Single-cell RNA sequencing and cross-species analysis revealed the role of T cell-driven inflammatory responses in the pathogenesis of aortic dissection

Abstract

Background

Aortic dissection (AD) is a life-threatening vascular condition characterized by acute inflammation and structural deterioration of the aortic wall. This study aimed to delineate the immune landscape, particularly T cell–mediated responses, and identify conserved inflammatory mechanisms driving AD pathogenesis across human and murine models.

Methods

Ascending aortic tissues and plasma were collected from patients with AD and normal controls. CD45⁺ immune cells were isolated using magnetic-activated cell sorting, followed by single-cell RNA sequencing (scRNA-seq) via the 10x Genomics Chromium platform. Data processing and downstream bioinformatics analyses were performed using Cell Ranger and Seurat pipelines, including cell clustering, differential expression, and pathway enrichment analyses. To validate transcriptomic findings, a β-aminopropionitrile (BAPN)-induced AD mouse model was established, and aortic tissues were subjected to eukaryotic transcriptome sequencing. Differentially expressed genes (DEGs) were identified and functionally annotated via GO and KEGG analyses, while transcription factor-target networks were constructed to reveal key regulators. Cross-species integration of human and murine transcriptomic datasets was conducted to identify conserved DEGs and signaling pathways. Quantitative PCR and ELISA were performed on human samples to validate transcriptomic results and assess systemic inflammatory responses.

Results

scRNA-seq analysis revealed a distinct immune landscape in AD tissues characterized by significant T cell enrichment and activation. Disease-associated T cell clusters exhibited elevated expression of cytotoxic and inflammatory genes such as GZMA, GZMB, IFIT1, and IFI6, indicating enhanced adaptive immune activity within the aortic wall. Bulk transcriptomic analysis further demonstrated upregulation of pro-inflammatory mediators (IL1B, CXCL8, CCL2, NFKBIA) and enrichment in immune-related pathways, including TNF, NF-κB, and IL-17 signaling. In the murine AD model, transcriptomic profiling identified 4,427 DEGs, primarily involved in inflammatory responses, leukocyte migration, extracellular matrix remodeling, and apoptosis. Transcription factor analysis highlighted Nfkb1, Jun, Fos, and Stat3 as key regulatory hubs orchestrating these processes. Cross-species integration revealed 51 conserved DEGs between human and mouse datasets, predominantly enriched in IL-17 and cytokine-cytokine receptor interaction pathways. qRT-PCR validation in human aortic tissues confirmed significant upregulation of IL1B, IL6, MAPK10, MAPK12, MMP9, MMP13, S100A8, and S100A9 in AD samples, while ELISA demonstrated elevated serum levels of IFN-γ, IL-10, IL-6, TNF-α, IL-17 A, and IL-8, indicative of a systemic pro-inflammatory state.

Conclusion

Collectively, our integrative multi-omics analyses demonstrate that aortic dissection is driven by coordinated immune remodeling involving both adaptive and innate immune cells, accompanied by conserved inflammatory transcriptional programs across human and murine datasets. Despite limited changes in circulating cytokines, tissue-level analyses reveal robust activation of TNF, NF-κB, and IL-17-associated pathways, underscoring the importance of local vascular immune responses in AD pathogenesis.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12872-026-06275-w.

Introduction

Aortic dissection (AD) is a life-threatening vascular condition characterized by a tear in the aortic intima, allowing blood to enter the medial layer and create a false lumen.AD typically presents with sudden, severe chest or back pain, often described as tearing or stabbing [1]. Globally, the incidence of AD ranges from 2.0 to 3.5 per 100,000 person-years, with variations observed across different regions. In the United States and Europe, studies report incidences of 2.6 to 3.5 per 100,000 person-years. A study from China involving approximately 510,000 participants found an incidence of 2.35 per 100,000 person-years [1–3].The significant mortality and regional variations in aortic dissection incidence highlight the importance of elucidating its underlying mechanisms.

The pathogenesis of AD involves complex interactions between genetic predispositions, mechanical stress, and immune responses. Recent studies have highlighted the significant role of immune modulation in the development and progression of AD [1]. These immune cells contribute to extracellular matrix degradation, smooth muscle cell apoptosis, and the release of pro-inflammatory cytokines, all of which compromise aortic integrity [4]. Recent research utilizing single-cell RNA sequencing has elucidated the role of immune cells in AD. T lymphocytes also play a significant role in AD [5]. Studies have identified an expansion of T cells, particularly Th17-like T cells, in AD tissues. These cells secrete pro-inflammatory cytokines like IL-32 and TNF-α, which can recruit macrophages and exacerbate inflammation, leading to extracellular matrix degradation and aortic wall weakening [6]. Additionally, elevated levels of Th17 cells and associated cytokines, such as IL-17, have been observed in AD tissues, suggesting their involvement in promoting inflammation and matrix degradation. Regulatory T cells (Tregs), which typically suppress inflammation, have been shown to limit aneurysm formation and rupture in mouse models. Depletion of Tregs increases susceptibility to aortic aneurysms, suggesting their protective role in maintaining aortic integrity [7–10]. Importantly, recent evidence also indicates that monocyte–macrophage populations represent major contributors to aortic inflammatory responses, often showing pronounced infiltration and activation in diseased aortic tissue. Therefore, both innate immune cells and adaptive immune cells are considered to play complementary roles in AD-associated immune dysregulation, rather than a single dominant immune axis [11–13].

