Biology subjects

Pasqualini, F.

Publications and source records attributed to Pasqualini, F..

7 recordsLinked to original sources

bioRxiv2026

Disruption of endothelial stability directly impacts vascular neighboring cells in Hereditary Hemorrhagic Telangiectasia

BACKGROUNDHereditary hemorrhagic telangiectasia (HHT) is a genetic disorder caused by pathogenic variants in the endothelial TGF{beta}/BMP pathway, crucial for the vascular arterial-venous differentiation. Vascular defects result in fragile and malformed vessels. The precise mechanisms driving vascular network failure remain incompletely understood, complicating the design of targeted therapies. METHODSNasal telangiectasias from HHT patients carrying variants in ACVRL1 or ENG were used to perform scRNA-seq (2 ACVRL1- and 1 ENG-patient) and spatial transcriptomics (1 ACVRL1 and 1 ENG) to uncover endothelial cells (EC) populations. Vascular characteristics within biopsies were evaluated using transmission electron microscopy (TEM) (1 ACVRL1 and 1 ENG) and histological analyses (23 ACVRL1 and 7 ENG), with particular attention to regions exhibiting varying degrees of damage. RESULTSComparing our HHT tissues with healthy donor from the literature, we identified cellular heterogeneity within EC populations, revealing two distinct venous clusters: a stable, quiescent population (Mature Vein) and an activated, pro-inflammatory population (HHT Vein). The coexistence of these two clusters suggests cellular diversity within the biopsy, further validated by TEM and histology, revealing a juxtaposition of well-organized collagen and cellular architecture with severely disrupted, fibrotic regions. Moreover, cellular crosstalk analyses allowed us to identify critical ligands in ECs that interact with fibroblasts and mural cells. In particular, we found Midkine (MDK) lost in HHT Vein ECs with further validation in vitro, suggesting its potential role in cellular stability. Furthermore, spatial transcriptomics allowed to further uncover pathologic phenotypes in cells neighboring HHT Vein ECs. CONCLUSIONSHHT biopsies exhibit localized inflamed and fibrotic vascular areas with the presence of different transcriptional sub-populations of EC. Within the same tissue, stable and activated ECs can be distinguished. The pathologic-like EC cluster, present exclusively in the HHT samples, may contribute to vascular leakage through the loss of important ligands involved in cellular communication.

cell biology↗

bioRxiv2025

Bioimage analysis for multiplexed FUCCI acquisitions powered by deep learning

The FUCCI sensor fluorescently labels cell cycle phases, which is essential to assess normal and abnormal cell-cycle progression in physiological and pathological conditions of developing organisms. However, accurate cell-cycle decoding is challenging in the low signal-to-noise conditions typical of multiplexed live cell imaging. To address this challenge, we developed deep learning networks that integrate FUCCI signals with a cytoplasmic alpha-tubulin fluorescent reporter. Our approach outperforms existing methods for both segmenting and classifying FUCCI nuclei, even in low signal-to-noise conditions. The resulting high-accuracy segmentation enables robust automated tracking. We leverage this to introduce a dynamic time warping analysis that determines cell cycle pseudotime from incomplete tracks and can detect cell cycle arrest. We provide pre-trained networks for multichannel FUCCI analysis, offering a powerful tool for studies in cancer research, development, and mechanobiology.

bioengineering↗

bioRxiv2025

Vertically Integrated System for Tracking and Assessing cell-cycle aware phenotypes under confinement

Quantitative cell biology often studies migration and the cell-cycle (CC) in separate assays, limiting mechanistic insights, particularly under geometric confinement. Here, we introduce a vertically integrated platform for simultaneously tracking single-cell migration and assessing CC under confinement. Our system integrates cell engineering via multiplexed sensors for cell-cycle, actin, and tubulin, as well as photopatterned engineered extracellular matrix (ECM) islands of defined sizes. It also features an automated, high-throughput pattern-aware imaging pipeline (Fab2Mic) that enables on-pattern, joint migration-CC assessment in the same live cells. Since the local microenvironment plays a critical role in metastasis by constraining cell behaviors within spatial boundaries, we used an HT1080 fibrosarcoma model as an illustrative case. Where static phenotyping yielded 40% G1 and 60% S/G2/M, with larger cell areas and tubulin spread in the S/G2/M phase, dynamic phenotyping via live-cell imaging confirmed CC-linked motility, with faster instantaneous velocities in G1, exemplifying the CC-migration correlations. These phenotypes were modulated by the spatial confinement imposed by the engineered ECM islands. Stronger confinement reduced cell area and tubulin spread and increased the frequency of abnormal CC events, particularly Long G1 states on smaller engineered ECM islands. It also induced a confinement-specific S/G2/M-G1 mitotic slippage, observed only under our confined conditions. Together, this vertically integrated system suggests that confinement may continuously tune migration-CC coupling and provides a deployable pipeline for CC-aware mechanobiology and screening. Moreover, we stress how dynamic imaging provides access to variables that are difficult or impossible to infer from static snapshots, including velocity and CC timing.

