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PUPAID: A R + ImageJ pipeline for thorough and semi-automated processing and analysis of multi-channel immunofluorescence data

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  • Paul Régnier
  • Camille Montardi
  • Anna Maciejewski-Duval
  • Cindy Marques
  • David Saadoun

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.

Suggested Citation

  • Paul Régnier & Camille Montardi & Anna Maciejewski-Duval & Cindy Marques & David Saadoun, 2024. "PUPAID: A R + ImageJ pipeline for thorough and semi-automated processing and analysis of multi-channel immunofluorescence data," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-18, September.
    • Handle: RePEc:plo:pone00:0308970
    DOI: 10.1371/journal.pone.0308970
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    References listed on IDEAS

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    1. Yael Amitay & Yuval Bussi & Ben Feinstein & Shai Bagon & Idan Milo & Leeat Keren, 2023. "CellSighter: a neural network to classify cells in highly multiplexed images," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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