Embryo Timelapses Can Be Compiled and Quantified to Understand Canonical Histone Dynamics Across Multiple Cell Cycles

In the last decade, computational processing of big datasets has facilitated the analyses of unprecedented quantities of biological data. Thus, automation and big data analysis have been revolutionary in detecting and quantifying subtle phenotypes in cell biological contexts. Analyzing similar quantities of data in larger and more complicated biological systems such as developing embryos has been more challenging due to experimental limitations on both ensemble data collection and analysis. These challenges include photosensitivity of living samples and of the fluorescently tagged proteins under study, collectively limiting the number of images that can be acquired in a single timelapse series. Here we present a streamlined workflow to quantify dynamics of fluorescently labeled proteins over the course of several cell cycles in early embryos, taking advantage of the stereotypical nature of early development that is inherent for many organisms. We benchmark this pipeline studying a fluorescently labeled histone during early embryonic development of the nematode Caenorhabditis elegans. Our strategy allowed us to overcome biological and experimental variation among our timelapse series and quantify nuclear accumulation rate, chromatin incorporation, and turnover/stability of canonical histones. We find that histone proteins are broadly stable in early C. elegans development. Thus, changes in genome regulation occurring in early development do not manifest in gross changes in histone metabolism. Our method enabling characterization of cumulative protein dynamics over several cell cycles of developmental time with high temporal resolution can be applied to expand our understanding of diverse cellular and developmental processes.