Evidence map›Paper›PMID 37083820›Full record

ArticlePLoS computational biology2023

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

Zichen Wang, Parth Natekar, Challana Tea, Sharon Tamir, Hiroyuki Hakozaki, Johannes Schöneberg

Abstract read
In one paragraph

Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Diffusive spreading across dynamic mitochondrial network architectures.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Temporal networks in biology and medicine: a survey on models, algorithms, and tools.Network modeling and analysis in health informatics and bioinformatics · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zichen WangDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.ORCID 0000-0003-4759-3053
Parth NatekarDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.ORCID 0000-0003-4299-0885
Challana TeaDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.
Sharon TamirDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.
Hiroyuki HakozakiDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.ORCID 0009-0008-2503-0480
Johannes SchönebergDepartment of Pharmacology, University of California, San Diego, San Diego, California, United States of America.ORCID 0000-0001-7083-1828

Funding

Decode Mitochondrial Morphology Dynamics to Predict Cell Fate DecisionsDP2GM150022 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SCHOENEBERG, JOHANNES · 2022 to 2025
$2.4M
Modeling and Analysis of the Spatio-Temporal Dynamics of the Mitochondrial NetworkR01GM148765 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Johannes Schoeneberg · 2023 to 2026
$1.2M
NIGMS NIH HHS DP2 GM150022NIGMS NIH HHS R01 GM148765
6 · The paper itself

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) 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.

Indexed as

Induced Pluripotent Stem CellsComputer SimulationHumansMicroscopy, FluorescenceMitochondriaMitochondrial DynamicsSoftware

Identifiers

PMID37083820
PMCPMC10184899

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.