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BCL9 regulates CD226 and CD96 checkpoints in CD8+ T cells to improve PD-1 response in cancer.
To date, the overall response rate of PD-1 blockade remains unsatisfactory, partially due to limited understanding of tumor immune microenvironment (TIME). B-cell lymphoma 9 (BCL9), a key transcription co-activator of the Wnt pathway, is highly expressed in cancers. By genetic depletion and pharmacological inhibition of BCL9 in tumors, we found that BCL9 suppression reduced tumor growth, promoted CD8+ T cell tumor infiltration, and enhanced response to anti-PD-1 treatment in mouse colon cancer models. To determine the underlying mechanism of BCL9's role in TIME regulation, single-cell RNA-seq was applied to reveal cellular landscape and transcription differences in the tumor immune microenvironment upon BCL9 inhibition. CD155-CD226 and CD155-CD96 checkpoints play key roles in cancer cell/CD8+ T cell interaction. BCL9 suppression induces phosphorylation of VAV1 in CD8+ T cells and increases GLI1 and PATCH expression to promote CD155 expression in cancer cells. In The Cancer Genome Atlas database analysis, we found that BCL9 expression is positively associated with CD155 and negatively associated with CD226 expression. BCL9 is also linked to adenomatous polyposis coli (APC) mutation involved in patient survival following anti-PD-1 treatment. This study points to cellular diversity within the tumor immune microenvironment affected by BCL9 inhibition and provides new insights into the role of BCL9 in regulating CD226 and CD96 checkpoints.
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Pinpointing Cell Identity in Time and Space.
Mammalian cells display a broad spectrum of phenotypes, morphologies, and functional niches within biological systems. Our understanding of mechanisms at the individual cellular level, and how cells function in concert to form tissues, organs and systems, has been greatly facilitated by centuries of extensive work to classify and characterize cell types. Classic histological approaches are now complemented with advanced single-cell sequencing and spatial transcriptomics for cell identity studies. Emerging data suggests that additional levels of information should be considered, including the subcellular spatial distribution of molecules such as RNA and protein, when classifying cells. In this Perspective piece we describe the importance of integrating cell transcriptional state with tissue and subcellular spatial and temporal information for thorough characterization of cell type and state. We refer to recent studies making use of single cell RNA-seq and/or image-based cell characterization, which highlight a need for such in-depth characterization of cell populations. We also describe the advances required in experimental, imaging and analytical methods to address these questions. This Perspective concludes by framing this argument in the context of projects such as the Human Cell Atlas, and related fields of cancer research and developmental biology.
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scBrainScope: cross-species multidimensional brain atlas.
As one of the most evolutionarily complex and functionally diverse organs, the brain is characterized by its intricate structure, developmental alterations, and cellular heterogeneity. Although extensive brain atlases exist, there remains an urgent need for an integration platform that brings together gene expression profiles and analysis tools to explore features across species, brain regions, developmental stages, and pathological conditions. Thus, we developed scBrainScope, a detailed and interactive transcriptomic atlas of the brain. Our platform brings together single cell sequencing data from 135 species, covering 433 brain regions, 198 developmental stages, and 100 neurological diseases. In addition, we compiled 737 bulk RNA sequencing datasets from 275 species, along with 1154 spatial datasets of brain, spinal cord, and embryonic tissues. scBrainScope comprises six atlas modules (AtlasScope, RegionScope, TissueScope, SpaceScope, PathoScope, and AgeScope) and three analytical modules (sPandora, ePandora, and cPandora). Together, these tools enable researchers to investigate cell identity, gene programs, and spatial organization at multiple scales and dimensions. scBrainScope is freely available at http://8.142.154.29/scBrainScope or http://www.brainscopes.org, offering an interactive, data-rich resource for neuroscience, evolutionary biology, and translational medicine.
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Benchmarking atlas-level data integration in single-cell genomics.
Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.
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The Transcriptomic Signature of Donkey Ovarian Tissue Revealed by Cross-Species Comparative Analysis at Single-Cell Resolution.
