Multiplexed immunofluorescence tissue imagingenables precise spatial assessment of protein expression in medical resection specimens. However, tissue sections are stained with a mixture of antibodies , DNA and RNA markers, the detection of weak or broken edges due to fluorescent membrane staining artifacts between touching or overlapping cells is a long term studied problem, and is an active research topic in biomedical image analysis. Sometimes detecting these kinds of edges which are even lacking discrimination or judgment by human visual intelligence. We have built a GPU client-server and have developed a hybrid system combining the stochastic random-reaction-seed (RRS) method and deep neural learning U-net to identify cell-membrane accurately and automatically. Furthermore, we have designed a high performance machine-learning AI-pipeline in quantifying spatial distribution of cell phenotypes from tissue images with various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section .
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 .
R
immunofluorescence tissue imagingenables precise spatial assessment of protein expression in medical resection specimens. However, tissue sections are
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
R
antibodies
protein
R
markers, the detection of weak or broken
biomarkers are introducing various complexities, including spurious
D
membrane
R
is a long term studied problem, and is an active research topic in biomedical image analysis. Sometimes detecting these kinds of edges which are even lacking discrimination or judgment by human visual intelligence. We have built a GPU client-server and have developed a hybrid system combining the
. We have developed the RRScell method harnessing the
R
method
algorithm
R
identify cell-membrane
extract single-cell resolution profiling-map of gene expression over a million cells tissue section
R
we have designed a high performance machine-learning AI-pipeline
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)
R
various complexities, and extract single-cell or even subcellular resolution profiling-map of protein and RNA expression over a million cells tissue section
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