Live cell-lineage tracing and machine learning reveal patterns of organ regeneration.
eLife 7:e30823 (2018)
Despite the intrinsically stochastic nature of damage, sensory organs recapitulate normal architecture during repair to maintain function. Here we present a quantitative approach that combines live cell-lineage tracing and multifactorial classification by machine learning to reveal how cell identity and localization are coordinated during organ regeneration. We use the superficial neuromasts in larval zebrafish, which contain three cell classes organized in radial symmetry and a single planar-polarity axis. Visualization of cell-fate transitions at high temporal resolution shows that neuromasts regenerate isotropically to recover geometric order, proportions and polarity with exceptional accuracy. We identify mediolateral position within the growing tissue as the best predictor of cell-fate acquisition. We propose a self-regulatory mechanism that guides the regenerative process to identical outcome with minimal extrinsic information. The integrated approach that we have developed is simple and broadly applicable, and should help define predictive signatures of cellular behavior during the construction of complex tissues.
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Publication type
Article: Journal article
Document type
Scientific Article
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Keywords
Developmental Biology ; Stem Cells ; Zebrafish; Zebrafish Lateral-line; Progenitor Cells; Gene-expression; Enhancer Trap; Neuromasts; Notch; Migration; Latent; Glia
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Language
english
Publication Year
2018
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2018
ISSN (print) / ISBN
2050-084X
e-ISSN
2050-084X
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Volume: 7,
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Article Number: e30823
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eLife Sciences Publications
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Sheraton House, Castle Park, Cambridge, Cb3 0ax, England
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Reviewing status
Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
30204 - Cell Programming and Repair
Research field(s)
Enabling and Novel Technologies
Stem Cell and Neuroscience
PSP Element(s)
G-503800-001
G-500100-001
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Erfassungsdatum
2018-06-20