Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.

Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.

Background: Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature. Methods: Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. In vitro and in vivo experiments were conducted to validate the role of TUBA1C, a key gene within the signature. Results: A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion in vitro, and reduced tumor growth in vivo. Conclusion: This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.

Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis.

Background: Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development. Methods: We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics. Results: MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene. Conclusion: This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

Integrin subunit beta 6 is a potential diagnostic marker for acute kidney injury in patients with diabetic kidney disease: a single cell sequencing data analysis.

BACKGROUND: Diabetic kidney disease (DKD), a prevalent complication of diabetes mellitus, is often associated with acute kidney injury (AKI). Thus, the development of preventive and therapeutic strategies is crucial for delaying the progression of AKI and DKD. METHODS: The GSE183276 dataset, comprising the data of 20 healthy controls and 12 patients with AKI, was downloaded from the Gene Expression Omnibus (GEO) database to analyze the AKI group. For analyzing the DKD group, the GSE131822 dataset, comprising the data of 3 healthy controls and 3 patients with DKD, was downloaded from the GEO database. The common differentially expressed genes (DEGs) in renal tubular epithelial cells (TECs) were subjected to enrichment analyses. Next, a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database to analyze gene-related regulatory networks. Finally, the AKI animal models and the DKD and AKI cell models were established, and the reliability of the identified genes was validated using quantitative real-time polymerase chain reaction analysis. RESULTS: Functional analysis was performed with 40 common DEGs in TECs. Eight hub genes were identified using the PPI and gene-related networks. Finally, validation experiments with the in vivo animal model and the in vitro cellular model revealed the four common DEGs. Four DEGs that share molecular mechanisms in the pathogenesis of DKD and AKI were identified. In particular, the expression of Integrin Subunit Beta 6(ITGB6), a hub and commonly upregulated gene, was upregulated in the in vitro models. CONCLUSION: ITGB6 may serve as a biomarker for early AKI diagnosis in patients with DKD and as a target for early intervention therapies.

Single-cell RNA sequencing and immune repertoire analysis revealed dynamic immune characteristics associated with peripheral blood during sepsis.

Sepsis is a potentially fatal condition arising from an abnormal immune response to an infection, which can result in organ failure and even death. To explore the mechanism underlying the dysregulated immune response during sepsis and identify potential therapeutic targets, single-cell RNA sequencing (scRNA-seq) and immune repertoire analysis were conducted to depict the cellular landscape of peripheral blood cells in septic mice. We observed significant alterations in the number and proportion of peripheral blood cell populations driven by sepsis. By combining single-cell gene expression profiles and B cell receptor (BCR) repertoire analysis, we discerned that infection inflicted serious damage on the antigen presentation ability of B cells and the diversity of BCR in a short time. In addition, we found that the cecal ligation and puncture procedure in mice inhibited the communication signals of CD4+ and CD8+ T cells and decreased the interactions between B cells and other cells. Our study provides detailed insights into the dynamic changes in the biological characteristics of peripheral blood cells driven by sepsis and provides important advances in our understanding of immune disorders during sepsis.

Inflammation-induced loss of CFTR-expressing airway ionocytes in non-eosinophilic asthma.

BACKGROUND AND OBJECTIVE: Severe asthma is a heterogeneous disease with subtype classification according to dominant airway infiltrates, including eosinophilic (Type 2 high), or non-eosinophilic asthma. Non-eosinophilic asthma is further divided into paucigranulocytic or neutrophilic asthma characterized by elevated neutrophils, and mixed Type 1 and Type 17 cytokines in the airways. Severe non-eosinophilic asthma has few effective treatments and many patients do not qualify for biologic therapies. The cystic fibrosis transmembrane conductance regulator (CFTR) is dysregulated in multiple respiratory diseases including cystic fibrosis and chronic obstructive pulmonary disease and has proven a valuable therapeutic target. We hypothesized that the CFTR may also play a role in non-eosinophilic asthma. METHODS: Patient-derived human bronchial epithelial cells (hBECs) were isolated and differentiated at the air-liquid interface. Single cell RNA-sequencing (scRNAseq) was used to identify epithelial cell subtypes and transcriptional activity. Ion transport was investigated with Ussing chambers and immunofluorescent quantification of ionocyte abundance in human airway epithelial cells and murine models of asthma. RESULTS: We identified that hBECs from patients with non-eosinophilic asthma had reduced CFTR function, and did not differentiate into CFTR-expressing ionocytes compared to those from eosinophilic asthma or healthy donors. Similarly, ionocytes were also diminished in the airways of a murine model of neutrophilic-dominant but not eosinophilic asthma. Treatment of hBECs from healthy donors with a neutrophilic asthma-like inflammatory cytokine mixture led to a reduction in ionocytes. CONCLUSION: Inflammation-induced loss of CFTR-expressing ionocytes in airway cells from non-eosinophilic asthma may represent a key feature of disease pathogenesis and a novel drug target.