Donkeys (Equus asinus) hold significant agricultural value in China, particularly for their hides and meat, which possess notable medicinal and dietary importance. However, their reproductive efficiency remains suboptimal compared with other livestock. Ovarian function is a key determinant of fertility, yet the molecular mechanisms underlying donkey ovarian biology remain largely unexplored. To address this gap, we performed single-cell RNA sequencing of donkey ovaries, generating a high-resolution transcriptomic atlas comprising 17,423 cells. Cross-species comparative analysis revealed a high degree of evolutionary conservation in core ovarian cell types, including endothelial, epithelial, immune, and smooth muscle cells, among vertebrates. In contrast, granulosa and theca cells exhibited distinct transcriptional profiles across species, reflecting lineage-specific adaptations. Notably, we identified key genes with donkey-specific expression patterns, including NR3C1 in endothelial cells, LIPE in granulosa cells, and DHRS9 in theca interna cells. Furthermore, an in vitro cumulus-oocyte complex model demonstrated the critical role of GATM in mammalian oocyte maturation. Collectively, these findings provide a comprehensive characterization of ovarian cell-type conservation and species-specific adaptations, offering key molecular insights into the mechanisms underlying cross-species differences in reproductive efficiency.
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Multiomics Analysis Reveals CTHRC1+ CAFs Drive Immunosuppressive Niches and Predict Immunotherapy Resistance in Gastric Cancer.
Cancer-associated fibroblasts (CAFs) orchestrate immune-excluded tumor microenvironment (TME), but the CAF heterogeneity remains incompletely understood in gastric cancer (GC). In this study, we integrated multicohort single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk transcriptomic data to construct a comprehensive atlas of the GC TME. Unsupervised clustering identified eight transcriptionally distinct CAF subpopulations, among which CTHRC1+ CAFs were selectively enriched in tumors and showed the strongest association with T cell exclusion. Pseudotemporal trajectory analysis, gene regulatory network inference, and cell-cell communication analysis revealed that basic helix-loop-helix family member e41 (BHLHE41) serves as a key transcription factor driving CTHRC1+ CAF differentiation, whereas spatial analyses demonstrated these fibroblasts contribute to fibrotic niches at the tumor-stroma interface through macrophage migration inhibitory factor (MIF)-mediated signaling. Finally, we developed and validated a CTHRC1+ cancer-associated fibroblast-related risk signature (CRS) that accurately predicts immunotherapy response across independent cohorts. These findings establish CTHRC1+ CAFs as a critical stromal determinant of immune exclusion in GC, suggesting that targeting the CTHRC1+ CAF-MIF axis or applying CRS-guided patient stratification may enhance immunotherapy efficacy.
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Single-cell RNA-seq reveals the communications between extracellular matrix-related components and Schwann cells contributing to the earlobe keloid formation.
Keloid is a major type of skin fibrotic disease, with one prominent feature of extensive accumulation of extracellular matrix (ECM) components, and another feature of pain/itching, which is closely related to the peripheral nervous system (PNS). However, the molecular pathogenesis of these two prominent features still needs to be further explored. In the present study, we performed single-cell RNA sequencing (scRNA-seq) on clinical earlobe keloid samples and adjacent normal skin samples and constructed a keloid atlas of 31,379 cells. All cells were clustered into 13 major cell types using cell-type-specific markers. Among them, fibroblast, vascular endothelial cells, and smooth muscle cells were defined as the ECM-related populations according to their ECM-associated functions. Also, we found that Schwann cells (SCs) were the main neuron cells of PNS in the skin. Interestingly, the cell proportions of ECM-related populations, as well as SC were increased significantly in the earlobe keloid compared to the adjacent normal tissues, suggesting an important role of these cell types in the development of the earlobe keloid. Comprehensive cell-cell interaction analysis at the single-cell level revealed a strong interaction between SC and ECM-related subgroups which might be mediated by SEMA3C signaling pathways and MK/PTN gene family, which are found to be mainly involved in promoting cell proliferation and migration. Moreover, further exploration of the interactions of ECM-related populations and SC in different keloids, including earlobe keloid, back keloid, and chest keloid revealed an increasing amount of TGFβ-TGFβ receptor interactions in chest/back keloids as compared to earlobe keloid, which suggested the anatomic site-specific pathogenesis in different keloids. Altogether, these findings suggested the interactions between ECM-related populations and SC contributing to the earlobe keloid formation and helped us to better understand the pathogenesis of keloids.
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Integrative multi-omics analysis identifies stromal-immune crosstalk as a determinant of immunotherapy efficacy and establishes a prognostic signature in gastric cancer.
