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.

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.

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'.

The site of breast cancer metastases dictates their clonal composition and reversible transcriptomic profile.

Intratumoral heterogeneity is a driver of breast cancer progression, but the nature of the clonal interactive network involved in this process remains unclear. Here, we optimized the use of optical barcoding to visualize and characterize 31 cancer subclones in vivo. By mapping the clonal composition of thousands of metastases in two clinically relevant sites, the lungs and liver, we found that metastases were highly polyclonal in lungs but not in the liver. Furthermore, the transcriptome of the subclones varied according to their metastatic niche. We also identified a reversible niche-driven signature that was conserved in lung and liver metastases collected during patient autopsies. Among this signature, we found that the tumor necrosis factor-α pathway was up-regulated in lung compared to liver metastases, and inhibition of this pathway affected metastasis diversity. These results highlight that the cellular and molecular heterogeneity observed in metastases is largely dictated by the tumor microenvironment.

Chromosomes distribute randomly to, but not within, human neutrophil nuclear lobes.

The proximity pattern and radial distribution of chromosome territories within spherical nuclei are random and non-random, respectively. Whether this distribution pattern is conserved in the partitioned or lobed nuclei of polymorphonuclear cells is unclear. Here we use chromosome paint technology to examine the chromosome territories of all 46 chromosomes in hundreds of single human neutrophils - an abundant and famously polymorphonuclear immune cell. By comparing the distribution of chromosomes to randomly shuffled controls and validating with orthogonal chromosome conformation capture technology, we show for the first time that human chromosomes randomly distribute to neutrophil nuclear lobes, while maintaining a non-random radial distribution within these lobes. Furthermore, we demonstrate that chromosome length correlates with three-dimensional volume not only in neutrophils but other human immune cells. This work demonstrates that chromosomes are largely passive passengers during the neutrophil lobing process but are able to subsequently maintain their macro-level organization within lobes.

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.

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.

An Erg-driven transcriptional program controls B cell lymphopoiesis.

B lymphoid development is initiated by the differentiation of hematopoietic stem cells into lineage committed progenitors, ultimately generating mature B cells. This highly regulated process generates clonal immunological diversity via recombination of immunoglobulin V, D and J gene segments. While several transcription factors that control B cell development and V(D)J recombination have been defined, how these processes are initiated and coordinated into a precise regulatory network remains poorly understood. Here, we show that the transcription factor ETS Related Gene (Erg) is essential for early B lymphoid differentiation. Erg initiates a transcriptional network involving the B cell lineage defining genes, Ebf1 and Pax5, which directly promotes expression of key genes involved in V(D)J recombination and formation of the B cell receptor. Complementation of Erg deficiency with a productively rearranged immunoglobulin gene rescued B lineage development, demonstrating that Erg is an essential and stage-specific regulator of the gene regulatory network controlling B lymphopoiesis.

TAPAS: Towards Automated Processing and Analysis of multi-dimensional bioimage data.

Modern microscopy is based on reproducible quantitative analysis, image data should be batch-processed by a standardized system that can be shared and easily reused by others. Furthermore, such system should require none or minimal programming from the users. We developed TAPAS (Towards an Automated Processing and Analysis System). The goal is to design an easy system for describing and exchanging processing workflows. The protocols are simple text files comprising a linear list of commands used to process and analyse the images. An extensive set of 60 modules is already available, mostly based on the tools proposed in the 3D ImageJ Suite. We propose a wizard, called TAPAS menu, to help the user design the protocol by listing the available modules and the parameters associated. Most modules will have default parameters values for most common tasks. Once the user has designed the protocol, he/she can apply the protocol to a set of images, that can be either stored locally or on a OMERO database. An extensive documentation including the list of modules, various tutorials and link to the source code is available at https://imagej.net/TAPAS.

Author Correction: Structural basis for delta cell paracrine regulation in pancreatic islets.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Structural basis for delta cell paracrine regulation in pancreatic islets.

Little is known about the role of islet delta cells in regulating blood glucose homeostasis in vivo. Delta cells are important paracrine regulators of beta cell and alpha cell secretory activity, however the structural basis underlying this regulation has yet to be determined. Most delta cells are elongated and have a well-defined cell soma and a filopodia-like structure. Using in vivo optogenetics and high-speed Ca2+ imaging, we show that these filopodia are dynamic structures that contain a secretory machinery, enabling the delta cell to reach a large number of beta cells within the islet. This provides for efficient regulation of beta cell activity and is modulated by endogenous IGF-1/VEGF-A signaling. In pre-diabetes, delta cells undergo morphological changes that may be a compensation to maintain paracrine regulation of the beta cell. Our data provides an integrated picture of how delta cells can modulate beta cell activity under physiological conditions.

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images.

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).

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.

NucleiNet: A convolutional encoder-decoder network for bio-image denoising.

