Spatial Transcriptomics Resolve an Emphysema-specific Lymphoid Follicle B Cell Signature in COPD
Bibliographic record
Abstract
RATIONALE: Within chronic obstructive pulmonary disease (COPD), emphysema is characterized by a significant yet partially understood B cell immune component. OBJECTIVE: To characterize the transcriptomic signatures from lymphoid follicles (LFs) in ever-smokers without COPD and COPD patients with varying degrees of emphysema. METHODS: Lung sections from 40 COPD patients and ever-smokers were used for LF proteomic and transcriptomic spatial profiling. Formalin and OCT-fixed lung samples obtained from biopsies or lung explants, were assessed for LF presence. Emphysema measurements were obtained from clinical chest CT scans. High confidence transcriptional (HCT) target intersection analyses were conducted to resolve emphysema-induced transcriptional networks. MEASUREMENTS AND MAIN RESULTS: Overall, 115 LFs from ever-smokers and GOLD 1-2 and GOLD 3-4 patients were analyzed. No LFs were found in never-smokers. Differential gene expression analysis revealed significantly increased expression of LF assembly and B cell markers genes in subjects with severe emphysema. HCT analysis revealed activation of abnormal B cell activity signature in LFs (q-value: 2.56E-111). LFs from GOLD 1-2 COPD patients with emphysema showed significantly increased expression of genes associated with antigen presentation, inflammation, and B cell activation and proliferation. LFs from GOLD 1-2 COPD patients without emphysema showed an anti-inflammatory profile. The extent of centrilobular emphysema was significantly associated with genes involved in B cell maturation and antibody production. Protein-RNA network analysis showed that LFs in emphysema have a unique signature skewed towards chronic B cell activation. CONCLUSIONS: An off-targeted B cell activation within LFs is associated with autoimmune-mediated emphysema pathogenesis.
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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 | 0.000 |
| 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".