SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies have emerged as powerful tools for understanding cellular identity and function within the natural spatial context of tissues. Traditional transcriptomics techniques, such as bulk and single-cell RNA sequencing, lose this spatial information, which is critical for addressing many biological questions. Here, we present a protocol for high-resolution spatial transcriptomics using fixed frozen mouse lung sections mounted on 10X Genomics Xenium slides. This method integrates multiplexed fluorescent in situ hybridization (FISH) with high-throughput imaging to reveal the spatial distribution of mRNA molecules in lung tissue sections, allowing detailed analysis of gene expression changes in a mouse model of pulmonary hypertension (PH). We compared two tissue preparation methods, fixed frozen and fresh frozen, for compatibility with the Xenium platform. Our fixed frozen approach, utilizing a free-floating technique to mount thin lung sections onto Xenium slides at room temperature, preserved tissue integrity and maximized the imaging area, resulting in high-fidelity spatial transcriptomics data. Using a predesigned 379-gene mouse panel, we identified 40 major lung cell types. We detected key cellular changes in PH, including an increase in arterial endothelial cells (AECs) and fibroblasts, alongside a reduction in capillary endothelial cells (CAP1 and CAP2). Through differential gene expression analysis, we observed markers of endothelial-to-mesenchymal transition and fibroblast activation in PH lungs. High-resolution spatial mapping further confirmed increased arterialization in the distal microvasculature. These findings underscore the utility of spatial transcriptomics in preserving the native tissue architecture and enhancing our understanding of cellular heterogeneity in disease. Our protocol provides a reliable method for integrating spatial and transcriptomic data using fixed frozen lung tissues, offering significant potential for future studies in complex diseases such as PH.

Spatial Transcriptomic Analysis of Acute Heart Rejection Model

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and [formula omitted]-sums clustering

High-resolution spatial transcriptomics requires computational methods to accurately assign transcripts to individual cells. We present SMURF (Segmentation and Manifold UnRolling Framework), a cross-platform soft-segmentation algorithm that uses deep learning to map mRNAs from capture spots to nearby nuclei. SMURF also unrolls complex tissue architectures by projecting cells onto Cartesian coordinates, enabling analyses of cell-type organization and gene expression gradients. We show that SMURF assigns mRNAs to single cells more accurately than existing approaches and robustly unrolls complex tissues to reveal zonated transcriptional programs and cell-type organization across multiple tissues and spatial transcriptomic technologies. To showcase the biological insights enabled by SMURF, we segment over 400,000 cells from the mouse ileum using Visium HD data. We identify zonated gene expression programs along the maturing intestinal villus and the transcription factors that regulate them. Importantly, we show that gene expression gradients along the proximal-distal axis of the intestine accumulate in the upper villus and that upper villus gene expression is reprogrammed by environmental signals in the lumen, suggesting that environmental inputs are major determinants of regional transcriptional identity. Together, these results establish SMURF as a powerful framework for analyzing gene expression of cells within native tissue contexts.

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method designed to reconstruct single-cell spatial neighborhoods and facilitate 3D tissue visualization using spatial and single-cell RNA sequencing data. scHolography employs a high-dimensional transcriptome-to-space projection that infers spatial relationships among cells, defining spatial neighborhoods and enhancing analyses of cell–cell communication. When applied to both human and mouse datasets, scHolography enables quantitative assessments of spatial cell neighborhoods, cell–cell interactions, and tumor-immune microenvironment. Together, scHolography offers a robust computational framework for elucidating 3D tissue organization and analyzing spatial dynamics at the cellular level.

MotivationSpatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs.ResultsSPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression.Availability and implementationThe SPRITE software package is available at https://github.com/sunericd/SPRITE. Code for generating experiments and analyses in the manuscript is available at https://github.com/sunericd/sprite-figures-and-analyses.

