@article {68716, title = {PEELing: an integrated and user-centric platform for cell-surface proteomics analysis}, journal = {Bioinformatics}, year = {2025}, month = {2025 Aug 6}, abstract = {

Summary:\ Molecular compartmentalization is vital for cellular physiology. Spatially-resolved proteomics allows biologists to survey protein composition and dynamics with subcellular resolution. Here we present PEELing, an integrated package and user-friendly web service for analyzing spatially-resolved proteomics data. PEELing assesses data quality using curated or user-defined references, performs cutoff analysis to remove contaminants, connects to databases for functional annotation, and generates data visualizations-providing a streamlined and reproducible workflow to explore spatially-resolved proteomics data.

Availability and implementation:\ PEELing and its tutorial are publicly available at https://peeling.janelia.org/ (Zenodo DOI: 10.5281/zenodo.15692517). A Python package of PEELing is available at https://github.com/JaneliaSciComp/peeling/ (Zenodo DOI: 10.5281/zenodo.15692434).

Contact:Technical support for PEELing: peeling@janelia.hhmi.org.

bioRxiv Preprint:\ https://doi.org/10.1101/2023.04.21.537871

}, doi = {10.1093/bioinformatics/btaf439}, url = {https://doi.org/10.1093/bioinformatics/btaf439}, author = {Xi Peng and Jody Clements and Zuzhi Jiang and Stephan Preibisch and Jiefu Li} } @article {69373, title = {Connectome-driven neural inventory of a complete visual system}, journal = {Nature}, year = {2024}, month = {2025 Mar 26}, abstract = {

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain{\textquoteright}s volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly{\textquoteright}s visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the 53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

}, doi = {10.1038/s41586-025-08746-0}, url = {https://doi.org/10.1038/s41586-025-08746-0}, author = {Aljoscha Nern and Frank Loesche and Shin-ya Takemura and Laura E Burnett and Marisa Dreher and Eyal Gruntman and Judith Hoeller and Gary B Huang and Michal Januszewski and Nathan C Klapoetke and Sanna Koskela and Kit D Longden and Zhiyuan Lu and Stephan Preibisch and Wei Qiu and Edward M Rogers and Pavithraa Seenivasan and Arthur Zhao and John Bogovic and Brandon S Canino and Jody Clements and Michael Cook and Samantha Finley-May and Miriam A Flynn and Imran Hameed and Kenneth J Hayworth and Gary Patrick Hopkins and Philip M Hubbard and William T Katz and Julie Kovalyak and Shirley A Lauchie and Meghan Leonard and Alanna Lohff and Charli A Maldonado and Caroline Mooney and Nneoma Okeoma and Donald J Olbris and Christopher Ordish and Tyler Paterson and Emily M Phillips and Tobias Pietzsch and Jennifer Rivas Salinas and Patricia K Rivlin and Ashley L Scott and Louis A Scuderi and Satoko Takemura and Iris Talebi and Alexander Thomson and Eric T Trautman and Lowell Umayam and Claire Walsh and John J Walsh and C Shan Xu and Emily A Yakal and Tansy Yang and Ting Zhao and Jan Funke and Reed George and Harald F Hess and Gregory S X E Jefferis and Christopher Knecht and Wyatt Korff and Stephen M Plaza and Sandro Romani and Stephan Saalfeld and Louis K Scheffer and Stuart Berg and Gerald M Rubin and Michael B Reiser} } @article {68800, title = {A Connectome of the Male Drosophila Ventral Nerve Cord}, journal = {bioRxiv}, year = {2023}, month = {2023 Jun 06}, abstract = {

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the\ Drosophila\ central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

