Abstract
Spheroid cultures derived from explanted cancer specimens are an increasingly utilized resource for studying complex biological processes like tumor cell invasion and metastasis, representing an important bridge between the simplicity and practicality of 2-dimensional monolayer cultures and the complexity and realism of in vivo animal models. Temporal imaging of spheroids can capture the dynamics of cell behaviors and microenvironments, and when combined with quantitative image analysis methods, enables deep interrogation of biological mechanisms. This paper presents a comprehensive open-source software framework for Temporal Analysis of Spheroid Imaging (TASI) that allows investigators to objectively characterize spheroid growth and invasion dynamics. TASI performs spatiotemporal segmentation of spheroid cultures, extraction of features describing spheroid morpho-phenotypes, mathematical modeling of spheroid dynamics, and statistical comparisons of experimental conditions. We demonstrate the utility of this tool in an analysis of non-small cell lung cancer spheroids that exhibit variability in metastatic and proliferative behaviors.
Highlights
3-dimensional spheroid models of cancer have been widely used to investigate mechanisms of invasion and metastasis[1,2,3,4] and the impact of drugs on metastatic potential[5,6,7]
Temporal Analysis of Spheroid Imaging (TASI) is published as open-source software under an Apache 2.0 license
Full documentation on using TASI and the formatting of inputs and outputs is described in the Github repository
Summary
3-dimensional spheroid models of cancer have been widely used to investigate mechanisms of invasion and metastasis[1,2,3,4] and the impact of drugs on metastatic potential[5,6,7]. The relative ease in imaging spheroid models makes them especially amenable to investigating temporal processes where dynamic behaviors and interactions can be captured. Software for analyzing of collective cell migration has been developed in smaller models for developmental biology, using cell tracking to extract quantitative features describing migration patterns[30]. Cultures derived from neoplastic cells often exhibit irregular shapes, chaining and branching behaviors, and can be highly dynamic, making automatic delineation difficult[5,22,25], and leading investigators to perform manual segmentations that are not objective or repeatable[31,32]. Most spheroid analysis software only measures basic size and shape features, which is insufficient to discriminate different patterns of invasion[33]. Leader and follower cell migration behavior were widely studied, most were conducted in 2-dimensional models and there were no standard features selected to quantify the morphology differences between them. Many researchers still used manual selection to quantify the morphology features of these cells, which limited the number of cells studied and was time-consuming and subjective
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