Cellular crosstalk in organotypic vasculature: mechanisms of diabetic cardiorenal complications and SGLT2i responses
Diabetes mellitus (DM) is a global chronic metabolic disease that imposes significant healthcare burdens, social costs, and premature mortality, which pose a critical threat to public health . Among its most severe complications is diabetic panvascular disease (DPD), characterized by widespread atherosclerosis affecting both macrovessels and microvessels in the heart, brain, kidneys, eyes, and peripheral systems. Particularly, DPD impairs the function of many vital organs, such as the heart and kidneys . The vasculature, one of the first functional systems to develop during embryogenesis, is also the first to be affected by DPD . It primarily comprises two cell types: endothelial cells (ECs) lining the lumen and mural cells (pericytes and vascular smooth muscle cells [VSMCs]) surrounding the ECs . As the innermost layer of the vasculature, ECs act as an important interface between organ parenchyma and circulating blood and regulate vascular tone, host defense, and metabolite exchange by sensing the circulatory environment . The heterogeneity and functional characterization of ECs are crucial for understanding physiological adaptation to the onset of diseases. Mural cells, which partially cover ECs, are essential for vascular formation and maturation. These cells influence EC behavior through cell-to-cell interactions (CCIs), thus regulating vascular stability, permeability, and remodeling . Recently, significant evidence has highlighted the heterogeneity of vascular cells, particularly ECs, in aspects including morphology, gene expression, function, metabolism, and proliferative potential . The recent development of single-cell RNA sequencing (scRNA-seq) has revolutionized the identification of vascular cell subtypes and the characterization of cellular heterogeneity in complex tissues with unprecedented resolution . However, current studies on EC have primarily focused on molecular characteristics and gene expression profiles of different EC subtypes, often overlooking their complex communication network with the vasculature’s outer layer and the tissue microenvironment. This gap hinders a comprehensive understanding of vasculature physiological functions and the co-morbidity mechanisms underlying panvascular disease (PVD). Multicellular organisms depend on cellular activity coordination, which is mediated by cellular communication networks among different cell types . As a key interface between the circulatory system and various organ environments, the signaling interactions (e.g., CCI) of the vasculature have not been systematically explored to date. Identification of common CCI patterns and the underlying molecular mechanisms of the vasculature in both normal and diseased conditions could pave the way for developing more targeted and effective therapeutic strategies for PVD. This study systematically integrated and annotated single-cell transcriptomic data from eight tissues: the heart, brain, hypothalamus, lung, liver, kidney, aorta, and retina, sampled from young to old mice (4 weeks–96 weeks). This yielded 63 distinct cell types. Based on cellular annotation, the cellular communication characteristics of the vasculature across tissues and cell types in different physiological states were comprehensively analyzed. High-frequency signaling pathways involved in EC communication shared across multiple tissues were identified. ECs were found to be the major cell type mediating CCIs in the vasculature, with their functions dependent on high-frequency receptor–ligand pairs. Moreover, varying degrees of CCI heterogeneity were observed across different EC subtypes. Notably, we focused on the heterogeneity/conservativeness of communication in major target organs (heart and kidney) during DPD. Four significant communication modules were identified, and the roles of CCI mediators and sodium–glucose cotransporter 2 inhibitors (SGLT2i) in diabetic cardiorenal complications were verified. These findings provide valuable insights into the pathological mechanisms of DPD and offer guidance for the clinical treatment of diabetic cardiorenal complications. 

Dataset resource By manually searching the Gene Expression Omnibus (GEO) database using the keywords “scRNA-seq,” “snRNA-seq,” “single-cell RNA sequencing,” and “single-nucleus RNA sequencing,” a total of 14 mice scRNA-seq and snRNA-seq datasets were retrieved (Table ). These datasets include 8 tissues from the heart, brain, hypothalamus, kidney, lung, liver, retina, and aorta, which covered an age span of 4 weeks–96 weeks. scRNA-seq/snRNA-seq data preprocessing Cell Ranger version 3.1.0 software ( https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest ) was used to demultiplex the FASTQ reads and align them to the mouse transcriptome (mm10, provided by 10x Genomics) with the default parameters. The output of this pipeline contains a digital gene-barcode matrix for each sample. Then, all of the matrices were subjected to Seurat version 4.3  for further data processing. Cells with more than 6,000 or less than 200 detected genes as well as those with a mitochondrial transcription ratio > 5% were discarded. Cell doublets (occasional pairs of cells that are not dissociated during sample preparation) were identified using the scDblFinder  and then discarded. After log(x + 1)-transformation, size factors were estimated using Scran version 1.16.0 , and normalization was performed. Clustering based on the regulon matrix We performed clustering on the normalized cell-gene matrix. Briefly, upon obtaining the matrix, the dimensionality was reduced by principal component analysis (PCA) based on the z-transformed expression levels of the identified regulons. Then, batch effects derived from technical and biological covariates, including the batch and harvested time, were corrected using harmony . Next, the graph-based clustering of the PCA reduced data with the Louvain algorithm after computing the nearest neighbor graph was used to partition the cells. The resulting clusters were visualized in a 2-D embedding produced by uniform manifold approximation and projection (UMAP, version 0.4.6). Finally, the resulting coordinates of UMAP and cluster tags for each cell were assigned to the expression matrix after the size factor correction step for downstream analysis. Estimation of the cell proportion The significance of changes in the proportion of each cell type was calculated as described previously . In brief, the detected number of each cell type was modeled as a random count variable using a Poisson process. The rate of detection was modeled by providing the total number of captured cells in a given mouse as an offset variable, with the condition of each mouse (wild-type or mutant) provided as a covariate. Compositional analysis We utilized the scCODA  software to assess changes in cell type proportions across different conditions. This tool employs a Bayesian framework to tackle the common challenge of low replication in single-cell studies. Specifically, it models cell-type counts with a hierarchical Dirichlet-Multinomial model. This model effectively accounts for the uncertainty in cell-type proportions and mitigates the negative correlative bias by simultaneously modeling all measured cell-type proportions. To guarantee a uniquely identifiable solution and enhance interpretability, scCODA selects a specific cell type as the reference (in our analysis, the ‘reference_cell_type’ parameter was set to ‘automatic’). Cell-cell communication analysis To elucidate the global communication patterns among cells, we employed CellChat  version 0.5.0, a computational tool designed to quantitatively infer cell-cell communication networks from single-cell RNA sequencing (scRNA-seq) data. This analysis was conducted using a curated list of ligand-receptor pairs provided by CellChat. Overrepresentation test for gene ontology (GO) terms We utilized g: Profiler  to conduct an overrepresentation analysis for differentially expressed genes (DEGs) identified in each cell type. The background gene set for the analysis consisted of all genes that were tested for differential expression within the respective cluster. Pathways exhibiting an adjusted P -value (false discovery rate, FDR) below 0.05 were considered significantly enriched. GSEA enrichment To investigate the broad signatures of cell-type-specific responses, we performed gene set enrichment analysis(GSEA) using the Python implementation of gseapy  version 0.10.1. The reference gene set collections H (hallmark gene sets) and C5 (ontology gene sets) were obtained from the Molecular Signature Database (MSigDB version 7.1) . Protein-protein interaction (PPI) network construction The STRING database was used to construction PPI network. Specifically, genes were upload and the ‘Multiple protein’ function was used. Next, the MCL clustering algorithm was used to cluster the network. Animal experiments Six-week-old male ApoE −/− mice (C57BL/6J) and male C57BL/6J mice were purchased from Jiangsu GemPharmatech Co. Ltd. (SCXK(SU)2018-0008). The mice were housed in a specific pathogen-free (SPF)-grade animal facility, provided with sterilized water and feed, at a temperature of 24 °C with a 12/12 h light cycle. After a 1-week acclimatization period, ApoE −/− mice were fed a high-fat diet (HFD), whereas C57BL/6J mice were fed a standard diet. After 6 weeks on the HFD, ApoE −/− mice received intraperitoneal injections of streptozotocin (STZ) (30 mg/kg/d) for 5 consecutive days to induce a type 2 diabetes mellitus (T2DM) model. C57BL/6J mice were injected with an equal volume of citrate solution as a control group (CON group). The criteria for establishing the T2DM mouse model included a fasting blood glucose (FBG) level > 16.7 mmol/L, along with symptoms such as polyuria, polydipsia, and polyphagia . The ApoE −/− mice were then randomly divided into two groups: the T2DM model group (T2DM group) and the dapagliflozin group (DAPA group). Mice in the DAPA group received dapagliflozin (DAPA) at 1 mg/kg/d via gastric gavage , while mice in the model and control groups received an equal volume of water via gastric gavage. After eight weeks, the mice were sacrificed by intraperitoneal injection of sodium pentobarbital (30 mg/kg) following a 12-h fasting period. Blood, heart, and kidney tissues were then collected for further analysis. The animal experiment was approved by the Animal Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences (Approval No. 2021XLC046-1). Hematoxylin–eosin (H&E) staining The whole hearts and kidneys of mice were fixed in 4% paraformaldehyde for 24 h at 25 °C. After fixation, the tissues were embedded in paraffin and sectioned at a thickness of 4 μm along the long axis. Paraffin-embedded tissue sections were deparaffinized using xylene and rehydrated through a graded series of alcohol concentrations. H&E staining was performed to assess structural changes in the heart and kidney tissues. Tissue dissociation and cell isolation Flash frozen heart tissue was homogenized in a Dounce homogenizer in Lysis Buffer with 1mM DTT and 1U/µL RNase inhibitor and incubated on ice for 5 min. The suspension was filtered through a 40 μm filter to remove debris and centrifuged at 4 °C 500 g for 5 min. After the nucleis were resuspended in 300 µL Lysis buffer and 300 µL RB buffer in a 2 mL tube, the mixture is centrifuged by density gradient to separate the nuclei from cell debris, collect the intermediate layer washed by RB buffer. Finally, the cell number and viability were assessed by an automatic cell counter. Droplet-based snRNA-seq using the 10x genomics chromium platform Single cells were suspended in PBS containing 0.04% BSA. snRNA-seq libraries were prepared using Chromium Single Cell 3′ Reagent Kits v2 (10x Genomics) according the manufacturer’s protocol. The target cell recovery for each library was 6,000. The generated libraries were sequenced on an Illumina HiSeq2500. Statistical analysis Statistical analysis and data visualization were performed using GraphPad Prism (V9.5.0). Data are expressed as mean ± standard deviation (SD). For data that conformed to a normal distribution, comparisons between the two groups were conducted using a two-tailed Student’s t-test. For data that did not conform to a normal distribution, the Mann-Whitney U test was used for comparisons between two groups. For comparisons across multiple groups, one-way analysis of variance (ANOVA) was used, followed by Bonferroni correction for multiple comparisons, provided the data met normality assumptions, otherwise, a Kruskal-Wallis test was applied. Statistical significance was defined as a P < 0.05. All independent experiments were repeated at least three times. 

