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Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.
In brief: This review synthesizes the technical landscape of current spatial transcriptomic platforms, addresses computational challenges unique to ovarian tissue, and surveys biological discoveries across ovarian development, aging, follicle dynamics, and cancer, providing a practical framework to guide platform selection and analytical strategy in reproductive biology. Abstract: The ovary is a structurally complex organ whose function depends on precisely coordinated interactions among multiple cell types. Spatially resolved transcriptomics (ST) has emerged as a powerful complement to single-cell RNA sequencing (scRNA-seq), enabling gene expression profiling within intact tissue and preserving the spatial context that dissociation-based methods inherently lack. This review provides a comprehensive overview of the major ST platforms, including sequencing-based technologies (Visium, Visium HD, Stereo-seq, and GeoMx) and imaging-based technologies (Xenium, MERSCOPE, and CosMx), with a focus on their distinct technical features, resolution trade-offs, and suitability for ovarian research. We survey 40 published studies applying ST to ovarian biology, spanning atlases, ovarian aging, follicle development and ovulation, and ovarian cancer. We also discuss typical computational analyses as well as their challenges specific to ovary, including cell segmentation of morphologically diverse cell populations, deconvolution of mixed-cell capture spots in sequencing-based platforms, quality control, batch correction, and spatially aware downstream analyses encompassing trajectory inference, cell-cell interaction modeling, gene regulatory network reconstruction, and more. Across these biological contexts, multimodal integration, pairing ST with scRNA-seq, spatial proteomics, or chromatin accessibility profiling, has proven increasingly valuable for resolving the full molecular complexity of ovarian biology. Nevertheless, some challenges persist, and no single platform is universally optimal for all research questions. Thoughtful alignment between biological objectives, tissue scale, and platform capability will be critical for advancing ST from descriptive mapping toward mechanistic and clinically translatable discovery.
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Focusing on scRNA-seq-Derived T Cell-Associated Genes to Identify Prognostic Signature and Immune Microenvironment Status in Low-Grade Glioma.
BACKGROUND: The clinical outcomes of low-grade glioma (LGG) are associated with T cell infiltration, but the specific contribution of heterogeneous T cell types remains unclear. METHOD: To study the different functions of T cells in LGG, we mapped the single-cell RNA sequencing results of 10 LGG samples to obtain T cell marker genes. In addition, bulk RNA data of 975 LGG samples were collected for model construction. Algorithms such as TIMER, CIBERSORT, QUANTISEQ, MCPCOUTER, XCELL, and EPIC were used to depict the tumor microenvironment landscape. Subsequently, three immunotherapy cohorts, PRJEB23709, GSE78820, and IMvigor210, were used to explore the efficacy of immunotherapy. RESULTS: The Human Primary Cell Atlas was used as a reference dataset to identify each cell cluster; a total of 15 cell clusters were defined and cells in cluster 12 were defined as T cells. According to the distribution of T cell subsets (CD4+ T cell, CD8+ T cell, Naïve T cell, and Treg cell), we selected the differentially expressed genes. Among the CD4+ T cell subsets, we screened 3 T cell-related genes, and the rest were 28, 4, and 13, respectively. Subsequently, according to the T cell marker genes, we screened six genes for constructing the model, namely, RTN1, HERPUD1, MX1, SEC61G, HOPX, and CHI3L1. The ROC curve showed that the predictive ability of the prognostic model for 1, 3, and 5 years was 0.881, 0.817, and 0.749 in the TCGA cohort, respectively. In addition, we found that risk scores were positively correlated with immune infiltration and immune checkpoints. To this end, we obtained three immunotherapy cohorts to verify their predictive ability of immunotherapy effects and found that high-risk patients had better clinical effects of immunotherapy. CONCLUSION: This single-cell RNA sequencing combined with bulk RNA sequencing may elucidate the composition of the tumor microenvironment and pave the way for the treatment of low-grade gliomas.
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Single-Nucleus RNA Sequencing Reveals WRKY12a Regulating Secondary Cell Wall Formation During Xylem Differentiation in Rubber Tree.
