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

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

doi:10.1101/2022.08.16.504049

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…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 ).…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Natekar,

Wang,

Arora

et al. 2023

Preprint

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Imaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present MitoSpace: a novel mitochondrial phenotypic atlas that leverages self-supervised deep learning to create a semantically meaningful latent space from images without relying on any data labels for training. Our approach employs a dataset of ~100,000 microscopy images of Cal27 and HeLa cells treated with 25 drugs affecting mitochondria, but can be generalized to any cell type, cell organelle, or drug library with no changes to the methodology. We demonstrate how MitoSpace enhances our understanding of the range of mitochondrial phenotypes induced by pharmacological interventions. We find that i) self-supervised learning can automatically uncover the semantic landscape of drug induced latent mitochondrial phenotypes and can map individual cells to the correct functional area of the drug they are treated with, ii) the traditional classification of mitochondrial morphology along a fragmented to fused axis is more complex than previously thought, with additional axes being identified, and iii) latent spaces trained in a self-supervised manner are superior to those trained with supervised models, and generalize to other cell types and drug conditions without explicit training on those cell types or drug conditions. Future applications of MitoSpace include creating mitochondrial biomarkers for drug discovery and determining the effects of unknown drugs and diseases for diagnostic purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Natekar,

Wang,

Arora

et al. 2023

Preprint

Self Cite

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“…These dynamics are proposed to enable the sharing of mitochondrial proteins and other molecules, evening out heterogeneous delivery of nuclear-encoded proteins and complementing mutant protein copies from nearby mitochondrial DNA copies with those from more distant copies, among other advantages from sharing molecules [31, 37, 39]. Mitochondrial networks can range from fragmented [18] to highly connected structures [9, 43], depending on the cell type and environment. We build on previous quantitative modeling of mitochondrial network dynamics, using a two-dimensional spatial lattice [31] and an aspatial agent-based model [8], to explore how fusion and fission levels control the spread of particles through the mitochondrial network.…”

Section: Discussionmentioning

confidence: 99%

“…The level of connectivity for mitochondria is controlled by both fission and fusion events [16][17][18][19], as well as organelle movement [20][21][22]. Relatively frequent fission leads to more fragmented mitochondria, while relatively frequent fusion leads to a more connected mitochondrial network.…”

Section: Introductionmentioning

confidence: 99%

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

Chuphal,

Brown

2024

Preprint

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Mitochondrial network structure is controlled by the dynamical processes of fusion and fission, which merge and split mitochondrial tubes into structures including branches and loops. To investigate the impact of mitochondrial network dynamics and structure on the spread of proteins and other molecules through mitochondrial networks, we used stochastic simulations of two distinct quantitative models that each included mitochondrial fusion and fission, and particle diffusion via the network. Better-connected mitochondrial networks and networks with faster dynamics exhibit more rapid particle spread on the network, with little further improvement once a network has become well-connected. As fragmented networks gradually become better connected, particle spread either steadily improves until the networks become well-connected for slow-diffusing particles or plateaus for fast-diffusing particles. We compared model mitochondrial networks with both end-to-end and end-to-side fusion, which form branches, to non-branching model networks that lack end-to-side fusion. To achieve the optimum (most rapid) spread that occurs on well-connected branching networks, non-branching networks require much faster fusion and fission dynamics. Thus the process of end-to-side fusion, which creates branches in mitochondrial networks, enables rapid spread of particles on the network with relatively slow fusion and fission dynamics. This modeling of protein spread on mitochondrial networks builds towards mechanistic understanding of how mitochondrial structure and dynamics regulate mitochondrial function.

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“…The authors defined the critical state as a pre-disease state which act as a tripping point which can be helpful to prevent the disease deterioration. In [8], Wang et al developed a tool, MitoTNT, to analyse the dynamics of Mitochondria network in the cell that rapidly changes through fission, fusion, and motility.…”

Section: Introductionmentioning

confidence: 99%

Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

Sahu,

2023

Preprint

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The search for biomarkers of diseases has traditionally involved analyzing disease data as a whole. But this approach has largely ignored the significance of disease progression at different stages. Acknowledging the fact that different genes may be responsible for disease progression in different stages, we have developed a novel approach to analyse the temporal graphs that employs a modified version of betweenness centrality, known as ''transition centrality''. Applying this measure to the stage-wise data of three common diseases, namely Alzheimer's disease, Parkinson's disease, and Human breast cancer, we identified successfully a set of genes that may be key players in disease progression. The findings reveal that stage-specific genes can be identified using transition centrality as the specificity of these genes in a particular stage was validated through an extensive review of the relevant literature. Remarkably, this analysis yielded a number of promising disease-related genes for each of the diseases that have been studied, such as RARA and NOS1 in Alzheimer's disease, LRRK2 in Parkinson's disease, and BRCA1, ACTB, TYMS, and many others in human breast cancer. Our results demonstrate that the transition centrality analysis can be utilized to identify stage-specific genes of diseases.

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

Cited by 4 publications

References 38 publications

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

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

Mitochondrial network branching enables rapid protein spread with slower mitochondrial dynamics

Identifying Stage-Specific Genes in Disease Progression using Transition Centrality on Disease Temporal Graphs

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