Tissue signatures of human macrophages during homeostasis and activation
Bibliographic record
Abstract
Human macrophages (MΦs) reside in tissues and develop tissue-specific identities. While studies in mice have identified molecular signatures for site-specific MΦ differentiation, less is known about the transcriptional profiles of human MΦs in distinct sites, including mucosal tissues and lymphoid organs during homeostasis and activation. Here, we use multimodal single-cell sequencing and ex vivo stimulation assays to define tissue signatures for populations of human MΦs isolated from lungs, small intestine, spleen, bone marrow, and lymph nodes obtained from individual organ donors. Our results reveal distinct tissue-adapted gene and protein profiles of metabolic, adhesion, and immune interaction pathways, which are specific to MΦs and not monocytes isolated from the same sites and exhibit homology to murine MΦs from the same sites. Tissue-adapted MΦs remained responsive to polarizing cytokine stimuli ex vivo, with upregulation of expected transcripts and secreted proteins, while retaining tissue-specific profiles. Patterns of chromatin accessibility in tissue MΦs identified from single-nucleus assay for transposase-accessible chromatin by sequencing reflected gene expression signatures and indicate that differential utilization of transcription factors may drive stable tissue-adapted profiles. Together, our findings show how human MΦ identity is coupled to their site of residence for mucosal and lymphoid organs and is intrinsically maintained during activation and polarization.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".