Analysis of the microglia transcriptome across the human lifespan using single cell RNA sequencing
Bibliographic record
Abstract
BACKGROUND: Microglia are tissue resident macrophages with a wide range of critically important functions in central nervous system development and homeostasis. METHOD: In this study, we aimed to characterize the transcriptional landscape of ex vivo human microglia across different developmental ages using cells derived from pre-natal, pediatric, adolescent, and adult brain samples. We further confirmed our transcriptional observations using ELISA and RNAscope. RESULTS: We showed that pre-natal microglia have a distinct transcriptional and regulatory signature relative to their post-natal counterparts that includes an upregulation of phagocytic pathways. We confirmed upregulation of CD36, a positive regulator of phagocytosis, in pre-natal samples compared to adult samples in situ. Moreover, we showed adult microglia have more pro-inflammatory signature compared to microglia from other developmental ages. We indicated that adult microglia are more immune responsive by secreting increased levels of pro-inflammatory cytokines in response to LPS treatment compared to the pre-natal microglia. We further validated in situ up-regulation of IL18 and CXCR4 in human adult brain section compared to the pre-natal brain section. Finally, trajectory analysis indicated that the transcriptional signatures adopted by microglia throughout development are in response to a changing brain microenvironment and do not reflect predetermined developmental states. CONCLUSION: In all, this study provides unique insight into the development of human microglia and a useful reference for understanding microglial contribution to developmental and age-related human disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".