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. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation.

Many proteins contain disordered regions of low-sequence complexity, which cause aging-associated diseases because they are prone to aggregate. Here, we study FUS, a prion-like protein containing intrinsically disordered domains associated with the neurodegenerative disease ALS. We show that, in cells, FUS forms liquid compartments at sites of DNA damage and in the cytoplasm upon stress. We confirm this by reconstituting liquid FUS compartments in vitro. Using an in vitro "aging" experiment, we demonstrate that liquid droplets of FUS protein convert with time from a liquid to an aggregated state, and this conversion is accelerated by patient-derived mutations. We conclude that the physiological role of FUS requires forming dynamic liquid-like compartments. We propose that liquid-like compartments carry the trade-off between functionality and risk of aggregation and that aberrant phase transitions within liquid-like compartments lie at the heart of ALS and, presumably, other age-related diseases.

Neuronal migration prevents spatial competition in retinal morphogenesis.

The concomitant occurrence of tissue growth and organization is a hallmark of organismal development1-3. 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. Here, to understand how cell movements that are key for growth and organization are orchestrated, we study the emergence of photoreceptor neurons that occur during the peak of retinal growth, using zebrafish, human tissue and human organoids. Quantitative imaging reveals that successful retinal morphogenesis depends on the active bidirectional translocation of photoreceptors, leading to a transient transfer of the entire cell population away from the apical proliferative zone. This pattern of migration is driven by cytoskeletal machineries that differ depending on the direction: microtubules are exclusively required for basal translocation, whereas actomyosin is involved in apical movement. Blocking the basal translocation of photoreceptors induces apical congestion, which hampers the apical divisions of progenitor cells and leads to secondary defects in lamination. Thus, photoreceptor migration is crucial to prevent competition for space, and to allow concurrent tissue growth and lamination. This shows that neuronal migration, in addition to its canonical role in cell positioning4, can be involved in coordinating morphogenesis.

Event-driven acquisition for content-enriched microscopy.

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

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. 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.

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. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.

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. 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 F1-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.

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 2.0, an efficient, user-friendly digital hematopathology workflow integrated within QuPath software, which serves as BM quantifier for 5 mutually exclusive compartments (bone, hematopoietic, adipocytic, and interstitial/microvasculature areas and other) and derives the cellularity of human BM trephine biopsies. Instance segmentation of individual adipocytes is realized through the adaptation of the machine-learning-based algorithm StarDist. We calculated BM compartments and adipocyte size distributions of hematoxylin and eosin images obtained from 250 bone specimens, from control subjects and patients with acute myeloid leukemia or myelodysplastic syndrome, at diagnosis and follow-up, and measured the agreement of cellularity estimates by MarrowQuant 2.0 against visual scores from 4 hematopathologists. The algorithm was capable of robust BM compartment segmentation with an average mask accuracy of 86%, maximal for bone (99%), hematopoietic (92%), and adipocyte (98%) areas. MarrowQuant 2.0 cellularity score and hematopathologist estimations were highly correlated (R2 = 0.92-0.98, intraclass correlation coefficient [ICC] = 0.98; interobserver ICC = 0.96). BM compartment segmentation quantitatively confirmed the reciprocity of the hematopoietic and adipocytic compartments. MarrowQuant 2.0 performance was additionally tested for cellularity assessment of specimens prospectively collected from clinical routine diagnosis. After special consideration for the choice of the cellularity equation in specimens with expanded stroma, performance was similar in this setting (R2 = 0.86, n = 42). Thus, we conclude that these validation experiments establish MarrowQuant 2.0 as a reliable tool for BM cellularity assessment. We expect this workflow will serve as a clinical research tool to explore novel biomarkers related to BM stromal components and may contribute to further validation of future digitalized diagnostic hematopathology workstreams.

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. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .

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. 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.

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. Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images. We validate our approach on simulated and experimental datasets acquired with two different microscopy modalities and also compare the results to non-learned methods. Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role. Finally, we make our implementation freely available as open source software in Python.

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. One unique SLE target is the Smith antigen (Sm), a nuclear ribonucleoprotein complex. Sm response occurs in 25% of patients with SLE. To simplify analysis of the disease and its associated autoantibody repertoire, we focused on this subset [referred to here as "Sm positive", Sm+]. We analyzed the memory B cell repertoire and identified a V region, Vκ4-1, which was significantly overrepresented in the Sm+ SLE subset. Antibodies that express Vκ4-1 are enriched in antinuclear (ANA) positive specificities and often associated with speckled ANA pattern that is a characteristic of Sm binding. In healthy individuals Vκ4-1 B cells are enriched in the unswitched memory population. Unswitched memory B cells resemble mouse marginal zone B cells and this population is decreased in all SLE patients. Moreover, we found a similar decrease in healthy African American donors. African Americans have a significantly higher prevalence of SLE compared to Caucasians. Thus, reduced unswitched memory B cell compartment may represent a new susceptibility marker for SLE.