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d7002e62">Understanding cellular heterogeneity within complex tissue environments is critical for advancing our knowledge of biological processes and disease mechanisms. Spatial transcriptomics technologies, such as Xenium, provide detailed spatial mapping of gene expression at the single-cell level, revealing intricate tissue organization and distinct cell populations. However, translating these spatial insights into actionable molecular information often requires further downstream analysis of specific cells of interest. To address this, we introduce an integrated workflow combining SLACS (Spatially-resolved Laser-Activated Cell Sorting) with Xenium-derived spatial data, enabling targeted isolation and in-depth transcriptomic analysis of defined cell populations. SLACS technology is a novel cell sorting method that utilizes laser-based activation for precise, spatially guided isolation of individual cells. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d7002e64">In this study, we applied SLACS to samples previously analyzed using Xenium spatial transcriptomics, focusing on cell populations identified as functionally relevant or rare based on their spatial gene expression profiles. The targeted cells were isolated using SLACS, followed by high-resolution RNA-seq analysis to further characterize their unique transcriptomic signatures. Utilizing two distinct breast cancer samples analyzed via Xenium, we demonstrate the effective identification of regions of interest (ROIs) for SLACS-based isolation and subsequent high-resolution transcriptomic analysis. We applied Xenium spatial transcriptomics analysis of two breast cancer samples: one luminal A subtype and one triple-negative breast cancer. The Xenium platform provided detailed maps of gene expression, highlighting specific cell populations and spatially distinct ROIs. Using custom cell type annotations generated with the spacexr R package (RCTD), we identified key cell types, such as luminal progenitor cells, cancer-associated fibroblasts (CAFs), and cells undergoing epithelial-to-mesenchymal transition (EMT).

Spatial transcriptomics technologies are becoming increasingly high-resolution, enabling gene expression measurement at the subcellular level. Here, we present subcellular expression localization analysis (ELLA), a statistical framework for modeling subcellular mRNA localization and detecting spatially variable genes within cells. ELLA uses an over-dispersed nonhomogeneous Poisson process to model spatial count data with a unified cellular coordinate system to anchor diverse cellular morphologies, demonstrating effective type I error control and high power in simulations. In real data applications, ELLA identifies genes with distinct subcellular localization and associate these patterns to key mRNA characteristics: nuclear-enriched genes exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, while cytoplasmic- or membrane-enriched genes frequently encode ribosomal proteins or contain signal peptides. ELLA also uncovers dynamic subcellular localization changes across the cell cycle. Overall, ELLA is a powerful, robust, and scalable tool for subcellular spatial expression analysis across high-resolution spatial transcriptomics platforms.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. The spatial architecture of cell types and gene expression are the basis of cell-cell interactions, biological function and disease pathology, but are not well investigated in the human motor cortex, a key ALS-relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, the brain regional vulnerability of ALS-associated genes and the genetic link between region-specific genes and ALS risk remain largely unclear. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics. We benchmarked SpatialE against another enrichment tool (multimodal intersection analysis) using spatial transcriptomics data from both human and mouse brain tissues. To investigate regional vulnerability, we analysed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function variants detected by whole-genome sequencing in ALS patients and controls, then analysed differential gene expression in the TargetALS RNA-sequencing dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than multimodal intersection analysis in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogeneous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 of the motor cortex, with abundant expression of upper motor neurons and layer 5 excitatory neurons. Burden analyses of rare loss-of-function variants in Layer 5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6814 ALS patients and 3324 controls (P = 0.029). Gene expression analyses in CNS tissues revealed downregulation of NOMO1 in ALS, which is consistent with a loss-of-function disease mechanism. In conclusion, our integrated spatial transcriptomics and genomic analyses identified regional brain vulnerability in ALS and the association of a layer 5 gene (NOMO1) with ALS risk.

Transcriptomics covers the in-depth analysis of RNA molecules in cells or tissues and plays an essential role in understanding cellular functions and disease mechanisms. Advances in spatial transcriptomics (ST) in recent times have revolutionized the field by combining gene expression data with spatial information, enabling the analysis of RNA molecules within their tissue context. The evolution of spatial transcriptomics, particularly the integration of artificial intelligence (AI) in data analysis, and its diverse applications have been found to be superior methods in developmental research. Spatial transcriptomics technologies, along with single-cell RNA sequencing (scRNA-seq), offer unprecedented possibilities to unravel intricate cellular interactions within tissues. It emphasizes the importance of accurate cell localization for in-depth discoveries and developments via highthroughput spatial transcriptome profiling. The integration of artificial intelligence in spatial transcriptomics analysis is a key focus, showcasing its role in detecting spatially variable genes, clustering cell populations, communication analysis, and enhancing data interpretation. The evolution of AI methods tailored for spatial transcriptomics is highlighted, addressing the unique challenges posed by spatially resolved transcriptomic data. Applications of spatial transcriptomics integrated with other omics data, such as genomics, proteomics, and metabolomics, provide a detailed view of molecular processes within tissues and emerge in diverse applications. Integrating spatial transcriptomics with AI represents a transformative approach to understanding tissue architecture and cellular interactions. This innovative synergy not only enhances our understanding of gene expression patterns but also offers a holistic view of molecular processes within tissues, with profound implications for disease mechanisms and therapeutic development.

Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.

Spatially resolved technologies are transforming cancer research by enabling the simultaneous visualization of molecular and cellular processes within tissue contexts. We performed a comprehensive benchmarking of various multiplex immunofluorescence (IF) platforms, including COMET™ and MILAN, and spatial transcriptomics technologies, including RNAscope™ and Xenium, and their combinations to evaluate their performance, compatibility, and potential for deep phenotypic profiling. Tissues from multiple cancer types were subjected to multiplex IF using the COMET and MILAN platforms. Complementary spatial transcriptomics analyses were performed using RNAscope and Xenium on the same sections. Performance metrics and cross-platform reproducibility were systematically assessed and the added value of evaluating protein and RNA markers simultaneously was also evaluated. To facilitate data processing and integration, we developed a novel bioinformatics software, DISSCOVERY, specifically designed for the integrated analysis of high-dimensional multiplex IF and spatial transcriptomics data. Each multiplex IF and spatial transcriptomics platform demonstrated unique strengths and limitations, both individually and in combination. Initial evaluations focused on the performance of each platform independently, followed by assessments of their combined application on the same tissue section. Notably, the combination of the Xenium and MILAN platforms provided unparalleled flexibility and scalability, enabling high-throughput analysis of both large marker panels and extensive sample sets. The COMET platform, which integrates RNAscope with multiplex IF, also proved highly valuable for its speed and automation, particularly in validating newly discovered mechanisms. Integration of these data through the DISSCOVERY software enabled seamless cross-platform analysis, uncovering spatial co-localization patterns and gene-protein associations. This approach allowed us to identify novel cellular niches and functional interactions across various cancer types, highlighting the powerful synergy of multiplex IF, IHC and spatial transcriptomics for advancing our understanding of cancer biology. Overall, our benchmarking underscores the complementary capabilities of multiplex IF and spatial transcriptomics technologies in dissecting the tumor microenvironment. The DISSCOVERY pipeline streamlines data integration, facilitating multi-omic insights into cancer biology. These findings provide a roadmap for selecting and combining spatial technologies to advance cancer research. Citation Format: Jonathan Chui, Asier Antoranz, Jon Pey, Nikolina Dubroja, Madhavi Dipak Andhari, Chiara Caprioli, Katy Vandereyken, Carmen Bravo González-Blas, Julie Morscio, Alexandre Arnould, Pouya Nazari, Gautam Shankar, Bart de Moor, Thierry Voet, Francesca M. Bosisio, Frederik De Smet. Technological benchmarking of integrated multiome analysis using multiplex immunofluorescence and spatial transcriptomics across cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2073.

Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information. To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

High-resolution spatial transcriptomics requires new computational methods to accurately assign transcripts to individual cells. We developed SMURF (Segmentation and Manifold UnRolling Framework), a novel, cross-platform, soft-segmentation algorithm that maps mRNAs from barcoded capture spots to nearby nuclei. SMURF also “unrolls” complex tissue architectures by projecting cells onto Cartesian coordinates, enabling analysis of cell-type organization and gene-expression gradients in intact tissues. We benchmarked SMURF and found it assigns mRNAs to single cells more accurately than existing approaches. We evaluated SMURF’s ability to unroll complex tissues at cell-type resolution across multiple tissues and platforms. This analysis revealed previously unrecognized zonation of gene-expression programs, identified the transcription factors that regulate these patterns, and provided evidence that regional gene expression at the intestinal tip is reprogrammed by luminal environmental signals. Together, these results establish SMURF as a powerful framework for analyzing gene expression of cells within their native tissue contexts.