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1
Li, Youcheng, Leann Lac, Qian Liu, and Pingzhao Hu. "ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning." PLOS Computational Biology 20, no. 6 (June 27, 2024): e1012254. http://dx.doi.org/10.1371/journal.pcbi.1012254.
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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.
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Chen, Tsai-Ying, Li You, Jose Angelito U. Hardillo, and Miao-Ping Chien. "Spatial Transcriptomic Technologies." Cells 12, no. 16 (August 10, 2023): 2042. http://dx.doi.org/10.3390/cells12162042.
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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.
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Lv, Zhuo, Shuaijun Jiang, Shuxin Kong, Xu Zhang, Jiahui Yue, Wanqi Zhao, Long Li, and Shuyan Lin. "Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants." Plants 13, no. 12 (June 18, 2024): 1679. http://dx.doi.org/10.3390/plants13121679.
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“Omics” typically involves exploration of the structure and function of the entire composition of a biological system at a specific level using high-throughput analytical methods to probe and analyze large amounts of data, including genomics, transcriptomics, proteomics, and metabolomics, among other types. Genomics characterizes and quantifies all genes of an organism collectively, studying their interrelationships and their impacts on the organism. However, conventional transcriptomic sequencing techniques target population cells, and their results only reflect the average expression levels of genes in population cells, as they are unable to reveal the gene expression heterogeneity and spatial heterogeneity among individual cells, thus masking the expression specificity between different cells. Single-cell transcriptomic sequencing and spatial transcriptomic sequencing techniques analyze the transcriptome of individual cells in plant or animal tissues, enabling the understanding of each cell’s metabolites and expressed genes. Consequently, statistical analysis of the corresponding tissues can be performed, with the purpose of achieving cell classification, evolutionary growth, and physiological and pathological analyses. This article provides an overview of the research progress in plant single-cell and spatial transcriptomics, as well as their applications and challenges in plants. Furthermore, prospects for the development of single-cell and spatial transcriptomics are proposed.
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Gorbunova, Vera. "COMPARATIVE TRANSCRIPTOMIC OF LONGEVITY." Innovation in Aging 7, Supplement_1 (December 1, 2023): 432. http://dx.doi.org/10.1093/geroni/igad104.1423.
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Abstract Transcriptome analysis provides a nuanced view into the changes that occur in cells and tissues. Transcriptome changes rapidly and reproducibly in response to physiological influences and environmental insults. Recent years have seen an exponential increase in transcriptome data at bulk, single cell and spatial resolution that allows insights into the mechanisms and regulatory pathways of aging and longevity. In this session Drs. Gorbunova (University of Rochester) and Gladyshev (Harvard Medical School) will discuss comparative transcriptomics of longevity across species with diverse lifespans that revealed unique signatures of longevity and the integration of transcriptome and proteome data. Dr. Gladyshev will discuss development of transcriptomic clocks of measuring biological aging. Dr. Artyomov will discuss single-cell resolution approaches to reveal aspects of immune aging in humans, and Dr. Palovics will present the use of transcriptomics to understand rejuvenating effects of heterochronic parabiosis.
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Callaway, Edward M., Hong-Wei Dong, Joseph R. Ecker, Michael J. Hawrylycz, Z. Josh Huang, Ed S. Lein, John Ngai, et al. "A multimodal cell census and atlas of the mammalian primary motor cortex." Nature 598, no. 7879 (October 6, 2021): 86–102. http://dx.doi.org/10.1038/s41586-021-03950-0.
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AbstractHere we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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Adabbo, Bruno, Simona Migliozzi, Luciano Garofano, Young Taek Oh, Sakir H. Gultekin, Fulvio D'Angelo, Evan R. Roberts, et al. "EPCO-27. RECONSTRUCTION OF THE SPATIAL ECOSYSTEM OF GLIOBLASTOMA REVEALS RECURRENT RELATIONSHIPS BETWEEN TUMOR CELL STATES AND TUMOR MICROENVIRONMENT." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v129. http://dx.doi.org/10.1093/neuonc/noad179.0490.
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Abstract Glioblastoma multiforme (GBM) is the most aggressive form of primary brain tumor, with no curative treatment options. Multiple studies have characterized at single cell resolution the GBM as being composed of transcriptional cell states interconnected with components in the tumor immune microenvironment (TME). Our group proposed and validated the first single cell guided functional classification of GBM in four tumor-intrinsic cell states which informed clinical outcome and delivered therapeutic options. However, single cell technologies lack the spatial relationships among the cell states of GBM and between GBM cell states and TME. Spatially resolved transcriptomic technologies are emerging as powerful tools to reconstruct the spatial architecture of a tissue. We performed spatial transcriptomics of multicellular regions of interest (ROI) in 8 IDH wild-type GBM with both CosMx Spatial Molecular Imager, which analyzes 1,000 RNA probes and 64 proteins at single cell resolution, and GeoMx Digital Spatial Profiler which profiles the whole transcriptome (~18,000 genes) at ROI resolution. We integrated the two platforms to define single cell states and non-malignant cells and developed a computational deconvolution approach for CosMx spatial data which utilized GeoMx ROI resolved transcriptomic profiles as a-priori information to predict cell type abundances. Spatial deconvolution of CosMx derived single cells revealed spatial segregation of the tumor cell clones and cellular states and highlighted recurrent patterns of cell states, distinct TME cell types associated with coherent histopathological features across multiple samples. Our studies established a scalable approach to resolve the transcriptional heterogeneity of GBM and reconstruct the architecture of GBM cell states and tumor microenvironment.
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He, Jiang, Bin Wang, Justin He, Renchao Chen, Benjamin Patterson, Sudhir Tattikota, Timothy Wiggin, et al. "Abstract LB333: Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE." Cancer Research 84, no. 7_Supplement (April 5, 2024): LB333. http://dx.doi.org/10.1158/1538-7445.am2024-lb333.
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Abstract The advent of spatial transcriptomics has enabled a revolution in how complex tissues are studied. However, samples with lower quality RNA due to degradation, protein crosslinking, or high RNase content remain challenging for spatial transcriptomic measurement. In particular, formalin fixed, paraffin embedded (FFPE) tissues are the most widely used sample types in clinical and molecular diagnosis, yet they are notoriously difficult for single-cell transcriptomic analysis. To accurately profile the gene expression in FFPE samples in situ, a spatial transcriptomics technique with high detection efficiency and single molecule resolution is required. The Vizgen® MERSCOPE® Platform for spatial genomics is built on Multiplexed Error Robust in situ Hybridization (MERFISH) technology and directly profiles the transcriptome of intact tissues with high sensitivity in high-quality samples. Here we present an updated workflow to perform MERFISH in low and high-quality samples. We demonstrated its application in more than 5 FFPE sample types from mouse and human, including archival samples. For each tissue type, hundreds of thousands of cells were captured using the updated MERSCOPE Platform workflow, generating 100s million counts and their spatial information for profiled genes in each sample. The updated workflow involves streamlined sample preparation and chemistry optimization to improve sensitivity. MERSCOPE accurately profiled gene expression in situ and mapped cell types in archival human samples across a range of low and high RNA qualities. We compared the performance of MERSCOPE imaging using the updated protocol to the previous version and observed a significant increase in gene counts per 100 micron2 of tissue. We also demonstrated increased reproducibility between replicates with the streamlined workflow and chemistry. Furthermore, we demonstrated the updated workflow is compatible with simultaneous protein-based cell boundary staining. Finally, we constructed a spatially resolved single-cell atlas across low-quality archival breast and lung tumor types, mapped and cataloged different cell types within the tumor microenvironment, and systematically characterized the gene expression among cells. Spatially resolved transcriptomic profiling of low-quality samples at single-cell level provides enormous opportunities in cancer research. These improvements will enable new genomic inquiries into previously intractable tissues like FFPE, leading to new biological insights into cancer progression. Citation Format: Jiang He, Bin Wang, Justin He, Renchao Chen, Benjamin Patterson, Sudhir Tattikota, Timothy Wiggin, Lizi Maziashvili, Peter Reinhold, Manisha Ray, George Emanuel. Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB333.
