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Journal articles on the topic 'Spatial transcriptomic'

Author: Grafiati

Published: 21 September 2024

Last updated: 26 July 2025

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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 (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 var

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Lv, Zhuo, Shuaijun Jiang, Shuxin Kong, et al. "Advances in Single-Cell Transcriptome Sequencing and Spatial Transcriptome Sequencing in Plants." Plants 13, no. 12 (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

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Chen, Tsai-Ying, Li You, Jose Angelito U. Hardillo, and Miao-Ping Chien. "Spatial Transcriptomic Technologies." Cells 12, no. 16 (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 applic

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Gorbunova, Vera. "COMPARATIVE TRANSCRIPTOMIC OF LONGEVITY." Innovation in Aging 7, Supplement_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

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Callaway, Edward M., Hong-Wei Dong, Joseph R. Ecker, et al. "A multimodal cell census and atlas of the mammalian primary motor cortex." Nature 598, no. 7879 (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 orga

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Lee, Sumin, and Amos Lee. "Abstract 2079: Spatially guided single-cell analysis integrating spatial transcriptomics and spatial cell sorting for in-depth profiling." Cancer Research 85, no. 8_Supplement_1 (2025): 2079. https://doi.org/10.1158/1538-7445.am2025-2079.

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Spatial transcriptomics technologies, such as Xenium, provide detailed spatial mapping of gene expression at the single-cell level, revealing intricate tissue organization and distinct cell populations. However, translating these spatial insights into actionable molecular information often requires further downstream analysis of specific cells of interest. To address this, we introduce an integrated workflow combining SLACS (Spatially-resolved Laser-Activated Cell Sorting) with Xenium-derived spatial data, enabling targeted isolation and in-depth transcriptomic analysis of defined cell populat

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Danaher, Patrick, Michael Patrick, Shanshan He, et al. "Abstract 751: High-resolution and AI-enabled single-cell spatial transcriptomics and histopathology integrated to reveal tumor differentiation and immune exclusion in skin squamous cell carcinoma." Cancer Research 85, no. 8_Supplement_1 (2025): 751. https://doi.org/10.1158/1538-7445.am2025-751.

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Abstract Skin squamous cell carcinoma (SCC) is characterized by heterogeneity in differentiation states and immune exclusion within the tumor microenvironment (TME). Using the Bruker Spatial Biology CosMx® Whole Transcriptome (WTX) panel, which profiles approximately 19, 000 genes at single-cell resolution, we examined spatial gene expression in FFPE SCC sections. Individual single cell boundaries were defined utilizing a trained AI cell segmentation model. H&E staining on the same tissue provided histopathological context, enabling the integration of molecular and morphological findings.

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Zhou, Jun, Shengxi Wang, Ming Liu, and Zhaopei Li. "Effect of cryoablation on the spatial transcriptomic landscape of the immune microenvironment in non-small cell lung cancer." Journal of Cancer Research and Therapeutics 20, no. 7 (2024): 2141–47. https://doi.org/10.4103/jcrt.jcrt_1887_24.

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ABSTRACT Background: Cryoablation induces antitumor immune responses. Spatial transcriptomic landscape technology has been used to determine the micron-level panoramic transcriptomics of tissue slices in situ. Methods: The effects of cryoablation on the immune microenvironment in non-small cell lung cancer (NSCLC) were explored by comparing the Whole Transcriptome Atlas (WTA) panel of immune cells before and after cryoablation using the spatial transcriptomic landscape. Results: The bioinformatics analysis showed that cryoablation significantly affected the WTA of immune cells, particularly ge

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He, Shanshan, Liang Zhang, Michael Patrick, et al. "Abstract 2068: Mapping the spatial whole transcriptome from normal to tumor tissue in renal clear cell carcinoma: tumorigenesis and microenvironmental shifts at single-cell resolution." Cancer Research 85, no. 8_Supplement_1 (2025): 2068. https://doi.org/10.1158/1538-7445.am2025-2068.

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Abstract Renal clear cell carcinoma (ccRCC) develops through significant molecular and spatial reprogramming, transforming normal kidney tissue into a malignant state and reshaping the tumor microenvironment. To investigate the transition from normal to cancerous tissue, we utilized the CosMx® Whole Transcriptome (WTX) panel to perform high-resolution spatial transcriptomic imaging of FFPE sections containing ccRCC and adjacent normal kidney. H&E staining on the same tissue sections enabled direct correlation of molecular profiles with histopathological features, uncovering novel insights

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Tu, Wenqian, and Lihua Zhang. "Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning." PLOS Computational Biology 21, no. 4 (2025): e1012948. https://doi.org/10.1371/journal.pcbi.1012948.

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Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial transcriptomics data coming from different platforms usually has different resolutions. Moreover, current methods do not consider the heterogeneity of spatial structures within and across slices when modeling spatial transcriptomics data with graph-based methods. In this study, we propose a community-enhanced graph contrastive learning-based method na

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Zhang, Liang, Shanshan He, Michael Patrick, et al. "Abstract 2067: Single-cell spatial whole transcriptome reveals tumor heterogeneity and stromal dynamics in invasive ductal carcinoma of the breast." Cancer Research 85, no. 8_Supplement_1 (2025): 2067. https://doi.org/10.1158/1538-7445.am2025-2067.

