Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 56 for “"Spatial Transcriptomics"”.
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Quantitative spatial transcriptomics of the developing brain
… techniques that quantitatively investigate the spatial dynamics of cell heterogeneity across large tissue areas at a single-cell level are lacking. Applying this capability to the developing brain would enable reconstruction of a full spatial transcriptomic map of neural heterogeneity and the …
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Fundamental representations of regions and interactions in spatial transcriptomics
… the fundamental unit of biology, it is their spatial coordination that gives rise to the tissue architectures underlying both health and disease. Spatial transcriptomics technologies offer a unique window into this coordination by simultaneously capturing the spatial and molecular identities …
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Computational and Statistical Methods for Analysis of Spatial Transcriptomics Data
Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatiallydefined measurement spots across a biological tissue. We show how measuring cells in their native environment has the …
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Integrating Spatial Transcriptomics Data for Cross-Species Molecular Region Comparison
… of different biological processes and functions. Spatially resolved transcriptomics (SRT) technologies present the ability to measure gene expression of single cells within tissues, enabling the detection of unique spatial molecular patterns in the brain. Several computational methods that rely on …
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Spatial Transcriptomics Analysis Reveals Transcriptomic and Cellular Topology Associations in Breast and Prostate Cancers
… gene expression, of given cells relate to their spatial layout, i.e., topology, in the tumor microenvironment (TME). However, with the advent of spatial transcriptomics (ST) and integrative bioinformatics analysis techniques, we are now able to better understand the TME of common cancers. Method: …
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Advances in spatial modeling for complex data with applications to symbolic data and spatial transcriptomics.
Spatial statistical modeling is commonly used to analyze and draw inference from data collected across geographic space, providing insight into underlying spatial processes across environmental, biological, epidemiological, and other scientific applications. Spatial data are increasingly complex, …
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Alteration of differentiation in glioblastoma: from spatial transcriptomics approaches to the identification of a suppressor event
… valuable insights, they lack the essential spatial context that is critical for unravelling the heterogeneity of glioblastoma. This limitation impedes the understanding of interactions among distinct subpopulations and their intricate relationships with the neuronal microenvironment. To gain …
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Profiling the dynamics of the active transcriptome in the juvenile and adult brain using spatial transcriptomics
… lacked comprehensive information about the spatial dynamics of cell type-specific gene expression over the course of brain maturation. This information is important for understanding the dynamics of gene expression in the context of changing tissue architecture over the course of maturation. …
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Understanding tumor cell plasticity in spatial transcriptomics with graph attention networks and walk-based pseudotime analysis
… strategies and patient outcomes. Utilizing spatial transcriptomics extrinsic driving factors of plasticity can be probed. We introduce PlastiNet, which uses a graphical attention-based network to create a spatial aware embedding. The utility of our approach is validated in model systems, …
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Integrative Approaches in Genomic Analysis: Advancing Epigenetic Prediction, Twas Methodology, and Cell-Type Deconvolution in Spatial Transcriptomics
… In Chapter 4, we turn our attention to spatial transcriptomics, which measures gene expression in situ on slices of tissue. We present a novel zero-inflated hierarchical generalized transformation (ZI-HGT) model that functions as a noisy transformation for cell-type deconvolution with …
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Integration of Spatial Transcriptomics with Chromatin Images Using Graph-Based Autoencoder Identifies Joint Biomarkers for Alzheimer’s Disease
… nuclear morphology, as well as gene expression. Spatial transcriptomic technologies, such as STARmap and 10x Visium, allow the joint measurement of these different modalities in whole tissue sections.1,2 However, methods for jointly analyzing the different spatial data modalities in 3D are still …
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Development and application of innovative technologies to unravel neuronal systems: From optogenetics to neural interfaces to spatial transcriptomics
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Probabilistic Dynamical Modelling of Spatiotemporal Cell Trajectories During Neural Development
… for the analysis of single-cell and spatial transcriptomics data and I show how they can be applied to more effectively map the rules of brain cell development in health and disease. Cell2fate is an RNA velocity model for inference of transcriptional dynamics from spliced and …
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Generative modeling of the tumor microenvironment : deconvolution, completion, and integration
… diverse views of tissue state, including spatial transcriptomics, spatial proteomics, and multi-sequence medical imaging. However, the data collected by these platforms is often incomplete, aggregated, or distributed across unpaired modalities. These acquisition constraints create three …
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INTEGRATIVE GENOMICS UNVEILS TRANSCRIPTION FACTOR ROLES IN CELLULAR FATEAND SPATIALLY RESOLVED TRIPLE-NEGATIVE BREAST CANCER IMMUNITY CAPACITY
… We propose a novel clinical workflow combining spatial transcriptomics, RNA sequencing, and Immunohistochemistry to analyze TNBC, focusing on PD-L1 status. This method revealed sub-tumoral variations and identified LY6D as a new diagnostic marker, highlighting the potential of spatial …
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Sensitive Multiplexed MicroRNA Spatial Profiling and Data Classification Framework Applied to Murine Breast Tumors
… them particularly useful as biomarkers. As the spatial transcriptomics field advances, protocols that enable highly sensitive and spatially resolved detection become necessary to maximize the information gained from samples. This is especially true of miRNAs where the location of where they are …
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Spatial transcriptomic analysis of low-grade intestinal T-cell lymphoma and lymphoplasmacytic inflammatory bowel disease in cats
… samples from cats with LGITL and IBD using spatial transcriptomics, formalin-fixed paraffin-embedded cat jejunum and ileum samples were used, six with a diagnosis of LGITL and six with a diagnosis of IBD. Endoscopy biopsies and full-thickness biopsy and necropsy samples were included. Three …
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Investigating the spatiotemporal dynamics of mononuclear phagocytes in solid tumours.
… single-cell RNA sequencing, confocal imaging and spatial transcriptomics to precisely track tumour MNP entry and egress. This enabled the distinction of rapidly replenished, monocyte-derived (md)TAMs that are enriched at the tumour core from resident-like (r)TAMs that interact with fibroblasts at …
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Transcriptional Characterisation of Human Musculoskeletal Development in vivo and in vitro
… transcriptome of human tissues, in particular spatial transcriptomics, single cell RNA sequencing and in-situ sequencing, and discuss their application to date in the musculoskeletal system. In Chapter 2, I form a detailed healthy reference tissue ‘atlas’ of the human first trimester hindlimb …
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Single cell and spatial analysis characterize metabolic DPEP1+ fibroblasts in PDAC
… targets. Here, we used single cell and spatial transcriptomics to comprehensively characterize the TME content of human PDAC. We characterized CAFs metabotypes and a novel CAF population expressing DPEP1, COMP, CST1, which involved in glutathione metabolism, termed "gluCAFs” and was …
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