Unraveling Comorbidities Contribution to Cardiac Diastolic Dysfunction and Heart Failure.

Background: Heart failure with preserved ejection fraction (HFpEF) is a major public health problem characterized by multiple simultaneous comorbidities whose specific contribution is challenging to disentangle in humans, leading to a generalized therapeutic approach that may not account for the underlying pathology.

Methods: We followed distinct mouse models of major HFpEF comorbidities for 2.5 years to unveil their specific contribution to the syndrome.

Plasma membrane remodeling determines adipocyte expansion and mechanical adaptability.

Adipocytes expand massively to accommodate excess energy stores and protect the organism from lipotoxicity. Adipose tissue expandability is at the center of disorders such as obesity and lipodystrophy; however, little is known about the relevance of adipocyte biomechanics on the etiology of these conditions. Here, we show in male mice in vivo that the adipocyte plasma membrane undergoes caveolar domain reorganization upon lipid droplet expansion.

The G4 resolvase Dhx36 modulates cardiomyocyte differentiation and ventricular conduction system development.

Extensive genetic studies have elucidated cardiomyocyte differentiation and associated gene networks using single-cell RNA-seq, yet the intricate transcriptional mechanisms governing cardiac conduction system (CCS) development and working cardiomyocyte differentiation remain largely unexplored. Here we show that mice deleted for Dhx36 (encoding the Dhx36 helicase) in the embryonic or neonatal heart develop overt dilated cardiomyopathy, surface ECG alterations related to cardiac impulse propagation, and (in the embryonic heart) a lack of a ventricular conduction system (VCS). Heart snRNA-seq and snATAC-seq reveal the role of Dhx36 in CCS development and in the differentiation of working cardiomyocytes.

T Cell Homeostasis Disturbances in a Cohort of Long-Term Elite Controllers of HIV Infection.

Elite controllers (ECs) are people living with HIV (PLWH) able to control HIV replication without antiretroviral therapy and have been proposed as a model of a functional HIV cure. Much evidence suggests that this spontaneous control of HIV has a cost in terms of T cell homeostasis alterations. We performed a deep phenotypic study to obtain insight into T cell homeostasis disturbances in ECs maintaining long-term virologic and immunologic control of HIV (long-term elite controllers; LTECs).

A specific natural killer cells phenotypic signature associated to long term elite control of HIV infection.

Elite controllers (ECs) are an exceptional group of people living with HIV (PLWH) that control HIV replication without therapy. Among the mechanisms involved in this ability, natural killer (NK)-cells have recently gained much attention. We performed an in-deep phenotypic analysis of NK-cells to search for surrogate markers associated with the long term spontaneous control of HIV.

SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks.

Summary: Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data.

A deep learning-based tool for the automated detection and analysis of caveolae in transmission electron microscopy images.

Caveolae are nanoscopic and mechanosensitive invaginations of the plasma membrane, essential for adipocyte biology. Transmission electron microscopy (TEM) offers the highest resolution for caveolae visualization, but provides complicated images that are difficult to classify or segment using traditional automated algorithms such as threshold-based methods. As a result, the time-consuming tasks of localization and quantification of caveolae are currently performed manually.

Caveolin-1 dolines form a distinct and rapid caveolae-independent mechanoadaptation system.

In response to different types and intensities of mechanical force, cells modulate their physical properties and adapt their plasma membrane (PM). Caveolae are PM nano-invaginations that contribute to mechanoadaptation, buffering tension changes. However, whether core caveolar proteins contribute to PM tension accommodation independently from the caveolar assembly is unknown.

Chromatin dynamics through mouse preimplantation development revealed by single molecule localisation microscopy.

Most studies addressing chromatin behaviour during preimplantation development are based on biochemical assays that lack spatial and cell-specific information, crucial during early development. Here, we describe the changes in chromatin taking place at the transition from totipotency to lineage specification, by using direct stochastical optical reconstruction microscopy (dSTORM) in whole-mount embryos during the first stages of mouse development. Through the study of two post-translational modifications of Histone 3 related to active and repressed chromatin, H3K4me3 and H3K9me3 respectively, we obtained a time-course of chromatin states, showing spatial differences between cell types, related to their differentiation state.

RXRs control serous macrophage neonatal expansion and identity and contribute to ovarian cancer progression.

Tissue-resident macrophages (TRMs) populate all tissues and play key roles in homeostasis, immunity and repair. TRMs express a molecular program that is mostly shaped by tissue cues. However, TRM identity and the mechanisms that maintain TRMs in tissues remain poorly understood.

Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves.

Background: Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves.

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees.

Tox_(R)CNN: Deep learning-based nuclei profiling tool for drug toxicity screening.

Toxicity is an important factor in failed drug development, and its efficient identification and prediction is a major challenge in drug discovery. We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. Deep learning algorithms obtain abstract representations of images through an automated process, allowing them to efficiently classify complex patterns, and have become the state-of-the art in machine learning for computer vision.

Von Hippel-Lindau Protein Is Required for Optimal Alveolar Macrophage Terminal Differentiation, Self-Renewal, and Function.

The rapid transit from hypoxia to normoxia in the lung that follows the first breath in newborn mice coincides with alveolar macrophage (AM) differentiation. However, whether sensing of oxygen affects AM maturation and function has not been previously explored. We have generated mice whose AMs show a deficient ability to sense oxygen after birth by deleting Vhl, a negative regulator of HIF transcription factors, in the CD11c compartment (CD11cΔVhl mice).

Pulmonary Artery-Vein Classification in CT Images Using Deep Learning.

Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time consuming, difficult to standardize, and thus not feasible for large clinical studies or useful in real-world clinical decision making.

Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis.

Advances in flow cytometry (FCM) increasingly demand adoption of computational analysis tools to tackle the ever-growing data dimensionality. In this study, we tested different data input modes to evaluate how cytometry acquisition configuration and data compensation procedures affect the performance of unsupervised phenotyping tools. An analysis workflow was set up and tested for the detection of changes in reference bead subsets and in a rare subpopulation of murine lymph node CD103 dendritic cells acquired by conventional or spectral cytometry.

Pluripotency Surveillance by Myc-Driven Competitive Elimination of Differentiating Cells.

The mammalian epiblast is formed by pluripotent cells able to differentiate into all tissues of the new individual. In their progression to differentiation, epiblast cells and their in vitro counterparts, embryonic stem cells (ESCs), transit from naive pluripotency through a differentiation-primed pluripotent state. During these events, epiblast cells and ESCs are prone to death, driven by competition between Myc-high cells (winners) and Myc-low cells (losers).

ESC-Track: A computer workflow for 4-D segmentation, tracking, lineage tracing and dynamic context analysis of ESCs.

Embryonic stem cells (ESCs) can be established as permanent cell lines, and their potential to differentiate into adult tissues has led to widespread use for studying the mechanisms and dynamics of stem cell differentiation and exploring strategies for tissue repair. Imaging live ESCs during development is now feasible due to advances in optical imaging and engineering of genetically encoded fluorescent reporters; however, a major limitation is the low spatio-temporal resolution of long-term 3-D imaging required for generational and neighboring reconstructions. Here, we present the ESC-Track (ESC-T) workflow, which includes an automated cell and nuclear segmentation and tracking tool for 4-D (3-D + time) confocal image data sets as well as a manual editing tool for visual inspection and error correction.

Implementation and Performance of Automated Software for Computing Right-to-Left Ventricular Diameter Ratio From Computed Tomography Pulmonary Angiography Images.

Objective: The aim of this study was to prospectively test the performance and potential for clinical integration of software that automatically calculates the right-to-left ventricular (RV/LV) diameter ratio from computed tomography pulmonary angiography images.

Methods: Using 115 computed tomography pulmonary angiography images that were positive for acute pulmonary embolism, we prospectively evaluated RV/LV ratio measurements that were obtained as follows: (1) completely manual measurement (reference standard), (2) completely automated measurement using the software, and (3 and 4) using a customized software interface that allowed 2 independent radiologists to manually adjust the automatically positioned calipers.

Results: Automated measurements underestimated (P < 0.

Automatic Synthesis of Anthropomorphic Pulmonary CT Phantoms.

The great density and structural complexity of pulmonary vessels and airways impose limitations on the generation of accurate reference standards, which are critical in training and in the validation of image processing methods for features such as pulmonary vessel segmentation or artery-vein (AV) separations. The design of synthetic computed tomography (CT) images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image is differentiated unequivocally. This work demonstrates a complete framework to generate computational anthropomorphic CT phantoms of the human lung automatically.

Automated axial right ventricle to left ventricle diameter ratio computation in computed tomography pulmonary angiography.

Background And Purpose: Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE.

Right ventricle segmentation from cardiac MRI: a collation study.

Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation.

A benchmark for comparison of cell tracking algorithms.

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.