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Longitudinal single-cell analysis identifies a chemotherapy-suppressed proliferative program associated with adverse prognosis in neuroblastoma.
BACKGROUND: High-risk neuroblastoma frequently relapses after intensive therapy, but the treatment-associated malignant-neuroblast programs that inform baseline tumor risk remain incompletely defined. We used longitudinal single-cell/single-nucleus RNA-seq data to identify post-treatment transcriptional changes in malignant neuroblasts and evaluated their prognostic relevance in diagnostic bulk RNA-seq cohorts. METHODS: We analyzed a longitudinal single-cell/single-nucleus RNA-seq atlas of diagnostic (DX) and post-treatment (PTX) neuroblastoma samples. Neuroblast cells were aggregated into donor- and treatment-state-level pseudo-bulk profiles, and paired differential expression analysis was performed to identify PTX-regulated malignant-neuroblast programs. PTX-down and PTX-up gene sets were functionally characterized by Hallmark over-representation analysis and Hallmark pathway activities were further estimated by GSVA in bulk cohorts. We then projected the PTX-down program onto diagnostic bulk RNA-seq cohorts by calculating a PTX-down proliferation score as the mean cohort-wise z-score of PTX-down genes. Prognostic value was evaluated in a primary diagnostic bulk RNA-seq validation cohort and three independent external diagnostic bulk RNA-seq cohorts. Clinical model increment, cross-cohort meta-analysis, clinical feature association, and Hallmark correlation analyses were performed. RESULTS: Paired pseudo-bulk analysis showed that PTX malignant-neuroblast profiles had reduced activity of a proliferative-replication program enriched for E2F targets, G2/M checkpoint, MYC targets, DNA repair, and mitotic spindle pathways, together with relative enrichment of adaptive-state features related to hypoxia, inflammatory signaling, apoptosis, and epithelial-mesenchymal transition. In the primary validation cohort, high PTX-down proliferation score was associated with inferior OS and EFS and remained prognostic after adjustment for age, COG-defined high-risk classification, MYCN status, and INSS stage. Adding the PTX-down score improved C-index and 1-, 3-, and 5-year time-dependent AUC beyond both COG and clinical models. Across four bulk RNA-seq cohorts, higher PTX-down score was consistently associated with adverse OS (random-effects pooled HR = 1.83, 95% CI 1.20-2.80; P = 0.005). The score was also higher in tumors with MYCN amplification, stage 4 disease, and age ≥ 18 months. Core-gene and Hallmark correlation analyses defined the PTX-down score as an E2F/G2M/MYC-driven proliferative axis. CONCLUSION: Longitudinal single-cell analysis identifies a chemotherapy-suppressed proliferative program in malignant neuroblasts. Projection of this program onto diagnostic bulk tumors captures high-risk biology, improves prognostic discrimination, and predicts adverse outcome in neuroblastoma.
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Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.
PURPOSE: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development. MATERIALS AND METHODS: We acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes. RESULTS: In OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA. CONCLUSION: SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.
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Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery.
Crop productivity depends on activity of meristems that produce optimized plant architectures, including that of the maize ear. A comprehensive understanding of development requires insight into the full diversity of cell types and developmental domains and the gene networks required to specify them. Until now, these were identified primarily by morphology and insights from classical genetics, which are limited by genetic redundancy and pleiotropy. Here, we investigated the transcriptional profiles of 12,525 single cells from developing maize ears. The resulting developmental atlas provides a single-cell RNA sequencing (scRNA-seq) map of an inflorescence. We validated our results by mRNA in situ hybridization and by fluorescence-activated cell sorting (FACS) RNA-seq, and we show how these data may facilitate genetic studies by predicting genetic redundancy, integrating transcriptional networks, and identifying candidate genes associated with crop yield traits.
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Systematic screening identifies a TEAD4-S100A13 axis modulating cisplatin sensitivity of oral squamous cell carcinoma cells.
BACKGROUND: This study aimed to screen prognosis-related S100 protein family members in human paxpillomaviruses (HPV)-negative oral squamous cell carcinoma (OSCC) and their molecular regulations. METHODS: Bioinformatic screening was conducted based on single-cell RNA-seq data from Puram 2017 dataset and bulk-seq data from the Cancer Genome Atlas (TCGA). HPV-negative OSCC cell lines CAL-27 and SCC-4 were used as in vitro cell models. RESULTS: Among 21 S100 protein family member genes, S100A13 upregulation was associated with unfavorable progression-free survival and disease-specific survival of OSCC patients. Gene Set Enrichment Analysis showed that the higher S100A13 expression group had elevated genes enriched in DNA repair and oxidative phosphorylation. S100A13 knockdown increased cisplatin sensitivity, while its overexpression decreased the sensitivity of CAL-27 and SCC-4 cells. S100A13 gene had complex alternative transcription patterns. ENST00000440685 is one of the major protein-coding transcripts and was the only transcript elevated in the tumor group. TEAD4 could bind to the promoter of ENST00000440685 and increase its transcription. TEAD4 overexpression alleviated the tumor-suppressive effect of cisplatin in terms of colony formation, the expression of apoptotic proteins, and DNA damage. However, S100A13 knockdown partly abrogated the protective effects of TEAD4 overexpression. CONCLUSION: This study revealed a novel TEAD4-S100A13 axis that might modulate cisplatin sensitivity of OSCC tumor cells.
