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Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification

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Abstract

The most widely used method of identification of microbial morphology and structure is microscopy, but it can be difficult to distinguish between pathogens with a similar appearance. Existing fluorescent staining methods require a combination of a variety of fluorescent materials to meet this demand. In this study, unique concentration-dependent fluorescent carbon dots (CDs) were synthesized for the identification and quantification of pathogens. The emission wavelength of the CDs could be tuned spanning the full visible region by virtue of aggregation-induced narrowing of bandgaps. This tunable emission wavelength of the specific concentration response to diverse microbes can be used to distinguish microorganisms with a similar appearance, even in a same genus. A hyperspectral microscopy system was demonstrated to distinguish Aspergillus flavus and A. fumigatus based on the results above. The identification accuracy of the two similar-looking pathogens can be close to 100%, and the relative proportions and spatial distributions can also be profiled from the mixture of the pathogens. This technique can provide a solution to the fast detection of microorganisms and is potentially applicable to a wide range of problems in areas such as healthcare, food preparation, biotechnology, and health emergency.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) (Nos. 61935010, 61975069, 21905253, and 51973200), the China Postdoctoral Science Foundation (Nos. 2018M640681 and 2019T120632), the Natural Science Foundation of Henan (No. 202300410372), Key-Area Research and Development Program of Guangdong Province (No. 2020B090922006), Guangdong Project of Science and Technology Grants (No. 2018B030323017), Guangzhou science and technology project (Nos. 201903010042 and 201904010294), Youth project of science and technology research program of Chongqing Education Commission of China (No. KJQN202001322), and the Science and Technology Development Fund from Macau SAR (File Nos. 0125/2018/A3 and 0071/2019/AMJ).

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Authors and Affiliations

  1. Green Catalysis Center, and College of Chemistry, Zhengzhou University, Zhengzhou, 450001, China

    Yongqiang Zhang, Jingkun Yu, Rui Fu & Siyu Lu

  2. Department of Optoelectronic Engineering, Jinan University, Guangzhou, 510632, China

    Kunxing Liu, Siqi Zhu & Zhenqiang Chen

  3. Institute of Applied Physics and Materials Engineering, University of Macau, Taipa, Macao, 999078, China

    Shuangpeng Wang

  4. School of electronic information and electrical engineering, Chongqing University of Arts and Sciences, Chongqing, 402160, China

    Haifeng Chen

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  1. Yongqiang Zhang
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Correspondence to Siqi Zhu, Shuangpeng Wang or Siyu Lu.

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Zhang, Y., Liu, K., Yu, J. et al. Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification. Nano Res. 15, 6399–6406 (2022). https://doi.org/10.1007/s12274-021-3776-2

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  • DOI: https://doi.org/10.1007/s12274-021-3776-2

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