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.

Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis.

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis.

Single-cell analysis reveals that TCF7L2 facilitates the progression of ccRCC via tumor-associated macrophages.

BACKGROUND: Tumor-associated macrophages (TAMs) play an important role in the recurrence and progression of clear cell renal cell carcinoma (ccRCC). However, the specified mechanism has not been elucidated. METHODS: Single-cell and transcriptome analysis were applied to characterize the heterogeneity of TAMs. SCENIC would infer regulators of different subsets of TAMs. The CellChat algorithm was used to infer macrophage-tumor interaction networks, whereas pseudo-time traces were used to parse cell evolution and dynamics. RESULTS: In this study, single-cell transcriptomic data of ccRCC were analyzed. Notably, the macrophages were clustered to select the cluster with a tendency toward M2-type TAM, which has an impact on the occurrence and metastasis of ccRCC. This macrophage cluster was defined as "TAM2". And this study revealed that TCF7L2 as a potential transcription factor regulating TAM2 transcriptional heterogeneity and differentiation. Pseudotime traces showed TCF7L2 trajectories during TAM2 cell cluster development. In addition, the results of cell interaction showed that TAM2 had the highest number and strength of interactions with cancer cells and endothelial cells. In vitro experiments, this study found that TCF7L2 was highly expressed in TAMs and promoted the polarization of macrophages to M2-type macrophages. And then overexpression of TCF7L2 in macrophages markedly promoted ccRCC invasion and proliferation. CONCLUSION: TCF7L2 could play a key role in the progression of ccRCC via enhancing TAMs recruitment and M2-type polarization.

Single-cell RNA sequencing profiling of mouse cardiac cells in response to retinoic acid.

Congenital heart disease (CHD) is the leading cause of birth defect-related mortality. CHD is a multifactorial, complex disease involving environmental factors playing important roles. To elucidate the cardiac cellular and molecular mechanisms of cardiac malformation, we administered pregnant mice with a single dose of all-trans retinoic acid (RA) at E8.5, as the CHD model. We performed single-cell RNA sequencing on cardiac cells from developing mouse hearts spanning from E8.5 to E17.5 after RA administration. A total of 69,447 cells were obtained from seven developmental stages ranging from E8.5 to E17.5. RA significantly impacted various CM subpopulations, particularly the outflow tract CMs at E9.0 by reduction of Tdgf1 expression. RA also influences the transition of endocardial-to-mesenchymal cells by decreasing the Stmn2 levels, which may contribute to abnormal valve development. In addition, RA altered the metabolic pattern of epicardial cells at E11.5 and promoted its differentiation potential. Taken together, these results are valuable for the development of preventive and therapeutic strategies for CHDs.

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.

Integrating transcriptomics, eQTL, and Mendelian randomization to dissect monocyte roles in severe COVID-19 and gout flare.

Introduction: There are considerable similarities between the pathophysiology of gout flare and the dysregulated inflammatory response in severe COVID-19 infection. Monocytes are the key immune cells involved in the pathogenesis of both diseases. Therefore, it is critical to elucidate the molecular basis of the function of monocytes in gout and COVID-19 in order to develop more effective therapeutic approaches. Methods: The single-cell RNA sequencing (scRNA-seq), large-scale genome-wide association studies (GWAS), and expression quantitative trait loci (eQTL) data of gout and severe COVID-19 were comprehensively analyzed. Cellular heterogeneity and intercellular communication were identified using the scRNA-seq datasets, and the monocyte-specific differentially expressed genes (DEGs) between COVID-19, gout and normal subjects were screened. In addition, the correlation of the DEGs with severe COVID-19 and gout flare was analyzed through GWAS statistics and eQTL data. Results: The scRNA-seq analysis exhibited that the proportion of classical monocytes was increased in both severe COVID-19 and gout patient groups compared to healthy controls. Differential expression analysis and MR analysis showed that NLRP3 was positively associated with the risk of severe COVID-19 and involved 11 SNPs, of which rs4925547 was not significantly co-localized. In contrast, IER3 was positively associated with the risk of gout and involved 9 SNPs, of which rs1264372 was significantly co-localized. Discussion: Monocytes have a complex role in gout flare and severe COVID-19, which underscores the potential mechanisms and clinical significance of the interaction between the two diseases.

Effect of fentanyl on HIV expression in peripheral blood mononuclear cells.

