang, Y., Petukhov, V., Biederstedt, E., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.03.527053 · Cited by 3
Romani, F., Sauret-Gueto, S., Rebmann, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.17.545419 · Cited by 3
Marghi, Y. M., Gala, R., Baftizadeh, F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.02.560574 · Cited by 3
Lee, D., & Benton, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.22.568274 · Cited by 3
scATAcat: Cell-type annotation for scATAC-seq dataPreprint
Altay, A., & Vingron, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.24.577073 · Cited by 3
Decoding cell identity with multi-scale explainable deep learningPreprint
Zhu, J., Zhang, Z., Xiang, Y., Xie, B., Dong, X., Xie, L., Zhou, P., Yao, R., Wang, X., Li, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.05.578922 · Cited by 3
Cell differentiation controls iron assimilation in a choanoflagellatePreprint
Leon, F., Espinoza-Esparza, J. M., Deng, V., Coyle, M. C., Espinoza, S., & Booth, D. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.25.595918 · Cited by 3
Huang, S., Rizzo, D., Wu, S. J., Xu, Q., Ziane, L., Alghamdi, N., Stafford, D. A., Daigle, T. L., Tasic, B., Zeng, H., et al. (2024) · Research Square · doi:10.1101/2024.06.05.597539 · Cited by 5
Self-citationThe Gene Expression Landscape of Disease GenesPreprint
García‐González, J., Cote, A. C., Garcia-Gonzalez, S., Liou, L., & O’Reilly, P. F. (2024) · medRxiv · doi:10.1101/2024.06.20.24309121 · Cited by 3
Jiang, S., Wang, L., Yun, Z., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.22.614319 · Cited by 3
Evolutionary trends in the emergence of skeletal cell types
Damatac, A. M., Koska, S., Ullrich, K. K., Domazet‐Lošo, T., Klimovich, A. V., & Kaucká, M. (2025) · Evolution Letters · doi:10.1101/2024.09.26.615131 · Cited by 6
scPlantFormer: A Lightweight Foundation Model for Plant Single-Cell Omics AnalysisPreprint
Zhang, X., Xu, J., Chen, D., & Chen, L. (2024) · Research Square · doi:10.21203/rs.3.rs-5219487/v1 · Cited by 3
Discovery of neuronal cell types by pairing whole cell reconstructions with RNA expression profilesPreprint
Ferreira, T., Eddison, M., Copeland, M., Lay, M. L., Weldon, M., Baka, J., Tenshaw, E., Stark, A. K., Schauder, D., Olbris, D. J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.30.630829 · Cited by 3
Sequestration-based neural networks that operate out of equilibriumPreprint
Nakamura, E., Bisso, F. B., Gispert, I., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.16.654484 · Cited by 3
Human Neocortical Glutamatergic Neurons Revealed Through Multimodal ProfilingPreprint
Dalley, R. A., Walling-Bell, S., Miller, J. A., Waleboer, F., Thijssen, J., McCutcheon, A., Gelfand, E. C., Mallory, M., Radaelli, C., Liu, X.-P., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.08.698257 · Cited by 6
Chi, Y., Marini, S., & Wang, G. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.038 · Cited by 2
Kamaguchi, R., Amemori, S., Amemori, K.-I., & Osakada, F. (2025) · Neuroscience Research · doi:10.1016/j.neures.2025.02.007 · Cited by 2
Towards a mathematical framework for modelling cell fate dynamics
Vittadello, S. T., Diaz, L., Liu, Y., Zanca, A., & Stumpf, M. P. H. (2025) · Journal of Mathematical Biology · doi:10.1007/s00285-025-02287-w · Cited by 7
Emerging tools to advance neuroethology in butterflies and moths
Cicconardi, F., Farnworth, M. S., Grob, R., Shodja, D. N., Bacquet, C. N., el Jundi, B., Martin, A., & Montgomery, S. H. (2025) · Journal of Comparative Physiology A · doi:10.1007/s00359-025-01785-y · Cited by 6
Yamada, T., Sarropoulos, I., & Sepp, M. (2026) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2026.01.010 · Cited by 2
Single-cell and spatial transcriptomics in plants: from cell states to inter-tissue coordination
Phua, D. Y. Z., Li, X., & Nolan, T. M. (2026) · Journal of Experimental Botany · doi:10.1093/jxb/erag241 · Cited by 5
Kim, H., Xu, C., Washington, C., Shi, C., Lowman, M., & Kebschull, J. M. (2026) · Neuron · doi:10.1016/j.neuron.2026.05.005 · Cited by 6
Evolution of Local Circuit Neurons in Two Sensory Thalamic Nuclei in Amniotes
Pritz, M. B. (2023) · Brain Behavior and Evolution · doi:10.1159/000530316 · Cited by 2
Zhang, C., Hu, Y., & Gao, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-42883-8 · Cited by 3
