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Resolving floral development dynamics using genome and single-cell temporal transcriptome of Dendrobium devonianum.
Dendrobium devonianum, a species of the Orchidaceae family, is notable for its unique floral characteristics, which include two yellow spots and purple tips on its labellum, as well as fringed edges. However, the molecular mechanisms underlying flower pattern formation in D. devonianum remain poorly understood, hindering advancements in its breeding process. Here, a chromosome-scale genome of D. devonianum was presented for the first time, revealing two significant polyploidization events. Additionally, a high-resolution single-cell transcriptomic atlas was constructed, capturing 11 distinct cell clusters. Expression patterns of MADS-box genes were identified through temporal and spatial bulk RNA-Seq, revealing alignment with the ABCDE model of flower formation. Meanwhile, mass spectrometry imaging and scRNA analyses showed that the yellow spots were primarily associated with carotenoid biosynthesis gene expression, while the purple colour is predominantly linked to anthocyanin biosynthesis gene expression. These genes were mainly expressed in the epidermis and vascular cells. Developmental trajectory analyses of epidermal cells further uncovered a gene regulatory network and several transcription factors likely responsible for fringes formation along the labellum margin. This study provides valuable insights into the molecular mechanisms driving floral colour differentiation and structural traits in D. devonianum, contributing to a deeper understanding of orchid evolution, diversification and breeding.
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Single-cell Atlas reveals core function of CPVL/MSR1 expressing macrophages in the prognosis of triple-negative breast cancer.
BACKGROUND: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with the worst prognosis among all subtypes. The impact of distinct cell subpopulations within the tumor microenvironment (TME) on TNBC patient prognosis has yet to be clarified. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) integrated with bulk RNA sequencing (bulk RNA-seq), we applied Cox regression models to compute hazard ratios, and cross-validated prognostic scoring using a GLMNET-based Cox model. Cell communication analysis was used to elucidate the potential mechanisms of CPVL and MSR1. Ultimately, RNA interference-mediated gene knockdown was utilized to validate the impact of specific genes on the polarization of tumor-associated macrophages (TAMs). RESULTS: Our findings revealed that the function of immune cells is more pivotal in prognosis, with TAMs showing the strongest correlation with TNBC patient outcomes, compared with other immune cells. Additionally, we identified CPVL and MSR1 as critical prognostic genes within TAMs, with CPVL expression positively correlated with favorable outcomes and MSR1 expression associated with poorer prognosis. Mechanistically, CPVL may contribute to favorable prognosis by inhibiting the SPP1-CD44 ligand-receptor and promoting CXCL9-CXCR3, C3-C3AR1 ligand-receptor, through which TAMs interact with other cells such as monocytes, neutrophils, and T cells. Moreover, cytokines including IL-18, IFNγR1, CCL20, and CCL2, along with complement-related gene like TREM2 and complement component CFD, may participate in the process of CPVL or MSR1 regulating macrophage polarization. Furthermore, RT-PCR experiments confirmed that CPVL is positively associated with M1-like TAM polarization, while MSR1 is linked to M2-like TAM polarization. Finally, the prognostic significance of these two genes is also validated in HER2-positive breast cancer subtypes. CONCLUSIONS: CPVL and MSR1 are potential biomarkers for macrophage-mediated TNBC prognosis, suggesting the therapeutic potential of macrophage targeting in TNBC.
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Establishment of a Seven-Gene Signature Associated with CD8+ T Cells through the Utilization of Both Single-Cell and Bulk RNA-Sequencing Techniques in Clear Cell Renal Cell Carcinoma.
