@article {68717, title = {Simultaneous photoactivation and high-speed structural tracking reveal diffusion-dominated motion in the endoplasmic reticulum}, journal = {bioRxiv}, year = {2023}, month = {2023 Apr 25}, abstract = {

The endoplasmic reticulum (ER) is a structurally complex, membrane-enclosed compartment that stretches from the nuclear envelope to the extreme periphery of eukaryotic cells. The organelle is crucial for numerous distinct cellular processes, but how these processes are spatially regulated within the structure is unclear. Traditional imaging-based approaches to understanding protein dynamics within the organelle are limited by the convoluted structure and rapid movement of molecular components. Here, we introduce a combinatorial imaging and machine learning-assisted image analysis approach to track the motion of photoactivated proteins within the ER of live cells. We find that simultaneous knowledge of the underlying ER structure is required to accurately analyze fluorescently-tagged protein redistribution, and after appropriate structural calibration we see all proteins assayed show signatures of Brownian diffusion-dominated motion over micron spatial scales. Remarkably, we find that in some cells the ER structure can be explored in a highly asymmetric manner, likely as a result of uneven connectivity within the organelle. This remains true independently of the size, topology, or folding state of the fluorescently-tagged molecules, suggesting a potential role for ER connectivity in driving spatially regulated biology in eukaryotes.

}, doi = {10.1101/2023.04.23.537908}, url = {https://doi.org/10.1101/2023.04.23.537908}, author = {Matteo Dora and Christopher J. Obara and Tim Abel and Jennifer Lippincott-Schwarz and David Holcman} } @article {68315, title = {Motion of single molecular tethers reveals dynamic subdomains at ER-mitochondria contact sites}, journal = {bioRxiv}, year = {2022}, month = {2022 Sep 03}, abstract = {

To coordinate cellular physiology, eukaryotic cells rely on the inter-organelle transfer of molecules at specialized organelle-organelle contact sites1,2. Endoplasmic reticulum-mitochondria contact sites (ERMCSs) are particularly vital communication hubs, playing key roles in the exchange of signaling molecules, lipids, and metabolites3. ERMCSs are maintained by interactions between complementary tethering molecules on the surface of each organelle4,5. However, due to the extreme sensitivity of these membrane interfaces to experimental perturbation6,7, a clear understanding of their nanoscale structure and regulation is still lacking. Here, we combine 3D electron microscopy with high-speed molecular tracking of a model organelle tether, VAPB, to map the structure and diffusion landscape of ERMCSs. From EM reconstructions, we identified subdomains within the contact site where ER membranes dramatically deform to match local mitochondrial curvature. In parallel live cell experiments, we observed that the VAPB tethers that mediate this interface were not immobile, but rather highly dynamic, entering and leaving the site in seconds. These subdomains enlarged during nutrient stress, indicating ERMCSs can readily remodel under different physiological conditions. An ALS-associated mutation in VAPB altered the normal fluidity of contact sites, likely perturbing effective communication across the contact site and preventing remodeling. These results establish high speed single molecule imaging as a new tool for mapping the structure of contact site interfaces and suggest that the diffusion landscape of VAPB is a crucial component of ERMCS homeostasis.

}, doi = {10.1101/2022.09.03.505525}, url = {https://doi.org/10.1101/2022.09.03.505525}, author = {Christopher J. Obara and Jonathon Nixon-Abell and Andrew S. Moore and Federica Riccio and David P. Hoffman and Gleb Shtengel and C. Shan Xu and Kathy Schaefer and H. Amalia Pasolli and Jean-Baptiste Masson and Harald F. Hess and Christopher P. Calderon and Craig Blackstone and Jennifer Lippincott-Schwartz} } @booklet {67224, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, year = {2020}, abstract = {

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

}, url = {https://arxiv.org/abs/1907.00770}, author = {Artur Speiser and Lucas-Raphael M{\"u}ller and Ulf Matti and Christopher J. Obara and Wesley R. Legant and Jonas Ries and Jakob H. Macke and Srinivas C. Turaga} }