Disentangling neural cell diversity using single-cell transcriptomics
Abstract
Cellular specialization is particularly prominent in mammalian nervous systems, which are composed of millions to billions of neurons that appear in thousands of different 'flavors' and contribute to a variety of functions. Even in a single brain region, individual neurons differ greatly in their morphology, connectivity and electrophysiological properties. Systematic classification of all mammalian neurons is a key goal towards deconstructing the nervous system into its basic components. With the recent advances in single-cell gene expression profiling technologies, it is now possible to undertake the enormous task of disentangling neuronal heterogeneity. High-throughput single-cell RNA sequencing and multiplexed quantitative RT-PCR have become more accessible, and these technologies enable systematic categorization of individual neurons into groups with similar molecular properties. Here we provide a conceptual and practical guide to classification of neural cell types using single-cell gene expression profiling technologies.
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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 [7]
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 [10]
Limitations of single-cell transcriptomics mean that the complete developmental dendrogram of midbrain DA neurons still needs refinement.
“However, because of the limitations of single cell transcriptomics (Box 1; reviewed in [10]), the complete developmental dendrogram of midbrain DA neurons remains to be further refined.”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [10]
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- Authors
- Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R.
- Affiliations
- Northwestern University (US)
- Allen Institute for Brain Science (US)
- Iowa State University (US)
- Corresponding author
- Awatramani, Rajeshwar
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2016-08-26
- Volume
- 19
- Issue
- 9
- Pages
- 1131-1141
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2516756255
- Cited by
- 293
- References
- 152
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 10 · 2023: 21 · 2022: 25 · 2021: 32 · 2020: 36 · 2019: 41 · 2018: 59 · 2017: 39 · 2016: 4
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Vetenskapsrådet
- Canadian Institutes of Health Research
- National Institute of Neurological Disorders and Stroke (R21NS092034, R01NS096240)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- neuronal cell types, neuronal heterogeneity, single-cell RNA sequencing, single-cell gene expression profiling, mammalian nervous system
- MeSH terms
- Animals, Brain, Cells, Cultured, Humans, Nerve Net, Neurons, Gene Expression Profiling, Transcriptome
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