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Jiang, Peng. "Abstract IA002: Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets." Molecular Cancer Therapeutics 22, no. 12_Supplement (December 1, 2023): IA002. http://dx.doi.org/10.1158/1535-7163.targ-23-ia002.
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Abstract My talk will present three computational frameworks we developed to study cytokine signaling activities and cell-cell communications during the antitumor immune response, using tumor single-cell and spatial transcriptomics. The basic immunology tool to study cytokine signaling mostly measures cytokine release, which is transient and does not represent downstream target activities. Therefore, we first developed the CytoSig platform, providing a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine, and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation, and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor. Then, based on CytoSig, we developed Tres, a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as TGF-β1, TRAIL, and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in multiple cancer types using bulk T cell transcriptomic data from pre-treatment patient tumors or infusion/pre-manufacture samples for cellular immunotherapies. Further, Tres identified FIBP as a candidate immunotherapy target to potentiate adoptive cell therapy efficacies. FIBP knockout in T cells enhanced adoptive cell therapy by down-regulating T cells' cholesterol metabolism. Last, I will briefly show our SpaCET framework for deconvolving cell fractions and identifying cell-cell interactions in tumor spatial transcriptomics data. SpaCET resolved several challenges in spatial transcriptomics analysis that previous methods did not address sufficiently. Through coupling cell fractions with ligand-receptor co-expression analysis, SpaCET reveals that intercellular interactions tend to be located at the tumor-immune boundaries. Citation Format: Peng Jiang. Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA002.
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Ali, Abdullah Mahmood, and Azra Raza. "scRNAseq and High-Throughput Spatial Analysis of Tumor and Normal Microenvironment in Solid Tumors Reveal a Possible Origin of Circulating Tumor Hybrid Cells." Cancers 16, no. 7 (April 8, 2024): 1444. http://dx.doi.org/10.3390/cancers16071444.
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Metastatic cancer is a leading cause of death in cancer patients worldwide. While circulating hybrid cells (CHCs) are implicated in metastatic spread, studies documenting their tissue origin remain sparse, with limited candidate approaches using one–two markers. Utilizing high-throughput single-cell and spatial transcriptomics, we identified tumor hybrid cells (THCs) co-expressing epithelial and macrophage markers and expressing a distinct transcriptome. Rarely, normal tissue showed these cells (NHCs), but their transcriptome was easily distinguishable from THCs. THCs with unique transcriptomes were observed in breast and colon cancers, suggesting this to be a generalizable phenomenon across cancer types. This study establishes a framework for HC identification in large datasets, providing compelling evidence for their tissue residence and offering comprehensive transcriptomic characterization. Furthermore, it sheds light on their differential function and identifies pathways that could explain their newly acquired invasive capabilities. THCs should be considered as potential therapeutic targets.
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He, Jiang, Justin He, Timothy Wiggin, Rob Foreman, Renchao Chen, Nicolas Fernandez, and George Emanuel. "Abstract 4195: Spatially resolved single cell transcriptomic profiling in formalin-fixed paraffin-embedded (FFPE) tissues." Cancer Research 83, no. 7_Supplement (April 4, 2023): 4195. http://dx.doi.org/10.1158/1538-7445.am2023-4195.
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Abstract Formalin-fixed paraffin-embedded (FFPE) tissues are the most widely used clinical sample types in histology and molecular diagnosis, but these samples are often challenging for single-cell transcriptomic analysis due to RNA degradation and protein crosslinking. A spatial transcriptomics technique with high detection efficiency and single molecule resolution is required in order to accurately profile the gene expression in FFPE samples in situ. Vizgen’s MERSCOPE platform, built on multiplexed error robust in situ hybridization MERFISH technology, directly profiles intact tissue’s transcriptome with subcellular spatial resolution. Here, we demonstrate the FFPE MERSCOPE workflow in tissues from 10 mouse and human samples, including archival clinical samples. In each sample, hundreds of thousands of cells were captured with >100 million transcript counts, generating detailed spatial transcriptomic data for the profiled genes in each sample. A comparison of FFPE and matched fresh frozen samples indicated that the FFPE workflow performs similarly in detection efficiency as compared to the fresh frozen protocol. We further demonstrated the MERSCOPE FFPE workflow is compatible with protein imaging by performing simultaneous protein-based cell boundary staining with MERFISH to accurately profile gene expression and map cell types in archival clinical human samples. Finally, we constructed a spatially resolved single cell atlas across eight major tumor types, mapped and cataloged different cell types within the tumor microenvironment and systematically characterized the gene expression among cells. This study demonstrates the potential for spatially resolved transcriptomic profiling of FFPE samples at single cell level to contribute to a wide range of biomedical research areas, including many applications to study human diseases. Citation Format: Jiang He, Justin He, Timothy Wiggin, Rob Foreman, Renchao Chen, Nicolas Fernandez, George Emanuel. Spatially resolved single cell transcriptomic profiling in formalin-fixed paraffin-embedded (FFPE) tissues. [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 4195.
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Shengquan, Chen, Zhang Boheng, Chen Xiaoyang, Zhang Xuegong, and Jiang Rui. "stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics." Bioinformatics 37, Supplement_1 (July 1, 2021): i299—i307. http://dx.doi.org/10.1093/bioinformatics/btab298.
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Abstract Motivation Single-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of transcriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling and spatial organization mapping of cells simultaneously. Among the technologies, imaging-based methods can offer higher spatial resolutions, while they are limited by either the small number of genes imaged or the low gene detection sensitivity. Although several methods have been proposed for enhancing spatially resolved transcriptomics, inadequate accuracy of gene expression prediction and insufficient ability of cell-population identification still impede the applications of these methods. Results We propose stPlus, a reference-based method that leverages information in scRNA-seq data to enhance spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-nearest-neighbor. stPlus outperforms baseline methods with higher gene-wise and cell-wise Spearman correlation coefficients. We also introduce a clustering-based approach to assess the enhancement performance systematically. Using the data enhanced by stPlus, cell populations can be better identified than using the measured data. The predicted expression of genes unique to scRNA-seq data can also well characterize spatial cell heterogeneity. Besides, stPlus is robust and scalable to datasets of diverse gene detection sensitivity levels, sample sizes and number of spatially measured genes. We anticipate stPlus will facilitate the analysis of spatial transcriptomics. Availability and implementation stPlus with detailed documents is freely accessible at http://health.tsinghua.edu.cn/software/stPlus/ and the source code is openly available on https://github.com/xy-chen16/stPlus.
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Lee, Youjin, Derek Bogdanoff, Yutong Wang, George C. Hartoularos, Jonathan M. Woo, Cody T. Mowery, Hunter M. Nisonoff, et al. "XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment." Science Advances 7, no. 17 (April 2021): eabg4755. http://dx.doi.org/10.1126/sciadv.abg4755.
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Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.
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Gupta, Anushka, Stephen Williams, Lauren Gutgasell, Benton Veire, Ace Santiago, Hardeep Singh, Rena Chan, et al. "Spatially resolved whole-transcriptome analysis with simultaneous highly multiplexed immune cell epitope detection in multiple cancer tissues." Journal of Immunology 210, no. 1_Supplement (May 1, 2023): 251.04. http://dx.doi.org/10.4049/jimmunol.210.supp.251.04.
