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
Committee Chair
Shankar Mukherji
Degree
Doctor of Philosophy (PhD)
Author's Department
Physics
Document Type
Dissertation
Date of Award
8-14-2023
Language
English (en)
DOI
https://doi.org/10.7936/96ej-8x36
Author's ORCID
https://orcid.org/0000-0003-2902-7867
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 Theses and Dissertations. 3127.
The definitive version is available at https://doi.org/10.7936/96ej-8x36