Single-cell bio-analytical techniques play a pivotal role in contemporary biological and biomedical research. Among current high-throughput single-cell imaging methods, coherent Raman imaging offers both high bio-compatibility and high-throughput information-rich capabilities, offering insights into cellular composition, dynamics, and function. Coherent Raman imaging finds its value in diverse applications, ranging from live cell dynamic imaging, high-throughput drug screening, fast antimicrobial susceptibility testing, etc. In this thesis, I first present a deep learning algorithm to solve the inverse problem of getting a chemically labeled image from a single-shot femtosecond stimulated Raman scattering (SRS) image. This method allows high-speed, high-throughput tracking of lipid droplet dynamics and drug response in live cells. Second, I provide image-based single-cell analysis in an engineered Escherichia coli (E. coli) population, confirming the chemical composition and subcellular structure organization of individual engineered E. coli cells. Additionally, I unveil metabolon formation in engineered E. coli by high-speed spectroscopic SRS and two-photon fluorescence imaging.
Lastly, I present stimulated Raman-activated cell ejection (S-RACE) by integrating high-throughput SRS imaging, in situ image decomposition, and high-precision laser-induced cell ejection. I demonstrate the automatic imaging-identification-sorting workflow in S-RACE and advance its compatibility with versatile samples ranging from polymer particles, single live bacteria/fungus, and tissue sections.
Collectively, these efforts demonstrate the valuable capability of SRS in high-throughput single-cell imaging and sorting, opening opportunities for a wide range of biological and biomedical applications.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47944 |
| Date | 18 January 2024 |
| Creators | Zhang, Jing |
| Contributors | Cheng, Ji-Xin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-sa/4.0/ |
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