<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-14947</full_dataset_link><description>Aging is a major, yet unmodifiable, risk factor for cardiovascular disease, leading to vascular alterations, increased cardiac fibrosis, and inflammation, all of which contribute to impaired cardiac function. To investigate the spatial impact of aging, we applied an integrative approach combining single-nucleus RNA sequencing and spatial transcriptomics in 12-week-old and 18-month-old mice. We systematically mapped the aging heart and identified larger vessel-associated niches as key hotspots for activated macrophages and fibroblasts in aged hearts. These niches, surrounding arteries, were also enriched in senescent cells. Our findings suggest 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.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Library Construction - For spatial transcriptomics, tissue sections were placed onto Visium Spatial Gene Expression Slides (10X Genomics, PN-1000187) and processed according to the Visium Spatial Gene Expression User Guide. H&amp;E staining was performed as suggested by 10X Genomics. Brightfield imaging of tissue sections was performed using a Fritz brightfield digital microscope (Precipoint). Image stitching and processing were conducted using NIS-Elements software (Nikon).</sample_protocol><sample_protocol>Sequencing - Next-generation sequencing libraries were prepared following standard protocols provided in the Visium User Guide. Libraries were loaded at a concentration of 300 pM and sequenced on an Illumina NovaSeq 6000 System using parameters recommended by 10X Genomics.</sample_protocol><sample_protocol>Nucleic Acid Extraction - Frozen heart tissue samples were embedded in OCT compound (Tissue-Tek) and subsequently cryosectioned at 10 µm thickness using a Cryostar NX70 cryostat (Thermo Fisher Scientific). Tissue sections were immediately transferred onto pre-chilled Visium Tissue Optimization Slides (10X Genomics, PN-1000193) to determine the optimal permeabilization time. Following manufacturer recommendations (10X Genomics), optimization experiments established an ideal permeabilization window at either 12 or 18 minutes for efficient tissue digestion and RNA release.</sample_protocol><sample_protocol>Sample Collection - Mouse heart tissues were immediately frozen in liquid nitrogen after OCT compound (Tissue-Tek) embedding. Ten-micrometer tissue cryosections were stained with hematoxylin and eosin (H&amp;E) and the appropriate tissue regions were selected for further processing.</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>Sequence Alignment - The spatial transcriptomics FASTQ files and histology images were processed and mapped to the mouse reference genome (GRCm38, “refdata-gex-mm10-2020-A”) using the SpaceRanger software (v2.0.1) using the default parameters.</data_protocol><data_protocol>Data Transformation - . A permissive filtering was applied to the count matrices. We remove spots with a low number of UMI counts (&lt; 500) and genes (&lt; 300) as well as genes lowly expressed (&lt; 3 spots). Mitochondrial and hemoglobin genes were excluded from the analysis, as they do not provide relevant spatial information. Normalization was performed using the SCTransform function from Seurat (vst.flavor=\"v2\" and min_cells=3). The raw counts were also normalized and logarithmize using a scaling factor of 10,000 using the sc.pp.normalize_total and sc.pp.log1p functions from Scanpy.</data_protocol><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>Vascular niches as the primary hotspots for cardiac aging</name><description>Aging is a major, yet unmodifiable, risk factor for cardiovascular disease, leading to vascular alterations, increased cardiac fibrosis, and inflammation, all of which contribute to impaired cardiac function. To investigate the spatial impact of aging, we applied an integrative approach combining single-nucleus RNA sequencing and spatial transcriptomics in 12-week-old and 18-month-old mice. We systematically mapped the aging heart and identified larger vessel-associated niches as key hotspots for activated macrophages and fibroblasts in aged hearts. These niches, surrounding arteries, were also enriched in senescent cells. Our findings suggest 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.</description><dates><release>2025-10-31T00:00:00Z</release><modification>2025-10-31T02:01:43.947Z</modification><creation>2025-03-18T20:32:13.55Z</creation></dates><accession>E-MTAB-14947</accession><cross_references><ENA>ERP170502</ENA><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>