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Fluorescence microscopy is a key driver of discoveries in the life sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how content-aware image restoration based on deep learning extends the range of biological phenomena observable by microscopy. We demonstrate on eight concrete examples how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how near isotropic resolution can be achieved with up to tenfold under-sampling along the axial direction, and how tubular and granular structures smaller than the diffraction limit can be resolved at 20-times-higher frame rates compared to state-of-the-art methods. All developed image restoration methods are freely available as open source software in Python, FIJI, and KNIME.

Epithelial folding transforms simple sheets of cells into complex three-dimensional tissues and organs during animal development. Epithelial folding has mainly been attributed to mechanical forces generated by an apically localized actomyosin network, however, contributions of forces generated at basal and lateral cell surfaces remain largely unknown. Here we show that a local decrease of basal tension and an increased lateral tension, but not apical constriction, drive the formation of two neighboring folds in developing Drosophila wing imaginal discs. Spatially defined reduction of extracellular matrix density results in local decrease of basal tension in the first fold; fluctuations in F-actin lead to increased lateral tension in the second fold. Simulations using a 3D vertex model show that the two distinct mechanisms can drive epithelial folding. Our combination of lateral and basal tension measurements with a mechanical tissue model reveals how simple modulations of surface and edge tension drive complex three-dimensional morphological changes. Epithelial folding has mainly been linked to forces acting in the apical actomyosin network of cells. Here, the authors show using live imaging that two distinct mechanisms, changes in basal surface tension and changes in lateral surface tension, drive the formation of two folds in the Drosophila wing disc.

Elimination of autoreactive developing B cells is an important mechanism to prevent autoantibody production. However, how B cell receptor (BCR) signaling triggers apoptosis of immature B cells remains poorly understood.We show that BCR stimulation up-regulates the expression of the lysosomal-associated transmembrane protein 5 (LAPTM5), which in turn triggers apoptosis of immature B cells through two pathways. LAPTM5 causes BCR internalization, resulting in decreased phosphorylation of SYK and ERK. In addition, LAPTM5 targets the E3 ubiquitin ligase WWP2 for lysosomal degradation, resulting in the accumulation of its substrate PTEN. Elevated PTEN levels suppress AKT phosphorylation, leading to increased FOXO1 expression and up-regulation of the cell cycle inhibitor p27Kip1 and the proapoptotic molecule BIM. In vivo, LAPTM5 is involved in the elimination of autoreactive B cells and its deficiency exacerbates autoantibody production. Our results reveal a previously unidentified mechanism that contributes to immature B cell apoptosis and B cell tolerance.

The identification of cell borders (‘segmentation’) in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae , current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application ( www.quantsysbio.com/data-and-software ) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually. Current cell segmentation methods for Saccharomyces cerevisiae face challenges under a variety of standard experimental and imaging conditions. Here the authors develop a convolutional neural network for accurate, label-free cell segmentation.

Background High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for analysing centriole function in health and disease. Centriole detection in human tissue culture cells has been achieved typically by manual determination of organelle number per cell. However, manual cell scoring of centrioles has a low throughput and is not reproducible. Published semi-automated methods tally the centrosome surrounding centrioles and not centrioles themselves. Furthermore, such methods rely on hard-coded parameters or require a multichannel input for cross-correlation. Therefore, there is a need for developing an efficient and versatile pipeline for the automatic detection of centrioles in single channel immunofluorescence datasets. Results We developed a deep-learning pipeline termed CenFind that automatically scores cells for centriole numbers in immunofluorescence images of human cells. CenFind relies on the multi-scale convolution neural network SpotNet , which allows the accurate detection of sparse and minute foci in high resolution images. We built a dataset using different experimental settings and used it to train the model and evaluate existing detection methods. The resulting average F 1 -score achieved by  CenFind is > 90% across the test set, demonstrating the robustness of the pipeline. Moreover, using the StarDist -based nucleus detector, we link the centrioles and procentrioles detected with CenFind to the cell containing them, overall enabling automatic scoring of centriole numbers per cell. Conclusions Efficient, accurate, channel-intrinsic and reproducible detection of centrioles is an important unmet need in the field. Existing methods are either not discriminative enough or focus on a fixed multi-channel input. To fill this methodological gap, we developed CenFind , a command line interface pipeline that automates cell scoring of centrioles, thereby enabling channel-intrinsic, accurate and reproducible detection across experimental modalities. Moreover, the modular nature of CenFind enables its integration in other pipelines. Overall, we anticipate CenFind to prove critical for accelerating discoveries in the field.

In rectal cancer, there is a need for improved pretreatment biomarkers applicable to biopsies. Tumor budding (TB) is a histological feature used in colon cancer and, due to its link to epithelial-mesenchymal transition (EMT), is hypothesized to be a potential marker for therapy resistance. As EMT-related processes are also seen in other morphological features beyond TB, we investigated epithelial marker downregulation in tumor tissue, as well as morphological features such as tumor cluster size and finger-like projections. We therefore leveraged five colon cancer images to establish a hyperplex immunofluorescence workflow and a validation cohort consisting of rectal cancer pre-treatment biopsies. We built a custom image analysis pipeline to detect and segment tumor buds and other morphological features and correlated them with molecular expression intensities. We found correlations of epithelial marker downregulation and morphological transition states, both at the invasion front and at the tumor center. We furthermore observed a link between morpho-molecular transitions of nuclear CDX2 expression and tumor cluster size, which in turn informs a novel biomarker. Finally, quantification of these CDX2-based morpho-molecular transition states in rectal biopsies showed that downregulation of CDX2 expression in relation to tumor cluster size is associated with worse disease-free survival.

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to trade-offs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase-contrast with the functional specificity of fluorescence. We develop surveillance for events of interest in label-free microscopy using dynamics-informed neural networks that trigger smart fluorescence acquisitions upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging. Stepp and colleagues present hybrid-EDA, an event-driven acquisition (EDA) that enables gentle investigation of rare mitochondrial events. This approach combines continuous, low-phototoxicity phase-contrast surveillance with event-triggered fluorescence imaging, powered by dynamics-aware machine-learning event detection.

Sample-induced image-degradation remains an intricate wave-optical problem in light-sheet microscopy. Here we present biobeam, an open-source software package that enables simulation of operational light-sheet microscopes by combining data from 105-106 multiplexed and GPU-accelerated point-spread-function calculations. The wave-optical nature of these simulations leads to the faithful reproduction of spatially varying aberrations, diffraction artifacts, geometric image distortions, adaptive optics, and emergent wave-optical phenomena, and renders image-formation in light-sheet microscopy computationally tractable.

The human epidermal growth factor receptor 2 (HER2) gene amplification status is a crucial marker for evaluating clinical therapies of breast or gastric cancer. We propose a deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two RetinaNet-based object localization networks which are trained (1) to detect and classify interphase nuclei into distinct classes normal , low-grade and high-grade and (2) to detect and classify FISH signals into distinct classes HER2 or centromere of chromosome 17 (CEN17) . By independently classifying each nucleus twice, the two-step pipeline provides both robustness and interpretability for the automated detection of the HER2 amplification status. The accuracy of our deep learning-based pipeline is on par with that of three pathologists and a set of 57 validation images containing several hundreds of nuclei are accurately classified. The automatic pipeline is a first step towards assisting pathologists in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective studies, and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.