Multiplex Detection and Quantification of Virus Co-Infections Using Label-free Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms

• Published in: ACS sensors Vol. 10; no. 2; pp. 1298 - 1311
• Main Authors: Yang, Yanjun, Cui, Jiaheng, Kumar, Amit, Luo, Dan, Murray, Jackelyn, Jones, Les, Chen, Xianyan, Hülck, Sebastian, Tripp, Ralph A., Zhao, Yiping
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 28.02.2025
• Subjects: Coinfection - diagnosis; Coinfection - virology; Deep Learning; Humans; Nanotubes - chemistry; Saliva - virology; Silver - chemistry; Spectrum Analysis, Raman - methods; Viruses - isolation & purification; silver nanorods; deep learning; multiplex detection and quantification; virus coinfection; surface-enhanced Raman spectroscopy (SERS); label-free diagnostics; one model multiple tasks
• ISSN: 2379-3694; 2379-3694
• DOI: 10.1021/acssensors.4c03209

Multiple respiratory viruses can concurrently or sequentially infect the respiratory tract, making their identification crucial for diagnosis, treatment, and disease management. We present a label-free diagnostic platform integrating surface-enhanced Raman scattering (SERS) with deep learning for rapid, quantitative detection of respiratory virus coinfections. Using sensitive silica-coated silver nanorod array substrates, over 1.2 million SERS spectra are collected from 11 viruses, nine two-virus mixtures, and four three-virus mixtures at various concentrations in saliva. A deep learning model, MultiplexCR, is developed to simultaneously predict virus species and concentrations from SERS spectra. It achieves an impressive 98.6% accuracy in classifying virus coinfections and a mean absolute error of 0.028 for concentration regression. In blind tests, the model demonstrates consistent high accuracy and precise concentration predictions. This SERS-MultiplexCR platform completes the entire detection process in just 15 min, offering significant potential for rapid, point-of-care diagnostics in infection detection, as well as applications in food safety and environmental monitoring.