High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

Sebastián L. Vega, Er Liu, Varun Arvind, Jared Bushman, Hak Joon Sung, Matthew L. Becker, Sophie Lelièvre, Joachim Kohn, Pierre Alexandre Vidi, Prabhas V. Moghe

Research output: Contribution to journalArticlepeer-review

Abstract

Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high-content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer-mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative “imaging-derived” parsing index, which reflects the dynamic evolution of the high-dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions.

Original languageEnglish (US)
Pages (from-to)11-23
Number of pages13
JournalExperimental cell research
Volume351
Issue number1
DOIs
StatePublished - Feb 1 2017

All Science Journal Classification (ASJC) codes

  • Cell Biology

Keywords

  • Biomaterials
  • Differentiation
  • Image informatics
  • Imaging
  • Mesenchymal stem cells
  • Nuclear
  • Oncogenic transformation
  • Organization
  • Stem cells

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