Description
- Abstract:
- Ovarian cancer is an aggressive disease with wide-reaching global incidence, with over 300,000 instances and over 200,000 deaths each year. Due to the lack of effective early detection strategies for ovarian cancers, most ovarian cancer patients are diagnosed with advanced-stage cancer. Exposure to per- and polyfluoroalkyl substances (PFAS) have been shown to alter normal ovarian function and induce resistance to platinum-based chemotherapy in ovarian cancer cell lines. Extracellular vesicles (EVs) are a target of research for early cancer detection because of their unique signature in tumor microenvironments and their critical role in intercellular communication We analyzed physical and biochemical differences between controls and an experimental, PFAS-exposed sample of EVs derived from human ovarian cancer cell lines. We isolated EVs from supernatants collected from NIH OVCAR and Caov-3 cell lines exposed to different concentrations of PFAS, creating three experimental groups: 1 exposed to PFAS, 1 exposed to an equal volume of methanol (vehicle control), and 1 with no exposure (untreated control). Samples underwent surface-enhanced Raman spectroscopy (SERS) and interferometric scattering microscopy (iSCAT). We calibrated a StellarNet HYPER-Nova spectrometer with a 785nm excitation laser for this protocol and identified that Raman spectra were optimally captured at 1-3mW. We identified the need for and attached a brightfield imaging arm to aid in accurately assessing sample dropped onto gold-coated SERS-chips. Physical quantification of EVs of following NTA revealed a statistically significant increase in EV concentration in PFAS-exposed samples as compared with the two control groups. Furthermore, SERS analysis showed that spectral signatures of the methanol and PFAS EVs differed when compared to control group. Finally, iSCAT confirmed differences in EV concentration between PFAS and both controls. We have identified next steps. Firstly, we will enhance sensitivity and reproducibility by engineering customized SERS substrates and refine measurement protocols (laser power, acquisition time, sample prep). Secondly, we will implement AI-driven analytics—building a machine-learning pipeline that performs both classification (to distinguish PFAS treatment groups) and regression (to track dose- and time-dependent changes in EV spectra) across multiple time points.
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Citation
Ranjana Ramesh, Utkan Demirci, Ugur Parlatan, et al.,
"Advancing Raman spectroscopy to understand the biochemical composition of EVs in PFAS-exposed human ovarian cancer cells"
(2026).
Summer Research Symposium.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/a357-dc90
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Summer Research Symposium
Each year, Brown University showcases the research of its undergraduates at the Summer Research Symposium. More than half of the student-researchers are UTRA recipients, while others receive funding from a variety of Brown-administered and national programs and fellowships and go …...