Murine models are instrumental in elucidating AD pathophysiology under controlled conditions. However, these models may not fully recapitulate the human disease’s complexity. Species-specific differences in immune system components and responses can limit the translatability of findings. Moreover, the genetic homogeneity of inbred mouse strains does not reflect the genetic diversity observed in human populations, potentially overlooking critical gene-environment interactions pertinent to AD [14–16]. In addition, transcriptomic data generated from different platforms capture biological signals at distinct resolutions, which should be considered when performing cross-species integration analyses [17, 18]. To address these limitations, our study integrates scRNA-seq analyses of human AD tissues with murine model investigations.

In this study, we aim to conduct a comprehensive analysis of immune cell heterogeneity in aortic dissection (AD) by leveraging single-cell RNA sequencing (scRNA-seq). By isolating CD45⁺ immune cells and applying unsupervised clustering algorithms, we seek to delineate distinct immune cell subsets and characterize their transcriptional profiles. In addition, we will investigate the functional pathways and intercellular communication networks that underlie immune-mediated mechanisms contributing to AD pathogenesis. Rather than focusing solely on single-cell–resolved cellular specificity, this study also emphasizes tissue-level conserved transcriptional signatures across human and murine datasets [17, 19]. Comparative analyses between human and murine datasets will be performed to identify evolutionarily conserved immune responses implicated in disease progression. Insights derived from murine models will guide the validation of key findings in human tissues, thereby facilitating the discovery of clinically relevant therapeutic targets. This integrative approach is expected to advance our understanding of AD immunopathology and inform the development of novel immunomodulatory strategies.

Materials and methods

We confirm that the study was conducted in accordance to the ARRIVE guidelines. Research involving human research participants has been performed in accordance with the Declaration of Helsinki.All methods were performed in accordance with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Study population and data collection

Human Ethics and Consent to Participate declarations: The study was approved by the Ethics Committee of Shanghai Chest Hospital, Shanghai Jiaotong University School of Medicine (IRB No.: IS24142; 26th December 2023). and informed consent was obtained from all participants (or their legally authorized guardians in cases of minors/incapacitated individuals) prior to study enrollment.Clinical trial number: not applicable.The inclusion criteria were as follows: (1) patients with AD > 18 years of age; (2) obvious clinical manifestations of AD, such as sudden onset of severe chest, back, or abdominal pain; and (3) false lumen or free intima detected using echocardiography, computed tomography angiography, magnetic resonance imaging, or aortic angiography. Patients with aortic trauma, pseudoaneurysm, ischemic heart disease, cancer, or infectious diseases were excluded. A case-control study was established to compare serum inflammatory cytokines levels of patients with AD and healthy controls. A total of 33 patients with AD and 29 healthy controls were recruited. Blood samples of patients with AD were collected before surgery within 2 h of admission. Blood samples from patients with AD and healthy controls were collected; plasma was separated by centrifugation immediately and stored at − 80℃ until use.

Clinical tissue sample collection

The study was approved by the Ethics Committee of Shanghai Chest Hospital, Shanghai Jiaotong University School of Medicine (IRB No.: IS24142; 26th December 2023). and informed consent was obtained from all participants (or their legally authorized guardians in cases of minors/incapacitated individuals) prior to study enrollment.Ascending aortic tissue specimens were collected from four patients undergoing cardiovascular surgery. Two samples were obtained from individuals diagnosed with Stanford type A AD, while the remaining two were derived from patients undergoing isolated aortic valve replacement, serving as normal controls. All tissue samples were harvested intraoperatively under sterile conditions, ensuring freshness and minimizing ischemic damage. Immediately post-excision, the specimens were immersed in pre-chilled sterile PBS and promptly transported on ice to the laboratory for subsequent processing.

Preparation of single-cell suspensions and MACS sorting of immune cells

Ascending aortic tissues were minced into ~ 1–2 mm³ fragments and enzymatically digested in a pre-chilled PBS-based solution containing 1 mg/mL collagenase IV and 0.1 mg/mL DNase I. Digestion was carried out at 37 °C for 30 min. The reaction was terminated by adding an equal volume of DMEM supplemented with 10% FBS. The resulting suspension was sequentially filtered through 100 μm and 40 μm strainers to remove tissue debris and aggregates. Cells were centrifuged at 300 g for 5 min at 4 °C and washed twice with cold PBS. Cell concentration and viability were measured using an automated cell counter.

