Dataset Open Access

Raw and processed data for publication "Quantitative microscopy reveals dynamics and fate of clustered IRE1alpha"

Belyy, Vladislav; Tran, Ngoc Han; Walter, Peter

This dataset contains all raw and processed data for our PNAS paper titled "Quantitative microscopy reveals dynamics and fate of clustered IRE1alpha". Using the associated analysis code (10.5281/zenodo.3544482), all figures in the paper can be reproduced from the data in this upload.

Abstract of the paper is included below:

"The endoplasmic reticulum (ER) membrane-resident stress sensor IRE1 governs the most evolutionarily conserved branch of the unfolded protein response. Upon sensing an accumulation of unfolded proteins in the ER lumen, IRE1 activates its cytoplasmic kinase and ribonuclease (RNase) domains to transduce the signal. IRE1 activity correlates with its assembly into large clusters, yet the biophysical characteristics of IRE1 clusters remain poorly characterized. We combined super-resolution microscopy, single-particle tracking, fluorescence recovery and photoconversion to examine IRE1 clustering quantitatively in living human and mouse cells. Our results revealed that (1) by contrast to qualitative impressions gleaned from microscopic images, IRE1 clusters comprise only a small fraction (~5%) of the total IRE1 in the cell. (2) IRE1 clusters have complex topologies that display features of higher-order organization. (3) IRE1 clusters contain a diffusionally constrained core, indicating that they are not phase-separated liquid condensates.  (4) IRE1 molecules in clusters remain diffusionally accessible to the free pool of IRE1 molecules in the general ER network. (5) When IRE1 clusters disappear at later timepoints of ER stress as IRE1 signaling attenuates, their constituent molecules are released back into the ER network and not degraded. (6) IRE1 cluster assembly and disassembly are mechanistically distinct. (7) IRE1 clusters’ mobility is nearly independent of cluster size. Taken together, these insights define the clusters as dynamic assemblies with unique properties. The analysis tools developed for this study will be widely applicable to investigations of clustering behaviors in other signaling proteins."

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