Sanquin Research and Landsteiner Laboratory, Academic Medical Centre, Amsterdam, The Netherlands.
Saxion, Academy Life Science Engineering and Design, University of Applied Science, Enschede, The Netherlands.
Cytometry A. 2024 Oct;105(10):763-771. doi: 10.1002/cyto.a.24894. Epub 2024 Sep 9.
Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by combining Artificial Intelligence (AI) with IFC. Our study demonstrates that this approach is highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.
红细胞吞噬作用是一个由吞噬细胞识别、吞噬和消化抗体包被或受损红细胞的过程。了解吞噬作用背后的动力学机制对于理解特定情况下的细胞过程至关重要。已经使用了几种技术来研究吞噬作用。其中，一种有趣的方法是使用成像流式细胞术（IFC）来区分细胞或颗粒的内化和结合。然而，这种方法需要繁琐的分析。在这里，我们介绍了一种将人工智能（AI）与 IFC 相结合来分析吞噬作用过程的新方法。我们的研究表明，这种方法非常适合研究红细胞吞噬作用，可以对内化、结合和非结合的红细胞进行分类。验证实验表明，我们的流水线具有很高的准确性和重现性。
