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- Title
MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data.
- Authors
Wang, Zichen; Natekar, Parth; Tea, Challana; Tamir, Sharon; Hakozaki, Hiroyuki; Schöneberg, Johannes
- Abstract
Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) temporal network is implicated in numerous diseases ranging from cancer to neurodegeneration. 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. Here we present MitoTNT, the first-in-class software for Mitochondrial Temporal Network Tracking in 4D live-cell fluorescence microscopy data. MitoTNT uses spatial proximity and network topology to compute an optimal tracking assignment. To validate the accuracy of tracking, we created a reaction-diffusion simulation to model mitochondrial network motion and remodeling events. We found that our tracking is >90% accurate for the ground-truth simulations and agrees well with published motility results for experimental data. We used MitoTNT to quantify 4D mitochondrial networks from human induced pluripotent stem cells. First, we characterized sub-fragment motility and analyzed network branch motion patterns. We revealed that the skeleton node motion is correlated along branch and uncorrelated in time. Second, we identified fission and fusion events with high spatiotemporal resolution. 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. Finally, we developed graph-based transport simulations that model how material would distribute on experimentally measured mitochondrial temporal networks. We showed that pharmacological perturbations increase network reachability but decrease network resilience through a combination of altered mitochondrial fission/fusion dynamics and motility. MitoTNT's easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analysis aim at making temporal network tracking accessible to the wider mitochondria research community. Author summary: Mitochondria are the powerhouses of the cell and assume critical roles in cell fate, cell signaling, and cellular health. Mitochondria dysfunctions underlie many human diseases, including metabolic diseases, cancer, diabetes, and neurodegenerative diseases. Contrary to the bean-shaped textbook depiction, mitochondria assume large interconnected and dynamic network structures in the cells. These four-dimensional temporal networks (space and time) could only recently be captured through advanced imaging methods such as lattice light-sheet microscopy (LLSM). The current challenge then becomes the lack of analysis tools to extract quantitative information from the resulting enormous LLSM datasets. Here, we present the software MitoTNT (Mitochondrial Temporal Network Tracking) to solve this data analysis problem. MitoTNT uses the distance and topology information in the mitochondrial network to track the mitochondrial network over time. MitoTNT achieves >90% tracking accuracy in synthetic ground-truth datasets and agrees well with previously published motility values from experimental data. Using MitoTNT, we uncovered distinct network movement patterns, fission and fusion preferences, and network-level responses to drugs. To make temporal mitochondrial network tracking widely accessible, MitoTNT comes with easy-to-use tracking module, interactive 4D visualization capability, and powerful post-tracking analysis.
- Subjects
TIME-varying networks; FLUORESCENCE microscopy; INDUCED pluripotent stem cells; PLURIPOTENT stem cells; MITOCHONDRIA; CELL anatomy
- Publication
PLoS Computational Biology, 2023, Vol 19, Issue 4, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1011060