Abstract
Elucidating the regulatory mechanisms of human cardiac aging remains a great challenge. Here, using human heart tissues from 74 individuals ranging from young (≤35 years) to old (≥65 years), we provide an overview of the histological, cellular and molecular alterations underpinning the aging of human hearts. We decoded aging-related gene expression changes at single-cell resolution and identified increased inflammation as the key event, driven by upregulation of ARID5A, an RNA-binding protein. ARID5A epi-transcriptionally regulated Mitochondrial Antiviral Signaling Protein (MAVS) mRNA stability, leading to NF-κB and TBK1 activation, amplifying aging and inflammation phenotypes. The application of gene therapy using lentiviral vectors encoding shRNA targeting ARID5A into the myocardium not only mitigated the inflammatory and aging phenotypes but also bolstered cardiac function in aged mice. Altogether, our study provides a valuable resource and advances our understanding of cardiac aging mechanisms by deciphering the ARID5A-MAVS axis in post-transcriptional regulation.
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Data availability
The raw sequence data reported in the Article have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation, Beijing Institute of Genomics, Chinese Academy of Sciences (GSA for Human: HRA004053, https://ngdc.cncb.ac.cn/gsa-human/browse/HRA004053). The data and resources used in this study are publicly available and can be accessed through the following links: hg19 reference genome: https://ftp.ensembl.org/pub/grch37/release-87/; Aging Atlas: https://ngdc.cncb.ac.cn/aging/age_related_genes; DisGeNET: https://disgenet.com; KEGG: https://www.genome.jp/kegg/; and GO: https://www.geneontology.org/. These resources provide comprehensive data and annotations that support the analyses and findings presented in this study.
Code availability
The code used to reproduce the analyses and figures described in this study is available via GitHub at https://github.com/YandongZheng/scHumanHeatAgingAtlas.
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Acknowledgements
We would like to acknowledge J. Jia (Institute of Biophysics, Chinese Academy of Sciences) for his support in single-nucleus sorting by FACS. We also thank S. Li, H. Qin and J. Hao (Institute of Zoology, Chinese Academy of Sciences) for their assistance with tissue sectioning and photography, as well as Y. Deng (China National Center for Bioinformation) for helping with lentivirus centrifugation. In addition, we are grateful to J. Chen, X. Li (China National Center for Bioinformation), L. Bai, Q. Chu, L. Tian, J. Lu, Y. Yang, J. Li, S. Qiao and R. Bai (Institute of Zoology, Chinese Academy of Sciences) for their management support. This work was supported by the National Key Research and Development Program of China (2022YFA1103700 to W.Z.), the National Natural Science Foundation of China (32341001 to G.-H.L., 82125011 to J.Q., 81921006 to G.-H.L., 32121001 to W.Z., 92149301 to G.-H.L., 92168201 to G.-H.L., 82361148131 to W.Z., 82322025 to S.M., 82241205 to W.J., 82170487 to W.J.), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA0460403 to W.Z.), Beijing Natural Science Foundation (JQ24044 to W.Z.), Shenzhen Medical Research Fund (D2401003 to Y. Fan), CAS Youth Interdisciplinary Team to W.Z., the National Key Research and Development Program of China (2020YFA0804000 to G.-H.L., 2021YFF1201000 to W.Z., the STI2030-Major Projects-2021ZD0202400 to S.W.), the National Natural Science Foundation of China (82330044 to G.-H.L., 92049304, 82192863 to W.Z., 82122024 to S.W., 82071588 to S.W., 82271600 to S.M., 82070332 to Y. Yao, 82370315 to Y. Yao, 82270691 to G.X., 82070674 to G.X, 82361148130 to G.-H.L.), CAS Project for Young Scientists in Basic Research (YSBR-076 to G.-H.L., YSBR-012 to W.Z., YSBR-073 to Y. Yang), the Program of the Beijing Natural Science Foundation (5242024 to Y. Fan, JQ24039 to W.J.), Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (JYY2023-13 to W.Z.), Youth Innovation Promotion Association of CAS (2022083 to S.M.), the Informatization Plan of Chinese Academy of Sciences (CAS-WX2022SDC-XK14 to G.