Literature DB >> 31423780

Ultrasimple Single-Cell Detection of Multiple Cytokines by a Nanowell Chip Integrated with Encoded Microarrays.

Mohammed A A Abdullah1, Jun Wang1,2.   

Abstract

Cytokine production is often regarded as the marker of immune cells' activation status. The spectrum and temporal secretion of cytokines are dramatically varied between cell phenotypes and even within the same phenotype. Multiparameter analysis of individual immune cell's cytokine secretion has always been a challenging and complicated process that needs special facilities in a laboratory setting. Herein, we present an ultrasimple method with high sensitivity and high robustness to quantify cytokine expression at the single-cell resolution. A microchip is developed based on poly(dimethylsiloxane) nanowells on sticky tape, while each nanowell is integrated with a DNA-antibody convertible microarray. Only pipetting is needed for the whole single-cell analysis process. The sensitivity of the assay is evaluated by measuring various concentrations of six recombinant cytokine proteins, which was found comparable to conventional methods. Once single cells are loaded to nanowells and incubated there, a Fluorinert FC-40 is used to isolate nanowells; so, cytokines from those cells are captured by separate microarrays. The rest of the sandwich enzyme-linked immunosorbent assay detection process is also executed simply by pipetting of various reagents. This method is validated by measuring cytokine production from hundreds of single cells. It has simplified a typically sophisticated multiplex single-cell assay into an instrument-free, point-of-detection technology, and thus it may find a broad utility in clinical diagnostics.

Entities:  

Keywords:  microchip; multiplex; point-of-care diagnosis; simple method; single-cell analysis

Mesh:

Substances:

Year:  2019        PMID: 31423780      PMCID: PMC6765397          DOI: 10.1021/acssensors.9b00765

Source DB:  PubMed          Journal:  ACS Sens        ISSN: 2379-3694            Impact factor:   7.711


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