Effector and stem-like memory cell fates are imprinted in distinct lymph node niches directed by CXCR3 ligands.

T cells dynamically interact with multiple, distinct cellular subsets to determine effector and memory differentiation. Here, we developed a platform to quantify cell location in three dimensions to determine the spatial requirements that direct T cell fate. After viral infection, we demonstrated that CD8+ effector T cell differentiation is associated with positioning at the lymph node periphery. This was instructed by CXCR3 signaling since, in its absence, T cells are confined to the lymph node center and alternatively differentiate into stem-like memory cell precursors. By mapping the cellular sources of CXCR3 ligands, we demonstrated that CXCL9 and CXCL10 are expressed by spatially distinct dendritic and stromal cell subsets. Unlike effector cells, retention of stem-like memory precursors in the paracortex is associated with CCR7 expression. Finally, we demonstrated that T cell location can be tuned, through deficiency in CXCL10 or type I interferon signaling, to promote effector or stem-like memory fates.

Macrophages provide a transient muscle stem cell niche via NAMPT secretion.

Skeletal muscle regenerates through the activation of resident stem cells. Termed satellite cells, these normally quiescent cells are induced to proliferate by wound-derived signals1. Identifying the source and nature of these cues has been hampered by an inability to visualize the complex cell interactions that occur within the wound. Here we use muscle injury models in zebrafish to systematically capture the interactions between satellite cells and the innate immune system after injury, in real time, throughout the repair process. This analysis revealed that a specific subset of macrophages 'dwell' within the injury, establishing a transient but obligate niche for stem cell proliferation. Single-cell profiling identified proliferative signals that are secreted by dwelling macrophages, which include the cytokine nicotinamide phosphoribosyltransferase (Nampt, which is also known as visfatin or PBEF in humans). Nampt secretion from the macrophage niche is required for muscle regeneration, acting through the C-C motif chemokine receptor type 5 (Ccr5), which is expressed on muscle stem cells. This analysis shows that in addition to their ability to modulate the immune response, specific macrophage populations also provide a transient stem-cell-activating niche, directly supplying proliferation-inducing cues that govern the repair process that is mediated by muscle stem cells. This study demonstrates that macrophage-derived niche signals for muscle stem cells, such as NAMPT, can be applied as new therapeutic modalities for skeletal muscle injury and disease.

Orphan nuclear receptors-induced ALT-associated PML bodies are targets for ALT inhibition.

Orphan nuclear receptors (NRs), such as COUP-TF1, COUP-TF2, EAR2, TR2 and TR4, are implicated in telomerase-negative cancers that maintain their telomeres through the alternative lengthening of telomeres (ALT) mechanism. However, how telomere association of orphan NRs is involved in ALT activation remains unclear. Here, we demonstrate that telomeric tethering of orphan NRs in human fibroblasts initiates formation of ALT-associated PML bodies (APBs) and features of ALT activity, including ALT telomere DNA synthesis, telomere sister chromatid exchange, and telomeric C-circle generation, suggesting de novo ALT induction. Overexpression of orphan NRs exacerbates ALT phenotypes in ALT cells, while their depletion limits ALT. Orphan NRs initiate ALT via the zinc finger protein 827, suggesting the involvement of chromatin structure alterations for ALT activation. Furthermore, we found that orphan NRs and deficiency of the ALT suppressor ATRX-DAXX complex operate in concert to promote ALT activation. Moreover, PML depletion by gene knockout or arsenic trioxide treatment inhibited ALT induction in fibroblasts and ALT cancer cells, suggesting that APB formation underlies the orphan NR-induced ALT activation. Importantly, arsenic trioxide administration abolished APB formation and features of ALT activity in ALT cancer cell line-derived mouse xenografts, suggesting its potential for further therapeutic development to treat ALT cancers.

Transient demyelination causes long-term cognitive impairment, myelin alteration and network synchrony defects.