}, doi = {10.1101/2023.06.05.543757}, url = {https://doi.org/10.1101/2023.06.05.543757}, author = {Shin-ya Takemura and Kenneth J Hayworth and Gary B Huang and Michal Januszewski and Zhiyuan Lu and Elizabeth C Marin and Stephan Preibisch and C Shan Xu and John Bogovic and Andrew S Champion and Han S J Cheong and Marta Costa and Katharina Eichler and William Katz and Christopher Knecht and Feng Li and Billy J Morris and Christopher Ordish and Patricia K Rivlin and Philipp Schlegel and Kazunori Shinomiya and Tomke Sturner and Ting Zhao and Griffin Badalamente and Dennis Bailey and Paul Brooks and Brandon S Canino and Jody Clements and Michael Cook and Octave Duclos and Christopher R Dunne and Kelli Fairbanks and Siqi Fang and Samantha Finley-May and Audrey Francis and Reed George and Marina Gkantia and Kyle Harrington and Gary Patrick Hopkins and Joseph Hsu and Philip M Hubbard and Alexandre Javier and Dagmar Kainmueller and Wyatt Korff and Julie Kovalyak and Dominik Krzeminski and Shirley A Lauchie and Alanna Lohff and Charli Maldonado and Emily A Manley and Caroline Mooney and Erika Neace and Matthew Nichols and Omotara Ogundeyi and Nneoma Okeoma and Tyler Paterson and Elliott Phillips and Emily M Phillips and Caitlin Ribeiro and Sean M Ryan and Jon Thomson Rymer and Anne K Scott and Ashley L Scott and David Shepherd and Aya Shinomiya and Claire Smith and Alia Suleiman and Satoko Takemura and Iris Talebi and Imaan F M Tamimi and Eric T Trautman and Lowell Umayam and John J Walsh and Tansy Yang and Gerald M Rubin and Louis K Scheffer and Jan Funke and Stephan Saalfeld and Harald F Hess and Stephen M Plaza and Gwyneth M Card and Gregory S X E Jefferis and Stuart Berg} } @article {68635, title = {OME-Zarr: a cloud-optimized bioimaging file format with international community support.}, journal = {Histochemistry and Cell Biology}, volume = {160}, year = {2023}, month = {2023 Feb 25}, pages = {223-251}, abstract = {

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the format itself {\textendash} OME-Zarr {\textendash} along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain {\textemdash} the file format that underlies so many personal, institutional, and global data management and analysis tasks.

}, doi = {10.1007/s00418-023-02209-1}, url = {https://doi.org/10.1007/s00418-023-02209-1}, author = {Josh Moore and Daniela Basurto-Lozada and S{\'e}bastien Besson and John Bogovic and Eva M. Brown and Jean-Marie Burel and Gustavo de Medeiros and Erin E. Diel and David Gault and Satrajit S. Ghosh and Ilan Gold and Yaroslav O. Halchenko and Matthew Hartley and Dave Horsfall and Mark S. Keller and Mark Kittisopikul and Gabor Kovacs and Ayb{\"u}ke K{\"u}pc{\"u} Yolda{\c s} and Albane le Tournoulx de la Villegeorges and Tong Li and Prisca Liberali and Melissa Linkert and Dominik Lindner and Joel L{\"u}thi and Jeremy Maitin-Shepard and Trevor Manz and Matthew McCormick and Khaled Mohamed and William Moore and Bugra {\"O}zdemir and Constantin Pape and Lucas Pelkmans and Martin Prete and Tobias Pietzsch and Stephan Preibisch and Norman Rzepka and David R. Stirling and Jonathan Striebel and Christian Tischer and Daniel Toloudis and Petr Walczysko and Alan M. Watson and Frances Wong and Kevin A. Yamauchi and Omer Bayraktar and Muzlifah Haniffa and Stephan Saalfeld and Jason R. Swedlow} } @article {68466, title = {A scalable implementation of the recursive least-squares algorithm for training spiking neural networks}, journal = {Frontiers in Neuroinformatics}, year = {2023}, month = {2023 Jun 27}, abstract = {

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of\ \>\ 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for\ in-silico\ study of the dynamics and connectivity underlying multi-area computations.

}, doi = {10.3389/fninf.2023.1099510}, url = {https://doi.org/10.3389/fninf.2023.1099510}, author = {Benjamin J. Arthur and Christopher M. Kim and Susu Chen and Stephan Preibisch and Ran Darshan} } @article {68193, title = {Architecture and dynamics of a novel desmosome-endoplasmic reticulum organelle}, journal = {bioRxiv}, year = {2022}, month = {2022 Jul 08}, abstract = {

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signaling, and lipid transfer. Using high-resolution volume electron microscopy, we find that the ER forms a previously unknown association with keratin intermediate filaments and desmosomal cell-cell junctions. Peripheral ER assembles into mirror image-like arrangements at desmosomes and exhibits nanometer proximity to keratin filaments and the desmosome cytoplasmic plaque. ER tubules exhibit stable associations with desmosomes, and perturbation of desmosomes or keratin filaments alters ER organization and mobility. These findings indicate that desmosomes and the keratin cytoskeleton pattern the distribution of the ER network. Overall, this study reveals a previously unknown subcellular architecture defined by the structural integration of ER tubules with an epithelial intercellular junction.

