Tissue-wide profiling of human lungs reveals spatial sequestration of macrophages in tuberculosis
Bibliographic record
Abstract
Abstract The immune response to human tuberculosis (TB), particularly in the context of complex lung pathology, remains incompletely understood. Here, we employed whole-slide spatial proteomics to map immune cell organization in TB-affected human lung tissues. Our analysis revealed pronounced spatial segregation of major immune cell populations in non-necrotizing TB lesions. At the tissue level, macrophages and lymphocytes formed distinct cellular communities associated with specific pathological features. At the lesion level, macrophages and B cells showed an inverse relationship in both abundance and spatial distribution. Proinflammatory T cells preferentially accumulated in macrophage-rich lesions but remained largely separated from macrophages. Interestingly, lesions exhibiting clear segregation between T cells and macrophages were more common in subclinical TB than in active disease. These findings suggest that spatial isolation of macrophages from effector lymphocytes may help temper inflammation and potentially prevent lesion progression to necrosis, while also enabling immune evasion by Mycobacterium tuberculosis . One Sentence Summary Xiao et al. reveal spatial segregation of immune cells in TB-lung tissue and link the microenvironmental dynamics to disease states of tuberculosis.
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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".