Research

We are a small multidisciplinary team that uses a combination of cellular, molecular, genetic, biomechanical, live imaging and computational approaches to understand the development of the posterior lateral line primordium. This includes the use state-of-the-art microscopy, image processing and the development of multi-scale computational models to understand the self-organization of cell-fate, morphogenesis and migration of the lateral line primordium.

Research Approaches

Using multiscale imaging and image processing to visualize and characterize dynamics of morphogenesis and collective migration of the posterior lateral line primordium
Understanding cell signaling, regulatory networks and coordination of cell fate, morphogenesis and collective migration
Mechanics and quantitative analysis of collective migration
Development of multiscale computational models of the self-organization of cell fate, morphogenesis, and collective migration of the posterior lateral line primordium

Refer to Figure 1 caption. — **Figure 1.** Examples of cutting edge microscopy at different scales. A. primordium at 32 hpf. B. deposited neuromasts at 48 hpf. C. Di SPIM images. E Confocal slices with nuclei and membranes. F. Fluorescent primordium on somites. G. Sequential frames from time lapse movie. H. Depth coded confocal slices showing isolated primordium cells.

Refer to Figure 2 caption. — **Figure 2.** Schematics of regulatory networks. A. Basic Wnt FGF regulatory network. B. Sequential formation and deposition of neuromasts. C. Changes in deposition pattern following changes in Wnt and FGF signaling. D. Regulatory network in epithelial rosettes. E. Regulatory network that specifies sensory hair cell progenitor.

Refer to Figure 3 caption. — **Figure 3.** Analysis of primordium images to understand mechanics of migration. A. Normarski image. B. PIV analysis. C. Confocal slice. D. Segmentation analysis to identify nodes at cell edges. E. Movement analysis based on centroids.

Refer to Figure 4 caption. — **Figure 4.** Agent-based Models of the self-organization of cell fate, morphogenesis and collective migration. A. Model of how a polarized migratory response to a self-generated local gradient of cxcl12a determines directional migration of the primordium. (Dalle Nogare et al 2014 ( https://doi.org/10.1242/dev.106690 .) B. A model that uses measured parameters including initial cell number, proliferation rate, the rate at which the Wnt system shrinks, and migration speed to predict the pattern of neuromast formation and deposition by the migrating primordium (Dalle Nogare and Chitnis 2017 ( https://doi.org/10.1016/j.mod.2017.04.005 ) C. A model that shows how mutually inhibitory interactions between Wnt and Fgf signaling meet theoretical requirements of reaction-diffusion patterning with local activation of Wnt coupled with its indirect long range inhibition system that can determine self-organization (Semin Cell Dev Biol. 2020 Apr;100:186-198. doi: 10.1016/j.semcdb.2019.12.015. Epub 2019 Dec 31).