Improved Computer Vision Algorithms for High-Throughput Targeting of Single Cells in Intact Tissue for Automated Microinjections

Abstract

Microinjection is a technique for organism-level and cellular-level manipulation of biological systems. The precise nature of microinjection permits the ability to target single cells in intact tissue which has enabled the study of cell-type related phenomena in development and disease progression. We envisioned the use of single-cellular microinjection as a tool for tagging cells with unique oligonucleotide barcodes that can be used during post-injection transcriptomic analysis to relate the transcriptomic reads with originally injected cells. For this process to be viable, we needed a system that was capable of precisely identifying the locations of cells in 3D tissue, assessing their feasibility for injection, and conducting rapid and large-scale microinjection into the identified cells. In this thesis, we report the development of such system. Our automated system uses computer vision algorithms to identify the 3D position of epifluorescent cells in intact tissue slices and assign them a quality metric to prioritize injections. The system guides a robotic micromanipulator to these cells and attempts injections while another computer vision algorithm and Kalman filter are used to improve the robot’s positioning accuracy. Additionally, cell impalement and cell filling detection algorithms were developed to evaluate injection success. We discovered, through a microinjection parameter sweep, an optimum combination of parameters to enable successful microinjection into a variety of cell types and tissue types. We used the optimized parameters to demonstrate automated tagging of single cells with a fluorescently labeled antibody targeting the nuclear pore complex proteins as a precursor step to fluorescence-based nuclei sorting and later transcriptomic analysis.