Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy

作者：Yuetao Li, Jiabao Xu, Xiaofei Yi, Xiaobo Li, Yanjun Luo, Andrew Glidle, P. Summersgill, Simon Allen, Tim Ryan, Xiaochen Liu, Wei Yu, Xiaobing Chu, Shiyu Chen, Qian Zhang, Xiaogang Xu, Xiaoting Hua, Qiwen Yang, Julien Reboud, Yunsong Yu, Wei E. Huang, Jonathan M. Cooper, Huabing Yin · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-66996-y · 被引用次数：6 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Bacterial Identification and Susceptibility Testing、Microfluidic and Bio-sensing Technologies

Antimicrobial resistance (AMR) is a critical global health challenge, demanding rapid and accurate diagnostics to guide timely antimicrobial therapy. Current diagnosis is hindered by prolonged culturing and difficulties detecting low pathogen loads. Here, we present a culture-free diagnostic platform that integrates microfluidics, Raman micro-spectroscopy, and deep learning to deliver "sample-to-report" testing within 20 min. The microfluidic enrichment system employs dialysis-dielectrophoresis (DEP) technology to rapidly isolate pathogens directly from clinical samples with a detection limit as low as <2 colony forming unit (CFU)/ml. Combining a single-cell Raman fingerprint database of 342 clinical isolates from 29 bacterial and 7 fungal species with a 1D ResNet deep learning model, our approach achieved 95.1% accuracy in lab settings. Validated in a 305-patient clinical study involving primary urine and other clinical samples, it demonstrated 95.4% agreement with traditional culture methods and 98.5% sensitivity in diagnosing infections. While broader validation is needed for clinical implementation, the integrated, rapid diagnosis pipeline, as well as broad-spectrum detection, offer a promising solution for next-generation diagnostics for combating AMR.