Introduction: Illicit drug use, particularly the synthetic opioid fentanyl, presents a significant global health challenge. Previous studies have shown that fentanyl enhances viral replication; yet, the mechanisms by which it affects HIV pathogenesis remain unclear. This study investigated the impact of fentanyl on HIV replication in CD4+ T lymphocytes. Methods: CD4+ T lymphocytes from HIV-negative donors were activated, infected with HIVNL4-3, and treated with fentanyl. HIV proviral DNA and p24 antigen expression were quantified using real-time PCR and ELISA, respectively. Single-cell RNA libraries were analyzed to identify differentially expressed genes. Results: Results indicated that fentanyl treatment increased HIV p24 expression and proviral DNA levels, and naltrexone mitigated these effects. Single-cell RNAseq analysis identified significantly altered gene expression in CD4+ T lymphocytes. Discussion: The results of our findings suggest that fentanyl promotes HIV replication ex vivo, emphasizing the need for a deeper understanding of opioid-virus interactions to develop better treatment strategies for individuals with HIV and opioid use disorder.

Omics in allergy and asthma.

This review explores the transformative impact of omics technologies on allergy and asthma research in recent years, focusing on advancements in high-throughput technologies related to genomics and transcriptomics. In particular, the rapid spread of single-cell RNA sequencing has markedly advanced our understanding of the molecular pathology of allergic diseases. Furthermore, high-throughput genome sequencing has accelerated the discovery of monogenic disorders that were previously overlooked as ordinary intractable allergic diseases. We also introduce microbiomics, proteomics, lipidomics, and metabolomics, which are quickly growing areas of research interest, although many of their current findings remain inconclusive as solid evidence. By integrating these omics data, we will gain deeper insights into disease mechanisms, leading to the development of precision medicine approaches that promise to enhance treatment outcomes.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Altered immune cell in human severe acute pancreatitis revealed by single-cell RNA sequencing.

Background: Severe acute pancreatitis (SAP) is characterized by inflammation, with inflammatory immune cells playing a pivotal role in disease progression. This study aims to understand variations in specific immune cell subtypes in SAP, uncover their mechanisms of action, and identify potential biological markers for predicting Acute Pancreatitis (AP) severity. Methods: We collected peripheral blood from 7 untreated SAP patients and employed single-cell RNA sequencing for the first time to construct a transcriptome atlas of peripheral blood mononuclear cells (PBMCs) in SAP. Integrating SAP transcriptomic data with 6 healthy controls from the GEO database facilitated the analysis of immune cell roles in SAP. We obtained comprehensive transcriptomic datasets from AP samples in the GEO database and identified potential biomarkers associated with AP severity using the "Scissor" tool in single-cell transcriptomic data. Results: This study presents the inaugural construction of a peripheral blood single-cell atlas for SAP patients, identifying 20 cell subtypes. Notably, there was a significant decrease in effector T cell subsets and a noteworthy increase in monocytes compared to healthy controls. Moreover, we identified a novel monocyte subpopulation expressing high levels of PPBP and PF4 which was significantly elevated in SAP. The proportion of monocyte subpopulations with high CCL3 expression was also markedly increased compared to healthy controls, as verified by flow cytometry. Additionally, cell communication analysis revealed insights into immune and inflammation-related signaling pathways in SAP patient monocytes. Finally, our findings suggest that the subpopulation with high CCL3 expression, along with upregulated pro-inflammatory genes such as S100A12, IL1B, and CCL3, holds promise as biomarkers for predicting AP severity. Conclusion: This study reveals monocytes' crucial role in SAP initiation and progression, characterized by distinct pro-inflammatory features intricately linked to AP severity. A monocyte subpopulation with elevated PPBP and CCL3 levels emerges as a potential biomarker and therapeutic target.

Identification and Verification of Key Genes in Colorectal Cancer Liver Metastases Through Analysis of Single-Cell Sequencing Data and TCGA Data.

BACKGROUND: Colorectal cancer (CRC) is highly prevalent worldwide, with more patients experiencing colorectal cancer liver metastases (CRLM). This study aimed to identify key genes in CRLM through single-cell sequencing data reanalysis and experimental validation. METHODS: The study analyzed single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional enrichment analysis. The Cancer Genome Atlas (TCGA) data enabled bulk-RNA expression and survival prognosis analysis. Real-time polymerase chain reaction (qPCR) detected mRNA expression, whereas Western blot determined protein levels. Cell function experiments assessed SPARC's impact on CRC cell behavior. RESULTS: Cluster analysis showed 23 classes among 17 CRLM samples, representing six cell types. A GO and KEGG analysis identified interleukin-1 beta (IL1B), CD2 molecule (CD2), and C-X-C motif chemokine ligand 8 (CXCL8) as significant prognostic factors in CRC. Secreted protein acidic and cysteine rich (SPARC) was one of the top differentially expressed genes (DEGs) in tissue stem cells, confirmed in primary and metastatic lesions. Metastatic lesions showed higher expression of SPARC and CRC stem cell marker leucine-rich repeat containing G protein-coupled receptor 5 (LGR5), which was significantly correlated positively with LGR5 expression. Knockdown of SPARC reduced CRC cell sphere- and colony-formation, invasion, and migration abilities. Overexpression of SPARC significantly increased the malignancy of CRC cells. CONCLUSIONS: Several key genes were identified in the process of CRLM. In CRLM samples and those corresponding to CRC stem cells, SPARC was significantly upregulated. In the therapy of CRLM, SPARC might be a potential target.

