Intravital Imaging of Tumor Cell Motility in the Tumor Microenvironment Context Protocol First Online: 11 March 2018
Part of the
Methods in Molecular Biology
book series (MIMB, volume 1749) Abstract
Cancer cell motility and invasion are key features of metastatic tumors. Both are highly linked to tumor microenvironmental parameters, such as collagen architecture or macrophage density. However, due to the genetic, epigenetic and microenvironmental heterogeneities, only a small portion of tumor cells in the primary tumor are motile and furthermore, only a small portion of those will metastasize. This creates a challenge in predicting metastatic fate of single cells based on the phenotype they exhibit in the primary tumor. To overcome this challenge, tumor cell subpopulations need to be monitored at several timescales, mapping their phenotype in primary tumor as well as their potential homing to the secondary tumor site. Additionally, to address the spatial heterogeneity of the tumor microenvironment and how it relates to tumor cell phenotypes, large numbers of images need to be obtained from the same tumor. Finally, as the microenvironment complexity results in nonlinear relationships between tumor cell phenotype and its surroundings, advanced statistical models are required to interpret the imaging data. Toward improving our understanding of the relationship between cancer cell motility, the tumor microenvironment context and successful metastasis, we have developed several intravital approaches for continuous and longitudinal imaging, as well as data classification via support vector machine (SVM) algorithm. We also describe methods that extend the capabilities of intravital imaging by postsacrificial microscopy of the lung as well as correlative immunofluorescence in the primary tumor.
Key words Tumor microenvironment Motility Intravital imaging Correlative immunofluorescence Invadopodia Invasion 4D multiphoton fluorescent microscopy Second harmonic generation Photoconvertible proteins Support vector machine classification Electronic Supplementary Material:
The online version of this chapter (
) contains supplementary material, which is available to authorized users. https://doi.org/10.1007/978-1-4939-7701-7_14 Notes Acknowledgments
We thank Dr. Aviv Bergman for his contribution in establishing the SVM algorithm for classification. This work was supported by grants from the NIH 5K99CA172360 and Concern Foundation Award to B.G.
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