Biomechanics-Driven 3D Architecture Inference from Histology Using CellSqueeze3D

Conventional 2D analysis of hematoxylin and eosin (H&E)-stained images is fundamentally limited by the tissue thickness, as cellular overlap and morphological changes in the compressed perspective obscure distinct cell boundaries. To address this, it develops CellSqueeze3D, a computational framework that reconstructs the 3D spatial distribution and size of individual cells from a single H&E-stained section. Founded on the principle that 2D cell compression preserves 3D geometry, the method employs a hybrid Particle Swarm Optimization (PSO) approach with biomechanical constraints to infer biologically plausible reconstructions. Validation shows that the nuclear-to-cytoplasmic (N/C) ratio distribution derived from the predicted cell radii differs significantly from random assignments (p = 1.39e-80). By employing projected cell boundaries, the 3D-informed cellular classifier surpassed traditional methods (AUC increases of 0.136 and 0.069). The resulting morphological metrics also revealed strong associations with key gene expression patterns, providing prognostic insights. Furthermore, cellular and nuclear size indices from CellSqueeze3D significantly predict the mutation status of 21 genes in TCGA cohorts, achieving a median AUROC above 0.65 in fivefold cross-validation. This study demonstrates that fully utilizing the previously untapped 3D spatial information from a single slice significantly enhances computational pathology and quantitative tissue phenotyping.