Biology subjects

Zanders, L.

Publications and source records attributed to Zanders, L..

3 recordsLinked to original sources

bioRxiv2025

Vascular niches are the primary hotspots for aging within the multicellular architecture of cardiac tissue

BackgroundAging is a major, yet unmodifiable risk factor for cardiovascular diseases, leading to vascular alterations, increased cardiac fibrosis, and inflammation, all of which contribute to impaired cardiac function. However, the microenvironment inciting age-related alterations withing the multicellular architecture of the cardiac tissue is unknown. MethodsWe investigated local microenvironments in aged mice hearts applying an integrative approach combining single-nucleus RNA sequencing and spatial transcriptomics in 12-week-old and 18-month-old mice. We defined distinct cardiac niches and studied changes in their cellular composition and functional characteristics. ResultsIntegration of spatial transcriptomics data across young and aged hearts allowed us to identify 11 cardiac niches, which were characterized by distinct cellular composition and functional signatures. Aging did not alter the overall proportions of cardiac niches but leads to distinct regional changes, particularly in the left ventricle. Whereas cardiomyocyte-enriched niches show disrupted circadian clock gene expression, vascular niches showed major changes in pro-inflammatory and pro-fibrotic signatures and altered cellular composition. We particularly identified larger vessel-associated cellular niches as key hotspots for activated fibroblasts and macrophages in aged hearts, with interactions of both cell types through the C3:C3ar1 axis. These niches were also enriched in senescence cells exhibiting high expression of immune evasion mechanisms that may impair senescent cell clearance. ConclusionOur findings indicate that the microenvironment around the vasculature is particularly susceptible to age-related changes and serves as a primary site for inflammation-driven aging, so called "inflammaging". This study provides new insights into how aging reshapes cardiac cellular architecture, highlighting vessel-associated niches as potential therapeutic targets for age-related cardiac dysfunction.

physiology↗

bioRxiv2025

Deciphering human heart failure with preserved ejection fraction (HFpEF) at single cell resolution

BACKGROUNDHeart failure with preserved ejection fraction (HFpEF) is a complex and growing condition, representing over half of all heart failure cases. Despite its high morbidity and mortality, its heterogeneity and limited therapeutic options pose significant challenges. Understanding the molecular mechanisms driving HFpEF is essential for the development of new therapies to improve patient outcomes. METHODSWe performed single-nucleus RNA sequencing of nuclei obtained from endomyocardial biopsies of six patients with HFpEF. The obtained dataset was integrated with a dataset of 12 healthy human hearts and their transcriptomic differences were analyzed. RESULTSAfter quality control and integration of the datasets, nine major cardiac cell types were annotated. HFpEF cardiomyocytes were characterized by a reduction in genes associated with aerobic respiration and fatty acid metabolism and showed an upregulation of RHOA/ROCK1 signaling, which was validated using immunofluorescence staining in human HFpEF myocardial sections. Endothelial cells exhibited signs of increased apoptosis, SEMA3 signaling and signs of reduced VEGFA signaling as well as a reactivation of a fetal gene signature. In line with a prominent role of cardiac fibrosis in HFpEF, we observed increased signs of fibroblast activation and proliferation, and reduced signs of IFN{gamma} signaling in HFpEF which was most pronounced in activated fibroblasts. Treatment of human cardiac fibroblast with rhIFN{gamma} resulted in decreased collagen contents. Macrophages from HFpEF myocardium showed a pro-inflammatory transcriptomic signature and showed increased expression of MHC-II molecules. This was associated with signs of an increased IFN{gamma} response. CONCLUSIONOur results provide insights into the transcriptional diversity of HFpEF recapitulating structural, functional, and molecular hallmarks of the disease and provide mechanistic insights which might represent therapeutic targets and biomarkers to improve outcome of patients with HFpEF. CLINICAL PERSPECTIVEO_ST_ABSWhat is new?C_ST_ABSO_LIWe provide a single-nucleus RNA sequencing (snRNA-Seq) dataset from human HFpEF myocardium and demonstrate feasibility of snRNA-Seq from endomyocardial biopsies C_LIO_LIThe snRNA-Seq data confirms signs of known molecular hallmarks of HFpEF, such as metabolic changes, inflammation and fibrosis C_LIO_LIWe identify signs of regulating cellular mechanisms underlying these hallmarks, such as cytoskeleton remodeling via RhoA/ROCK1 in cardiomyocytes, and differential interferon gamma signaling in stromal and immune cells C_LI What are the clinical implications?O_LIWe provide several cell type-specific cellular mechanisms which might serve as biomarkers or therapeutic targets in the treatment of HFpEF C_LI

cell biology↗

bioRxiv2025

Decoding heart failure subtypes with neural networks via differential explanation analysis

Single-cell transcriptomics offers critical insights into the molecular mechanisms of heart failure with reduced or preserved ejection fraction. However, understanding these mechanisms is hindered by the growing complexity of single-cell data and the difficulty in unmasking meaningful differential genes signatures among heart failure types. Machine learning, particularly deep neural networks, address these challenges by learning transcriptional patterns, reconstructing expression profiles and effectively classifying cells but often lacks interpretability. Recent advances in explainable AI (XAI) offer tools to clarify model decisions. Yet pinpointing differentially regulated genes with these tools remains challenging. In this study, we introduce a novel method to identify differentially explained genes (DXGs) based on importance scores derived from custom-built neural networks. We highlight the superiority of DXGs in identifying heart failure subtypes-specific pathways that provide new insights into different types of heart failure. Offering a robust foundation for future research and therapeutic exploration in expanding transcriptome atlases.

bioinformatics↗