By manually searching the Gene Expression Omnibus (GEO) database using the keywords “scRNA-seq,” “snRNA-seq,” “single-cell RNA sequencing,” and “single-nucleus RNA sequencing,” a total of 14 mice scRNA-seq and snRNA-seq datasets were retrieved (Table ). These datasets include 8 tissues from the heart, brain, hypothalamus, kidney, lung, liver, retina, and aorta, which covered an age span of 4 weeks–96 weeks. 

Cell Ranger version 3.1.0 software ( https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest ) was used to demultiplex the FASTQ reads and align them to the mouse transcriptome (mm10, provided by 10x Genomics) with the default parameters. The output of this pipeline contains a digital gene-barcode matrix for each sample. Then, all of the matrices were subjected to Seurat version 4.3  for further data processing. Cells with more than 6,000 or less than 200 detected genes as well as those with a mitochondrial transcription ratio > 5% were discarded. Cell doublets (occasional pairs of cells that are not dissociated during sample preparation) were identified using the scDblFinder  and then discarded. After log(x + 1)-transformation, size factors were estimated using Scran version 1.16.0 , and normalization was performed. 

We performed clustering on the normalized cell-gene matrix. Briefly, upon obtaining the matrix, the dimensionality was reduced by principal component analysis (PCA) based on the z-transformed expression levels of the identified regulons. Then, batch effects derived from technical and biological covariates, including the batch and harvested time, were corrected using harmony . Next, the graph-based clustering of the PCA reduced data with the Louvain algorithm after computing the nearest neighbor graph was used to partition the cells. The resulting clusters were visualized in a 2-D embedding produced by uniform manifold approximation and projection (UMAP, version 0.4.6). Finally, the resulting coordinates of UMAP and cluster tags for each cell were assigned to the expression matrix after the size factor correction step for downstream analysis. 

The significance of changes in the proportion of each cell type was calculated as described previously . In brief, the detected number of each cell type was modeled as a random count variable using a Poisson process. The rate of detection was modeled by providing the total number of captured cells in a given mouse as an offset variable, with the condition of each mouse (wild-type or mutant) provided as a covariate. 

We utilized the scCODA  software to assess changes in cell type proportions across different conditions. This tool employs a Bayesian framework to tackle the common challenge of low replication in single-cell studies. Specifically, it models cell-type counts with a hierarchical Dirichlet-Multinomial model. This model effectively accounts for the uncertainty in cell-type proportions and mitigates the negative correlative bias by simultaneously modeling all measured cell-type proportions. To guarantee a uniquely identifiable solution and enhance interpretability, scCODA selects a specific cell type as the reference (in our analysis, the ‘reference_cell_type’ parameter was set to ‘automatic’). 

To elucidate the global communication patterns among cells, we employed CellChat  version 0.5.0, a computational tool designed to quantitatively infer cell-cell communication networks from single-cell RNA sequencing (scRNA-seq) data. This analysis was conducted using a curated list of ligand-receptor pairs provided by CellChat. 

We utilized g: Profiler  to conduct an overrepresentation analysis for differentially expressed genes (DEGs) identified in each cell type. The background gene set for the analysis consisted of all genes that were tested for differential expression within the respective cluster. Pathways exhibiting an adjusted P -value (false discovery rate, FDR) below 0.05 were considered significantly enriched. 

To investigate the broad signatures of cell-type-specific responses, we performed gene set enrichment analysis(GSEA) using the Python implementation of gseapy  version 0.10.1. The reference gene set collections H (hallmark gene sets) and C5 (ontology gene sets) were obtained from the Molecular Signature Database (MSigDB version 7.1) . 

The STRING database was used to construction PPI network. Specifically, genes were upload and the ‘Multiple protein’ function was used. Next, the MCL clustering algorithm was used to cluster the network. 

Six-week-old male ApoE −/− mice (C57BL/6J) and male C57BL/6J mice were purchased from Jiangsu GemPharmatech Co. Ltd. (SCXK(SU)2018-0008). The mice were housed in a specific pathogen-free (SPF)-grade animal facility, provided with sterilized water and feed, at a temperature of 24 °C with a 12/12 h light cycle. After a 1-week acclimatization period, ApoE −/− mice were fed a high-fat diet (HFD), whereas C57BL/6J mice were fed a standard diet. After 6 weeks on the HFD, ApoE −/− mice received intraperitoneal injections of streptozotocin (STZ) (30 mg/kg/d) for 5 consecutive days to induce a type 2 diabetes mellitus (T2DM) model. C57BL/6J mice were injected with an equal volume of citrate solution as a control group (CON group). The criteria for establishing the T2DM mouse model included a fasting blood glucose (FBG) level > 16.7 mmol/L, along with symptoms such as polyuria, polydipsia, and polyphagia . The ApoE −/− mice were then randomly divided into two groups: the T2DM model group (T2DM group) and the dapagliflozin group (DAPA group). Mice in the DAPA group received dapagliflozin (DAPA) at 1 mg/kg/d via gastric gavage , while mice in the model and control groups received an equal volume of water via gastric gavage. After eight weeks, the mice were sacrificed by intraperitoneal injection of sodium pentobarbital (30 mg/kg) following a 12-h fasting period. Blood, heart, and kidney tissues were then collected for further analysis. The animal experiment was approved by the Animal Ethics Committee of Xiyuan Hospital, China Academy of Chinese Medical Sciences (Approval No. 2021XLC046-1). 

The whole hearts and kidneys of mice were fixed in 4% paraformaldehyde for 24 h at 25 °C. After fixation, the tissues were embedded in paraffin and sectioned at a thickness of 4 μm along the long axis. Paraffin-embedded tissue sections were deparaffinized using xylene and rehydrated through a graded series of alcohol concentrations. H&E staining was performed to assess structural changes in the heart and kidney tissues. 

