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

Kranich, Jan; Chlis, Nikolaos-Kosmas; Rausch, Lisa; Latha, Ashretha; Schifferer, Martina; Kurz, Tilman; Kia, Agnieszka Foltyn-Arfa; Simons, Mikael; Theis, Fabian J. und Brocker, Thomas (2020): In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning. In: Journal of Extracellular Vesicles, Bd. 9, Nr. 1, 1792683

Abstract

Thein vivodetection 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 vivocombined 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 cellsin 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 EVsin vivo.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin ; Medizin > Munich Cluster for Systems Neurology (SyNergy)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198