Open AccessIn vivo identification of apoptotic and extracellular vesicle‐bound live cells using image‐based deep learning
Author(s) -
Kranich Jan,
Chlis NikolaosKosmas,
Rausch Lisa,
Latha Ashretha,
Schifferer Martina,
Kurz Tilman,
FoltynArfa Kia Agnieszka,
Simons Mikael,
Theis Fabian J.,
Brocker Thomas
Publication year - 2020
Publication title -
journal of extracellular vesicles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.94
H-Index - 68
ISSN - 2001-3078
DOI - 10.1080/20013078.2020.1792683
Subject(s) - in vivo , phosphatidylserine , flow cytometry , apoptosis , microbiology and biotechnology , microvesicles , chemistry , biology , microrna , biochemistry , gene , phospholipid , membrane
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 .