SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

Relation equivariant graph neural networks to explore the mosaic-like tissue architecture of kidney diseases on spatially resolved transcriptomics

MOTIVATION

Chronic kidney disease (CKD) and acute kidney injury (AKI) are prominent public health concerns affecting more than 15% of the global population. The ongoing development of spatially resolved transcriptomics (SRT) technologies presents a promising approach for discovering the spatial distribution patterns of gene expression within diseased tissues. However, existing computational tools are predominantly calibrated and designed on the ribbon-like structure of the brain cortex, presenting considerable computational obstacles in discerning highly heterogeneous mosaic-like tissue architectures in the kidney. Consequently, timely and cost-effective acquisition of annotation and interpretation in the kidney remains a challenge in exploring the cellular and morphological changes within renal tubules and their interstitial niches.

RESULTS

We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), designed for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, REGNN integrates equivariance to handle n-dimensional symmetries of the spatial area, while additionally leveraging Positional Encoding to strengthen relative spatial relations of the nodes uniformly distributed in the lattice. Given the limited availability of well-labeled spatial data, this framework implements both graph autoencoder and graph self-supervised learning strategies. On heterogeneous samples from different kidney conditions, REGNN outperforms existing computational tools in identifying tissue architectures within the 10× Visium platform. This framework offers a powerful graph deep learning tool for investigating tissues within highly heterogeneous expression patterns and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases.

AVAILABILITY AND IMPLEMENTATION

REGNN is publicly available at https://github.com/Mraina99/REGNN.

OmniClust: A versatile clustering toolkit for single-cell and spatial transcriptomics data

In recent years, RNA transcriptome sequencing technology has been continuously evolving, ranging from single-cell transcriptomics to spatial transcriptomics. Although these technologies are all based on RNA sequencing, each sequencing technology has its own unique characteristics, and there is an urgent need to develop an algorithmic toolkit that integrates both sequencing techniques. To address this, we have developed OmniClust, a toolkit based on single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data. OmniClust employs deep learning algorithms for feature learning and clustering of spatial transcriptomics data, while utilizing machine learning algorithms for clustering scRNA-seq data. OmniClust was tested on 12 spatial transcriptomics benchmark datasets, demonstrating high clustering accuracy across multiple clustering evaluation metrics. It was also evaluated on four scRNA-seq benchmark datasets, achieving high clustering accuracy based on various clustering evaluation metrics. Furthermore, we applied OmniClust to downstream analyses of spatial transcriptomics and single-cell RNA breast cancer data, showcasing its potential to uncover and interpret the biological significance of cancer transcriptome data. In summary, OmniClust is a clustering tool designed for both single-cell transcriptomics and spatial transcriptomics data, demonstrating outstanding performance.

CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells' molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions.

SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection

Technological advances have enabled us to profile multiple omics layers with spatial information, significantly enhancing spatial domain detection and advancing a variety of biomedical research fields. Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. Extensive experiments reveal that SMOPCA outperforms existing single-modal and multimodal dimension reduction and clustering methods, across both single-cell and spatial multi-omics datasets derived from diverse technologies and tissue structures.

Advances in spatial transcriptomics and its application in the musculoskeletal system

While bulk RNA sequencing and single-cell RNA sequencing have shed light on cellular heterogeneity and potential molecular mechanisms in the musculoskeletal system in both physiological and various pathological states, the spatial localization of cells and molecules and intercellular interactions within the tissue context require further elucidation. Spatial transcriptomics has revolutionized biological research by simultaneously capturing gene expression profiles and in situ spatial information of tissues, gradually finding applications in musculoskeletal research. This review provides a summary of recent advances in spatial transcriptomics and its application to the musculoskeletal system. The classification and characteristics of data acquisition techniques in spatial transcriptomics are briefly outlined, with an emphasis on widely-adopted representative technologies and the latest technological breakthroughs, accompanied by a concise workflow for incorporating spatial transcriptomics into musculoskeletal system research. The role of spatial transcriptomics in revealing physiological mechanisms of the musculoskeletal system, particularly during developmental processes, is thoroughly summarized. Furthermore, recent discoveries and achievements of this emerging omics tool in addressing inflammatory, traumatic, degenerative, and tumorous diseases of the musculoskeletal system are compiled. Finally, challenges and potential future directions for spatial transcriptomics, both as a field and in its applications in the musculoskeletal system, are discussed.

Combining spatial transcriptomics with tissue morphology

Spatial transcriptomics has transformed our understanding of tissue architecture by preserving the spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. This review introduces a framework for categorizing methods that combine spatial transcriptomics with tissue morphology, focusing on either translating or integrating morphological features into spatial transcriptomics. Translation involves using morphology to predict gene expression, creating super-resolution maps or inferring genetic information from H&E-stained samples. Integration enriches spatial transcriptomics by identifying morphological features that complement gene expression. We also explore learning strategies and future directions for this emerging field.

ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis

Recent technological advances enable mapping of tissue spatial organization at single-cell resolution, but methods for analyzing spatially continuous microenvironments are still lacking. We introduce ONTraC, a graph neural network-based framework for constructing spatial trajectories at niche-level. Through benchmarking analyses using multiple simulated and real datasets, we show that ONTraC outperforms existing methods. ONTraC captures both normal anatomical structures and disease-associated tissue microenvironment changes. In addition, it identifies tissue microenvironment-dependent shifts in gene expression, regulatory network, and cell-cell interaction patterns. Taken together, ONTraC provides a useful framework for characterizing the structural and functional organization of tissue microenvironments.

m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics

MOTIVATION

Spatial clustering is a key analytical technique for exploring spatial transcriptomics data. Recent graph neural network-based methods have shown promise in spatial clustering but face notable challenges. One significant issue is that analyzing the functions and complex mechanisms of organisms from a single scale is difficult and most methods focus exclusively on the single-scale representation of transcriptomic data, potentially limiting the discriminative power of extracted features for spatial domain clustering. Furthermore, classical clustering algorithms are often applied directly to latent representation, making it a worthwhile endeavor to explore a tailored clustering method to further improve the accuracy of spatial domain annotation.

RESULTS

To address these limitations, we propose m2ST, a novel dual multi-scale graph clustering method. m2ST first uses a multi-scale masked graph autoencoder to extract representations across different scales from spatial transcriptomic data. To effectively compress and distill meaningful knowledge embedded in the data, m2ST introduces a random masking mechanism for node features and uses a scaled cosine error as the loss function. Additionally, we introduce a tailored multi-scale clustering framework that integrates scale-common and scale-specific information exploration into the clustering process, achieving more robust annotation performance. Shannon entropy is finally utilized to dynamically adjust the importance of different scales. Extensive experiments on multiple spatial transcriptomic datasets demonstrate the superior performance of m2ST compared to existing methods.

AVAILABILITY AND IMPLEMENTATION

https://github.com/BBKing49/m2ST.

Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes

Spatial transcriptomics (ST) data provide spatially informed gene expression profiles. However, power is limited for spatially informed differential gene expression (DGE) of complex diseases such as Alzheimer disease (AD), due to small sample sizes of ST data. Conversely, single-nucleus RNA sequencing (snRNA-seq) data offer larger sample sizes for cell-type-specific (CTS) analyses but lack spatial information. Here, we integrated ST and snRNA-seq data to enhance the power of spatially informed CTS DGE analysis of AD-related phenotypes. We first utilized the CeLEry tool to infer six cortical layers of ∼1.5 million cells in the snRNA-seq data that were profiled from the dorsolateral prefrontal cortex (DLPFC) tissue of 436 postmortem brains. Then, we conducted cortical layer- and cell-type-specific (LCS) and CTS DGE analyses based on the linear mixed model, for β-amyloid, tangle density, and cognitive decline. We identified 138 LCS significant genes with false discovery rate (FDR) q <0.05, including 103 for β-amyloid, 24 for tangle density, and 25 for cognitive decline. The majority of these LCS significant genes, including known AD risk genes such as APOE, KCNIP3, and CTSD, cannot be detected by CTS analyses. We also identified 2 genes shared across all 3 phenotypes and 10 shared between 2 phenotypes. Gene set enrichment analyses with the LCS DGE results of microglia in cortical layer 6 of β-amyloid identified 12 significant AD-related pathways. In conclusion, incorporating spatial information with snRNA-seq data enhanced the power of spatially informed DGE analyses. These identified LCS significant genes not only help illustrate the pathogenesis of AD but they also provide potential targets for developing therapeutics of AD.

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo-relations, as well as inference of intra- and inter-cellular molecular networks that drive disease progression. We also discuss strategies to address major challenges, including data sparsity, high-dimensionality, scalability, and heterogeneity. Furthermore, we outline how spatial multi-omics enables novel insights into disease mechanisms, advancing precision medicine and informing targeted therapies. KEY POINTS: Advancements in spatial multi-omics facilitate our understanding of tumour spatiotemporal heterogeneity. AI-driven multimodal models uncover complex molecular interactions that underlie cellular behaviours and tissue dynamics. Combining multi-omics technologies and AI-enabled bioinformatics tools helps predict critical disease stages, such as pre-cancer, advancing precision medicine, and informing targeted therapeutic strategies.

