biostudies-arrayexpressCarlos TorrojaMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-16848Early diastolic dysfunction precedes the development of HFpEF, but the molecular events that initiate this process remain poorly defined. HFpEF arises in the setting of systemic comorbidities such as obesity, hypertension, hyperglycemia, and sleep apnea, which often coexist and make it difficult to distinguish their individual contributions to early cardiac remodelling. Here, we aimed to define the early, comorbidity-specific mechanisms that initiate diastolic dysfunction across major HFpEF-associated conditions.biostudies-arrayexpressSample Collection - Subsets of animals were sacrificed at predefined time points. HG mice and a subset of untreated control mice were sacrificed at 25 weeks of age, as this stage corresponds to the onset of diastolic dysfunction in this model12. OB, SAH, and CIH, along with age-matched CTL mice, were sacrificed at 50 weeks, when these models begin to show diastolic function impairments. Additional groups of OB, SAH, CIH, and HG mice, together with their respective age-matched CTL groups, were monitored until they developed HFpEF or died naturally. HFpEF was defined as the presence of diastolic dysfunction assessed by echocardiography and lung congestion detected by ultrasound (prominent B-lines in at least half of one hemithorax and/or moderate pleural effusion), in the presence of preserved ejection fraction (EF). Mice fitting the definition of HFpEF were euthanized Nuclei isolation was performed following Cui et al. protocol16, with slight modifications. Briefly, frozen left ventricles were homogenized in lysis buffer with a mechanical probe and then further disrupted using a Dounce homogenizer. The homogenate was sequentially filtered through progressively finer strainers to remove tissue debris, and nuclei were collected and purified through a sucrose-based density separation. Isolated nuclei were then stained with DAPI and subjected to flow cytometry sorting to select DAPI-positive nuclei. Sorted nuclei were collected, resuspended in a suitable buffer, and their integrity was assessed. Samples with >90% intact cells were considered acceptable for downstream single-nucleus RNA sequencing. Nuclei were adjusted to a final concentration of ~1,000 nuclei/µL and their concentration and integrity were assessed using the Countess 3 cell counter (Thermo Fisher Scientific).Sequencing - Pooled libraries were sequenced at a loading concentration of 700 pM using paired-end reads (28 bp Read1, 10 bp Index1, 10 bp Index2, and 90 bp Read2) on a P3 flow cell (100 cycles) of the NextSeq 2000 platform (Illumina). FASTQ files for each sample were generated using the cellranger mkfastq pipeline (10x Genomics)Sample Treatment - A total of 288 C57BL/6 male and female mice, aged 8–10 weeks at baseline, were used in this study. They were assigned to one of the following experimental groups using simple randomization: obesity (OB), systemic arterial hypertension (SAH), chronic intermittent hypoxia (CIH), or chronic hyperglycemia (HG). Untreated animals served as controls (CTL group). Following our previous protocol12, OB mice were fed a high fat diet (HFD) [45 kcal% (24 g%) palm oil-based fat, 35 kcal% (41 g%) carbohydrate, 20 kcal% (24 g%) protein; based on OpenSource Diets No. D12451, Research Diet Services, Wijk bij Duurstede, The Netherlands]. Blood glucose (BG) levels were monitored monthly. For the SAH model, mice were fed a high-salt diet (HS, 8% NaCl; based on OpenSource Diets No. D12451, Research Diet Ser- vices, Wijk bij Duurstede, The Netherlands). A noninvasive tail cuff was used to assess blood pressure levels in conscious mice, with monthly assessment performed to verify the establishment of hypertension. CIH mice were exposed to chronic hypoxia (FiO2 10%) during 8 hours in their light cycle, 5 days per week. Throughout the hypoxic exposure, animals had free access to standard chow diet and water. For the HG model, mice were injected with streptozotocin (STZ, 50mg/kg, 0.05 mol/L in citrate buffer, pH 4.5, Sigma, St. Louis, USA) i.p for five consecutive days. BG levels were monitored before STZ injection and every 4-8 weeks to confirm the induction of hyperglycaemia. Body weight (BW) was monitored monthly across all models.Library Construction - Dual-indexed snRNA-seq libraries were generated with the Chromium Next GEM Single Cell 3′ GEM