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Unraveling microglial spatial organization in the developing human brain with DeepCellMap, a deep learning approach coupled with spatial statistics

Author

Listed:
  • Theo Perochon

    (École Normale Supérieure)

  • Zeljka Krsnik

    (University of Zagreb)

  • Marco Massimo

    (King’s College London)

  • Yana Ruchiy

    (Karolinska Institutet)

  • Alejandro Lastra Romero

    (Karolinska Institutet)

  • Elyas Mohammadi

    (Karolinska Institutet)

  • Xiaofei Li

    (Karolinska Institutet)

  • Katherine R. Long

    (King’s College London)

  • Laura Parkkinen

    (University of Oxford)

  • Klas Blomgren

    (Karolinska Institutet)

  • Thibault Lagache

    (Université Paris Cité)

  • David A. Menassa

    (Karolinska Institutet
    University of Oxford)

  • David Holcman

    (École Normale Supérieure
    University of Cambridge, DAMPT and Churchill College)

Abstract

Mapping cellular organization in the developing brain presents significant challenges due to the multidimensional nature of the data, characterized by complex spatial patterns that are difficult to interpret without high-throughput tools. Here, we present DeepCellMap, a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline is designed to map microglial organization during normal and pathological brain development and has the potential to be adapted to any cell type. Using DeepCellMap, we capture the morphological diversity of microglia, identify strong coupling between proliferative and phagocytic phenotypes, and show that distinct spatial clusters rarely overlap as human brain development progresses. Additionally, we uncover an association between microglia and blood vessels in fetal brains exposed to maternal SARS-CoV-2. These findings offer insights into whether various microglial phenotypes form networks in the developing brain to occupy space, and in conditions involving haemorrhages, whether microglia respond to, or influence changes in blood vessel integrity. DeepCellMap is available as an open-source software and is a powerful tool for extracting spatial statistics and analyzing cellular organization in large tissue sections, accommodating various imaging modalities. This platform opens new avenues for studying brain development and related pathologies.

Suggested Citation

  • Theo Perochon & Zeljka Krsnik & Marco Massimo & Yana Ruchiy & Alejandro Lastra Romero & Elyas Mohammadi & Xiaofei Li & Katherine R. Long & Laura Parkkinen & Klas Blomgren & Thibault Lagache & David A., 2025. "Unraveling microglial spatial organization in the developing human brain with DeepCellMap, a deep learning approach coupled with spatial statistics," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56560-z
    DOI: 10.1038/s41467-025-56560-z
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    References listed on IDEAS

    as
    1. Thibault Lagache & Gabriel Lang & Nathalie Sauvonnet & Jean-Christophe Olivo-Marin, 2013. "Analysis of the Spatial Organization of Molecules with Robust Statistics," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-7, December.
    2. Takahiro Masuda & Roman Sankowski & Ori Staszewski & Chotima Böttcher & Lukas Amann & Sagar & Christian Scheiwe & Stefan Nessler & Patrik Kunz & Geert Loo & Volker Arnd Coenen & Peter Christoph Reinac, 2019. "Author Correction: Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution," Nature, Nature, vol. 568(7751), pages 4-4, April.
    3. Thibault Lagache & Alexandre Grassart & Stéphane Dallongeville & Orestis Faklaris & Nathalie Sauvonnet & Alexandre Dufour & Lydia Danglot & Jean-Christophe Olivo-Marin, 2018. "Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    4. Takahiro Masuda & Roman Sankowski & Ori Staszewski & Chotima Böttcher & Lukas Amann & Sagar & Christian Scheiwe & Stefan Nessler & Patrik Kunz & Geert Loo & Volker Arnd Coenen & Peter Christoph Reinac, 2019. "Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution," Nature, Nature, vol. 566(7744), pages 388-392, February.
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