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Research Article

Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification

Yongqiang Zhang1, Kunxing Liu2, Jingkun Yu1, Haifeng Chen4, Rui Fu1, Siqi Zhu2( ), Zhenqiang Chen2, Shuangpeng Wang3( ), Siyu Lu1 ( )
Green Catalysis Center, and College of Chemistry, Zhengzhou University, Zhengzhou 450001, China
Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
Institute of Applied Physics and Materials Engineering, University of Macau, Taipa, Macao 999078, China
School of electronic information and electrical engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China
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Graphical Abstract

Concentration-dependent wavelength-tunable carbon dots (CWT-CDs) were developed as the only stain for microorganism identification. By establishing the characteristic databases with spatial hyperspectral information through the hyperspectral microscopy system, using the support vector machine classifier to learn and train these databases, the identification accuracy of pathogenic microorganisms could be close to 100%, and the relative proportions and spatial distributions of different types of microorganisms in mixed populations could also be indexed.

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.

Keywords

carbon dotsconcentration-dependent wavelength-tunablehyperspectral imagingpathogens rapid detectionmachine-learning

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References

1

Mannoor, M. S.; Zhang, S. Y.; Link, A. J.; McAlpine, M. C. Electrical detection of pathogenic bacteria via immobilized antimicrobial peptides. Proc. Natl. Acad. Sci. USA 2010, 107, 19207–19212.
Crossref Google Scholar
2
Infoplease Staff. Common infectious diseases worldwide[Online]. 2017. https://www.infoplease.com/math-science/health/diseases/common-infectious-diseases-worldwide (accessed Jun 5, 2021).
3

Lazcka, O.; Del Campo, F. J.; Munoz, F. X. Pathogen detection: A perspective of traditional methods and biosensors. Biosens. Bioelectron. 2007, 22, 1205–1217.
Crossref Google Scholar
4

Borst, A.; Box, A. T. A.; Fluit, A. C. False-positive results and contamination in nucleic acid amplification assays: Suggestions for a prevent and destroy strategy. Eur. J. Clin. Microbiol. Infect. Dis. 2004, 23, 289–299.
Crossref Google Scholar
5

Jyoti, A.; Ram, S.; Vajpayee, P.; Singh, G.; Dwivedi, P. D.; Jain, S. K.; Shanker, R. Contamination of surface and potable water in South Asia by Salmonellae: Culture-independent quantification with molecular beacon real-time PCR. Sci. Total Environ. 2010, 408, 1256–1263.
Crossref Google Scholar
6

Li, M.; Gu, J. D. Advances in methods for detection of anaerobic ammonium oxidizing (anammox) bacteria. Appl. Microbiol. Biotechnol. 2011, 90, 1241–1252.
Crossref Google Scholar
7

Brandily, M. L.; Monbet, V.; Bureau, B.; Boussard-Plédel, C.; Loréal, O.; Adam, J. L.; Sire, O. Identification of foodborne pathogens within food matrices by IR spectroscopy. Sens. Actuat. B: Chem. 2011, 160, 202–206.
Crossref Google Scholar
8

Moreirinha, C.; Almeida, A.; Saraiva, J. A.; Delgadillo, I. High-pressure processing effects on foodborne bacteria by mid-infrared spectroscopy analysis. LWT 2016, 73, 212–218.
Crossref Google Scholar
9

Bai, H. T.; Chen; H.; Hu, R.; Li, M.; Lv, F. T.; Liu, L. B.; Wang, S. Supramolecular conjugated polymer materials for in situ pathogen detection. ACS Appl. Mater. Interfaces 2016, 8, 31550–31557.
Crossref Google Scholar
10

Doria, G.; Conde, J.; Veigas, B.; Giestas, L.; Almeida, C.; Assunção, M.; Rosa, J.; Baptista, P. V. Noble metal nanoparticles for biosensing applications. Sensors 2012, 12, 1657–1687.
Crossref Google Scholar
11

