Quantitative and unbiased lung alveolar septum assessment in an LPS experimental mouse model using 2D-spatial correlation image analysis from hematoxylin and eosin slides.

Introduction

Quantitative assessment of lung tissue architecture is essential for evaluating disease progression in experimental models of acute lung injury (ALI). However, conventional methods for measuring alveolar septum thickness rely on manual procedures that are time-consuming, labor-intensive, and, observer-dependent, compromises comparability across studies and reproducibility in translational biomedical research and drug development.Method: We present a novel, fully automated approach based on two-dimensional spatial autocorrelation function (2D-ACF) analysis to quantify septum thickness from hematoxylin and eosin (H&E)-stained lung sections. After validation with simulated data, the method was applied to a murine model of ALI induced by lipopolysaccharide (LPS) instillation under normal (ND) or high-fat diet (HFD) conditions.

Results

The 2D-ACF provided an unbiased estimate of mean septum thickness across entire tissue images. LPS instillation increased thickness more than sixfold versus controls, with further enlargement in the HFD+Instilled group, capturing additive effects of metabolic stress across >400 images. Manual measurements showed substantial inter-observer variability, especially in injured lungs, whereas the 2D-ACF was observer-independent and correlated strongly with traditional measurements in the instilled group (r = 0.89, p < 0.001). Discussion: The 2D-ACF framework offers a reproducible, scalable, and unbiased tool for lung pathology studies and other tissues exhibiting spatial heterogeneity in histological architecture.