Flash frozen heart tissue was homogenized in a Dounce homogenizer in Lysis Buffer with 1mM DTT and 1U/µL RNase inhibitor and incubated on ice for 5 min. The suspension was filtered through a 40 μm filter to remove debris and centrifuged at 4 °C 500 g for 5 min. After the nucleis were resuspended in 300 µL Lysis buffer and 300 µL RB buffer in a 2 mL tube, the mixture is centrifuged by density gradient to separate the nuclei from cell debris, collect the intermediate layer washed by RB buffer. Finally, the cell number and viability were assessed by an automatic cell counter. 

Single cells were suspended in PBS containing 0.04% BSA. snRNA-seq libraries were prepared using Chromium Single Cell 3′ Reagent Kits v2 (10x Genomics) according the manufacturer’s protocol. The target cell recovery for each library was 6,000. The generated libraries were sequenced on an Illumina HiSeq2500. 

Statistical analysis and data visualization were performed using GraphPad Prism (V9.5.0). Data are expressed as mean ± standard deviation (SD). For data that conformed to a normal distribution, comparisons between the two groups were conducted using a two-tailed Student’s t-test. For data that did not conform to a normal distribution, the Mann-Whitney U test was used for comparisons between two groups. For comparisons across multiple groups, one-way analysis of variance (ANOVA) was used, followed by Bonferroni correction for multiple comparisons, provided the data met normality assumptions, otherwise, a Kruskal-Wallis test was applied. Statistical significance was defined as a P < 0.05. All independent experiments were repeated at least three times. 

