CD163 and Tim-4 identify resident intestinal macrophages across sub-tissular regions that are spatially regulated by TGF-β
Bibliographic record
Abstract
Abstract In bodily organs, macrophages are localised in poorly understood tissular and sub-tissular niches associated with defined macrophage ontogeny and activity. In the intestine, a paradigm is emerging that long-lived macrophages are dominantly present in the muscular layer, while highly monocyte-replenished populations are found in the lamina propria beneath the epithelial barrier. Whether longevity is restricted in such a simplified manner has not been well explored. Moreover, the impact of specific gut-associated factors on long-lived macrophage functionality and niche occupancy is unknown. We generated sc-RNA-Seq data from wild-type and Ccr2 −/− mice to identify phenotypic features of long-lived macrophage populations in distinct intestinal niches and identified CD163 as a useful marker to distinguish submucosal/muscularis (S/M) from lamina propria (LP) macrophages. Challenging the emerging paradigm, long-lived macrophages, identified by Tim-4 expression, were found in the LP and S/M. Long-lived LP macrophages are restrained in their response to proinflammatory stimulation compared to short-lived populations in the same location, and to the long-lived population within the S/M. Employing a novel Timd4 cre Tgfbr2 fl/fl mouse line we demonstrate distinct functions of TGF-β on long-lived macrophages in these two compartments. Importantly, in Timd4 cre Tgfbr2 fl/fl mice, zonation of CD163 + macrophages in the S/M was lost, suggesting TGF-β plays an unappreciated role in positioning of macrophages in the tissue. These data highlight the importance of considering ontogeny and niche when assessing the action of key intestinal regulatory signals.
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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 | 0.001 |
| 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".