A sample-to-answer digital microfluidic multiplexed PCR system for syndromic pathogen detection in respiratory tract infection

Abstract

Timely identification of infectious pathogens is crucial for the accurate diagnosis, management, and treatment of syndromic respiratory diseases. Nevertheless, the implementation of rapid, precise, and automated point-of-care testing (POCT) remains a significant challenge. This study introduces an advanced digital microfluidic (DMF) POCT testing system designed for the rapid molecular syndromic testing of multiple respiratory pathogens from a single untreated sample. The system seamlessly integrates magnetic beads-based nucleic acid extraction, PCR amplification, and real-time fluorescence analysis in an automatic run, facilitating sample-to-answer detection within 80 min. It accommodates various sample types, including nasopharyngeal swabs, oropharyngeal swabs, bronchoalveolar lavage fluid, and sputum. A facile sample loading method has been developed to reduce hands-on time to less than 1 min. The system exhibits high sensitivity (200–628 copies per mL) for 15 pathogens and has the capacity for up to 32 multiplexed tests per run. Validation with 255 clinical samples confirms its high sensitivity and specificity. The DMF-based system significantly reduces manual labour, enhances rapid POCT for respiratory infections, and, with optimized manufacturing processes, lowers costs for large-scale production. The system can be applied and improve clinical management near the patients as well as in resource-limited settings.

Graphical abstract: A sample-to-answer digital microfluidic multiplexed PCR system for syndromic pathogen detection in respiratory tract infection

Supplementary files

Article information

Article type
Paper
Submitted
26 Aug 2024
Accepted
18 Jan 2025
First published
05 Feb 2025

Lab Chip, 2025, Advance Article

A sample-to-answer digital microfluidic multiplexed PCR system for syndromic pathogen detection in respiratory tract infection

H. Bai, J. Hu, T. Liu, L. Wan, C. Dong, D. Luo, F. Li, Z. Yuan, Y. Tang, T. Chen, S. Wang, H. Gou, Y. Zhou, B. Ying, J. Huang and W. (. Hu, Lab Chip, 2025, Advance Article , DOI: 10.1039/D4LC00704B

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