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• W4385955997 abstract "The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets." @default.
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• W4385955997 date "2023-09-01" @default.
• W4385955997 modified "2023-10-17" @default.
• W4385955997 title "S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue" @default.
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• W4385955997 doi "https://doi.org/10.1016/j.patter.2023.100829" @default.
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