A two‐step human culture system replicates intestinal monocyte maturation cascade: Conversion of tissue‐like inflammatory monocytes into macrophages
Bibliographic record
Abstract
Abstract Monocyte maturation program into macrophages (MΦ) is well defined in murine gut under homeostatic or inflammatory conditions. Obviously, in vivo tracking of monocytes in inflamed tissues remains difficult in humans. Furthermore, in vitro models fall short in generating the surrogates of transient extravasated tissue inflammatory monocytes. Here, we aimed to unravel environmental cues that replicated the human monocyte “waterfall” process in vitro by first, generating tissue‐like inflammatory monocytes, which were then shifted toward MΦ. Purified CD14 + CD16 − monocytes, cultured with granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), IFN‐γ and IL23, differentiated into CD14 + CD163 − cells that displayed a monocyte‐like morphology. In vitro generated inflammatory CD14 + CD163 − (inflammatory monocyte‐like cells) cells promoted IL‐1β‐dependent memory Th17 and Th17/Th1 responses, like the CD14 + CD163 − mo‐like cells that accumulate in inflamed colon of Crohn's disease patients. Next, in vitro generated inflammatory monocyte‐like cells converted to functional CD163 + MΦ following exposure to TGF‐β and IL10. Gene set enrichment analysis further revealed a shared molecular signature between converted CD163 + MΦ and MΦ detected in various inflamed nonlymphoid and lymphoid diseased tissues. Our findings propose a two‐step in vitro culture that recapitulates human monocyte maturation cascade in inflamed tissue. Manipulation of this process might open therapeutic avenues for chronic inflammatory disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".