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

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.

The New Era of High-Throughput Nanoelectrochemistry

“…The general approach for the development of machine learning algorithms is to build a training process, including several stages such as data importing, extraction of defined features, model training, and evaluation of the model . Hu et al demonstrated the use of a machine learning algorithm to obtain automated classification of metabolites using a nanopore platform . Taniguchi et al reported the identification of several polystyrene nanoparticles by combining solid-state nanopore sensing with a machine learning algorithm .…”

“… 65 Hu et al demonstrated the use of a machine learning algorithm to obtain automated classification of metabolites using a nanopore platform. 66 Taniguchi et al reported the identification of several polystyrene nanoparticles by combining solid-state nanopore sensing with a machine learning algorithm. 67 The same group has demonstrated that such approach can also be employed for accurate detection of different types of viruses.…”

Machine learning assisted single-molecule sensing of per- and polyfluoroalkyl carboxylic acids: Quantification without standards

“…Frequency-modulated multi-dimensional feature extraction for 100% classi cation accuracy An inherent advantage of SMS over ensemble methods was the ability to record multi-dimensional features of the signal generated by an individual molecule 36 . The use of ve or more signal features was demonstrated recently as an effective approach to distinguish structurally similar compounds, e.g., achieving 92.4%-99.9% accuracy for the determination of saccharides 37 , riboses 38 , alditols 39 or benzenediols 40 . Herein, frequency modulation (using ve low-pass lters of 2000, 800, 500, 200 or 100 Hz, as well as the wavelet transform) was applied to extend the eight common features of singlemolecule signals, i.e., the magnitude (ΔI/I 0 ), duration (τ on ), standard deviation (I σ ), peak-to-peak (I pp ) of current blockade, and the peak (H peak ), full width at half maximum (H FWHM ), skewness (H skew ), kurtosis (H kurt ) of the all-points histograms of current blockade, to a total number of 43 (τ on remained constant at all frequencies), Fig.…”

The new era of high throughput nanoelectrochemistry

“…67 Hu et al demonstrated the use of a machine learning algorithm to obtain automated classification of metabolites using a nanopore platform. 68 Taniguchi et al reported the identification of several polystyrene nanoparticles by combining solid-state nanopore sensing with a machine learning algorithm. 69 The same group has demonstrated that such an approach can also be employed for accurate detection of different types of viruses.…”