MoS2-Plasmonic Nanocavities for Raman Spectra of Single Extracellular Vesicles Reveal Molecular Progression in Glioblastoma

材料科学 纳米技术 等离子体子 多路复用 人口 分子诊断学 光电子学 生物信息学 生物 人口学 社会学
作者
Mahsa Jalali,Carolina del Real Mata,Laura Montermini,Olivia Jeanne,Imman I. Hosseini,Zonglin Gu,Cristiana Spinelli,Yao Lü,Nadim Tawil,Marie Christine Guiot,Zhimin He,Sebastian Wachsmann‐Hogiu,Ruhong Zhou,Kevin Petrecca,Walter Reisner,Janusz Rak,Sara Mahshid
出处
期刊:ACS Nano [American Chemical Society]
卷期号:17 (13): 12052-12071 被引量:40
标识
DOI:10.1021/acsnano.2c09222
摘要

Extracellular vesicles (EVs) are continually released from cancer cells into biofluids, carrying actionable molecular fingerprints of the underlying disease with considerable diagnostic and therapeutic potential. The scarcity, heterogeneity and intrinsic complexity of tumor EVs present a major technological challenge in real-time monitoring of complex cancers such as glioblastoma (GBM). Surface-enhanced Raman spectroscopy (SERS) outputs a label-free spectroscopic fingerprint for EV molecular profiling. However, it has not been exploited to detect known biomarkers at the single EV level. We developed a multiplex fluidic device with embedded arrayed nanocavity microchips (MoSERS microchip) that achieves 97% confinement of single EVs in a minute amount of fluid (<10 μL) and enables molecular profiling of single EVs with SERS. The nanocavity arrays combine two featuring characteristics: (1) An embedded MoS2 monolayer that enables label-free isolation and nanoconfinement of single EVs due to physical interaction (Coulomb and van der Waals) between the MoS2 edge sites and the lipid bilayer; and (2) A layered plasmonic cavity that enables sufficient electromagnetic field enhancement inside the cavities to obtain a single EV level signal resolution for stratifying the molecular alterations. We used the GBM paradigm to demonstrate the diagnostic potential of the SERS single EV molecular profiling approach. The MoSERS multiplexing fluidic achieves parallel signal acquisition of glioma molecular variants (EGFRvIII oncogenic mutation and MGMT expression) in GBM cells. The detection limit of 1.23% was found for stratifying these key molecular variants in the wild-type population. When interfaced with a convolutional neural network (CNN), MoSERS improved diagnostic accuracy (87%) with which GBM mutations were detected in 12 patient blood samples, on par with clinical pathology tests. Thus, MoSERS demonstrates the potential for molecular stratification of cancer patients using circulating EVs.
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