StereoMM: a graph fusion model for integrating spatial transcriptomic data and pathological images

Spatial omics technologies, generating high-throughput and multimodal data, have necessitated the development of advanced data integration methods to facilitate comprehensive biological and clinical treatment discoveries. Based on the cross-attention concept, we developed an AI learning based toolchain called StereoMM, a graph based fusion model that can incorporate omics data such as gene expression, histological images, and spatial location. StereoMM uses an attention module for omics data interaction and a graph autoencoder to integrate spatial positions and omics data in a self-supervised manner. Applying StereoMM across various cancer types and platforms has demonstrated its robust capability. StereoMM outperforms competitors in identifying spatial regions reflecting tumour progression and shows promise in classifying colorectal cancer patients into deficient mismatch repair and proficient mismatch repair groups. The comprehensive inter-modal integration and efficiency of StereoMM enable researchers to construct spatial views of integrated multimodal features efficiently, advancing thorough tissue and patient characterization.

A Robust Kernel-Based Workflow for Niche Trajectory Analysis

Niche trajectory analysis is a promising framework for modeling spatially continuous variations of the tissue microenvironment. However, the existing approach is limited by its requirement of cell-type annotation as a necessary input, which can lead to unwanted technical variations. To overcome this limitation, a new kernel-based strategy is presented that models the structural composition of a niche as a continuous function in the gene expression space, thereby obviating the need for cell-type annotation. Further integration with cell-type deconvolution analysis extends its application to datasets with any spatial resolution. Applying this strategy to real datasets indicates enhanced performance in robustness and accuracy and provides new insights into injury or disease-associated tissue microenvironment changes. As such, a useful tool for spatial transcriptomics data analysis is provided.

Learning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE

Spatially resolved transcriptomics (SRT) is poised to advance the understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here PASSAGE (Phenotype Associated Spatial Signature Analysis with Graph-based Embedding), a rationally-designed deep learning framework is presented for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, PASSAGE's unique capability in calling sophisticated signatures has been demonstrated in multiple real-world cases. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Spatially resolved mapping of cells associated with human complex traits

Depicting spatial distributions of disease-relevant cells is crucial for understanding disease pathology1,2. Here we present genetically informed spatial mapping of cells for complex traits (gsMap), a method that integrates spatial transcriptomics data with summary statistics from genome-wide association studies to map cells to human complex traits, including diseases, in a spatially resolved manner. Using embryonic spatial transcriptomics datasets covering 25 organs, we benchmarked gsMap through simulation and by corroborating known trait-associated cells or regions in various organs. Applying gsMap to brain spatial transcriptomics data, we reveal that the spatial distribution of glutamatergic neurons associated with schizophrenia more closely resembles that for cognitive traits than that for mood traits such as depression. The schizophrenia-associated glutamatergic neurons were distributed near the dorsal hippocampus, with upregulated expression of calcium signalling and regulation genes, whereas depression-associated glutamatergic neurons were distributed near the deep medial prefrontal cortex, with upregulated expression of neuroplasticity and psychiatric drug target genes. Our study provides a method for spatially resolved mapping of trait-associated cells and demonstrates the gain of biological insights (such as the spatial distribution of trait-relevant cells and related signature genes) through these maps.

Spatially Aware Domain Adaptation Enables Cell Type Deconvolution from Multi-Modal Spatially Resolved Transcriptomics

Spatially Resolved Transcriptomics (SRT) offers unprecedented opportunities to elucidate the cellular arrangements within tissues. Nevertheless, the absence of deconvolution methods that simultaneously model multi-modal features has impeded progress in understanding cellular heterogeneity in spatial contexts. To address this issue, SpaDA is developed, a novel spatially aware domain adaptation method that integrates multi-modal data (i.e., transcriptomics, histological images, and spatial locations) from SRT to accurately estimate the spatial distribution of cell types. SpaDA utilizes a self-expressive variational autoencoder, coupled with deep spatial distribution alignment, to learn and align spatial and graph representations from spatial multi-modal SRT data and single-cell RNA sequencing (scRNA-seq) data. This strategy facilitates the transfer of cell type annotation information across these two similarity graphs, thereby enhancing the prediction accuracy of cell type composition. The results demonstrate that SpaDA surpasses existing methods in cell type deconvolution and the identification of cell types and spatial domains across diverse platforms. Moreover, SpaDA excels in identifying spatially colocalized cell types and key marker genes in regions of low-quality measurements, exemplified by high-resolution mouse cerebellum SRT data. In conclusion, SpaDA offers a powerful and flexible framework for the analysis of multi-modal SRT datasets, advancing the understanding of complex biological systems.