Organotypic mapping of the vasculature using a single-cell approach To comprehensively analyze the cellular composition and function of the organotypic vasculature, 14 published scRNA-seq and snRNA-seq datasets were integrated, which included 7 organs closely associated with vascular diseases—the heart, brain, lung, kidney, liver, hypothalamus, and retina—along with the aorta (Table A and B). After rigorous quality control and data integration, a single-cell organotypic atlas of the vasculature was constructed, comprising 321,358 cells from 56 mice across 43 samples (Fig. A). Clustering analysis, performed using UMAP, revealed 63 cell types, which were classified into tissue-specific and tissue-sharing cell types based on their distribution across tissues (Fig. B, Figure ). Regarding tissue-specific cell types, the kidney, hypothalamus, and retina, due to their unique anatomical structures, contributed to more distinct cell types, including podocytes, oligodendrocyte progenitor cells, and bipolar cells. For tissue-sharing cell types, six major categories were identified: ECs, immune cells, neurons, muscle/fiber cells, epidermal cells, and mural cells. Notably, ECs were further subdivided into lymphatic endothelial cells (EC lym., characterized by lgfbp5) and vascular EC (VEC, characterized by Alb), which were further classified into arterial EC (EC art.), vein ECs (EC vein), capillary ECs (EC cap.), and pericyte ECs (EC per., characterized by Emcn). Additionally, two specific EC subtypes were identified in the kidney: glomeruli ECs (EC glo.) and angiogenic ECs (EC ang.). To characterize the distribution of each cell type across organs, heat maps were generated to visualize the proportion of each cell type in different organs (Fig. C). Shared cellular communication patterns in the organotypic vasculature CCI is an essential process for maintaining life activities and responding to environmental changes in multicellular organisms . To comprehensively characterize CCI in the organotypic vasculature, CellChat was used for the first time to map the CCI network across organs. Given the various shared cell types among multiple tissues, the cell types were initially classified based on the communication frequency pathways. The results showed that > 50% of the CCI pathways were found in only one to three organs (low-frequency CCI pathways), while only eight CCI pathways were shared across eight tissues (high-frequency CCI pathways). These high-frequency pathways included ANGPT, CDH, COLLAGEN, FGF, JAM, LAMININ, PECAM1, and PTPRM (Table A). This suggests that CCI is highly tissue-specific, with significant differences in communication patterns across tissues. To further analyze the functional differences between low- and high-frequency CCI pathways, GO enrichment analysis was performed for the genes encoding ligands of these pathways. The results showed that the ligands of low-frequency CCI pathways were strongly enriched in biological processes of inflammation, immunity, and cytokines, whereas the ligands of high-frequency CCI pathways were enriched in fundamental biological processes such as growth, development, differentiation, and organ formation (Fig. A, Table B). This finding suggests that low-frequency CCI pathways may be associated with tissue-specific immune regulation, whereas high-frequency CCI pathways may significantly contribute to maintaining basic biological functions. This study determined the cell types initiating CCI pathways with different frequencies in each tissue. The results indicated that most cell types initiated CCI pathways with a frequency > 3, albeit most of the CCI pathways were low-frequency. Given the strong association of the ligands of low-frequency CCI pathways with inflammation and immunity, the study prioritized the proportion of immune cells in each tissue. The results showed that immune cells were > 35% of the population in three out of eight tissues, which was consistent with the frequency of immune-related CCI pathways (Fig. B). The immune system is the body’s primary defense mechanism, tasked with recognizing foreign invaders and coordinating an immune response swiftly. Therefore, signaling mediators with high specificity are essential for the CCI pathways of immune cells, including soluble cytokines, chemokines, exosomes, and microvesicles . This partly explains the correlation between the number of low-frequency CCI pathways and the tissues with high immune cell ratios. To better understand the distribution characteristics of CCI pathways with different sharing frequencies across tissues, the expressions of receptor and ligand genes associated with these CCI pathways in different tissues were further analyzed (Fig. C, Figure A, Table B). Notably, we found that as the sharing frequency increased, the expressions of cardiac and renal receptor genes also increased. Moreover, the expression of high-frequency CCI receptors (e.g., Sdc1, Jam3, Dag1, etc.) was significantly higher than that of low-frequency CCI receptors (e.g., Adipor1, Cd40, Npr1, etc.) in multiple tissues, while no significant difference was observed in ligand expression (Fig. C, Figure A). This suggests that tissues require more pathways associated with basic life functions. Subsequently, receptor protein expression for different sharing frequencies in each was assessed using the tissue-based map of the human proteome  (Fig. D, Figure B). The results showed that receptor and ligand protein levels for high-frequency CCI pathways were higher than those for low-frequency CCI pathways in several tissues, which was consistent with the gene expression trend. The elevated expression of high-frequency receptor–ligand proteins significantly contribute to inter-tissue communication. In addition, the vast differences in biological functions and tissue structures contribute to significant differences in CCI pathways across tissues (Figure C, D). For example, in the kidney, which contains specialized cells such as those in renal tubules and glomeruli, CCI primarily occurs via direct cellular contact, involving few CCI pathways . Conversely, the aorta, the largest artery in the vasculature, has numerous cell populations and requires high-intensity CCI to regulate vascular tone and diastolic function , thus involving several CCI pathways. Characterization of receptor and ligand communication in multiple cell types of the organotypic vasculature We have recognized identified eight inter-organ shared CCI pathways within the organotypic vasculature, which are largely mediated by receptors and ligands expressed in cell types common to all eight tissues. To explore these shared communication patterns, we examined the characteristics of the receptors and ligands in shared cell types across tissues (Fig. A). The results showed that several shared ligand pairs and their functions were observed in all cell types. However, there are differences in the number of enriched terms for ligand genes among different cell types (Fig. B), which may contribute to the specificity of the CCI pathways (Figure A, B, and Figure A). For example, the specific CCI pathways in immune cells include pathways such as IL-6, CD226, and CCL, which are related to inflammation factors and immune responses , while the CCI pathways in neuronal cells primarily involve MAG and ENHO pathways, which are associated with axon growth and neuronal function (Figure B) . Considering that the sharing between EC and immune-associated cells across tissues was the most frequent (Fig. C), we further explored the functions of the receptors and ligands in these two cell types. It was found that EC ligands may interact with receptors on multiple cell types, including stem, immune, and muscle cells, which were involved in multiple biological processes such as stem cell differentiation and immune regulation. In contrast, ligands from immune-associated cells primarily interact with other immune cells to ensure the precise regulation of the immune response (Fig. D). These CCI characteristics reflect the functional differences of distinct cell types in maintaining homeostasis. To further explore the distribution characteristics of receptors and ligands across different tissues, we examined the cumulative distribution of receptors and ligands in ECs, immune-related cells, fibroblasts, and neurons in each tissue (Fig. E, Figure B-D). The results revealed that the vascular tissue contained the highest number of receptors and ligands from structural cells, including ECs and fibroblasts  (Fig. E, Figure C). This finding aligns with previous studies, indicating that ECs and fibroblasts significantly contribute to maintaining vascular structure and function. Additionally, the aorta and lungs were rich in the receptors and ligands of immune cells (Figure B). The aorta, as a vascular tissue, is continuously exposed to shear and metabolic stresses, thus regulating the migration and activating immune cells, such as monocytes, in the blood vessels to maintain vascular homeostasis . Similarly, the lungs are enriched in immune cells, including macrophages, dendritic cells, and T cells, owing to their direct exposure to the external environment, where they perform immune defense functions . Notably, among all tissues, neurons were only identified in the brain, hypothalamus, retina, and aorta. However, the aorta exhibited an almost complete absence of receptors or ligands for neurons (Figure D). This finding suggests that although each cell type has unique communication pathways, the overall organ function may primarily depend on receptor–ligand pairs with high-frequency expressions. To test this hypothesis, we examined the expression levels of receptor–ligand pairs with different frequencies, using the heart as an example. The results showed that the expression levels of high-frequency receptor–ligand pairs were significantly higher than those of low-frequency pairs, regardless of whether the cells acted as signal senders or receivers (Fig. F). This finding suggests that high-frequency receptor–ligand pairs may be the primary drivers of the heart’s basic functions. Further observations of the expressions of receptor–ligand protein expression in each tissue produced similar results (Fig. G). These findings suggest that in healthy conditions, organ function primarily depends on high-frequency receptor–ligand pairs, whereas low-frequency receptor–ligand pairs may contribute to specific pathological conditions or unique physiological events. Notably, under disease conditions, high-frequency receptor–ligand pairs could be the key targets for addressing multi-organ co-morbidities. Characteristics of CCI of ECs in the organotypic vasculature ECs are the only cell type common to all tissues, and the analysis of communication patterns and receptor–ligand characteristics highlights their fundamental, extensive, and central role in the CCI of the organotypic vasculature. To further analyze the communication characteristics of ECs, the CCI strength of ECs as signal sources (senders) and targets (receivers) were statistically analyzed across eight tissues (Fig. A, Figure A, B), in order to gain a comprehensive understanding of the communication characteristics of ECs in the organotypic vasculature at the tissue level, including their specific roles and primary communication targets. The results showed that the number and proportion of incoming and outgoing signals for ECs were similar in each organ, with outgoing signals constituting the majority. The main targets of EC communication were vascular structural cells, including fibroblasts, pericytes, and ECs themselves. Notably, the number of EC communications differed significantly between tissues. To exclude the effect of cell numbers on communication distribution, the correlation between cell numbers and communication frequency in each tissue was analyzed (Fig. B). The results indicated that CCI distribution was independent of cell count but closely related to the specific physiological functions and microenvironments of each tissue. For example, despite similar cell counts in the aorta and kidney, EC communications were observed to be more frequent in the aorta, this observation is consistent with the aorta’s physiological role as a major artery, which requires intense CCI to maintain vascular tone and diastolic function [ – ]. To further explore the communication pathways of ECs, the primary communication pathways of ECs in the organotypic vasculature were analyzed (Fig. C). The analysis revealed that LAMININ, COLLAGEN, and JAM associated with the basement membrane and cell adhesion—were the major pathways of EC communication. Moreover, functional enrichment analysis of receptors and ligands revealed that EC communication was closely related to the PI3K-Akt signaling pathway, cell adhesion molecules, and extracellular matrix (ECM)-receptor interactions (Fig. D). These findings indicate that ECs play a critical role in maintaining vascular and tissue homeostasis through processes such as tissue repair, cell migration, and immune response. EC are among the most abundant and heterogeneous cell types in the body. Based on function and structure, they can be categorized into VEC and EC lym. VECs are further categorized into EC art., EC-vein, and EC-cap . Although existing studies have highlighted tissue specificity and gene expression profiles of each EC subtype, the cellular communication patterns among these subtypes remain unexplored. Therefore, the CCIs of various EC subtypes across tissues were analyzed (Fig. E). The results revealed both shared CCI pathways as well as unique CCI pathways in incoming and outgoing signaling of different EC subtypes across tissues (Fig. F, Figure C, D). For example, in the aorta, VECs (including EC art., EC vein, and EC cap.) were primarily involved in BMP, JAM, and NOTCH signaling pathways associated with angiogenesis. Conversely, EC-lym showed communication patterns resembling those of mesothelial cells and neurons, primarily participating in lymphoid development and immunoregulatory signaling, such as RELN, IL2, and NRXN (Fig. G, F). These findings highlight the functional heterogeneity of ECs from a communication perspective and offer new insights into their tissue-specific functions within the organotypic vasculature. Changes in the CCI of the heart and kidney during DM The shared CCI patterns of the panvascular system and their mediators across tissues under physiological conditions have been explored. However, in complex, high-mortality diseases, simultaneous or sequential pathologic changes across multiple organs often involve intricate intercellular interference mechanisms that remain largely unknown. DPD is a vascular disease characterized by widespread atherosclerosis in patients with diabetes, affecting multiple critical target organs, including the heart, kidneys, and brain . Highly complex interactions are observed in the heart and kidneys, the organs most affected by DM . Over 40% of patients with diabetes experience heart failure, and nearly half of patients with heart failure suffer from chronic kidney disease . Understanding the mechanisms behind cardiorenal complications is essential for revealing DPD pathogenesis. To analyze CCI changes in the heart and kidney during DM, four additional single-cell datasets from mice with diabetes were selected (Table ), along with two heart datasets from the previously mentioned single-cell organotypic atlas of vasculatures and categorized into control and diabetes groups. The CCI changes in the heart and kidney between control and diabetic conditions were visualized using CellChat to explore the key communication mediators of diabetic cardiorenal complications (Fig. A, Figure S6A). The results showed that DM significantly changed the CCI patterns of the heart and kidney. Specifically, among all pathways enriched with differentially expressed genes (DEGs) (Diabetes group vs. Control group), diabetes-induced DEGs accounted for a higher proportion of CCI-related pathways, suggesting that intercellular interactions play an important role in the pathological processes of diabetes-induced heart disease. Conversely, the CCI of renal cells was less responsive to diabetes (Fig. B, C). Further analysis revealed that the CCI of macrophages, EC vein, and EC lym. in the heart was significantly affected by DM, which aligns with previous findings showing that DM alters the function and structure of the heart by changing the communication patterns of these key cells . However, in the kidney, macrophages and EC veins were more affected by DM (Fig. D, Figure S6B, C), Additionally, we found that organ-specific cell types, such as cardiomyocytes in the heart and principal cells in the kidney, exhibited smaller changes in quantity and strength of CCI compared to ECs and immune cells. This suggests that organ-specific cell types may retain their core intercellular signaling structures to combat the pathological microenvironment changes in DM. In contrast, ECs and immune cells showed significant alterations in communication, who act as primary drivers of diabetic tissue injury through dynamic CCI remodeling. Notably, although the magnitude of CCI changes in the fibroblasts of the heart and kidney was small, the number of such CCIs was significantly higher than in other cell types (Fig. D, Figure S6B, C). Fibrosis is one of the common pathological mechanisms in diabetic heart disease and diabetic nephropathy . Substantial evidence indicates that diabetes-associated fibrogenesis is primarily driven by hyperglycemia-induced fibroblast activation, with ECs and macrophages identified as key cellular sources of pro-fibrotic mediators [ , , ]. These cells orchestrate fibroblast phenotypic modulation through paracrine signaling and direct Intercellular interactions, thereby promoting fibrotic responses characterized by excessive extracellular matrix synthesis and collagen deposition . We speculate that the high CCI count of fibroblasts may be related to their response to communication signals emitted by ECs and immune cells. To further understand the aberrant communication mechanisms in the heart and kidney during DM, the CCIs between macrophages, fibroblasts, and various EC subtypes were analyzed in detail (Fig. E, Figure S6D). In the heart, DM reduced the crosstalk between fibroblasts and macrophages, with receptor–ligand pairs associated with immune cell infiltration and fibroblast activation (e.g., Mif-CD74_Cxcr4 and Spp1-Itga9_Itga1) significantly reduced. Conversely, the CCI between fibroblasts and EC cap. increased, accompanied by an increase in receptor–ligand pairs involved in angiogenesis and ECM remodeling (Fig. F). In the kidney, DM primarily enhanced the crosstalk between different EC subtypes, leading to a significant increase in receptor–ligand pairs related to collagen fiber formation, including Col4a3-Cd44 and Col4a3-Itga1_Itgb1 (Figure S6D, E). These changes indicated that diabetes promotes renal fibrosis and vascular remodeling by altering the communication patterns between different EC subtypes. To identify key communication pathways involved in diabetic cardiorenal complications, we selected 26 CCI pathways that exhibit changes in both the heart and kidney under diabetic conditions (Fig. G). Subsequently, we employed the STRING database to perform PPI and enrichment analyses of the corresponding receptor–ligand genes within these pathways (Fig. H, I). The results showed that these pathways primarily involved four key modules: BMP signaling, collagen synthesis, neuron-related signaling, and angiogenesis (Figure S6F). Key nodes in each module, such as Bmp6, Tgfb1, Col4a1, and Egfr, may contribute to the cardiorenal complications of DM. Further analysis of the gene expression levels of these core receptor–ligand pairs in the heart and kidney, as obtained from the database, showed that Bmp6 and its receptor Bmpr1a were significantly altered in diabetic hearts and kidneys (Fig. J, Figure S6G). These genes could be potential targets for diabetic cardiorenal complications. BMP signaling mediates aberrant CCI in diabetic cardiorenal complications and the significant effect of SGLT2i Hyperglycemia-induced cardiorenal damage is well established. DM doubles the risk of cardiorenal syndrome and significantly increases the risk of adverse cardiovascular events, end-stage renal disease, and death . Therefore, developing comprehensive therapeutic strategies for diabetic cardiorenal complications is crucial to improving patient outcomes and prognosis. Large clinical studies have demonstrated that the antihyperglycemic agent SGLT2i effectively reduces the risk of heart failure and improves renal outcomes, including end-stage renal disease, elevated creatinine, and death from nephropathy [ – ]. To further validate the role of the identified key communication signals in diabetic cardiorenal complications and to explore the mechanisms underlying the cardiorenal benefits of SGLT2i from a CCI perspective, we selected ApoE −/− mice with a background of lipid metabolism disorders (C57BL/6J genetic background) as model mice. T2DM was quickly induced by intraperitoneal injection of low-dose STZ and HFD feeding , and DAPA was used as a therapeutic intervention (Fig. A). The results showed that mice in the T2DM group exhibited significant weight loss and had markedly elevated blood glucose levels compared to the CON group, along with pathological changes in cardiac and renal tissues, including myocardial fracture and deformation, increased glomerular volume, and infiltration of inflammatory cells. After treatment with DAPA, the blood glucose levels of mice in the DAPA group were restored to normal, whereas the pathological changes in cardiac and renal tissues were significantly improved, with clear morphology and arrangement of cardiomyocytes, as well as critical improvement in glomerular hypertrophy and inflammatory cell infiltration (Fig. B-D, Fig.S7, TableS4). Also, while we did not conduct additional measurements of the renal parameter differences between the mouse groups, previous studies have demonstrated that DAPA exhibits significant individual renal protective effects in T2DM mice, including reducing the urine albumin/creatinine ratio and serum urea levels . To further analyze the effects of T2DM and SGLT2i treatment on the CCI between cardiac and renal cells, snRNA-seq of mouse hearts was performed, and intergroup differences in the strength and number of CCIs were compared using CellChat. We focused on the CCI pathways that were abnormal due to T2DM and were normalized by DAPA treatment. The results revealed that the LAMININ, PTPRM, and COLLAGEN pathways had high scores of CCI strength but few interactions. Conversely, numerous interactions were observed in the SEMA3, ADGRG, and ADGRL pathways, although their interaction strength was significantly reduced (Fig. E). Notably, both the strength and number of interactions of the BMP pathway were significantly higher than average, suggesting its critical role in CCI during diabetic cardiorenal complications. Subsequently, differential expression levels of receptor–ligand genes in these pathways were analyzed, thus revealing the most significant changes for the ADGRG, SEMA3, and BMP pathways (Fig. E). Particularly, the receptor–ligand genes of the BMP signaling pathway were significantly upregulated in the T2DM group and downregulated in the DAPA group (Fig. F). These results align with earlier findings and further confirm the central role of BMP signaling in diabetic cardiorenal complications . To further validate the role of BMP signaling in the cardiorenal protective mechanism of SGLT2i, changes in key pathway BMP molecules in the heart were analyzed. The results showed that BMP6 and its receptor Bmpr1a were significantly upregulated in the T2DM group and downregulated in the DAPA group (Fig. F). This was consistent with previous findings on diabetic nephropathy , suggesting that BMP6 may be a novel target for the cardiorenal protective mechanism of SGLT2i. Our analysis indicates that SGLT2i may restore abnormal CCI in the heart and kidney in DM by regulating the BMP signaling, thus exerting protective effects on both organs. This provides new insights into the cardiac and renal benefits of SGLT2i and offers a potential target for developing precise therapeutic strategies against cardiorenal complications in DM. 

