Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression.

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets.

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards.

Neuronal migration prevents spatial competition in retinal morphogenesis.

The concomitant occurrence of tissue growth and organization is a hallmark of organismal development. This often means that proliferating and differentiating cells are found at the same time in a continuously changing tissue environment. How cells adapt to architectural changes to prevent spatial interference remains unclear.

CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets.

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.

MarrowQuant 2.0: A Digital Pathology Workflow Assisting Bone Marrow Evaluation in Experimental and Clinical Hematology.

Bone marrow (BM) cellularity assessment is a crucial step in the evaluation of BM trephine biopsies for hematologic and nonhematologic disorders. Clinical assessment is based on a semiquantitative visual estimation of the hematopoietic and adipocytic components by hematopathologists, which does not provide quantitative information on other stromal compartments. In this study, we developed and validated MarrowQuant 2.

Deep neural network automated segmentation of cellular structures in volume electron microscopy.

Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with automated segmentation of intracellular substructures in electron microscopy (ASEM), a new pipeline to train a convolutional neural network to detect structures of a wide range in size and complexity.

LAPTM5 mediates immature B cell apoptosis and B cell tolerance by regulating the WWP2-PTEN-AKT pathway.

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.

EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300 μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains.

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings.

Deep learning-enhanced light-field imaging with continuous validation.

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction.

3D FIB-SEM reconstruction of microtubule-organelle interaction in whole primary mouse β cells.

Microtubules play a major role in intracellular trafficking of vesicles in endocrine cells. Detailed knowledge of microtubule organization and their relation to other cell constituents is crucial for understanding cell function. However, their role in insulin transport and secretion is under debate.

A convolutional neural network segments yeast microscopy images with high accuracy.

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.

Practical sensorless aberration estimation for 3D microscopy with deep learning.

Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise accurate results at fast processing speeds. However, collecting ground truth microscopy data for training the network is typically very difficult or even impossible thereby limiting this approach in practice.

Rod nuclear architecture determines contrast transmission of the retina and behavioral sensitivity in mice.

Rod photoreceptors of nocturnal mammals display a striking inversion of nuclear architecture, which has been proposed as an evolutionary adaptation to dark environments. However, the nature of visual benefits and the underlying mechanisms remains unclear. It is widely assumed that improvements in nocturnal vision would depend on maximization of photon capture at the expense of image detail.

Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues.

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).

Content-aware image restoration: pushing the limits of fluorescence microscopy.

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.

Differential lateral and basal tension drive folding of Drosophila wing discs through two distinct mechanisms.

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.

The influence of body composition on exercise-associated skin temperature changes after resistance training.

Resistance exercise leads to an increase in skin temperature (T) in the area of the exercised muscle. Infrared thermography seems to be applicable to identify these primary used functional muscles with measuring T changes. The aim of the current study was to investigate the influence of body composition on T patterns after resistance exercise.

Biobeam-Multiplexed wave-optical simulations of light-sheet microscopy.

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.

Origins and specificity of auto-antibodies in Sm+ SLE patients.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease accompanied by production of autoantibodies directed to a variety of self-proteins and nucleic acids. The genetic basis of SLE is also complex with at least 40 susceptibility loci identified. This complexity suggests that there are a variety of SLE manifestations; nevertheless, SLE is treated as a single disease clinically.

Self-Antigen-Driven Thymic B Cell Class Switching Promotes T Cell Central Tolerance.

B cells are unique antigen-presenting cells because their antigen presentation machinery is closely tied to the B cell receptor. Autoreactive thymic B cells can efficiently present cognate self-antigens to mediate CD4 T cell-negative selection. However, the nature of thymocyte-thymic B cell interaction and how this interaction affects the selection of thymic B cell repertoire and, in turn, the T cell repertoire are not well understood.

Two- and three-dimensional co-culture models of soft tissue healing: pericyte-endothelial cell interaction.

The demographic change in western countries towards an older population is being shadowed by an increased appearance of chronic diseases influencing soft tissue healing in a negative manner. Although various promising therapeutic approaches are available for treating chronic wounds, no in vitro model exists that successfully allows the analysis of interacting cells and of the effect of therapeutic drugs within a wound. Granulation tissue assures wound stability, neo-angiogenesis and revascularization finally leading to functional soft tissue repair.