Deep Learning‐Enhanced Potentiometric Aptasensing with Magneto‐Controlled Sensors. Issue 3 (13th December 2022)

• Record Type: Journal Article
• Authors: Mou, Junsong; Ding, Jiawang; Qin, Wei
• Is Part Of: Angewandte Chemie international edition. Volume 62:Issue 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-13
• Subjects: Aptamer -- Biosensor -- Deep Learning -- Magnetic Fields -- Potentiometry; Chemistry -- Periodicals; 540
• DOI: 10.1002/anie.202210513
• Languages: English
• ISSNs: 1433-7851

Abstract

Abstract: Bioelectronic sensors that report charge changes of a biomolecule upon target binding enable direct and sensitive analyte detection but remain a major challenge for potentiometric measurement, mainly due to Debye Length limitations and the need for molecular‐level platforms. Here, we report on a magneto‐controlled potentiometric method to directly and sensitively measure the target‐binding induced charge change of DNA aptamers assembled on magnetic beads using a polymeric membrane potentiometric ion sensor. The potentiometric responses of the negatively charged aptamer, serving as a receptor and reporter, were dynamically controlled and modulated by applying a magnetic field. Based on a potentiometric array, this non‐equilibrium measurement technique combined with deep learning algorithms allows for rapidly and reliably classifying and quantifying diverse small molecules using antibiotics as models. This potentiometric strategy opens new modalities for sensing applications. Abstract : Aptamer‐assembled magnetic beads can be directly measured by using a magneto‐controlled potentiometric ion sensor. The potentiometric aptasensing system enables rapid, efficient discrimination and quantification of multiple analytes with the assistance of deep learning models.