-H.L.), New Cornerstone Science Foundation through the XPLORER PRIZE (2021-1045 to G.-H.L.), Excellent Young Talents Program of Capital Medical University (12300927 to S.W.), The Project for Technology Development of Beijing-affiliated Medical Research Institutes (11000023T000002036310 to S.W.), Excellent Young Talents Training Program for the Construction of Beijing Municipal University Teacher Team (BPHR202203105 to S.W.), Initiative Scientific Research Program, Institute of Zoology, Chinese Academy of Sciences (2023IOZ0102 to S.M.), Scientific Research Key Program of Beijing Municipal Commission of Education (KZ202110025033 to Y. Yao), the Fundamental Research Funds for the Central Universities (number 2023SCU12055 to G.X.), the Beijing Nova Program (Z201100006820104 to Y. Yang), the China Postdoctoral Science Foundation (number 2022M712262 to G.X.) and Beijing Anzhen Hospital Major Science and Technology Innovation Fund (number KCZD202203 to W.J., KCQY202201 to W.J.).
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Authors and Affiliations
Contributions
W.Z., G.-H.L. and J.Q. conceptualized the study and supervised all experiments and bioinformatics analyses. Y. Fan performed the hCF and hCE cell culture, nuclei isolation for snRNA-seq and cardiac aging phenotypic and mechanistic analyses, including immunofluorescence and immunohistochemistry staining, qRT-PCR, western blotting, plasmid construction and RIP-qPCR. Y. Zheng performed bioinformatic analyses. Y. Zhang performed hCMs differentiation, cell culture and the corresponding phenotypic analyses. Y. Yao and G.X. collected human cardiac tissues. C.L. wrote the paper. J.H. and X.W. performed PAR-CLIP-seq library construction and the mRNA lifetime analysis. Q.J. helped with the PAR-CLIP-seq data analysis. S.Z. and S.F. helped with immunofluorescence and immunohistochemistry staining, qRT-PCR and western blotting. J.L. helped with intramyocardial injection of lentiviruses in mice. L.-Z.L. helped with nuclei isolation for snRNA-seq. C.W. helped with plasmid construction. S.W., S.M., M.S., Y. Feng, Y. Yang, G.Z., X.-L.T. and W.S. helped with the supervision of the project. X.X., W.J., J.Z., J.W., H.Z. and J.Y. helped with the collection of human cardiac tissues. W.Z., G.-H.L., J.Q., Y. Fan, Y. Zheng, Y. Zhang and C.L. wrote and reviewed the paper. All authors have agreed and reviewed the paper.
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Extended data
Extended Data Fig. 1 Phenotypic analysis in young and aged human hearts.
a, Echocardiographic analysis of left ventricular ejection fraction (LVEF) in human subjects across different groups. Data are presented as mean ± s.e.m., with n = 26 individuals per group. Detailed information for all individuals is summarized in Supplementary Table 1. b, Representative images of the hematoxylin-eosin (H&E) stained sections of right atrial tissue from young and elderly individuals are depicted. The tissue procurement methodology for these samples strictly adhered to the protocol utilized for single-cell nuclear sequencing specimens. Scale bars, 800 µm. c, Immunofluorescence staining of H3K9me3, cTnT and Hoechst in young and aged human RA tissues. Scale bars, 10 μm and 5 μm (zoomed-in image). d, Immunohistochemical staining of HP1α in young- and aged- human tissues. Scale bars, 20 μm and 5 μm (zoomed-in image). e, Immunohistochemical staining of LAP2β in young- and aged- human RA tissues. Scale bars, 20 μm and 5 μm (zoomed-in image). f, Immunofluorescence staining of cTnT and WGA in young and aged human cardiac tissues. Scale bars, 25 μm and 5 μm (zoomed-in image). g, RT-qPCR analysis of NPPA, NPPB, MYH6 and MYH7 expression in young and aged human RA tissues. All data are presented as the mean ± s.e.m. For Extended Data Figs. 1c–f and g, n = 10 and 8 respectively for both the young and aged groups. *P < 0.05, **P < 0.01. Intergroup differences were assessed using a two-tailed unpaired Student’s t-test.