Exploring the Latent Information in Spatial Transcriptomics Data via Multi-View Graph Convolutional Network Based on Implicit Contrastive Learning

Latest developments in spatial transcriptomics enable thoroughly profiling of gene expression while preserving tissue microenvironment. Connecting gene expression with spatial arrangement is key for precise spatial domain identification, enhancing the comprehension of tissue microenvironments and biological processes. However, accurately analyzing spatial domains with similar gene expression and histological features is still challenging. This study introduces STMIGCL, a novel framework that leverages a multi-view graph convolutional network and implicit contrastive learning. First, it creates neighbor graphs from gene expression and spatial coordinates, and then combines these with gene expression through multi-view learning to learn low-dimensional representations. To further refine the obtained low-dimensional representations, a graph contrastive learning method with contrastive enhancement in the latent space is employed, aiming to better capture critical information in the data and improve the accuracy and discriminative power of the embeddings. Finally, an attention mechanism is used to adaptively integrate different views, capturing the importance of spots in various views to obtain the final spot representation. Experimental data confirms that STMIGCL significantly enhances spatial domain recognition precision and outperforms all baseline methods in tasks such as trajectory inference and Spatially Variable Genes (SVGs) recognition.

SpaNorm: spatially-aware normalization for spatial transcriptomics data

Normalization of spatial transcriptomics data is challenging due to spatial association between region-specific library size and biology. We develop SpaNorm, the first spatially-aware normalization method that concurrently models library size effects and the underlying biology, segregates these effects, and thereby removes library size effects without removing biological information. Using 27 tissue samples from 6 datasets spanning 4 technological platforms, SpaNorm outperforms commonly used single-cell normalization approaches while retaining spatial domain information and detecting spatially variable genes. SpaNorm is versatile and works equally well for multicellular and subcellular spatial transcriptomics data with relatively robust performance under different segmentation methods.

Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large spatial transcriptomics (ST) data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conquer framework especially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulations, HERGAST consistently outperforms other methods across all settings with more than a 10% increase in average adjusted rand index (ARI). In real-world datasets, HERGAST's high-precision spatial clustering identifies SPP1+ macrophages intermingled within colorectal tumors, while the enhanced gene expression signals reveal unique spatial expression patterns of key genes in breast cancer.

Histology-Based Virtual RNA Inference Identifies Pathways Associated with Metastasis Risk in Colorectal Cancer

Colorectal cancer (CRC) remains a major health concern, with over 150,000 new diagnoses and more than 50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (e.g., Visium, Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented Virtual RNA Inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST dataset to date, comprising 45 patients and more than 300,000 Visium spots from primary tumors. Using state-of-the-art architectures (UNI, ResNet-50, ViT, and VMamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications

Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Understanding complex biological systems requires tracing cellular dynamic changes across conditions, time, and space. However, integrating multi-sample data in a unified way to explore cellular heterogeneity remains challenging. Here, we present Stereopy, a flexible framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this framework, we devise a universal container, a scope controller, and an integrative transformer tailored for multi-sample multimodal data storage, management, and processing. Stereopy showcases three representative applications: investigating specific cell communities and genes responsible for pathological changes, detecting spatiotemporal gene patterns by considering spatial and temporal features, and inferring three-dimensional niche-based cell-gene interaction network that bridges intercellular communications and intracellular regulations. Stereopy serves as both a comprehensive bioinformatics toolbox and an extensible framework that empowers researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data

In recent years, spatial transcriptomics (ST) has emerged as an innovative technology that enables the simultaneous acquisition of gene expression information and its spatial distribution at the single-cell or regional level, providing deeper insights into cellular interactions and tissue organization, this technology provides a more holistic view of tissue organization and intercellular dynamics. However, existing methods still face certain limitations in data representation capabilities, making it challenging to fully capture complex spatial dependencies and global features. To address this, this paper proposes an innovative spatial multi-scale graph convolutional network (SGTB) based on large language models, integrating graph convolutional networks (GCN), Transformer, and BERT language models to optimize the representation of spatial transcriptomics data. The Graph Convolutional Network (GCN) employs a multi-layer architecture to extract features from gene expression matrices. Through iterative aggregation of neighborhood information, it captures spatial dependencies among cells and gene co-expression patterns, thereby constructing hierarchical cell embeddings. Subsequently, the model integrates an attention mechanism to assign weights to critical features and leverages Transformer layers to model global relationships, refining the ability of learned representations to reflect variations in spatial patterns. Finally, the model incorporates the BERT language model, mapping cell embeddings into textual inputs to exploit its deep semantic representation capabilities for high-dimensional feature extraction. These features are then fused with the embeddings generated by the Transformer, further optimizing feature learning for spatial transcriptomics data. This approach holds significant application value in improving the accuracy of tasks such as cell type classification and gene regulatory network construction, providing a novel computational framework for deep mining of spatial multi-scale biological data.

