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

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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Publication Details

Journal
PLoS ONE
Published
2024-09-19
DOI
https://doi.org/10.1371/journal.pone.0308970
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.70
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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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PLoS ONE
Single-cell and spatial transcriptomics
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article

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

Paul Régnier, Anna Maciejewski‐Duval, Cindy Marques, Camille Montardi, David Saadoun
article en
3 citations

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.

PLoS ONEVol. 19(9)
Inserm (FR), Université Paris-Saclay (FR), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Centre de Référence des Maladies Autoinflammatoires et des Amyloses (FR), Hôpital Ambroise-Paré (FR), Immunologie - Immunopathologie - Immunothérapie (FR)
Quality Education
Openalex Percentile: Top 30%
Single-cell and spatial transcriptomics
0.70
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