Extended Data Fig. 2 Quality control and cell type analysis of human cardiac snRNA-seq.
a, Histograms showing the frequency distribution of unique molecular identifiers (UMIs) (left) and genes (right) detected in each sample. b, The scatter plot showing the proportion of mitochondrial genes in all cells of human cardiac tissue. The color key ranging from yellow to red indicates an increase in the proportion of mitochondrial genes. Cells with low quality are colored in gray. c, The plot showing the distribution of quality control metrics across samples in snRNA-seq data obtained from human cardiac tissue. The height of each bar represents the median number of genes detected per cell for the corresponding sample, with yellow and green dots indicating sequencing saturation and mapping rate to the genome, respectively. d, The dot plot shows the proportion of mitochondrial genes per cell in each sample of human heart tissue. e, The heatmap showing the number of nuclei in all samples used for single-nucleus transcriptome sequencing in this study. f, UMAP plot showing the distribution of cells in young and aged groups of human cardiac tissue. g, The dot plot depicts the expression levels of representative marker genes for each cell type, where dot size and color reflect the percentage of cell expression and the average expression level of the certain gene, respectively. h, UMAP plot showing the expression of representative marker gene in T cells and NKT cells. i, UMAP plot showing the distribution of subclusters of T cells and NKT cells. Left panel, the cells are colored by different subclusters or cell types. Right panel, violin plots showing the expression of representative marker genes in NKT cells. j, The dot plot depicts a subset of aging-related differential genes identified based on M-specific recognition. where dot size and color reflect the percentage of cell expression and the average expression level of the certain gene, respectively. k, Bar plot showing the proportion of each cell type in the human heart across young and aged groups. Each point represents a sample. The data from 16 young samples and 16 aged samples. l, Bar plot showing the proportion of CD3E, and CD247 positive cells in human cardiac tissue across age groups. Data are derived from 16 young samples and 16 aged samples. Statistical significance was assessed using an unpaired two-tailed Student’s t-test.
Extended Data Fig. 3 Aged cardiac cells show increased inflammation, fibrosis and transcriptional noise.
a, UMAP plot showing the distribution of subclusters of fibroblasts. b, The bar plot showing the fold change in cell proportion for fibroblast subcluster (aged vs. young), with purple bars indicating up-regulation in aged individuals and green bars indicating down-regulation. c, Heatmaps showing the average AUC scores of TBK1/IKK activation, NF-κB signaling pathway, SASP-related genes, inflammatory-related genes and cardiac fibrosis-related genes. d, The bar plots display enriched representative Gene Ontology (GO) terms or pathways for indicated fibroblast subsets upregulated or downregulated with aging. Colors indicate different cell subpopulation, and the length of the bar indicates −log10 (p-value). e, Immunofluorescence staining of Collagen I, cTnT and WGA in young- and aged- human cardiac tissues. Scale bars, 20 μm and 5 μm (zoomed-in image). Data are presented as mean ± s.e.m., n = 10, *P < 0.05. f, The box plots showing the transcriptional noise of each cell type in the human cardiac tissue between young and aged groups. The y-axis value of each box indicates the interquartile range, the horizontal line within the box is the median, and the whiskers represent 1.5 times the interquartile range. The gray color indicates young cells, while red represents aged cells. Data are derived from 16 young samples and 16 aged samples. The P-value is indicated above each boxplot (Wilcoxon rank-sum test). g, The scatter plots depict the ratio of transcriptional noise between old and young samples in human heart tissue, where the X-axis represents the 1-Spearman correlation coefficient, and the Y-axis indicates the Euclidean distance between cells. The magnitude of the dots is aligned with the -log10(adjusted p-values) from the differential transcriptional noise tests specific to each cell type, with the red lines indicating the fit of the linear regression model. h, Top, the heatmap displaying the row Z-score expression levels of genes with high Pearson’s correlation coefficients (correlation coefficient > 0.6 and FDR < 0.05) between the coefficient of variation and expression levels in cardiomyocytes, fibroblasts, and endothelial cells. The bins are ordered based on the coefficient of variation ranking within each group. Bottom, bar plot displaying the representative Gene Ontology (GO) terms and pathways that were enriched for genes exhibiting high Pearson’s correlation coefficients in corresponding cell types.