Single-cell suspensions were then adjusted to 1 × 10⁷ cells/mL for MACS. CD45⁺ immune cells were labeled with 10 µL of CD45-conjugated microbeads per 1 × 10⁷ cells and incubated at 4 °C for 15 min with intermittent mixing. Unbound beads were removed by washing, and CD45 + cells were isolated using a MACS column under a magnetic field. Purity was confirmed by flow cytometry, and RNA integrity was validated for downstream single-cell RNA sequencing.

Single-cell RNA sequencing

Single-cell RNA sequencing was performed using the 10x Genomics Chromium platform, which integrates microfluidics and cell barcoding to enable high-throughput transcriptomic profiling. Enriched immune cells were adjusted to a concentration of 70–1200 cells/µL, ensuring > 90% viability. Cell suspensions, barcoded gel beads, and oil reagents were loaded onto a Chromium microfluidic chip. Individual cells were encapsulated with barcoded gel beads in nanoliter-scale droplets. Within each droplet, cells were lysed and released mRNA was captured by oligo-dT primers on the gel beads, followed by reverse transcription to generate cDNA tagged with UMIs and cell barcodes. Droplets were subsequently disrupted, cDNA was purified and PCR-amplified. Amplified cDNA underwent fragmentation, end repair, and adaptor ligation to construct sequencing libraries. Libraries were quality-checked using a bioanalyzer and quantified by qPCR. Sequencing was performed on an Illumina NovaSeq 6000 platform.

Data processing

Raw data were processed using Cell Ranger for demultiplexing, quality control, and alignment. Cells with < 200 genes or > 20% mitochondrial gene content were excluded. Data normalization and identification of highly variable genes were conducted using the Seurat package. Dimensionality reduction was performed via PCA and visualized using UMAP. Clustering was achieved using Louvain algorithm with a resolution of 0.5. Cell types were annotated based on canonical markers. Differential expression was analyzed with FindMarkers, and functional enrichment (GO, KEGG, Reactome, WikiPathways) was visualized using ggplot2, pheatmap, and ComplexHeatmap. Transcription factor-target networks were constructed and visualized via Sankey and trend plots.

Quantitative real-time polymerase chain reaction

Total RNA was extracted from frozen aortic tissue samples using Trizol reagent, followed by chloroform-mediated phase separation and isopropanol precipitation. The RNA pellet was washed with ethanol, air-dried, and dissolved in RNase-free water. cDNA was synthesized using a commercial reverse transcription kit according to the manufacturer’s protocol. qRT-PCR was performed using SYBR Green chemistry on the QuantStudio 6 Flex system. The reaction was carried out in a 10 µL volume with appropriate concentrations of primers, cDNA template, and fluorescent dye. Amplification involved initial denaturation and 40 cycles of denaturation, annealing, and extension, followed by melting curve analysis to confirm product specificity. All reactions were run in triplicate to ensure reproducibility. Relative mRNA expression levels were quantified using the 2^–ΔΔCT method, normalized to β-actin as the internal control. The primers used for qRT-PCR analysis were designed based on human gene sequences obtained from the NCBI database. The following primer sequences were used: β-actin (F: 5’-GTCCACCGCAAATGCTTCTA-3’; R: 5’- TGCTGTCACCTTCACCGTTC3’); Ccl2( F: 5’-CTCATAGCAGCCACCTTCATTC-3’, R: 5’-GATCACAGCTTCTTTGGGACAC-3’); Cxcl3 (F: 5’-CGTCCGTGGTCACTGAACTG-3’, F: 5’-GTCAGTTGGTGCTCCCCTTG-3’); IL-1β (F: 5’-ACGATGCACCTGTACGATCACT-3’, R: 5’-GAGAACACCACTTGTTGCTCCA-3’); IL-6 (F: 5’- TCAGCCCTGAGAAAGGAGACAT-3’, R: 5’-GCTCTGGCTTGTTCCTCACTACT-3’); Lcn2 (F: 5’-ACAAAGACCCGCAAAAGATGT-3’, R: 5’-GTCCTGATCCAGTAGTCACACTTCT-3’); Mapk10 (F: 5’- GAACTGATGGATGCCAACTTATGT-3’, R: 5’-GTAATAACGTGTCACCACATATGGA-3’); Mapk12 (F: 5’-ATTCACTCCTGATGAGACCCTG-3’, R: 5’-GGTACACGAGGAACTGGATCC-3’); Mmp13 (F: 5’-ATCATGATCTCTTTTGGAATTAAGG-3’, R: 5’-AACAAGTTGTAGCCTTTGGAACTAC-3’); Mmp9 (F: 5’-TCGAACTTTGACAGCGACAAG-3’, R: 5’-TCAGTGAAGCGGTACATAGGGT-3’; S100a8 (F: 5’-AAGTCCGTGGGCATCATGTT-3’, R: 5’-TCAGGTCATCCCTGTAGACGG-3’); and S100a9 (F: 5’-GCAACATAGAGACCATCATCAAC-3’, R: 5’-CTCCATGATGTGTTCTATGACCTTT-3’).

