Bayesian sparse regression analysis documents the diversity of spinal inhibitory interneurons
Open accessAbstract
Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of ~50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell-type characterization in the nervous system and elsewhere.
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Combinatorial expression of 19 TFs has been proposed to generate multiple subpopulations of V1 interneurons.
“Moreover, the combinatorial expression of 19 TFs have been proposed to generate multiple subpopulations of V1 interneurons (Bikoff et al., 2016; Gabitto et al., 2016; Sweeney et al., 2018).”
From Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord (2019) · cites this paper as Gabitto et al., 2016
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- Authors
- Gabitto, M. I., Pakman, A., Bikoff, J. B., Abbott, L. F., Jessell, T. M., & Paninski, L.
- Affiliations
- Howard Hughes Medical Institute (US)
- Columbia University (US)
- Corresponding author
- Gabitto, Mariano I.
- Paninski, Liam
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 165
- Issue
- 1
- Pages
- 220-233
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4831714
- OpenAlex
- W2289759715
- Cited by
- 73
- References
- 59
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 11 · 2023: 2 · 2022: 6 · 2021: 7 · 2020: 6 · 2019: 11 · 2018: 10 · 2017: 7 · 2016: 5
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- NIH (NS033245, MH093338)
- Harold and Leila Y. Mathers Foundation
- Brain Research Foundation
- Gatsby
- Swartz
- Mathers Foundations
- ONR (N00014-14-1-0243)
- ARO MURI (W911NF-12-1-0594)
- 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 › Gene expression and cancer classification
- Keywords
- cell type diversity, sparse Bayesian inference, transcription factor expression, ventral spinal cord, estimation uncertainty, experimental design optimization, neuronal cell types
- MeSH terms
- Animals, Bayes Theorem, Spinal Cord, Transcription Factors, Mice, Transcriptome, Renshaw Cells
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