Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments

• Published in: Molecular biology of the cell Vol. 31; no. 14; pp. 1512 - 1524
• Main Authors: Morone, Diego, Marazza, Alessandro, Bergmann, Timothy J., Molinari, Maurizio
• Format: Journal Article
• Language: English
• Published: The American Society for Cell Biology 01-07-2020
• ISSN: 1059-1524; 1939-4586
• DOI: 10.1091/mbc.E20-04-0269

LysoQuant is a deep learning approach to segmentation and classification of fluorescent images capturing cargo delivery within endolysosomes for clearance. It yields information on various parameters of the activity of lysosome-driven pathways such as ER-phagy.
Endolysosomal compartments maintain cellular fitness by clearing dysfunctional organelles and proteins from cells. Modulation of their activity offers therapeutic opportunities. Quantification of cargo delivery to and/or accumulation within endolysosomes is instrumental for characterizing lysosome-driven pathways at the molecular level and monitoring consequences of genetic or environmental modifications. Here we introduce LysoQuant, a deep learning approach for segmentation and classification of fluorescence images capturing cargo delivery within endolysosomes for clearance. LysoQuant is trained for unbiased and rapid 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. In our selected examples, LysoQuant successfully determines the magnitude of mechanistically distinct catabolic pathways that ensure lysosomal clearance of a model organelle, the endoplasmic reticulum, and of a model protein, polymerogenic ATZ. It does so with accuracy and velocity compatible with those of high-throughput analyses.