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Abstract The tumor microenvironment is composed of highly heterogeneous niches, often with varying degrees of immune infiltration. The spatial distribution of immune cells with respect to malignant cells can directly impact patient prognosis and overall survival outcomes. The Visium CytAssist Spatial Gene Expression assay uses a whole transcriptome probe-based approach, termed RTL, to detect and quantify mRNA expression with spatial context. Although examination of the tumor microenvironment with an RTL-based spatial assay can provide significant transcriptomic information concerning regions of interest, immune cells frequently have extremely low mRNA expression levels and can be difficult to detect. The use of antibody-conjugated probes specific to immune cell epitopes, which are highly expressed, can enhance data recovered from these tumor samples, enabling spatially accurate detection of immune populations. The Visium CytAssist Spatial Proteogenomic Solution enables identification of immune-specific epitopes via antibody-conjugated probes from the same tissue slide used for transcriptomic analysis. Using the CytAssist workflow, we showcase the ability to comprehensively resolve immune cells associated with multiple immune and tumor tissues, including an array of human breast cancer punches. Spatial expression patterns of immune markers map back to distinct morphological features within the samples, allowing identification of differentially-expressed genes associated with those areas. Overall, these data highlight the value of Visium CytAssist Spatial Proteogenomic Solution in immuno-oncology studies, through the integration of spatially resolved transcriptomic and immune cell marker data.
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Yin, Yifeng, Jerald Sapida, David Sukovich, David Patterson, and Augusto Tentori. "Abstract 3645: Unraveling spatial complexity of the tumor microenvironment: A whole transcriptomic perspective with Visium HD." Cancer Research 84, no. 6_Supplement (March 22, 2024): 3645. http://dx.doi.org/10.1158/1538-7445.am2024-3645.
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Abstract In recent years, advances in spatial transcriptomics have revolutionized our understanding of the tumor microenvironment, providing crucial insights into the complex interplay of different cell types within cancer tissues. In this study, we employed the new, high definition Visium spatial transcriptomics assay (Visium HD) to investigate the intricate molecular landscape of prostate cancer at single-cell scale resolution and across the whole transcriptome. Our research focused on deciphering the spatial heterogeneity of gene expression patterns within the tumor microenvironment, shedding light on the interactions between cancer cells, stromal cells, and vasculature. The Visium HD assay enables an unbiased exploration of the transcriptome on FFPE tissue sections mounted on standard glass slides. Combining gene expression data with H&E images from the same section, it allows precise characterization of local molecular features and disease states at single-cell scale. We analyzed a set of human prostate adenocarcinoma samples with the Visium HD assay, and identified spatially regulated gene signatures associated with specific cell types and functional pathways. Our findings have recapitulated previously identified molecular markers, including VEGFA and MYC which have been implicated in tumor progression and angiogenesis. The spatial gene expression patterns had good correlation with histological annotations of cancerous and normal tissue within the H&E image. However, Visium HD allowed us to further observe molecular processes and pathways specific to this adenocarcinoma sample. This integrative approach offers valuable implications for personalized cancer therapy and the development of targeted interventions tailored to the spatial context of individual tumors. Our study underscores the significance of understanding the spatial organization of the whole transcriptome in cancer tissues, and highlights the potential of the Visium HD platform as a powerful tool for unraveling the complexities of the tumor microenvironment. These insights pave the way for the development of innovative therapeutic strategies and precision medicine approaches, ultimately contributing to improved outcomes for cancer patients. Citation Format: Yifeng Yin, Jerald Sapida, David Sukovich, David Patterson, Augusto Tentori. Unraveling spatial complexity of the tumor microenvironment: A whole transcriptomic perspective with Visium HD [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 3645.
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Duan, Hao, Qingchen Zhang, Feifei Cui, Quan Zou, and Zilong Zhang. "MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks." PLOS Computational Biology 20, no. 9 (September 5, 2024): e1012409. http://dx.doi.org/10.1371/journal.pcbi.1012409.
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Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition.
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Bae, Sungwoo, Hongyoon Choi, and Dong Soo Lee. "Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images." Nucleic Acids Research 49, no. 10 (February 22, 2021): e55-e55. http://dx.doi.org/10.1093/nar/gkab095.
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Abstract Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.
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Lein, Ed, Lars E. Borm, and Sten Linnarsson. "The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing." Science 358, no. 6359 (October 5, 2017): 64–69. http://dx.doi.org/10.1126/science.aan6827.
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The stereotyped spatial architecture of the brain is both beautiful and fundamentally related to its function, extending from gross morphology to individual neuron types, where soma position, dendritic architecture, and axonal projections determine their roles in functional circuitry. Our understanding of the cell types that make up the brain is rapidly accelerating, driven in particular by recent advances in single-cell transcriptomics. However, understanding brain function, development, and disease will require linking molecular cell types to morphological, physiological, and behavioral correlates. Emerging spatially resolved transcriptomic methods promise to fill this gap by localizing molecularly defined cell types in tissues, with simultaneous detection of morphology, activity, or connectivity. Here, we review the requirements for spatial transcriptomic methods toward these goals, consider the challenges ahead, and describe promising applications.
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Noronha, Katelyn J., Jennifer M. Garbarino, Daniel Massucci, Abigail R. Tyree, and Colin Ng. "Abstract 4407: Simultaneous spatial epigenomic and transcriptomic analysis of gastric adenocarcinoma reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level." Cancer Research 84, no. 6_Supplement (March 22, 2024): 4407. http://dx.doi.org/10.1158/1538-7445.am2024-4407.
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Abstract Recent advances in spatial transcriptomics and spatial proteomics have enabled increasingly complex questions on the nature of gene regulation and expression in cellular subtypes in tumor tissue and the tumor microenvironment. However, most spatial omics techniques do not profile the epigenomic landscape responsible for downstream gene expression. Furthermore, current spatial technologies have yet to profile the epigenome and transcriptome simultaneously, and thus it remains a challenge to correlate multi-omics data across sections of extremely heterogenous tumor tissue. Recently, co-profiling of spatial epigenomics and transcriptomics using principles of Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq) has been demonstrated on normal brain tissue. Joint spatial profiling of chromatin states and whole transcriptome in tissue allows for parallel characterization of gene regulation programs across all cell types, while preserving the tissue architecture for greater understanding of the cellular environment. Here we present the first application of spatial ATAC-seq and spatial transcriptomics on the same tissue section to characterize the tumor microenvironment of an invasive gastric adenocarcinoma (GAC) and adjacent normal tissue. GAC is the fifth most common cancer and commonly exhibits mutations in epigenetic modifiers, including ARID1A and MLL1-4. Distinct spatial clusters representing different cell subtypes were identified via both spatial chromatin accessibility and spatial transcriptomics. Spatial ATAC-seq profiling of accessible regulatory elements correlated well with RNA expression of target genes. Spatial patterns of transcription factor motif accessibility also correlated well with the observed transcriptional program of tumor tissue. When compared to adjacent normal tissue, spatial co-profiling of chromatin accessibility and the transcriptome revealed that the epigenetic landscape is significantly altered in tumorigenesis of GAC. Future work will focus on development of co-profiling of histone modifications and the transcriptome to enable the study of another layer of the epigenomic landscape, especially as targeting epigenetic modifiers such as EZH2 has been identified as a potential therapeutic strategy in GACs. Overall, we present a solution to profile multiple layers of gene regulation and expression with spatial context, which can be applied to most tumor types for better understanding of tumorigenesis and the consequences of new targeted therapies. Citation Format: Katelyn J. Noronha, Jennifer M. Garbarino, Daniel Massucci, Abigail R. Tyree, Colin Ng. Simultaneous spatial epigenomic and transcriptomic analysis of gastric adenocarcinoma reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level [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 4407.
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Jiang, Rui, Zhen Li, Yuhang Jia, Siyu Li, and Shengquan Chen. "SINFONIA: Scalable Identification of Spatially Variable Genes for Deciphering Spatial Domains." Cells 12, no. 4 (February 13, 2023): 604. http://dx.doi.org/10.3390/cells12040604.
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Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.
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Saqib, Jahanzeb, Beomsu Park, Yunjung Jin, Junseo Seo, Jaewoo Mo, and Junil Kim. "Identification of Niche-Specific Gene Signatures between Malignant Tumor Microenvironments by Integrating Single Cell and Spatial Transcriptomics Data." Genes 14, no. 11 (October 31, 2023): 2033. http://dx.doi.org/10.3390/genes14112033.
