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Analysis of the Mechanism Underlying Radiotherapy Resistance Caused by Oligodendroglia Cells in Glioblastoma by Applying the Single-cell RNA Sequencing Technology.
BACKGROUND: Glioblastoma (GBM) is an aggressive malignancy. The inherent resistance of GBM to radiotherapy poses great challenges for clinical treatment. OBJECTIVES: The primary objective of this study is to explore the molecular mechanisms of radiotherapy resistance in GBM and identify the key influencing factors that contribute to this phenomenon. METHODS: The single-cell RNA sequencing (scRNA-seq) data of GBM were downloaded from the Gene Expression Omnibus (GEO) database. Cells were clustered using the Seurat R package, and the clusters were annotated using the CellMarker database. Pseudotime analysis was conducted using Monocle2. Marker scores were calculated based on the RNA-seq data of GBM from the UCSC database, and the enrichment of Hallmark gene sets was measured with the AUCell package. Furthermore, the most frequently mutated genes were identified using the simple nucleotide variation data from The Cancer Genome Atlas (TCGA) applying the maftools package. RESULTS: This study identified two oligodendrocyte subsets (ODC3 and ODC4) as radiotherapy- resistant groups in GBM. Enrichment and Pseudotime analysis revealed that the inflammatory response and immune activation pathways were enriched in ODC3, while the cell division and interferon response pathways were enriched in ODC4. The enrichment scores of hallmark gene sets further confirmed that ODC3 and ODC4 subpopulations developed radiotherapy resistance via distinct molecular mechanisms. Analysis of gene mutation frequencies showed that TP53 exhibited the most significant change in mutation frequency, indicating that it was an important risk factor involved in radiotherapy resistance in GBM. CONCLUSION: We identified two ODC subpopulations that exhibited resistance to radiotherapy, providing a new perspective and potential targets for personalized treatment strategies for GBM.
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Single-cell RNA-sequencing atlas of bovine caudal intervertebral discs: Discovery of heterogeneous cell populations with distinct roles in homeostasis.
Back and neck pain are significant healthcare burdens that are commonly associated with pathologies of the intervertebral disc (IVD). The poor understanding of the cellular heterogeneity within the IVD makes it difficult to develop regenerative IVD therapies. To address this gap, we developed an atlas of bovine (Bos taurus) caudal IVDs using single-cell RNA-sequencing (scRNA-seq). Unsupervised clustering resolved 15 unique clusters, which we grouped into the following annotated partitions: nucleus pulposus (NP), outer annulus fibrosus (oAF), inner AF (iAF), notochord, muscle, endothelial, and immune cells. Analyzing the pooled gene expression profiles of the NP, oAF, and iAF partitions allowed us to identify novel markers for NP (CP, S100B, H2AC18, SNORC, CRELD2, PDIA4, DNAJC3, CHCHD7, and RCN2), oAF (IGFBP6, CTSK, LGALS1, and CCN3), and iAF (MGP, COMP, SPP1, GSN, SOD2, DCN, FN1, TIMP3, WDR73, and GAL) cells. Network analysis on subpopulations of NP and oAF cells determined that clusters NP1, NP2, NP4, and oAF1 displayed gene expression profiles consistent with cell survival, suggesting these clusters may uniquely support viability under the physiological stresses of the IVD. Clusters NP3, NP5, oAF2, and oAF3 expressed various extracellular matrix (ECM)-associated genes, suggesting their role in maintaining IVD structure. Lastly, transcriptional entropy and pseudotime analyses found that clusters NP3 and NP1 had the most stem-like gene expression signatures of the NP partition, implying these clusters may contain IVD progenitor cells. Overall, results highlight cell type diversity within the IVD, and these novel cell phenotypes may enhance our understanding of IVD development, homeostasis, degeneration, and regeneration.
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Comprehensive analysis of autophagy-related prognostic genes in breast cancer using bulk and single-cell RNA sequencing.
OBJECTIVE: This study aimed to utilize single-cell RNA sequencing (scRNA-seq) to elucidate the autophagic landscape in breast cancer and to develop a prognostic model for breast cancer patients based on traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: We analyzed scRNA-seq data from the GSE75688 dataset to explore the expression patterns of autophagy-related genes (ARGs) across distinct cellular clusters. ARGs were retrieved from the GeneCards database, and bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA). Cox proportional hazards regression was employed to construct a prognostic risk model based on ARGs. Patients were subsequently stratified into high-risk and low-risk groups according to their risk scores. For external validation, we used gene expression data from the GSE20685 and GSE48390 datasets. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the 3-gene signature. RESULTS: Using the FindClusters function in Seurat, all cells were grouped into four distinct clusters, highlighting the intratumoral heterogeneity within the samples. Significant differences in autophagy scores were observed among the clusters. Fifteen differentially expressed autophagy-related genes were identified, and a prognostic signature consisting of three autophagy-related genes - FEZ1, STX11, and ADAMTSL1 - was developed. Based on this model, patients were classified into high- and low-risk groups, with a statistically significant difference in survival between the two groups (log-rank test, P = 0.0011). The model demonstrated robust predictive performance with an AUC of 0.761 in the external validation dataset. A nomogram incorporating the 3-gene signature and clinical factors showed strong prognostic discrimination. CONCLUSION: This study uncovered significant variation in autophagy levels among different breast cancer cell clusters. Furthermore, we established a novel 3-gene autophagy-related prognostic model that effectively stratifies patient risk and provides a potential tool for personalized prognosis in breast cancer.
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hECA v2.0: an AI-ready ensemble cell atlas of single-cell RNA and ATAC sequencing data.
With the growing accumulation of scattered single-cell data and the rapid advancement of artificial intelligence (AI), there is a pressing need for a high-quality, well-organized, and AI-ready single-cell data resources to support large-scale model. Here, we present version 2.0 of human Ensemble Cell Atlas (hECA), a cell atlas incorporating both single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data. It expands the scRNA-seq data collection to 10,831,024 human cells with unified labels, and adds the new modality of scATAC-seq profiles with 1,450,511 cells. The data cover 42 human organs and tissues. To ensure cross-dataset consistency and quality, we standardized gene expression and chromatin accessibility matrices, harmonized cellular metadata, and manually re-annotated cell types based on the unified Hierarchical Annotation Framework (uHAF). The strength of the dataset has been shown in pre-training the large generative cellular AI model scMulan. hECA2.0 provides a well-structured and ready-to-use data resource, serving as a robust data foundation for AI-driven single-cell research.
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Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2.
The recent emergence of multi-sample multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that individual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-sample multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ individuals, we demonstrate that scMerge2 enables multi-sample multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.
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COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets. RESULTS: In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments. AVAILABILITY AND IMPLEMENTATION: The COME is freely available in GitHub (https://github.com/cindyway/COME).
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stAPAminer: Mining Spatial Patterns of Alternative Polyadenylation for Spatially Resolved Transcriptomic Studies.
Alternative polyadenylation (APA) contributes to transcriptome complexity and gene expression regulation and has been implicated in various cellular processes and diseases. Single-cell RNA sequencing (scRNA-seq) has enabled the profiling of APA at the single-cell level; however, the spatial information of cells is not preserved in scRNA-seq. Alternatively, spatial transcriptomics (ST) technologies provide opportunities to decipher the spatial context of the transcriptomic landscape. Pioneering studies have revealed potential spatially variable genes and/or splice isoforms; however, the pattern of APA usage in spatial contexts remains unappreciated. In this study, we developed a toolkit called stAPAminer for mining spatial patterns of APA from spatially barcoded ST data. APA sites were identified and quantified from the ST data. In particular, an imputation model based on the k-nearest neighbors algorithm was designed to recover APA signals, and then APA genes with spatial patterns of APA usage variation were identified. By analyzing well-established ST data of the mouse olfactory bulb (MOB), we presented a detailed view of spatial APA usage across morphological layers of the MOB. We compiled a comprehensive list of genes with spatial APA dynamics and obtained several major spatial expression patterns that represent spatial APA dynamics in different morphological layers. By extending this analysis to two additional replicates of the MOB ST data, we observed that the spatial APA patterns of several genes were reproducible among replicates. stAPAminer employs the power of ST to explore the transcriptional atlas of spatial APA patterns with spatial resolution. This toolkit is available at https://github.com/BMILAB/stAPAminer and https://ngdc.cncb.ac.cn/biocode/tools/BT007320.
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Analyses of single-cell and bulk RNA-sequencing datasets identify biomarker-based molecular subtypes and a prognostic signature in lung adenocarcinoma.
