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A single-cell transcriptomic atlas identifies hierarchical skeletal progenitors and zonal patterning in mouse mandibular cartilage.
Skeletal and synovial joint development involves distinct skeletal stem/progenitor populations. However, in neural crest-derived temporomandibular joint (TMJ), skeletal stem/progenitor lineage dynamics and ossification programs remain poorly defined. Here, we performed single-cell RNA sequencing (scRNA-seq) on mouse mandibular cartilage at embryonic day 16.5 (E16.5), postnatal day 0 (P0), and 13 weeks to construct a high-resolution atlas of skeletal stem/progenitor populations. At embryonic and postnatal stages, the outer perichondrium contained Lgr5+Pthlh+ cells expressing Dlx5, consistent with an intramembranous ossification-associated program. In contrast, a previously uncharacterized Crabp1+ subpopulation within the inner perichondrium expressed Nrg1, Lsamp, Tac1, and Igfbp5, consistent with an early chondrogenic-associated transcriptional state. These results suggest spatially distinct ossification-associated programs and reveal the transcriptional regulation underlying TMJ development. Our findings provide a model for understanding neural crest-derived TMJ skeletal stem/progenitor cell regulation and may inform future studies of TMJ cartilage biology and regeneration strategies within this specialized tissue.
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Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia.
BACKGROUND: T-cell suppression in patients with Acute myeloid leukemia (AML) limits tumor cell clearance. This study aimed to explore the role of T-cell senescence-related genes in AML progression using single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), and survival data of patients with AML in the TCGA database. METHODS: The Uniform Manifold Approximation and Projection (UMAP) algorithm was used to identify different cell clusters in the GSE116256, and differentially expressed genes (DEGs) in T-cells were identified using the FindAllMarkers analysis. GSE114868 was used to identify DEGs in AML and control samples. Both were crossed with the CellAge database to identify aging-related genes. Univariate and multivariate regression analyses were performed to screen prognostic genes using the AML Cohort in The Cancer Genome Atlas (TCGA) Database (TCGA-LAML), and risk models were constructed to identify high-risk and low-risk patients. Line graphs showing the survival of patients with AML were created based on the independent prognostic factors, and Receiver Operating Characteristic Curve (ROC) curves were used to calculate the predictive accuracy of the line graph. GSE71014 was used to validate the prognostic ability of the risk score model. Tumor immune infiltration analysis was used to compare differences in tumor immune microenvironments between high- and low-risk AML groups. Finally, the expression levels of prognostic genes were verified using polymerase chain reaction (RT-qPCR). RESULTS: 31 AMLDEGs associated with aging identified 4 prognostic genes (CALR, CDK6, HOXA9, and PARP1) by univariate, multivariate, and stepwise regression analyses with risk modeling The ROC curves suggested that the line graph based on the independent prognostic factors accurately predicted the 1-, 3-, and 5-year survival of patients with AML. Tumor immune infiltration analyses suggested significant differences in the tumor immune microenvironment between low- and high-risk groups. Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). RT-qPCR verified that prognostic gene expression was consistent with the data prediction results. CONCLUSION: CALR, CDK6, HOXA9, and PARP1 predicted disease progression and prognosis in patients with AML. Based on these, we developed and validated a new AML risk model with great potential for predicting patients' prognosis and survival.
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Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.
Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.
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A senescence-based prognostic gene signature for colorectal cancer and identification of the role of SPP1-positive macrophages in tumor senescence.
BACKGROUND: Senescence is significantly associated with cancer prognosis. This study aimed to construct a senescence-related prognostic model for colorectal cancer (CRC) and to investigate the influence of senescence on the tumor microenvironment. METHODS: Transcriptome and clinical data of CRC cases were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Senescence-related prognostic genes detected by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a model. The efficacy of the model was validated using the receiver operating characteristic (ROC) curve and survival analysis. Differentially expressed genes (DEGs) were identified and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed. CIBERSORT and Immuno-Oncology Biological Research (IOBR) were used to investigate the features of the tumor microenvironment. Single-cell RNA-seq data were used to investigate the expression levels of model genes in various cell types. Immunofluorescence staining for p21, SPP1, and CD68 was performed with human colon tissues. RESULTS: A seven-gene (PTGER2, FGF2, IGFBP3, ANGPTL4, DKK1, WNT16 and SPP1) model was finally constructed. Patients were classified as high- or low-risk using the median score as the threshold. The area under the ROC curve (AUC) for the 1-, 2-, and 3-year disease-specific survival (DSS) were 0.731, 0.651, and 0.643, respectively. Survival analysis showed a better 5-year DSS in low-risk patients in the construction and validation cohorts. GO and KEGG analyses revealed that DEGs were enriched in extracellular matrix (ECM)-receptor interactions, focal adhesion, and protein digestion and absorption. CIBERSORT and IOBR analyses revealed an abundance of macrophages and an immunosuppressive environment in the high-risk subgroup. Low-risk patients had higher response rates to immunotherapy than high-risk patients. ScRNA-seq data revealed high expression of SPP1 in a subset of macrophages with strong senescence-associated secretory phenotype (SASP) features. Using CRC tumor tissues, we discovered that SPP1+ macrophages were surrounded by a large number of senescent tumor cells in high-grade tumors. CONCLUSION: Our study presents a novel model based on senescence-related genes that can identify CRC patients with a poor prognosis and an immunosuppressive tumor microenvironment. SPP1+ macrophages may correlate with cell senescence leading to poor prognosis.