Generic and scalable data analysis procedures are highly demanded by the increasing number of multi-dimensional biomedical data. However, especially for time-lapse biological data, the high level of noise prevents for automated high-throughput analysis methods. The rapid developing of machine-learning methods and particularly deep-learning methods provide new tools and methodologies that can help in the denoising of such data. Using a convolutional encoder-decoder network, one can provide a scalable bio-image platform, called NucleiNet, to automatically segment, classify and track cell nuclei. The proposed method can achieve 0.99 F-score and 0.99 pixel-wise accuracy on C. elegans dataset, which means that over 99% of nuclei can be successfully detected with no merging nuclei found.

Rapid Synaptogenesis in the Nucleus Accumbens Is Induced by a Single Cocaine Administration and Stabilized by Mitogen-Activated Protein Kinase Interacting Kinase-1 Activity.

BACKGROUND: Repeated cocaine exposure produces new spine formation in striatal projection neurons (SPNs) of the nucleus accumbens. However, an acute exposure to cocaine can trigger long-lasting synaptic plasticity in SPNs leading to behavioral alterations. This raises the intriguing question as to whether a single administration of cocaine could enduringly modify striatal connectivity. METHODS: A three-dimensional morphometric analysis of presynaptic glutamatergic boutons and dendritic spines was performed on SPNs 1 hour and 1 week after a single cocaine administration. Time-lapse two-photon microscopy in adult slices was used to determine the precise molecular-events sequence responsible for the rapid spine formation. RESULTS: A single injection triggered a rapid synaptogenesis and persistent increase in glutamatergic connectivity in SPNs from the shell part of the nucleus accumbens, specifically. Synapse formation occurred through clustered growth of active spines contacting pre-existing axonal boutons. Spine growth required extracellular signal-regulated kinase activation, while spine stabilization involved transcription-independent protein synthesis driven by mitogen-activated protein kinase interacting kinase-1, downstream from extracellular signal-regulated kinase. The maintenance of new spines driven by mitogen-activated protein kinase interacting kinase-1 was essential for long-term connectivity changes induced by cocaine in vivo. CONCLUSIONS: Our study originally demonstrates that a single administration of cocaine is able to induce stable synaptic rewiring in the nucleus accumbens, which will likely influence responses to subsequent drug exposure. It also unravels a new functional role for cocaine-induced extracellular signal-regulated kinase pathway independently of nuclear targets. Finally, it reveals that mitogen-activated protein kinase interacting kinase-1 has a pivotal role in cocaine-induced connectivity.

A novel toolbox to investigate tissue spatial organization applied to the study of the islets of Langerhans.

Thanks to the development of new 3D Imaging techniques, volumetric data of thick samples, especially tissues, are commonly available. Several algorithms were proposed to analyze cells or nuclei in tissues, however these tools are limited to two dimensions. Within any given tissue, cells are not likely to be organized randomly and as such have specific patterns of cell-cell interaction forming complex communication networks. In this paper, we propose a new set of tools as an approach to segment and analyze tissues in 3D with single cell resolution. This new tool box can identify and compute the geographical location of single cells and analyze the potential physical interactions between different cell types and in 3D. As a proof-of-principle, we applied our methodology to investigation of the cyto-architecture of the islets of Langerhans in mice and monkeys. The results obtained here are a significant improvement in current methodologies and provides new insight into the organization of alpha cells and their cellular interactions within the islet's cellular framework.

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.

A-type Lamins Form Distinct Filamentous Networks with Differential Nuclear Pore Complex Associations.

The nuclear lamina is a universal feature of metazoan nuclear envelopes (NEs) [1]. In mammalian cells, it appears as a 10-30 nm filamentous layer at the nuclear face of the inner nuclear membrane (INM) and is composed primarily of A- and B-type lamins, members of the intermediate filament family [2]. While providing structural integrity to the NE, the lamina also represents an important signaling and regulatory platform [3]. Two A-type lamin isoforms, lamins A and C (LaA and LaC), are expressed in most adult human cells. Encoded by a single gene, these proteins are largely identical, diverging only in their C-terminal tail domains. By contrast with that of LaC, the unique LaA tail undergoes extensive processing, including farnesylation and endo-proteolysis [4, 5]. However, functional differences between LaA and LaC are still unclear. Compounding this uncertainty, the structure of the lamina remains ill defined. In this study, we used BioID, an in vivo proximity-labeling method to identify differential interactors of A-type lamins [6]. One of these, Tpr, a nuclear pore complex (NPC) protein, is highlighted by its selective association with LaC. By employing superresolution microscopy, we demonstrate that this Tpr association is mirrored in enhanced interaction of LaC with NPCs. Further superresolution studies visualizing both endogenous A- and B-type lamins have allowed us to construct a nanometer-scale model of the mammalian nuclear lamina. Our data indicate that different A- and B-type lamin species assemble into separate filament networks that together form an extended composite structure at the nuclear periphery providing attachment sites for NPCs, thereby regulating their distribution.