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bioRxiv · 10.1101/2023.03.17.533167

S3-CIMA: Supervised spatial single-cell image analysis for the identification of disease-associated cell type compositions in tissue

Babaei, S.·Christ, J. C.·Makky, A.·Zidane, M.·Wistuba- Hamprecht, K.·Schuerch, C. M.·Claassen, M.
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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.

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Babaei, S., Christ, J. C., Makky, A., Zidane, M., Wistuba- Hamprecht, K., Schuerch, C. M., Claassen, M.. 2023-03-21. S3-CIMA: Supervised spatial single-cell image analysis for the identification of disease-associated cell type compositions in tissue. https://doi.org/10.1101/2023.03.17.533167

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