<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>David John</submitter><organism>Mus musculus</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-17510</full_dataset_link><description>Aging is a major risk factor for cardiovascular diseases, yet the contribution of the lymphatic vasculature to cardiac aging remains largely unexplored. Here, we show that aging reduces lymphatic vessel density in human and mice hearts and induces morphological changes, including the formation of zipper-like, tighter endothelial junctions. These alterations are accompanied by immune cell infiltration, fibrinogen and amyloid accumulation, and myocardial edema. Experimental reduction of cardiac lymphatics in young mice, achieved by Flt4 (VEGFR3) depletion or overexpression of soluble Flt4, reproduces some age-related cardiac phenotypes, such as inflammation and impaired lymphatic integrity. Mechanistically, we found that aging induces the selective up-regulation of nuclear interleukin 33 (IL33) in lymphatic endothelial cells. In contrast to the extracellular, cardioprotective form of IL33, nuclear IL33 promotes lymphatic cell death and junctional remodeling. A targeted screen of pro-lymphatic factors identified VEGFC as an age-sensitive regulator that both declines in the aging heart and suppresses IL33. Cardiac Vegfc overexpression or Il33 silencing restores lymphatic vessel density, reduces macrophage infiltration, and improves tissue homeostasis in aged hearts. Collectively, these findings establish cardiac lymphatic dysfunction and VEGFC deficiency as key features of cardiac aging, highlighting potential therapeutic entry points for age-associated heart disease.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Sample Collection - Aged male and female C57Bl/6J wildtype mice were purchased from Janvier (Le Genest SaintIsle, France) and from Charles River (Sulzfeld, Germany). Homozygosity of these inbred mice was controlled by Janvier and Charles River using exome sequencing. Mice have been kept in a 12h-day-night cycyle. All animal experiments have been executed in accordance to the guidelines from Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes and were approved by the local authorities by the state of Hesse (Regierungspräsidium Darmstadt).</sample_protocol><sample_protocol>Nucleic Acid Extraction - Resulting non-cardiomyocyte cellular suspensions were loaded on a 10X Chromium Controller (10X Genomics) according to manufacturer’s protocol. Murine scRNA-seq libraries were prepared using Chromium Single Cell 3′ v3 Reagent Kit (10X Genomics), according to manufacturer’s protocols. Briefly, the initial step consisted in generating cell partitioning droplets where individual cells were isolated together with gel beads coated with unique primers bearing 10X cell barcodes, UMI (unique molecular identifiers) and poly(dT) sequences. Reverse transcription reactions were engaged to generate barcoded full-length cDNA followed by the disruption of emulsions using the recovery agent and cDNA clean up with DynaBeads MyOne Silane Beads (37002D, Thermo Fisher Scientific). Bulk cDNA was amplified using a Biometra Thermocycler Professional Basic Gradient with 96-Well Sample Block (98 °C for 3 minutes; cycled 14×: 98 °C for 15 s, 67 °C for 20 s, and 72 °C for 1 minute; 72 °C for 1 minute; held at 4 °C). Amplified cDNA product was cleaned with the SPRIselect Reagent Kit (B23318, Beckman Coulter). Indexed sequencing libraries were constructed using the reagents from the Chromium Single Cell 3′ v3 Reagent Kit as follows: fragmentation, end repair and A-tailing; size selection with SPRIselect; adaptor ligation; post-ligation cleanup with SPRIselect; sample index PCR and cleanup with SPRI select beads.</sample_protocol><sample_protocol>Library Construction - Single cell RNA libraries were generated using the Gem-X Kit (1000686, 10X Genomics), using standard GEM construction, amplification, fragmentation and indexing procedures.</sample_protocol><sample_protocol>Sequencing - Libraries were sequenced on the Illumina NovaSeq 6000 platform using a S4 flowcell kit (PE100, depth of 50,000 reads/nucleus) with the following specifications: Read 1: 28 bp, Read 2: 90 bp, Index 1: 10 bp, Index 2: 10 bp.</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - Data processing was performed using Scanpy (v1.10.1)44 operated under Python 3.11.8. he count matrices were processed individually and then combined into one AnnData object. Genes lowly express (&lt; 3 cells) and cells with low number of genes (&lt;800) and high mitochondrial content (>7.5%) were excluded from the analysis. After quality control, gene expression values were normalized and logarithmize using a scaling factor of 10,000. The processed data was integrated with the endothelial cells from a public dataset from young and old mice to increase the statistical power18. The integration was performed using BBKNN45 and the Leiden algorithm was used to cluster the data. Annotation was performed using a curated list of markers46,47 and the uniform manifold approximation and projection (UMAP) embeddings were computed. Differential expression analysis was performed using the Wilcoxon Rank Sum test and MAST with Benjamini-Hochhberh correction for multiple testing.</data_protocol><data_protocol>Sequence Alignment - The FASTQ files were mapped to the mouse reference genome (GRCm38) using the CellRanger (v7.0.0) software from 10x.</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq 6000</instrument_platform><study_type>RNA-seq of coding RNA from single cells</study_type><species>Mus musculus</species><pubmed_authors>David John</pubmed_authors></additional><is_claimable>false</is_claimable><name>Age-Associated Loss of Lymphatic Vessels Promotes Cardiac Inflammation (sc-RNA-SEQ)</name><description>Aging is a major risk factor for cardiovascular diseases, yet the contribution of the lymphatic vasculature to cardiac aging remains largely unexplored. Here, we show that aging reduces lymphatic vessel density in human and mice hearts and induces morphological changes, including the formation of zipper-like, tighter endothelial junctions. These alterations are accompanied by immune cell infiltration, fibrinogen and amyloid accumulation, and myocardial edema. Experimental reduction of cardiac lymphatics in young mice, achieved by Flt4 (VEGFR3) depletion or overexpression of soluble Flt4, reproduces some age-related cardiac phenotypes, such as inflammation and impaired lymphatic integrity. Mechanistically, we found that aging induces the selective up-regulation of nuclear interleukin 33 (IL33) in lymphatic endothelial cells. In contrast to the extracellular, cardioprotective form of IL33, nuclear IL33 promotes lymphatic cell death and junctional remodeling. A targeted screen of pro-lymphatic factors identified VEGFC as an age-sensitive regulator that both declines in the aging heart and suppresses IL33. Cardiac Vegfc overexpression or Il33 silencing restores lymphatic vessel density, reduces macrophage infiltration, and improves tissue homeostasis in aged hearts. Collectively, these findings establish cardiac lymphatic dysfunction and VEGFC deficiency as key features of cardiac aging, highlighting potential therapeutic entry points for age-associated heart disease.</description><dates><release>2026-08-14T00:00:00Z</release><modification>2026-08-14T14:43:57.251Z</modification><creation>2026-08-13T20:09:01.373Z</creation></dates><accession>E-MTAB-17510</accession><cross_references><ENA>ERP203682</ENA><Biostudies>E-MTAB-7895</Biostudies><Biostudies>E-MTAB-9817</Biostudies><Biostudies>E-MTAB-9816</Biostudies><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005684</EFO><EFO>EFO_0004917</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>