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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.

Spatial transcriptomics technologies profile gene expression across tissue sections while retaining spatial information, yet most platforms capture signals from multiple cells per measurement location, requiring computational methods to determine the underlying cellular composition. Current deconvolution approaches either depend on single-cell reference atlases—which may be unavailable for rare tissues or specific disease contexts—or employ unsupervised methods that struggle with complex spatial patterns and typically need manual specification of how many cell types to identify. We developed Attention-Guided Enhanced Deconvolution (AGED), a two-stage framework combining probabilistic modeling with neural attention architectures for reference-free analysis. The first stage uses a Performer-based network with linear-complexity attention to systematically evaluate models with different numbers of cell types, automatically selecting the optimal configuration through composite scoring of reconstruction quality, cluster separation, and topic diversity. The second stage, Attention-Guide, stars with hierarchical probabilistic initialization. It progressively refines cell type features through multiple attention mechanisms: cross-attention based on statistical priors, spatial attention aggregating neighborhood information, and collaborative attention capturing theme-gene associations. A dynamic gating mechanism enables the model to balance global statistical patterns with locally data-driven features, while regularization promotes sparse solutions consistent with biological reality—each position containing only a few dominant cell types. Testing on Mouse Olfactory Bulb (MOB) tissue, AGED automatically identified four anatomical structures and achieved superior reconstruction performance (r=0.86) with characteristic sparsity patterns. When similarly applied to human pancreatic ductal adenocarcinoma (PDAC) and human thymus tissue, it revealed detailed relationships between dissected structures and cell types. The learned cell type distributions aligned well with established neuroanatomical boundaries and known molecular markers, indicating the attention-based refinement maintains biological interpretability throughout training. This framework offers a practical solution for spatial transcriptomics analysis across diverse experimental systems without requiring matched single-cell data.

Clear cell renal cell carcinoma (ccRCC) is characterized by high recurrence and metastatic potential, leading to poor clinical outcomes. There is a critical need to identify reliable prognostic biomarkers and therapeutic targets to improve patient stratification and personalized treatment. This study integrated single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data to identify prognostic genes and therapeutic targets. Prognostic modeling and validation were performed using The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. In addition, functional analyses were conducted to explore the biological roles of candidate genes. Seven prognostic genes (CYFIP2, MPPED2, HHLA2, ADAM8, ATP1A1, ARC, and MXD3) were identified and used to construct a risk model that stratified patients into high- and low-risk groups. The high-risk group exhibited significantly poorer survival, a finding validated in both TCGA and ICGC datasets. A nomogram incorporating risk score and age improved survival prediction accuracy, with Area Under the Curve (AUC) values of 0.79, 0.75, and 0.78 at 1, 3, and 5 years, respectively. ATP1A1 was highly expressed in endothelial cells and was significantly associated with M1 macrophages; thus, it was selected as a potential therapeutic target. Functional analyses revealed its role in angiogenesis inhibition and M1 macrophage polarization. The risk model and nomogram demonstrate strong prognostic value and may aid in clinical risk stratification for ccRCC. ATP1A1 emerges as a potential therapeutic target, with functional implications in angiogenesis and immune modulation. These findings highlight the clinical relevance of the identified gene signatures and support the development of personalized treatment strategies for ccRCC patients.

The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix Factorization (JGR-NMF). An adaptive neighborhood graph construction strategy is introduced by applying an n th-power transformation to the spot adjacency probability matrix, thereby automatically optimizing the neighborhood size for individual spots. Furthermore, a JGR-NMF framework is developed, integrating this adaptively constructed kNN graph with the spatial adjacency matrix. Evaluations conducted on two breast cancer datasets, one Mouse Kidney dataset and one Mouse Embryo dataset, demonstrate that JGR-NMF significantly outperforms five state-of-the-art baseline methods in spatial domain identification. Systematic ablation studies further confirm the critical role of graph regularization in enhancing model performance.

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.