3.1 Construction of AAA models and associated physiopathologic characteristics
The weights of all mice were dynamically monitored for 4 weeks after the implantation of minipumps. There was no difference in body weight between the two groups with an increase in feeding time (Supplementary Fig. 2A). One mouse died of AAA rupture on day 26 in the AAA group, while all other mice survived. There was no significant difference in the survival rate between the two groups (P = 0.373, Supplementary Fig. 2B). No significant differences were found in creatine kinase (CK), creatine kinase MB (CKMB), lactic dehydrogenase (LDH), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and total cholesterol (TC) between the two groups. Interestingly, the levels of blood glucose (GLU) and total triglyceride (TG) were lower in the AAA group, which may be due to a reduction in food intake (Supplementary Fig. 2C).
Ultrasound examination revealed that the intraluminal diameter and intima thickness of the AAA group tended to increase not accompanied by alternation of cardiac function after continuous pumping of Ang II for 28 days (Fig. 1A-1C, Table.1). HE staining results revealed that the basic structure of the artery had been destroyed, and inflammatory cell infiltration was mainly located in the adventitia and media of the artery wall in the AAA group. Masson staining results revealed that SMCs showed a disorderly arrangement accompanied by secondary collagen hyperplasia in the AAA group. EVG staining revealed that the break of elastin layer in the AAA group. Small new elastic fibers were observed in the crevasse (Fig. 1D). Semi-quantitative analysis indicated that there was a tendency for elastin degradation to increase (Fig. 1E). The expression of CD68 and MCP-1 increased in the AAA group, which revealed infiltration of macrophages/monocytes in the arterial wall tissue. Moreover, the expression of cytokines, including TNF-α and IL-1β, increased in the arterial wall tissue (Fig. 1F-J). Additionally, the expression of MMP-2 and MMP-9 increased in the AAA group as was expected (Fig. 1K-M).
3.2 Single cell sequencing identified a total of 25 cell clusters representing eight types of cells
The overall quality parameters of the sc-RNA sequencing data were as follows: A total of 13,779 and 14,086 cells were obtained in the CTR and AAA groups, respectively. The median number of genes per cell was 2734 and 1818 in the CTR and AAA groups, respectively (Supplementary Fig. 3A). Cell activity was an important factor that affected the results. After the removal of low-activity and dead cells, the distribution of the number of genes detected (nFeature_RNA), the total quantity distribution of UMI detected (nCount_RNA), and the percentage of mitochondrial gene expression (Percent.mito) in single cells were obtained (Supplementary Fig. 3B). Cells with mRNA >500 and a proportion of <25% of mitochondrial genes met the standard and were used for subsequent data analysis.
The non-linear dimensionality reduction method of t-distributed stochastic neighbor embedding (t-SNE) was used to visualize the cell subpopulation classification results. A total of 25 cell clusters were isolated from the AAA and CTR groups. In the development of AAA, the cell types, the number of clusters, and the proportion of cells in each group were altered (Fig. 2A).
Different types of cells have specific marker genes. Each type of cell was identified by marker genes [11, 17] and a heatmap indicated that each type of cell was reasonably and distinctly divided (Fig. 2B). A total of eight groups of different cell types were identified (Fig. 2C). The main cell types included: 1) fibroblasts (Fbs) [markers: collagen type III alpha 1 chain (Col3a1), collagen type I alpha 1 (Col1a1), decorin (Dcn), gelsolin (Gsn); clusters 0, 1, 2, 6, 7, 12, 15, 22, 23, 24]; 2) monocyte/macrophages (Mo/Møs) [markers: Cd68, allograft inflammatory factor 1 (Aif1), complement component 1, q subcomponent, beta polypeptide (C1qb); clusters 5, 8, 16, 18, 19, 20]; 3) ECs [markers: cadherin 5 (Cdh5), platelet/endothelial cell adhesion molecule 1 (Pecam1), fatty acid-binding protein 4 (Fabp4); clusters 4, 9, 11]; 4) SMCs [markers: myosin, heavy polypeptide 11 (Myh11), actin, alpha 2, smooth muscle, aorta (Acta2), transgelin (Tagln); clusters 3, 10]; 5) T leukomonocytes (markers: Cd3d, Cd3g, Cd28; cluster 13); 6) B leukomonocytes (markers: Cd79a, Cd79b, Cd28; cluster 21); 7) dendritic cells (DCs) [markers: Cd209a, Cd74, interferon-induced transmembrane protein 1 (Ifitm1); cluster 14]; 8) granulocytes (Gra) [markers: S100 calcium-binding protein A8 (S100a8), C-C motif chemokine receptor 1 (Ccr1), lymphocyte antigen 6 complex, locus G (Ly6g); cluster 17]. The visualization of marker genes confirmed that the markers we selected covered virtually all cells (Fig. 2D) and showed a high degree of differentiation (Fig. 2E). During the formation of AAA, the number of different types of cells in the artery wall tissue was altered: the cell numbers of clusters 1, 2, 3, 5, 8, 9, 10, 12, 17, 18, and 22 increased, while those of clusters 0, 4, 6, 7, 11, 13, 16, 19, 20, 21, 23, and 24 decreased. The cell numbers of clusters 14 and 15 were almost unchanged (Fig. 2F). As for the composition of cells, Fbs and ECs accounted for the largest proportion of artery wall tissue, followed by Mo/Møs and T cells, with B cells, DCs, SMCs, and Gras occupying the smallest proportion in the CTR group. While Fbs and Mo/Møs accounted for the largest proportion of artery wall tissue, followed by SMCs and ECs, and T cells, B cells, DCs, and Gras occupied the smallest proportion in the AAA group (Fig. 2G).
