Electrochemical Impedance-Based Detection of Pancreatic Cancer Biomarker Glypican1 and Mucin1 Using Electric Field-Lysed Extracellular Vesicles for Analysis: A Proof of Concept
IF 4.3 2区 综合性期刊Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu
{"title":"Electrochemical Impedance-Based Detection of Pancreatic Cancer Biomarker Glypican1 and Mucin1 Using Electric Field-Lysed Extracellular Vesicles for Analysis: A Proof of Concept","authors":"Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu","doi":"10.1109/JSEN.2025.3542298","DOIUrl":null,"url":null,"abstract":"Glypican1 and mucin1 antigens are prominent biomarkers for the prognosis and diagnosis of pancreatic cancer. Their presence within the extracellular vesicles (EVs) opens the possibilities for oncology care through the development of minimally invasive biomarker-assisted screening tools. Traditionally, EV antigen quantification relies on ultracentrifugation (UC) and chemical lysis, which are time-consuming, equipment-dependent, and often compromise EV integrity, damaging surface intact biomarkers. This study integrates EV isolation and electric field (EF) lysis into a unified platform. The lysates were then analyzed using an electrochemical impedance spectroscopy (EIS)-based sensor to detect glypican-1 (GPC1) and mucin-1 (MUC1). ELISA confirms the EF lysis of the immobilized EV and shows an increase in the antigen concentration by 2.5 times (compared to the pre-lysed sample). Hence, EF lysis makes the sensor more sensitive than traditional methods. To enhance the electric lysis process, we applied varying voltages of a sinusoidal signal to the screen printed gold electrode (SPGE)-immobilized EVs. The lysate was subsequently used to quantify the GPC1 and MUC1 antigens through EIS. The results indicate that a 50-mV sinusoidal signal is sufficient to effectively lyse EVs, confirmed by western blotting. The nanoparticle tracking analyzer (NTA) results showed the successful isolation of <inline-formula> <tex-math>$10^{{9}}$ </tex-math></inline-formula> EVs from <inline-formula> <tex-math>$100~\\mu $ </tex-math></inline-formula>L of serum using CD63 antibody. The developed EIS sensor can detect GPC1 and MUC1 with an LOD of 0.053 and 0.033 pg/mL, respectively, from EV lysate, showing minimal nonspecific binding in the negative control. Beyond GPC1 and MUC1, the approach is adaptable for detecting other EV-associated biomarkers, enabling broader applications in early cancer detection and disease monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10566-10574"},"PeriodicalIF":4.3000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10907770/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Glypican1 and mucin1 antigens are prominent biomarkers for the prognosis and diagnosis of pancreatic cancer. Their presence within the extracellular vesicles (EVs) opens the possibilities for oncology care through the development of minimally invasive biomarker-assisted screening tools. Traditionally, EV antigen quantification relies on ultracentrifugation (UC) and chemical lysis, which are time-consuming, equipment-dependent, and often compromise EV integrity, damaging surface intact biomarkers. This study integrates EV isolation and electric field (EF) lysis into a unified platform. The lysates were then analyzed using an electrochemical impedance spectroscopy (EIS)-based sensor to detect glypican-1 (GPC1) and mucin-1 (MUC1). ELISA confirms the EF lysis of the immobilized EV and shows an increase in the antigen concentration by 2.5 times (compared to the pre-lysed sample). Hence, EF lysis makes the sensor more sensitive than traditional methods. To enhance the electric lysis process, we applied varying voltages of a sinusoidal signal to the screen printed gold electrode (SPGE)-immobilized EVs. The lysate was subsequently used to quantify the GPC1 and MUC1 antigens through EIS. The results indicate that a 50-mV sinusoidal signal is sufficient to effectively lyse EVs, confirmed by western blotting. The nanoparticle tracking analyzer (NTA) results showed the successful isolation of $10^{{9}}$ EVs from $100~\mu $ L of serum using CD63 antibody. The developed EIS sensor can detect GPC1 and MUC1 with an LOD of 0.053 and 0.033 pg/mL, respectively, from EV lysate, showing minimal nonspecific binding in the negative control. Beyond GPC1 and MUC1, the approach is adaptable for detecting other EV-associated biomarkers, enabling broader applications in early cancer detection and disease monitoring.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice