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Updated: May 25, 2025
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
Rapid and high-sensitivity screening of pregnancy complications by profiling circulating placental extracellular
Carlos Palma1,2, Mostafa Kamal Masud2,3, Dominic Guanzon1,2
1Translational Extracellular Vesicles in Obstetrics and Gynae-Oncology Group, University of Queensland Centre for Clinical Research, Faculty of Medicine, Royal Brisbane and Women's Hospital, The University of Queensland, Herston, QLD 4006, Australia.
Herein, we developed a specific, rapid sensor to quantify placental extracellular vesicle (EV) protein biomarkers of early pregnancy complications. A distinct tetraspanin CD9 and placental alkaline phosphatase (PLAP) expression pattern was observed via targeted multiple reaction monitoring of EVs from maternal plasma collected before 18 weeks of gestation. A classification model was developed using training and validation patient sets, distinguishing between individuals at high risk of developing complications from those with normal pregnancies, achieving 80% sensitivity, 90% specificity, 89% positive predictive value (PPV), and 82% negative predictive value (NPV). Superparamagnetic nanoflowers that captured target EVs (CD9+/PLAP+) were used to construct a 4-flex glass strip nanozymatic readout system. The sensor analyzes plasma for EVs, identifying gestational diabetes mellitus risk with a 95% combined sensitivity, 100% specificity, 100% PPV, and 96% NPV. This nanoplatform identifies individuals at risk of developing pregnancy complications with a >90% classification accuracy, exhibiting potential for clinical applications.
Herein, we developed a specific, rapid sensor to quantify placental extracellular vesicle (EV) protein biomarkers of early pregnancy complications. A distinct tetraspanin CD9 and placental alkaline phosphatase (PLAP) expression pattern was observed via targeted multiple reaction monitoring of EVs from maternal plasma collected before 18 weeks of gestation. A classification model was developed using training and validation patient sets, distinguishing between individuals at high risk of developing complications from those with normal pregnancies, achieving 80% sensitivity, 90% specificity, 89% positive predictive value (PPV), and 82% negative predictive value (NPV). Superparamagnetic nanoflowers that captured target EVs (CD9+/PLAP+) were used to construct a 4-flex glass strip nanozymatic readout system. The sensor analyzes plasma for EVs, identifying gestational diabetes mellitus risk with a 95% combined sensitivity, 100% specificity, 100% PPV, and 96% NPV. This nanoplatform identifies individuals at risk of developing pregnancy complications with a >90% classification accuracy, exhibiting potential for clinical applications.
