High-throughput screening (HTS) is a technique for searching large libraries of chemical or genetic perturbants, to find new treatments for a disease or to better understand disease pathways. As automated image analysis for cultured cells has improved, microscopy has emerged as one of the most powerful and informative ways to analyze screening samples. However, many diseases and biological pathways can be better studied in whole animals-particularly diseases that involve organ systems and multicellular interactions, such as metabolism and infection. The worm Caenorhabditis elegans is a well-established and effective model organism, used by thousands of researchers worldwide to study complex biological processes. Samples of C. elegans can be robotically prepared and imaged by high-throughput microscopy, but existing image-analysis methods are insuf- ficient for most assays. In this project, image-analysis algorithms that are capable of scoring high-throughput assays of C. elegans will be developed. The algorithms will be tested and refined in three high-throughput screens, which will uncover chemical and genetic regulators of fat metabolism and infection: (1) A C. elegans viability assay to identify modulators of infection. The proposed algorithms use a probabilistic shape model of C. elegans in order to identify and mea- sure individual worms even when the animals touch or cross. These methods are the basis for quantifying many other phenotypes, including body morphology and subtle variations in reporter signal levels. (2) A C. elegans lipid assay to identify genes that regulate fat metabolism. The algorithms proposed for illumination correction, level-set-based foreground segmentation, well-edge detection, and artifact removal will result in improved or- business in high-throughput experiments. (3) A fluorescence gene expression assay to identify regulators of the response of the C. elegans host to Staphylococcus aureus infection. The proposed techniques for constructing anatomical maps of C. elegans will make it possible to quantify a variety of changes in fluorescent localization patterns in a biologically relevant way. In addition to discovering new metabolism- and infection-related drugs and genetic regulators through these specific screens, this work will provide the C. elegans community with (a) a new framework for extracting mor- phological features from C. elegans for quantitative analysis of this organism, and (b) a versatile, modular, open-source toolbox of algorithms enabling the discovery of genetic pathways, chemical probes, and drug can- didates in whole organism high-throughput screens relevant to a variety of diseases. This work is a close collaboration with C. elegans experts Fred Ausubel and Gary Ruvkun at Massachusetts General Hospital/Harvard Medical School, with Polina Golland and Tammy Riklin-Raviv, experts in model-based segmentation and statistical image analysis at MIT's Computer Science and Artificial Intelligence Laboratory, and with Anne Carpenter, developer of open-source image analysis software at the Broad Institute.
Large-scale screening experiments that test the effects of thousands of chemicals or genetic perturbants by microscopy and image analysis can discover new treatments and help biomedical scientists understand dis- ease mechanisms. Microscopy screens of cultured cells are routine, but researchers wish to study complex processes like metabolism and infection in a whole animal like the tiny worm Caenorhabditis elegans, for which existing image analysis methods are insufficient. The goal of this research is to develop open-source software to automatically identify and measure C. elegans in microscopy images, thereby making it possible for researchers worldwide to screen a wide variety of complex biological processes related to human disease.
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