Tumor immune microenvironment constituents, such as CD8+ T cells, have emerged as crucial focal points for cancer immunotherapy. Given the absence of reliable biomarkers for clear cell renal cell carcinoma (ccRCC), we aimed to ascertain a molecular signature that could potentially be linked to CD8+ T cells. The differentially expressed genes (DEGs) linked to CD8+ T cells were identified through an analysis of single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database. Subsequently, immune-associated genes were obtained from the InnateDB and ImmPort datasets and were cross-referenced with CD8+ T-cell-associated DEGs to generate a series of DEGs linked to immune response and CD8+ T cells. Patients with ccRCC from the Cancer Genome Atlas (TCGA) were randomly allocated into testing and training groups. A gene signature was established by conducting LASSO-Cox analysis and subsequently confirmed using both the testing and complete groups. The efficacy of this signature in evaluating immunotherapy response was assessed on the IMvigor210 cohort. Finally, we employed various techniques, including CIBERSORT, ESTIMATE, ssGSEA, and qRT-PCR, to examine the immunological characteristics, drug responses, and expression of the signature genes in ccRCC. Our findings revealed 206 DEGs linked to immune response and CD8+ T cells, among which 65 genes were correlated with overall survival (OS) in ccRCC. A risk assessment was created utilizing a set of seven genes: RARRES2, SOCS3, TNFSF14, XCL1, GRN, CLDN4, and RBP7. The group with a lower risk showed increased expression of CD274 (PD-L1), suggesting a more favorable response to anti-PD-L1 treatment. The seven-gene signature demonstrated accurate prognostic prediction for ccRCC and holds potential as a clinical reference for treatment decisions.
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Integrated multi-omics analysis identifies DARS2, MRTO4, and MRPL37 as novel biomarkers and potential therapeutic targets for bladder cancer.
BACKGROUND: Bladder cancer (BC) is the predominant malignant tumor in the urinary system globally, with its intricate molecular features greatly influencing patient outcomes and treatment response.Identifying novel biomarkers and therapeutic targets is essential for improving patient management. METHODS: This study utilized advanced bioinformatics techniques to analyze data from multiple public databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), IEU OpenGWAS project, eQTLGen, FinnGen, and DepMap. The analytical framework integrates the limma package for the identification of differentially expressed genes (DEGs), employs Weighted Gene Co-expression Network Analysis (WGCNA) to ascertain genes associated with BC, utilizes Mfuzz-based clustering to categorize gene expression profiles, implements univariate Cox regression modeling, conducts Kaplan-Meier (KM) survival analysis for the selection of prognostic genes, and applies Mendelian Randomization (MR) analysis to ascertain core genes. The expression patterns of core genes were verified through single - cell RNA sequencing (scRNA - seq) analysis. Receiver operating characteristic (ROC) analysis was carried out to evaluate the diagnostic and prognostic capacities of the core genes. Consensus clustering analysis further confirmed the risk stratification potential of these genes. Gene Set Enrichment Analysis (GSEA) was applied to explore the potential mechanisms of these genes in BC, the associations between these genes and the immune microenvironment, and their predictive effectiveness for immunotherapy response. Moreover, a web - based nomogram tool was constructed to predict patient prognosis. Ultimately, MR mediation analysis was utilized to further probe into the mediating mechanism of immune cells in the progression of core genes and BC. RESULTS: Comprehensive analysis identified DARS2, MRTO4, and MRPL37 as core genes in BC. The expression of these genes was notably elevated in neoplastic tissues compared to that in normal bladder epithelia, with a progressive escalation in expression levels concomitant with the advancement of tumor stage. Analyses employing Receiver Operating Characteristic (ROC) curves and consensus clustering corroborated their robust potential for diagnostic and prognostic purposes. These genes potentially facilitate BC progression via the oxidative phosphorylation (OXPHOS) and Myc target pathways, and are significantly associated with the infiltration of immune cells such as activated CD4 T cells, effector memory CD4 T cells, and immature dendritic cells (iDCs). Furthermore, the developed online nomogram tool exhibited high accuracy. Finally, the results of the mediation analysis confirmed that MRPL37 promotes the occurrence and development of BC via HLA-DR⁺ CD4⁺ T cells (βmed = 0.083, βmedp = 36.3%, p = 0.046). CONCLUSION: This research underscores the substantial functions of DARS2, MRTO4, and MRPL37 in the development of BC and their potential as therapeutic targets, offering a foundation for future investigations and the development of targeted therapies.
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High-resolution anatomical and spatial transcriptome analyses reveal two types of meristematic cell pools within the secondary vascular tissue of poplar stem.