Dissection of tumoral niches using spatial transcriptomics and deep learning

This study introduces TG-ME, an innovative computational framework that integrates transformer with graph variational autoencoder (GraphVAE) models for dissection of tumoral niches using spatial transcriptomics data and morphological images. TG-ME effectively identifies and characterizes niches in bench datasets and a high resolution NSCLC dataset. The pipeline consists in different stages that include normalization, spatial information integration, morphological feature extraction, gene expression quantification, single cell expression characterization, and tumor niche characterization. For this, TG-ME leverages advanced deep learning techniques that achieve robust clustering and profiling of niches across cancer stages. TG-ME can potentially provide insights into the spatial organization of tumor microenvironments (TME), highlighting specific niche compositions and their molecular changes along cancer progression. TG-ME is a promising tool for guiding personalized treatment strategies by uncovering microenvironmental signatures associated with disease prognosis and therapeutic outcomes.

Neurogenesis drives hippocampal formation-wide spatial transcription alterations in health and Alzheimer's disease

The mechanism by which neurogenesis regulates the profile of neurons and glia in the hippocampal formation is not known. Further, the effect of neurogenesis on neuronal vulnerability characterizing the entorhinal cortex in Alzheimer's disease (AD) is unknown. Here, we used in situ sequencing to investigate the spatial transcription profile of neurons and glia in the hippocampal circuitry in wild-type mice and in familial AD (FAD) mice expressing varying levels of neurogenesis. This approach revealed that in addition to the dentate gyrus, neurogenesis modulates the cellular profile in the entorhinal cortex and CA regions of the hippocampus. Notably, enhancing neurogenesis in FAD mice led to partial restoration of neuronal and cellular profile in these brain areas, resembling the profile of their wild-type counterparts. This approach provides a platform for the examination of the cellular dynamics in the hippocampal formation in health and in AD.

Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data

Advances in spatially resolved transcriptomics (SRT) have led to the emergence of numerous computational methods for identifying spatial domains and spatially variable genes (SVGs); however, a comprehensive assessment of existing methods is lacking. We comprehensively benchmarked 19 methods for detecting spatial domains and domain-specific SVGs from SRT data, using 30 real-world datasets covering six SRT technologies and 27 synthetic datasets. We first evaluated the performance of these methods on spatial domain identification in terms of accuracy, stability, generalizability, and scalability. Results reveal that there is no single method that works best for all datasets, and the optimal method depends on the data, especially the SRT platform. Further, we proposed a quantitative strategy to evaluate domain-specific SVG recognition results and assessed the impact of spatial domains on SVG detection. We found that SVG detection based on spatial domains identified by different GNN methods have high accuracy but low concordance. Generally, the more accurate the recognized spatial domains, the higher the number and accuracy of domain-specific SVGs detected. Moreover, integrating spatial clustering results from different methods can lead to more robust and better clustering and SVG results. Practical guidelines were provided for choosing appropriate methods for spatial domain and domain-specific SVG identification.

Spatially informed graph transformers for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT) has emerged as a powerful technique for mapping gene expression landscapes within spatial contexts. However, significant challenges persist in effectively integrating gene expression with spatial information to elucidate the heterogeneity of biological tissues. Here, we present a Spatially informed Graph Transformers framework, SpaGT, which leverages both node and edge channels to model spatially aware graph representation for denoising gene expression and identifying spatial domains. Unlike conventional graph neural networks, which rely on static, localized convolutional aggregation, SpaGT employs a structure-reinforced self-attention mechanism that iteratively evolves topological structural information and transcriptional signal representation. By replacing graph convolution with global self-attention, SpaGT enables the integration of both global and spatially localized information, thereby improving the detection of fine-grained spatial domains. We demonstrate that SpaGT achieves superior performance in identifying spatial domains and denoising gene expression data across diverse platforms and species. Furthermore, SpaGT facilitates the discovery of spatially variable genes with significant prognostic potential in cancer tissues. These findings establish SpaGT as a powerful tool for unraveling the complexities of biological tissues.

Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder

Domain identification is a critical problem in spatially resolved transcriptomics data analysis, which aims to identify distinct spatial domains within a tissue that maintain both spatial continuity and expression consistency. The degree of coupling between expression data and spatial information in different datasets often varies significantly. Some regions have intact and clear boundaries, while others exhibit blurred boundaries with high intra-domain expression similarity. However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. Spot2vector encodes and integrates spatial and expression information, enabling effective identification of domains with diverse spatial patterns across spatially resolved transcriptomics data generated by different platforms. The decoders enable us to decipher the distribution and generation mechanisms of data while improving expression quality through denoising. Extensive validation and analyses demonstrate that Spot2vector excels in enhancing domain identification accuracy, effectively reducing data dimensionality, improving expression recovery and denoising, and precisely capturing spatial gene expression patterns.

mcDETECT: Decoding 3D Spatial Synaptic Transcriptomes with Subcellular-Resolution Spatial Transcriptomics

