Label-free quantitative phenotyping of hepatic stellate cell activation using holotomography with AI-enabled subcellular segmentation

Abstract

Hepatic stellate cell (HSC) activation is a central driver of liver fibrosis, yet its quantitative characterization in living cells remains limited by endpoint assays that rely on fixation and labeling. Here we introduce a label-free framework that combines three-dimensional holotomography (HT) with automated, AI-assisted analysis to study HSC activation dynamics in live cells. Using refractive index tomography, we non-invasively visualize hallmark structural features of HSC activation, including lipid droplet depletion, cytoskeletal remodeling, and changes in cellular morphology. Correlative fluorescence imaging validates the biological relevance of HT-derived features, while longitudinal imaging reveals continuous activation trajectories at single-cell resolution. Automated segmentation of whole cells and subcellular organelles enables scalable extraction of multi-parametric biophysical descriptors, defining a quantitative phenotypic fingerprint that distinguishes quiescent and activated states. Together, this work establishes holotomography-based quantitative phenotyping as a powerful approach for studying HSC activation and, more broadly, dynamic cell-state transitions in living systems.