3.3 Immune cells differentiate to promote inflammation during the development of AAA
The transformation of immune cells in the formation of AAA deserves priority attention. Mo/Møs comprised the majority of the immune cells. The proportions of Mo/Mø1, Mo/Mø2, and Mo/Mø4 increased, whereas that of Mo/Mø3, Mo/Mø5, and Mo/Mø6 decreased (Fig. 3A). We selected several M1 marker genes [Cd86, Il1b, toll like receptor 2 (Tlr2), class II major histocompatibility complex transactivator (Ciita)] and M2 marker genes [arginase 1 (Arg1), Cd163, stabilin 1 (Stab1), and mannose receptor C-type 1 (Mrc1)] to infer the phenotypic transition of macrophages during AAA formation. The t-SNE feature indicated that M2 marker genes, including Cd163 and Mrc1, were mainly expressed in the CTR group. Stab1 was highly expressed in both groups, whereas Arg1 was barely expressed in either group. M1 marker genes were irregularly expressed in both the AAA and CTR groups (Fig. 3B-C). The violin plots further indicated that the expression of M1 marker genes in each cluster seemed to be irregular, while M2 marker genes were highly expressed in Mo/Mø3, 5, and 6 (Fig. 3D). The results suggested that the number of M2-type macrophages gradually decreased and that macrophages were polarized in a direction that promoted inflammation as the disease progressed. Type M2 macrophages could be further divided into three subgroups (namely M2a, M2b, and M2c) based on the expression of marker genes . In our study, M2b was rarely expressed in the arterial tissue. M2a was expressed in all CTR clusters (Mo/Mø3, Mo/Mø5, and Mo/Mø6). M2c was predominantly expressed in Mo/Mø5. M2a and M2c macrophages comprised the primary proportion of macrophages in the CTR group and decreased as AAA progressed (Fig. 3E-F).
T and B cells are important components of the immune cells. In this experiment, fewer T and B cells were identified in the AAA group (Fig. 3A). During the development of AAA, the expression of Cd3 and Cd8 increased significantly, while Cd4 expression was similar in both groups (Fig. 3G). Bioinformatic analysis further revealed the functional alternations of T lymphocytes. Ribosome-related components, translation, T cell differentiation and activation, protein binding, and the structural constituent of ribosomes accounted for the majority of the GO enrichment terms. During the formation of AAA, the protein synthesis of T lymphocytes increased, and the differentiation and activation of cells was vibrant (Supplementary Fig. 4A). KEGG pathway analysis suggested that protein synthesis (ribosome) and T cell activation and differentiation were the most critical mechanism alterations (Supplementary Fig. 4B).
After activation, B lymphocytes can differentiate into plasma cells. In addition to synthesizing and secreting various immunoglobulins, plasma cells also highly express Cd38 and Cd27, while there is a low expression of Cd5, which is consistent with the current results (Fig. 3H). Further bioinformatic analysis suggested that ribosome-related components, immune system process, translation, cytoplasmic translation, mRNA processing, and protein binding accounted for the majority of the GO enrichment terms (Supplementary Fig. 4C). KEGG pathway analysis suggested that the B cell receptor signaling pathway, protein synthesis (ribosome) pathway, and NF-κB signaling pathway were critical pathways in the AAA process. Additionally, the activation of B cells also affected the differentiation and function of T lymphocytes (Supplementary Fig. 4D).
