Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis

Area of Science:

• Neuroscience
• Computational Biology
• Developmental Biology

Background:

• Neuronal morphogenesis involves complex neurite dynamics and dendritic branching during development.
• High-throughput imaging generates vast datasets of neuronal morphology, but quantification remains challenging.

Purpose of the Study:

• To introduce a novel framework, Temporal Topological Morphology Descriptor (TTMD), for quantifying neuronal structural trajectories.
• To enable automated analysis of neuronal development and identification of pathological alterations.

Main Methods:

• Combining persistent topology with time-resolved neuronal imaging.
• Applying TTMD to Drosophila sensory neuron datasets to analyze developmental stages and growth dynamics.
• Utilizing TTMD for automated tracking of branch emergence and retraction.

Main Results:

• TTMD successfully decodes developmental stages, branching, and mutant-specific growth dynamics from topology.
• Topological Morphology Descriptor (TMD) resolves major developmental transitions in Class I neurons.
• TTMD reveals subtle alterations in Class III neuron branch dynamics caused by actin pathway mutations.

Conclusions:

• Temporal topology offers a unified and interpretable approach to neuronal morphogenesis.
• TTMD facilitates automated analysis of developmental neuroanatomy.
• The framework aids in identifying pathological alterations in neuronal structure.