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Systematic inference of super-resolution cell spatial profiles from histology images

ABSTRACT: Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly predict the super-resolution cell spatial profiles from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk, which is validated on independent longitudinal cohorts. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

ORGANISM(S): Homo sapiens

PROVIDER: GSE287979 | GEO | 2025/01/29

REPOSITORIES: GEO

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Project description:Spatial organization of the transcriptome has emerged as a powerful means for regulating the post-transcriptional fate of RNA in eukaryotes; however, whether prokaryotes use RNA spatial organization as a mechanism for post-transcriptional regulation remains unclear. Here we used super-resolution microscopy to image the E. coli transcriptome and observed a genome-wide spatial organization of RNA: mRNAs encoding inner-membrane proteins are enriched at the membrane, whereas mRNAs encoding outer-membrane, cytoplasmic and periplasmic proteins are distributed throughout the cytoplasm. Membrane enrichment is caused by co-translational insertion of signal peptides recognized by the signal-recognition particle. Our time-resolved RNA-sequencing and live-cell super-resolution imaging experiments revealed a physiological consequence of this spatial organization and the underlying mechanism: membrane localization enhances degradation rates of inner-membrane-protein mRNAs by placing them in proximity to membrane-bound RNA degradation enzymes. Together, these results demonstrate that the bacterial transcriptome is spatially organized and that this organization shapes the posttranscriptional Spatial organization of the transcriptome has emerged as a powerful means for regulating the post-transcriptional fate of RNA in eukaryotes; however, whether prokaryotes use RNA spatial organization as a mechanism for post-transcriptional regulation remains unclear. Here we used super-resolution microscopy to image the E. coli transcriptome and observed a genome-wide spatial organization of RNA: mRNAs encoding inner-membrane proteins are enriched at the membrane, whereas mRNAs encoding outer-membrane, cytoplasmic and periplasmic proteins are distributed throughout the cytoplasm. Membrane enrichment is caused by co-translational insertion of signal peptides recognized by the signal-recognition particle. Our time-resolved RNA-sequencing and live-cell super-resolution imaging experiments revealed a physiological consequence of this spatial organization and the underlying mechanism: membrane localization enhances degradation rates of inner-membrane-protein mRNAs by placing them in proximity to membrane-bound RNA degradation enzymes. Together, these results demonstrate that the bacterial transcriptome is spatially organized and that this organization shapes the post-transcriptional dynamics of mRNAs.

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Systematic inference of super-resolution cell spatial profiles from histology images

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