Single-cell Sequencing Datasets Research Articles

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains incompletely understood. By analyzing single-cell RNA sequencing data from over 600,000 cells in gastric cancer (GSE163558 and GSE183904), colorectal cancer (GSE205506), and lung cancer (GSE207422), we identified neutrophil subsets in primary gastric cancer that are associated with the treatment response to ICIs. Specifically, we focused on neutrophils with high expression of CD44 (CD44_NEU), which are abundant during tumor progression and exert significant influence on the gastric cancer immune microenvironment. Machine learning analysis revealed 22 core genes associated with CD44_NEU, impacting inflammation, proliferation, migration, and oxidative stress. In addition, multiple immunofluorescence staining and gastric cancer spatial transcriptome data (GSE203612) showed a correlation between CD44_NEU and T-cell infiltration in gastric cancer tissues. A risk score model derived from seven essential genes (AQP9, BASP1, BCL2A1, PLEK, PDE4B, PROK2, and ACSL1) showed better predictive capability for patient survival compared to clinical features alone, and integrating these scores with clinical variables resulted in a prognostic nomogram. Overall, this study highlights the heterogeneity of TANs, particularly the CD44_NEU critical influence on immunotherapy outcomes, paving the way for personalized treatment strategies.

Identification of a Novel Signature Based on Ferritinophagy-Related Genes to Predict Prognosis in Lung Adenocarcinoma: Focus on AHNAK2.

Lung adenocarcinoma (LUAD) accounts for over 40% of all non-small cell lung cancer (NSCLC) cases and continues to be difficult to treat despite advancements in diagnostics and therapies. Ferritinophagy, a newly recognized autophagy process linked to ferroptosis, has been associated with LUAD development. Recent studies have shown a dysregulation of genes related to ferritinophagy in LUAD, indicating its potential as a therapeutic target. We constructed a predictive model using seven genes associated with ferritinophagy. The model's accuracy was evaluated across three independent gene expression datasets. We analyzed the biological functions, immune environment, mutations, and drug sensitivities in groups with high and low risk. Utilizing a single-cell sequencing (scRNA-seq) dataset, we confirmed the expression of the model genes and identified a subtype of epithelial cells expressing AHNAK2. We further investigated the impact of the ferritinophagy-related gene AHNAK2 on LUAD cell proliferation, invasion, migration, and ferroptosis in vitro. Our prediction model, comprising seven genes (AHNAK2, ARNTL2, CD27, LTB, SLC15A1, SLC2A1, and SYT1), has shown potential in predicting the prognosis of individuals diagnosed with LUAD. Notably, AHNAK2 impedes ferroptosis, promoting LUAD progression in vitro. Our research suggests that ferritinophagy-associated genes are promising prognostic markers for LUAD and lay the groundwork for further exploration of ferritinophagy's role in LUAD. Furthermore, we present AHNAK2 as a novel regulator of ferroptosis, which requires further investigation to understand its mechanism.

Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence.

Diabetic foot ulcers (DFUs) are a severe complication of diabetes mellitus (DM), which often lead to hospitalization and non-traumatic amputations in the United States. Diabetes prevalence estimates in South Texas exceed the national estimate and the number of diagnosed cases is higher among Hispanic adults compared to their non-Hispanic white counterparts. San Antonio, a predominantly Hispanic city, reports significantly higher annual rates of diabetic amputations compared to Texas. The late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk. The aim of this study was to identify molecular factors related to the severity of DFUs by leveraging a multimodal approach. We first utilized electronic health records (EHRs) from two large demographic groups, encompassing thousands of patients, to identify blood tests such as cholesterol, blood sugar, and specific protein tests that are significantly associated with severe DFUs. Next, we translated the protein components from these blood tests into their ribonucleic acid (RNA) counterparts and analyzed them using public bulk and single-cell RNA sequencing datasets. Using these data, we applied a machine learning pipeline to uncover cell-type-specific and molecular factors associated with varying degrees of DFU severity. Our results showed that several blood test results, such as the Albumin/Creatinine Ratio (ACR) and cholesterol and coagulation tissue factor levels, correlated with DFU severity across key demographic groups. These tests exhibited varying degrees of significance based on demographic differences. Using bulk RNA-Sequenced (RNA-Seq) data, we found that apolipoprotein E (APOE) protein, a component of lipoproteins that are responsible for cholesterol transport and metabolism, is linked to DFU severity. Furthermore, the single-cell RNA-Seq (scRNA-seq) analysis revealed a cluster of cells identified as keratinocytes that showed overexpression of APOE in severe DFU cases. Overall, this study demonstrates how integrating extensive EHRs data with single-cell transcriptomics can refine the search for molecular markers and identify cell-type-specific and molecular factors associated with DFU severity while considering key demographic differences.

