ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology.

Autor: Lu M; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.; Cambridge Infinitus Research Centre, University of Cambridge, Cambridge, UK., Christensen CN; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.; Artificial Intelligence Group, Department of Computer Science and Technology, University of Cambridge, Cambridge, UK., Weber JM; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.; Delft Bioinformatics Lab, Intelligent Systems Department, Delft University of Technology, Delft, the Netherlands., Konno T; UK Dementia Research Institute at the University of Cambridge and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK., Läubli NF; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK., Scherer KM; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK., Avezov E; UK Dementia Research Institute at the University of Cambridge and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK., Lio P; Artificial Intelligence Group, Department of Computer Science and Technology, University of Cambridge, Cambridge, UK., Lapkin AA; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK., Kaminski Schierle GS; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.; Cambridge Infinitus Research Centre, University of Cambridge, Cambridge, UK., Kaminski CF; Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK. cfk23@cam.ac.uk.; Cambridge Infinitus Research Centre, University of Cambridge, Cambridge, UK. cfk23@cam.ac.uk.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2023 Apr; Vol. 20 (4), pp. 569-579. Date of Electronic Publication: 2023 Mar 30.
DOI: 10.1038/s41592-023-01815-0
Abstrakt: The ability to quantify structural changes of the endoplasmic reticulum (ER) is crucial for understanding the structure and function of this organelle. However, the rapid movement and complex topology of ER networks make this challenging. Here, we construct a state-of-the-art semantic segmentation method that we call ERnet for the automatic classification of sheet and tubular ER domains inside individual cells. Data are skeletonized and represented by connectivity graphs, enabling precise and efficient quantification of network connectivity. ERnet generates metrics on topology and integrity of ER structures and quantifies structural change in response to genetic or metabolic manipulation. We validate ERnet using data obtained by various ER-imaging methods from different cell types as well as ground truth images of synthetic ER structures. ERnet can be deployed in an automatic high-throughput and unbiased fashion and identifies subtle changes in ER phenotypes that may inform on disease progression and response to therapy.
(© 2023. The Author(s) under exclusive license to Springer Nature America, Inc.)
Databáze: MEDLINE
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