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基于甲基化的金吸附行为可识别癌症来源的细胞外囊泡 DNA。

Methylation dependent gold adsorption behaviour identifies cancer derived extracellular vesicular DNA.

机构信息

Centre for Personalised Nanomedicine, Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia.

出版信息

Nanoscale Horiz. 2020 Sep 1;5(9):1317-1323. doi: 10.1039/d0nh00258e. Epub 2020 Jun 12.

Abstract

Extracellular vesicles (EV) play a major role in intercellular communication by transmitting cellular materials (e.g. protein, RNA) among distant cells. Recent evidence suggests that they could also contribute to carrying DNA which could inform on the mutational status of the parent tumour DNA. Thus, the fundamental analysis of evDNA could open a better understanding of tumour metastasis and provide new pathways for noninvasive detection and monitoring of cancer. To explore the potential of evDNA for diagnostics, the isolation of pure evDNA from body fluids free of cfDNA contamination is crucial. Herein, we use a liposome based model system to develop an improved evDNA isolation protocol free from cfDNA contamination and evaluate the methylation dependent physicochemical properties of evDNA to develop a simple test for detecting cancer evDNA. Using a highly sensitive multiplex microelectrode device, we demonstrate that serum-evDNA derived from cancer patients show different solution and surface based properties than normal evDNA due to their different methylation landscape (i.e. methylscape). This microdevice allows simultaneous analysis of multiple samples in a single platform from as low as 500 pg μL of evDNA.

摘要

细胞外囊泡 (EV) 通过在远距离细胞之间传递细胞物质(例如蛋白质、RNA),在细胞间通讯中发挥重要作用。最近的证据表明,它们还可能有助于携带 DNA,从而提供有关亲本肿瘤 DNA 突变状态的信息。因此,对 evDNA 的基本分析可以更好地了解肿瘤转移,并为癌症的非侵入性检测和监测提供新途径。为了探索 evDNA 在诊断中的潜力,必须从无 cfDNA 污染的体液中分离出纯 evDNA。在此,我们使用基于脂质体的模型系统开发了一种改进的 evDNA 分离方案,该方案可避免 cfDNA 污染,并评估 evDNA 的甲基化依赖性物理化学特性,以开发一种用于检测癌症 evDNA 的简单测试。使用高度灵敏的多路微电极装置,我们证明源自癌症患者的血清-evDNA 由于其不同的甲基化图谱(即甲基景观),与正常 evDNA 相比,在溶液和表面上具有不同的特性。该微设备允许从低至 500pg μL 的 evDNA 中对单个平台上的多个样本进行同时分析。

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