What is LysoQuant?
LysoQuant is a deep learning approach for the segmentation and classification of fluorescence images capturing cargo delivery within endolysosomes for clearance.
LysoQuant is trained for unbiased and fast recognition with human-level accuracy. The pipeline informs on a series of quantitative parameters such as endolysosome number, size, shape, position within cells, and occupancy, which report on the activity of lysosome-driven pathways.
Why is it important to study lysosomes?
Lysosomes have long been considered static organelles, ensuring the degradation and recycling of cellular wastes. However, they are all but static, and their activity, intracellular distribution, number, as well as their capacity to welcome cargo are regulated by various signaling pathways and cellular needs (Baabio & Bonifacino, 2019; Bright, Davis, et al., 2016; Huotari & Helenius, 2011).
Lysosomes substantially contribute to tissue and organ homeostasis by clearing damaged cell organelles and proteins. Cumulating knowledge expands the number of diseases directly and indirectly linked to their dysfunction, from rare lysosomal storage disorders (Marques & Saftig, 2019) to more frequent cancers, metabolic and neurodegenerative diseases (Fraldi, Klein, et al., 2016; Gilleron, Gerdes, et al., 2019; Kimmelman & White, 2017).
Monitoring lysosome biogenesis, plasticity, mobility within the cell, and turnover, as well as quantifying cargo delivery to and accumulation within endolysosomes, is instrumental in characterizing lysosome-driven pathways at the molecular level and monitoring consequences of genetic or environmental modifications.
When you use this tool, please cite the following:
Deep learning approach for quantification of organelles and misfolded polypeptides delivery within degradative compartments.
Diego Morone, Alessandro Marazza, Timothy J. Bergmann, and Maurizio Molinari
Molecular Biology of the Cell 2020 31:14, 1512-1524