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial–mesenchymal transition (EMT) pathway.

Helicobacter pylori induces GBA1 demethylation to inhibit ferroptosis in gastric cancer.

This research investigates potential therapeutic targets for gastric cancer, focusing on ferroptosis-related genes. Gastric cancer is known for its lower survival rates, necessitating new treatment strategies. This study employed Mendelian randomization to identify ferroptosis-related genes and methylation sites in gastric cancer, examining correlations between Helicobacter pylori infection, GBA1 gene expression, and promoter methylation with single-cell datasets and the TCGA-STAD database. We used Helicobacter pylori-infected gastric cancer cell models and used next-generation sequencing to monitor methylation changes pre- and post-infection. GBA1 expression levels were assessed via qRT-PCR and Western blot both before and after infection. The effect of Helicobacter pylori on GC cell proliferation was analyzed using CCK-8 and EdU assays after knocking down the GBA1 gene. The association between Helicobacter pylori infection and ferroptosis, including its reversibility after GBA1 knockdown, was evaluated using FerrOrange, GSH, MDA, and C11-BODIPY assays. Mass spectrometry measured the impact of Helicobacter pylori and GBA1 knockdown on lipid metabolism. An in vivo subcutaneous tumor-bearing model was also established to confirm these findings. Mendelian randomization analysis revealed that high GBA1 expression and reduced methylation levels of its promoter are risk factors for gastric cancer. Single-cell sequencing and TCGA-STAD datasets indicated a positive correlation between Helicobacter pylori infection and GBA1 expression, with a concurrent negative correlation between GBA1 promoter methylation and GBA1 expression. In gastric cancer cell lines, Helicobacter pylori infection was observed to enhance GBA1 expression and decrease methylation levels at its promoter. Additionally, Helicobacter pylori promoted GC cell proliferation, an effect mitigated by knocking down GBA1. Infection also reduced lipid peroxidation, increased glutathione levels, and impeded ferroptosis in GC cells; however, these effects were reversed following GBA1 knockdown. Changes in sphingolipid metabolism induced by I were detected in GC cell lines. In vivo experiments using a subcutaneous tumor-bearing model demonstrated that Helicobacter pylori infection fosters tumorigenesis in GC cells. Our study demonstrates that Helicobacter pylori infection triggers demethylation and upregulation of GBA1, subsequently inhibiting ferroptosis in gastric cancer cells. These findings suggest that targeting the GBA1 pathway may offer a novel therapeutic approach for managing gastric cancer.

Single-cell and Spatial Transcriptomic Analyses Implicate Formation of the Immunosuppressive Microenvironment during Breast Tumor Progression.

Ductal carcinoma in situ and invasive ductal carcinoma represent two stages of breast cancer progression. A multitude of studies have shown that genomic instability increases during tumor development, as manifested by higher mutation and copy number variation rates. The advent of single-cell and spatial transcriptomics has enabled the investigation of the subtle differences in cellular states during the tumor progression at single-cell level, thereby providing more nuanced understanding of the intercellular interactions within the solid tumor. However, the evolutionary trajectory of tumor cells and the establishment of the immunosuppressive microenvironment during breast cancer progression remain unclear. In this study, we performed an exploratory analysis of the single-cell sequencing dataset of 13 ductal carcinoma in situ and invasive ductal carcinoma samples. We revealed that tumor cells became more malignant and aggressive during their progression, and T cells transited to an exhausted state. The tumor cells expressed various coinhibitory ligands that interacted with the receptors of immune cells to create an immunosuppressive tumor microenvironment. Furthermore, spatial transcriptomics data confirmed the spatial colocalization of tumor and immune cells, as well as the expression of the coinhibitory ligand-receptor pairs. Our analysis provides insights into the cellular and molecular mechanism underlying the formation of the immunosuppressive landscape during two typical stages of breast cancer progression.