Discovery of GJC1 as a prognostic biomarker in glioma cells: insights into its cell-cycle relationship and differential expression in non-neuronal cells.

Background: Gliomas, originating from the most common non-neuronal cells in the brain (glial cells), are the most common brain tumors and are associated with high mortality and poor prognosis. Glioma cells exhibit a tendency to disrupt normal cell-cycle regulation, leading to abnormal proliferation and malignant growth. This study investigated the predictive potential of GJC1 in gliomas and explored its relationship with the cell cycle. Methods: Retrospective analysis of RNA-seq and single-cell sequencing data was conducted using the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The differential expression of GJC1 in gliomas with various pathological features and in different non-neuronal cell groups was analyzed. Functional data were examined using gene set variation analysis (GSVA). Furthermore, CellMiner was used to evaluate the relationship between GJC1 expression and predicted treatment response across these databases. Results: GJC1 expression was enriched in high-grade gliomas and 1p/19q non-codeletion gliomas. GJC1 enrichment was observed in classical and mesenchymal subtypes within the TCGA glioma subtype group. In single-cell subgroup analysis, GJC1 expression was higher in glioma tissues compared to other non-neuronal cells. Additionally, the TCGA classical subtype of glioma cells exhibited more GJC1 expression than the other subgroups. GJC1 emerged as an independent prognostic factor for overall survival in glioma. GSVA unveiled potential mechanisms by which GJC1 may impact cell-cycle regulation in glioma. Finally, a significant correlation was observed between GJC1 expression and the sensitivity of multiple anti-cancer drugs. Conclusion: These findings confirmed GJC1 as a novel biomarker and provided insights into the differential gene expression in non-neuronal cells and the impact of the cell cycle on gliomas. Consequently, GJC1 may be used to predict glioma prognosis and has potential therapeutic value.

APP-CD74 axis mediates endothelial cell-macrophage communication to promote kidney injury and fibrosis.

Background: Kidney injuries often carry a grim prognosis, marked by fibrosis development, renal function loss, and macrophage involvement. Despite extensive research on macrophage polarization and its effects on other cells, like fibroblasts, limited attention has been paid to the influence of non-immune cells on macrophages. This study aims to address this gap by shedding light on the intricate dynamics and diversity of macrophages during renal injury and repair. Methods: During the initial research phase, the complexity of intercellular communication in the context of kidney injury was revealed using a publicly available single-cell RNA sequencing library of the unilateral ureteral obstruction (UUO) model. Subsequently, we confirmed our findings using an independent dataset from a renal ischemia-reperfusion injury (IRI) model. We treated two different types of endothelial cells with TGF-ß and co-cultured their supernatants with macrophages, establishing an endothelial cell and macrophage co-culture system. We also established a UUO and an IRI mouse model. Western blot analysis, flow cytometry, immunohistochemistry and immunofluorescence staining were used to validate our results at multiple levels. Results: Our analysis revealed significant changes in the heterogeneity of macrophage subsets during both injury processes. Amyloid ß precursor protein (APP)-CD74 axis mediated endothelial-macrophage intercellular communication plays a dominant role. In the in vitro co-culture system, TGF-ß triggers endothelial APP expression, which subsequently enhances CD74 expression in macrophages. Flow cytometry corroborated these findings. Additionally, APP and CD74 expression were significantly increased in the UUO and IRI mouse models. Immunofluorescence techniques demonstrated the co-localization of F4/80 and CD74 in vivo. Conclusion: Our study unravels a compelling molecular mechanism, elucidating how endothelium-mediated regulation shapes macrophage function during renal repair. The identified APP-CD74 signaling axis emerges as a promising target for optimizing renal recovery post-injury and preventing the progression of chronic kidney disease.

