Kranich J, Chlis NK, Rausch L, Latha A, Schifferer M, Kurz T, Kia AFA, Simons M, Theis FJ, Brocker T (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 9
Article Number: 1792683
Journal Issue: 1
DOI: 10.1080/20013078.2020.1792683
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.
APA:
Kranich, J., Chlis, N.-K., Rausch, L., Latha, A., Schifferer, M., Kurz, T.,... Brocker, T. (2020). In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning. Journal of Extracellular Vesicles, 9(1). https://dx.doi.org/10.1080/20013078.2020.1792683
MLA:
Kranich, Jan, et al. "In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning." Journal of Extracellular Vesicles 9.1 (2020).
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