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Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline.
Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis.
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Deciphering the cellular and molecular landscapes of Wnt/β-catenin signaling in mouse embryonic kidney development.
BACKGROUND: The Wnt/β-catenin signaling pathway is critical in kidney development, yet its specific effects on gene expression in different embryonic kidney cell types are not fully understood. METHODS: Wnt/β-catenin signaling was activated in mouse E12.5 kidneys in vitro using CHIR99021, with RNA sequencing performed afterward, and the results were compared to DMSO controls (dataset GSE131240). Differential gene expression in ureteric buds and cap mesenchyme following pathway activation (datasets GSE20325 and GSE39583) was analyzed. Single-cell RNA-seq data from the Mouse Cell Atlas was used to link differentially expressed genes (DEGs) with kidney cell types. β-catenin ChIP-seq data (GSE39837) identified direct transcriptional targets. RESULTS: Activation of Wnt/β-catenin signaling led to 917 significant DEGs, including the upregulation of Notum and Apcdd1 and the downregulation of Crym and Six2. These DEGs were involved in kidney development and immune response. Single-cell analysis identified 787 DEGs across nineteen cell subtypes, with Macrophage_Apoe high cells showing the most pronounced enrichment of Wnt/β-catenin-activated genes. Gene expression profiles in ureteric buds and cap mesenchyme differed significantly upon β-catenin manipulation, with cap mesenchyme showing a unique set of DEGs. Analysis of β-catenin ChIP-seq data revealed 221 potential direct targets, including Dpp6 and Fgf12. CONCLUSION: This study maps the complex gene expression driven by Wnt/β-catenin signaling in embryonic kidney cell types. The identified DEGs and β-catenin targets elucidate the molecular details of kidney development and the pathway's role in immune processes, providing a foundation for further research into Wnt/β-catenin signaling in kidney development and disease.
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CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis.
BACKGROUND: Finding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. RESULTS: Here, we developed the R package "CeTF" that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems - for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle. CONCLUSION: This preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor ( http://bioconductor.org/packages/CeTF ) and GitHub ( http://github.com/cbiagii/CeTF ).
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Multiomics Identification of Radioresistance-Associated Biomarkers and Prognostic Model Construction in Rectal Cancer.
BACKGROUND: Radiotherapy remains a cornerstone in the local management of rectal cancer (RC); however, resistance to radiation significantly compromises therapeutic efficacy and adversely affects patient prognosis. Identification of biomarkers associated with radioresistance and the development of a prognostic model based on radiotherapy-related genes in RC remain critical for enhancing treatment outcomes. METHODS: Data related to RC were obtained from public repositories, including bulk RNA-seq data from 993 patients across four Gene Expression Omnibus (GEO) and one The Cancer Genome Atlas (TCGA) cohort, as well as single-cell RNA-seq data from six samples. A prognostic model was developed using differential expression analysis, functional enrichment analysis, and least absolute shrinkage and selection operator regression analysis. Associations between risk score and prognosis were assessed through gene set variation analysis, gene set enrichment analysis, and construction of a nomogram to identify potential therapeutic targets for RC. RESULTS: Prognosis-related genes were determined through analysis of clinical data from patients with RC in the GEO and TCGA datasets, leading to the development of a risk score model. The risk score demonstrated significant associations with immune cell infiltration, chemotherapy drug sensitivity, and multiple signaling pathways. Protein expression levels of the key genes in patients with RC were verified using the Human Protein Atlas database. Furthermore, immunohistochemical evaluation in animal models provided additional validation. CONCLUSION: Molecular characteristics and mechanisms underlying radiotherapy response in RC were clarified through multiomics analysis. Five key genes were identified as potentially related to radiotherapy sensitivity in RC. These prognostic genes may serve as novel biomarkers and potential targets for diagnosis, prognostic evaluation, and clinical management of RC.
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scPlantLLM: A Foundation Model for Exploring Single-cell Expression Atlases in Plants.
