{"title":"CRISPR technology combined with isothermal amplification methods for the diagnosis of Candida albicans infection.","authors":"Runde Liu, Wenxiang Ji, Min Jiang, Jilu Shen","doi":"10.1016/j.cca.2024.120106","DOIUrl":null,"url":null,"abstract":"<p><p>Since Candida albicans, a type of fungus, causes severe infections that pose a significant threat to human health, its rapid detection is critical in clinical antifungal therapy. Traditional fungal diagnostic approaches are largely based on the culture method. This method is time-consuming and laborious, taking about 48-72 h, and cannot identify emerging species, making it unsuitable for critically ill patients with bloodstream infections, sepsis, and so on. Other antigen or nucleic acid amplification-based methods were also found to be unsuitable for Point-of-Care Testing (POCT) diagnosis due to various limitations. Therefore, establishing a new approach for the rapid diagnosis of Candida spp is imperative. Herein, we proposed a novel diagnostic method for invasive fungi detection. Specifically, we created a new CRISPR diagnostic platform for Candida albicans-specific Internal Transcriptional Spacer 2 (ITS2) gene by combining the DNase cleavage activity of Cas12a with Recombinase Polymerase Amplification (RPA). Furthermore, to achieve rapid on-site detection under low-resource conditions, we used a transverse lateral flow strip with a single target to visualize the Cas12a single enzyme digestion product. We designated the platform as a rapid molecular detection tool that integrates RPA and the CRISPR-Cas12a technology. The entire platform can accurately identify Candida albicans within 50 minwhile remaining unaffected by other fungi or bacteria. Furthermore, the detection limit of the platform could reach 10<sup>2</sup> CFU/ml. Moreover, this approach offers additional benefits, including easy operation, low set-up cost, and broad applicability for Candida albicans detection across medical institutions at all levels, especially in township health centers in resource-poor regions.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120106"},"PeriodicalIF":3.2000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinica Chimica Acta","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.cca.2024.120106","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/21 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Since Candida albicans, a type of fungus, causes severe infections that pose a significant threat to human health, its rapid detection is critical in clinical antifungal therapy. Traditional fungal diagnostic approaches are largely based on the culture method. This method is time-consuming and laborious, taking about 48-72 h, and cannot identify emerging species, making it unsuitable for critically ill patients with bloodstream infections, sepsis, and so on. Other antigen or nucleic acid amplification-based methods were also found to be unsuitable for Point-of-Care Testing (POCT) diagnosis due to various limitations. Therefore, establishing a new approach for the rapid diagnosis of Candida spp is imperative. Herein, we proposed a novel diagnostic method for invasive fungi detection. Specifically, we created a new CRISPR diagnostic platform for Candida albicans-specific Internal Transcriptional Spacer 2 (ITS2) gene by combining the DNase cleavage activity of Cas12a with Recombinase Polymerase Amplification (RPA). Furthermore, to achieve rapid on-site detection under low-resource conditions, we used a transverse lateral flow strip with a single target to visualize the Cas12a single enzyme digestion product. We designated the platform as a rapid molecular detection tool that integrates RPA and the CRISPR-Cas12a technology. The entire platform can accurately identify Candida albicans within 50 minwhile remaining unaffected by other fungi or bacteria. Furthermore, the detection limit of the platform could reach 102 CFU/ml. Moreover, this approach offers additional benefits, including easy operation, low set-up cost, and broad applicability for Candida albicans detection across medical institutions at all levels, especially in township health centers in resource-poor regions.
期刊介绍:
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.