<i>In vitro</i> and <i>in vivo</i> derived macrophages occupy distinct phenotypic states
Bibliographic record
Abstract
SUMMARY Monocyte-derived macrophages (MDMs) are widely used to model human macrophage biology in vitro and standardized polarization conditions were proposed to recapitulate the different macrophage activation states. Although surface markers specific for distinct MDM populations have been identified, a systematic analysis of surface marker expression on these consensus MDM populations has not previously been reported. Here, we use mass cytometry to perform an in-depth characterization of MDM surface profiles and determine how these markers evolve with time. We also compared the phenotypes found in vitro with patient-derived tumor-associated macrophages (TAMs) and found that although MDMs and TAMs shared most markers investigated, the cell-surface signatures markedly differed in terms of expression levels and combinations. Our high-dimensional, single-cell analyses clarifies the surface expression profile of the core in vitro differentiated macrophage populations and highlights some limitations of the in vitro system to represent the complexity of in vivo polarized tumor-associated macrophage phenotypes. These findings provide new opportunities to improve the models used to study macrophage biology and give a pathway to improve our understanding of the in vivo complexity of these systems.
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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 | 0.000 | 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) | 0.002 | 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".