Single-cell RNA sequencing (scRNA-seq) provides unprecedented insights into plant cellular diversity by enabling high-resolution analyses of gene expression at the single-cell level. However, the complexity of scRNA-seq data, including challenges in batch integration, cell type annotation, and gene regulatory network (GRN) inference, demands advanced computational approaches. To address these challenges, we developed scPlantLLM, a Transformer model trained on millions of plant single-cell data points. Using a sequential pretraining strategy incorporating masked language modeling and cell type annotation tasks, scPlantLLM generates robust and interpretable single-cell data embeddings. When applied to Arabidopsis thaliana datasets, scPlantLLM excels in clustering, cell type annotation, and batch integration, achieving an accuracy of up to 0.91 in zero-shot learning scenarios. Furthermore, the model demonstrates an ability to identify biologically meaningful GRNs and subtle cellular subtypes, showcasing its potential to advance plant biology research. Compared to traditional methods, scPlantLLM outperforms in key metrics such as adjusted rand index (ARI), normalized mutual information (NMI), and silhouette score (SIL), highlighting its superior clustering accuracy and biological relevance. scPlantLLM represents a foundation model for exploring plant single-cell expression atlases, offering unprecedented capabilities to resolve cellular heterogeneity and regulatory dynamics across diverse plant systems. The code used in this study is available at https://github.com/compbioNJU/scPlantLLM.
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FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets.
Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology. We present FIRM, a re-scaling algorithm which accounts for the effects of cell type compositions, and achieve accurate integration of scRNA-seq datasets across multiple tissue types, platforms and experimental batches. Compared with existing state-of-the-art integration methods, FIRM provides accurate mixing of shared cell type identities and superior preservation of original structure without overcorrection, generating robust integrated datasets for downstream exploration and analysis. FIRM is also a facile way to transfer cell type labels and annotations from one dataset to another, making it a reliable and versatile tool for scRNA-seq analysis, especially for cell atlas data integration.
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Atlas-Level Single-Cell and Spatial Transcriptomics Data Integration via PRIME.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have enabled atlas-scale cellular cartography, with consortium efforts now assembling millions of cells across diverse tissues, donors, and technologies to build comprehensive references for cell identify and disease mechanism, yet the scientific value of these atlases hinges on robust computational integration across heterogeneous data sources. Unlike pairwise batch correction, atlas-level integration must jointly reconcile heterogeneous and often hierarchically nested batch effects across many datasets whose cell-type compositions are highly imbalanced, all while preserving subtle biological variation and remaining computationally tractable at the scale of millions of cells. Existing approaches often prioritize either batch mixing or preservation of local biological structure, and most cannot natively accommodate spatial coordinates. Here we introduce PRIME (Projection-based Robust Integration via Manifold Embedding), an ensemble integration framework that combines random-projection-based consensus anchoring, graph-Laplacian correction, and optional spatial-neighborhood regularization. Across multiple random projections of the expression manifold, PRIME uses consensus voting to keep only cell pairs that repeatedly matched, reducing false anchors caused by projection-specific distortions. For ST, PRIME couples this expression-based anchor graph with a coordinate-derived spatial neighborhood graph in a unified graph-Laplacian objective with closed-form solution, enabling simultaneous cross-batch alignment and local spatial coherence. Based on extensive benchmarking spanning diverse datasets, we show that PRIME consistently outperforms state-of-the-art methods in both batch correction and biological conservation across scRNA-seq and ST integration scenarios and downstream tasks including trajectory inference, spatial-domain preservation, and perturbation-response analysis. Particularly, when integrating a human hematopoiesis benchmark spanning eight donors and approximately 33,000 cells, PRIME preserves biologically coherent developmental trajectories in human hematopoiesis. It also maintains cortical laminar architecture across dorsolateral prefrontal cortex sections in a ST dataset and recovers known drug-target relationships in a perturbation atlas of more than 1 million cells while suppressing batch-associated confounders. Together, these results establish PRIME as a versatile and scalable framework for atlas-level integration of scRNA-seq and ST across diverse biological applications.
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A novel risk score model based on fourteen chromatin regulators-based genes for predicting overall survival of patients with lower-grade gliomas.
