TY - JOUR
T1 - Intelligent Probabilistic System for Digital Tracing Cellular Origin of Individual Clinical Extracellular Vesicles
AU - Xiao, Xia
AU - Wu, Kun
AU - Yan, An
AU - Wang, Jun Gang
AU - Zhang, Zhanxia
AU - Li, Di
N1 - Publisher Copyright:
© 2021 American Chemical Society.
PY - 2021/7/27
Y1 - 2021/7/27
N2 - Extracellular vesicles (EVs) are small vesicles secreted by various cell types to mediate cell-to-cell communication through the transfer of macromolecules. EVs carry multiple cargo molecules that reflect the origins of their donor cells; thus, they can be considered reliable biomarkers for early cancer diagnosis. However, the diverse cellular origin of EV masks the detection signals generated by both tumor- and nontumor-derived cells. Thereby, the capability to recognize the cellular origin of EVs is the prerequisite for their diagnostic applications. In the present study, we develop an intelligent probabilistic system for tracing the cellular origin of individual EVs using single-molecule multicolor imaging. Through the analysis of the expression profile of two typical membrane protein markers, CD9 and CD63, on single EVs, accurate and rapid probabilistic recognition of EVs derived from individual tumor and nontumor cells in clinical samples is achieved. The correlation between cellular origin and surface protein phenotyping on single EVs is also exemplified. The proposed system holds great potential for advancing EVs as reliable clinical indicators and exploring their biological functions.
AB - Extracellular vesicles (EVs) are small vesicles secreted by various cell types to mediate cell-to-cell communication through the transfer of macromolecules. EVs carry multiple cargo molecules that reflect the origins of their donor cells; thus, they can be considered reliable biomarkers for early cancer diagnosis. However, the diverse cellular origin of EV masks the detection signals generated by both tumor- and nontumor-derived cells. Thereby, the capability to recognize the cellular origin of EVs is the prerequisite for their diagnostic applications. In the present study, we develop an intelligent probabilistic system for tracing the cellular origin of individual EVs using single-molecule multicolor imaging. Through the analysis of the expression profile of two typical membrane protein markers, CD9 and CD63, on single EVs, accurate and rapid probabilistic recognition of EVs derived from individual tumor and nontumor cells in clinical samples is achieved. The correlation between cellular origin and surface protein phenotyping on single EVs is also exemplified. The proposed system holds great potential for advancing EVs as reliable clinical indicators and exploring their biological functions.
UR - https://www.scopus.com/pages/publications/85111296847
U2 - 10.1021/acs.analchem.1c01971
DO - 10.1021/acs.analchem.1c01971
M3 - 文章
AN - SCOPUS:85111296847
SN - 0003-2700
VL - 93
SP - 10343
EP - 10350
JO - Analytical Chemistry
JF - Analytical Chemistry
IS - 29
ER -
