BME PhD Dissertation Defense - Yuying Tan

  • Starts: 3:00 pm on Friday, March 24, 2023

Title: “Uncovering Cancer Metabolic Signatures by High-Content Stimulated Raman Scattering (SRS) Imaging”

Advisory Committee: Darren Roblyer, PhD – BU BME, ECE (Chair) Ji-Xin Cheng, PhD – BU BME, ECE (Advisor) Hadi Nia, PhD – BU BME, MSE Muhammad Zaman, PhD – BU BME, MSE Wilson W. Wong, PhD – BU BME

Abstract: Cancer is still one of the most serious health problems worldwide and cancer resistance to chemotherapy, the most wildly use therapeutic strategy for cancer, mounts the biggest challenges for current anti-cancer treatment. The unique characteristics of chemo-resistant cancer cell such as metabolic hallmark can largely facilitate surmounting this difficulty by serving as a therapeutic target to fight against chemo-resistance. However, the understanding of cancer metabolism is still limited, partly resulting from the lack of suitable analytic approaches. My dissertation work applied recently developed stimulated Raman scattering (SRS) imaging on cancer cells to uncover their metabolic signatures for the development of more effective cancer therapy.

Taking advantage of SRS imaging, we uncovered that cisplatin-resistant cell have increased fatty acid (FA) uptake, accompanied with reduced glucose uptake and lipogenesis. This metabolic reprograming from glucose to FA dependent anabolic and energy metabolism enables us to develop a rapid diagnostic method for cisplatin-resistance and a therapeutic strategy for cisplatin-resistant cancer. Moreover, we used SRS imaging to estimate the ratio of saturated (SFAs) and unsaturated fatty acids (UFAs) in cancer cell and revealed the role of Stearoyl Co-A desaturase 1 (SCD) on maintaining the intracellular balance of SFAs and UFAs. The unbalance SFAs/UFAs leaded to endoplasmic reticulum (ER) stress, presented as stiff and disorganized ER structure in SRS imaging. This ER stress induced cancer cell apoptosis in vitro, suggesting the therapeutic potential of targeting the lipid balance. To further dissect the metabolic features and reprograming in cancer cells, we developed a high-content hyperspectral SRS (h2SRS) imaging approach by introducing sparsity-driven hyperspectral image decomposition to SRS image post-processing. h2SRS can simultaneously map five major biomolecules involving protein, carbohydrate, FA, cholesterol, and nucleic acid at the single cell level, revealing the acute and adapted metabolic reprograming induced by chemotherapy in cancer cells. This approach accelerates the discoveries of new therapeutic targets against chemo-resistance and benefit the exploration of cellular metabolism study.

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