Name: Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development (Video) | JoVE Journal
Uploaded: 2025-10-03T13:10:00.000+00:00
Duration: 9 min 48 s
Description: Learn flow cytometry and single-cell RNA sequencing to characterize microglia activation in early postnatal mouse cerebellum. This method reveals cellular heterogeneity and enhances understanding of brain development.

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5 minutes

This work was supported by the Canadian Institutes of Health Research (487354) and the Fonds de Recherche du Qu&#233;bec (333845).

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The authors have declared that no conflict of interest exists.

This study presents a detailed and optimized protocol that combines flow cytometry and single-cell RNA sequencing (scRNA-seq) for the isolation and characterization of microglial cells during early postnatal brain development. While flow cytometry remains a cost-effective and accessible technique, its integration with single-cell transcriptomics provides complementary insights by linking protein-level marker expression with gene-level transcriptional profiles. The protocol addresses the technical challenges specific to immature brain tissue, including efficient dissociation, debris and myelin removal, and optimized immunostaining strategies, to ensure reproducibility and reliability in the analysis of microglial populations across key developmental time points.
Flow cytometry was used to identify microglial subsets based on the expression of selected surface and intracellular markers. Rather than inferring functional states, subsets were distinguished by their marker expression profiles, such as CD80+/CD86+/iNOS+ ; CD206+/Arg1+ ; CD86+/CD64+ or CD163+/CD206+ phenotypes. These marker-defined populations reflect molecular heterogeneity within the microglia compartment and provide a practical approach for high-throughput characterization. While these surface marker-based classifications may not capture the full complexity of microglia diversity, they remain informative, especially in contexts where high-dimensional molecular tools may not be available. Binary classifications such as the M1/M2 paradigm have been deliberately avoided, in recognition of the spectrum-like, context-dependent nature of microglial phenotypes, as described in recent literature12.
These observations are consistent with previous studies highlighting the role of microglia in both normal brain maturation and in response to environmental or pathological stimuli13,14. The ability to differentiate microglia heterogeneity offers valuable insights into how microglial cell polarization may contribute to neurodevelopmental disorders15,16.
Although single-cell RNA sequencing offers higher resolution and allows the identification of transcriptionally distinct microglial cell subpopulations, its cost and analytical complexity limit its accessibility. In the current dataset collected at postnatal day 15 following perinatal injury, the peak of inflammatory response is largely resolved, resulting in a low number of activated microglia. Consequently, dimensionality reduction techniques such as UMAP did not recover well-separated microglial subclusters, and cluster-level transcriptional analysis was not included in this protocol manuscript.
A significant innovation of this protocol is its optimization for isolating and characterizing microglial cells from the cerebellum during early postnatal stages (P3 to P30). This region and age-specific context present distinct challenges, including low cell yield, myelin content, and increased tissue fragility. To address these constraints, the protocol integrates an adapted dissociation workflow, efficient debris and myelin removal, and carefully tuned immunostaining conditions suited for small-volume cerebellar tissue. Despite the increasing recognition of cerebellar microglia as functionally and transcriptionally distinct from those in other brain regions, detailed protocols for their isolation and analysis remain limited. The current method offers a reliable and accessible workflow, particularly tailored for developmental studies of cerebellar microglia.
The main objective of this manuscript is to provide a reproducible and adaptable methodological framework rather than to report specific biological discoveries. The workflow is suitable for adaptation to other developmental stages or experimental models in which transcriptionally distinct microglial states may be more prominent. In such contexts, flow cytometry remains a valuable tool, especially when integrated with complementary assays.
One of the key strengths of this protocol is its adaptability across various developmental stages, ranging from postnatal day 3 (P3) to postnatal day 30 (P30). This allows for longitudinal analysis of microglia phenotype as brain maturation progresses. The combined use of flow cytometry and scRNA-seq makes it possible to study not only surface markers, but also intracellular signaling pathways and transcription factors involved in microglial cells.
Nonetheless, enzymatic digestion may affect the expression of some surface markers, necessitating careful optimization of digestion conditions for specific experimental goals17,18. Additionally, neither flow cytometry nor single-cell sequencing provides population-level data, which may not fully capture spatial and functional nuances of microglial cell interactions within their native environment. Incorporating single-cell spatial techniques could help address this limitation by preserving the spatial context of microglial cells. Future studies combining this protocol with imaging techniques, such as immunohistochemistry, in vivo imaging, western blot, or single-cell spatial transcriptomics, could offer complementary insights into microglial cell dynamics.
This method opens new avenues for investigating how early-life insults, such as inflammation, hypoxia, or environmental stressors, influence microglial cell phenotypes and contribute to long-term neurodevelopmental outcomes. By enabling precise and reliable characterization of microglial cell activation states, it facilitates the identification of therapeutic targets to modulate microglial responses in early life, aiming to prevent or mitigate neurodevelopmental disorders.
In conclusion, this protocol offers a robust and accessible framework for studying microglial cell diversity during early brain development. The combination of flow cytometry and single-cell RNA sequencing allows for flexible application across various developmental stages and experimental models, facilitating deeper exploration of microglia biology in both physiological and pathological contexts.

