Year: 2020
Type: article
Abstract: ABSTRACT Proper identification and annotation of cells in mammalian tissues is of paramount importance to biological research. Various approaches are currently used to identify and label cell types of interest in complex tissues. In this report, we generated an artificial intelligence (AI) deep learning model that uses image segmentation to predict cardiomyocyte nuclei in mouse heart sections without a specific cardiomyocyte nuclear label. This tool can annotate cardiomyocytes highly sensitively and specifically (AUC 0.94) using only cardiomyocyte structural protein immunostaining and a global nuclear stain. We speculate that our method is generalizable to other tissues to annotate specific cell types and organelles in a label-free way.
Source: Circulation Research
Institution: The University of Texas Southwestern Medical Center
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: bronze
Awards: G4082330795, G6766700596
OpenAlex ID: W3029474394