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Updated: Apr 23, 2026

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
Published on: December 3, 2019
RNA-sequencing reveals oligodendrocyte and neuronal transcripts in microglia relevant to central nervous system
Anne C Solga1, Winnie W Pong1, Jason Walker2
1Department of Neurology, Washington University School of Medicine, St. Louis MO.
Abstract:
Expression profiling of distinct central nervous system (CNS) cell populations has been employed to facilitate disease classification and to provide insights into the molecular basis of brain pathology. One important cell type implicated in a wide variety of CNS disease states is the resident brain macrophage (microglia). In these studies, microglia are often isolated from dissociated brain tissue by flow sorting procedures [fluorescence-activated cell sorting (FACS)] or from postnatal glial cultures by mechanic isolation. Given the highly dynamic and state-dependent functions of these cells, the use of FACS or short-term culture methods may not accurately capture the biology of brain microglia. In the current study, we performed RNA-sequencing using Cx3cr1(+/GFP) labeled microglia isolated from the brainstem of 6-week-old mice to compare the transcriptomes of FACS-sorted versus laser capture microdissection (LCM). While both isolation techniques resulted in a large number of shared (common) transcripts, we identified transcripts unique to FACS-isolated and LCM-captured microglia. In particular, ∼50% of these LCM-isolated microglial transcripts represented genes typically associated with neurons and glia. While these transcripts clearly localized to microglia using complementary methods, they were not translated into protein. Following the induction of murine experimental autoimmune encephalomyelitis, increased oligodendrocyte and neuronal transcripts were detected in microglia, while only the myelin basic protein oligodendrocyte transcript was increased in microglia after traumatic brain injury. Collectively, these findings have implications for the design and interpretation of microglia transcriptome-based investigations.
Insights
Comparing microglia isolation methods, laser capture microdissection (LCM) revealed unique transcripts not seen with fluorescence-activated cell sorting (FACS). These findings impact the interpretation of microglia gene expression studies in central nervous system (CNS) diseases.
Area of Science:
- Neuroscience
- Immunology
- Molecular Biology
Background:
- Microglia, the resident immune cells of the central nervous system (CNS), are crucial in brain pathology.
- Current methods like fluorescence-activated cell sorting (FACS) or short-term culture may not fully represent microglia biology due to their dynamic nature.
Purpose of the Study:
- To compare the transcriptomes of microglia isolated using fluorescence-activated cell sorting (FACS) versus laser capture microdissection (LCM).
- To investigate how different isolation techniques affect the understanding of microglia gene expression in CNS conditions.
Main Methods:
- RNA sequencing was performed on Cx3cr1(+/GFP) labeled microglia from mouse brainstem.
- Microglia were isolated using both FACS and LCM techniques for comparative transcriptomic analysis.
- Complementary methods were used to validate transcript localization.
Main Results:
- Both FACS and LCM yielded common transcripts, but unique transcripts were identified in each isolation method.
- Approximately 50% of LCM-isolated microglial transcripts were associated with neuronal and glial genes, though not translated to protein.
- Disease models (experimental autoimmune encephalomyelitis and traumatic brain injury) showed altered oligodendrocyte and neuronal transcripts in microglia.
Conclusions:
- The choice of microglia isolation technique significantly influences transcriptome profiles.
- LCM captures unique transcripts, including non-translated neuronal and glial genes, which may be missed by FACS.
- These findings are critical for accurate interpretation of microglia gene expression data in CNS disease research.

