An Image-Based Data-Driven Analysis of Cellular Architecture in a Developing Tissue

Abstract

Quantitative microscopy is becoming increasingly crucial in efforts to disentangle the complexity of organogenesis, yet adoption of the potent and ever-growing toolbox provided by modern data science has been slow, primarily because data-driven tools focus on "big data" whereas microscopy produces "rich data". We tackle this issue using a newly developed algorithm for point cloud-based morphometry to unpack the rich information encoded in high-resolution 3D image data into a straightforward numerical representation. This allowed us to employ machine learning for multi-modal data integration of cell morphology, intracellular organization, gene expression and annotated contextual knowledge that facilitates data exploration. We apply these techniques to construct and explore a quantitative atlas of cellular architecture for the zebrafish posterior lateral line primordium, an experimentally tractable model of complex self-organized organogenesis. In doing so, we are able to retrieve both previously established and novel biologically relevant patterns, demonstrating the potential of our data-driven approach.