3.4 Transformation in the function and phenotype of SMCs and ECs during the formation of AAA
SMCs and ECs are important components of the artery wall tissue. Corresponding functional and phenotypic changes in SMCs and ECs also occurred during the disease process of AAA. The numbers of EC1 and EC3 generally decreased, while that of EC2 increased during AAA formation, and numbers of both SMC1 and SMC2 increased (Fig. 4A). The top 10 marker genes of ECs were identified for each cluster relative to all other clusters. The expression patterns of marker genes in EC1 and EC3 were similar, which was different from that of EC2 (Fig. 4B). Phlogosis and damage-related genes, including vascular cell adhesion molecule-1 (Vcam1), von Willebrand factor (Vwf), intercellular adhesion molecule 1 (Icam1), endothelin 1 (Edn1), Serpine1, and prostaglandin I2 synthase (Ptgis) [19–22], were highly expressed in EC2. Proliferation and regeneration-related genes, including endoglin (Eng), kinase insert domain receptor (Kdr), and chromodomain helicase DNA-binding protein 5 (Chd5) [23–25], were highly expressed in EC1 and 3 (Fig. 4C), which was further confirmed by the violin plot (Fig. 4D).
Similar phenotypic and functional changes were observed in the SMC. The expression of secretory SMC marker genes [secreted phosphoprotein 1 (Spp1), matrix Gla protein (Mgp), epiregulin (Ereg), and elastin (Eln)] increased in SMC1 and SMC2, while the expression of contractile SMC marker genes [Acta2, Tagln, caldesmon 1 (Cald1), and Myh11] remained unchanged during AAA formation (Fig. 4E), which implied a transition of SMCs from contractile to secretory during the formation of AAA.
3.5 The cell heterogeneity and DEG expression of Fbs in AAA
Fibroblasts were identified into most clusters in our study. During the AAA process, the amount of Fb1, Fb4, Fb5, Fb9, and Fb10 decreased while Fb2, Fb3, Fb6, and Fb8 increased, Fb7 was basically unchanged (Fig. 5A). The top 10 marker genes were identified for each cluster relative to all the other clusters. Clusters with the same trends in amount had similar gene expression mode (Fig. 5B). Further screening of DEGs was necessary. A total of 411 upregulated genes and 605 downregulated genes were identified (Fig. 5C). GO enrichment analysis revealed that Fbs were predominantly involved in the binding of collagen and the synthesis and degradation of ECM (Fig. 5D). KEGG pathway analysis revealed that oxidative phosphorylation, ECM-receptor interaction, and PI3K-Akt signaling pathway may be the pivotal signaling pathways that participate in the formation of AAA (Fig. 5E).
3.6 Single cell trajectory analysis predicted cell differentiation of Fbs in AAA
The process of phenotypic and functional alternations in the cells was gradual. Therefore, it was not clear which pattern of Fbs was responsible for AAA initiation. The pseudo-time of Fbs with gene expression profiles of different clusters was reconstituted (Fig. 6A). Gene expression could be roughly divided into seven stages according to the different nodes (Fig. 6B). The typical direction of Fb differentiation was the transformation from stage 1 to stage 4. The t-SNE map also reflected the direction of cell differentiation; dark-colored cells continued to transform into light-colored cells (Fig. 6C). We identified that Fb5 was the starting point of differentiation and ultimately differentiated into Fb2 and Fb6. The heatmap displays the alternation tendencies of the top 50 critical genes. Overall, we found three different patterns of genetic changes, namely, Cluster 1: The expression of genes first increased and then decreased with the development of AAA, including that of ribosomal protein L37a (Rpl37a), ribosomal protein S20 (Rps20), and ribosomal protein S27 (Rps27); Cluster 2: The expression of genes decreased with the development of AAA, including that of myelin and lymphocyte protein, T cell differentiation protein (Mal) and noncompact myelin-associated protein (Ncmap); Cluster 3: The expression of genes increased with the development of AAA, including that of complement component factor h (Cfh), biglycan (Bgn), and v-maf musculoaponeurotic fibrosarcoma oncogene family, protein B (Mafb) (Fig. 6D). The top six genes displaying the most distinct expression change were considered to have determined the outcome of Fb differentiation. The relative expression levels of the top six genes were shown over pseudotime by Monocle2 in cluster mode (Fig. 6E) and state mode (Fig. 6F).
As we selected the top 50 genes which determined the direction of Fbs differentiation, further bioinformatics studies were necessary. The overlap of the 3 clusters was shown in the circus plot and the blue curves link genes that belong to the same enriched ontology term (Fig. 6G). The heatmap of GO enrichment terms shown that genes listed in cluster 3 (up expressed) mainly participated in negative regulation of immune system process, regulation of complement cascade and antigen processing and present; genes in cluster 2 (down expressed) mainly participated in myelination; genes in cluster 1 (first up and then down expressed) mainly participated in SRP-dependent cotranslational protein targeting to membrane, negative regulation of peptidase activity, post-translational protein phosphorylation, platelet degranulation, protein localization to membrane, response to endoplasmic reticulum stress, vesicle organization and response to metal ion (Fig. 6H). To further capture the relationships between the terms, a subset of enriched terms had been selected and rendered as a network plot (Fig. 6I). Although no common GO enrichment terms were found between the 3 clusters, it seemed that cluster 1 was associated with cluster 3 according part of the GO terms, which involved in regulation of the immune system, phosphorylation of post-translational protein and degranulation of platelets.
