Sessile droplet array for sensitive profiling of multiple extracellular vesicle immuno-subtypes
- Authors
- Lee, Eunjeong; Shin, Suyeon; Yim, Sang-Gu; Lee, Gyeong Won; Shim, Yujin; Kim, Yoon-Jin; Yang, Seung Yun; Kim, Anmo J.; Choi, Sungyoung
- Issue Date
- Dec-2022
- Publisher
- ELSEVIER ADVANCED TECHNOLOGY
- Keywords
- Sessile droplet; Extracellular vesicle; Multiclass cancer classification; Cancer diagnosis
- Citation
- BIOSENSORS & BIOELECTRONICS, v.218, pp.1 - 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- BIOSENSORS & BIOELECTRONICS
- Volume
- 218
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/172799
- DOI
- 10.1016/j.bios.2022.114760
- ISSN
- 0956-5663
- Abstract
- The sensitive detection of the multiple immuno-subtypes of cancer-specific extracellular vesicles (EVs) has emerged as a promising method for multiclass cancer diagnosis; however, its limitations in sensitivity, accessibility, and multiple detection of EV subtypes have hindered its further implementation. Here, we present a platform for sensitive EV detection enabled by sessile droplet array (eSD) that exploits enhanced immuno-capture of EVs via evaporation-driven radial flows in a sessile droplet. Compared to a micro-well without internal flows, this platform demonstrates significantly enhanced EV capture and detection by detecting low levels of EVs with a detection limit of 384.7 EVs per microliter, which is undetectable in the micro-well. In addition, using a small sample consumption of -0.2 mu L. plasma per droplet, the platform detects EV immuno-subtypes against seven different antibodies in patient plasma samples of different cancer types (liver, colon, lung, breast and prostate cancers). Further, using the profiling data, the platform exhibits a sensitivity of 100% (95% confidence interval (CI): 83-100%) and a specificity of 100% (95% CI: 40-100%) for the diagnosis of cancer, and classified cancer types with an overall accuracy of 96% (95% CI: 86-100%) using a two-staged algorithm based on quadratic discriminant analysis technique for machine learning.
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