IGHG1+ malignant epithelial Cell-myCAF crosstalk via MIF-CD74/APP-CD74 drives early brain metastasis in NSCLC: Delineated via primary tumor-brain metastasis single-cell and spatial transcriptomics.

Prognostic biomarker panels identified through bulk sequencing approaches have shown utility in localized prostate cancer but are limited by underlying molecular heterogeneity. Spatial transcriptomics offers a complementary approach to investigate spatial gene expression patterns and the tissue- and cell-type-associated localization of their constituent biomarker genes. Using publicly available data, we analyzed biomarker genes from four prognostic panels (Oncotype DX, Prolaris, Decipher, and ProClass-an in-house candidate panel) across 37 tissue sections from two patients with localized high-grade disease. Analyses were performed to quantify biomarker gene abundance across tissue sections using the Visium Spatial Platform, assess spatial variability using global Moran's I, and identify biomarker localization to specific biological niches through spatial co-expression network analysis. Tissue type composition varied markedly between tissue sections. Several genes from Oncotype DX (KLK2, AZGP1, TPM2, GSN, FLNC, COL1A1) and Decipher (ANO7, MYBPC1) were consistently spatially variable across the samples (mean global Moran's I > 0.1). In contrast, cell cycle-associated genes, predominantly in the Prolaris panel, exhibited weak expression limited to a small proportion of sample spots. ProClass genes also showed limited expression, impeding robust spatial analysis. Weighted gene co‑expression network analysis identified spatial modules closely aligned to histopathological annotations, linking most Oncotype DX genes to stromal networks, whereas Decipher genes spanned diverse networks. Spatial transcriptomics revealed significant variability in biomarker gene expression across highly heterogeneous prostate cancer tissue sections from two patients, providing proof-of-concept for its potential use in prognostic biomarker panel research. Several Oncotype DX genes were notably spatially variable, predominantly localizing to stromal regions, whilst the majority of biomarker genes from other panels exhibited non-spatial patterns. Although low-abundance genes may be impacted by current technological limitations, integrating spatial data into biomarker research holds promise for developing "spatially robust" panels to provide reliable prognostic information amidst molecular heterogeneity in prostate cancer.

Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment.

Integrated single‑cell and spatial transcriptomics reveal the colorectal cancer microenvironment.

R-loop-driven molecular subtypes reveal prognostic and immunogenomic features in uterine corpus endometrial carcinoma.

The immunodynamics of pulmonary fibrosis.

The relationship between thyroid function and pancreatic disease has been observed clinically, yet causality remains unestablished. We applied bidirectional Mendelian randomization using genetic instruments from genome-wide association studies encompassing over 500,000 individuals to determine causal relationships. We demonstrate that genetic liability to hypothyroidism substantially protects against acute pancreatitis (odds ratio 0.37, 95 % CI 0.17-0.80). Genetically elevated basal metabolic rate increases acute pancreatitis risk (OR 1.16) while decreasing chronic pancreatitis risk (OR 0.77), revealing divergent pathophysiological mechanisms. No causal relationship exists between thyroid function and pancreatic cancer. To elucidate underlying mechanisms, we performed multi-omics analysis including bulk RNA sequencing from 172 pancreatic adenocarcinomas, single-cell RNA sequencing from acute pancreatitis (32,830 cells), chronic pancreatitis (30,426 cells), and pancreatic cancer (95,751 cells), and GeoMx spatial transcriptomics (253 regions). High metabolic gene expression predicts favorable cancer survival (hazard ratio 0.52, P = 0.0015). Single-cell analysis reveals myeloid-specific metabolic gene downregulation in chronic pancreatitis and 31-fold upregulation of the thyroid hormone-inactivating enzyme DIO3 in tumor epithelial cells. Spatial transcriptomics demonstrates that PPARGC1A downregulation occurs in preneoplastic lesions before malignant transformation. These findings establish thyroid function as a causal determinant of pancreatitis susceptibility, identify cell type-specific mechanisms including local thyroid hormone inactivation and metabolic reprogramming, and demonstrate that patient-derived organoids better preserve prognostically favorable metabolic phenotypes than cell lines. Thyroid function represents a potentially modifiable risk factor for inflammatory pancreatic disease.

Interleukin-1β Drives Disease Progression in Arrhythmogenic Cardiomyopathy.

