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. We used the Keras library in R to train a convolutional neural network with a total of 36,000 TEM image crops obtained from adipocytes previously annotated manually by an expert. The resulting model can differentiate caveolae from non-caveolae regions with a 97.44% accuracy. The predictions of this model are further processed to obtain caveolae central coordinate detection and cytoplasm boundary delimitation. The model correctly finds negligible caveolae predictions in images from caveolae depleted Cav1-/- adipocytes. In large reconstructions of adipocyte sections, model and human performances are comparable. We thus provide a new tool for accurate caveolae automated analysis that could speed up and assist in the characterization of the cellular mechanical response.

The Clinical outcomes, healthcare resource utilization, and related costs (COHERENT) model. Application in heart failure patients.

INTRODUCTION AND OBJECTIVES: Composite endpoints are widely used but have several limitations. The Clinical outcomes, healthcare resource utilization and related costs (COHERENT) model is a new approach for visually displaying and comparing composite endpoints including all their components (incidence, timing, duration) and related costs. We aimed to assess the validity of the COHERENT model in a patient cohort. METHODS: A color graphic system displaying the percentage of patients in each clinical situation (vital status and location: at home, emergency department [ED] or hospital) and related costs at each time point during follow-up was created based on a list of mutually exclusive clinical situations coded in a hierarchical fashion. The system was tested in a cohort of 1126 patients with acute heart failure from 25 hospitals. The system calculated and displayed the time spent in each clinical situation and health care resource utilization-related costs over 30 days. RESULTS: The model illustrated the times spent over 30 days (2.12% in ED, 23.6% in index hospitalization, 2.7% in readmissions, 65.5% alive at home, and 6.02% dead), showing significant differences between patient groups, hospitals, and health care systems. The tool calculated and displayed the daily and cumulative health care-related costs over time (total, €4 895 070; mean, €144.91 per patient/d). CONCLUSIONS: The COHERENT model is a new, easy-to-interpret, visual display of composite endpoints, enabling comparisons between patient groups and cohorts, including related costs. The model may constitute a useful new approach for clinical trials or observational studies, and a tool for benchmarking, and value-based health care implementation.

Caveolin1 and YAP drive mechanically induced mesothelial to mesenchymal transition and fibrosis.

Despite their emerging relevance to fully understand disease pathogenesis, we have as yet a poor understanding as to how biomechanical signals are integrated with specific biochemical pathways to determine cell behaviour. Mesothelial-to-mesenchymal transition (MMT) markers colocalized with TGF-ß1-dependent signaling and yes-associated protein (YAP) activation across biopsies from different pathologies exhibiting peritoneal fibrosis, supporting mechanotransduction as a central driving component of these class of fibrotic lesions and its crosstalk with specific signaling pathways. Transcriptome and proteome profiling of the response of mesothelial cells (MCs) to linear cyclic stretch revealed molecular changes compatible with bona fide MMT, which (i) overlapped with established YAP target gene subsets, and were largely dependent on endogenous TGF-ß1 signaling. Importantly, TGF-ß1 blockade blunts the transcriptional upregulation of these gene signatures, but not the mechanical activation and nuclear translocation of YAP per se. We studied the role therein of caveolin-1 (CAV1), a plasma membrane mechanotransducer. Exposure of CAV1-deficient MCs to cyclic stretch led to a robust upregulation of MMT-related gene programs, which was blunted upon TGF-ß1 inhibition. Conversely, CAV1 depletion enhanced both TGF-ß1 and TGFBRI expression, whereas its re-expression blunted mechanical stretching-induced MMT. CAV1 genetic deficiency exacerbated MMT and adhesion formation in an experimental murine model of peritoneal ischaemic buttons. Taken together, these results support that CAV1-YAP/TAZ fine-tune the fibrotic response through the modulation of MMT, onto which TGF-ß1-dependent signaling coordinately converges. Our findings reveal a cooperation between biomechanical and biochemical signals in the triggering of MMT, representing a novel potential opportunity to intervene mechanically induced disorders coursing with peritoneal fibrosis, such as post-surgical adhesions.

Tumor-stroma biomechanical crosstalk: a perspective on the role of caveolin-1 in tumor progression.

Tumor stiffening is a hallmark of malignancy that actively drives tumor progression and aggressiveness. Recent research has shed light onto several molecular underpinnings of this biomechanical process, which has a reciprocal crosstalk between tumor cells, stromal fibroblasts, and extracellular matrix remodeling at its core. This dynamic communication shapes the tumor microenvironment; significantly determines disease features including therapeutic resistance, relapse, or metastasis; and potentially holds the key for novel antitumor strategies. Caveolae and their components emerge as integrators of different aspects of cell function, mechanotransduction, and ECM-cell interaction. Here, we review our current knowledge on the several pivotal roles of the essential caveolar component caveolin-1 in this multidirectional biomechanical crosstalk and highlight standing questions in the field.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies.

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.