Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Spatial domain identification, a pivotal task in spatial transcriptomics (ST) research, seeks to elucidate the spatial distribution relationships among diverse cell types and complex tissue architectures. Recent advancements in spatial transcriptomics technology have substantially improved our understanding of cellular spatial arrangements, intercellular interactions, and the local regulation of gene expression. However, conventional algorithms often face challenges in handling high-dimensional, sparse spatial transcriptomic data, which are highly susceptible to noise interference. To address these issues effectively, we propose a novel Transformer-based spatial domain recognition algorithm that synergizes the strengths of Transformer models and graph neural networks. This approach is specifically designed to overcome the limitations of high-dimensional sparse data. By representing spatial transcriptome data through a Transformer encoder structure, our model harnesses self-attention mechanisms and inter-layer dependencies to capture complex intercellular relationships. The self-attention mechanism enables global learning of dependencies across spatial regions, thereby enhancing the modeling of intricate tissue structures. Moreover, during training, we incorporate data augmentation and regularization techniques to mitigate overfitting risks and improve generalization capabilities across varied datasets. Experimental results indicate that our model achieves state-of-the-art performance on multiple datasets, significantly boosting the accuracy of spatial domain recognition and demonstrating heightened robustness in distinguishing cell types at different resolutions.

The rapid advancement of spatial transcriptomics (ST) sequencing technology has made it possible to capture gene expression with spatial coordinate information at the cellular level. Although many methods in ST data analysis can detect spatially variable genes (SVGs), these methods often fail to identify genes with explicit spatial expression patterns due to the lack of consideration for spatial domains. Considering spatial domains is crucial for identifying SVGs as it focuses the analysis of gene expression changes on biologically relevant regions, aiding in the more accurate identification of SVGs associated with specific cell types. Existing methods for identifying SVGs based on spatial domains predefine spot similarity before training, which prevents adaptive learning and limits generalizability across different tissues or samples. This limitation may also lead to inaccurate identification of specific genes at boundary regions. To address these issues, we present GAADE, an unsupervised neural network architecture based on graph-structured data representation learning. GAADE stacks encoder/decoder layers and integrates a self-attention mechanism to reconstruct node attributes and graph structure, effectively capturing spatial domain structures of different sections. Consequently, we confine the identification of SVGs within spatial domains. By performing differential expression analysis on spots within the target spatial domain and their multi-order neighbors, GAADE detects genes with enriched expression patterns within defined domains. Comparative evaluations with five other popular methods on ST datasets across four different species, regions and tissues demonstrate that GAADE exhibits superior performance in detecting SVGs and capturing the extent of spatial gene expression variation.

TOGAR: Token-gated generative refinement for high-fidelity spatial transcriptomics and robust spatial domain clustering.

Hepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pathogenesis and limitations of therapeutic approaches. To improve the prognosis of HCC patients, we analyzed single-cell transcriptome and spatial transcriptome on liver tissues from HCC patients. We applied Non-negative Matrix Factorization (NMF) method on spatial transcriptome and found a critical pathway associated with HCC through GO over-representation analysis. According to the pathway activity score, the cells in single-cell transcriptome were divided into three groups. Then we conducted hdWGCNA and selected HCC-related co-expressed gene modules which showed significant intergroup differences. Integrative machine learning algorithms on bulk transcriptome were employed for core genes selection. We respectively evaluated their AUC values as independent diagnostic markers and retained a core gene. We conducted a survival analysis using this gene. Next, we performed immune infiltration analysis, tumor microenvironmental analysis and drug sensitivity analysis. Finally, pseudotime analysis, differentiation potential analysis and cell-cell communication analysis were applied to decipher the mechanisms underlying tumor initiation and progression. Single-cell transcriptome analysis revealed the high heterogeneity of cells in patients with HCC. The GO over-representation analysis based on NMF indicated that cytoplasmic translation path was enriched in most NMF components. The SSR2 gene passed the integrative machine learning algorithms and possessed excellent diagnosis (AUC > 0.8) and prognostic abilities (K-M curve, P = 0.007). Further analysis identified SSR2 as the top-ranked gene by average expression level in NK cells compared to all other immune cell clusters. Its expression showed a consistent downward trend during NK cell maturation.NK cells with high expression of SSR2 exhibited stronger cell-cell communication than those with low expression of SSR2. NK cells mainly communicated with other cells through MIF-mediated signaling pathway. In hepatocellular carcinoma (HCC), the high expression of the SSR2 gene in NK cells is accompanied by enhanced cytoplasmic translation pathway, which promotes MIF secretion and activates its downstream signaling pathways, thereby driving the disruption of the tumor immune microenvironment and the progression of the disease.

Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction. Here, we developed an STMGraph, a universal dual-view dynamic deep learning framework that combines dual-remask (MASK-REMASK) with dynamic graph attention model (DGAT) to exploit ST data outperforming pre-existing tools. The dual-remask mechanism masks the embeddings before encoding and decoding, establishing dual-decoding-view to share features mutually. DGAT leverages self-supervision to update graph linkage relationships from two distinct perspectives, thereby generating a comprehensive representation for each node. Systematic benchmarking against 10 state-of-the-art tools revealed that the STMGraph has the optimal performance with high accuracy and robustness on spatial domain clustering for the datasets of diverse ST platforms from multi- to sub-cellular resolutions. Furthermore, STMGraph aggregates ST information cross regions by dual-remask to realize the batch-effects correction implicitly, allowing for spatial domain clustering of ST multi-slices. STMGraph is platform independent and superior in spatial-context-aware to achieve microenvironmental heterogeneity detection, spatial domain clustering, batch-effects correction, and new biological discovery, and is therefore a desirable novel tool for diverse ST studies

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.

ABSTRACTDespite enabling high‐resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene‐level interpretability, and fall short in capturing structure‐discriminative genes or long‐range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual‐view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic‐driven gene selection with dual‐view embedding fusion to address these gaps. SAGE integrates non‐negative matrix factorization (NMF)‐based topic modeling with classifier‐based importance scoring to identify highly spatially informative genes, and fuses a local expression graph with a topic‐driven non‐local graph via consensus refinement and contrastive graph representation learning to jointly learn spatial and functional embeddings. Evaluated on 34 real‐world datasets, SAGE not only outperforms existing methods in clustering accuracy but also reveals functionally coherent regions and interpretable gene expression patterns. In case studies, SAGE reveals spatial heterogeneity associated with a pre‐malignant activation state in human breast cancer. Moreover, in zebrafish melanoma, it refines the tumor–muscle interface into transcriptionally distinct subdomains and uncovers shared vascular signatures between anatomically separate tissues. Together, these results demonstrate that SAGE can be used not only for accurate spatial domain delineation across diverse ST platforms, but also for dissecting microenvironmental niches and long‐range tissue interactions underlying disease progression.

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

Recent advancements in spatial transcriptomics technology have enabled the capture of gene expression profiles while maintaining spatial information. Accurately identifying spatial clustering plays a pivotal role in analyzing spatial transcriptomics data and understanding tissue microenvironments. However, current spatial domain identification methods cannot explore the complex relationship of gene expression profiles and spatial topology. To alleviate this issue, we propose STMCCL, a novel self-supervised learning framework that jointly trains a masked autoencoder and cluster-guided contrastive learning. This framework extracts informative latent representations from gene expression profiles and spatial information. Specifically, we first use data augmentation strategies to build augmented views and employ a masked encoder to generate a feature view. Then, encoders are applied to learn view-unique embeddings of each view. Furthermore, we introduce a multiple cluster-perspectives module that considers both geometric and structural relationships between clusters to produce more reliable cluster assignments. Finally, to derive more discriminative positives and negatives, the cluster-guided contrastive module calculates the confidence of each sample based on the initial cluster. Comprehensive experiments on 7 public datasets demonstrate that STMCCL outperforms the state-of-the-art baselines with finer-scale spatial domain identification.

Spatial transcriptomics is revolutionizing our understanding of tumor ecosystems, providing insights into the spatial dimensions of cell locations, their organization, and interactions within tissues. A key challenge in this field is integrating histological annotations with spatial domains identified through unsupervised clustering methods like SpaGCN. This integration is crucial for accurate spatial domain identification and further analyses. However, clustering results often vary based on parameters such as resolution, leading to inconsistencies with histological annotations. This variation necessitates optimized parameter selection to achieve the best clustering outcomes. We here introduce SpatialCS, a novel tool specifically designed to both visualize and quantify the alignment between histological annotations and the clustering results obtained from spatial transcriptomics data. SpatialCS employs a new index based on the asymmetric Wallace indices. This index penalizes spot pairs that are clustered together but do not share the same histology annotation type, with penalties scaled according to their transcriptional similarity. Additionally, we propose a purity index, derived by calculating the average transcriptional entropy of all clusters. This index serves as a measure of the homogeneity within each cluster. Furthermore, SpatialCS includes a unique visualization function. This function projects histological annotations onto the clustering results as outlines, making it easier to assess the consistency between histology annotations and clustering outcomes. We have applied SpatialCS to Visium spatial transcriptomics data generated on four colorectal cancer liver metastases, each accompanied by comprehensive spot-level histological annotations in both tumor and normal compartments. The histologic annotations include but not limited to are tumor cells, tumor cell cluster subtypes, tumor stroma, tumor in the vessels, portal tract, individual bile ducts, portal vein and liver cell plates and non-neoplastic stroma. The application of SpatialCS has proven its effectiveness in identifying the most consistent clustering results, which were further confirmed by experienced pathologists. Notably, the output of SpatialCS offers helpful guidance for improving histological annotations. Together, SpatialCS not only provides a quantitative approach to evaluate the consistency between histological annotations and unsupervised clustering analysis but also showcases its potential to significantly enhance histological annotations. Citation Format: Yanshuo Chu, Kyung Serk Cho, Isha Khanduri, Tieling Zhou, Jiahui Jiang, Yunhe Liu, Xinmiao Yan, Guangsheng Pei, Yibo Dai, Yang Liu, Ruiping Wang, Akshaya S Jadhav, Sharia Hernandez, Humam Kadara, Luisa Maren Solis Soto, Scott Kopetz, Dipen Maheshbhai Maru, Linghua Wang. SpatialCS: A tool to effectively integrate histological annotations and spatial transcriptomics analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7417.

The identification of tumor cells is pivotal for understanding tumor heterogeneity and the tumor microenvironment. Recent advances in spatially resolved transcriptomics (SRT) have revolutionized the way that transcriptomic profiles are characterized and have enabled the simultaneous quantification of transcript locations in intact tissue samples. SRT is a promising alternative method to study gene expression patterns in spatial domains. Nevertheless, the precise detection of tumor regions within intact tissue remains a great challenge. A common strategy for identifying tumor cells is via tumor-specific marker gene expression signatures, which are highly dependent on marker accuracy. Another effective approach is through aneuploid copy number alterations, as most types of cancer exhibit copy number abnormalities. Here, we introduce a novel computational method, called TUSCAN (TUmor Segmentation and Classification ANalysis in spatial transcriptomics), which constructs a spatial copy number variation profile to improve the accuracy of tumor region identification. TUSCAN combines gene information from SRT data and hematoxylin-and-eosin-staining image to annotate tumor sections and other benign tissues. We benchmark the performance of TUSCAN and several existing methods through the application to multiple datasets from different SRT platforms. We demonstrate that TUSCAN can effectively delineate tumor regions, with improved accuracy compared to other approaches. Additionally, the output of TUSCAN provides interpretable clonal evolution inferences that may lead to novel insights into disease development and potential druggable targets.

stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data

Spatial transcriptomics is an emerging technology that allows for analysis of cellular and molecular heterogeneity at spatial resolution. The accurate identification of pathological regions in spatial transcriptomics data is essential for understanding tissue heterogeneity and disease progression. We introduce UPSST, a comprehensive framework that integrates tissue morphology, imputes gene expression, and clusters spatial regions using a graph attention neural network (GAT). UPSST was evaluated across multiple spatial transcriptomics datasets, achieving high performance, such as achieving an Adjusted Rand Index (ARI) of 0.737 and a Fowlkes-Mallows Index (FMI) of 0.818 on slice 151671 of the LIBD human dorsolateral prefrontal cortex (DLPFC) dataset. These results highlight the robustness and precision of our approach in identifying pathology domains. Additionally, UPSST facilitates downstream analyses such as differential and enrichment analysis, which are crucial for deriving biological insights. In conclusion, UPSST offers a powerful and reliable tool for spatial transcriptomics analysis, advancing the identification of pathological regions with high accuracy.

Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.