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Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.
BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanisms linking OTA exposure to colorectal cancer (CRC) pathogenesis remain poorly understood. METHODS: We employed an integrative computational approach combining network toxicology, multi-cohort transcriptomic analysis, and machine learning to identify potential connections between OTA and CRC. Functional enrichment analysis was conducted to characterize the associated biological processes. Molecular docking and molecular dynamics simulations were used to computationally assess the predicted binding potential and conformational stability of OTA-target complexes. Immune cell infiltration patterns were assessed using CIBERSORT, and protein-level expression patterns were examined using publicly available Human Protein Atlas (HPA) data. Single-cell RNA-sequencing (scRNA-seq) data from three public cohorts were integrated to characterize cell-type-specific expression patterns and to explore intercellular communication via CellChat. scTenifoldKnk-based in silico perturbation analysis of MET and PLAU was performed in their respective high-expression cell populations to generate functional hypotheses. RESULTS: Intersection of OTA-predicted targets with CRC-associated genes identified 81 candidate genes enriched in metabolic and microenvironment remodeling pathways. Integration of machine learning frequency, SHAP importance, and PPI topological centrality pinpointed five core genes: MET, PLAU, TOP2A, FABP4, and ADH1B. Molecular docking and dynamics simulations suggested favorable predicted binding and conformational stability for selected OTA-target complexes. Immune infiltration analysis revealed a myeloid-enriched microenvironment, with MET, PLAU, and TOP2A positively correlating with pro-tumorigenic subsets, while FABP4 and ADH1B were associated with lymphoid populations. HPA immunohistochemistry data were consistent with the upregulation of MET, PLAU, and TOP2A, alongside the downregulation of FABP4 and ADH1B at the protein level. Single-cell analysis revealed distinct cell-type preferences: MET in colonic epithelial cells and fibroblasts, PLAU in dendritic cells, TOP2A in proliferative epithelial and exhausted CD8+ T cells, FABP4 in vascular endothelial cells, and ADH1B in conventional dendritic cells (cDC1). CellChat analysis revealed active PLAU-mediated communication between dendritic cells and stromal populations. scTenifoldKnk-based in silico perturbation of PLAU in its native high-expression cells perturbed immune pathways, whereas MET perturbation in its high-expression cells disrupted cell adhesion and ECM-interaction pathways, suggesting their complementary roles in immune modulation and tissue architecture maintenance. CONCLUSIONS: This integrated computational study prioritized a five-gene framework potentially linking OTA-related predicted targets with CRC-associated metabolic and immune-related molecular alterations. These findings are supported by immune contexture inference, external protein-level reference data, computational structural analyses, and single-cell-based in silico perturbation results, providing a hypothesis-generating framework for subsequent experimental investigations into the potential toxicological relevance of OTA in CRC.
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Using single cell atlas data to reconstruct regulatory networks.
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.
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Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer.
BACKGROUND: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure. METHOD: Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes. RESULT: TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response. CONCLUSION: In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.
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Dissociation and inDrops microfluidic encapsulation of human gut tissues for single-cell atlasing studies.
In droplet-based single-cell RNA-sequencing (scRNA-seq) experiments, cells, along with some of their surrounding buffer and ambient material, are encapsulated into droplets for mRNA capture and barcoding. This protocol details the steps for human gut tissue dissociation using cold active protease, and subsequent isolation of single epithelial cells, with enrichment of viability through washes. Next, the steps for encapsulation on the inDrops scRNA-seq platform are described. This procedure has been demonstrated to be applicable to polyps, cancers, and inflamed tissues. For complete details on the use and execution of this protocol, please refer to Chen et al. (2021).
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Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.
