In Situ Photoacoustic Visualization of Pneumonia Induced by MRSA and Specific Identifying Tumor-Homing Bacteria.
Qinghua WangLing-Ling WuQingyang ZhangRao WeiXiangchuan MengXiaowan HanNianzeng XingJian-Dong JiangHai-Yu HuPublished in: ACS applied bio materials (2023)
Optical imaging holds great promise for monitoring bacterial infectious processes and drug resistance with high temporal-spatial resolution. Currently, the diagnosis of deep-seated bacterial infections in vivo with fluorescence imaging, including near-infrared (NIR) fluorescence imaging technology, remains a significant challenge due to its limited tissue penetration depth. In this study, we developed a highly specific targeting probe, Cy7-Neo-NO 2 , by conjugating a bacterial 16S rRNA-targeted moiety, neomycin, with a bacterial nitroreductase (NTR)-activated NIR photoacoustic (PA) scaffold using our previously developed caged photoinduced electron transfer (a-PeT) approach. This conjugation effectively resolved probe aggregation issues in physiological conditions and substantially enhanced its reactivity toward bacterial NTR. Notably, Cy7-Neo-NO 2 enabled the first in situ photoacoustic imaging of pneumonia induced by methicillin-resistant Staphylococcus aureus (MRSA), as well as the detection of bacteria within tumors. Furthermore, upon NIR irradiation, Cy7-Neo-NO 2 successfully inhibited MRSA growth through a synergistic effect combining photothermal therapy and photodynamic therapy. Our results provided an effective tool for obtaining exceptional PA agents for accurate diagnosis, therapeutic evaluation of deep-seated bacterial infections in vivo , and intratumoral bacteria-specific recognition.
Keyphrases
- fluorescence imaging
- photodynamic therapy
- methicillin resistant staphylococcus aureus
- staphylococcus aureus
- high resolution
- electron transfer
- intensive care unit
- single molecule
- optical coherence tomography
- big data
- mass spectrometry
- positron emission tomography
- respiratory failure
- extracorporeal membrane oxygenation
- mechanical ventilation
- deep learning
- acute respiratory distress syndrome
- sensitive detection