Neuronal cell-type classification: challenges, opportunities and the path forward
Abstract
Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity - and thereby pave the way for understanding the structure, function and development of brain circuits - is to group neurons into types, which can then be analysed systematically and reproducibly. However, neuronal classification has been challenging both technically and conceptually. New high-throughput methods have created opportunities to address the technical challenges associated with neuronal classification by collecting comprehensive information about individual cells. Nonetheless, conceptual difficulties persist. Borrowing from the field of species taxonomy, we propose principles to be followed in the cell-type classification effort, including the incorporation of multiple, quantitative features as criteria, the use of discontinuous variation to define types and the creation of a hierarchical system to represent relationships between cells. We review the progress of classifying cell types in the retina and cerebral cortex and propose a staged approach for moving forward with a systematic cell-type classification in the nervous system.
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Single-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 [3]
Graded or continuous variation has been observed in a number of locations in the mammalian nervous system and represents one of the classic complications of cell typing.
“These kinds of graded or continuous variation have been observed in a number of locations in the mammalian nervous system and represent one of the classic complications of cell typing18.”
From Whole-brain annotation and multi-connectome cell typing of Drosophila (2024) · cites this paper as [18]
Neuronal diversity is organized hierarchically, and relationships among neuronal types can be considered analogous to species relationships.
“Transcriptional analysis of individual cells from different regions of the brain suggests that neuronal diversity is organized hierarchically, which has led to the suggestion that the relationships that exist among different neuronal types can be considered, in practical terms, analogous to those of species”
From Development and Functional Diversification of Cortical Interneurons (2018) · cites this paper as Zeng and Sanes, 2017
To understand the roles of t-types in cortical computations, it is necessary to know their anatomy, connectivity, and electrophysiology.
“However, to understand the roles of t-types in cortical computations, it is necessary to know their anatomy, connectivity, and electrophysiology7.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [7]
The current view is that a neuronal cell type is characterized by a common genetic profile that gives rise to distinct physiological and anatomical properties, including patterns of connectivity.
“The current view is that a neuronal cell type is characterized by a common genetic profile that gives rise to distinct physiological and anatomical properties, including patterns of connectivity7,9.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [7]
Most existing neural taxonomies are based on either transcriptomic or morpho-electric criteria, because it has been technically challenging to study both aspects in the same set of cells.
“Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [7]
Many molecular, anatomical, and physiological studies have revealed a broad spectrum of neuronal cell types in different cortical and hippocampal regions.
“Many molecular, anatomical, and physiological studies have revealed a broad spectrum of neuronal cell types in different cortical and hippocampal regions”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Zeng and Sanes, 2017
Neurons display substantial multi-dimensional variations, some of which are discrete and others continuous.
“First, neurons display substantial multi-dimensional variations, of which some are discrete whereas others seem continuous13,14.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [14]
Methods that use extra labels can reveal heterogeneity between chosen populations, yet those differences might not co-vary with other features or show discontinuities across a wider set of cortical cells.
“these approaches may successfully identify heterogeneity in features between specific populations under investigation, but these differences may not necessarily co-vary with other features or display discontinuities when examined in the context of a broader set of cortical cells—significant criteria for cell-type classification1.”
From Classification of electrophysiological and morphological neuron types in the mouse visual cortex (2019) · cites this paper as [1]
Rigorous and quantitative definitions of a neuron type have remained elusive.
“The notion of a ‘neuron type’, with similar properties among its members, as the basic unit of brain circuits has been an important concept for over a century; however, rigorous and quantitative definitions have remained surprisingly elusive1–5.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [3]
Identifying molecularly distinct neuronal populations requires measuring multiple markers in individual cells.
“Indeed, the identification of molecularly distinct neuronal populations requires the measurement of multiple markers in individual cells (reviewed in [10–12]).”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [11]
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- Authors
- Zeng, H., & Sanes, J. R.
- Affiliations
- Allen Institute for Brain Science (US)
- Harvard University (US)
- Corresponding author
- Zeng, Hongkui
- Sanes, Joshua R.
- Venue
- Nature Reviews Neuroscience
- Published
- 2017-08-03
- Volume
- 18
- Issue
- 9
- Pages
- 530-546
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W2744478946
- Semantic Scholar
- 816368967365895a1af42309bf3895b0fbcecb42
- Cited by
- 841
- References
- 30
- Citations by year
- 2026: 64 · 2025: 98 · 2024: 118 · 2023: 114 · 2022: 93 · 2021: 102 · 2020: 94 · 2019: 78 · 2018: 69 · 2017: 11
- FWCI
- 6.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Eye Institute (R01EY023173)
- 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 › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- neuronal heterogeneity, hierarchical classification, discontinuous variation, retina, cerebral cortex, high-throughput single-cell analysis, brain circuits
- MeSH terms
- Animals, Cerebral Cortex, Humans, Neurons, Retina
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