The secondary vascular tissue emanating from meristems is central to understanding how vascular plants such as forest trees evolve, grow, and regulate secondary radial growth. However, the overall molecular characterization of meristem origins and developmental trajectories from primary to secondary vascular tissues in woody tree stems is technically challenging. In this study, we combined high-resolution anatomic analysis with a spatial transcriptome (ST) technique to define features of meristematic cells in a developmental gradient from primary to secondary vascular tissues in poplar stems. The tissue-specific gene expression of meristems and derived vascular tissue types were accordingly mapped to specific anatomical domains. Pseudotime analyses were used to track the origins and changes of meristems throughout the development from primary to secondary vascular tissues. Surprisingly, two types of meristematic-like cell pools within secondary vascular tissues were inferred based on high-resolution microscopy combined with ST, and the results were confirmed by in situ hybridization of, transgenic trees, and single-cell sequencing. The rectangle shape procambium-like (PCL) cells develop from procambium meristematic cells and are located within the phloem domain to produce phloem cells, whereas fusiform shape cambium zone (CZ) meristematic cells develop from fusiform metacambium meristematic cells and are located inside the CZ to produce xylem cells. The gene expression atlas and transcriptional networks spanning the primary transition to secondary vascular tissues generated in this work provide new resources for studying the regulation of meristem activities and the evolution of vascular plants. A web server (https://pgx.zju.edu.cn/stRNAPal/) was also established to facilitate the use of ST RNA-seq data.
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Single-Cell Transcriptomics of Mtb/HIV Co-Infection.
Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) co-infection continues to pose a significant healthcare burden. HIV co-infection during TB predisposes the host to the reactivation of latent TB infection (LTBI), worsening disease conditions and mortality. There is a lack of biomarkers of LTBI reactivation and/or immune-related transcriptional signatures to distinguish active TB from LTBI and predict TB reactivation upon HIV co-infection. Characterizing individual cells using next-generation sequencing-based technologies has facilitated novel biological discoveries about infectious diseases, including TB and HIV pathogenesis. Compared to the more conventional sequencing techniques that provide a bulk assessment, single-cell RNA sequencing (scRNA-seq) can reveal complex and new cell types and identify more high-resolution cellular heterogeneity. This review will summarize the progress made in defining the immune atlas of TB and HIV infections using scRNA-seq, including host-pathogen interactions, heterogeneity in HIV pathogenesis, and the animal models employed to model disease. This review will also address the tools needed to bridge the gap between disease outcomes in single infection vs. co-infection. Finally, it will elaborate on the translational benefits of single-cell sequencing in TB/HIV diagnosis in humans.
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HeartMAP: A multi-chamber spatial framework for cardiac cell-cell communication.
Understanding cell-cell communication within and between the four distinct cardiac chambers is fundamental to elucidating cardiac function and disease mechanisms. Each chamber exhibits unique cellular and molecular characteristics that reflect specialised physiological roles, yet existing frameworks for mapping chamber-specific intercellular networks have remained limited. Here, we present HeartMAP (Heart Multi-chamber Analysis Platform), a computational framework that infers cardiac cell-cell communication networks at chamber resolution through integration of single-cell RNA-seq co-expression patterns and ligand-receptor interaction databases. Using a dataset of 287,269 cells from seven healthy human heart donors (Single Cell Portal SCP498), we identified chamber-specific cell populations, communication networks, and therapeutic targets. HeartMAP employs a progressive three-tier analytical approach comprising basic pipeline analysis, advanced communication modelling and multi-chamber atlas construction to reveal both conserved and chamber-specific signalling pathways; cross-chamber correlation analysis demonstrated the highest similarity between ventricles (r = 0.985) and the lowest between left atrium and left ventricle (r = 0.870), reflecting functional specialisation. Communication hub analysis identified atrial cardiomyocytes and adipocytes as key signalling centres with hub scores of 0.037-0.047, while differential expression analysis revealed over 150 significantly different genes per chamber pair. These findings establish a molecular foundation for precision cardiology approaches, enabling chamber-specific therapeutic strategies that could improve treatment outcomes for cardiovascular diseases. HeartMAP is freely available as a Python package than can be installed using "pip install heartmap", the package's documentation can be found on https://pypi.org/project/heartmap/, it can also be accessed via a user-friendly web interface freely available at https://huggingface.co/spaces/Tumo505/heartmap-cell-analysis.
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Single-cell transcriptomics reveals cellular dynamics and chemokine CXCL2-mediated smooth muscle cell proliferation in arterial repair.
BACKGROUND: Percutaneous coronary intervention (PCI) remains the primary treatment for coronary artery disease (CAD), yet post-procedural arterial injury triggers cellular change and pathological inflammation, leading to thrombosis and restenosis. Recent studies have highlighted the chemokine CXCL2 play an important role in the immune response to tissue repair. However, the cellular mechanisms and the role of chemokine CXCL2 underlying arterial repair after PCI remain poorly understood. METHODS: Single-cell RNA sequencing (scRNA-seq) was used to characterize the heterogeneity and gene expression profiles of cells in a femoral artery injury (FAI) model. Animal models of FAI and cellular experiments were used to validate the effects of CXCL2 on smooth muscle cell proliferation. RESULTS: (1) Mesenchymal stem cells (MSCs), smooth muscle cells (SMCs), and macrophages play pivotal roles in arterial repair. And distinct subpopulations within these cell types were identified, each exhibiting unique functional characteristics and temporal dynamics during repair. (2) Notably, we identified an inflammatory SMC subpopulation (SMC2) that actively secretes chemokine CXCL2, which promotes SMC proliferation and mediates arterial remodeling after injury, suggesting its potential as a therapeutic target. CONCLUSIONS: This study provides a comprehensive cell atlas of the injured artery, offering valuable insights into the complex cellular interactions, signaling pathways and immune responses involved in orchestrating vascular repair. Our findings show chemokine CXCL2 as a key mediator of SMC proliferation and provide a roadmap for developing CXCL2-targeted therapies to improve vascular outcomes in PCI patients.
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scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.
Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.
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CC/CXC chemokine risk signature at single-cell resolution: a machine learning model for precision stratification in cervical cancer.
BACKGROUND: Advanced cervical cancer has a poor prognosis due to chemoresistance and immunosuppression, while the prognostic value of chemokines-related genes (CRGs) remains underexplored. This study aimed to develop a prognostic signature based on CRGs and explore its clinical utility in cervical cancer risk stratification, microenvironment characterization, and therapeutic response prediction. METHODS: We integrated bulk transcriptomic data from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) cohort and Gene Expression Omnibus (GEO) datasets, immune infiltration analysis, drug sensitivity prediction, and single-cell RNA sequencing (scRNA-seq) analysis. Machine learning algorithms were employed to identify prognostic CRGs and construct a risk signature. Validation was performed using an independent GEO cohort. Immune cell infiltration was quantified using CIBERSORT. Enrichment analyses [Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark] were conducted on differentially expressed genes (DEGs) between risk groups. scRNA-seq data were processed using Seurat for cell type annotation and CRG expression profiling, while CellChat was used to analyze chemokine-mediated cell-cell communication. RESULTS: Univariate Cox analysis identified 15 CRGs associated with cervical cancer prognosis. A robust 14-gene CRG-derived risk signature was constructed. The signature demonstrated high prognostic accuracy for overall survival (OS) in the TCGA-CESC cohort [1-year area under the curve (AUC): 0.966; 3-year AUC: 0.980; 5-year AUC: 0.976] and was validated in the GEO cohorts. High-risk patients exhibited worse OS, disease-specific survival (DSS), and progression-free survival (PFI). Risk scores correlated significantly with advanced T stage (P<0.05), International Federation of Gynecology and Obstetrics (FIGO) stage IV (P<0.05), and older age (≤55 years, P<0.05). High-risk patients displayed an immunosuppressive microenvironment characterized by reduced CD8+ T cells and M1 macrophages, along with increased chemoresistance. Enrichment analysis linked high-risk profiles to cytokine signaling, epithelial-mesenchymal transition, and glycolysis. scRNA-seq analysis revealed distinct CRG expression patterns localized to specific cellular niches: CCL22 in granulocyte-monocyte progenitors (GMPs), CCL5 in natural killer (NK)/T cells, CXCL9 in dendritic cells (DCs)/macrophages, and CXCL2/CXCL3 in endothelial cells/macrophages. Cell-cell communication identified active CXCL/CCL-mediated communication networks, particularly between epithelial cells and stromal components (smooth muscle cells, fibroblasts), highlighting their role in tumor-stroma crosstalk. A nomogram incorporating the risk score showed high predictive accuracy for 1-, 3-, and 5-year OS. CONCLUSIONS: This study constructed the first CRGs-derived risk signature and revealed its role in tumor-immune-stromal crosstalk at single-cell resolution. The signature reflects tumor-immune interactions and therapeutic vulnerabilities, providing a basis for clinical risk stratification and personalized immunotherapy strategies.