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.

Quantitative phase imaging (QPI) has proven to be a valuable tool for advanced biological and pharmacological research, providing phase information for the study of cell features and physiology in label-free conditions. The next step for QPI to become a gold standard is the quantitative assessment of the phase gradients over the different microscopy setups. Given the large variety of QPI systems, a systematic comparison is a challenging task, and requires a calibration target representative of the living samples. In this paper, we introduce a tailor-made 3D-printed phantom derived from phase images of eukaryotic cells. It comprises typical morphologies and optical thicknesses found in biological cultures and is characterized with digital holographic microscopy (reference measurements). The performance of three different full field QPI optical systems, in terms of optical path difference and dry mass accuracy, were evaluated. This phantom opens up other possibilities for the validation of reconstruction algorithms and post-processing routines, and paves the way for calibration targets designed ad hoc for specific biological questions.

Neuronal dendrites must relay synaptic inputs over long distances, but the mechanisms by which activity-evoked intracellular signals propagate over macroscopic distances remain unclear. Here, we discovered a system of periodically arranged endoplasmic reticulum-plasma membrane (ER-PM) junctions tiling the plasma membrane of dendrites at ∼1 μm intervals, interlinked by a meshwork of ER tubules patterned in a ladder-like array. Populated with Junctophilin-linked plasma membrane voltage-gated Ca channels and ER Ca-release channels (ryanodine receptors), ER-PM junctions are hubs for ER-PM crosstalk, fine-tuning of Ca homeostasis, and local activation of the Ca/calmodulin-dependent protein kinase II. Local spine stimulation activates the Ca modulatory machinery, facilitating signal transmission and ryanodine-receptor-dependent Ca release at ER-PM junctions over 20 μm away. Thus, interconnected ER-PM junctions support signal propagation and Ca release from the spine-adjacent ER. The capacity of this subcellular architecture to modify both local and distant membrane-proximal biochemistry potentially contributes to dendritic computations.

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 “bridge” 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.

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

The point spread function (PSF) is fundamental to any type of microscopy, most importantly so for single-molecule localization techniques, where the exact PSF shape is crucial for precise molecule localization at the nanoscale. Optical aberrations and fixed fluorophore dipoles often result in non-isotropic and distorted PSFs, impairing and biasing conventional fitting approaches. Further, PSF shapes are deliberately modified in PSF engineering approaches for providing improved sensitivity, e.g., for 3D localization or determination of dipole orientation. As this can lead to highly complex PSF shapes, a tool for visualizing expected PSFs would facilitate the interpretation of obtained data and the design of experimental approaches. To this end, we introduce a comprehensive and accessible computer application that allows for the simulation of realistic PSFs based on the full vectorial PSF model. Our tool incorporates a wide range of microscope and fluorophore parameters, including orientationally constrained fluorophores, as well as custom aberrations, transmission and phase masks, thus enabling an accurate representation of various imaging conditions. An additional feature is the simulation of crowded molecular environments with overlapping PSFs. Further, our app directly provides the Cramér–Rao bound for assessing the best achievable localization precision under given conditions. Finally, our software allows for the fitting of custom aberrations directly from experimental data, as well as the generation of a large dataset with randomized simulation parameters, effectively bridging the gap between simulated and experimental scenarios, and enhancing experimental design and result validation.

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.

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 – OME-Zarr – 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 — the file format that underlies so many personal, institutional, and global data management and analysis tasks.

The endoplasmic reticulum (ER) forms a dynamic network that contacts other cellular membranes to regulate stress responses, calcium signalling and lipid transfer. Here, 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 nanometre 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, mobility and expression of ER stress transcripts. These findings indicate that desmosomes and the keratin cytoskeleton regulate the distribution, function and dynamics 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.