Spatial transcriptomics (ST) has shown great potential for unraveling the molecular mechanisms of neurodegenerative diseases. However, most existing analyses of ST data focus on bulk or single-cell resolution, overlooking subcellular compartments such as synapses, which are fundamental structures of the brain's neural network. Here we present mcDETECT, a novel framework that integrates machine learning algorithms and in situ ST (iST) with targeted gene panels to study synapses. mcDETECT identifies individual synapses based on the aggregation of synaptic mRNAs in three-dimensional (3D) space, allowing for the construction of single-synapse spatial transcriptome profiles. By benchmarking the synapse density measured by volume electron microscopy and genetic labeling, we demonstrate that mcDETECT can faithfully and accurately recover the spatial location of single synapses using iST data from multiple platforms, including Xenium, Xenium 5K, MERSCOPE, and CosMx. Based on the subsequent transcriptome profiling, we further stratify total synapses into various subtypes and explore their pathogenic dysregulation associated with Alzheimer's disease (AD) progression, which provides potential targets for synapse-specific therapies in AD progression.

SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics

Understanding the spatial locations of cell within tissues is crucial for unraveling the organization of cellular diversity. Recent advancements in spatial resolved transcriptomics (SRT) have enabled the analysis of gene expression while preserving the spatial context within tissues. Spatial domain characterization is a critical first step in SRT data analysis, providing the foundation for subsequent analyses and insights into biological implications. Graph neural networks (GNNs) have emerged as a common tool for addressing this challenge due to the structural nature of SRT data. However, current graph-based deep learning approaches often overlook the instability caused by the high sparsity of SRT data. Masking mechanisms, as an effective self-supervised learning strategy, can enhance the robustness of these models. To this end, we propose SpaMask, dual masking graph autoencoder with contrastive learning for SRT analysis. Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. We conducted a comprehensive evaluation of SpaMask on eight datasets from five different platforms. Compared to existing methods, SpaMask achieves superior clustering accuracy and effective batch correction.

Quantitative characterization of cell niches in spatially resolved omics data

Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning

Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial transcriptomics data coming from different platforms usually has different resolutions. Moreover, current methods do not consider the heterogeneity of spatial structures within and across slices when modeling spatial transcriptomics data with graph-based methods. In this study, we propose a community-enhanced graph contrastive learning-based method named Tacos to integrate multiple spatial transcriptomics data. We applied Tacos to several real datasets coming from different platforms under different scenarios. Systematic benchmark analyses demonstrate Tacos's superior performance in integrating different slices. Furthermore, Tacos can accurately denoise the spatially resolved transcriptomics data.

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

MOTIVATION

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets.

RESULTS

In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of 30 synthesized and 74 real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

AVAILABILITY AND IMPLEMENTATION

The source code of this benchmarking framework is available at both Github (https://github.com/XiDsLab/svg-benchmark) and Zenodo (https://doi.org/10.5281/zenodo.15031083). In addition, all real and synthetic datasets considered in this study are also publicly available at Zenodo (https://doi.org/10.5281/zenodo.7227771).

Protocol to boost the robustness and accuracy of spatial transcriptomics algorithms using ensemble techniques

Spatial transcriptomics enhances our understanding of cellular organization by mapping gene expression data to precise tissue locations. Here, we present a protocol for using weighted ensemble method for spatial transcriptomics (WEST), which uses ensemble techniques to boost the robustness and accuracy of existing algorithms. We describe steps for preprocessing data, obtaining embeddings from individual algorithms, and ensemble integrating all embeddings as a similarity matrix. We then detail procedures for using the similarity matrix to identify spatial domains and obtain new embeddings. For complete details on the use and execution of this protocol, please refer to Cai et al.1.

SPARROW reveals microenvironment-zone-specific cell states in healthy and diseased tissues

Spatially resolved transcriptomics technologies have advanced our understanding of cellular characteristics within tissue contexts. However, current analytical tools often treat cell-type inference and cellular neighborhood identification as separate and hard clustering processes, limiting comparability across scales and samples. SPARROW addresses these challenges by jointly learning latent embeddings and soft clusterings of cell types and cellular organization. It outperformed state-of-the-art methods in cell-type inference and microenvironment zone delineation and uncovered zone-specific cell states in human and mouse tissues that competing methods missed. By integrating spatially resolved transcriptomics and single-cell RNA sequencing (scRNA-seq) data in a shared latent space, SPARROW achieves single-cell spatial resolution and whole-transcriptome coverage, enabling the discovery of both established and unknown microenvironment zone-specific ligand-receptor interactions in the human tonsil. Overall, SPARROW is a computational framework that provides a comprehensive characterization of tissue features across scales, samples, and conditions.

