Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model.

Journal: Nano letters
PMID: 40241681

Abstract

Discriminating low-abundance hydroxylation is a crucial and unmet need for early disease diagnostics and therapeutic development due to the small hydroxyl group with 17.01 Da. While single-molecule surface-enhanced Raman spectroscopy (SERS) sensors can detect hydroxylation, subsequent data analysis suffers from signal fluctuations and strong interference from citrates. Here, we used our plasmonic particle-in-pore sensor, occurrence frequency histogram of the single-molecule SERS spectra, and a one-dimensional convolutional neural network (1D-CNN) model to achieve single-molecule discrimination of hydroxylation. The histogram extracted spectral features of the whole data set to overcome the signal fluctuations and helped the citrate-replaced particle-in-pore sensor to generate clean signals of the hydroxylation for model training. As a result, the discrimination of single-molecule SERS signals of proline and hydroxyproline was successful by the 1D-CNN model with 96.6% accuracy for the first time. The histogram further validated that the features extracted by the 1D-CNN model corresponded to hydroxylation-induced spectral changes.

Authors

  • Yingqi Zhao
    Public Health Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Kuo Zhan
    Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Aapistie 5 A, 90220 Oulu, Finland.
  • Pei-Lin Xin
    Key Laboratory for Advanced Materials, Shanghai Key Laboratory of Functional Materials Chemistry, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China.
  • Zuyan Chen
    The Biomimetics and Intelligent Systems (BISG) research unit, Faculty of Information Technology and Electronic Engineering, University of Oulu, 90220 Oulu, Finland.
  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Francesco De Angelis
    Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy.
  • Jian-An Huang
    Research Unit of Health Sciences and Technology (HST), Faculty of Medicine University of Oulu, Oulu, 999018, Finland.

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