2. Academic Validation
  3. AIstain: Enhancing microglial phagocytosis analysis through deep learning

AIstain: Enhancing microglial phagocytosis analysis through deep learning

  • Cell Rep Methods. 2025 Oct 17:101207. doi: 10.1016/j.crmeth.2025.101207.
Alexander Zähringer 1,  Janaki Manoja Vinnakota 2,  Tobias Wertheimer 2,  Philipp Saalfrank 2,  Marie Follo 3,  Florian Ingelfinger 2,  Robert Zeiser 4
Affiliations

Affiliations

  • 1 Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany. Electronic address: alexander.zaehringer@uniklinik-freiburg.de.
  • 2 Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany.
  • 3 Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany; Lighthouse Core Facility, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany.
  • 4 Department of Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany; Signaling Research Centers BIOSS and CIBSS, Center for Integrative Biological Signaling Studies, Albert-Ludwigs-University Freiburg, Freiburg im Breisgau, Germany. Electronic address: robert.zeiser@uniklinik-freiburg.de.
PMID: 41109218 DOI: 10.1016/j.crmeth.2025.101207
Abstract

Investigating microglial phagocytosis is essential for understanding the mechanisms underlying brain health and disease. Dysregulation of phagocytosis is implicated in various neurological disorders, necessitating accurate analysis. Leveraging advances in deep learning, this study explores the application of a U-Net-based neural network for image cytometry to enhance the analysis of microglial phagocytosis. Murine microglia were imaged using the Olympus ScanR system, generating a substantial dataset for training a U-Net. The U-Net (AIstain) demonstrated superior performance in cell detection compared to live cell staining and the established segmentation tools SAM2 and Cellpose 3. Additionally, the model's applicability can be extended to Other cell types, including leukemia and breast Cancer cells, highlighting its versatility. AIstain provides a straightforward approach for the analysis of live cell images and microglial phagocytosis. This method enhances the precision of the results while simultaneously reducing the complexity of the experiment, thus facilitating substantial progress in the domain of neurobiological research.

Keywords

CP: computational biology; U-Net; artificial intelligence; microglia; neural network; neuroinflammation; phagocytosis.

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