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DEP-track: a motion-aware framework for large-scale cell tracking and crossover frequency estimation in
Sena Lee1, Seungyeop Choi2,3, Yerin Lee1
1Department of Precision Medicine, Wonju College of Medicine, Wonju, Republic of Korea.
Frontiers in Bioinformatics
|May 11, 2026
Summary
DEP-Track automates single-cell analysis in dielectrophoresis (DEP) experiments by preserving cell trajectories and estimating crossover frequencies. This enables scalable, high-throughput dielectric phenotyping with enhanced accuracy and reproducibility.
Area of Science:
- Biophysics
- Cell Biology
- Computational Biology
Background:
- Dielectrophoresis (DEP) is crucial for cell analysis, but current methods struggle with scalability and reproducibility, especially in long-term, frequency-modulated experiments.
- Manual inspection and repeated measurements limit throughput, statistical power, and single-cell tracking accuracy in conventional DEP workflows.
- Automated, precise analysis of single-cell responses under DEP is needed for advanced cell characterization.
Purpose of the Study:
- To develop DEP-Track, a novel computational framework for automated, large-scale trajectory preservation and crossover frequency estimation in frequency-modulated DEP microscopy.
- To enable precise, single-cell level estimation of crossover frequencies, overcoming limitations of conventional DEP analysis.
- To establish a scalable and reproducible workflow for high-throughput dielectric phenotyping.
Main Methods:
- DEP-Track integrates anchor-free cell detection with motion-aware trajectory association to maintain cell identity across motion transitions.
- It unifies velocity-based and trajectory-based estimation methods for crossover frequency determination under varying frequencies.
- The framework processes long-term time-lapse imaging data (e.g., 13,200 frames) for continuous single-cell tracking.
Main Results:
- DEP-Track successfully tracked hundreds of cells continuously over extended periods, enabling population-scale analysis without repeated experiments.
- Automated extraction of statistically consistent crossover frequencies at the single-cell level was achieved from repeated crossover events.
- Estimated crossover frequencies demonstrated strong agreement with conventional methods and prior measurements, validating analytical accuracy.
Conclusions:
- DEP-Track provides a scalable, reproducible computational solution for analyzing single-cell responses in DEP experiments.
- The framework facilitates high-throughput dielectric phenotyping by enabling precise, single-cell crossover frequency estimation.
- This advancement transforms DEP analysis, paving the way for more efficient and powerful cell characterization techniques.