The early postnatal period is a critical phase for brain development, characterized by complex cellular and molecular processes that lay the foundation for cognitive, emotional, and behavioral functions1. However, this period is also marked by vulnerability, as disruptions can alter neurodevelopmental trajectories and lead to long-term neurological or psychiatric disorders. Microglia, the brain's resident immune cells, play a central role in shaping the developing brain through synaptic pruning, phagocytosis, immune surveillance, and responses to environmental insults2,3. These cells exhibit remarkable plasticity, adopting transcriptional profiles that vary according to brain region, development stage, and pathological context4,5.
Accurate analysis of microglia activation in the developing brain requires robust and repeatable methodologies capable of analyzing their dynamic phenotypes in the context of immature brain tissues. Flow cytometry provides a reliable solution, enabling the high-throughput characterization of cellular populations and marker expression6. However, its application in studying microglia from early postnatal brains is technically challenging due to the delicate nature of the tissue and the need for efficient cell isolation and enrichment protocols7,8.
The protocol presented here combines flow cytometry and single-cell RNA sequencing (scRNA-seq) to isolate and characterize microglia from early postnatal mouse brains, with a particular focus on the cerebellum from postnatal day 3 (P3) to postnatal day 30 (P30)9, a region and developmental stage for which detailed methodological references remain limited. 
This workflow includes enzymatic dissociation, density gradient centrifugation for debris removal and cell enrichment, and immunostaining using a panel of extracellular and intracellular markers. To characterize microglia diversity, a range of surface and intracellular markers is employed10,11. Instead of assigning fixed labels (inflammatory or reparative), the protocol defines subpopulations based on discrete marker expression profiles. For example, microglia expressing CD80, CD86, and iNOS, CD206 and Arg1, CD86 and CD64, or CD163 and CD206 are identified and analyzed as molecularly distinct subsets. These markers reflect different activated microglia states10,11and are presented as expression profiles. 
This multiparametric gating strategy enables precise identification of microglial subsets based on surface marker expression, offering a streamlined framework for analyzing immune heterogeneity during postnatal brain development. To extend phenotypic profiling and uncover transcriptional diversity, single-cell RNA sequencing is performed following cell isolation. This study establishes a consistent and scalable workflow to advance understanding of neurodevelopmental processes and the long-term impact of microglial activation. By integrating optimized flow cytometry and scRNA-seq within a developmentally and regionally informed framework, this protocol serves as a powerful tool for investigating early-life microglial biology and its relevance to neurodevelopmental outcomes.

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flow cytometry and single-cell analysis for characterizing microglia activation in early postnatal mouse brain development

Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

Microglia, the brain's resident immune cells, exhibit region and context-specific transcriptional profiles during development and disease. This protocol presents two complementary methods for studying microglial populations in mouse cerebellar tissue: flow cytometry and single-cell RNA sequencing.
As the first method used, the flow cytometry-based protocol is optimized for early postnatal brains, ensuring robust cell isolation and consistency across experimental conditions. It begins with tissue dissociation using enzymatic digestion, followed by myelin removal via Percoll density gradient centrifugation to yield a high-quality neural cell suspension, and a gating strategy based on CD45 and CD11b expression. Live/dead staining ensures cell viability, and fluorochrome-conjugated antibodies are used to profile the expression of selected surface markers on microglia. Compensating controls are performed using latex beads with validated gating strategies using fluorescence minus one (FMO) control.
The second method involves single-cell RNA sequencing using 10X Genomics, following the same upstream isolation steps, which enable transcriptomic profiling of microglia across conditions. Microglial clusters are identified using gene expression analysis, and differential expression analysis is conducted between experimental groups. A random forest classifier is applied solely to distinguish male and female samples when multiplexed.
Together, these reproducible and adaptable protocols provide a robust framework for investigating microglial diversity in the cerebellum during early brain development and its potential alteration following experimental perturbations.

