Department of Pharmacology, University of California, San Diego, San Diego, California, United States of America.
Department of Chemistry and Biochemistry, University of California, San Diego, San Diego, California, United States of America.
PLoS Comput Biol. 2023 Apr 21;19(4):e1011060. doi: 10.1371/journal.pcbi.1011060. eCollection 2023 Apr.
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) 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 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 nodes and is 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 analyses aim at making temporal network tracking accessible to the wider mitochondria research community.
线粒体在细胞中形成一个网络,通过裂变、融合和运动迅速变化。这种四维(4D:x,y,z,时间)网络的失调与从癌症到神经退行性疾病等多种疾病有关。虽然晶格光片显微镜最近使得在 4D 中对线粒体进行成像成为可能,但缺乏对这些数据集进行定量分析的方法。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中线粒体时间网络跟踪的一流软件。MitoTNT 使用空间接近度和网络拓扑结构来计算最佳的跟踪分配。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,我们的跟踪对于真实模拟的准确性>90%,并且与实验数据的已发表运动结果吻合良好。我们使用 MitoTNT 从人诱导多能干细胞中定量 4D 线粒体网络。首先,我们描述了亚片段运动,并分析了网络分支运动模式。我们发现,分支节点的骨架节点运动沿分支节点相关,并且在时间上不相关。其次,我们以高时空分辨率识别了裂变和融合事件。我们发现,靠近裂变/融合部位的线粒体骨架节点的移动速度几乎是随机骨架节点的两倍,微管在介导选择性裂变/融合中起作用。最后,我们开发了基于图的传输模拟,该模拟可以模拟物质在实验测量的线粒体时间网络上的分布。我们表明,药物处理通过改变线粒体裂变/融合动力学和运动来增加网络可达性,但降低网络弹性。MitoTNT 易于使用的跟踪模块、交互式 4D 可视化功能和强大的跟踪后分析旨在使时间网络跟踪更容易为更广泛的线粒体研究界所接受。