High-Throughput Automated Single-Cell Imaging Analysis Reveals Dynamics of Glioblastoma Stem Cell Population During State Transition.

ABSTRACT: Cancer stem cells (CSCs) are a heterogeneous and dynamic self-renewing population that stands at the top of tumor cellular hierarchy and contribute to tumor recurrence and therapeutic resistance. As methods of CSC isolation and functional interrogation advance, there is a need for a reliable and accessible quantitative approach to assess heterogeneity and state transition dynamics in CSCs. We developed a high-throughput automated single cell imaging analysis (HASCIA) approach for the quantitative assessment of protein expression with single-cell resolution and applied the method to investigate spatiotemporal factors that influence CSC state transition using glioblastoma (GBM) CSCs (GSCs) as a model system. We were able to validate the quantitative nature of this approach through comparison

SUBMITTER: Chumakova AP 

PROVIDER: S-EPMC7927898 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature