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Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.
Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.
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Belimumab modulates type I interferon signalling in the treatment of systemic lupus erythematosus.
OBJECTIVES: To examine the effects of belimumab on the immune atlas in patients with systemic lupus erythematosus (SLE). METHODS: We present a single-cell RNA-seq profile of peripheral blood mononuclear cells from six patients with active SLE before and after drug treatment initiation. Three of these patients received belimumab combined with conventional therapy, while three received conventional therapy alone, and served as the control group. RESULTS: We found that belimumab significantly decreased the number of CD16+ monocytes after 8 weeks of treatment, whereas the opposite was observed in the control group. Compared to conventional therapy, belimumab elicited a significant reduction in IFN-stimulated gene (ISG) activity in monocytes and low-density granulocytes (LDGs). Notably, the expansion of unique subpopulations enriched among ISGs was inhibited in patients treated with belimumab. Moreover, the transcription and expression of BAFF-R and B-cell maturation antigen, two BAFF receptors, was increased in plasmacytoid dendritic cells (pDCs), B cells and plasma cells. However, the expression of BAFF-R was inhibited in monocytes and T cells in patients with SLE. CONCLUSIONS: These results revealed a novel mechanism of belimumab action, advancing our understanding of the immune atlas in SLE patients before and after belimumab-targeting treatment.
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Comprehensive analysis of clinical features, mRNA splicing, and immunological role of REEP5 in esophageal squamous cell carcinoma.
Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy within the digestive system, characterized by high incidence and mortality rates. The biological role of REEP5 in ESCC progression remains poorly understood, despite its associations with various diseases, potentially accelerating tumor malignancy. We retrieved RNA-seq data and clinical information from 179 ESCC patients from the Gene Expression Omnibus (GEO) and 93 patients from The Cancer Genome Atlas (TCGA) databases. Bioinformatics analyses were conducted to explore the biological functions of REEP5 in ESCC, its role in the tumor microenvironment, and its prognostic value. Additionally, utilizing single-cell RNA-seq (scRNA-seq) data from 3 ESCC patients in the GEO database, we performed cluster analyses to investigate cell-specific expression differences of REEP5 between cancerous and adjacent non-cancerous tissues. Molecular biology experiments were also conducted to validate REEP5 expression disparities between tumor and non-tumor tissues. Compared to normal tissues, REEP5 was significantly enriched in ESCC tissues. High REEP5 expression was closely associated with poor prognosis in ESCC patients. Gene Ontology (GO) analysis revealed strong correlations between REEP5 and processes such as mRNA splicing and protein stabilization. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicated positive correlations between REEP5 and mRNA spliceosome assembly and disassembly. Pearson correlation analysis demonstrated positive associations between REEP5 and cancer-inhibitory immune checkpoints CTLA-4, TIM-3, and HVEM. Single-cell clustering and CIBERSORT analysis showed that REEP5 expression was closely related to T-cell infiltration in ESCC, with significant enrichment effects observed in CD8+ T-cell infiltration. REEP5 expression is closely correlated with the pathological and molecular pathology of ESCC, potentially playing a crucial role in Mast cell or T-cell-mediated immune responses in ESCC. Therefore, REEP5 holds promise as a novel therapeutic target for ESCC.
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A single-cell transcriptomic and anatomic atlas of mouse dorsal raphe Pet1 neurons.
Among the brainstem raphe nuclei, the dorsal raphe nucleus (DR) contains the greatest number of Pet1-lineage neurons, a predominantly serotonergic group distributed throughout DR subdomains. These neurons collectively regulate diverse physiology and behavior and are often therapeutically targeted to treat affective disorders. Characterizing Pet1 neuron molecular heterogeneity and relating it to anatomy is vital for understanding DR functional organization, with potential to inform therapeutic separability. Here we use high-throughput and DR subdomain-targeted single-cell transcriptomics and intersectional genetic tools to map molecular and anatomical diversity of DR-Pet1 neurons. We describe up to fourteen neuron subtypes, many showing biased cell body distributions across the DR. We further show that P2ry1-Pet1 DR neurons - the most molecularly distinct subtype - possess unique efferent projections and electrophysiological properties. These data complement and extend previous DR characterizations, combining intersectional genetics with multiple transcriptomic modalities to achieve fine-scale molecular and anatomic identification of Pet1 neuron subtypes.
