Mutation detection of urinary cell-free DNA via catch-and-release isolation on nanowires for liquid biopsy

• Main Authors: Takahashi, Hiromi, Yasui, Takao, Hirano, Masaki, Shinjo, Keiko, Miyazaki, Yusuke, Shinoda, Wataru, Hasegawa, Takeshi, Natsume, Atsushi, Kitano, Yotaro, Ida, Mikiko, Zhang, Min, Shimada, Taisuke, Paisrisarn, Piyawan, Zhu, Zetao, Ohka, Fumiharu, Aoki, Kosuke, Rahong, Sakon, Nagashima, Kazuki, Yanagida, Takeshi, Baba, Yoshinobu
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Cell-free DNA; Extracellular vesicles
• Online Access: https://hdl.handle.net/10356/174543
• Institution: Nanyang Technological University

Cell-free DNA (cfDNA) and extracellular vesicles (EVs) are molecular biomarkers in liquid biopsies that can be applied for cancer detection, which are known to carry information on the necessary conditions for oncogenesis and cancer cell-specific activities after oncogenesis, respectively. Analyses for both cfDNA and EVs from the same body fluid can provide insights into screening and identifying the molecular subtypes of cancer; however, a major bottleneck is the lack of efficient and standardized techniques for the isolation of cfDNA and EVs from clinical specimens. Here, we achieved catch-and-release isolation by hydrogen bond-mediated binding of cfDNA in urine to zinc oxide (ZnO) nanowires, which also capture EVs by surface charge, and subsequently we identified genetic mutations in urinary cfDNA. The binding strength of hydrogen bonds between single-crystal ZnO nanowires and DNA was found to be equal to or larger than that of conventional hydrophobic interactions, suggesting the possibility of isolating trace amounts of cfDNA. Our results demonstrated that nanowire-based cancer screening assay can screen cancer and can identify the molecular subtypes of cancer in urine from brain tumor patients through EV analysis and cfDNA mutation analysis. We anticipate our method to be a starting point for more sophisticated diagnostic models of cancer screening and identification.