Secondary xylem differentiation determines wood structure and function in perennial plants, yet its regulatory mechanisms remain poorly understood in Hevea brasiliensis. Here, we generated a single-nucleus RNA sequencing (snRNA-seq) atlas from the cambium to mature xylem region in stems, resolving transcriptionally distinct cell populations corresponding to cambium, xylem mother cells, and fibre-vessel cells. Pseudotime analysis reconstructed the developmental trajectories from cambium to late xylem cell states and revealed stage-specific activation of secondary cell wall (SCW) biosynthesis programmes. Gene regulatory network analysis identified HbWRKY12a as a fibre-vessel enriched transcription factor functioning as a key regulator in SCW formation. Molecular assays demonstrated that HbWRKY12a directly binds and activates HbMYB1R1c, which subsequently modulates lignin-associated SCW deposition. Heterologous overexpression of either HbWRKY12a or HbMYB1R1c in Arabidopsis resulted in significant reductions in stem diameter and plant height. Both overexpression lines exhibited markedly decreased SCW thickness in fibre, vessels, and pith cells. These findings reveal a WRKY-MYB cascade that mediates SCW formation in H. brasiliensis, extending the classical NAC-MYB regulatory framework and providing molecular targets for improving xylem properties and stress resilience in tropical perennial crops.
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Characterizing malignant prognostic signatures in primary glioma based on single-cell and bulk transcriptome sequencing.
Glioma, characterized by its highly invasive nature, presents significant challenges in prognosis and treatment resistance. The advent of single-cell RNA sequencing (scRNA-seq) has facilitated a more nuanced understanding of the cellular and molecular landscapes of glioma cells. In this study, conducting a comprehensive analysis of scRNA-seq and bulk RNA-seq data, employing Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) methods, we identified three malignant prognostic signatures: IGFBP2, MDK, and RARRES2. The predictive accuracy of this model was validated across both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. Additionally, we explored the correlation of these signatures with drug responsiveness and immune cell infiltration. Differential expression validation and functional analyses of RARRES2 were performed using external Gene Expression Omnibus (GEO) datasets and in-house samples. In both glioma and pan-cancer contexts, RARRES2 expression is significantly positively correlated with the infiltration of M2-like macrophages, NK cells, and CD8+ T cells. Given that RARRES2 receptors are predominantly found in myeloid and glioma cells, we hypothesize that RARRES2 may regulate tumor progression through autocrine pathways and influence macrophage recruitment and differentiation via paracrine pathways. Collectively, our findings provide valuable insights into potential novel prognostic markers for glioma, potentially enhancing the accuracy of prognostic predictions and serving as promising therapeutic targets.
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Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis.
UNLABELLED: As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4+ T/CD8+ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project. SIGNIFICANCE: scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.
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Single-cell transcriptomic landscape of metabolic reprogramming in kidney allograft rejection.
INTRODUCTION: Chronic rejection remains a major barrier to long-term survival after renal transplantation. However, our understanding of the transcriptional programs and metabolic alterations that shape the functions of human kidney cell populations during rejection is still limited. OBJECTIVES: To generate a comprehensive, cell type-specific transcriptomic map of metabolic states in healthy and transplanted human kidney samples. METHODS: We performed single-cell RNA sequencing on 33 kidney samples, constructing an atlas comprising 82,661 cells. RESULTS: Analysis of average metabolic pathway activity across cell subpopulations revealed distinct metabolic profiles. These differences provided insights into both unique abnormalities and shared features underlying various rejection states. We identified downregulation of mitochondrial programs in multiple non-proximal tubular cell populations compared with their counterparts in healthy kidneys. In contrast, both proximal tubular (PT) cells and T cells exhibited relatively elevated oxidative phosphorylation-related transcriptional scores, though the mechanisms driving this shift were specific to cell type and rejection status. Additionally, rejection-associated macrophages demonstrated altered amino acid metabolism, accompanied by enhanced IL-1 signaling. CONCLUSION: This single-cell atlas, together with a metabolically based stratification of chronic kidney transplant rejection, offers novel insights into the heterogeneous nature of rejection and provides valuable guidance for the development of targeted therapies.
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Single-cell transcriptomic resources for tracing neurogenesis and cell fate specification in sea urchin embryos.
We generated a developmental stage-specific single-cell RNA-sequencing atlas of the Western Pacific sea urchin Hemicentrotus pulcherrimus, uncovering new gene modules associated with neurogenesis and identifying Delta/Notch-sensitive regulators of neuronal differentiation. Pharmacological perturbation of this pathway revealed neurogenic genes that are inconspicuous under normal conditions and clarified the roles of regional specifiers in maintaining progenitor states. To promote broader accessibility, we developed an interactive web platform within HpBase with Kana, enabling gene expression exploration even for researchers without computational expertise. Furthermore, we performed a cell-type deconvolution method that links bulk RNA sequencing to the single-cell reference, allowing rapid visualization of cell-type composition changes from bulk data alone. These integrated resources and analytical tools not only provide mechanistic insights into echinoderm neurodevelopment but also establish a generalizable workflow for combining bulk and single-cell transcriptomics in non-model organisms, empowering developmental and evolutionary biologists with practical strategies for cell-type-level resolution in complex systems.