Elucidating disease pathogenesis using formalin-fixed paraffin-embedded samples: Integrating genomics, spatial transcriptomics, and histology – A focus on vascular anomalies

Spatial transcriptomics technologies such as Xenium, MERFISH, and Visium HD enable high-resolution profiling of gene expression while preserving tissue architecture. However, most computational methods for spatial analysis do not explicitly model local tissue context, such as boundaries, neighborhoods, or gradients. Here, we present SpNeigh (https://github.com/jinming-cheng/SpNeigh/), an R package for spatial neighborhood analysis and spatially aware differential expression modeling. SpNeigh includes tools for boundary detection, spatial neighborhood extraction, distance-based weighting, and gradient-based statistical testing. It supports both region-based differential expression and smooth spatial modeling using spline-based regression, along with a spatial enrichment index that identifies genes enriched near defined spatial features. We demonstrate the utility of SpNeigh across multiple platforms and tissues, including mouse brain, human breast cancer, and human liver, revealing intermediate populations at tissue interfaces, immune microenvironment differences, and spatially zonated gene expression patterns. SpNeigh offers a flexible and interpretable framework for dissecting spatial gene expression dynamics in complex tissues.

MYB: A potential therapeutic target in triple-negative breast cancer based on the PI3K/AKT signaling pathway.

Advances and limitations in angiogenesis assays: Integrating in vitro, in vivo, and emerging technologies.

Pathway-level somatic mutation burden reflects diffuse genomic accumulation rather than selective transcriptional disruption in ovarian serous carcinoma: Evidence from bulk, spatial, and multi-region genomic analyses.

Di-(2-ethylhexyl) terephthalate promotes breast cancer progression: Multi-omics integrated experimental validation.

Spatial transcriptomics captures gene expression with spatial resolution, but its high dimensionality complicates spatial domain identification. While deep learning excels in feature extraction, its limited interpretability underscores the need for dimensionality reduction techniques that preserve spatial and biological relevance. In this study, we propose a novel contrastive graph-regularized non-negative matrix factorization (CGNMF) model for interpretable dimensionality reduction in spatial transcriptomics analysis. Our approach integrates graph regularization with a self-supervised contrastive learning framework to enhance both feature representation and spatial structure preservation. Specifically, we construct positive and negative sample pairs by jointly considering gene expression similarity and spatial proximity, enabling the model to learn discriminative representations that reflect both transcriptomic and spatial characteristics. The contrastive learning component is incorporated into the graph-regularized non-negative matrix factorization framework, effectively guiding the factorization process towards biologically and spatially coherent dimensions. This integration facilitates the automatic delineation of spatial domains and improves interpretability. We benchmark CGNMF against seven spatial domain identification methods using three publicly available datasets. Evaluations based on clustering metrics showed that CGNMF consistently outperformed existing methods. Notably, CGNMF successfully identified biologically relevant functional regions that are overlooked by current approaches, highlighting its robustness and utility in spatial domain identification tasks.

Cotton fiber derived from the ovule epidermis provides a natural source for the textile industry. Transcriptional features of the ovule epidermis contribute critical signals and guide fiber development. This study applied the 10× Genomics Visium spatial transcriptome platform to cotton ovules at 1 d post-anthesis (1 DPA), generating high-resolution, tissue-specific gene expression profiles during early ovule development. Following data normalization, dimensionality reduction, and clustering with Seurat, ovule cross-sections were segmented into seven distinct tissue groups based on anatomical features: nucellus/embryo sac; inner integument micropylar end; inner integument chalaza; outer integument chalazal end; outer integument chalaza; outer integument micropylar end; and funicle. These clusters reveal unique transcriptional signatures that closely correspond with the developmental functions of each tissue region. The cotton fiber condensation region on the outer integument chalazal end is characterized by primary cell biosynthesis, while the outer integument micropylar end is enriched with lipid transportation associated with fiber yield. The resulting 1-DPA cotton ovule spatial transcriptome atlas (1-COSTA) captures key gene expression patterns linked to fiber and lint yield regulation. To facilitate data exploration, the 1-COSTA database was established with a user-friendly web interface built on the R Shiny Server, enabling researchers to access core Seurat visualization and analysis tools including 3D expression visualization of genes in a code-free manner. This resource offers an invaluable reference for understanding spatial gene regulation in cotton fiber development and seed yield.

Spatial multi-omics technologies are fundamentally shifting breast cancer research from static molecular inventories toward high-resolution spatial atlases of the tumor microenvironment (TME). By integrating spatial transcriptomics, spatial proteomics, and emerging metabolomic approaches, researchers can now map tumor cell heterogeneity, stromal cell subtypes, immune cell topography, and tertiary lymphoid structures (TLSs) within their native tissue context. These spatial insights have enabled the construction of prognostic models based on tumor-immune interface distances, improved prediction of chemotherapy and immunotherapy responses, and facilitated the design of niche‑specific combination therapies targeting, for example, fibroblasts‑rich or immunosuppressive regions. Despite these advances, major challenges remain, including multi‑modal data integration, batch effects, high costs, and the lack of standardized protocols for clinical implementation. Overcoming these bottlenecks is essential to establish "spatial pathology" as a routine tool in precision oncology, ultimately guiding more effective, personalized treatment strategies for breast cancer patients. Importantly, it should be noted that the clinical readiness of spatial features varies substantially: assessment of TLS on routine H&E sections is near-implementation, while high-dimensional spatial transcriptomic and proteomic signatures still require standardized protocols, prospective validation, and cost reduction before entering routine diagnostics.