Enzyme-linked immunosorbent assay for inflammatory cytokine

Levels of inflammatory cytokines in AD patients and healthy controls were measured using ELISA kits following the manufacturer’s protocol including IFN-α, IFN-γ, IL-10, IL-17 A, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8 and TNFα. Briefly, standards and diluted plasma samples were added to 96-well plates pre-coated with capture antibodies and incubated at 37 °C. After washing, biotin-labeled detection antibodies were added, followed by streptavidin-HRP incubation. Color development was performed using TMB substrate in the dark, and the reaction was stopped with stop solution. Absorbance was measured at 450 nm using a microplate reader. All steps were conducted at room temperature or 37 °C, with appropriate washing between incubations to ensure specificity and accuracy. Results reflect relative cytokine concentrations based on the standard curve.

Mouse model of aortic dissection and tissue sample collection

This study has been reported in accordance with the ARRIVE 2.0 Guidelines(Animal Research: Reporting of In Vivo Experiments), including detailed methodological descriptions to enhance reproducibility. and protocols were approved by the Ethics Committee of Tianjin Medical University General Hospital (IRB No.: 2024DW121; 28th November 2024).Efforts were made to minimize animal suffering and ensure humane treatment. All animals were provided by Beijing Vital River Laboratory Animal Technology Co., Ltd.

(1) A total of 20 male SPF-grade C57BL/6J mice (3 weeks old) were randomly divided into a control group and an AD model group, with 10 mice in each group. All animals were housed under sterile conditions with free access to standard rodent chow and distilled water. Environmental parameters were maintained at a 12 h light/dark cycle, 55 ± 5% humidity, and ambient noise levels below 85 dB. Following a 1-week acclimatization period, experimental procedures were initiated. To induce aortic dissection, mice in the model group were administered BAPN at a final concentration of 0.2% (w/v) in their drinking water, continuously for 4 weeks. BAPN impairs collagen cross-linking in the aortic wall, thereby promoting the development of aortic dissection. Mice in the control group received standard care and were provided with untreated drinking water. All animals that were sacrificed by Cervical Dislocation in this study, animals were anesthetized with ketamine (50 mg/kg IP).and then Cervical vertebrae were manually dislocated via axial traction. Thoracic aortas were carefully isolated after PBS perfusion, photographed, and immediately frozen in liquid nitrogen for subsequent analyses.

Eukaryotic transcriptome sequencing

Total RNA was extracted from mouse aortic tissue using TRIzol reagent or an RNA extraction kit. After tissue homogenization, chloroform was added for phase separation. The aqueous phase was collected, and RNA was precipitated with isopropanol, washed with 75% ethanol, and dissolved in RNase-free DEPC-treated water. RNA purity and concentration were assessed using a NanoDrop spectrophotometer. RNA integrity was evaluated using an Agilent Bioanalyzer or agarose gel electrophoresis. mRNA was isolated from total RNA using oligo(dT) magnetic beads. The purified mRNA was fragmented into 200–500 bp. First-strand cDNA was synthesized using reverse transcriptase and primers, followed by second-strand cDNA synthesis. cDNA fragments were end-repaired, A-tailed, and ligated to sequencing adapters. Adapter-ligated fragments were amplified using high-fidelity DNA polymerase under limited PCR cycles. Amplified libraries were purified, quantified, and quality-checked. Qualified libraries were sequenced on the Illumina NovaSeq platform with paired-end reads. Each sample generated at least 30 million reads to ensure data quality for transcriptomic analysis.

Statistics

Data analysis was performed using SPSS 26.0, and graphs were generated with GraphPad Prism 9. Results are expressed as mean ± standard deviation. For normally distributed data, one-way ANOVA followed by Dunnett’s test was applied; non-normally distributed data were analyzed using non-parametric tests. Statistical significance was set at p < 0.05.

Results

Immune cell remodeling and T-cell activation in AD

A total of nine major clusters were identified, indicating cellular heterogeneity in aortic tissue, with comparable overall structure but altered cluster density between groups (Fig. 1A). Major immune and stromal cell types were annotated, including T cells, macrophages, endothelial cells, smooth muscle cells, and fibroblasts, confirming reliable cell classification (Fig. 1B). In AD samples, immune cell composition was significantly altered, with pronounced changes in macrophage- and T cell-related clusters, suggesting immune microenvironment remodeling (Fig. 1C). Cluster annotation was supported by canonical marker genes. Distinct expression signatures were observed across clusters, including ADCYAP1, CPA3, and CTSG in mast cell-related populations, FCGR3B, ADGRG3, S100P, CMTM2, and MME in myeloid/neutrophil-associated clusters, and AFF3 and BANK1 in lymphocyte-related clusters, confirming robust cell-type identification (Fig. 1D).