}, doi = {10.1101/2022.07.07.499185}, url = {https://doi.org/10.1101/2022.07.07.499185}, author = {Navaneetha Krishnan Bharathan and William Giang and Jesse S. Aaron and Satya Khuon and Teng-Leong Chew and Stephan Preibisch and Eric T. Trautman and Larissa Heinrich and John Bogovic and Davis Bennett and David Ackerman and Woohyun Park and Alyson Petruncio and Aubrey V. Weigel and Stephan Saalfeld and COSEM Project Team and A. Wayne Vogl and Sara N. Stahley and Andrew P. Kowalczyk} } @article {68021, title = {Long-term imaging reveals behavioral plasticity during C. elegans dauer exit}, journal = {BMC Biology}, volume = {20}, year = {2022}, month = {2022 Dec 13}, pages = {277}, abstract = {

During their lifetime, animals must adapt their behavior to survive in changing environments. This ability requires the nervous system to adjust through dynamic expression of neurotransmitters and receptors but also through growth, spatial reorganization and connectivity while integrating external stimuli. For instance, despite having a fixed neuronal cell lineage, the nematode\ Caenorhabditis elegans{\textquoteright}\ nervous system remains plastic throughout its development. Here, we focus on a specific example of nervous system plasticity, the\ C. elegans\ dauer exit decision. Under unfavorable conditions, larvae will enter the non-feeding and non-reproductive dauer stage and adapt their behavior to cope with a new environment. Upon improved conditions, this stress resistant developmental stage is actively reversed to resume reproductive development. However, how different environmental stimuli regulate the exit decision mechanism and thereby drive the larva{\textquoteright}s behavioral change is unknown. To fill this gap, we developed a new open hardware method for long-term imaging (12h) of\ C. elegans\ larvae. We identified dauer-specific behavioral motifs and characterized the behavioral trajectory of dauer exit in different environments to identify key decision points. Combining long-term behavioral imaging with transcriptomics, we find that bacterial ingestion triggers a change in neuropeptide gene expression to establish post-dauer behavior. Taken together, we show how a developing nervous system can robustly integrate environmental changes, activate a developmental switch and adapt the organism{\textquoteright}s behavior to a new environment.

}, doi = {10.1186/s12915-022-01471-4}, url = {https://doi.org/10.1186/s12915-022-01471-4}, author = {Friedrich Preusser and Anika Neuschulz and Jan Philipp Junker and Nikolaus Rajewsky and Stephan Preibisch} } @article {67774, title = {RS-FISH: Precise, interactive, fast, and scalable FISH spot detection}, journal = {Nature Methods}, volume = {19}, year = {2022}, month = {2022 Nov 17}, pages = {1563-7}, abstract = {

Fluorescent in-situ hybridization (FISH)-based methods are powerful tools to study molecular processes with subcellular resolution, relying on accurate identification and localization of diffraction-limited spots in microscopy images. We developed the Radial Symmetry-FISH (RS-FISH) software that accurately, robustly, and quickly detects single-molecule spots in two and three dimensions, making it applicable to several key assays, including single-molecule FISH (smFISH), spatial transcriptomics, and spatial genomics. RS-FISH allows interactive parameter tuning and scales to large sets of images as well as tera-byte sized image volumes such as entire brain scans using straight-forward distributed processing on workstations, clusters, and in the cloud.

}, doi = {10.1038/s41592-022-01669-y}, author = {Ella Bahry and Laura Breimann and Marwan Zouinkhi and Leo Epstein and Klim Kolyvanov and Xi Long and Kyle I S Harrington and Timoth{\'e}e Lionnet and Stephan Preibisch} } @article {67871, title = {Image-based representation of massive spatial transcriptomics datasets.}, journal = {bioRxiv}, year = {2021}, month = {2021 Dec 07}, abstract = {

We present STIM, an imaging-based computational framework for exploring, visualizing, and processing high-throughput spatial sequencing datasets. STIM is built on the powerful ImgLib2, N5 and BigDataViewer (BDV) frameworks enabling transfer of computer vision techniques to datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM{\textquoteright}s capabilities by representing, visualizing, and automatically registering publicly available spatial sequencing data from 14 serial sections of mouse brain tissue.

}, doi = {10.1101/2021.12.07.471629}, url = {https://doi.org/10.1101/2021.12.07.471629}, author = {Stephan Preibisch and Nikos Karaiskos and Nikolaus Rajewsky} }