Biology subjects

Aslani, S.

Publications and source records attributed to Aslani, S..

6 recordsLinked to original sources

bioRxiv2026

Multiscale mapping of venous remodelling in idiopathic pulmonary fibrosis

The integrity of the pulmonary vasculature is a key determinant of lung health, yet challenges in visualisation and quantification have hindered interrogation of its pathophysiological role in chronic lung disease. Here, we align recent advances in microscale image acquisition with novel computer vision-based vessel segmentation models to demonstrate expansion of the bronchial and pulmonary veins across varying severities of tissue remodelling in idiopathic pulmonary fibrosis (IPF). Relating these imaging findings to molecular data in control, mild, and severe fibrosis, we show that bronchial venous endothelial cells expand beyond their physiological peribronchial niche even in mild disease, acquiring persistent angiogenic, inflammatory, and matrix-remodelling programmes that define a specialised fibrovascular-immune interface. Finally, we relate these microscale and molecular observations to clinical CT imaging, demonstrating that intrapulmonary vein enlargement independently associates with worsened survival across three IPF cohorts. Collectively, our multimodal, multiscale approach connects previously unresolved three-dimensional venous architecture to its molecular endothelial correlate, establishing venous enlargement as a prognostically significant and clinically relevant feature of IPF. Our analytical approach also demonstrates how biological discoveries made in intact ex vivo human organs can translate into measurable phenotypes in living patients, providing a template for other organs and diseases.

pathology↗

bioRxiv2025

Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segmentation requires the effective propagation and integration of information across different scales. However, most existing segmentation methods are optimised for single-scale imaging modalities, limiting their ability to capture and analyse small functional units throughout complete human organs. To facilitate multiscale biomedical image segmentation, we utilised Hierarchical Phase-Contrast Tomography (HiP-CT), an advanced imaging modality that can generate 3D multiscale datasets from high-resolution volumes of interest (VOIs) at ca. 1 {micro}m/voxel to whole-organ scans at ca. 20 {micro}m/voxel. Building on these hierarchical multiscale datasets, we developed a deep learning-based segmentation pipeline that is initially trained on manually annotated high-resolution HiP-CT data and then extended to lower-resolution whole-organ scans using pseudo-labels generated from high-resolution predictions and multiscale image registration. As a case study, we focused on glomeruli in human kidneys, benchmarking four 3D deep learning models for biomedical image segmentation on a manually annotated high-resolution dataset extracted from VOIs, at 2.58 to ca. 5 {micro}m/voxel, of four human kidneys. Among them, nnUNet demonstrated the best performance, achieving an average test Dice score of 0.906, and was subsequently used as the baseline model for multiscale segmentation in the pipeline. Applying this pipeline to two low-resolution full-organ data at ca. 25 {micro}m/voxel, the model identified 1,019,890 and 231,179 glomeruli in a 62-year-old donor without kidney diseases and a 94-year-old hypertensive donor, enabling comprehensive morphological analyses, including cortical spatial statistics and glomerular distributions, which aligned well with previous anatomical studies. Our results highlight the effectiveness of the proposed pipeline for segmenting small functional units in multiscale bioimaging datasets and suggest its broader applicability to other organ systems.

bioinformatics↗

bioRxiv2024

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focusing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.

bioinformatics↗

bioRxiv2023

HDAC6/aggresome processing pathway importance for inflammasome formation is context dependent

The inflammasome is a large multiprotein complex that assembles in the cell cytoplasm in response to stress or pathogenic infection. Its primary function is to defend the cell and promote the secretion of pro-inflammatory cytokines, including IL-1{beta} and IL-18. It was shown that in immortalized bone marrow derived macrophages (iBMDMs) inflammasome assembly is dependent on the deacetylase HDAC6 and the aggresome processing pathway (APP), a cellular pathway involved in the disposal of misfolded proteins. Here we used primary BMDMs from mice in which HDAC6 is ablated or impaired and found that inflammasome activation was largely normal. We also used human peripheral blood mononuclear cells and monocytes cell lines expressing a synthetic protein blocking HDAC6-ubiquitin interaction and impairing the APP and found that inflammasome activation was moderately affected. Finally, we used a novel HDAC6 degrader and showed that inflammasome activation was partially impaired in human macrophage cell lines with depleted HDAC6. Our results therefore show that HDAC6 importance in inflammasome activation is context dependent.

immunology↗

bioRxiv2023

A rationally designed inactivated Salmonella Typhimurium vaccine induces strong and long-lasting immune responses in pigs

Salmonella enterica subspecies enterica serovar Typhimurium (S.Tm) poses a considerable threat to public health due to its zoonotic potential. Human infections are mostly foodborne, and pork and pork products are ranked among the top culprits for transmission. In addition, the high percentage of antibiotic resistance, especially in monophasic S.Tm, limits treatment options when needed. Better S.Tm control would therefore be of benefit both for farm animals and for safety of the human food chain. A promising pre-harvest intervention is vaccination. In this study we tested safety and immunogenicity of an oral inactivated S.Tm vaccine, which has been recently shown to generate an "evolutionary trap" and to massively reduce S.Tm colonization and transmission in mice. We show that this vaccine is highly immunogenic and safe in post-weaning pigs and that administration of a single oral dose results in a strong and long-lasting serum IgG response. This has several advantages over existing - mainly live - vaccines against S.Tm, both in improved seroconversion and reduced risk of vaccine-strain persistence and reversion to virulence.

immunology↗

bioRxiv2022

Combined oral vaccination with niche competition can generate sterilizing immunity against entero-pathogenic bacteria

Colonization of the intestinal lumen precedes invasive infection for a wide range of enteropathogenic and opportunistic pathogenic bacteria. Here we show that combining oral vaccination with engineered or selected niche-competitor strains permits pathogen exclusion and strain replacement in the mouse gut lumen. This approach can be applied both prophylactically to prevent invasion of non-typhoidal Salmonella strains, or therapeutically to displace an established Escherichia coli. Both intact adaptive immunity and metabolic niche competition are necessary for efficient vaccine-enhanced competition. Our findings imply that mucosal antibodies have evolved to work in the context of gut microbial ecology, by influencing the outcome of competition. This has broad implications for the elimination of pathogenic and antibiotic-resistant bacterial reservoirs, and for rational microbiota engineering.

microbiology↗