Magnetic-Nanowaxberry-Based Simultaneous Detection of Exosome and Exosomal Proteins for the Intelligent Diagnosis of Cancer
Magnetic-Nanowaxberry-Based Simultaneous Detection of Exosome and Exosomal Proteins for the Intelligent Diagnosis of Cancer
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DOI：
10.1021/acs.analchem.1c03957
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发表时间：
2021-11-01
影响因子：
7.4
通讯作者：
Qu, Lingbo
中科院分区：
文献类型：
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作者：
Ding, Lihua;Liu, Li-E;Qu, Lingbo
外泌体浓度和外泌体蛋白被认为是有前途的癌症生物标志物。本文合成了一种具有巨大表面积和强亲和力的杨梅状磁珠（magnetic-candaxberry），用于与适体偶联以捕获和回收外泌体。随后，我们开发了一种荧光测定法，通过使用三色探针识别EGFR和EpCAM或自发地锚到脂质双层，来灵敏、准确和同时定量外泌体和癌症相关的外泌体蛋白[表皮生长因子受体（EGFR）和上皮细胞粘附分子（EpCAM）]。在这种设计中，由于双重识别策略，可以避免可溶性蛋白质的干扰。此外，基于脂质的外泌体浓度定量可以提高准确性。同时检测模式可节省样品，简化操作步骤。因此，该测定显示出高灵敏度（EGFR的检测限低至0.96 pg/mL，EpCAM的检测限低至0.19 pg/mL，外泌体的检测限低至2.4 × 10（4）个颗粒/μ L）、高特异性和令人满意的准确度。更重要的是，这项技术成功地用于分析血浆中的外泌体，以区分癌症患者和健康个体。为了提高诊断效率，使用深度学习来挖掘隐藏在所提出的方法获得的数据中的潜在模式。智能诊断癌症的准确率可达96.0%。该研究为开发用于外泌体分析和智能疾病诊断的新型生物传感器提供了新的途径。
Exosome concentration and exosomal proteins are regarded as promising cancer biomarkers. Herein, a waxberry-like magnetic bead (magnetic-nanowaxberry) which has huge surface area and strong affinity was synthesized to couple with aptamer for exosome capture and recovery. Subsequently, we developed a fluorescent assay for the sensitive, accurate, and simultaneous quantification of exosome and cancer-related exosomal proteins [epidermal growth factor receptor (EGFR) and epithelial cell adhesion molecule (EpCAM)] by using triple-colored probes to recognize EGFR and EpCAM or spontaneously anchor to the lipid bilayer. In this design, the interference of soluble proteins can be avoided due to the dual recognition strategy. Moreover, the lipid-based quantification of exosome concentration can improve the accuracy. Besides, the simultaneous detection mode can save samples and simplify the operation steps. Consequently, the assay shows high sensitivity (the limits of detection are down to 0.96 pg/mL for EGFR, 0.19 pg/mL for EpCAM, and 2.4 x 10(4) particles/ mu L for exosome), high specificity, and satisfactory accuracy. More importantly, this technique is successfully used to analyze exosomes in plasma to distinguish cancer patients from healthy individuals. To improve the diagnostic efficacy, the deep learning was used to exploit the potential pattern hidden in data obtained by the proposed method. Also, the accuracy for the intelligent diagnosis of cancer can achieve 96.0%. This study provides a new avenue for developing new biosensors for exosome analysis and intelligent disease diagnosis.