Structural, Compositional, and Dielectric State Profiling in Label-Free Single-Cell Monitoring

Individual cells sense and transition between functional states, and the distribution of these states over time determines drug response, disease progression, and cell manufacturing outcomes. However, repeated measurement is difficult with label-based acquisition, as photobleaching, phototoxicity, and probe-induced perturbation accumulate. Label-free monitoring that leverages intrinsic physical signals circumvents these constraints, shifting the analytical burden from label chemistry to instrumental drift. In this review, we organize this field into three measurement modalities, including imaging-based, vibrational spectroscopy-based, and electrical sensing-based, each linked to distinct intrinsic state variables. Anchoring each modality to biological state variables such as structural organization, molecular composition, or dielectric architecture enables a physics-grounded framework that clarifies how intrinsic signals map onto functional cellular phenotypes in longitudinal monitoring. We describe the measurement principle, drift sources, and feature space for each modality, and evaluate representative platforms against a frame spanning design, feature definition, quantitative performance, and validation practice. Dominant analytical constraints differ systematically across modalities, motivating integrative architectures in which complementary modalities resolve ambiguities that no single modality can disentangle. We further discuss shared requirements for calibration, standardized reporting, and multimodal integration, and outline requirements in hardware miniaturization, edge inference, and artificial intelligence-guided molecular attribution to support scalable quantitative single-cell phenotyping.