What Is Your Conceptual Definition of “Cell Type” in the Context of a Mature Organism?
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There are several axes of variation that determine cellular identity.
“While it may be tempting to assume that the clusters detected in single‐cell data represent cell types, there are several axes of variation that determine cellular identity (Wagner et al, 2016; Clevers et al, 2017).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Clevers et al, 2017
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“While it may be tempting to assume that the clusters detected in single-cell data represent cell types, there are several axes of variation that determine cellular identity (Wagner et al , 2016; Clevers et al , 2017).”
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“However, the definition of this term remains subject to intense debate (Clevers et al , 2017 ).”
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
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“The definition of cell types and the difference with cellular state are receiving renewed interest and scrutiny with the advent of quantitative single-cell techniques such as scRNA-seq (see e.g. ref (53)).”
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Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 213 · see citing works
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“The extent to which malignant cell populations can be described as distinct ‘‘cell types’’ or ‘‘cell states’’ remains elusive and inspires further study into temporally regulated oncogenic regulatory elements and lineage tracing of malignant cell populations (Clevers et al., 2017).”
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“The quantification of cell identity is crucial to understanding development, disease and homeostasis, yet the notion of cell identity remains poorly defined 1 .”
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“…limit their ability to identify complex transcriptional regulations that may vary among different cell types or states [hereafter, we will use “cell state” to describe a group of defined subpopulation of cells without distinguishing the differences between “cell type” and cell state ( 12 , 13 )].”
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“Thus, although scRNA-seq methods have unleashed a torrent of new analysis tools, a conceptual biological approach is required for distinguishing cell types from cell states (Clevers et al., 2017).”
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“…term cell state here instead of cell type for the compelling reason that distinctions in these cell groupings by this technology does not always mean a distinction in cell morphology, physiology, function, or cellular location, key characteristics of the use of cell type ( Clevers et al., 2017 ).”
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“The concept of cell “ type ” has many definitions, reflecting the long history of cell biology and accumulation of experimental methods (Clevers et al., 2017).”
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“But a satisfactory, exhaustive classification of cell types based on function is often unattainable in practice (Lundberg and Uhlen, 2017; Sanes, 2017).”
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“Clustering is often used as an unsupervised method that can use latent variables — for example using a categorical variable such as cell type, which was not directly measured on the data but enables simple interpretations [39] (Hackathon 3).”
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“Cell-state inference Cells of a particular type are likely to occupy a continuum of states, owing to the cell cycle, or differentiation, or spatial location, for example (Wagner et al., 2016; Clevers et al., 2017).”
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“Tying transcriptionally distinct clusters to function or morphology may be key to making this distinction ( Clevers et al., 2017 ).”
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“While being an incredibly useful description to investigate complex multicellular organisms, there is no consistent and standard definition of cell types (15–17) .”
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Immune cell type divergence in a basal chordatePreprint
Scully, T. D., Pickett, C. J., Gort-Freitas, N. A., Davidson, B., & Klein, A. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.20.655184 · Cited by 1 · see citing works
Self-citationInterpretation of Biological Data at a Glance
Cho, S. (2020) · Journal of Biotechnology and Bioindustry · doi:10.37503/jbb.2020.8.38
Chapter 10. Post-Genomics, Cell Reprogramming, and the Obstacle to Biomedicine
Germain, P., & Testa, G. (2020) · Hermann eBooks · doi:10.3917/herm.metho.2020.01.0409
de Vries, D. H. (2022) · doi:10.33612/diss.255266474
Walz, W. (2023) · Springer eBooks · doi:10.1007/978-3-031-48105-5_19
Tukacs, V., Fladhus, E. K., Mittli, D., Lörincz, M. L., Eberwine, J. H., Kékesi, K. A., & Juhász, G. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.16.594495
“The model implies that the measured transcript copy number results from the interplay of transcriptional burst frequency, size, and mRNA degradation contributing to the stochastic mRNA availability that is integral to the cellular phenotype 14,15,16,17 .”
BackgroundSelf-citationRoy, G., Syed, R., Lazaro, O., Robertson, S., McCabe, S., Rodriguez, D., Mawla, A. M., Johnson, T. S., & Kalwat, M. A. (2024) · eLife · doi:10.7554/elife.96713.1.sa3
Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learningPreprint
Roy, G., Syed, R., Lazaro, O., Robertson, S., McCabe, S., Rodriguez, D., Mawla, A. M., Johnson, T. S., & Kalwat, M. A. (2024) · eLife · doi:10.7554/elife.96713.1
Roy, G., Syed, R., Lazaro, O., et al. (2024) · eLife · doi:10.7554/elife.96713.1.sa1
- Authors
- Clevers, H., Rafelski, S., Elowitz, M. B., Klein, A., Shendure, J., Trapnell, C., Lein, E., Lundberg, E. K., Uhlén, M., Martinez-Arias, A., Sanes, J. R., Blainey, P. C., Eberwine, J. H., Kim, J., & Love, J. C.
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 4
- Issue
- 3
- Pages
- 255-259
- ISSN
- 2405-4712 (ISSN-L)
- OpenAlex
- W2612589628
- Cited by
- 108
- References
- 0
- Citations by year
- 2026: 3 · 2025: 19 · 2024: 19 · 2023: 8 · 2022: 12 · 2021: 15 · 2020: 12 · 2019: 9 · 2018: 3 · 2017: 7 · 2016: 1
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- cell identity, cellular heterogeneity, cell fate, Purkinje cells, Paneth cells, chief cells
- MeSH terms
- Cells, Humans
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