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

Autor: Fabian J. Theis, Martina Schifferer, Thomas Brocker, Lisa Rausch, Mikael Simons, Nikolaos-Kosmas Chlis, Ashretha Latha, Agnieszka Foltyn-Arfa Kia, Tilman Kurz, Jan Kranich
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Journal of extracellular vesicles 9(1), 1792683-(2020). doi:10.1080/20013078.2020.1792683
Journal of Extracellular Vesicles, Vol 9, Iss 1 (2020)
Journal of Extracellular Vesicles
article-version (VoR) Version of Record
J. Extra. Vesicles 9:1792683 (2020)
DOI: 10.1080/20013078.2020.1792683
Popis: SUMMARYThe 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 Dendritic cell exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs in vivo.
Databáze: OpenAIRE
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