ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2019
High-Throughput Automated Single-Cell Imaging Analysis Reveals Dynamics of Glioblastoma Stem Cell Population During State Transition.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
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- Asymmetric cell division promotes therapeutic resistance in glioblastoma stem cells.JCI insight · 2021Article
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- Cancer Stem Cell Plasticity - A Deadly Deal.Frontiers in molecular biosciences · 2020Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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 of the protein expression levels determined by HASCIA to those determined by immunoblotting. A virtue of HASCIA was exemplified by detection of a subpopulation of SOX2-low cells, which expanded in fraction size during state transition. HASCIA also revealed that GSCs were committed to loose stem cell state at an earlier time point than the average SOX2 level decreased. Functional assessment of stem cell frequency in combination with the quantification of SOX2 expression by HASCIA defined a stable cutoff of SOX2 expression level for stem cell state. We also developed an approach to assess local cell density and found that denser monolayer areas possess higher average levels of SOX2, higher cell diversity, and a presence of a sub-population of slowly proliferating SOX2-low GSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of GSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available at https://hascia.lerner.ccf.org. © 2019 International Society for Advancement of Cytometry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.