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UiT The Arctic University of Norway

3 ARTICLES PUBLISHED IN JoVE

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JoVE Journal

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy
Chun A. Changou 1,2, Deanna L. Wolfson 2, Balpreet Singh Ahluwalia 2,3, Richard J. Bold 4,5, Hsing-Jien Kung 5,6, Frank Y.S. Chuang 1,2,5
1Department of Biochemistry and Molecular Medicine, University of California, Davis , 2NSF Center for Biophotonics Science & Technology, University of California, Davis , 3University of Tromsø, 4Department of Surgery (Division of Surgical Oncology), University of California, Davis , 5UC Davis Comprehensive Cancer Center, University of California, Davis , 6Department of Biological Chemistry, University of California, Davis

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

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Biochemistry

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
David André Coucheron 1, Øystein Ivar Helle 1, Cristina Ionica Øie 2, Jean-Claude Tinguely 1, Balpreet Singh Ahluwalia 1
1Department of Physics and Technology, UiT The Arctic University of Norway, 2Vascular Biology Research Group, Department of Medical Biology, UiT The Arctic University of Norway

Chip-based super-resolution optical microscopy is a novel approach to fluorescence microscopy and offers advantages in cost effectiveness and throughput. Here, the protocols for chip preparation and imaging are shown for TIRF microscopy and localization-based super-resolution microscopy.

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Biology

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
Abhinanda Ranjit Punnakkal 1, Gustav Godtliebsen 2, Ayush Somani 1, Sebastian Andres Acuna Maldonado 3, Åsa Birna Birgisdottir 2,4, Dilip K. Prasad 1, Alexander Horsch 1, Krishna Agarwal 3
1Department of Computer Science, UiT The Arctic University of Norway, 2Department of Clinical Medicine, UiT The Arctic University of Norway, 3Department of Physics and Technology, UiT The Arctic University of Norway, 4Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway

This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells.

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