Background: Low grade gliomas(LGGs) present vexatious management issues for neurosurgeons. Chromatin regulators (CRs) are emerging as a focus of tumor research due to their pivotal role in tumorigenesis and progression. Hence, the goal of the current work was to unveil the function and value of CRs in patients with LGGs. Methods: RNA-Sequencing and corresponding clinical data were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) database. A single-cell RNA-seq dataset was sourced from the Gene Expression Omnibus (GEO) database. Altogether 870 CRs were retrieved from the published articles in top academic journals. The least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression analysis were applied to construct the prognostic risk model. Patients were then assigned into high- and low-risk groups based on the median risk score. The Kaplan-Meier (K-M) survival curve and receiver operating characteristic curve (ROC) were performed to assess the prognostic value. Sequentially, functional enrichment, tumor immune microenvironment, tumor mutation burden, drug prediction, single cell analysis and so on were analyzed to further explore the value of CR-based signature. Finally, the expression of signature genes were validated by immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR). Results: We successfully constructed and validated a 14 CRs-based model for predicting the prognosis of patients with LGGs. Moreover, we also found 14 CRs-based model was an independent prognostic factor. Functional analysis revealed that the differentially expressed genes were mainly enriched in tumor and immune related pathways. Subsequently, our research uncovered that LGGs patients with higher risk scores exhibited a higher TMB and were less likely to be responsive to immunotherapy. Meanwhile, the results of drug analysis offered several potential drug candidates. Furthermore, tSNE plots highlighting the magnitude of expression of the genes of interest in the cells from the scRNA-seq assay. Ultimately, transcription expression of six representative signature genes at the mRNA level was consistent with their protein expression changes. Conclusion: Our findings provided a reliable biomarker for predicting the prognosis, which is expected to offer new insight into LGGs management and would hopefully become a promising target for future research.
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Metacells untangle large and complex single-cell transcriptome networks.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells represent an important hurdle for downstream analyses, interpretation and visualization. RESULTS: We develop a framework called SuperCell to merge highly similar cells into metacells and perform standard scRNA-seq data analyses at the metacell level. Our systematic benchmarking demonstrates that metacells not only preserve but often improve the results of downstream analyses including visualization, clustering, differential expression, cell type annotation, gene correlation, imputation, RNA velocity and data integration. By capitalizing on the redundancy inherent to scRNA-seq data, metacells significantly facilitate and accelerate the construction and interpretation of single-cell atlases, as demonstrated by the integration of 1.46 million cells from COVID-19 patients in less than two hours on a standard desktop. CONCLUSIONS: SuperCell is a framework to build and analyze metacells in a way that efficiently preserves the results of scRNA-seq data analyses while significantly accelerating and facilitating them.
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Comprehensive single-cell transcriptomic analysis reveals fibroblast subpopulations and the prognostic association of COMT in prostate cancer progression， COMT ， COMT.
Prostate cancer is a heterogeneous malignancy with a complex tumor microenvironment (TME) composed of various cellular components, including fibroblasts. These fibroblasts, particularly cancer-associated fibroblasts (CAFs), are crucial in shaping the TME and influencing cancer progression. Catechol-O-methyltransferase (COMT), a key enzyme involved in the metabolism of catecholamines and oxidative stress regulation, has recently been implicated in cancer biology. This study aims to explore the molecular landscape of fibroblasts in prostate cancer and evaluate the prognostic significance of COMT expression in this context. We performed an integrated single-cell RNA sequencing (scRNA-seq) analysis on prostate cancer samples from Gene Expression Omnibus (GEO) databases. Fibroblast subpopulations were identified through clustering, and functional gene signatures for each subgroup were characterized. Prognostic analysis was carried out using univariate and multivariate Cox regression to identify genes associated with patient survival, culminating in a risk score model using data from the Cancer Genome Atlas (TCGA). Additionally, immunofluorescence assays were used to validate COMT expression in tumor-derived fibroblasts. Our single-cell sequencing analysis revealed three distinct fibroblast subpopulations, each with unique gene expression profiles linked to extracellular matrix remodeling, immune modulation, and cellular stress responses. COMT was identified as a key gene in tumor-derived fibroblasts, with its expression significantly higher in tumor samples compared to normal tissues. The risk score model, based on COMT and other fibroblast-associated genes (QSOX1, TAX1BP3, CCDC66, MTCH1, ARL2BP), successfully stratified patients into high-risk and low-risk groups, with higher risk scores correlating with poorer survival outcomes. Immunostaining confirmed the overexpression of COMT in tumor-derived fibroblasts, consistent with bioinformatics analysis. This study underscores the significant role of fibroblasts, particularly CAFs, in prostate cancer progression. Our findings highlight COMT as a critical regulator of the tumor microenvironment and a promising prognostic marker. The integration of single-cell RNA-seq with clinical data offers new insights into fibroblast heterogeneity and the potential for COMT as a therapeutic target in prostate cancer. Further research is needed to validate these findings and explore the mechanistic role of COMT in prostate cancer progression and therapeutic resistance.