Identification and validation of ferroptosis-related biomarkers in intervertebral disc degeneration.

Introduction: Ferroptosis plays a significant role in intervertebral disc degeneration (IDD). Understanding the key genes regulating ferroptosis in IDD could reveal fundamental mechanisms of the disease, potentially leading to new diagnostic and therapeutic targets. Methods: Public datasets (GSE23130 and GSE70362) and the FerrDb database were analyzed to identify ferroptosis-related genes (DE-FRGs) involved in IDD. Single-cell RNA sequencing data (GSE199866) was used to validate the specific roles and expression patterns of these genes. Immunohistochemistry and Western blot analyses were subsequently conducted in both clinical samples and mouse models to assess protein expression levels across different tissues. Results: The analysis identified seven DE-FRGs, including MT1G, CA9, AKR1C1, AKR1C2, DUSP1, CIRBP, and KLHL24, with their expression patterns confirmed by single-cell RNA sequencing. Immunohistochemistry and Western blot analysis further revealed that MT1G, CA9, AKR1C1, AKR1C2, DUSP1, and KLHL24 exhibited differential expression during the progression of IDD. Additionally, the study highlighted the potential immune-modulatory functions of these genes within the IDD microenvironment. Discussion: Our study elucidates the critical role of ferroptosis in IDD and identifies specific genes, such as MT1G and CA9, as potential targets for diagnosis and therapy. These findings offer new insights into the molecular mechanisms underlying IDD and present promising avenues for future research and clinical applications.

Immunosuppressive tumor microenvironment in gastric signet-ring cell carcinoma.

Gastric signet-ring cell carcinoma (GSRCC) is a subtype of gastric cancer with distinct phenotype and high risk of peritoneal metastasis. Studies have shown that early GSRCC has a good prognosis, while advanced GSRCC is insensitive to radiotherapy, chemotherapy or immune checkpoint blockade therapy. With technological advancement of single-cell RNA sequencing analysis and cytometry by time of flight mass cytometry, more detailed atlas of tumor microenvironment (TME) in GSRCC and its association with prognosis could be investigated extensively. Recently, two single-cell RNA sequencing studies revealed that GSRCC harbored a unique TME, manifested as highly immunosuppressive, leading to high immune escape. The TME of advanced GSRCC was enriched for immunosuppressive factors, including the loss of CXCL13 +-cluster of differentiation 8+-Tex cells and declined clonal crosstalk among populations of T and B cells. In addition, GSRCC was mainly infiltrated by follicular B cells. The increased proportion of SRCC was accompanied by a decrease in mucosa-associated lymphoid tissue-derived B cells and a significant increase in follicular B cells, which may be one of the reasons for the poor prognosis of GSRCC. By understanding the relationship between immunosuppressive TME and poor prognosis in GSRCC and the underlying mechanism, more effective immunotherapy strategies and improved treatment outcomes of GSRCC can be anticipated.

ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.

BACKGROUND: Single-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility. RESULTS: In response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. We demonstrate the application of scRNAbox by analyzing two publicly available datasets. CONCLUSION: ScRNAbox is a comprehensive end-to-end pipeline designed to streamline the processing and analysis of scRNAseq data. By responding to the pressing demand for a user-friendly, HPC solution, scRNAbox bridges the gap between the growing computational demands of scRNAseq analysis and the coding expertise required to meet them.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling.

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Delving deeper into the mechanisms fundamental to HIV-associated immunopathology using single-cell sequencing techniques: A scoping review of current literature.