3.7 Analysis of marker genes revealed different subtypes of increased fibroblasts in function and spatial distribution
It was necessary to identify Fbs through the expression of specific markers. Seven different terms were identified, namely, normal quiescent state [Vim and caveolin 1 (Cav1)] , active state [S100a8, fibroblast activation protein alpha (Fap), Acta2, platelet-derived growth factor receptor beta (Pdgfrb)] [27, 28], antigen presentation [Cd74 and histocompatibility 2, class II antigen A, beta 1 (H2-Ab1)] , phlogosis (Il1b and Il6) , angiogenesis (Des) , extracellular matrix synthesis [platelet-derived growth factor receptor alpha (Pdgfra) and fibulin 1 (Fbln1)]  and tissue repair [fibronectin 1 (Fn1)] . The dot plot showed the expression of characteristic genes in different Fb clusters (Fig. 7A). Based on the results from single-cell trajectory analysis, Fb5 might be the starting point of differentiation (Fig. 6C) and was selected as the control cluster. Semi-quantitative analysis showed the difference in the expression of related functional genes between Fb5 and increased Fbs in the expression of related functional genes (Fig. 7B). The expression of marker genes in increased Fbs was compared with that of Fb5, and the values of fold change (FC) were obtained. The increased Fbs were redefined according to the FC values (Fig. 7C). The criteria were as follows: 0 (0.5<FC value<2); + (2≤FC value<4); ++ (4≤FC value<8); +++ (8≤FC value); - (0.25<FC value≤0.5); -- (0.125<FC value≤0.25); --- (FC value≤0.125). Fb2 highly expressed Cd74 and H2-Ab1 and was defined as an antigen presenting Fb (apFb). Fb6 highly expressed in Fn1 and was defined as tissue repair Fb (trFb). Fb8 highly expressed Des and was defined as vascular Fb (vFb). Fb3 only highly expressed in activation-related markers and was defined as activation Fb (acFb). Immunofluorescence confirmed the presence of 4 Fbs in AAA tissue (Fig. 7D), Fb2 co-expressed Vim and Cd74 and can be observed in the whole artery layer of AAA tissue; Fb3 highly co-expressed Fbln1 and Vim, and mainly distributed in the intima of arteries; Fb6 highly co-expressed Fn1 and Vim, which was mainly observed around thrombus; Fb8 high co-expressed Des and Vim. As the smallest subgroup, it was hardly been discovered for its small amount.
3.8 The ligand–receptor interaction analysis revealed the procollagen synthesis effect of Fbs on SMCs
Cell-cell communication was investigated by ligand–receptor analysis according to CellPhoneDB. The results indicated that the intensity of the interaction between different types of cells varied greatly. Meanwhile, the Fbs had the closest connection with the SMCs (Fig. 8A). We further processed the ligand–receptor analysis between SMCs and increased Fbs (Fb2, Fb3, Fb6, and Fb8) during AAA formation. Fb3 and Fb6 were found to have more connections with SMCs than Fb2 and Fb8 (Fig. 8B). Four distinct Fb clusters influenced the function and phenotype of SMC through multiple ligand–receptor pairs (Fig. 8C-F). Different Fb/SMC pairs showed specific ligand–receptor characteristics. We found that CD74- macrophage migration inhibitory factor (Mif), Cd74- COPI coat complex subunit alpha (Copa), and CD74- amyloid beta precursor protein (App) displayed important associations between Fb2 and SMCs, confirming the antigen-presenting effect of Fb2. The Fn1–a5b1 complex, Fbn1–a5b1 complex, Fn1–a8b1 complex, Fn1–aVb1 complex, and Fn1–aVb5 complex were critical links between Fb6 and SMC, which demonstrated that Fb6 played an important role in tissue repair during disease progression. Alternatively, increased collagen synthesis was the most prominent feature of the selected Fbs, except Fb8. Additionally, the expression of Spp1- a9b1 complex and fibroblast growth factor receptors (Fgfr1)-neural cell adhesion molecule 1 (Ncam1) commonly increased significantly while neuropilin-1 (Nrp1)-vascular endothelial growth factor B (Vegfb) and Pdgfr complex- platelet-derived growth factor D (Pdgfd) were decreased in 4 Fbs.