{"id":20084,"date":"2020-07-07T11:00:28","date_gmt":"2020-07-07T09:00:28","guid":{"rendered":"https:\/\/irb.usi.ch\/?p=20084"},"modified":"2020-08-04T17:59:15","modified_gmt":"2020-08-04T15:59:15","slug":"new-paper-from-molinaris-lab-deep-learning-to-monitor-lysosomal-activity","status":"publish","type":"post","link":"https:\/\/irb.usi.ch\/it\/news\/new-paper-from-molinaris-lab-deep-learning-to-monitor-lysosomal-activity\/","title":{"rendered":"New paper from Molinari\u2019s Lab: Deep Learning to monitor Lysosomal activity"},"content":{"rendered":"

Lysosomes clear from cells damaged organelles and macromolecules thus giving substantial contribution to tissues and organs homeostasis. Cumulating knowledge expands the number of rare lysosomal storage disorders directly and indirectly linked to lysosomal dysfunction.<\/p>\n

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.<\/p>\n

Quantitative approaches to monitor lysosomal activity, i.e., the magnitude of delivery within lysosomes of cargo to be removed from cells (being it macromolecules like proteins or larger structures such as organelle fragments) or the accumulation within lysosomes of unprocessed material are expected to contribute to the understanding of the mechanistic details of lysosome-driven pathways and may find application for diagnostic and therapeutic purposes.<\/p>\n

A recent study, resulting from the collaboration between the group of Maurizio Molinari and Diego Morone at the Imaging Facility of the IRB, established a deep learning approach, called LysoQuant, for segmentation and classification of fluorescence images capturing cargo delivery and\/or accumulation within lysosomes. LysoQuant is trained for unbiased and rapid recognition with human-level accuracy and the pipeline informs on a series of quantitative parameters such as lysosome 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 (ER), and of a model protein, polymerogenic ATZ, whose intra-hepatic accumulation is the major cause of pediatric liver transplant resulting from a rare disease condition. The accuracy and velocity of LysoQuant performance is compatible with high throughput analyses.<\/p>\n

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Endolysosomal compartments maintain cellular fitness by clearing from cells dysfunctional organelles and proteins. 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 provides fast recognition with human-level accuracy of fluorescence images capturing cargo delivery within endolysosomes, thus weakening the boundary between the worlds of manual accurate annotation and high-throughput screenings.
Credits: Diego Morone, Alessandro Marazza, Timothy J. Bergmann, Maurizio Molinari – Institute for Research in Biomedicine, Switzerland<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

Lysosomes clear from cells damaged organelles and macromolecules thus giving substantial contribution to tissues and organs homeostasis. Cumulating knowledge expands the number of rare lysosomal storage disorders directly and indirectly linked to lysosomal dysfunction. Lysosomes have for long time been considered static organelles ensuring degradation and recycling of cellular wastes. However, they are all but […]<\/p>\n","protected":false},"author":1,"featured_media":20085,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[16],"tags":[],"yoast_head":"\nNew paper from Molinari\u2019s Lab: Deep Learning to monitor Lysosomal activity - IRB USI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/irb.usi.ch\/it\/news\/new-paper-from-molinaris-lab-deep-learning-to-monitor-lysosomal-activity\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New paper from Molinari\u2019s Lab: Deep Learning to monitor Lysosomal activity - IRB USI\" \/>\n<meta property=\"og:description\" content=\"Lysosomes clear from cells damaged organelles and macromolecules thus giving substantial contribution to tissues and organs homeostasis. Cumulating knowledge expands the number of rare lysosomal storage disorders directly and indirectly linked to lysosomal dysfunction. Lysosomes have for long time been considered static organelles ensuring degradation and recycling of cellular wastes. However, they are all but […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/irb.usi.ch\/it\/news\/new-paper-from-molinaris-lab-deep-learning-to-monitor-lysosomal-activity\/\" \/>\n<meta property=\"og:site_name\" content=\"IRB USI\" \/>\n<meta property=\"article:published_time\" content=\"2020-07-07T09:00:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-08-04T15:59:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/irb.usi.ch\/images\/mmo_paper_may2020.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2220\" \/>\n\t<meta property=\"og:image:height\" content=\"1755\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Tempo di lettura stimato\">\n\t<meta name=\"twitter:data1\" content=\"2 minuti\">\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"https:\/\/irb.usi.ch\/it\/#website\",\"url\":\"https:\/\/irb.usi.ch\/it\/\",\"name\":\"IRB USI\",\"description\":\"Institute for Research in Biomedicine\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":\"https:\/\/irb.usi.ch\/it\/?s={search_term_string}\",\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"it-IT\"},{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/irb.usi.ch\/it\/news\/new-paper-from-molinaris-lab-deep-learning-to-monitor-lysosomal-activity\/#primaryimage\",\"inLanguage\":\"it-IT\",\"url\":\"https:\/\/irb.usi.ch\/images\/mmo_paper_may2020.jpg\",\"width\":2220,\"height\":1755,\"caption\":\"Endolysosomal compartments maintain cellular fitness by clearing from cells dysfunctional organelles and proteins. 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 provides fast recognition with human-level accuracy of fluorescence images capturing cargo delivery within endolysosomes, thus weakening the boundary between the worlds of manual accurate annotation and high-throughput screenings. Credits: Diego Morone, Alessandro Marazza, Timothy J. 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