Single-Molecule Sensing of Acidic Catecholamine Metabolites Using a Programmable Nanopore

Area of Science:

• Analytical Chemistry
• Nanotechnology
• Biomarker Detection

Background:

• Acidic catecholamine metabolites are vital diagnostic markers for various diseases.
• Their similar chemical structures pose significant challenges for precise sensing strategies.
• Single-molecule analysis offers a promising approach for differentiating these complex molecules.

Purpose of the Study:

• To develop and apply a novel sensing technique for distinguishing acidic catecholamine metabolites.
• To investigate the capability of the programmable nano-reactor for stochastic sensing (PNRSS) in analyzing these metabolites.
• To explore the potential of PNRSS in identifying specific binding modes and pH-dependent behaviors.

Main Methods:

• Utilized the programmable nano-reactor for stochastic sensing (PNRSS) technique.
• Adapted PNRSS with a phenylboronic acid (PBA) adaptor for metabolite capture.
• Analyzed three acidic catecholamine metabolites: DOPAC, DHMA, and VMA.
• Employed a custom machine learning algorithm for automated event classification.

Main Results:

• PNRSS successfully differentiated between structurally similar acidic catecholamine metabolites at the single-molecule level.
• 3,4-dihydroxymandelic acid (DHMA) exhibited two distinct, resolvable binding modes.
• The study observed direct pH regulation of DHMA's binding modes due to PNRSS's high resolution.
• An automated classification system was developed using machine learning.

Conclusions:

• PNRSS is a powerful technique for the high-resolution, single-molecule analysis of acidic catecholamine metabolites.
• The method enables the differentiation of molecules with subtle structural differences and the observation of dynamic binding behaviors.
• This approach holds potential for advancing disease diagnostics through improved metabolite sensing.