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Mapping Organism-wide Single Cell mRNA Expression Linked to Extracellular Vesicle Biogenesis, Secretion, and Cargo.
Extracellular vesicles (EVs) are functional lipid-bound nanoparticles trafficked between cells and found in every biofluid. It is widely claimed that EVs can be secreted by every cell, but the quantity and composition of these EVs can differ greatly among cell types and tissues. Defining this heterogeneity has broad implications for EV-based communication in health and disease. Recent discoveries have linked single-cell EV secretion to the expression of genes encoding EV machinery and cargo. To gain insight at single-cell resolution across an entire organism, we compared the abundance, variance, and co-expression of 67 genes involved in EV biogenesis and secretion, or carried as cargo, across >44 000 cells obtained from 117 cell populations in the Tabula Muris. Our analysis provides both novel holistic and cell population-specific insight into EV biology. The highest overall expression of EV genes occurs in secretory cells of the pancreas and perhaps more surprisingly, multiple non-neuronal cell populations of the brain. We find that the most abundant EV genes encode the most abundant EV cargo proteins (tetraspanins and syndecans), but these genes are highly differentially expressed across functionally distinct cell populations. Expression variance identifies dynamic and constitutively expressed EV genes while co-expression analysis reveals novel insights into cell population-specific coordination of expression. Results of our analysis illustrate the diverse transcriptional regulation of EV genes which could be useful for predicting how individual cell populations might communicate via EVs to influence health and disease.
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Integrative analysis of bulk and single-cell transcriptomics identifies factors related to immunosuppressive microenvironment to predict unfavorable prognosis in inflammatory breast cancer.
INTRODUCTION: Inflammatory breast cancer (IBC) is a rare invasive tumor and characterized by the formation of tumor emboli within dermal lymphatic vessels. The tumor microenvironment (TME) is a key factor in IBC aggressiveness, but its heterogeneity and intercellular role remain incompletely understood. METHODS: Weighted gene coexpression network analysis (WGCNA) was performed to identify immune infiltration-associated genes. We also depicted cellular communication networks and specific ligand-receptor signaling pathways using the single-cell transcriptomics analysis of IBC and non-IBC samples. Finally, we verified the expression of key proteins by immunohistochemistry. RESULTS: Through the intersection of WGCNA module genes and differentially expressed genes between pathological complete response and residual disease samples, GZMB was identified as hub gene which is related to immune infiltration and efficacy of neoadjuvant therapy in IBC. In IBC cohort, patients with high expression of GZMB harbored more immunosuppressive cells thus showed unfavorable prognosis compared with GZMB-low expression group (p<0.05). Subsequent to dimension reduction and clustering, 12 clusters were identified to construct the single-cell atlas between IBC and non-IBC samples. Cellular communication analysis unveiled the heterogeneity of cell communication in IBC. The proportion of immune cells was significantly lower than that of malignant epithelial cells in the cellular composition of IBC. Moreover, it indicated that SPP1 and plasmacytoid dendritic cells were specific in IBC and associated with an immunosuppressive microenvironment in IBC. Immunohistochemical analysis suggested that protein levels of GZMB and SPP1 tended to be higher in the samples from patients with residual disease compared to the patient achieving pathological complete response, though this observation is based on an extremely small sample size. DISCUSSION: This study identifies GZMB and SPP1 as potential immunosuppressive-related prognostic biomarkers in IBC patients, reveals the key role of plasmacytoid dendritic cells in remodeling of immunosuppressive microenvironment in IBC.
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The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells.
Improving the selectivity and effectiveness of drugs represents a crucial issue for future therapeutic developments in immuno-oncology. Traditional bulk transcriptomics faces limitations in this context for the early phase of target discovery as resulting gene expression levels represent the average measure from multiple cell populations. Alternatively, single cell RNA sequencing can dive into unique cell populations transcriptome, facilitating the identification of specific targets. Here, we generated Tumor-Infiltrating regulatory T cells (TI-Tregs) and exhausted T cells (Tex) gene signatures from a single cell RNA-seq pan-cancer T cell atlas. To overcome noise and sparsity inherent to single cell transcriptomics, we then propagated the gene signatures by diffusion in a protein-protein interaction network using the Patrimony high-throughput computing platform. This methodology enabled the refining of signatures by rescoring genes based on their biological connectivity and shed light not only on processes characteristics of TI-Treg and Tex development and functions but also on their immunometabolic specificities. The combined use of single cell transcriptomics and network propagation may thus represent an innovative and effective methodology for the characterization of cell populations of interest and eventually the development of new therapeutic strategies in immuno-oncology.