Intratumoral tertiary lymphoid structure (TLS) fosters B-cell differentiation and antibody production, yet the intrinsic programs sustaining these functions remain unclear. By integrating spatial transcriptomics and single-cell RNA sequencing of colorectal cancer, we delineate a B-cell trajectory enriched for endoplasmic reticulum protein processing, autophagy, NF-κB signaling, and N-linked glycosylation, highlighting SSR4 as a progressively induced TRAP-complex component. SSR4 is further upregulated in B cells from anti-PD-1 responders. B-cell-specific Ssr4 deletion leads to peripheral B-cell loss, reduced antibody output, exacerbated colitis, and impaired mucosal immunity. Mechanistically, SSR4 interacts with DDOST to regulate BAFFR N-glycosylation, thereby sustaining B-cell activation and LTα1β2 expression via NF-κB signaling. In ApcMin/+ mice, Ssr4 deficiency accelerates tumorigenesis, disrupts TLS maturation, lowers serum IgG1, and increases high-mannose (HM) Ig. Conversely, SSR4 overexpression in CHO cells reduces high-mannose glycans and enhances IgG1 ADCC and CDC. These findings identify SSR4 as a central regulator of B-cell glycosylation and TLS-dependent anti-tumor immunity, offering translational implications for immunotherapy and antibody engineering.

Spatial transcriptomics enable fine-scale characterization of spatial heterogeneity and cellular niches within tissues, and have substantially advanced our understanding of tissue architecture and functional organization. However, existing spatial transcriptomic integration methods often struggle to effectively capture the rich morphological information provided by the histology and thus further limit their capacity for comprehensive cross-modality learning. In this paper, we present SYMOL, a unified synergistic self-supervised multimodal framework that integrates spatial coordinates, gene expression, and histological images covering both multichannel immunohistochemistry (IHC) and hematoxylin and eosin (H&E) stains for effective spatial transcriptomic integration and representation learning. Specifically, SYMOL extracts distinct visual characteristics via several pretrained large vision models and synergistically aggregates cross-modal features into unified morphology-aware embeddings. Comprehensive benchmarking on multiple publicly available spatial transcriptomic data sets with multichannel IHC images and H&E images shows that SYMOL consistently surpasses state-of-the-art methods in various downstream tasks, including cellular niche identification, multislice integration, cross-data set label transfer, and gene expression enhancement. In addition, SYMOL accurately delineates tumor microenvironment in lung tissues with histopathological imaging and enables fine-scale mapping of cellular niches in the mouse brain, thereby demonstrating both clinical relevance and robustness in complex neuroanatomical settings.

Bone and skeletal muscle are essential components of musculoskeletal system, enabling movement, load-bearing, and systemic homeostasis. These tissues communicate through dynamic bone-muscle crosstalk mediated by cytokines, growth factors, and extracellular-matrix (ECM) proteins. The spatial organization of these mediators is critical for maintaining tissue integrity, and its disruption contributes to diseases, such as osteoporosis, sarcopenia, and metabolic syndrome. Despite this importance, spatial transcriptomics (ST) studies of bone-muscle interactions remain limited. Here, we applied 10x Genomics Visium ST with computational tools, e.g., SMART and CellChat, to deconvolute cell-type composition and characterize cell-cell communication networks and ligand-receptor (L-R) interactions in mouse femur and adjacent skeletal muscle. We identified eight major cell types (erythroid cells, endothelial cells, skeletal muscle cells, osteoblasts, myeloid cells, monocytes/macrophages, mesenchymal stem cells, and adipocytes) with distinct spatial transcriptional profiles and thirteen CellChat-inferred pathways, such as ECM-receptor related (e.g., COLLAGEN, TENASCIN, THBS) and secreted-signaling involved (e.g., VEGF) pathways. Representative L-R pairs include Col1a1/Col1a2-Sdc4, mediating osteoblast-to-muscle interactions, and Col4a1-Sdc4, facilitating muscle-to-osteoblast interactions in COLLAGEN, Tnxb-Sdc4 in TENASCIN, supporting muscle-to-osteoblast/muscle/myeloid/endothelial communication, Comp-Sdc4 in THBS, driving monocyte/macrophage-to-osteoblast/muscle signaling, and Vegfa-Vegfr1/Vegfr2 in VEGF, mediating muscle-to-endothelial/myeloid signaling. Immunostaining validated colocalization of several representative L-R pairs with their corresponding cells. Additionally, independent mouse and human bone scRNA-seq datasets reproduced most of the pathways and L-R pairs identified in ST, underscoring the robustness and cross-species relevance of our findings. Together, we present an initial spatially resolved transcriptome-wide map of bone-muscle intercellular communication, providing novel insights into molecular crosstalk and establishing groundwork for future studies in musculoskeletal disorders.