Back

Neurogenesis Leads Early Development in Zebrafish

Wang, Z.; Tian, L.; Li, B.

2025-11-12 neuroscience
10.1101/2025.11.12.687769 bioRxiv
Show abstract

Vertebrate early neurogenesis is a highly conserved process fundamental to brain function and the emergence of intelligence. However, the cellular dynamics bridging gastrulation and organogenesis remain elusive due to observational challenges. We developed a live-cell imaging platform for transgenic zebrafish that provides, for the first time, a continuous reconstruction of early neurogenesis across subcellular to organismal scales. Our analysis reveals that neurogenesis is a precisely orchestrated process. Neuronal cell bodies initially coalesce into discrete, linearly arranged clusters extending from the brain along the spinal cord. From these hubs, axons radiate outward to innervate the central nervous system and peripheral tissues, including the yolk sac surface. A primary pioneer neuron projects from the brain, coursing ventrally in parallel to the body axis. Secondary neurons then interconnect, forming a pervasive network that is subsequently refined through selective axonal apoptosis. The emergence of frequent Ca2+; flashes only after structural maturation indicates that functionality is contingent upon an established scaffold. We also observe concurrent material transport and a slow, directional flow of Ca2+; along axons, suggesting complementary signaling modalities. Furthermore, neurogenesis exhibits precise spatiotemporal coupling with histogenesis, particularly with the developing lateral line and vasculature. Our work, with refined spatial and time resolution, defines the kinetic pathway of early neurogenesis and underscores the critical interplay of subsystems in embryogenesis, offering fundamental insights for neural health and bio-inspired intelligence.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.