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MSdb: An integrated expression atlas of human musculoskeletal system.
The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.
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Integrated Transcriptomic and Functional Analyses Identify SERPINE1 as a Ferroptosis-Associated Mediator of Glioblastoma Radioresistance.
BACKGROUND: Radiotherapy failure remains a major challenge in glioblastoma (GBM), yet the stress-adaptive programs that enable tumor cells to survive irradiation-induced lethal injury remain incompletely defined. This study investigated whether serpin family E member 1 (SERPINE1) contributes to GBM radioresistance by modulating ferroptosis-associated lipid peroxidation, a process linked to iron-dependent oxidative injury. METHODS: Radiotherapy-treated GBM cohorts from TCGA (n = 93; 56 radiosensitive and 37 radioresistant cases), the Chinese Glioma Genome Atlas (CGGA)-325 (n = 100), and CGGA-693 (n = 193), as well as Clinical Proteomic Tumor Analysis Consortium (CPTAC) protein data and the Gene Expression Omnibus (GEO) single-cell RNA sequencing (scRNA-seq) dataset GSE131928, were analyzed using differential gene expression, weighted gene co-expression network analysis (WGCNA), machine-learning feature selection, FerrDb annotation, survival analysis, pathway enrichment, scRNA-seq, and tumor microenvironment (TME) signature profiling. Radiosensitive disease was defined as a complete or partial response after radiotherapy, whereas radioresistant disease was defined as stable or progressive disease. Statistical analyses included Wilcoxon rank-sum tests, Spearman correlations, log-rank tests, multivariable Cox regression for overall survival (OS), and time-dependent receiver operating characteristic (ROC) analysis. Functional validation was performed in two GBM cell lines (U87 and U251) using SERPINE1 knockdown or overexpression, X-ray irradiation, ferrostatin-1 (Fer-1), and RAS-selective lethal 3 (RSL3). RESULTS: Integrative screening identified SERPINE1 as a ferroptosis-associated mediator of radioresistance and poor survival. SERPINE1 expression was elevated in primary GBM tumors (n = 199) compared with normal tissues (n = 18). SERPINE1-high tumors showed enrichment of ferroptosis driver, suppressor, and marker gene signatures, consistent with a ferroptosis-stressed but ferroptosis-defensive state. Single-cell analysis localized SERPINE1 mainly to mesenchymal-like malignant cells, where high expression co-occurred with hypoxia, extracellular matrix remodeling, radioresistance, and ferroptosis-defense programs. In U87 cells, SERPINE1 knockdown enhanced radiosensitivity and increased irradiation-induced lipid reactive oxygen species (ROS), malondialdehyde (MDA), and Fe2+ accumulation, whereas SERPINE1 overexpression in U251 cells attenuated ferroptosis-associated lipid peroxidation and iron accumulation and improved post-irradiation cell survival. Fer-1 partially rescued the radiosensitizing effect of SERPINE1 depletion, whereas RSL3 weakened SERPINE1-mediated radioprotection. CONCLUSIONS: SERPINE1 appears to link mesenchymal adaptation, redox stress regulation, and ferroptosis-associated lipid peroxidation defense in GBM radioresistance. These findings support further evaluation of SERPINE1-guided ferroptosis-based radiosensitization strategies. Public datasets were obtained from TCGA, CGGA, CPTAC, and GEO (GSE131928).
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Integrative analysis of bulk and single-cell RNA-seq reveals the molecular characterization of the immune microenvironment and oxidative stress signature in melanoma.
