Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model

TL;DR

This study demonstrates a label-free method combining plasmonic nanopore sensors and deep learning to accurately discriminate between proline and hydroxylated proline at the single-molecule level, aiding disease monitoring.

Contribution

It introduces a novel approach using histogram analysis and CNNs to overcome signal fluctuations in single-molecule SERS detection of amino acids.

Findings

Achieved 96.6% accuracy in discrimination

Effectively reduced citrate interference in SERS signals

Demonstrated ligand exchange improves signal clarity

Abstract

Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy.