Search Results (56)

Undifferentiated sarcomas (USs), including undifferentiated pleomorphic sarcoma (UPS), are aggressive mesenchymal malignancies with limited molecular biomarkers for prognostic assessment and therapeutic stratification. Expression-based markers may provide insight into tumor aggressiveness and clinical outcomes. Here, we performed integrative transcriptomic and spatial analyses to identify differentially expressed genes (DEGs). By comparing normal tissues with sarcoma tumors and sarcoma tumors with cell lines. Intersection and clustering analyses were conducted to define shared expression programs, which revealed a subset of DEGs enriched in epithelial-mesenchymal transition (EMT)-related pathways. CosMx spatial transcriptomics was applied to xenograft tumors derived from two UPS cell lines to resolve tumor-intrinsic signatures. The National Cancer Center Cohort samples were used for validation, and immunohistochemistry confirmed the expression in thirty US tissues. Spatial transcriptomic profiling identified mesenchymal tissue–driven gene expression programs in UPS xenografts. Across bulk RNA-seq and spatial data, epithelial membrane protein 3 (EMP3) consistently emerged as highly expressed in US tissues and cell lines. EMP3 is a robust mesenchymal-associated biomarker linked to EMT, tumor progression, and clinical outcomes in USs, supporting its potential utility as a prognostic indicator and therapeutic target. Full article

Tooth development or odontogenesis is a complex morphogenetic process that requires tightly regulated interactions between the oral epithelium and mesenchyme of neural crest origin. In this narrative review, we compile existing knowledge regarding gene regulatory networks and epigenetic factors throughout tooth development from initiation to eruption. Signaling between the epithelium and mesenchyme is mediated by four conserved pathways—Wnt/β-catenin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Sonic hedgehog (Shh)—which operate iteratively and interact through extensive crosstalk at each developmental stage. Transcription factors, such as PAX9, MSX1, PITX2, and LEF1, interpret these signals to control cell fate decisions and differentiation. Epigenetic modifications, including DNA methylation, histone modifications, and microRNA-mediated regulation, provide additional layers of control that fine-tune gene expression programs. Unlike existing reviews that address these regulatory mechanisms separately, here we integrate signaling pathways, transcription factor networks, epigenetic regulation, human genetic disorders, dental stem cell biology, and recent single-cell transcriptomic insights into a unified framework. We discuss opportunities to apply developmental biology knowledge towards regenerative dentistry goals, including iPSC-derived dental models and spatially resolved multi-omics approaches, while acknowledging the considerable gap between preclinical findings and clinical applications. Full article

Chronic crop diseases caused by uncultured, obligate, or host-dependent pathogens challenge traditional pathogen-centric paradigms that often interpret symptoms as direct outcomes of pathogen toxins, effectors, or tissue colonization. Here, we advance a host-centric immune framework that reframes disease as an emergent consequence of dysregulated host immune network activity, including prolonged activation, signaling miscoordination, and systemic physiological disruption. Using citrus huanglongbing (HLB) as a primary exemplar and canola clubroot as a parallel system, we synthesize evidence that persistent immune stimulation can drive self-damaging outputs, including sustained reactive oxygen species accumulation, chronic vascular and transport dysfunction, hormone imbalance, and growth–defense trade-offs. While many observations derive from transcriptomic, physiological, and genetic studies conducted under controlled experimental conditions, the available evidence collectively suggests that persistent immune activation may contribute substantially to disease-associated decline in these systems. We argue that pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) operate as an integrated immune network whose feedback structure can become destabilized under chronic infection, generating immune states that are simultaneously harmful and often ineffective at pathogen clearance. We further discuss how panomic profiling, spatially resolved analyses, and network inference can diagnose host immune states at tissue and cell-type resolution, and how genome editing enables causal tests and rational immune tuning strategies that optimize defense amplitude, timing, and localization rather than indiscriminately amplifying resistance. By centering the host immune system as both a source of protection and pathology, this framework provides a conceptual and practical roadmap for understanding and engineering resilience in HLB, clubroot, and other chronic crop diseases in which pathogen biology remains experimentally opaque. Full article

Background: Spatially resolved transcriptomics (SRT) enables simultaneous measurement of gene expression and spatial location, but the existing domain detection methods are limited by over-reliance on spot-to-spot proximity, rigid pre-alignment requirements for multi-slice datasets, and inadequate mitigation of batch effects. This study aims to develop a unified method for accurate spatial domain identification across both single-slice and multi-slice SRT datasets. Methods: We propose a novel method named spatially resolved transcriptomics heterogeneous graph contrastive learning (stHGCL), which integrates a spot–gene heterogeneous graph, a dual-stage encoder (comprising LightGCN and GCN), and a neighborhood-driven contrastive learning module. The heterogeneous graph captures high-order structural information through spot–gene connections mediated by shared genes; the dual-stage encoder refines spot embeddings by fusing gene expression and spatial location; contrastive learning enhances intra-cluster compactness and mitigates batch effects. Results: stHGCL was validated on seven benchmark datasets from platforms including 10x Visium, BaristaSeq, STARmapSeq, Slide-seq, and Stereo-seq. It outperformed nine single-slice and eight multi-slice state-of-the-art methods. It achieved the highest mean Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) scores and could accurately delineate complex spatial domains with distinct boundaries, and even achieved cross-slice spatial domain detection for unaligned multi-slice datasets. Ablation studies confirmed the effectiveness of its main modules. Conclusions: stHGCL effectively captures high-order structural and spatial information and mitigates batch effects. It provides a robust scalable solution for unified spatial domain detection in SRT, facilitating insights into the spatial domains across both single-slice and multi-slice experimental paradigms. Full article

Background: The JinEr mushroom results from the heterogeneous symbiosis of Naematelia aurantialba and Stereum hirsutum, with low-temperature storage being key for postharvest quality preservation. However, the species-specific low-temperature response patterns remain unclear. Methods: An integrated approach combining metabolomics, transcriptomics (dual-genome alignment), and spatially resolved enzyme assays was used to dissect responses at 0 °C and 4 °C. Results: The two fungi displayed distinct stress response tendencies under the studied conditions. N. aurantialba showed enhanced stress defense (DNA repair, antioxidant pathways) with defense-related enzyme activities concentrated in its apical/middle enrichment regions. S. hirsutum was observed to maintain overall metabolic activity at the pathway level, and its metabolic enzyme activities were predominant in the basal region. The symbiotic system exhibited temperature-dependent plasticity stress responses. Storage at 0 °C induced a survival-oriented response with slower crude polysaccharide degradation. In contrast, storage at 4 °C supported active metabolic defense coordination but more pronounced polysaccharide loss. Conclusions: These observed defense- and metabolism-biased differential responses suggest a cold stress-specific coordination working model within the symbiotic system under postharvest cold stress. A temperature of 0 °C is more suitable for enabling JinEr mushroom postharvest storage to retain polysaccharides. This study advances our understanding of heterogeneous symbiotic fungi’s postharvest biology and provides a temperature-targeted theoretical basis for storage optimization. Full article

The vascular cambium serves as the fundamental meristem for wood formation. It determines wood biomass and structural properties by balancing self-renewal with the bidirectional production of xylem and phloem. This process is controlled by a complex network of peptides, transcription factors, and phytohormones. These regulatory networks coordinate cambial stem cell activity, balancing cell division and differentiation. Additionally, layers of regulation such as chromatin state, protein stability, and non-coding RNAs add significant complexity to these networks. Emerging single-cell and spatial transcriptomics, together with quantitative modeling, now resolve cambial heterogeneity, predicting the dynamic characteristics of wood formation. This review synthesizes current knowledge of cambial regulation, highlighting how feedback loops, spatial gradients, and dynamic signaling networks collectively orchestrate the predictive potential for improving cambial activity and wood formation. Full article

