The metastatic profile of the cancer cell is considered to be one of the most problematic characteristics from the pathogenic point of view. Because the metastatic cancer cells often show higher mobility compared to the non-metastatic cancer cells, distinguishing the metastatic cancer cell by their images can contain a clue to understanding the molecular process of the cellular metastasis-associated behaviors. In this study, we suggest a deep-learning approach to classify the metastatic cancer cells and non-metastatic cancer cells by their single-cell images acquired by phase-contrast microscopy.
The movement of intracellular vesicle contains essential biomedical information, mediating drug delivery and virus transmission. However, due to the interaction between vesicles and cytoskeletal networks, the trajectories of vesicle transport are often too complicated to understand the details. Particularly, identifying active transport via cytoskeletal network from random motion requires time-consuming mathematical methods. In this paper, we propose a machine learning approach to categorize the vesicle transport into active transport and random movement, using the features computed from the vector analysis of 3D vesicle transport trajectories. This approach is expected to simplify the process for vesicle transport data analysis.
The movement of vesicle in a living cell includes essential information for understanding the details of the intracellular transport. Although the vesicle tracking method has allowed us to understand precise movement of a single nanoparticle from the physical point of view, the whole cell-level transport has still not been clearly explained with the analysis of only a few representative vesicle movements. In this study, as an initial attempt to gain insight into cell-level vesicle transport, we adopted a computer vision technique to analyze the overall intracellular vesicle transport. In detail, we propose an algorithm to estimate and visualize the ow of the entire endocytic vesicles in terms of convergence and divergence with respect to the geometric cell center. In this algorithm, optical ow of the fluorescent nanoparticles in a living cell is computed using Lucas-Kanade method. Then, the direction of vesicle movement regarding the geometric center of the cell is calculated and mapped to visualize either converging or diverging movement, based on four-quadrant inverse tangent. With this suggested method, it is expected that we can gain insight into cell-level vesicle transport, which can help design and quantitatively evaluate various biomedical applications, including drug delivery.
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