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

TL;DR

The RRScell method combines stochastic algorithms and deep learning to automatically and accurately profile single-cell gene expression and phenotypes in multiplexed immunofluorescence tissue images, enabling detailed spatial analysis.

Contribution

This paper introduces RRScell, a novel AI-driven approach integrating RRS and U-net for high-precision single-cell profiling in complex tissue images, with an embedded markerUMAP for spatial phenotyping.

Findings

Accurately profiles over a million cells in tissue sections.

Provides robust extraction of cell morphology and biomarker content.

Enables efficient spatial analysis of high-dimensional tissue data.

Abstract

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.