Human immunodeficiency virus (HIV) infection has evolved into an established global pandemic over the past four decades; however, despite massive research investment globally, the precise underlying mechanisms which are fundamental to HIV-related pathogenesis remain unclear. Single cell ribonucleic acid (RNA) sequencing methods are increasingly being used for the identification of specific cell-type transcriptional changes in HIV infection. In this scoping review, we have considered information extracted from fourteen published HIV-associated single-cell RNA sequencing-related studies, hoping to throw light on the underlying mechanisms of HIV infection and pathogenesis, and to explore potential candidate biomarkers for HIV disease progression and antiviral treatment. Generally, HIV positive individuals tend to manifest disturbances of frequency of multiple cellular types, and specifically exhibit diminished levels of CD4+ T-cells and enriched numbers of CD8+ T-cells. Cell-specific transcriptional changes tend to be linked to cell permissiveness, hyperacute or acute HIV infection, viremia, and cell productivity. The transcriptomes of CD4+ T-cell and CD8+ T-cell subpopulations are also observed to change in HIV-positive diabetic individuals, spontaneous HIV controllers, individuals with high levels of HIV viremia, and those in an acute phase of HIV infection. The transcriptional changes seen in B cells, natural killer (NK) cells, and myeloid dendritic cells (mDCs) of HIV-infected individuals demonstrate that the humoral immune response, antiviral response, and immune response regulation, respectively, are all altered following HIV infection. Antiretroviral therapy (ART) plays a crucial role in achieving immune reconstitution, in improving immunological disruption, and in mitigating immune system imbalances in HIV-infected individuals, while not fully restoring inherent cellular transcription to levels seen in HIV-negative individuals. The preceding observations not only illustrate compelling advances in the understanding of HIV-associated immunopathogenesis, but also identify specific cell-type transcriptional changes that may serve as potential biomarkers for HIV disease monitoring and therapeutic targeting.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing.

Background: Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life. Method: The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations. Result: The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies. Conclusions: We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

Integrative analysis of bulk and single-cell RNA sequencing reveals sphingolipid metabolism and immune landscape in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by its aggressive behavior and complex molecular heterogeneity, posing significant challenges for treatment and prognostication. This study offers a comprehensive analysis of ccRCC by leveraging both bulk and single-cell RNA sequencing data, with a specific aim to unravel the complexities of sphingolipid metabolism and the intricate dynamics within the tumor microenvironment (TME). By examining ccRCC samples sourced from public databases, our investigation delves deep into the genetic and transcriptomic landscape of this cancer type. Employing advanced analytical techniques, we have identified pivotal patterns in gene expression and cellular heterogeneity, with a special focus on the roles and interactions of various immune cells within the TME. Significantly, our research has unearthed insights into the dynamics of sphingolipid metabolism in ccRCC, shedding light on its potential implications for tumor progression and strategies for immune evasion. A novel aspect of this study is the development of a risk score model designed to enhance prognostic predictions for ccRCC patients, which is currently pending external validation to ascertain its clinical utility. Despite its contributions, the study is mindful of its limitations, including a reliance on observational data from public sources and a primary focus on RNA sequencing data, which may constrain the depth and generalizability of the findings. The study does not encompass critical aspects, such as protein expression, posttranslational modifications, and comprehensive metabolic profiles. Moreover, its retrospective design underscores the necessity for future prospective studies to solidify these preliminary conclusions. Our findings illuminate the intricate interplay between genetic alterations, sphingolipid metabolism, and immune responses in ccRCC. This research not only enhances our understanding of the molecular foundations of ccRCC but also paves the way for the development of targeted therapies and personalized treatment modalities. The study underlines the importance of cautious interpretation of results and champions ongoing research using diverse methodologies to thoroughly comprehend and effectively combat this formidable cancer type.

Characteristics and transcriptional regulators of spontaneous epithelial-mesenchymal transition in genetically unperturbed patient-derived non-spindled breast carcinoma.