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Integrated single-cell and bulk RNA sequencing analyses identify an immunotherapy nonresponse-related fibroblast signature in gastric cancer.
Factors that determine nonresponse to immune checkpoint inhibitor (ICI) remain unclear. The protumor activities of cancer-associated fibroblasts (CAFs) suggest that they are potential therapeutic targets for cancer treatment. There is, however, a lack of CAF-related signature in predicting response to immunotherapy in gastric cancer (GC). Single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data of GC immunotherapy were downloaded from the Gene Expression Omnibus database. Bulk RNA-seq data were obtained from The Cancer Genome Atlas. The R package 'Seurat' was used for scRNA-seq data processing. Cellular infiltration, receptor-ligand interactions, and evolutionary trajectory analysis were further explored. Differentially expressed genes affecting overall survival were obtained using the limma package. Weighted Gene Correlation Network Analysis was used to identify key modules of immunotherapy nonresponder. Prognostic model was constructed by univariate Cox and least absolute contraction and selection operator analysis using the intersection of activated fibroblast genes (AFGs) with key module genes. The differences in clinicopathological features, immune microenvironment, immunotherapy prediction, and sensitivity to small molecule agents between the high- and low-risk groups were further investigated. Based on scRNA-seq, we finally identified 20 AFGs associations with the prognosis of GC patients. AFGs' high expression levels were correlated with both poor prognosis and tumor progression. Three genes ( FRZB , SPARC , and FKBP10 ) were identified as immunotherapy nonresponse-related fibroblast genes and used to construct the prognostic signature. This signature is an independent significant risk factor affecting the clinical outcomes of GC patients. Remarkably, there were more CD4 memory T cells, resting mast cells, and M2 macrophages infiltrating in the high-risk group, which was characterized by higher tumor immune exclusion. Moreover, patients with higher risk scores were more prone to not respond to immunotherapy but were more sensitive to various small molecule agents, such as memantine. In conclusion, this study constructed a fibroblast-associated ICI nonresponse gene signature, which could predict the response to immunotherapy. This study potentially revealed a novel way to overcome immune resistance in GC.
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State transition and intercellular communication of synovial fibroblasts in response to chronic and acute shoulder injuries unveiled by single-cell transcriptomic analyses.
PURPOSE: We aimed to investigate the heterogeneity of synovial fibroblasts and their potential to undergo cell state transitions at the resolution of single cells. MATERIALS AND METHODS: We employed the single-cell RNA sequencing (scRNA-seq) approach to comprehensively map the cellular landscape of the shoulder synovium in individuals with chronic rotator cuff tears (RCTs) and acute proximal humerus fractures (PHFs). Utilizing unbiased clustering analysis, we successfully identified distinct subpopulations of fibroblasts within the synovial environment. We utilized Monocle 3 to delineate the trajectory of synovial fibroblast state transition. And we used CellPhone DB v2.1.0 to predict cell-cell communication patterns within the synovial microenvironment. RESULTS: We identified eight main cell clusters in the shoulder synovium. Unbiased clustering analysis identified four synovial fibroblast subpopulations, with diverse biological functions associated with protein secretion, ECM remodeling, inflammation regulation and cell division. Lining, mesenchymal, pro-inflammatory and proliferative fibroblasts subsets were identified. Combining the results from StemID and characteristic gene features, mesenchymal fibroblasts exhibited characteristics of fibroblast progenitor cells. The trajectory of synovial fibroblast state transition showed a transition from mesenchymal to pro-inflammatory and lining phenotypes. In addition, the cross talk between fibroblast subclusters increased in degenerative shoulder diseases compared to acute trauma. CONCLUSION: We successfully generated the scRNA-seq transcriptomic atlas of the shoulder synovium, which provides a comprehensive understanding of the heterogeneity of synovial fibroblasts, their potential to undergo state transitions, and their intercellular communication in the context of chronic degenerative and acute traumatic shoulder diseases.
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A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing.
