Lysosomes have for long time been considered static organelles ensuring 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 (Ballabio & Bonifacino, 2019, Bright, Davis et al., 2016, Huotari & Helenius, 2011). By clearing from cells damaged organelles and proteins, they give substantial contribution to tissues and organs homeostasis. 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 quantification of cargo delivery to and/or accumulation within endolysosomes is instrumental to characterize lysosome-driven pathways at the molecular level and to monitor consequences of genetic or environmental modifications.
LysoQuant is a deep learning approach for 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 and the pipeline informs on a series of quantitative parameters such as endolysosome number, size, shape, position within cells and occupancy, which report on activity of lysosome-driven pathways.
When you use this tool, please cite
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