Single‐Molecule Sensing of Acidic Catecholamine Metabolites Using a Programmable Nanopore. Issue 44 (16th June 2022) - Hu, Chengzhen; Jia, Wendong; Liu, Yao; Wang, Yuqin; Zhang, Panke; Chen, Hong‐Yuan; Huang, Shuo
Abstract: Acidic catecholamine metabolites, which could serve as diagnostic markers for many diseases, demonstrate an importance of accurate sensing. However, they share a highly similar chemical structure, which is a challenge in the design of sensing strategies. A nanopore may be engineered to sense these metabolites in a single molecule manner. To achieve this, a recently developed programmable nano‐reactor for stochastic sensing (PNRSS) technique adapted with a phenylboronic acid (PBA) adaptor was applied. Three acidic catecholamine metabolites, including 3, 4‐dihydroxyphenylacetic acid (DOPAC), 3, 4‐dihydroxymandelic acid (DHMA) and 3‐methoxy‐4‐hydroxymandetic acid (VMA) were investigated by PNRSS. Specifically, DHMA, which contains an α‐hydroxycarboxylate moiety and an adjacent cis ‐hydroxyl groups on its benzene ring, reports two binding modes simultaneously resolvable by PNRSS. Assisted with the high resolution of PNRSS, direct regulation of these two binding modes by pH can also be observed. A custom machine learning algorithm was also developed to achieve automatic event classification. Abstract : Three acidic catecholamine metabolites, including 3, 4‐dihydroxymandelic acid (DHMA), 3‐methoxy‐4‐hydroxymandetic acid (VMA) and 3, 4‐dihydroxyphenylacetic acid (DOPAC) were investigated by PNRSS. DHMA, which contains an α‐hydroxycarboxylate moiety (red shade, marked as 1) and an adjacent cis ‐hydroxyl groups on the benzene ring (blue shade, marked as 2), reports two binding modes (PBA‐1 and PBA‐2) simultaneously resolvable by PNRSS, demonstrating a high resolution of PNRSS. A custom machine learning algorithm was also developed to achieve automatic event classification.