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

2024-12-25

AI Summary

Discriminating proline (Pro) and hydroxyproline (Hyp) at the single-molecule level—critical for monitoring disease-associated hydroxylation modifications—remains challenging due to poor label-free specificity and signal instability in conventional plasmonic nanopore sensors, particularly under citrate interference. Method: We propose a novel strategy integrating plasmonic nanopores, surface-enhanced Raman spectroscopy (SERS), and ligand-exchange kinetics control. We introduce a histogram-based feature extraction method utilizing single-molecule SERS peak frequencies and dynamically modulate molecular occupancy in plasmonic hotspots to suppress citrate-induced artifacts and enhance signal-to-noise ratio. Classification is automated via convolutional neural networks. Contribution/Results: This approach achieves highly specific Pro/Hyp discrimination at the single-molecule level, with 96.6% classification accuracy. It establishes a new paradigm for real-time, label-free detection and therapeutic response assessment of low-abundance hydroxylation modifications in clinical settings.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Bio-inspired Learning

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Proline Hydroxyproline Discrimination
• Single Molecule Level
• Plasmonic Nanopore Sensor

Innovation

• Plasmonic Nanopore Sensor
• Convolutional Neural Network
• Label-free Single Molecule Detection