Sastry, R. K.; Anshul, S.; Rao, N. H. Nanotechnology in food processing sector-an assessment of emerging trends. J. Food Sci. Technol. 2013, 50, 831–841.
Crossref Google Scholar
12

Aurich, K.; Wesche, J.; Palankar, R.; Schlüter, R.; Bakchoul, T.; Greinacher, A. Magnetic nanoparticle labeling of human platelets from platelet concentrates for recovery and survival studies. ACS Appl. Mater. Interfaces 2017, 9, 34666–34673.
Crossref Google Scholar
13

Zhou, Q.; Liu, X. X.; Tian, Y.; Wu, M.; Niu, Z. W. Mussel-inspired polydopamine coating on tobacco mosaic virus: One-dimensional hybrid nanofibers for gold nanoparticle growth. Langmuir 2017, 33, 9866–9872.
Crossref Google Scholar
14

Li, S. Y.; Wang, F.; Li, X. Q.; Chen, J.; Zhang, X. H.; Wang, Y. C.; Liu, J. W. Dipole orientation matters: Longer-circulating choline phosphate than phosphocholine liposomes for enhanced tumor targeting. ACS Appl. Mater. Interfaces 2017, 9, 17736–17744.
Crossref Google Scholar
15

Zhou, C. C.; Xu, W. H.; Zhang, P. B.; Jiang, M. J.; Chen, Y. C.; Kwok, R. T. K.; Lee, M. M. S.; Shan, G. G.; Qi, R. L.; Zhou, X. et al. Engineering sensor arrays using aggregation-induced emission luminogens for pathogen identification. Adv. Funct. Mater. 2019, 29, 1805986.
Crossref Google Scholar
16

Zhang, Y. Q.; Li, C. F.; Fan, Y.; Wang, C. B.; Yang, R. F.; Liu, X. Y.; Zhou, L. A self-quenching-resistant carbon nanodot powder with multicolored solid-state fluorescence for ultra-fast staining of various representative bacterial species within one minute. Nanoscale 2016, 8, 19744–19753.
Crossref Google Scholar
17

Zhang, Y. Q.; Liu, X. Y.; Fan, Y.; Guo, X. Y.; Zhou, L.; Lv, Y.; Lin, J. One-step microwave synthesis of N-doped hydroxyl-functionalized carbon dots with ultra-high fluorescence quantum yields. Nanoscale 2016, 8, 15281–15287.
Crossref Google Scholar
18

Ru, Y.; Sui, L. Z.; Song, H. Q.; Liu, X. J.; Tang, Z. Y.; Zang, S. Q.; Yang, B.; Lu, S. Y. Rational design of multicolor-emitting chiral carbonized polymer dots for full-color and white circularly polarized luminescence. Angew. Chem., Int. Ed. 2021, 60, 14091–14099.
Crossref Google Scholar
19

Lan, M. H.; Zhao, S. J.; Wu, S. L.; Wei, X. F.; Fu, Y. Z.; Wu, J. J.; Wang, P. F.; Zhang, W. J. Optically tunable fluorescent carbon nanoparticles and their application in fluorometric sensing of copper ions. Nano Res. 2019, 12, 2576–2583.
Crossref Google Scholar
20

Wang, B. Y.; Yu, J. K.; Sui, L. Z.; Zhu, S. J.; Tang, Z. Y.; Yang, B.; Lu, S. Y. Rational design of multi-color-emissive carbon dots in a single reaction system by hydrothermal. Adv. Sci. 2021, 8, 2001453.
Crossref Google Scholar
21

Li, W. D.; Zhao, Y. X.; Liu, Y.; Sun, M. Z.; Waterhouse, G. I. N.; Huang, B. L.; Zhang, K.; Zhang, T. R.; Lu, S. Y. Exploiting Ru-induced lattice strain in CoRu nanoalloys for robust bifunctional hydrogen production. Angew. Chem., Int. Ed. 2021, 60, 3290–3298.
Crossref Google Scholar
22

