Biology subjects

Casanova, R.

Publications and source records attributed to Casanova, R..

7 recordsLinked to original sources

bioRxiv2026

Mural cell contractility regulates vessel diameter by controlling cell morphology and vessel coverage

Mural cells are key regulators of vascular architecture, yet how their contractility and morphology jointly influence vessel diameter in vivo remains poorly understood. Here, using high-resolution live imaging and single-cell morphometric analysis in zebrafish, we show that vascular smooth muscle cells (vSMCs) and pericytes undergo a developmental reduction in cell size through the progressive retraction of actin-rich primary and secondary processes during vascular remodelling. By specifically manipulating RhoA activity to alter mural cell contractility and shape, we uncover vessel-specific roles for mural cells in diameter regulation. We find that vSMC contractility is dispensable for the initial constriction of the dorsal aorta but is required to stabilise its diameter following vasoconstriction and to maintain vascular tone. In contrast, pericyte contractility is dispensable for both the constriction and stabilisation of intersegmental vessels. In the brain vasculature, vessel diameter is governed by the balance between contractile force and the extent of vessel coverage by vSMCs. Together, our findings redefine the role of mural cell contractility in the control of vessel constraint, demonstrating that morphology and vessel coverage, rather than contractility alone, are key determinants of vascular diameter in vivo.

cell biology↗

bioRxiv2025

Exploiting pair correlation function to describe biological tissue structure

Multiplexed imaging technologies now enable the simultaneous profiling of hundreds to thousands of molecular targets in intact tissues, providing unprecedented insight into cellular heterogeneity and spatial organization. While data generation has rapidly matured, the quantitative analysis of spatial structure remains challenging and poorly standardized, particularly across biological length scales. Existing approaches, such as distance-based metrics, neighborhood analyses and graph neural networks, either capture only local interactions or sacrifice interpretability for predictive power. Here we introduce PCF-SiM (Pair Correlation Function Sigmoid Modeling), a scalable and interpretable framework that leverages parametric modeling of the pair correlation function to quantify spatial organization in multiplexed imaging data. PCF-SiM compresses complex spatial patterns into a small set of biologically meaningful parameters, enabling robust comparisons across cell types, samples and conditions. Applying PCF-SiM to diverse public spatial transcriptomics datasets, we demonstrate its ability to detect condition-dependent tissue remodeling in a mouse colitis model. We further extend the framework with a co-scaling strategy that identifies cell types participating in shared spatial structures. Using newly generated clinical datasets from Hashimotos thyroiditis and uveal melanoma liver metastases, PCF-SiM reveals hierarchical organization of autoimmune infiltrates and coordinated spatial interactions between lymphatic endothelial cells and tumor-infiltrating lymphocytes. Finally, we show that reliable inference of tissue-scale architecture requires whole-slide imaging, exposing intrinsic limitations of tumor microarray-based spatial analyses. Together, PCF-SiM provides a principled and interpretable approach for spatial analysis of multiplexed imaging data.

bioinformatics↗

bioRxiv2024

Transposable element methylation state predicts age and disease

Transposable elements (TEs) are DNA sequences that expand selfishly in the genome, possibly causing severe cellular damage. While normally silenced, TEs have been shown to activate during aging. DNA methylation is one of the main mechanisms by which TEs are silenced and has been used to train highly accurate age predictors. Yet, one common criticism of such predictors is that they lack interpretability. In this study, we investigate the changes in TE methylation that occur during human aging. We find that evolutionarily young LINE1s (L1s), the only known TEs capable of autonomous transposition in humans, undergo the fastest loss of methylation, suggesting an active mechanism of de-repression. We then show that accurate age predictors can be trained on both methylation of individual TE copies and average methylation of TE families genome wide. Lastly, we show that while old L1s gradually lose methylation during the entire lifespan, demethylation of young L1s only happens late in life and is associated with cancer.

genomics↗

bioRxiv2024

SMAC mimetics overcome apoptotic resistance in ovarian cancer through MSLN-TNF alpha axis

Resistance to chemotherapy and PARPi inhibitors remains a critical challenge in the treatment of epithelial ovarian cancer, mainly due to disabled apoptotic responses in tumor cells. Given mesothelins pivotal role in ovarian cancer and its restricted expression in healthy tissues, we conducted a drug-screening discovery analysis across a range of genetically modified cancer cells to unveil mesothelins therapeutic impact. We observed enhanced cell death in cancer cells with low mesothelin expression when exposed to a second mitochondria-derived activator of caspases (SMAC) mimetics, and demonstrated a compelling synergy when combined with chemotherapy in ex vivo patient-derived cultures and zebrafish tumor xenografts. Mechanistically, the addition of the SMAC mimetics drug birinapant to either carboplatin or paclitaxel triggered the activation of the Caspase 8-dependent apoptotic program facilitated by TNFLJ signaling. Multimodal analysis of neoadjuvant-treated patient samples further revealed an association between tumor-associated macrophages and the activation of TNFLJ-related pathways. Our proposed bimodal treatment shows promise in enhancing the clinical management of patients by harnessing the potential of SMAC mimetics alongside conventional chemotherapy.

cancer biology↗

bioRxiv2023

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

systems biology↗

bioRxiv2023

Standardization of suspension and imaging mass cytometry readouts for clinical decision making

Suspension and imaging mass cytometry are single-cell, proteomic-based methods used to characterize tissue composition and structure. Data assessing the consistency of these methods over an extended period of time are still sparse and are needed if mass cytometry-based methods are to be used clinically. Here, we present experimental and computational pipelines developed within the Tumor Profiler clinical study, an observational clinical trial assessing the relevance of cutting-edge technologies in guiding treatment decisions for advanced cancer patients. By using aliquots of frozen antibody panels, batch effects between independent experiments performed within a time frame of one year were minimized. The inclusion of well-characterized reference samples allowed us to assess and correct for batch effects. A systematic evaluation of a test tumor sample analyzed in each run showed that our batch correction approach consistently reduced signal variations. We provide an exemplary analysis of a representative patient sample including an overview of data provided to clinicians and potential treatment suggestions. This study demonstrates that standardized suspension and imaging mass cytometry measurements generate robust data that meet clinical requirements for reproducibility and provide oncologists with valuable insights on the biology of patient tumors.

cancer biology↗

bioRxiv2023

Statistical modeling and analysis of multiplexed imaging data

The rapid development of multiplexed imaging technologies has enabled the spatial cartography of various healthy and tumor tissues. However, the lack of adequate statistical models has hampered the use of multiplexed imaging to efficiently compare tissue composition across sample groups, for instance between healthy and tumor tissue samples. Here, we developed two statistical models that accurately describe the distribution of cell counts observed in a given field of view in an imaging experiment. The parameters of these distributions are directly linked to the field of view size and also to properties of the studied cell type such as cellular density and spatial aggregation. Using these models, we identified statistical tests that have improved statistical power for differential abundance testing of tissue composition compared to the commonly used rank-based test. Our analysis revealed that spatial aggregation is the main determinant of statistical power and that to have sufficient power to detect differences in cell counts when cells are highly aggregated may require sampling of hundreds of fields of view. To overcome this challenge, we provide a new stratified sampling strategy that might significantly reduce the number of required samples.

bioinformatics↗