Immune checkpoint inhibitors like pembrolizumab exhibit variable efficacy in metastatic gastric cancer (GC). This study aimed to identify molecular drivers of pembrolizumab response, explore mechanisms of immune checkpoint inhibitors (ICIs) efficacy, and develop a prognostic signature. Transcriptomic analysis of pembrolizumab-treated GC (TIGER database) identified 165 response-associated differentially expressed genes (DEGs). Functional annotation and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) revealed that responder-upregulated genes (R-DEGs) were enriched in immune activation pathways and mainly localized to CD8 + T/NK cells. In contrast, non-responder-upregulated genes (D-DEGs) were linked to extracellular matrix (ECM) remodeling and mainly expressed in fibroblasts/endothelial cells. CellChat analysis demonstrated that key DEGs mediate immune-stromal crosstalk via MHC-I and collagen/laminin signaling. A prognostic signature (Lasso-StepCox[forward] Riskscore; LSR: APOD, APOH, BATF2, GJA1, MAGED1, SLC5A1, SLCO2A1, VWF, VCAN) was derived and validated in four independent GC cohorts from the GEO and Cancer Genome Atlas (TCGA) database. Multi-omics analyses showed that LSR-high tumors exhibited aggressive clinicopathological features, increased stromal components, reduced cytotoxic immune infiltration, diminished tumor mutational burden (TMB), and poorer prognosis. Immunohistochemistry (IHC) and spatial transcriptomics in GC showed that stromal VWF/VCAN expression correlates with reduced CD8⁺ T cell granzyme B expression, suggesting T cell dysfunction. High VWF expression in GC predicted poor survival, and a combined VWF/VCAN score showed enhanced prognostic stratification. This study highlights stromal-immune crosstalk as a driver of pembrolizumab resistance and provides a signature as a clinical tool for prognosis and personalized therapy in metastatic GC.
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Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes.
Gastric cancer (GC) is a common digestive system cancer, characterized by a significant mortality rate. Mitochondria is an indispensable organelle in eukaryotic cells. It was previously revealed that a series of nucleus-encoded mitochondrial genes (NMG) mutations and dysfunctions potentially contribute to the initiation and progression of GC. However, the correlation between NMG mutations and survival outcomes for GC patients is still unclear. In this study, NMG expression profile and clinical information in GC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through consistent clustering and functional enrichment analysis, we have identified three NMG clusters and three gene clusters that are associated with patterns of immune cell infiltration. Prognostic genes were identified through Univariate Cox regression analysis. The principal component analysis was conducted to set up a scoring system. Subsequently, the Single‑cell RNA sequencing (scRNA-seq) data of GC patients and cancer cell drug sensitivity data were retrieved from the GEO database. Patients with high NMG scores exhibited increased microsatellite instability status and a heightened tumor mutation rate compared to those with low NMG scores. Survival analysis revealed that GC samples with high NMG scores could achieve a better prognosis. Additionally, These patients were observed to be more responsive to immunotherapy. Moreover, we delved into prognostic genes at the level of single cells, revealing that MRPL4 and MRPL37 exhibit high expression in epithelial cells, while TPM1 demonstrates high expression in tissue stem cells. Utilizing cancer cell drug sensitivity data from the Drug Sensitivity in Cancer (GDSC) database, we noted a heightened sensitivity to chemotherapy in the high NMG group. Furthermore, we discovered a significant enrichment of cuproptosis-related genes in clusters with high NMG scores. Consequently, employing the scoring system could facilitate the prediction of GC patients' sensitivity to cuproptosis-induced therapy. Our study confirmed the potency of this scoring system as a therapeutic response biomarker for gastric cancer, potentially informing clinical treatment strategies.
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A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito.
The female Aedes aegypti mosquito's remarkable ability to hunt humans and transmit pathogens relies on her unique biology. Here, we present the Aedes aegypti Mosquito Cell Atlas, a comprehensive single-nucleus RNA sequencing dataset of more than 367,000 nuclei from 19 dissected tissues of adult female and male Aedes aegypti, providing cellular-level resolution of mosquito biology. We identify novel cell types and expand our understanding of sensory neuron organization of chemoreceptors to all sensory tissues. Our analysis uncovers male-specific cells and sexually dimorphic gene expression in the antenna and brain. In female mosquitoes, we find that glial cells in the brain, rather than neurons, undergo the most extensive transcriptional changes following blood feeding. Our findings provide insights into the cellular basis of mosquito behavior and sexual dimorphism. The Aedes aegypti Mosquito Cell Atlas resource enables systematic investigation of cell type-specific expression across all mosquito tissues.