Fingerprinting Fluorescent In Situ Hybridization Enables Multiplexed Identification of Pathogenic Bacteria

致病菌 荧光团 荧光 生物 计算生物学 细菌 荧光显微镜 多路复用 荧光寿命成像显微镜 显微镜 瓶颈 生物系统 多路复用 费斯特共振能量转移 纳米技术 共焦显微镜 DNA微阵列 高通量筛选 化学 微生物学 杂交探针 DNA 荧光原位杂交 共域化
作者
Wenxing Li,Liang Wu,Chenbin Liu,Wen Chen,Yazhou Wu,Shuchang Xu,Jie Deng,Danyu Tian,Jian Wan,Song Hu,Dongsheng Mao,Xiaoli Zhu
出处
期刊:ACS Nano [American Chemical Society]
卷期号:20 (11): 9214-9224 被引量:2
标识
DOI:10.1021/acsnano.5c18844
摘要

hybridization (FISH) is a highly specific technique for pathogenic bacteria detection that requires no culturing and provides simultaneous information on pathogenic bacteria abundance, morphology, and spatial localization. However, the limited sensitivity and poor multiplexing capacity of conventional FISH have hindered its broader application. Herein, we proposed a fingerprinting FISH (FinFISH) strategy driven by DNA self-assembly for multiplexed pathogenic bacteria detection. Using respiratory pathogens as representative models, FinFISH employs three distinct fluorophores in combinatorial labeling to generate identifiable fluorescent fingerprints for each species. In this work, FAM, Cy3, and Cy5 were selected as the fluorescent reporters because they represent well-established fluorophore combinations for multicolor imaging and combinatorial encoding, with minimal spectral overlap under standard fluorescence microscopy conditions. This strategy enables pathogen detection far beyond the limitations imposed by fluorescence channel numbers, effectively overcoming the throughput bottleneck of conventional imaging systems while offering high scalability for further expansion. Additionally, a custom-designed enclosed chip featuring multichannel reaction chambers improves parallel sample processing and simplifies experimental operation. Experimental results demonstrated that FinFISH not only performed well in identifying pathogenic bacteria within simulated sputum and urine samples but also proved applicable to clinical samples. Moreover, FinFISH provides additional semiquantitative insights into mixed infections. With future integration of expanded probe design and artificial intelligence (AI)-assisted analysis, FinFISH has the potential to advance clinical pathogenic bacteria diagnostics, microbial colocalization studies, and spatial analysis of intratumoral bacteria.
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