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

Stem Cell Reports. 2022 Apr 12;17(4):911-923. doi: 10.1016/j.stemcr.2022.02.011. Epub 2022 Mar 17.

Abstract

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.

Keywords: K-means; cell heterogeneity; cell migration; decision tree; neural stem cells; neuroblasts; principal component analysis; time-lapse imaging.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Movement / physiology
  • Neural Stem Cells* / physiology
  • Neurogenesis / physiology
  • Neurons*
  • Time-Lapse Imaging

Grant support