BACKGROUND: Immune checkpoint blockade has improved outcomes for a subset of patients with lung adenocarcinoma (LUAD), yet primary and acquired resistance remain common. Given the heterogeneity of the tumor microenvironment (TME), biomarkers that reflect both tumor-intrinsic programs and immune context are needed for prognostic stratification and for generating clinically relevant hypotheses. The aim of this study was to identify immune-associated genes and construct a prognostic signature for LUAD by integrating single-cell and bulk transcriptomic data. METHODS: We integrated single-cell RNA sequencing (scRNA-seq; GSE131907) with bulk transcriptomic profiles from The Cancer Genome Atlas (TCGA)-LUAD to identify immune-associated genes and construct a prognostic signature. Major cell populations were delineated and annotated using reference-based methods, and immune-associated differentially expressed genes (DEGs) were derived from the scRNA-seq analysis. Genes consistently dysregulated in both scRNA-seq and bulk data were carried forward for functional enrichment, survival analyses, and molecular subtype discovery. A multigene risk score was built using Cox regression and validated in an independent Gene Expression Omnibus (GEO) cohort (GSE13213) and evaluated using Kaplan-Meier analysis, time-dependent discrimination metrics, and multivariable adjustment for clinical covariates. Immune infiltration and immune functional states were characterized using Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATE), single-sample gene set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), and Tumor Immune Dysfunction and Exclusion (TIDE). Protein-level evidence was examined using the Human Protein Atlas (HPA), and messenger RNA (mRNA) expression was assessed in paired clinical specimens by reverse transcription quantitative polymerase chain reaction (RT-qPCR) (n=10 pairs). RESULTS: Across datasets, we identified immune-associated genes consistently altered in LUAD and enriched for antigen presentation and immune response pathways. A four-gene signature (HLA-DRB5, LDHA, ENO1, and TIMP1) stratified patients into low- and high-risk groups with significantly different overall survival (OS) and remained informative after adjustment for major clinical variables. High-risk tumors displayed an immune-suppressive phenotype, including reduced immune infiltration and attenuated antigen-presentation programs, together with higher predicted immune evasion signals in computational immunotherapy-response inference. CONCLUSIONS: Our integrative analysis nominates a four-gene immune-associated signature that captures prognostic heterogeneity in LUAD and is linked to distinct TME states. These findings provide candidate biomarkers and testable hypotheses for immunotherapy-related mechanisms, which require validation in independent immunotherapy-treated cohorts and functional experiments.
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Revealing a cancer-associated fibroblast-based risk signature for pancreatic adenocarcinoma through single-cell and bulk RNA-seq analysis.
PURPOSE: Proliferation of stromal connective tissue is a hallmark of pancreatic adenocarcinoma (PAAD). The engagement of activated cancer-associated fibroblasts (CAFs) contributes to the progression of PAAD through their involvement in tumor fibrogenesis. However, the prognostic significance of CAF-based risk signature in PAAD has not been explored. METHODS: The single-cell RNA sequencing (scRNA-seq) data sourced from GSE155698 within the Gene Expression Omnibus (GEO) database was supplemented by bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and microarray data retrieved from the GEO database. The scRNA-seq data underwent processing via the Seurat package to identify distinct CAF clusters utilizing specific CAF markers. Differential gene expression analysis between normal and tumor samples was conducted within the TCGA-PAAD cohort. Univariate Cox regression analysis pinpointed genes associated with CAF clusters, identifying prognostic CAF-related genes. These genes were utilized in LASSO regression to craft a predictive risk signature. Subsequently, integrating clinicopathological traits and the risk signature, a nomogram model was constructed. RESULTS: Our scRNA-seq analysis unveiled four distinct CAF clusters in PAAD, with two linked to PAAD prognosis. Among 207 identified DEGs, 148 exhibited significant correlation with these CAF clusters, forming the basis of a seven-gene risk signature. This signature emerged as an independent predictor in multivariate analysis for PAAD and demonstrated predictive efficacy in immunotherapeutic outcomes. Additionally, a novel nomogram, integrating age and the CAF-based risk signature, exhibited robust predictability and reliability in prognosticating PAAD. Moreover, the risk signature displayed substantial correlations with stromal and immune scores, as well as specific immune cell types. CONCLUSIONS: The prognosis of PAAD can be accurately predicted using the CAF-based risk signature, and a thorough analysis of the PAAD CAF signature may aid in deciphering the patient's immunotherapy response and presenting fresh cancer treatment options.
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Evolutionary genetic algorithm identifies IL2RB as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer.
Identifying robust predictive biomarkers to stratify colorectal cancer (CRC) patients based on their response to immune-checkpoint therapy is an area of unmet clinical need. Our evolutionary algorithm Atlas Correlation Explorer (ACE) represents a novel approach for mining The Cancer Genome Atlas (TCGA) data for clinically relevant associations. We deployed ACE to identify candidate predictive biomarkers of response to immune-checkpoint therapy in CRC. We interrogated the colon adenocarcinoma (COAD) gene expression data across nine immune-checkpoints (PDL1, PDCD1, CTLA4, LAG3, TIM3, TIGIT, ICOS, IDO1 and BTLA). IL2RB was identified as the most common gene associated with immune-checkpoint genes in CRC. Using human/murine single-cell RNA-seq data, we demonstrated that IL2RB was expressed predominantly in a subset of T-cells associated with increased immune-checkpoint expression (P < 0.0001). Confirmatory IL2RB immunohistochemistry (IHC) analysis in a large MSI-H colon cancer tissue microarray (TMA; n = 115) revealed sensitive, specific staining of a subset of lymphocytes and a strong association with FOXP3+ lymphocytes (P < 0.0001). IL2RB mRNA positively correlated with three previously-published gene signatures of response to immune-checkpoint therapy (P < 0.0001). Our evolutionary algorithm has identified IL2RB to be extensively linked to immune-checkpoints in CRC; its expression should be investigated for clinical utility as a potential predictive biomarker for CRC patients receiving immune-checkpoint blockade.