Fig. 1.

Single-cell transcriptomic profiling reveals immune landscape alterations and T cell enrichment in AD. A UMAP visualization of unsupervised clustering of single cells from control and AD samples. B UMAP plot annotated by major cell types based on canonical marker gene expression. C Bar plots showing the relative proportions of each cell cluster in control and AD groups. D Heatmap depicting representative marker gene expression across clusters. Red indicates higher expression, and blue indicates lower expression. T cell-associated markers are prominently expressed in specific clusters enriched in AD samples. E Reclustering analysis of T-cell populations. F Distribution of T-cell subpopulations across control and AD samples. G Heatmap of T-cell subtype-specific marker gene expression.

AD and control cells showed partial transcriptional separation, indicating global expression differences between the two groups (Fig. 1E). Immune populations, particularly T cells and macrophages, were expanded in AD tissues, indicating enhanced immune activation (Fig. 1F). T-cell subclustering identified distinct subsets with heterogeneous transcriptional profiles. One subset showed enrichment of cell cycle and proliferation-associated genes, including KIF15, TROAP, GTSE1, UBE2C, and TSHZ2, suggesting increased proliferative activity. Another subset was characterized by XIST and MTRNR2L12, indicating transcriptional diversity among T-cell states. Additional differentially expressed genes, including EGR3 and AL109930.1, further supported functional heterogeneity of T-cell populations in AD (Fig. 1G). Overall, these results demonstrate a marked remodeling of the immune landscape in AD aortic tissues, characterized by altered immune cell composition, expanded inflammatory populations, and heterogeneous activation states of T cells. Although the overall T-cell proportion remains relatively stable, T-cell functional states are significantly remodeled in AD, reflecting immune activation at the transcriptional and subset level rather than at the bulk compositional level.

Immune activation and pathway enrichment reveal T cell–driven mechanisms in AD

Transcriptomic profiling was conducted on aortic tissues from AD patients and controls to identify differential gene expression and related functional pathways. Bulk or pseudo-bulk RNA sequencing data were analyzed using integrative bioinformatics approaches, including GO and KEGG enrichment analyses and protein–protein interaction (PPI) network construction [14, 15].Differential gene expression analysis revealed a distinct transcriptomic signature in AD tissues (Fig. 2A), with prominent upregulation of immune-related genes, including IL1B, CXCL8, CCL2, and NFKBIA. These genes are primarily involved in leukocyte recruitment, cytokine signaling, and inflammatory activation. Conversely, several downregulated genes were linked to vascular structure and smooth muscle cell function, suggesting alterations in vessel wall maintenance. GO enrichment analysis (Fig. 2B&D) indicated that upregulated genes were predominantly associated with immune-related biological processes, including “leukocyte chemotaxis,” “inflammatory response,” “cytokine-mediated signaling pathway,” and “T cell activation.” Downregulated genes were enriched in pathways related to extracellular matrix organization and vascular development. The GO chord diagram (Fig. 2C) demonstrated the pleiotropic nature of several DEGs—such as IL1B, CXCL2, CD14, and PTGS2—that appeared across multiple enriched immune-related GO terms, suggesting their multifaceted roles in vascular inflammation. KEGG pathway analysis (Fig. 2E&G) identified significant enrichment of upregulated DEGs in canonical immune and inflammatory signaling pathways, including “TNF signaling,” “NF-kappa B signaling,” “IL-17 signaling,” and “chemokine signaling.” These pathways are central to immune cell recruitment and activation, as well as cytokine production and matrix degradation. The KEGG chord diagram (Fig. 2F) highlighted the convergence of several key genes (NFKBIA, CCL20, IL1B) across multiple pathways, underscoring their functional centrality. To further explore molecular interactions, a PPI network was constructed (Fig. 2H&I). This network revealed highly connected nodes, with IL1B, JUN, CXCL8, and NFKBIA functioning as key hubs. These proteins are central mediators of transcriptional responses to cytokine stimulation and play pivotal roles in the orchestration of inflammatory signaling cascades. The top 25 hub genes formed a densely interconnected subnetwork enriched in immune-associated functions.

Fig. 2.

Transcriptomic profiling and functional enrichment analysis of AD. A Heatmap depicting the top 20 differentially expressed genes between case and control groups. B GO (Gene Ontology) enrichment analysis results for all significantly altered biological processes between case and control groups. C GO chord diagram illustrating relationships between the top enriched GO terms and associated differentially expressed genes. D Bar plot of the top significantly enriched GO terms ranked by statistical significance. E KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis showing all significantly altered pathways between case and control groups. F KEGG chord diagram demonstrating connections between key enriched pathways and corresponding differentially expressed genes. G Bar plot of the top significantly enriched KEGG pathways ranked by statistical significance. H Protein-protein interaction (PPI) network constructed using differentially expressed genes filtered by p-value < 0.05 and fold change > 1.5 between case and control groups. I Top 25 hub proteins within the PPI network ranked by degree of connectivity.

