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基于 DNA 电路的免疫分析用于超灵敏蛋白质模式分类。

DNA circuit-based immunoassay for ultrasensitive protein pattern classification.

机构信息

Gulliver Laboratory, ESPCI Paris Université PSL, 10 rue Vauquelin, 75005 Paris, France.

出版信息

Analyst. 2024 Oct 7;149(20):5052-5062. doi: 10.1039/d4an00728j.

DOI:10.1039/d4an00728j
PMID:39206940
Abstract

Cytokines are important immune modulators, and pivotal biomarkers for the diagnostic of various diseases. In standard analytical procedure, each protein is detected individually, using for instance gold standard ELISA protocols or nucleic acid amplification-based immunoassays. In recent years, DNA nanotechnology has been employed for creating sophisticated biomolecular systems that perform neuromorphic computing on molecular inputs, opening the door to concentration pattern recognition for biomedical applications. This work introduces immuno-PUMA (i-PUMA), an isothermal amplification-based immunoassay for ultrasensitive protein detection. The assay couples the convenience of supported format of an ELISA protocol with the computing capabilities of a DNA/enzyme circuit. We demonstrate a limit of detection of 2.1 fM, 8.7 fM and 450 aM for IL12, IL4 and IFNγ cytokines, respectively, outperforming the traditional ELISA format. i-PUMA's versatility extends to molecular computation, allowing the creation of 2-input perceptron-like classifiers for IL12 and IL4, with tunable weight sign and amplitude. Overall, i-PUMA represents a sensitive, low-cost, and versatile immunoassay with potential applications in multimarker-based sample classification, complementing existing molecular profiling techniques.

摘要

细胞因子是重要的免疫调节剂,也是各种疾病诊断的关键生物标志物。在标准分析程序中,每种蛋白质都是单独检测的,例如使用金标准 ELISA 方案或基于核酸扩增的免疫测定法。近年来,DNA 纳米技术已被用于创建复杂的生物分子系统,这些系统可以对分子输入进行神经形态计算,为生物医学应用打开了浓度模式识别的大门。这项工作介绍了基于免疫- PUMA(i-PUMA)的超灵敏蛋白质检测等温扩增免疫测定法。该测定法将 ELISA 方案的支持格式的便利性与 DNA/酶回路的计算能力相结合。我们分别证明了对于 IL12、IL4 和 IFNγ 细胞因子,检测限分别为 2.1 fM、8.7 fM 和 450 aM,优于传统的 ELISA 格式。i-PUMA 的多功能性扩展到分子计算,允许为 IL12 和 IL4 创建具有可调权重符号和幅度的 2 输入类神经感知器分类器。总体而言,i-PUMA 是一种灵敏、低成本且多功能的免疫测定法,具有基于多标记物的样品分类的潜在应用,补充了现有的分子分析技术。

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