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UiT The Arctic University of Norway

3 ARTICLES PUBLISHED IN JoVE

JoVE Journal

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Chun A. Changou^1,2, Deanna L. Wolfson², 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.

Biochemistry

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

David André Coucheron¹, Øystein Ivar Helle¹, Cristina Ionica Øie², Jean-Claude Tinguely¹, Balpreet Singh Ahluwalia¹

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.

Biology

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Abhinanda Ranjit Punnakkal¹, Gustav Godtliebsen², Ayush Somani¹, Sebastian Andres Acuna Maldonado³, Åsa Birna Birgisdottir^2,4, Dilip K. Prasad¹, Alexander Horsch¹, Krishna Agarwal³

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.