In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning. Issue 1 (1st January 2020)

• Record Type: Journal Article
• Authors: Kranich, Jan; Chlis, Nikolaos-Kosmas; Rausch, Lisa; Latha, Ashretha; Schifferer, Martina; Kurz, Tilman; Foltyn-Arfa Kia, Agnieszka; Simons, Mikael; Theis, Fabian J.; Brocker, Thomas
• Is Part Of: Journal of extracellular vesicles. Volume 9:Issue 1(2020)
• Publication Date: 2020-01-01
• Subjects: Extracellular Vesicles -- exosomes -- dendritic cells -- viral Infection -- irradiation -- apoptosis; Cells -- Mechanical properties -- Periodicals; Transport Vesicles; Cells -- Mechanical properties; Periodicals; Fulltext; Internet Resources; Periodicals; Electronic journals; Periodicals; 571.63
• DOI: 10.1080/20013078.2020.1792683
• Languages: English
• ISSNs: 2001-3078

Abstract

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 .