线粒体在细胞内形成一个网络，通过分裂、融合和动力学迅速变化。这个四维（4D：x，y，z，时间）的时间网络的失调与许多疾病有关，从癌症到神经退行性疾病。虽然晶格光片显微镜最近使得能够以4D成像线粒体，但对所得数据的定量分析方法缺乏。在这里，我们提出了MitoTNT，这是第一个用于4D活细胞荧光显微镜数据的线粒体时间网络跟踪的一流软件。MitoTNT利用空间接近度和网络拓扑来计算最优的跟踪分配。为了验证跟踪的准确性，我们创建了一个反应扩散模拟来模拟线粒体网络运动和重塑事件。我们发现我们的跟踪准确率>90%的基本真实模拟，并与实验数据的已发表的运动结果吻合。我们使用MitoTNT量化来自人体诱导多能干细胞的4D线粒体网络。首先，我们表征了子片段的运动，并分析了网络分支运动模式。我们揭示了骨架结点运动沿着分支相关，在时间上不相关。其次，我们用高空间分辨率鉴定了分裂和融合事件。我们发现接近分裂/融合地点的线粒体骨架结点移动速度几乎是随机骨架结点的两倍，并且微管在调节选择性分裂/融合方面发挥作用。最后，我们开发了基于图形的运输模拟，对实验测量的线粒体时间网络如何分布物质进行建模。我们发现，药理干扰通过改变线粒体分裂/融合动力学和动力学，增加了网络的可达性，但减少了网络的弹性。MitoTNT易于使用的跟踪模块、交互式的4D可视化能力和强大的跟踪后分析旨在使时间网络跟踪对更广泛的线粒体研究社区可达。版权所有：©2023年王等人。本文是根据创作共用许可证发布的开放获取文章，允许在任何媒介中不受限制地使用、分发和复制，只要原作者和来源得到认可。

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.Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.