Flow Cytometry and Single-Cell Analysis for Characterizing Microglia Activation in Early Postnatal Mouse Brain Development
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".