ORCID
https://orcid.org/0000-0003-2902-7867
Date of Award
8-14-2023
Degree Name
Doctor of Philosophy (PhD)
Degree Type
Dissertation
Abstract
Eukaryotic cells are building blocks to complex living systems, characterized by membrane-bound organelles. Studying how eukaryotic organelles react to cellular growth and size increase is crucial, but it demands biochemical and biophysical manipulations, as well as quantitative observation tools in microscopy. We developed a multi-color yeast strain with tagged fluorescent proteins, enabling systematic measurements of 6 organelles inside each cell using spectral confocal microscopy. These measurements provided insights into how organelle biogenesis is coordinated with cellular size and growth rate regulation via different signaling pathways. To explore cellular growth under dynamic conditions, I utilized deep learning for organelle recognition using low-power bright field fluorescent microscopy. This technique maintains high spectral resolution while minimizing photodamage and enabling timelapse acquisition. Training output statistics demonstrated excellent fidelity to the target. The work in this thesis lays a foundation for capturing how organelle dynamics and cellular growth interrelate
Language
English (en)
Chair and Committee
Shankar Mukherji
Recommended Citation
Wang, Shixing, "Multi-color Fluorescent Microscopy and Deep Learning for Studying Eukaryotic Organelles: Unveiling Cellular Growth in a System Biology Perspective" (2023). Arts & Sciences Electronic Theses and Dissertations. 3127.
https://openscholarship.wustl.edu/art_sci_etds/3127