Joint trajectory inference for single-cell genomics using deep learning with a mixture prior

Trajectory inference methods are essential for analyzing the developmental paths of cells in single-cell sequencing datasets. It provides insights into cellular differentiation, transitions, and lineage hierarchies, helping unravel the dynamic processes underlying development and disease progression. However, many existing tools lack a coherent statistical model and reliable uncertainty quantification, limiting their utility and robustness. In this paper, we introduce VITAE (Variational Inference for Trajectory by AutoEncoder), a statistical approach that integrates a latent hierarchical mixture model with variational autoencoders to infer trajectories. The statistical hierarchical model enhances the interpretability of our framework, while the posterior approximations generated by our variational autoencoder ensure computational efficiency and provide uncertainty quantification of cell projections along trajectories. Specifically, VITAE enables simultaneous trajectory inference and data integration, improving the accuracy of learning a joint trajectory structure in the presence of biological and technical heterogeneity across datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. Furthermore, we apply VITAE to jointly analyze three distinct single-cell RNA sequencing datasets of the mouse neocortex, unveiling comprehensive developmental lineages of projection neurons. VITAE effectively reduces batch effects within and across datasets and uncovers finer structures that might be overlooked in individual datasets. Additionally, we showcase VITAE's efficacy in integrative analyses of multiomic datasets with continuous cell population structures.

Role and mechanism of LINC02390 and its potential target genes, CLECL1 and CD69, in immune microenvironment of lung adenocarcinoma.

Targeted therapy and immunotherapy has brought new hope to patients with lung adenocarcinoma (LUAD) with their applications. However, the prognosis of LUAD patients is still unpromising. It is particularly important to find the biomarkers that can predict the prognosis of LUAD. In our previous study, we found that patients with high expression of LINC02390 had a better prognosis. The clinical significance of LINC02390 and its potential target genes, CLECL1 and CD69, in the prognosis of LUAD and its role in the immune microenvironment were explored. Through the survival analysis, LINC02390 and its potential target genes, CLECL1 and CD69, were identified as good prognostic factors for LUAD. According to GO and KEGG analyses, LINC02390-related genes were identified potentially involved in immune-related signaling pathways. Gene mutations and their relationship with immune cell infiltration were verified through the online cbioportal and TIMER database. CD69 was found to positively associate with CD8 + T cells and CLECL1 was also positively associated with CD4 + T cells. A high expression of CD69 in CD8 + T cells was identified through the single-cell sequencing dataset GSE111894. Finally, CLECL1 and CD69 were lowly expressed in clinical tissue samples with LUAD by immunohistochemical staining. LINC02390 and its possible target genes, CLECL1 and CD69, may be potential targets for the immunotherapy in LUAD patients.

Exploring the molecular mechanisms and shared potential drugs between rheumatoid arthritis and arthrofibrosis based on large language model and synovial microenvironment analysis

Rheumatoid arthritis (RA) and arthrofibrosis (AF) are both chronic synovial hyperplasia diseases that result in joint stiffness and contractures. They shared similar symptoms and many common features in pathogenesis. Our study aims to perform a comprehensive analysis between RA and AF and identify novel drugs for clinical use. Based on the text mining approaches, we performed a correlation analysis of 12 common joint diseases including arthrofibrosis, gouty arthritis, infectious arthritis, juvenile idiopathic arthritis, osteoarthritis, post infectious arthropathies, post traumatic osteoarthritis, psoriatic arthritis, reactive arthritis, rheumatoid arthritis, septic arthritis, and transient arthritis. 5 bulk sequencing datasets and 4 single-cell sequencing datasets of RA and AF were integrated and analyzed. A novel drug repositioning method was found for drug screening, and text mining approaches were used to verify the identified drugs. RA and AF performed the highest gene similarity (0.77) and functional ontology similarity (0.84) among all 12 joint diseases. We figured out that they share the same key pathogenic cell including CD34 + sublining fibroblasts (CD34-SLF) and DKK3 + sublining fibroblasts (DKK3-SLF). Potential therapeutic target database (PTTD) was established with the differential expressed genes (DEGs) of these key pathogenic cells. Based on the PTTD, 15 potential drugs for AF and 16 potential drugs for RA were identified. This work provides a new perspective on AF and RA study which enhances our understanding of their pathogenesis. It also shed light on their underlying mechanism and open new avenues for drug repositioning studies.

