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Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.
BACKGROUND: Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. OBJECTIVE: This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. METHODS: Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. RESULTS: 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. CONCLUSIONS: LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.
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Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq).
Single-cell epigenome and transcriptome profiling enables the dissection of gene regulatory networks, offering a powerful approach to characterize cellular heterogeneity and regulatory landscapes of cell states. Here we describe a single-cell ultra-high-throughput multiplexed sequencing (SUM-seq) assay for scalable and cost-effective simultaneous profiling of chromatin accessibility and gene expression in single nuclei. SUM-seq combines sample-specific accessible DNA and mRNA in situ barcoding with droplet-based microfluidic barcoding, introducing sample multiplexing and means to resolve multinucleated droplets for multiomic single-cell library preparation. In comparison with existing methods for multimodal profiling of chromatin accessibility and gene expression from the same cell, SUM-seq offers increased throughput and an unmatched multiplexing capability. This permits substantial scaling of the number of samples and nuclei assayed in one experiment, adhering to the needs of large-scale atlas projects, time-course experiments and perturbation screens while considerably reducing costs. We provide guidelines for experimental design and sample handling to accommodate various settings and sample types. Moreover, we discuss potential applications and provide guidelines for data processing. From sample collection to library preparation, the assay can be completed in 2-3 days, followed by sequencing and 1 day of data processing. Although the protocol can be implemented by researchers with general molecular biology skills, prior experience with single-cell assays is recommended.
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Trypanosoma cruzi cell atlas as a single-cell resource for understanding parasite population heterogeneity and differentiation.
Trypanosoma cruzi, the causative agent of Chagas disease, exhibits a complex life cycle with multiple hosts, stages and differentiation steps. We present a cell atlas for the T. cruzi life cycle, based on single cell transcriptomes for over 31,000 cells and population-based transcriptomics. The atlas reveals many life cycle associated genes and can be utilised to accurately annotate life cycle stages. It provides detailed insights into cell heterogeneity, including cell-specific repertoires of surface antigens in trypomastigotes, with key implications for immune responses. Enabled by single-cell resolution, we define the transcriptomic changes that occur across the epimastigote to metacyclic trypomastigote differentiation axis. Furthermore, we provide comprehensive UTR annotation, identifying previously unannotated transcripts as well as revealing alternative polyadenylation and an unanticipated complexity of reverse strand and antisense transcripts. This T. cruzi atlas provides a comprehensive resource and unlocks a range of new avenues for research on this important human pathogen.
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Single-cell transcriptome analysis of liver immune microenvironment changes induced by microplastics in mice with non-alcoholic fatty liver.
Recent studies have discovered that tiny particles of microplastics (MPs) at the nano-scale level can enter the body of organisms from the environment, potentially causing metabolic ailments. However, further investigation is required to understand the alterations in the immune microenvironment associated with non-alcoholic fatty liver disease (NAFLD) occurrence following exposure to MPs. Experiments were performed using mice, which were given a normal chow or high-fat diet (NCD or HFD, respectively) plus free drinking of sterile water with or without MPs, respectively. Employing an impartial technique known as unbiased single-cell RNA-sequencing (scRNA-seq), the cellular (single-cell) pathology landscape of NAFLD and related changes in the identified immune cell populations induced following MPs plus HFD treatment were assessed. The results showed that mice in the HFD groups had remarkably greater NAFLD activity scores than those from the NCD groups. Moreover, administration of MPs plus HFD further worsened the histopathological changes in the mice's liver, leading to hepatic steatosis, inflammatory cell infiltrations and ballooning degeneration. Following the construction of a sing-cell resolution transcriptomic atlas of 43,480 cells in the mice's livers of the indicated groups, clear cellular heterogeneity and potential cell-to-cell cross-talk could be observed. Specifically, we observed that MPs exacerbated the pro-inflammatory response and influenced the stemness of hepatocytes during HFD feeding. Importantly, treatment with MPs significantly increase the infiltration of the infiltrating liver-protecting Vsig4+ macrophages in the liver of the NAFLD mouse model while remarkably decreasing the angiogenic S100A6+ macrophage subpopulation. Furthermore, mice treated with MPs plus HFD exhibited significantly increased recruitment of CD4+ cells and heightened exhaustion of CD8+ T cells than those from the control group, characteristics typically associated with the dysregulation of immune homeostasis and severe inflammatory damage. Overall, this study offers valuable perspectives into comprehending the potential underlying cellular mechanisms and regulatory aspects of the microenvironment regarding MPs in the development of NAFLD.
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Single-cell RNA sequencing of intestinal Behçet's disease identifies putative pathogenic programs and potential therapeutic targets.
Intestinal Behçet's disease is a rare, refractory subtype of systemic vasculitis, characterized by deep ileocecal ulcers and extensive clinical overlap with Crohn's disease, while the cellular and molecular mechanisms underlying its pathogenesis remain poorly characterized. This study aimed to delineate the single-cell transcriptomic landscape of intestinal Behçet's disease, characterize its putative disease-associated transcriptomic signatures, and identify potential candidates for differential diagnosis and targeted treatment. We performed single-cell RNA sequencing on paired inflamed and histologically normal terminal ileum biopsy specimens from 4 patients with active intestinal Behçet's disease, and integrated our dataset with a public single-cell dataset from 12 Crohn's disease patients and 6 healthy controls for systematic bioinformatic analysis. We thereby constructed the first single-cell transcriptomic atlas of human intestinal Behçet's disease, profiling 98,119 high-quality cells to identify 3 major cell lineages, 20 distinct cell populations, and 41 functionally defined cell subtypes. Our analysis indicated that intestinal Behçet's disease may be characterized by robust stromal compartment activation, extracellular matrix remodeling, and potentially distinct epithelial antimicrobial signatures, which showed notable differences from the prominent epithelial barrier dysfunction and interferon-driven immune activation observed in Crohn's disease in this parallel intra-disease comparison framework. We further identified predicted pathogenic crosstalk between endothelial cells and neutrophils, which may be mediated by collagen/laminin-CD44 axes. Our findings thereby characterize the potential pathogenic features of intestinal Behçet's disease, and provide hypothesis-generating clues for the clinical management of this rare disorder.
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scTrans: Sparse attention powers fast and accurate cell type annotation in single-cell RNA-seq data.
Cell type annotation is crucial in single-cell RNA sequencing data analysis because it enables significant biological discoveries and deepens our understanding of tissue biology. Given the high-dimensional and highly sparse nature of single-cell RNA sequencing data, most existing annotation tools focus on highly variable genes to reduce dimensionality and computational load. However, this approach inevitably results in information loss, potentially weakening the model's generalization performance and adaptability to novel datasets. To mitigate this issue, we developed scTrans, a single cell Transformer-based model, which employs sparse attention to utilize all non-zero genes, thereby effectively reducing the input data dimensionality while minimizing information loss. We validated the speed and accuracy of scTrans by performing cell type annotation on 31 different tissues within the Mouse Cell Atlas. Remarkably, even with datasets nearing a million cells, scTrans efficiently perform cell type annotation in limited computational resources. Furthermore, scTrans demonstrates strong generalization capabilities, accurately annotating cells in novel datasets and generating high-quality latent representations, which are essential for precise clustering and trajectory analysis.
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Construction of monocyte-related prognosis model based on comprehensive analysis of bulk RNA-seq and single-cell RNA-seq in high-grade serous ovarian cancer.
High-grade serous ovarian cancer (HGSOC) is a common subtype of ovarian cancer with high mortality. Finding a new biomarker is useful for the diagnosis and treatment of HGSOC. The scRNA and bulk RNA data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The monocyte-related clusters were identified and annotated by Seruat and SingleR package. The Kaplan-Meier and receiver operating characteristic curve was used to determine the prognosis. The differentially expressed genes were determined by limma. The single sample Gene Set Enrichment Analysis, Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes were used for the enrichment function. The correlation between drug activity and gene expression was assessed by rcellminer and rcellminer Data package. We identified 9 cell types and obtained 37 differentially expressed marker genes of monocyte. A2M, CD163, and FPR1 were screened out as hub genes and used to construct risk model in HGSOC through univariate and multivariate cox analysis. Single sample Gene Set Enrichment Analysis showed risk score was related to B cell and T cell signal pathways, and further analysis showed most immune checkpoint genes expressions were upregulated in high-risk score group. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis exhibited that hub gene related genes were involved in signal receptor binding and cytokine-cytokine interaction. Low A2M expression and high expression of CD163 and FPR1 were associated with poor prognosis. Gene Set Enrichment Analysis revealed that A2M promoted tumor development through enhancing immune cell related signal pathways, while CD163 and FPR1 inhibited tumor development through activated carcinogenic signal pathways. Drug sensitivity analysis revealed that these hub genes could be potential therapeutic targets for the treatment of HGSOC. We constructed a risk model for the overall survival and explored the potential mechanism of monocyte in HGSOC.
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Single-cell atlas of gastric cancer reveals malignant epithelial evolution and regulatory reprogramming of the tumor microenvironment.
