Cell density during differentiation can alter the phenotype of bone marrow-derived macrophages
Bibliographic record
Abstract
BACKGROUND: Bone marrow-derived macrophages (BMDMs) are widely used primary cells for studying macrophage function. However, despite numerous protocols that are currently available, lack of a notable consensus on generating BMDMs may obscure the reliability in comparing findings from different studies or laboratories. FINDINGS: In this study, we addressed the effect of cell density on the resulting macrophage population. With reference to previously published methods, bone marrow cells from wild type C57BL/6 mice were plated at either 4 × 10(5) cells or 5 × 10(6) cells per 10 cm and cultured in 20% L-cell conditioned media for 7 days, after which they were analyzed for cell surface markers, production of proinflammatory cytokines, and responsiveness to polarizing signals. Reproducibly, cells plated at lower density gave a pure population of CD11b(+)F4/80(+) macrophages (97.28 ± 0.52%) with majority being Ly-6C(-)Ly-6G(-) and c-Fms(+), while those plated at higher density produced less CD11b(+)F4/80(+) cells and a considerably higher proportion of CD11b(+)F4/80(+)CD11c(+) (68.72 ± 2.52%) and Ly-6C(-)Ly-6G(+) (71.10 ± 0.90%) cells. BMDMs derived from higher plating density also secreted less proinflammatory cytokines such as IL-6, IL-12 and TNF-α and were less phagocytic, and had a different pattern of expression for M1- and M2-related genes upon LPS or IL-4 stimulation. CONCLUSIONS: Overall, our findings indicate that altering cell density during BMDM differentiation can give rise to distinct macrophage populations that could vary the outcome of a functional study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".