Yang, S. H.; Sun, X. H.; Wang, Z. Y.; Wang, X. Y.; Guo, G. S.; Pu, Q. S. Anomalous enhancement of fluorescence of carbon dots through lanthanum doping and potential application in intracellular imaging of ferric ion. Nano Res. 2018, 11, 1369–1378.
Crossref Google Scholar
23

Bao, X.; Yuan, Y.; Chen, J. Q.; Zhang, B. H.; Li, D.; Zhou, D.; Jing, P. T.; Xu, G. Y.; Wang, Y. L.; Holá, K. et al. In vivo theranostics with near-infrared-emitting carbon dots-highly efficient photothermal therapy based on passive targeting after intravenous administration. Light Sci. Appl. 2018, 7, 91.
Crossref Google Scholar
24

Song, H. Q.; Wu, M.; Tang, Z. Y.; Tse, J. S.; Yang, B.; Lu, S. Y. Single atom Ruthenium-doped CoP/CDs nanosheets via splicing of carbon-dots for robust hydrogen production. Angew. Chem., Int. Ed. 2021, 60, 7234–7244.
Crossref Google Scholar
25

Qu, S. N.; Liu, X. Y.; Guo, X. Y.; Chu, M. H.; Zhang, L.; Shen, D. Z. Amplified spontaneous green emission and lasing emission from carbon nanoparticles. Adv. Funct. Mater. 2014, 24, 2689–2695.
Crossref Google Scholar
26

Qu, D.; Zheng, M.; Li, J.; Xie, Z. G.; Sun, Z. C. Tailoring color emissions from N-doped graphene quantum dots for bioimaging applications. Light Sci. Appl. 2015, 4, e364.
Crossref Google Scholar
27

Song, H. Q.; Li, Y. H.; Shang, L.; Tang, Z. Y.; Zhang, T. R.; Lu, S. Y. Designed controllable nitrogen-doped carbon-dots-loaded MoP nanoparticles for boosting hydrogen evolution reaction in alkaline medium. Nano Energy 2020, 72, 104730.
Crossref Google Scholar
28

Zhang, M. R.; Su, R. G.; Zhong, J.; Fei, L.; Cai, W.; Guan, Q. W.; Li, W. J.; Li, N.; Chen, Y. S.; Cai, L. L. et al. Red/orange dual-emissive carbon dots for pH sensing and cell imaging. Nano Res. 2019, 12, 815–821.
Crossref Google Scholar
29

Liu, Y.; Li, X.; Zhang, Q. H.; Li, W. D.; Xie, Y.; Liu, H. Y.; Shang, L.; Liu, Z. Y.; Chen, Z. M.; Gu, L. et al. A general route to prepare low-ruthenium-content bimetallic electrocatalysts for pH-universal hydrogen evolution reaction by using carbon quantum dots. Angew. Chem., Int. Ed. 2020, 132, 1735–1743.
Crossref Google Scholar
30

Wang, Z. G.; Fu, B. S.; Zou, S. W.; Duan, B.; Chang, C. Y.; Yang, B.; Zhou, X.; Zhang, L. Facile construction of carbon dots via acid catalytic hydrothermal method and their application for target imaging of cancer cells. Nano Res. 2016, 9, 214–223.
Crossref Google Scholar
31

Yan, J. A., Xian, L. D.; Chou, M. Y. Structural and electronic properties of oxidized graphene. Phys. Rev. Lett. 2009, 103, 086802.
Crossref Google Scholar
32

Zhang, Y. Q.; Hu, Y. S.; Lin, J.; Fan, Y.; Li, Y. T.; Lv, Y.; Liu, X. Y. Excitation wavelength independence: Toward low-threshold amplified spontaneous emission from carbon nanodots. ACS Appl. Mater. Interfaces 2016, 8, 25454–25460.
Crossref Google Scholar
33

Zhang, Y. Q.; Zhuo, P.; Yin, H.; Fan, Y.; Zhang, J. H.; Liu, X. Y.; Chen, Z. Q. Solid-state fluorescent carbon dots with aggregation-induced yellow emission for white light-emitting diodes with high luminous efficiencies. ACS Appl. Mater. Interfaces 2019, 11, 24395–24403.
Crossref Google Scholar
34

