A Novel <i>Ex Vivo</i> Approach for Investigating Profibrotic Macrophage Polarization Using Murine Precision-Cut Lung Slices
Bibliographic record
Abstract
ABSTRACT Idiopathic pulmonary fibrosis (IPF) is fatal interstitial lung disease characterized by excessive scarring of the lung tissue and declining respiratory function. Given its short prognosis and limited treatment options, novel strategies to investigate emerging experimental treatments are urgently needed. Macrophages, as the most abundant immune cell in the lung, have key implications in wound healing and lung fibrosis. However, they are highly plastic and adaptive to their surrounding microenvironment, and thus to maximize translation of research to lung disease, there is a need to study macrophages in multifaceted, complex systems that are representative of the lung. Precision-cut lung slices (PCLS) are living tissue preparations derived from the lung that are cultured ex vivo, which bypass the need for artificial recapitulation of the lung milieu and architecture. Our objective was to establish and validate a moderate-throughput, biologically- translational, viable model to study profibrotic polarization of macrophages in the lung using murine PCLS. To achieve this, we used a polarization cocktail (PC), consisting of IL-4, IL-13, and IL-6, over a 72-hour time course. We first demonstrated no adverse effects of the PC on PCLS viability and architecture. Next, we showed that multiple markers of macrophage profibrotic polarization, including Arginase-1, CD206, YM1, and CCL17 were induced in PCLS following PC treatment. Through tissue microarray-based histological assessments, we directly visualized and quantified Arginase-1 and CD206 staining in PCLS in a moderate-throughput manner. We further delineated phenotype of polarized macrophages, and using high-plex immunolabelling with the Iterative Bleaching Extends Multiplexity (IBEX) method, showed that the PC effects both interstitial and alveolar macrophages. Substantiating the profibrotic properties of the system, we also showed expression of extracellular matrix components and fibrotic markers in stimulated PCLS. Finally, we demonstrated that clodronate treatment diminishes the PC effects on profibrotic macrophage readouts. Overall, our findings support a suitable complex model for studying ex vivo profibrotic macrophage programming in the lung, with future capacity for investigating experimental therapeutic candidates and disease mechanisms in pulmonary fibrosis.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".