Label-free Neutrophil Enrichment from Patient-derived Airway Secretion Using Closed-loop Inertial Microfluidics
Author(s)Qu, Yanyan; Lee, Janet S.; Ryu, Hyunryul; Choi, Kyungyong; Kwon, Taehong; Han, Jongyoon; ... Show more Show less
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Airway secretions contain a large number of immune-related cells, e.g., neutrophils, macrophages, and lymphocytes, which can be used as a major resource to evaluate a variety of pulmonary diseases, both for research and clinical purposes. However, due to the heterogeneous and viscous nature of patient mucus, there is currently no reliable dissociation method that does not damage the host immune cells in the patient airway secretion. In this research, we introduce a sample preparation method that uses inertial microfluidics for the patient's immune assessment. Regardless of the heterogeneous fluidic properties of the clinical samples, the proposed method recovers more than 95% of neutrophils from airway secretion samples that are diluted 1,000-fold with milliliters of clean saline. By recirculating the concentrated output stream to the initial sample reservoir, a high concentration, recovery, and purity of the immune cells are provided; recirculation is considered a trade-off to the single-run syringe-based operation of inertial microfluidics. The closed-loop operation of spiral microfluidics provides leukocytes without physical or chemical disturbance, as demonstrated by the phorbol 12-myristate 13-acetate (PMA)-induced elastase release of sorted neutrophils. Keywords: Immunology and Infection, Issue 136, Inertial microfluidics, airway secretion, label-free cell sorting, heterogeneous biofluid, neutrophil enrichment, patient sample preparation
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Research Laboratory of Electronics
Journal of Visualized Experiments
Ryu, Hyunryul, et al. “Label-Free Neutrophil Enrichment from Patient-Derived Airway Secretion Using Closed-Loop Inertial Microfluidics.” Journal of Visualized Experiments, no. 136, June 2018. © 2018 Journal of Visualized Experiments.
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