TRACI: a standardized pipeline for drift-correction and single-cell migration analysis of IncuCyte time-lapse data
Year: 2026
Type: article
Abstract: Background Quantitative single-cell migration analysis from time-lapse imaging data is important for studying cell motility in processes such as cancer invasion and tissue regeneration. The IncuCyte live-cell imaging system enables high-throughput longitudinal monitoring of cell migration, but its analytical utility is limited by frame-to-frame stage drift and jitter, which distort apparent cell trajectories, and by the absence of integrated single-cell tracking tools. Methods TRACI (Tracking and Registration-based Analysis of Cell migration from IncuCyte data) is a standardized, drift-aware analysis pipeline implemented in a combined Java- and Python-based framework with a graphical user interface. The pipeline integrates image organization, phase cross-correlation drift correction, coordinate-based single-cell tracking and group-level quantitative analysis. To demonstrate its utility, we applied TRACI to patient-derived glioblastoma stem cell (GSC) lines imaged under epidermal growth factor (EGF)-supplemented and EGF-depleted conditions. Results Without drift correction, no significant differences in accumulated migration distance were detected between EGF-supplemented and EGF-depleted conditions. Following drift correction, EGF withdrawal significantly reduced accumulated distance in both GSC lines tested. This effect was entirely masked in the uncorrected data. Conclusions TRACI is released as open-source software under a non-commercial license and is freely available via GitHub, supporting transparency, reproducibility and community-driven adaptation. By providing a standardized workflow that integrates drift correction, single-cell tracking and quantitative analysis within an accessible framework, TRACI facilitates reproducible analysis of IncuCyte time-lapse migration data across multiple experimental conditions.
Source: F1000Research
Institution: The Netherlands Cancer Institute
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W7204477464