Easy isolation of F4/80 positive macrophages from mouse tissues
Bibliographic record
Abstract
Abstract Macrophages are important and also have critical roles in the development and homeostasis of tissues and organs. Our understanding of macrophages is constantly evolving and expanding, as they are actively studied in many areas of research, including infectious diseases, wound healing, and tumor immunology. Adding to the complexity of macrophage research, subsets of macrophages can be identified throughout the body with diverse phenotypes and functions. Furthermore, macrophage frequency can be highly variable across different tissues, presenting a challenge to obtain highly pure macrophages. To address these challenges, we have developed a simple method to isolate macrophages by targeting F4/80, a well-established murine macrophage marker. Starting with a single-cell suspension from mouse peritoneal cavity, lung, or spleen, F4/80-positive cells were labeled with an antibody complex that links F4/80-positive cells to magnetic particles, then separated using an EasySep™ magnet. Using this method, F4/80-positive cells were enriched from 37.3 ± 9.3% to 94.4 ± 2.9% (mean ± SD; n = 12) from peritoneal lavage fluids, 26.5 ± 2.7% to 94.3 ± 2.8 % (n = 9) from lungs, and 8.0 ± 2.4% to 88.8 ± 3.4% (n = 18) from spleens. Protocols have been optimized to accommodate a range of sample sizes from the various tissue sources. EasySep™-isolated F4/80-positive macrophages are functional, as demonstrated by their ability to uptake FITC-dextran and secrete inflammatory mediators upon activation. Overall, the EasySep™ Mouse F4/80 Positive Selection Kit enables simple and easy isolation of F4/80-positive macrophages in under 25 minutes, streamlining the experimental workflows for researchers studying macrophage biology.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".