Explore the vast range of titles available.

Spatial transcriptomics is revolutionizing the exploration of intratissue heterogeneity in cancer, yet capturing cellular niches and their spatial relationships remains challenging. We introduce SpottedPy, a Python package designed to identify tumor hotspots and map spatial interactions within the cancer ecosystem. Using SpottedPy, we examine epithelial-mesenchymal plasticity in breast cancer and highlight stable niches associated with angiogenic and hypoxic regions, shielded by CAFs and macrophages. Hybrid and mesenchymal hotspot distribution follows transformation gradients reflecting progressive immunosuppression. Our method offers flexibility to explore spatial relationships at different scales, from immediate neighbors to broader tissue modules, providing new insights into tumor microenvironment dynamics.

Background Spatial transcriptomics technologies are revolutionizing our understanding of intra-tumor heterogeneity and the tumor microenvironment by revealing single-cell molecular profiles within their spatial tissue context. The rapid development of spatial transcriptomics methods, each with unique characteristics, makes it challenging to select the most suitable technology for specific research objectives. Here, we compare four imaging-based approaches—RNAscope HiPlex, Molecular Cartography, Merscope, and Xenium—alongside Visium, a sequencing-based method. These technologies were employed to study cryosections of medulloblastoma with extensive nodularity (MBEN), a tumor chosen for its distinct microanatomical features. Results Our analysis reveals that automated imaging-based spatial transcriptomics methods are well-suited to delineate the intricate MBEN microanatomy and capture cell-type-specific transcriptome profiles. We devise approaches to compare the sensitivity and specificity of different methods, along with their unique attributes, to guide method selection based on the research objective. Furthermore, we demonstrate how reimaging slides after the spatial transcriptomics analysis can significantly improve cell segmentation accuracy and integrate additional transcript and protein readouts, expanding the analytical possibilities and depth of insight. Conclusions This study underscores important distinctions between spatial transcriptomics technologies and offers a framework for evaluating their performance. Our findings support informed decisions regarding methods and outline strategies to improve the resolution and scope of spatial transcriptomic analyses, ultimately advancing spatial transcriptomics applications in solid tumor research.

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

Spatial transcriptomics technologies permit the study of the spatial distribution of RNA at near-single-cell resolution genome-wide. However, the feasibility of studying spatial allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating spatial ASE. To tackle the challenges presented by cell type mixtures and a low signal to noise ratio, we implement a hierarchical model involving additive mixtures of spatial smoothing splines. We apply our method to allele-resolved Visium and Slide-seq from the mouse cerebellum and hippocampus and report new insight into the landscape of spatial and cell type-specific ASE therein.

Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo’s utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.

With the rapid accumulation of spatial transcriptomics (ST) data across diverse tissues, individuals, and technological platforms, there is an urgent need for a robust and reliable multi‐slice integration framework to enable 3D spatial domain identification. However, existing methods largely focus on 2D spatial domain identification within individual slices and fail to adequately account for inter‐slice spatial correlations and batch effect correction, thereby limiting the accuracy of cross‐slice 3D spatial domain identification. In this study, SpaBatch is presented, a novel framework for integrating and analyzing multi‐slice ST data, which effectively corrects batch effects and enables cross‐slice 3D spatial domain identification. To demonstrate the power of SpaBatch, SpaBatch is applied to eight real ST datasets, including human cortical slices from different individuals, mouse brain slices generated using two different techniques, mouse embryo slices, human embryonic heart slices, HER2+ breast cancer tissues and mouse hypothalamic slices profiled using the MERFISH platforms. Comprehensive validation demonstrates that SpaBatch consistently outperforms state‐of‐the‐art methods in 3D spatial domain identification while effectively correcting batch effects. Moreover, SpaBatch efficiently captures conserved tissue architectures and cancer‐associated substructures across slices, and leverages limited annotations to predict spatial domain in unannotated sections, highlighting its potential for tissue‐structure interpretation and developmental biology studies. All code and public datasets used in this study are available at: https://github.com/wenwenmin/SpaBatch. SpaBatch is an end‐to‐end multi‐slice spatial transcriptomics data integration framework. It simultaneously performs embedding learning, spatial feature denoising and reconstruction, batch effect correction, and spatial domain optimization, effectively correcting batch effects and achieving accurate 3D spatial domain identification. Validation across multiple datasets shows that SpaBatch outperforms existing methods and demonstrates broad applicability and robustness across spatial transcriptomics data from different species, platforms, and tissue types.

The gut microbiota influences host immunity and metabolism, and changes in its composition and function have been implicated in several non‐communicable diseases. Here, comparing germ‐free (GF) and specific pathogen‐free (SPF) mice using spatial transcriptomics, single‐cell RNA sequencing, and targeted bile acid metabolomics across multiple organs, we systematically assessed how the gut microbiota's absence affected organ morphology, immune homeostasis, bile acid, and lipid metabolism. Through integrated analysis, we detect marked aberration in B, myeloid, and T/natural killer cells, altered mucosal zonation and nutrient uptake, and significant shifts in bile acid profiles in feces, liver, and circulation, with the alternate synthesis pathway predominant in GF mice and pronounced changes in bile acid enterohepatic circulation. Particularly, autophagy‐driven lipid droplet breakdown in ileum epithelium and the liver's zinc finger and BTB domain‐containing protein (ZBTB20)‐Lipoprotein lipase (LPL) (ZBTB20‐LPL) axis are key to plasma lipid homeostasis in GF mice. Our results unveil the complexity of microbiota–host interactions in the crosstalk between commensal gut bacteria and the host. We present a multi‐organ single‐cell, spatial transcriptomics, and BA omics atlas of specific pathogen‐free (SPF) and germ‐free (GF) mice. We found plasma cell aggregation displays significant tissue heterogeneity depending on the gut microbiota. GF mice exhibit impaired follicular and marginal zone B cell maturation, linked to microbiota‐mediated modulation of Cr2 gene expression. The microbiota regulates the development and survival of neutrophils in the bone marrow, influences the development and differentiation of T cells in the thymus, and modulates intraepithelial γδ T cell composition and lipid absorption in the small intestine. The absence of microbiota in GF mice alters the intestinal mucosa zonation and triggers coordinated dynamics in intestinal lipid absorption, transport, chylomicron synthesis, lipid droplet formation, lipolysis, and fatty acid oxidation in the small intestine enterocytes. The liver microbiota‐dependent zinc finger and BTB domain‐containing protein (ZBTB20)‐Lipoprotein lipase (LPL) axis plays a role in plasma lipid homeostasis. Highlights Single‐cell, spatial transcriptomics, and bile acidomics atlases in germ‐free mice. Marked aberration and tissue heterogeneity in B, myeloid, and T/NK cells in germ‐free mice. Microbiota shapes mucosal zonation and modulates lipid dynamics of the small intestine. Germ‐free mice show liver bile acid synthesis and ileal reabsorption anomalies.

Spatial transcriptomics (ST) measures gene expression while preserving spatial context within tissues. One of the key tasks in ST analysis is spatial domain detection, which remains challenging due to the complex structure of ST data and the varying performance of individual clustering methods. To address this, we propose SpatialESD, a Spatial EnSemble Domain detection method that integrates results from different spatial domain detection methods to improve spatial domain detection. SpatialESD captures both direct cooccurrence patterns and multiscale indirect relationships between clusters, improving the robustness and accuracy of spatial domain detection. We evaluated SpatialESD on simulated datasets and multiple 10x Visium spatial transcriptomics datasets, including human brain, breast cancer, and ovarian cancer samples. The results show that SpatialESD consistently outperforms individual methods and the existing EnSDD ensemble method in terms of clustering accuracy and stability. Based on the identified domains, we further detected domain‐specific differentially expressed genes and performed trajectory and cell–cell interaction analyses. These results reveal spatial patterns of gene expression and cellular communication, offering insights into tissue organization and disease mechanisms. Overall, SpatialESD provides a reliable and effective solution for spatial domain detection in ST data and facilitates downstream biological discovery.

Adamantinomatous craniopharyngioma (ACP), a benign yet highly recurrent and therapy—resistant intracranial tumor, remains a considerable clinical challenge because of its complex pathological structure, infiltrative growth, and limited treatment options. Here, integrated spatially resolved multiomics is employed—including single‐cell spatial transcriptomics via CosMx SMI and spatially resolved metabolomics via AFADESI‐MSI, accompanied by bulk metabolomics and functional validation—to unravel the driving factors of ACP progression and recurrence. Analysis results reveal three interdependent biological hallmarks: first, the spatial segregation and molecular heterogeneity of 10 distinct tumor epithelial cell subpopulations within the ACP, each of which presents unique transcriptional signatures; second, in tumor regions and recurrent tumor epithelium tissues, stronger transporter‐mediated choline/ethanolamine uptake from cystic fluid and significant upregulation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) synthesis is observed, creating the enhanced “cystic fluid–tumor cell” and “choline/ethanolamine–PC/PE” metabolic axis, and demonstrating the spatial metabolic remodeling of ACP; and third, this metabolic axis directly couples to autophagy activation of corresponding regions in ACP tissue, which is validated by multi‐immunohistochemistry for Beclin1 and GABARAP. Together, these findings reveal metabolic remodeling and autophagic activation as critical drivers of ACP progression and recurrence and provide an opportunity for precise biomarker‐driven treatment of this intractable tumor. Multiomics integration analysis reveals the “cystic fluid–tumor cell” metabolic coupling that mediates active choline/ethanolamine uptake of tumor cells from cystic fluid and PC/PE synthesis pathways reprogramming that mediating autophagy pathway activation within ACP.

UTP4 is a critical component of ribosome biogenesis, and its dysregulation may contribute to cancer development. However, the role of UTP4 in cancer remains unclear. The present study comprehensively investigated the expression and prognostic significance of UTP4 across multiple cancers, with a particular focus on gastric cancer (GC). Integrated bioinformatics analysis of public datasets, including The Cancer Genome Atlas, revealed that UTP4 is frequently overexpressed in various tumors and associated with poor prognosis. Further analysis uncovered its correlations with genetic mutations, immune infiltration and immune checkpoint expression. Based on these findings and CRISPR-Cas9 screening predictions, the functional role of UTP4 in GC cells was experimentally validated. The results demonstrated that UTP4 knockdown significantly inhibited cell proliferation, migration and invasion. These findings highlight UTP4 as a novel pan-cancer biomarker and potential therapeutic target, providing a foundation for further clinical investigations.