Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast

Article Article scientifique Article Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast eng 2026-02-26 author Zelic Marija author Gligorovski Vojislav author Labbaf Farzaneh author Labagnara Marco author Oesterle Roxane author Brenna Greta author Massard Fanny author Chethan Samir G author Li Wanlan 403509 Département de biologie moléculaire et cellulaire (MOCEL) author Martin Sophie 0000-0002-5317-2557 403314 Département de biologie moléculaire et cellulaire (MOCEL) author Hauf Silke author Rahi Sahand Jamal vbag067 10.1093/bioadv/vbag067 2635-0041 https://academic.oup.com/bioinformaticsadvances/advance-article/doi/10.1093/bioadv/vbag067/8499673 Tracking and lineage tracing are widely needed tasks in biological image analysis. For cells that grow and divide, tracking is challenging because cells change in number, shape, and size throughout a recording. Longer intervals between images make tracking more difficult. Consequently, tracking has to be performed between consecutive or temporally close images, which leads to exponentially decreasing tracking accuracy and high sensitivity to error rates. For budding yeast, this challenge is further heightened by the similarity of cells in colonies, their dense packing, asymmetric cell divisions, and movement due to colony growth. A related task, lineage tracing, is similarly challenging without fluorescent markers since a new daughter cell can be surrounded by multiple potential mother cells. Here, we present neural networks for budding yeast tracking and lineage tracing, named LYN-track and LYN-trace, respectively, which leverage fine geometric features of cells and their neighborhoods. To train and test the algorithms, we recorded and annotated budding and fission yeast timelapse microscopy movies (78,852 frame-to-frame tracklets, 2,512 images), which we make available. On these and existing datasets, our neural network-based methods demonstrate robust, above state-of-the-art performance. Both tools are integrated into graphical user interfaces (GUIs) and can be retrained with custom data. Dewey 570 7daed2ee-1311-4824-9df9-2e51260a966a Département de biologie moléculaire et cellulaire 16484 false 1410 Groupe Sophie Martin Bioinformatics advances 474644e6-8091-453a-a60b-4738e57676d8 Article (Published version) Public vbag067.pdf 2321956 application/pdf CC BY-4.0 53bd486a9ba3abdadfa12a70cbbe5a45941b7bd0de92273a8caf9e4561854ee2 openAccess Faculté des sciences / Section de biologie / Département de biologie moléculaire et cellulaire

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Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast	Bioinformatics advances			2026	6	124

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