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• W2887012956 abstract "ABSTRACT Cancer stem cells (CSCs) are a heterogeneous and dynamic population that stands at the top of tumor cellular hierarchy and is responsible for maintenance of the tumor microenvironment. 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 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) CSC 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 CSCs 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 quantification of SOX2 expression by HASCIA defined a stable cut-off 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 CSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of CSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available at https://hascia.lerner.ccf.org ." @default.
• W2887012956 created "2018-08-22" @default.
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• W2887012956 date "2018-07-31" @default.
• W2887012956 modified "2023-09-27" @default.
• W2887012956 title "High-throughput Automated Single Cell Imaging Analysis Reveals Dynamics Of Glioblastoma Stem Cell Population During State Transition" @default.
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• W2887012956 doi "https://doi.org/10.1101/381715" @default.
• W2887012956 hasPublicationYear "2018" @default.
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