A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain

Mariela D. Petkova, Michał Januszewski, Tim Blakely, Kristian J. Herrera, Gregor F.P. Schuhknecht, Robert Tiller, Jinhan Choi, Richard L. Schalek, Jonathan Boulanger-Weill, Adi Peleg, Yuelong Wu, Shuohong Wang, Jakob Troidl, Sumit Kumar Vohra, Donglai Wei, Zudi Lin, Armin Bahl, Juan Carlos Tapia, Nirmala Iyer, Zachary T. Miller et al. (50 additional authors not shown)

Abstract: h3>Abstract</h3> <p>We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (<i>vglut2a</i>) and inhibitory (<i>gad1b</i>) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing &gt;180,000 segmented soma, &gt;40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.</p>

External IDs:doi:10.1101/2025.06.10.658982