Computational Analyses Reveal Deregulated Clock Genes Associated with Breast Cancer Development in Night Shift Workers.

Breast cancer (BC) is the leading cause of cancer death among women worldwide. Women employed in shift jobs face heightened BC risk due to prolonged exposure to night shift work (NSW), classified as potentially carcinogenic by the International Agency for Research on Cancer (IARC). This risk is linked to disruptions in circadian rhythms governed by clock genes at the cellular level. However, the molecular mechanisms are unclear. This study aimed to assess clock genes as potential BC biomarkers among women exposed to long-term NSW. Clock gene expression was analysed in paired BC and normal breast tissues within Nurses' Health Studies I and II GEO datasets. Validation was performed on additional gene expression datasets from healthy night shift workers and women with varying BC susceptibility, as well as single-cell sequencing datasets. Post-transcriptional regulators of clock genes were identified through miRNA analyses. Significant alterations in clock gene expression in BC compared to normal tissues were found. BHLHE40, CIART, CLOCK, PDPK1, and TIMELESS were over-expressed, while HLF, NFIL3, NPAS3, PER1, PER3, SIM1, and TEF were under-expressed. The downregulation of PER1 and TEF and upregulation of CLOCK correlated with increased BC risk in healthy women. Also, twenty-six miRNAs, including miR-10a, miR-21, miR-107, and miR-34, were identified as potential post-transcriptional regulators influenced by NSW. In conclusion, a panel of clock genes and circadian miRNAs are suggested as BC susceptibility biomarkers among night shift workers, supporting implications for risk stratification and early detection strategies.

Comprehensive analysis of shared risk genes and immunity-metabolisms between non-alcoholic fatty liver disease and atherosclerosis via bulk and single-cell transcriptome analyses

Objectiveand design: Considering the clinical link between non-alcoholic fatty liver disease (NAFLD) and atherosclerosis (AS), we performed bioinformatics analysis to uncover their pathogenic interrelationship. Methods and resultsData from the U.S. National Health and Nutritional Examination Survey (NHANES) 1999–2018 were included. Among 4851 participants in NHANES, NAFLD was significantly associated with atherosclerotic cardiovascular disease risk (ASCVD risk) (OR = 2.32, 95%CI: 2.04–2.65, P < 0.0001). We conducted WGCNA analysis for NAFLD (GSE130970) and AS (GSE28829) and identified three modules positively related to NAFLD severity and two modules accelerating atherosclerosis plaque progression. 198 key-modules genes were obtained via overlapping these modules. Next, we mined the disease-controlled differentially expressed genes (DEGs) from NAFLD (GSE89632) and AS (GSE100927), respectively. The final common risk genes (ACP5, TP53I3, RPS6KA1, TYMS, TREM2, CA12, and IFI27) were defined by intersecting the upregulated DEGs with 198 genes and validated in new datasets (GSE48452 and GSE43292). Importantly, they showed good diagnostic ability for NAFLD and AS. Immune infiltration analysis showed both illnesses have dysregulated immunity. Analysis of single-cell sequencing datasets NAFLD (GSE179886) and AS (GSE159677) uncovered different abnormal expressions of seven common genes in different immune cells while highlighting metabolic disturbances including upregulation of fatty acid biosynthesis, downregulation of fatty acid degradation and elongation. ConclusionWe found 7 shared hub genes with good diagnostic ability and depicted the landscapes of immune and metabolism involved in NAFLD and AS. Our results provided a comprehensive association between them and may contribute to developing potential intervention strategies for targeting both disorders based on these risk factors.

