Analysis of phenotypic switches of aortic smooth muscle cells in single cell level during hyperlipidemic condition
Abstract
The osteochondrogenic switch of vascular smooth muscle cells (VSMCs) is a pivotal cellular process in atherosclerotic calcification. Here, we explore the regulatory role of TXNIP in the phenotypical transitioning of VSMCs toward osteochondrogenic cells responsible for atherosclerotic calcification. The atherosclerotic phenotypes of Txnip-/- mice were analysed in combination with single-cell RNA-sequencing (scRNA-seq). The atherosclerotic phenotypes of Tagln-Cre; Txnipflox/flox mice (SMC-specific Txnip ablation model), and the mice transplanted with the bone marrow of Txnip-/- mice were analysed. Public dataset GSE159677 was reanalysed to define the gene expression of TXNIP in the VSMCs of human calcified atherosclerotic plaques. In vitro mechanism study was performed using primary cultured VSMCs. Atherosclerotic lesions of Txnip-/- mice presented significantly increased calcification and deposition of collagen content. Subsequent scRNA-seq analysis identified the modulated VSMC and osteochondrogenic clusters, which were VSMC-derived populations. The osteochondrogenic cluster was markedly expanded in Txnip-/- mice. Bone and cartilage formation pathways and BMP signalling were enriched in the VSMC-derived cells of Txnip-/- mice. TXNIP was downregulated in the modulated VSMC and osteochondrogenic clusters of human calcified atherosclerotic lesions. Tagln-Cre; Txnipflox/flox mice recapitulated the calcification and collagen-rich atherosclerotic phenotypes of Txnip-/- mice. Suppression of TXNIP in cultured VSMCs accelerates osteodifferentiation and upregulates BMP signalling. Treatment with the BMP signalling inhibitor K02288 abrogated the effect of TXNIP suppression on osteodifferentiation. Our results suggest that TXNIP is a novel regulator of atherosclerotic calcification by suppressing BMP signalling to inhibit the transition of VSMCs toward an osteochondrogenic phenotype.
Cell lineage diversity and intratumoral heterogeneity in gastric cancer
Abstract
Single-cell transcriptomic profiles analysis has proposed new insights for understanding the behavior of human gastric cancer (GC). GC offers a unique model of intratumoral heterogeneity. However, the specific classes of cells involved in carcinogenetic passage, and the tumor microenvironment of stromal cells was poorly understood. We characterized the heterogeneous cell population of precancerous lesions and gastric cancer at the single-cell resolution by RNA sequencing. We identified 10 gastric cell subtypes and showed the intestinal and diffuse-type cancer were characterized by different cell population. We found that the intestinal and diffuse-type cancer cells have the differential metaplastic cell lineages: intestinal-type cancer cells differentiated along the intestinal metaplasia lineage while diffuse-type cancer cells resemble de novo pathway. We observed an enriched CCND1 mutation in premalignant disease state and discovered cancer-associated fibroblast cells harboring pro-stemness properties. In particular, tumor cells could be categorized into previously proposed molecular subtypes and harbored specific subtype of malignant cell with high expression level of epithelial-myofibroblast transition which was correlated with poor clinical prognosis. In addition to intratumoral heterogeneity, the analysis revealed different cellular lineages were responsible for potential carcinogenetic pathways. Single-cell transcriptomes analysis of gastric pre-cancerous lesions and cancer may provide insights for understanding GC cell behavior, suggesting potential targets for the diagnosis and treatment of GC.
Metabolomics-driven approaches for understanding the biological networks underlying disease
Abstract
Metabolic profiling is particulary important in human disease where small molecules such as endogenous metabolites or lipids play fundamental signalling roles. The metabolic profile is perturbed in a characteristic fashion in disease, and this shift in position can be readily visualized and modeled using chemometric techniques. The metabolic profile of biofluids or tissues shows changes of their composition in response to disease-induced stress due to the system's attempt to maintain homeostasis. Understanding the biochemical reason for such a shift in metabolic space leads to the identification of disease biomarkers or therapeutic targets.
Due to the complexity of biological process, integration of multiple data from one or more omics not only can help unravel the underlying molecular mechanism of disease but also identify the signature of pathway/networks characteristic for specific disease that can be used for diagnosis, prognosis, and targeted therapy. Therefore, metabolomics-drived omics or biological approaches have the potential to map biochemical changes in human disease and provide an opportunity to unveil the biological networks underlying disease. This approach can also improve our understanding of the biological processes that are associated with a certain phenotype and also allow studying how the dysregulation of specific biological pathways is progagated across the biological system.
Recent developments in the metabolic phenotyping have contributed to unravelling the pathophysiology of diseases. The necessity of implementing metabolomics-driven omics approaches is required to integrate metabolic data and deal with the functional complexity of human disease in pathological conditions. Therefore, metabolomics-driven approach might not only lead to a better understanding the biological networks underlying disease but could also be key methodologies to predict diseases and individual drug responses.
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