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Integrative analysis of bulk multiomics and single-cell transcriptomics reveals the value of neddylation in Skin Cutaneous Melanoma.
BACKGROUND: Melanoma is recognized as the most aggressive type of skin cancer, characterized by its high mortality rates and significant therapeutic challenges. OBJECTIVE: The primary aim of this research is to analyze cellular heterogeneity in SKCM through single-cell RNA sequencing (scRNA-seq) and construct a neddylation-related prognostic model. METHODS: The scRNA-seq and bulk data of Skin Cutaneous Melanoma (SKCM) samples were obtained from Gene Expression Omnibus and Cancer Genome Atlas Program databases. The nonnegative matrix factorization clustering algorithm was employed to stratify malignant cells into different neddylation cell subtypes in SKCM. RESULTS: The scRNA-seq analysis classified 31,894 high-quality cells into eight major cell types, revealing significant cellular distribution patterns within the TME. We observed that neddylation activity was significantly elevated in SKCM tissues compared to normal counterparts. Based on the expression profile of prognostic NRGs, the malignant cells were stratified into four neddylation cell subtypes in SKCM. A neddylation-related prognostic signature (NRPS) was established based on marker genes of neddylation-C3 cell subtype. The low-risk group activated immune-related signaling pathways, while the high-risk group activated various cancer-related pathways. Notably, the low-risk group exhibited better overall survival, higher immune cell infiltration, and response rates to immunotherapy compared to the high-risk group. CONCLUSION: This investigation emphasizes the crucial function of NRGs in the prognosis of SKCM, while also underscoring the importance of understanding the TME and its cellular heterogeneity. The distinct cellular composition and functional attributes within the TME highlight the potential for immunotherapy responses that can be stratified by risk.
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Integrated single-cell and bulk transcriptomics identify autophagy-related immune-suppressive subtypes and a prognostic signature in colorectal cancer.
BACKGROUND: Colorectal cancer still causes many cancer deaths, and patient outcomes differ a lot. The tumor microenvironment can shape tumor growth and treatment response. Autophagy is a cell recycling process linked to tumor survival and immune control, but its cell-type pattern in colorectal cancer tissue and its clinical meaning are not clear. We mapped autophagy activity across cell types, defined autophagy-based subtypes, and tested their value for risk stratification. METHODS: Single-cell RNA sequencing data from colorectal cancer tumor tissues were integrated to evaluate autophagy activity across malignant, stromal, and immune cell populations. Subsequently, autophagy-related genes were curated from multiple public databases and analyzed in bulk transcriptomic cohorts obtained from The Cancer Genome Atlas and The Gene Expression Omnibus. Additionally, unsupervised consensus clustering was applied to define autophagy-based molecular subtypes. Multiple machine learning algorithms were evaluated to construct an autophagy-related prognostic model, and feature importance was assessed using explainable modeling approaches. Furthermore, immune microenvironment characteristics, genomic alterations, and stemness features were systematically analyzed. Key genes were further validated in clinical colorectal cancer specimens and through in vitro functional experiments. RESULTS: Autophagy activity varied across the tumor microenvironment. Malignant and stromal cells showed higher autophagy levels than immune cells. High-autophagy tumors showed stronger tumor-stroma interactions, weaker immune communication, and an immune-suppressive microenvironment. Using 47 prognostic autophagy-related genes, we identified four subtypes with different overall survival, genomic alteration patterns, stemness signatures, and immune landscapes. The prognostic model performed well across independent cohorts. GOLGA2 (Golgi autoantigen, golgin subfamily A member 2) was the top risk gene and was upregulated in tumor tissues. Functional assays showed that GOLGA2 promotes HCT116 cell proliferation and migration. CONCLUSION: Autophagy is closely tied to an immune-suppressive tumor microenvironment in colorectal cancer. The autophagy-based subtypes and prognostic model support patient risk stratification. Furthermore, GOLGA2 was a top-ranked gene in the signature and showed tumor-promoting effects in vitro, so it may warrant further study as a potential target.
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scParadise: tunable, highly accurate multi-level cell type annotation, unknown cell type identification, and modality imputation.