Transcriptomic validation of inflammatory and remodeling-related gene signatures in BAPN-induced AD model

Transcriptomic profiling of aortic tissues from control and BAPN-induced AD mice was performed to assess global gene expression changes at the tissue level. High-quality sequencing data were obtained, and principal component patterns demonstrated clear separation between control and AD groups, with high intra-group reproducibility (Fig. 3A-B).

Fig. 3.

Transcriptomic landscape and functional enrichment analysis of aortic tissues in AD model mice. A Gross morphological comparison of aortic tissues from control and AD model mice, showing clear dilation and structural distortion in the AD group. B Heatmap of the sample-to-sample correlation matrix, indicating high intra-group consistency and distinct expression profiles between control and AD groups. C Bar plot summarizing the number of upregulated and downregulated DEGs. D Circos plot of GO enrichment analysis showing major biological processes associated with DEGs, including inflammatory response and extracellular matrix organization. E GO chord diagram illustrating the relationships between top enriched GO terms and their associated DEGs. F Top ranked GO terms based on statistical enrichment, predominantly involving immune regulation and vascular remodeling. G Circos plot of KEGG pathway enrichment, highlighting significant pathways such as TNF signaling and NF-κB signaling in AD tissues. H KEGG chord diagram linking key DEGs to multiple enriched signaling pathways. I Top enriched KEGG pathways related to inflammation, apoptosis, and immune signaling. J Sankey diagram showing TF-target regulatory relationships altered between AD and control groups. K Differentially expressed transcription factors ranked by their regulatory influence. L Quantitative summary of the number of downstream target genes regulated by key TFs in the AD condition. M Interaction network of the top 30 DEGs based on regulatory and functional importance

Differential expression analysis identified a large number of DEGs, reflecting broad transcriptional remodeling in the diseased aorta (Fig. 3C). Functional enrichment analyses showed that upregulated genes were consistently associated with inflammatory response, leukocyte migration, extracellular matrix organization, and oxidative stress-related processes (Fig. 3D-F). KEGG pathway analysis further indicated enrichment in TNF, IL-17, NF-κB, and Toll-like receptor signaling pathways (Fig. 3G-I), which are well-established mechanisms in aortic dissection and serve here as validation of disease-associated transcriptional activation at the bulk tissue level.

To further explore coordinated transcriptional regulation, transcription factor–target interaction analysis was performed based on bulk RNA-seq data. Key TFs such as Nfkb1, Jun, Fos, and Stat3 were identified as central regulatory nodes associated with inflammatory and stress-response gene programs (Fig. 3J-L). This analysis provides a tissue-level overview of regulatory activity changes, complementing previously reported single-cell-based regulatory landscapes.

Finally, PPI network analysis of DEGs highlighted highly connected inflammatory and matrix-related genes, including Tnf, Il6, Mmp9, and Ccl2 (Fig. 3M), further supporting the activation of immune and extracellular matrix remodeling processes in AD.

Overall, these results provide bulk-level transcriptomic confirmation of established pathological features of AD and serve as an independent validation of inflammation- and remodeling-associated gene programs observed in single-cell studies.

Cross-species transcriptomic integration reveals conserved inflammatory signatures and immune pathway activation in AD

To explore shared transcriptional alterations between human and murine AD, we performed cross-species comparison using bulk RNA-seq data from BAPN-induced mouse aortas and pseudo-bulk aggregated human single-cell transcriptomic data. This analysis was designed to identify tissue-level conserved gene expression signatures rather than cell-type-specific regulatory programs. A total of 51 overlapping differentially expressed genes (DEGs) were identified between the two species, indicating the presence of conserved transcriptional responses associated with AD pathology (Fig. 4A). Protein–protein interaction (PPI) analysis of these shared genes revealed a connected network enriched in inflammation- and remodeling-related molecules, including IL1B, CXCL3, and MMP19 (Fig. 4B). These genes are involved in inflammatory signaling, leukocyte recruitment, and extracellular matrix regulation, suggesting coordinated activation of vascular injury-related pathways. Functional enrichment analysis of the shared DEGs demonstrated significant involvement in immune-related pathways, including TNF, IL-17, and cytokine-cytokine receptor interaction signaling pathways (Fig. 4C). These pathways represent common inflammatory responses observed in both human and murine AD tissues and support the existence of conserved inflammatory transcriptional programs at the tissue level. To further validate transcriptomic findings in human samples, qPCR analysis was performed on aortic tissues from 33 AD patients and 29 controls. Several inflammatory and matrix remodeling genes, including IL1B, IL6, MAPK10, MAPK12, MMP9, MMP13, S100A8, and S100A9, were significantly upregulated in AD tissues, whereas other genes such as CCL2, CXCL3, and LCN2 showed variable expression patterns (Fig. 4D). These results confirm the activation of inflammatory and remodeling-related gene programs in human AD tissues. Serum cytokine profiling further demonstrated systemic inflammatory alterations in AD patients, with significantly elevated levels of IFN-α, IFN-γ, IL-17 A, IL-2, IL-4, IL-5, IL-6, and IL-8 compared with controls. IL-10 and TNF-α showed increasing trends but did not reach statistical significance, while IL-1β showed no significant difference between groups. These findings support the presence of both systemic immune activation and local vascular inflammatory responses in AD (Table 1). Overall, these results suggest that AD is characterized by conserved tissue-level inflammatory transcriptional programs across species, accompanied by substantial systemic cytokine dysregulation.

Fig. 4.

Cross-species transcriptomic integration identifies conserved inflammatory pathways and validates immune activation in AD. A Venn diagram showing 51 overlapping DEGs identified by integrating bulk RNA-seq data from a murine AD model and human single-cell RNA-seq data. B PPI network constructed from the intersecting DEGs using STRING. C KEGG pathway enrichment analysis of the intersecting DEGs. D Quantitative PCR validation of selected DEGs in human AD and control aortic tissues (*p < 0.05; **p < 0.01; ***p < 0.001)

Table 1.

Expressions of serum inflammatory cytokines in AD patients and controls (mean ± SD, pg/mL)

Group	AD group (n = 33)	Control group (n = 29)	P value
IFN-α	2.79 ± 3.50	1.25 ± 0.87	< 0.001
IFN-γ	3.50 ± 4.43	1.05 ± 0.62	0.004
IL-10	28.28 ± 73.24	3.51 ± 2.85	0.069
IL-17 A	6.85 ± 7.33	1.61 ± 1.32	< 0.001
IL-1β	1.94 ± 1.27	1.68 ± 1.04	0.383
IL-2	1.85 ± 1.23	0.73 ± 0.43	< 0.001
IL-4	2.40 ± 1.27	1.02 ± 0.52	< 0.001
IL-5	1.85 ± 1.95	1.22 ± 0.56	0.028
IL-6	111.43 ± 201.13	6.80 ± 12.09	0.006
IL-8	66.29 ± 95.16	9.98 ± 6.91	0.002
TNF-α	6.37 ± 14.73	1.05 ± 1.03	0.054

Discussion

Recent studies have highlighted the significant role of T cells in the pathogenesis of AD. scRNA-seq analyses of human AD tissues have revealed an increased proportion of T cells, particularly CD4 + Th17-like cells, in the dissected aorta compared to normal tissues. These T cells exhibit enhanced cytotoxic properties and are implicated in promoting inflammation and tissue remodeling within the aortic wall [6]. In murine models, similar findings have been observed. Angiotensin II-infused mice, a common model for studying AD, show increased infiltration of T cells in the aortic adventitia. These T cells contribute to the inflammatory milieu by secreting pro-inflammatory cytokines and interacting with other immune cells, such as macrophages, to exacerbate vascular inflammation [20–22]. The activation and recruitment of T cells in AD are mediated by various chemokines and cytokines, including IL-6 and MCP-1. These molecules not only attract T cells to the site of injury but also promote their activation and proliferation, further amplifying the inflammatory response [23–25]. Collectively, these findings underscore the pivotal role of T cells in the development and progression of AD, highlighting them as potential targets for therapeutic intervention. Importantly, our analysis suggests that although the overall proportion of T cells remains relatively stable, their transcriptional states and functional heterogeneity are markedly altered in AD. In this study, scRNA-seq analysis of Stanford type A AD and control samples revealed significant alterations in immune cell composition within the aortic microenvironment. Notably, both T cell- and monocyte/macrophage-related clusters contributed to immune remodeling in AD tissues, with T cell-enriched clusters exhibiting increased expression of canonical T cell markers and immune effector genes. These findings indicate a coordinated involvement of innate and adaptive immune compartments rather than a T cell-dominant response alone, underscoring the complexity of immune dysregulation in AD. Transcriptional profiling showed that T cells in AD display a pro-inflammatory, cytotoxic phenotype, suggesting their direct involvement in vascular inflammation and tissue remodeling processes.

