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Spatially defined multicellular functional units in colorectal cancer revealed from single cell and spatial transcriptomics - scRNAseq v2

GSE260798 Mus musculus Expression profiling by high throughput sequencing 9 samples Submitted 2024/10/18 Platform GPL21273
Summary
While advances in single cell genomics have helped to chart the cellular components of tumor ecosystems, it has been more challenging to characterize their specific spatial organization and functional interactions. Here, we combine single cell RNA-seq, spatial transcriptomics by Slide-seq, and in situ multiplex RNA analysis, to create a detailed spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression. We profiled inducible genetic CRC mouse models that recapitulate key features of human CRC, assigned cell types and epithelial expression programs to spatial tissue locations in tumors, and computationally used them to identify the regional features spanning different cells in the same spatial niche. We find that tumors were organized in cellular neighborhoods, each with a distinct composition of cell subtypes, expression programs, and local cellular interactions. Comparing to scRNA-seq and Slide-seq data from human CRC, we find that both cell composition and layout features were conserved between the species, with mouse neighborhoods correlating with malignancy and clinical outcome in human patient tumors, highlighting the relevance of our findings to human disease. Our work offers a comprehensive framework that is applicable across various tissues, tumors, and disease conditions, with tools for the extrapolation of findings from experimental mouse models to human diseases.
Published in
Spatially defined multicellular functional units in colorectal cancer revealed from single cell and spatial transcriptomics
Avraham-Davidi I, Mages S, Klughammer J et al. · bioRxiv : the preprint server for biology 2025 · PMID 40667258 · doi:10.1101/2022.10.02.508492
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Direct links to NCBI, no account and no request form: the whole study as GSE260798_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 9 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1083530 and SRA study SRP493197. Searching any of these in the dataset finder brings you back here.

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