Multiplexed detection platform implemented with magnetic encoding and deep learning-based decoding for quantitative analysis of exosomes from cancers.

Although a number of biosensing technologies have been reported for the detection of cancer-derived exosomes used as early diagnosis markers for cancers, it is necessary to identify them with various biomarkers to distinguish the stages and types of cancers owing to the extreme heterogeneity of cancer. Here, we developed a new multiplexed assay platform for the detection of exosomes using magnetic encoded microparticles (MEMPs), which can recognize multiple proteins expressed on exosomes, and a deep learning-based decoding algorithm. This platform, in which the accuracy of the decoding algorithm was evaluated to be 93 %, was applied to detect exosomes from four types of cancer cell lines and plasma from patients with cancer using three cancer biomarkers: PD-L1 (Programmed Death-Ligand 1), EpCAM (Epithelial cell adhesion molecule), and EGFR (Epidermal growth factor receptor). The limit of detections (LODs) of this platform when applied to the detection of exosomes from MDA-MB-231 cell line were calculated as 4.03 × 106 mL−1 for PD-L1, 1.00 ×107 mL−1 for EpCAM, and 7.17×106 mL−1 for EGFR, respectively. In a clinical study, four types of samples from patients with cancer (n = 92) showed higher signals than those of healthy controls (n = 18). Based on these results, we confirmed that this platform can distinguish patients with cancer from healthy individuals. [Display omitted] • Development of a multiplex detection platform using magnetic particle encoding and a deep learning-based decoding algorithm. • Proposal of a novel detection method through microparticle rotation. • Detection of exosomes from various cancer cell lines using multiple biomarkers. • Confirmation of the potential for cancer diagnosis through exosome detection in cancer patient plasma. [ABSTRACT FROM AUTHOR]