Related Experiment Video
Updated: Sep 11, 2026
Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
Published on: December 16, 2017
Partaker: deep-learning-based single-cell-resolution analysis of multi-dimensional, long-term, microfluidic-based
Henrique H Libutti-NúÑez1, Bukola A Akindipe1, Hamed Rastaghi1
1Department of Nanoengineering, North Carolina Agricultural and Technical State University, Greensboro, 27401, NC, USA.
Motivation:
Deep-learning segmentation models for microbial time-lapse fluorescence microscopy already exist, but they are often difficult to use consistently across experiments and are not packaged with unified workflows for combining models, quantifying multi-channel fluorescence, and scaling analyses to large microfluidic imaging datasets.
Results:
To address these challenges, we developed Partaker, an easy-to-use Python-based graphical tool for deep-learning segmentation, multi-channel fluorescence quantification, and morphological analysis of microbial cells over time. Partaker supports extensible model integration, efficient handling of large imaging datasets, and interactive visualization, enabling time-resolved analysis of population fluorescence distributions from per-cell measurements. We demonstrate performance using a two-strain validation experiment (housekeeping and inducible) and show robust recovery of population-level fluorescence dynamics with single-cell resolution.
Availability And Implementation:
Partaker is implemented in Python and is available on GitHub (https://github.com/SamOliveiraLab/partaker). A versioned release of the software is archived on Zenodo (https://doi.org/10.5281/zenodo.18844425). Documentation, example datasets, and installation instructions are provided in the repository.
