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 IEEE Sensors Journal Pub Date : 2025-02-27 DOI:10.1109/JSEN.2025.3542298
Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu
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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.
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基于电化学阻抗的胰腺癌生物标志物Glypican1和Mucin1的检测使用电场裂解的细胞外囊泡进行分析:概念验证
Glypican1和mucin1抗原是胰腺癌预后和诊断的重要生物标志物。它们存在于细胞外囊泡(ev)中,通过开发微创生物标志物辅助筛查工具,为肿瘤治疗开辟了可能性。传统上,EV抗原定量依赖于超离心(UC)和化学裂解,这是耗时的,依赖于设备,并且经常损害EV的完整性,破坏表面完整的生物标志物。本研究将EV隔离和电场裂解整合为一个统一的平台。然后使用基于电化学阻抗谱(EIS)的传感器分析裂解物,检测glypican-1 (GPC1)和mucin-1 (MUC1)。ELISA证实了固定化EV的EF裂解,并显示抗原浓度增加了2.5倍(与预裂解样品相比)。因此,EF裂解使传感器比传统方法更灵敏。为了加强电解过程,我们在丝网印刷金电极(SPGE)固定化电动汽车上施加不同电压的正弦信号。裂解液随后通过EIS定量GPC1和MUC1抗原。结果表明,经western blotting证实,50 mv的正弦信号足以有效地溶解ev。纳米颗粒跟踪分析仪(NTA)结果显示,利用CD63抗体从100~\mu $ L的血清中成功分离出10^{{9}}$ ev。所开发的EIS传感器在EV裂解液中检测GPC1和MUC1的LOD分别为0.053和0.033 pg/mL,阴性对照中显示最小的非特异性结合。除了GPC1和MUC1外,该方法还适用于检测其他ev相关生物标志物,从而在早期癌症检测和疾病监测中获得更广泛的应用。
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
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
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