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Integrated Multi-Omics Analysis Identifies SRI as a Critical Target Promoting Gastric Cancer Progression and Associated with Poor Prognosis.
Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0-C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion.
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A Comprehensive Immune Cell Atlas of Cystic Kidney Disease Reveals the Involvement of Adaptive Immune Cells in Injury-Mediated Cyst Progression in Mice.
BACKGROUND: Inducible disruption of cilia-related genes in adult mice results in slowly progressive cystic disease, which can be greatly accelerated by renal injury. METHODS: To identify in an unbiased manner modifier cells that may be influencing the differential rate of cyst growth in injured versus non-injured cilia mutant kidneys at a time of similar cyst severity, we generated a single-cell atlas of cystic kidney disease. We conducted RNA-seq on 79,355 cells from control mice and adult-induced conditional Ift88 mice (hereafter referred to as cilia mutant mice) that were harvested approximately 7 months post-induction or 8 weeks post 30-minute unilateral ischemia reperfusion injury. RESULTS: Analyses of single-cell RNA-seq data of CD45+ immune cells revealed that adaptive immune cells differed more in cluster composition, cell proportion, and gene expression than cells of myeloid origin when comparing cystic models with one another and with non-cystic controls. Surprisingly, genetic deletion of adaptive immune cells significantly reduced injury-accelerated cystic disease but had no effect on cyst growth in non-injured cilia mutant mice, independent of the rate of cyst growth or underlying genetic mutation. Using NicheNet, we identified a list of candidate cell types and ligands that were enriched in injured cilia mutant mice compared with aged cilia mutant mice and non-cystic controls that may be responsible for the observed dependence on adaptive immune cells during injury-accelerated cystic disease. CONCLUSIONS: Collectively, these data highlight the diversity of immune cell involvement in cystic kidney disease.
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A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation.
The human brain undergoes protracted postnatal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell-type-specific gene expression dynamics. Here, using single-nucleus RNA sequencing on temporal lobe tissue, including samples of African ancestry, we build a joint pediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between pediatric and adult cell subtypes, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in pediatric tissue. The resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.
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Unifying multimodal single-cell data with a mixture-of-experts β -variational autoencoder framework.
Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. We present UniVI (Unifed Variational Inference), a scalable mixture-of-experts β -variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or pre-annotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a non-hematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to tri-modal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell-type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, tri-modal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.
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Understanding the immune microenvironment of ovarian cancer.
Ovarian cancer remains the most lethal gynecological malignancy, yet immune checkpoint inhibitors (ICIs) demonstrate limited clinical efficacy due to profound CD8+- T cell exhaustion within a highly heterogeneous tumor immune microenvironment (TME). Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of this complex ecosystem by resolving rare immune subsets, reconstructing lineage trajectories, and mapping dynamic intercellular signaling networks. Emerging single-cell atlases reveal that terminally exhausted T cells evolve from plastic progenitor populations, notably GZMK+- subsets, while ascitic fluid functions as a distinct immunomodulatory reservoir that continuously shapes intratumoral immune dynamics. Furthermore, multi-compartment transcriptomic profiling uncovers myeloid-lymphoid crosstalk that actively sustains localized immunosuppression and drives therapeutic resistance. These high-resolution molecular insights are increasingly leveraged to accurately predict ICI responsiveness, rationally design novel combinatorial regimens, and strategically guide next-generation adoptive cellular therapies. This review summarizes the scRNA-seq-driven advances in characterizing the ovarian cancer TME, emphasizing how single-cell resolution dismantles immunosuppressive barriers to T cell reinvigoration and establishes a robust translational framework for precision immunotherapy.
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Integrated single cell transcriptomics analysis for unraveling heterogeneity and plasticity of root cells for sustainable and regenerative agriculture.
The plant root system architecture (RSA) functions in anchorage, acquisition of water and mineral nutrients, and exhibits pronounced phenotypic plasticity in response to the spatiotemporal heterogeneity of the soil environment. Resolving the regulatory networks that underpin root development is therefore a prerequisite for improving stress tolerance and yield. Single-cell RNA sequencing (scRNA-seq) resolves transcriptional landscapes at cellular resolution, discriminating root zonation, lineage trajectories and cell-type-restricted responses to environmental signals. Coupling scRNA-seq to epigenomic, proteomic and metabolomic profiling of the same cell populations links chromatin state to transcript, protein and metabolite output and therefore exposes the regulatory layers that govern root development and plasticity. Machine-learning models trained on single-cell matrices assist cell-type annotation, gene regulatory network inference and prioritization of candidate loci for precision breeding, although their output remains contingent on reference datasets that are still sparse for crop species. This review examines what scRNA-seq, spatial transcriptomics and machine learning have so far established about root cellular heterogeneity and regulatory architecture. This delimits the technical constraints that presently bound their application to crop improvement including protoplasting bias, transcript dropout and incomplete state of crop reference atlases to support sustainable and regenerative agriculture.
