Fully automated cellular-resolution vertebrate screening platform with parallel animal processing

The zebrafish larva is an optically-transparent vertebrate model with complex organs that is widely used to study genetics, developmental biology, and to model various human diseases. In this article, we present a set of novel technologies that significantly increase the throughput and capabilities of our previously described vertebrate automated screening technology (VAST). We developed a robust multi-thread system that can simultaneously process multiple animals. System throughput is limited only by the image acquisition speed rather than by the fluidic or mechanical processes. We developed image recognition algorithms that fully automate manipulation of animals, including orienting and positioning regions of interest within the microscope's field of view. We also identified the optimal capillary materials for high-resolution, distortion-free, low-background imaging of zebrafish larvae.National Institutes of Health (U.S.) (Director's New Innovator Award DP2 OD002989)National Institutes of Health (U.S.) (Transformative Research Award R01 NS073127)David & Lucile Packard Foundation (Award in Science and Engineering)Broad Institute of MIT and Harvard (SPARC Award)Foxconn International Holdings Ltd.Athinoula A. Martinos Center for Biomedical Imaging (Training Grant

Resolving clustered worms via probabilistic shape models

The roundworm Caenorhabditis elegans is an effective model system for biological processes such as immunity, behavior, and metabolism. Robotic sample preparation together with automated microscopy and image analysis has recently enabled high-throughput screening experiments using C. elegans. So far, such experiments have been limited to per-image measurements due to the tendency of the worms to cluster, which prevents extracting features from individual animals. We present a novel approach for the extraction of individual C. elegans from clusters of worms in high-throughput microscopy images. The key ideas are the construction of a low-dimensional shape-descriptor space and the definition of a probability measure on it. Promising segmentation results are shown.National Institutes of Health (U.S.) (Grant R01 AI072508)National Institutes of Health (U.S.) (Grant P01 AI083214)National Institutes of Health (U.S.) (Grant R01 AI085581)National Institutes of Health (U.S.) (Grant U54 EB005149