Xiaoya Liu, Hai Peng, Lisha Gong, Hong Zhang, Chenglong Zhao, Weiju Lai, Gang An, Xianxian Zhao
{"title":"Reliable and Precise Lipoprotein Detection based on the Self-Priming Hairpin-Triggered Cas12a/crRNA based Signaling Strategy","authors":"Xiaoya Liu, Hai Peng, Lisha Gong, Hong Zhang, Chenglong Zhao, Weiju Lai, Gang An, Xianxian Zhao","doi":"10.1039/d4an01167h","DOIUrl":null,"url":null,"abstract":"Cardiovascular disease, intimately linked to dyslipidemia, is one of the leading global causes of mortality. Dyslipidaemia often presents as an elevated concentration of low-density lipoprotein (LDL) and a decreased concentration of high-density lipoprotein (HDL). Therefore, accurately measuring the levels of LDL and HDL particles is crucial for assessing the risks of developing cardiovascular diseases. However, conventional approaches can commonly quantify HDL/LDL particles by detecting the cholesterol or protein molecules within them, which possibly fail to report the amounts of intact particles. In addition, these approaches are sometimes tedious and time-consuming, therefore, highlighting the need for a novel method for precise and effective identification of intact HDL and LDL particles. We have devised a technique that allows accurately and sensitively determining the levels of intact HDL and LDL in a sample without the need for isolation. This method relies on antibody-based immobilization and a self-priming hairpin-triggered Cas12a/crRNA signaling strategy. Based on the elegant design, this technique can be employed to directly and precisely measure the concentration of “actual” HDL and LDL particles, rather than the cholesterol content inside HDL and LDL. The approach has detection limits of 12.3 mg/dL and 5.4 mg/dL for HDL and LDL, respectively, and is also suitable for analyzing lipoproteins in clinical samples. Hence, this platform exhibits immense potential in clinical applications and health management.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"1 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analyst","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1039/d4an01167h","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Cardiovascular disease, intimately linked to dyslipidemia, is one of the leading global causes of mortality. Dyslipidaemia often presents as an elevated concentration of low-density lipoprotein (LDL) and a decreased concentration of high-density lipoprotein (HDL). Therefore, accurately measuring the levels of LDL and HDL particles is crucial for assessing the risks of developing cardiovascular diseases. However, conventional approaches can commonly quantify HDL/LDL particles by detecting the cholesterol or protein molecules within them, which possibly fail to report the amounts of intact particles. In addition, these approaches are sometimes tedious and time-consuming, therefore, highlighting the need for a novel method for precise and effective identification of intact HDL and LDL particles. We have devised a technique that allows accurately and sensitively determining the levels of intact HDL and LDL in a sample without the need for isolation. This method relies on antibody-based immobilization and a self-priming hairpin-triggered Cas12a/crRNA signaling strategy. Based on the elegant design, this technique can be employed to directly and precisely measure the concentration of “actual” HDL and LDL particles, rather than the cholesterol content inside HDL and LDL. The approach has detection limits of 12.3 mg/dL and 5.4 mg/dL for HDL and LDL, respectively, and is also suitable for analyzing lipoproteins in clinical samples. Hence, this platform exhibits immense potential in clinical applications and health management.