In the adult brain, activity-dependent myelin plasticity is required for proper learning and memory consolidation. Myelin loss, alteration, or even subtle structural modifications can therefore compromise the network activity, leading to functional impairment. In multiple sclerosis, spontaneous myelin repair process is possible, but it is heterogeneous among patients, sometimes leading to functional recovery, often more visible at the motor level than at the cognitive level. In cuprizone-treated mouse model, massive brain demyelination is followed by spontaneous and robust remyelination. However, reformed myelin, although functional, may not exhibit the same morphological characteristics as developmental myelin, which can have an impact on the activity of neural networks. In this context, we used the cuprizone-treated mouse model to analyze the structural, functional, and cognitive long-term effects of transient demyelination. Our results show that an episode of demyelination induces despite remyelination long-term cognitive impairment, such as deficits in spatial working memory, social memory, cognitive flexibility, and hyperactivity. These deficits were associated with a reduction in myelin content in the medial prefrontal cortex (mPFC) and hippocampus (HPC), as well as structural myelin modifications, suggesting that the remyelination process may be imperfect in these structures. In vivo electrophysiological recordings showed that the demyelination episode altered the synchronization of HPC-mPFC activity, which is crucial for memory processes. Altogether, our data indicate that the myelin repair process following transient demyelination does not allow the complete recovery of the initial myelin properties in cortical structures. These subtle modifications alter network features, leading to prolonged cognitive deficits in mice.

EM-stellar: benchmarking deep learning for electron microscopy image segmentation.

MOTIVATION: The inherent low contrast of electron microscopy (EM) datasets presents a significant challenge for rapid segmentation of cellular ultrastructures from EM data. This challenge is particularly prominent when working with high-resolution big-datasets that are now acquired using electron tomography and serial block-face imaging techniques. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. RESULTS: We present EM-stellar, a platform that is hosted on Google Colab that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets. Using EM-stellar we show that the performance of any DL method is dependent on the properties of the images being segmented. It also follows that no single DL method performs consistently across all performance evaluation metrics. AVAILABILITY AND IMPLEMENTATION: EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https://github.com/cellsmb/em-stellar). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Shifting meiotic to mitotic spindle assembly in oocytes disrupts chromosome alignment.

Mitotic spindles assemble from two centrosomes, which are major microtubule-organizing centers (MTOCs) that contain centrioles. Meiotic spindles in oocytes, however, lack centrioles. In mouse oocytes, spindle microtubules are nucleated from multiple acentriolar MTOCs that are sorted and clustered prior to completion of spindle assembly in an "inside-out" mechanism, ending with establishment of the poles. We used HSET (kinesin-14) as a tool to shift meiotic spindle assembly toward a mitotic "outside-in" mode and analyzed the consequences on the fidelity of the division. We show that HSET levels must be tightly gated in meiosis I and that even slight overexpression of HSET forces spindle morphogenesis to become more mitotic-like: rapid spindle bipolarization and pole assembly coupled with focused poles. The unusual length of meiosis I is not sufficient to correct these early spindle morphogenesis defects, resulting in severe chromosome alignment abnormalities. Thus, the unique "inside-out" mechanism of meiotic spindle assembly is essential to prevent chromosomal misalignment and production of aneuploidy gametes.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making.

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening.

DiAna, an ImageJ tool for object-based 3D co-localization and distance analysis.

We present a new plugin for ImageJ called DiAna, for Distance Analysis, which comes with a user-friendly interface. DiAna proposes robust and accurate 3D segmentation for object extraction. The plugin performs automated object-based co-localization and distance analysis. DiAna offers an in-depth analysis of co-localization between objects and retrieves 3D measurements including co-localizing volumes and surfaces of contact. It also computes the distribution of distances between objects in 3D. With DiAna, we furthermore introduce an original method, which allows for estimating the statistical significance of object co-localization. DiAna offers a complete and intuitive 3D image analysis tool for biologists.

OpenSegSPIM: a user-friendly segmentation tool for SPIM data.