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A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.
BACKGROUND: The tumour heterogeneous make-up of immune cell infiltrates is a key factor for the therapy response and prognosis of hepatocellular carcinoma (HCC). However, it is still a major challenge to comprehensively understand the tumour immune microenvironment (TIME) at the genetic and cellular levels. METHODS: HCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database, and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was performed to evaluate the abundance of immune infiltrating cells. We employed weighted gene coexpression network analysis (WGCNA) to construct a gene coexpression network. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were further used to construct a risk model. Moreover, the expression levels of model genes were assessed by qPCR. RESULTS: We defined 25 cell clusters based on the scRNA-seq dataset (GSE149614), and the clusters were labelled as various cell types by marker genes. Then, we constructed a weighted coexpression network and identified a total of 6 modules, among which the brown module was most highly correlated with tumours. Moreover, we found that the brown module was most closely related to monocytes (cluster 21). Through univariate Cox and LASSO analyses, we constructed a 3-gene risk model (RiskScore = 0.257*Expression CSTB + 0.263* Expression TALDO1 + 0.313* Expression CLTA). This risk model showed excellent predictive efficacy for prognosis in the TCGA-LIHC and ICGC cohorts. Additionally, patients with high risk scores were found to be less likely to benefit from immunotherapy. CONCLUSIONS: We developed a 3-gene signature (including CLTA, TALDO1 and CSTB) based on the heterogeneity of the TIME to predict the survival outcome and immunotherapy response.
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Multi-Omics Analysis and Experimental Validation Uncover Neddylation-Associated Biomarkers and Cellular Landscape in Lung Adenocarcinoma.
BACKGROUND: Accumulating evidence suggests that neddylation contributes to tumor initiation and progression. Nonetheless, its biological functions and regulatory mechanisms in lung adenocarcinoma (LUAD) remain poorly understood. METHODS: Bioinformatics analyses integrated single-cell RNA sequencing (scRNA-seq) datasets from the Gene Expression Omnibus (GEO) with bulk RNA-seq data from The Cancer Genome Atlas (TCGA). The Seurat package was used for scRNA-seq processing, including cell clustering, annotation, cell-cell interaction analysis, and pseudotime trajectory inference. The survival and pROC packages were used for Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) analyses, respectively. Tissue microarray (TMA) and immunohistochemistry (IHC) analyses were performed to validate biomarker expression. Functional assays, including Cell Counting Kit-8 (CCK-8), colony formation, wound healing, and Transwell assays, were conducted in A549 and NCI-H1975 cells with DNAJB1 knockdown. RESULTS: Eight major cell populations were identified at the single-cell level, among which T/NK cells, particularly CD8+ effector memory T cells (CD8+ Tem), exhibited strong associations with neddylation. T/NK cells displayed extensive ligand-receptor-mediated interactions with other cell types. A transcription factor (TF)-target regulatory network was constructed, revealing that Surfactant Protein C (SFTPC), Lipopolysaccharide-Induced TNF Factor (LITAF), Serine/Threonine Kinase 17B (STK17B), Enah/Vasp-Like Protein (EVL), FYN Proto-Oncogene, Src Family Tyrosine Kinase (FYN), and Lymphocyte-Specific Protein 1 (LSP1) were associated with favorable prognosis, whereas DNAJB1 correlated with poor survival in LUAD patients. Pseudotime trajectories further delineated dynamic differentiation pathways of T/NK subsets and differential TF target expression across states. IHC analysis showed elevated DNAJB1 expression in tumors compared with adjacent normal tissues. Moreover, silencing DNAJB1 significantly inhibited proliferation, migration, and invasion in A549 and NCI-H1975 cells. CONCLUSION: This study identifies neddylation-associated cellular phenotypes and clinically relevant biomarkers in LUAD, providing new mechanistic insights and highlighting potential therapeutic targets for LUAD treatment.
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scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data.
Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.
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Signal peptidase complex catalytic subunit SEC11A upregulation is a biomarker of poor prognosis in patients with head and neck squamous cell carcinoma.