This protocol effectively utilizes flow cytometry to characterize microglial cell populations in mouse brain samples, based on the expression of selected surface and intracellular markers. The following sections detail the results obtained from the key steps of the workflow, highlighting the distribution and heterogeneity of microglia subsets in response to experimental conditions.
Compensation and gating strategy validation 
Compensation beads were used to calibrate fluorescence signals and correct for spectral overlap between fluorochromes. Positive and negative populations for each fluorochrome were clearly separated, allowing accurate compensation matrix generation. Gating thresholds were initially validated using Fluorescence Minus One (FMO) control during panel optimization to distinguish true signal from background, particularly for poor or overlapping markers. These thresholds were subsequently reused across experiments using the same staining panel and cytometer settings to ensure consistency. Isotype controls were included to monitor potential non-specific binding, particularly for intracellular markers, but were not used for gating decisions.
Microglial cell identification 
The live/dead (Amcyan) cell-gating strategy successfully excluded dead cells. The gating strategy effectively isolated microglial cell populations based on CD45 (AF700) and CD11b (FITC) expression. The forward scatter (FSC) and side scatter (SSC) parameters were optimized (FSC = 300, SSC = 200) to center the microglia population within the dot plot (Figure 5).
Microglial cell phenotyping 
Dot plot analysis enabled the identification of microglia subpopulations based on differential expression of surface and intracellular markers. Using specific gating strategies in FlowJo software, a subset of microglial cells expressing CD80 (Super Bright 436), CD86 (PE), and iNOS (PE-eFluor 610) was identified (Figure 6A). Double gating was also used to define subsets expressing CD206 (APC) and Arg1 (PE-Cy7) (See Figure 6B). Additional populations were characterized by co-expression of CD86 (PE) and CD64 (PerCP-eFluor 710), as well as CD163 (Super Bright 600) and CD206 (APC) (Figure 6C,D). These phenotypic profiles reflect marker-defined heterogeneity within the microglia population and demonstrate the capacity of this protocol to distinguish multiple transcriptionally and immunologically distinct subsets without inferring fixed functional states.
Microglia single-cell analysis 
Uniform Manifold Approximation and Projection (UMAP) was used to visualize cellular heterogeneity and identify the microglial cell clusters among other brain cell types. Each dot represents a single cell, and cluster is color-coded based on transcriptional similarity (Figure 2).
Differential expression analysis was performed within the microglia cluster to identify genes modulated under experimental conditions. Genes with an adjusted P-value ≤ 0.05 were considered differentially expressed.
A volcano plot was generated to visualize these differentially expressed genes of microglial cells, highlighting those with both high fold change and statistical significance (Figure 4).
To compare these single cells result with flow cytometry data, the expression of microglia markers CD45 and CD11b (Ptprc and Itgam) was examined. A UMAP shows their expression within different cell populations (Figure 7).
Finally, the expression of selected immune-related markers was assessed to characterize transcriptional heterogeneity among microglial cells. A Violin plot displays the expression of genes such as CD80 and NOS-, as well Mrc1, Arg1, Fcgr1, and CD163 at the single-cell level (Figure 8). These marker expression patterns allow for comparison with flow cytometry-based profiling and highlight the molecular diversity of microglial subsets across experimental conditions.
<img alt="Density and scatter plots illustrating cell quality metrics: percent.mt, log10GenesPerUMI, nGene, nUMI." class="xfigimg" src="/files/ftp_upload/68427/68427fig1.jpg" data-altupdatedat="1762509930000" data-alt="Figure 1"> 
Figure 1: Quality control of single-cell transcriptomic data. (A) Distribution of mitochondrial RNA content across cells. Cells with &gt;30% mitochondrial RNA were excluded to remove potentially stressed or dying cells. (B) Cells with a log10GenesPerUMI &gt; 0.75 were filtered out to ensure a consistent gene-to-UMI ratio and reduce noise from low-complexity libraries. (C) Cells with fewer than 500 detected transcripts (nUMI) or more than 300 detected genes (nGENE) were also excluded to eliminate poor-quality or multiple cells. (D) Correlation plot of NUMI versus nGene illustrating the impact of filtering thresholds. Detected doublets were removed. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="UMAP chart of cell clusters; includes neuronal, endothelial, and immune cells; data visualization." class="xfigimg" src="/files/ftp_upload/68427/68427fig2.jpg" data-altupdatedat="1762509930000" data-alt="Figure 2"> 
Figure 2: UMAP visualization of single-cell transcriptomes. UMAP plot showing the distribution of single cells based on transcriptomic profiles obtained from two individual animals. Each dot represents one cell, color-coded by cluster identity. The microglial cell cluster is identified among other cell populations. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Random Forest ROC curve, AUC=99.7%, diagram, model sensitivity and specificity analysis." class="xfigimg" src="/files/ftp_upload/68427/68427fig3.jpg" data-altupdatedat="1762509930000" data-alt="Figure 3"> 