BACKGROUND: The immune microenvironment and oxidative stress of melanoma show significant heterogeneity, which affects tumor growth, invasion and treatment response. Single-cell and bulk RNA-seq data were used to explore the heterogeneity of the immune microenvironment and oxidative stress of melanoma. METHODS: The R package Seurat facilitated the analysis of the single-cell dataset, while Harmony, another R package, was employed for batch effect correction. Cell types were classified using Uniform Manifold Approximation and Projection (UMAP). The Secreted Signaling algorithm from CellChatDB.human was applied to elucidate cell-to-cell communication patterns within the single-cell data. Consensus clustering analysis for the skin cutaneous melanoma (SKCM) samples was executed with the R package ConsensusClusterPlus. To quantify immune infiltrating cells, we utilized CIBERSORT, ESTIMATE, and TIMERxCell algorithms provided by the R package Immuno-Oncology Biological Research (IOBR). Single nucleotide variant (SNV) analysis was conducted using Maftools, an R package specifically designed for this purpose. Subsequently, the expression levels of PXDN and PAPSS2 genes were assessed in melanoma tissues compared to adjacent normal tissues. Furthermore, in vitro experiments were conducted to evaluate the proliferation and reactive oxygen species expression in melanoma cells following transfection with siRNA targeting PXDN and PAPSS2. RESULTS: Malignant tumor cell populations were reclassified based on a comprehensive single-cell dataset analysis, which yielded six distinct tumor subsets. The specific marker genes identified for these subgroups were then used to interrogate the Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort, derived from bulk RNA sequencing data, resulting in the delineation of two immune molecular subtypes. Notably, patients within the cluster2 (C2) subtype exhibited a significantly more favorable prognosis compared to those in the cluster1 (C1) subtype. An alignment of immune characteristics was observed between the C2 subtype and unique immune functional tumor cell subsets. Genes differentially expressed across these subtypes were subsequently leveraged to construct a predictive risk model. In vitro investigations further revealed elevated expression levels of PXDN and PAPSS2 in melanoma tissue samples. Functional assays indicated that modulation of PXDN and PAPSS2 expression could influence the production of reactive oxygen species (ROS) and the proliferative capacity of melanoma cells. CONCLUSION: The constructed six-gene signature can be used as an immune response and an oxidative stress marker to guide the clinical diagnosis and treatment of melanoma.
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scPlantDB: a comprehensive database for exploring cell types and markers of plant cell atlases.
Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled the comprehensive profiling of gene expression patterns at the single-cell level, offering unprecedented insights into cellular diversity and heterogeneity within plant tissues. In this study, we present a systematic approach to construct a plant single-cell database, scPlantDB, which is publicly available at https://biobigdata.nju.edu.cn/scplantdb. We integrated single-cell transcriptomic profiles from 67 high-quality datasets across 17 plant species, comprising approximately 2.5 million cells. The data underwent rigorous collection, manual curation, strict quality control and standardized processing from public databases. scPlantDB offers interactive visualization of gene expression at the single-cell level, facilitating the exploration of both single-dataset and multiple-dataset analyses. It enables systematic comparison and functional annotation of markers across diverse cell types and species while providing tools to identify and compare cell types based on these markers. In summary, scPlantDB serves as a comprehensive database for investigating cell types and markers within plant cell atlases. It is a valuable resource for the plant research community.
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Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis.
The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine 'human liver' scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.
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Spatiotemporal analysis reveals distinct inflammatory programs underlying chronic colitis.
Inflammatory bowel disease (IBD) is a complex disorder that is often resistant to immunomodulatory treatments. Here, to understand how immune, epithelial, and stromal compartments are rewired during disease initiation and progression, we leveraged T cell transfer and Il10-/- spontaneous colitis models, including anti-IL-12p40 intervention, and integrated time-course transcriptomic analyses at bulk, single-cell, and spatial resolution. These well-established models exhibited conserved features of chronic inflammation, including neutrophil infiltration, and impaired tissue regeneration. Comparison of murine transcriptional programs and human IBD datasets revealed neutrophil-associated inflammation and cytokine signaling as the most conserved pathways across species. We identified spatial heterogeneity in inflammatory modules and described three gene programs with differential spatial and temporal distributions, including one corresponding to tertiary lymphoid structures. When used together, these models recapitulate complementary aspects of human disease at both cellular and transcriptional levels. This high-resolution spatiotemporal atlas will guide future translational research aimed at optimizing therapeutic strategies for IBD.