Detecting Rhythmic Gene Expression in Single-cell Transcriptomics.

An autonomous, environmentally synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNA seq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single-cell RNA seq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

Association of circulating cytokine levels and tissue-infiltrating myeloid cells with achalasia: results from Mendelian randomization and validation through clinical characteristics and single-cell RNA sequencing.

BACKGROUND: Achalasia is a rare motility disorder of the esophagus often accompanied by immune dysregulation, yet specific underlying mechanisms remain poorly understood. METHODS: We utilized Mendelian randomization (MR) to explore the causal effects of cytokine levels on achalasia, with cis-expression/protein quantitative trait loci (cis-eQTLs/pQTLs) for 47 cytokines selected from a genome-wide association study (GWAS) meta-analysis and GWAS data for achalasia obtained from FinnGen. For cytokines significantly linked to achalasia, we analyzed their plasma concentrations and expression differences in the lower esophageal sphincter (LES) using enzyme-linked immunosorbent assay and single-cell RNA sequencing (scRNA-seq) profiling, respectively. We further employed bioinformatics approaches to investigate underlying mechanisms. RESULTS: We revealed positive associations of circulating Eotaxin, macrophage inflammatory protein-1b (MIP1b), soluble E-selectin (SeSelectin) and TNF-related apoptosis-inducing ligand (TRAIL) with achalasia. When combining MR findings with scRNA-seq data, we observed upregulation of TRAIL (OR = 2.70, 95% CI, 1.20-6.07), encoded by TNFSF10, in monocytes and downregulation of interleukin-1 receptor antagonist (IL-1ra) (OR = 0.70, 95% CI 0.59-0.84), encoded by IL1RN, in FOS_macrophages in achalasia. TNFSF10high monocytes in achalasia displayed activated type I interferon signaling, and IL1RNlow FOS_macrophages exhibited increased intercellular communications with various lymphocytes, together shaping the proinflammatory microenvironment of achalasia. CONCLUSIONS: We identified circulating Eotaxin, MIP1b, SeSelectin and TRAIL as potential drug targets for achalasia. TNFSF10high monocytes and IL1RNlow macrophages may play a role in the pathogenesis of achalasia.

Integrative analyses of bulk and single-cell RNA-seq reveals the correlation between SPP1+ macrophages and resistance to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma.

Neoadjuvant chemoimmunotherapy (NACI) has significant implications for the treatment of esophageal cancer. However, its clinical efficacy varies considerably among patients, necessitating further investigation into the underlying mechanisms. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology facilitates the analysis of patient heterogeneity at the cellular level, particularly regarding treatment outcomes. In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression Omnibus (GEO) database. After performing dimensionality reduction, clustering, and annotation on the scRNA-seq data, we employed CellChat to investigate differences in cell-cell communication among samples from distinct efficacy groups. The results indicated that macrophages in the non-responder exhibited stronger cell communication intensity compared to those in responders, with SPP1 and GALECTIN signals showing the most significant differences between the two groups. This finding underscores the crucial role of macrophages in the efficacy of NACI. Subsequently, reclustering of macrophages revealed that Mac-SPP1 may be primarily responsible for treatment resistance, while Mac-C1QC appears to promote T cell activation. Finally, we conducted transcriptome sequencing on ESCC tissues obtained from 32 patients who underwent surgery following NACI. Utilizing CIBERSORT, CIBERSORTx, and WGCNA, we analyzed the heterogeneity of tumor microenvironment among different efficacy groups and validated the correlation between SPP1+ macrophages and resistance to NACI in ESCC using publicly available transcriptome sequencing datasets. These findings suggest that SPP1+ macrophages may represent a key factor contributing to resistance against NACI in ESCC.

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.

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.

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.

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.

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.