Issue 17, 2022

Droplet microfluidics for functional temporal analysis and cell recovery on demand using microvalves: application in immunotherapies for cancer

Abstract

Most common methods of cellular analysis employ the top-down approach (investigating proteomics or genomics directly), thereby destroying the cell, which does not allow the possibility of using the same cell to correlate genomics with functional assays. Herein we describe an approach for single-cell tools that serve as a bottom-up approach. Our technology allows functional phenotyping to be conducted by observing the cytotoxicity of cells and then probe the underlying biology. We have developed a droplet microfluidic device capable of trapping droplets in the array and releasing the droplet of interest selectively using microvalves. Each droplet in the array encapsulates natural killer cells (NK cells) and tumour cells for real-time monitoring of burst kinetics and spatial coordination during killing by single NK cells. Finally, we use the microvalve actuation to selectively release droplets with the desired functional phenotype such as for fast and serial killing of target tumour cells by NK cells. From this perspective, our device allows for investigating first interactions and real-time monitoring of kinetics and later cell recovery on demand for single-cell omic analysis such as single-cell RNA sequencing (scRNA), which to date, is primarily based on in-depth analyses of the entire transcriptome of a relatively low number of cells.

Graphical abstract: Droplet microfluidics for functional temporal analysis and cell recovery on demand using microvalves: application in immunotherapies for cancer

Supplementary files

Article information

Article type
Paper
Submitted
11 May 2022
Accepted
05 Jul 2022
First published
18 Jul 2022

Lab Chip, 2022,22, 3258-3267

Author version available

Droplet microfluidics for functional temporal analysis and cell recovery on demand using microvalves: application in immunotherapies for cancer

S. N. Agnihotri, G. S. Ugolini, M. R. Sullivan, Y. Yang, A. De Ganzó, J. W. Lim and T. Konry, Lab Chip, 2022, 22, 3258 DOI: 10.1039/D2LC00435F

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