Advanced Photonics Center, Southeast University, Nanjing, 210096, China.
Department of Hematology and Oncology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China.
Small. 2019 Oct;15(43):e1901014. doi: 10.1002/smll.201901014. Epub 2019 Sep 3.
Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So far, they have been recognized as potential biomarkers for cancer diagnostics. However, the present single-variate analysis of exosomes has greatly limited the accuracy and specificity of diagnoses. Besides, most diagnostic approaches focus on bulk analysis using lots of exosomes and tend to be less accurate because they are vulnerable to impure extraction and concentration differences of exosomes. To address these challenges, a quantitative analysis platform is developed to implement a sequential quantification analysis of multiple exosomal surface biomarkers at the single-exosome level, which utilizes DNA-PAINT and a machine learning algorithm to automatically analyze the results. As a proof of concept, the profiling of four exosomal surface biomarkers (HER2, GPC-1, EpCAM, EGFR) is developed to identify exosomes from cancer-derived blood samples. Then, this technique is further applied to detect pancreatic cancer and breast cancer from unknown samples with 100% accuracy.
外泌体是富含体液（如尿液、血液和唾液）的内体衍生囊泡。到目前为止，它们已被认为是癌症诊断的潜在生物标志物。然而，目前对外泌体的单变量分析极大地限制了诊断的准确性和特异性。此外，大多数诊断方法侧重于使用大量外泌体进行批量分析，因为它们容易受到外泌体不纯提取和浓度差异的影响，所以准确性较差。为了解决这些挑战，开发了一种定量分析平台，以在单个外泌体水平上实现对外泌体表面多个标志物的顺序定量分析，该平台利用 DNA-PAINT 和机器学习算法自动分析结果。作为概念验证，开发了四种外泌体表面标志物（HER2、GPC-1、EpCAM、EGFR）的分析方法，以鉴定来自癌症衍生血液样本的外泌体。然后，该技术进一步应用于从未知样本中以 100%的准确率检测胰腺癌和乳腺癌。