BACKGROUND: Although tumor cells undergoing epithelial-mesenchymal transition (EMT) typically exhibit spindle morphology in experimental models, such histomorphological evidence of EMT has predominantly been observed in rare primary spindle carcinomas. The characteristics and transcriptional regulators of spontaneous EMT in genetically unperturbed non-spindled carcinomas remain underexplored. METHODS: We used primary culture combined with RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), and in situ RNA-seq to explore the characteristics and transcription factors (TFs) associated with potential spontaneous EMT in non-spindled breast carcinoma. RESULTS: Our primary culture revealed carcinoma cells expressing diverse epithelial-mesenchymal traits, consistent with epithelial-mesenchymal plasticity. Importantly, carcinoma cells undergoing spontaneous EMT did not necessarily exhibit spindle morphology, even when undergoing complete EMT. EMT was a favored process, whereas mesenchymal-epithelial transition appeared to be crucial for secondary tumor growth. Through scRNA-seq, we identified TFs that were sequentially and significantly upregulated as carcinoma cells progressed through the EMT process, which correlated with increasing VIM expression. Once upregulated, the TFs remained active throughout the EMT process. ZEB1 was a key initiator and sustainer of EMT, as indicated by its earliest significant upregulation in the EMT process, its exact correlation with VIM expression, and the reversal of EMT and downregulation of EMT-upregulated TFs upon ZEB1 knockdown. The correlation between ZEB1 and vimentin expression in triple-negative breast cancer and metaplastic breast carcinoma tumor cohorts further highlighted its role. The immediate upregulation of ZEB2 following that of ZEB1, along with the observation that the knockdown of ZEB1 or ZEB2 downregulates both ZEB1 and ZEB2 concomitant with the reversal of EMT, suggests their functional cooperation in EMT. This finding, together with that of a lack of correlation of SNAI1, SNAI2, and TWIST1 expression with the mesenchymal phenotype, indicated EMT-TFs have a context-dependent role in EMT. Upregulation of EMT-related gene signatures during EMT correlated with poor patient outcomes, highlighting the biological importance of the model. Elevated EMT gene signatures and increased ZEB1 and ZEB2 expression in vimentin-positive compared to vimentin-negative carcinoma cells within the corresponding primary tumor tissue confirmed ZEB1 and ZEB2 as intrinsic, instead of microenvironmentally-induced, EMT regulators, and vimentin as an in vivo indicator of EMT. CONCLUSIONS: Our findings provide insights into the characteristics and transcriptional regulators of spontaneous EMT in primary non-spindled carcinoma.

Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations.

Background: Glioma is a highly heterogeneous malignancy of the central nervous system. This heterogeneity is driven by various molecular processes, including neoplastic transformation, cell cycle dysregulation, and angiogenesis. Among these biomolecular events, inflammation and stress pathways in the development and driving factors of glioma heterogeneity have been reported. However, the mechanisms of glioma heterogeneity under stress response remain unclear, especially from a spatial aspect. Methods: This study employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore the impact of oxidative stress response genes in oligodendrocyte precursor cells (OPCs). Our analysis identified distinct pathways activated by oxidative stress in two different types of gliomas: high- and low- grade (HG and LG) gliomas. Results: In HG gliomas, oxidative stress induced a metabolic shift from oxidative phosphorylation to glycolysis, promoting cell survival by preventing apoptosis. This metabolic reprogramming was accompanied by epithelial-to-mesenchymal transition (EMT) and an upregulation of stress response genes. Furthermore, SCENIC (Single-Cell rEgulatory Network Inference and Clustering) analysis revealed that oxidative stress activated the AP1 transcription factor in HG gliomas, thereby enhancing tumor cell survival and proliferation. Conclusion: Our findings provide a novel perspective on the mechanisms of oxidative stress responses across various grades of gliomas. This insight enhances our comprehension of the evolutionary processes and heterogeneity within gliomas, potentially guiding future research and therapeutic strategies.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases.

It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by "Harmony" algorithm. The data was standardized and normalized by using "Seurat" package, and "SingleR" algorithm was used for cell grouping annotation. The "Findmarker" function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Upregulation of CKS2 in immunosuppressive cells is associated with metastasis and poor prognosis in prostate cancer: a single-cell RNA-sequencing analysis.

Background: Metastasis worsens prostate cancer (PCa) prognosis, with the immunosuppressive microenvironment playing a key role in bone metastasis. This study aimed to investigate how an immunosuppressive environment promotes PCa metastasis and worsens prognosis of patients with PCa. Methods: Candidate oncogenes were identified through analysis of the Gene Expression Omnibus (GEO) database. A prognostic model was developed for the purpose of identifying target genes. A single-cell RNA sequencing data from GEO database was used to analyze the localization of target genes in the tumor microenvironment. A pan-cancer analysis was conducted to study the cancer-causing potential of target genes across different types of tumors. Results: Fifty-one genes were found to be differentially expressed in bone metastasis compared to non-metastatic PCa, with CKS2 identified as the most significant gene associated with poor prognosis. CKS2 was shown to be linked to an immunosuppressive microenvironment and osteoclastic bone metastases, as shown by its negative correlation with immune cell infiltration and osteoblast-related gene expression. Moreover, CKS2 was found in immunosuppressive cells and was linked to bone metastasis in PCa. It was also overexpressed in different types of tumors, making it as an oncogenic gene. Conclusions: This research offers a new perspective on the potential utility of CKS2 as a therapeutic target for the prevention of metastatic PCa.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Background: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Methods: Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. Results: We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. Conclusions: This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.