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Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning

Overview
Journal Anal Chem
Specialty Chemistry
Date 2019 Feb 12
PMID 30741527
Citations 12
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Abstract

CD8 T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8 T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8 T cells from naı̈ve CD8 T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.

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References
1.
Gordonov S, Hwang M, Wells A, Gertler F, Lauffenburger D, Bathe M . Time series modeling of live-cell shape dynamics for image-based phenotypic profiling. Integr Biol (Camb). 2015; 8(1):73-90. PMC: 5058786. DOI: 10.1039/c5ib00283d. View

2.
Chen C, Mahjoubfar A, Tai L, Blaby I, Huang A, Niazi K . Deep Learning in Label-free Cell Classification. Sci Rep. 2016; 6:21471. PMC: 4791545. DOI: 10.1038/srep21471. View

3.
Pandey R, Zhou R, Bordett R, Hunter C, Glunde K, Barman I . Integration of diffraction phase microscopy and Raman imaging for label-free morpho-molecular assessment of live cells. J Biophotonics. 2018; 12(4):e201800291. PMC: 6447451. DOI: 10.1002/jbio.201800291. View

4.
BARER R . Determination of dry mass, thickness, solid and water concentration in living cells. Nature. 1953; 172(4389):1097-8. DOI: 10.1038/1721097a0. View

5.
Ettinger A, Wittmann T . Fluorescence live cell imaging. Methods Cell Biol. 2014; 123:77-94. PMC: 4198327. DOI: 10.1016/B978-0-12-420138-5.00005-7. View