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.
Authors
- Paul Régnier (ORCID: https://orcid.org/0000-0002-9713-1132)
- Anna Maciejewski‐Duval
- Cindy Marques (ORCID: https://orcid.org/0000-0002-1361-0575)
- Camille Montardi (ORCID: https://orcid.org/0009-0000-4922-5591)
- David Saadoun (ORCID: https://orcid.org/0000-0003-3628-9996)
Institutions
- 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)
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