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Kranich, J.* ; Chlis, N.-K. ; Rausch, L.* ; Latha, A.* ; Schifferer, M.* ; Kurz, T.* ; Foltyn-Arfa Kia, A.* ; Simons, M.* ; Theis, F.J. ; Brocker, T.*

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

J. Extra. Vesicles 9:1792683 (2020)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Extracellular Vesicles ; Apoptosis ; Dendritic Cells ; Exosomes ; Irradiation ; Viral Infection
ISSN (print) / ISBN 2001-3078
e-ISSN 2001-3078
Quellenangaben Band: 9, Heft: 1, Seiten: , Artikelnummer: 1792683 Supplement: ,
Verlag Taylor & Francis
Verlagsort [S.l.]
Begutachtungsstatus