To comprehensively analyze the cellular composition and function of the organotypic vasculature, 14 published scRNA-seq and snRNA-seq datasets were integrated, which included 7 organs closely associated with vascular diseases—the heart, brain, lung, kidney, liver, hypothalamus, and retina—along with the aorta (Table A and B). After rigorous quality control and data integration, a single-cell organotypic atlas of the vasculature was constructed, comprising 321,358 cells from 56 mice across 43 samples (Fig. A). Clustering analysis, performed using UMAP, revealed 63 cell types, which were classified into tissue-specific and tissue-sharing cell types based on their distribution across tissues (Fig. B, Figure ). Regarding tissue-specific cell types, the kidney, hypothalamus, and retina, due to their unique anatomical structures, contributed to more distinct cell types, including podocytes, oligodendrocyte progenitor cells, and bipolar cells. For tissue-sharing cell types, six major categories were identified: ECs, immune cells, neurons, muscle/fiber cells, epidermal cells, and mural cells. Notably, ECs were further subdivided into lymphatic endothelial cells (EC lym., characterized by lgfbp5) and vascular EC (VEC, characterized by Alb), which were further classified into arterial EC (EC art.), vein ECs (EC vein), capillary ECs (EC cap.), and pericyte ECs (EC per., characterized by Emcn). Additionally, two specific EC subtypes were identified in the kidney: glomeruli ECs (EC glo.) and angiogenic ECs (EC ang.). To characterize the distribution of each cell type across organs, heat maps were generated to visualize the proportion of each cell type in different organs (Fig. C). 

CCI is an essential process for maintaining life activities and responding to environmental changes in multicellular organisms . To comprehensively characterize CCI in the organotypic vasculature, CellChat was used for the first time to map the CCI network across organs. Given the various shared cell types among multiple tissues, the cell types were initially classified based on the communication frequency pathways. The results showed that > 50% of the CCI pathways were found in only one to three organs (low-frequency CCI pathways), while only eight CCI pathways were shared across eight tissues (high-frequency CCI pathways). These high-frequency pathways included ANGPT, CDH, COLLAGEN, FGF, JAM, LAMININ, PECAM1, and PTPRM (Table A). This suggests that CCI is highly tissue-specific, with significant differences in communication patterns across tissues. To further analyze the functional differences between low- and high-frequency CCI pathways, GO enrichment analysis was performed for the genes encoding ligands of these pathways. The results showed that the ligands of low-frequency CCI pathways were strongly enriched in biological processes of inflammation, immunity, and cytokines, whereas the ligands of high-frequency CCI pathways were enriched in fundamental biological processes such as growth, development, differentiation, and organ formation (Fig. A, Table B). This finding suggests that low-frequency CCI pathways may be associated with tissue-specific immune regulation, whereas high-frequency CCI pathways may significantly contribute to maintaining basic biological functions. This study determined the cell types initiating CCI pathways with different frequencies in each tissue. The results indicated that most cell types initiated CCI pathways with a frequency > 3, albeit most of the CCI pathways were low-frequency. Given the strong association of the ligands of low-frequency CCI pathways with inflammation and immunity, the study prioritized the proportion of immune cells in each tissue. The results showed that immune cells were > 35% of the population in three out of eight tissues, which was consistent with the frequency of immune-related CCI pathways (Fig. B). The immune system is the body’s primary defense mechanism, tasked with recognizing foreign invaders and coordinating an immune response swiftly. Therefore, signaling mediators with high specificity are essential for the CCI pathways of immune cells, including soluble cytokines, chemokines, exosomes, and microvesicles . This partly explains the correlation between the number of low-frequency CCI pathways and the tissues with high immune cell ratios. To better understand the distribution characteristics of CCI pathways with different sharing frequencies across tissues, the expressions of receptor and ligand genes associated with these CCI pathways in different tissues were further analyzed (Fig. C, Figure A, Table B). Notably, we found that as the sharing frequency increased, the expressions of cardiac and renal receptor genes also increased. Moreover, the expression of high-frequency CCI receptors (e.g., Sdc1, Jam3, Dag1, etc.) was significantly higher than that of low-frequency CCI receptors (e.g., Adipor1, Cd40, Npr1, etc.) in multiple tissues, while no significant difference was observed in ligand expression (Fig. C, Figure A). This suggests that tissues require more pathways associated with basic life functions. Subsequently, receptor protein expression for different sharing frequencies in each was assessed using the tissue-based map of the human proteome  (Fig. D, Figure B). The results showed that receptor and ligand protein levels for high-frequency CCI pathways were higher than those for low-frequency CCI pathways in several tissues, which was consistent with the gene expression trend. The elevated expression of high-frequency receptor–ligand proteins significantly contribute to inter-tissue communication. In addition, the vast differences in biological functions and tissue structures contribute to significant differences in CCI pathways across tissues (Figure C, D). For example, in the kidney, which contains specialized cells such as those in renal tubules and glomeruli, CCI primarily occurs via direct cellular contact, involving few CCI pathways . Conversely, the aorta, the largest artery in the vasculature, has numerous cell populations and requires high-intensity CCI to regulate vascular tone and diastolic function , thus involving several CCI pathways. 

