Issue 6, 2023

Magnetic-nanowaxberry-based microfluidic ExoSIC for affinity and continuous separation of circulating exosomes towards cancer diagnosis

Abstract

Exosomes are seen as promising biomarkers for minimally invasive liquid biopsies and disease surveillance. However, the complexity of body fluids, inherent heterogeneity, and tiny size of exosomes impede their extraction, consequently restricting their clinical application. In this study, in order to efficiently isolate exosomes from clinical samples, an irregular serpentine channel microfluidic chip (ExoSIC) was designed to continuously separate exosomes from plasma based on a magnetic-nanowaxberry (MNWB). In the ExoSIC, irregular serpentine microchannels are utilized to increase fluid chaotic mixing, hence improving exosome capture efficiency. In comparison to commonly used spherical magnetic particles, the designed MNWB can not only enhance the capture efficiency of exosomes, but also possess a size-exclusion effect to improve exosome purity. Consequently, the ExoSIC exhibited a large yield (24 times higher than differential centrifugation), optimum purity (greater than precipitation and similar to differential centrifugation), and high specificity. Furthermore, the ExoSIC was utilized for plasma-based cancer diagnosis by multiplex monitoring of five exosomal biomarkers (exosomal concentration, EGFR, EpCAM, SAA1 and FV), and the AUC reached 0.791. This work provides a comprehensive framework for exosome-based cancer diagnostics in order to meet clinical requirements for exosome isolation and downstream analysis.

Graphical abstract: Magnetic-nanowaxberry-based microfluidic ExoSIC for affinity and continuous separation of circulating exosomes towards cancer diagnosis

Supplementary files

Article information

Article type
Paper
Submitted
25 Oct 2022
Accepted
01 Feb 2023
First published
15 Feb 2023

Lab Chip, 2023,23, 1694-1702

Magnetic-nanowaxberry-based microfluidic ExoSIC for affinity and continuous separation of circulating exosomes towards cancer diagnosis

L. Ding, X. Liu, Z. Zhang, L. Liu, S. He, Y. Wu, C. Y. Effah, R. Yang, A. Zhang, W. Chen, M. Yarmamat, L. Qu, X. Yang and Y. Wu, Lab Chip, 2023, 23, 1694 DOI: 10.1039/D2LC00996J

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