Pruning-Assisted Modeling of Network Graph Connectivity from Spatial Transcriptomic Data.

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.

For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers. Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 456.

Spatial transcriptomic technologies are transforming our ability to profile gene expression, by mapping it to the native architecture of the tissues. However, the inherent complexity and multimodality of the problem demand robust analytical computational tools. In this study, we benchmarked seven state-of-the art clustering algorithms that cover different methodological approaches-conventional statistics, Machine Learning, and Deep Learning-based on their capacity to integrate gene expression, histology, and spatial proximity to identify spatial domains in spatial transcriptomic data. Our comparative analysis implemented on a wellannotated human brain reference dataset shows that algorithms that integrate spatial adjacency (GraphST and BayesSpace) significantly outperform the others, with GraphST achieving the best performance. As a proof of principle, we applied GraphST to a highgrade serous ovarian cancer sample. The model revealed transcriptionally distinct subregions within tumor and stromal compartments not distinguishable by histological assessment alone. Furthermore, by integrating cell-type deconvolution with spatial T-cell receptor (TCR) clonotyping, we discovered heterogeneous immune niches, characterized by domain-specific clonal expansion. Tumor cores exhibited highly diverse TCR repertoires, while stromal margins were dominated by a few expanded clones, suggesting differential immune-selection pressure. These findings highlight the potential of spatially informed Deep Learning methods to dissect the tumor microenvironment and identify actionable targets for immune-based anticancer therapies.

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.

Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development andplant biology as well asto investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.

Pluripotent cells have the potential to differentiate into all of the cell types of an animal. This unique cell state is governed by an interconnected network of transcription factors. Among these, Oct4 plays an essential role both in the development of pluripotent cells in the embryo and in the self-renewal of its in vitro counterpart, embryonic stem (ES) cells. Furthermore, Oct4 is one of the four Yamanaka factors and its overexpression alone can generate induced pluripotent stem (iPS) cells. Recent reports underscore Oct4 as an essential regulator of opposing cell state transitions, such as pluripotency establishment and differentiation into embryonic germ lineages. Here we discuss these recent studies and the potential mechanisms underlying these contrasting functions of Oct4.

The direct transformation of one somatic cell type into another somatic cell type would be beneficial for producing isogenic replacement cells for therapeutic use. Various approaches for altering cell fate are being developed, including methods for differentiating stem cells isolated from somatic tissues. This chapter describes a procedure for turning one somatic cell type (the "donor" cell) into another somatic "target" cell type using cellular extracts. The method also can be used to promote differentiation of a somatic stem cell along a specific pathway. The procedure involves permeabilization of the donor cell, incubation of the permeabilized cell in a nuclear and cytoplasmic extract derived from the target cell type, the resealing of donor cell membrane, and culture. Cells can be analyzed for induction of new gene and protein expression, as well as for the establishment of cellular functions specific to the target cell type. We also describe a slight modification of the procedure to allow analysis of extract-induced chromatin remodeling in nuclei purified from somatic cells. Because large numbers of cells and nuclei can be treated in cell extracts and because extracts can be fractionated or supplemented with various agents, this system constitutes a powerful tool to examine the molecular mechanisms of nuclear reprogramming and of cell differentiation, at least as they take place in vitro.

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.

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.

The c-kit receptor is involved in the adhesion of mouse primordial germ cells to somatic cells in culture

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

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>

Transcript diversity including splicing and alternative 3' end usage is crucial for cellular identity and adaptation, yet its spatial coordination remains poorly understood. Here we present SPLISOSM (spatial isoform statistical modeling), a method for detecting isoform-resolution patterns from spatial transcriptomics data. SPLISOSM uses multivariate testing with nonparametric kernels to account for spot-level and isoform-level dependencies, achieving high statistical power on sparse data. In the mouse brain, we identify over 1,000 spatially variable transcript diversity events, primarily in synaptic signaling pathways linked to neuropsychiatric disorders, and uncover both known and previously unknown regulatory relationships with region-specific RNA binding proteins. We further show that these patterns are evolutionarily conserved between mouse and human prefrontal cortex. Analysis of human glioblastoma highlights pervasive transcript diversity in antigen presentation and adhesion genes associated with specific microenvironmental conditions. Together, we present a comprehensive spatial splicing analysis in the brain under normal and neoplastic conditions.