Bio-Protocol (Jun 2024)

Flow Cytometry Analysis of Microglial Phenotypes in the Murine Brain During Aging and Disease

  • Jillian Cox,
  • Kevin Pham,
  • Alex Keck,
  • Zsabre Wright,
  • Manu Thomas,
  • Willard Freeman,
  • Sarah Ocañas

DOI
https://doi.org/10.21769/BioProtoc.5018
Journal volume & issue
Vol. 14, no. 12

Abstract

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Microglia, the brain's primary resident immune cell, exists in various phenotypic states depending on intrinsic and extrinsic signaling. Distinguishing between these phenotypes can offer valuable biological insights into neurodevelopmental and neurodegenerative processes. Recent advances in single-cell transcriptomic profiling have allowed for increased granularity and better separation of distinct microglial states. While techniques such as immunofluorescence and single-cell RNA sequencing (scRNA-seq) are available to differentiate microglial phenotypes and functions, these methods present notable limitations, including challenging quantification methods, high cost, and advanced analytical techniques. This protocol addresses these limitations by presenting an optimized cell preparation procedure that prevents ex vivo activation and a flow cytometry panel to distinguish four distinct microglial states from murine brain tissue. Following cell preparation, fluorescent antibodies were applied to label 1) homeostatic, 2) disease-associated (DAM), 3) interferon response (IRM), and 4) lipid-droplet accumulating (LDAM) microglia, based on gene markers identified in previous scRNA-Seq studies. Stained cells were analyzed by flow cytometry to assess phenotypic distribution as a function of age and sex. A key advantage of this procedure is its adaptability, allowing the panel provided to be enhanced using additional markers with an appropriate cell analyzer (i.e., Cytek Aurora 5 laser spectral flow cytometer) and interrogating different brain regions or disease models. Additionally, this protocol does not require microglial cell sorting, resulting in a relatively quick and straightforward experiment. Ultimately, this protocol can compare the distribution of microglial phenotypic states between various experimental groups, such as disease state or age, with a lower cost and higher throughput than scRNA-seq.