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For cells that grow and divide, tracking is challenging because cells change in number, shape, and size throughout a recording. As the time interval between images increases, it becomes more difficult to establish correspondences between cells across timepoints. Consequently, tracking has to be performed between consecutive or temporally close images, which leads to exponentially decreasing tracking accuracy and thus high sensitivity to error rates. For budding yeast, this challenge is further heightened by the similarity of cells in colonies, their dense packing, the asymmetric nature of cell divisions, and movement due to growth of the colony. A related task, lineage tracing, is similarly challenging without fluorescent markers due to multiple potential mother cells surrounding a new daughter cell. Here, we present neural networks for budding yeast tracking and lineage tracing, named LYN-track and LYN-trace, respectively. These methods leverage fine geometric features of cells and their neighborhoods. To train and test the algorithms, we recorded and annotated new budding and fission yeast microscopy movies (78,852 frame-to-frame tracklets, 2,512 images), which we make freely available. On these and existing datasets, our neural network-based methods demonstrate robust, above state-of-the-art performance. Both tools have been integrated into graphical user interfaces (GUIs), available on Github, and can be straightforwardly retrained with custom data if desired.<\/jats:p>","DOI":"10.64898\/2026.01.09.698579","type":"posted-content","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:20:20Z","timestamp":1768015220000},"source":"Crossref","is-referenced-by-count":0,"title":["Love-thy-neighbor: Neural networks for tracking and lineage tracing in budding yeast"],"prefix":"10.64898","author":[{"given":"Marija","family":"Zelic","sequence":"first","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vojislav","family":"Gligorovski","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzaneh","family":"Labbaf","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Labagnara","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roxane","family":"Oesterle","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Greta","family":"Brenna","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanny","family":"Massard","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir G.","family":"Chethan","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Fralin Life Sciences Institute, Center for the Mathematics of Biosystems, Virginia Tech, Blacksburg, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanlan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Molecular and Cellular Biology, Faculty of Science, University of Geneva, Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie G.","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Molecular and Cellular Biology, Faculty of Science, University of Geneva, Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silke","family":"Hauf","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, Fralin Life Sciences Institute, Center for the Mathematics of Biosystems, Virginia Tech, Blacksburg, VA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahand Jamal","family":"Rahi","sequence":"additional","affiliation":[{"name":"Laboratory of the Physics of Biological Systems, Institute of Physics, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026011209351401000_2026.01.09.698579v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.01.018"},{"key":"2026011209351401000_2026.01.09.698579v1.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2001255117"},{"key":"2026011209351401000_2026.01.09.698579v1.3","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1038\/s41467-023-38959-8","article-title":"Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor","volume":"14","year":"2023","journal-title":"Nature Communications"},{"key":"2026011209351401000_2026.01.09.698579v1.4","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1038\/s42255-023-00741-x","article-title":"Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle","volume":"5","year":"2023","journal-title":"Nat. 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DeeperGCN: All you need to train deeper GCNs. arXiv 2006.07739 (2020)."}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.01.09.698579","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:08:08Z","timestamp":1782277688000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.01.09.698579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,9]]},"references-count":53,"URL":"https:\/\/doi.org\/10.64898\/2026.01.09.698579","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1093\/bioadv\/vbag067","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2026,1,9]]},"subtype":"preprint"}}