Abstract
Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. Image acquisitions performed by confocal microscopy notably allow excellent lateral resolution and more than 10 parameter measurements when using spectral or multiplex imaging. Analysis of such complex images can be very challenging and easily lead to bias and misinterpretation. Here, we have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation. It offers many features, such as filtering of spurious data points, selection of cell subsets on scatterplot, visualization of scatterplot selections back into the image, statistics of selected data and data annotation. Our application allows to characterize labeled cells, from their phenotype to their number and location in the tissue, as well as their interaction with other cells. SAPHIR is available from: https://github.com/elodiegermani/SAPHIR
Highlights
Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research
We provided new Figures with a better resolution and new options implemented in Shiny Analytical Plot of Histological Image Results (SAPHIR) to increase the visualization properties of the app
Advances in optical microscopy allow image acquisitions with more than 10 channel measurements using spectral fluorescence imaging or multiplex imaging combined with z-axis optical slices of tissue sections ranging from 10 μm to more than 200 μm when clearing methods are used[1,2,3,4]
Summary
Study of cell populations in tissues using immunofluorescence is a powerful method for both basic and medical research. We have developed the Shiny Analytical Plot of Histological Image Results (SAPHIR), an R shiny application for histo-cytometry using scatterplot representation of data extracted by segmentation It offers many features, such as filtering of spurious data points, selection of cell subsets on scatterplot, visualization of scatterplot selections back into the image, statistics of selected data and data annotation. Advances in optical microscopy allow image acquisitions with more than 10 channel measurements using spectral fluorescence imaging or multiplex imaging combined with z-axis optical slices of tissue sections ranging from 10 μm to more than 200 μm when clearing methods are used[1,2,3,4] Analysis of such complex images is very challenging due to the size and complexity of data. In existing software, this scatterplot representation is rarely interactive with the image itself, this would allow to locate selection results back into the image and to filter or correct results and fine-tune the gates defining cell populations to obtain in return a better visualization of them into the image
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