A Spatial Transcriptomic Atlas of Fibrosing Interstitial Lung Diseases
Bibliographic record
Abstract
Abstract Rationale Fibrosing interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic hypersensitivity pneumonitis (CHP), are characterized by progressive lung scarring. While single-cell RNA-seq (scRNA-seq) has provided insights into the cellular landscape of normal and diseased lungs, a comprehensive spatial map of these diseases remains lacking. Thus, we sought to fill this gap by generating a spatial transcriptomic atlas of fibrosing ILDs. Methods We used formalin-fixed, paraffin-embedded surgical lung biopsies from treatment-naïve patients with IPF (n=10), NSIP (n=8), CHP (n=10), or unclassified ILDs (n=17). Spatial transcriptomics was performed with the Visium platform (10X Genomics) to capture a 6.5 mm by 6.5 mm square area. After performing quality control, we integrated single-cell annotations from the Integrated Human Lung Atlas and used “cell2location” to map cell type proportions on tissues. We used non-negative matrix factorization (NMF) and ‘scanpy’ to identify co-localizing cell types and differentially expressed genes between groups, respectively. Results Cell type mapping was consistent with histological findings, as we identified known marker genes for each cell type among the top differentially expressed genes. We also identified genes such as DDIT4 and TSC22D3, SFTPC, and TIMP1 and TAGLN that were strongly associated with fibroblasts in IPF, CHP and NSIP, respectively. While no cell types were specifically enriched in a particular ILD subtype, NMF (R=7) revealed differential co-localization patterns across conditions. For example, in CHP, AT1 and AT2 cells formed two distinct factors, a pattern not observed in IPF or NSIP. Conclusion This ongoing study provides the first large scale spatial transcriptomic atlas of fibrosing ILDs. We successfully integrated scRNA-seq data to predict cell type proportions within spatial contexts. Additional plans include the generation of an algorithm to reclassify unclassifiable cases and a correlation analysis between clinical outcomes and spatial gene expression patterns, independent of the ILD subtype.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".