In single-cell biology, the main limitation has shifted from data generation to converting sparse, heterogeneous single-cell RNA sequencing (scRNA-seq) datasets into accurate cell types, interpretable multi-omic states, and reproducible conclusions. To address these challenges, we present scParadise, which transforms scRNA-seq data to a new scientific knowledge. scParadise comprises three integrated tools: scAdam, a multi-level cell type annotation tool with unknown cell type identification; scEve, a cross-tissue modality imputation tool; and scNoah, a standardized benchmarking tool. Using scParadise, we corrected annotation mistakes in the Tabula Muris Senis atlas, showing that cells labeled as granulocytes are exclusively neutrophils and that presumably annotated macrophages actually represent a range of different cell types. Moreover, we identify three previously unknown natural killer T (NKT) cell subsets by imputing protein expression across tissues, which include CD56dim CD3+, CD56dim CD3+ CD4+, and CD56dim CD3+ CD8+ cell subsets in human visceral adipose tissue, which we verify by flow cytometry. These new subsets engage in obesity-related tumor necrosis factor-centric crosstalk with myeloid and adipose progenitors, thereby illuminating a new paradigm for immune-stromal interactions that contribute to chronic inflammation and impaired adipogenesis.
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Metamorphosis of the purple sea urchin Heliocidaris crassispina: Single-cell transcriptomic datasets.
Metamorphosis is an obligate developmental transition for most marine invertebrates with a biphasic life cycle, representing a critical ontogenetic phase. This process involves extensive morphological and physiological changes that incur high mortality, thereby regulating population dynamics. This study establishes a single-cell transcriptomic atlas of larval and juvenile Heliocidaris crassispina, providing a comprehensive analysis at single-cell resolution. eight distinct cell types were identified: digestive, epithelial, germ, immune, muscle, nerve, proliferating, and skeletogenic cells. These findings establish a foundation for future research into invertebrate metamorphosis at the single-cell level.
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NeuRoDev resolves lifelong temporal and cellular variation in human cortical gene expression.
Understanding how the human brain develops and functions requires direct analysis of human cells. Single-cell atlases open unprecedented opportunities to survey cell physiological molecular states as the brain develops. However, technical challenges limit their potential. We present NeuRoDev, a computational resource with highly curated transcriptomic data and novel analytical tools to investigate neuronal and glial development in the human cortex. NeuRoDev compresses ∼1M single-cell transcriptomes into integrative summary networks of reproducible cell clusters that capture temporal and cellular variation across all stages of human brain development. It provides a reference framework to directly interrogate cellular maturation dynamics, contextualize gene function, and interpret experimental organoid models. We use NeuRoDev to investigate developmental variation in cell physiology, reconstruct genesis and maturation dynamics in neuronal and glial cells, and interpret time-series data from human organoids. NeuRoDev is provided as a freely available software package and web applications for interactive data analysis.
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Unveiling the formation of the primordial follicle pool in goats: insights from single-cell omics analysis.
In brief: In brief The establishment of the primordial follicle pool governs female reproductive lifespan but is poorly understood in ruminants. This study provides the first single-cell atlas of goat ovarian development, revealing key cellular interactions and identifying LUZP2 as a candidate oocyte-associated regulator potentially involved in primordial follicle formation. Abstract: Mammalian primordial follicles, essential for female reproduction, form a finite nonrenewable pool perinatally, with species-specific developmental variations. This study employed single-cell RNA sequencing to map the transcriptional landscape of primordial follicle formation in goats, focusing on fetal (embryonic day 75 [E75], E140) and postnatal (postpartum day 0) stages using over 37,000 ovarian cells. These stages encompassed the midfetal stage (after syncytial rupture and the beginning of primordial follicle formation) to the formation of the primordial follicle pool in goats. Seven ovarian cell types were identified, with oocyte and granulosa cell subtypes analyzed in depth. Key interactions between oocytes, proliferative granulosa cells, and granulosa cells-1 via tight junctions, ECM-receptor signaling, and endocytosis were implicated in primordial follicle assembly. Transcription factor analysis identified LUZP2 as a novel oocyte-enriched transcriptional regulator potentially involved in primordial follicle formation, and its expression pattern was validated by real-time quantitative PCR and immunofluorescence. Overall, this study provides the first single-cell transcriptomic atlas of goat ovarian development, delineating dynamic gene expression patterns and cellular interactions during primordial follicle pool establishment. These findings advance understanding of conserved and species-specific mechanisms governing female reproductive lifespan in mammals.
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scRNA-seq reveals different cell clusters in the testes of Mongolian cattle and EGR1/FOS/JUN regulation in Sertoli cells.
Mongolian cattle maintain stable reproductive performance under long-term cold and nutrient-limited conditions, but the molecular basis of their testicular function remains unclear. In this research, a single-cell RNA-sequencing (scRNA-seq) atlas of Mongolian cattle testes was generated and analyzed. The results showed that scRNA-seq identified major germ and somatic cell types, in particular Sertoli cells, which was composed of four subclusters representation different developmental stages. Transcriptomic analysis and pseudotime reconstruction indicated that Early Growth Response 1 (EGR1) might be a potential regulator of Sertoli cell maturation. To investigate the function of EGR1 in Sertoli cells, the methods of siRNA knockdown, overexpression, immunohistochemistry, and ChIP–qPCR were used and the results showed that EGR1 could directly bind to and activate FOS and JUN, joining an EGR1–FOS/JUN-Nectin-2 regulatory pathway in Sertoli cells to maintain the reproductive capacity. The results provided insights into the cellular and molecular mechanisms underlying the reproductive performance of Mongolian cattle.
