Oliveira Lab Publishes Partaker: A Tool for Single-Cell Bacterial Analysis in Microfluidic Time-Lapse Imaging

We're excited to share that our paper, "Partaker: deep-learning-based single-cell-resolution analysis of multi-dimensional, long-term, microfluidic-based time-lapse microscopy data," is officially published in Bioinformatics!

This one has been a long time in the making, and we couldn't be more excited to finally share it. Partaker brings deep-learning segmentation, multi-channel fluorescence quantification, and scalable analysis together into one unified pipeline, solving a problem that's frustrated microfluidic imaging researchers for years: tools that don't generalize or scale across datasets.

Huge congratulations to lead author Henrique Libutti-Nunez and to co-authors Bukola Akindipe, Hamed Rastaghi, Nona Hashemi, and Samuel Oliveira for the incredible work behind this publication.

Read the paper: https://doi.org/10.1093/bioinformatics/btag675

Citation: Libutti-Nunez, H., Akindipe, B. A., Rastaghi, H., Hashemi, N., & Oliveira, S. M. D. (2026). Partaker: deep-learning-based single-cell-resolution analysis of multi-dimensional, long-term, microfluidic-based time-lapse microscopy data. Bioinformatics, btag675. https://doi.org/10.1093/bioinformatics/btag675