Single-Cell Transcriptome Analysis Reveals Mesenchymal Stem Cells in Cavernous Hemangioma

Revealing the Mechanism of Esculin in Treating Renal Cell Carcinoma Based on Network Pharmacology and Experimental Validation

PURPOSE

This study aims to explore the potential mechanisms of esculin in the treatment of renal cell carcinoma (RCC).

METHODS

We employed network pharmacology to predict the potential mechanisms and targets of esculin in RCC. Molecular docking techniques were then employed to validate the predicted targets. Additionally, a series of in vitro experiments were conducted to verify the anticancer effects of esculin on RCC cells, including the CCK-8 assay, EdU assay, wound healing assay, apoptosis assay, and Western blot.

RESULTS

Network pharmacology and molecular docking results identified GAPDH, TNF, GSK3B, CCND1, MCL1, IL2, and CDK2 as core targets. GO and KEGG analyses suggested that esculin may influence apoptotic processes and target the PI3K/Akt pathway in RCC. Furthermore, the CCK-8 assay demonstrated that esculin inhibited RCC cell viability. Microscopic observations revealed that following esculin treatment, there was an increase in cell crumpling, a reduction in cell density, and an accumulation of floating dead cells. Additionally, with increasing esculin concentrations, the proportion of EdU-positive cells decreased, the wound closure ratio decreased, the proportion of PI-positive cells increased, the expression levels of BAX and cleaved-caspase-3 proteins increased, and the expression level of Bcl2 protein decreased. These findings suggested that esculin inhibits the proliferation and migration of RCC cells while promoting apoptosis. Moreover, esculin was found to target GAPDH and inhibit the PI3K/Akt pathway.

CONCLUSIONS

This study is the first to elucidate the therapeutic effects of esculin on RCC cells. The results provide evidence supporting the clinical application of esculin and introduce a promising new candidate for RCC treatment.

Re-analysis of single-cell RNA-seq data reveals the origin and roles of cycling myeloid cells

Cycling myeloid cells (CMCs) are often detected from various tissues using single-cell RNA sequencing (scRNA-seq) datasets, however, their research value was not noticed before. For the first time, our study preliminarily revealed the origin, differentiation, and roles of CMCs in physiological processes. Particularly, subgroup a of cycling myeloid cells (aCMCs) were conclusively identified as belonging to a specific cell type. In an active state, aCMCs rapidly proliferate during the early stages of an embryonic development. With an individual maturing, most aCMCs differentiate into specialized cells, while a small portion of them enter an inactive or dormant state. Under pathological conditions, aCMCs restore their proliferative and differentiation capacities via activation or revival. The present study has set the stage for future research on CMCs by linking them with progenitors of immune cells, and provided a crucial starting point to understand the origin, differentiation, and roles of CMCs in various physiological and pathological processes, particularly those related to traumatic injury, cancer, and pathogen infection, leading to develop targeted therapies or interventions.

Single-cell sequencing advances in research on mesenchymal stem/stromal cells

Mesenchymal stem/stromal cells (MSCs), originating from the mesoderm, represent a multifunctional stem cell population capable of differentiating into diverse cell types and exhibiting a wide range of biological functions. Despite more than half a century of research, MSCs continue to be among the most extensively studied cell types in clinical research projects globally. However, their significant heterogeneity and phenotypic instability have significantly hindered their exploration and application. Single-cell sequencing technology emerges as a powerful tool to address these challenges, offering precise dissection of complex cellular samples. It uncovers the genetic structure and gene expression status of individual contained cells on a massive scale and reveals the heterogeneity among these cells. It links the molecular characteristics of MSCs with their clinical applications, contributing to the advancement of regenerative medicine. With the development and cost reduction of single-cell analysis techniques, sequencing technology is now widely applied in fundamental research and clinical trials. This study aimed to review the application of single-cell sequencing in MSC research and assess its prospects.

