Immunology-Immunopathology-Immunotherapy (i3) Laboratory, INSERM UMR-S 959, Sorbonne Université, Paris, France.
Biotherapy Unit (CIC-BTi), Inflammation-Immunopathology-Biotherapy Department (DHU i2B), Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, France.
PLoS One. 2024 Sep 19;19(9):e0308970. doi: 10.1371/journal.pone.0308970. eCollection 2024.
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.
PUPAID 是一个用 R 和 ImageJ 语言编写的工作流程，专门用于对多通道免疫荧光数据进行半自动处理和分析。该工作流程旨在以简单、易懂但全面的方式，从自动分割的细胞（这里定义为感兴趣区域，AOI）中提取荧光信号，这些细胞位于整个多层幻灯片（或最终裁剪的部分）上（这里定义为感兴趣区域，ROI）。包含的（但可选的）基于 R Shiny 的交互式应用程序使 PUPAID 也适合不熟悉 R 编程的科学家。此外，我们表明，与已经发表的最先进的方法（如 StarDist 或 Cellpose）相比，PUPAID 可以识别出更多的细胞，尤其是在高密度区域。为了实现更广泛的可能性和下游兼容性，单细胞信息以 FCS 文件（基于单细胞的细胞仪数据的标准化文件格式）的形式导出，以便可以使用任何第三方细胞仪分析软件或任何以 FCS 文件作为输入的分析工作流程打开。
