  Multiplexed immuno-fluorescence tissue imaging, allowing simultaneous
detection of molecular properties of cells, is an essential tool for
characterizing the complex cellular mechanisms in translational research and
clinical practice. New image analysis approaches are needed because tissue
section stained with a mixture of protein, DNA and RNA biomarkers are
introducing various complexities, including spurious edges due to fluorescent
staining artifacts between touching or overlapping cells. We have developed the
RRScell method harnessing the stochastic random-reaction-seed (RRS) algorithm
and deep neural learning U-net to extract single-cell resolution profiling-map
of gene expression over a million cells tissue section accurately and
automatically. Furthermore, with the use of manifold learning technique UMAP
for cell phenotype cluster analysis, the AI-driven RRScell has equipped with a
marker-based image cytometry analysis tool (markerUMAP) in quantifying spatial
distribution of cell phenotypes from tissue images with a mixture of
biomarkers. The results achieved in this study suggest that RRScell provides a
robust enough way for extracting cytometric single cell morphology as well as
biomarker content in various tissue types, while the build-in markerUMAP tool
secures the efficiency of dimension reduction, making it viable as a general
tool in the spatial analysis of high dimensional tissue image.