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

Spatially resolved transcriptomics (SRT) simultaneously measure spatial location, histology images, and transcriptional profiles of cells or regions in undissociated tissues. Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. MuCST accurately identifies spatial domains and is applicable to diverse datasets platforms. Overall, MuCST provides an alternative for integrative analysis of multi-modal SRT data ( https://github.com/xkmaxidian/MuCST ).

Graph-Based 3-Dimensional Spatial Gene Neighborhood Networks of Single Cells in Gels and Tissues

Objective: We developed 3-dimensional spatially resolved gene neighborhood network embedding (3D-spaGNN-E) to find subcellular gene proximity relationships and identify key subcellular motifs in cell-cell communication (CCC). Impact Statement: The pipeline combines 3D imaging-based spatial transcriptomics and graph-based deep learning to identify subcellular motifs. Introduction: Advancements in imaging and experimental technology allow the study of 3D spatially resolved transcriptomics and capture better spatial context than approximating the samples as 2D. However, the third spatial dimension increases the data complexity and requires new analyses. Methods: 3D-spaGNN-E detects single transcripts in 3D cell culture samples and identifies subcellular gene proximity relationships. Then, a graph autoencoder projects the gene proximity relationships into a latent space. We then applied explainability analysis to identify subcellular CCC motifs. Results: We first applied the pipeline to mesenchymal stem cells (MSCs) cultured in hydrogel. After clustering the cells based on the RNA count, we identified cells belonging to the same cluster as homotypic and those belonging to different clusters as heterotypic. We identified changes in local gene proximity near the border between homotypic and heterotypic cells. When applying the pipeline to the MSC-peripheral blood mononuclear cell (PBMC) coculture system, we identified CD4+ and CD8+ T cells. Local gene proximity and autoencoder embedding changes can distinguish strong and weak suppression of different immune cells. Lastly, we compared astrocyte-neuron CCC in mouse hypothalamus and cortex by analyzing 3D multiplexed-error-robust fluorescence in situ hybridization (MERFISH) data and identified regional gene proximity differences. Conclusion: 3D-spaGNN-E distinguished distinct CCCs in cell culture and tissue by examining subcellular motifs.

Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

Spatial cellular context is crucial in shaping intratumor heterogeneity. However, understanding how each tumor establishes its unique spatial landscape and what factors drive the landscape for tumor fitness remains significantly challenging. Here, we analyzed over 2 million cells from 50 tumor biospecimens using spatial single-cell imaging and single-cell RNA sequencing. We developed a deep learning-based strategy to spatially map tumor cell states and the architecture surrounding them, which we referred to as Spatial Dynamics Network (SDN). We found that different tumor cell states may be organized into distinct clusters, or 'villages', each supported by unique SDNs. Notably, tumor cell villages exhibited village-specific molecular co-dependencies between tumor cells and their microenvironment and were associated with patient outcomes. Perturbation of molecular co-dependencies via random spatial shuffling of the microenvironment resulted in destabilization of the corresponding villages. This study provides new insights into understanding tumor spatial landscape and its impact on tumor aggressiveness.

Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets

Dimensionality reduction greatly facilitates the exploration of cellular heterogeneity in single-cell RNA sequencing data. While most of such approaches are data-driven, it can be useful to incorporate biologically plausible assumptions about the underlying structure or the experimental design. We propose the boosting autoencoder (BAE) approach, which combines the advantages of unsupervised deep learning for dimensionality reduction and boosting for formalizing assumptions. Specifically, our approach selects small sets of genes that explain latent dimensions. As illustrative applications, we explore the diversity of neural cell identities and temporal patterns of embryonic development.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Graph neural networks for single-cell omics data: a review of approaches and applications

Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data have introduced substantial difficulties to traditional computational methods. The intricate non-Euclidean networks of intracellular and intercellular signaling molecules within single-cell datasets, coupled with the complex, multimodal structures arising from multi-omics joint analysis, pose significant challenges to conventional deep learning operations reliant on Euclidean geometries. Graph neural networks (GNNs) have extended deep learning to non-Euclidean data, allowing cells and their features in single-cell datasets to be modeled as nodes within a graph structure. GNNs have been successfully applied across a broad range of tasks in single-cell data analysis. In this survey, we systematically review 107 successful applications of GNNs and their six variants in various single-cell omics tasks. We begin by outlining the fundamental principles of GNNs and their six variants, followed by a systematic review of GNN-based models applied in single-cell epigenomics, transcriptomics, spatial transcriptomics, proteomics, and multi-omics. In each section dedicated to a specific omics type, we have summarized the publicly available single-cell datasets commonly utilized in the articles reviewed in that section, totaling 77 datasets. Finally, we summarize the potential shortcomings of current research and explore directions for future studies. We anticipate that this review will serve as a guiding resource for researchers to deepen the application of GNNs in single-cell omics.

