ArticlePloS one2024
PUPAID: A R + ImageJ pipeline for thorough and semi-automated processing and analysis of multi-channel immunofluorescence data.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Photorespiration is linked to DNA methylation by formate as a one-carbon source.Nature plants · 2026Article
- A semi-automated pipeline integrating ImageJ/Fiji and StarDist for the reproducible quantification of cellular and optical density metrics in immunofluorescence images of brain tissue.Frontiers in neuroanatomy · 2026Article
- Müller Glia Co-Regulate Barrier Permeability with Endothelial Cells in an Vitro Model of Hyperglycemia.International journal of molecular sciences · 2024Article
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Authors and funding
5 authors.
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Abstract
PUPAID is a workflow written in R + ImageJ languages which is dedicated to the semi-automated processing and analysis of multi-channel immunofluorescence data. The workflow is designed to extract fluorescence signals within automatically-segmented cells, defined here as Areas of Interest (AOI), on whole multi-layer slides (or eventually cropped sections of them), defined here as Regions of Interest (ROI), in a simple and understandable yet thorough manner. The included (but facultative) R Shiny-based interactive application makes PUPAID also suitable for scientists who are not fluent with R programming. Furthermore, we show that PUPAID identifies significantly more cells, especially in high-density regions, as compared to already published state-of-the-art methods such as StarDist or Cellpose. For extended possibilities and downstream compatibility, single cell information is exported as FCS files (the standardized file format for single cell-based cytometry data) in order to be openable using any third-party cytometry analysis software or any analysis workflow which takes FCS files as input.
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