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Sorting single T cells based on secreted cytokines and surface markers using hydrogel nanovials

View ORCID ProfileDoyeon Koo, Robert Dimatteo, Sohyung Lee, Joseph de Rutte, Dino Di Carlo
doi: https://doi.org/10.1101/2022.04.28.489940
Doyeon Koo
1Department of Bioengineering, University of California, Los Angeles, CA 90095
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Robert Dimatteo
2Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095
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Sohyung Lee
2Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095
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Joseph de Rutte
1Department of Bioengineering, University of California, Los Angeles, CA 90095
3Partillion Bioscience Corporation, Los Angeles, CA 90095
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Dino Di Carlo
1Department of Bioengineering, University of California, Los Angeles, CA 90095
4Department of Mechanical and Aerospace Engineering, Los Angeles, CA 90095
5California NanoSystems Institute, Los Angeles, CA 90095
6Jonsson Comprehensive Cancer Center, Los Angeles, CA 90095
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  • For correspondence: dicarlo@ucla.edu
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Abstract

Immune cell function is intrinsically linked to secreted factors which enable cells to communicate with neighboring or distant cells to coordinate a response. The ability to secrete cytokines also can help define the population of cells with therapeutic potential in emerging cell therapies, such as chimeric antigen receptor (CAR)-T cell therapies. Polyfunctional cells that can secrete more than one cytokine have been found to play an outsized role in therapeutic efficacy. While there are a variety of techniques to analyze cellular secretions from individual polyfunctional cells, there are no widely-available approaches to sort viable cells based on this phenotype. Here, we apply lab on a particle technology to the analysis and sorting of T cells based on a combination of secreted factors, interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α) and interleukin 2 (IL-2) and surface markers (CD8+ and CD4+). Cells are selectively loaded into the antibody-functionalized cavity of micro-hydrogel particles, called nanovials, where secreted cytokines are captured and fluorescently stained. By leveraging standard fluorescence activated cell sorters and using fluorescence pulse area/height information we can distinguish between fluorescence signals on the nanovial cavities and on cells, and are able to process greater than 1 million nanovials in one hour of sorting. The frequency of multi-cytokine secreting cells was correlated with surface marker expression, and biased towards CD4+ T cells. CD8+ cells that secreted more than one cytokine, were biased towards IFN-γ and TNF-α with fewer CD8+ cells secreting IL-2. The majority of cells with a polyfunctional phenotype that were sorted remained viable and regrew following sorting. This nanovial cytokine secretion assay can be applied to sort antigen-specific T cells or CAR-T cells based on their functional engagement with cognate antigens or peptide-major histocompatibility complexs (MHCs), enabling discovery of functional CARs or T cell receptors and deeper investigation into the molecular underpinnings of single T cell function.

Competing Interest Statement

J.D. is an employee of Partillion Bioscience which is commercializing nanovial technology. All of the authors are inventors on patent applications owned by the University of California. J.D., D.D. and the University of California have financial interests in Partillion Bioscience.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 30, 2022.
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Sorting single T cells based on secreted cytokines and surface markers using hydrogel nanovials
Doyeon Koo, Robert Dimatteo, Sohyung Lee, Joseph de Rutte, Dino Di Carlo
bioRxiv 2022.04.28.489940; doi: https://doi.org/10.1101/2022.04.28.489940
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Sorting single T cells based on secreted cytokines and surface markers using hydrogel nanovials
Doyeon Koo, Robert Dimatteo, Sohyung Lee, Joseph de Rutte, Dino Di Carlo
bioRxiv 2022.04.28.489940; doi: https://doi.org/10.1101/2022.04.28.489940

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