Extended Data Fig. 4 The factorization model based on machine learning and generalized linear model algorithm in cardiac snRNA-seq.
a, Schematic diagram of the generation of MlGlmCells factorization model that separates the dual effects of age and gender across different samples, which is based on machine learning and generalized linear model algorithm. Materials sourced from the Freepik website. b, Bar plot showing the prediction precision of age factorization for each cell type using the MlGlmCells model. TP: true positive (young samples that have been correctly identified), FN: false negative (young samples that have been misidentified), TN: true negative (aged samples that have been correctly identified), FP: false positive (aged samples that have been misidentified). c, Scatter plots showing the accuracy of age factorization performed by the MlGlmCells model for each cell type. d, Bar plot showing the prediction precision of gender factorization for each cell type using the MlGlmCells model. TP: true positive (male samples that have been correctly identified), FN: false negative (male samples that have been misidentified), TN: true negative (female samples that have been correctly identified), FP: false positive (female samples that have been misidentified). e, Scatter plots showing the accuracy of gender factorization performed by the MlGlmCells model for each cell type. f, Venn diagram showing the comparison of the number of differential genes analyzed by Seurat model and the MlGlmCells model. Panel a created with BioRender.com.
Extended Data Fig. 5 Aging-associated DEG analysis reveals elevated inflammatory and cellular interaction in the aged heart.
a, Heatmap showing the number of upregulated (purple) and downregulated (green) aging-related DEGs in each cell type. b, Augur was used to prioritize cell types in human cardiac tissue under the influence of age factors. c, The network showing representative GO terms and pathways of aging-related upregulated (up) and downregulated (bottom) DEGs between aged and young human cardiac tissue. The node size indicates the total number of hits for a specific term, and lines connect those with a similarity > 0.3. d, The bar plots display enriched representative Gene Ontology (GO) terms or pathways for aging-related up-regulated (left) and down-regulated (right) differentially expressed genes (DEGs) in cardiomyocytes, fibroblasts, and endothelial cells in human heart tissue. e, Bar plot showing the proportion of CDKN1A- (left), CDKN2A- (middle), and CDKN2B-positive (right) cells in human cardiac tissue across age groups. The data were acquired from 16 young samples and 16 aged samples. Statistical significance was assessed using an unpaired two-tailed t-test. f, The circular heatmap showing the expression fold change (aged vs. young) of inflammation-related gene set for each cell types. The red indicates up-regulation in the aged samples, while green denotes downregulation. The intensity of color reflects the level of overexpression in aged cells for each gene. g, The circular heatmap showing the expression fold change (aged vs. young) of senescence-associated secretory phenotype (SASP)-related genes for each cell types. The red indicates up-regulation in the aged samples, while green denotes downregulation. The intensity of color reflects the level of overexpression in aged cells for each gene. h, Immunohistochemical staining of S100A9 in young- and aged- human right cardiac tissues. Scale bars, 20 μm and 5 μm (zoomed-in image). Data are presented as mean ± s.e.m., n = 10, **P < 0.01. Statistical analysis was conducted by using unpaired student’s t-test (two-tailed).