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RBMS3 promotes ferroptosis in colorectal cancer and is suppressed by M2 tumor-associated macrophages via STAT3 activation.
BACKGROUND: Colon adenocarcinoma (COAD) shows marked clinical heterogeneity, and robust biomarkers with mechanistic relevance are still needed for prognostic stratification. RBMS3 has been implicated in tumor biology, but its pan-cancer significance, prognostic value in COAD, and regulatory mechanisms remain insufficiently defined. METHODS: We performed integrated analyses of The Cancer Genome Atlas pan-cancer and COAD cohorts, followed by external validation in GEO datasets. Prognostic associations were evaluated by Cox and Kaplan-Meier analyses, and clinicopathological relevance was assessed in TCGA-COAD. A prognostic nomogram combining RBMS3 and clinical variables was constructed. Single-cell RNA-seq analysis was used to characterize RBMS3-associated functional programs. In vitro experiments in HCT116 cell and THP-1-derived M2 macrophage co-culture systems were conducted to validate ferroptosis-related effects and upstream STAT3-dependent regulation. RESULTS: RBMS3 was significantly dysregulated across multiple tumor types and showed prognostic relevance in pan-cancer analysis, with a prominent adverse survival association in COAD. GEO cohorts confirmed decreased RBMS3 expression in tumor samples relative to normal controls, and low RBMS3 expression was associated with unfavorable prognosis, supporting its tumor-suppressive prognostic role. In TCGA-COAD, RBMS3 expression correlated with T stage and pathological stage, and integration of RBMS3 with staging factors improved prognostic stratification in a nomogram model. Single-cell analysis showed enrichment of ferroptosis-related pathways in RBMS3-positive tumor cells. Functionally, RBMS3 overexpression in HCT116 cells reduced viability, decreased GPX4, increased ACSL4, and elevated intracellular Fe ion levels, indicating molecular features consistent with enhanced ferroptotic sensitivity. Mechanistically, M2 macrophages suppressed RBMS3 expression in tumor cells with concomitant STAT3 activation, while pharmacological STAT3 inhibition partially restored RBMS3 expression. CONCLUSION: RBMS3 is a clinically relevant prognostic biomarker in COAD and is linked to ferroptosis-associated tumor-cell programs. The M2 macrophage-STAT3-RBMS3 axis provides a mechanistic connection between the immune microenvironment and tumor intrinsic regulation, highlighting RBMS3 as a potential target for prognostic modeling and translational intervention in COAD.
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CD52 mRNA expression predicts prognosis and response to immune checkpoint blockade in melanoma.
The immune-modulating protein CD52 attenuates lymphocyte function and is associated with autoimmune disorders, for example, multiple sclerosis (MS). CD52 represents a therapeutic target in MS and chronic lymphocytic leukemia (CLL). Its expression has prognostic and predictive value in CLL and is prognostic in breast cancer. Its significance in melanoma is unclear. We analyzed CD52 mRNA expression data from tumor bulk tissues of N = 445 untreated melanoma patients from The Cancer Genome Atlas (TCGA) Research Network and of N = 121 melanoma patients undergoing anti-PD-1 immune checkpoint blockade (ICB) with regard to outcome (overall survival [OS], disease control [DC], and progression-free survival [PFS]), single-cell RNA-Seq data of N = 4645 cells from N = 19 melanoma tissues, and N = 15,457 cells from normal skin provided by N = 5 donors. Higher CD52 mRNA expression was associated with favorable OS (hazard ratio (HR) = 0.820, [95% CI 0.734-0.916], p < .001) in non-ICB-treated melanoma and with PFS (HR = 0.875, [95% CI 0.775-0.989], p = .033) and DC (p = .005) in ICB-treated melanoma. CD52 expression correlated significantly with distinct immune cell subsets and correlated negatively with immune checkpoint expression in T cells. Moreover, our results suggest CD52 expression by a certain type of tissue-resident macrophages. CD52 mRNA was expressed in a small subgroup (8%) of immune checkpoint coexpressing melanoma cells. CD52 expression is associated with features of ICB response in melanoma. Concomitant ICB and anti-CD52 treatment requires critical review.
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An integrated single-cell reference atlas of the human endometrium.
The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal-epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development.
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Using DenseFly algorithm for cell searching on massive scRNA-seq datasets.
BACKGROUND: High throughput single-cell transcriptomic technology produces massive high-dimensional data, enabling high-resolution cell type definition and identification. To uncover the expressional patterns beneath the big data, a transcriptional landscape searching algorithm at a single-cell level is desirable. RESULTS: We explored the feasibility of using DenseFly algorithm for cell searching on scRNA-seq data. DenseFly is a locality sensitive hashing algorithm inspired by the fruit fly olfactory system. The experiments indicate that DenseFly outperforms the baseline methods FlyHash and SimHash in classification tasks, and the performance is robust to dropout events and batch effects. CONCLUSION: We developed a method for mapping cells across scRNA-seq datasets based on the DenseFly algorithm. It can be an efficient tool for cell atlas searching.