Generating and Co-culturing Murine Primary Microglia and Cortical Neurons
Bibliographic record
Abstract
Microglia are tissue-resident macrophages of the central nervous system (CNS), performing numerous functions that support neuronal health and CNS homeostasis. They are a major population of immune cells associated with CNS disease activity, adopting reactive phenotypes that potentially contribute to neuronal injury during chronic neurodegenerative diseases such as multiple sclerosis (MS). The distinct mechanisms by which microglia regulate neuronal function and survival during health and disease remain limited due to challenges in resolving the complex in vivo interactions between microglia, neurons, and other CNS environmental factors. Thus, the in vitro approach of co-culturing microglia and neurons remains a valuable tool for studying microglia-neuronal interactions. Here, we present a protocol to generate and co-culture primary microglia and neurons from mice. Specifically, microglia were isolated after 9-10 days in vitro from a mixed glia culture established from brain homogenates derived from neonatal mice between post-natal days 0-2. Neuronal cells were isolated from brain cortices of mouse embryos between embryonic days 16-18. After 4-5 days in vitro, neuronal cells were seeded in 96-well plates, followed by the addition of microglia to form the co-culture. Careful timing is critical for this protocol as both cell types need to reach experimental maturity to establish the co-culture. Overall, this co-culture can be useful for studying microglia-neuron interactions and can provide multiple readouts, including immunofluorescence microscopy, live imaging, as well as RNA and protein assays.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".