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Advanced High-Content-Screening Applications of Clonogenicity in Cancer
Author(s) -
Hector Esquer,
Qiong Zhou,
Adedoyin D. Abraham,
Daniel V. LaBarbera
Publication year - 2020
Publication title -
slas discovery
Language(s) - English
Resource type - Journals
eISSN - 2472-5560
pISSN - 2472-5552
DOI - 10.1177/2472555220926921
Subject(s) - clonogenic assay , cancer stem cell , cancer research , high content screening , cancer cell , cancer , stem cell , computational biology , biology , cell , microbiology and biotechnology , genetics
Since its first report in 1956 by Puck and Marcus, the clonogenic assay has not been completely adapted into high-content-screening (HCS) workflows despite the numerous automated systems available. Initially, clonogenic assays were used to observe the effects of radiation on cell survival, particularly with cancer cells. The clonogenic assay has since been well characterized as a measure of cancer stem cell (CSC) stemness, demonstrating that a single CSC can generate clonogenic colonies. CSCs are highly tumorigenic with an unlimited proliferation potential and capacity to generate malignant tumors. Furthermore, CSCs are also known to resist conventional chemotherapy as well as more contemporary targeted therapies alike. Therefore, given the complexity of CSCs and their clinical relevance, new methods must follow to more effectively study and characterize CSC mechanisms that allow them to proliferate and persist, and to develop drugs and other therapies that can more effectively target these populations. Herein, we present a HCS method to quantify the number and size of colonies in 2D and 3D culture models and to distinguish colonies based on fluorescent markers using an Opera Phenix high-content-screening system. In addition, we present a method to scan at low magnification and rescan at a higher magnification to capture in greater detail colonies or even single cells of interest. These methods can be adapted to numerous applications or other imaging systems to study CSC biology using high-content analysis and for high-throughput drug discovery.

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