Spatial Transcriptomics Technologies Research Articles

Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding.

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

Dimension-agnostic and granularity-based spatially variable gene identification using BSP

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling.

In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics.

Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.

Chromatin accessibility profiling of targeted cell populations with laser capture microdissection coupled to ATAC-seq

Spatially resolved omics technologies reveal context-dependent cellular regulatory networks in tissues of interest. Beyond transcriptome analysis, information on epigenetic traits and chromatin accessibility can provide further insights on gene regulation in health and disease. Nevertheless, compared to the enormous advancements in spatial transcriptomics technologies, the field of spatial epigenomics is much younger and still underexplored. In this study, we report laser capture microdissection coupled to ATAC-seq (LCM-ATAC-seq) applied to fresh frozen samples for the spatial characterization of chromatin accessibility. We first demonstrate the efficient use of LCM coupled to in situ tagmentation and evaluate its performance as a function of cell number, microdissected areas, and tissue type. Further, we demonstrate its use for the targeted chromatin accessibility analysis of discrete contiguous or scattered cell populations in tissues via single-nuclei capture based on immunostaining for specific cellular markers.

SiGra: single-cell spatial elucidation through an image-augmented graph transformer

Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27–50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.

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

Background:Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background. Methods:We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots’ embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets. Results:We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters. Conclusion:We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated four distinct state-of-the-art deep learning architectures, which include ResNet101, Inception-v3, EfficientNet (with six different variants), and vision transformer (with two different variants), all without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.

Spatial Transcriptomic Technologies.

Spatial transcriptomic technologies enable measurement of expression levels of genes systematically throughout tissue space, deepening our understanding of cellular organizations and interactions within tissues as well as illuminating biological insights in neuroscience, developmental biology and a range of diseases, including cancer. A variety of spatial technologies have been developed and/or commercialized, differing in spatial resolution, sensitivity, multiplexing capability, throughput and coverage. In this paper, we review key enabling spatial transcriptomic technologies and their applications as well as the perspective of the techniques and new emerging technologies that are developed to address current limitations of spatial methodologies. In addition, we describe how spatial transcriptomics data can be integrated with other omics modalities, complementing other methods in deciphering cellar interactions and phenotypes within tissues as well as providing novel insight into tissue organization.

SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics

Recent advancements in spatial transcriptomic technologies have enabled the measurement of whole transcriptome profiles with preserved spatial context. However, limited by spatial resolution, the measured expressions at each spot are often from a mixture of multiple cells. Computational deconvolution methods designed for spatial transcriptomic data rarely make use of the valuable spatial information as well as the neighboring similarity information. Here, we propose SONAR, a Spatially weighted pOissoN-gAmma Regression model for cell-type deconvolution with spatial transcriptomic data. SONAR directly models the raw counts of spatial transcriptomic data and applies a geographically weighted regression framework that incorporates neighboring information to enhance local estimation of regional cell type composition. In addition, SONAR applies an additional elastic weighting step to adaptively filter dissimilar neighbors, which effectively prevents the introduction of local estimation bias in transition regions with sharp boundaries. We demonstrate the performance of SONAR over other state-of-the-art methods on synthetic data with various spatial patterns. We find that SONAR can accurately map region-specific cell types in real spatial transcriptomic data including mouse brain, human heart and human pancreatic ductal adenocarcinoma. We further show that SONAR can reveal the detailed distributions and fine-grained co-localization of immune cells within the microenvironment at the tumor-normal tissue margin in human liver cancer.

Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse

BackgroundOpting for or against the administration of adjuvant chemotherapy in therapeutic management of stage II colon cancer remains challenging. Several studies report few survival benefits for patients treated with adjuvant therapy and additionally revealing potential side effects of overtreatment, including unnecessary exposure to chemotherapy-induced toxicities and reduced quality of life. Predictive biomarkers are urgently needed. We, therefore, hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers.MethodsThe spatial tissue composition of stage II colon cancer patients was examined by a novel spatial transcriptomics technology with sub-cellular resolution, namely in situ sequencing. A panel of 176 genes investigating specific cancer-associated processes such as apoptosis, proliferation, angiogenesis, stemness, oxidative stress, hypoxia, invasion and components of the tumour microenvironment was designed to examine differentially expressed genes in tissue of relapsed versus non-relapsed patients. Therefore, FFPE slides of 10 colon cancer stage II patients either classified as relapsed (5 patients) or non-relapsed (5 patients) were in situ sequenced and computationally analysed.ResultsWe identified a tumour gene signature that enables the subclassification of tissue into neoplastic and non-neoplastic compartments based on spatial expression patterns obtained through in situ sequencing. We developed a computational tool called Genes-To-Count (GTC), which automates the quantification of in situ signals, accurately mapping their position onto the spatial tissue map and automatically identifies neoplastic and non-neoplastic tissue compartments. The GTC tool was used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer.ConclusionsIn depth spatial in situ sequencing showed potential to provide a deeper understanding of the underlying mechanisms involved in the recurrence of disease and revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our open-access GTC-tool allowed us to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.

A standard for sharing spatial transcriptomics data

Spatial transcriptomic technologies have the potential to reveal critical relationships between the function of genes and cells and their spatial organization. Here, we provide a sharing model for spatial transcriptomics data with the aim of establishing a set of primary data and metadata needed to reproduce analyses and facilitate computational methods development.

Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism.

Recent advances in spatial transcriptomics technologies have enabled gene expression profiles while preserving spatial context. Accurately identifying spatial domains is crucial for downstream analysis and it requires the effective integration of gene expression profiles and spatial information. While increasingly computational methods have been developed for spatial domain detection, most of them cannot adaptively learn the complex relationship between gene expression and spatial information, leading to sub-optimal performance. To overcome these challenges, we propose a novel deep learning method named Spatial-MGCN for identifying spatial domains, which is a Multi-view Graph Convolutional Network (GCN) with attention mechanism. We first construct two neighbor graphs using gene expression profiles and spatial information, respectively. Then, a multi-view GCN encoder is designed to extract unique embeddings from both the feature and spatial graphs, as well as their shared embeddings by combining both graphs. Finally, a zero-inflated negative binomial decoder is used to reconstruct the original expression matrix by capturing the global probability distribution of gene expression profiles. Moreover, Spatial-MGCN incorporates a spatial regularization constraint into the features learning to preserve spatial neighbor information in an end-to-end manner. The experimental results show that Spatial-MGCN outperforms state-of-the-art methods consistently in several tasks, including spatial clustering and trajectory inference.

SCS: cell segmentation for high-resolution spatial transcriptomics.

Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10-15 cells per spot, recent technologies provide a much denser spot placement leading to subcellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present subcellular spatial transcriptomics cell segmentation (SCS), which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new subcellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells and provided more realistic cell size estimation. Subcellular analysis of RNAs using SCS spot assignments provides information on RNA localization and further supports the segmentation results.

