Mapping isoforms and regulatory mechanisms from spatial transcriptomics data with SPLISOSM.

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

Spatial transcriptomics is an emerging technology widely applied to the analyses of tissue architecture and corresponding biological functions. Substantial computational methods have been developed for analyzing spatial transcriptomics data. These methods generate embeddings from gene expression and spatial locations for spot clustering or tissue architecture segmentation. Although the hyperparameters used to produce an embedding can be tuned for a given training set, a fixed embedding has variable performance from case to case due to data distributions. Therefore, selecting an effective embedding for new data in advance would be useful. For this purpose, we developed an embedding evaluation method named message passing-Moran’s I with maximum filtering (MP-MIM), which combines message passing-based embedding transformation with spatial autocorrelation analysis. We applied a graph convolution to aggregate spatial transcriptomics data and employed global Moran’s I to measure spatial autocorrelation and select the most effective embedding to infer tissue architecture. Sixteen spatial transcriptomics samples generated from the human brain were used to validate our method. The results show that MP-MIM can accurately identify high-quality embeddings that produce a high correlation between the predicted tissue architecture and the ground truth. Overall, our study provides a novel method to select embeddings for new test data and enhance the usability of deep learning tools for spatial transcriptome analyses.

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.

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.

ObjectiveThe tumor microenvironment (TME) plays a crucial role in tumor progression. Recent advancements in single-cell sequencing technology have identified Tc17 cells, a newfound subset of CD8+ T cells with a phenotype similar to Th17, within the gastric cancer (GC) microenvironment. However, the role of Tc17 cells are unclear.Materials and methodWe collected 296,879 cells from the single-cell sequencing data of 65 GC samples, along with spatial transcriptomic data from 10 GC samples, to further explore the role of Tc17 cells. Multicolor immunohistochemical staining demonstrated the presence of Tc17 cells in the GC TME. SCENIC analysis was performed to identify the specific transcription factors of Tc17. Pseudotime analysis of T cells was conducted to decipher the differentiation trajectory of Tc17. Additionally, cell-cell interaction analysis revealed the interactions between Tc17 cells and tumor cells. Finally, functional validation through CCK-8 proliferation assays, wound Healing assays, and transwell assays conclusively demonstrated the tumor-promoting effects of IL-17A and IL-26 on gastric tumor cells.ResultsOur results indicate that Tc17 cells are absent from blood and exclusively found in tissues, with greater enrichment in tumor tissues compared to normal tissues. Among CD8+ T cells, Tc17 exhibits the weakest cytotoxic capacity and the highest anti-inflammatory profile, suggesting reduced tumor-killing ability. Pseudotime analysis revealed that Tc17 cells ultimately progress towards a state of exhaustion, losing their capacity to eliminate tumor cells. Survival analysis further correlates the presence of Tc17 cells with poor prognosis. Notably, a robust interaction between Tc17 cells and tumor cells was observed in GC. We found that tumor cells can recruit Tc17 cells via the CXCL16-CXCR6 axis, this finding was validated on spatial transcriptome data. And in turn, Tc17 cells may promote tumor progression by secreting IL-17A and IL-26, as functionally corroborated by CCK-8 Assay, wound healing, and transwell assay in vitro.ConclusionsThese findings reveal the immunosuppressive role of Tc17 cells in gastric cancer, provide a new perspective for understanding the immunosuppressive mechanism of the gastric cancer tumor microenvironment, and provide a potential target for the development of targeted therapeutic strategies

