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Multiplexed immuno-fluorescence tissue imaging, allowing simultaneousdetection of molecular properties of cells, is an essential tool forcharacterizing the complex cellular mechanisms in translational research andclinical practice. New image analysis approaches are needed because tissuesection stained with a mixture of protein, DNA and RNA biomarkers areintroducing various complexities, including spurious edges due to fluorescentstaining artifacts between touching or overlapping cells. We have developed theRRScell method harnessing the stochastic random-reaction-seed (RRS) algorithmand deep neural learning U-net to extract single-cell resolution profiling-mapof gene expression over a million cells tissue section accurately andautomatically. Furthermore, with the use of manifold learning technique UMAPfor cell phenotype cluster analysis, the AI-driven RRScell has equipped with amarker-based image cytometry analysis tool (markerUMAP) in quantifying spatialdistribution of cell phenotypes from tissue images with a mixture ofbiomarkers. The results achieved in this study suggest that RRScell provides arobust enough way for extracting cytometric single cell morphology as well asbiomarker content in various tissue types, while the build-in markerUMAP toolsecures the efficiency of dimension reduction, making it viable as a generaltool in the spatial analysis of high dimensional tissue image.

RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue