RESUMEN
We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, providing a method for linking 3D segmented nuclei to their surrounding cells in 3D digital organs. We found that the nuclear-to-cell volume ratio varies between different ovule tissues and during the development of a tissue. Finally, we extended the PlantSeg 3D segmentation pipeline with a proofreading tool that uses 3D segmented nuclei as seeds to correct cell segmentation errors in difficult-to-segment tissues.
Asunto(s)
Núcleo Celular , Aprendizaje Profundo , Imagenología Tridimensional , Programas Informáticos , Núcleo Celular/metabolismo , Imagenología Tridimensional/métodos , Animales , Algoritmos , Arabidopsis , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Plants produce the major part of terrestrial biomass and are long-term deposits of atmospheric carbon. This capacity is to a large extent due to radial growth of woody species - a process driven by cambium stem cells located in distinct niches of shoot and root axes. In the model species Arabidopsis thaliana, thousands of cells are produced by the cambium in radial orientation generating a complex organ anatomy enabling long-distance transport, mechanical support and protection against biotic and abiotic stressors. These complex organ dynamics make a comprehensive and unbiased analysis of radial growth challenging and asks for tools for automated quantification. Here, we combined the recently developed PlantSeg and MorphographX image analysis tools, to characterize tissue morphogenesis of the Arabidopsis hypocotyl. After sequential training of segmentation models on ovules, shoot apical meristems and adult hypocotyls using deep machine learning, followed by the training of cell type classification models, our pipeline segments complex images of transverse hypocotyl sections with high accuracy and classifies central hypocotyl cell types. By applying our pipeline on both wild type and phloem intercalated with xylem (pxy) mutants, we also show that this strategy faithfully detects major anatomical aberrations. Collectively, we conclude that our established pipeline is a powerful phenotyping tool comprehensively extracting cellular parameters and providing access to tissue topology during radial plant growth.
Asunto(s)
Proteínas de Arabidopsis , Arabidopsis , Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Desarrollo de la Planta , Arabidopsis/metabolismo , Meristema/metabolismo , Aprendizaje AutomáticoRESUMEN
Lateral root formation determines to a large extent the ability of plants to forage their environment and thus their growth. In Arabidopsis thaliana and other angiosperms, lateral root initiation requires radial cell expansion and several rounds of anticlinal cell divisions that give rise to a central core of small cells, which express different markers than the larger surrounding cells. These small central cells then switch their plane of divisions to periclinal and give rise to seemingly morphologically similar daughter cells that have different identities and establish the different cell types of the new root. Although the execution of these anticlinal and periclinal divisions is tightly regulated and essential for the correct development of the lateral root, we know little about their geometrical features. Here, we generate a four-dimensional reconstruction of the first stages of lateral root formation and analyze the geometric features of the anticlinal and periclinal divisions. We identify that the periclinal divisions of the small central cells are morphologically dissimilar and asymmetric. We show that mother cell volume is different when looking at anticlinal vs. periclinal divisions and the repeated anticlinal divisions do not lead to reduction in cell volume, although cells are shorter. Finally, we show that cells undergoing a periclinal division are characterized by a strong cell expansion. Our results indicate that cells integrate growth and division to precisely partition their volume upon division during the first two stages of lateral root formation.
Asunto(s)
Arabidopsis/anatomía & histología , Arabidopsis/crecimiento & desarrollo , Diferenciación Celular , División Celular , Proliferación Celular , Raíces de Plantas/anatomía & histología , Raíces de Plantas/crecimiento & desarrollo , Arabidopsis/genética , Variación Genética , Genotipo , Microscopía Fluorescente/métodos , Raíces de Plantas/genéticaRESUMEN
A fundamental question in biology is how morphogenesis integrates the multitude of processes that act at different scales, ranging from the molecular control of gene expression to cellular coordination in a tissue. Using machine-learning-based digital image analysis, we generated a three-dimensional atlas of ovule development in Arabidopsis thaliana, enabling the quantitative spatio-temporal analysis of cellular and gene expression patterns with cell and tissue resolution. We discovered novel morphological manifestations of ovule polarity, a new mode of cell layer formation, and previously unrecognized subepidermal cell populations that initiate ovule curvature. The data suggest an irregular cellular build-up of WUSCHEL expression in the primordium and new functions for INNER NO OUTER in restricting nucellar cell proliferation and the organization of the interior chalaza. Our work demonstrates the analytical power of a three-dimensional digital representation when studying the morphogenesis of an organ of complex architecture that eventually consists of 1900 cells.
Asunto(s)
Arabidopsis/crecimiento & desarrollo , Proliferación Celular , Flores/crecimiento & desarrollo , Regulación del Desarrollo de la Expresión Génica , Arabidopsis/genética , Flores/genética , Óvulo Vegetal/genética , Óvulo Vegetal/crecimiento & desarrolloRESUMEN
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. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.
Asunto(s)
Anticuerpos Antivirales/sangre , COVID-19/diagnóstico , Inmunoensayo , Inmunoglobulina A/sangre , Inmunoglobulina G/sangre , Inmunoglobulina M/sangre , Microscopía/métodos , SARS-CoV-2/inmunología , COVID-19/inmunología , COVID-19/virología , Prueba de COVID-19/métodos , Técnica del Anticuerpo Fluorescente , Ensayos Analíticos de Alto Rendimiento , Humanos , Procesamiento de Imagen Asistido por Computador/estadística & datos numéricos , Sueros Inmunes/química , Aprendizaje Automático , Sensibilidad y EspecificidadRESUMEN
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.
Asunto(s)
Arabidopsis/anatomía & histología , Imagenología Tridimensional/métodos , Células Vegetales , Programas Informáticos , Arabidopsis/citología , Redes Neurales de la ComputaciónRESUMEN
Patients affected by nephrogenic diabetes insipidus (NDI) can present with hypernatremic dehydration, and first-line rehydration schemes are completely different from those largely applied in usual conditions determining a mild to severe hypovolemic dehydration/shock. In reporting the case of a patient affected by NDI and presenting with severe dehydration triggered by acute pharyngotonsillitis and vomiting, we want to underline the difficulties in managing this condition. Restoring the free-water plasma amount in patients affected by NDI may not be easy, but some key points can help in the first line management of these patients: (1) hypernatremic dehydration should always be suspected; (2) even in presence of severe dehydration, skin turgor may be normal and therefore the skinfold recoll should not be considered in the dehydration assessment; (3) decreased thirst is an important red flag for dehydration; (4) if an incontinent patient with NDI appears to be dehydrated, it is important to place the urethral catheter to accurately measure urine output and to be guided in parenteral fluid administration; (5) if the intravenous route is necessary, the more appropriate fluid replenishment is 5% dextrose in water with an infusion rate that should slightly exceed the urine output; (6) the 0.9% NaCl solution (10 mL/kg) should only be used to restore the volemia in a shocked NDI patient; and (7) it could be useful to stop indomethacin administration until complete restoration of hydration status to avoid a possible worsening of a potential prerenal acute renal failure.