Spatial transcriptomics is a rapidly emerging field that allows for the study of gene expression within a tissue sample. This technology has the potential to provide valuable insights into the molecular mechanisms underlying cancer progression and could be particularly useful in identifying specific regional cancer progression. By studying the regional spatial trajectory of cancer progression, we can develop more targeted therapies and better predict the likelihood of metastasis. Additionally, analyzing gene expression patterns in specific regions of the tissue can reveal the molecular mechanisms driving cancer growth in those areas, potentially uncovering new therapeutic targets. In this study, we created a spatial trajectory analysis method, named as stLearn - PSeudo-Time-Space (PSTS) algorithm, to investigate specific regional cancer progression. Spatial trajectory analysis is a statistical method that allows for the identification of patterns of transitional gene expression over time and space within a tissue sample. In detail, this method incorporates spatial features, such as tissue sub-structure locations and gene expression changes, to model the trajectory from a single snapshot of transcriptomics data. Also, we evaluated the effectiveness of PSTS by utilizing the cancer progression model in human breast cancer samples. Using this approach, we were able to identify specific regions within the breast tissue that displayed distinct progression branching trajectories patterns of transitional gene expression associated with ductal carcinoma in situ and invasive ductal carcinoma. These regions were found to be characterized by a high level of cellular diversity and were associated with aggressive forms of breast cancer. The results of this study suggest that spatial transcriptomics and spatial trajectory analysis could be a powerful tool for identifying specific regional cancer progression and may help to inform the biomarkers for the development of targeted therapies for breast cancer. Overall, this study highlights the potential of spatial transcriptomics and spatial trajectory analysis to provide valuable insights into the molecular mechanisms underlying cancer progression and could have significant implications for the diagnosis and treatment of cancer. Citation Format: Duy Pham, Quan Nguyen. A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB076.

<p>Gene expression programs and neighborhood violent crime rates in lung tumors. <b>A,</b> Heatmap of genes correlated with violent crime rates per 1,000 (<i>P</i> < 0.05). Average normalized gene expressions per tumor sample were extracted from spatial transcriptomics data and checked for correlation with violent crime per 1,000. Housekeeping genes were removed and data were log-transformed and hierarchically clustered prior to visualization. From (left to right), samples are ordered from lowest violent crime ranking to highest violent crime ranking. <b>B,</b> Heatmap of genes correlated with overall crime rates (<i>P</i> < 0.05). Average normalized gene expressions per sample were extracted from spatial transcriptomics data and checked for correlation with violent crime per 1,000. Housekeeping genes were removed and data was log-transformed and hierarchically clustered prior to visualization. From (left to right), samples are ordered from lowest violent crime ranking to highest violent crime ranking. <b>C,</b> Pathway analysis of genes upregulated with violent crime rates per 1,000 (<i>P</i> < 0.05). Upregulated genes were extracted from the violent crime heatmap, and functional annotation of genes was performed in DAVID using Biocarta, Kegg, and Wikipathways analysis. <b>D,</b> Pathway analysis of genes upregulated with overall crime (<i>P</i> < 0.05). Upregulated genes were extracted from the violent crime heatmap, and functional annotation of genes was performed in DAVID using Biocarta, Kegg, and Wikipathways analysis. Abbreviation. T, tumor.</p>

BackgroundUnderstanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.MethodsWe investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.ResultsWe identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54–2.93], PYY [OR = 1.87, 95% CI: 1.43–2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73–0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99–4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67–0.86] and TPM3 [OR = 0.47, 95% CI: 0.34–0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80–0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82–0.86] and early onset disease [OR = 0.71, 95% CI: 0.68–0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions.ConclusionOur findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

Parsing brain structure is crucial in neuroscience research, and the advent of spatial transcriptomics offers unprecedented molecular opportunities. In this study, we utilized 10X Visium spatial transcriptomics to analyze the mouse hindbrain, focusing on the MO::L1 and MO::L2/3 regions. By integrating artificial priors with the SpaceFlow method, we identified spatial organization and conducted differential gene expression analysis. Key findings include the identification of TGF-beta and Hippo signaling pathways, crucial for neurodevelopment and injury repair. Our results highlight the roles of differentially expressed genes in developmental processes, cellular regulation, and signaling pathways, providing insights into brain region functions. Additionally, we discuss current research gaps, suggesting that future studies should explore dynamic gene expression changes and leverage advanced computational models to enhance our understanding of the nervous system, thereby advancing precision medicine.

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Functional interactions within and between different types of somatic cells are crucial for executing complex organ-level biological processes in multicellular organisms. Spatial transcriptomic technologies have allowed for high throughput characterization of cell communities and associated cellular processes in the tissue contexts. However, analytical resources for characterization and quantitative inference of spatial interactions among somatic cells that can potentially impact complex biological functions in tissue microenvironment are still limited. Here, we describe a framework to use network graph-based spatial statistical models on spatially annotated molecular data to gain insights into cellular relationship and connectivity in the local tumor microenvironment and evaluate the effects of network graph connectivity on the model inference.

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.

