Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA289151 | ENA |
REPOSITORIES: ENA
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Project description:Integrating high-dimensional transcriptomics and image analysis tools into early safety screening
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Project description:Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (V)
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Project description:Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (IV)
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Project description:Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (I)
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Project description:Integrating high-dimensional transcriptomics and image analysis tools into early safety screening (III)
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Project description:Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution, hindering unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop Reverse-padlock Amplicon Encoding FISH (RAEFISH), an image-based spatial transcriptomics method with whole-genome coverage and single-molecule resolution in intact tissues. We demonstrate spatial profiling of 23,000 human or 22,000 mouse transcripts in single cells and tissue sections. Our analyses reveal transcript-specific subcellular localization, cell-type-specific and cell-type-invariant zonation-dependent transcriptomes, and gene programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.
2025-07-22 | GSE302564 | GEO
Project description:Systematic whole-protein screening and comprehensive profiling of antigen-specific CD4+ T cells are crucial for advancing vaccine design and cancer immunotherapies, yet remain technically challenging. Here, we present a high-throughput platform that utilizes large-scale class II single-chain trimer libraries to detect antigen-specific CD4+ T cells, while simultaneously profiling their antigen specificity, TCRα/β sequences, MHC restriction, whole transcriptomes, and patient/timepoint origins at single-cell resolution. Upon rigorous platform validation, we screened the full SARS-CoV-2 spike receptor binding domain in a longitudinal cohort of 22 participants, identifying 2,188 antigen-specific CD4+ T cells and revealing key metrics defining the immunogenicity of class II-restricted viral antigens. We further extended the platform to whole-protein screening of HPV-16 E6/E7 in a cohort of precancerous patients, uncovering HPV-specific CD4 TCRs that, upon extensive characterization, demonstrated strong therapeutic potential. By integrating high-throughput antigen screening with high-dimensional, multi-modal cellular characterization, our approach provides an unprecedented window into CD4+ T cell immunity, potentially guiding vaccine design and next-generation TCR-based cancer immunotherapies.
2026-03-11 | GSE324489 | GEO
Project description:Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor identifies regenerative signatures in heart failure, unbiasedly screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.
2025-07-11 | GSE300692 | GEO
Project description:Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor identifies regenerative signatures in heart failure, unbiasedly screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.
2025-07-11 | GSE300585 | GEO
Project description:Artificial intelligence has significantly advanced computational biology. Recent developments in omics technologies, such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), provide detailed genomic data alongside tissue histology. However, current computational models often focus on either omics- or image-based analysis, lacking integration of both. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin (H&E) images and transcriptomics using tissue patches from Visium data. For transcriptomics, we created 'sentences' by concatenating top-expressed gene symbols from tissue patches. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building on OmiCLIP, we created the Loki platform, which offers five key functions: tissue alignment, tissue annotation based on bulk RNA-seq or marker genes, cell type decomposition, image–transcriptomics retrieval, and ST gene expression prediction from H&E images. Compared with 22 state-of-the-art models on 5 simulations, 19 public, and 4 in-house experimental datasets, Loki demonstrated consistent accuracy and robustness in all tasks.
2025-04-01 | GSE293199 | GEO