Figure 3: ROC curve of Random Forest classifier evaluated by cross-validation. Receiver Operating Characteristic (ROC) curve illustrating the performance of the Random Forest model used to distinguish microglia from other brain cell types based on gene expression profiles. The model was trained on a set of ~1000 cells with known labels and evaluated using 10-fold cross-validation. Among the five statistical models tested (Random Forest, logistic regression, naïve Bayes, support vector machine, and decision tree), the Random Forest model achieved the highest classification performance. The area under the curve (AUC) reflects the model's accuracy. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Volcano plot of microglia DEGs; Log2 fold change vs. -log10 P; data visualization of gene expression." class="xfigimg" src="/files/ftp_upload/68427/68427fig4.jpg" data-altupdatedat="1762509930000" data-alt="Figure 4"> 
Figure 4: Differential gene expression analysis in microglial cells. Volcano plot representing the log2 fold change versus the adjusted P-Value for genes differentially expressed in microglia between the two experimental groups (control vs. cerebellar brain injury). Upregulated gene is more highly expressed in the group of interest (ident.1), and downregulated genes show reduced expression compared to the reference group (ident.2). Key significantly differentially expressed genes are labeled. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Flow cytometry diagram showing cell gating process for microglia selection in CD11b, CD45 analysis." class="xfigimg" src="/files/ftp_upload/68427/68427fig5.jpg" data-altupdatedat="1762509930000" data-alt="Figure 5"> 
Figure 5: Gating strategy for microglia identification by flow cytometry. (A) Gating strategy applied to cerebellar cells from a control postnatal day 3 (P3) mouse. (B) Singlets were selected to exclude doublets. (C) Viable cells were identified using a viability dye; events with low FSC-A were excluded to remove debris and ensure analysis of intact cells. (D) Microglia were identified as CD45+ and CD11b+ with two distinct populations: CD45low-CD11bint for quiescent microglia and CD45int -CD11bhigh for activated microglia. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Flow cytometry diagram of CD80, CD86, iNOS analysis for immune cell profiling." class="xfigimg" src="/files/ftp_upload/68427/68427fig6.jpg" data-altupdatedat="1762509930000" data-alt="Figure 6"> 
Figure 6. Characterization of microglia marker expression profiles by flow cytometry. (A) Sequential gating of CD45+ CD11b+ cells based on CD80, CD86, and iNOS expression. (B) Identification of a subset expressing CD206, followed by gating based on Arg1 expression. (C) Identification of a subset expressing CD86, followed by gating based on CD64 expression. (D) Identification of a subset expressing CD163, followed by gating based on CD206 expression. These gating strategies illustrate the phenotypic heterogeneity of microglia populations based on surface and intracellular marker expression. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="UMAP plot, scRNA-seq data, Ptprc vs. Itgam expression, cell type distribution, bioinformatics analysis." class="xfigimg" src="/files/ftp_upload/68427/68427fig7.jpg" data-altupdatedat="1762509930000" data-alt="Figure 7"> 
Figure 7: Confirmation of microglia identity using Itgam and Ptprc expression in single-cell RNA sequencing data. UMAP displays the transcriptional landscape of cerebellar cells at P15, with gene expression of Itgam and Ptprc overlaid across clusters. Co-expression of these canonical myeloid markers supports the identification of microglia populations and aligns with markers used in flow cytometry, providing cross-validation between transcriptomic and protein-level analyses. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Microglia gene expression violin plot, comparing Veh and CBI treatments for Cd80, Nos2, Fcgr1." class="xfigimg" src="/files/ftp_upload/68427/68427fig8.jpg" data-altupdatedat="1762509930000" data-alt="Figure 8"> 
Figure 8: Expression profiles of selected immune-related genes in microglial cells identified by single-cell RNA sequencing. Violin plots display the distribution of gene expression for CD80, NOS2, Mrc1, Arg1, Fcgr1, and CD163 across individual microglial cells. These markers are commonly associated with immune-related processes and illustrate the transcriptional heterogeneity observed within the microglial population. Data are shown for both control and cerebellar brain injury (CBI) conditions. <a href="https://www.jove.com/files/ftp_upload/68427/68427fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Composition of solutions used for neural cell isolation and staining. This table provides a detailed composition of the solutions required for cell isolation and staining, including their respective concentrations and components. <a href="https://www.jove.com/files/ftp_upload/68427/Table%201.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Antibody mix concentrations for extracellular staining. This table details concentrations of antibodies and live/dead solution used for extracellular staining, specifying the required volumes and dilutions for each antibody in the staining mix for one sample. <a href="https://www.jove.com/files/ftp_upload/68427/Table%202.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Antibody mix concentrations for intracellular staining. This table details concentrations of antibodies used for intracellular staining, specifying the required volumes and dilutions for each antibody in the staining mix for one sample. <a href="https://www.jove.com/files/ftp_upload/68427/Table%203.xlsx" target="_blank">Please click here to download this Table.</a>