We have recognized identified eight inter-organ shared CCI pathways within the organotypic vasculature, which are largely mediated by receptors and ligands expressed in cell types common to all eight tissues. To explore these shared communication patterns, we examined the characteristics of the receptors and ligands in shared cell types across tissues (Fig. A). The results showed that several shared ligand pairs and their functions were observed in all cell types. However, there are differences in the number of enriched terms for ligand genes among different cell types (Fig. B), which may contribute to the specificity of the CCI pathways (Figure A, B, and Figure A). For example, the specific CCI pathways in immune cells include pathways such as IL-6, CD226, and CCL, which are related to inflammation factors and immune responses , while the CCI pathways in neuronal cells primarily involve MAG and ENHO pathways, which are associated with axon growth and neuronal function (Figure B) . Considering that the sharing between EC and immune-associated cells across tissues was the most frequent (Fig. C), we further explored the functions of the receptors and ligands in these two cell types. It was found that EC ligands may interact with receptors on multiple cell types, including stem, immune, and muscle cells, which were involved in multiple biological processes such as stem cell differentiation and immune regulation. In contrast, ligands from immune-associated cells primarily interact with other immune cells to ensure the precise regulation of the immune response (Fig. D). These CCI characteristics reflect the functional differences of distinct cell types in maintaining homeostasis. To further explore the distribution characteristics of receptors and ligands across different tissues, we examined the cumulative distribution of receptors and ligands in ECs, immune-related cells, fibroblasts, and neurons in each tissue (Fig. E, Figure B-D). The results revealed that the vascular tissue contained the highest number of receptors and ligands from structural cells, including ECs and fibroblasts  (Fig. E, Figure C). This finding aligns with previous studies, indicating that ECs and fibroblasts significantly contribute to maintaining vascular structure and function. Additionally, the aorta and lungs were rich in the receptors and ligands of immune cells (Figure B). The aorta, as a vascular tissue, is continuously exposed to shear and metabolic stresses, thus regulating the migration and activating immune cells, such as monocytes, in the blood vessels to maintain vascular homeostasis . Similarly, the lungs are enriched in immune cells, including macrophages, dendritic cells, and T cells, owing to their direct exposure to the external environment, where they perform immune defense functions . Notably, among all tissues, neurons were only identified in the brain, hypothalamus, retina, and aorta. However, the aorta exhibited an almost complete absence of receptors or ligands for neurons (Figure D). This finding suggests that although each cell type has unique communication pathways, the overall organ function may primarily depend on receptor–ligand pairs with high-frequency expressions. To test this hypothesis, we examined the expression levels of receptor–ligand pairs with different frequencies, using the heart as an example. The results showed that the expression levels of high-frequency receptor–ligand pairs were significantly higher than those of low-frequency pairs, regardless of whether the cells acted as signal senders or receivers (Fig. F). This finding suggests that high-frequency receptor–ligand pairs may be the primary drivers of the heart’s basic functions. Further observations of the expressions of receptor–ligand protein expression in each tissue produced similar results (Fig. G). These findings suggest that in healthy conditions, organ function primarily depends on high-frequency receptor–ligand pairs, whereas low-frequency receptor–ligand pairs may contribute to specific pathological conditions or unique physiological events. Notably, under disease conditions, high-frequency receptor–ligand pairs could be the key targets for addressing multi-organ co-morbidities. 

ECs are the only cell type common to all tissues, and the analysis of communication patterns and receptor–ligand characteristics highlights their fundamental, extensive, and central role in the CCI of the organotypic vasculature. To further analyze the communication characteristics of ECs, the CCI strength of ECs as signal sources (senders) and targets (receivers) were statistically analyzed across eight tissues (Fig. A, Figure A, B), in order to gain a comprehensive understanding of the communication characteristics of ECs in the organotypic vasculature at the tissue level, including their specific roles and primary communication targets. The results showed that the number and proportion of incoming and outgoing signals for ECs were similar in each organ, with outgoing signals constituting the majority. The main targets of EC communication were vascular structural cells, including fibroblasts, pericytes, and ECs themselves. Notably, the number of EC communications differed significantly between tissues. To exclude the effect of cell numbers on communication distribution, the correlation between cell numbers and communication frequency in each tissue was analyzed (Fig. B). The results indicated that CCI distribution was independent of cell count but closely related to the specific physiological functions and microenvironments of each tissue. For example, despite similar cell counts in the aorta and kidney, EC communications were observed to be more frequent in the aorta, this observation is consistent with the aorta’s physiological role as a major artery, which requires intense CCI to maintain vascular tone and diastolic function [ – ]. To further explore the communication pathways of ECs, the primary communication pathways of ECs in the organotypic vasculature were analyzed (Fig. C). The analysis revealed that LAMININ, COLLAGEN, and JAM associated with the basement membrane and cell adhesion—were the major pathways of EC communication. Moreover, functional enrichment analysis of receptors and ligands revealed that EC communication was closely related to the PI3K-Akt signaling pathway, cell adhesion molecules, and extracellular matrix (ECM)-receptor interactions (Fig. D). These findings indicate that ECs play a critical role in maintaining vascular and tissue homeostasis through processes such as tissue repair, cell migration, and immune response. EC are among the most abundant and heterogeneous cell types in the body. Based on function and structure, they can be categorized into VEC and EC lym. VECs are further categorized into EC art., EC-vein, and EC-cap . Although existing studies have highlighted tissue specificity and gene expression profiles of each EC subtype, the cellular communication patterns among these subtypes remain unexplored. Therefore, the CCIs of various EC subtypes across tissues were analyzed (Fig. E). The results revealed both shared CCI pathways as well as unique CCI pathways in incoming and outgoing signaling of different EC subtypes across tissues (Fig. F, Figure C, D). For example, in the aorta, VECs (including EC art., EC vein, and EC cap.) were primarily involved in BMP, JAM, and NOTCH signaling pathways associated with angiogenesis. Conversely, EC-lym showed communication patterns resembling those of mesothelial cells and neurons, primarily participating in lymphoid development and immunoregulatory signaling, such as RELN, IL2, and NRXN (Fig. G, F). These findings highlight the functional heterogeneity of ECs from a communication perspective and offer new insights into their tissue-specific functions within the organotypic vasculature. 

