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Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells

Moosung Lee, Young-Ho Lee, Jinyeop Song, Geon Kim, YoungJu Jo, HyunSeok Min, Chan Hyuk Kim, YongKeun Park
doi: https://doi.org/10.1101/539858
Moosung Lee
aDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
bKAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea
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Young-Ho Lee
cDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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Jinyeop Song
aDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
bKAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea
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Geon Kim
aDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
bKAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea
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YoungJu Jo
aDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
bKAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea
dPresent address: Department of Applied Physics, Stanford University, Stanford, CA 94305, USA
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HyunSeok Min
eTomocube Inc., Daejeon, South Korea
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Chan Hyuk Kim
cDepartment of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
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  • For correspondence: kimchanhyuk@kaist.ac.kr yk.park@kaist.ac.kr
YongKeun Park
aDepartment of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
bKAIST Institute for Health Science and Technology, KAIST, Daejeon, South Korea
eTomocube Inc., Daejeon, South Korea
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  • For correspondence: kimchanhyuk@kaist.ac.kr yk.park@kaist.ac.kr
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Abstract

We propose and experimentally validate a label-free, volumetric, and automated assessment method of immunological synapse dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed approach enables automatic and quantitative spatiotemporal analyses of immunological synapse kinetics regarding morphological and biochemical parameters related to the total protein densities of immune cells, thus providing a new perspective for studies in immunology.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 04, 2019.
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Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells
Moosung Lee, Young-Ho Lee, Jinyeop Song, Geon Kim, YoungJu Jo, HyunSeok Min, Chan Hyuk Kim, YongKeun Park
bioRxiv 539858; doi: https://doi.org/10.1101/539858
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Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of chimeric antigen receptor T cells
Moosung Lee, Young-Ho Lee, Jinyeop Song, Geon Kim, YoungJu Jo, HyunSeok Min, Chan Hyuk Kim, YongKeun Park
bioRxiv 539858; doi: https://doi.org/10.1101/539858

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