MitoTNT

MitoTNT tracks mitochondrial networks in 4D live-cell fluorescence microscopy, quantifying temporal dynamics such as fission, fusion, motility, and inter-network transport in datasets including lattice light-sheet microscopy.

Key Features:

• Temporal Network Tracking: Tracks mitochondrial network changes over time, capturing fission, fusion, and motility events.
• Spatial Proximity and Network Topology Optimization: Leverages spatial proximity and network topology to compute an optimal tracking solution.
• Accuracy Benchmarking: Demonstrates greater than 90% accuracy in dynamic spatial mitochondria simulations.
• Imaging Modality Support: Operates on 4D live-cell fluorescence microscopy datasets, including lattice light-sheet microscopy.
• Correlated Movement Pattern Identification: Identifies correlated movement patterns within mitochondrial networks.
• Local Fission and Fusion Fingerprinting: Provides fingerprints of local fission and fusion events at granular spatial resolution.
• Asymmetric Dynamics Analysis: Analyzes asymmetric fission and fusion dynamics.
• Cross-Network Transport Detection: Detects transport patterns across mitochondrial networks.
• Pharmacological Response Analysis: Quantifies network-level responses to pharmacological manipulations.
• Implementation and Extensibility: Implemented in Python with an extendable architecture for research adaptation.

Scientific Applications:

• Correlated Movement Patterns: Detects coordinated mitochondrial movements to study spatially correlated dynamics within cells.
• Local Fission and Fusion Dynamics: Characterizes local fission and fusion events to investigate mechanisms of mitochondrial remodeling.
• Asymmetric Dynamics: Enables analysis of asymmetric fission and fusion behaviors.
• Cross-Network Transport Patterns: Reveals transport and connectivity patterns across mitochondrial networks to study intracellular distribution.
• Pharmacological Response Analysis: Measures network-level changes in response to pharmacological perturbations for drug and toxicology studies.

Methodology:

MitoTNT computes optimal temporal tracks by leveraging spatial proximity and network topology, was benchmarked on dynamic spatial mitochondria simulations reporting >90% accuracy, and is implemented in Python.