Longitudinal micro-CT as an outcome measure of interstitial lung disease in TNF-transgenic mice

INTRODUCTION: Rheumatoid arthritis associated interstitial lung disease (RA-ILD) is a debilitating condition with poor survival prognosis. High resolution computed tomography (CT) is a common clinical tool to diagnose RA-ILD, and is increasingly being adopted in pre-clinical studies. However, murine models recapitulating RA-ILD are lacking, and CT outcomes for inflammatory lung disease have yet to be formally validated. To address this, we validate μCT outcomes for ILD in the tumor necrosis factor transgenic (TNF-Tg) mouse model of RA. METHODS: Cross sectional μCT was performed on cohorts of male TNF-Tg mice and their WT littermates at 3, 4, 5.5 and 12 months of age (n = 4–6). Lung μCT outcomes measures were determined by segmentation of the μCT datasets to generate Aerated and Tissue volumes. After each scan, lungs were obtained for histopathology and 3 sections stained with hematoxylin and eosin. Automated histomorphometry was performed to quantify the tissue area (nuclei, cytoplasm, and extracellular matrix) and aerated area (white space) within the tissue sections. Spearman’s correlation coefficients were used to evaluate the extent of association between μCT imaging and histopathology endpoints. RESULTS: TNF-Tg mice had significantly greater tissue volume, total lung volume and mean intensity at all timepoints compared to age matched WT littermates. Histomorphometry also demonstrated a significant increase in tissue area at 3, 4, and 5.5 months of age in TNF-Tg mice. Lung tissue volume was correlated with lung tissue area (ρ = 0.81, p<0.0001), and normalize lung aerated volume was correlated with normalized lung air area (ρ = 0.73, p<0.0001). CONCLUSIONS: We have validated in vivo μCT as a quantitative biomarker of ILD in mice. Further, development of longitudinal measures is critical for dissecting pathologic progression of ILD, and μCT is a useful non-invasive method to study lung inflammation in the TNF-Tg mouse model.