Alegria, Andrew2025-01-072025-01-072024-06https://hdl.handle.net/11299/269199University of Minnesota Ph.D. dissertation. June 2024. Major: Mechanical Engineering. Advisors: Suhasa Kodandaramaiah, Daryl Gohl. 1 computer file (PDF); xii, 113 pages.Microinjection is a widely used technique for transgenesis, mutagenesis, cell labeling, cryopreservation, and in vitro fertilization in multiple single and multicellular organisms. Microinjection requires specialized skills acquired for each target organism and involves rate limiting and labor-intensive preparatory steps. Here we constructed a machine vision (MV) guided generalized robot that fully automates the process of microinjection in fruit fly (Drosophila melanogaster) embryos. The robot uses machine learning (ML) models trained to detect individual embryos in images of agar plates, and models trained to identify specific anatomical locations within each embryo in 3-dimensional (3-D) space using dual view microscopes. The robot uses this information to serially perform microinjection in each detected embryo without any human intervention. We constructed and used two such robots to automatically microinject tens of thousands of Drosophila embryos for various experiments. We systematically optimized robotic microinjection by determining optimal values for depth of microinjection, speed of microinjection, and volume of microinjection. We validated the use of the robot by performing routine transgenesis and mutagenesis with proficiency comparable to highly skilled human practitioners while achieving up to 4x increases in microinjection throughput in Drosophila. The automated microinjection robot was utilized to microinject pools of over 20,000 uniquely barcoded plasmids into 1,713 embryos in two days to rapidly generate more than 400 unique transgenic Drosophila lines. This high throughput microinjection experiment enabled a novel measurement of the number of independent germline integration events per successfully injected embryo. The robot was used to perform a saturated mutagenesis screen of the apterous (ap) enhancer region where >21,000 microinjections were performed. From this experiment we were able to screen the ap enhancer region, creating dozens of null or hypomorphic mutations. Lastly, we demonstrated the versatility of the automated microinjection robot by carrying out microinjections in several different organisms with diverse egg morphologies, including aphids, black solider flies, and carp. Thus, the robot should have utility in a wide variety of organisms.enComputer visionGeneticsMachine learningRoboticsThesis or Dissertation