Integrating Bulk Transcriptome and Single-Cell RNA Sequencing Data Reveals the Landscape of the Immune Microenvironment in Thoracic Aortic Aneurysms

Peripheral Blood Gene Expression Profiling Reveals Molecular Pathways Associated with Cervical Artery Dissection

Cervical artery dissection (CeAD) is the primary cause of ischemic stroke in young adults. Monogenic heritable connective tissue diseases account for fewer than 5% of cases of CeAD. The remaining sporadic cases have known risk factors. The clinical, radiological, and histological characteristics of systemic vasculopathy and undifferentiated connective tissue dysplasia are present in up to 70% of individuals with sporadic CeAD. Genome-wide association studies identified CeAD-associated genetic variants in the non-coding genomic regions that may impact the gene transcription and RNA processing. However, global gene expression profile analysis has not yet been carried out for CeAD patients. We conducted bulk RNA sequencing and differential gene expression analysis to investigate the expression profile of protein-coding genes in the peripheral blood of 19 CeAD patients and 18 healthy volunteers. This was followed by functional annotation, heatmap clustering, reports on gene-disease associations and protein-protein interactions, as well as gene set enrichment analysis. We found potential correlations between CeAD and the dysregulation of genes linked to nucleolar stress, senescence-associated secretory phenotype, mitochondrial malfunction, and epithelial-mesenchymal plasticity.

Elucidating key immunological biomarkers and immune microenvironment dynamics in aging-related intracranial aneurysm through integrated multi-omics analysis

BACKGROUND

The exact cause of intracranial aneurysms (IA) is still unclear. However, pro-inflammatory factors are known to contribute to IA progression. The specific changes in the immune microenvironment of IAs remain largely unexplored.

METHODS

This study analyzed single-cell sequencing data from a male mouse model of brain aneurysm, focusing on samples before and after elastase-induced Willis aneurysms. The data helped identify eight distinct cell subpopulations: fibroblasts, macrophages, NK cells, endothelial cells, B cells, granulocytes, and monocytes. The study also involved bulk RNA sequencing of 97 IA samples, utilizing ssGSEA and CIBERSORT algorithms for analysis. Intercellular communication among these cells was inferred to understand the immune dynamics in IA.

RESULTS

The study found that fibroblasts and macrophages are predominant in various disease states of IA. Notably, the onset of IA was marked by a significant increase in fibroblasts and a decrease in macrophages. There was a marked increase in cellular interactions, especially involving macrophages, at the onset of the disease. Through enrichment analysis, 12 potential immunogenic biomarkers were identified. Of these, Rgs1 emerged as a critical molecule in IA formation, confirmed through secondary validation in a single-cell sequencing dataset.

CONCLUSION

This comprehensive analysis of immune cell composition and intercellular communication in IA tissues highlights the significant roles of macrophages and the molecule Rgs1. These findings shed light on the physiological and pathological conditions of IA, offering new insights into its immune microenvironment.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

Identification of potential diagnostic biomarkers for tenosynovial giant cell tumour by integrating microarray and single-cell RNA sequencing data

PURPOSE

Tenosynovial giant cell tumour (TGCT) is a benign hyperplastic and inflammatory disease of the joint synovium or tendon sheaths, which may be misdiagnosed due to its atypical symptoms and imaging features. We aimed to identify biomarkers with high sensitivity and specificity to aid in diagnosing TGCT.

METHODS

Two scRNA-seq datasets (GSE210750 and GSE152805) and two microarray datasets (GSE3698 and GSE175626) were downloaded from the Gene Expression Omnibus (GEO) database. By integrating the scRNA-seq datasets, we discovered that the osteoclasts are abundant in TGCT in contrast to the control. The single-sample gene set enrichment analysis (ssGSEA) further validated this discovery. Differentially expressed genes (DEGs) of the GSE3698 dataset were screened and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Osteoclast-specific up-regulated genes (OCSURGs) were identified by intersecting the osteoclast marker genes in the scRNA-seq and the up-regulated DEGs in the microarray and by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The expression levels of OCSURGs were validated by an external dataset GSE175626. Then, single gene GSEA, protein-protein interaction (PPI) network, and gene-drug network of OCSURGs were performed.

RESULT

22 seurat clusters were acquired and annotated into 10 cell types based on the scRNA-seq data. TGCT had a larger population of osteoclasts compared to the control. A total of 159 osteoclast marker genes and 104 DEGs (including 61 up-regulated genes and 43 down-regulated genes) were screened from the scRNA-seq analysis and the microarray analysis. Three OCSURGs (MMP9, SPP1, and TYROBP) were finally identified. The AUC of the ROC curve in the training and testing datasets suggested a favourable diagnostic capability. The PPI network results illustrated the protein-protein interaction of each OCSURG. Drugs that potentially target the OCSURGs were predicted by the DGIdb database.