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The tumor microenvironment significantly affects the transcriptomic states of tumor cells. Single-cell RNA sequencing (scRNA-seq) helps elucidate the transcriptomes of individual cancer cells and their neighboring cells. However, cell dissociation results in the loss of information on neighboring cells. To address this challenge and comprehensively assess the gene activity in tissue samples, it is imperative to integrate scRNA-seq with spatial transcriptomics. In our previous study on physically interacting cell sequencing (PIC-seq), we demonstrated that gene expression in single cells is affected by neighboring cell information. In the present study, we proposed a strategy to identify niche-specific gene signatures by harmonizing scRNA-seq and spatial transcriptomic data. This approach was applied to the paired or matched scRNA-seq and Visium platform data of five cancer types: breast cancer, gastrointestinal stromal tumor, liver hepatocellular carcinoma, uterine corpus endometrial carcinoma, and ovarian cancer. We observed distinct gene signatures specific to cellular niches and their neighboring counterparts. Intriguingly, these niche-specific genes display considerable dissimilarity to cell type markers and exhibit unique functional attributes independent of the cancer types. Collectively, these results demonstrate the potential of this integrative approach for identifying novel marker genes and their spatial relationships.
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Li, Zhuliu, Tianci Song, Jeongsik Yong, and Rui Kuang. "Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion." PLOS Computational Biology 17, no. 4 (April 7, 2021): e1008218. http://dx.doi.org/10.1371/journal.pcbi.1008218.
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High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x,y) spatial coordinates (x-mode andy-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.
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Dries, Ruben, Jiaji Chen, Natalie del Rossi, Mohammed Muzamil Khan, Adriana Sistig, and Guo-Cheng Yuan. "Advances in spatial transcriptomic data analysis." Genome Research 31, no. 10 (October 2021): 1706–18. http://dx.doi.org/10.1101/gr.275224.121.
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Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell–cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
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Mirchia, Kanish, Soo-Jin Cho, Alyssa T. Reddy, Line Jacques, Melike Pekmezci, Arie Perry, David Raleigh, and Harish Vasudevan. "EPCO-04. SPATIAL TRANSCRIPTOMIC ANALYSIS OF MALIGNANT PERIPHERAL NERVE SHEATH TUMORS REVEALS THERAPEUTICALLY TARGETABLE MOLECULAR SIGNATURES IN REGIONS UNDERGOING HISTOPATHOLOGIC TRANSFORMATION." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v124. http://dx.doi.org/10.1093/neuonc/noad179.0469.
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Abstract Malignant peripheral nerve sheath tumors (MPNSTs) evolve from plexiform neurofibromas (pNF) in patients with neurofibromatosis type-1 (NF-1) yet the cellular and transcriptomic mechanisms underlying this transformation remain unclear. Here, we perform spatial gene expression profiling on fifteen MPNSTs to correlate histologic observations with transcriptomic programs and identify mechanisms underlying malignant transformation. METHODS: Fifteen MPNSTs, including tumors with a histopathologically defined transition zone between adjacent low-grade and high-grade areas (n=3), were retrospectively identified. Spatial transcriptomic profiling was performed using Visium followed by downstream processing with SpaceRanger, Seurat, Harmony, and monocle. RESULTS: 50,807 unique spatial transcriptomes were distributed across 17 unique clusters, of which 12 were enriched for tumor cells and 5 for non-tumor microenvironment cells based on histologic evaluation, unsupervised cell-type assignment, marker gene expression, and cell signature gene sets. Transcriptomic clusters in low-grade areas were enriched for genes regulating mononuclear cell migration and cell-cell adhesion, while high-grade regions were enriched for Ras/Raf/MEK/ERK target genes, anti-apoptotic programs, and extracellular matrix reorganization genes. Trajectory inference beginning in low grade regions revealed spatially organized molecular signatures of malignant transformation enriched for cell cycle progression within histopathologically homogeneous regions. Moreover, the intermediate transformation zone between low-grade neurofibromatous and high-grade MPNST histopathologic regions was marked by loss of cell differentiation genes and increased cell cycle progression. CONCLUSIONS: Spatial transcriptomic analysis elucidates the cellular and gene expression patterns underlying progression from pNF to MPNST. Distinct cell populations exist across low-grade, transformation zone, and high-grade regions in MPNSTs highlighted by alterations in Ras/Raf/MEK/ERK signaling, cell differentiation and cell cycle programs, shedding light on targets for combinatorial molecular therapy to decrease the risk of malignant transformation and serving as a biomarker of aggressive peripheral nerve tumors to complement conventional histopathology.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (May 27, 2022): 583. http://dx.doi.org/10.12688/f1000research.110492.1.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, the system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there is no genomic and transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: externa and main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for both P. reticulata and Rhizocephala. Based on the results obtained, the spatial heterogeneity of protein-coding genes expression in different regions of P. reticulata adult female body was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in the metamorphosis”, even in their reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of this amazing parasites.
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Nesterenko, Maksim, and Aleksei Miroliubov. "From head to rootlet: comparative transcriptomic analysis of a rhizocephalan barnacle Peltogaster reticulata (Crustacea: Rhizocephala)." F1000Research 11 (January 9, 2023): 583. http://dx.doi.org/10.12688/f1000research.110492.2.
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Background: Rhizocephalan barnacles stand out in the diverse world of metazoan parasites. The body of a rhizocephalan female is modified beyond revealing any recognizable morphological features, consisting of the interna, a system of rootlets, and the externa, a sac-like reproductive body. Moreover, rhizocephalans have an outstanding ability to control their hosts, literally turning them into “zombies”. Despite all these amazing traits, there are no genomic or transcriptomic data about any Rhizocephala. Methods: We collected transcriptomes from four body parts of an adult female rhizocephalan Peltogaster reticulata: the externa, and the main, growing, and thoracic parts of the interna. We used all prepared data for the de novo assembly of the reference transcriptome. Next, a set of encoded proteins was determined, the expression levels of protein-coding genes in different parts of the parasite’s body were calculated and lists of enriched bioprocesses were identified. We also in silico identified and analyzed sets of potential excretory / secretory proteins. Finally, we applied phylostratigraphy and evolutionary transcriptomics approaches to our data. Results: The assembled reference transcriptome included transcripts of 12,620 protein-coding genes and was the first for any rhizocephalan. Based on the results obtained, the spatial heterogeneity of protein-coding gene expression in different regions of the adult female body of P. reticulata was established. The results of both transcriptomic analysis and histological studies indicated the presence of germ-like cells in the lumen of the interna. The potential molecular basis of the interaction between the nervous system of the host and the parasite's interna was also determined. Given the prolonged expression of development-associated genes, we suggest that rhizocephalans “got stuck in their metamorphosis”, even at the reproductive stage. Conclusions: The results of the first comparative transcriptomic analysis for Rhizocephala not only clarified but also expanded the existing ideas about the biology of these extraordinary parasites.
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Ritter, M., C. Blume, B. Patel, Y. Tang, A. Patel, N. Berghaus, Z. Seferbekova, et al. "OS10.8.A APPLICATIONS OF NOVEL FFPE BASED TECHNOLOGIES FOR THE DIAGNOSTICS OF GLIOMAS." Neuro-Oncology 25, Supplement_2 (September 1, 2023): ii23. http://dx.doi.org/10.1093/neuonc/noad137.068.
