Abstract

The endoplasmic reticulum (ER) is a highly dynamic polygonal membrane network composed of interconnected tubules and sheets (cisternae) that forms the first compartment in the secretory pathway involved in protein translocation, folding, glycosylation, quality control, lipid synthesis, calcium signalling, and metabolon formation. Despite its central role in this plethora of biosynthetic, metabolic and physiological processes, there is little quantitative information on ER structure, morphology or dynamics. Here we describe a software package (AnalyzER) to automatically extract ER tubules and cisternae from multi-dimensional fluorescence images of plant ER. The structure, topology, protein-localisation patterns, and dynamics are automatically quantified using spatial, intensity and graph-theoretic metrics. We validate the method against manually-traced ground-truth networks, and calibrate the sub-resolution width estimates against ER profiles identified in serial block-face SEM images. We apply the approach to quantify the effects on ER morphology of drug treatments, abiotic stress and over-expression of ER tubule-shaping and cisternal-modifying proteins.

Quantitative study of endoplasmic reticulum (ER) structure and dynamics has been a challenge. Here, the authors introduce software to automatically extract ER network elements from multi-dimensional fluorescence images of plant ER and to quantify structure, topology, protein localization and dynamics.

Details

Title
Quantitative analysis of plant ER architecture and dynamics
Author
Pain, Charlotte 1   VIAFID ORCID Logo  ; Kriechbaumer Verena 1   VIAFID ORCID Logo  ; Kittelmann Maike 1   VIAFID ORCID Logo  ; Hawes, Chris 1   VIAFID ORCID Logo  ; Fricker, Mark 2   VIAFID ORCID Logo 

 Oxford Brookes University, Department of Biological and Medical Sciences, Oxford, UK (GRID:grid.7628.b) (ISNI:0000 0001 0726 8331) 
 University of Oxford, Department of Plant Sciences, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948) 
Publication year
2019
Publication date
Dec 2019
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2187022700
Copyright
This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.