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Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties

Neuronal migration is a highly dynamic process, and multiple cell movement metrics can be extracted from time-lapse imaging datasets. However, these parameters alone are often insufficient to evaluate the heterogeneity of neuroblast populations. We developed an analytical pipeline based on reducing...

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Detalles Bibliográficos
Autores principales: Ferreira, Aymeric, Bressan, Cedric, Hardy, Simon V., Saghatelyan, Armen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023771/
https://www.ncbi.nlm.nih.gov/pubmed/35303437
http://dx.doi.org/10.1016/j.stemcr.2022.02.011
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author Ferreira, Aymeric
Bressan, Cedric
Hardy, Simon V.
Saghatelyan, Armen
author_facet Ferreira, Aymeric
Bressan, Cedric
Hardy, Simon V.
Saghatelyan, Armen
author_sort Ferreira, Aymeric
collection PubMed
description Neuronal migration is a highly dynamic process, and multiple cell movement metrics can be extracted from time-lapse imaging datasets. However, these parameters alone are often insufficient to evaluate the heterogeneity of neuroblast populations. We developed an analytical pipeline based on reducing the dimensions of the dataset by principal component analysis (PCA) and determining sub-populations using k-means, supported by the elbow criterion method and validated by a decision tree algorithm. We showed that neuroblasts derived from the same adult neural stem cell (NSC) lineage as well as across different lineages are heterogeneous and can be sub-divided into different clusters based on their dynamic properties. Interestingly, we also observed overlapping clusters for neuroblasts derived from different NSC lineages. We further showed that genetic perturbations or environmental stimuli affect the migratory properties of neuroblasts in a sub-cluster-specific manner. Our data thus provide a framework for assessing the heterogeneity of migrating neuroblasts.
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spelling pubmed-90237712022-04-23 Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties Ferreira, Aymeric Bressan, Cedric Hardy, Simon V. Saghatelyan, Armen Stem Cell Reports Article Neuronal migration is a highly dynamic process, and multiple cell movement metrics can be extracted from time-lapse imaging datasets. However, these parameters alone are often insufficient to evaluate the heterogeneity of neuroblast populations. We developed an analytical pipeline based on reducing the dimensions of the dataset by principal component analysis (PCA) and determining sub-populations using k-means, supported by the elbow criterion method and validated by a decision tree algorithm. We showed that neuroblasts derived from the same adult neural stem cell (NSC) lineage as well as across different lineages are heterogeneous and can be sub-divided into different clusters based on their dynamic properties. Interestingly, we also observed overlapping clusters for neuroblasts derived from different NSC lineages. We further showed that genetic perturbations or environmental stimuli affect the migratory properties of neuroblasts in a sub-cluster-specific manner. Our data thus provide a framework for assessing the heterogeneity of migrating neuroblasts. Elsevier 2022-03-17 /pmc/articles/PMC9023771/ /pubmed/35303437 http://dx.doi.org/10.1016/j.stemcr.2022.02.011 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Ferreira, Aymeric
Bressan, Cedric
Hardy, Simon V.
Saghatelyan, Armen
Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title_full Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title_fullStr Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title_full_unstemmed Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title_short Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
title_sort deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023771/
https://www.ncbi.nlm.nih.gov/pubmed/35303437
http://dx.doi.org/10.1016/j.stemcr.2022.02.011
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