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Abstract BACKGROUND Due to the lack of consistent tumour-cell specific markers, the diffuse brain invasion of glioblastoma (GB) presents a significant diagnostic challenge, especially for specimens with a low tumour cell fraction or scarce tissue. The only common alteration found in most GB is the gain of chromosome 7 and loss of chromosome 10. The emergence of new technologies such as spatial and single nucleus transcriptomics that allow for detection of copy number alterations (CNVs) may allow us to push the diagnostic boundaries. MATERIAL AND METHODS We performed 10x Visium spatial transcriptomic analysis of FFPE tissue from bulk resections of 14 FFPE GB and stereotactic biopsies of 6 GB, 7 IDH mutant astrocytomas, 4 oligodendrogliomas and 1 diffuse glioma NOS. Visium tissue spots were then deconvolved using data from single nucleus transcriptomic data from nuclei isolated from FFPE tissue. Fresh frozen and FFPE single nucleus transcriptomic libraries were also generated from 4 GB with matching fresh-frozen and FFPE tissue available. RESULTS The infiltration zone, the tumour core and healthy brain tissue could be differentiated using the inferred CNVs of chromosome 7 and chromosome 10. Additionally, inferred CNVs of tissue fragments smaller than 1 mm2 were in accordance with Infinium MethylationEPIC array analysis (a well-established method for CNV detection) from the same tumour tissue. Using the spatial transcriptomics approach, the loss of chromosome 1p and 19q, which is characteristic for oligodendroglioma was also detected. To overcome the low resolution of Visium, deconvolution approaches using a single cell dataset can be used to determine the tumour content of a given tissue more precisely. To test if the single nuclei data from FFPE samples is suitable for deconvolution of spatial transcriptomics, we compared fresh-frozen and FFPE single nuclei data from matching tumours. As expected, FFPE and fresh frozen nuclei clustered together according to cell types and tumours in the UMAP space. CONCLUSION Spatial transcriptomics can be useful for brain tumour diagnostic workup, especially in cases where a limited amount of tissue prevents analysis by standard molecular diagnostic METHODS . Additionally, CNVs inferred from the spatial data as well as the spatial expression of marker genes help to better distinguish tumour infiltration zones from the core tumour and healthy brain tissue in the case of GB. This analysis can be further supported by deconvolution with a reference set generated from single nucleus transcriptomic analysis of FFPE tissue.
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Lee, Amos C., Sumin Lee, and Sunghoon Kwon. "Abstract 6781: Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6781. http://dx.doi.org/10.1158/1538-7445.am2023-6781.
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Abstract Cancer is spatially heterogeneous in terms of genetic molecules. Revealing genomic, epigenomic, transcriptomic, and epitranscriptomic features that are unique to the malignant cell populations within cancer lead to cancer biomarker discoveries that can be translated into diagnostic or therapeutic tools. Here, we introduce spatially-resolved laser-activated cell sorting (SLACS) technology coupled to next generation sequencing and mass spectrometry that analyzes genetic molecules from regions of interest within spatial context. Specifically, we demonstrate application of SLACS in various cancers such as breast cancer, glioblastoma, glioma, meningioma, multiple myeloma, and leukemia. Examples of discovering cancer biomarker and their mechanism of action within spatial context are also provided. Specifically, we describe Select-seq which isolates the regions of interest as small as single cells from the immunofluorescence stained tissue and obtains the full-length transcriptome data. We demonstrated Select-seq on tumor tissue section from triple negative breast cancer patient who have received neoadjuvant chemotherapy. Select-seq produced full-length spatial transcriptome data, with which we analyzed the transcriptomic and epitranscriptomic features including alternative splicing variant, complementarity-determining region analysis of B cells, and even adenosine-to-inosine base editing. We were also able to analyze the immune- and stem-cell-like microniches, in which the complex mechanisms behind ferroptosis inhibition might suggest therapeutic options for the TNBC patients. Bridging spatial technologies to omics technologies, the discovery of specific markers within spatial context will provide insights into the next generation diagnostics and cancer therapeutics such as cancer vaccines. Citation Format: Amos C. Lee, Sumin Lee, Sunghoon Kwon. Spatial omics using spatially-resolved laser-activated cell sorting for cancer biomarker discovery [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 6781.
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Wood, Colin Stuart, Joao Da Silva Filho, Andrew Cameron, Assya Legrini, Holly Leslie, Tengyu Zhang, Yoana Doncheva, et al. "Abstract 5072: Multi-omic, multi-scale characterisation of colorectal cancer defines spatiotemporal patterns of recurrence." Cancer Research 84, no. 6_Supplement (March 22, 2024): 5072. http://dx.doi.org/10.1158/1538-7445.am2024-5072.
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Abstract Aim: Patients who undergo intended curative resection of colorectal cancer (CRC) have a 20-25% chance of metachronous recurrence, the site and timing of which are unpredictable and resistant to current treatment. The biological basis for such heterogeneous disease behavior remains to be elucidated. Bulk transcriptomic profiling and subsequently single-cell RNAseq have provided insight in the epithelial subtypes and immune microenvironment. Recently spatially resolved transcriptomic assessment allow molecular profiling of tissue while preserving tissue architecture. Utilizing ST technology we sought to perform deep characterization of a large cohort of patients with primary CRC with the intention to decipher the biological determinants of spatio-temporal patterns of recurrence. Methods: 750 patients who underwent resection of CRC with mature follow up were studied. Bulk transcriptomic, genomic and multiplex immune characterization was performed using a TMA format. Of these, 28 patients were assessed using single cell spatial transcriptomics (Nanostring CosMx Spatial Molecular Imager (SMI, 1000plex gene panel)), 120 tumors underwent regional whole transcriptome profiling of epithelium and TME (Nanostring GeoMx Digital Spatial Profiler using FFPE tissue). We used image analysis and bioinformatics to integrate these complex datasets in over 120000 single-cells in the context of their spatial tissue architecture and clinicopatholgical outcome and recurrence data. Results: Using the CosMx SMI we characterized cells with complete topographic detail and defined 2 unique epithelial cell states defined. Each epithelial state had distinct spatial properties including cell size and morphology, average distance to nearest lymphocyte, neighboring cell types and stromal neighborhood. This epithelial signature was integrated in GeoMx samples and were found to predict recurrence (p<0.05). The Epithelial, Fibroblast and Immune GeoMx transcriptomic compartments were expanded and grouped using unsupervised clustering. Each compartment demonstrated groups of patients where specific spatially derived signatures could predict site and time of recurrence (p<0.05). Conclusions: By maintaining the tissue structure, we have directly measured cellular interactions and captured cells commonly missed during dissociative studies whilst defining novel molecular subtypes of CRC with clinical relevance. Novel platforms such as CosMx allow deeper characterization of unique cell types and cellular interactions that may pave the way for novel therapeutics and precision medicine for patients with CRC. Citation Format: Colin Stuart Wood, Joao Da Silva Filho, Andrew Cameron, Assya Legrini, Holly Leslie, Tengyu Zhang, Yoana Doncheva, Claire Kennedy-Dietrich, Matthias Marti, Joanne Edwards, Paul Horgan, Campbell Roxburgh, Colin Steele, Nigel Jamieson. Multi-omic, multi-scale characterisation of colorectal cancer defines spatiotemporal patterns of recurrence [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 5072.
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Jamshidi, Raehannah, Lyra Griffiths, Rich Johnston, Vaunita Parihar, Frank Schneider, and Adam Marcus. "Abstract 1153: Using spatial transcriptomics to dissect cell to cell cooperation in lung adenocarcinoma." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1153. http://dx.doi.org/10.1158/1538-7445.am2024-1153.
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Abstract Lung cancer is the second most common cancer in the United States and the leading cause of cancer related deaths in this country. Unfortunately, efficacious treatments for lung cancer remain suboptimal. Lung cancer is characterized by several distinct subtypes of which lung adenocarcinoma is the most common, comprising about 40% of this malignancy. The hallmark gene mutations of lung adenocarcinoma include TP53, RAS, and STK11. LKB1 is a serine-threonine kinase (coded by the gene STK11) that largely functions as a tumor suppressor, and is mutated in 20-30% of non-small cell lung cancers (NSCLCs). The diversity between and within lung cancer subtypes, as well as within a patient (i.e. metastases vs primary tumor), makes treating this cancer very challenging. Lung adenocarcinoma, in particular, has collective invasion packs of cells adjacent to the primary tumor that correlate with metastatic disease in mouse models. We hypothesize that the transcriptomic profile of the collective invasion packs in lung adenocarcinoma patients varies significantly from the adjacent primary tumor and represents a targetable metastatic sub-population. This work will help to identify specific cell signaling pathways that have the translational potential to develop novel therapeutics for metastatic disease, ultimately improving patient outcomes through precision medicine. Utilizing patient lung adenocarcinoma samples with KRAS and KRAS+LKB1 mutations, we identified regions of interest including bulk tumor and surrounding invasion packs. Then, using GeoMx digital spatial profiling technology (by Nanostring) and next-gen sequencing, we generated transcriptomic profiles from bulk tumor and invasion packs. Preliminary results indicate region specific transcriptomic differences, highlighting the heterogeneity of these cell populations. More specifically, collective invasion packs exhibit upregulation of gene networks involving immune cell differentiation, function, oxidative phosphorylation, tumor invasiveness, and mitochondrial structure. To discern the metastatic potential, transcriptomic profiles and biological functions will be compared between invasion packs, tumor bulk vs invasion packs, and finally, inter-patient differences. Successful completion of this project will characterize transcriptomes of metastatic lung cancer and has the potential to ultimately identify biomarkers of aggressive disease. Lastly, this foundational study will pave the way for future studies on the transcriptomic landscape of metastatic pediatric cancers and cancer predisposition syndromes. Citation Format: Raehannah Jamshidi, Lyra Griffiths, Rich Johnston, Vaunita Parihar, Frank Schneider, Adam Marcus. Using spatial transcriptomics to dissect cell to cell cooperation in lung adenocarcinoma [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 1153.
