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Astrocyte Heterogeneity in Multiple Sclerosis From Reactive Glia to Therapeutic Targets
No abstract available.
Read PDFGALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics
Spatial transcriptomics alignment is challenged by technical variations, including geometric distortions from tissue preparation and platform-driven differences in resolution and modality. These issues create diverse alignment scenarios, from matched and mismatched resolutions to cross-modality integration, …
Read PDFDecoding wound healing: cellular insights and technological advances
Wound healing is a complex process involving spatiotemporal patterning of cellular activity across four overlapping phases: hemostasis, inflammation, proliferation, and remodeling, which restore anatomic and functional tissue integrity. Recent advances in cell-based technologies have increased …
Read PDFSystematic data-driven genome-scale metabolic model reduction for bioprocess modeling: CHO culture case study
Genome-scale metabolic models (GEMs) enable mechanistic insight into cellular metabolism, but their size and underdetermination hinder use in dynamic bioprocess simulation and real-time digital twins. Compact models are essential, yet existing reduction strategies either neglect …
Read PDFApplications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges
Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, high-throughput single-cell and spatial transcriptomics is one of …
Read PDFEmpowering multifaceted analysis of spatial transcriptomics data with RGAST
Spatial transcriptomics (ST) enables mapping gene expression in native tissue context to resolve architecture and cellular interactions, but current analytical workflows rely on separate algorithms for distinct tasks. We present RGAST (Relational Graph Attention network …
Read PDFA comprehensive survey of computer vision methods for spatial transcriptomics
Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven …
Read PDFMulti-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis
Sepsis remains one of the most complex and lethal syndromes in pediatric critical care, driven by dysregulated and heterogeneous host responses to infection. Despite decades of biomarker research, few biomarkers have been translated into routine …
Read PDFAdvances in deciphering intratumoral versus peritumoral heterogeneity of infiltrating lymphocytes in pancreatic cancer: a spatial perspective
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, characterized by a profoundly immunosuppressive and spatially heterogeneous tumor microenvironment. Recent research has focused on the distinct topographic distribution of tumor-infiltrating lymphocytes (TILs) across …
Read PDFDeveloping a Single-Cell Spatial Transcriptomics Workflow for In Vivo Evaluation of Implanted Biomaterials
In vivo evaluation of biomaterials largely relies on histology to assess biocompatibility and foreign body responses. While effective for capturing end-stage outcomes, these methods offer limited insight into the cellular mechanisms driving tissue remodeling, hindering …
Read PDFThe niche-centric view: Redefining inflammatory disease beyond molecular signatures
No abstract available.
Read PDFAdvances in the pathophysiological study of brain development: application of cerebral organoid combined with Spatial omics technology
Understanding the complexities of the human brain development remains one of the most formidable challenges in neuroscience, constrained by the limitations of traditional models and the inaccessibility of brain tissue. The advent of cerebral organoids …
Read PDFFuture Leaders in Stress: the next generation of stress neurobiology
No abstract available.
Read PDFDECODE: deep learning-based common deconvolution framework for various omics data
Deconvolution algorithms estimate cell-type abundances from tissue-level data, enabling systematic cellular analysis of large cohorts. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for various omics data …
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