![]() Received: SeptemAccepted: MaPublished: April 21, 2023Ĭopyright: © 2023 Wang et al. Haugh, North Carolina State University, UNITED STATES To make temporal mitochondrial network tracking widely accessible, MitoTNT comes with an easy-to-use tracking module, an interactive 4D visualization capability, and powerful post-tracking analyses.Ĭitation: Wang Z, Natekar P, Tea C, Tamir S, Hakozaki H, Schöneberg J (2023) MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data. Using MitoTNT, we uncovered distinct network movement patterns, fission and fusion preferences, and network-level responses to drugs. MitoTNT achieves >90% tracking accuracy in synthetic ground-truth datasets and agrees well with previously published motility values from experimental data. MitoTNT uses the distance and topology information in the mitochondrial network to track the mitochondria over time. Here, we present the software MitoTNT ( Mitochondrial Temporal Network Tracking) to solve this data analysis problem. The current challenge has been the lack of analysis tools to extract quantitative information from the enormous LLSM datasets. These four-dimensional networks (space and time) could only recently be captured through advanced imaging methods such as lattice light-sheet microscopy (LLSM). Contrary to the bean-shaped textbook depiction, mitochondria form large interconnected and dynamic network structures in cells. Mitochondria dysfunctions underlie many human diseases, including cancer, diabetes, cardiovascular and neurodegenerative diseases. Mitochondria are the powerhouses of the cell and assume critical roles in cell fate, cell signaling, and cellular health. MitoTNT’s easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analyses aim at making temporal network tracking accessible to the wider mitochondria research community. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. ![]() We found that mitochondrial skeleton nodes near the fission/fusion sites move nearly twice as fast as random skeleton nodes and that microtubules play a role in mediating selective fission/fusion. Second, we identified fission and fusion events with high spatiotemporal resolution. We revealed that the skeleton node motion is correlated along branch nodes and is uncorrelated in time. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. We found that our tracking is >90% accurate for ground-truth simulations and agrees well with published motility results for experimental data. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. While lattice light-sheet microscopy has recently made it possible to image mitochondria in 4D, quantitative analysis methods for the resulting datasets have been lacking. Dysregulation of this four-dimensional (4D: x,y,z,time) network is implicated in numerous diseases ranging from cancer to neurodegeneration. Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility.
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