Extended Data Fig. 6 The intercellular inflammatory interactions are enhanced in the aged heart.
a, The heatmap showing the expression levels of inflammatory cytokines in human cardiac immune cells, with purple indicating up-regulation and green indicating down-regulation. b, The circle plot showing the top 100 highly expressed ligand-receptor interactions between cardiac immune cells and cardiac parenchymal cell and fibroblasts in the old vs. young groups. The gene types of ligands and receptor are colored by yellow, green and black, and are annotated on the right. The ligand-receptor interaction events are indicated by connecting lines with arrows. The thickness of the line is positively correlated with the expression level of the ligand, and the size of the arrow is positively correlated with the expression level of the receptor. Purple lines indicated upregulated ligand and/or receptor in the old group, green lines indicated ligand-receptor pairs downregulated in the old group. c, Bar plot showing the frequency of ligand-receptor pairs. d, Sankey diagram showing the potential cell-cell interaction between cardiac cells and immune cells. The width of the connection line is proportional to the average difference multiple of the receptor ligand pairs in the old and young groups. Purple connecting lines indicate that both ligands and receptors are up-regulated in the older group, and green indicates down-regulated. Left panel, the cell-cell interaction between immune cells and cardiomyocytes. Right panel, the cell-cell interaction between immune cells and cardiac stromal cells.
Extended Data Fig. 7 Increased inflammation levels and ARID5A expression in aged human hearts.
a, The circular heatmap showing the expression fold change (aged vs. young) of top 50 age-related down-regulated DEGs with the highest frequency of occurrence across multiple cell types. The intensity of color reflects the level of expression in aged cells for each gene. b, Network visualizing the up- and down-regulated aging-related DEGs across all cell types, which overlapped with genes annotated in the Aging Atlas database. Node size represents the frequency at which DEGs appear across various cell types, while red and blue parts of the nodes represent up- and downregulated genes, respectively. c, The dendrogram depicts the hierarchical clustering of gene co-expression modules based on topological overlap matrix (TOM) in single-nucleus transcriptome sequencing data of human cardiac tissue. d, The violin plots showing the AUC scores of hub genes from module 2 for each cell type in human cardiac tissue across young and aged groups. The dots within the violin plots represent the median, while the whiskers indicate a range of 1.5 times the interquartile distance. e, The bar plot showing the enriched GO terms or pathways for hub genes from module 2.
Extended Data Fig. 8 ARID5A drives senescence and functional decline in hCEs and regulates the expression of MAVS.
a, UMAP plot highlighting the expression of ARID5A. b, Western blotting analysis of ARID5A and P21Cip1 expression in the earlier passage (EP) and later passage (LP) of human cardiac endothelial cells. c, SA-β-Gal staining in the EP and LP human cardiac endothelial cells. ***P < 0.001. Scale bars, 50 μm. d, Tube formation analysis of the EP and LP human cardiac endothelial cells. Scale bars, 100 μm. e, RT-qPCR analysis of ARID5A and CDKN1A expression in human cardiac endothelial cells at earlier passage infected with lentiviruses expressing sgNTC or sgARID5A using a CRISPRa system. f-i, Western blotting analysis of ARID5A (f), SA-β-Gal staining (g), tube formation (h), and cell migration (i) in human cardiac endothelial cells at early passage infected with lentiviruses expressing sgNTC or sgARID5A using a CRISPRa system. Scale bars: 50 μm in (g), 100 μm in (h), and 50 μm in (i). j-m, Western blotting analysis of ARID5A (j), SA-β-Gal staining (k), tube formation assay (l), and cell migration assay (m) in human cardiac endothelial cells at late passage infected with lentiviruses expressing sgNTC or sgARID5A using a CRISPRi system. Scale bars: 50 μm in (k), 100 μm in (l), and 50 μm in (m). n, RT-qPCR analysis of ARID5A and CDKN1A1 expression in human cardiac endothelial cells at late passage infected with lentiviruses expressing sgNTC or sgARID5A using a CRISPRi system. o, UMAP plot highlighting the expression of MAVS in right atrium. p, Western blot analysis of MAVS expression in Flag-MAVS overexpression and Flag-Luc (control) cardiomyocytes. q, Immunofluorescence staining of cTnT in Flag-MAVS overexpression and Flag-Luc (control) cardiomyocytes. Scale bars, 10 μm. r, Western blot analysis of P21Cip1 expression in Flag-MAVS overexpression and Flag-Luc (control) cardiomyocytes. s, SA-β-gal staining in Flag-MAVS overexpression and Flag-Luc (control) cardiomyocytes. Scale bars, 25 μm. t, RT-qPCR analysis of IL6 expression in human cardiac fibroblasts transfected with siNTC or siMAVS. These cells were pre-infected with lentiviruses expressing Flag-tagged Gal4 or ARID5A. All data are presented as the mean ± s.e.m. For Extended Data Figs. 7b–t, n = 3 independent experiments, *P < 0.05, **P < 0.01, ***P < 0.001. Statistical analysis was conducted by using unpaired student’s t-test (two-tailed).
