DEP-track: a motion-aware framework for large-scale cell tracking and crossover frequency estimation in dielectrophoresis

Precise and scalable analysis of single-cell responses under dielectrophoresis (DEP) remains challenging, particularly in long-term experiments involving frequency modulation and dense cell populations. Conventional DEP workflows rely heavily on manual trajectory inspection or repeated measurements, limiting throughput, reproducibility, and statistical power. Here, we present DEP-Track, a motion-aware computational framework designed for automated large-scale trajectory preservation and crossover frequency estimation from frequency-modulated DEP microscopy data, where the crossover frequency is defined as the point at which the direction of DEP-induced cell motion reverses. The framework integrates anchor-free cell detection with motion-aware trajectory association to maintain single-cell identity across abrupt polarity-induced motion transitions over tens of thousands of frames. By unifying velocity-based estimation under fixed frequencies and trajectory-based estimation under continuous frequency modulation, DEP-Track enables automated extraction of statistically consistent estimates of crossover frequency at the single-cell level from repeated crossover events within a single experiment. In long-term time-lapse imaging experiments (13,200 frames), hundreds of cells were continuously tracked, enabling population-scale analysis without repeated experimental runs. Importantly, this study focuses exclusively on estimating the crossover frequency at the single-cell level. The estimated crossover frequencies showed strong agreement with conventional analysis workflows and previously reported measurements, confirming analytical accuracy and reproducibility. By transforming DEP analysis into a scalable and reproducible computational workflow, DEP-Track establishes a framework for high-throughput dielectric phenotyping based on crossover frequency.