SEA‐AD: Scientific analysis and open access resources targeting early changes in Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) is the most common form of dementia, representing ∼70% of all dementia cases, and affecting much of the aged population. Currently our understanding of AD neuropathology is largely centered on characteristic deposition of certain neuropathological proteins and identification of histologically defined neuronal populations. Molecular tools to characterize the transcriptome, epigenome, and spatial organization of single cells in complex brain tissues allow a more refined look at how the neurotypical brain changes in AD than had been possible previously. Linking molecular, cellular, genomic, cognitive, and pathological indicators of AD progression will be essential for understanding disease mechanisms.MethodsThe Seattle Alzheimer’s Disease Brain Cell Atlas (SEA‐AD) brings together experts in AD research and large‐scale molecular/anatomical brain mapping to modernize AD tissue banking methods, and to combine traditional and quantitative neuropathology with emerging genotyping, single nucleus transcriptomics, single nucleus epigenomics, and spatial transcriptomics technologies. Applied to multiple brain regions in donors spanning different stages of AD pathology, these techniques will identification of cell type specific molecular pathways and yield valuable insights into selective vulnerability or resistance to pathology. Two well characterized cohorts (Adult Changes in Thought (ACT) and the University of Washington ADRC) provided 84 subjects of varying degrees of AD severity, including cognitively normal subjects.ResultsThe initial focus of SEA‐AD was on integrating and interpreting relationships between all collected multimodal data in middle temporal gyrus (MTG). Identified cell types and molecular pathways of interest are presented separately and are both consistent with and expand upon results found from ROSMAP and other cohorts. The complete SEA‐AD MTG atlas is now freely and publicly available at brain‐map.org and includes access to all data and associated donor metadata, tools for visualization and exploration of molecular data, exploration of pathology images, a tool for assigning cell types to community‐based ‐omics datasets, comprehensive documentation, and user support.ConclusionsSEA‐AD has developed a high resolution, high‐quality, publicly accessible atlas of aging and AD, and is designed to better understand the complex and likely heterogenous nature of AD.

An information theoretic approach to detecting spatially varying genes

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.

Abstract 4708: Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays

Abstract The tumor microenvironment is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. Although examination of singular biospecimens is sufficient for diagnostic purposes, it is inadequate and cost prohibitive when scaling for complex and overarching studies. Thus, high density multi-tumor tissue microarrays (TMAs) have been a practical and effective solution for high-throughput molecular analysis of tissues. Introduced more than a decade ago, TMAs have been instrumental in the recent study of tumor biology, the development of diagnostic tests, the establishment of quality control, and the investigation and identification of oncological biomarkers. Here, we demonstrate the pairing of the 10x Genomics Visium Cytassist Spatial Gene Expression Solution and Xenium In-Situ Platform on multi-tumor TMAs to screen for common biomarkers among a cohort of samples. Spatial transcriptomics technology has proven valuable in mapping the whole transcriptome with spatial context (Visium), whereas In Situ (Xenium) enables high-throughput cellular characterization at single-cell resolution. With the addition of our CytAssist platform, we expand on the pre-existing standard Visium solution by facilitating the retrieval of RNA transcriptomic information from tissues placed on standard or archival slides. On the other hand, the novel Xenium platform compliments whole transcriptome Visium data by unlocking the potential to assign transcripts to a particular cell with spatial context and subcellular resolution. The combination of spatial transcriptomics and targeted in situ data with FFPE TMAs promotes a high-throughput method to accelerate the uncovering of molecular signatures suitable to understanding the tumor microenvironment. We showcase the ability to spatially and comprehensively resolve individual oncogene and tumor suppressor genes associated with multiple tumors from a cohort of cancer patients and from multiple different tumor samples. In addition, these markers are mapped back to distinct morphological features within each tissue core, and use differential gene expression data to identify distinct cell types throughout the different patient tissues. By combining the throughput of TMA samples and depth of the Visium and Xenium platforms, the strategy enables greater insights into cell-type specifics while also expanding the spectrum of biospecimen types that can be analyzed. Citation Format: Syrus Mohabbat, Hardeep Singh, Stephen R. Williams, Lauren M M. Gutgesell, David J. Sukovich, Govinda M. Kamath, Hanyoup Kim, Amanda Janesick, Robert Shelansky, Ghezal Beliakoff, Augusto M. Tentori, Albert Kim, Cedric R. Uytingco, Sarah Taylor. Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4708.

Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer entity characterized by a heterogeneous genetic landscape and an immunosuppressive tumor microenvironment. Recent advances in high-resolution single-cell sequencing and spatial transcriptomics technologies have enabled an in-depth characterization of both malignant and host cell types and increased our understanding of the heterogeneity and plasticity of PDAC in the steady state and under therapeutic perturbation. In this Review we outline single-cell analyses in PDAC, discuss their implications on our understanding of the disease and present future perspectives of multimodal approaches to elucidate its biology and response to therapy at the single-cell level.

A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue.