Understanding cellular heterogeneity has been a challenge in biology. Current bio-analytical methods such as mass spectrometry or fluorescence-based detection would destruct the sample or perturb the functions of targeted molecules. In situ imaging of bio-molecules at single cell level resolves the phenotypes at metabolomics domain, which can address the challenges in studying cellular heterogeneity. Stimulated Raman scattering (SRS) microscopy provides a label-free approach to identify molecules based on the signature of molecular vibrations. However, there are several challenges to overcome in order to use SRS as a single-cell analysis platform with high throughput, high content, and high sensitivity. My thesis work aims to overcome above-mentioned difficulties. To fulfill the first unmet need of single cell metabolic analysis, we developed an SRS flow cytometry, and demonstrated the discrimination of particles at a throughput of up to 11,000 particles per second, which is a four orders of magnitude improvement compared to conventional spontaneous Raman flow cytometry. Next, we addressed the second unmet need of single-cell metabolic analysis through the development of SRS imaging cytometry. Using this platform, we studied the response of human pancreatic cancer to drug-induced and starvation-induced stress, and discovered lipid-facilitated protrusion as a metabolic biomarker for stress-resistant cancer cells. Lastly, to probe low-concentration bio-molecules using fingerprint Raman bands, we utilized pre-resonance enhancement to increase the SRS signal by two orders of magnitude, and demonstrated ultra-sensitive imaging of retinoids in cells. We demonstrated in situ imaging of retinoid level in cancer cells and during neuronal development. Collectively, these efforts demonstrate SRS cytometry as a high-throughput, high-content, and high-sensitivity single-cell analysis platform with broad applications. / 2022-01-28T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/39329 |
| Date | 29 January 2020 |
| Creators | Huang, Kai-Chih |
| Contributors | Cheng, Ji-Xin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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