Dr. Livia P. Mendes Profile

Dr. Livia P. Mendes

at Northeastern Univ

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Publications (2)

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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.

Proceedings Article | 23 February 2018 Paper

Applications of quantitative time lapse holographic imaging to the development of complex pharmaceutical nano formulations

Ed Luther, Livia Mendes, Jiayi Pan, Daniel Costa, Can Sarisozen, Vladimir Torchilin

Proceedings Volume 10503, 105032G (2018) https://doi.org/10.1117/12.2288504

KEYWORDS: Holography, Image segmentation, Tumors, Standards development, Imaging systems, Image processing, Holograms, 3D image processing, Etching

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We rely on in vitro cellular cultures to evaluate the effects of the components of multifunctional nano-based formulations under development. We employ an incubator-adapted, label-free holographic imaging cytometer HoloMonitor M4® (Phase Holographic Imaging, Lund, Sweden) to obtain multi-day time-lapse sequences at 5- minute intervals. An automated stage allows hand-free acquisition of multiple fields of view.

Our system is based on the Mach-Zehnder interferometry principle to create interference patterns which are deconvolved to produce images of the optical thickness of the field of view. These images are automatically segmented resulting in a full complement of quantitative morphological features, such as optical volume, thickness, and area amongst many others. Precise XY cell locations and the time of acquisition are also recorded.

Visualization is best achieved by novel 4-Dimensional plots, where XY position is plotted overtime time (Z-directions) and cell-thickness is coded as color or gray scale brightness. Fundamental events of interest, i.e., cells undergoing mitosis or mitotic dysfunction, cell death, cell-to-cell interactions, motility are discernable. We use both 2D and 3D models of the tumor microenvironment.

We report our new analysis method to track feature changes over time based on a 4-sample version of the Kolmogorov-Smirnov test. Feature A is compared to Control A, and Feature B is compared to Control B to give a 2D probability plot of the feature changes over time. As a result, we efficiently obtain vectors quantifying feature changes over time in various sample conditions, i.e., changing compound concentrations or multi-compound combinations.

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