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Brain-derived extracellular vesicles: Potential diagnostic biomarkers for central nervous system diseases
Psychiatry and Clinical Neurosciences ( IF 11.9 ) Pub Date : 2023-10-25 , DOI: 10.1111/pcn.13610
Xiao Xu 1 , Zoya Iqbal 2 , Limei Xu 1 , Caining Wen 1 , Li Duan 2 , Jiang Xia 3 , Ningning Yang 4 , Yuanmin Zhang 1, 5 , Yujie Liang 1, 5
Affiliation  

Extracellular vesicles (EVs) are membrane-enclosed nanovesicles secreted by cells into the extracellular space and contain functional biomolecules, e.g. signaling receptors, bioactive lipids, nucleic acids, and proteins, which can serve as biomarkers. Neurons and glial cells secrete EVs, contributing to various physiological and pathological aspects of brain diseases. EVs confer their role in the bidirectional crosstalk between the central nervous system (CNS) and the periphery owing to their distinctive ability to cross the unique blood–brain barrier (BBB). Thus, EVs in the blood, cerebrospinal fluid (CSF), and urine can be intriguing biomarkers, enabling the minimally invasive diagnosis of CNS diseases. Although there has been an enormous interest in evaluating EVs as promising biomarkers, the lack of ultra-sensitive approaches for isolating and detecting brain-derived EVs (BDEVs) has hindered the development of efficient biomarkers. This review presents the recent salient findings of exosomal biomarkers, focusing on brain disorders. We summarize highly sensitive sensors for EV detection and state-of-the-art methods for single EV detection. Finally, the prospect of developing advanced EV analysis approaches for the non-invasive diagnosis of brain diseases is presented.

中文翻译:

脑源性细胞外囊泡:中枢神经系统疾病的潜在诊断生物标志物

细胞外囊泡(EV)是由细胞分泌到细胞外空间的膜封闭的纳米囊泡,含有功能性生物分子,例如信号受体、生物活性脂质、核酸和蛋白质,可作为生物标志物。神经元和神经胶质细胞分泌 EV,导致脑部疾病的各种生理和病理方面。由于电动汽车具有穿越独特血脑屏障(BBB)的独特能力,因此它们在中枢神经系统(CNS)和外周之间的双向串扰中发挥着作用。因此,血液、脑脊液 (CSF) 和尿液中的 EV 可以成为有趣的生物标志物,从而实现中枢神经系统疾病的微创诊断。尽管人们对评估 EV 作为有前途的生物标志物产生了巨大的兴趣,但缺乏用于分离和检测脑源性 EV (BDEV) 的超灵敏方法阻碍了高效生物标志物的开发。这篇综述介绍了外泌体生物标志物的最新发现,重点关注脑部疾病。我们总结了用于 EV 检测的高灵敏度传感器和用于单个 EV 检测的最先进方法。最后,提出了开发先进的 EV 分析方法用于脑部疾病无创诊断的前景。
更新日期:2023-10-25
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