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Spatial atlas of gastric cancer reveals macrophage-driven stromal activation via ICAM1⁺ smooth muscle cells.
BACKGROUND: Stromal remodeling constitutes a hallmark of gastric cancer (GC) progression, yet the intercellular signaling networks orchestrating this process remain incompletely understood. METHODS: Single-cell RNA sequencing (scRNA-seq) data from 172,763 cells (GSE167297: n = 30,365; OMIX001073: n = 142,398) were integrated with spatial transcriptomic profiles from 10 GC samples (GSE251950). The analyses encompassed single-cell integration, spatial deconvolution, spatial architecture reconstruction, identification of tertiary lymphoid structures (TLSs), and inference of intercellular communication networks. Clinical relevance was evaluated through survival analyses across multiple independent public cohorts. Key computational findings were validated using multiplex immunofluorescence staining, quantitative real-time PCR, and transwell co-culture migration assays. RESULTS: A single-cell–resolved spatial atlas of the GC tumor microenvironment (TME) was constructed. Spatial–cellular integration revealed aberrant immune cell redistribution, with enrichment of CD4⁺CXCL13⁺TOX2⁺ and CD8_Trm-ZNF683⁺ T cells in paratumor regions, indicating potential roles in immune surveillance and tumor restraint. Spatial architecture delineated six distinct microenvironmental niches: C0 (immune-active, TLS-enriched), C1 (dendritic cell-dominant, immunoregulatory), C2 (immune–stromal interface), C3/C4 (epithelial-rich tumor cores), and C5 (stromal-supportive). TLSs were predominantly localized within the immune-active C0 niche, representing mature immune hubs. Within the stromal C5 niche, a coordinated stromal activation mechanism was identified, mediated by a macrophage–smooth muscle cell (SMC)–fibroblast axis driven by synergistic macrophage-derived ITGB2–ICAM1 and OSM–OSMR signaling. Immunofluorescence co-localization and qPCR assays confirmed elevated expression of OSM, OSMR, ITGB2, and ICAM1 in tumor tissues. Functionally, Functionally, transwell co-culture assays demonstrated that macrophage-derived OSM and ITGB2 were associated with enhanced SMCs proliferation and migratory capacity, while silencing either molecule significantly attenuated this effect. This dual signaling axis promoted ICAM1⁺ SMC proliferation and activation through PI3K/Akt pathway engagement and extracellular matrix (ECM) remodeling, resulting in extensive stromal reorganization and a pro-tumorigenic microenvironment. Across multiple cohorts, concurrent activation of ITGB2 and OSM pathways was associated with significantly poorer overall survival compared with activation of either pathway alone, underscoring the prognostic and therapeutic relevance of this interaction. CONCLUSIONS: This study reconstructs the GC spatial architecture at single-cell resolution, and reveals a previously unrecognized macrophage-driven stromal activation program mediated by convergent ITGB2–ICAM1 and OSM–OSMR signaling. ICAM1⁺ SMCs emerge as key effectors and potential targets for stromal-directed therapy.
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CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity.
Defining cell classes is central to the analysis of growing single-cell RNA sequencing (scRNA-seq) atlases. Marker genes are most often identified by differential expression (DE) methods that assess genes one at a time, ignoring the redundancy and complementarity revealed when genes are considered jointly. Working with binarized expression data, we instead seek discriminating panels of genes that together are specific to a cell type, framing marker-panel selection as a variant of the minimal set-covering problem in combinatorial optimization. This formulation efficiently searches the vast space of candidate panels, exploits the large cell numbers typical of scRNA-seq, and is robust to zero-inflation. Using blood and brain data, we show that our method, CellCover, reduces gene redundancy and captures cell-class-specific signals distinct from those found by DE. Transfer-learning experiments across mouse, primate, and human data demonstrate that CellCover identifies conserved cell classes in neocortical neurogenesis and tracks developmental progression in progenitors and neurons. Examining outer radial glia markers across mammals, we find that transcriptomic elements of this key cell type likely arose in rodent gliogenic precursors before the full program emerged in the primate lineage.
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scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation.
INTRODUCTION: Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. OBJECTIVES: We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. METHODS: Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. RESULTS: scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. CONCLUSION: scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.