Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology
Bibliographic record
Abstract
Tuberculosis (TB) typically causes lung destruction and fibrosis, leading to ∼1.3 million deaths annually. The cellular drivers of human TB immunopathology remain poorly defined. We performed single-cell RNA sequencing and spatial transcriptomics on lung tissues from TB-infected and TB-negative individuals, identifying 30 distinct immune, parenchymal, and stromal cell subsets. Several were linked to TB pathology and corroborated through immunohistochemistry, flow cytometry, and independent human datasets. Fibroblasts were identified as major drivers in both active TB granuloma and TB-diseased lung tissue. In particular, the MMP1+CXCL5+ fibroblast subset, expressing a myofibroblast-like gene signature, was associated with severe disease and higher bacterial burden in nonhuman primate granulomas. Network analyses revealed cross talk between MMP1+CXCL5+ fibroblasts and SPP1+ macrophages within the granuloma cuff, which has been reported in other disease contexts, and may play an important role in TB immunopathology. Our findings highlight previously unappreciated cell populations and potential targets for novel TB therapies.
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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 | 0.000 |
| 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".