In Small (Weinheim an der Bergstrasse, Germany)
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.
Chen Chen, Zong Shenfei, Liu Yun, Wang Zhuyuan, Zhang Yizhi, Chen Baoan, Cui Yiping
DNA-PAINT, biomarkers, cancer diagnosis, exosomes, machine learning