@article {69880, title = {DeepPD: Joint phase and object estimation from phase diversity with neural calibration of a deformable mirror}, journal = {arXiv}, year = {2025}, month = {2025 Apr 19}, abstract = {

Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.

}, doi = {10.48550/arxiv.2504.14157}, url = {https://doi.org/10.48550/arxiv.2504.14157}, author = {Magdalena C. Schneider and Courtney Johnson and C{\'e}dric Allier and Larissa Heinrich and Diane Adjavon and Joren Husic and Patrick La Riviere and Stephan Saalfeld and Hari Shroff} } @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 {69473, title = {SciJava Ops: An Improved Algorithms Framework for Fiji and Beyond}, journal = {Front. Bioinform.}, volume = {4}, year = {2024}, month = {2024 Sep 26}, abstract = {

Decades of iteration on scientific imaging hardware and software has yielded an explosion in not only the size, complexity, and heterogeneity of image datasets but also in the tooling used to analyze this data. This wealth of image analysis tools, spanning different programming languages, frameworks, and data structures, is itself a problem for data analysts who must adapt to new technologies and integrate established routines to solve increasingly complex problems. While many {\textquotedblleft}bridge{\textquotedblright} layers exist to unify pairs of popular tools, there exists a need for a general solution to unify new and existing toolkits. The SciJava Ops library presented here addresses this need through two novel principles. Algorithm implementations are declared as plugins called Ops, providing a uniform interface regardless of the toolkit they came from. Users express their needs declaratively to the Op environment, which can then find and adapt available Ops on demand. By using these principles instead of direct function calls, users can write streamlined workflows while avoiding the translation boilerplate of bridge layers. Developers can easily extend SciJava Ops to introduce new libraries and more efficient, specialized algorithm implementations, even immediately benefitting existing workflows. We provide several use cases showing both user and developer benefits, as well as benchmarking data to quantify the negligible impact on overall analysis performance. We have initially deployed SciJava Ops on the Fiji platform, however it would be suitable for integration with additional analysis platforms in the future.

}, doi = {10.3389/fbinf.2024.1435733}, url = {https://doi.org/10.3389/fbinf.2024.1435733 }, author = {Gabriel J. Selzer and Curtis T. Rueden and Mark C. Hiner and Edward L. Evans III and David Kolb and Marcel Wiedenmann and Christian Birkhold and Tim-Oliver Buchholz and Stefan Helfrich and Brian Northan and Alison Walter and Johannes Schindelin and Tobias Pietzsch and Stephan Saalfeld and Michael R. Berthold and Kevin W. Eliceiri} } @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 {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 {67994, title = {Petascale pipeline for precise alignment of images from serial section electron microscopy.}, journal = {bioRxiv}, year = {2022}, month = {2022 Mar 27}, abstract = {

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

}, doi = {10.1101/2022.03.25.485816}, url = {https://doi.org/10.1101/2022.03.25.485816}, author = {Sergiy Popovych and Thomas Macrina and Nico Kemnitz and Manuel Castro and Barak Nehoran and Zhen Jia and J. Alexander Bae and Eric Mitchell and Shang Mu and Eric T. Trautman and Stephan Saalfeld and Kai Li and Sebastian Seung} } @article {67849, title = {Rapid reconstruction of neural circuits using tissue expansion and lattice light sheet microscopy}, journal = {eLife}, year = {2022}, month = {2022 Oct 26}, abstract = {

Electron microscopy (EM) allows for the reconstruction of dense neuronal connectomes but suffers from low throughput, limiting its application to small numbers of reference specimens. We developed a protocol and analysis pipeline using tissue expansion and lattice light-sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many samples with single synapse resolution and molecular contrast. We validate this approach in\ Drosophila, demonstrating that it yields synaptic counts similar to those obtained by EM, can be used to compare counts across sex and experience, and to correlate structural connectivity with functional connectivity. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.

}, doi = {10.7554/eLife.81248}, url = {https://doi.org/10.7554/eLife.81248}, author = {Joshua L. Lillvis and Hideo Otsuna and Xiaoyu Ding and Igor Pisarev and Takashi Kawase and Jennifer Colonell and Konrad Rokicki and Cristian Goina and Ruixuan Gao and Amy Hu and Kaiyu Wang and John Bogovic and Daniel E. Milkie and Edward S. Boyden and Stephan Saalfeld and Paul W. Tillberg and Barry J. Dickson} } @article {67243, title = {Expansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organization}, journal = {bioRxiv}, year = {2021}, month = {2021 Mar 8}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed\ Expansion-Assisted Iterative\ Fluorescence\ In\ Situ\ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 {\textmu}m) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine novel spatially and molecularly defined subregions. EASI-FISH also facilitates iterative re-analysis of scRNA-Seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, doi = {10.1101/2021.03.08.434304}, url = {https://doi.org/10.1101/2021.03.08.434304}, author = {Yuhan Wang and Mark Eddison and Greg Fleishman and Martin Weigert and Shengjin Xu and Frederick E. Henry and Tim Wang and Andrew L. Lemire and Uwe Schmidt and Hui Yang and Konrad Rokicki and Cristian Goina and Karel Svoboda and Eugene W. Myers and Stephan Saalfeld and Wyatt Korff and Scott M. Sternson and Paul W. Tillberg} }