Cargando…

Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns

Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random...

Descripción completa

Detalles Bibliográficos
Autores principales: Lupperger, Valerio, Marr, Carsten, Chapouton, Prisca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748264/
https://www.ncbi.nlm.nih.gov/pubmed/33290409
http://dx.doi.org/10.1371/journal.pbio.3000708
_version_ 1783625077776973824
author Lupperger, Valerio
Marr, Carsten
Chapouton, Prisca
author_facet Lupperger, Valerio
Marr, Carsten
Chapouton, Prisca
author_sort Lupperger, Valerio
collection PubMed
description Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brain hemispheres revealed weakly aggregated, nonrandom division patterns in space and time. Analyzing divisions at 2 time points allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle reentries in roughly 15% of newly born NSCs. In agent-based simulations of NSC populations, this redividing activity sufficed to induce aggregated spatiotemporal division patterns that matched the ones observed experimentally. In contrast, omitting redivisions leads to a random spatiotemporal distribution of dividing cells. Spatiotemporal aggregation of dividing stem cells can thus emerge solely from the cells’ history.
format Online
Article
Text
id pubmed-7748264
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-77482642021-01-04 Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns Lupperger, Valerio Marr, Carsten Chapouton, Prisca PLoS Biol Short Reports Regulation of quiescence and cell cycle entry is pivotal for the maintenance of stem cell populations. Regulatory mechanisms, however, are poorly understood. In particular, it is unclear how the activity of single stem cells is coordinated within the population or if cells divide in a purely random fashion. We addressed this issue by analyzing division events in an adult neural stem cell (NSC) population of the zebrafish telencephalon. Spatial statistics and mathematical modeling of over 80,000 NSCs in 36 brain hemispheres revealed weakly aggregated, nonrandom division patterns in space and time. Analyzing divisions at 2 time points allowed us to infer cell cycle and S-phase lengths computationally. Interestingly, we observed rapid cell cycle reentries in roughly 15% of newly born NSCs. In agent-based simulations of NSC populations, this redividing activity sufficed to induce aggregated spatiotemporal division patterns that matched the ones observed experimentally. In contrast, omitting redivisions leads to a random spatiotemporal distribution of dividing cells. Spatiotemporal aggregation of dividing stem cells can thus emerge solely from the cells’ history. Public Library of Science 2020-12-08 /pmc/articles/PMC7748264/ /pubmed/33290409 http://dx.doi.org/10.1371/journal.pbio.3000708 Text en © 2020 Lupperger et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Short Reports
Lupperger, Valerio
Marr, Carsten
Chapouton, Prisca
Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title_full Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title_fullStr Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title_full_unstemmed Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title_short Reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
title_sort reoccurring neural stem cell divisions in the adult zebrafish telencephalon are sufficient for the emergence of aggregated spatiotemporal patterns
topic Short Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748264/
https://www.ncbi.nlm.nih.gov/pubmed/33290409
http://dx.doi.org/10.1371/journal.pbio.3000708
work_keys_str_mv AT luppergervalerio reoccurringneuralstemcelldivisionsintheadultzebrafishtelencephalonaresufficientfortheemergenceofaggregatedspatiotemporalpatterns
AT marrcarsten reoccurringneuralstemcelldivisionsintheadultzebrafishtelencephalonaresufficientfortheemergenceofaggregatedspatiotemporalpatterns
AT chapoutonprisca reoccurringneuralstemcelldivisionsintheadultzebrafishtelencephalonaresufficientfortheemergenceofaggregatedspatiotemporalpatterns