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Misra, Adwiteeya, Cameron D. Baker, Elizabeth M. Pritchett, Kimberly N. Burgos Villar, John M. Ashton, and Eric M. Small. "Characterizing Neonatal Heart Maturation, Regeneration, and Scar Resolution Using Spatial Transcriptomics." Journal of Cardiovascular Development and Disease 9, no. 1 (December 21, 2021): 1. http://dx.doi.org/10.3390/jcdd9010001.
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The neonatal mammalian heart exhibits a remarkable regenerative potential, which includes fibrotic scar resolution and the generation of new cardiomyocytes. To investigate the mechanisms facilitating heart repair after apical resection in neonatal mice, we conducted bulk and spatial transcriptomic analyses at regenerative and non-regenerative timepoints. Importantly, spatial transcriptomics provided near single-cell resolution, revealing distinct domains of atrial and ventricular myocardium that exhibit dynamic phenotypic alterations during postnatal heart maturation. Spatial transcriptomics also defined the cardiac scar, which transitions from a proliferative to secretory phenotype as the heart loses regenerative potential. The resolving scar is characterized by spatially and temporally restricted programs of inflammation, epicardium expansion and extracellular matrix production, metabolic reprogramming, lipogenic scar extrusion, and cardiomyocyte restoration. Finally, this study revealed the emergence of a regenerative border zone defined by immature cardiomyocyte markers and the robust expression of Sprr1a. Taken together, our study defines the spatially and temporally restricted gene programs that underlie neonatal heart regeneration and provides insight into cardio-restorative mechanisms supporting scar resolution.
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Mirchia, Kanish, Abrar Choudhury, Tara Joseph, Janeth Ochoa Birrueta, Joanna Phillips, Aparna Bhaduri, Elizabeth Crouch, Arie Perry, and David Raleigh. "EPCO-48. THE SINGLE-CELL AND SPATIAL TRANSCRIPTOMIC ARCHITECTURE OF MENINGEAL SOLITARY FIBROUS TUMORS PHENOCOPIES CEREBRAL VASCULAR DEVELOPMENT AND HOMEOSTASIS." Neuro-Oncology 25, Supplement_5 (November 1, 2023): v135. http://dx.doi.org/10.1093/neuonc/noad179.0510.
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Abstract INTRODUCTION Meningeal solitary fibrous tumors (SFTs) are rare mesenchymal neoplasms that are associated with local recurrence and distant metastasis. The cell states and spatial transcriptomic architecture of SFTs are unknown. METHODS Single-cell RNA sequencing or spatial transcriptomic sequencing were performed on 40,022 cells or 23,682 spatial transcriptomes from 12 meningeal SFT samples. Datasets were corrected for batch effects using Harmony and integrated using uniform maniform approximation and projection (UMAP). Clusters were defined using automated cell type classification, cell signature gene sets, cell cycle analysis, and differentially expressed marker genes. Trajectory analyses were performed using RNA velocity and pseudotime. Cell-cell communication analysis was performed using CellChat. RESULTS were validated using immunofluorescence and immunohistochemistry and generalized using comparisons to 30,934 single-cell transcriptomes from 6 meningioma samples, 139,134 perinatal human brain vascular single-cell transcriptomes, 84,138 adult human brain vascular single-cell transcriptomes, and DNA methylation profiles from 8 SFT and 221 meningioma samples. RESULTS Unsupervised hierarchical clustering of DNA methylation profiles revealed molecular distinction of meningeal SFTs from meningiomas. Deconvolution of vascular single-cell types showed SFT cell clusters resembling perinatal or adult fibroblasts, perinatal mitotic endothelia, and adult venous or arterial endothelia. UMAP and trajectory analyses showed SFTs were comprised of 8 interchangeable tumor cell states that were associated with cell adhesion (VCAM1, NCAM2), cell stress (EGR1), cell signaling (NOTCH3), extracellular matrix remodeling (ECM) (ADAMTS6, PLCG2), or protein synthesis pathways (RPL27A, RPL37). Cell-cell communication analyses identified single-cell and spatial interactions between VCAM1- or NCAM2-expressing SFT cells and endothelia or immature neurons in the tumor microenvironment, respectively. Single-cell deconvolution demonstrated evolution of ECM and protein synthesis SFT cells in paired primary/recurrent samples, and evolution of ECM SFT cells and macrophages in paired primary/metastatic samples. CONCLUSIONS The single-cell and spatial transcriptomic architecture of meningeal SFT reveals dynamic cell states that phenocopy cerebral vascular development and homeostasis.
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Wu, Yuesong, Aoqi Xie, Ian Loveless, Madison George, Kendyll Gartrelle, Julie Clark, Daniel Salas-Escabillas, et al. "Abstract C107: Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer." Cancer Research 84, no. 2_Supplement (January 16, 2024): C107. http://dx.doi.org/10.1158/1538-7445.panca2023-c107.
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Abstract Pancreatic cancer is the third leading cause of cancer-related deaths in the US, with a 5-year survival rate of 12%. Black or African American patients (BAA) have a 20% higher incidence of pancreatic cancer, and recent studies suggest that there are biological factors that may influence this disparity, in addition to complex socioeconomic factors. Pancreatic tumors are highly enriched with stroma and exhibit significant inter- and intra-patient morphological and molecular heterogeneity. To improve the care for all patients, we need to better understand the disease mechanisms by comprehensively mapping different spatial locations within individual patients’ tumors and determining whether there are common patterns of pathogenesis across diverse patient populations. In this study, we utilized the 10X Visium spatial transcriptomics platform on eight treatment-naïve primary pancreatic tumors. We tested multiple published bioinformatic packages, including SpatialDE, SPARK-X, Spatial PCA, and Cottrazm, and optimized a multi-sample integrated analysis pipeline that allows us to automatically segment tumor lesions, infer major cell types in regions of interest on tissues, detect spatially variant genes, and assess associations between tissue morphological phenotypes and transcriptomic features. Importantly, our study cohort includes four White patients and four BAA patients. Through the comparison of spatial transcriptomes between White and BAA patients, we identified several genes displaying differential expression in tumor lesions of distinct racial groups, including Trefoil Factor 1 (TFF1) and CYP3A5. These genes present potential candidates for further investigation to elucidate the biological mechanisms underlying racial disparities in pancreatic cancer. Citation Format: Yuesong Wu, Aoqi Xie, Ian Loveless, Madison George, Kendyll Gartrelle, Julie Clark, Daniel Salas-Escabillas, Rupen Shah, David Kwon, Ralph Francescone, Débora B. Vendramini Costa, Howard Crawford, Brian Theisen, Yuehua Cui, Ling Huang, Nina G. Steele. Use of spatial transcriptomics to identify molecular features associated with African American heritage in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C107.