UNLABELLED: OpenSegSPIM is an open access and user friendly 3D automatic quantitative analysis tool for Single Plane Illumination Microscopy data. The software is designed to extract, in a user-friendly way, quantitative relevant information from SPIM image stacks, such as the number of nuclei or cells. It provides quantitative measurement (volume, sphericity, distance, intensity) on Light Sheet Fluorescent Microscopy images. AVAILABILITY AND IMPLEMENTATION: freely available from http://www.opensegspim.weebly.com Source code and binaries under BSD License. CONTACT: lgole@imcb.a-star.edu.sg or wmyu@imcb.a-star.edu.sg or sohail.ahmed@imb.a-star.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp.

In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform).

The architecture of Rhodobacter sphaeroides chromatophores.

The chromatophores of Rhodobacter (Rb.) sphaeroides represent a minimal bio-energetic system, which efficiently converts light energy into usable chemical energy. Despite extensive studies, several issues pertaining to the morphology and molecular architecture of this elemental energy conversion system remain controversial or unknown. To tackle these issues, we combined electron microscope tomography, immuno-electron microscopy and atomic force microscopy. We found that the intracellular Rb. sphaeroides chromatophores form a continuous reticulum rather than existing as discrete vesicles. We also found that the cytochrome bc1 complex localizes to fragile chromatophore regions, which most likely constitute the tubular structures that interconnect the vesicles in the reticulum. In contrast, the peripheral light-harvesting complex 2 (LH2) is preferentially hexagonally packed within the convex vesicular regions of the membrane network. Based on these observations, we propose that the bc1 complexes are in the inter-vesicular regions and surrounded by reaction center (RC) core complexes, which in turn are bounded by arrays of peripheral antenna complexes. This arrangement affords rapid cycling of electrons between the core and bc1 complexes while maintaining efficient excitation energy transfer from LH2 domains to the RCs.

A generic classification-based method for segmentation of nuclei in 3D images of early embryos.

BACKGROUND: Studying how individual cells spatially and temporally organize within the embryo is a fundamental issue in modern developmental biology to better understand the first stages of embryogenesis. In order to perform high-throughput analyses in three-dimensional microscopic images, it is essential to be able to automatically segment, classify and track cell nuclei. Many 3D/4D segmentation and tracking algorithms have been reported in the literature. Most of them are specific to particular models or acquisition systems and often require the fine tuning of parameters. RESULTS: We present a new automatic algorithm to segment and simultaneously classify cell nuclei in 3D/4D images. Segmentation relies on training samples that are interactively provided by the user and on an iterative thresholding process. This algorithm can correctly segment nuclei even when they are touching, and remains effective under temporal and spatial intensity variations. The segmentation is coupled to a classification of nuclei according to cell cycle phases, allowing biologists to quantify the effect of genetic perturbations and drug treatments. Robust 3D geometrical shape descriptors are used as training features for classification. Segmentation and classification results of three complete datasets are presented. In our working dataset of the Caenorhabditis elegans embryo, only 21 nuclei out of 3,585 were not detected, the overall F-score for segmentation reached 0.99, and more than 95% of the nuclei were classified in the correct cell cycle phase. No merging of nuclei was found. CONCLUSION: We developed a novel generic algorithm for segmentation and classification in 3D images. The method, referred to as Adaptive Generic Iterative Thresholding Algorithm (AGITA), is freely available as an ImageJ plug-in.

Multiple binding of repressed mRNAs by the P-body protein Rck/p54.

Translational repression is achieved by protein complexes that typically bind 3' UTR mRNA motifs and interfere with the formation of the cap-dependent initiation complex, resulting in mRNPs with a closed-loop conformation. We demonstrate here that the human DEAD-box protein Rck/p54, which is a component of such complexes and central to P-body assembly, is in considerable molecular excess with respect to cellular mRNAs and enriched to a concentration of 0.5 mM in P-bodies, where it is organized in clusters. Accordingly, multiple binding of p54 proteins along mRNA molecules was detected in vivo. Consistently, the purified protein bound RNA with no sequence specificity and high nanomolar affinity. Moreover, bound RNA molecules had a relaxed conformation. While RNA binding was ATP independent, relaxing of bound RNA was dependent on ATP, though not on its hydrolysis. We propose that Rck/p54 recruitment by sequence-specific translational repressors leads to further binding of Rck/p54 along mRNA molecules, resulting in their masking, unwinding, and ultimately recruitment to P-bodies. Rck/p54 proteins located at the 5' extremity of mRNA can then recruit the decapping complex, thus coupling translational repression and mRNA degradation.

TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization.

MOTIVATION: The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images. RESULTS: Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization. AVAILABILITY: TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence. CONTACT: thomas.boudier@snv.jussieu.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spot Spine, a freely available ImageJ plugin for 3D detection and morphological analysis of dendritic spines.

Background: Dendritic spines are tiny protrusions found along the dendrites of neurons, and their number is a measure of the density of synaptic connections. Altered density and morphology is observed in several pathologies, and spine formation as well as morphological changes correlate with learning and memory. The detection of spines in microscopy images and the analysis of their morphology is therefore a prerequisite for many studies. We have developed a new open-source, freely available, plugin for ImageJ/FIJI, called Spot Spine, that allows detection and morphological measurements of spines in three dimensional images. Method: Local maxima are detected in spine heads, and the intensity distribution around the local maximum is computed to perform the segmentation of each spine head. Spine necks are then traced from the spine head to the dendrite. Several parameters can be set to optimize detection and segmentation, and manual correction gives further control over the result of the process. Results: The plugin allows the analysis of images of dendrites obtained with various labeling and imaging methods. Quantitative measurements are retrieved including spine head volume and surface, and neck length. Conclusion: The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.

Coupled mechanical mapping and interference contrast microscopy reveal viscoelastic and adhesion hallmarks of monocyte differentiation into macrophages.

Monocytes activated by pro-inflammatory signals adhere to the vascular endothelium and migrate from the bloodstream to the tissue ultimately differentiating into macrophages. Cell mechanics and adhesion play a crucial role in macrophage functions during this inflammatory process. However, how monocytes change their adhesion and mechanical properties upon differentiation into macrophages is still not well understood. In this work, we used various tools to quantify the morphology, adhesion, and viscoelasticity of monocytes and differentiatted macrophages. Combination of atomic force microscopy (AFM) high resolution viscoelastic mapping with interference contrast microscopy (ICM) at the single-cell level revealed viscoelasticity and adhesion hallmarks during monocyte differentiation into macrophages. Quantitative holographic tomography imaging revealed a dramatic increase in cell volume and surface area during monocyte differentiation and the emergence of round and spread macrophage subpopulations. AFM viscoelastic mapping showed important stiffening (increase of the apparent Young's modulus, E0) and solidification (decrease of cell fluidity, ß) on differentiated cells that correlated with increased adhesion area. These changes were enhanced in macrophages with a spread phenotype. Remarkably, when adhesion was perturbed, differentiated macrophages remained stiffer and more solid-like than monocytes, suggesting a permanent reorganization of the cytoskeleton. We speculate that the stiffer and more solid-like microvilli and lamellipodia might help macrophages to minimize energy dissipation during mechanosensitive activities. Thus, our results revealed viscoelastic and adhesion hallmarks of monocyte differentiation that may be important for biological function.

Software for drift compensation, particle tracking and particle analysis of high-speed atomic force microscopy image series.

Atomic force microscopy (AFM) image acquisition is performed by raster-scanning a faint tip with respect to the sample by the use of a piezoelectric stage that is guided by a feedback system. This process implies that the resulting images feature particularities that distinguish them from images acquired by other techniques, such as the drift of the piezoelectric elements, the unequal image contrast along the fast- and the slow-scan axes, the physical contact between the tip of nondefinable geometry and the sample, and the feedback parameters. Recently, high-speed AFM (HS-AFM) has been introduced, which allows image acquisition about three orders of magnitude faster (500-100 ms frame rate) than conventional AFM (500 s to 100 s frame rate). HS-AFM produces image sequences, large data sets, which report biological sample dynamics. To analyze these movies, we have developed a software package that (i) adjusts individual scan lines and images to a common contrast and z-scale, (ii) filters specifically those scan lines where increased or insufficient force was applied, (iii) corrects for piezo-scanner drift, (iv) defines particle localization and angular orientation, and (v) performs particle tracking to analyze the lateral and rotation displacement of single molecules.

