MorphCell learns reconstructable shape representations with explicit scale control for 3D cell morphology

Abstract Three-dimensional (3D) cell morphology provides a measurable phenotype of cellular state and function, but representing it across biological systems and imaging modalities remains difficult. Existing approaches do not jointly provide transferable representations, complete surface reconstruction, geometric interpretation and explicit control of physical scale. Here we introduce MorphCell, a self-supervised framework that uses cell-surface point clouds to learn shape-driven representations independently of physical scale. MorphCell combines cross-view reconstruction pretraining with spherical self-reconstruction. The former captures geometric relationships between surface regions, whereas the latter recovers complete 3D morphology from individual representations. Pretrained on non-biological object surfaces, MorphCell transfers to cellular datasets without biological task-specific training and outperforms existing point-cloud representations in morphological classification. The learned representations capture both global contour and local surface variation, enabling geometric interpretation through reconstruction, biophysical descriptors and saliency analysis. By retaining physical scale as a separate variable for controlled fusion, MorphCell further reveals that shape and scale contribute differently across biological distinctions. This framework provides a general approach for representing, reconstructing and interpreting 3D cellular morphology across imaging modalities and biological contexts.