BACKGROUND: Accurate prognostic risk stratification for lung adenocarcinoma (LUAD) remains a critical challenge. This study employs single-cell pseudo-time series analysis to identify pseudo-time differential genes (PTDGs) associated with LUAD development and constructs tumor progression-related prognostic risk stratification for patients with LUAD. METHODS: This study conducted quality control for single-cell RNA sequencing (RNA-seq) data firstly. Then, malignant tumor cells of the epithelium were identified using cell-cluster markers and InferCNV. Trajectory analysis with Monocle identified PTDGs, further refined to 2,597 candidate genes. The prognostic signature of PTDGs was constructed by using Cox, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses. In addition, multicenter datasets and immunohistochemistry analysis were used to evaluate the expression of the identified PTDGs between the two groups. RESULTS: The developmental trajectory of the LUAD epithelium was plotted, and 13 PTDGs were identified to be highly associated with the prognosis of LUAD. Based on the prognostic risk score, The Cancer Genome Atlas (TCGA)-LUAD patients were well stratified into low- and high-risk groups. The PTDGs-based risk model demonstrated good performance in the training, internal and external validation datasets, as well as being compared with existing risk score formulas. In addition, multi-center datasets and immunohistochemistry revealed a significant up-regulation of DDIT4, FURIN, PTTG1 and RIPK2 at transcriptional and protein expression levels in LUAD tissues compared to normal tissues. CONCLUSIONS: PTDGs are potential biological markers for prognostic risk stratification of patients with LUAD.
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Integration of Bulk and Single-Cell RNA-Seq Data to Construct a Prognostic Model of Membrane Tension-Related Genes for Colon Cancer.
BACKGROUND: The plasma membrane provides a highly dynamic barrier for cancer cells to interact with their surrounding microenvironment. Membrane tension, a pivotal physical property of the plasma membrane, has attracted widespread attention since it plays a role in the progression of various cancers. This study aimed to identify a prognostic signature in colon cancer from membrane tension-related genes (MTRGs) and explore its implications for the disease. METHODS: Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database, and then applied to the differentially expressed gene analysis. By implementing a univariate Cox regression and a LASSO-Cox regression, we developed a prognostic model based on four MTRGs. The prognostic efficacy of this model was evaluated in combination with a Kaplan-Meier analysis and receiver operating characteristic (ROC) curve analysis. Moreover, the relationships between the signature and immune cell infiltration, immune status, and somatic mutation were further explored. Lastly, by utilizing single-cell RNA-seq data, cell type annotation, pseudo-time analysis, drug sensitivity, and molecular docking were implemented. RESULTS: We constructed a 4-MTRG signature. The risk score derived from the model was further validated as an independent variable for survival prediction. Two risk groups were divided based on the risk score calculated by the 4-MTRG signature. In addition, we observed a significant difference in immune cell infiltration, such as subsets of CD4 T cells and macrophages, between the high- and low-risk groups. Moreover, in the pseudo-time analysis, TIMP1 was found to be more highly expressed with the progression of time. Finally, three small molecule drugs, elesclomol, shikonin, and bryostatin-1, exhibited a binding potential to TIMP-1. CONCLUSIONS: The novel 4-MTRG signature is a promising biomarker in predicting clinical outcomes for colon cancer patients, and TIMP1, a member of the signature, may be a sensitive regulator of the progression of colon cancer.
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A new prognostic model for glioblastoma multiforme based on coagulation-related genes.
BACKGROUND: Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored. METHODS: RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups. RESULTS: A 5-gene prognostic signature (PLAUR, GP6, C5AR1, SERPINA5, F2RL2) was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients. CONCLUSIONS: An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.
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Integrative analysis of miRNA and mRNA sequencing data reveals potential regulatory mechanisms of ACE2 and TMPRSS2.
Development of novel approaches for regulating the expression of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) is becoming increasingly important within the context of the ongoing COVID-19 pandemic since these enzymes play a crucial role in cell infection. In this work we searched for putative ACE2 and TMPRSS2 expression regulation networks mediated by various miRNA isoforms (isomiR) across different human organs using publicly available paired miRNA/mRNA-sequencing data from The Cancer Genome Atlas (TCGA) project. As a result, we identified several miRNA families targeting ACE2 and TMPRSS2 genes in multiple tissues. In particular, we found that lysine-specific demethylase 5B (JARID1B), encoded by the KDM5B gene, can indirectly affect ACE2 / TMPRSS2 expression by repressing transcription of hsa-let-7e / hsa-mir-125a and hsa-mir-141 / hsa-miR-200 miRNA families which are targeting these genes.
