TY - JOUR
T1 - Physiologic patient derived 3d spheroids for anti-neoplastic drug screening to target cancer stem cells
AU - Bregenzer, Michael E.
AU - Davis, Ciara
AU - Horst, Eric N.
AU - Mehta, Pooja
AU - Novak, Caymen M.
AU - Raghavan, Shreya
AU - Snyder, Catherine S.
AU - Mehta, Geeta
N1 - Funding Information:
This work is supported primarily by DOD OCRP Early Career Investigator Award W81XWH-13-1-0134(GM), DOD Pilot award W81XWH-16-1-0426 (GM), DOD Investigator Initiated award W81XWH-17-OCRP-IIRA (GM), Rivkin Center for Ovarian Cancer and Michigan Ovarian Cancer Alliance. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number P30CA046592. CMN is supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1256260. MEB is supported by the Department of Education Graduate Assistance in Areas of National Need (GAANN) Fellowship.
Publisher Copyright:
© 2019 Journal of Visualized Experiments.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/7
Y1 - 2019/7
N2 - In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.
AB - In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.
KW - Cancer Research
KW - Cancer stem cells
KW - Drug screening
KW - Hanging drop spheroids
KW - High throughput
KW - Issue 149
KW - Patient derived
KW - Precision medicine
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U2 - 10.3791/59696
DO - 10.3791/59696
M3 - Article
C2 - 31329171
AN - SCOPUS:85070439179
VL - 2019
JO - Journal of visualized experiments : JoVE
JF - Journal of visualized experiments : JoVE
SN - 1940-087X
IS - 149
M1 - e59696
ER -