@article {69707, title = {Pearling Drives Mitochondrial DNA Nucleoid Distribution}, journal = {bioRxiv}, year = {2024}, month = {12/2024}, abstract = {

The regular distribution of mitochondrial DNA-containing nucleoids is essential for mitochondrial function and genome inheritance; however, the underlying mechanisms remain unknown. Our data reveal that mitochondria frequently undergo spontaneous and reversible pearling - a biophysical instability in which tubules undulate into regularly spaced beads. We discovered that pearling imposes a characteristic length scale, simultaneously mediating nucleoid disaggregation and establishing inter-nucleoid distancing with near-maximally achievable precision. Cristae invaginations play a dual role: lamellar cristae density determines pearling frequency and duration, and preserves the resulting nucleoid spacing after recovery. The distribution of mitochondrial genomes is thus fundamentally governed by the interplay between spontaneous pearling and cristae ultrastructure.

}, doi = {10.1101/2024.12.21.629917}, url = {https://www.biorxiv.org/content/early/2024/12/22/2024.12.21.629917}, author = {Landoni, Juan C. and Lycas, Matthew D. and Macuada, Josefa and Jaccard, Romeo and Obara, Christopher J. and Moore, Andrew S. and Ben Nejma, Sheda and Hoffman, David and Lippincott-Schwartz, Jennifer and Marshall, Wallace and Sturm, Gabriel and Manley, Suliana} } @article {67226, title = {Deep learning enables fast and dense single-molecule localization with high accuracy}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Sep 03}, abstract = {

Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2020.10.26.355164}, url = {https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.355164}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Kreshuk, Anna and Macke, Jakob H. and Ries, Jonas and Turaga, Srinivas C.} } @article {67222, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, journal = {arXiv e-prints}, year = {2019}, month = {06/2019}, pages = {arXiv:1907.00770}, 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.

}, keywords = {Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Statistics - Machine Learning}, url = {https://arxiv.org/abs/1907.00770}, author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Ries, Jonas and Macke, Jakob H. and Turaga, Srinivas C.} }