Quantitative neuronal morphometry by supervised and unsupervised learning.

We present a protocol to characterize the morphological properties of individual neurons reconstructed from microscopic imaging. We first describe a simple procedure to extract relevant morphological features from digital tracings of neural arbors. Then, we provide detailed steps on classification, clustering, and statistical analysis of the traced cells based on morphological features. We illustrate the pipeline design using specific examples from zebrafish anatomy. Our approach can be readily applied and generalized to the characterization of axonal, dendritic, or glial geometry. For complete context and scientific motivation for the studies and datasets used here, refer to Valera et al. (2021).

A neuronal blueprint for directional mechanosensation in larval zebrafish.

Animals have a remarkable ability to use local cues to orient in space in the absence of a panoramic fixed reference frame. Here we use the mechanosensory lateral line in larval zebrafish to understand rheotaxis, an innate oriented swimming evoked by water currents. We generated a comprehensive light-microscopy cell-resolution projectome of lateralis afferent neurons (LANs) and used clustering techniques for morphological classification. We find surprising structural constancy among LANs. Laser-mediated microlesions indicate that precise topographic mapping of lateral-line receptors is not essential for rheotaxis. Recording neuronal-activity during controlled mechanical stimulation of neuromasts reveals unequal representation of water-flow direction in the hindbrain. We explored potential circuit architectures constrained by anatomical and functional data to suggest a parsimonious model under which the integration of lateralized signals transmitted by direction-selective LANs underlies the encoding of water-flow direction in the brain. These data provide a new framework to understand how animals use local mechanical cues to orient in space.

Live Morphometric Classification of Sensory Neurons in Larval Zebrafish.

Sensory systems convey environmental information to the brain. A comprehensive description of neuronal anatomy and connectivity is essential to understand how sensory information is acquired, transmitted, and processed. Here we describe a high-resolution live imaging technique to quantify the architecture of sensory neurons in larval zebrafish. This approach is ideal to assess neuronal-circuit plasticity and regeneration.

Hair cell identity establishes labeled lines of directional mechanosensation.

Directional mechanoreception by hair cells is transmitted to the brain via afferent neurons to enable postural control and rheotaxis. Neuronal tuning to individual directions of mechanical flow occurs when each peripheral axon selectively synapses with multiple hair cells of identical planar polarization. How such mechanosensory labeled lines are established and maintained remains unsolved. Here, we use the zebrafish lateral line to reveal that asymmetric activity of the transcription factor Emx2 diversifies hair cell identity to instruct polarity-selective synaptogenesis. Unexpectedly, presynaptic scaffolds and coherent hair cell orientation are dispensable for synaptic selectivity, indicating that epithelial planar polarity and synaptic partner matching are separable. Moreover, regenerating axons recapitulate synapses with hair cells according to Emx2 expression but not global orientation. Our results identify a simple cellular algorithm that solves the selectivity task even in the presence of noise generated by the frequent receptor cell turnover. They also suggest that coupling connectivity patterns to cellular identity rather than polarity relaxes developmental and evolutionary constraints to innervation of organs with differing orientation.