Nanoparticles are widely explored in oncology as delivery platforms for cytotoxic drugs and molecularly defined therapeutic agents, including immunomodulators. While advances in nanomaterial engineering have enabled precise control over physicochemical properties, biological responses to nanoparticles remain difficult to predict and often diverge across experimental systems. Recent omics studies reveal that nanoparticle exposure induces coordinated cellular programs that extend beyond overt toxicity and are strongly shaped by delivery context, cellular state, and microenvironmental conditions. Importantly, these responses cannot be attributed solely to the payload, as nanocarriers themselves frequently engage stress, metabolic, and immune-related pathways, giving rise to non-additive and context-dependent effects. This Perspective proposes omics-based functional fingerprinting as a conceptual framework to interpret nanoparticle biology in cancer. Functional fingerprints are defined as integrated biological response states arising from nanocarrier–payload systems and resolving through transcriptomic, proteomic, metabolomic, and emerging single-cell or spatial approaches. By explicitly distinguishing carrier-dependent, payload-induced, and composite response programs, functional fingerprinting provides a means to reconcile heterogeneous observations and move beyond material-centered classification. Incorporating biological resolution and context awareness into nanoparticle profiling is expected to improve mechanistic interpretation, safety assessment, and the rational design of more predictive nanomedicine strategies. Full article

RNA sequencing (scRNA-seq) has emerged as a transformative technology for dissecting cellular heterogeneity and immune complexity in livestock species. This review summarizes recent advances in the application of single-cell transcriptomics to livestock health, with a particular focus on immune system organization and host–pathogen interactions in cattle, pigs, poultry, and small ruminants. We highlight the development of large-scale, multi-tissue cell atlases—such as the Cattle Cell Atlas and resources generated through the Farm Animal Genotype-Tissue Expression (FarmGTEx) consortium—that provide foundational reference frameworks for livestock genomics. These atlases have enabled the identification of tissue- and species-specific immune cell populations, clarified cellular tropism of major bacterial and viral pathogens, and revealed distinctive immunological features, including the prominent role of γδ T cells in ruminant immunity. We discuss how single-cell immune receptor sequencing has advanced monoclonal antibody discovery and informed rational vaccine design. Key technical and analytical challenges, including incomplete genome annotations, tissue processing constraints, and cross-platform data integration, are critically assessed. Finally, we outline future directions integrating spatial transcriptomics and multi-omics approaches to further resolve immune function within tissue contexts. Collectively, these advances position single-cell transcriptomics as a central framework for improving disease resistance, vaccine efficacy, and translational research in livestock health. Full article

Understanding the intimate interactions between plants and their microbiota at the cellular level is essential for unlocking the full potential of plant holobionts in agricultural systems. Traditional bulk and microbial community-level sequencing approaches reveal broad community patterns but fail to resolve how distinct plant cell types interact with or regulate microbial colonization, as well as the diverse antagonistic and synergistic interactions and responses existing between various microbial populations. Recent advances in single-cell and spatial multi-omics have transformed our understanding of plant cell identities as well as gene regulatory programs and their dynamic regulation in response to environmental stresses and plant development. In this review, we highlight the single-cell discoveries that uncover the plant cell-type-specific microbial perception, immune activation, and symbiotic differentiation, particularly in roots, nodules, and leaves. We further discuss how integrating transcriptomic, epigenomic, and spatial data can reconstruct multilayered interaction networks that connect plant cell-type-specific regulatory states with microbial spatial niches and inter-kingdom signaling (e.g., ligand–receptor and metabolite exchange), providing a foundation for developing new strategies to engineer crop–microbiome interactions to support sustainable agriculture. We conclude by outlining key methodological challenges and future research priorities that point toward building a fully integrated cellular interactome of the plant holobiont. Full article

Background: Hypoxic–ischemic (HI) brain injury triggers a dynamic, multi-phase response involving early microglial efferocytosis followed by extracellular matrix (ECM) deposition and scar formation. Arginase-1 (ARG1), a key enzyme in tissue repair, is implicated in both processes, yet its role in neonatal microglia remains poorly defined. We characterize ARG1-linked pathways in neonatal microglia, identifying distinct efferocytic and fibrotic phases post-HI. Methods: HI was induced in P9 mice using the Vannucci model, and brains were collected at 24 h (D1) and 5 days (D5). Spatially resolved single-cell transcriptomics (seqFISH) was performed using a targeted panel enriched for microglial, ARG1-pathway, efferocytosis, and profibrotic genes. Cell segmentation, clustering, and spatial mapping were conducted using Navigator and Seurat. Differential expression, GSEA, and enrichment analyses were used to identify time- and injury-dependent pathways. Results: Spatial transcriptomics identified 12 transcriptionally distinct cell populations with preserved neuroanatomical organization. HI caused the expansion of microglia and astrocytes and the loss of glutamatergic neurons by D5. Microglia rapidly activated regenerative and profibrotic programs—including TGF-β, PI3K–Akt, cytoskeletal remodeling, and migration—driven by early DEGs such as Cd44, Reln, TGF-β1, and Col1a2. By D5, microglia adopted a collagen-rich fibrotic state with an upregulation of Bgn, Col11a1, Anxa5, and Npy. Conclusion: Neonatal microglia transition from early efferocytic responses to later fibrotic remodeling after HI, driven by the persistent activation of PI3K–Akt, TGF-β, and Wnt/FZD4 pathways. These findings identify microglia as central regulators of neonatal scar formation and highlight therapeutic targets within ARG1-linked signaling. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Compared with traditional two-dimensional (2D) models, patient-derived CRC organoids more faithfully preserve the genomic, transcriptomic, and architectural features of primary tumors, making them a powerful intermediate platform bridging basic discovery and clinical translation. Over the past several years, organoid systems have rapidly expanded beyond conventional epithelial-only cultures toward increasingly complex architectures, including immune-organoid co-culture models and mini-colon systems that enable long-term, spatially resolved tracking of tumor evolution. These advanced platforms, combined with high-throughput technologies and clustered regularly interspaced short palindromic repeats (CRISPR)-based functional genomics, have substantially enhanced our ability to dissect CRC mechanisms, identify therapeutic vulnerabilities, and evaluate drug responses in a physiologically relevant context. However, current models still face critical limitations, such as the lack of systemic physiology (e.g., gut–liver or gut–brain axes), limited standardization across platforms, and the need for large-scale, prospective clinical validation. These gaps highlight an urgent need for next-generation platforms and computational frameworks. The development of high-throughput multi-omics, CRISPR-based perturbation, drug screening technologies, and artificial intelligence-driven predictive approaches will offer a promising avenue to address these challenges, accelerating mechanistic studies of CRC, enabling personalized therapy, and facilitating clinical translation. In this perspective, we propose a roadmap for CRC organoid research centered on two major technical pillars: advanced organoid platforms, including immune co-culture and mini-colon systems, and mechanistic investigations leveraging multi-omics and CRISPR-based functional genomics. We then discuss translational applications, such as high-throughput drug screening, and highlight emerging computational and translational strategies that may support future clinical validation and precision medicine. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer and is marked by pronounced intra-tumoural heterogeneity that complicates therapeutic response. Patient-derived organoids offer a physiologically relevant model to capture this diversity and evaluate treatment effects. When integrated with spatial transcriptomics, they might enable the mapping of spatially resolved transcriptional and isoform-level changes within the tumour microenvironment. Methods: We established a robust workflow for generating patient-derived ccRCC organoids, that are not passaged and retain original cellular components. These retain key features of the original tumours, including cancer cell, stromal, and immune components. Results: Spatial transcriptomic profiling revealed multiple transcriptionally distinct regions within and across organoids, reflecting the intrinsic heterogeneity of ccRCC. Isoform-level analysis identified spatially variable expression of glutaminase (GLS) isoforms, with heterogeneous distributions of both the GAC and KGA variants. Treatment with NUC-7738, a phosphoramidate derivative of 3′-deoxyadenosine, induced marked transcriptional remodelling of organoids, including alterations in ribosomal and mitochondrial gene expression. Conclusions: This study demonstrates that combining long-read spatial transcriptomics with patient-derived organoid models provides a powerful and scalable approach for dissecting gene and isoform-level heterogeneity in ccRCC and for elucidating spatially resolved transcriptional responses to novel therapeutics. Full article

