Histological Quantification to Determine Lung Fungal Burden in Experimental Aspergillosis

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2018-03-09
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American English
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JoVE
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The quantification of lung fungal burden is critical for the determination of the relative levels of immune protection and fungal virulence in mouse models of pulmonary fungal infection. Although multiple methods are used to assess fungal burden, quantitative polymerase chain reaction (qPCR) of fungal DNA has emerged as a technique with several advantages over previous culture-based methods. Currently, a comprehensive assessment of lung pathology, leukocyte recruitment, fungal burden, and gene expression in mice with invasive aspergillosis (IA) necessitates the use of a significant number of experimental and control animals. Here the quantification of lung histological staining to determine fungal burden using a reduced number of animals was examined in detail. Lung sections were stained to identify fungal structures with Gomori's modified methanamine silver (GMS) staining. Images were taken from the GMS-stained sections from 4 discrete fields of each formalin-fixed paraffin-embedded lung. The GMS stained areas within each image were quantified using an image analysis program, and from this quantification, the mean percentage of stained area was determined for each sample. Using this strategy, eosinophil-deficient mice exhibited decreased fungal burden and disease with caspofungin therapy, while wild-type mice with IA did not improve with caspofungin. Similarly, fungal burden in mice lacking γδ T cells were also improved by caspofungin, as measured by qPCR and GMS quantification. GMS quantification is therefore introduced as a method for the determination of relative lung fungal burden that may ultimately reduce the quantity of experimental animals required for comprehensive studies of invasive aspergillosis

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Stolz, D. J., Sands, E. M., Amarsaikhan, N., Tsoggerel, A., & Templeton, S. P. (2018). Histological Quantification to Determine Lung Fungal Burden in Experimental Aspergillosis. Journal of visualized experiments : JoVE, (133), 57155. doi:10.3791/57155
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Journal of Visualized Experiments
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PMC
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