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High-Throughput Microfluidic Extraction of Platelet-free Plasma for MicroRNA and Extracellular Vesicle Analysis.

Authors :
Leong SY
Lok WW
Goh KY
Ong HB
Tay HM
Su C
Kong F
Upadya M
Wang W
Radnaa E
Menon R
Dao M
Dalan R
Suresh S
Lim DW
Hou HW
Source :
ACS nano [ACS Nano] 2024 Feb 27; Vol. 18 (8), pp. 6623-6637. Date of Electronic Publication: 2024 Feb 13.
Publication Year :
2024

Abstract

Cell-free RNAs and extracellular vesicles (EVs) are valuable biomarkers in liquid biopsies, but they are prone to preanalytical variabilities such as nonstandardized centrifugation or ex vivo blood degradation. Herein, we report a high-throughput and label-free inertial microfluidic device (ExoArc) for isolation of platelet-free plasma from blood for RNA and EV analysis. Unlike conventional inertial microfluidic devices widely used for cell sorting, a submicrometer size cutoff (500 nm) was achieved which completely removed all leukocytes, RBCs, platelets, and cellular debris based on differential lateral migration induced by Dean vortices. The single-step operation also reduced platelet-associated miRNAs (∼2-fold) compared to centrifugation. We clinically validated ExoArc for plasma miRNA profiling (39 samples) and identified a 7-miRNA panel that detects non-small cell lung cancer with ∼90% sensitivity. ExoArc was also coupled with size exclusion chromatography (SEC) to isolate EVs within 50 min with ∼10-fold higher yield than ultracentrifugation. As a proof-of-concept for EV-based transcriptomics analysis, we performed miRNA analysis in healthy and type 2 diabetes mellitus (T2DM) subjects ( n = 3 per group) by coupling ExoArc and ExoArc+SEC with quantitative polymerase chain reaction (RT-qPCR) assay. Among 293 miRNAs detected, plasmas and EVs showed distinct differentially expressed miRNAs in T2DM subjects. We further demonstrated automated in-line EV sorting from low volume culture media for continuous EV monitoring. Overall, the developed ExoArc offers a convenient centrifugation-free workflow to automate plasma and EV isolation for point-of-care diagnostics and quality control in EV manufacturing.

Details

Language :
English
ISSN :
1936-086X
Volume :
18
Issue :
8
Database :
MEDLINE
Journal :
ACS nano
Publication Type :
Academic Journal
Accession number :
38348825
Full Text :
https://doi.org/10.1021/acsnano.3c12862