The inflammation-intestinal metaplasia (IM)-carcinoma cascade has been proposed as a framework for gastric cancer (GC) development, yet the cell-level heterogeneity and microenvironmental remodeling underlying this progression remain poorly characterized. Here, we constructed a single-cell transcriptomic atlas by integrating scRNA-seq data from chronic gastritis (superficial, CGS), IM, cancer-adjacent, and tumor tissues through a unified analytical pipeline. Seven major cell lineages were resolved. Relative to CGS, IM and GC tissues exhibited a progressive contraction of epithelial compartments accompanied by expansion of immune and stromal populations. Copy number variation (CNV) inference identified two tumor-restricted malignant epithelial subgroups-one biased toward differentiation and the other enriched for inflammatory and epithelial-mesenchymal transition (EMT) signatures-as well as putative proto-malignant intermediates that coexisted with phenotypically normal epithelium. Cell-cell communication analysis indicated broadly augmented crosstalk between epithelial cells and T cells, myeloid cells, and fibroblasts, with prominent involvement of a CD44-extracellular matrix (ECM) axis. Pseudotime trajectory analysis placed malignant epithelium at late positions along gastric and pyloric mucosal cell differentiation backbones, coinciding with increasing CNV burden and enrichment of stem-like transcriptional programs. Gene regulatory network analysis revealed coordinated activity of lineage-specification modules (HNF4/CDX, NR1H4/ESRRA), proliferative regulons (MYC/TFDP1), and inflammatory/EMT-associated programs (FOSL1/REL/NF-κB). In independent cohorts, elevated expression of several malignant-epithelium-associated transcription factors-including HNF4A, KLF3, FOSL1, TCF7L2, BCL3, RELB, ONECUT2, and MAF-correlated with unfavorable overall survival. Collectively, these findings provide single-cell-resolution evidence consistent with the proposed three-stage model of gastric carcinogenesis and highlight candidate transcriptional regulators warranting further investigation as potential early-detection biomarkers.
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Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data.
A major challenge in single-cell RNA sequencing (scRNA-seq) analysis is recovering biologically meaningful cell ontology trees and conserved gene modules across datasets. Data integration and batch-effect correction methods have enabled effective analyses of multiple datasets but often fail to disentangle cell states in heterogeneous samples, such as cancer and the immune system. Here, we present Super Single-Cell Clustering (SuperSCC), a computational framework that utilizes machine learning models to discover cell identities and gene modules across multiple datasets without the need for data integration. Notably, SuperSCC can be implemented at both the cell lineage and cell state levels, thereby allowing the creation of hierarchies of cell programs with specific cell identities and gene modules. This information can be used to identify shared rare populations across datasets regardless of batch effects and has advantages for mapping cell labels from reference to query datasets. We used SuperSCC to perform atlas-level data analysis with more than 90 datasets and built cell state maps of complex tissues, such as the human lung, in healthy and diseased states. SuperSCC outperforms existing approaches in identifying cellular contexts, achieves higher annotation accuracy, and identifies gene modules that indicate conserved immune cell statuses in the lung microenvironment.
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Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the molecular feature of tumor-associated neutrophils of head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a lethal malignancy with a high recurrence and distant metastasis rate, posing significant challenges to patient prognosis. Recent studies suggest that tumor-associated neutrophils (TANs) can modulate immune cell infiltration and influence tumor initiation and progression. However, the potential clinical significance of TANs in HNSCC remains insufficiently explored. METHODS: TANs-specific marker genes were identified via single-cell sequencing data from HNSCC. Based on data from The Cancer Genome Atlas (TCGA), a prognostic risk model was constructed using TANs cell marker genes, and the model was validated with data from the Gene Expression Omnibus (GEO) database. The associations between the TANs signature and clinical characteristics, functional pathways, immune cell infiltration, immune checkpoint expression, and responses to immunotherapy and chemotherapy, were then investigated. Cell counting kit-8(CCK-8), Transwell, and wound healing assays were conducted to assess the functional role of TANs marker molecules. RESULTS: TANs characteristic genes were identified from single-cell sequencing data from HNSCC patients. On the basis of these characteristic genes, a tumor-associated neutrophils-associated signature (NRS) was developed and validated across internal and external cross-platform cohorts through comprehensive procedures. The NRS demonstrated robust and reliable performance in predicting overall survival. Additionally, patients with a low NRS showed enhanced immune cell infiltration, active lipid metabolism, and increased sensitivity to immunotherapy. In contrast, patients with a high NRS exhibited poor prognostic outcomes, advanced clinical stages, and significant associations with HNSCC metastasis and progression. Furthermore, we identified a TANs-associated biomarker, OLR1, and validated that OLR1 promotes HNSCC proliferation, invasion, and migration through CCK-8, Transwell invasion, and wound healing assays. CONCLUSION: This study has developed a promising TANs-based tool that may aid in personalized treatment and prognostic management for patients with HNSCC.