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Akilesh, Shreeram, Kammi J. Henriksen, Roberto F. Nicosia, Charles E. Alpers, and Kelly D. Smith. "Spatial Transcriptomic Profiling of Collapsing Glomerulopathy." Journal of the American Society of Nephrology 32, no. 10S (October 2021): 519. http://dx.doi.org/10.1681/asn.20213210s1519a.
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Choe, Kyongho, Unil Pak, Yu Pang, Wanjun Hao, and Xiuqin Yang. "Advances and Challenges in Spatial Transcriptomics for Developmental Biology." Biomolecules 13, no. 1 (January 12, 2023): 156. http://dx.doi.org/10.3390/biom13010156.
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Development from single cells to multicellular tissues and organs involves more than just the exact replication of cells, which is known as differentiation. The primary focus of research into the mechanism of differentiation has been differences in gene expression profiles between individual cells. However, it has predominantly been conducted at low throughput and bulk levels, challenging the efforts to understand molecular mechanisms of differentiation during the developmental process in animals and humans. During the last decades, rapid methodological advancements in genomics facilitated the ability to study developmental processes at a genome-wide level and finer resolution. Particularly, sequencing transcriptomes at single-cell resolution, enabled by single-cell RNA-sequencing (scRNA-seq), was a breath-taking innovation, allowing scientists to gain a better understanding of differentiation and cell lineage during the developmental process. However, single-cell isolation during scRNA-seq results in the loss of the spatial information of individual cells and consequently limits our understanding of the specific functions of the cells performed by different spatial regions of tissues or organs. This greatly encourages the emergence of the spatial transcriptomic discipline and tools. Here, we summarize the recent application of scRNA-seq and spatial transcriptomic tools for developmental biology. We also discuss the limitations of current spatial transcriptomic tools and approaches, as well as possible solutions and future prospects.
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Jeon, Hyeongseon, Juan Xie, Yeseul Jeon, Kyeong Joo Jung, Arkobrato Gupta, Won Chang, and Dongjun Chung. "Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives." Biomolecules 13, no. 2 (January 24, 2023): 221. http://dx.doi.org/10.3390/biom13020221.
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Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.
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Wu, Zhichao, Karen Dazelle, Hye-Jung Chung, and Kenneth Aldape. "TMIC-25. SPATIAL TRANSCRIPTOMIC LANDSCAPE OF DIFFUSE GLIOMA." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii276—vii277. http://dx.doi.org/10.1093/neuonc/noac209.1069.
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Abstract Single-cell studies have suggested heterogeneity of neoplastic cell states in diffuse gliomas. IDH-mutant astrocytoma and oligodendrogliomas (IDH-glioma) is described by cell states resembling including oligodendrocytes(OC), astrocytes (AC), and cycling neural progenitor cells (NPC); and IDH-wildtype glioblastoma (GBM-IDHwt) is marked by cell states resembling AC, NPC, oligodendrocyte progenitors (OPC) and mesenchymal (MES). Collective experience indicates that cell states are dynamic and can transition, at times recapturing brain development and inflammatory wound response. However, the spatial architecture of cell states, cell types and their interactions are yet to be investigated. We quantified the transcriptomics of 1,941 regions of interest (ROIs) from 19 IDH-glioma (non-1p/19q-codeleted n = 11, 1p/19q-codeleted n = 8) and 11 GBM-IDHwt tumors using the NanoString GeoMx Cancer Transcriptome Atlas (CTA, n = 1,811 genes) to study their spatial heterogeneity. Where appropriate, ROIs were selected to represent tumor core, infiltration edge, pseudopalisading necrosis, microvascular proliferation, and histological normal areas, if available. CTA profiling showed strong inter- and intra-tumor heterogeneity. Genes involved in oncogenesis and extra-cellular matrix such as VEGFA and VCAN, and macrophages (including CD74 and CCL3/1) showed the highest spatially variation. We identified substantial tumor immune environmental differences across ROIs including immune code, monocyte-like rich and lymphocyte-rich groups. The GBM-IDHwt cell states OPC and NPC were highly positively correlated (p-value = 5.65e-122) across ROIs suggesting spatial co-localization, but MES cell states were negatively correlated with the other three GBM cell states. M0 macrophage abundance was positively correlated with the MES state but negatively correlated with other cell states in GBM-IDHwt. The MES state was significantly lower in infiltrating edge of the tumor (p-value = 1.39e-34) but higher in the pseudopalisading necrosis region (p-value = 7.66e-30). Overall, this spatial transcriptomics dataset provides spatially informed relationships of neoplastic cell states and the immune microenvironment in diffuse glioma.
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Williams, Cameron Gerard, Jessica A. Engel, Megan S. F. Soon, Evan Murray, Fei Chen, and Ashraful Haque. "Studying lymphocyte differentiation in the spleen via spatial transcriptomics." Journal of Immunology 206, no. 1_Supplement (May 1, 2021): 98.55. http://dx.doi.org/10.4049/jimmunol.206.supp.98.55.
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Abstract Immune cell positioning within secondary lymphoid tissues likely affects cell-cell interaction and subsequent immune responses. Techniques such as intra-vital imaging and multiplex immunohistochemistry provide insight into this, but require specialist reagents. To examine cell-cell interactions in dense tissue at whole-genome scale, we tested the feasibility of a single-cell spatial transcriptomics method, Slide-seq2. We first confirmed using murine gut tissue that small tertiary lymphoid structures, particularly rich in B cells, could be identified and examined at a cellular level in the small intestine. We then hypothesized that microanatomical alterations could be detected. To test this, we compared mouse spleens before and 7 days after infection with blood-stage malaria parasites. To increase the molecular resolution of our data, we integrated Slide-seq2 data with high-depth, droplet-based, scRNA-seq data, generated via Chromium controller from 10× Genomics. We found that Slide-seq2 produced sufficiently rich data to map cell types from an scRNA-seq reference. Some spatially defined transcriptomes appeared to derive from mixtures of cell types, indicating that further deconvolution of spatially resolved transcriptomic data was required. Via unsupervised clustering of whole transcriptomes, we confirmed that T and B cell zones within naïve mice became less ordered at the peak of malaria infection, reflecting T and B cell interaction. Ongoing analyses aim to define novel splenic T and B cell interactions during malaria, both in extra follicular areas as well as within the germinal center.
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da Costa, André Luiz N. Targino, Jingxian Liu, Chia-Kuei Mo, Erik Storrs, Austin N. Southard-Smith, Reyka G. Jayasinghe, Julia T. Wang, et al. "Abstract 2341: Morph: A feature extraction toolset for spatial transcriptomics." Cancer Research 84, no. 6_Supplement (March 22, 2024): 2341. http://dx.doi.org/10.1158/1538-7445.am2024-2341.
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Abstract Accurately defining spatial characteristics of tumors has been a challenge in cancer research. Specifically, there is still a lack of spatial transcriptomic (ST) bioinformatic methods that infer tumor boundaries, a necessity for tumor microenvironment (TME) analyses, that are fully automated and handle non-rectangular grids (like the one found in Visium). Here we introduce Morph, a toolset that not only addresses these limitations, but also accurately extracts tumor regions, layers surrounding them, and distances related to such regions. Morph was tested on a dataset composed of 117 ST slides across 6 different cancer types, including primary and metastatic tumors. More broadly, Morph is a computationally efficient tool based on mathematical morphology that is designed to work without any approximations on ST slides (that can be composed of spots in a hexagonal lattice) of any resolution. The toolset accepts a variety of input types, such as tumor purity and manual annotation, and can perform a diverse set of morphological operations, such as erosion, dilation, opening and closing, using a number of structuring elements with different shapes and sizes, such as a hexagon of side 1. Moreover, Morph runs quickly (seconds per sample). Its main focus is to unveil the aforementioned features of any spatially distinct tumor region (here defined as microregion) for various downstream analyses, such as spatially-varying copy number variations, cell-type distribution, and tumor-microenvironment interaction. Overall, we developed a spatial transcriptomics toolset that is not limited to any specific technology platform, and that can accurately extract ST features facilitating the discovery of spatial transcriptomic and interaction patterns. Citation Format: André Luiz N. Targino da Costa, Jingxian Liu, Chia-Kuei Mo, Erik Storrs, Austin N. Southard-Smith, Reyka G. Jayasinghe, Julia T. Wang, Michael D. Iglesia, Michael Wendl, Siqi Chen, Andrew Houston, Alla Karpova, Yize Li, Li Ding. Morph: A feature extraction toolset for spatial transcriptomics [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 2341.