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Abstract Invasive ductal carcinoma (IDC) is the most common and aggressive form of breast cancer, characterized by tissue invasion and metastasis, contributing to significant morbidity and mortality. The tumor microenvironment (TME), composed of stromal and immune cells, plays a critical role in IDC progression and therapy resistance. However, the spatial organization and molecular heterogeneity within the IDC TME remain underexplored. Spatial transcriptomics enables the preservation of tissue architecture while revealing molecular signatures at single-cell resolution, offering new insights in

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Adabbo, Bruno, Simona Migliozzi, Luciano Garofano, 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 (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 s

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He, Jiang, Bin Wang, Justin He, et al. "Abstract LB333: Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE." Cancer Research 84, no. 7_Supplement (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 d

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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 (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 collecte

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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 (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 transcriptome

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He, Jiang, Justin He, Timothy Wiggin, et al. "Abstract 4195: Spatially resolved single cell transcriptomic profiling in formalin-fixed paraffin-embedded (FFPE) tissues." Cancer Research 83, no. 7_Supplement (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 trans

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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 (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, in

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Lee, Youjin, Derek Bogdanoff, Yutong Wang, et al. "XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment." Science Advances 7, no. 17 (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 p

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Gupta, Anushka, Stephen Williams, Lauren Gutgasell, 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 (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 o

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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 (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. Theref

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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 (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.

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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 (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, sheddi

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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 (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 corr

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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 (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. Impleme

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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 (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-

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Noronha, Katelyn, Molly Wetzel, Gumaro Rojas, et al. "Abstract 5319: Simultaneous spatial epigenomic and transcriptomic analysis of human breast cancer reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level." Cancer Research 85, no. 8_Supplement_1 (2025): 5319. https://doi.org/10.1158/1538-7445.am2025-5319.

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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. Co-profiling of spatia

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Hoang, Margaret, Yi Cui, Shanshen He, et al. "Abstract 2078: Single-cell spatial transcriptomics in colon adenocarcinoma reveals tumor heterogeneity and immune microenvironmental shifts." Cancer Research 85, no. 8_Supplement_1 (2025): 2078. https://doi.org/10.1158/1538-7445.am2025-2078.

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Abstract Colon adenocarcinoma (CRC) features complex molecular changes and a dynamic remodeling of the tumor microenvironment (TME). Understanding spatial organization of gene expression in tumor and adjacent normal tissues is crucial for insights into tumorigenesis, immune modulation, and therapeutic resistance. Traditional methods like single-cell RNA sequencing (scRNA-seq) provide cellular insights but lack spatial context. Spatial transcriptomics addresses this gap, offering high-resolution, whole transcriptome analysis within tissue architecture. This study applies 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 (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 affe

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Sifakis, Joseph J., Sophia Madejski, and Samantha J. Riesenfeld. "Abstract B050: Spatially aware transcriptomic topic modeling reveals novel signals of spatial organization in glioblastoma." Clinical Cancer Research 31, no. 13_Supplement (2025): B050. https://doi.org/10.1158/1557-3265.aimachine-b050.

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Abstract Background: Spatial transcriptomics (ST) enables high-resolution mapping of gene expression across tissue architecture and can reveal the organization of cellular states in complex tissues, such as the tumor microenvironment. Using ST data from glioblastoma (GBM) patients, a large recent study1 identified robust gene programs in a spatially oblivious manner and used them to assign cell states to spatial spots and assess cell-state spatial organization. The results characterized GBM tumors as generally spatially heterogeneous, with some hypoxia-associated structured regions. However, r

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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 (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 sp

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Mirchia, Kanish, Soo-Jin Cho, Alyssa T. Reddy, et al. "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 (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 retrosp

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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 (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 cov

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Ritter, M., C. Blume, B. Patel, et al. "OS10.8.A APPLICATIONS OF NOVEL FFPE BASED TECHNOLOGIES FOR THE DIAGNOSTICS OF GLIOMAS." Neuro-Oncology 25, Supplement_2 (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 transcri

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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 rhizocephala

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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

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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

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Shao, Xin, Chengyu Li, Haihong Yang, et al. "Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk." Nature Communications 13, no. 1 (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 s

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Shang, Lulu, and Xiang Zhou. "Spatially aware dimension reduction for spatial transcriptomics." Nature Communications 13, no. 1 (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 co

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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.

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Rocque, Brittany, Kate Guion, Pranay Singh, et al. "Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease." Scientific Reports 14, no. 1 (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

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Johnston, Kevin G., Bereket T. Berackey, Kristine M. Tran, et al. "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 ou

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Pont, Frédéric, Juan Pablo Cerapio, Pauline Gravelle, et al. "Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics." BMC Bioinformatics 24, no. 1 (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 diffe

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Wirth, Johannes, Nina Huber, Kelvin Yin, et al. "Spatial transcriptomics using multiplexed deterministic barcoding in tissue." Nature Communications 14, no. 1 (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 allowe

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Dongsheng, Chen. "SCAR: Single-cell and Spatially-resolved CAncer Resources." August 23, 2023. https://doi.org/10.5281/zenodo.8275819.

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SCAR is the first cancer database combining single-cell transcriptome and spatial transcriptomic datasets covering the most cancer types, with 348 cancer subtypes available to date. Furthermore, SCAR includes the spatial transcriptomic data of 21 organs and 34 types of single-cell omics techniques. On the other hand, SCAR also provides a wide range of analytical and visualization tools in tumor cell types classification, biomarker selection, survival curve prediction etc. It will allow users to comprehensively evaluate the tumor microenvironment and immunological response for cancer studies. S

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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 transcriptom

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Xu, Zhicheng, Weiwen Wang, Tao Yang, 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 specie

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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 (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. Fu

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Zhang, Chao, Renchao Chen, and Yi Zhang. "Accurate inference of genome-wide spatial expression with iSpatial." July 13, 2022. https://doi.org/10.5281/zenodo.6828511.

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Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Due to 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 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 their spatial distribution. Furth

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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 dat

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Ren, Honglei, Benjamin L. Walker, Zixuan Cang, and Qing Nie. "Identifying multicellular spatiotemporal organization of cells with SpaceFlow." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-31739-w.

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AbstractOne major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing

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