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In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.


ABSTRACT: The in vivo detection of dead cells remains a major challenge due to technical hurdles. Here, we present a novel method, where injection of fluorescent milk fat globule-EGF factor 8 protein (MFG-E8) in vivo combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identify apoptotic cells in vivo. However, unexpectedly, these analyses also revealed that the great majority of PS+ cells were not apoptotic, but rather live cells associated with PS+ extracellular vesicles (EVs). During acute viral infection apoptotic cells increased slightly, while up to 30% of lymphocytes were decorated with PS+ EVs of antigen-presenting cell (APC) exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs in vivo.

SUBMITTER: Kranich J 

PROVIDER: S-EPMC7480589 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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<i>In vivo</i> identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning.

Kranich Jan J   Chlis Nikolaos-Kosmas NK   Rausch Lisa L   Latha Ashretha A   Schifferer Martina M   Kurz Tilman T   Foltyn-Arfa Kia Agnieszka A   Simons Mikael M   Theis Fabian J FJ   Brocker Thomas T  

Journal of extracellular vesicles 20200716 1


The <i>in vivo</i> detection of dead cells remains a major challenge due to technical hurdles. Here, we present a novel method, where injection of fluorescent milk fat globule-EGF factor 8 protein (MFG-E8) <i>in vivo</i> combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identi  ...[more]

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