UCSF

The formation of repeated patterns is a recurring theme during tissue development. Well-studied models include the pigmentation of fish skins, the spacing and orientation of hair follicles, and the connection of sensory neurons. How do seemingly complex, yet highly organized patterns emerge? What rules underlie the development of these cellular patterns? A major challenge is to uncover rules in the face of “biological noise”, arising from stochastic variability of individual cells, local warping of the developing tissue, and specimen-to-specimen differences.

Here, I investigated rules governing neuronal pattern formation in the context of Drosophila visual circuit development, where the outer photoreceptors, R1-R6, resort in the lamina layer of the brain to form the stereotyped neural superposition (NSP) circuit. To hunt for signals within the biological noise, I developed a data-driven, standardized coordinate system to characterize the ensemble behaviors of photoreceptors. With this, I was able to identify rules that govern cell-type-specific extension velocities of the photoreceptor pair R3/R4, which uniquely exhibit asymmetric targeting. Specifically, I found that the extension speeds of the R3 and R4 growth cones are inherent to their cell identities. Further, to showcase the predictive power of the coordinate system, I made a computational prediction that extension angles can be explained by a weighted nearest-neighbor repulsion model. My work provides a new case study of how pattern formation rules — hidden within phenotypic variability — can be inferred by quantitative analysis during the development of living organisms.