A Deep Learning-based tool for the automated detection and analysis of caveolae in Transmission Electron Microscopy images

• Published in: Computational and structural biotechnology journal Vol. 21; pp. 224 - 237
• Main Authors: Aboy-Pardal, María C.M., Jimenez-Carretero, Daniel, Terrés-Domínguez, Sara, Pavón, Dácil M., Sotodosos-Alonso, Laura, Jiménez-Jiménez, Víctor, Sánchez-Cabo, Fátima, Pozo, Miguel A. Del
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023; Research Network of Computational and Structural Biotechnology; Elsevier
• Subjects: Caveolae; Deep learning; TEM; Deep learning; Caveolae; TEM
• ISSN: 2001-0370; 2001-0370
• DOI: 10.1016/j.csbj.2022.11.062

[Display omitted] Caveolae are nanoscopic and mechanosensitive invaginations of the plasma membrane, essential for adipocyte biology. Transmission electron microscopy (TEM) offers the highest resolution for caveolae visualization, but provides complicated images that are difficult to classify or segment using traditional automated algorithms such as threshold-based methods. As a result, the time-consuming tasks of localization and quantification of caveolae are currently performed manually. We used the Keras library in R to train a convolutional neural network with a total of 36000 TEM image crops obtained from adipocytes previously annotated manually by an expert. The resulting model can differentiate caveolae from non-caveolae regions with a 97.44% accuracy. The predictions of this model are further processed to obtain caveolae central coordinate detection and cytoplasm boundary delimitation. The model correctly finds negligible caveolae predictions in image tiles from caveolae depleted Cav1-/- adipocytes. In large reconstructions of adipocyte sections, model and human performances are comparable. We thus provide a new tool for accurate caveolae automated analysis that could speed up and assist in the characterization of the cellular mechanical response.