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NVUAtlas: A Comprehensive Single-Nucleus RNA-Seq Resource for the Human Neurovascular Unit in Alzheimer's Disease.
BACKGROUND: Neurovascular unit (NVU) dysfunction is being recognized as one of the earliest contributors to Alzheimer's disease (AD) pathogenesis. However, systematic investigation of NVU dysfunction is currently limited by lack of access to molecular level information due to underrepresentation of vascular and mural cells in standard single-nucleus RNA sequencing (snRNA-seq) datasets. Consequently, existing transcriptomic atlases lack the resolution necessary to capture the coordinated intercellular signaling and dysfunction across vascular components of NVU, including endothelial cells and pericytes. METHODS: We constructed the Human NVU Atlas by integrating 11 publicly available snRNA-seq datasets, including vascular-enriched samples, from the human prefrontal cortex. This comprehensive dataset aggregates over 4.2 million nuclei from 748 donors, including AD patients and age-matched controls. We utilized a unified probabilistic pipeline based on deep generative models (SCVI) to perform batch-aware integration and employed an ensemble of supervised and deep-learning classifiers to rigorously re-annotate cell types. Differential expression and ligand-receptor interaction analyses were subsequently performed to identify cell-type-specific disruptions in males versus females. RESULTS: The atlas successfully curated vascular populations from 11 studies to assemble the largest publically available NVU cohort of endothelial cells (2.8%) and pericytes (1.9%) alongside astrocytes and neurons. Differential expression analysis revealed that while neurons predominantly exhibited gene downregulation in AD, vascular cells displayed a pattern of transcriptional hyperactivity with significant gene upregulation. We also identified pronounced sex-specific vulnerabilities; females exhibited distinct inflammatory signatures and downregulation of basement membrane collagen genes (e.g., COL4A1 , COL4A2 ) in pericytes, whereas these changes were not observed in males. Moreover, cell-cell interaction analysis revealed a widespread loss of collagen-integrin signaling between pericytes and neurons, suggesting the involvement of extracellular matrix disruptions in NVU dysfunction observed in AD. CONCLUSION: The Human NVU Atlas provides a high-resolution, integrated transcriptomic framework for dissecting the cellular heterogeneity of the neurovascular unit. By uncovering sex-specific vascular mechanisms and disrupted intercellular communication, this resource highlights the critical role of vascular cells in AD progression and serves as a foundational reference for investigating cerebrovascular contribution to AD.
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A single-cell multi-omics atlas defines the cellular and molecular landscape of gastric intestinal metaplasia.
BACKGROUND: Gastric intestinal metaplasia (GIM) is a precancerous lesion closely associated with gastric cancer (GC) risk. A systematic characterization of the molecular profiles across distinct cell types during GIM progression is crucial for devising new intervention strategies and enabling early prevention for GC. METHODS: This study integrates single-cell transcriptomics and proteomics data from gastric tissues of patients with mild chronic non-atrophic gastritis (CNAG) and moderate-to-severe GIM. RESULTS: We identified differentially expressed genes (DEGs) and proteins (DEPs) that show consistent changes at both transcriptional and protein levels across epithelial, stromal, and immune cells. Notably, most proteins were downregulated in GIM tissues, potentially linked to reduced expression of RNA splicing-related genes. Epithelial cells in GIM lesions exhibited intestinal-type subpopulations, including goblet and enterocytes, likely originating from gastric isthmus stem cells. Despite overall protein downregulation, the ubiquitin-like protein NEDD8 was markedly upregulated in metaplastic tissues, especially in H. pylori-positive tissues. Pathways involved in cytoskeleton maintenance, extracellular matrix stability, and cell adhesion were downregulated, while several macrophage-expressed DEGs/DEPs (e.g., BST2, CYBB, ITGB2) were elevated. CONCLUSIONS: This work delineates dynamic transcriptional and translational alterations during the progression from mild gastritis to GIM. It implies that chronic inflammatory injury causes suppress protein synthesis, further driving metaplastic transformation early gastric precancerous lesions.