Environmental cues influence the highly dynamic morphology of microglia. Strategies to characterize these changes usually involve user-selected morphometric features, which preclude the identification of a spectrum of context-dependent morphological phenotypes. Here we develop MorphOMICs, a topological data analysis approach, which enables semiautomatic mapping of microglial morphology into an atlas of cue-dependent phenotypes and overcomes feature-selection biases and biological variability. We extract spatially heterogeneous and sexually dimorphic morphological phenotypes for seven adult mouse brain regions. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration mouse models, with females showing a faster morphological shift in affected brain regions. Remarkably, microglia morphologies reflect an adaptation upon repeated exposure to ketamine anesthesia and do not recover to control morphologies. Finally, we demonstrate that both long primary processes and short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs opens a new perspective to characterize microglial morphology.
We thank the scientific service units at ISTA, in particular M. Schunn’s team at the preclinical facility, and especially our colony manager S. Haslinger, for excellent support. We are also grateful to the ISTA Imaging & Optics Facility, and in particular C. Sommer for helping with the data file conversions. We thank R. Erhart from the ISTA Scientific Computing Unit for improving the script performance. We thank M. Maes, B. Nagy, S. Oakeley and M. Benevento and all members of the Siegert group for constant feedback on the project and on the manuscript. This research was supported by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions program (754411 to R.J.A.C.), and by the European Research Council (grant no. 715571 to S.S.). L.K. was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne, from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology. L.-H.T. was supported by NIH (grant no. R37NS051874) and by the JPB Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Colombo G, Cubero RJ, Kanari L, et al. A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Nature Neuroscience. 2022;25(10):1379-1393. doi:10.1038/s41593-022-01167-6
Colombo, G., Cubero, R. J., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., … Siegert, S. (2022). A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-022-01167-6
Colombo, Gloria, Ryan J Cubero, Lida Kanari, Alessandro Venturino, Rouven Schulz, Martina Scolamiero, Jens Agerberg, et al. “A Tool for Mapping Microglial Morphology, MorphOMICs, Reveals Brain-Region and Sex-Dependent Phenotypes.” Nature Neuroscience. Springer Nature, 2022. https://doi.org/10.1038/s41593-022-01167-6.
G. Colombo et al., “A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes,” Nature Neuroscience, vol. 25, no. 10. Springer Nature, pp. 1379–1393, 2022.
Colombo G, Cubero RJ, Kanari L, Venturino A, Schulz R, Scolamiero M, Agerberg J, Mathys H, Tsai L-H, Chachólski W, Hess K, Siegert S. 2022. A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes. Nature Neuroscience. 25(10), 1379–1393.
Colombo, Gloria, et al. “A Tool for Mapping Microglial Morphology, MorphOMICs, Reveals Brain-Region and Sex-Dependent Phenotypes.” Nature Neuroscience, vol. 25, no. 10, Springer Nature, 2022, pp. 1379–93, doi:10.1038/s41593-022-01167-6.
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