Immunologic landscape of human hepatic hemangiomas and epithelioid hemangioendotheliomas

BACKGROUND

The missing requirement for resection for the majority of hepatic hemangiomas (HH) and tissue scarcity for rare diseases such as hepatic epithelioid hemangioendotheliomas (HEHE) complicate the characterization of the spatial immunovascular niche of these benign and malignant vascular neoplastic diseases.

METHODS

Two tissue cohorts containing 98 HHs and 13 HEHEs were used to study entity-specific and disease stage-specific endothelial cell (EC) phenotype and immune cell abundance. Using semiquantitative assessment, annotation-based cell classifiers, digital cell detection on whole slides, and tissue microarrays, we quantified 23 immunologic and vascular niche-associated markers and correlated this with clinicopathologic data.

RESULTS

Both HH and HEHE ECs were characterized by a CD31high, CD34high, FVIII-related antigenhigh expression phenotype with entity-specific expression differences of sinusoidal EC markers Stabilin1, Stabilin2, CD32, and Lymphatic Vessel Endothelial Hyaluronan Receptor 1 (LYVE-1). Cell detection identified an HH margin-prevailing immunologic response dominated by Myeloperoxidase+ (MPO+) macrophages, CD3+ and CD8+ T cell subsets, and B cells (CD20+, CD79A+). In HEHE, increased CD68+ and CD20+ cell demarcation of lesion margins was observed, while CD3+ and CD8+ T cells were equally detectable both marginally and intralesionally. Stage-specific pairwise correlation analysis of HH and HEHE revealed disease entity-specific immunologic infiltration patterns as seen by high CD117+ cell numbers in HH, while HEHE samples showed increased CD3+ T cell infiltration.

CONCLUSIONS

ECs in HH and HEHE share a continuous EC expression phenotype, while the expression of sinusoidal EC markers is more highly retained in HEHE. These phenotypic differences are associated with a unique and disease-specific immunovascular landscape.

CTRP6 promotes the macrophage inflammatory response, and its deficiency attenuates LPS-induced inflammation

Macrophages play critical roles in inflammation and tissue homeostasis, and their functions are regulated by various autocrine, paracrine, and endocrine factors. We have previously shown that CTRP6, a secreted protein of the C1q family, targets both adipocytes and macrophages to promote obesity-linked inflammation. However, the gene programs and signaling pathways directly regulated by CTRP6 in macrophages remain unknown. Here, we combine transcriptomic and phosphoproteomic analyses to show that CTRP6 activates inflammatory gene programs and signaling pathways in mouse bone marrow-derived macrophages (BMDMs). Treatment of BMDMs with CTRP6 upregulated proinflammatory, and suppressed the antiinflammatory, gene expression. We also showed that CTRP6 activates p44/42-MAPK, p38-MAPK, and NF-κB signaling pathways to promote inflammatory cytokine secretion from BMDMs, and that pharmacologic inhibition of these signaling pathways markedly attenuated the effects of CTRP6. Pretreatment of BMDMs with CTRP6 also sensitized and potentiated the BMDMs response to lipopolysaccharide (LPS)-induced inflammatory signaling and cytokine secretion. Consistent with the metabolic phenotype of proinflammatory macrophages, CTRP6 treatment induced a shift toward aerobic glycolysis and lactate production, reduced oxidative metabolism, and elevated mitochondrial reactive oxygen species production in BMDMs. Importantly, in accordance with our in vitro findings, BMDMs from CTRP6-deficient mice were less inflammatory at baseline and showed a marked suppression of LPS-induced inflammatory gene expression and cytokine secretion. Finally, loss of CTRP6 in mice also dampened LPS-induced inflammation and hypothermia. Collectively, our findings suggest that CTRP6 regulates and primes the macrophage response to inflammatory stimuli and thus may have a role in modulating tissue inflammatory tone in different physiological and disease contexts.

LIDER: cell embedding based deep neural network classifier for supervised cell type identification

Background

Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation.

Methods

Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding.

Results

LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.