Ritter, K. A.; Lyding, J. W. The influence of edge structure on the electronic properties of graphene quantum dots and nanoribbons. Nat. Mater. 2009, 8, 235–242.
Crossref Google Scholar
35

Zhu, S. J.; Song, Y. B.; Wang, J.; Wan, H.; Zhang, Y.; Ning, Y.; Yang, B. Photoluminescence mechanism in graphene quantum dots: Quantum confinement effect and surface/edge state. Nano Today 2017, 13, 10–14.
Crossref Google Scholar
36

Kasprzyk, W.; Bednarz, S.; Żmudzki, P.; Galica, M.; Bogdał, D. Novel efficient fluorophores synthesized from citric acid. RSC Adv. 2015, 5, 34795–34799.
Crossref Google Scholar
37

Song, Y. B.; Zhu, S. J.; Zhang, S. T.; Fu, Y.; Wang, L.; Zhao, X. H.; Yang, B. Investigation from chemical structure to photoluminescent mechanism: A type of carbon dots from the pyrolysis of citric acid and an amine. J. Mater. Chem. C 2015, 3, 5976–5984.
Crossref Google Scholar
38

Shi, L.; Yang, J. H.; Zeng, H. B.; Chen, Y. M.; Yang, S. C.; Wu, C.; Zeng, H.; Yoshihito, O.; Zhang, Q. Q. Carbon dots with high fluorescence quantum yield: The fluorescence originates from organic fluorophores. Nanoscale 2016, 8, 14374–14378.
Crossref Google Scholar
39

Zhang, J.; Yang, L.; Yuan, Y.; Jiang, J.; Yu, S. H. One-pot gram-scale synthesis of nitrogen and sulfur embedded organic dots with distinctive fluorescence behaviors in free and aggregated states. Chem. Mater. 2016, 28, 4367–4374.
Crossref Google Scholar
40

Lv, P. Y.; Sun, S. L.; Lin, C. Q.; Liu, G. R. Space moving target detection and tracking method in complex background. Infrared Phys. Technol. 2018, 91, 107–118.
Crossref Google Scholar
41

Zhang, X. M.; Gong, W. Y.; Xu, X. B. Magnetic ring multi-defect stereo detection system based on multi-camera vision technology. Sensors 2020, 20, 392.
Crossref Google Scholar
42

Loong, T. W. Understanding sensitivity and specificity with the right side of the brain. BMJ 2003, 327, 716–719.
Crossref Google Scholar
43

Suk, H. I.; Wee, C. Y.; Lee, S. W. Shen, D. G. Supervised discriminative group sparse representation for mild cognitive impairment diagnosis. Neuroinformatics 2015, 13, 277–295.
Crossref Google Scholar
44

Akram, M. U.; Khalid, S.; Khan, S. A. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit. 2013, 46, 107–116.
Crossref Google Scholar
45

Kim, J.; Lee, B. Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine. Hum. Brain Mapp. 2018, 39, 3728–3741.
Crossref Google Scholar
46
Ding, H. Z.; Xu, J. H.; Jiang, L.; Dong, C.; Meng, Q.; Rehman, S. U.; Wang, J. F.; Ge, Z. S.; Osipov, V. Y.; Bi, H. Fluorine-defects induced solid-state red emission of carbon dots with an excellent thermosensitivity. Chin. Chem. Lett., in press, https://doi.org/10.1016/j.cclet.2021.04.033.
Nano Research
Volume 15 Issue 7,
July 2022
Pages 6399-6406
DOI: 10.1007/s12274-021-3776-2
Cite this article:
Zhang Y, Liu K, Yu J, et al. Single stain hyperspectral imaging for accurate fungal pathogens identification and quantification. Nano Research, 2022, 15(7): 6399-6406. https://doi.org/10.1007/s12274-021-3776-2
Topics:
Carbon quantum dots

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Received: 06 May 2021
Revised: 22 July 2021
Accepted: 26 July 2021
Published: 06 September 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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