Work

Smart microfluidic device for automated exosome quantification and classification using deep learning

Year: 2025

Type: article

Abstract: Microfluidic resistive pulse sensing offers a streamlined alternative for detecting and analyzing microparticles across various fields, including environmental science, chemistry, biomedicine, and disease diagnostics. This study introduces a novel microfluidic platform designed to analyze and characterize the physical properties of cancer-derived exosomes through automated, efficient analysis. The platform employs capillary- and vacuum-chamber-assisted passive-driven fluid injection, enabling fully automated operation. To ensure precise measurements, the platform incorporates hydrodynamic particle focusing without the need for a sheath and utilizes a reference gate to minimize noise during resistive pulse detection. The proposed microfluidic chip accurately measures the size, concentration, and zeta potential of exosomes derived from MCF-7, MDA-MB-231, and MCF-10A cell lines with high sensitivity. Furthermore, the microfluidic platform is fabricated through a cost-effective soft lithography method. Moreover, a deep learning-based model is applied to classify and distinguish exosomes based on the collected data, achieving an impressive accuracy of 96.6%. The microfluidic platform combines high sensitivity with advanced classification capabilities. Moreover, the proposed microfluidic devices’ compact and minimal operation advances Lab on a Chip for practical applications.

Source: Sensors and Actuators B Chemical

DOI: 10.1016/j.snb.2025.137919