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. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
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.
The connectomics community is acquiring volumetric electron microscopy (EM) images of the brain at an unprecedented rate with the aim of mapping out and understanding in detail the physical ...correlates of information processing in animals. Reliable automatic segmentation is urgently needed for upcoming whole-brain data sets (>100 terabytes (TB) per volume). Manual analysis, despite impressive progress in collaborative annotation1, will not scale to this massive task. We present an algorithm and software package to segment such data sets with low error rates. The software is made available open source in the Supplementary Software and at online repositories, and we also provide precompiled binaries (see Supplementary Note 1).
The remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training. In ...contrast, feature-based learning methods, such as the Random Forest, require little training data, but rarely reach the segmentation accuracy of CNNs. This work bridges the two approaches in a transfer learning setting. We show that a CNN can be trained to correct the errors of the Random Forest in the source domain and then be applied to correct such errors in the target domain without retraining, as the domain shift between the Random Forest predictions is much smaller than between the raw data. By leveraging a few brushstrokes as annotations in the target domain, the method can deliver segmentations that are sufficiently accurate to act as pseudo-labels for target-domain CNN training. We demonstrate the performance of the method on several datasets with the challenging tasks of mitochondria, membrane and nuclear segmentation. It yields excellent performance compared to microscopy domain adaptation baselines, especially when a significant domain shift is involved.
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 LZTR1L580P 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 LZTR1L580P-specific disease mechanism provoking cardiac hypertrophy. The variant is predicted to alter the binding affinity of the dimerization domains facilitating the formation of linear LZTR1 polymers. LZTR1 complex dysfunction results in the accumulation of RAS GTPases, thereby provoking global pathological changes of the proteomic landscape ultimately leading to cellular hypertrophy. Furthermore, our data show that cardiomyocyte-specific MRAS degradation is mediated by LZTR1 via non-proteasomal pathways, whereas RIT1 degradation is mediated by both LZTR1-dependent and LZTR1-independent pathways. Uni- or biallelic genetic correction of the LZTR1L580P missense variant rescues the molecular and cellular disease phenotype, providing proof of concept for CRISPR-based therapies.
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•LZTR1L580P in homozygosity is causative for Noonan syndrome and hypertrophic cardiomyopathy•LZTR1L580P fosters assembly of LZTR1 polymers, resulting in complex dysfunction•Pathological LZTR1 results in impaired RAS GTPase degradation, causing cellular hypertrophy•CRISPR correction of one allele is sufficient to normalize cardiac disease phenotype
Using patient-specific and CRISPR-Cas9-corrected iPSC cardiomyocytes, Busley et al. describe an LZTR1L580P-specific disease mechanism provoking Noonan syndrome-associated cardiac hypertrophy. Mutation-induced polymerization of LZTR1 complexes results in the accumulation of RAS GTPases, leading to molecular and cellular impairments associated with cardiac hypertrophy, whereas CRISPR correction of the missense variant rescues the disease phenotype.
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". Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.
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). These are tethered to the endoplasmic reticulum, providing insights into DMV biogenesis and spatial coordination of SARS-CoV-2 replication. Live cell imaging combined with an infection sensor reveals profound remodeling of cytoskeleton elements. Pharmacological inhibition of their dynamics suppresses SARS-CoV-2 replication. We thus report insights into virus-induced cytopathic effects and provide alongside a comprehensive publicly available repository of 3D datasets of SARS-CoV-2-infected cells for download and smooth online visualization.
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•Integrative imaging approaches reveal SARS-CoV-2-induced cellular alterations•SARS-CoV-2 extensively remodels the cellular endomembrane system•Pharmacological inhibition of cytoskeleton remodeling restricts viral replication•We provide a comprehensive repository of virus-induced ultrastructural cell changes
Cortese et al. use integrative imaging techniques to generate a publicly available repository of morphological alterations induced by SARS-CoV-2 in lung cells. Accumulation of ER-derived double-membrane vesicles, the viral replication organelle, occurs concomitantly with cytoskeleton remodeling and Golgi fragmentation. Pharmacological alteration of cytoskeleton dynamics restricts viral replication and spread.
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. Visualizing neuroid cells by correlative x-ray and electron microscopy revealed secretory vesicles and cellular projections enwrapping choanocyte microvilli and cilia. Our data show a communication system that is organized around sponge digestive chambers, using conserved modules that became incorporated into the pre- and postsynapse in the nervous systems of other animals.
IMPORTANCE: School and daycare closures were enforced as measures to confine the novel coronavirus disease 2019 (COVID-19) pandemic, based on the assumption that young children may play a key role in ...severe acute respiratory coronavirus 2 (SARS-CoV-2) spread. Given the grave consequences of contact restrictions for children, a better understanding of their contribution to the COVID-19 pandemic is of great importance. OBJECTIVE: To describe the rate of SARS-CoV-2 infections and the seroprevalence of SARS-CoV-2 antibodies in children aged 1 to 10 years, compared with a corresponding parent of each child, in a population-based sample. DESIGN, SETTING, AND PARTICIPANTS: This large-scale, multicenter, cross-sectional investigation (the COVID-19 BaWü study) enrolled children aged 1 to 10 years and a corresponding parent between April 22 and May 15, 2020, in southwest Germany. EXPOSURES: Potential exposure to SARS-CoV-2. MAIN OUTCOMES AND MEASURES: The main outcomes were infection and seroprevalence of SARS-CoV-2. Participants were tested for SARS-CoV-2 RNA from nasopharyngeal swabs by reverse transcription–polymerase chain reaction and SARS-CoV-2 specific IgG antibodies in serum by enzyme-linked immunosorbent assays and immunofluorescence tests. Discordant results were clarified by electrochemiluminescence immunoassays, a second enzyme-linked immunosorbent assay, or an in-house Luminex-based assay. RESULTS: This study included 4964 participants: 2482 children (median age, 6 range, 1-10 years; 1265 boys 51.0%) and 2482 parents (median age, 40 range, 23-66 years; 615 men 24.8%). Two participants (0.04%) tested positive for SARS-CoV-2 RNA. The estimated SARS-CoV-2 seroprevalence was low in parents (1.8% 95% CI, 1.2–2.4%) and 3-fold lower in children (0.6% 95% CI, 0.3-1.0%). Among 56 families with at least 1 child or parent with seropositivity, the combination of a parent with seropositivity and a corresponding child with seronegativity was 4.3 (95% CI, 1.19-15.52) times higher than the combination of a parent who was seronegative and a corresponding child with seropositivity. We observed virus-neutralizing activity for 66 of 70 IgG-positive serum samples (94.3%). CONCLUSIONS AND RELEVANCE: In this cross-sectional study, the spread of SARS-CoV-2 infection during a period of lockdown in southwest Germany was particularly low in children aged 1 to 10 years. Accordingly, it is unlikely that children have boosted the pandemic. This SARS-CoV-2 prevalence study, which appears to be the largest focusing on children, is instructive for how ad hoc mass testing provides the basis for rational political decision-making in a pandemic.
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. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum. Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. The comprehensively annotated experimental data and pre-trained networks are provided for immediate use by the community.
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