Filošević, A., Matić, I. S., Martinović, L. S., & Pavlić, S. D. (2024) · Future Pharmacology · doi:10.3390/futurepharmacol4010009 · Cited by 2
Chen, R., Nie, P., Wang, J., & Wang, G. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1865 · Cited by 3
Single‐cell genomics meets systems neuroscience: Insights from mapping the brain circuitry of stress
Hanchate, N. K. (2025) · Journal of Neuroendocrinology · doi:10.1111/jne.70005 · Cited by 2
Hierarchy of Chaotic Dynamics in Random Modular Networks
Kuśmierz, Ł., Pereira, U., Lu, Z., Mastrovito, D., & Mihalaş, Ş. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.148402 · Cited by 4
A practical guide to sequencing in neuropsychiatric research
Minerva, A. R., DeBerardine, M., Lin, R., Kim, H. J. J., Rogers, F. D., Rashford, R. L., & Peña, C. J. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00041-0 · Cited by 2
Translating single-cell genomics into cell types
Tegner, J. N. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-022-00600-6 · Cited by 2
A medley of gene expression adds new depth to zebrafish brain maps
Schulze, L., Zada, D., & Lovett-Barron, M. (2023) · Science Advances · doi:10.1126/sciadv.adg8660 · Cited by 2
Maj, C., Eberts, A., Schumacher, J., & Dasmeh, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-53098-w · Cited by 2
Multiplexed imaging to reveal tissue dendritic cell spatial localisation and function
Rocca, G. L., Galli, M., Celant, A., Stucchi, G., Marongiu, L., Cozzi, S., Innocenti, M., & Granucci, F. (2024) · FEBS Letters · doi:10.1002/1873-3468.14962 · Cited by 2
Building a learnable universal coordinate system for single-cell atlas with a joint-VAE modelPreprint
Gao, H., Hua, K., Wei, L., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.09.459281 · Cited by 2
Intrinsic neural diversity quenches the dynamic volatility of neural networksPreprint
Hutt, A., Rich, S., Valiante, T. A., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.25.505270 · Cited by 2
Frank, M. M., Sitko, A. A., Suthakar, K., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.509230 · Cited by 2
scTOP: physics-inspired order parameters for cellular identification and visualizationPreprint
Yampolskaya, M., Herriges, M. J., Ikonomou, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.25.525581 · Cited by 2
Cahill, R., Wang, Y., Xian, R. P., Lee, A. J., Zeng, H., Yu, B., Tasic, B., & Abbasi-Asl, R. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.10.531984 · Cited by 2
Self-citationIdentifying determinants of synaptic specificity by integrating connectomes and transcriptomes
Yoo, J., Dombrovski, M., Mirshahidi, P., Nern, A., LoCascio, S. A., Zipursky, S. L., & Kurmangaliyev, Y. Z. (2023) · doi:10.1101/2023.04.03.534791 · Cited by 39
Martini, L., Amprimo, G., Carlo, S. D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.28.546812 · Cited by 2
2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neuronsPreprint
Shi, J., Nutkovich, B., Kushinsky, D., Rao, B. Y., Herrlinger, S. A., Tsivourakis, E., Mihaila, T. S., Paredes, M. E. C., Malina, K. C.-K., O’Toole, C. K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.21.586118 · Cited by 2
scELMo: Embeddings from Language Models are Good Learners for Single-cell Data AnalysisPreprint
Zhao, H., Liu, T., Chen, T., Zheng, W., & Luo, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4014784/v1 · Cited by 2
Wei, W.-Q., Li, S., Zhang, D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.08.598051 · Cited by 2
Liu, Y., Zhao, S., Yun, Z., Xiong, F., & Peng, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.22.614330 · Cited by 2
Cai, L., Fan, T., Qu, X., Zhang, Y., Gou, X., Ding, Q., Feng, W., Cao, T., Lv, X., Liu, X., et al. (2024) · eLife · doi:10.1101/2024.09.23.614432 · Cited by 9
PointTree: Automatic and accurate reconstruction of long-range axonal projections of single-neuronPreprint
Cai, L., Fan, T., Qu, X., Zhang, Y., Gou, X., Ding, Q., Feng, W., Cao, T., Lv, X., Liu, X., et al. (2024) · eLife · doi:10.7554/elife.102840.1 · Cited by 2
Cell Type Specific Enhancers for Dorsolateral Prefrontal CortexPreprint
He, J., Phan, B. N., Kerkhoff, W. G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.01.626253 · Cited by 2