Nf-core/airrflow: An adaptive immune receptor repertoire analysis workflow employing the Immcantation framework.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

Energy Metabolism Dysregulation in Myocardial Infarction: An Integrative Analysis of Ischemic Cardiomyopathy.

Myocardial metabolism is closely related to functional changes after myocardial infarction (MI). This study aimed to present an integrative examination of human ischemic cardiomyopathy. We used both GSE121893 single-cell suspension sequencing and GSE19303 transcription microarray data sets from the GEO database, along with a murine MI model for full-spectrum metabolite detection. Through a systematic investigation that involved differential metabolite identification and functional enrichment analysis, we shed light on the pivotal role of energy metabolism dysregulation in the progression of MI. Our findings revealed an association between the core regulatory genes CDKN1A, FOS, ITGB4, and MAP2K1 and the underlying pathophysiology of the disease. These genes are identified as critical elements in the complex landscape of myocardial ischemic disorder, highlighting novel insights into therapeutic targets and the intricate biological mechanisms involved. This analysis provides a framework for future research on the metabolic alterations associated with MI.

Investigation into the role of H2-Ab1 in vascular remodeling in pulmonary arterial hypertension via Bioinformatics

BackgroundPulmonary arterial hypertension (PAH) is a progressive disease of vascular remodeling characterized by persistent pulmonary arterial pressure elevation, which can lead to right heart failure and premature death. Given the complex pathogenesis and poor prognosis of PAH, the identification and investigation of biomarkers become increasingly critical for advancing further understanding of the disease.MethodsPAH-related datasets, GSE49114, GSE180169 and GSE154959, were downloaded from the publicly available GEO database. By performing WGCNA on the GSE49114 dataset, a total of 906 PAH-related key module genes were screened out. By carrying out differential analysis on the GSE180169 dataset, a total of 576 differentially expressed genes were identified. Additionally, the GSE154959 single-cell sequencing dataset was also subjected to differential analysis, leading to the identification of 34 DEGs within endothelial cells. By taking intersection of the above three groups of DEGs, five PAH-related hub genes were screened out, namely Plvap, Cyp4b1, Foxf1, H2-Ab1, and H2-Eb1, among which H2-Ab1 was selected for subsequent experiments.ResultsA SuHx mouse model was prepared using the SU5416/hypoxia method, and the successful construction of the model was evaluated through Hematoxylin-Eosin staining, hemodynamic detection, fulton index, and Western Blot (WB). The results of WB and qRT-PCR demonstrated a significant upregulation of H2-Ab1 expression in SuHx mice. Consistent with the results of bioinformatics analysis, a time-dependent increase was observed in H2-Ab1 expression in hypoxia-treated mouse pulmonary artery endothelial cells (PAECs). To investigate whether H2-Ab1 affects the development and progression of PAH, we knocked down H2-Ab1 expression in PAECs, and found that its knockdown inhibited the viability, adhesion, migration, and angiogenesis, while concurrently promoted the apoptosis of PAECs.ConclusionH2-Ab1 could regulate the proliferation, apoptosis, adhesion, migration, and angiogenesis of PAECs.

Identifying the Pattern Characteristics of Anoikis-Related Genes in Keloid.

Objective: Anoikis is a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. Approach: We downloaded three keloid fibroblast (KF) RNA sequencing (RNA-seq) datasets from the Gene Expression Omnibus (GEO) and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. Weighted correlation network analysis was used to construct the coexpression network and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single-cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA-seq for gene-level validation and the correlation between KD immune infiltration and anoikis. Results: Our study comprehensively analyzed the role of ARGs in KD for the first time. The least absolute shrinkage and selection operator (LASSO) regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3, and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in patients with KD. Innovation: We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. Conclusions: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

Diagnosis and Molecular Characterization of Potential RNA Binding Protein Involved in the Pathogenesis of Liver Ischemia Reperfusion Injury.

