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The Application of Laser Microdissection in Molecular Detection and Identification of Aspergillus fumigatus from Murine Model of Acute Invasive Pulmonary Aspergillosis
Authors:Chong Wang  Ping Zhan  Le Wang  Rong Zeng  Yongnian Shen  Guixia Lv  Dongmei Li  Shuwen Deng  Weida Liu
Affiliation:1. Department of Mycology, Institute of Dermatology, Chinese Academy of Medical Science and Peking Union Medical College, No. 12, Jiang Wangmiao Street, Nanjing, 210042, People’s Republic of China
2. Jiangsu Key Laboratory of Molecular Biology for Skin Diseases and STIs, Nanjing, People’s Republic of China
3. Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC, USA
4. Shanghai Institute of Medical Mycology, Changzheng Hospital, Second Military Medical University, Shanghai, People’s Republic of China
Abstract:Invasive aspergillosis (IA) is a major concern in patients with severe immune deficiency. As antifungal susceptibility varies in different fungal pathogens, accurate and timely identification of species is becoming imperative for guidance of therapy and reducing high mortality rates in patients with IA. But, in fact, the diagnosis is challenging and new validated techniques are required for the detection and identification of clinically relevant isolates. The laser capture microdissection (LCM) system enables analysis of cytologically and/or phenotypically defined cell types from heterogeneous tissue and has been used in diagnosis and fungal species identification in pulmonary aspergillosis of white storks. To establish the experimental foundation for clinical application of the system, we microdissected and collected Blankophor-stained single hyphal strands from tissue cryosections of murine model of invasive pulmonary aspergillosis (IPA) with A. fumigatus by LCM, subsequently processed for DNA extraction, PCR sequencing, and species molecular identification. The sensitivity of LCM–PCR sequencing was 89 % (89/100), and the specificity was 100 %. Moreover, the positive predictive value and negative predictive value were 100 and 78.43 %, respectively. The result approved that the LCM-based methods had the potential for accurately diagnosis and rapidly identification fungal pathogens of IPA.
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