Live cell-lineage tracing and machine learning reveal patterns of organ regeneration
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-03-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/30823 |
| _version_ | 1811201326400405504 |
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| author | Oriol Viader-Llargués Valerio Lupperger Laura Pola-Morell Carsten Marr Hernán López-Schier |
| author_facet | Oriol Viader-Llargués Valerio Lupperger Laura Pola-Morell Carsten Marr Hernán López-Schier |
| author_sort | Oriol Viader-Llargués |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T02:19:01Z |
| format | Article |
| id | doaj.art-7b8f26c13b5d4f44b8b89e32366ab7ae |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T02:19:01Z |
| publishDate | 2018-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-7b8f26c13b5d4f44b8b89e32366ab7ae2022-12-22T03:52:10ZengeLife Sciences Publications LtdeLife2050-084X2018-03-01710.7554/eLife.30823Live cell-lineage tracing and machine learning reveal patterns of organ regenerationOriol Viader-Llargués0Valerio Lupperger1Laura Pola-Morell2Carsten Marr3Hernán López-Schier4https://orcid.org/0000-0001-7925-7439Unit Sensory Biology & Organogenesis, Helmholtz Zentrum München, Neuherberg, Germany; Laboratory of Sensory Cell Biology, Centre for Genomic Regulation, Barcelona, SpainInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, GermanyUnit Sensory Biology & Organogenesis, Helmholtz Zentrum München, Neuherberg, Germany; Laboratory of Sensory Cell Biology, Centre for Genomic Regulation, Barcelona, SpainInstitute of Computational Biology, Helmholtz Zentrum München, Neuherberg, GermanyUnit Sensory Biology & Organogenesis, Helmholtz Zentrum München, Neuherberg, Germany; Laboratory of Sensory Cell Biology, Centre for Genomic Regulation, Barcelona, SpainDespite 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.https://elifesciences.org/articles/30823regenerationsensory organneuromastmachine learninglive imagingclonal analysis |
| spellingShingle | Oriol Viader-Llargués Valerio Lupperger Laura Pola-Morell Carsten Marr Hernán López-Schier Live cell-lineage tracing and machine learning reveal patterns of organ regeneration eLife regeneration sensory organ neuromast machine learning live imaging clonal analysis |
| title | Live cell-lineage tracing and machine learning reveal patterns of organ regeneration |
| title_full | Live cell-lineage tracing and machine learning reveal patterns of organ regeneration |
| title_fullStr | Live cell-lineage tracing and machine learning reveal patterns of organ regeneration |
| title_full_unstemmed | Live cell-lineage tracing and machine learning reveal patterns of organ regeneration |
| title_short | Live cell-lineage tracing and machine learning reveal patterns of organ regeneration |
| title_sort | live cell lineage tracing and machine learning reveal patterns of organ regeneration |
| topic | regeneration sensory organ neuromast machine learning live imaging clonal analysis |
| url | https://elifesciences.org/articles/30823 |
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