Liver ischemia-reperfusion is one of the common complications after liver surgery. Uncontrolled liver ischemia-reperfusion will lead to many serious consequences such as surgical failure. It is an urgent clinical problem to search for diagnostic markers and explore its potential pathogenesis. In this study, we focus on 1411 candidate RNA binding protein. Through several GEO (Gene Expression Omnibus) online datasets, we construct a diagnostic model and perform interactive validation. We evaluate the efficacy of the prognostic model. Using bioinformatics methods, we predicted the relevant signaling pathways of liver ischemia-reperfusion and key genes. We also evaluated the association of RNA binding protein with immune cell infiltration. Single cell sequencing datasets were used to explore the expression profiles of key genes at the single cell level. Machine learning algorithm is used to predict key gene RNA binding domains. ROC (Receiver Operating Characteristic) and DCA (Decision Curve Analysis) curves showed that the above diagnostic model had good and stable diagnostic efficacy and clinical practicability. We identified three key genes (BTG2, CCNL1 and DNAJB1) in liver ischemia-reperfusion. DNAJB1, BTG2 and CCNL1 are mainly expressed in immune cells such as macrophages and T cells, and are closely related to inflammatory pathways such as TNF-α, highlighting their importance in hepatic ischemia reperfusion. We identified RNA-binding domains of the above three genes. We found that the expression of DNAJB1, CCNL1 and BTG2 in the ischemia-reperfusion group were significantly higher than those in the sham operation group. Our study revealed the importance of the candidate RNA binding protein in liver ischemia reperfusion injury and provided new insights into the therapeutic of hepatic ischemia-reperfusion injury.

FOS+ Macrophages Promote Chronic Rejection of Cardiac Transplantation.

Chronic rejection remains the leading cause of progressive decline in graft function. Accumulating evidence indicates that macrophages participate in chronic rejection dependent on CD40-CD40L. The FOS family members are critical in inflammatory and immune responses. However, the mechanisms underlying the role of FOS family members in chronic rejection remain unclear. In this study, we aimed to elucidate the role and underlying mechanisms of FOS-positive macrophages regulated by CD40 that mediate chronic allograft rejection. We downloaded publicly accessible chronic rejection kidney transplant single-cell sequencing datasets from the gene expression omnibus database. Differentially expressed genes between the CD40hi and CD40low macrophage chronic rejection groups were analyzed. We established a chronic rejection mouse model by using CTLA-4-Ig. We treated bone marrow-derived macrophages with an anti-CD40 antibody. We assessed expression of the FOS family by flow cytometry, real-time quantitative polymerase chain reaction, Western blotting, and immunofluorescence. We identified altered signaling pathways by using RNA sequencing analysis. We detected DNA specifically bound to transcription factors by using ChIP-sequencing, with detection of the degree of graft fibrosis and survival. FOS was highly expressed on CD40hi macrophages in patients with chronic transplantrejection. Mechanistically, we showed that CD40 activated NF-κB2 translocation into the nucleus to upregulate c-Fos and FosB expression, thus promoting chronic rejection of cardiac transplant.We showed thatNF-κB2 regulated c-Fos and FosB expression by binding to the c-fos and fosb promoter regions. Inhibition of c-Fos/activator protein-1 decreased graft fibrosis and prolonged graft survival. CD40 may activate transcription factor NF-κB2 translocation into the nucleus of macrophages to upregulate c-Fos and FosB expression, thus promoting chronic rejection of cardiac transplant. Inhibition of c-Fos/activator protein-1 decreased grafts fibrosis and prolonged graft survival.

Comprehensive analysis of cuproptosis-related genes involved in immune infiltration and their use in the diagnosis of hepatic ischemia‒reperfusion injury: an experimental Study.