Extended Data Fig. 9 Published data showed a correlation between ARID5A and MAVS expression.
a, UMAP plot showing the distribution of human ventricular cells derived from published data. Fifteen cell types were identified. b, Visualization of gene expression of ARID5A in published data. c, Visualization of gene expression of MAVS in published data. d, The violin plots showing the expression of ARID5A in human left ventricular different cell types across young and aged groups. The dataset includes 3 young samples (comprising 54,692 cells) and 15 aged samples (comprising 176,194 cells). Statistical significance was assessed using the Wilcoxon rank-sum test. e, The violin plots showing the expression of MAVS in human left ventricular different cell types across young and aged groups. The dataset includes 3 young samples (comprising 54,692 cells) and 15 aged samples (comprising 176,194 cells). Statistical significance was assessed using the Wilcoxon rank-sum test. f, The violin plots showing the total expression of ARID5A in human left ventricle across young and aged groups. The dataset includes 3 young samples (comprising 54,692 cells) and 15 aged samples (comprising 176,194 cells). Statistical significance was assessed using the Wilcoxon rank-sum test. g, The violin plots showing the total expression of MAVS in human left ventricle across young and aged groups. The dataset includes 3 young samples (comprising 54,692 cells) and 15 aged samples (comprising 176,194 cells). Statistical significance was assessed using the Wilcoxon rank-sum test. h, The scatter plot showing the correlation between ARID5A and MAVS in cardiac cells. i-m, Ridgeline plots showing the AUC scores of cardiac fibrosis-related genes (i), SASP-related genes (j), inflammatory-related genes (k), NF-κB signaling pathway (l) and activation of IRF3/IRF7 mediated by TBK1/IKK epsilon genes (m) in different cell types of young and aged human cardiac tissues. The inner plot: expression of all young and old cells; the outer heatmap: fold change (aged versus young) per cell type. Box plots indicate the median (centre line), 25th and 75th percentiles (box bounds), 1.5 times interquartile range (whiskers), and minima/maxima within the whisker range (dots/caps). The dataset includes 3 young samples (comprising 54,692 cells) and 15 aged samples (comprising 176,194 cells). Statistical significance was assessed using the Wilcoxon rank-sum test.
Extended Data Fig. 10 Silencing the expression of ARID5A attenuates cardiac inflammation.
a, Western-blot analysis of ARID5A expression in young and aged mouse heart. Data are presented as mean ± SEM, n = 6 in each group. **P < 0.01. Statistical analysis was conducted by using unpaired student’s t-test (two-tailed). b, Immunofluorescence staining of F4/80 in cardiac tissues of young-shGL2, aged-shGL2, and aged-shARID5A mice. Scale bars, 20 μm and 5 μm (zoomed-in image). Data are presented as mean ± s.e.m., n = 7, **P < 0.01. Statistical analysis was conducted by using one-way ANOVA.
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Fan, Y., Zheng, Y., Zhang, Y. et al. ARID5A orchestrates cardiac aging and inflammation through MAVS mRNA stabilization. Nat Cardiovasc Res 4, 602–623 (2025). https://doi.org/10.1038/s44161-025-00635-z
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DOI: https://doi.org/10.1038/s44161-025-00635-z
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