A Microfluidic Platform with an Embedded Miniaturized Electrochemical Sensor for On-Chip Plasma Extraction Followed by In Situ High-Sensitivity C-Reactive Protein (hs-CRP) Detection.

Abstract

Blood testing is a clinical diagnostic tool to evaluate physiological conditions, the immune system response, or the presence of infection from whole blood samples. Although conventional blood testing can provide rich biological information, it usually requires complicated and tedious whole blood processing steps operated by benchtop instruments and well-experienced technicians, limiting its usage in point-of-care (POC) settings. To address the above problems, we propose a microfluidic platform for on-chip plasma extraction directly from whole blood and in situ biomarker detection. Herein, we chose C-reactive protein (CRP) as the target biomarker, which can be used to predict fatal cardiovascular disease (CVD) events such as heart attacks and strokes. To achieve a rapid, undiluted, and high-purity on-chip plasma extraction, we combined two whole blood processing methods: (1) anti-D immunoglobulin-assisted sedimentation, and (2) membrane filtration. To perform in situ CRP detection, we fabricated a three-dimensional (3D) microchannel with an embedded electrochemical (EC) sensor, which has a modular design to attach the blood collector and buffer reservoir with standard Luer connectors. As a proof of concept, we first confirmed that the dual plasma extraction design achieved the same purity level as the standard centrifugation method with smaller sample (100 µL of plasma extracted from 400 µL of whole blood) and time (7 min) requirements. Next, we validated the functionalization protocol of the EC sensor, followed by evaluating the detection of CRP spiked in plasma and whole blood. Our microfluidic platform performed on-chip plasma extraction directly from whole blood and in situ CRP detection at a 0.1-10 μg/mL concentration range, covering the CVD risk evaluation level of the high-sensitivity CRP (hs-CRP) test. Based on the above features, we believe that this platform constitutes a flexible way to integrate the processing of complex samples with accurate biomarker detection in a sample-to-answer POC platform, which can be applied in CVD risk monitoring under critical clinical situations.