STForte: tissue context-specific encoding and consistency-aware spatial imputation for spatially resolved transcriptomics

Encoding spatially resolved transcriptomics (SRT) data serves to identify the biological semantics of RNA expression within the tissue while preserving spatial characteristics. Depending on the analytical scenario, one may focus on different contextual structures of tissues. For instance, anatomical regions reveal consistent patterns by focusing on spatial homogeneity, while elucidating complex tumor micro-environments requires more expression heterogeneity. However, current spatial encoding methods lack consideration of the tissue context. Meanwhile, most developed SRT technologies are still limited in providing exact patterns of intact tissues due to limitations such as low resolution or missed measurements. Here, we propose STForte, a novel pairwise graph autoencoder-based approach with cross-reconstruction and adversarial distribution matching, to model the spatial homogeneity and expression heterogeneity of SRT data. STForte extracts interpretable latent encodings, enabling downstream analysis by accurately portraying various tissue contexts. Moreover, STForte allows spatial imputation using only spatial consistency to restore the biological patterns of unobserved locations or low-quality cells, thereby providing fine-grained views to enhance the SRT analysis. Extensive evaluations of datasets under different scenarios and SRT platforms demonstrate that STForte is a scalable and versatile tool for providing enhanced insights into spatial data analysis.

MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder

Spatial transcriptomics (ST) technology provides gene expression profiles with spatial context, offering critical insights into cellular interactions and tissue architecture. A core task in ST is spatial domain identification, which involves detecting coherent regions with similar spatial expression patterns. However, existing methods often fail to fully exploit spatial information, leading to limited representational capacity and suboptimal clustering accuracy. Here, we introduce MAEST, a novel graph neural network model designed to address these limitations in ST data. MAEST leverages graph masked autoencoders to denoise and refine representations while incorporating graph contrastive learning to prevent feature collapse and enhance model robustness. By integrating one-hop and multi-hop representations, MAEST effectively captures both local and global spatial relationships, improving clustering precision. Extensive experiments across diverse datasets, including the human brain, mouse hippocampus, olfactory bulb, brain, and embryo, demonstrate that MAEST outperforms seven state-of-the-art methods in spatial domain identification. Furthermore, MAEST showcases its ability to integrate multi-slice data, identifying joint domains across horizontal tissue sections with high accuracy. These results highlight MAEST's versatility and effectiveness in unraveling the spatial organization of complex tissues. The source code of MAEST can be obtained at https://github.com/clearlove2333/MAEST.

Multi-Manifolds fusing hyperbolic graph network balanced by pareto optimization for identifying spatial domains of spatial transcriptomics

Identifying spatial domains for spatial transcriptomics is crucial for achieving comprehensive insights into the pathogenesis of gene expression. Increasingly, computational methods based on graph neural networks are being developed for spatial transcriptomics. However, previous methods have solely focused on the Euclidean manifold. To effectively exploit and explore the informative and deeper topological structures of inherent manifolds, we presented a Multi-Manifolds fusing hyperbolic graph network, balanced by Pareto optimization, for identifying spatial domains in Spatial Transcriptomics (MManiST). First, we developed multi-manifolds encoders for distinct manifolds using the hyperbolic neural network. Features from different manifolds were then combined using an attention mechanism, with multiple reconstruction losses balanced by Pareto optimization. Extensive experiments on commonly used benchmark datasets show that our method consistently outperforms seven state-of-the-art methods. Additionally, we investigated the validity of each component and the impact of fusion methods in ablation experiments.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10× Visium samples from a cohort of melanoma metastasis and NanoString CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community. Significance: The spatialGE web application enables user-friendly exploratory analysis of spatial transcriptomics data by using a point-and-click interface to guide users from data input to discovery of spatial patterns, facilitating hypothesis generation.

STModule: identifying tissue modules to uncover spatial components and characteristics of transcriptomic landscapes

Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

Leveraging Multi-omics to Disentangle the Complexity of Ovarian Cancer

To better understand ovarian cancer lethality and treatment resistance, sophisticated computational approaches are required that address the complexity of the tumor microenvironment, genomic heterogeneity, and tumor evolution. The ovarian cancer tumor ecosystem consists of multiple tumors and cell types that support disease growth and progression. Over the last two decades, there has been a revolution in -omic methodologies to broadly define components and essential processes within the tumor microenvironment, including transcriptomics, metabolomics, proteomics, genome sequencing, and single-cell analyses. While most of these technologies comprehensively characterize a single biological process, there is a need to understand the biological and clinical impact of integrating multiple -omics platforms. Overall, multi-omics is an intriguing analytic framework that can better approximate biological complexity; however, data aggregation and integration pipelines are not yet sufficient to reliably glean insights that affect clinical outcomes.