CardioVinci: building blocks for virtual cardiac cells using deep learning.

Advances in electron microscopy (EM) such as electron tomography and focused ion-beam scanning electron microscopy provide unprecedented, three-dimensional views of cardiac ultrastructures within sample volumes ranging from hundreds of nanometres to hundreds of micrometres. The datasets from these samples are typically large, with file sizes ranging from gigabytes to terabytes and the number of image slices within the three-dimensional stack in the hundreds. A significant bottleneck with these large datasets is the time taken to extract and statistically analyse three-dimensional changes in cardiac ultrastructures. This is because of the inherently low contrast and the significant amount of structural detail that is present in EM images. These datasets often require manual annotation, which needs substantial person-hours and may result in only partial segmentation that makes quantitative analysis of the three-dimensional volumes infeasible. We present CardioVinci, a deep learning workflow to automatically segment and statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils and Z-discs with minimal manual annotation. The workflow encodes a probabilistic model of the three-dimensional cardiomyocyte using a generative adversarial network. This generative model can be used to create new models of cardiomyocyte architecture that reflect variations in morphologies and cell architecture found in EM datasets. This article is part of the theme issue 'The cardiomyocyte: new revelations on the interplay between architecture and function in growth, health, and disease'.

Direct image-based correlative microscopy technique for coupling identification and structural investigation of bacterial symbionts associated with metazoans.

Coupling prokaryote identification with ultrastructural investigation of bacterial communities has proven difficult in environmental samples. Prokaryotes can be identified by using specific probes and fluorescence in situ hybridization (FISH), but resolution achieved by light microscopes does not allow ultrastructural investigation. In the case of symbioses involving bacteria associated with metazoan tissues, FISH-based studies often indicate the co-occurrence of several bacterial types within a single host species. The ultrastructure is then relevant to address host and bacterial morphology and the intra- or extracellular localization of symbionts. A simple protocol for correlative light and electron microscopy (CLEM) is presented here which allows FISH-based identification of specific 16S rRNA phylotypes and transmission electron microscopy to be performed on a same sample. Image analysis tools are provided to superimpose images obtained and generate overlays. This procedure has been applied to two symbiont-bearing metazoans, namely, aphids and deep-sea mussels. The FISH protocol was modified to take into account constraints associated with the use of electron microscopy grids, and intense and specific signals were obtained. FISH signals were successfully overlaid with bacterial morphotypes in aphids. We thus used the method to address the question of symbiont morphology and localization in a deep-sea mussel. Signals from a type I methanotroph-related phylotype were associated with morphotypes displaying the stacked internal membranes typical for this group and three-dimensional electron tomography was performed, confirming for the first time the correspondence between morphology and phylotype. CLEM is thus feasible and reliable and could emerge as a potent tool for the study of prokaryotic communities.

Electron tomography of early melanosomes: implications for melanogenesis and the generation of fibrillar amyloid sheets.

Melanosomes are lysosome-related organelles (LROs) in which melanins are synthesized and stored. Early stage melanosomes are characterized morphologically by intralumenal fibrils upon which melanins are deposited in later stages. The integral membrane protein Pmel17 is a component of the fibrils, can nucleate fibril formation in the absence of other pigment cell-specific proteins, and forms amyloid-like fibrils in vitro. Before fibril formation Pmel17 traffics through multivesicular endosomal compartments, but how these compartments participate in downstream events leading to fibril formation is not fully known. By using high-pressure freezing of MNT-1 melanoma cells and freeze substitution to optimize ultrastructural preservation followed by double tilt 3D electron tomography, we show that the amyloid-like fibrils begin to form in multivesicular compartments, where they radiate from the luminal side of intralumenal membrane vesicles. The fibrils in fully formed stage II premelanosomes organize into sheet-like arrays and exclude the remaining intralumenal vesicles, which are smaller and often in continuity with the limiting membrane. These observations indicate that premelanosome fibrils form in association with intralumenal endosomal membranes. We suggest that similar processes regulate amyloid formation in pathological models.