Cardiac fibrosis is a major component of heart failure (HF) and develops when reparative wound healing becomes chronic, leading to excessive extracellular matrix accumulation. Cardiac fibroblasts (CFs), the main regulators of matrix remodeling, are heterogeneous in developmental origins, regional localizations, and activation states. This diversity determines whether tissue repair resolves normally or progresses into maladaptive scarring that disrupts myocardial structure and function after injuries. Recent single-cell and spatial transcriptomic studies show that CFs exist in distinct yet interrelated molecular states in murine models and human cardiac tissue with specialized roles in matrix production, angiogenesis, immune signaling, and mechanical sensing. These insights redefine cardiac fibrosis as a dynamic and context-dependent process rather than a uniform cellular response. Although CFs are promising targets for preventing HF progression and enhancing cardiac remodeling, translation into effective therapies remains limited by the unclear heterogeneity of pathological fibroblasts, the lack of distinctive CF markers, and the broad activity of fibrogenic signaling pathways. In this review, we discuss the dynamics of CF activations during the development and progression of HF and assess the underlying pathways and mechanisms contributing to cardiac dysfunction. Additionally, we highlight the potential of targeting CFs for developing therapeutic strategies. These include nonspecific suppression of fibroblast activity and targeted modulation of the signaling pathways and cell populations that sustain chronic remodeling. Furthermore, we assess regenerative approaches that can reprogram fibroblasts or modulate their paracrine functions to restore functional myocardium. Integrating antifibrotic and regenerative strategies with advances in precision drug discovery and gene delivery offers a path toward reversing established fibrosis and achieving recovery in HF. Full article

Wound healing is a complex, multi-phase process requiring coordinated interactions among diverse cell types and molecular pathways to restore tissue integrity. Dysregulation can lead to chronic non-healing wounds or excessive scarring, posing major clinical and economic burdens. Single-omics interrogate individual molecular layers, such as the genome, transcriptome, proteome, metabolome, or epigenome, and have revealed key cellular players, but provide a limited view of dynamic wound repair. Single-cell technologies provide higher resolution to single-omic data by resolving cell-type and state-specific heterogeneity, enabling precise characterization of cellular populations. Multi-omics integrates multiple molecular layers at single-cell resolution, reconstructing regulatory networks, epigenetic landscapes, and cell–cell interactions underlying healing outcomes. Recent advances in single-cell and spatial multi-omics have revealed fibroblast subpopulations with distinct fibrotic or regenerative roles and immune–epithelial interactions critical for re-epithelialization. Integration with computational tools and artificial intelligence (AI) continues to reveal cellular interactions, predict healing outcomes, and guide development of personalized therapies. Despite technical and translational challenges, including data integration and cost, multi-omics are increasingly shaping the future of precision wound care. This review highlights how multi-omics is redefining understanding of wound biology and fibrosis and explores emerging applications such as smart biosensors and predictive models with potential to transform wound care. Full article

Conventional cell-type deconvolution methods, such as Robust Cell-Type Decomposition (RCTD), SPOTlight, Spatial Cellular Estimator for Tumors (SpaCET), and cell2location, often encounter limitations when applied to Visium datasets that include reference profiles with multiple minor cell types. This highlights the necessity for more advanced computational approaches to resolve such challenges. To address this issue, we have employed and refined tensor decomposition (TD)-based unsupervised feature extraction (FE) to integrate multiple Visium datasets, providing a robust platform for spatial gene expression profiling (spatial transcriptomics). Notably, TD-based unsupervised FE successfully retrieves singular value vectors that correspond with spatial distribution; neighboring spots are assigned vectors with comparable values. Additionally, TD-based unsupervised FE demonstrates successful interference of cell-type fractions within individual Visium spots, enabling effective deconvolution even when referencing single-cell RNA-seq datasets containing several minor cell types—a scenario where conventional methods, such as RCTD, SPOTlight, SpaCET, and cell2location, typically prove ineffective. The findings of this study suggest that TD-based unsupervised FE has broad applicability for diverse deconvolution tasks. Full article