MotivationCapturing spatial structure is fundamental to the analysis of spatial transcriptomics data. However, most existing methods focus on clustering within individual tissue slices and often ignore the high inter-slice similarity inherent in multi-slice datasets.ResultsTo address this limitation, we propose STransfer, a novel transfer learning framework that combines graph convolutional networks (GCNs) with positive pointwise mutual information (PPMI) to model both local and global spatial dependencies. An attention-based module is introduced to fuse features from multiple graphs into unified node representations, facilitating the learning of low-dimensional embeddings that jointly encode gene expression and spatial context. By transferring knowledge from labeled slices to adjacent unlabeled ones, STransfer significantly enhances clustering accuracy while reducing manual annotation costs. Extensive experiments demonstrate that STransfer consistently outperforms state-of-the-art methods in both spatial modeling and cross-slice transfer performance.Availability and implementationThe code for STransfer has been uploaded to GitHub: https://github.com/Saki-JSU/Publications/tree/main/STransfer.

T cells are the central players in antitumor immunity, and effective tumor killing depends on their ability to infiltrate into the tumor microenvironment (TME) while maintaining normal cytotoxicity. However, late-stage tumors develop immunosuppressive mechanisms that impede T cell movement and induce exhaustion. Investigating T cell migration in human tumors in vivo could provide novel insights into tumor immune escape, although it remains a challenging task. In this study, we developed ReMiTT, a computational method that leverages spatial transcriptomics data to track T cell migration patterns within tumor tissue. Applying ReMiTT to multiple tumor samples, we identified potential migration trails. On these trails, chemokines that promote T-cell trafficking display an increasing trend. Additionally, we identified key genes and pathways enriched on these migration trails, including those involved in cytoskeleton rearrangement, leukocyte chemotaxis, cell adhesion, leukocyte migration, and extracellular matrix (ECM) remodeling. Furthermore, we characterized the phenotypes of T cells along these trails, showing that the migrating T cells are highly proliferative. Our findings introduce a novel approach for studying T cell migration and interactions within the tumor microenvironment (TME), offering valuable insights into tumor-immune dynamics.

Cardiac remodeling involves diverse types of myeloid-derived cells, including macrophages and neutrophils. While the general significance of myeloid cells is recognized, precise roles of these cells remain to be fully elucidated due to their heterogeneity. Characterization of myeloid cells based on gene expression profile is crucial for uncovering how myeloid cells regulate cardiac remodeling. We performed single cell RNA sequencing (scRNA-seq) analysis to characterize CD11b+ myeloid cell populations in infarcted hearts. To investigate the importance of unique population that specifically express GARP, we generated myeloid cell-specific GARP knockout (CKO) mice and subjected them to myocardial infarction (MI). Cardiac remodeling, function and cellular events were assessed using histological analysis, echocardiography, flow cytometry and immuno-staining. We also re-analyzed published spatial transcriptome data to characterize this unique cell population in human hearts. ScRNA-seq analysis revealed a distinct myeloid cell population that was separate from macrophages and neutrophils. This population specifically expressed GARP, a docking receptor and activator of latent TGF-βs. GARP-expressing myeloid cells (GEM cells) exhibited the gene expression profile characteristic of fibrocytes, fibroblast-like myeloid cells. CKO mice showed significantly ameliorated cardiac fibrosis (fibrotic area (%): fl/flcontrol 45.5±14.8 vs. CKO 26.7±4.9, P<0.05) and improved cardiac function (Ejection Fraction (%): fl/flcontrol 22.4±11.7 vs. CKO 36.6±7.0, P<0.05) 14 days after MI. Mechanistically, GARP gene deletion resulted in the suppression of TGF-β signaling in cardiomyocytes and reduced neutrophil infiltration into myocardium, accompanied by decreased chemokine production. Additionally, cardiomyocyte apoptosis decreased in CKO mice (TUNEL+ cardiomyocytes (%): fl/flcontrol 1.66±0.32 vs. CKO 0.78±0.38, P<0.01), proposing the importance of myeloid GARP in cardiomyocyte death. Pseudotime analysis of scRNA-seq revealed that GEM cells were derived from heart-resident fibrocytes. Finally, a spatial transcriptome data from MI patients suggested the presence of CD11b+GARP+ cells in post-infarcted human hearts. These findings newly unveil GARP-expressing myeloid cells, differentiated from resident fibrocytes, exhibited a significant pathophysiologyical role in cardiac remodeling. This suggests that GEM cells could be a promising therapeutic target against post-infarct heart failure.