Transcriptomic analyses have provided valuable insights into the molecular mechanisms underlying aortic tissue remodeling in AD. Bulk RNA sequencing of human AD tissues has identified DEGs associated with inflammation, extracellular matrix degradation, and vascular smooth muscle cell (VSMC) dysfunction. Notably, genes such as MMP9, IL-6, and IL-1β are upregulated, indicating active tissue remodeling and inflammation [26–28]. Single-cell transcriptomic studies have further delineated the heterogeneity of cell populations within the aortic wall. In human ascending aorta samples, scRNA-seq has identified various cell types, including VSMCs, endothelial cells, fibroblasts, and immune cells, each exhibiting distinct gene expression profiles. A notable finding is the identification of synthetic VSMCs characterized by the expression of STEAP4, which may contribute to pathological remodeling through interactions with neutrophils and fibroblasts via the CXCL12-CXCR4/ACKR3 axis [29–31]. In murine models, transcriptomic analyses have corroborated these findings, revealing similar patterns of gene expression changes associated with inflammation and extracellular matrix degradation [32, 33]. These studies collectively enhance our understanding of the cellular and molecular alterations in the aortic wall during AD, providing potential targets for therapeutic intervention. In our study, RNA-seq of murine AD models identified over 4,400 differentially expressed genes, highlighting extensive transcriptomic reprogramming linked to inflammation and vascular remodeling. GO and KEGG enrichment confirmed activation of immune responses, leukocyte migration, and stress pathways including TNF and IL-17 signaling. Transcription factor analyses revealed key regulators, Nfkb1, Jun, Stat3, orchestrating inflammatory and apoptotic gene networks. Morphological and transcriptomic data validated the model’s fidelity to human AD pathology. Importantly, this analysis is performed at the tissue level and is intended to capture global transcriptional reprogramming rather than cell-type-specific regulatory mechanisms, thereby providing a complementary perspective to single-cell-resolved datasets.

Inflammatory cytokines play a central role in the pathogenesis of AD. Elevated levels of pro-inflammatory cytokines such as IL-6, IL-1β, and TNF-α have been detected in the plasma and aortic tissues of AD patients. These cytokines contribute to the recruitment and activation of immune cells, degradation of the extracellular matrix, and apoptosis of VSMCs, leading to weakening of the aortic wall [34–36]. In this study, single-cell and bulk transcriptomic analyses consistently demonstrate pronounced inflammatory activation characterized by immune cell enrichment and cytokine signaling pathway activation. scRNA-seq data demonstrate expanded T cell clusters with elevated expression of pro-inflammatory and cytotoxic markers, including granzymes and interferon-stimulated genes, indicating an activated phenotype within the AD microenvironment. Bulk RNA-seq and integrative analyses identify significant upregulation of inflammatory mediators such as IL1B, IL6, TNF, CXCL8, and NFKBIA, which are centrally involved in leukocyte recruitment and vascular inflammation. Gene ontology and KEGG analyses highlight enrichment of TNF, NF-κB, IL-17, and chemokine signaling pathways, reflecting a coordinated inflammatory response. Importantly, IL-17 pathway enrichment observed in transcriptomic analyses reflects tissue-level signaling activation rather than systemic elevation of IL-17 A in circulation. Serum cytokine profiling further supports a systemic inflammatory state, including increased IFN-γ, IL-6, TNF-α, IL-17 A, and IL-8 levels. This discrepancy suggests that cytokine signaling in AD is partially compartmentalized, with local vascular inflammatory responses not fully mirrored by peripheral blood measurements.

Cross-species transcriptomic analyses have identified conserved molecular mechanisms underlying AD pathogenesis. By integrating bulk RNA-seq data from murine models with human single-cell transcriptomic datasets, researchers have identified overlapping DEGs involved in inflammation and immune responses. Notably, genes such as IL1B, CXCL3, and MMP19 are consistently upregulated across species, indicating conserved inflammatory transcriptional programs associated with AD. PPI network analyses of these intersecting genes reveal densely connected modules centered around key inflammatory mediators, highlighting coordinated activation of immune-related pathways. Functional enrichment further demonstrates involvement in TNF, IL-17, and cytokine-cytokine receptor interaction signaling pathways, suggesting shared inflammatory responses across species. This study does not aim to identify novel pathogenic mechanisms of AD, but rather to validate the robustness and cross-species conservation of previously reported inflammatory pathways at the tissue level.

Conclusion

In conclusion, this study reveals that aortic dissection is characterized by coordinated immune remodeling involving both T cells and monocyte/macrophage populations, together with extensive inflammatory-related transcriptional reprogramming. Cross-species analyses further identify conserved tissue-level inflammatory signatures across human and murine datasets. These findings provide a comprehensive framework for understanding immune-driven mechanisms in AD pathogenesis.

Authors’ contributions

Conceptualization, S.Z. and N.-S.W.; Data curation, Y.-G.Z.; Formal analysis, S.Z. and Y.-G.Z.; Investigation: S.Z.and Y.-D.S.; Methodology, S.Z. and N.-S.W.; Project administration, N.-S.W.; Resources, S.Z. and M.-F.Z.; Supervision, N-S.W.;Visualization, S.Z. and M.-F.Z.; Writing—review and editing, S.Z. and N.-S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research had no funding.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2025) in National Genomics Data Center (Nucleic Acids Res 2025), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA015051) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.

Declarations

Competing interests

The authors declare no competing interests.