High-plex IF imaging: inflamed colon

The rapid development of highly multiplexed microscopy systems has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging, and there is a need for more robust and generalizable tools for evaluating the cellular constituents and underlying stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping – an algorithm used widely in hyperspectral image analysis – to compress the channel dimension of high-plex immunofluorescence (IF) images. As many high-plex IF imaging experiments probe unique sets of markers, existing cell and pixel classification models do not typically generalize well. Here, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in the image. The class maps calculated through pSAM not only yield pixel classifications but can be combined with instance segmentation algorithms to phenotype cells in high-plex IF images. In a dataset of colon biopsies imaged with a 13-plex staining panel, sixteen pSAM class maps were computed and combined with instance segmentation of cells to provide cell class predictions. Finally, pSAM detected a diverse set of structure and immune cells when applied to a novel dataset of kidney biopsies imaged with a 44-marker panel. In summary, pSAM is a powerful and readily generalizable tool for evaluating cellular constituents of high-plex IF image data.