Fingerprinting Fluorescent <i>In Situ</i> Hybridization Enables Multiplexed Identification of Pathogenic Bacteria

Fluorescence <i>in situ</i> 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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Publication Details

Journal
ACS Nano
Published
2026-03-11
DOI
https://doi.org/10.1021/acsnano.5c18844
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
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article

Fingerprinting Fluorescent In Situ Hybridization Enables Multiplexed Identification of Pathogenic Bacteria

Dongsheng Mao, Chenbin Liu, Wen Chen, Wenxing Li et al.
ACS Nano
Biosensors and Analytical Detection
article

Fingerprinting Fluorescent In Situ Hybridization Enables Multiplexed Identification of Pathogenic Bacteria

Dongsheng Mao, Chenbin Liu, Wen Chen, Wenxing Li, Yazhou Wu, Danyu Tian, Xiaoli Zhu, Liang Wu, Song Hu, Shuchang Xu, Jian Wan, Jie Deng
article en

Abstract

Fluorescence <i>in situ</i> 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.

ACS NanoVol. 20(11)
Shanghai Medical College of Fudan University (CN), Shanghai Tenth People's Hospital (CN), Shanghai Children's Hospital (CN), Pudong New Area People's Hospital (CN)
Openalex Percentile: Top 28%
Biosensors and Analytical Detection
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