bioengineering↗

bioRxiv2025

MicroSplit: Semantic Unmixing of Fluorescent Microscopy Data

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscopes optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce Micro[S]plit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmix them by computational means, allowing faster imaging and reduced photon exposure. We show that Micro[S]plit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of Micro[S]plit networks, which are trained for each splitting task at hand, across various datasets and noise levels and show its utility to image more, to image faster, and to improve downstream analysis. We provide Micro[S]plit along with all associated training and evaluation datasets as open resources, enabling life scientists to immediately benefit from the potential of computational multiplexing and thus help accelerate the rate of their scientific discovery process.

bioinformatics↗

bioRxiv2024

CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies

Cell cycle progression, migration, and proliferation shape development and regeneration, but simultaneous live-cell imaging remains challenging as conventional fluorescent cell cycle indicators (FUCCI) monopolize the green and red channels used by most structural and functional biosensors. To overcome this, we integrated a spectrally re-engineered FUCCI variant, open-source analysis software, and four-color human stem cell reporter lines into CALIPERS: a method for Cell-cycle-Aware Live-cell Imaging in Phenotyping and Regeneration Studies.

bioengineering↗

bioRxiv2023

SEM2: A computational framework to model multiscale mechanics with subcellular elements

Modeling multiscale mechanics in shape-shifting biological tissues in embryos, traditional, or engineered cell culture platforms (organoids, organs-on-chips) is both important and challenging. In fact, it is difficult to model relevant tissue-level structural changes mediated by discrete events at the cellular and subcellular levels, such as migration and proliferation. To accomplish this, we leveraged the subcellular element modeling (SEM) method, where ensembles of coarse-grained particles interacting via empirically defined potentials are used to model individual cells while preserving cell rheology. However, an explicit treatment of multiscale mechanics in SEM was missing. Here, we introduced SEM2, an extended version of the open-source software SEM++ and LAMMPS, enabling new analyses and visualization of particle-level stress and strain. We demonstrated various functionalities of SEM2 by simulating cell creep, migration, and proliferation in scenarios that recapitulate classical and engineered cell culture platforms. For every scenario, we highlight key mechanobiology that emerges spontaneously from particle interactions and discuss recent experimental evidence as qualitative validations of our simulations. The code for SEM2 is available on GitHub at https://github.com/Synthetic-Physiology-Lab/sem2.

bioengineering↗

bioRxiv2022

Regulation of inflammation and protection against invasive pneumococcal infection by the long pentraxin PTX3

Streptococcus pneumoniae is a major pathogen in children, elderly subjects and immunodeficient patients. PTX3 is a fluid phase pattern recognition molecule (PRM) involved in resistance to selected microbial agents and in regulation of inflammation. The present study was designed to assess the role of PTX3 in invasive pneumococcal infection. In a murine model of invasive pneumococcal infection, PTX3 was strongly induced in non-hematopoietic (particularly, endothelial) cells. The IL-1{beta}/MyD88 axis played a major role in regulation of the Ptx3 gene expression. Ptx3-/- mice were more susceptible to invasive pneumococcal infection. Although high concentrations of PTX3 had opsonic activity in vitro, no evidence of PTX3-enhanced phagocytosis was obtained in vivo. In contrast, Ptx3-deficient mice showed enhanced recruitment of neutrophils and inflammation. Using P-selectin deficient mice, we found that protection against pneumococcus was dependent upon PTX3-mediated regulation of neutrophil inflammation. In humans, PTX3 genetic polymorphisms were associated with invasive pneumococcal infections. Thus, this fluid phase PRM plays an important role in tuning inflammation and resistance against invasive pneumococcal infection.

immunology↗