Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

CBIO-04. MUTATION-SPECIFIC NON-CANONICAL PATHWAY OF PTEN AS A DISTINCT THERAPEUTIC TARGET FOR GLIOBLASTOMA

The dominant-negative effect of PTEN mutation has been described previously, suggesting that aberrant gain of function attributed to mutation might be more disastrous than deletion in respect to malignant potential. In present study, we explored the functional implications of hot spot mutations of PTEN in GBM tumors. Subcellular location of PTEN is important for its distinct function and spatial distribution within the cytoplasm is known to be associated with cellular locomotion. We evaluated the subcellular compartmentalization of different PTEN mutants and found that some PTEN mutants located at cellular edges of chemotaxing cells. Moreover, these PTEN mutations exhibited invasive phenotype, which was not disrupted by PI3K inhibitor, but microtubule inhibitors. This finding suggests that cytoskeletal assembly as a novel non-canonical pathway of PTEN, thus unraveling a novel therapeutic vulnerability of PTEN. Mutation-specific therapeutic options should be considered in treating GBM patients with PTEN mutations.

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline's usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

Automated cell tracking using StarDist and TrackMate

The ability of cells to migrate is a fundamental physiological process involved in embryonic development, tissue homeostasis, immune surveillance, and wound healing. Therefore, the mechanisms governing cellular locomotion have been under intense scrutiny over the last 50 years. One of the main tools of this scrutiny is live-cell quantitative imaging, where researchers image cells over time to study their migration and quantitatively analyze their dynamics by tracking them using the recorded images. Despite the availability of computational tools, manual tracking remains widely used among researchers due to the difficulty setting up robust automated cell tracking and large-scale analysis. Here we provide a detailed analysis pipeline illustrating how the deep learning network StarDist can be combined with the popular tracking software TrackMate to perform 2D automated cell tracking and provide fully quantitative readouts. Our proposed protocol is compatible with both fluorescent and widefield images. It only requires freely available and open-source software (ZeroCostDL4Mic and Fiji), and does not require any coding knowledge from the users, making it a versatile and powerful tool for the field. We demonstrate this pipeline’s usability by automatically tracking cancer cells and T cells using fluorescent and brightfield images. Importantly, we provide, as supplementary information, a detailed step-by-step protocol to allow researchers to implement it with their images.

Bio-Inspired Locomotion of Circular Robots With Diametrically Translating Legs

In this paper, we develop an analytical framework for designing the locomotion of mobile robots with a circular core and equispaced diametral legs, each having a radial translational degree of freedom. The mechanism has resemblance with certain cellular locomotion. The robot travels by radial actuation of the legs in a sequential and synchronized manner. Two elementary regimes of motion are first designed using the geometry and degrees of freedom of the mechanism. Overall motion of the robot is generated by repeated switching between the two regimes. The paper addresses both kinematics and kinetics of the mechanism, enabling the prediction of trajectories and computation of constraint as well as actuation forces. Simulation results are provided in support of the theory developed.