@article {70451, title = {The dark side of channelrhodopsins: intrinsic ion conductance of apo-opsins}, journal = {Research Square}, year = {2026}, month = {2026 Jun 09}, abstract = {

Opsins are known as proteins that bind retinal to form light-sensing rhodopsins, which mediate diverse forms of photoreception and are widely used for optogenetic control of cellular activity. Yet many opsins lack canonical retinal-binding features or, in their native biological contexts, are expressed where chromophore availability is limited, raising the question whether retinal-free opsins are silent. Here we show that retinal-free ion-transporting microbial opsins are not inert but adopt intrinsic, light-independent functional states alongside their canonical light-driven activity. Using developmental phenotyping in transgenic Drosophila melanogaster, electrophysiology in Xenopus oocytes and mammalian cells, together with structural and computational analyses, we demonstrate robust light-independent ion conductance in multiple channel-type opsins and an ion-pumping opsin. Structural modeling and molecular dynamics simulations reveal conformational rearrangements in transmembrane helices and internal water networks that are consistent with stabilization of apo-conductive states. Together, these findings establish apo-opsin activity as a defined functional state of microbial opsins, expand the current framework of rhodopsin biology, and highlight the need to consider chromophore-independent activity in both basic research and optogenetic applications.

}, doi = {10.21203/rs.3.rs-9039624/v1}, url = {https://www.doi.org10.21203/rs.3.rs-9039624/v1}, author = {Gao, Shiqiang and Zhang, Chong and Ma, Jie and Fukuda, Masahiro and Lin, Yiechang and Zhu, Mo and Duan, Xiao and Zhou, Lingbo and Zettner, Maximilian and Yang, Shang and Wang, Yanhui and Yu-Strzelczyk, Jing and Zheng, Qiang and Dannh{\"a}user, Sven and Kobayashi, Kazuhiro and Tajima, Seiya and Nakamura, Seiwa and Kim, Suhyang and Takeno, Yuka and Gee, Christine and Shi, Jiachen and He, Yuge and Kittel, Robert and Heckmann, Manfred and Nagel, Georg and Shen, Yin and Cory, Ben and Kato, Hideaki} } @article {65178, title = {ilastik: interactive machine learning for (bio)image analysis.}, journal = {Nature Methods}, volume = {16}, year = {2019}, month = {2019 Sep 30}, pages = {1226-32}, abstract = {

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

}, issn = {1548-7105}, doi = {10.1038/s41592-019-0582-9}, author = {Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N and Kausler, Bernhard X and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A and Kreshuk, Anna} } @article {48647, title = {The candidate multi-cut for cell segmentation.}, journal = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)}, year = {2017}, month = {2017 Jul 04}, abstract = {

Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allows joint selection and clustering of segment candidates from a merge-tree. This way, we overcome the respective limitations of the individual methods: (1) the space of possible segmentations is not constrained to candidates of a merge-tree, and (2) the decision for clustering can be made on candidates larger than superpixels, using features over larger contexts. We solve the optimization problem of selecting and clustering of candidates using an integer linear program. On datasets of 2D light microscopy of cell populations and 3D electron microscopy of neurons, we show that our method generalizes well and generates more accurate segmentations than merge-tree or Multi-Cut methods alone.

}, doi = {10.1109/ISBI.2018.8363658}, author = {Funke, Jan and Zhang, Chong and Pietzsch, Tobias and Gonzalez Ballester, Miguel A and Saalfeld, Stephan} }