In the current study, we aimed to investigate the expression of the five microsomal signal peptidase complex (SPC) subunit genes (SEC11A, SEC11C, SPCS1, SPCS2, and SPCS3) in head and neck squamous cell carcinoma (HNSC) and to explore their prognostic value. Data from the HNSC subset of The Cancer Genome Atlas (TCGA) and one previous single-cell RNA-seq dataset was used. Subgroup analysis was conducted in tumors from different anatomic sites. Gene set enrichment analysis (GSEA), and immune cell infiltration analysis were performed to check the influence of SEC11A on the tumor microenvironment. Among the genes significantly upregulated in the tumor group, only SEC11A expression (as a continuous variable) is independently associated with poorer progression-free survival (PFS) (HR: 2.075, 95%CI: 1.447-2.977, p<0.001) and disease-specific survival (DSS) (HR: 2.023, 95%CI: 1.284-3.187, p = 0.002). Subgroup analysis confirmed the prognostic value in tumors from three anatomic origins, including laryngeal squamous cell carcinoma, oral cavity-related squamous cell carcinoma, and oropharynx-related squamous cell carcinoma. SEC11A is expressed in all subtypes of cells in the tumor microenvironment. Its expression showed a moderate positive correlation with its gene-level copy number (Pearson's r = 0.53, p<0.001). SEC11A expression was negatively correlated with CD8+ T cells and B cells, but was positively correlated with cancer-associated fibroblast and myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment. In summary, SEC11A upregulation is a result of gene amplification in head and neck squamous cell carcinoma. Its upregulation might serve as an independent prognostic biomarker and a predictor of the infiltration of certain types of immune cells.
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Single-cell transcriptomic profiling uncovers cellular complexity and microenvironment in gastric tumorigenesis associated with Helicobacter pylori.
INTRODUCTION: Helicobacter pylori (H. pylori) infection is the main risk for gastric cancer (GC). However, the cellular heterogeneity and underlying molecular mechanisms in H. pylori-driven gastric tumorigenesis are poorly understood. OBJECTIVE: Here, we generated a single-cell atlas of gastric tumorigenesis comprising 18 specimens of gastritis, gastric intestinal metaplasia (IM) and GC with or without H. pylori infection. METHODS: Single-cell RNA sequencing (scRNA-seq) was performed. Immunofluorescence, immunohistochemistry and qRT-PCR analysis were applied in a second human gastric tissues cohort for validation. Bioinformatics analyses of public TCGA and GEO datasets were applied. RESULTS: Single-cell RNA profile highlights cellular heterogeneity and alterations in tissue ecology throughout the progression of gastric carcinoma. Various cell lineages exhibited unique cancer-associated expression profiles, such as tumor-like epithelial cell subset (EPC), inflammatory cancer-associated fibroblasts (iCAFs) and Tumor-associated macrophage (TAM). Notably, we revealed that the specific epithelial subset enterocytes from the precancerous lesion GIM, exhibited elevated expression of genes related to lipid metabolism, and HNF4G was predicted as its specific transcription factor. Furthermore, we identified differentially expressed genes in H. pylori-positive and negative epithelial cells, fibroblasts and myeloid cells were identified. Futhermore, H. pylori-positive specimens exhibited enriched cell-cell communication, characterized by significantly active TNF, SPP1, and THY1 signaling networks. CONCLUSIONS: Our study provides a comprehensive landscape of the gastric carcinogenesis ecosystem and novel insights into the molecular mechanisms of different cell types in H. pylori-induced GC.
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Single-cell RNA sequencing technology was employed to construct a risk prediction model for genes associated with pyroptosis and ferroptosis in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common subtypes of non-small cell lung cancer, and its mortality rate remains high. As a programmed cell death mode closely related to inflammation, pyroptosis has been found to play an important regulatory role in the tumor immune microenvironment and tumor progression in recent years. However, the characteristics and prognostic value of apoptosis in different cell types in LUAD have not been systematically elucidated. METHOD: This study integrated single-cell RNA sequencing (scRNA seq), bulk transcriptome data, and clinical information to comprehensively analyze the expression patterns and functional roles of pyroptosis related genes (PRGs) in LUAD. We conducted high-resolution clustering analysis on the GSE189357 single-cell dataset, constructed a protein interaction network, and established a prognostic model in conjunction with iron death related genes. Experimental validation of key genes through RNA sequencing and qPCR. RESULT: This study identified 17 different cell types, among which the pyroptosis activity of myeloid dendritic cells and neutrophils was significantly increased. Differentially expressed PRGs are enriched in immune related pathways, such as inflammasome assembly and NOD like receptor signaling pathways. The seven gene prognostic model we constructed (CXCL2, SLC7A5, CAV1, IFNG, EPAS1, TNFAIP3, CYBB) can effectively distinguish high-risk LUAD patients. Functional enrichment analysis revealed that the IL-17 signaling pathway is active in high-risk populations, suggesting that apoptosis may promote the progression of LUAD through immune dysfunction. Immune infiltration and pseudo time trajectory analysis further reveal the close correlation between PRGs expression and the dynamic state of immune cells. RNA sequencing and qPCR confirmed the differential expression of core genes in tumor tissues. CONCLUSION: This study developed a single-cell atlas of pyroptosis in LUAD and identified prognostic biomarkers with potential translational value. Our findings reveal the interaction mechanism between pyroptosis, ferroptosis, and tumor immunity, providing new ideas and targets for precise treatment of LUAD.