The shared CCI patterns of the panvascular system and their mediators across tissues under physiological conditions have been explored. However, in complex, high-mortality diseases, simultaneous or sequential pathologic changes across multiple organs often involve intricate intercellular interference mechanisms that remain largely unknown. DPD is a vascular disease characterized by widespread atherosclerosis in patients with diabetes, affecting multiple critical target organs, including the heart, kidneys, and brain . Highly complex interactions are observed in the heart and kidneys, the organs most affected by DM . Over 40% of patients with diabetes experience heart failure, and nearly half of patients with heart failure suffer from chronic kidney disease . Understanding the mechanisms behind cardiorenal complications is essential for revealing DPD pathogenesis. To analyze CCI changes in the heart and kidney during DM, four additional single-cell datasets from mice with diabetes were selected (Table ), along with two heart datasets from the previously mentioned single-cell organotypic atlas of vasculatures and categorized into control and diabetes groups. The CCI changes in the heart and kidney between control and diabetic conditions were visualized using CellChat to explore the key communication mediators of diabetic cardiorenal complications (Fig. A, Figure S6A). The results showed that DM significantly changed the CCI patterns of the heart and kidney. Specifically, among all pathways enriched with differentially expressed genes (DEGs) (Diabetes group vs. Control group), diabetes-induced DEGs accounted for a higher proportion of CCI-related pathways, suggesting that intercellular interactions play an important role in the pathological processes of diabetes-induced heart disease. Conversely, the CCI of renal cells was less responsive to diabetes (Fig. B, C). Further analysis revealed that the CCI of macrophages, EC vein, and EC lym. in the heart was significantly affected by DM, which aligns with previous findings showing that DM alters the function and structure of the heart by changing the communication patterns of these key cells . However, in the kidney, macrophages and EC veins were more affected by DM (Fig. D, Figure S6B, C), Additionally, we found that organ-specific cell types, such as cardiomyocytes in the heart and principal cells in the kidney, exhibited smaller changes in quantity and strength of CCI compared to ECs and immune cells. This suggests that organ-specific cell types may retain their core intercellular signaling structures to combat the pathological microenvironment changes in DM. In contrast, ECs and immune cells showed significant alterations in communication, who act as primary drivers of diabetic tissue injury through dynamic CCI remodeling. Notably, although the magnitude of CCI changes in the fibroblasts of the heart and kidney was small, the number of such CCIs was significantly higher than in other cell types (Fig. D, Figure S6B, C). Fibrosis is one of the common pathological mechanisms in diabetic heart disease and diabetic nephropathy . Substantial evidence indicates that diabetes-associated fibrogenesis is primarily driven by hyperglycemia-induced fibroblast activation, with ECs and macrophages identified as key cellular sources of pro-fibrotic mediators [ , , ]. These cells orchestrate fibroblast phenotypic modulation through paracrine signaling and direct Intercellular interactions, thereby promoting fibrotic responses characterized by excessive extracellular matrix synthesis and collagen deposition . We speculate that the high CCI count of fibroblasts may be related to their response to communication signals emitted by ECs and immune cells. To further understand the aberrant communication mechanisms in the heart and kidney during DM, the CCIs between macrophages, fibroblasts, and various EC subtypes were analyzed in detail (Fig. E, Figure S6D). In the heart, DM reduced the crosstalk between fibroblasts and macrophages, with receptor–ligand pairs associated with immune cell infiltration and fibroblast activation (e.g., Mif-CD74_Cxcr4 and Spp1-Itga9_Itga1) significantly reduced. Conversely, the CCI between fibroblasts and EC cap. increased, accompanied by an increase in receptor–ligand pairs involved in angiogenesis and ECM remodeling (Fig. F). In the kidney, DM primarily enhanced the crosstalk between different EC subtypes, leading to a significant increase in receptor–ligand pairs related to collagen fiber formation, including Col4a3-Cd44 and Col4a3-Itga1_Itgb1 (Figure S6D, E). These changes indicated that diabetes promotes renal fibrosis and vascular remodeling by altering the communication patterns between different EC subtypes. To identify key communication pathways involved in diabetic cardiorenal complications, we selected 26 CCI pathways that exhibit changes in both the heart and kidney under diabetic conditions (Fig. G). Subsequently, we employed the STRING database to perform PPI and enrichment analyses of the corresponding receptor–ligand genes within these pathways (Fig. H, I). The results showed that these pathways primarily involved four key modules: BMP signaling, collagen synthesis, neuron-related signaling, and angiogenesis (Figure S6F). Key nodes in each module, such as Bmp6, Tgfb1, Col4a1, and Egfr, may contribute to the cardiorenal complications of DM. Further analysis of the gene expression levels of these core receptor–ligand pairs in the heart and kidney, as obtained from the database, showed that Bmp6 and its receptor Bmpr1a were significantly altered in diabetic hearts and kidneys (Fig. J, Figure S6G). These genes could be potential targets for diabetic cardiorenal complications. 

Hyperglycemia-induced cardiorenal damage is well established. DM doubles the risk of cardiorenal syndrome and significantly increases the risk of adverse cardiovascular events, end-stage renal disease, and death . Therefore, developing comprehensive therapeutic strategies for diabetic cardiorenal complications is crucial to improving patient outcomes and prognosis. Large clinical studies have demonstrated that the antihyperglycemic agent SGLT2i effectively reduces the risk of heart failure and improves renal outcomes, including end-stage renal disease, elevated creatinine, and death from nephropathy [ – ]. To further validate the role of the identified key communication signals in diabetic cardiorenal complications and to explore the mechanisms underlying the cardiorenal benefits of SGLT2i from a CCI perspective, we selected ApoE −/− mice with a background of lipid metabolism disorders (C57BL/6J genetic background) as model mice. T2DM was quickly induced by intraperitoneal injection of low-dose STZ and HFD feeding , and DAPA was used as a therapeutic intervention (Fig. A). The results showed that mice in the T2DM group exhibited significant weight loss and had markedly elevated blood glucose levels compared to the CON group, along with pathological changes in cardiac and renal tissues, including myocardial fracture and deformation, increased glomerular volume, and infiltration of inflammatory cells. After treatment with DAPA, the blood glucose levels of mice in the DAPA group were restored to normal, whereas the pathological changes in cardiac and renal tissues were significantly improved, with clear morphology and arrangement of cardiomyocytes, as well as critical improvement in glomerular hypertrophy and inflammatory cell infiltration (Fig. B-D, Fig.S7, TableS4). Also, while we did not conduct additional measurements of the renal parameter differences between the mouse groups, previous studies have demonstrated that DAPA exhibits significant individual renal protective effects in T2DM mice, including reducing the urine albumin/creatinine ratio and serum urea levels . To further analyze the effects of T2DM and SGLT2i treatment on the CCI between cardiac and renal cells, snRNA-seq of mouse hearts was performed, and intergroup differences in the strength and number of CCIs were compared using CellChat. We focused on the CCI pathways that were abnormal due to T2DM and were normalized by DAPA treatment. The results revealed that the LAMININ, PTPRM, and COLLAGEN pathways had high scores of CCI strength but few interactions. Conversely, numerous interactions were observed in the SEMA3, ADGRG, and ADGRL pathways, although their interaction strength was significantly reduced (Fig. E). Notably, both the strength and number of interactions of the BMP pathway were significantly higher than average, suggesting its critical role in CCI during diabetic cardiorenal complications. Subsequently, differential expression levels of receptor–ligand genes in these pathways were analyzed, thus revealing the most significant changes for the ADGRG, SEMA3, and BMP pathways (Fig. E). Particularly, the receptor–ligand genes of the BMP signaling pathway were significantly upregulated in the T2DM group and downregulated in the DAPA group (Fig. F). These results align with earlier findings and further confirm the central role of BMP signaling in diabetic cardiorenal complications . To further validate the role of BMP signaling in the cardiorenal protective mechanism of SGLT2i, changes in key pathway BMP molecules in the heart were analyzed. The results showed that BMP6 and its receptor Bmpr1a were significantly upregulated in the T2DM group and downregulated in the DAPA group (Fig. F). This was consistent with previous findings on diabetic nephropathy , suggesting that BMP6 may be a novel target for the cardiorenal protective mechanism of SGLT2i. Our analysis indicates that SGLT2i may restore abnormal CCI in the heart and kidney in DM by regulating the BMP signaling, thus exerting protective effects on both organs. This provides new insights into the cardiac and renal benefits of SGLT2i and offers a potential target for developing precise therapeutic strategies against cardiorenal complications in DM. 

