Partaker: deep-learning-based single-cell-resolution analysis of multi-dimensional, long-term, microfluidic-based time-lapse microscopy data

Clicks: 17
ID: 328196
2026
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Abstract
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.
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openalex_W7212121101 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Henrique Hiram Libutti-Núñez, Bukola A. Akindipe, Hamed Rastaghi, Nona Hashemi, Samuel M. D. Oliveira
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag675
URL
https://doi.org/10.1093/bioinformatics/btag675
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