Hepatic ischemia reperfusion injury (HIRI) is a common injury not only during liver transplantation but also during major hepatic surgery. HIRI causes severe complications and affects the prognosis and survival of patients. Cuproptosis, a newly identified form of cell death, plays an important role in a variety of illnesses. However, its role in HIRI remains unknown. The GSE151648 dataset was mined from the Gene Expression Omnibus (GEO) database, and differences were analyzed for intersections. Based on the differentially expressed genes (DEGs), functional annotation, differentially expressed cuproptosis-related genes (DE-CRGs) identification and lasso logistic regression were conducted. Correlation analysis of DE-CRGs and immune infiltration was further conducted, and DE-CRGs were applied to construct an HIRI diagnostic model. The hierarchical clustering method was used to classify the specimens of HIRI, and functional annotation was conducted to verify the accuracy of these DE-CRGs in predicting HIRI progression. The GSE14951 microarray dataset and GSE171539 single-cell sequencing dataset were chosen as validation datasets. At the same time, the significance of DE-CRGs was verified using a mouse model of HIRI with cuproptosis inhibitors and inducers. Finally, a network of transcription-factor-DE-CRGs and miRNA-DE-CRGs was constructed to reveal the regulation mechanisms. And potential drugs for DE-CRGs were predicted using Drug Gene Interaction Database (DGIdb). Overall, 2390 DEGs and 19 DE-CRGs were identified. Through machine learning algorithms, 8 featured DE-CRGs (GNL3, ALAS1, TSC22D2, KLF5, GTF2B, DNTTIP2, SLFN11 and HNRNPU) were screened, and 2 cuproptosis-related subclusters were defined. Based on the 8 DE-CRGs obtained from the HIRI model (AUC=0.97), the nomogram model demonstrated accuracy in predicting HIRI. Eight DE-CRGs were highly expressed in HIRI samples and were negatively related to immune cell infiltration. A higher level of immune infiltration and expression of CRG group B was found in the HIRI population. Differences in cell death and immune regulation were found between the 2 groups. The diagnostic value of the 8 DE-CRGs was confirmed in the validation of two datasets. The identification of 7 DE-CRGs (SLFN11 excluded) by HIRI animal model experiments was also confirmed. Using hTFtarget, miRWalk and DGIDB database, we predicted that 17 transcription factors, 192 miRNAs and 10 drugs might interact with the DE-CRGs. This study shows that cuproptosis may occur in HIRI and is correlated with immune infiltration. Additionally, a cuproptosis-related predictive model was constructed for studying the causes of HIRI and developing targeted treatment options for HIRI.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy.

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

Screening of potential drugs for the treatment of diabetic kidney disease using single-cell transcriptome sequencing and connectivity map data

ObjectiveTo explore the feasibility of screening potential drugs for the treatment of diabetic kidney disease (DKD) using a single-cell transcriptome sequencing dataset and Connectivity Map (CMap) database screening. MethodsA DKD single-nucleus transcriptome sequencing dataset was analyzed using Seurat 4.0 to obtain specific podocyte subclusters and differentially expressed genes (DEGs) related to DKD. These DEGs were subsequently subjected to a search against the CMap database to screen for drug candidates. Cell and animal experiments were conducted to evaluate the efficacy of the top 3 drug candidates. ResultsInitially, we analyzed the DKD single-nucleus transcriptome sequencing dataset to obtain intrinsic renal cells such as podocytes, endothelial cells, mesangial cells, proximal tubular cells, collecting duct cells and immune cells. Podocytes were further divided into four subclusters, among which the proportion of POD_1 podcytes was significantly greater in DKD kidneys than in control kidneys (34.0 % vs. 3.4 %). The CMap database was searched using the identified DEGs in the POD_1 subcluster, and the drugs, including tozasertib, paroxetine, and xylazine, were obtained. Cell-based experiments showed that tozasertib, paroxetine and xylazine had no significant podocyte toxicity in the concentration range of 0.01–50 μM. Tozasertib, paroxetine, and xylazine all reversed the advanced glycation end products (AGEs)-induced decrease in podocyte marker levels, but the effect of paroxetine was more prominent. Animal experiments showed that paroxetine decreased urine ALB/Cr levels in DKD model mice by approximately 51.5 % (115.7 mg/g vs. 238.8 mg/g, P < 0.05). Histopathological assessment revealed that paroxetine attenuated basement membrane thickening, restored the number of foot processes of podocytes, and reduced foot process fusion. In addition, paroxetine also attenuated renal tubular-interstitial fibrosis. Mechanistically, paroxetine inhibited the expression of GRK2 and NLRP3, decreased the phosphorylation level of p65, restored NRF2 expression, and relieved inflammation and oxidative stress. ConclusionThis strategy based on single-cell transcriptome sequencing and CMap data can facilitate the identification and aid the rapid development of clinical DKD drugs. Paroxetine, screened by this strategy, has excellent renoprotective effects.