CONCLUSION

MMP9, SPP1, and TYROBP were identified as osteoclast-specific up-regulated genes of the tenosynovial giant cell tumour via bioinformatic analysis, which had a reasonable diagnostic efficiency and served as potential drug targets.

Machine Learning and Omics Analysis in Aortic Aneurysm

Aortic aneurysm is a life-threatening condition and mechanisms underlying its formation and progression are still incompletely understood. Omics approach has brought new insights to identify a broad spectrum of biomarkers and better understand cellular and molecular pathways involved. Omics generate a large amount of data and several studies have highlighted that artificial intelligence (AI) and techniques such as machine learning (ML)/deep learning (DL) can be of use in analyzing such complex datasets. However, only a few studies have so far reported the use of ML/DL for omics analysis in aortic aneurysms. The aim of this study is to summarize recent advances on the use of ML/DL for omics analysis to decipher aortic aneurysm pathophysiology and develop patient-tailored risk prediction models. In the light of current knowledge, we discuss current limits and highlight future directions in the field.

Identification of monocyte-associated pathways participated in the pathogenesis of pulmonary arterial hypertension based on omics-data

Pulmonary arterial hypertension (PAH) is one kind of chronic and uncurable diseases that can cause heart failure. Immune microenvironment plays a significant role in PAH. The aim of this study was to assess the role of immune cell infiltration in the pathogenesis of PAH. Differentially expressed genes based on microarray data were enriched in several immune-related pathways. To evaluate the immune cell infiltration, based on the microarray data sets in the GEO database, we used both ssGSEA and the CIBERSORT algorithm. Additionally, single-cell RNA sequencing (scRNA-seq) data was used to further explicit the specific role and intercellular communications. Then receiver operating characteristic curves and least absolute shrinkage and selection operator were used to discover and test the potential diagnostic biomarkers for PAH. Both the immune cell infiltration analyses based on the microarray data sets and the cell proportion in scRNA-seq data exhibited a significant downregulation in the infiltration of monocytes in PAH. Then, the intercellular communications showed that the interaction weighs of most immune cells, including monocytes changed between the control and PAH groups, and the ITGAL-ITGB2 and ICAM signaling pathways played critical roles in this process. In addition, ITGAM and ICAM2 displayed good diagnosis values in PAH. This study implicated that the change of monocyte was one of the key immunologic features of PAH. Monocyte-associated ICAM-1 and ITGAL-ITGB2 signaling pathways might be involved in the pathogenesis of PAH.

Multi-omics in thoracic aortic aneurysm: the complex road to the simplification

BACKGROUND

Thoracic aortic aneurysm (TAA) is a serious condition that affects the aorta, characterized by the dilation of its first segment. The causes of TAA (e.g., age, hypertension, genetic syndromes) are heterogeneous and contribute to the weakening of the aortic wall. This complexity makes treating this life-threatening aortopathy challenging, as there are currently no etiological therapy available, and pharmacological strategies, aimed at avoiding surgical aortic replacement, are merely palliative. Recent studies on novel therapies for TAA have focused on identifying biological targets and etiological mechanisms of the disease by using advanced -omics techniques, including epigenomics, transcriptomics, proteomics, and metabolomics approaches.

METHODS

This review presents the latest findings from -omics approaches and underscores the importance of integrating multi-omics data to gain more comprehensive understanding of TAA.

RESULTS

Literature suggests that the alterations in TAA mediators frequently involve members of pro-fibrotic process (i.e., TGF-β signaling pathways) or proteins associated with cell/extracellular structures (e.g., aggrecans). Further analyses often reported the importance in TAA of processes as inflammation (PCR, CD3, leukotriene compounds), oxidative stress (chromatin OXPHOS, fatty acids), mitochondrial respiration and glycolysis/gluconeogenesis (e.g., PPARs and HIF1a). Of note, more recent metabolomics studies added novel molecular markers to the list of TAA-specific detrimental mediators (proteoglycans).

CONCLUSION

It is increasingly clear that integrating data from different -omics branches, along with clinical data, is essential as well as complicated both to reveal hidden relevant information and to address complex diseases such as TAA. Importantly, recent progresses in metabolomics highlighted novel potential and unprecedented marks in TAA diagnosis and therapy.