SynapseNet: Deep Learning for Automatic Synapse Reconstruction.

Electron microscopy is an important technique for the study of synaptic morphology and its relation to synaptic function. The data analysis for this task requires the segmentation of the relevant synaptic structures, such as synaptic vesicles, active zones, mitochondria, presynaptic densities, synaptic ribbons, and synaptic compartments. Previous studies were predominantly based on manual segmentation, which is very time-consuming and prevented the systematic analysis of large datasets.

A spatiotemporal atlas of mouse gastrulation and early organogenesis to explore axial patterning and project in vitro models onto in vivo space.

During gastrulation, mouse epiblast cells form the three germ layers that establish the body plan and initiate organogenesis. While single-cell atlases have advanced our understanding of lineage diversification, spatial aspects of differentiation remain poorly defined. Here, we applied spatial transcriptomics to mouse embryos at embryonic (E) E7.

Highly adaptable deep-learning platform for automated detection and analysis of vesicle exocytosis.

Activity recognition in live-cell imaging is labor-intensive and requires significant human effort. Existing automated analysis tools are largely limited in versatility. We present the Intelligent Vesicle Exocytosis Analysis (IVEA) platform, an ImageJ plugin for automated, reliable analysis of fluorescence-labeled vesicle fusion events and other burst-like activity.

FeatureForest: the power of foundation models, the usability of random forests.

Analysis of biological images relies heavily on segmenting the biological objects of interest in the image before performing quantitative analysis. Deep learning (DL) is ubiquitous in such segmentation tasks, but can be cumbersome to apply, as it often requires a large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models. More recently, large foundation models, such as SAM, have shown promising results on scientific images.

Segment Anything for Microscopy.

Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation.

Mutation-induced LZTR1 polymerization provokes cardiac pathology in recessive Noonan syndrome.

Noonan syndrome patients harboring causative variants in LZTR1 are particularly at risk to develop severe and early-onset hypertrophic cardiomyopathy. In this study, we investigate the mechanistic consequences of a homozygous variant LZTR1 by using patient-specific and CRISPR-Cas9-corrected induced pluripotent stem cell (iPSC) cardiomyocytes. Molecular, cellular, and functional phenotyping in combination with in silico prediction identify an LZTR1-specific disease mechanism provoking cardiac hypertrophy.

Current Progress and Challenges in Large-Scale 3D Mitochondria Instance Segmentation.

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 1,986 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

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 cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process.

Volume electron microscopy.

Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

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 cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process.

Convolutional networks for supervised mining of molecular patterns within cellular context.

Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol.

OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies.

The rapid pace of innovation in biological imaging and the diversity of its applications have prevented the establishment of a community-agreed standardized data format. We propose that complementing established open formats such as OME-TIFF and HDF5 with a next-generation file format such as Zarr will satisfy the majority of use cases in bioimaging. Critically, a common metadata format used in all these vessels can deliver truly findable, accessible, interoperable and reusable bioimaging data.

Profiling cellular diversity in sponges informs animal cell type and nervous system evolution.

The evolutionary origin of metazoan cell types such as neurons and muscles is not known. Using whole-body single-cell RNA sequencing in a sponge, an animal without nervous system and musculature, we identified 18 distinct cell types. These include nitric oxide–sensitive contractile pinacocytes, amoeboid phagocytes, and secretory neuroid cells that reside in close contact with digestive choanocytes that express scaffolding and receptor proteins.

Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii.

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera: A semi-quantitative, high throughput, microscopy-based assay expands existing approaches to measure SARS-CoV-2 specific antibody levels in human sera.

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA, and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS-CoV-2 serological assays.

Integrative Imaging Reveals SARS-CoV-2-Induced Reshaping of Subcellular Morphologies.

Pathogenesis induced by SARS-CoV-2 is thought to result from both an inflammation-dominated cytokine response and virus-induced cell perturbation causing cell death. Here, we employ an integrative imaging analysis to determine morphological organelle alterations induced in SARS-CoV-2-infected human lung epithelial cells. We report 3D electron microscopy reconstructions of whole cells and subcellular compartments, revealing extensive fragmentation of the Golgi apparatus, alteration of the mitochondrial network and recruitment of peroxisomes to viral replication organelles formed by clusters of double-membrane vesicles (DMVs).

Accurate and versatile 3D segmentation of plant tissues at cellular resolution.

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells.

The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning.

Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose an efficient algorithm for graph partitioning, the "Mutex Watershed".