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A high-content image-based method for quantitatively studying context-dependent cell population dynamics

delete2016-07-25
delete53
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OA
AI
C
Colleen M. Garvey
E
Erin Spiller
D
Danika Lindsay
C
Chun-Te Chiang
N
Nathan C. Choi
D
David B. Agus
P
Parag Mallick
J
Jasmine Foo
S
Shannon M. Mumenthaler *
DOI:10.1038/srep29752delete
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摘要

摘要

En 中文
Tumor progression results from a complex interplay between cellular heterogeneity, treatment response, microenvironment and heterocellular interactions. Existing approaches to characterize this interplay suffer from an inability to distinguish between multiple cell types, often lack environmental context, and are unable to perform multiplex phenotypic profiling of cell populations. Here we present a high-throughput platform for characterizing, with single-cell resolution, the dynamic phenotypic responses (i.e. morphology changes, proliferation, apoptosis) of heterogeneous cell populations both during standard growth and in response to multiple, co-occurring selective pressures. The speed of this platform enables a thorough investigation of the impacts of diverse selective pressures including genetic alterations, therapeutic interventions, heterocellular components and microenvironmental factors. The platform has been applied to both 2D and 3D culture systems and readily distinguishes between (1) cytotoxic versus cytostatic cellular responses; and (2) changes in morphological features over time and in response to perturbation. These important features can directly influence tumor evolution and clinical outcome. Our image-based approach provides a deeper insight into the cellular dynamics and heterogeneity of tumors (or other complex systems), with reduced reagents and time, offering advantages over traditional biological assays.
Keyword:
TUMOR MICROENVIRONMENT
RESISTANCE
EVOLUTION
HETEROGENEITY
INHIBITORS
PHENOTYPES
MORPHOLOGY
TRACKING
REVEALS
IMPACT
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Scientific Reports 封面图
Scientific Reports
IF:
3.9
论文数:
28.0W
被引数:
83.5W

机构

U
university of southern california
学者数:
4.7W
论文数: 3.8W
被引数: 51
U
University of Minnesota Twin Cities
学者数:
3.7W
论文数: 3.1W
被引数: 58
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引用论文

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