S³-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue.

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

SUBMITTER: Babaei S 

PROVIDER: S-EPMC10500029 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature