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Detecting cancer microbiota using unmapped RNA reads on spatial transcriptomics
Recently, the study of microbiota has emerged as a novel area in cancer research. While next-generation sequencing (NGS) technologies are becoming more prevalent in the area, the acquisition of spa...
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Data-driven discovery of cancer-specific targets for hepatocellular carcinoma using spatial transcriptomics
Hepatocellular carcinoma (HCC) is frequently diagnosed in advanced stages, with limited options available for systemic treatment. Given the significant investment of time, the high costs, and the...
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SpatialSPM: Statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets
Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset
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A Spatial Transcriptomics based Label-Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis
Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cel
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IAMSAM : Image-based Analysis of Molecular signatures using the Segment-Anything Model
Spatial transcriptomics is a cutting-edge technique that combines gene expression data with spatial information, allowing researchers to…
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Deep learning-based mapping of tertiary lymphoid structure scores from H&E images of renal cell carcinoma trained by spatial transcriptomics data
Tertiary lymphoid structures are organized aggregates of immune cells present in the tumor microenvironment (TME) in which novel targets as well as beneficial biomarkers for immunotherapy in cancer...
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Peptide-based theranostics targeting ANTXR1 in cancer stroma
NTXR1 is one of the pancreatic ductal adenocarcinoma (PDAC) stroma-specific cell surface markers (Ref 1), which is also overexpressed in the stroma of various cancer types. Radiotheranostics refers...
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Mapping cell types in the tumor microenvironment from tissue images via deep learning trained by spatial transcriptomics of lung adenocarcinoma
Profiling heterogeneous cell types in the tumor microenvironment (TME) is important for cancer immunotherapy. Here, we propose a method and validate in independent samples for mapping cell types in th
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GeneDART: Extending gene coverage in image-based spatial transcriptomics by deep learning-based domain adaptation with barcode-based RNA-sequencing data
Spatial transcriptomics (ST) technologies provide comprehensive biological insights regarding cell-cell interactions and peri-cellular microenvironments. ST technologies are divided into two categorie
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