Live cell-lineage tracing and machine learning reveal patterns of organ regeneration
Abstract
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.
Article and author information
Author details
Funding
European Research Council (2007_205095)
- Hernán López-Schier
AGAUR (2009-SGR-305)
- Hernán López-Schier
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Zebrafish were maintained under standard conditions. Experiments with wild-type, mutant and transgenic embryos of undetermined sex were conducted in accordance with institutional guidelines and under a protocol approved by the Ethical Committee of Animal Experimentation of the Parc de Recerca Biomedica de Barcelona, Spain, and protocol number Gz.:55.2-1-54-2532-202-2014 by the "Regierung von Oberbayern", Germany.
Copyright
© 2018, Viader-Llargués et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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