(F-235) Single Extracellular Vesicle SERS for Molecular Profiling in Glioblastoma

Introduction:: For cancers with low survival rates due to failure of therapy, technologies enabling monitoring of the molecular transformation are key to enhance the survival rate. As cancer cells release extracellular vesicles (EVs), they carry molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. Real-time monitoring of complex cancers such as glioblastoma (GBM) presents a major technological challenge due to tumour EV scarcity, heterogeneity, and intrinsic complexity. As a tool for molecular profiling of EVs, surface-enhanced Raman spectroscopy (SERS) produces a label-free spectroscopic fingerprint. Using single EV (sEV) SERS, we have previously demonstrated the ability to detect biomarkers at the EV level. Even though the differentiation of EVs is very important for early-stage diagnosis, determining the molecular characteristics underlying EVs and monitoring their changes are essential in the allocation of the possible molecular changes responsible for the development or existence of resistant cells in tumors. We proposed to advance the sEV SERS technology for molecular profiling of single EVs to break the diagnostic and therapeutic gridlock in brain cancer by focusing on the sEV representation of the resistance to temozolomide (TMZ) chemotherapy underlying the lethal recurrence of GBM.


Materials and Methods:: In this study, we used single EV Surface-enhanced Raman spectroscopy (sEV SERS) technology to identify the specific molecular traits that are correlated with different EV populations. SERS is a phenomenon that occurs when small objects are optically excited near an intense and sharp plasmon resonance frequency, resulting in an enhancement of the electromagnetic field surrounding them. To enhance the SERS enhancement factor for recording the signal from a single EV, we designed a layered hybrid plasmonic platform. A combined bottom-up (chemical vapour deposition) and top-down (ebeam lithography) approach was used to develop the hybrid plasmonic nanocavity platform as a sEV SERS analyzer. We investigated cells using well established and control models developed previously at McGill University Health Center (RI-MUHC). The blood patient samples were recruited from Montreal Neurological Institute (MNI). The sample preparations and handling were according the well-established protocols previously developed in lab for sEV entrapment.

We explored the molecular changes that contribute to the cellular evolution of xenograft models in a series of human glioma stem cells (GSCs) with mesenchymal molecular characteristics using sEV SERS technology. GSC 1123 parental and genetically modified cells to express representation of O6-methylguanine DNA methyltransferase (MGMT) upregulation, were used to identify the correlated molecular traits compared with the parental cells. This observation demonstrated the set of molecular traits responsible to distinguish the susceptible and resistant cells.


Results, Conclusions, and Discussions:: As a result of stratified molecular changes that distinguish parental cells from genetically modified cells, we quantified the sEV SERS results of circulating EVs in the blood of patients with GBM. The quantification of the underlying molecular traits demonstrated 96% accuracy on par with clinical pathology tests.

This observation demonstrated the set of molecular traits responsible to distinguish the susceptible and resistant cells via multidimentional and label-free single EV SERS method. As a result of stratified molecular changes that distinguish parental cells from genetically modified cells, we quantified the single EV SERS results of circulating EVs in the cerebrospinal fluid (CSF) and plasma of patients with glioblastoma (GBM) and plasma of these patients. The quantification of the underlying molecular traits demonstrated the probability of MGMT-related molecular alterations on par with clinical pathology tests.

In conclusion, it is important to determine the dynamics and nature of a tumor in a minimally invasive way. In particular, for patients with GBM, defining the underlying molecular characteristics of the tumor could lead to more personalized care and new treatment approaches. The use of single extracellular vesicle (sEV) SERS in combination with analysis tools such as SVM demonstrated great potential in the differentiating molecular progressions leading to tumor resistance. The hybrid plasmonic nanostructure platform designed for sEV SERS can enhance the SERS signal and provide label-free molecular profiling of EVs, which carry molecular fingerprints of underlying resistance to therapy. By monitoring the molecular changes in sEVs using SERS, we were able to distinguish susceptible and resistant cells and identify molecular traits responsible for it. Furthermore, the quantification of underlying molecular traits demonstrated the probability of MGMT-related molecular alterations, in a cohort of GBM patient plasma samples. This technique has the potential to aid in cancer molecular progression monitoring based on liquid biopsy and can help identify SERS spectral features that distinguish the resistance molecular traits in a pool of EVs.


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