Estimating the completeness of large-scale single-cell sequencing projectsPreprint
Miihkinen, M., Chu, Y., Vakkilainen, S., Akimov, Y. A., & Aittokallio, T. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631769 · Cited by 1
Tian, A., Kim, S., Baidouri, H., Li, J., Cheng, X., Vranka, J. A., Li, Y., Chen, R., & Raghunathan, V. K. (2025) · Scientific Data · doi:10.1038/s41597-025-04528-5 · Cited by 2
Consequences of training data composition for deep learning models in single-cell biologyPreprint
Nadig, A., Thoutam, A., Hughes, M., Gupta, A., Navia, A. W., Fusi, N., Raghavan, S., Winter, P. S., Amini, A. P., & Crawford, L. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.639127 · Cited by 5
Kim, H., Xu, C., Washington, C., Shi, C., Lowman, M., & Kebschull, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661405 · Cited by 1
Single-cell map of the female brain across reproductive transitionsPreprint
Demarchi, L., Tickerhoof, M. C., Kameshwar, A. K. S., Rocks, D., Ouldibbat, L., Milosevic, A., Suzuki, M., & Kundaković, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.03.697507 · Cited by 4
Life-stage-specific specialities in the cell atlases of the Clytia hemisphaerica planula and medusaPreprint
Ferraioli, A., Ramon-Mateu, J., Meynadier, M., Lamonerie, T., Pagnotta, S., Chevalier, S., Iglesias, M., Najle, S. R., Sebé-Pedrós, A., Arguel, M., et al. (2026) · eLife · doi:10.64898/2026.02.16.705741 · Cited by 2
Multimodal alignments of in vivo imaging and spatial biology datasets at cellular resolutionPreprint
Wang, L., Jiang, X., Sun, X., Chattree, G., Cetin, A. H., Cai, X., Paul, E., Chrapkiewicz, R., Hernandez, O., Ke, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.28.719500 · Cited by 4
A cell type-resolved proteomic atlas of the human bodyPreprint
Weiss, C. A. M., Sjöstedt, E., Debnath, A., Ben‐Moshe, S., Diedrich, L., Zwahlen, M., Oeller, M., Wahle, M., Skarwan, E., Heymann, T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.26.727663 · Cited by 4
Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example
Defard, T., Desrentes, A., Fouillade, C., & Mueller, F. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4184-2_5 · Cited by 1
New algorithms for unsupervised cell clustering from scRNA-seq data
Robles, M., Díaz-Riaño, J., Forigua, C., Ojeda, S., Guio, L., Siaucho, P., Guzmán-Porras, J. J., Orjuela, D. A. G., Naranjo, A., Anaya, S. J. M., et al. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag121 · Cited by 2
Diversification of dentate gyrus granule cell subtypes is regulated by Nrg1 nuclear back-signaling
Rajebhosale, P., Jiang, L., Ressa, H. J., Johnson, K. R., Desai, N. S., Jone, A., Role, L. W., & Talmage, D. A. (2025) · Life Science Alliance · doi:10.26508/lsa.202403169 · Cited by 1
Toward a probabilistic definition of neural cell types
Andriatsilavo, M., & Hassan, B. A. (2025) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2025.103035 · Cited by 2
Matsuda, K., Wang, C., Kakinuma, H., Toyoda, A., Shiraki, T., Kawakami, K., & Kubo, F. (2026) · Nature Communications · doi:10.1038/s41467-026-71371-6 · Cited by 1
Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons
Yang, L., Liu, F., Hahm, H., Okuda, T., Li, X., Zhang, Y., Kalyanaraman, V., Heitmeier, M. R., & Samineni, V. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60360-w · Cited by 3
Metadata
- Authors
- Zeng, H.
- Affiliations
- Allen Institute for Brain Science (US)
- Corresponding author
- Zeng, Hongkui
- ORCID
- Zeng, Hongkui
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 185
- Issue
- 15
- Pages
- 2739-2755
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC9342916
- OpenAlex
- W4286449197
- Semantic Scholar
- fcb96c79ea7a1b1e583da762c3405f5296bcb8d6
- Cited by
- 439
- References
- 219
- Citations by year
- 2026: 95 · 2025: 126 · 2024: 121 · 2023: 83 · 2022: 13
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 385 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIMH (U19MH114830)
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- cell types, cell type classification, mammalian brain, cell function, cellular heterogeneity
- MeSH terms
- Animals, Brain, Humans, Mammals, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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