Library and Gel Bead Kit v3.1, following the manufacturer’s protocol. Library fragment size was assessed with a High Sensitivity DNA chip on a 2100 Bioanalyzer (Agilent Technologies), and concentrations were quantified using the Qubit fluorometer (Thermo Fisher Scientific). Individual libraries from each sample were diluted to a final concentration of 10 nM and subsequently pooledNucleic Acid Extraction - Each nuclei suspension was then loaded into a single port of a Chromium Next GEM Chip G (10x Genomics), targeting the recovery of approximately 10,000 nuclei per sample. Single nuclei were encapsulated into emulsion droplets using the Chromium Controller (10x Genomics),OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - Downstream analysis and quality control were carried out in R using the Scater (v1.26.1)17 and Seurat (v4.4.0)18 packages. Cells were retained if they met the following criteria: total UMI counts between 800 and 20,000, detection of at least 500 genes, mitochondrial gene content below 15%, a UMI contribution (relative to the sample’s median) greater than 0.2, gene expression complexity (percentage of reads from the top 50 expressed genes) under 60%, and hemoglobin gene set expression below 0.1%. Mitochondrial genes and hemoglobin genes. Potential doublets were identified and removed using scDblFinder (v1.12.0)19. After filtering and quality control, 50,764 high-quality single nuclei remained. Gene expression counts were normalized, and the top 2000 variable genes were selected using the variance-stabilizing transformation (VST) method. Data integration across samples was performed using the RPCA approach implemented in Seurat’s IntegrateData function. Dimensionality reduction and clustering were conducted based on 15 principal components, excluding those strongly correlated with cell cycle effects. Cluster marker identification and differential expression analysis between experimental groups were performed using the MAST algorithm. Only genes expressed in at least 30% of cells were tested for cluster markers, and those detected in at least 10% of cells were included in the differential expression analysis. Cell–cell communication was inferred using the LIANA framework, which integrates multiple ligand–receptor inference methods (natmi, connectome, logfc, and sca) to generate consensus predictions. Consensus ranks were obtained through robust rank aggregation (RRA), where lower RRA scores indicate interactions consistently ranked higher than expected by chance. Only clusters containing at least five cells were included in the analysis. Interactions were filtered based on the aggregate_rank (treated as a p-value) and retained only when the ligand and/or receptor genes showed significant differential expression (adjusted p-value < 0.05) between conditions. Netplots and dotplots were adapted to display the most relevant intercluster interactions. Netplots depict the number of significant connections between clusters, while dotplots show the expression levels (logFC) of ligand and receptor genes as outline and fill colors, respectively: green indicates upregulation, blue downregulation, white logFC = 0, and grey denotes genes not included in the differential expression analysis.Sequence Alignment - Sequencing data were processed using the Cell Ranger pipeline (v6.1.1, 10x Genomics), aligning reads to the GRCm38 mouse reference genome.MetabolomicsUnknownTranscriptomicsGenomicsProteomicsIllumina HiSeq 2000RNA-seq of coding RNA from single cellsMus musculusCarlos TorrojaLaura LalagunaAntonella AusielloEnrique LarafalseComorbidity-Specific Molecular Programs of Early Diastolic Dysfunction Identify Glucocorticoid Receptor Activation as a Causal Driver in ObesityEarly diastolic dysfunction precedes the development of HFpEF, but the molecular events that initiate this process remain poorly defined. HFpEF arises in the setting of systemic comorbidities such as obesity, hypertension, hyperglycemia, and sleep apnea, which often coexist and make it difficult to distinguish their individual contributions to early cardiac remodelling. Here, we aimed to define the early, comorbidity-specific mechanisms that initiate diastolic dysfunction across major HFpEF-associated conditions.2026-07-03T00:00:00Z2026-07-03T01:03:41.968Z2026-04-02T10:41:54.448ZE-MTAB-16848ERP191721EFO_0002944EFO_0004170EFO_0005684EFO_0004917EFO_0005518EFO_0003816EFO_0004184EFO_0003969