The vasculature plays a crucial role as a barrier and a distributed organ in maintaining the normal function of various organs and tissues in a healthy state. However, dysfunction of vascular-related cells, particularly in pathological conditions, significantly contributes to vascular pathologies including atherosclerosis, which can lead to PVD. Although endothelial dysfunction and hypoxia  are known pathogenic mechanisms of PVD, current endothelial protective agents, and anti-hypoxic therapies have shown limited clinical efficacy. Therefore, a deeper understanding of the vasculature’s functional characteristics and its changes in pathological states is essential for optimizing existing treatments and developing new therapeutic strategies. This study systematically explored the CCI patterns of the organotypic vasculature from a single-cell perspective for the first time and demonstrated the central role of ECs in the intercellular signaling within the vasculature using a frequency-based mathematical model. These findings offer a crucial foundation for understanding the common physiological mechanisms of the panvascular system. The dynamic network of CCI and inter-organ collaboration is essential for maintaining organ function and microenvironment homeostasis. Over the past decade, several computational tools, including CellphoneDB , ICELLNET , and CellChat , have been developed to infer CCIs from gene expression data and enable detailed characterization of CCI networks in key target organs [ – ]. However, most studies have focused on CCIs within specific organs under physiological or pathological conditions. This has left a gap in understanding inter-organ communication and collaboration. By integrating scRNA-seq/snRNA-seq data from multiple organs, we demonstrated the feasibility of quantifying multi-organ CCI for the first time. Our analysis identified numerous specific CCI pathways and a few overlapping CCI features across different organs to highlight key inter-organ communication mechanisms. Based on the frequency-based mathematical model, pathways essential for life activities, such as cell development and differentiation, were found to overlap across all organs. This aligns with the well-established concept that despite differences in function and morphology, organs share a common evolutionary trajectory in basic signaling and biological processes . Notably, immune-related CCI pathways were significantly increased only in organs with high immune requirements (e.g., lung, aorta, and liver). Previous studies have demonstrated that CCI in immune cell populations is primarily driven by specialized receptors and secreted ligands, which are numerous in immune system-related organs than in non-immune organs . Our study supports these findings. Additionally, we identified various shared receptor–ligand pairs across different cell types, which may explain the inter-organ CCI signaling. Notably, high-frequency receptor–ligand pairs (e.g. Lama4-Dag1, Col4a1-Sdc1, etc.) were expressed at significantly higher levels than low-frequency pairs (e.g. Adipoq-Adipor1, Cd40lg-Cd40, etc., ) across various organs and cell types. This suggests that high-frequency receptor–ligand pairs play a dominant role in intercellular signaling within the vascular system. Therefore, these high-frequency receptor–ligand pairs may be potential targets for the development of PVD drugs. As the innermost layer of the vasculature, ECs serve as the interface for communication between the vascular system and external environment, playing a critical role in maintaining vascular homeostasis. This includes regulating blood pressure and blood flow, influencing angiogenesis, changing vascular permeability, and mediating immune and inflammatory responses. Therefore, abnormalities of the ECs would directly impact the vascular system and lead to the development of vascular diseases. For example, endothelial dysfunction is the earliest detectable pathologic change in atherosclerosis . In DM, hyperglycemia first causes abnormalities in EC metabolism and then vascular damage . By analyzing the communication patterns and characteristics of receptors and ligands of the organotypic vasculature, the intra- and inter-organ communication characteristics of ECs were summarized for the first time. We found that ECs mainly acted as signal senders in the organotypic vasculature, whereas their ligands are more likely to interact with receptors of vascular structural cells, such as fibroblasts, pericytes, and ECs themselves. LAMININ, COLLAGEN, and JAM, which are associated with the cell basement membrane and cell adhesion, are the main communication pathways of EC. They include PI3K-Akt signaling, cell adhesion molecules, ECM-receptor interactions, and other biological functions. This finding is consistent with a recent study on the organotypic atlas of vascular cells. Specifically, various vascular and organotypic communication pathways related to cell adhesion signaling are observed across ECs and their surrounding cells in vascular beds and tissues . Additionally, significant differences were observed in the communication patterns between different EC subtypes. For example, BMP and NOTCH signaling pathways, which are closely related to angiogenesis, were predominantly observed in VECs, while immune-related signals such as RELN and IL2 were primarily specific to EC lym. These findings highlight the functional heterogeneity of ECs from a communication perspective and offer new insights into understanding the tissue-specific functions of ECs in panvascular organs. Another key outcome of this study was the exploration of common changes in the communication between the heart and kidney during DM. We present the first quantitative analysis of CCI in both the hearts and kidneys of diabetic mice, focusing on the shared intercellular communication changes in a diabetic context. The heart was more sensitive to the diabetic environment and exhibited more changes in CCI than the kidney. Previous studies revealed significant changes in the number and abundance of metabolites in the heart in response to DM, and our results further support this finding . Specifically, this study revealed several CCIs across various EC subtypes in the heart and kidney during DM, along with a significant increase in receptor–ligand pairs associated with angiogenesis, basement membrane formation, and fibrosis. These findings suggest that EC dysfunction contributes to initiating vascular complications in DM . Further analysis of the communication pathways and the corresponding functions of receptors and ligands in both the heart and kidney during DM highlighted BMP signaling as a key factor in the cardiorenal complications of DM. BMP is a subgroup of the transforming growth factor β (TGFβ) superfamily, and its signaling pathways include both the classical Smad-dependent pathway and non-classical Smad-independent pathways, both of which are involved in bone formation as well as the development of organs such as the heart and kidney . Numerous studies have demonstrated the significant role of BMP signaling in DM and vascular complications . Activation of BMP signaling triggers the endoplasmic reticulum stress response in pancreatic islets, which promotes dysfunction of pancreatic β-cell dysfunction and elevated glucose levels . Elevated BMP levels are positively associated with the development of coronary artery disease in DM, vascular calcification, diabetic retinopathy, and diabetic nephropathy [ – ]. Our analysis also revealed that the expression of BMP6, a key molecule in BMP signaling, was significantly increased in the heart during DM, which further supported the key role of BMP signaling in the cardiorenal complications of DM. CCI is a key mechanism for maintaining organismal adaptation and homeostasis in multicellular organisms. Changes in CCI have been identified as key contributors to organ co-morbidities, which offer new perspectives for drug development and application. The cardiorenal benefits of SGLT2i, a first-line hypoglycemic agent, are well-supported by clinical evidence, which demonstrates potential cardiorenal protection by improving metabolic communication in the kidney and regulating the gut-renal axis . However, the effect of SGLT2i on the CCI between heart and kidney cells remains unclear. By combining our analysis of shared CCIs between the heart and kidney during DM, we explored the cardiorenal protective mechanism of SGLT2i from a CCI perspective. Using the heart as an example, we performed SnRNA-seq and CCI analyses by focusing on the CCI pathways restored by DAPA treatment. Notably, the BMP signaling pathway emerged as a critical mediator of cardiorenal CCIs in DM, as indicated by its prominent ranking in terms of CCI strength, number, and magnitude of changes. Previous studies have demonstrated that SGLT2i improved aberrant CCIs in diabetic nephropathy and inhibited EC-derived Bmp6 signaling . Based on the inhibitory effects of DAPA on the receptors and ligands of the BMP signaling pathway in the heart, we hypothesize that SGLT2i may restore the disrupted CCI between the heart and kidney in DM by regulating BMP signaling to achieve the potential protection of the heart and kidney. Overall, we explored the CCI patterns in panvascular organs under normal conditions and during DM, which offer new insights into the physiological mechanisms of the panvascular system and the characteristics of DPD. We highlighted the key role of BMP signaling in diabetic cardiorenal complications and the protective effects of SGLT2i. Moreover, this study introduced a mathematical model and algorithm based on frequency screening to quantify cell-type interactions across organs and identify key CCI pathways and receptor–ligand pairs. This novel research paradigm can guide future investigations into CCI in multiorgan and panvascular lesions and inform the development of drugs targeting CCI mediators. However, while we focused on communication abnormalities in diabetic cardiorenal complications and the systemic effects of DAPA, the lack of deeper exploration of specific injury-associated cell subtypes remains a limitation. Future studies integrating spatial omics could further dissect the cellular drivers of Diabetic cardiorenal complications and the cardiorenal benefits of SGLT2i. 

This study constructed a comprehensive organotypic single-cell atlas of the vasculature by integrating scRNA-seq data from eight organs and systematically analyzed vascular CCI characteristics. A frequency-based mathematical model highlighted the critical role of high-frequency receptor–ligand pairs in organ function specificity and multi-organ co-morbidities. Moreover, the core role of ECs in vasculature was identified. Using the heart and kidney as examples, BMP6 was shown to promote cardiorenal complications and mediate the protective effects of SGLT2i. These findings provide a scientific basis for understanding DPD comorbidity mechanisms and the action mechanism of SGLT2i. 

Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 Supplementary Material 4 Supplementary Material 5 