This protocol combines flow cytometry and single-cell RNA sequencing to isolate and characterize microglial cell states in the cerebellum of early postnatal mouse brains. It uses enzymatic dissociation, Percoll centrifugation, and immunostaining to reveal microglia heterogeneity and improve understanding of their roles in cerebellar development and disease.

Microglia, the brain's resident immune cells, exhibit region and context-specific transcriptional profiles during development and disease. This protocol ...

flow-cytometry-single-cell-analysis-for-characterizing-microglia

Research

JoVE Journal

Neuroscience

Microglia are resident macrophages that provide the first line of defense and immune surveillance of the central nervous system. MicroRNAs are regulatory molecules that play an important role in many physiological processes including activation and differentiation of macrophages. In this article, we describe the method for measurement of microRNAs in microglia.

Detection of MicroRNAs in Microglia by Real-time PCR in Normal CNS and During Neuroinflammation

One key to successful investigation of microglial biology is the preservation of microglial immunofunction ex vivo during isolation from CNS tissue. Isolating microglia via rotary shaking results in highly pure and immunofunctional cell cultures as assessed by fluorescent imaging, immunocytochemistry, and ELISA following microglia activation with the proinflammatory stimuli lipopolysaccharide (LPS) and Pam3CSK4 (Pam).

Isolation of Cortical Microglia with Preserved Immunophenotype and Functionality From Murine Neonates

Microglia activation and microgliosis are key responses to chronic neurodegeneration. Here, we present methods for in vivo, long-term visualization of retinal CX3CR1-GFP+ microglial cells by confocal ophthalmoscopy, and for threshold and morphometric analyses to identify and quantify their activation. We monitor microglial changes during early stages of age-related glaucoma.

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

This protocol provides an analysis of the macrophage subpopulations in the adult mouse central nervous system by flow cytometry and is helpful for the study of multiple markers expressed by these cells.

Analysis of Microglia and Monocyte-derived Macrophages from the Central Nervous System by Flow Cytometry

A protocol for the isolation of primary microglia from murine brains is presented. This technique aids in furthering the current understanding of neurological conditions. Density gradient centrifugation and magnetic separation are combined to produce sufficient yield of a highly pure sample. Furthermore, we outline the steps for characterization of microglia.

Characterization and Isolation of Mouse Primary Microglia by Density Gradient Centrifugation

Primary microglia cultures are commonly used to evaluate new anti-inflammatory molecules. The present protocol describes a reproducible and relevant method to magnetically isolate microglia from neonate pups.

Magnetic Isolation of Microglial Cells from Neonate Mouse for Primary Cell Cultures

Here, we present a protocol to isolate and purify primary microglia in animal models of demyelinating diseases, utilizing columnar magnetic-activated cell sorting.

Isolation of Mouse Primary Microglia by Magnetic-Activated Cell Sorting in Animal Models of Demyelination

This work describes a protocol for the quantification of global histone modifications using intranuclear flow cytometry in isolated brain microglia. The work also contains the microglia isolation protocol that was used for data collection.

Quantification of Global Histone Post Translational Modifications Using Intranuclear Flow Cytometry in Isolated Mouse Brain Microglia

Here we present two protocols to quantify microglial engulfment of vGLUT1-positive synapses and pHRodo Red-labeled crude synaptosomes using flow cytometry.

Quantification of Microglial Engulfment of Synaptic Material Using Flow Cytometry

The present protocol describes how to isolate and purify primary hippocampal microglia from adult mice, followed by instructions for conducting whole-cell patch-clamp recordings on these acutely isolated cells.

Isolation and Whole-Cell Patch-Clamp Recording of Hippocampal Microglia from Adult Mice

Learn flow cytometry and single-cell RNA sequencing to characterize microglia activation in early postnatal mouse cerebellum. This method reveals cellular heterogeneity and enhances understanding of brain development.

Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development

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