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Chen, Ce, Yining Ge, and Lingli Lu. "Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants." Frontiers in Plant Science 14 (August 11, 2023). http://dx.doi.org/10.3389/fpls.2023.1185377.
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Single-cell and spatial transcriptomics have diverted researchers’ attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.
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Shao, Xin, Chengyu Li, Haihong Yang, Xiaoyan Lu, Jie Liao, Jingyang Qian, Kai Wang, et al. "Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk." Nature Communications 13, no. 1 (July 30, 2022). http://dx.doi.org/10.1038/s41467-022-32111-8.
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AbstractSpatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.
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Shang, Lulu, and Xiang Zhou. "Spatially aware dimension reduction for spatial transcriptomics." Nature Communications 13, no. 1 (November 23, 2022). http://dx.doi.org/10.1038/s41467-022-34879-1.
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AbstractSpatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.
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Danan, Charles H., Kay Katada, Louis R. Parham, and Kathryn E. Hamilton. "Spatial transcriptomics add a new dimension to our understanding of the gut." American Journal of Physiology-Gastrointestinal and Liver Physiology, December 6, 2022. http://dx.doi.org/10.1152/ajpgi.00191.2022.
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The profound complexity of the intestinal mucosa demands a spatial approach to the study of gut transcriptomics. Although single-cell RNA sequencing has revolutionized our ability to survey the diverse cell types of the intestine, knowledge of cell type alone cannot fully describe the cells that make up the intestinal mucosa. During homeostasis and disease, dramatic gradients of oxygen, nutrients, extracellular matrix proteins, morphogens, and microbiota collectively dictate intestinal cell state, and only spatial techniques can articulate differences in cellular transcriptomes at this level. Spatial transcriptomic techniques assign transcriptomic data to precise regions in a tissue of interest. In recent years, these protocols have become increasingly accessible, and their application in the intestinal mucosa has exploded in popularity. In the gut, spatial transcriptomics typically involve the application of tissue sections to spatially barcoded RNA sequencing or laser capture microdissection followed by RNA sequencing. In combination with single-cell RNA sequencing, these spatial sequencing approaches allow for the construction of spatial transcriptional maps at pseudo-single-cell resolution. In this review, we describe the spatial transcriptomic technologies recently applied in the gut and the previously unattainable discoveries that they have produced.
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Rocque, Brittany, Kate Guion, Pranay Singh, Sarah Bangerth, Lauren Pickard, Jashdeep Bhattacharjee, Sofia Eguizabal, et al. "Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease." Scientific Reports 14, no. 1 (February 13, 2024). http://dx.doi.org/10.1038/s41598-024-53993-2.
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AbstractSingle cell and spatially resolved ‘omic’ techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome ‘spot’ on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell–cell interactions predicted using ligand–receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell–cell interactions in biobanked clinical samples with advanced liver disease.
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Pont, Frédéric, Juan Pablo Cerapio, Pauline Gravelle, Laetitia Ligat, Carine Valle, Emeline Sarot, Marion Perrier, et al. "Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics." BMC Bioinformatics 24, no. 1 (January 27, 2023). http://dx.doi.org/10.1186/s12859-023-05150-1.
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Abstract Background: The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called ’spatial transcriptomics’. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce. Results: Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies. Conclusions: Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.
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Wirth, Johannes, Nina Huber, Kelvin Yin, Sophie Brood, Simon Chang, Celia P. Martinez-Jimenez, and Matthias Meier. "Spatial transcriptomics using multiplexed deterministic barcoding in tissue." Nature Communications 14, no. 1 (March 18, 2023). http://dx.doi.org/10.1038/s41467-023-37111-w.
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AbstractSpatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.
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Johnston, Kevin G., Bereket T. Berackey, Kristine M. Tran, Alon Gelber, Zhaoxia Yu, Grant R. MacGregor, Eran A. Mukamel, Zhiqun Tan, Kim N. Green, and Xiangmin Xu. "Single-cell spatial transcriptomics reveals distinct patterns of dysregulation in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer’s risk gene mutation." Molecular Psychiatry, August 5, 2024. http://dx.doi.org/10.1038/s41380-024-02651-0.
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AbstractThe R47H missense mutation of the TREM2 gene is a known risk factor for development of Alzheimer’s Disease. In this study, we analyze the impact of the Trem2R47H mutation on specific cell types in multiple cortical and subcortical brain regions in the context of wild-type and 5xFAD mouse background. We profile 19 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H; 5xFAD genotypes using MERFISH spatial transcriptomics, a technique that enables subcellular profiling of spatial gene expression. Spatial transcriptomics and neuropathology data are analyzed using our custom pipeline to identify plaque and Trem2R47H-induced transcriptomic dysregulation. We initially analyze cell type-specific transcriptomic alterations induced by plaque proximity. Next, we analyze spatial distributions of disease associated microglia and astrocytes, and how they vary between 5xFAD and Trem2R47H; 5xFAD mouse models. Finally, we analyze the impact of the Trem2R47H mutation on neuronal transcriptomes. The Trem2R47H mutation induces consistent upregulation of Bdnf and Ntrk2 across many cortical excitatory neuron types, independent of amyloid pathology. Spatial investigation of genotype enriched subclusters identified spatially localized neuronal subpopulations reduced in 5xFAD and Trem2R47H; 5xFAD mice. Overall, our MERFISH spatial transcriptomics analysis identifies glial and neuronal transcriptomic alterations induced independently by 5xFAD and Trem2R47H mutations, impacting inflammatory responses in microglia and astrocytes, and activity and BDNF signaling in neurons.
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Mao, Guangyao, Yi Yang, Zhuojuan Luo, Chengqi Lin, and Peng Xie. "SpatialQC: automated quality control for spatial transcriptome data." Bioinformatics, July 25, 2024. http://dx.doi.org/10.1093/bioinformatics/btae458.
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Abstract Motivation The advent of spatial transcriptomics has revolutionized our understanding of the spatial heterogeneity in tissues, providing unprecedented insights into the cellular and molecular mechanisms underlying biological processes. Although quality control (QC) critical for downstream data analyses, there is currently a lack of specialized tools for one-stop spatial transcriptome QC. Here, we introduce SpatialQC, a one-stop QC pipeline, which generates comprehensive QC reports and produces clean data in an interactive fashion. SpatialQC is widely applicable to spatial transcriptomic techniques. Availability and implementation source code and user manuals are available via https://github.com/mgy520/spatialQC, and deposited on Zenodo (https://doi.org/10.5281/zenodo.12634669). Supplementary information Supplementary data are available at Bioinformatics online.
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Xu, Zhicheng, Weiwen Wang, Tao Yang, Ling Li, Xizheng Ma, Jing Chen, Jieyu Wang, et al. "STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization." Nucleic Acids Research, November 11, 2023. http://dx.doi.org/10.1093/nar/gkad933.
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Abstract Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.
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Zhang, Chao, Renchao Chen, and Yi Zhang. "Accurate inference of genome-wide spatial expression with iSpatial." Science Advances 8, no. 34 (August 26, 2022). http://dx.doi.org/10.1126/sciadv.abq0990.
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Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Because of the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here, we developed a new algorithm named iSpatial to derive the spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we provide a computational approach to reveal spatial organization of the entire transcriptome at single-cell resolution. With numerous high-quality datasets available in the public domain, iSpatial provides a unique way to understand the structure and function of complex tissues and disease processes.
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Fan, Zhen, Runsheng Chen, and Xiaowei Chen. "SpatialDB: a database for spatially resolved transcriptomes." Nucleic Acids Research, November 12, 2019. http://dx.doi.org/10.1093/nar/gkz934.
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Abstract Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.