Affinity molecular assay for detecting Candida albicans using chitin affinity and RPA-CRISPR/Cas12a

Shimei Shen, Wen Wang, Yuanyan Ma, Shilei Wang, Shaocheng Zhang, Xuefei Cai, Liang Chen, Jin Zhang, Yalan Li, Xiaoli Wu, Jie Wei, Yanan Zhao (), Ailong Huang (), Siqiang Niu () and Deqiang Wang ()
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Shimei Shen: Chongqing Medical University
Wen Wang: Chongqing Medical University
Yuanyan Ma: Chongqing Medical University
Shilei Wang: Chongqing Hospital of Traditional Chinese Medicine
Shaocheng Zhang: The Second Affiliated Hospital of Chengdu Medical College (Nuclear Industry 416 Hospital)
Xuefei Cai: Chongqing Medical University
Liang Chen: University at Buffalo
Jin Zhang: Chongqing Medical University
Yalan Li: Chongqing Medical University
Xiaoli Wu: Chongqing Medical University
Jie Wei: Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University)
Yanan Zhao: University at Buffalo
Ailong Huang: Chongqing Medical University
Siqiang Niu: The First Affiliated Hospital of Chongqing Medical University
Deqiang Wang: Chongqing Medical University

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Invasive fungal infections (IFIs) pose a significant threat to immunocompromised individuals, leading to considerable morbidity and mortality. Prompt and accurate diagnosis is essential for effective treatment. Here we develop a rapid molecular diagnostic method that involves three steps: fungal enrichment using affinity-magnetic separation (AMS), genomic DNA extraction with silicon hydroxyl magnetic beads, and detection through a one-pot system. This method, optimized to detect 30 CFU/mL of C. albicans in blood and bronchoalveolar lavage (BAL) samples within 2.5 h, is approximately 100 times more sensitive than microscopy-based staining. Initial validation using clinical samples showed 93.93% sensitivity, 100% specificity, and high predictive values, while simulated tests demonstrated 95% sensitivity and 100% specificity. This cost-effective, highly sensitive technique offers potential for use in resource-limited clinical settings and can be easily adapted to differentiate between fungal species and detect drug resistance.

Date: 2024
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DOI: 10.1038/s41467-024-53693-5

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