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Single-cell Profiling of Dynamic Cytokine Secretion and the Phenotype of Immune Cells

By Xingyue An, Victor G. Sendra, Ivan Liadi, Balakrishnan Ramesh, Gabrielle Romain, Cara L. Haymaker, Melisa A. Martinez-Paniagua, Yanbin Lu, Laszlo G. Radvanyi, Badrinath Roysam and Navin Varadarajan


Natural killer (NK) cells are a highly heterogeneous population of innate lymphocytes that constitute our first line of defense against several types of tumors and microbial infections. Understanding the heterogeneity of these lymphocytes requires the ability to integrate their underlying phenotype with dynamic functional behaviors. We have developed and validated a single-cell methodology that integrates cellular phenotyping and dynamic cytokine secretion based on nanowell arrays and bead-based molecular biosensors. We demonstrate the robust passivation of the polydimethylsiloxane (PDMS)-based nanowells arrays with polyethylene glycol (PEG) and validated our assay by comparison to enzyme-linked immunospot (ELISPOT) assays. We used numerical simulations to optimize the molecular density of antibodies on the surface of the beads as a function of the capture efficiency of cytokines within an open-well system. Analysis of hundreds of individual human peripheral blood NK cells profiled ex vivo revealed that CD56dimCD16+ NK cells are immediate secretors of interferon gamma (IFN-?) upon activation by phorbol 12-myristate 13-acetate (PMA) and ionomycin (< 3 h), and that there was no evidence of cooperation between NK cells leading to either synergistic activation or faster IFN-? secretion. Furthermore, we observed that both the amount and rate of IFN-? secretion from individual NK cells were donor-dependent. Collectively, these results establish our methodology as an investigational tool for combining phenotyping and real-time protein secretion of individual cells in a high-throughput manne

Topics: Cytokines, Killer cells, lymphocytes, polyethlene glycol, enzyme-linked immunosorbent assay, phorbols
Publisher: 'Public Library of Science (PLoS)'
Year: 2017
DOI identifier: 10.1371/journal.pone.0181904.URL:
OAI identifier:

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