Green, Rebecca, Ochoa, Stacy, Chow, Tiffany, Hendel, Jeff, Zhao, Zhiling, Khaliullin, Renat, Desai, Arshad, Oegema, Karen, Wang, Shaohe
[
International Worm Meeting,
2019]
An important challenge is to functionally classify the ~2000 genes (>1400 conserved) that control cell-fate specification and morphogenesis during embryogenesis. Here, we perform a 4D high-content screen by filming embryogenesis using two custom-engineered C. elegans reporter strains, following individual RNAi-based knockdown (>20,000 individual movies). We monitor (1) changes in cell fate specification, by dynamically tracking fluorescently labeled endoderm, mesoderm and ectoderm nuclei, and (2) morphogenic changes during epithelial and neuronal development by monitoring tissue position and tissue shape. Consistent and timely analysis of 20,000 movies requires automation, however, the range and complexity of 4D developmental phenotypes are not easily captured by existing automated methods. To address this challenge, we manually curated a pilot set of 500 genes (>7000 movies) and used this reference to guide the development of custom automated analysis algorithms; this effort ensured that our final automated analysis method captured observed phenotypes across a spectrum of developmental defects. For each RNAi condition, our automated analysis yields phenotypic signatures consisting of >100 continuous parameters. To evaluate the phenotypic similarity between RNAi conditions, we measure the distance between phenotypes in continuous space. To correct for the fact that a strict measure of Euclidean distance penalizes genes with more severe phenotypes, we measure the angle between the average phenotypes for the two conditions (phenotypic angle of deviation; PAD). Finally, we optimized the set of parameters used for automated comparison by assessing performance of the algorithm on a manually-annotated set of phenotypic groups. Our resulting automated method effectively identifies genes whose knockdown leads to similar phenotypes; this allows partitioning of genes into functional groups that are predicted to reflect developmental pathways and will yield a systems-level view of embryonic development. This work represents the first fully automated high-content screen of an intact developing organism and is the most complex morphological profiling effort to date.
Desai, Arshad, Biggs, Ronald, Oegema, Karen, Zhao, Zhiling, Wang, Shaohe, Ochoa, Stacy, Green, Rebecca, Gerson-Gurwitz, Adina, Khaliullin, Renat
[
International Worm Meeting,
2015]
­Embryogenesis is a complex process requiring coordination of cell division, signaling, migration, differentiation, and death. Systematically defining the genetic pathways that drive these morphogenetic events during embryogenesis is an important current challenge. Our goal is to construct a comprehensive functional network map of essential developmental genes for the model metazoan, C. elegans. To this end, we have developed a 4D-high-content screening based approach to functionally classify ~2600 developmental genes, using two-specifically engineered marker strains that readout defects in (1) germ layer specification and positioning and (2) cell shape changes and cell migration during morphogenesis. Following RNAi of targets, we image C. elegans embryos throughout the developmental time course (~10hrs) using a CV1000 spinning disk confocal high-content imaging system, which enables collection of developmental data for 50-100 embryos in a single experiment. To date, we have completed a pilot set of >500 genes. Among these, we have recovered expected phenotypes for well described developmental genes as well as severe developmental phenotypes for many uncharacterized genes, validating our overall experimental approach. This pilot data set is being used to develop custom data management algorithms (cropping, orienting, and indexing embryos) and data analysis protocols, including: manual and automated scoring of phenotypic features (Imaris and custom). Using this approach, each individual embryogenesis movie is scored and genes are clustered according to phenotypic profiles. When complete, this will be the first systems-level view of embryonic development in a complex multicellular organism. We anticipate such an effort will translate to higher organisms and help reveal the genetic basis for congenital defects, such as neural tube, craniofacial, and ventral body wall closure abnormalities.