Dr. Nina Filiczak Profile

Dr. Nina Filiczak

at Northeastern Univ

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Proceedings Article | 4 March 2019 Presentation + Paper

Advancing methods for the analysis of glioblastoma cell motion using quantitative time lapse holographic imaging and cellular tomography

Ed Luther, Livia Mendes, Nina Filiczak, Aditi Jhaveri, Lara Milane, Vladimir Torchilin

Proceedings Volume 10881, 108810Q (2019) https://doi.org/10.1117/12.2510236

KEYWORDS: Holography, Tomography, Refractive index, Visualization, Motion analysis

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Glioblastomas are brain cancers with very poor patient prognosis. We have developed a Glioblastoma U87 MR model, using 4-dimensional imaging in multi-day tracking experiments. The cells have a tendency to form long-term cellular associations, and quantifying their motility by standard approaches is difficult. We cultured the cells in a structured environment (wound healing template), separated the X and Y information to define cumulative directionality plots providing a metric of the overall cell population movement analyzed by holographic imaging cytometry. With cellular tomography, we obtained 3D time lapse tomographs of cells at 0.2 um resolution, enabling sub-cellular analysis at levels not previously possible. Even in label-free cultures, sub-cellular components can be distinguished and color-coded based on differences of their refractive index values. We discovered that there are numerous mitochondria present, both single and also actively undergoing fission and fusion processes. Many thin mitochondrial networks are present within the cytoplasm, and also extending away from the cell in tunneling nanotubes. There is fusion of these networks to form larger structures that form connections between cells. Substances can be seen moving bi-directionally between cells. After several days of culture, the cells form large multicellular and highly connected spheroids. This is evident in widefield stitched images of the spheroids. While the tendency of U87 cells to form spheroids was previously known, the combined results from our multi-modality quantitative imaging platforms provide new insights into the cellular dynamics of glioblastoma cells, and the networks that they form. This knowledge is being applied to the development anti-glioblastoma treatments.

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