Single-cell transcriptomics reveals how Shenfu Qiangxin pill ameliorates HFpEF by modulating cardiac cellular heterogeneity

Figures

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  Fig. 1. The UPLC-Q-TOF–MS/MS analysis of SFQX. A Chromatogram in positive (upper) and negative (lower) ion modes. B Numbers of different types of compounds

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  Fig. 2. SFQX attenuates the clinical manifestations exhibited by HFpEF mice. A Schematic diagram of HFpEF model and drug intervention. B Body weight. C Heart weight/tibia length (HW/TL) ratio. D Lung wet-to-dry weight ratio. E Representative echocardiographic images (M-mode, pulse-wave Doppler, and tissue Doppler). Quantitative analysis of F LVEF (%), G E/A, and H E/e’. I Blood pressure. J Running distance. Concentration of K NT-proBNP and ANP. L Representative images of gross cardiac morphology and H&E. (n = 3). M Representative images of WGA staining. (scale bar = 50 μm). N Quantification of cardiomyocyte cross-sectional area in each group. 6 cardiomyocytes per mouse (n = 3 mice per group). Data are presented as mean ± SD (n = 6). *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 3. SFQX ameliorates cardiac metabolic stress in HFpEF mice. A Glucose tolerance test in each group. B Quantitative analysis of fasting glucose and area under the curve (AUC) for the glucose tolerance test, n = 6. C mRNA expression of glycolysis-related genes in the heart, n = 4. D mRNA expression levels of genes related to fatty acid activation, transport, and β-oxidation in each group, n = 4. Data are presented as mean ± SD. *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 4. Single-cell atlas of the heart in HFpEF mice. A Visualization of cell populations via Uniform Manifold Approximation and Projection (UMAP), with groups color-coded. B Projection of cell type-specific marker gene expression onto a bubble chart. C Proportion of main cell types in Control, HFpEF and SFQX group. D Comparative analysis of gene expression for all cell types: HFpEF vs. Control and SFQX vs. HFpEF. E The strength and number of interactions in the Control, HFpEF, and SFQX groups. F The intercellular interaction network in the three groups. Edge width represents the interaction strength, and the edges are color-coded based on the signal source. G Relative information flow for each signaling pathway in the three groups, defined as the sum of communication probabilities across all subpopulation pairs. H Schematic diagram of SFQX restoring intercellular communication in HFpEF

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  Fig. 5. SFQX modulates macrophage subsets to mitigate inflammatory responses. A UMAP embedding of macrophages, colored by manually annotated subsets. B Marker genes for each macrophage cluster. C Abundance of macrophage subsets in Control, HFpEF, and SFQX groups. D Flow cytometry analysis, and E quantification of LY6C+/CCR2+ cells, respectively, n = 3. Identification of differentially expressed genes (DEGs) in macrophages: F HFpEF versus Control, and G SFQX treatment versus HFpEF. H Inference of the pseudotime trajectories of macrophages, color-coded as per the cell subsets. I Analysis of cellular state proportions along pseudotime trajectories across three groups. J LEGENDplex™ quantification of inflammatory cytokines in serum, n = 6. K Inflammatory factor mRNA expression levels, n = 4. Data are presented as mean ± SD. *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 6. SFQX modulates fibroblast subsets to reduce cardiac fibrosis. A UMAP embedding of fibroblasts (FBs), colored by manually annotated subsets. B Marker genes for each FB cluster. C Abundance of FB subsets in Control, HFpEF, and SFQX groups. Identification of DEGs in FBs: D HFpEF versus Control, and E SFQX treatment versus HFpEF. F, G Bio-functional analysis of the DEGs. H Representative images of Masson and Sirius red. I, J Western blot analysis of Collagen III, Collagen I, and α-SMA protein expressions. Data are presented as mean ± SD (n = 3). *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 7. SFQX modulates endothelial cell subsets to improve angiogenic disorders. A UMAP embedding of endothelial cells (ECs), colored by manually annotated subsets. B Marker genes for each EC cluster. C Abundance of EC subsets in Control, HFpEF, and SFQX groups. D Heatmap of average expression levels for key genes in multiple energy metabolism pathways. E, F Western blot analysis of VEGFA and VEGFR2 protein expressions (n = 3). G qPCR analysis of Vegfa, Vegfr-2, and Fgf2 mRNA expressions (n = 4). H Cardiac microvascular network visualized by lectin staining. Data are presented as mean ± SD. *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 8. SFQX restores lymphatic endothelial homeostasis to promote cardiac lymphatic drainage. A Whole heart clearing and whole-mount immunofluorescence staining for LYVE1 (lymphatic endothelial hyaluronic acid receptor 1). B Reconstruction and quantification of the 3D cardiac lymphatic system. C The concentration of VEGFC in serum (n = 6). qPCR analysis of D Vegfc, E Vegfd, F Lyve1, G Vegfr-3, and H Pdpn mRNA expressions (n = 4). I–J Western blot analysis of the protein level of LYVE1, PROX1 and VEGFR-3 in the heart (n = 3). K, L Assessment of cardiac lymphatic drainage function using Evans blue dye (n = 4). M Quantification of cardiac edema by the wet-to-dry heart weight ratio (n = 4). Data are presented as mean ± SD. *p < 0.05, **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. HFpEF group

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  Fig. 9. Schematic diagram of SFQX modulating cardiac cell heterogeneity for the treatment of HFpEF