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

Resistance exercise leads to an increase in skin temperature (Tskin) in the area of the exercised muscle. Infrared thermography seems to be applicable to identify these primary used functional muscles with measuring Tskin changes. The aim of the current study was to investigate the influence of body composition on Tskin patterns after resistance exercise. 38 male subjects (19-32 years, BMI 20.4-55.2 kg/m2) participated. Body fat percentage and biceps skinfold thickness were calculated. The subjects were divided into two groups: lean group (LG) with body fat percentage < 25%, obese group (OG) with body fat percentage ≥ 25%. All participants completed three sets with ten repetitions of unilateral biceps curl at 50% of the one repetition maximum. To represent exercise-induced changes of Tskin to rest (Trest), the algebraic difference of each time point to Trest was calculated. The resulting delta values (∆) are as follows: immediately after the first, second, and third set (∆Tset1,∆Tset2,∆Tset3), and at 1,2,3,4,5,6,7,8,9,10,15,20,25,30 min after the third set (∆T1-∆T30). The maximum positive difference to Trest was defined as ∆Tmax, and the time to reach ∆Tmax was defined as Time to ∆Tmax. LG and OG differed significantly at Trest (32.8 ±â€¯0.9 vs. 31.1 ±â€¯1.4 °C), ∆Tmax (1.9 ±â€¯0.4 vs. 0.9 ±â€¯0.8 °C), Time to ∆Tmax (4.5 ±â€¯2.0 vs. 17.6 ±â€¯10.2 min) and at ∆Tset2 to ∆T15 (p < 0.005). Correlations between body composition (BMI, body fat percentage, biceps skinfold thickness) and Trest, ∆Tset2, ∆Tset3, ∆Tmax (-0.47 

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. As one of the first steps in developing a model for human granulation tissue, we examined microvascular endothelial cells and pericytes in conventional 2D and in 3D spheroid co-cultures. We determined which parameters could be used in a standardized manner and whether the cultures were responsive to hypoxia and to erythropoietin supplementation. The read-out parameters of cell migration, cell density, rate of apoptotic cells, spatial cell distribution in the spheroid and spheroid volume were shown to be excellent analytic measures. In addition, quantification of hypoxia-related genes identified a total of 13 genes that were up-regulated in spheroids after hypoxia. As these parameters delivered reliable results in the present approach and as the general morphological distribution of pericytes and endothelial cells within the spheroid occurred in a typical manner, we believe that this basic in vitro model will serve for the future study of diverse aspects of soft tissue healing.

Antibodies that bind complex glycosaminoglycans accumulate in the Golgi.

Light (L) chains that edit anti-DNA heavy (H) chains rescue B-cell development by suppressing DNA binding. However, exceptional editor L chains allow B cells to reach splenic compartments even though their B-cell receptors remain autoreactive. Such incompletely edited B cells express multireactive antibodies that accumulate in the Golgi and are released as insoluble, amyloid-like immune complexes. Here, we examine examples of incomplete editing from the analysis of variable to joining (VJ) gene junction of the variable (Vλx) editor L chain. When paired with the anti-DNA heavy chain, VH56R, the Vλx variants yield antibodies with differing specificities, including glycosaminoglycan reactivity. Our results implicate these specificities in the evasion of receptor editing through intracellular sequestration of IgM and the release of insoluble IgM complexes. Our findings can be extrapolated to human L chains and have implications for understanding a latent component of the Ig repertoire that could exert pathogenic and protective functions.

Apoptotic marginal zone deletion of anti-Sm/ribonucleoprotein B cells.

CD40L is excessively produced in both human and murine lupus and plays a role in lupus pathogenesis. To address how excess CD40L induces autoantibody production, we crossed CD40L-transgenic mice with the anti-DNA H-chain transgenic mouse lines 3H9 and 56R, well-characterized models for studying B-cell tolerance to nuclear antigens. Excess CD40L did not induce autoantibody production in 3H9 mice in which anergy maintains self-tolerance, nor did it perturb central tolerance, including deletion and receptor editing, of anti-DNA B cells in 56R mice. In contrast, CD40L/56R mice restored a large number of marginal zone (MZ) B cells reactive to Sm/ribonucleoprotein (RNP) and produced autoantibody, whereas these B cells were deleted by apoptosis in MZ of 56R mice. Thus, excess CD40L efficiently blocked tolerance of Sm/RNP-reactive MZ B cells, leading to production of anti-Sm/RNP antibody implicated in the pathogenesis of lupus. These results suggest that self-reactive B cells such as anti-Sm/RNP B cells, which somehow escape tolerance in the bone marrow and migrate to MZ, are tolerized by apoptotic deletion in MZ and that a break in this tolerance may play a role in the pathogenesis of lupus.

Alternative mechanisms of receptor editing in autoreactive B cells.

Pathogenic anti-DNA antibodies expressed in systemic lupus erythematosis bind DNA mainly through electrostatic interactions between the positively charged Arg residues of the antibody complementarity determining region (CDR) and the negatively charged phosphate groups of DNA. The importance of Arg in CDR3 for DNA binding has been shown in mice with transgenes coding for anti-DNA V(H) regions; there is also a close correlation between arginines in CDR3 of antibodies and DNA binding. Codons for Arg can readily be formed by V(D)J rearrangement; thereby, antibodies that bind DNA are part of the preimmune repertoire. Anti-DNAs in healthy mice are regulated by receptor editing, a mechanism that replaces κ light (L) chains compatible with DNA binding with κ L chains that harbor aspartic residues. This negatively charged amino acid is thought to neutralize Arg sites in the V(H). Editing by replacement is allowed at the κ locus, because the rearranged VJ is nested between unrearranged Vs and Js. However, neither λ nor heavy (H) chain loci are organized so as to allow such second rearrangements. In this study, we analyze regulation of anti-DNA H chains in mice that lack the κ locus, κ-/κ- mice. These mice show that the endogenous preimmune repertoire does indeed include a high frequency of antibodies with Arg in their CDR3s (putative anti-DNAs) and they are associated mainly with the editor L chain λx. The editing mechanisms in the case of λ-expressing B cells include L chain allelic inclusion and V(H) replacement.

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. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.