MitoTNT: Mitochondrial Temporal Network Tracking for 4D live-cell fluorescence microscopy data

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.

LiveLattice: Real-time visualization of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

“…In the case of 4D particle tracking, pyLattice ParticleTracking 26 can be applied for particle segmentation and tracking. In the case of 4D analyses of mitochondrial motility and dynamics, pyLattice MitoTNT 27 can be used to reliably track and extract functional information from mitochondrial networks.…”

Obesity causes mitochondrial fragmentation and dysfunction in white adipocytes due to RalA activation

“…MitoGraph was used to segment the mitochondria in each cell. Based on the segmented mitochondria skeleton, we used MitoTNT to track mitochondria and perform motility calculations with a published protocol 82 . Mitochondria displaying high motility were used for further fusion and fission dynamic analysis.…”

Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes

“…Mitochondria form a three dimensional network in the cell that changes over time through fission, fusion, and motility 5,39 . Consequently, it is established that the mitochondrial network naturally displays a wide range of morphologies in control conditions (Figure 1A).…”

“…In traditional high-content phenotypic profiling 2,3 , a set of features is manually defined and quantified from each microscopic image. Examples include size, shape, and texture of an organelle 2,3,4,5 . Such feature sets can then be processed to find a subset of features that constitute a 'fingerprint' of each individual sample condition, for example healthy versus diseased.…”