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Engineering and evaluation of precision-glycosylated clickable albumin nanoplatform for targeting the tumor microenvironment
doi: https://doi.org/10.7150/thno.123973 https://www.thno.org/ms/acceptms Abstract Rationale: Glycosylation of drug delivery vehicles enables selective tumor microenvironment (TME) targeting but is l…

Spatial transcriptomics unveils landscape of resistance to concurrent chemo-radiotherapy in hypopharyngeal squamous cell carcinoma: the role of SPP1+ macrophages
doi: https://doi.org/10.1101/2024.07.09.602476 Abstract Hypopharyngeal squamous cell carcinoma (SCC) is a highly aggressive cancer with a poor prognosis, particularly in advanced stages where concurr…

Analysis of Unmapped RNA-seq Data from Cancer Spatial Transcriptome to Decipher Cancer Microbiome
doi: https://doi.org/10.1101/2024.06.09.598160 Abstract Recent research increasingly emphasizes the importance of the microbiome in the development and progression of cancer. Thus, exploring the micr…

Spatial Transcriptomics Reveals Spatially Diverse Cancer-Associated Fibroblast in Lung Squamous Cell Carcinoma Linked to Tumor Progression
doi: https://doi.org/10.1101/2024.05.16.594592 Abstract While cancer-associated fibroblasts (CAFs) are crucial in influencing tumor growth and immune responses in lung cancer, we still lack a compreh…

CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities
doi: https://doi.org/10.1101/2024.05.08.593094 Abstract Large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have transformed biomedical research into a data-driven fie…

Generation of Super-resolution Images from Barcode-based Spatial Transcriptomics Using Deep Image Prior
doi: https://doi.org/10.1016/j.crmeth.2024.100937 Abstract Spatial transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. How…

SpatialSPM: Statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets
doi: https://doi.org/10.1101/2023.06.26.546605 Abstract Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insight…

Mapping cell types in the tumor microenvironment from tissue images via deep learning trained by spatial transcriptomics of lung adenocarcinoma
doi: https://doi.org/10.1101/2023.03.04.531083 Abstract Profiling heterogeneous cell types in the tumor microenvironment (TME) is important for cancer immunotherapy. Here, we propose a method and val…

IAMSAM : Image-based Analysis of Molecular signatures using the Segment-Anything Model
doi: https://doi.org/10.1101/2023.05.25.542052 Abstract Spatial transcriptomics is a cutting-edge technique that combines gene expression data with spatial information, allowing researchers to study …
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