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Detection of sepsis in patient blood samples using CD64 expression in a microfluidic cell separation device

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Abstract

A microfluidic affinity separation device was developed for the detection of sepsis in critical care patients. An affinity capture method was developed to capture cells based on changes in CD64 expression in a single, simple microfluidic chip for sepsis detection. Both sepsis patient samples and a laboratory CD64+ expression model were used to validate the microfluidic assay. Flow cytometry analysis showed that the chip cell capture had a linear relationship with CD64 expression in laboratory models. The Sepsis Chip detected an increase in upregulated neutrophil-like cells when the upregulated cell population is as low as 10% of total cells spiked into commercially available aseptic blood samples. In a proof of concept study, blood samples obtained from sepsis patients within 24 hours of diagnosis were tested on the chip to further validate its performance. On-chip CD64+ cell capture from 10 patient samples (619 ± 340 cells per chip) was significantly different from control samples (32 ± 11 cells per chip) and healthy volunteer samples (228 ± 95 cells per chip). In addition, the on-chip cell capture has a linear relationship with CD64 expression indicating our approach can be used to measure CD64 expression based on total cell capture on Sepsis Chip. Our method has proven to be sensitive, accurate, rapid, and cost-effective. Therefore, this device is a promising detection platform for neutrophil activation and sepsis diagnosis.

Graphical abstract: Detection of sepsis in patient blood samples using CD64 expression in a microfluidic cell separation device

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Publication details

The article was received on 05 Sep 2017, accepted on 08 Nov 2017 and first published on 09 Nov 2017


Article type: Paper
DOI: 10.1039/C7AN01471F
Citation: Analyst, 2018, Advance Article
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    Detection of sepsis in patient blood samples using CD64 expression in a microfluidic cell separation device

    Y. Zhang, W. Li, Y. Zhou, A. Johnson, A. Venable, A. Hassan, J. Griswold and D. Pappas, Analyst, 2018, Advance Article , DOI: 10.1039/C7AN01471F

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