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| Home > Publications Database > In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning. |
| Home > Publications Database > In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning. |
| Journal Article | DZNE-2020-01408 |
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2020
Co-Action Publ.
[S.l.]
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Please use a persistent id in citations: doi:10.1080/20013078.2020.1792683
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.
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