Single-cell genomics is rapidly advancing our knowledge of the diversity of cell phenotypes, including both cell types and cell states. Driven by single-cell/-nucleus RNA sequencing (scRNA-seq), comprehensive cell atlas projects characterizing a wide range of organisms and tissues are currently underway. As a result, it is critical that the transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell types by surface protein expression to defining diseases by their molecular drivers. Here, we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the nonlinear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that optimally capture the cell type identity represented in complete scRNA-seq transcriptional profiles. The marker genes selected provide an expression barcode that serves as both a useful tool for downstream biological investigation and the necessary and sufficient characteristics for semantic cell type definition. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and noncoding RNAs in neuronal cell type identity.
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Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types.
BACKGROUND: It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level. METHODS: Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism. RESULTS: For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF. CONCLUSIONS: We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.
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Single-Cell RNA Sequencing Revealed a 3-Gene Panel Predicted the Diagnosis and Prognosis of Thyroid Papillary Carcinoma and Associated With Tumor Immune Microenvironment.
OBJECTIVE: The objective of this research was to screen prognostic related genes of thyroid papillary carcinoma (PTC) by single-cell RNA sequencing (scRNA-seq), to construct the diagnostic and prognostic models based on The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) data, and to evaluate the association between tumor immune microenvironment and the prognostic model. METHOD: The differentially expressed genes (DEGs) and tumor evolution were analyzed by scRNA-seq based on public databases. The potential regulatory networks of DEGs related to prognosis were analyzed by multi-omics data in the THCA. Logistic regression and Cox proportional hazards regression were utilized to construct the diagnosis and prognostic model of PTC. The performance of the diagnostic model was verified by bulk RNA sequencing (RNA-seq) of our cohort. The tumor immune microenvironment associated with the prognostic model was evaluated using multi-omics data. In addition, qRT-PCR was performed on tumor tissues and adjacent normal tissues of 20 patients to verify the expression levels of DEGs. RESULTS: The DEGs screened by scRNA-seq can distinguish between tumor and healthy samples. DEGs play different roles in the evolution from normal epithelial cells to malignant cells. Three DEGs ((FN1, CLU, and ANXA1)) related to prognosis were filtered, which may be regulated by DNA methylation, RNA methylation (m6A) and upstream transcription factors. The area under curve (AUC) of the diagnostic model based on 3-gene in the validation of our RNA-seq was 1. In the prognostic model based on 3-gene, the overall survival (OS) of high-risk patients was shorter. Combined with the clinical information of patients, a nomogram was constructed by using tumor size (pT) and risk score to quantify the prognostic risk. The age and tumor size of high-risk patients in the prognostic model were greater. In addition, the increase of tumor mutation burden (TMB) and diversity of T cell receptor (TCR), and the decrease of CD8+ T cells in high-risk group suggest the existence of immunosuppressive microenvironment. CONCLUSION: We applied the scRNA-seq pipeline to focus on epithelial cells in PTC, simulated the process of tumor evolution, and revealed a prognostic prediction model based on 3 genes, which is related to tumor immune microenvironment.
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Comprehensive analysis reveals a prognostic and therapeutic biomarker CD3D in the breast carcinoma microenvironment.
Breast carcinoma (BRCA) is the most common carcinoma among women worldwide. Despite the great progress achieved in early detection and treatment, morbidity and mortality rates remain high. In the present study, we make a systematic analysis of BRCA using TCGA database by applying CIBERSORT and ESTIMATE computational methods, uncovered CD3D as a prognostic biomarker by intersection analysis of univariate COX and protein-protein interaction (PPI). It revealed that high CD3D expression was strongly associated with poor survival of BRCA, based on The Cancer Genome Atlas (TCGA) database and online websites. Gene Set Enrichment Analysis (GSEA) revealed that the high CD3D expression group was mainly enriched for the immune-related pathways and the low CD3D expression group was mainly enriched for metabolic-related activities. Based on CIBERSORT analysis, the difference test and correlation test suggested that CD3D had a strong correlation with T cells, particularly CD8 + T cells, which indicated that CD3D up-regulation may increase T cell immune infiltration in the TME and induce antitumor immunity by activating T lymphocytes. Furthermore, the correlation analysis showed that CD3D expression had a strongly positive correlation with immune checkpoints, which indicating that the underlying mechanism involves CD3D mediated regulation of T cell functions in BRCA, and single cell RNA-seq analysis revealed that CD3D correlate with CD8 + T cells and it is itself highly expressed in CD8 + T cells. In summary, we identified a prognostic biomarker CD3D in BRCA, which was associated with lymphocyte infiltration, immune checkpoints and could be developed for innovative therapeutics of BRCA.